2 Printed on paper containing at least 30% postconsumer recovered fiber. 
U.S. Environmental Protection Agency 

Office of Water (4303T) 

1200 Pennsylvania Avenue, NW 

Washington, DC 20460 

EPA-821-B-03-002
Economic and Environmental Benefits Analysis Document For The
 


Final Effluent Limitations 
 


Guidelines and Standards 
 


For The 
 


Metal Products & Machinery 
 


Point Source Category
 


EPA-821-B-03-002 
 


Christine Todd Whitman 
 


Administrator
 


G. Tracy Mehan, III
 


Assistant Administrator, Office of Water
 


Geoffrey H. Grubbs
 


Director, Office of Science and Technology
 


Sheila E. Frace
 


Director, Engineering and Analysis Division
 


Nicolaas Bouwes
 


Chief, Economic and Environmental Assessment Branch
 


William Anderson
 


Technical Coordinator
 


Lynne Tudor 

Economist 

James C. Covington, III
 


Economist
 


February 2003
 


U.S. Environmental Protection Agency
 


Office of Water
 


Washington, DC 20460
 

ACKNOWLEDGMENTS AND DISCLAIMER
 


The Agency would like to acknowledge the contributions of William Anderson, 

James C. Covington, III, Lynne Tudor, Lynn Zipf, and Nicolass Bouwes to development of 

this Economic and Environmental Benefits Analysis document. In addition, EPA 

acknowledges the contribution of Abt Associates, Westat, Eastern Research Group, and 

Science Application International Corporation. 

Neither the United States government nor any of its employees, contractors, 

subcontractors, or other employees makes any warranty, expressed or implied, or assumes 

any legal liability or responsibility for any third party’s use of, or the results of such use of, 

any information, apparatus, product, or process discussed in this report, or represents that its 

use by such a third party would not infringe on privately owned rights. References to 

proprietary technologies are not intended to be an endorsement by the Agency. 

Questions or comments regarding this economic document should be addressed to:
 


Mr. James C. Covington, III.
 


Economist
 


Engineering and Analysis Division (4303T)
 


U.S. Environmental Protection Agency
 


1200 Pennsylvania Avenue, N.W.
 


Washington, DC 20460
 


(202) 566 - 1034
 


covington.james@epa.gov
 

MP&M EEBA Table of Contents 

Table of Contents
 


EXECUTIVE SUMMARY 

ES.1 Overview of Facilities Evaluated for Regulation under the MP&M Point Source Category and Its Effluent
 


Discharges ................. ................. ................. ................. ............... ES-1
 


ES.2 Description of the Final Rule ................. ................. ................. ................. ES-3
 


ES.3 Economic Impacts and Social Costs of the Final Rule ................. ................. .............. ES-3
 


ES.3.1 Economic Impacts ................. ................. ................. ................. ... ES-4
 


ES.3.2 Social Costs ................. ................. ................. ................. ........ ES-7
 


ES.4 National Benefits of the Final Rule ................. ................. ................. ............ ES-8
 


ES.4.1 Reduced Human Health Risk ................. ................. ................. .......... ES-10
 


ES.4.2 Ecological, Recreational, and Nonuser Benefits ................. ................. ............. ES-14
 


ES.4.3 POTW Impacts ................. ................. ................. ................. .... ES-15
 


ES.4.4 Total Estimated Benefits of the Final MP&M Rule ................. ................. .......... ES-16
 


ES.5 National Benefits-Costs Comparison ................. ................. ................. .......... ES-16
 


ES.6 Ohio Case Study ................. ................. ................. ................. ........ ES-18
 


ES.6.1 Benefits ................. ................. ................. ................. .......... ES-18
 


ES.6.2 Social Costs ................. ................. ................. ................. ....... ES-20
 


ES.6.3 Comparing Monetized Benefits and Costs ................. ................. ................. ES-20
 


PART I: INTRODUCTION AND BACKGROUND INFORMATION 

Chapter 1: Introduction 

1.1 Purpose ................. ................. ................. ................. ................. . 1-1
 


1.2 Introduction ................. ................. ................. ................. ............... 1-1
 


1.3 Readers’ Aids ................. ................. ................. ................. .............. 1-3
 


Chapter 2: The MP&M Industry and the Need for Regulation 

2.1 Overview of Facilities Evaluated for Regulation under the MP&M Point Source Category .............. . . . ..... 2-1
 


2.2 MP&M Discharges and the Need for Regulation ................. ................. ................. .... 2-3
 


2.2.1 Baseline MP&M Discharges for Regulated Facilities ................. ................. ............ 2-4
 


2.2.2 Discharges under the MP&M Regulation ................. ................. ................. .... 2-4
 


2.3 Addressing Market Imperfections ................. ................. ................. ............... 2-5
 


2.4 Overlap with Other Effluent Guidelines ................. ................. ................. ........... 2-6
 


2.5 Meeting Legislative and Litigation-Based Requirements ................. ................. .............. 2-9
 


Glossary ................. ................. ................. ................. ................. ... 2-11
 


Acronyms ................. ................. ................. ................. ................. .. 2-13
 


Chapter 3: Profile of the MP&M Industries 

3.1 Data Sources ................. ................. ................. ................. .............. 3-2
 


3.2 	Overview of the MP&M Industry and Industry Trends ................. ................. ............... 3-3
 


3.2.1 Aerospace ................. ................. ................. ................. ........... 3-7
 


3.2.2 Aircraft ................. ................. ................. ................. ............. 3-7
 


3.2.3 Electronic Equipment ................. ................. ................. ................. .. 3-7
 


3.2.4 Hardware ................. ................. ................. ................. ............ 3-8
 


3.2.5 Household Equipment ................. ................. ................. ................. .. 3-8
 


3.2.6 Instruments ................. ................. ................. ................. .......... 3-9
 


3.2.7 Iron and Steel ................. ................. ................. ................. ........ 3-9
 


3.2.8 Job Shops ................. ................. ................. ................. ........... 3-9
 


3.2.9 Mobile Industrial Equipment ................. ................. ................. ............. 3-9
 


3.2.10 Motor Vehicle and Bus & Truck ................. ................. ................. ........ 3-10
 


3.2.11 Office Machine ................. ................. ................. ................. ..... 3-10
 


3.2.12 Ordnance ................. ................. ................. ................. .......... 3-10
 


3.2.13 Precious Metals and Jewelry ................. ................. ................. ............ 3-11
 


Table of Contents-1 
MP&M EEBA Table of Contents 

3.2.14 Printed Wiring Boards ................. ................. ................. ................ 3-11
 


3.2.15 Railroad ................. ................. ................. ................. ........... 3-11
 


3.2.16 Ships and Boats ................. ................. ................. ................. ..... 3-11
 


3.2.17 Stationary Industrial Equipment ................. ................. ................. ......... 3-12
 


3.3 	Characteristics of MP&M Manufacturing Sectors ................. ................. ................. . 3-12
 


3.3.1 Domestic Production ................. ................. ................. ................. .. 3-13
 


3.3.2 Industry/Market Structure ................. ................. ................. ............... 3-18
 


3.3.3 Financial Condition and Performance ................. ................. ................. ...... 3-24
 


3.4 	Characteristics of MP&M Non-Manufacturing Sectors ................. ................. .............. 3-25
 


3.4.1 Domestic Production ................. ................. ................. ................. .. 3-25
 


3.4.2 Industry Structure and Competitiveness ................. ................. ................. .... 3-28
 


3.5 	Characteristics of All MP&M Sectors ................. ................. ................. ........... 3-30
 


3.5.1. Eight-firm Concentration Ratio ................. ................. ................. .......... 3-30
 


3.5.2 Risk Normalized Return on Assets ................. ................. ................. ........ 3-31
 


3.6 Characteristics of MP&M Facilities ................. ................. ................. ............ 3-32
 


Glossary ................. ................. ................. ................. ................. ... 3-38
 


Acronyms ................. ................. ................. ................. ................. .. 3-40
 


References ................. ................. ................. ................. ................. . 3-41
 


Chapter 4: Regulatory Options 

4.1 Subcategorization ................. ................. ................. ................. .......... 4-1
 


4.2 Technology Options ................. ................. ................. ................. ........ 4-3
 


4.3 BPT/BAT Options for Direct Dischargers ................. ................. ................. ........ 4-3
 


4.4 PSES Options for Indirect Dischargers ................. ................. ................. ........... 4-3
 


4.5 NSPS and PSNS Options for New Sources ................. ................. ................. ........ 4-4
 


4.6 Summary of the Final Rule and Regulatory Alternatives ................. ................. .............. 4-4
 


Glossary ................. ................. ................. ................. ................. .... 4-5
 


Acronyms ................. ................. ................. ................. ................. ... 4-6
 


PART II: COSTS AND ECONOMIC IMPACTS 

Chapter 5: Facility Impact Analysis 

5.1 Data Sources ................. ................. ................. ................. .............. 5-2
 


5.2 	Methodology ................. ................. ................. ................. .............. 5-2
 


5.2.1 Converting Engineering Compliance Costs and Survey Financial Data to Current Year Dollar Values ........ 5-3
 


5.2.2 Market-level Impacts and Cost Pass-through Analysis ................. ................. ........... 5-4
 


5.2.3 Impact Measures for Private Facilities ................. ................. ................. ...... 5-5
 


5.2.4 Impact Measures for Railroad Line Maintenance Facilities ................. ................. ...... 5-12
 


5.2.5 Impact Measures for Government-owned Facilities ................. ................. ............ 5-12
 


5.3 	Results ................. ................. ................. ................. ................. . 5-14
 


5.3.1 Baseline Closures ................. ................. ................. ................. ... 5-14
 


5.3.2 Price Increases ................. ................. ................. ................. ...... 5-15
 


5.3.3 Overview of Impacts ................. ................. ................. ................. .. 5-16
 


5.3.4 Results for Indirect Dischargers ................. ................. ................. .......... 5-18
 


5.3.5 Results for Direct Dischargers ................. ................. ................. ........... 5-19
 


5.3.6 Results for Private Facilities ................. ................. ................. ............ 5-20
 


5.3.7 Results for Government-Owned Facilities ................. ................. ................. .. 5-21
 


Glossary ................. ................. ................. ................. ................. ... 5-25
 

Acronyms ................. ................. ................. ................. ................. .. 5-26
 

References ................. ................. ................. ................. ................. . 5-27
 

Chapter 6: Employment Effects 

6.1 Job Losses Due to Closures ................. ................. ................. ................. ... 6-2
 


6.2 Job Gains Due to Compliance Requirements ................. ................. ................. ...... 6-3
 


6.3 Net Effects on Employment ................. ................. ................. ................. .. 6-5
 


Glossary ................. ................. ................. ................. ................. .... 6-7
 


Table of Contents-2 
MP&M EEBA Table of Contents 

Acronym ................. ................. ................. ................. ................. ... 6-8
 

References ................. ................. ................. ................. ................. .. 6-9
 

Chapter 7: Government and Community Impact Analysis
 


7.1 Impacts on Governments ................. ................. ................. ................. ..... 7-1
 


7.1.1 Impacts on Governments that Operate MP&M Facilities ................. ................. ......... 7-1
 


7.1.2 POTW Administrative Costs ................. ................. ................. .............. 7-2
 


7.2 Community Impacts of Facility Closures ................. ................. ................. ......... 7-6
 


Glossary ................. ................. ................. ................. ................. .... 7-7
 


Acronyms ................. ................. ................. ................. ................. ... 7-8
 


Chapter 8: Foreign Trade Impacts 

8.1 Data sources ................. ................. ................. ................. .............. 8-1
 


8.2 Methodology ................. ................. ................. ................. .............. 8-2
 


8.3 Results ................. ................. ................. ................. ................. .. 8-3
 


References ................. ................. ................. ................. ................. .. 8-5
 


Chapter 9: Firm Level, New Source and Industry Impacts
 


9.1 Firm Level Impacts ................. ................. ................. ................. ......... 9-1
 


9.1.1 Sources ................. ................. ................. ................. ............. 9-1
 


9.1.2 Methodology ................. ................. ................. ................. ......... 9-2
 


9.1.3 Results ................. ................. ................. ................. .............. 9-2
 


9.2 New Source Impacts ................. ................. ................. ................. ........ 9-3
 


9.2.1 Methodology ................. ................. ................. ................. ......... 9-4
 


9.2.2 Results ................. ................. ................. ................. .............. 9-5
 


9.3 Industry Level Impacts ................. ................. ................. ................. ...... 9-7
 


Glossary ................. ................. ................. ................. ................. .... 9-9
 


Acronyms ................. ................. ................. ................. ................. .. 9-10
 


References ................. ................. ................. ................. ................. . 9-11
 


Chapter 10: Small Entity Impact Analysis 

10.1 Defining Small Entities ................. ................. ................. ................. .... 10-2
 


10.2 Methodology ................. ................. ................. ................. ............ 10-4
 


10.3 Results ................. ................. ................. ................. ................. 10-4
 


10.3.1 Number of Affected Small Entities ................. ................. ................. ....... 10-4
 


10.3.2 Impacts on Facilities Owned by Small Entities ................. ................. ............... 10-5
 


10.3.3 Impacts on Small Firms ................. ................. ................. ............... 10-6
 


10.4 Consideration of Small Entity Impacts in Developing the Final Rule ................. ................. ... 10-7
 


Glossary ................. ................. ................. ................. ................. ... 10-8
 


Acronyms ................. ................. ................. ................. ................. .. 10-9
 


References ................. ................. ................. ................. ................. 10-10
 


Chapter 11: Social Costs 

11.1 Components of Social Costs ................. ................. ................. ................. 11-1
 


11.2 Resource Costs of Compliance ................. ................. ................. ............... 11-2
 


11.3 POTW Administration Costs ................. ................. ................. ................ 11-4
 


11.4 Social Costs of Unemployment ................. ................. ................. ............... 11-5
 


11.4.1 Social Cost of Worker Dislocation ................. ................. ................. ....... 11-5
 


11.4.2 Cost of Administering Unemployment Benefits Programs ................. ................. ...... 11-6
 


11.4.3 Total Cost of Unemployment ................. ................. ................. ........... 11-6
 


11.5 Total Social Costs ................. ................. ................. ................. ........ 11-7
 


Glossary ................. ................. ................. ................. ................. ... 11-8
 


References ................. ................. ................. ................. ................. . 11-9
 


Table of Contents-3 
MP&M EEBA Table of Contents 

PART III: BENEFITS 

Chapter 12: Benefit Overview 

12.1 MP&M Pollutants ................. ................. ................. ................. ........ 12-2
 


12.1.1 Characteristics of MP&M Pollutants ................. ................. ................. ..... 12-2
 


12.1.2 Effects of MP&M Pollutants on Human Health ................. ................. .............. 12-3
 


12.1.3 Environmental Effects of MP&M Pollutants ................. ................. ................ 12-7
 


12.1.4 Effects of MP&M Pollutants on Economic Productivity ................. ................. ....... 12-8
 


12.2 Linking the Regulation to Beneficial Outcomes ................. ................. ................. .. 12-9
 


12.3 Qualitative and Quantitative Benefits Assessment ................. ................. ................ 12-11
 


12.3.1 Overview of Benefit Categories ................. ................. ................. ........ 12-11
 


12.3.2 Human Health Benefits ................. ................. ................. ............... 12-13
 


12.3.3 Ecological Benefits ................. ................. ................. ................. 12-13
 


12.3.4 Economic Productivity Benefits ................. ................. ................. ........ 12-14
 


12.3.5 Methods for Valuing Benefit Events ................. ................. ................. ..... 12-14
 


Glossary ................. ................. ................. ................. ................. .. 12-16
 

Acronyms ................. ................. ................. ................. ................. . 12-19
 

References ................. ................. ................. ................. ................. 12-20
 

Chapter 13: Human Health Benefits 

13.1 Methodology & Data Sources ................. ................. ................. ................ 13-2
 


13.1.1 Cancer from Fish Consumption ................. ................. ................. .......... 13-3
 


13.1.2 Cancer from Drinking Water Consumption ................. ................. ................. 13-8
 


13.1.3 Exposures above Non-cancer Health Thresholds ................. ................. ............ 13-10
 


13.1.4 Human Health AWQC ................. ................. ................. ............... 13-14
 


13.2 Results ................. ................. ................. ................. ................ 13-17
 


13.2.1 Fish Consumption Cancer Results ................. ................. ................. ....... 13-17
 


13.2.2 Drinking Water Consumption Cancer Results ................. ................. ............... 13-19
 


13.2.3 Non-cancer Health Threshold Results ................. ................. ................. .... 13-19
 


13.2.4 Human Health AW QC Results ................. ................. ................. ......... 13-21
 


13.3 Limitations and Uncertainties ................. ................. ................. ............... 13-22
 


13.3.1 Sample Design & Analysis of Benefits by Location of Occurrence ................. ............... 13-22
 


13.3.2 In-Waterway Concentrations of MP&M Pollutants ................. ................. .......... 13-23
 


13.3.3 Joint Effects of Pollutants ................. ................. ................. ............. 13-23
 


13.3.4 Background Concentrations of MP&M Pollutants ................. ................. ........... 13-23
 


13.3.5 Downstream Effects ................. ................. ................. ................. 13-24
 


13.3.6 Exposed Fishing Population ................. ................. ................. ........... 13-24
 


13.3.7 Treatment of Cancer Latency ................. ................. ................. .......... 13-25
 


13.3.8 Treatment of Cessation Lag ................. ................. ................. ........... 13-25
 


13.3.9 Use of Mean Individual Exposure Parameters ................. ................. .............. 13-26
 


13.3.10 Cancer Potency Factors ................. ................. ................. .............. 13-26
 


Glossary ................. ................. ................. ................. ................. .. 13-27
 

Acronyms ................. ................. ................. ................. ................. . 13-28
 

References ................. ................. ................. ................. ................. 13-29
 

Chapter 14: Lead-Related Benefits 

14.1 Overview of Lead-Related Health Effects ................. ................. ................. ....... 14-2
 


14.1.1 Children Under Age One ................. ................. ................. ............... 14-3
 


14.1.2 Children Between the Ages of One and Seven ................. ................. ............... 14-3
 


14.1.3 Adults ................. ................. ................. ................. ............ 14-4
 


14.2 Health Benefits to Children ................. ................. ................. ................. . 14-4
 


14.2.1 PbB Distribution of Exposed Children ................. ................. ................. .... 14-5
 


14.2.2 Relationship Between PbB Levels and IQ ................. ................. ................. 14-12
 


14.2.3 Value of Children's Intelligence ................. ................. ................. ........ 14-12
 


14.2.4 Value of Additional Educational Resources ................. ................. ................ 14-14
 


14.2.5 Changes in Neonatal Mortality ................. ................. ................. ......... 14-17
 


Table of Contents-4 
MP&M EEBA Table of Contents 

14.3 Adult Health Benefits ................. ................. ................. ................. .... 14-17
 


14.3.1 Estimating Changes in Adult PbB Distribution Levels ................. ................. ........ 14-20
 


14.3.2 Male Health Benefits ................. ................. ................. ................ 14-22
 


14.3.3 Female Health Benefits ................. ................. ................. ............... 14-26
 


14.4 Lead-Related Benefit Results ................. ................. ................. ............... 14-28
 


14.4.1 Preschool Age Children Lead-Related Benefit Results ................. ................. ........ 14-28
 


14.4.2 Adult Lead-Related Benefit Results ................. ................. ................. ..... 14-29
 


14.5 Limitations and Uncertainties ................. ................. ................. ............... 14-31
 


14.5.1 Excluding Older Children ................. ................. ................. ............. 14-31
 


14.5.2 Compensatory Education Costs ................. ................. ................. ........ 14-32
 


14.5.3 Dose-Response Relationships ................. ................. ................. .......... 14-32
 


14.5.4 Absorption Function for Ingested Lead in Fish Tissue ................. ................. ........ 14-32
 


14.5.5 Economic Valuation ................. ................. ................. ................. 14-33
 


Glossary ................. ................. ................. ................. ................. .. 14-35
 

Acronyms ................. ................. ................. ................. ................. . 14-38
 

References ................. ................. ................. ................. ................. 14-39
 

Chapter 15: Recreational Benefits 

15.1 Ecological Improvements from the MP&M Regulation ................. ................. ............. 15-3
 


15.1.1 Overview of Ecological Improvements ................. ................. ................. .... 15-3
 


15.1.2 Quantification of Ecological Improvements ................. ................. ................. 15-3
 


15.1.3 Benefiting Reaches ................. ................. ................. ................. .. 15-4
 


15.1.4 Geographic Characteristics of MP&M Reaches ................. ................. .............. 15-6
 


15.2 Valuing Economic Recreational Benefits ................. ................. ................. ....... 15-6
 


15.2.1 Transferring Values from Surface Water Valuation Studies ................. ................. ..... 15-6
 


15.2.2 Recreational Fishing ................. ................. ................. ................. . 15-9
 


15.2.3 Wildlife Viewing ................. ................. ................. ................. .. 15-13
 


15.2.4 Recreational Boating ................. ................. ................. ................. 15-17
 


15.2.5 Nonuse Benefits ................. ................. ................. ................. ... 15-20
 


15.3 Summary of Recreational Benefits ................. ................. ................. ........... 15-20
 


15.4 Limitations and Uncertainties Associated with Estimating Recreational Benefits .............. . . . ......... 15-22
 


Glossary ................. ................. ................. ................. ................. .. 15-26
 


Acronyms ................. ................. ................. ................. ................. . 15-28
 


References ................. ................. ................. ................. ................. 15-29
 


Chapter 16: POTW Benefits 

16.1 Reduced Interference with POTW Operations ................. ................. ................. ... 16-2
 


16.2 Assessing Benefits from Reduced Sludge Contamination ................. ................. ............ 16-2
 


16.2.1 Data Sources ................. ................. ................. ................. ....... 16-2
 


16.2.2 Sludge Generation, Treatment, and Disposal Practices ................. ................. ........ 16-4
 


16.2.3 Overview of Improved Sludge Quality Benefits ................. ................. .............. 16-7
 


16.2.4 Sludge Use/Disposal Costs and Practices ................. ................. ................. .. 16-8
 


16.2.5 Quantifying Sludge Benefits ................. ................. ................. ........... 16-10
 


16.3 Estimated Savings in Sludge Use/Disposal Costs ................. ................. ................. 16-15
 


16.4 Methodology Limitations ................. ................. ................. ................. . 16-16
 


Glossary ................. ................. ................. ................. ................. .. 16-18
 


Acronyms ................. ................. ................. ................. ................. . 16-19
 


References ................. ................. ................. ................. ................. 16-20
 


Chapter 17: Environmental Justice & Protection of Children 

17.1 Demographic Characteristics of Populations Living in the Counties Near MP&M Facilities .............. . . . . 17-1
 


17.2 Protection of Children from Environmental Health and Safety Risks ................. ................. ... 17-3
 


Glossary ................. ................. ................. ................. ................. ... 17-4
 


Reference ................. ................. ................. ................. ................. .. 17-5
 


Table of Contents-5 
MP&M EEBA Table of Contents 

PART IV: COMPARISON OF COSTS AND BENEFITS 

Chapter 18: MP&M Benefit/Cost Comparison 

18.1 Estimating National Level Benefits and Costs ................. ................. ................. ... 18-1
 


18.2 Social Costs ................. ................. ................. ................. ............. 18-2
 


18.3 Benefits ................. ................. ................. ................. ................ 18-2
 


18.4 Comparing Monetized Benefits and Costs ................. ................. ................. ...... 18-2
 


Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

19.1 Estimated Social Costs ................. ................. ................. ................. ..... 19-1
 


19.1.1 Compliance Costs for MP&M Facilities ................. ................. ................. ... 19-1
 


19.1.2 Government Administrative Costs ................. ................. ................. ........ 19-2
 


19.1.3 Cost of Unemployment ................. ................. ................. ................ 19-2
 


19.1.4 Total Social Costs ................. ................. ................. ................. ... 19-3
 


19.2 Estimated Benefits ................. ................. ................. ................. ........ 19-4
 


19.2.1 Human Health Benefits ................. ................. ................. ................ 19-4
 


19.2.2 Recreational Benefits ................. ................. ................. ................. . 19-5
 


19.2.3 Avoided Sewage Sludge Disposal or Use Costs ................. ................. ............... 19-6
 


19.2.4 Total Monetized Benefits ................. ................. ................. ............... 19-7
 


19.3 Comparison of Estimated Benefits and Costs ................. ................. ................. ..... 19-7
 


Glossary ................. ................. ................. ................. ................. .. 19-10
 


Acronym ................. ................. ................. ................. ................. .. 19-11
 


PART V: OHIO CASE STUDY 

Chapter 20: Baseline Conditions in Ohio 

20.1 Overview of Ohio’s Geography, Population, and Economy ................. ................. .......... 20-2
 


20.2 Profile of MP&M Facilities in Ohio ................. ................. ................. ........... 20-3
 


20.3 Ohio’s Water Resources ................. ................. ................. ................. ... 20-6
 


20.3.1 Aquatic Life Use ................. ................. ................. ................. .... 20-8
 


20.3.2 Water Recreation in Ohio ................. ................. ................. ............. 20-11
 


20.3.3 Commercial Fishing in Ohio ................. ................. ................. ........... 20-12
 


20.3.4 Surface Water Withdrawals ................. ................. ................. ........... 20-12
 


20.4 Surface Water Quality in Ohio ................. ................. ................. .............. 20-12
 


20.4.1 Use Attainment in Streams and Rivers in Ohio ................. ................. .............. 20-13
 


20.4.2 Lake Erie and Other Lakes Use Attainment ................. ................. ................ 20-13
 


20.4.3 Causes and Sources of Use Non-Attainment in Ohio ................. ................. ......... 20-14
 


20.5 Effects of Water Quality Impairments on Water Resource Services ................. ................. ... 20-15
 


20.5.1 Effect of Water Quality Impairment on Life Support for Animals and Plants .............. . . . ....... 20-15
 


20.5.2 Effect of Water Quality Impairment on Recreational Services ................. ................. .. 20-17
 


20.6 Presence and Distribution of Endangered and Threatened Species in Ohio ................. .............. 20-18
 


20.6.1 E&T Fish ................. ................. ................. ................. ......... 20-19
 


20.6.2 E&T Mollusks ................. ................. ................. ................. ..... 20-19
 


20.6.3 Other Aquatic E&T Species ................. ................. ................. ........... 20-20
 


Glossary ................. ................. ................. ................. ................. .. 20-24
 

Acronyms ................. ................. ................. ................. ................. . 20-27
 

References ................. ................. ................. ................. ................. 20-28
 

Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model RUM Analysis 

21.1 Methodology ................. ................. ................. ................. ........... 21-2
 


21.1.1 Overview ................. ................. ................. ................. .......... 21-2
 


21.1.2 Modeling the Site Choice Decision ................. ................. ................. ....... 21-3
 


21.1.3 Modeling Trip Participation ................. ................. ................. ............ 21-6
 


21.1.4 Calculating W elfare Changes from Water Quality Improvements ................. ................. 21-9
 


21.1.5 Extrapolating Results to the State Level ................. ................. ................. .. 21-10
 


Table of Contents-6 
MP&M EEBA Table of Contents 

21.2 Data ................. ................. ................. ................. ................. . 21-10
 


21.2.1 The Ohio Data ................. ................. ................. ................. .... 21-11
 


21.2.2 Estimating the Price of Visits to Sites ................. ................. ................. .... 21-14
 


21.2.3 Site Characteristics ................. ................. ................. ................. 21-14
 


21.3 Site Choice Model Estimates ................. ................. ................. ............... 21-17
 


21.3.1 Fishing Model ................. ................. ................. ................. .... 21-18
 


21.3.2 Boating Model ................. ................. ................. ................. .... 21-19
 


21.3.3 Swimming Model ................. ................. ................. ................. .. 21-20
 


21.3.4 Viewing (Near-water Activity) Model ................. ................. ................. ... 21-20
 


21.4 Trip Participation Model ................. ................. ................. ................. .. 21-20
 


21.5 Estimating Benefits from Reduced MP&M Discharges in Ohio ................. ................. ..... 21-23
 


21.5.1 Benefiting Reaches in Ohio ................. ................. ................. ........... 21-23
 


21.5.2 Estimating Recreational Benefits in Ohio ................. ................. ................. 21-24
 


21.6 Limitations and Uncertainty ................. ................. ................. ................ 21-25
 


21.6.1 One-State Approach ................. ................. ................. ................. 21-25
 


21.6.2 Including One-Day Trips Only ................. ................. ................. ......... 21-26
 


21.6.3 Nonuse Benefits ................. ................. ................. ................. ... 21-26
 


21.6.4 Potential Sources of Survey Bias ................. ................. ................. ....... 21-26
 


Glossary ................. ................. ................. ................. ................. .. 21-28
 

Acronyms ................. ................. ................. ................. ................. . 21-30
 

References ................. ................. ................. ................. ................. 21-31
 

Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

22.1 Benefits of the Final Regulation ................. ................. ................. ............... 22-1
 


22.1.1 Human Health Benefits (Other than Lead) ................. ................. ................. .. 22-2
 


22.1.2 Lead-Related Benefits ................. ................. ................. ................. 22-3
 


22.1.3 Economic Productivity Benefits ................. ................. ................. ......... 22-4
 


22.1.4 Total Monetized Benefits ................. ................. ................. ............... 22-4
 


22.2 Social Costs of the Final Regulation ................. ................. ................. ........... 22-5
 


22.2.1 Baseline and Post-Compliance Closures ................. ................. ................. ... 22-5
 


22.2.2 Compliance Costs for MP&M Facilities ................. ................. ................. ... 22-6
 


22.2.3 Total Social Costs ................. ................. ................. ................. ... 22-7
 


22.3 Comparison of Monetized Benefits and Costs in Ohio ................. ................. .............. 22-7
 


Glossary ................. ................. ................. ................. ................. ... 22-8
 


Acronyms ................. ................. ................. ................. ................. .. 22-9
 


APPENDICES 

Appendix A: Detailed Economic Impact Analysis Information 

A.1 MP&M SIC and NAICS Codes ................. ................. ................. ................A-1
 


A.1.1 SIC Codes by Sector ................. ................. ................. ................. ... A-1
 


A.1.2 Bridge Between NAICS and SIC codes ................. ................. ................. ..... A-7
 


A.2 Annual Establishment “Births” and “Deaths” in MP&M Industries ................. ................. .... A-26
 


A.3 Description of MP&M Surveys ................. ................. ................. ............... A-28
 


A.3.1 Screener Surveys ................. ................. ................. ................. .... A-28
 


A.3.2 Ohio Screener Surveys ................. ................. ................. ................. A-28
 


A.3.3 Detailed MP&M Industry Surveys ................. ................. ................. ........ A-28
 


A.3.4 Iron and Steel Survey ................. ................. ................. ................. . A-29
 


A.3.5 Municipality Survey ................. ................. ................. ................. . . A-29
 


A.3.6 Federal Facility Survey ................. ................. ................. ................ A-29
 


A.3.7 POTW Survey ................. ................. ................. ................. ...... A-29
 


References ................. ................. ................. ................. ................. . A-31
 

Appendix B: Cost Pass-Through Analysis 

B.1 The Choice of Sector-Specific CPT Coefficients ................. ................. ................. ... B-1
 


B.2 Econometric Analysis ................. ................. ................. ................. ....... B-2
 


Table of Contents-7 
MP&M EEBA Table of Contents 

B.2.1 Framework ................. ................. ................. ................. .......... B-3
 


B.2.2 Data Used to Estimate the Regression Equation ................. ................. ................ B-4
 


B.2.3 Regression Results ................. ................. ................. ................. ....B-6
 


B.3 Market Structure Analysis ................. ................. ................. ................. ...B-9
 


B.3.1 Measures Descriptions ................. ................. ................. ................. . B-9
 


B.3.2 Results ................. ................. ................. ................. ............ B-13
 


B.4 Validation of Econometrically-Estimated CPT Coefficients ................. ................. .......... B-16
 


B.4.1 Other Metal Products ................. ................. ................. ................. . B-17
 


B.4.2 Job Shops ................. ................. ................. ................. .......... B-17
 


B..3 Motor Vehicle ................. ................. ................. ................. ....... B-18
 


B.4.4 Aircraft ................. ................. ................. ................. ........... B-18
 


B.4.5 Mobile Industrial Equipment ................. ................. ................. ........... B-18
 


B.4.6 Aerospace ................. ................. ................. ................. .......... B-18
 


B.5 Adjusting Estimates of Compliance CPT Potential ................. ................. ................. B-18
 


Attachment B.A: Selected Review of CPT Literature ................. ................. ................. . . B-20
 

B.A.1 Ashenfelter et al. (1998), “Identifying the Firm-Specific Cost Pass-Through Rate.” ................ . ... B-20
 

B.A.2 Exchange Rate Pass-Through ................. ................. ................. ........... B-20
 

B.A.3 Tax Pass-Through ................. ................. ................. ................. ... B-20
 

B.A.4 Studies Cited ................. ................. ................. ................. ....... B-20
 

Acronyms ................. ................. ................. ................. ................. . . B-22
 

Appendix C: Summary of Moderate Impact Thresholds by Sector 

C.1 Developing Threshold Values for Pre-Tax Return on Assets (PTRA) ................. ................. .... C-1
 


C.2 Developing Threshold Values for Interest Coverage Ratio (ICR) ................. ................. ........C-2
 


C.3 Summary of Results ................. ................. ................. ................. .........C-4
 


References ................. ................. ................. ................. ................. . . C-5
 


Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

D.1 Analytic Concepts Underlying Analysis of Capital Outlays ................. ................. ........... D-2
 


D.2 Specifying Variables for the Analysis ................. ................. ................. .......... D-4
 


D.3 Selecting the Regression Analysis Dataset ................. ................. ................. ....... D-7
 


D.4 Specification of Models to be Tested ................. ................. ................. ........... D-8
 


D.4.1 Linear Model Specification ................. ................. ................. ............. D-9
 


D.4.2 Log-Linear Model Specification ................. ................. ................. ........ D-10
 


D.4.3 Sensitivity Analysis ................. ................. ................. ................. . D-12
 


D.5 Model Validation ................. ................. ................. ................. ........ D-12
 


Attachment D.A: Bibliography of Literature Reviewed for this Analysis ................. ................. ... D-17
 


Attachment D.B: Historical Variables Contained in the Value Line Investment Survey Dataset .............. . . . .. D-18
 


Appendix E: Calculation of Capital Cost Components 

E.1 Calculation of One-Time Capital Cost Components ................. ................. ................. . E-1
 


Appendix F: Administrative Costs 

F.1 	Effluent Guidelines Permitting Requirements ................. ................. ................. ...... F-1
 


F.1.1 NPDES Basic Industrial Permit Program ................. ................. ................. .... F-1
 


F.1.2 Pretreatment Program ................. ................. ................. ................. .. F-2
 


F.2 	POTW Administrative Cost Methodology ................. ................. ................. ........ F-2
 


F.2.1 Data Sources ................. ................. ................. ................. ......... F-2
 


F.2.2 Overview of Methodology ................. ................. ................. ............... F-3
 


F.3 	Unit Costs of Permitting Activities ................. ................. ................. .............. F-4
 


F.3.1 Permit Application and Issuance ................. ................. ................. ........... F-4
 


F.3.2 Inspection ................. ................. ................. ................. ........... F-7
 


F.3.3 Monitoring ................. ................. ................. ................. .......... F-7
 


F.3.4 Enforcement ................. ................. ................. ................. ......... F-9
 


F.3.5 Repermitting ................. ................. ................. ................. ........ F-10
 


F.4 POTW Administrative Costs by Option ................. ................. ................. ......... F-10
 


Appendix F Exhibits ................. ................. ................. ................. ........... F-12
 


Table of Contents-8 
MP&M EEBA Table of Contents 

References ................. ................. ................. ................. ................. . F-25
 

Appendix G: Extrapolation Methods 

G.1 Using Raking to Adjust MP&M Facility Sample Weights ................. ................. .............G-2
 


G.1.1 Data Sources ................. ................. ................. ................. ........ G-2
 


G.1.2 Raking Adjustment ................. ................. ................. ................. .... G-3
 


G.2 Methodology for Developing Sample-Weighted Estimates for Sites with More Than One MP&M Facility ........ G-7
 


G.3 Methodology for Extrapolation of Ohio Case Study Results to the National Level ............... . . .......... G-13
 


G.3.1 Change in Pollutant Loads ................. ................. ................. .............. G-14
 


G.3.2 Level of Recreational Activities on Reaches Affected by MP&M Discharges .............. . . . ........ G-14
 


G.3.3 Differences in Household Income ................. ................. ................. ........ G-14
 


G.4 Results ................. ................. ................. ................. ................. G-15
 


Glossary ................. ................. ................. ................. ................. ... G-17
 


Appendix H: Fate and Transport Model for DW and Ohio Analyses 

H.1 Model Equations ................. ................. ................. ................. .......... H-1
 


H.2 Model Assumptions ................. ................. ................. ................. ........ H-3
 


H.2.1 Steady Flow Conditions Exist Within the Stream or River Reach ................. ................. . H-3
 


H.2.2 Longitudinal Dispersion of the Pollutant Is Negligible ................. ................. .......... H-3
 


H.2.3 Flow Geometry, Suspension of Solids, and Reaction Rates Are Constant Within a River Reach ............H-4
 


H.3 Hydrologic Linkages ................. ................. ................. ................. ....... H-4
 


H.4 Associating Risk with Exposed Populations ................. ................. ................. ...... H-4
 


H.5 Data Sources ................. ................. ................. ................. .............H-4
 


H.5.1 Pollutant Loading Data Used in the Drinking Water Risk Analysis ................. ................. . H-4
 


H.5.2 Pollutant Loading Data Used in the Ohio Case Study Analysis ................. ................. .... H-4
 


Glossary ................. ................. ................. ................. ................. .... H-8
 


Acronyms ................. ................. ................. ................. ................. ... H-9
 


References ................. ................. ................. ................. ................. . H-10
 


Appendix I: Environmental Assessment 

I.1 MP&M Pollutant Characterization ................. ................. ................. .............. I-4
 


I.1.1 Identifying MP&M Pollutants ................. ................. ................. ............. I-4
 


I.1.2 Physical-Chemical Characteristics and Toxicity Data of MP&M Pollutants .............. . . . ............ I-9
 


I.1.3 Grouping MP&M Pollutants Based on Risk to Aquatic Receptors ................. ................. . I-21
 


I.1.4 Assumptions and Limitations ................. ................. ................. ............ I-23
 


I.2. 	Methodology ................. ................. ................. ................. ............. I-23
 


I.2.1 Sample Set Data Analysis and National Extrapolation ................. ................. .......... I-23
 


I.2.2 Water Quality Modeling ................. ................. ................. ................ I-23
 


I.2.3 Impact of Indirect Discharging Facilities on POTW Operations ................. ................. ... I-25
 


I.2.4 Assumptions and Limitations ................. ................. ................. ............ I-27
 


I.3 Data Sources ................. ................. ................. ................. ............. I-28
 


I.3.1 Facility-Specific Data ................. ................. ................. ................. . I-28
 


I.3.2 Water Body-Specific Data ................. ................. ................. ............... I-28
 


I.3.3 Information Used to Evaluate POTW Operations ................. ................. .............. I-29
 


I.4 Results ................. ................. ................. ................. ................. . I-33
 


I.4.1 Human Health Impacts ................. ................. ................. ................. I-34
 


I.4.2 Aquatic Life Effects ................. ................. ................. ................. ... I-37
 


I.4.3 POTW Effects ................. ................. ................. ................. ....... I-41
 


Glossary ................. ................. ................. ................. ................. ... I-44
 

Acronyms ................. ................. ................. ................. ................. .. I-48
 

References ................. ................. ................. ................. ................. . I-49
 

Appendix J: Spacial Distribution of MP&M Facilities and Recreational User 

Populations 

Table J.1 Distribution of MP&M Facilities and Participants of Water Based Recreation by State ................ . .. J-2
 

Figure J.1 Cumulative Distribution of Facilities and Participants ................. ................. ................. J-4
 

Table of Contents-9 
MP&M EEBA Table of Contents 

Appendix K: Selecting WPT Values for Benefits Transfer 

K.1 Desvousges et al., 1987. Option Price Estimates for Water Quality Improvements: A Contingent Valuation Study 

for the Monongahela River ................. ................. ................. ................. K-2
 

K.2 Farber and Griner, 2000. Valuing Watershed Quality Improvements Using Conjoint Analysis ................. K-3
 

K.3 Jakus et al., 1997. Do Sportfish Consumption Advisories Affect Reservoir Anglers’ Site Choice? .............. K-5
 

K.4 Lant and Roberts, 1990. Greenbelts in the Cornbelt: Riparian Wetlands, Intrinsic Values, and Market Failure .... K-6
 

K.5 Audrey Lyke, 1993. Discrete Choice Models to Value Changes in Environmental Quality: A Great Lakes Case 
 

Study ................. ................. ................. ................. ................. . K-7
 

K.6 Montgomery and Needelman, 1997. The Welfare Effects of Toxic Contamination in Freshwater Fish ........... K-8
 

K.7 Phaneuf et al., 1998. Valuing Water Quality Improvements Using Revealed Preference Methods When Corner 
 

Solutions are Present ................. ................. ................. ................. ...... K-8
 

Glossary ................. ................. ................. ................. ................. .. K-10
 

Acronyms ................. ................. ................. ................. ................. . K-11
 

References ................. ................. ................. ................. ................. K-12
 

Appendix L: Parameters Used in the IEUBK Model 

Table B-1: Description of Parameters Used in the IEUBK Lead Model ................. ................. ..... L-1
 

Appendix M: Sensitivity Analysis of Lead-Related Benefits 

M.1 Values for Quantified Lead-Related Health Effects ................. ................. ................. M-1 

M.2 Lead-Related Benefit Results ................. ................. ................. ................. M-2 

M.2.1 Preschool Age Children Lead-Related Benefits ................. ................. .............. M-2 

M.2.2 Adult Lead-Related Benefits ................. ................. ................. ............ M-3 

Appendix N: Analysis of the National Demand for Water-Based Recreation Survey 

N.1 Background Information ................. ................. ................. ................. .... N-1
 

N.2 Data Analysis ................. ................. ................. ................. ............ N-2
 

N.3 Participation in Water-Based Recreation by Activity Type ................. ................. ........... N-2
 

N.4 Allocation of Trips by Water Body Type ................. ................. ................. ....... N-11
 

N.5 One-Way Travel Distance ................. ................. ................. ................. . N-16
 

N.6 Individual Expenditures per Trip ................. ................. ................. ............. N-19
 

N.7 Distribution of Direct Costs for Single-day Trips ................. ................. ................. . N-22
 

N.8 Profile of Boating Trips ................. ................. ................. ................. ... N-27
 

N.9 Profile of Fishing Trips ................. ................. ................. ................. ... N-30
 

Table of Contents-10 
MP&M EEBA Executive Summary 

Executive Summary 

INTRODUCTION 

EPA is promulgating effluent limitations guidelines and
 


standards for the Metal Products and Machinery (MP&M)
 


industry. This document presents EPA’s economic and
 


environmental analyses supporting the final rule. The
 


Executive Summary provides an overview of the costs and
 


benefits of the regulation.
 


Overall, EPA finds that the final rule has modest economic
 


impacts and benefits. The estimated social cost of the final
 


rule is $13.8 million annually (2001$). The total benefits that
 


can be valued in dollar terms in the categories traditionally
 


analyzed for effluent guidelines range from around $1.0 to
 


$1.5 million annually (2001$), based on alternative
 


extrapolation methods.
 


EPA recognizes that estimates of both costs and benefits are
 


uncertain. To supplement the national level analysis
 


performed for the final MP&M regulation, EPA conducted a
 


more detailed case study of the expected State-level costs
 


and benefits of the MP &M rule in Ohio. In contrast to the
 


national-level analysis, the more detailed case study analysis
 


finds that the final regulation would achieve benefits
 


substantially exceeding estimated social costs. Comparing the midpoint estimate of social costs ($62,232) with the midpoint
 


EXECUTIVE SUMMARY CONTENTS 

ES.1 Overview of Facilities Evaluated for 

Regulation under the MP&M Point Source 

Category and Its Effluent Discharges ...... ES-1 

ES.2 Description of the Final Rule ............... ES-3 

ES.3 Economic Impacts and Social Costs of the 

Final Rule ................. .......... ES-3 

ES.3.1 Economic Impacts ................. ES-4 

ES.3.2 Social Costs ................. ..... ES-7 

ES.4 National Benefits of the Final Rule .......... ES-8 

ES.4.1 Reduced Human Health Risk ........ ES-10 

ES.4.2 Ecological, Recreational, and 

Nonuser Benefits ................. ... ES-14 

ES.4.3 Reduced POTW Impacts ........... ES-15 

ES.4.4 Total Estimated Benefits of the 

Final MP&M Rule ................. .. ES-16 

ES.5 National Benefit-Costs Comparison ......... ES-16 

ES.6 Ohio Case Study ................. ....... ES-18 

ES.6.1 Benefits ................. ........ ES-18 

ES.6.2 Social Costs ................. .... ES-20 

ES.6.3 Comparing Monetized Benefits 

and Costs ................. ......... ES-20 

estimate of monetizable benefits ($930,408) for Ohio, EPA estimates a net benefit of the final MP&M rule for Ohio is 

$868,178 (2001$). 

EPA notes that effluent limitations guidelines for the MP&M industry are technology-based. EPA is neither required to 

demonstrate environmental benefits of its technology-based rules, nor is it required to consider receiving water quality in 

setting technology-based effluent limitations guidelines and standards. EPA considers benefits as one of the factors that the 

Agency evaluates. 

Detailed descriptions of the analytic methodologies and results are presented in the Economic, Environmental, and Benefits 

Assessment for the Final Metal Products and Machinery Rule (EEBA). In addition, the EEBA presents costs, benefits, and 

economic impacts for alternatives to the final rule that were considered by EPA. 

ES.1 OVERVIEW OF FACILITIES EVALUATED FOR REGULATION UNDER THE MP&M 

POINT SOURCE CATEGORY AND ITS EFFLUENT DISCHARGES 

The MP&M Point Source Category regulates oily operations process wastewater discharges to surface waters from existing or 

new industrial facilities (including facilities owned and operated by federal, state, or local governments) engaged in 

manufacturing, rebuilding, or maintenance of metal parts, products, or machines for use in the sixteen Metal Product & 

Machinery (MP&M) industrial sectors. Please note the underlined language in the previous sentence as a facility may be 

subject to the MP&M effluent guidelines even if it is not in one of the MP&M industrial sectors. For example, EPA considers 

a facility performing machining part of the “Bus & Truck” MP&M industrial sector if it manufactures metal parts for truck 

trailers. Process wastewater means wastewater as defined at 40 CFR parts 122 and 401, and includes wastewater from air 

pollution control devices (see 40 CFR 438.2(g)). Oily operations are listed at 40 CFR 438.2(g) and defined in Appendix B to 

Part 438 (see also Section 4 of the TDD ). 

ES-1 
MP&M EEBA Executive Summary 

According to Statistics of U.S. Business, 1996, approximately 638,696 establishments operate in the MP&M industry sectors. 

Based on information in the MP&M survey database, approximately 44,000 facilities meet the definition of an MP&M 

facility. These 44,000 facilities include approximately 41,000 indirect dischargers (i.e., facilities discharging effluent to a 

publicly-owned sewage treatment works or POTWs) and 3,000 direct dischargers (i.e., facilities discharging effluent directly 

to a waterway under a NPDES permit). 

Table ES.1 reports the estimated number of MP&M facilities and total discharge flow (before final rule implementation) by 

type of facility. The largest number of sites, approximately 22,000, perform “rebuilding/maintenance only” and account for 

approximately 6 percent of the total estimated discharge flow for the industry. “Manufacturing only” contains the next largest 

number of facilities (15,400) and accounts, by far, for the largest percentage of the total estimated discharge flow for the 

industry (82 percent). 

Table ES.1: Number of MP&M Facilities and 

Total Discharge Flow by Type of Facility 

Type of Facility Number of 

Facilities 

Total Estimated 

Discharge Flow 

(million gal/yr) 

Percent of 

Facilities 

Percent of Total 

Discharge Flow 

Manufacturing & 

Rebuilding/Maintenance 

6,600 9,400 15.0% 12.0% 

Manufacturing only 15,400 64,100 35.0% 82.0% 

Rebuilding/Maintenance 

only 

22,000 4,700 50.0% 6.0% 

Total 44,000 78,200 100.0% 100.0% 

Source: U.S. EPA analysis. See Section 4 of the Technical Development Document for the final rule. 

Of the 43,858 water discharging facilities, 3,593 are predicted to close in the baseline, leaving 40,265 existing MP&M 

facilities that EPA estimates could be regulated.1 After accounting for subcategory and discharger class exclusions, EPA 

estimates that the final rule will regulate 2,382 of these facilities, all of which are direct dischargers. These regulated 

facilities represent 5.9 percent of the 40,265 facilities that could be potentially regulated. 

Table ES.2 summarizes information on the total number of MP&M facilities that were evaluated for the final rule, the number 

operating in the baseline, and the number and percent of facilities that will be regulated under the final rule. As reported in 

Table ES.2, no indirect dischargers are subject to the final regulation. The rule will regulate 2,382 direct dischargers in the 

Oily Wastes subcategory. 

Table ES.2: Number of MP&M Facilities Evaluated for the Final Rule 

and Regulated under the Final Rule 

Discharge Status MP&M 

Facilities 

Operating in 

the Baseline 

Regulated 

under the Final 

Rule 

Percent of Facilities 

Operating in the Baseline 

that are Regulated 

Direct dischargers 2,739 2,641 2,382 90% 

Indirect dischargers 41,162 37,652 0 0% 

All dischargers 43,858 40,265 2,382 6% 

Source: U.S. EPA analysis. 

1 These are facilities that are predicted to close due to weak financial performance under baseline conditions, i.e., in the absence of 

the final rule. EPA does not attribute the costs or the reduced discharges resulting from these baseline closures to the final rule, and 

therefore excludes these facilities from its analyses of the rule’s impacts. Baseline closures account for differences between the universe of 

facilities discussed in this report and the universe discussed in the Technical Development Document. 

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MP&M EEBA Executive Summary 

Several aspects of the MP&M industries as a whole and part of those industries evaluated for regulation under the final rule 

are important in understanding the need for the regulation, the likely distribution and occurrence of benefits, and the 

framework of the economic analysis for the regulation. 

Facilities in the relevant MP&M industries are located in every state, with a particular concentration in the heavy industrial 

regions along the Gulf Coast, both East and West Coasts and the Great Lakes Region. Moreover, MP&M facilities are 

frequently located in highly populated regions. Based on an analysis of in-scope sample facilities, around 35% of these 

facilities discharge to reaches located adjacent to counties with populations of at least 500 thousand people.2 

Discharges of these pollutants to surface waters and POTW s have a number of adverse effects, including degradation of 

aquatic habitats, reduced survivability and diversity of native aquatic life, and increased human health risk through the 

consumption of contaminated fish and water. 

Many MP&M facilities evaluated for the final regulation produce goods and services that serve multiple market sectors. It is 

not possible to associate regulatory costs and benefits to particular sectors, because EPA is not able to link regulated 

processes to specific sectors for facilities operating in multiple sectors. As a result, EPA’s cost and economic impact analyses 

are disaggregated by type of facility but not by sector. 

ES.2 DESCRIPTION OF THE FINAL RULE 

In order to address variations between products, raw materials processed, and other factors that result in distinctly different 

effluent characteristics, EPA proposed eight groupings called “subcategories” for the January 2001 proposal and June 2002 

Notice of Data Availability (NODA). EPA retained this subcategory structure for evaluating options for the final rule. 

Regulation of a category using subcategories allows each subcategory to have a uniform set of effluent limitations that take 

into account technological achievability and economic impacts unique to that subcategory (see Section 6 of the TDD). For 

the final rule, EPA is establishing limitations and standards only for direct dischargers in the Oily Wastes subcategory. The 

other seven subcategories (General Metals, Metal Finishing Job Shops, Non Chromium Anodizing, Printed Wiring Board, 

Railroad Line Maintenance, Shipbuilding Dry Docks, and Steel Forming & Finishing) were considered for regulation at 

proposal and for some of the alternative regulatory options, but are not further regulated under the final rule. 

EPA is establishing BPT pH limitations and daily maximum limitations for two pollutants, oil and grease as hexane 

extractable material (O&G (as HEM)) and total suspended solids (TSS), for direct dischargers in the Oily Wastes subcategory 

based on the proposed technology option (Option 6). The technology requirements include the following treatment measures: 

(1) in-process flow control and pollution prevention; and (2) oil-water separation by chemical emulsion breaking and 

skimming (see Section 9 of the TDD). This technology is available technology readily applicable to all facilities in the Oily 

Wastes subcategory. Approximately 42% of the direct discharging facilities in the Oily Wastes subcategory currently employ 

this technology already. 

EPA is promulgating BCT equivalent to BPT for facilities in the Oily Wastes subcategory and has decided not to establish 

BAT limitations. EPA is promulgating NSPS for new direct dischargers in the Oily Wastes subcategory at the BPT and BCT 

levels. 

ES.3 ECONOMIC IMPACTS AND SOCIAL COSTS OF THE FINAL RULE 

EPA assessed the economic impacts and social costs of the final rule using detailed financial and technical data from the 

MP &M surveys (see Section 3 of the TDD ). Engineering analyses of these facilities identified the pollution prevention and 

treatment systems needed to comply with the final rule and other regulatory alternatives. The estimated capital and annual 

operating and maintenance costs of these systems, incremental to the costs of systems already in place, represent the 

2 EPA is not able to characterize the location characteristics of all potentially-regulated MP&M facilities at the national level 

precisely, because the MP&M survey design was not intended to provide national results by location characteristics. 

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MP&M EEBA Executive Summary 

compliance costs of the rule.3 EPA analyzed the financial performance of the facilities evaluated for regulation under preregulation conditions (the baseline) and as subject to regulatory requirements. The Agency used a variety of measures to 

assess the economic impacts resulting from the final rule, both for the regulated MP&M facilities and for the firms and 

governments that own the facilities. The economic impact analysis also considered impacts for small entities in particular, 

and impacts on employment, foreign trade and communities. The results of the analyses for sample facilities were 

extrapolated using survey sample weights for each facility, to provide national-level results. 

ES.3.1 Economic Impacts 

Overall, EPA found the econom ic impact of the final rule to be modest. The following are EPA’s findings for different
 


categories of impacts.
 


a. Facility impacts
 


The facility impact analysis assesses how facilities will be affected financially by the final rule. Key outputs of the facility
 


impact analysis include expected facility closures in the MP&M industries, associated losses in employment, and the number
 


of facilities experiencing financial stress short of closure (“moderate impacts”). EPA performed economic impact analyses 

for three categories of facilities, using different methodologies to evaluate each of the groups. The three groups are: 

�	 	 Private MP&M Facilities. This group includes privately-owned facilities that do not perform railroad line 

maintenance and are not owned by governments. This major category includes private businesses in a wide range of 

sectors or industries, including facilities that manufacture and rebuild railroad equipment. Only facilities that repair 

railroad track and equipment along the railroad line are not included. 

� Railroad line maintenance facilities maintain and repair railroad track, equipment and vehicles. 

�	 	 Government-owned facilities include MP&M facilities operated by municipalities, State agencies and other public 

sector entities such as State universities. Many of these facilities repair, rebuild, and maintain buses, trucks, cars, 

utility vehicles (e.g., snow plows and street cleaners), and light machinery. 

The specific methodology used to assess impacts differed for each of the three types of MP&M facilities. For private MP&M 

facilities, EPA established thresholds for measures of financial performance and compared the facilities’ performance before 

and after compliance with each regulatory option with these thresholds. Impacts were measured at the operating company 

level for railroad line maintenance facilities, since firms are unlikely to keep track of financial performance at the facility level 

for these sites. For governments, EPA compared compliance costs with facilities’ baseline costs of service, and assessed the 

impact of the compliance costs on the government’s taxpayers and on its ability to finance compliance costs by issuing debt. 

EPA identified facilities that are financially weak and might be expected to close under baseline conditions. Of the estimated 

43,858 discharging facilities, 8.2 percent or 3,593 facilities were assessed as baseline closures. The 3,593 baseline closures 

include 3,511 indirect dischargers, or 8.5 percent of indirect dischargers, and 98 direct dischargers, or 3.6 percent of direct 

dischargers. These facilities were excluded from the post-compliance analysis of regulatory impacts. 

Table ES.3 summarizes the facility-level economic impact of the final rule. EPA estimates that the final rule will cause no 

facilities to close or to incur moderate financial stress short of closure. The final rule excludes all indirect discharging 

facilities and two percent of the direct discharging facilities from requirements. 

3 The annual equivalent of capital and other one-time costs is calculated by annualizing costs at a seven percent discount rate over an 

estimated 15 year equipment life. Annual compliance costs are annualized capital costs plus annual operating and maintenance (O&M) 

costs. 

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MP&M EEBA Executive Summary 

Table ES.3: Regulatory Impacts for All Facilities, Final Rule, National Estimates 

Totala Direct Indirect 

Number of facilities operating in the baseline: total 40,265 2,641 37,652 

private MP&M and railroad line maintenance 36,480 2,183 34,325 

government-owned 3,785 458 3,327 

Number of facilities with subcategory exclusions 37,883 259 37,652 

Percent of facilities operating in the baseline excluded or below 

cutoffs 94.1% 9.8% 100.0% 

Number of facilities operating subject to regulatory requirements 2,382 2,382 0 

Number of regulatory closures 0 0 0 

Percent of facilities operating in the baseline that are regulatory 

closures 0.0% 0.0% 0.0% 

Number of facilities experiencing moderate impacts 0 0 0 

Percent of facilities operating in the baseline that experience 

moderate impacts 0.0% 0.0% 0.0% 

a The total number of facilities does not sum to the number of facilities by subcategory because some facilities 

have an indirect and direct discharging operation within the same facility. 

Source: U.S. EPA analysis. 

Table ES.4 summarizes impacts for government-owned facilities in particular. Under the final rule, 88 percent of the 

government-owned facilities are excluded from requirements by subcategory exclusions. The compliance costs of the final 

rule do not result in significant budgetary impacts for any of the governments that operate MP&M facilities. 

Table ES.4: Regulatory Impacts for Government-Owned Facilities, 

Final Rule, National Estimates 

Number of government-owned facilities operating in the baseline 3,785 

Number of facilities with subcategory exclusions 3,327 

Percent of facilities operating in the baseline excluded 88% 

Number of facilities operating subject to regulatory requirements 458 

Number of facilities experiencing significant budgetary impactsa 0 

Percent of facilities operating in the baseline that experience 

significant budgetary impacts 0% 

a

 A government is judged to experience major budgetary impacts if (1) any of its 

MP&M facilities incur compliance costs exceeding 1% of baseline cost of service 

and (2) the government fails both the taxpayer impact and debt impact tests. 

Source: U.S. EPA analysis. 

b. Firm-level impacts
 


EPA examined the impacts of the final rule on firms that own MP&M facilities, as well as on the financial condition of the
 


facilities themselves. A firm that owns multiple MP&M facilities could experience adverse financial impacts at the firm level
 


if its facilities are among those that incur significant impacts at the facility level. The firm-level analysis is also used to 

assess impacts on small firms, as required by the Regulatory Flexibility Act. 

EPA compared compliance costs with revenue at the firm level as a measure of the relative burden of compliance costs. EPA 

applied this analysis only to MP&M facilities owned by private entities. EPA estimated firm-level compliance costs by 

summing costs for all facilities owned by the same firm that responded to the survey plus estimated compliance costs for 

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MP&M EEBA Executive Summary 

additional facilities for which respondents submitted voluntary information. The Agency was not able to estimate the national 

numbers of firms that own MP &M facilities precisely, because the sample weights based on the survey design represent 

numbers of facilities rather than firms. Most MP&M facilities (26,472 of 36,480, or 73 percent) are single-facility firms, 

however. These firms can be analyzed using the survey weights. In addition, from survey responses, EPA identified 389 

sample facilities that are owned by 276 multi-facility firms. It is not known how many multi-facility firms exist at the national 

level, so EPA included these 276 firms in the firm-level analysis without extrapolation to the national level. 

Table ES.5 shows the results of the firm-level analysis. The results represent a total of 26,748 MP&M firms (26,472 + 276), 

owning 26,861 facilities (26,472 owned by single-facility firms + 389 owned by multi-facility firms). 

Table ES.5: Firm Level Before-Tax Annual Compliance Costs 

as a Percent of Annual Revenues 

Number of 

Firms in the 

Analysis a 

Number and Percent with Before-Tax Annual Compliance Costs/Annual 

Revenues Equal to: 

0% >0% and <1% Over 1% 

Number % Number % Number % 

26,748 25,722 96.2% 1,027 3.8% 0 0% 

a Firms whose only MP&M facilities close in the baseline are excluded. 

Source: U.S. EPA analysis. 

None of the firms in the analysis incur after-tax costs of greater than 1 percent of their annual revenues. Of the 1,027 firms
 


that incur any costs at all, none own facilities that close or experience moderate impacts as a results of the final rule.
 


This analysis is likely to overstate costs at the firm level because it does not consider the actions a multi-facility firm might
 


take to reduce its compliance costs under the final rule, such as transferring functions among facilities to consolidate wet
 


processes and to take advantage of scale economies in wastewater treatment.
 


c. Employment effects
 


Potential changes in employment from the rule include: (1) job losses that occur when facilities close and (2) job gains
 


resulting from facilities’ compliance activities. EPA estimates that the final rule will cause no facilities to close and therefore
 


the final rule will cause no job losses. EPA estimates that the regulation will increase employment, with the manufacture and
 


installation of compliance equipment causing a short-term gain in direct employment of 20 FTEs. In addition, EPA estimates
 


that operation and maintenance of compliance equipment will cause a continuing direct requirement for 2 FTEs per year. The
 


net effect on direct employment of the regulation is an estimated increase in 47 FTE-years, a measure that reflects both the 

number and duration of jobs gained. Over the 15 year analysis period, the employment gain averages 3 FTEs per year. 

d. Community impacts
 


EPA also considered the potential impacts of changes in employment due to the regulation on the communities where MP&M
 


facilities are located. Given that no closures are predicted due to the final rule, EPA does not expect the rule to have 

significant impacts at the community level. 

e. Foreign trade impacts
 


The foreign trade impacts analysis allocates the value of changes in output for each facility that is projected to close to
 


exports, imports, or domestic sales, based on the dominant source of competition in each market as reported in the surveys. 

EPA does not expect any foreign trade impacts as a result of the final rule because no facility closures are expected. 

f. Impacts on new facilities 

The new facility analysis assessed whether revised or new discharge limits for newly constructed sources would create a 

barrier to entry by new businesses and new facilities. To assess the potential for barrier to entry, EPA compared, by 

subcategory and discharger status, the estimated annual incremental costs of meeting revised new source limits with the 

estimated annual revenue of new facilities. EPA based the estimates of annual revenue and incremental costs of meeting 

revised new source limits on information from the existing facility database. EPA used the findings from this analysis, in 

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MP&M EEBA Executive Summary 

terms of the estimated percentages of new facilities that would incur costs exceeding specified revenue thresholds, to decide 

whether to issue revised new source limits for the various industry subcategories and discharger classes. From this analysis, 

EPA concluded that the promulgation of revised new source limits for the Oily Wastes direct discharger subcategory would 

not create a barrier to entry and this information, in part, underlies EPA’s decision to promulgate new source limits for this 

subcategory as part of the final regulation. 

g. Impacts on small entities
 


Table ES.6 shows the total number of facilities operating in the baseline and the number owned by small entities. Overall,
 


approximately 73 percent of all MP&M facilities are owned by small entities. However, subcategory exclusions in the final
 


rule will exclude approximately 95 percent of the facilities owned by small entities. 

Table ES.6: Number and Percent of MP&M Facilities Owned by Small Entities 

Type of Facility 

Number of Facilities of 

all Sizes Operating in 

the Baseline 

Number of Facilities 

Owned by Small 

Entities 

Percent of Facilities 

Owned by Small 

Entities 

Private MP&Ma 36,480 27,418 75% 

Government-owned 3,785 1,962 52% 

Totala 40,265 29,380 73% 

a Excludes baseline closures 

Source: U.S. EPA analysis. 

EPA assessed impacts on small entities by comparing compliance costs to revenues for the small entities at the firm level and 
 

by analyzing the facility impact analysis results for facilities owned by small firms. These analyses indicate that no facilities 
 

will incur costs exceeding 1% of revenues, and only 1,019 facilities owned by small firms will incur any costs at all. None of 
 

these facilities incur moderate impacts or close as a result of the final rule. 
 

EPA estimates that 1,962 facilities are owned by small governments (those with populations less than 50,000). The 
 

subcategory exclusions in the final rule exclude 1,682 of these small government-owned MP&M facilities. Thus, the final 
 

rule covers 280 small government-owned facilities. Of these facilities, only 140 incur costs, and the average annual cost per 
 

facility is less than $30,000. All of the 140 facilities have costs less than 3 percent of baseline cost of service. EPA estimated 
 

no significant impacts for any of these facilities or the governments that own them, based on the analysis of change in site cost 
 

of service, impact on taxpayers, and impact on government debt levels. The total compliance cost for all the small 
 

government-owned facilities incurring costs under the final rule is $3.5 million. 
 

ES.3.2 Social Costs 

The social costs of the final rule represent the value of society’s resources used to comply with and administer the rule. EPA 

estimated three categories of social cost for the final regulation: 

� the cost of society’s economic resources used to comply with the final regulation, 

� the cost to governments of administering the final regulation, and 

� the social costs of unemployment resulting from the regulation. 

Resource costs of compliance are the value of society’s productive resources including labor, equipment, and materials 

expended to achieve the reductions in effluent discharges required by the final rule. The social costs of these resources are 

generally higher than the cost burden to facilities because facilities are able to deduct the costs from their taxable income and 

may offset some of the costs through increased prices to customers. The costs to society, however, are the full value of the 

resources used, whether they are paid for by the regulated facilities, by all taxpayers in the form of lost tax revenues, or passed 

on to customers through increased prices. 

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MP&M EEBA Executive Summary 

The main cost to government from administering the regulation is the cost to POTWs for writing permits, and for compliance 

monitoring and enforcement activities. POTWs could incur costs in writing new permits for previously unpermitted facilities, 

and writing revised permits for some facilities earlier than would otherwise be required. Because the final regulation excludes 

all indirect dischargers from coverage, EPA expects that the final rule will not increase POTW administrative costs. 

The loss of jobs from facility closures would represent a social cost of the regulation. From its facility impact analysis, EPA 

estimates that no facilities will close as a result of the regulation. Accordingly, EPA estimates a zero cost of unemployment 

for the final rule. EPA did not recognize possible savings in unemployment-related costs from jobs created by the rule as a 

negative cost (benefit) of the regulation. 

From this analysis EPA estimated a total annual social cost of $13.8 million annually (2001$) for the final rule (see Table 

ES.7). All of this cost results from the estimated resource cost of compliance. 

Table ES.7: Total Social Cost: Final Rule 

(millions, 2001$) 

Social Cost Categories Final Rule 

Resource cost of compliance expenditures $13.8 

Costs to POTWs of administering the rule $0.0 

Social costs of unemployment $0.0 

Total Social Cost $13.8 

Source: U.S. EPA analysis. 

ES.4 NATIONAL BENEFITS OF THE FINAL RULE 

The final regulation will reduce MP&M industry pollutant discharges to the nation’s surface waters with a number of 

consequent benefits to society, including: 

� improved quality of freshwater, estuarine, and marine ecosystems; 

� increased survivability and diversity of aquatic and terrestrial wildlife; and 

� reduced risks to human health through consumption of fish or water taken from affected waterways. 

Table ES.8 shows EPA estimates for reduction in pollutant discharges to U.S. waters under the final rule. Loadings are shown 

both in pounds of pollutant and in toxic-weighted pound equivalents. The latter measure reflects the relative toxicity of the 

various toxic pollutants. The regulation would result in a 80 percent reduction in total toxic-weighted pollutant lbs. 

equivalent per year. The estimated toxic weighted pollutant reductions range from 87 percent for priority metal pollutants to 1 

percent for arsenic. Reductions in pounds of pollutants (not toxic-weighted) range from 93 percent for oil and grease (O&G) 

to 5 percent for arsenic. As shown in Table ES.8, the final rule achieves modest reductions for arsenic, organics, biological 

oxygen demand (BOD), and chemical oxygen demand (COD), and significant reductions for toxic metals, other inorganics, 

bulk pollutants, and oil and grease. 

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MP&M EEBA Executive Summary 

Table ES.8: Summary of Discharges by Pollutant Type for Regulated MP&M Facilitiesa 

Pollutant Category 

Current Releases Releases under the Final Rule Percent Reduction Due to the 

Final Rule 

Pounds Pounds Eq. Pounds Pounds Eq. Pounds Pounds Eq. 

Priority Pollutants 

Metals 794 2,756 153 351 80.7% 87.3% 

Organics 336 58 268 45 20.2% 22.4% 

Arse nic 22 75 21 74 4.5% 1.3% 

Cyanide (CN) 0 0 - -

Nonconventional Pollutants 

Metals 25,863 417 16,428 158 36.5% 62.1% 

Organics 2,159 45 1,038 39 51.9% 13.3% 

Other inorganics 2,334 0.2 1,301 0.1 44.3% 50.0% 

Bulk pollutants 335,679 167,295 50.2% 

Conventional Pollutants 

BOD 263,419 165,567 37.1% 

COD 523,440 488,697 6.6% 

O&G 428,137 28,955 93.2% 

TSS 160,695 73,769 54.1% 

Total 3,351 667 80.1% 

0 0 

a Includes only direct discharging facilities in the Oily Wastes subcategory that continue to operate in the baseline and that are subject 

to the final rule. 

Source: U.S. EPA analysis. 

EPA assessed the benefits from the expected pollutant reductions in three broad classes: human health, ecological, and 

productivity benefits.4 EPA was able to assess benefits within these three classes with varying degrees of completeness and 

rigor. Where possible, EPA quantified the expected effects and estimated monetary values. Some benefit categories could 

not be monetized because of data limitations and a limited understanding of how society values certain water quality changes. 

EPA used sample facility data to estimate national benefits from the regulation. The Agency extrapolated findings from the 

sample facility analyses to the national level using two extrapolation methods: (1) traditional extrapolation and (2) 

post-stratification extrapolation. EPA traditionally uses a standard linear weighting technique (i.e., traditional extrapolation) 

to estimate national compliance costs, changes in pollutant removals, and national-level benefits of environmental regulations. 

However, using sample weights that are based only on facility-specific (e.g., engineering) characteristics without including 

non-facility factors can lead to a conditional bias in the estimation of national-level benefits. In particular, this approach 

omits consideration of important non-facility factors that influence the occurrence and size of benefits. Non-facility factors 

that are likely to affect the occurrence and size of benefits from reduced sample facility discharges and that are not reflected 

in the standard stratification and sample-weighting approach include the receiving water body type and size and the size of 

population residing in the vicinity of a sample facility. To address omission of these important non-facility factors (i.e., water 

body type and size, affected population, and co-occurrence MP&M discharges) in designing the MP&M facilities sample, 

EPA adjusted sampling weights through post-stratification using two variables: (1) receiving water body type and size and 

(2) the size of the population residing in the vicinity of the sample facility. The Agency used a commonly used post4

 EPA evaluated two productivity measures: (1) the reduction in pollutant interference at POTWs, and (2) pass-through of pollutants 

into the sludge, which limits options for POTW disposal of sewage sludge. Because the final rule only regulates direct discharges and thus 

does not affect POTW operations, productivity benefits were evaluated for alternative options only. 

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MP&M EEBA Executive Summary 

stratification method called “raking” to adjust original sample weights to reflect these benefits characteristics. Appendix G of 

this report provides detail on extrapolation methods used in this analysis. 

To supplement the national-level analysis performed for the final MP&M regulation, EPA also conducted a detailed case 

study of the expected state-level costs and benefits of the MP&M rule in Ohio. For several reasons, EPA judges that the Ohio 

case study is more robust than the national benefit analyses that EPA undertakes in support of effluent guideline development. 

These reasons include: (1) use of more detailed data on MP& M facilities than is possible at the national level; (2) use of more 

detailed and accurate water quality data than are usually available; (3) more accurate accounting for the presence and effect of 

multiple discharges to the same reach; (4) inclusion of data on non-MP&M discharges in the baseline and post compliance; 

(5) use of a first-order decay model to estimate in-stream concentrations in downstream water bodies; and (6) inclusion of an 

additional recreational benefit category (swimming) in the analysis. The Ohio case study analysis is presented in Chapters 20, 

21, and 22 of this report. 

ES.4.1 Reduced Human Health Risk 

EPA estimates that the final rule will prevent discharge of 18 pounds per year of carcinogens and 119 pounds per year of lead.
 


Also, the final rule will prevent discharge of an additional 6,900 pounds of 76 pollutants of concern that are known to cause
 


adverse human health effects. These reduced pollutant discharges from MP&M facilities are expected to result in reduced risk
 


of illness from consumption of contaminated fish, shellfish, and water.
 


EPA analyzed the following measures of health-related benefits: reduced cancer risk from fish and water consumption;
 


reduced risk of non-cancer toxic effects from fish and water consumption; lead-related health effects to children and adults;
 


and reduced occurrence of in-waterway pollutant concentrations in excess of levels of concern. The levels of concern include
 


human health-based ambient water quality criteria (AWQC) or documented toxic effect levels for those chemicals not covered
 


by water quality criteria. Although some health effects are relatively well understood and can be quantified and monetized in
 


a benefits analysis (e.g., cancer), others are less well understood, and may not be assessed with the same rigor or at all (e.g.,
 


systemic health effects). The Agency therefore monetized only two of these health benefits: (1) changes in the incidence of
 


cancer from fish and water consumption, and (2) changes in adverse health effects in children and adults from reduced lead
 


exposure.
 


The national-level analysis of human health benefits finds negligible monetized benefits from the final rule. However, because
 


of significant simplifications in the national level analysis, this finding should be recognized as potentially having substantial
 


error and should therefore be interpreted with caution. In particular, the national-level analysis: (1) is based only on limited
 


information on MP&M facilities at the national level; (2) accounts in only a very limited way for the presence and effect of
 


joint discharges on the same reach; (3) omits data on non-MP&M discharges in the baseline and post compliance; and (4)
 


omits consideration of the downstream effects of pollutant discharges.
 


In contrast to the national-level analysis, the methods and data used for the Ohio case study address a number of these analytic
 


weaknesses. This more rigorous analysis finds that the final regulation would achieve $0.5 million (2001$) in health-related
 


benefits in the state of Ohio alone. EPA estimates that this analysis provides a more accurate, albeit lower-bound, estimate of
 


health-related benefits than indicated by the simpler national-level analysis. Moreover, given (1) that Ohio represents only
 


about 6 percent of the total MP&M facility population and (2) that a substantial share of the total MP&M facility population
 


is located in other states with similar water body and population characteristics (e.g., the states of Illinois, Indiana, Michigan,
 


Pennsylvania), it is reasonable to expect that additional human health benefits would be estimated for the remainder of the
 


country if EPA were able to apply this more rigorous approach at the national level. Accordingly, EPA judges that the final
 


rule's human health benefits are higher than its social costs.
 


a. Benefits from reduced incidence of cancer cases
 


EPA assessed changes in the incidence of cancer cases from consumption of M P&M pollutants in fish tissue and drinking
 


water. The methodology for assessing human health benefits from reduced cancer incidence is presented in Chapter 13 of this
 


report. The Agency valued changes in incidence of cancer cases using a willingness-to-pay (WTP) of $6.5 million (2001$)
 


for avoiding premature mortality. This estimate of the value of a statistical life saved is recommended in EPA's Guidelines for 

Preparing Economic Analysis. This estimate does not include estimates of WTP to avoid morbidity prior to death. 

EPA estimated aggregate cancer risk from contaminated drinking water for populations served by drinking water intakes on 

water bodies to which MP& M facilities discharge. EPA based this analysis on six carcinogenic pollutants for which drinking 

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MP&M EEBA Executive Summary 

water criteria have not been published.5

 This analysis excludes seven carcinogens for which drinking water criteria have been 

published. EPA assumed that public drinking water treatment systems will remove these pollutants from the public water 

supply. To the extent that treatment for these seven pollutants may cause incidental removals of the chemicals without criteria, 

the analysis may overstate cancer-related benefits. 

Calculated in-stream concentrations provide the basis for estimating changes in cancer risk for populations served by affected 

drinking water intakes. EPA estimates that baseline MP&M discharges from in-scope facilities are associated with virtually 

zero annual cancer cases. The national-level analysis finds that the final regulation would lead to a marginal reduction in these 

cancer cases resulting from consumption of contaminated drinking water; correspondingly, monetary benefits estimated from 

reduced consumption of contaminated drinking water are essentially zero. 

EPA also estimated cancer risk from the consumption of contaminated fish for recreational and subsistence anglers and their 

families. EPA based this analysis on thirteen carcinogenic pollutants found in MP&M effluent discharges. Estimated 

contaminant concentrations in fish tissue are a function of predicted in-stream pollutant concentrations and pollutant 

bioconcentration factors. EPA used data on numbers of licensed fishermen by state and county, presence of fish consumption 

advisories, number of fishing trips per person per year, and average household size to estimate the affected population of 

recreational and subsistence anglers and their families. The analysis uses different fish consumption rates for recreational and 

subsistence anglers to estimate the change in cancer risk among these populations. 

EPA estimated that baseline MP&M discharges from in-scope facilities are associated with 0.03 annual cancer cases. The 

national-level analysis shows that the final option would lead to a marginal reduction in cancer cases among recreational and 

subsistence angler populations. The monetary benefits estimated from consumption of less contaminated fish by these 

populations are essentially negligible. 

The findings from the national analysis of changes in cancer risk for the final rule differ from the Ohio case study results. 

Based on the Ohio case study, the final option is expected to eliminate 0.01 cancer cases annually in the State of Ohio alone. 

This reduction translates into $14,500 (2001$) in annual benefits due to reduced cancer risk from consumption of 

contaminated fish tissue and drinking water (see Chapter 22 of this report for detail). 

The difference in the findings of the national and Ohio analyses results primarily from more comprehensive information on 

MP&M and non-MP&M facility discharges used in the Ohio case study analysis. The national-level analysis accounts only 

for the pollutant exposures from MP&M sample facilities. In contrast, the Ohio case study approach accounts for a broader 

baseline of pollutant exposure, including more thorough and detailed coverage of discharges from MP&M facilities and also 

estimated exposures from non-MP&M sources. As a result, the Ohio case study analysis more accurately reflects baseline 

health risk conditions. 

b. Reductions in systemic health effects 

The final rule can potentially achieve a wide range of non-cancer human health benefits (e.g., systemic effects, reproductive 

toxicity, and developmental toxicity) from reduced contamination of fish tissue and drinking water sources. The common 

approach for assessing the risk of non-cancer health effects from the ingestion of a pollutant is to calculate a hazard quotient 

by dividing an individual's oral exposure to the pollutant, expressed as a pollutant dose in milligrams per kilogram body 

weight per day (mg/kg-day), by the pollutant's oral reference dose (RfD). An RfD is defined as an estimate (with uncertainty 

spanning perhaps an order of magnitude) of a daily oral exposure that likely would not result in the occurrence of adverse 

health effects in humans, including sensitive individuals, during a lifetime. A hazard quotient less than one means that the 

pollutant dose to which an individual is exposed is less than the RfD, and, therefore, presumed to be without appreciable risk 

of adverse human health effects. EPA guidance for assessing exposures to mixtures of pollutants recommends calculating a 

hazard index (HI) by summing the individual hazard quotients for those pollutants in the mixture that affect the same target 

organ or system (e.g., the kidneys, the respiratory system). HI values are interpreted similarly to hazard quotients; values 

below one are generally considered to suggest that exposures are not likely to result in appreciable risk of adverse health 

effects during a lifetime, and values above one are generally cause for concern, although an HI greater than one does not 

necessarily suggest a likelihood of adverse effects. Chapter 13 of this report provides a detailed discussion of the 

methodology for assessing changes in systemic health effects associated with this rule. 

5 EPA included n-nitrosodimethylamine (NDMA) in its assessment of the baseline incidence of cancer cases. However, the Agency 

did not consider NDMA pollutant reductions in its benefits analysis due to limited wastewater sampling for that pollutant. 

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MP&M EEBA Executive Summary 

To evaluate the potential benefits of reducing the in-stream concentrations of 76 pollutants that cause non-cancer health
 


effects, EPA estimated target organ-specific HIs for drinking water and fish ingestion exposures in both the baseline and
 


post-compliance scenarios. Specifically, EPA calculated target-organ specific HIs for pollutants predicted in each MP&M
 


discharge reach; as a result, a separate HI was calculated for each target organ/exposure pathway (fish consumption and
 


drinking water)/reach combination. EPA then combined estimates of the numbers of individuals in the exposed populations
 


with the HIs for the populations to determine how many individuals might be expected to realize reduced risk of non-cancer
 


health effects in the post-compliance scenario.
 


The results of EPA's analysis suggest that hazard indices for individuals in the exposed populations may decrease after
 


facilities comply with today's rule. Increases in the percentage of exposed populations that would be exposed to no risk of
 


non-cancer adverse human health effects due to the MP&M discharges occur in both the fish and drinking water analyses. The
 


shift to lower hazard indices should be considered in conjunction with the finding that the hazard indices for incremental
 


exposures to pollutants discharged by MP&M facilities (for which reference doses are available) are less than one in the
 


baseline analysis for the entire population associated with sample facilities. Whether the incremental shifts in hazard indices
 


are significant in reducing absolute risks of non-cancer adverse human health effects is uncertain and will depend on the
 


magnitude of contaminant exposures for a given population from risk sources not accounted for in this analysis.
 


c. Benefits from reduced exposure to lead
 


EPA performed a separate analysis of benefits from reduced exposure to lead. This analysis differs from the analysis of
 


non-cancer adverse human health effects from exposure to other MP&M pollutants because it is based on dose-response
 


functions tied to specific health endpoints to which monetary values can be applied. Chapter 14 of this report presents the 

methodology for assessing benefits from reduced exposure to lead. 

Many lead-related adverse health effects are relatively common and are chronic in nature. These effects include, but are not 

limited to, hypertension, coronary heart disease, and impaired cognitive function. Lead is harmful to individuals of all ages, 

but the effects of lead on children are of particular concern. Children's rapid rate of development makes them more 

susceptible to neurobehavioral effects from lead exposure. The neurobehavioral effects on children from lead exposure 

include hyperactivity, behavioral and attention difficulties, delayed mental development, and motor and perceptual skill 

deficits. 

This analysis assessed benefits of reduced lead exposure from consumption of contaminated fish tissue to three sensitive 

populations: (1) preschool age children; (2) pregnant women; and (3) adult men and women. This analysis uses blood-lead 

levels as a biomarker of lead exposure. EPA estimated baseline and post-compliance blood lead levels in the exposed 

populations and then used changes in these levels to estimate benefits in the form of avoided health damages. 

EPA assessed neurobehavioral effects on children based on a dose response relationship for IQ decrements. Avoided 

neurological and cognitive damages are expressed as changes in overall IQ levels, including reduced incidence of extremely 

low IQ scores (<70, or two standard deviations below the mean) and reduced incidence of blood-lead levels above 20 mg/dL. 

The analysis uses the value of compensatory education that an individual would otherwise need and the impact of an 

additional IQ point on individuals' future earnings to value the avoided neurological and cognitive damages. The 

national-level analyses shows that implementation of the final option would not result in any changes in IQ loss across all 

exposed children. The final option does not reduce occurrences of extremely low IQ scores (<70) or incidences of blood-lead 

levels above 20 mg/dL. 

Prenatal exposure to lead is an important route of exposure. Fetal exposure to lead in utero due to maternal blood-lead levels 

may result in several adverse health effects, including decreased gestational age, reduced birth weight, late fetal death, 

neurobehavioral deficits in infants, and increased infant mortality. To assess benefits to pregnant women, EPA estimated 

changes in the risk of infant mortality due to changes in maternal blood-lead levels during pregnancy. The national-level 

analysis shows that the final option does not result in changes in maternal blood lead levels during pregnancy and as a result 

does not reduce neonatal mortality. 

The national-level analysis finds no benefits to children from reduced exposure to lead. However, as for the cancer risk 

analysis previously discussed, these findings differ from the more comprehensive analysis used in the Ohio case study. Using 

the more rigorous case study approach, EPA estimates that the final regulation will yield annual lead-related benefits for 

children in Ohio of $422,113 (2001$). This benefit value includes three components. First, reduced lead exposure is estimated 

to reduce neonatal mortality by 0.024 cases annually with an annual value of $162,094 (2001$). Second, reduced lead 

exposure will avoid the loss of an estimated 26.96 IQ points among preschool children in Ohio, which translates into 

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MP&M EEBA Executive Summary 

$253,934 (2001$) per year in benefits. Third, the annually avoided costs of compensatory education from incidence of IQ
 


below 70 and blood-lead levels above 20 g/dL among children amounts to approximately $5,345 (2001$).
 


Lead exposure has been shown to have adverse effects on the health of adults as well as children. The health effects in adults
 


that EPA quantified all derive from lead's effects on blood pressure. Quantified health effects include increased incidence of
 


hypertension (estimated for males only), initial coronary heart disease (CHD), strokes (initial cerebrovascular accidents and
 


atherothrombotic brain infarctions), and premature mortality. This analysis does not include other health effects associated
 


with elevated blood pressure and other adult health effects of lead, including nervous system disorders, anemia, and possible
 


cancer effects. EPA used cost of illness estimates (i.e., medical costs and lost work time) to estimate monetary value of
 


reduced incidence of hypertension, initial CHD , and strokes. EPA then used the value of a statistical life saved to value
 


changes in risk of premature mortality. The national-level analysis finds that the final rule will achieve no lead-related health
 


benefits among adults.
 


Again, the national analysis results differ from the Ohio case study results. Using the case study approach, EPA estimates that
 


the final regulation will achieve total lead-related benefits among Ohio adults of $117,393 (2001$). This value includes
 


benefits from reduced hypertension among adult males: a reduction of an estimated 9.4 cases annually, with benefits of
 


approximately $10,670 (2001$). In addition, reducing the incidence of initial CHD, strokes, and premature mortality among
 


adult males and females in Ohio would result in estimated benefits of $963, $2,115, and $103,645, respectively (see Chapter
 


22 of this report for detail).
 


Based on the national-level benefits analysis, EPA found that total benefits from reduced exposure to lead, for both children
 


and adults, are negligible under the final rule. However, based on the Ohio case study findings, benefits for children and
 


adults from reduced lead-related health effects of the final rule are estimated to total approximately $0.5 million (2001$)
 


annually in the state of Ohio alone (see Section H of today's final rule for detail). As in the cancer risk analysis, the difference
 


in the national and Ohio-based findings stems primarily from more comprehensive information on MP&M and non-MP&M
 


facility discharges used in Ohio.
 


d. Exceedances of human health-based AWQC
 


EPA also estimated the effect of MP&M facility discharges on the occurrence of pollutant concentrations in affected
 


waterways that exceed human health-based AW QCs. In a conceptual sense, this analysis and its findings are not additive to
 


the preceding analyses of change in cancer or lead-related health risks but are another way of quantitatively characterizing the
 


same possible benefit categories. This analysis compares the estimated baseline and post-compliance in-stream pollutant
 


concentrations in affected waterways to ambient water criteria for protection of human health. The comparison included
 


AW QC for protection of human health through consumption of organisms and consumption of water and organisms. Pollutant 

concentrations in excess of these values indicate potential risks to human health. 

EPA estimates that in-stream concentrations of 4 pollutants (i.e., arsenic, iron, manganese, and n-nitrosodimethylamine) will 

exceed human health criteria for consumption of water and organisms in 78 receiving reaches nationwide as the result of 

baseline MP&M pollutant discharges. EPA estimates that 23% of human health AWQC exceedances are caused by 

n-nitrosodimethylamine (NDMA). EPA did not consider NDMA pollutant reductions in its benefits analyses due to limited 

wastewater sampling data for that pollutant. EPA estimates that the final rule will not eliminate the occurrence of 

concentrations in excess of human health criteria for consumption of water and organisms and for consumption of organisms 

on any of the reaches on which baseline discharges are estimated to cause concentrations in excess of AWQC values. 

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MP&M EEBA Executive Summary 

ES.4.2 Ecological, Recreational, and Nonuser Benefits 

EPA expects the MP&M rule to improve aquatic species habitats by reducing concentrations of toxic contaminants such as
 


aluminum, cadmium, copper, lead, mercury, silver, and zinc in water. These improvements should enhance the quality and
 


value of water-based recreation, such as fishing, swimming, wildlife viewing, camping, waterfowl hunting, and boating. The
 


benefits from improved water-based recreation would be seen as increases in the increased value participants derive from a
 


day of recreation and the increased number of days that consumers of water-based recreation choose to visit the cleaner
 


waterways. This analysis measures the economic benefit to society from water quality improvements based on the increased
 


monetary value of recreational opportunities resulting from those improvements
 


a. Reduced aquatic life impacts
 


EPA quantified the ecological improvements of the final regulation by comparing estimates of in-waterway concentrations of
 


pollutants discharged by MP&M facilites with AWQC values for protection of aquatic species. Pollutant concentrations in
 


excess of acute and/or chronic AWQC limits for protection of aquatic life indicate potential adverse impacts to aquatic
 


species. EPA estimates that baseline in-stream concentrations of 9 pollutants (i.e., aluminum, cadmium, copper, lead,
 


manganese, mercury, nickel, silver, and zinc) will exceed the acute and chronic criterion for aquatic life in 353 reaches
 


nationwide. The final rule eliminates concentrations in excess of aquatic life AWQCs on nine of these reaches. EPA’s
 


analysis shows that none of the receiving reaches exceeding chronic or acute aquatic life AW QC at the baseline discharge
 


level will experience partial water quality improvements from reduced occurrence of AWQC exceedances for some
 


pollutants.
 


b. Recreational benefits
 


EPA assessed the recreational benefits from reduced occurrence of pollutant concentrations exceeding aquatic life and/or
 


human health AWQC values. Combining its findings from both the aquatic life and human health AWQC exceedance
 


analyses, EPA found that 394 stream reaches exceed chronic or acute aquatic life AWQC and/or human health AWQC values 

at baseline discharge levels. The Agency estimates that the final rule will eliminate exceedances on nine of these discharge 

reaches, leaving 384 reaches with concentrations of one or more pollutants exceeding AWQ C limits. None of these 384 

reaches will experience partial water quality improvements from reduced occurrence of some pollutant concentrations in 

excess of AW QC limits. 

EPA attached a monetary value to reduced exceedances based on increased values for three water-based recreation activities 

(fishing, wildlife viewing, and boating) and on nonuser values. EPA applied a benefits transfer approach to estimate the total 

WTP, including both use and nonuse values, for improvements in surface water quality. This approach builds upon a review 

and analysis of the surface water valuation literature. 

EPA first estimated the baseline value of water-based recreation for benefiting reaches, based on per-reach estimates of: 

� annual person-days of water-based recreation, and 

� per-day values of water-based recreation. 

The baseline per-day values of water-based recreation are based on studies by Walsh et. al (1992) and Bergstrom and Cordell 

(1991). The studies provide values per recreation day for a wide range of water-based activities, including fishing, boating, 

wildlife viewing, waterfowl hunting, camping, and picnicking. The mean values per recreational fishing, boating, and wildlife 

viewing day used in this analysis are $42.12, $48.30 and $26.28 (2001$), respectively. Applying facility weights and 

summing over all benefiting reaches provides a total baseline value for a given recreational activity for MP&M reaches 

expected to benefit from the elimination of pollutant concentrations in excess of AWQC limits. 

EPA then applied the percentage change in the recreational value of water resources implied by surface water valuation 

studies to estimate changes in values for all MP&M reaches in which the regulation eliminates AWQC exceedances by one or 

more MP&M pollutants. The Agency selected eight of the most comparable studies and calculated the changes in recreation 

values from water quality improvements (as percentage of the baseline) implied by those studies. Sources of estimates 

included Lyke (1993), Jakus et al. (1997), Montgomery and Needleman (1997), Paneuf et al. (1998), Desvousges et al. 

(1987), Lant and Roberts (1990), Farber and Griner (2000), and Tudor et al. (2002). EPA's reasoning for selecting each study 

is discussed in detail in Chapter 15 of this report. EPA took a simple mean of point estimates from all applicable studies to 

derive a central tendency value for percentage change in the water resource values due to water quality improvements. These 

studies yielded estimates of increased recreational value from water quality improvements expected from reduced MP&M 

ES-14 
MP&M EEBA Executive Summary 

discharges of 12, 9, and 18 percent for fishing, boating, and wildlife viewing respectively. Using all possible applicable 

valuation studies in developing a benefits transfer approach to valuing changes in the recreational value of water resources 

from reduced MP&M discharges, makes unit values more likely to be nationally representative, and avoids the potential bias 

inherent in using a single study to make estimates at the national level. 

Table ES.9 presents the estimated national recreational benefits of the final rule (2001$). The estimated increased value of 

recreational activities to users of water-based recreation is $537,197, $202,691, and $259,949 annually for fishing, boating, 

and wildlife viewing, respectively. The recreational activities considered in this analysis are stochastically independent; EPA 

calculated the total user value of enhanced water-based recreation opportunities by summing over the three recreation 

categories. The estimated increase in the total user value is $999,838 annually. 

EPA also estimated non-market nonuser benefits. These non-market nonuser benefits are not associated with current use of 

the affected ecosystem or habitat; instead, they arise from the value society places on improved water quality independent of 

planned uses or based on expected future use. Past studies have shown that nonuser values are a sizable component of the 

total economic value of water resources. EPA estimated average changes in nonuser value to equal one-half of the recreational 

use benefits based on study by A. Fisher and R. Raucher (1984). The estimated increase in nonuse value is $499,919 (2001$). 

A recent literature review finds that nonuse benefits are, on average, 1.9 to 2.5 times all use values, rather than 0.5 times 

recreational benefits alone as EPA has traditionally assumed for its nonuse benefit estimates (T. Brown, 1993). EPA's method 

for estimating nonuse benefits from water quality improvements resulting from reduced MP&M discharges is therefore likely 

to understate the true value of nonuse benefits. 

Table ES.9: Estimated Recreational Benefits from Reduced MP&M Discharges (thousands, 2001$) 

Recreational Activity 

Traditional 

Extrapolation 

Post-Stratification 

Extrapolation 

Recreational fishing $537 $350 

Recreational boating $203 $132 

Wildlife viewing and near-water recreation $260 $169 

Total recreational use benefits (fishing + boating + wildlife viewing) $1,000 $651 

Nonuser benefits (1⁄2 of total recreational use) $500 $326 

Total Recreational Benefits (2001$) $1,500 $977 

Source: U.S. EPA analysis. 

The recreational trips corresponding to the three activities considered in this analysis are stochastically independent; EPA 

calculated the total value of enhanced water-based recreation opportunities by summing the three recreation categories and 

nonuser value. The resulting increase in the value of water resources to consumers of water-based recreation and nonusers is 

$1,500 thousand (2001$) annually under the traditional extrapolation method and $977 thousand (2001$) annually under the 

post-stratification extrapolation method. 

ES.4.3 POTW Impacts 

The final rule only regulates direct dischargers. Therefore, the selected option does not affect POTW operation. For the 

alternative policy options that consider both direct and indirect dischargers, EPA evaluated two productivity measures 

associated with MP&M pollutants. The first measure is the reduction in pollutant interference at publicly-owned treatment 

works (POTW s). The second measure is pass-through of pollutants into the sludge, which limits options for POTW disposal 

of sewage sludge. These analyses are presented in Chapter 16 of this report. 

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MP&M EEBA Executive Summary 

ES.4.4 Total Estimated Benefits of the Final MP&M Rule 

Using the traditional extrapolation method, EPA estimates total benefits for the five monetized categories of approximately 

$1,500,000 (2001$) annually. This value understates the total benefits of the rule because the benefits analysis omits 

significant sources of benefits to society. Examples of benefit categories not reflected in this estimate include non-cancer 

health benefits other than benefits from reduced exposure to lead; other water-dependent recreational benefits, such as 

swimming and waterskiing benefits to recreational users from reduced concentration of conventional pollutants and 

nonconventional pollutants such as TKN; and reduced cost of drinking water treatment for the pollutants with drinking water 

criteria. In addition, as noted in the prior discussion, although the national-level benefits analysis finds negligible benefits 

from reduced health risk, the more rigorous analytic approach used for the Ohio case study found material health-related 

benefits approximately $0.5 million in the state of Ohio alone. 

ES.5 NATIONAL BENEFITS-COSTS COMPARISON 

The comparison of benefits and for the final rule is inevitably incomplete because EPA cannot value all of the benefits 

resulting from the final rule in dollar terms. A comparison of benefits and costs is thus limited by the lack of a comprehensive 

benefits valuation and also by uncertainties in the estimates. Bearing these limitations in mind, EPA presents a summary 

comparison of benefits and costs for the final rule in Table ES.10. The estimated social cost of the final rule is $13.8 million 

annually (2001$). The total benefits that can be valued in dollar terms in the categories traditionally analyzed for effluent 

guidelines range from $977,000 to $1,500,000 annually (2001$), based on the alternative extrapolation methods. 

As previously noted, EPA used more detailed information and a more comprehensive analytic method to estimate expected 

benefits of the final rule for the state of Ohio. This more rigorous analysis was undertaken to address certain issues in the 

national-level analysis and to supplement the national-level analysis performed for the final rule. The following section 

presents this analysis. The Ohio case study showed that the more rigorous analytic approach leads to a different conclusion 

from that found in the simpler, national-level analysis approach in particular, that the estimated state-level benefits exceed 

the estimated state-level cost. As previously discussed, given (1) that Ohio accounts for only about 6 percent of total MP&M 

facilities, and (2) that other states with substantial numbers of MP&M facilities have similar population and water body 

characteristics to Ohio, EPA estimates that use of the more rigorous approach nationally would yield a higher estimate of 

national benefits. On this basis, the Agency estimates that national benefits from the final rule may be comparable to its social 

costs. 

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MP&M EEBA Executive Summary 

Table ES.10: Comparison of National Annual Monetizable Benefits to Social Costs 

(thousands, 2001$) 

Benefit and Cost Categories Traditional Extrapolation Post-Stratification 

Extrapolation 

Benefit Categories 

Reduced Cancer Risk from Fish Consumption $0 $0 

Reduced Cancer Risk from Water Consumption $0 $0 

Reduced Risk from Exposure to Lead $0 $0 

Enhanced Water-Based Recreation $1,000 $651 

Nonuse Benefits $500 $326 

Total Monetized Benefits $1,500 $977 

Cost Categories 

Resource Costs of Compliance $13,825 $13,825 

Costs of Administering the Final Regulation $0 $0 

Social Costs of Unemployment $0 $0 

Total Monetized Costs $13,825 $13,825 

Net Monetized Benefits (Benefits Minus Costs) ($12,325) ($12,847) 

Source: U.S. EPA analysis. 

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MP&M EEBA Executive Summary 

ES.6 OHIO CASE STUDY 

Part V of this report presents a detailed case study of the expected state-level costs and benefits of the MP&M rule in Ohio. 

The case study assesses the costs and benefits of the final rule for facilities and water bodies located in Ohio. Ohio is among 

the ten states with the largest numbers of MP &M facilities. The state has a diverse water resource base and a more extensive 

water quality ecological database than many other states. EPA gathered data on MP&M facilities and on Ohio's baseline water 

quality conditions and water-based recreation activities to support the case study analysis. These data characterize current 

water quality conditions, water quality changes expected from the regulation, and the expected welfare changes from water 

quality improvements at water bodies affected by MP&M discharges. The case study also estimates the social costs of the 

final rule for facilities in Ohio and compares estimated social costs and benefits for the state. 

The case study analysis supplements the national-level analysis performed for the final MP&M regulation in two important 

ways. First, the analysis used improved data and methods to determine MP&M pollutant discharges from both MP&M 

facilities and other sources. In particular, EPA administered 1,600 screener questionnaires to augment information on the 

Ohio's MP& M facilities. The Agency also used information from the sampled M P&M facilities to estimate discharge 

characteristics of non-sampled MP&M facilities, as described in Appendix H of the EEBA report. The Agency assigned 

discharge characteristics to all non-MP&M industrial direct discharges based on the information provided in the EPA’s 

Permit Compliance System (PCS) database. Second, the analysis used an original travel cost study to value four recreational 

uses of water resources affected by the regulation: swimming, fishing, boating, and near-water activities. The added detail 

provides a more complete and reliable analysis of water quality changes from reduced MP&M discharges. The study provides 

more complete estimates of changes in human welfare resulting from reduced health risk, enhanced recreational opportunities, 

and improved economic productivity. 

EPA estimated human health benefits from reduced MP&M dischargers in Ohio using similar methodologies to those used for 

the national-level analysis. These methodologies are presented in Chapter 13 and 14 of the EEBA report. 

The case study analysis of recreational benefits combines water quality modeling with a random utility model (RUM) to 

assess how changes in water quality from the regulation will affect consumers' valuation of water resources. The RUM 

analysis addresses a wide range of pollutant types and effects, including water quality measures not often addressed in past 

recreational benefits studies. In particular, the model supports a more complete analysis of recreational benefits from 

reductions in nutrients and toxic pollutants (i.e., priority pollutants and nonconventional pollutants with toxic effects). 

EPA subjected this study to a formal peer review by experts in the natural resource valuation field. The peer review concluded 

that EPA had done a competent job, especially given the available data. As requested by the Agency, peer reviewers provided 

suggestions for further improvements in the analysis. Since the proposed rule analysis, the Agency made changes to the Ohio 

model and conducted additional sensitivity analyses suggested by the reviewers. The peer review report and EPA's response 

to peer reviewers' comments, along with the revised model, are in the docket for the rule. 

ES.6.1 Benefits 

The use of an original RUM in this case study allows the Agency to address limitations inherent in benefits transfer used in 

the analysis of recreational benefits at the national level. The use of benefits transfer often requires additional assumptions 

because water quality changes evaluated in the available recreation demand studies are only roughly comparable with the 

water quality measures evaluated for a particular rule. The RUM model estimates the effects of the specific water quality 

characteristics analyzed for the final MP&M regulation, such as presence of AWQC exceedances and concentrations of the 

nonconventional pollutant Total Kjeldahl Nitrogen (TKN). EPA estimates that this direct link between the water quality 

characteristics analyzed for the rule and the characteristics valued in the RUM analysis reduces uncertainty in benefit 

estimates and makes the analysis of recreational benefits more robust. 

The final MP&M regulation affects a broad range of pollutants, some of which are toxic to human and aquatic life but are not 

directly observable (i.e., priority and nonconventional pollutants). These unobservable toxic pollutants degrade aquatic 

habitats, decrease the size and abundance of fish and other aquatic species, increase fish deformities, and change watershed 

species composition. Changes in toxic pollutant concentrations may therefore affect recreationists' valuation of water 

resources, even if consumers are unaware of changes in ambient pollutant concentrations. 

The study used data from the National Demand Survey for Water-Based Recreation (NDS), conducted by U.S. EPA and the 

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MP&M EEBA Executive Summary 

National Forest Service, to examine the effects of in-stream pollutant concentrations on consumers' decisions to visit a 

particular water body. The analysis estimated baseline and post-compliance water quality at recreation sites actually visited by 

the surveyed consumers and at all other sites within the consumers' choice set, visited or not. The RUM analysis of consumer 

behavior then estimated the effect of ambient water quality and other site characteristics on the total number of trips taken for 

different water-based recreation activities and the allocation of these trips among particular recreational sites. The RUM 

analysis is a travel cost model, in which the cost to travel to a particular recreational site represents the "price" of a visit. 

EPA modeled two consumer decisions: (1) how many water-based recreational trips to take during the recreational season (the 

trip participation model); and (2) which recreation site to choose (the site choice model). Combining the trip participation 

model's prediction of trips under the baseline and post-compliance scenarios and the site choice model's per-trip welfare 

measure provides a measure of total welfare. EPA calculated each individual's seasonal welfare gain for each recreation 

activity from post-compliance water quality changes, and then used Census data to aggregate the estimated welfare change to 

the State level. The sum of estimated welfare changes over the four recreation activities yielded estimates of total welfare 

gain. 

EPA estimated other components of benefits in Ohio using similar methodologies to those used for the national-level analysis. 

In addition to the RUM study of recreational benefits, other analytical improvements included the following: (1) use of more 

detailed data on MP&M facilities, obtained from the 1,600 additional surveys; (2) use of data on non-MP&M discharges to 

estimate current baseline conditions in the state, and (3) use of a first-order decay model to estimate in-stream concentrations 

in the Ohio water bodies in the baseline and post-compliance. 

Appendix H of this report describes the water quality model used in this analysis and the approach and data sources used to 

estimate total pollutant loadings from all industrial and municipal sources to Ohio's water bodies. The Agency has concluded 

that the added level of detail results in more robust benefit estimates. 

Summing the monetary values over all benefit categories yields total monetized benefits of $930,408 (2001$) annually for the 

final rule, as shown in Table ES.11. Although more comprehensive than the national benefits analysis, the case study benefit 

estimates still omit important mechanisms by which society is likely to benefit from the final rule. Examples of benefit 

categories not reflected in the monetized benefits include non-cancer health benefits (other than lead-related benefits) and 

reduced costs of drinking water treatment. 

Table ES.11: Annual Benefits from Reduced MP&M Discharges in Ohio 

(thousands, 2001$) 

Benefit Category Mean Annual Benefits 

1. d cancer risk: 

Fish consumption 

Water consumption 

$15 

$0 

2. isk from exposure to lead: 

Children 

Adults 

$422 

$117 

3. ishing $153 

4. wimming $10 

5. Enhanced boating $0 

4. ildlife viewing $88 

5. Nonuse benefits (1⁄2 recreational use benefits) $125 

Total Monetized Benefits $930 

Reduce 

Reduced r 

Enhanced f 

Enhanced s 

Enhanced w 

Source: U.S. EPA analysis. 

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MP&M EEBA Executive Summary 

ES.6.2 Social Costs 

EPA also estimated the social costs of the final rule for MP&M facilities in Ohio. EPA developed engineering estimates of 

compliance costs for each Ohio facility, and annualized costs using a seven percent discount rate over a 15-year period. 

Estimating the frequency of baseline closures is necessary to assess the costs of regulation. Facilities assessed as baseline 

closures are not expected to incur compliance costs under the final MP&M regulation. The screener data collected for Ohio 

facilities did not provide financial data to perform an after-tax cash flow or net present value test, as done in the national 

analysis. EPA therefore used data from the national analysis to estimate the percentage of facilities assessed as baseline 

closures. EPA assumed that the frequency of baseline closures among Ohio facilities would be the same as that estimated in 

the national analysis for facilities with the same discharge status, subcategory, and flow category. For example, two percent 

of direct Oily Wastes facilities discharging less than one million gallons per year close in the baseline in the national data set; 

this same percentage is assumed for Ohio screener indirect dischargers in that flow and size category. EPA reduced the total 

estimated costs for screener facilities, by analysis category, based on the fraction of facilities assessed as baseline closures. 

EPA used the same methods as used in the national social cost analysis to estimate other components of social costs for the 

Ohio case study. Table ES.12 shows the total estimated social costs of the final rule for Ohio facilities. 

Table ES.12: Annual Social Costs for Ohio Facilities: 

Final Option 

(thousands, 2001$, costs annualized at 7%) 

Component of Social Costs Final Rule 

Resource value of compliance costs $62 

Government administrative costs $0 

Social cost of unemployment $0 

Total Social Cost $62 

Source: U.S. EPA analysis. 

ES.6.3 Comparing Monetized Benefits and Costs 

The Ohio case study shows substantial net positive benefits associated with the MP&M regulation. EPA estimates the social 

cost in Ohio of the final regulation to be $62 thousand annually ($2001). The sum total of benefits that can be valued in dollar 

terms is $930 thousand annually ($2001). Comparing the midpoint estimate of social costs ($62 thousand) with the midpoint 

estimate of monetizable benefits ($930 thousand) results in a net social benefit of $868 thousand. This represents a partial 

cost-benefit comparison because not all of the benefits resulting from the regulation can be valued in dollar terms (e.g., 

changes in systemic health risk). 

For the reasons previously discussed, EPA judges that the analytic approach and detailed data used for the Ohio case study 

provide a more robust and accurate benefits estimate than the data and approach used for the national-level analysis. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 1: Introduction 

Chapter 1: Introduction

INTRODUCTION

The U.S. Environmental Protection Agency (EPA) is 

promulgating effluent limitations guidelines and standards for 

the Metal Products and Machinery (MP&M) Point Source 

Category, under Sections 301, 304, 306, 307 and 501 of the 

Clean Water Act. EPA has determined that the final rule is 

CHAPTER CONTENTS

1.1 Purpose ................. ................. . 1-1 

1.2 Organization ................. .............. 1-1 

1.3 Readers’ Aids ................. ............. 1-3 

not likely to result in aggregate costs to the economy that exceed $100 million annually. The Agency therefore found that the 

final regulation is not a “significant regulatory action” as defined by Executive Order 12866 (58 FR 51735, October 4, 1993). 

1.1 PURPOSE

This Economic, Environmental, and Benefits Analysis report (EEBA) presents EPA’s economic and benefits analyses for the 

final MP&M regulation. These analyses supported EPA in developing the final regulation and in meeting the requirements of 

the following statutes and executive orders: 

�	 	 Executive Order 12866 “Regulatory Planning and Review”, which requires analysis of costs, benefits, and economic 

impacts of the final rule and regulatory alternatives; 

�	 	 Unfunded Mandates Reform Act (UMRA), which requires evaluation of impacts on governments, among other 

requirements; 

�	 	 Regulatory Flexibility Act as amended by the Small Business Regulatory Enforcement Fairness Act of 1996 

(RFA/SBREFA), which requires consideration of the rule’s impact on small firms and governments; 

�	 	 Executive Order 12898 “Federal Actions to Address Environmental Justice in Minority Populations and LowIncome Populations”; and 

� Executive Order 13084 “ Protection of Children from Environmental Health Risks and Safety Risks”. 

1.2 ORGANIZATION

This report is organized in five major parts, 22 chapters, and 14 appendices, as follows: 

Part I “Introduction and Background Information” (Chapters 1 though 4) describes the need for the regulation, provides a 

profile of the MP&M industry, and describes regulatory options evaluated and selected by the Agency for the final rule. 

Part II “Costs and Economic Impacts” (Chapters 5 through 11) presents EPA’s analysis of the economic impacts and social 

costs of the final rule. Chapter 5 presents the analysis of costs and impacts at the facility level. Chapters 6 through 9 present 

analyses of other types of economic impacts that derive from the facility-level analysis, including impacts on employment, 

governments (for EPA’s analyses under UMRA), communities, foreign trade, firms, and new facilities. Chapter 10 provides 

an analysis of impacts on small firms and governments, as required by RFA/SBREFA. Finally, Chapter 11 presents the social 

costs of the final rule. 

Part III “Benefits” (Chapters 12 through 17) provides EPA’s analysis of the environmental impacts and benefits of the final 

rule. Chapter 12 provides an overview of the benefits expected from the rule. Chapters 13 through 16 present EPA’s 

analyses of different components of the benefits analysis. These include human health benefits (except for lead-related) 

(Chapter 13), lead-related benefits (Chapter 14), recreational benefits (Chapter 15), and benefits to POTW s (Chapter 16). 

Chapter 17 presents an analysis of the environmental justice effects of the final rule, as required by Executive Order 12898. 

1-1
MP&M EEBA Part I: Introduction and Background Information Chapter 1: Introduction 

Part IV “Comparison of Costs and Benefits” (Chapters 18 and 19) compares the social costs and benefits for the final rule 

(Chapter 18) and for other regulatory alternatives evaluated by the Agency for the final rule (Chapter 19). 

Part V “ Ohio Case Study” (Chapters 20 through 22) provides a detailed case study of the final rule’s costs and benefits for 

the State of Ohio. This case study includes a more detailed and complete analysis of benefits, based on more complete 

information on the number and location of MP&M facilities and the characteristics of affected waters than was available for 

the national analyses. The case study also includes an original travel cost study to value recreational uses affected by the final 

rule. EPA believes that the case study provides more robust results because it avoids the uncertainties that result from the 

need to extrapolate sample facility results to the national level. The results of the case study generally confirm the overall 

results of the national analysis. 

Appendices to this report provide additional material in support of the analyses described in the chapters, including the 

following: 

� Appendix A: supporting material for the profile of the MP&M industries in Chapter 3; 

� Appendix B: description of the cost pass-through analysis; 

� Appendix C: description of the moderate impact analysis; 

� Appendix D: description of the methodology used to estimate capital outlays as part of the facility impact analysis; 

� Appendix E: description of the calculation of capital cost components; 

� Appendix F: description of the methodology used to estimate POTW administrative costs; 

� Appendix G: summary of the method used to extrapolate sample facility results to the national level; 

� Appendix H: description of fate and transport model for drinking water and Ohio analyses; 

� Appendix I: discussion of methodologies and results of the environmental assessment analysis; 

� Appendix J: analyses of spatial distribution of MP&M facility location and benefiting population; 

�	 	 Appendix K: description of the surface water valuation studies and specific values selected for assessing recreational 

benefits from the final regulation; 

� Appendix L: description of parameters in the IEUBK lead model; 

� Appendix M: sensitivity analysis of lead related benefits; and 

� Appendix N: analysis of the national demand for water-based recreation survey (NDS). 

The docket for the final rule, located at U.S. EPA Headquarters, provides additional supporting documentation, including: 

� copies of the literature cited in the report;
 


� documentation of the financial and economic portions of the MP&M Section 308 surveys;
 


� memorandums documenting supplementary analyses undertaken in support of regulation development, but that are
 


not included in the EEBA; and 

� datasets, spreadsheets, and programs used to perform the analyses. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 1: Introduction 

1.3 READERS’ AIDS

Each chapter includes a chapter-specific table of contents. A list of references is provided at the end of each chapter. 

Glossaries and lists of acronyms are also provided at the end of the chapters, and the first usage of items listed in them are 

denoted in the text with the following formats: 

� Glossary indicates that a term is defined in the chapter glossary, and 

� Acronym indicates that the acronym is included in the chapter list of acronyms. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 1: Introduction 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

Chapter 2: The MP&M Industry and
 

the Need for Regulation
 

INTRODUCTION 

The Metal Products and Machinery (MP&M) effluent 

guidelines establish limitations and standards only for direct 

dischargers in the Oily Wastes subcategory (40 CFR 438, 

Subpart A). EPA establishes industrial subcategories based 

on a number of considerations (see Chapter 4 and Section 6 

of the TDD). EPA evaluated seven other subcategories for 

the final rule: general metals, metal finishing job shops, non 

chromium anodizing, printed wiring board, railroad line 

maintenance, shipbuilding dry docks, and steel forming and 

finishing. EPA evaluated a number of options for these seven 

subcategories. Based on these analyses, EPA did not 

establish or revise limitations or standards for facilities in 

these seven subcategories. 

CHAPTER CONTENTS 

2.1 Overview of Facilities Evaluated for Regulation under 

the MP&M Point Source Category .............. 2-1 

2.2 MP&M Discharges and the Need For Regulation . . 2-3 

2.2.1 Baseline MP&M Discharges for Regulated 

Facilities ................. ......... 2-4 

2.2.2 Discharges under the MP&M Regulation . 2-4 

2.3 Addressing Market Imperfections .............. 2-5 

2.4 Overlap with Other Effluent Guidelines .......... 2-6 

2.5 Meeting Legislative and Litigation-Based 

Requirements ................. ............. 2-9 

Glossary ................. ................. ... 2-11 

Acronyms ................. ................. .. 2-13 

The facilities regulated under this rule produce, manufacture, rebuild, or maintain metal parts, products, or machines for use 

in sixteen different industrial sectors. These industrial sectors include: hardware, aircraft, aerospace, ordnance, electronic 

equipment, stationary industrial equipment, mobile industrial equipment, buses and trucks, motor vehicles, household 

equipment, instruments, office machines, railroads, ships and boats, precious metals and jewelry, and miscellaneous metal 

products. Most of the subcategories above serve multiple markets. EPA evaluated options that would have covered facilities 

in three additional industrial sectors: printed wiring boards, metal finishing job shops, and iron and steel. The final regulation 

does not cover facilities in these sectors. 

This chapter provides an overview of the MP&M industry evaluated for the final rule and presents the pollutant discharges 

from MP&M facilities subject to the final regulation. The chapter also reviews the reasons why EPA is regulating the 

industry’s effluent discharges including the need to reduce pollutant discharges from the MP&M industry, the issue of 

addressing market imperfections, other effluent guidelines that may overlap in coverage of the MP&M industry sectors 

evaluated for the final rule, and requirements that stem from the Clean Water Act (CWA) and litigation. 

2.1 OVERVIEW OF FACILITIES EVALUATED FOR REGULATION UNDER THE MP&M POINT 

SOURCE CATEGORY 

The MP&M Point Source Category regulates oily operations process wastewater discharges to surface waters from existing or 

new industrial facilities (including facilities owned and operated by Federal, State, or local governments) engaged in 

manufacturing, rebuilding, or maintenance of metal parts, products, or machines for use in the sixteen Metal Product & 

Machinery (MP&M) industrial sectors listed above. Please note the underlined language in the previous sentence as a facility 

may be subject to the MP&M effluent guidelines even if it is not in one of the MP&M industry sectors. For example, EPA 

considers a facility performing machining part of the “Bus & Truck” MP&M industry sector if it manufactures metal parts for 

truck trailers. Process wastewater means wastewater as defined at 40 CFR parts 122 and 401, and includes wastewater from 

air pollution control devices (see 40 CFR 438.2(g)). Oily operations are listed at 40 CFR 438.2(g) and defined in Appendix B 

to Part 438 (see also Section 4 of the TDD). 

As defined for this document, MP&M facilities: (1) produce metal parts, products, or machines for use in one of the 19 

industry sectors evaluated for coverage in the MP&M point source category; (2) use operations in one of the eight regulatory 

subcategories evaluated for coverage in the MP&M point source category; and (3) discharge process wastewater, either 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

directly or indirectly, to surface waters. MP&M facilities frequently produce products for multiple sectors and subcategories. 

As referred to in this document, MP&M facilities represent only a portion of all facilities in the industry sectors, since some 

facilities may perform operations that are not covered by one of the subcategories (i.e., part assembly or plastic molding), and 

some may not generate or discharge process wastewater. 

According to Statistics of U.S. Business, 1996, approximately 638,696 establishments operate in the MP&M industry sectors. 

Based on information in the MP&M survey database, approximately 44,000 facilities meet the definition of an MP&M 

facility. These 44,000 facilities include approximately 41,000 indirect dischargers (i.e., facilities discharging effluent to a 

publicly-owned sewage treatment works or POTWs) that would be subject to Pretreatment Standards for 

Existing Sources (PSES). The remaining 3,000 direct dischargers (i.e., they discharge effluent directly to a waterway 

under a National Pollutant Discharge Elimination System (NPDES) permit) and would thus be subject to Best Available 

Technology Economically Achievable (BAT ) and Best Practicable Control Technology Currently Available 

(BPT ) requirements. 

Table 2.1 reports the estimated number of MP&M facilities and total discharge flow (before final rule implementation) by 

type of facility. The largest number of sites, approximately 22,000, perform “rebuilding/maintenance only” and account for 

approximately 6 percent of the total estimated discharge flow for the industry. “Manufacturing only” contains the next largest 

number of facilities (15,400) and accounts, by far, for the largest percentage of the total estimated discharge flow for the 

industry (82 percent). 

Table 2.1: Number of MP&M Facilities and 

Total Discharge Flow by Type of Facility 

Type of Facility Number of 

Facilities 

Total Estimated 

Discharge Flow 

(million gal/yr) 

Percent of 

Facilities 

Percent of Total 

Discharge Flow 

Manufacturing & 

Rebuilding/Maintenance 6,600 9,400 15.0% 12.0% 

Manufacturing Only 15,400 64,100 35.0% 82.0% 

Rebuilding/Maintenance 

Only 22,000 4,700 50.0% 6.0% 

Total 44,000 78,200 100.0% 100.0% 

Source: U.S. EPA analysis. See Section 4 of the Technical Development Document for the final 

rule. 

Of the 43,858 water discharging facilities, 3,593 are predicted to close in the baseline, leaving 40,265 existing MP&M 

facilities that EPA estimates could be regulated.1 After accounting for subcategory and discharger class exclusions, EPA 

estimates that the final rule will regulate 2,382 of these facilities, all of which are direct dischargers. These regulated 

facilities represent 5.9 percent of the 40,265 facilities that could be potentially regulated. 

Table 2.2 summarizes information on the total number of MP&M facilities that were evaluated for the final rule, and the 

number that will be regulated under the final rule. As reported in Table 2.2, no indirect dischargers are subject to the final 

regulation. The rule will regulate 2,382 direct dischargers in the Oily Wastes subcategory. The rule excludes direct 

dischargers in the General Metals, Metal Finishing Job Shops, Non -Chromium Anodizing, Printed Wiring Board, Railroad 

Line Maintenance, Shipbuilding Dry Docks, and Steel Forming and Finishing subcategories (214 facilities, 12 facilities, 0 

facilities, 8 facilities, 6 facilities, 6 facilities, and 13 facilities, respectively) 2

. 

1 These are facilities that are predicted to close due to weak financial performance under baseline conditions, i.e., in the absence of 

the final rule. EPA does not attribute the costs or the reduced discharges resulting from these baseline closures to the final rule, and 

therefore excludes these facilities from its analyses of the rule’s impacts. Baseline closures account for differences between the universe of 

facilities discussed in this report and the universe discussed in the Technical Development Document. 

2 EPA excluded 3,511 indirect and 98 direct dischargers predicted to close in the baseline. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

Table 2.2: MP&M Facilities by Subcategory and Discharger Class, and 

Facilities Regulated Under the Final Rulea 

Subcategory 

Indirect Dischargers Direct Dischargers 

Evaluated for 

Regulation 

(# of facilities) 

Regulated under 

Final Rule 

(# of facilities) 

Evaluated for 

Regulation 

(# of facilities) 

Regulated under 

Final Rule 

(# of facilities) 

General Metals 10,244 0 214 0 

Metal Finishing Job Shop 1,479 0 12 0 

Non-Chromium Anodizing 93 0 0 0 

Printed Wiring Board 600 0 8 0 

Steel Forming & Finishing 12 0 13 0 

Oily Waste 24,394 0 2,382 2,382 

Railroad Line Maintenance 820 0 6 0 

Shipbuilding Dry Dock 9 0 6 0 

All Categories 37,652 0 2,641 2,382 

a Excludes facilities that close in the baseline. 

Source: U.S. EPA analysis. 

2.2 MP&M DISCHARGES AND THE NEED FOR REGULATION 

EPA is regulating the MP&M industry because the industry releases substantial quantities of pollutants, including toxic 

pollutant compounds (priority and nonconventional metals and organics) and conventional pollutants such as total 

suspended solids (TSS) and oil and grease (O&G). These MP&M industry pollutants are generally controlled by 

straightforward and widely-used treatment system technologies such as chemical precipitation and clarification (frequently 

referred to as the “lime and settle” process).3 

Discharges of these pollutants to surface waters and POTWs have a number of adverse effects, including degradation of 

aquatic habitats, reduced survivability and diversity of native aquatic life, and increased human health risk through the 

consumption of contaminated fish and water. In addition, many of these pollutants volatilize into the air, disrupt biological 

wastewater treatment systems, and contaminate sewage sludge. 

Metal constituents are of particular concern because of the large amounts present in MP&M effluents. Unlike some organic 

compounds and other wastes that are metabolized in activated sludge systems to relatively innocuous constituents, metals are 

chemical elements and cannot be eliminated. Moreover, in solution, some metals have a high affinity for biological uptake. 

Depending on site-specific conditions, metals form insoluble inorganic and organic complexes that partition to sewage sludge 

at POTW s or underlying sediment in aquatic ecosystems. The accumulated metal constituents can return to a bioavailable 

form upon land application of sewage sludge; dredging and resuspension of sediment; or as a result of seasonal, natural, or 

induced alteration of sediment chemistry. 

Benefits of reducing metal and other pollutant loads to the environment from MP&M facilities include reduced risk of cancer 

and systemic human health risks, improved recreation opportunities (e.g., fishing , swimming, boating, and other near-water 

recreational activities), improved aquatic and benthic habitats, and less costly sewage sludge disposal and increased beneficial 

use of the sludge.4 

3 See Chapter 12 and Appendix I for more detailed information on the pollutants of concern in the MP&M industry. 

4 Sewage sludge is also called biosolids. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

The goal of the MP &M regulation is to reduce pollutant discharges and to eliminate or reduce the level of risk and harm 

caused by them. These pollutant discharges and their harmful consequences are the externalities that the MP&M regulation 

addresses, as discussed in Section 2.3. 

2.2.1 Baseline MP&M Discharges for Regulated Facilities 

Table 2.3 provides an overview of the discharges from MP&M facilities that are regulated under the final rule. Loadings are 

defined as toxic-weighted loadings. This measure weights quantities of different pollutants by a measure of their relative 

toxicity. Toxic-weighted loadings measures the relative toxic effects of discharges containing different mixtures of pollutants. 

MP&M discharges also contain conventional pollutants with little or no toxic effects but that can have substantial adverse 

environmental impacts, such as O&G and some components of TSS. 

Table 2.3: Toxic-Weighted Discharges for Direct Discharging Facilities in the Oily 

Wastes Subcategory a 

# of Regulated Facilities 2,382 

Baseline Discharges 3,351 

Average Baseline Loadings per Facility 1.41 

Remaining Discharges Under Final Rule 668 

Average Discharges Under Final Rule per Facility 0.28 

Discharge Reductions Achieved by Final Rule 2,683 

(Pounds Equivalent) 

a Discharges discussed in this table are total discharges from the facility, and do not reflect POTW pollutant 

removals. EPA believes it is appropriate to analyze wastewater discharges disregarding POTW removals because 

indirect discharges present environmental risks that are not fully addressed by POTW treatment. The MP&M 

industry releases 89 pollutants that cause inhibition problems at POTWs and an additional 35 pollutants that 

volatilize to form hazardous air pollutants (HAPs) that may present a threat to human health or the environment. 

Other MP&M pollutants are found in POTW sludge. Only eight of these pollutants have land application 

pollutant criteria that limit the uses of sludge. 

b Excludes discharges from facilities that are projected to close in the baseline (327 lbs-equiv., or an average of 

4.4 lbs-equiv. per closing facility). 

Source: U.S. EPA analysis. 

As reported in Table 2.3, direct dischargers in the Oily Wastes subcategory currently release a total of 3,351 toxic weighted 

pounds per year, an average of 1.41 toxic weighted pounds per facility. After implementation of the final rule, EPA estimates 

that Oily Wastes direct dischargers will release only 668 toxic weighted pounds per year, an average of 0.28 toxic pounds per 

facility. EPA estimates that the final rule will reduce pollutant discharges by approximately 2,683 toxic weighted pounds per 

year. 

2.2.2 Discharges under the MP&M Regulation 

Reductions in toxic loadings result from treatment of effluents and pollution prevention at facilities that are subject to the 

regulation. Table 2.4 shows baseline and post-regulation loadings by type of pollutant, both as unweighted pounds and on a 

toxic-weighted basis, for facilities that are regulated under the final rule. The final rule eliminates 80.1 percent of the baseline 

toxic-weighted loadings from the facilities that are regulated, including 83.7 percent of the priority pollutants (87.3 percent 

of metals, 22.4 percent of organics, and 1.3 percent of arsenic) and 57.4 percent of the nonconventional pollutants (62.1 

percent of metals, 13.3 percent of organics, and 50.0 percent of “other inorganics”). The final rule also eliminates substantial 

fractions of the baseline discharges of conventional pollutants from the regulated facilities, including 6.6 percent of 

2-4 
MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

chemical oxygen demand (COD), 37.1 percent of biological oxygen demand (BOD), 93.2 percent of oil and grease 

(O&G ), and 54.1 percent of total suspended solids (TSS).5 

Table 2.4: Summary of Discharges by Pollutant Type for Facilities Regulated under the Final Rulea 

Pollutant Category 

Current Releases Releases under the Final Rule Final Rule Reductions 

Pounds Pounds Eq. Pounds Pounds Eq. Pounds Pounds Eq. 

Priority Pollutants 

Metals 794 2,756 153 351 641 2,405 

Organics 336 58 268 45 68 13 

Arsenic 22 75 21 74 1 1 

Cyanide (CN) 0 0 0 0 0 0 

Nonconventional Pollutants 

Metals 25,863 417 16,428 158 9,435 259 

Organics 2,159 45 1,038 39 1,121 6 

Other Inorganics 2,334 0.2 1,301 0.1 1,033 0.1 

Bulk Pollutants 335,679 167,295 168,384 

Conventional Pollutants 

BOD 263,419 165,567 97,852 

COD 523,440 488,697 34,743 

O&G 428,137 28,955 399,182 

TSS 160,695 73,769 86,926 

a Discharges discussed in this table are facility discharges and do not account for POTW removals. EPA believes it is appropriate to 

analyze wastewater discharges disregarding POTW removals because indirect discharges present environmental risks that are not fully 

addressed by POTW treatment. The MP&M industry releases 89 pollutants that cause inhibition problems at POTWs and an additional 

35 pollutants that volatilize to form hazardous air pollutants (HAPs) that may present a threat to human health or the environment. 

Other MP&M pollutants released by the industry are found in POTW sludge. Only eight of these pollutants have land application 

pollutant criteria that limit the uses of sludge. 

Source: U.S. EPA analysis. 

2.3 ADDRESSING MARKET IMPERFECTIONS 

Environmental legislation in general, and the CWA and the MP&M regulation in particular, seek to correct imperfections – 

uncompensated environmental externalities – in the functioning of the market economy. In manufacturing, rebuilding, and 

repairing metal products and machinery, MP&M facilities release pollutants that increase risks to human health and aquatic 

life and cause other environmental harm without accounting for the consequences of these actions on other parties (sometimes 

referred to as third parties) who do not directly participate in the business transactions of the business entities. 

5 It is not possible to provide an overall estimate of total pollutant pounds removed, because overlap among some of the pollutant 

categories would result in double-counting if the categories were summed. For example, TSS may include some of the priority pollutant 

and nonconventional metals discharges. Use of the toxic-weighted loadings avoids this double-counting, but does not include conventional 

pollutants. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

These costs are not borne by the responsible entities and are therefore external to the production and pricing decisions of the 

responsible entity. 

A profit-maximizing firm or a cost-minimizing government-owned facility will ignore these costs when deciding how much to 

produce and how to produce it. In addition, the externality is uncompensated because no party is compensated for the adverse 

consequences of the pollution releases. 

When these external costs are not accounted for in the production and pricing decisions of the responsible entities, their 

decisions will yield a mix and quantity of goods and services in the economy, and an allocation of economic resources to 

production activities, that are less than optimal. In particular, the quantity of pollution and related environmental harm caused 

by the activities of the responsible entities will, in general, exceed socially optimal levels. As a result, society will not 

maximize total social welfare. 

In addition, adverse distributional effects may accompany the uncompensated environmental externalities. If the 

distribution of pollution and environmental harm is not random among the U.S. population, but instead is concentrated among 

certain population subgroups based on socio-economic or other demographic characteristics, then the uncompensated 

environmental externalities may produce undesirable transfers of economic welfare among subgroups of the population. See 

Chapter 17: Environmental Justice and Protection of Children for more information. 

The goal of environmental legislation and implementing regulations, including the final MP&M rule that is the subject of this 

EEBA, is to correct these environmental externalities by requiring businesses and other polluting entities to reduce their 

pollution and environmental harm. Congress, in enacting the authorizing legislation, and EPA, in promulgating the 

implementing regulations, act on behalf of society to achieve a mix of goods and services and a level of pollution that more 

nearly approximates socially optimal levels. As a result, the mix and quantity of goods and services provided by the 

economy, the allocation of economic resources to those activities, and the quantity of pollution and environmental harm 

accompanying those activities will yield higher economic welfare to society. 

Requiring polluting entities to reduce levels of pollution and environmental harm is one approach to addressing the problem 

of environmental externalities. This approach imposes costs on the polluting entities in the form of compliance costs incurred 

to reduce pollution to allowed levels. A polluting entity will either incur the costs of meeting the regulatory limits or will 

determine that compliance is not in its best financial interest and will cease the pollution-generating activities. This approach 

to addressing the problem of environmental externalities will generally result in improved economic efficiency and net 

welfare gains for society if the cost of reducing the pollution and environmental harm activities is less than the value of 

benefits to society from the reduced pollution and environmental harm. 

It is theoretically possible to correct the market imperfection by means other than direct regulation. For example, negotiation 

and/or litigation could achieve an optimal allocation of economic resources and mix of production activities within the 

economy. However, the transaction costs of assembling the affected parties and involving them in the negotiation/litigation 

process, as well as the public goods character of the improvement sought by negotiation or litigation, make this approach 

impractical. 

2.4 OVERLAP WITH OTHER EFFLUENT GUIDELINES 

EPA has previously promulgated effluent guidelines regulations for 13 metals-related industries. In some instances, these 

industries may perform operations that are found in MP&M facilities. These effluent guidelines are: 

� Electroplating (40 CFR Part 413), 

� Iron & Steel Manufacturing (40 CFR Part 420), 

� Nonferrous Metals Manufacturing (40 CFR Part 421), 

� Ferroalloy Manufacturing (40 CFR Part 424), 

� Metal Finishing (40 CFR Part 433), 

� Battery Manufacturing (40 CFR Part 461), 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

� Metal Molding & Casting (40 CFR Part 464), 

� Coil Coating (40 CFR Part 465), 

� Porcelain Enameling (40 CFR Part 466), 

� Aluminum Forming (40 CFR Part 467), 

� Copper Forming (40 CFR Part 468), 

� Electrical & Electronic Components (40 CFR Part 469), and 

� Nonferrous Metals Forming & Metal Powders (40 CFR Part 471). 

In 1986, the Agency reviewed coverage of these regulations and identified a significant number of metals processing facilities 

discharging wastewater that these 13 regulations did not cover. From this review, EPA performed a more detailed analysis of 

these unregulated sites and identified the discharge of significant amounts of pollutants. This analysis resulted in the 

formation of the “Machinery Manufacturing and Rebuilding” (MM&R) point source category. In 1992, EPA changed the 

name of the category to “Metal Products and Machinery” (MP&M) to clarify coverage of the category (57 FR 19748). 

Only direct dischargers in the Oily Wastes subcategory will be regulated under the final regulations for 40 CFR Part 38. 

Table 2.5 shows the MP&M subcategories and the coverages that apply to each. EPA does not intend this table to be 

exhaustive, but rather to provide a general overview of the applicability of the Electroplating, Metal Finishing, and Metal 

Products & Machinery effluent guidelines. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry 

Table 2.5: Coverage by MP&M Subcategory 

Subcategory 

Continue Coverage under 40 CFR Part 413 

(Electroplating) 

Continue Coverage under 40 CFR Part 433 

(Metal Finishing) 

Coverage under 40 C

(Metal Products & M

General Metals (including 

Continuous Electroplaters) 

Existing indirect dischargers currently covered by Part 413. 

New and existing direct and indirect dischargers 

currently covered by Part 433. 

None 

Metal Finishing Job Shops Existing indirect dischargers currently covered 

by Part 413. 

New and existing direct and indirect dischargers 

currently covered by Part 433. 

None 

Non-Chromium Anodizers a Existing indirect dischargers that are currently 

covered by 413. 

New and existing direct and indirect dischargers 

currently covered by Part 433. 

None 

Printed Wiring Board (Printed Circuit Board) 

Existing indirect dischargers that are currently 

covered by 413. 

New and existing direct and indirect dischargers 

currently covered by Part 433. 

None 

Steel Forming & Finishing 

Wire Drawinga 

N/A N/A None 

Oily Wasteb N/A N/A All new and existing 

subcategory. (See 43

Railroad Line Maintenanceb N/A N/A None 

Shipbuilding Dry Docksb N/A N/A None 

a These facilities will continue to be subject to Part 420. b There are no national categorical pretreatment standards for these facilities. 

Source: U.S. EPA analysis. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

Figure 2.1 illustrates the relationship among the various metals industries effluent guidelines. 

Figure 2.1: Metals Industries Effluent Guidelines Covered Under 40CFR 

Source: U.S. EPA analysis. 

2.5 MEETING LEGISLATIVE AND LITIGATION-BASED REQUIREMENTS 

EPA’s effluent limitations guidelines and standards for the MP&M industry are under authority of the CWA, Sections 301, 

304, 306, 307, and 501. These CWA sections require the EPA Administrator to publish limitations and guidelines for 

controlling industrial effluent discharges consistent with the overall CWA objective to “restore and maintain the chemical, 

physical, and biological integrity of the Nation’s waters.” EPA’s MP&M industry regulation responds to these requirements. 

In addition, the MP&M regulation responds to the requirements of a consent decree entered by the Agency as a result of 

litigation. Section 304(m) of the CWA (33 U.S.C. 1314(m)), added by the Water Quality Act of 1987, required EPA to 

establish schedules for (i) reviewing and revising existing effluent limitations guidelines and standards, and (ii) promulgating 

new effluent guidelines. On January 2, 1990, EPA published an Effluent Guidelines Plan (55 FR 80), in which schedules 

were established for developing new and revised effluent guidelines for several industry categories. One of the industries for 

which the Agency established a schedule was the Machinery Manufacturing and Rebuilding Category (MM&R).6 

6 The name was changed to Metal Products and Machinery (MP&M) in 1992 to avoid confusion over what was covered by the rule. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

The Natural Resources Defense Council, Inc. (NRDC) and Public Citizen, Inc. challenged the Effluent Guidelines Plan in a 

suit filed in U.S. District Court for the District of Columbia (NRDC et al v. Reilly, Civ. No. 89-2980). The plaintiffs charged 

that EPA’s plan did not meet the requirements of Section 304(m). A Consent Decree in this litigation was entered by the 

Court on January 31, 1992. This plan required, among other things, that EPA propose effluent guidelines for the MP&M 

category by November, 1994 and take final action on these effluent guidelines by May, 1996. EPA filed a motion with the 

Court on September 28, 1994, requesting an extension until March 31, 1995, for the EPA Administrator to sign the proposed 

regulation and a subsequent four month extension for signature of the final regulation in September 1996. EPA published a 

proposal entitled, “Effluent Limitations Guidelines, Pretreatment Standards, and New Source Performance Standards: Metal 

Products and Machinery” (60 FR 28210) on May 30, 1995. 

EPA initially divided the industry into two phases based on industry sector, as the Agency believed that would make the 

regulation more manageable. The Phase I proposal included the following industry sectors: Aerospace; Aircraft; Electronic 

Equipment; Hardware; Mobile Industrial Equipment; Ordnance; and Stationary Industrial Equipment. At that time, EPA 

planned to propose a rule for the Phase II sectors approximately three years after the MP&M Phase I proposal. 

EPA received over 4,000 pages of public comment on the Phase I proposal. One area where commenters from all stakeholder 

groups (i.e., industry, environmental groups, and regulators) were in agreement was that EPA should not divide the MP&M 

industry into two separate regulations. Commenters raised concerns regarding the regulation of similar facilities with 

different compliance schedules and potentially different limitations for similar processes based solely on whether the facilities 

were in a Phase I or Phase II sector. Furthermore, a large number of facilities performed work in multiple sectors. In such 

cases, permit writers and control authorities (e.g., POTW s) would need to decide which MP&M rule (Phase I or 2) applied to 

a facility. 

Based on these comments, EPA decided to combine the two phases of the regulation into one proposal. EPA published a 

proposal entitled, “Effluent Limitations Guidelines, Pretreatment Standards, and New Source Performance Standards for the 

Metal Products and Machinery Point Source Category” (66 FR 424) on January 3, 2001. The proposal published in January 

2001 completely replaced the 1995 proposal. 

On June 5, 2002, EPA published a Notice of Data Availability (NODA) (67 FR 38752). In the NODA, EPA discussed major 

issues raised in comments on the 2001 proposal; suggested revisions to the technical and economic methodologies used to 

estimate compliance costs, pollutant loadings, and economic and environmental impacts; presented the results of these 

suggested methodology changes and incorporation of new (or revised) data; and summarized the Agency’s thinking on how 

these results could affect the Agency’s final decisions. 

This report addresses the 304(m) decree as amended, requiring the MP&M rules to be promulgated by February 14, 2003. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

GLOSSARY 

Best Available Technology Economically Achievable: Effluent limitations for direct dischargers, addressing priority
 


and nonconventional pollutants. BAT is based on the best existing economically achievable performance of plants in the
 


industrial subcategory or category. Factors considered in assessing BAT include the cost of achieving BAT effluent
 


reductions, the age of equipment and facilities involved, the processes employed, engineering aspects of the control
 


technology, potential process changes, non-water quality environmental impacts (including energy requirements), economic
 


achievability, and such factors as the Administrator deems appropriate. The Agency may base BAT limitations upon effluent
 


reductions attainable through changes in a facility's processes and operations. Where existing performance is uniformly
 


inadequate, EPA may base BAT upon technology transferred from a different subcategory within an industry or from another
 


industrial category.
 


Best Practicable Control Technology Currently Available: Effluent limitations for direct discharging facilities,
 


addressing conventional, toxic, and nonconventional pollutants. In specifying BPT, EPA considers the cost of achieving
 


effluent reductions in relation to the effluent reduction benefits. The Agency also considers the age of the equipment and
 


facilities, the processes employed and any required process changes, engineering aspects of the control technologies, non-


water quality environmental impacts (including energy requirements), and such other factors as the Agency deems
 


appropriate. Limitations are traditionally based on the average of the best performances of facilities within the industry of
 


various ages, sizes, processes, or other common characteristics. Where existing performance is uniformly inadequate, EPA
 


may require higher levels of control than currently in place in an industrial category if the Agency determines that the
 


technology can be practically applied.
 


bioavailable: Degree of ability to be absorbed and ready to interact in organism metabolism.
 


(http://www.epa.gov/OCEPAterms)
 


biological oxygen demand: A measure of the amount of oxygen consumed in the biological processes that break down
 


organic matter in water. The greater the BOD, the greater the degree of pollution.
 


(http://www.epa.gov/OCEPAterms/bterms.html)
 


chemical oxygen demand: A measure of the oxygen required to oxidize all compounds, both organic and inorganic, in
 


water. (http://www.epa.gov/OCEPAterms/cterms.html)
 


Clean Water Act: Act passed by the U.S. Congress to control water pollution. Formerly referred to as the Federal Water
 


Pollution Control Act of 1972 or Federal Water Pollution Control Act Amendments of 1972 (Public Law 92-500), 33 U.S.C.
 


1251 et. seq., as amended by: Public Law 96-483; Public Law 97-117; Public Laws 95-217, 97-117, 97-440, and 100-04.
 


conventional pollutants: Statutorily listed pollutants understood well by scientists. These may be in the form of organic
 


waste, sediment, acid, bacteria, viruses, nutrients, oil and grease, or heat. (http://www.epa.gov/OCEPAterms)
 


distributional effects: Occurs when the distribution of pollution and environmental harm is not random among the U.S.
 


population, but instead is concentrated among certain population subgroups based on socio-economic or other demographic
 


characteristics, then the uncompensated environmental externalities may produce undesirable transfers of economic welfare
 


among subgroups of the population.
 


externalities: Costs or benefits of market transactions that are not reflected in the prices buyers and sellers use to make
 


their decisions. An externality is a by-product of the production or consumption of a good or service that affects someone not
 


immediately involved in the transaction.
 


(http://www.enmu.edu/users/biced/home/glossary.html)
 


A type of market failure that causes inefficiency.
 


(http://www.amosweb.com/cgi-bin/gls_dsp.pl?term=externalities)
 


MP&M facilities: MP&M facilities are defined on the basis of three considerations: (1) they produce metal parts, products,
 


or machines for use in one of the 19 industry sectors evaluated for coverage in the MP&M point source category; (2) they use
 


operations in one of the eight regulatory subcategories evaluated for coverage in the MP&M point source category; and (3)
 


they discharge process wastewater, either directly or indirectly, to surface waters. In this document, the term “MP&M
 


facilities” refers to all facilities meeting the above definition, regardless of whether a facility’s industrial sector, subcategory,
 


or discharger category is covered by the final regulation. If the MP&M facilities are referred to as “regulated” facilities or
 


facilities “subject to the final regulation”, the use of the qualifier “regulated” or “subject to the final regulation” restricts
 


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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

the definition to include only those facilities in the industry sectors, subcategory, and discharger category covered by the final
 


regulation.
 


MP&M industry: The facilities and markets comprising the 19 industry sectors evaluated for coverage in the MP&M point
 


source category. In this document, the term “MP&M industry” refers to the full 19 industry sectors, regardless of whether an
 


industry sector is covered by the final regulation. If the MP&M industry is referred to as the regulated MP &M industry, the
 


use of the qualifier “regulated” restricts the definition to only the industry sectors, subcategory, and discharger category
 


covered by the final regulation.
 


nonconventional pollutants: Any pollutant not statutorily listed or which is poorly understood by the scientific
 


community.
 


(http://www.epa.gov/OCEPAterms)
 


oil and grease (O&G): These organic substances may include hydrocarbons, fats, oils, waxes and high-molecular fatty
 


acids. Oil and grease may produce sludge solids that are difficult to process. (http://www.epa.gov/owmitnet/reg.htm)
 


Pretreatment Standards for Existing Sources (PSES): Categorical pretreatment standards for existing indirect
 


dischargers, designed to prevent the discharge of pollutants that pass through, interfere with, or are otherwise incompatible
 


with the operation of POTWs. Standards are technology-based and analogous to BAT effluent limitations guidelines.
 


priority pollutants: 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,
 


sediment, groundwater or soil samples.
 


publicly-owned treatment works: A treatment works for municipal sewage or liquid industrial wastes that is owned by a
 


State or municipality.
 


socially optimal level: Situation in which it is impossible to make any individual better off without making someone else
 


worse off. Also referred to as Pareto optimal.
 


social welfare: The sum of the welfare of all participants in the society; measured by the sum of consumer surplus --the
 


value consumers derive from goods and services less the price they have to pay for the goods and services -- and producers'
 


surplus -- the revenue received by producers of goods and services less their costs of producing the goods and services.
 


third parties: Those affected by a by-product of the production or consumption of a good or service that are not
 


immediately involved in the transaction.
 


total suspended solids: A measure of the suspended solids in wastewater, effluent, or water bodies, determined by tests
 


for "total suspended non-filterable solids."
 


(http://www.epa.gov/OCEPAterms/tterms.html).
 


toxic-weighted pollutants: This measure weights quantities of different pollutants in effluents by a measure of their
 


relative toxicity. Toxic-weighted loadings measures the relative toxic effects of discharges containing different mixtures of
 


pollutants.
 


uncompensated: Where parties damaged by externalities receive no compensation for accepting the damage.
 


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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation 

ACRONYMS 

BAT : Best Available Technology Economically Achievable
 


BPT: Best Practicable Control Technology Currently Available
 


BOD: biological oxygen demand
 


COD: chemical oxygen demand
 


CWA: Clean Water Act
 


MM &R: Machinery Manufacturing and Rebuilding
 


MP&M: Metal Products and Machinery
 


NPDES: National Pollutant Discharge Elimination System
 


NRDC: Natural Resources Defense Council
 


O&G: oil and grease
 


POTW: publicly-owned treatment works
 


PSES: Pretreatment Standards for Existing Sources
 


TSS: total suspended solids
 


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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Chapter 3: Profile of the MP&M
 


Industry Sectors
 


INTRODUCTION 

The final MP&M rule will apply to facilities that 

manufacture, rebuild, or maintain metal parts, products or 

machines to be used in a large number of industrial sectors. 

Manufacturing is the series of unit operations necessary to 

produce metal products, and is generally performed in a 

production environment. Rebuilding/maintenance is the 

series of unit operations necessary to disassemble used metal 

products into components, replace the components or 

subassemblies or restore them to original function, and 

reassemble the metal product. These operations are intended 

to keep metal products in operating condition and can be 

performed in either a production or a non-production 

environment. Manufacturing and rebuilding/maintenance 

activities often occur at the same facilities. 

The MP&M industry encompasses a large number of 

industries that manufacture intermediate and final goods, 

support transportation and other vehicle services, and repair 

and maintain products and equipment. The health of the 

MP&M industry is generally tied to the overall economic 

performance of the economy. The MP&M industry includes 

manufacturing and non-manufacturing industries defined by 

224 4-digit Standard Industrial Classification (SIC) 

codes, which are grouped into nineteen industry sectors.1 Of 

the 224 SIC codes, 174 are manufacturing (SICs 20 through 

39) and 50 are non-manufacturing. All nineteen sectors 

include manufacturing industries, and eleven include nonmanufacturing industries as well. 

Although EPA evaluated regulatory options that would have 

covered facilities operating in any of the nineteen sectors, the 

final regulation covers facilities operating only in sixteen of 

those sectors. 

CHAPTER CONTENTS 

3.1 Data Sources ................. ............. 3-2 

3.2 Overview of the MP&M Industry and Industry 

Trends ................. ............... 3-3 

3.2.1 Aerospace ................. ........ 3-7 

3.2.2 Aircraft ................. .......... 3-7 

3.2.3 Electronic Equipment ................ 3-7 

3.2.4 Hardware ................. ........ 3-8 

3.2.5 Household Equipment ............... 3-8 

3.2.6 Instruments ................. ....... 3-9 

3.2.7 Iron and Steel ................. ..... 3-9 

3.2.8 Job Shops ................. ........ 3-9 

3.2.9 Mobile Industrial Equipment .......... 3-9 

3.2.10 Motor Vehicle and Bus & Truck ...... 3-10 

3.2.11 Office Machine ................. ... 3-10 

3.2.12 Ordnance ................. ....... 3-10 

3.2.13 Precious Metals and Jewelry .......... 3-11 

3.2.14 Printed Wiring Boards .............. 3-11 

3.2.15 Railroad ................. ........ 3-11 

3.2.16 Ships and Boats ................. .. 3-11 

3.2.17 Stationary Industrial Equipment ....... 3-12 

3.3 Characteristics of MP&M Manufacturing Sectors 3-12 

3.3.1 Domestic Production ............... 3-13 

3.3.2 Industry/Market Structure ............ 3-18 

3.3.3 Financial Condition and Performance . . 3-24 

3.4 Characteristics of MP&M Non-Manufacturing 

Sectors ................. ............. 3-25 

3.4.1 Domestic Production ............... 3-25 

3.4.2 Industry Structure and Competitiveness . 3-28 

3.5 Characteristics of All MP&M Sectors ......... 3-30 

3.5.1 Eight-firm Concentration Ratio ....... 3-30 

3.5.2 Risk Normalized Return on Assets ..... 3-31 

3.6 Characteristics of MP&M Facilities ........... 3-32 

Glossary ................. ................. ... 3-38 

Acronyms ................. ................. .. 3-40 

References ................. ................. . 3-41 

This chapter provides a profile of the industry sectors that were evaluated for coverage by the MP&M rule. The profile 

focuses on the economic characteristics of the sectors and the facilities within the sectors, which may affect the rule’s 

financial and economic impacts. It presents and interprets a wide variety of data associated with production, market structure, 

and competitiveness, for each sector and for the MP&M industry as a whole. 

1 Appendix A lists the nineteen sectors and their associated 4-digit SIC codes. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

3.1 DATA SOURCES 

This profile presents data from the Economic Censuses, Statistics of U.S. Businesses (SUSB), Annual Survey of 

Manufacturers (ASM ), U.S. Industry and Trade Outlook, EPA’s Sector Notebooks, and other sources, to characterize the 

MP&M sectors, including both dischargers and non-dischargers. 

The years 1988 and 1996 were chosen as the years for which data are presented because these are the base analysis years, 

respectively, for the MP&M Phase 1 sectors survey and the Phase 2 survey. In the cases when data for those years were not 

available, data from other years were used. 

This profile relies on industries defined by SICs, both because data collection for the MP& M sectors was defined by SICs and 

to allow use of historical data. The Census Bureau switched to use of the new North American Industry Classification 

System (NAICS) codes starting with the 1997 Economic Censuses. Data classified by NAICS code were converted to SIC 

format before being included in the profile. The conversion used a bridge containing the percentage of each NAICS code that 

needed to be assigned to each SIC code. For a detailed discussion of the bridge, see Appendix A. 

The Agency used survey data to characterize the facilities within the MP&M sectors that are potentially subject to the rule 

because they discharge process wastewater from MP&M operations. The survey provides data such as discharge type, small 

business status, sources of revenues, and financial performance. 

The survey requested information on the sectors from which each facility derives its revenues. Many facilities derive 

revenues from more than one sector. It is therefore difficult to link facility characteristics to a specific sector. Data on the 

potentially-regulated facilities are therefore summarized by the regulatory subcategories rather than by sectors. 

All monetary values are shown in real 2001 dollars. EPA used the Producer Price Index (PPI) for industrial commodities 

as a conversion tool. A PPI is an index that measures price changes, from the perspective of the seller, of a collection of 

goods and services that are important inputs for a specific industry or for the economy as a whole. This chapter uses the PPI 

for industrial commodities to inflate nominal values to real values. Later chapters include PPI’s that are sector specific. 

These PPI’s are derived from the average of the PPI’s for each component industry SIC code, weighted by industry output. 

Table 3-1 shows the PPI values for the relevant years for which prices were deflated. The PPI for industrial commodities 

increased slightly every year between 1988 and 1996. Total inflation for industrial commodities from 1988 to 1996 was 

19.8%. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.1: Producer Price Index for Industrial Commodities 

Year Producer Price Index (PPI) Percent Change 

1988 100.0 n/a 

1989 105.0 5.0% 

1990 108.9 3.8% 

1991 109.6 0.6% 

1992 110.4 0.8% 

1993 111.9 1.4% 

1994 113.5 1.4% 

1995 118.1 4.0% 

1996 119.8 1.4% 

1997 120.1 0.3% 

1998 117.4 -2.3% 

1999 119.0 1.4% 

2000 126.8 6.6% 

2001 127.7 0.7% 

Source: Bureau of Labor Statistics, Producer Price Index. 

3.2 OVERVIEW OF THE MP&M INDUSTRY AND INDUSTRY TRENDS 

This section provides a general overview of the MP&M industry. It describes the individual MP&M industry sectors, 

provides basic economic information about MP&M manufacturers, and summarizes recent industry trends. 

Figure 3-1 shows that MP&M facilities are located in every state. A few MP& M sectors such as shipbuilding are 

concentrated geographically. Transportation-related MP&M facilities are found throughout the country. Overall, MP&M 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

facilities are most concentrated in the heavy industrial regions along the Gulf Coast, both the East and West Coasts, and the 

Great Lakes Region (New York, Pennsylvania, Ohio, Indiana, Illinois, and Michigan). 

Figure 3.1: Number of MP&M Facilities by State in 1992 

Source: Department of Commerce, Bureau of the Census, Census of Manufacturers, Census of Transportation, Census of Wholesale 

Trade, Census of Retail Trade, Census of Service Industries, 1992. 

Table 3.2 lists the MP&M sectors and provides a brief description of the products and services produced by each. Appendix 

A provides a more detailed list of the 4-digit SIC codes in each sector. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.2: MP&M Sector Definitions 

Sector Sector Description 

Aerospace Metal parts or products such as missiles, space vehicles, satellites and associated launching 

equipment. 

Aircraft Metal parts or products including all types of aircraft for public, private or commercial use. 

aircraft parts and equipment as well as aircraft maintenance activities. 

Bus and Truck Metal parts or products including freight trucks and trailers as well as public, private and commercial 

buses. ludes all associated equipment including equipment specific to truck and bus terminals. 

Includes bus and truck maintenance activities. 

Electronic Equipment Metal parts or products including general electronic components such as tubes, capacitors, and 

transformers, as well as finished electronic equipment such as televisions, radios, and telephones. 

Hardware Metal parts or products such as tools, cutlery, valves and tubing, dies, springs, sheet metal, drums, and 

heat treating equipment. 

Household Equipment Metal parts or products including appliances such as refrigerators, laundry equipment, lighting 

equipment, cooking equipment, and vacuum cleaners. munication type radios and 

televisions are included in this sector. 

Iron and Steel Sites engaged in iron or steel manufacturing, forming and finishing. 

Instruments Metal parts or products such as laboratory and medical equipment, measuring devices, environmental 

and process controls, optical equipment, surgical and dental equipment, and pens. 

Metal Finishing Job Shop Facilities with more than 50 percent of their revenues coming from work on products not owned by 

the site. While there are SIC codes associated with some Metal Finishing Job Shops, they sell to a 

variety of markets and are not a market in and of themselves. 

Mobile Industrial Equipment Metal parts or products including tractors and other farm equipment, construction machinery and 

equipment, mining machinery and equipment, industrial cranes and hoists, and tracked military 

vehicles. 

Motor Vehicle Metal parts or products including private passenger vehicles and associated parts and accessories such 

as automobiles, motorcycles, utility trailers and recreational vehicles, and mobile homes. 

Office Machines Metal parts or products including office computer equipment, storage devices, printers, photocopiers 

and associated parts and accessories. 

Ordnance Metal parts or products including all small arms, artillery, and ammunition with the exception of 

missiles (aerospace). anufacture of explosives. 

Other Metal Products Metal parts or products including products and machinery not categorized into the other sectors (e.g., 

sporting goods, musical instruments). 

Precious Metals and Jewelry Metal parts or products including jewelry, silverware, trophies, and clocks as well as all associated 

parts and accessories. 

Printed Wiring Boards Metal parts or products including printed wiring boards and printed circuit boards. 

Railroad Metal parts or products including railcars, locomotives and associated parts and accessories as well as 

track, switching and terminal stations. 

Ships and Boats Metal parts or products including ships and boats for military, freight, and private recreation. 

submarines, ferries, tug boats, barges, yachts, and other recreational boats as well as all parts and 

accessories. lso includes rebuilding and maintenance activities performed at marinas, dry docks, and 

other on shore activities specifically related to ships and boats. 

Stationary Industrial 

Equipment 

Metal parts or products including all industrial machinery, such as turbines, oil field machinery, 

elevators and moving stairways, conveying equipment, chemical process industry equipment, pumps, 

compressors, blowers, industrial ovens, vending machines, commercial laundry equipment, 

commercial refrigeration and heating equipment, welding apparatus, motors, and generators. 

Includes 

Inc 

Non-com 

Does not include the chemical processing or the m 

Includes 

A 

Source: U.S. EPA analysis. 

Table 3.3 shows output by sector for manufacturers, non-manufacturers, and all MP&M firms. Output is a good indicator of 

the overall size of a market. In 1997, MP&M firms accounted for more than $2.8 trillion in output. Motor vehicles were the 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

largest single MP&M sector, accounting for 43 percent of all MP&M output. Ordnance is the smallest sector, with 0.2 

percent of MP&M output. 

The MP&M manufacturing and non-manufacturing sectors differ in several important ways. The manufacturing sector 

accounted for $1.6 trillion in output, equal to 57 percent of the total MP&M output. The non-manufacturing sector accounted 

for $1.2 trillion, or 43 percent of MP&M output. Although MP&M non-manufacturers’ revenues were nearly $400 billion 

smaller than manufacturers’ revenues, the MP&M non-manufacturers had three times as many facilities as the MP&M 

manufacturers. Also, although manufacturing output was relatively evenly divided among the different sectors, more than 86 

percent of non-manufacturing output came from the motor vehicle and bus and truck sectors. 

Table 3.3: MP&M Output and Share in 1997a

 (millions, 2001$) 

Sector 

Manufacturers Non-Manufacturers Sector Total 

Output b Share Output b Share Output b Share 

Aerospace 20,115.1 1.2% 20,115.1 0.7% 

Aircraft 105,163.8 6.4% 9,935.9 0.8% 115,099.7 4.0% 

Bus & Truck 15,118.4 0.9% 209,316.1 16.7% 224,434.5 7.8% 

Electronic Equipment 145,886.9 8.9% 145,886.9 5.1% 

Hardware 189,145.5 11.6% 189,145.5 6.6% 

Household Equipment 102,242.3 6.3% 2,847.7 0.2% 105,090.0 3.6% 

Instruments 141,548.0 8.7% 7,401.9 0.6% 148,949.9 5.2% 

Iron and Steel 20,403.0 1.2% 20,403.0 0.7% 

Job Shop c 15,360.2 0.9% 15,360.2 0.5% 

Mobile Industrial Equipment 54,704.7 3.3% 54,704.7 1.9% 

Motor Vehicle 366,448.7 22.4% 870,450.5 69.6% 1,236,899.2 42.8% 

Office Machine 119,783.0 7.3% 30,929.9 2.5% 150,712.9 5.2% 

Ordnance 5,778.8 0.4% 5,778.8 0.2% 

Other Metal Products 60,249.6 3.7% 22,040.7 1.8% 82,290.3 2.9% 

Precious Metals and Jewelry 9,760.7 0.6% 367.4 0.0% 10,128.1 0.4% 

Printed Wiring Boards 10,400.7 0.6% 10,400.7 0.4% 

Railroad d 8,412.6 0.5% 30,727.9 2.5% 39,140.5 1.4% 

Ships and Boats 18,081.1 1.1% 37,383.0 3.0% 55,464.1 1.9% 

Stationary Industrial Equipment 227,053.7 13.9% 29,747.1 2.4% 256,800.8 8.9% 

Total MP&M 1,635,656.8 100.0% 1,251,148.1 100.0% 2,886,804.9 100.0% 

Percent of total 56.7% 43.3% 100.0% 

a Data for 1996 were not available, so economic census data from 1997 were used. b Value of shipments for manufacturing industries; total sales for retail and wholesale trade; total receipts for service industries; total 

revenue for transportation. c Includes facilities in two SICs that are defined specifically as job shops (SICs 3471 and 3479.) Facilities reporting in other sectors 

may also operate as job shops, so these data are likely to understate the true output of MP&M job shops. d Non-manufacturing railroad data are estimated based on 1992 data. 

Source: Department of Commerce, Bureau of the Census, Census of Manufacturers, Census of Transportation, Census of Wholesale 

Trade, Census of Retail Trade, Census of Service Industries, 1997. 

The following sections describe the MP&M sectors and briefly discuss recent industry trends in each sector. The discussion is 

based on 2001 Value Line Industry Reports, U.S. Industry and Trade Outlook 2000 (DRI-McGraw Hill), EPA’s Sector 

Notebooks, and other sources. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

3.2.1 Aerospace 

The aerospace industry includes original equipment manufacturers (OEM ) and facilities that rebuild and repair aerospace 

equipment. The industry serves both military and commercial end-uses such as space vehicles for commercial communication 

satellites, although military applications dominate. Its products include guided missiles, space vehicles, and associated 

propulsion units and parts. The assembly of aerospace products draws on numerous other industries, including plastics, 

rubber, fabricated metals, metal casing, glass, textile, and electronic components. Aerospace products are typically produced 

by a prime contractor and several tiers of subcontractors. Final assembly is performed by relatively few facilities, only a 

small number compared with the numerous subassembly and parts manufacturers. Aerospace manufacturing is extremely 

capital intensive. 

The U.S. aerospace industry has consolidated substantially in recent years, due to declines in defense spending. The number 

of facilities and firms as well as sector value of shipments and employment decreased from 1988 to 1996 in the US. 

Growth in the industry is expected to come from lower cost air-to-air missiles, with strong focus on increasing efficiency in 

production by reducing costs. Consumer demand has also grown for direct-to-home television, voice and data transmission, 

and other satellite services, which have increased the commercial demand for space vehicles needed to launch satellites. 

The aerospace industry exports a substantial share of its output. Many North American and European governments with large 

defense budgets have been seeking to reduce their military budgets, while governments in South America (with smaller 

budgets) have been maintaining or increasing their defense spending. Substantial consolidation has occurred in the European 

aerospace industry, which has become more competitive with U.S. companies (U.S. EPA 1997; DRI/McGraw Hill 2000). 

3.2.2 Aircraft 

Trends in the aircraft sector are heavily influenced by changes in industry structure and in the international political-economic 

arena. Although new aircraft production increased substantially in 1998 and 1999, production weakened in 2001 because of 

the economic slowdown and then plummeted following the September 11th terrorist attacks. Airlines have reacted to falling 

ticket sales by cutting scheduled flights, reducing personnel, and delaying or cancelling investment in new aircraft. 

During the 1990's, there was substantial restructuring through mergers and consolidation in the aircraft manufacturing 

industry, including producers of both aircraft and aircraft parts nationally and internationally. Firms focused on improving 

efficiency through cost cutting efforts such as reduced staffing. In addition, there is a growing trend for U.S. producers to 

outsource many aircraft parts to firms in other nations, in order to bring down costs and compete internationally. 

In addition to aircraft manufacturing, this sector includes rebuilding and repair of aircraft at manufacturers’ facilities or at 

airports.2 The aircraft maintenance and repair industry has slowed with the post 9/11 decline in passenger travel. 

3.2.3 Electronic Equipment 

The electronic equipment sector can be divided into two general groups of industries: microelectronics manufacturers and 

telecommunications equipment manufacturers. 

Microelectronics industries manufacture a wide range of products, from electronic connectors to integrated circuit panels. 

These products are used as material inputs in many industries such as automotive, telecommunications, aerospace, computer, 

and medical equipment. Although the microelectronics industry covers a diverse array of products, producers, and end-uses, 

some general trends have been evident in the industry. A strong increase in the use of microelectronic products in industries 

throughout the economy has led to rapid growth in microelectronics manufacturing over the past two decades. Although the 

US is a major producer of consumer electronics, Japan is the world’s leading producer of consumer electronics, and U.S. 

firms face strong international competition for cutting edge technological advances in their products. Due to the high skill 

level necessary in the development of products, there is considerable competition for skilled labor. The recent economic 

slowdown has led to lowered demand for end-products that incorporate microelectronics. In response, the microelectronics 

2 The rule regulates wastewater generated from washing vehicles only when it occurs as a preparatory step prior to performing an 

MP&M unit operation (e.g., prior to disassembly to perform engine maintenance or rebuilding). The rule does not cover the washing of 

cars, aircraft, or other vehicles when it is performed only for aesthetic/cosmetic purposes. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

industry has reduced capacity and laid off workers to reduce costs. Despite this decline, microelectronics continue to be an 

increasingly necessary component of the global economy. 

Telecommunication industries focus on the production of network equipment, fiber optics, and wireless communication 

equipment. Much of the growth in the industry has come from the increasing use of fiber optics and wireless end-user 

devices. The telecommunications industries experienced rapid growth in the nineties; however, industry activity slowed 

considerably with the collapse of the telecommunications bubble. Telecom firms have reacted by cutting employees, reducing 

costs, and selling off portions of their firms. Most have continued their R&D efforts. 

3.2.4 Hardware 

The hardware sector consists of many different industries, which can be generally classified into three groups: building 

hardware, conventional hardware, and tooling hardware. 

Building hardware consists of a group of industries that manufacture metal building products, including fabricated structural 

metal, sheet metalwork, and architectural metalwork. This group of industries grew rapidly throughout the 1990's. The 

building products industry as a whole saw record sales in 1998 and again in 1999. Much of this growth is attributed to large 

highway projects funded by the Transportation Efficiency Act for the 21st Century. 

Conventional hardware includes products such as screws, industrial fasteners, and valves and hose fittings. The products 

produced in this industry are used in the production of manufactured goods. Trends in this industry, therefore, generally 

reflect trends in other manufacturing industries. One of the most important industries influencing conventional hardware is 

the auto industry. Hardware producers have experienced pressures from end users such as auto makers to reduce costs. The 

industry faces a continued trend of consolidation of firms and increasing global pressure from countries with low labor costs. 

Domestic producers of screws and industrial fasteners saw growth in the real value of shipments due to the strong U.S. 

economy in the late nineties. 

The tooling hardware sector also contains a variety of different industries that produce various types of tools for different 

uses. Because these industries also face continued globalization, many of them are impacted by changes in the global 

economy. The decline in Asian markets in 1998 and 1999 resulted in a sharp decline in the value of shipments for the 

machine tooling industries. Prior to the 1998 financial crisis, value of shipments were increasing annually. The market for 

the power-driven segment of hand tools has increased, however, despite troubled overseas markets. 

3.2.5 Household Equipment 

There are three general groups of industries included in the household equipment sector: household furniture, household 

appliances, and plumbing equipment. Generally speaking, factors that affect this sector are consistent across these three 

groups. Low unemployment and increased disposable income stimulated growth in each of these industries in the nineties. 

However, because purchases of household equipment are relatively expensive and discretionary, consumers cut back spending 

in the recent recession. All three household equipment industries face international competition, as imports account for a 

substantial share of domestic consumption. 

Metal furniture accounts for 20 percent of the household furniture industry. Metal components are increasingly being added 

to non-metal furniture. For example, there is a trend to increase the functionality of non-metal furniture by equipping 

recliners with heat and massage. This could increase the industry’s reliance on metal parts. The industry has integrated 

vertically, as large manufacturers have begun to open their own retail stores in an effort to differentiate their products. 

There are two groups of household appliance manufacturers. Major appliances such as washing machines and refrigerators 

are produced by relatively few firms. Smaller appliances are characterized by little product differentiation but considerable 

price competition and are manufactured by a larger number of companies. 

Finally, a significant characteristic of the plumbing equipment market is the extent of U.S. dependence on foreign imports. 

While the U.S. construction market has grown at a record pace in the past few years, increasing demand for plumbing 

equipment, much of the demand has been served by imports and this industry has a trade deficit. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

3.2.6 Instruments 

The instruments sector is characterized by a diverse array of technologically advanced products and intense global 

competition among many firms of varying sizes. The sector can be generally divided into industrial measuring and testing 

instruments, and medical instruments. 

In the industrial measuring industry, producers of laboratory instruments are typically integrated firms who have consolidated 

and reduced costs in response to pressures from medical and pharmaceutical customers. Producers of measuring devices are 

also facing pressures to consolidate. These firms have been hurt by low commodity prices during the past few years, which 

have led to reduced investment in measuring equipment by fuel and grain producers. Sales should rebound, however, if Asian 

economies and fuel prices continue to grow. Small companies still dominate the electronic test equipment industry, which is 

characterized by a high degree of product differentiation. Most of these firms are not large enough to export products. 

Sales for medical devices increased steadily throughout the 1990's, while employment remained relatively constant. The 

industry has historically been characterized by many small to mid-size firms and intense competition for technological 

innovation. Efforts to bring down health care costs is one of the primary challenges facing this industry. Pressure to reduce 

costs has reduced insurance companies’ willingness-to-pay for new equipment. As the population ages, however, demand for 

medical services and devices is expected to grow. The industry will likely continue to grow in the next few years, but at a 

slower pace than it has grown historically.. 

3.2.7 Iron and Steel 

The basic iron and steel industry is regulated under 40 CFR 420, and primary iron and steel works, blast furnaces and rolling 

mills are not affected by the MP&M rule. The MP&M rule will regulate facilities that perform MP&M operations or cold 

forming operations on steel wire, rod, bar, pipe, or tube. This subcategory does not include facilities that perform those 

operations on base materials other than steel, nor does it include wastewater from cold forming, electroplating or continuous 

hot dip coating of steel sheet, strip, or plates or wastewater from performing any hot steel forming operations. 

Events in the global steel industry in the past few years have had significant and possibly far reaching impacts on domestic 

producers. In 1998, the industry experienced a global steel crisis. This crisis was caused in part by the Asian financial crisis, 

which triggered a sharp decline in imports of steel by major steel importing countries of Asia. This led to a flood of steel 

imports into U.S., and U.S. steel imports rose 33 percent in 1998. The situation was made worse by global overcapacity 

largely derived from producers in Russia and Latin America. 

This flood of steel into the U.S. and Europe led to rapidly declining steel prices in both regions. Excess inventories that 

accrued during the surge of imports hurt domestic producers. The “unfair” trading prices resulted in over 20 nations taking 

formal trade protection actions such as import duties and price floors. The US Congress determined that foreign steel was 

being sold in the US at unfair prices, and reacted by enacting anti-dumping tariffs. The slowdown in the US economy has 

also negatively affected the steel industry. Most steel firms are being forced to focus on rationalizing capacity and cutting 

costs. 

3.2.8 Job Shops 

MP&M metal finishing job shops are defined as those facilities with more than 50 percent of their revenues coming from 

products not owned by the site. While there are specific SIC codes associated with some Metal Finishing Job Shops, they sell 

to a variety of markets and are not a market in and of themselves. 

3.2.9 Mobile Industrial Equipment 

Mobile industrial equipment includes a number of different industries that produce machinery for different purposes, 

including construction, farming, and mining. Growth in the construction equipment industry is typically tied to economic 

factors such as housing starts, employment, and consumer confidence. Shipments of construction equipment rose steadily 

during much of the 1990's. The 1998 Transportation Equity Act for the 21st Century was expected to stimulate further 

spending by federal, state, and local governments. However, the current recession has forced many industry buyers to cut 

back or cancel orders. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

The farm and mining machinery industries both have been suffering from low commodity prices. Both industries experienced 

growth in shipments throughout much of the 1990's, but were hit in 1999 by low prices. Farm equipment was hit hardest as 

the real value of shipments fell by 38 percent in 1999. Output is expected to continue to decrease until grain surpluses decline 

and agricultural prices rise. However, the consolidation of farms has also had a significant in impact on this industry. With 

the increase in farm size, there is growing dependence upon mechanization to farm more acres per farm. 

3.2.10 Motor Vehicle and Bus & Truck 

The major trend in the motor vehicle and bus and truck industries is the continual consolidation of firms into highly capital 

intensive globalized manufacturers. Motor vehicle manufacturers are no longer constrained within national boundaries, as 

mergers and joint ventures include some of the largest firms from different countries. Many foreign owned manufacturers 

have facilities located in the U.S., and relative production costs and exchange rates play a greater role in determining the 

location of production facilities than the national identity of parent companies. 

Manufacturers have increasingly standardized the design of motor vehicles and their parts. These changes have resulted in 

much less product differentiation among manufacturers, but also in greater product quality. However, greater product quality 

has resulted in a consistently sharp increase in price over the past three decades. This price increase may have reached its 

pinnacle in the mid-nineties, since prices declined in 1998 and 1999. Industry output for automobiles increased 1.3 percent 

between 1996 and 2000. Although the current recession has hurt car prices, manufacturers have used incentives such as zero 

percent financing to maintain sales volume. 

3.2.11 Office Machine 

The office machine sector experienced rapid growth in the nineties that reversed itself with the downturn in the economy. 

The industry experienced 7.8 percent growth in the real value of shipments between 1996 and 2000. While this growth was 

accomplished with only a 1.3 percent increase in total employment, production employment increased by 5.4 percent. The 

relative difference between total and production employment can be attributed to increasing reliance on the Internet for sales, 

thereby reducing the need for non-production sales staff. Despite this increase in production employment, the industry 

remained extremely capital intensive. The recent weakness of the US economy hit the office machine sector hard with 

business purchases of computers and computer accessories falling significantly. 

Firms in the office machine sector have undergone mergers and acquisitions to bring down costs in order to compete. Firms 

often rely on joint venturing agreements, and sometimes form alliances with past competitors to produce complementary 

components of new technologies. Consolidation also allows firms to diversify, providing a range of products such as PCs, 

software, and information technology to protect against the strong competition in the market for any one product. Firms have 

also increasingly outsourced production to electronics manufacturers more equipped to increase production and take 

advantage of economies of size, while the original firms utilize their resources for research and development of new 

technologies to stay competitive. 

Globalization is an important trend in this industry as machine components are produced in different countries. Despite the 

trend toward a globalized market, the U.S. has held a negative trade balance for over a decade. 

3.2.12 Ordnance 

The ordnance sector includes firms that manufacture small arms, including grenade launchers and heavy field machine guns; 

artillery, including naval, aircraft, anti-aircraft, tank, coast, and field artillery; and ammunition, including bullets, bombs, 

mines, torpedoes, grenades, depth charges, and chemical warfare projectiles. It does not include the chemical processing or 

manufacture of explosives. Overall, the industry has a high ratio of value added to total sales. 

The ordnance sector has contracted significantly since the end of the Cold War. Decreases in US government military 

spending have caused significant declines in ordnance production, leading to lower industry shipments and cutbacks in 

employment. Foreign customers, including foreign governments, buy over 80 percent of the ordnance manufactured in the 

US. Although shipments of military weaponry have declined, sales of small arms have increased in the US over the past few 

years. Recent military actions by the US will likely result in government weapons purchases that will benefit the industry. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

3.2.13 Precious Metals and Jewelry 

Domestic production in the precious metals and jewelry industry is dominated by many small firms with low capital intensity, 

mostly concentrated in the northeast US. It is influenced by trends in consumer behavior, the retail market, and global 

competition. Devaluation in the price of gold due to declining world prices has benefitted the industry because it reduces the 

cost of making jewelry. Increased disposable income fueled strong consumer spending on precious metals and costume 

jewelry in the nineties, but this trend has weakened with the recent economic downturn. 

Increases in spending have not always translated into gains for domestic producers. The lowering of tariffs has resulted in a 

steady increase in imports of costume jewelry, as labor-intensive production is often less expensive in developing countries. 

Domestic producers have also been hurt by the strong U.S. dollar, which makes U.S. exports more expensive. Another 

challenge comes from the retail market, which has put strong pressure on producers to bring down prices in order to compete. 

These challenges include consolidation of retailers, giving them greater purchasing power, increased Internet and television 

home shopping, and a decrease in the number of wholesalers. 

3.2.14 Printed Wiring Boards 

Printed wiring boards (also referred to as printed circuit boards) are the physical structures on which electronic components 

such as semiconductor and capacitors are mounted. Computers and comm unications are the largest uses for printed wiring 

boards. In addition, printed wiring boards are used in a wide array of other products, including toys, radios, television sets, 

electronic wiring in cars, guided-missile and airborne electronic equipment, biotechnology, medical devices, digital imaging 

equipment, and industrial control equipment. While some producers of PWBs produce them for their own use, most 

manufacturers are independent firms that sell PWBs to the open market. The majority of PWB manufacturers are small firms. 

The domestic PWB industry experienced considerable growth throughout the 1990's. Real industry output grew nine percent 

from 1996 to 2000. Growth was spurred by continual growth in end-use markets. In addition to the increased in value of 

shipments, U.S. firms saw a 5.6 percent increase in average hourly earnings and a 16.3 percent increase in capital 

expenditures over the same period. However, demand in the PC, telecommunications, and electronics sectors has 

weakened recently. In parallel, there is growing international competitive pressure for PWB makers to reduce production 

costs. Consequently, many of the larger PWB firms are looking to relocate offshore. 

3.2.15 Railroad 

Railroad service consists of both freight and passenger service. In the past few years, railroad companies have been focusing 

on improving the efficiency of their lines and services. There has been a continued trend toward consolidation of major 

freight railroads. Consequently, companies have reduced the number of lines and focused attention on increasing the capacity 

of fewer lines. Railroads have also begun to focus on guaranteeing deliveries at specific times, which will allow them to 

compete with the trucking industry. 

Since the 1980's railroad traffic increased by 50 percent, while the line network decreased by 39 percent. This was 

accomplished by increasing capital expenditures for equipment such as new locomotives with greater horsepower, installation 

of double tracks, and increases in the capacity of non-railroad owned freight cars. Consequently, freight service in the 

nineties saw the first increase in operating revenue since 1984, although this was coupled with sharp decreases in 

employment. Passenger service has undergone similar changes to increase efficiency by adding new locomotives and 

beginning a transition to high speed train service. Total industry output increased 7.6 percent per year from 1988 to 1996. 

Although transportation volume is sensitive to the generally poor macroeconomic situation, railways have succeeded in 

cutting costs to maintain earnings. 

3.2.16 Ships and Boats 

Ship manufacturing experienced continual declines throughout the 1990's. Despite efforts by the Federal Government to 

stimulate investment in converting the industry from production of military ships to merchant ships, the U.S. Navy remains 

the primary customer of shipbuilders. The U.S. Navy dramatically reduced its orders for new vessels since the end of the 

Cold War, and has decommissioned many ships and submarines. The Navy decreased its fleet by 208 ships from 1985 to 

1998. Although the Navy plans to add 66 new ships through construction and conversion from 2000 through 2004, this 

represents a decline of over 60 percent in the procurement of new ships since the 1980's. The ship building industry was 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

helped by the Oil Pollution Act of 1990, which required all oil tankers entering U.S. ports to have double hulls. General 

economic woes and instability in the Middle East are expected to hurt the ships and boats industry. 

This sector also manufactures recreational boats, with sales that reflect overall trends in recreational expenditures. The U.S. 

boat building business is the world’s leading supplier of recreational craft. Despite this, rapid growth in the market for 

smaller personal water craft (e.g., jet skis) has led to an increase in imports of boats. 

3.2.17 Stationary Industrial Equipment 

The stationary industrial equipment sector includes firms that manufacture machinery and machinery parts used for oil, paper,
 


and food production, printing and packaging, as well as heaters and air conditioners, electric generating equipment, and motor
 


generators. These industries also produce large metal-working machines used in making parts for other industries.
 


The industries supplying oil and gas production, paper production, and printing machinery were affected by similar global
 


factors, and consequently followed similar trends. Low petroleum prices affected oil production in 1998. Natural gas
 


production was influenced by the low oil prices, which put pressure on the gas industry to reduce costs in order to compete.
 


These factors led to a decline of 38 percent in real value of shipments for oil production equipment manufacturers in 1998 and
 


1999. The price of petroleum increased in 1999 and 2000 and machinery shipments rebounded by 9.2 percent. However,
 


natural gas prices fell in 2001, hurting the industry.
 


Paper manufacturing equipment has suffered from events overseas. Although the U.S. has seen a decline in the production of
 


paper throughout the latter half of the 1990's, the U.S. remains the largest producer of paper manufacturing machinery. The
 


industry therefore relies heavily on exports to sustain growth. With struggling economies overseas, the industry saw a decline
 


in value of shipments from 1996 to 2000. Printing machinery manufacturers realized strong growth during the first half of the
 


1990's due to increased demand for new digital presses, but a decline in exports resulted in slower growth for the later half of
 


the decade. Global events did not have such an impact on manufacturers of packaging machinery, as the U.S. is not only the
 


leading producer of this equipment but also its leading end-user.
 


A variety of industries manufacture equipment used to produce energy or to power equipment. Refrigeration, air
 


conditioning, and heating equipment sales tend to follow growth in housing starts and construction of new office buildings. A
 


number of factors contributed to strong growth in this industry throughout the 1990's including record housing starts, record
 


heat in the summer of 1999, replacement of chlorofluorocarbon (CFC) air conditioning units, and a large percentage of new
 


homes being built with central air conditioning. With 66 percent of the existing air conditioners containing CFC technology
 


still in operation, replacement of these machines provides an opportunity for growth in this industry in the future.
 


Manufacturers of turbines, transformers, and switchboards, all of which are used for the production of electricity, saw
 


considerable growth in the late 1990s as the domestic economy grew. This strength has been limited by the recent recession.
 


A number of advanced technologies have been developed to meet the demands of a deregulated industry. These new
 


technologies are capable of producing electricity from smaller facilities at competitive costs. Implementation of these
 


technologies is not expected to take place for a few years, however, until the effects of deregulation become clearer.
 


3.3 CHARACTERISTICS OF MP&M MANUFACTURING SECTORS 

The data in these analyses come primarily from the Annual Survey of Manufacturers and the Small Business Administration, 

although some data from the 1997 Census were used for important economic indicators that were not available in 1996. The 

multi-year analyses presented in this section cover a nine year period from 1988 to 1996, the base years for the original Phase 

1 and Phase 2 survey data. Although ideally data would have been presented for the ten year period from 1987 to 1996, OMB 

reclassified a number of 4-digit SIC industries in 1987. This made it difficult to compare SIC codes before and after this 

reclassification and resulted in incomplete data in the Annual Survey of Manufacturers for many SIC codes in 1987. Because 

the data were incomplete in 1987, 1988 was chosen as the first year of the time series. With the exception of data for nonmanufacturing sectors, single-year data focus on the year 1996, the base analysis year for the overall MP& M regulatory 

analysis. Because the Annual Survey of Manufacturers does not include data for non-manufacturing sectors, single-year data 

for these sectors are for 1997, the most recent year of the Economic Census. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

3.3.1 Domestic Production 

a. Output
 


The two most common measures of manufacturing output are value of shipments (VOS) and value added (VA).
 


Historical trends in these measures provide insight into the overall economic health of an industry. Value of shipments is the
 


sum of the receipts a manufacturer earns from the sale of its outputs. It is an indicator of the overall size of a market or the
 


size of a firm in relation to its market or competitors. Value added is the difference between the value of shipments and the
 


value of purchased non-labor inputs used to make the products sold. It is used to measure the value of production activity in a
 


particular industry. The ratio of VA to VOS is an indicator of the importance of the industry’s contribution to the total value
 


of the product. A ratio close to zero indicates that the value of the input materials is much more important than the value of 

industry processing. A ratio close to one indicates that industry processing is the primary source of value in the product. 

Table 3.4 presents Department of Commerce data on VOS and VA for the MP&M manufacturing sectors during the period
 


from 1988 to 1996. VOS for the entire MP&M manufacturing sector grew from 1.27 trillion dollars in 1988 to 1.51 trillion
 


dollars in 1996, for an average annual growth rate of 2.1 percent. VA for the entire industry grew at a slower annual rate of
 


1.5 percent, from 638 billion dollars to 720 billion dollars. In comparison, US GDP grew at 2.6 percent per year over the
 


same period.
 


Value added as a percent of value of shipments for the MP&M manufacturing industries as a whole was 48 percent in 1996.
 


This indicates that 48 percent of the value of their output was the result of MP&M processing and 52 percent was the cost of
 


purchased inputs. In general, MP&M processing is important to the value of MP&M output products.
 


Growth in the individual sectors was generally consistent with the overall trend in MP&M manufacturing of slow positive
 


growth. Fourteen of the nineteen sectors had positive growth in VOS, and thirteen had positive growth in VA. Railroad
 


equipment manufacturers enjoyed the largest average annual growth of 7.6 percent in VOS. Electronic equipment
 


experienced the next largest average growth, with annual growth in VOS averaging 5.1 percent. Only the aerospace and
 


ordnance industries experienced a large decline in VOS and VA over this period. Aerospace VOS declined 7.6 percent per
 


year and ordnance VOS declined 7.3 percent per year. Both decreases were attributable to cutbacks in government defense
 


spending at the end of the Cold War.
 


VA as a percent of VO S for the individual sectors varied substantially from the manufacturing average of 48 percent. The
 


ordnance sector had the highest ratio of VA to VOS, at 67.6 percent, and the instrument and printed wiring board sectors also
 


had high ratios. In these sectors, industry processing is the most important part of the value of the finished product. Sectors
 


with low ratios of VA to VOS included the iron and steel sector, railroad sector, bus and truck sector and especially the motor
 


vehicle sector, for which VA as a percent of VOS was equal to only 33.7 percent. The value of input materials was the most
 


important contributor to the value of products in these sectors.
 


b. Number of facilities and firms
 


The number of facilities and firms in an industry is an indicator of industry size and structure. Changes in the number of firms
 


and facilities can indicate whether or not the industry is experiencing growth, and changes in the ratio of facilities to firms can 

indicate whether an industry is becoming more integrated. 

This profile uses SUSB data to assess the number of firms and facilities in the MP&M manufacturing sector. The SUSB did 

not begin its survey until 1989, and it did not include firms in its survey until a year later. Thus, facilities data are presented 

in 1989 and firm data are presented in 1990. 

Table 3.5 shows the number of MP&M manufacturing facilities in 1989 and 1996 and the number of firms in 1990 and 1996. 

Overall, the number of firms grew 2.1 percent annually and the number of facilities grew 1.4 percent annually over this 

period. By 1996, there were 144,603 manufacturing firms and 153,354 facilities. The average number of facilities per firm 

was relatively constant, with only a minor decrease from 1.07 in 1990 to 1.06 in 1996. Most MP&M manufacturers are 

single-facility firms. 

Trends in the individual manufacturing sectors were generally consistent with overall trends in manufacturing. The aerospace 

industry was the only MP&M manufacturing sector to experience significant downsizing during this period, with firms and 

facilities decreasing annually by 4.1 and 4.2 percent, respectively. The iron and steel industry experienced a more modest 

decrease in number of firms and facilities. The number of firms and facilities in the printed wiring board sector grew the 

fastest, by a little over five percent annually. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.4: Real Value of Shipments and Value Added: MP&M Manufacturing Sectors (millions, 2001$) 

Sector 

Value of Industry Shipments Value Added by Manufacture 

1988 1996 

Average 

Annual 

Growth 

Rate 

1988 1996 

Average 

Annual 

Growth 

Rate 

Value Added as 

a % of Value of 

Shipments in 

1996 

Aerospace 35,991 19,111 -7.6% 24,167 10,645 -9.7% 55.7% 

Aircraft 101,554 88,897 -1.7% 51,692 48,204 -0.9% 54.2% 

Bus & Truck 9,843 14,362 4.8% 3,622 5,513 5.4% 38.4% 

Electronic Equipment 85,498 127,347 5.1% 48,862 67,071 4.0% 52.7% 

Hardware 152,597 180,756 2.1% 82,644 98,674 2.2% 54.6% 

Household Equipment 87,764 98,763 1.5% 42,595 45,551 0.8% 46.1% 

Instruments 118,322 136,377 1.8% 78,160 89,052 1.6% 65.3% 

Iron and Steel 19,396 19,963 0.4% 7,228 7,103 -0.2% 35.6% 

Job Shops 11,733 14,927 3.1% 6,967 8,307 2.2% 55.7% 

Mobile Industrial Equipment 45,150 56,159 2.8% 21,356 24,302 1.6% 43.3% 

Motor Vehicle 315,641 387,547 2.6% 107,025 130,627 2.5% 33.7% 

Office Machine 86,352 110,084 3.1% 43,008 43,849 0.2% 39.8% 

Ordnance 10,241 5,567 -7.3% 6,631 3,761 -6.8% 67.6% 

Other Metal Products 58,809 63,995 1.1% 36,039 37,431 0.5% 58.5% 

Precious Metals and Jewelry 10,790 9,242 -1.9% 5,018 4,403 -1.6% 47.6% 

Printed Wiring Boards 10,162 11,408 1.5% 5,927 6,997 2.1% 61.3% 

Railroad 4,195 7,533 7.6% 1,893 2,761 4.8% 36.7% 

Ships and Boats 18,802 16,666 -1.5% 10,086 8,424 -2.2% 50.5% 

Stationary Industrial Equipment 176,961 236,213 3.7% 97,388 125,443 3.2% 53.1% 

Total 1,359,801 1,604,916 2.1% 680,309 768,118 1.5% 47.9% 

US GDP 7,189,924 8,821,069 2.6% n/a n/a n/a n/a 

Source: Department of Commerce, Bureau of the Census, Annual Survey of Manufacturers; Economagic 

Horizontal integration varied substantially across sectors. The railroad sector, with 1.41 facilities per firm in 1996, was the 

most horizontally integrated, but the iron and steel and aerospace sectors also had high numbers of facilities per firm. The 

precious metals and jewelry sector had nearly a one to one ratio between facilities and firms, indicating a very low level of 

horizontal integration. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.5: Number of Firms and Facilities: MP&M Manufacturing Sectors 

Sector 

Number of Firms Number of Facilities Facilities per 

Firm 

1990 1996 

Average 

Annual 

Growth 

Rate 

1989 1996 

Average 

Annual 

Growth 

Rate 

1990a 1996 

Aerospace 109 85 -4.1% 143 106 -4.2% 1.33 1.25 

Aircraft 1,428 1,486 0.7% 1,633 1,691 0.5% 1.16 1.14 

Bus & Truck 889 953 1.2% 1,016 1,040 0.3% 1.11 1.09 

Electronic Equipment 5,649 6,180 1.5% 6,396 6,693 0.7% 1.08 1.08 

Hardware 34,984 37,832 1.3% 37,861 40,044 0.8% 1.06 1.06 

Household Equipment 6,787 7,563 1.8% 7,914 8,303 0.7% 1.11 1.10 

Instruments 7,963 9,730 3.4% 8,959 10,552 2.4% 1.10 1.08 

Iron and Steel 597 583 -0.4% 784 770 -0.3% 1.30 1.32 

Job Shop 4,798 5,280 1.6% 5,104 5,549 1.2% 1.04 1.05 

Mobile Industrial 

Equipment 3,318 3,341 0.1% 3,606 3,591 -0.1% 1.07 1.07 

Motor Vehicle 4,991 6,044 3.2% 5,977 7,024 2.3% 1.17 1.16 

Office Machine 1,828 2,002 1.5% 2,050 2,087 0.3% 1.07 1.04 

Ordnance 340 421 3.6% 385 442 2.0% 1.09 1.05 

Other Metal Products 11,517 13,819 3.1% 12,069 14,198 2.3% 1.03 1.03 

Precious Metals and 

Jewelry 3,719 3,867 0.7% 3,870 3,892 0.1% 1.01 1.01 

Printed Wiring Boards 1,034 1,452 5.8% 1,046 1,530 5.6% 1.06 1.05 

Railroad 147 152 0.6% 180 215 2.6% 1.27 1.41 

Ships and Boats 2,511 3,195 4.1% 2,708 3,310 2.9% 1.05 1.04 

Stationary Industrial 

Equipment 35,231 40,618 2.4% 37,261 42,317 1.8% 1.04 1.04 

Total 127,840 144,603 2.1% 138,962 153,354 1.4% 1.07 1.06 

a Calculated using data from 1990 for facilities and firms. 

Source: Small Business Administration, Statistics of U.S. Businesses. 

c. Employment 

Employment is a measure of the level and trend of activity in an industry. While employment growth generally signals 

economic strength in an industry, strong productivity growth and scale economies can yield growth in revenues that exceeds 

growth in employment. Changing patterns of labor utilization relative to output are particularly important in understanding 

how regulatory requirements may translate into job losses both in aggregate and at the community level. This profile presents 

DOC data on employment for 1988 and 1996. 

Table 3.6 shows that employment in the MP&M manufacturing sectors as a whole decreased modestly between 1988 and 

1997. Over those years, total employment dropped from 7.98 million to 7.55 million, an average decline of 0.7 percent 

annually. To put this in perspective, VOS for the entire MP&M manufacturing sector grew about 2.1% annually over the 

same period of time, signaling that growth in output has been driven by increases in capital expenditures and labor 

productivity, not by increases in employment. 

Although total MP&M industry employment declined over the analysis period, not all sectors experienced employment 

declines. Employment grew or stayed constant in ten of the nineteen sectors. However, while a number of sectors evidenced 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

large percentage and absolute losses in employment, no sectors showed large percentage gains and only two showed large 

absolute gains in employment . Employment shrank by 11.6 percent annually in the aerospace sector, 9.1 percent in the 

ordnance sector, and 5.6 percent in the aircraft sector, due to cutbacks in defense spending following the Cold W ar. The 

greatest absolute decline occurred in the aircraft sector, which lost almost 220,000 jobs. The largest percentage increase in 

employment was in the railroad sector, which gained just 2.1 percent annually. The largest absolute increase in employment 

over the nine years was in the stationary industrial equipment sector, which gained 127,100 jobs. 

Table 3.6: Employment: MP&M Manufacturing Sectors 

Sector 

Number of Employees 

1988 1996 Average Annual 

Growth Rate 

Aerospace 223,700 81,000 -11.9% 

Aircraft 596,600 376,800 -5.6% 

Bus & Truck 63,900 67,700 0.7% 

Electronic Equipment 602,500 604,800 0.0% 

Hardware 1,246,200 1,307,600 0.6% 

Household Equipment 584,900 570,600 -0.3% 

Instruments 886,500 753,800 -2.0% 

Iron and Steel 65,500 67,900 0.5% 

Job Shops 123,300 129,200 0.6% 

Mobile Industrial Equipment 232,400 232,600 0.0% 

Motor Vehicle 928,000 974,000 0.6% 

Office Machine 329,800 259,100 -3.0% 

Ordnance 86,500 40,200 -9.1% 

Other Metal Products 368,100 361,400 -0.2% 

Precious Metals and Jewelry 87,100 65,800 -3.4% 

Printed Wiring Boards 80,900 88,300 1.1% 

Railroad 25,900 30,600 2.1% 

Ships and Boats 182,900 141,300 -3.2% 

Stationary Industrial Equipment 1,269,800 1,396,900 1.2% 

Total 7,984,500 7,549,600 -0.7% 

Source: Department of Commerce, Bureau of the Census, Annual Survey of Manufacturers. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

d. Capital expenditures
 


Capital expenditures are an indicator of production characteristics and market structure. Capital expenditures are the amount
 


of money spent annually on capital, which includes equipment, machinery, vehicles, software, buildings, intellectual rights, or
 


any other permanent addition to a firm. Capital does not refer to input materials that are consumed in the course of 

production. New capital expenditures are needed to modernize, expand, and replace a firm’s existing production capacity to 

meet growing demand or to stay current with new regulations or changing technology. 

An industry with high capital stock compared to its employee payroll is considered capital intensive: its production relies 

more heavily on machinery, software, and other forms of capital than on labor. An industry with high capital requirements 

can have significant barriers to entry for new firms, making the market less competitive. 

Table 3.7 presents DOC data on new capital expenditures by MP&M manufacturing sector. 

Table 3.7: New Capital Expenditures: MP&M Manufacturing Sectors 

Sector 

Capital Expenditures 

(millions, 2001$) 

Capital Expenditures per Facility 

(2001$) 

1988 1996 Average Annual 

Growth Rate 1988/89 1996 Change from 

1988/89 to 1996 

Aerospace 1,310 522 -10.9% 9,160,839 4,924,528 -4,236,311 

Aircraft 3,015 2,156 -4.1% 1,846,295 1,274,985 -571,310 

Bus & Truck 161 213 3.6% 158,465 204,808 46,343 

Electronic Equipment 3,118 4,482 4.6% 487,492 669,655 182,163 

Hardware 3,517 5,624 6.0% 92,892 140,446 47,553 

Household Equipment 2,150 2,616 2.5% 271,670 315,067 43,396 

Instruments 4,002 4,832 2.4% 446,702 457,923 11,221 

Iron and Steel 420 623 5.0% 535,714 809,091 273,377 

Job Shops 353 772 10.3% 69,161 139,124 69,963 

Mobile Industrial Equipment 1,121 1,121 0.0% 310,871 312,169 1,299 

Motor Vehicle 5,697 12,840 10.7% 953,154 1,828,018 874,864 

Office Machine 3,044 3,109 0.3% 1,484,878 1,489,698 4,820 

Ordnance 196 91 -9.2% 509,091 205,882 -303,209 

Other Metal Products 1,768 1,999 1.5% 146,491 140,794 -5,697 

Precious Metals and Jewelry 93 156 6.7% 24,031 40,082 16,051 

Printed Wiring Boards 430 624 4.8% 411,090 407,843 -3,247 

Railroad 78 103 3.6% 433,333 479,070 45,736 

Ships and Boats 483 374 -3.1% 178,360 112,991 -65,369 

Stationary Industrial Equipment 4,333 7,222 6.6% 116,288 170,664 54,376 

Total 35,288 49,480 4.3% 253,940 322,652 68,712 

Source: Department of Commerce, Bureau of the Census, Annual Survey of Manufacturers. 

In general, the MP &M manufacturing sector is relatively capital intensive. In 1988, manufacturing capital expenditures were 

38.3 billion dollars. They increased by 4.3 percent annually to reach a total yearly investment in capital of 49.5 billion dollars 

in 1996. Average yearly capital expenditures per firm increased from $254,000 in 1988 to $353,000 in 1996. 

For the most part, changes in capital investment from 1988 to 1996 in the individual manufacturing sectors followed the trend 

for the MP&M manufacturing sectors as a whole. Capital expenditures in the job shop and motor vehicle sectors grew at over 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

10% annually. The only sectors for which spending on new capital declined were aerospace, aircraft, ordnance, and ships and 

boats. 

There was large variation in capital expenditures across the MP&M sectors. A few industries stood out as being extremely 

capital intensive. Aerospace firms spent an average of 4.8 million dollars on capital per firm in 1996, and the aircraft, motor 

vehicle, and office machine sectors each spent more than one million dollars per firm in 1996. The precious metal and 

jewelry sector had the lowest levels of capital investment, with only $40,000 spent per firm in 1996. 

3.3.2 Industry/Market Structure 

A number of factors play an important role in determining market structure for an industry, including the barriers that firms 

face in entering and exiting the market, the degree to which firms in the market are vertically and horizontally integrated, and 

the extent to which markets have been globalized. The following sections discuss these factors. 

a. Facility size
 


Facility size is an indicator of economies of scale. The presence of many large facilities can indicate that there are advantages
 


to building on a larger scale, such as dividing labor more efficiently, utilizing equipment more effectively, or getting bulk
 


discounts. 

Table 3.8 shows 1997 Census data on the distribution of manufacturing facilities and VOS by employment size category and 

MP&M sector. The MP&M industry is characterized by a large number of small facilities. The Census data indicate that, in 

1997, 98.6 percent of all facilities in the MP&M industry employed less than 500 employees. Those facilities, however, 

accounted for only 59 percent of the total value of shipments from the manufacturing industries. The 1.4 percent of facilities 

with 500 or more employees generated 41 percent of the total VOS from the manufacturing industries. These large facilities 

are likely to enjoy substantial economies of scale. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.8: Number of Facilities and Value of Shipments by Employment Size Category: 

MP&M Manufacturing Sectors in 1997 

Sector 

Number of Facilities Value of Shipments (millions, 2001$) 

1 to 19 20 to 

99 

100 to 

499 

500 to 

2,499 

2,500 

or 

more 

1 to 19 20 to 99 100 to 

499 

500 to 

2,499 

2,500 or 

more 

Aerospace 33 23 20 13 10 42 991 15,993 185 2,916 

Aircraft 898 441 270 72 32 716 10,087 74,417 2,744 17,913 

Bus & Truck 658 335 154 18 1 675 8,013 573 2,366 3,357 

Electronic 

Equipment 

3,450 2,086 1,019 205 21 2,945 37,794 37,178 14,258 54,660 

Hardware 26,065 11,854 2,686 189 0 20,669 79,456 3 69,005 25,619 

Household 

Equipment 

4,958 2,274 1,110 191 11 3,745 40,843 7,314 13,747 37,067 

Instruments 6,741 2,667 1,103 253 20 5,592 44,850 21,168 17,124 52,680 

Iron and Steel 278 292 205 13 0 528 11,072 0 4,530 4,254 

Job Shop 3,701 1,654 199 6 0 2,103 5,610 0 7,273 343 

Mobile Industrial 

Equipment 

2,116 990 383 90 9 1,937 17,351 8,913 7,219 26,684 

Motor Vehicle 4,004 1,874 1,206 324 79 3,495 54,221 231,896 13,404 119,088 

Office Machine 1,408 485 218 73 21 1,553 16,551 59,499 4,842 35,440 

Ordnance 298 77 40 17 2 168 1,472 935 522 2,686 

Other Metal 

Products 

11,265 2,375 611 68 3 5,382 21,579 7,473 12,511 16,812 

Precious Metals 

and Jewelry 

3,250 480 100 12 0 1,777 3,436 0 2,864 1,666 

Printed Wiring 

Boards 

801 412 156 20 0 526 4,322 0 2,022 3,334 

Railroad 81 71 54 13 1 99 2,659 1,222 678 3,714 

Ships and Boats 3,755 469 179 25 6 1,314 5,714 6,414 2,036 2,404 

Stationary 

Industrial 

Equipment 

30,513 8,555 2,344 343 12 18,793 86,348 10,837 50,039 69,921 

Total 104,274 37,413 12,056 1,947 227 72,061 452,366 483,834 227,368 480,558 

Source: Department of Commerce, Bureau of the Census, Census of Manufacturers, 1997. 

Although the majority of MP&M industry facilities are small, the distribution of facilities by employment size category varies
 


substantially among the 19 MP&M sectors. The aerospace, aircraft, motor vehicle, and railroad sectors all had proportionally
 


high numbers of large facilities. The aerospace sector, in particular, had large economies of scale, with 23 percent of its
 


facilities employing 500 or more employees. The hardware, job shop, other metal products, precious metal, and ships and
 


boats sectors had proportionally large numbers of small facilities. At least 93 percent of facilities in each of these sectors had
 


less than 100 employees.
 


b. Firm size
 


This profile uses firm employment size as an indicator of market power and barriers to entry. If the largest firms in an
 


industry own disproportionately many facilities or control a large portion of industry output, then they may have significant
 


market power. These firms can use their large production capacities to control and exploit markets. The presence of many 

large firms in an industry can also indicate that there are barriers to entry into that industry, such as capital requirements or 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

economies of scale, that give existing firms in the industry a competitive advantage. EPA used 1996 SUSB data to assess the 

competitiveness of the MP&M manufacturing industries. 

Table 3.9 presents the distribution of manufacturing firms, facilities, and VOS by firm employment size and MP&M sector. 

Overall, most MP&M manufacturing firms were small, but the firms that were big owned many facilities and had 

disproportionately high receipts. In 1996, 138,492 firms, equal to 96 percent of manufacturers, had fewer than 500 

employees. These small businesses owned 92 percent of all facilities but had total sales of only 418.3 billion dollars, equal to 

28 percent of total estimated receipts. In 1996, 6,111 firms had 500 or more employees. These firms owned eight percent of 

all facilities but had estimated receipts of 1.08 trillion dollars, equal to 72 percent of the total for manufacturers. It is likely 

that there are significant economies of scale in the MP&M manufacturing industries. 

Although MP&M manufacturing firms tend to be small, firm size varies significantly among individual sectors. The 

aerospace, iron and job shops, and railroad sectors had proportionally high numbers of large facilities. In the aerospace 

sector, 50 percent of facilities were owned by firms with 500 or more employees, and 38 percent of firms had 500 or more 

employees. In contrast, over 98 percent of firms in the job shop, other metal products, precious metals, and ships and boats 

sectors had less than 500 employees. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.9: Number of Firms, Facilities, and Estimated Receipts by Firm Employment Size Category, 1996: 

MP&M Manufacturing Sectors 

Sector 

Firms Facilities Estimated Receipts 

(millions, 2001$) 

1 to 99 100 to 

499 

500 or 

more 1 to 99 100 to 

499 

500 or 

more 1 to 99 100 to 

499 

500 or 

more 

Aerospace 51 2 32 51 2 53 n/a n/a 19,029 

Aircraft 1,209 135 142 1,212 158 321 2,453 2,840 93,860 

Bus & Truck 805 92 56 810 107 123 2,269 2,638 6,702 

Electronic 

Equipment 4,936 681 563 4,977 786 930 12,156 17,353 81,615 

Hardware 34,162 2,345 1,325 34,398 2,968 2,678 66,557 42,561 61,295 

Household 

Equipment 6,408 665 490 6,455 791 1,057 12,799 17,412 66,409 

Instruments 8,273 727 730 8,320 842 1,390 17,248 16,243 96,894 

Iron and Steel 362 108 113 368 153 249 2,015 4,426 12,737 

Job Shops 4,945 240 95 5,001 338 210 7,157 3,487 3,097 

Mobile Industrial 

Equipment 2,875 263 203 2,898 319 374 6,668 6,321 32,129 

Motor Vehicle 4,950 614 480 4,987 724 1,313 11,314 21,376 366,635 

Office Machine 1,662 167 173 1,668 180 239 5,373 7,535 64,424 

Ordnance 358 25 38 358 28 56 329 453 4,213 

Other Metal 

Products 13,097 492 230 13,152 602 444 13,568 10,738 30,677 

Precious Metals and 

Jewelry 3,747 86 34 3,753 89 50 3,559 2,019 2,148 

Printed Wiring 

Boards 1,250 137 65 1,258 150 122 2,231 2,402 5,769 

Railroad 99 24 29 101 30 84 326 496 6,271 

Ships and Boats 3,003 137 55 3,012 165 133 2,699 2,954 11,501 

Stationary 

Industrial 

Equipment 

37,669 1,691 1,258 37,835 2,002 2,480 52,700 35,623 119,210 

Total 129,861 8,631 6,111 130,614 10,434 12,306 221,420 196,877 1,084,612 

Source: Small Business Administration, Statistics of U.S. Businesses, 1996. 

c. Foreign trade

 

This profile uses two measures of foreign competitiveness: export dependence and import penetration. Export
 


dependence is the share of value of shipments that is exported. Import penetration is the share of domestic consumption met
 


by imports. For both measures, a high value indicates a relatively high openness to foreign markets and global competition.
 


This openness has benefits, including providing domestic consumers with a wider selection of products and services at lower
 


prices, and allowing domestic producers to make profits in foreign markets. It can have costs, too, if imports to domestic
 


consumers are unreliable or if foreign competition drives down prices for domestic producers. This profile uses 1996 data 

from the Department of Commerce to illustrate trends in foreign trade. 

Table 3.10 shows that overall, the U.S. is an importer of MP&M manufactured goods, with net imports of 75.7 billion dollars 

in 1996. In general, MP&M industry sectors face global competition, as illustrated by the number of sectors that had both a 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

high export dependence and import penetration. For example, in the precious metals sector, roughly 77 percent of U.S. 

consumption was met by imports, while almost 23 percent of U.S. production was sold as exports. 

Although overall the US has a large trade deficit in MP&M manufactured goods and services, the US was a net exporter in six 

of the eighteen sectors for which balance of trade data was available. Eighty one percent of production in the ordnance sector 

and 67 percent of production in the aircraft sector was consumed overseas. The aircraft sector had the highest absolute net 

exports, valued at 27.26 billion dollars. A few sectors, especially aerospace, ships and boats, iron and steel, and bus and truck, 

were relatively closed to global competition, with low levels of imports and exports. Foreign imports had the highest relative 

importance in the precious metals, office machine, and household equipment sectors. The motor vehicle sector had the 

highest absolute net imports, valued at 63.12 billion dollars. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.10: Trade Statistics, 1996: MP&M Manufacturing Sectors 

Sector 

Value of 

Imports 

(millions, 

2001$) 

Value of 

Exports 

(millions, 

2001$) 

Value of 

Shipments 

(millions, 2001$) 

Implied 

Domestic 

Consumptiona 

Import 

Penetrationb 

Export 

Dependencec 

(a) (b) (c) (d) (e) (f) (g) 

Aerospace 143 143 19,111 19,112 0.7% 0.7% 

Aircraft 14,015 41,278 88,896 61,633 22.7% 67.0% 

Bus & Truck 410 436 14,362 14,335 2.9% 3.0% 

Electronic 

Equipment 

31,478 30,615 127,347 128,211 24.6% 23.9% 

Hardware 26,753 20,560 180,756 186,949 14.3% 11.0% 

Household 

Equipment 

40,697 16,809 98,762 122,650 33.2% 13.7% 

Instruments 18,990 31,462 136,376 123,904 15.3% 25.4% 

Iron and Steel 937 263 19,963 20,637 4.5% 1.3% 

Job Shopd n/a n/a 14,927 n/a n/a n/a 

Mobile Industrial 

Equipment 

10,775 16,634 56,159 50,300 21.4% 33.1% 

Motor Vehicle 124,203 61,015 387,546 450,735 27.6% 13.5% 

Office Machine 67,082 47,783 110,084 129,384 51.8% 36.9% 

Ordnance 647 2,792 5,566 3,421 18.9% 81.6% 

Other Metal 

Products 

25,282 11,243 63,996 78,035 32.4% 14.4% 

Precious Metals and 

Jewelry 

15,839 4,607 9,243 20,474 77.4% 22.5% 

Printed Circuit 

Boards 

2,667 1,947 11,408 12,127 22.0% 16.1% 

Railroad 1,208 773 7,533 7,969 15.2% 9.7% 

Ships and Boats 1,081 1,080 16,666 16,666 6.5% 6.5% 

Stationary Industrial 

Equipment 

38,809 55,835 236,213 219,187 17.7% 25.5% 

Totale 421,015 345,274 1,604,915 1,680,656 25.1% 20.5% 

a Implied domestic consumption based on value of shipments, imports, and exports [column d + column b - column c]. b Import penetration based on implied domestic consumption and imports [column b / column e]. 

Export dependence based on value of shipments and exports [column c / column d]. d As explained in the text, job shops include only two SICs specific to job shops, and not facilities in other SICs that may be operating 

as job shops. 

e Components may not sum to totals due to rounding. 

Source: Department of Commerce, Bureau of the Census. 

d. Establishment births and deaths

 

The number of firms starting up and closing each year reflects the competitiveness of an industry. Industries with high
 


numbers of these “births” and “deaths” relative to the total number of firms in the industry are likely to have low barriers to
 


entry or exit. These industries are likely to be competitive. Industries with low number of births and deaths are more likely to
 


have significant barriers to entry and exit, such as capital requirements or economies of scale, that make the industries less
 


competitive. As discussed in previous sections, firms in less competitive industries can manipulate prices to generate profits, 

while firms in more competitive industries have little control over prices. This profile presents SUSB data from 1989 to 

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c 
MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

1997 on establishment births and deaths. These data are only available by three digit SIC code, making it impossible to 

calculate sector specific birth and deaths rates. However, data for the MP&M industry as a whole are presented. 

The M P&M manufacturing sector has an annual birth rate of 8.1 percent and an annual death rate of 7.8 percent, indicating 

that in general the MP&M manufacturing sector is relatively competitive. Three digit SIC industry birth and death rates are 

much more variable, ranging from 4 percent to up to 15 percent. For a more complete discussion, along with the three digit 

SIC birth and death rates, see Appendix A. 

3.3.3 Financial Condition and Performance 

Operating margin is a measure of industry financial performance. Operating margin is defined as VOS less annual payroll 

and cost of materials, as a percent of VOS, and thus measures pre-tax operating profitability before capital- and financingrelated charges. Firms with higher operating margins have more cushion against operating losses as a consequence of 

fluctuating input prices, and thus are likely to be more stable. 

Table 3.11 presents DOC data on operating margins for each MP&M manufacturing industry for the years 1988 and 1996, as 

well as the change in operating margin between the two years. In 1996, the average operating margin for the MP&M sectors 

was 29.6 percent. This was a slight increase from 1988, when the average operating margin for the MP&M manufacturers 

was 28.0 percent. Ten MP&M manufacturing sectors experienced increases in their operating margins during this time 

period, while nine industries experienced decreases. 

Instruments, other metal products, and ordnance were the most profitable sectors, according to this measure, with operating 

margins around 40 percent. The iron and steel, motor vehicle, railroad, and ships and boats sectors had the lowest operating 

margins, all near 22 percent. The greatest increases in operating margin occurred in the aircraft, ordnance, and bus & truck 

industries, which all gained between five and six percent. The greatest decrease occurred in the aerospace industry, which 

lost 3.5 percent. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.11: Operating Margina: MP&M Manufacturing Sectors in 1988 and 1996 

Sector 1988 1996 Change in Operating 

Margin 

Aerospace 32.4% 28.9% -3.5% 

Aircraft 20.6% 26.7% 6.1% 

Bus & Truck 18.5% 24.4% 5.9% 

Electronic Equipment 32.0% 33.8% 1.8% 

Hardware 27.5% 29.7% 2.2% 

Household Equipment 29.7% 29.5% -0.2% 

Instruments 37.1% 41.1% 4.0% 

Iron and Steel 23.2% 22.9% -0.3% 

Job Shops 31.8% 30.8% -1.0% 

Mobile Industrial Equipment 27.9% 28.2% 0.3% 

Motor Vehicle 20.9% 22.4% 1.5% 

Office Machine 31.7% 30.2% -1.5% 

Ordnance 34.3% 39.6% 5.3% 

Other Metal Products 41.9% 40.4% -1.5% 

Precious Metals and Jewelry 27.9% 28.2% 0.3% 

Printed Wiring Boards 37.2% 36.8% -0.4% 

Railroad 22.4% 22.1% -0.3% 

Ships and Boats 23.2% 22.5% -0.7% 

Stationary Industrial Equipment 29.4% 31.5% 2.1% 

All MP&M Manufacturersb 28.0% 29.6% 1.6% 

a Operating Margin is calculated as (value of shipments - cost of materials - payroll)/value of shipments. b Weighted average by VOS. 

Source: Department of Commerce, Bureau of the Census, Annual Survey of Manufacturers. 

3.4 CHARACTERISTICS OF MP&M NON-MANUFACTURING SECTORS 

Eleven of the 18 MP&M sectors include non-manufacturing industries. The non-manufacturing activities are defined by 50 

four-digit SIC codes: 26 transportation SIC codes, 18 service SIC codes, five retail trade SIC codes, and one wholesale trade 

SIC code. MP&M facilities may perform both manufacturing and non-manufacturing activities. 

The analyses presented in this section cover 1997 only, because the Census does not collect data annually for nonmanufacturing SICs as it does for manufacturers in the Annual Survey of Manufacturers. The profile is based on data from 

the 1997 Censuses of Transportation, Communications, and Utilities; Service Industries; Retail Trade; and Wholesale Trade. 

3.4.1 Domestic Production 

a. Output

 

This profile uses sales and receipts as a measure of output. The sum of the receipts a manufacturer earns from the sale of its
 


outputs	 is an indicator of the overall size of a market or the size of a firm in relation to its market or competitors. EPA used 

Department of Commerce data to assess sales and receipts for the MP&M non-manufacturing sectors. 

Table 3.12 shows sales and receipts by sector for MP&M non-manufacturers. The MP&M nonmanufacturing sector 

generated 1.25 trillion dollars in sales and receipts in 1997. Motor vehicle repair and maintenance, with sales and receipts of 

870 billion dollars, accounted for almost 70 percent of total sales and receipts. Bus and truck, with sales and receipts of 209 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

billion dollars, accounted for another 17 percent. These two vehicle sectors made up 87% of total non-manufacturing output. 

The smallest sector was precious metals and jewelry, which accounted for only 367 million dollars in sales and receipts. 

Table 3.12: Sales/Receipts: MP&M Non-Manufacturing Sectors in 1997 

(millions, 2001$) 

Sector Output a Share 

Aircraft 9,935.9 0.8% 

Bus & Truck 209,316.1 16.7% 

Household Equipment 2,848 0.2% 

Instruments 7,402 0.6% 

Motor Vehicle 870,451 69.6% 

Office Machine 30,930 2.5% 

Other Metal Products 22,041 1.8% 

Precious Metals and Jewelry 367 0.029% 

Railroadb 30,728 2.5% 

Ships and Boats 37,383 3.0% 

Stationary Industrial Equipment 29,747 2.4% 

Total 1,251,148 100.0% 

a Total sales for retail and wholesale trade, total receipts for service industries, total revenue for 

transportation. b Railroad sales/receipts is estimated from 1992 data. 

Source: Department of Commerce, Bureau of the Census, Census of Transportation, Census of 

Wholesale Trade, Census of Retail Trade, Census of Service Industries, 1997. 

b. Number of facilities and firms

 

The number of facilities and firms in an industry is an indicator of the size and structure of an industry. Increases and
 


decreases in the number of firms and facilities can indicate whether an industry is growing or shrinking, and changes in the
 


ratio of facilities and firms can indicate whether an industry is becoming more integrated and concentrated. This profile uses
 


SBA data to assess the number of facilities and firms in the non-manufacturing sector from 1989 to 1996. The SBA changed 

its survey to include firms in 1990, but data on the number of firms are not available from this source in 1989. 

Table 3.13 shows the number of facilities and firms in the MP&M non-manufacturing sectors in 1989/1990 and 1996, with 

average annual growth rates. The number of firms and facilities grew from 1989 to 1996 in all of the sectors. The average 

number of facilities per firm shrank slightly over this time period, from 1.13 to 1.11, due to the fact that the number of firms 

in the non-manufacturing sector grew at 4.5 percent per year while the number of facilities grew at only 3.6 percent per year. 

In general, most MP&M non-manufacturers are single facility firms. 

Although the number of facilities and firms increased for all of the sectors over this time period, not all industries grew at the 

same rate. The number of facilities in the other metal products sector grew at only 0.6 percent annually, and the number of 

facilities in the stationary industrial equipment and instruments sectors grew at 1.3 percent annually. In contrast, the number 

of facilities in the office machine sector grew by 20.2 percent annually and the number of firms in the office machine sector 

grew by 23.7 percent annually. 

Concentration varied across the sectors. Stationary industrial equipment was the most concentrated sector, with an average of 

1.45 facilities per firm in 1996. The other metal products, household equipment, and office machine sectors were the least 

concentrated sectors, with only 1.04, 1.06, and 1.07 facilities per firm, respectively. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.13: Number of Firms and Facilities: MP&M Non-Manufacturing Sectors 

Sector 

Number of Firms Number of Facilities Facilities per Firm 

1990 1996 

Average 

Annual 

Growth Rate 

1989 1996 

Average 

Annual 

Growth 

Rate 

1989/90 1996 

Aircraft 2,024 3,281 8.4% 2,463 4,062 7.4% 1.22 1.24 

Bus & Truck 74,719 113,840 7.3% 88,128 127,675 5.4% 1.18 1.12 

Household Equipment 3,234 3,706 2.3% 3,367 3,935 2.3% 1.04 1.06 

Instruments 7,214 7,444 0.5% 8,365 9,185 1.3% 1.16 1.23 

Motor Vehicle 183,986 213,355 2.5% 203,592 234,542 2.0% 1.11 1.10 

Office Machine 9,206 32,916 23.7% 9,714 35,150 20.2% 1.06 1.07 

Other Metal Products 32,865 36,290 1.7% 34,683 37,902 1.3% 1.06 1.04 

Precious Metals and Jewelry 1,379 1,625 2.8% 1,535 1,838 2.6% 1.11 1.13 

Railroada n/a n/a n/a n/a n/a n/a n/a n/a 

Ships and Boats 5,739 8,290 6.3% 6,561 9,262 5.0% 1.14 1.12 

Stationary Industrial Equipment 14,672 15,075 0.5% 20,880 21,791 0.6% 1.42 1.45 

Total 335,038 435,822 4.5% 379,288 485,342 3.6% 1.13 1.11 

a The railroad sector has only two non-manufacturing SIC codes, both of which were excluded from the 1997 Census. Thus no data on 

railroads is available. 

Source: Small Business Administration, Statistics of U.S. Businesses. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

c. Employment

 

Employment is a measure of the level and trend of activity in an industry. Payroll is a measure of the skill level of employees
 


and of their value to the production process. While employment growth is often correlated with economic strength in an
 


industry, strong productivity growth and scale economies can result in growth in revenues that could not be predicted from 

employment trends alone. Trends in labor utilization relative to output are important in understanding how regulatory 

requirements may translate into job losses both in aggregate and at the community level. 

Table 3.14 shows DOC data on employment and payroll for the non-manufacturing MP&M sectors in 1997. Total 

employment for the non-manufacturing sector was 5.99 million, and total payroll was $201 billion. Average yearly 

pay/employee was $33,610. 

The majority of total employment came from the motor vehicle and bus and truck sectors. The motor vehicle sector had 2.6 

million employees, and the bus and truck sector had 2.1 million employees. Together these two sectors accounted for over 78 

percent of total employment in the non-manufacturing sector. The precious metals and jewelry sector had the lowest 

employment, with only 5,599 employees. 

Workers in a few industries were highly compensated. The railroad sector paid its workers $58,851 per year, and the office 

machine sector paid $56,092 per year. On the other extreme, workers in the precious metals and jewelry sector earned only 

$20,121 per year. 

Table 3.14: Employment and Payroll, 1997: MP&M Non-Manufacturing Sectors 

Sector Employment Share Payroll 

(thousands, 2001$) Share Pay/Employee 

Aircraft 121,210 2.0% 3,286,985 1.6% 27,118 

Bus & Truck 2,106,432 35.1% 65,643,990 32.6% 31,164 

Household Equipment 25,455 0.4% 935,661 0.5% 36,757 

Instruments 76,970 1.3% 2,530,404 1.3% 32,875 

Motor Vehicle 2,622,049 43.7% 83,223,310 41.3% 31,740 

Office Machine 235,332 3.9% 13,200,240 6.6% 56,092 

Other Metal Products 226,069 3.8% 6,981,264 3.5% 30,881 

Precious Metals and Jewelry 5,599 0.1% 112,659 0.1% 20,121 

Railroad 197,421 3.3% 11,618,460 5.8% 58,851 

Ships and Boats 178,560 3.0% 7,221,006 3.6% 40,440 

Stationary Industrial Equipment 198,735 3.3% 6,701,350 3.3% 33,720 

Total 5,993,832 100.0% 201,455,328.90 100.0% 33,610.44 

Source: Department of Commerce, Census of Transportation, Census of Wholesale Trade, Census of Retail Trade, Census 

of Service Industries, 1997. 

3.4.2 Industry Structure and Competitiveness 

A number of factors play an important role in determining market structure for an industry, including the barriers that firms
 


face in entering and exiting the market, the degree to which firms in the market are vertically and horizontally integrated, and
 


the extent to which markets have been globalized. This profile shows facility size and firm size as measures of industry
 


structure and competitiveness in the MP&M non-manufacturing sector.
 


a. Facility size

 

Facility size is an indicator of economies of scale. The presence of many large facilities in an industry can indicate that there
 


are advantages to building on a larger scale, such as dividing labor more efficiently, utilizing equipment more effectively, or 

getting bulk discounts. EPA used data from the 1997 Census to assess facility size for manufacturing facilities. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Non-manufacturing facilities tend to be small. There were 255,602 non-manufacturing facilities, or 52.9 percent, that 

employed 4 employees or less. These facilities accounted for 7 percent of sales and receipts in the non-manufacturing 

MP &M sectors. Facilities with less than 20 employees accounted for 88 percent of all non-manufacturing facilities but 

generated only 24 percent of non-manufacturing revenues. Facilities with more than 100 employees employed less than one 

percent of total employees, but generated 17 percent of total revenues. Non-manufacturing MP&M facilities appear to 

experience significant economies of scale. 

Although the individual non-manufacturing sectors tended to have small facilities, there was some variation between sectors 

in facility size. The aircraft sector and the ships and boats sector had relatively large facilities, probably because these sectors 

are involved with large-scale transportation. For both sectors, 6.3 percent of facilities had more than 100 employees. In 

contrast, the other metal products and precious metals and jewelry sectors had mostly small facilities. Ninety four percent of 

facilities in the other metal products sector and 96 percent of facilities in the precious metals and jewelry sector had less than 

20 employees. 

Table 3.15 presents the number of facilities and total sales by facility employment size category for each category. 

Table 3.15: Number of Facilities and Sales/Receipts by Facility Employment Size Category: 

MP&M Non-Manufacturing Sectors in 1997 

Sector 

Number of Facilities Sales/Receipts (millions, $2001) 

0 to 4 5 to 9 10 to 19 20 to 99 100 or 

more 0 to 4 5 to 9 10 to 19 20 to 99 100 or 

more 

Aircraft 1,936 936 720 870 299 381 482 879 2,767 5,433 

Bus & Truck 67,959 24,548 19,355 21,294 3,573 15,924 16,044 24,189 75,663 81,036 

Household Equipment 1,886 735 456 305 37 358 411 551 1,072 457 

Instruments 5,535 1,737 988 711 131 1,017 936 1,064 2,473 1,917 

Motor Vehicle 126,505 58,372 28,184 23,021 2,548 41,209 45,438 62,030 388,395 180,200 

Office Machine 16,849 3,619 2,186 1,935 408 3,598 2,592 3,327 9,285 12,146 

Other Metal Products 21,564 7,585 3,813 2,136 138 3,810 3,856 4,444 7,178 2,118 

Precious Metals and 

Jewelry 

790 215 88 41 2 109 81 70 88 19 

Railroada n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 

Ships and Boats 2,605 930 848 1,046 366 2,578 1,580 2,161 9,535 19,380 

Stationary Industrial 

Equipment 

9,974 7,601 3,601 2,084 134 3,183 5,487 6,000 9,321 3,189 

Total 255,602 106,277 60,238 53,443 7,635 72,168 76,907 104,714 505,778 305,894 

a The non-manufacturing railroad sector is comprised of two SIC codes, both of which were excluded from the 1997 Census. 

Source: Department of Commerce, Bureau of the Census, Census of Transportation, Census of Wholesale Trade, Census of Retail 

Trade, Census of Service Industries, 1997. 

b. Firm size

 

This profile uses firm employment size as an indicator of market power and barriers to entry. The distribution of facilities and
 


output by firm size can indicate that the firms in an industry have market power. If the largest firms own disproportionately
 


many facilities, in which case they are considered horizontally integrated, or if the largest firms control a large portion of
 


industry output, then they may have significant market power. These firms can use their large capacities to control and
 


exploit markets. The presence of many large firms in an industry can also indicate that there are barriers to entry into that 

industry, such as capital requirements or economies of scale, that give existing firms in the industry a competitive advantage. 

Table 3.16 presents SUSB data on numbers of firms and facilities with estimated receipts by firm employment size category 

in 1996 for MP&M non-manufacturers. In general, although the majority of MP&M non-manufacturing firms were small, the 

larger firms owned many facilities and had disproportionately large market shares. The vast majority of non-manufacturing 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

firms – 427,173 firms or about 98 percent of non-manufacturers – employed fewer than 100 employees. However, these firms 

owned only 90 percent of all facilities and earned 610 billion dollars, only 58 percent of all revenues. The 2,338 firms with 

500 or more employees, equal to 0.54 percent of all non-manufacturers, owned 6.5 percent of all facilities and generated 207 

billion dollars, equal to 19.8 percent of total revenue. 

Firm size in the individual MP&M non-manufacturing sectors is relatively similar to the trends in the non-manufacturing 

sector as a whole. At least 94 percent of the firms in every sector had less than 100 employees. Although firm size varies 

little by sector, there were larger variations in receipts by firm size. The aircraft, instruments, and ships and boats sectors 

each had a small percentage of firms that controlled a large share of the market. In the aircraft sector, the largest 2.35 percent 

of firms generated 60.4 percent of total revenues. In the instruments sector, the largest 1.2 percent of firms generated 50.2 

percent of total revenues. In the ships and boats sector, the largest 2.6 percent of firms generated 57.4 percent of total 

revenues. 

Table 3.16: Number of Firms, Facilities, and Estimated Receipts by Firm Employment Size Category, 1996: 

MP&M Non-Manufacturing Sectors 

Sector 

Firms Facilities Estimated Receipts (millions, 

2001$) 

1 to 99 100 to 

499 

500 or 

more 1 to 99 100 to 

499 

500 or 

more 1 to 99 100 to 

499 

500 or 

more 

Aircraft 3,124 80 77 3,189 139 734 2,717 1,264 6,071 

Bus & Truck 111,038 2,001 801 112,751 4,334 10,590 79,331 23,943 66,113 

Household Equipment 3,669 19 18 3,700 23 212 2,032 275 873 

Instruments 7,277 76 91 7,536 206 1,443 3,119 562 3,715 

Motor Vehicle 209,814 3,010 531 216,707 7,119 10,716 465,989 186,083 87,113 

Office Machine 32,428 290 198 32,745 759 1,646 14,787 4,800 10,565 

Other Metal Products 35,788 284 218 36,205 567 1,130 16,749 2,308 4,610 

Precious Metals and Jewelry 1,615 6 4 1,661 105 72 269 0 0 

Railroad n/a n/a n/a n/a n/a n/a n/a n/a n/a 

Ships and Boats 7,833 243 214 8,000 519 743 9,087 6,122 20,493 

Stationary Industrial Equipment 14,587 302 186 16,331 1,359 4,101 15,422 4,606 7,739 

Total 427,173 6,311 2,338 438,825 15,130 31,387 609,502 229,963 207,294 

a The non-manufacturing railroad sector is comprised of two SIC codes, both of which were excluded from the 1997 Census. 

Source: Small Business Administration, Statistics of U.S. Businesses. 

3.5 CHARACTERISTICS OF ALL MP&M SECTORS 

This section presents additional market structure data for the MP&M industry as a whole. It includes eight-firm concentration 

data and risk-normalized return on assets (ROA) data as measures of industry competitiveness. 

3.5.1 Eight-firm Concentration Ratio 

The eight-firm concentration ratio (8-firm CR) is a measure of the degree to which the largest firms in an industry have 

market power. It is defined as the percentage of the value of total industry shipments that is produced by the top eight firms 

of a given industry. In general, an industry with a high 8-firm CR are likely to have larger entry and exit barriers and to be 

less competitive. Firms in this kind of industry have less incentive to compete and more ability to manipulate prices to 

increase their profits. It is more difficult for firms in a competitive, less concentrated industry to manipulate prices. This 

profile presents 8-firm CR data from the 1992 Census. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.17 shows the 8-firm CR for each sector in 1992. The aerospace and aircraft sectors were particularly concentrated, 

with the largest eight firms in each sector producing 92 percent and 85 percent of industry shipments, respectively. The motor 

vehicle, ordnance, and railroad sectors were also relatively concentrated. The job shop and hardware industries were the least 

concentrated, with only 19 percent and 25 percent of output, respectively, being produced by the eight largest firms. 

Table 3.17: ght-firm Concentration Ratio, 1992 

Sector 

8-firm Concentration Ratio 

Value Ranka 

Aerospace 92.29 19 

Aircraft 85.3 18 

Bus & Truck 42.51 7 

Electronic Equipment 47.27 9 

Hardware 24.52 2 

Household Equipment 54.22 10 

Instruments 44.2 8 

Iron and Steel 41.87 6 

Job Shop 19.26 1 

Mobile Industrial Equipment 58.56 13 

Motor Vehicle 77.30 17 

Office Machine 61.38 14 

Ordnance 76.90 16 

Other Metal Products 54.27 11 

Precious Metals and Jewelry 35.0 4 

Printed Circuit Boards 35.0 3 

Railroad 71.00 15 

Ships and Boats 58.20 12 

Stationary Industrial Equipment 41.16 5 

Ei 

a Rank is a comparison within the MP&M manufacturing sectors only. A rank of 1 

indicates the lowest level of concentration. 

Source: Department of Commerce, Bureau of the Census. 

3.5.2 Risk Normalized Return on Assets 

Firms’ abilities to enter and exit markets determine, in part, the competitiveness of an industry. If significant barriers to entry 

exist, potential entrants may be dissuaded and existing firms may enjoy market power. If few barriers to entry exist, existing 

firms are more likely to face competition for market share via price and other competitive tactics. Some important entry 

barriers for the MP &M industry are large capital requirements, economies of scale, and brand name recognition. Although 

data on barriers to entry are limited, the available data show that market power exists in some sectors. 

EPA used the risk normalized return on assets as an indicator of the existence of entry or exit barriers for each industry 3

. A 

firm’s return on assets is the profit the firm earns from investing in assets. Normally, firms in riskier industries tend to have 

higher ROA’s. However, barriers to entry or exit can allow firms to achieve higher ROA’s than would be predicted from their 

3 The risk normalized ROA only assigns MP&M industry sectors relative rankings and does not imply that they face high 

or low barriers to competition in absolute terms. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

risk level. The risk normalized return on assets measures the additional profit that firms earn above and beyond what their 

risk level predicts. EPA used data from Marketguide.com to calculate a risk normalized ROA. The agency calculated risk 

normalized ROA by dividing each firm’s ROA by its asset beta (a measure of the relative riskiness of the firm’s common 

stock) and averaging over the five-year period from 1996 to 2000. 

The electronic equipment, printed circuit board, and office machine industries had the lowest risk normalized ROA’s, 

indicating relatively weaker barriers to entry or exit for these industries. The instrument, other metal products, mobile 

industrial equipment, and motor vehicle industries had the highest ROA’s. These industries are likely to have significant 

barriers to entry and exit. 

Table 3.18 presents the average risk normalized return on assets for the period from 1996 to 2001, based on data from 

Marketguide.com. 

Table 3.18: Average Risk Normalized Return on Assets, 1996 to 2001 

Sector 

Risk-Normalized ROA (%) 

Value Rank 

Aerospace 13.19 8 

Aircraft 16.15 13 

Bus & Truck 12.31 7 

Electronic Equipment 7.21 1 

Hardware 17.18 15 

Household Equipment 12.02 5 

Instruments 19.64 18 

Iron and Steel 11.38 4 

Job Shop 13.44 9 

Mobile Industrial Equipment 18.13 17 

Motor Vehicle 18.10 16 

Office Machine 9.58 3 

Ordnance 12.30 6 

Other Metal Products 26.60 19 

Precious Metals and Jewelry 14.43 10 

Printed Circuit Boards 7.50 2 

Railroad 14.62 11 

Ships and Boats 16.11 12 

Stationary Industrial Equipment 16.78 14 

Source: www.marketguide.com 

3.6 CHARACTERISTICS OF MP&M FACILITIES 

This section uses survey data to characterize MP&M facilities. It includes data on facility revenue sources, discharge type, 

small business status, market type, and financial performance. These data are organized according to MP&M regulation 

subcategories based on unit operations performed and the nature of the waste generated. EPA determined that a basis exists 

for dividing the MP&M category into the following subcategories: General Metals, Non-Chromium Anodizing, Metal 

Finishing Job Shops, Printed Wiring Boards, Steel Forming and Finishing, Oily Wastes, Railroad Line Maintenance, and 

Shipbuilding Dry Dock. EPA did not generally define subcategories in terms of industrial sectors because many facilities 

perform operations covered by multiple sectors and, as a result, the industrial sectors are too broad for subcategorization. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.19 shows the national number of MP&M facilities that sell products to different combinations of sectors. The table 

shows that many MP&M facilities operate in multiple market sectors. Almost every combination of sectors shows overlap, 

and some MP&M facilities report revenues from three or more sectors. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of 

Table 3.19: Overlap of Sectors 

Sector 

Aerospace 

Aircraft 

Bus and Truck 

Electronic Equipment 

Hardware 

Household Equipment 

Instrument 

Mobile Industrial Equipment 

Motor Vehicle 

Office Machine 

Ordnance 

Other Metal Products 

Precious Non-Precious Metals 

Printed Circuit Boards 

Railroad 

Aerospace 1,828 

Aircraft 0 2,350 Bus and Truck 129 169 5,574 

Electronic Equipment 1,327 1,318 824 4,073 

Hardware 345 399 914 1,129 7,075 Household Equipment 289 317 477 898 1,600 2,635 

Instrument 1,046 1,126 398 1,680 678 610 4,965 

Mobile Industrial Equipment 47 116 1,511 704 738 417 404 2,467 Motor Vehicle 157 220 1,790 619 823 678 524 1,089 13,853 

Office Machine 265 349 198 622 515 477 356 159 223 1,088 Ordnance 132 119 52 204 86 77 202 80 153 89 481 

Other Metal Products 289 321 457 850 1,450 1,393 475 438 695 329 36 5,359 

Precious and NonPrecious Metals 47 47 0 36 47 24 47 12 36 36 0 92 1,651 

Printed Circuit Boards 160 164 0 164 160 160 4 0 0 375 0 160 0 1,229 Railroad 16 61 95 86 143 67 69 124 154 91 58 81 12 26 1,132 

Ship and Boat 102 0 237 146 191 156 104 138 245 138 25 78 12 0 48 

Stationary Industrial Equipment 

1,169 1,255 714 1,818 1,151 687 1,293 688 530 486 130 469 39 164 109 

Unknown 

Source: U.S. EPA analysis. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

The remainder of this profile focuses on MP&M industry facilities that discharge effluent. Out of a total population of 

638,696 MP&M industry establishments reported in the Statistics of U.S. Businesses for 1996, approximately seven percent, 

or 43,858 facilities, are effluent dischargers as identified by the MP&M surveys. 

Figure 3.2 shows the breakdown of MP &M facilities by discharge type. Of the effluent dischargers, 41,119 (94 percent) are 

indirect dischargers, meaning that they discharge into a sewer or a POTW, and 2,699 (6 percent) are direct dischargers that 

discharge directly into a surface water body. The remaining 40 facilities are both direct and indirect dischargers. 

Figure 3.2: Facilities by Discharge Type 

Source: U.S. EPA analysis. 

Figure 3.3 shows facilities by revenue source. Local governments or municipalities operate 3,785 facilities (9 percent). The 

remaining 40,073 facilities are privately owned. Of these, 17,428 facilities (40 percent) are rebuilding and maintenance 

facilities and 20,172 facilities (46 percent) are manufacturing facilities. 

Figure 3.3: Facilities by Revenue Source 

Source: U.S. EPA analysis. 

Figure 3.4 shows facilities by small entity status. Small Business Administration (SBA) thresholds were used to estimate the 

number of facilities that are likely to be owned by small businesses, as defined by the SBA. Using the methodology detailed in 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

the Small Entity Impact Analysis (see Chapter 10), EPA determined that 32,179 facilities (73 percent) are owned by small or 

potentially small entities. 

Figure 3.4: Facilities by Small Entity Status 

Source: U.S. EPA analysis. 

Figure 3.5 indicates that MP&M facilities derive approximately 22 percent of their revenues from export sales. Almost 78 

percent of MP&M revenues come from domestic non-government sources. Government purchases account for a very small 

share of MP&M revenues overall. 

Figure 3.5: 1996 Facility Revenues by Market Type (billions, 2001$) 

Export data were not available for Iron and Steel surveys. 

Source: U.S. EPA analysis. 

To characterize baseline financial performance across regulation subcategories, EPA used Pre-Tax Return on Assets 

(PTRA) as a measure of industry profitability. PTRA measures the return, before tax, to total capital that company 

management achieves from its deployed capital assets. Unlike the ROA measure noted above in section 3.5.2, the PTRA 

reported in this discussion is not adjusted for risk. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Table 3.20 shows that the printed wiring board subcategory has the highest median PTRA (13.4 percent ) of all the 

subcategories. The shipbuilding drydock subcategory has the lowest median PTRA (2.5 percent). The median PTRA for all of 

the MP&M facilities is 11.1 percent. 

Table 3.20: Financial Performance 

Subcategory Median Pre-Tax Return on Assets (PTRA) 

Shipbuilding Drydock 2.5% 

General Metals 11.5% 

Steel Forming & Finishing 9.1% 

Metal Finishing Job Shops 9.2% 

Non-Chromium Anodizer 9.0% 

Oily Wastes 9.6% 

Printed Wiring Boards 13.4% 

Railroad Line Maintenancea n/a 

a PTRA data was not available for railroad line maintenance because these facilities were treated 

as cost-centers in the survey analysis. 

Source: U.S. EPA analysis. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

GLOSSARY 

capital expenditures: expenditures for permanent additions and major alterations to facilities and equipment, as well as 

replacements and additions to capacity, which are ordinarily depreciated. Reported capital expenditures include work done on 

contract and expenditures for assets leased from other concerns through capital leases. Expenditures for land and cost of 

maintenance and repairs charged as current operating expenses are excluded 

concentration ratio: the percentage of output from a given industry that is produced by the largest firms in that industry. 

For example, the eight firm concentration ratio measures the percentage of output that is produced by the eight largest firms in 

an industry. The concentration ratio is a measure of industry competitiveness. 

employment: total number of full-time equivalent employees, including production workers and non-production workers. 

export dependence: the share of shipments by domestic producers that is exported; calculated by dividing the value of 

exports by the value of domestic shipments. 

import penetration: the share of all consumption in the U.S. that is provided by imports; calculated by dividing imports by 

reported or apparent domestic consumption (the latter calculated as domestic value of shipments minus exports plus imports). 

manufacturing: series of unit operations necessary to produce metal products; generally performed in a production 

environment. 

North American Industry Classification System: classification system adopted beginning in 1997 to replace SIC 

codes. NAICS codes will be used throughout North American and allow for greater comparability with the International 

Standard Industrial Classification System (ISIC), which is developed and maintained by the United Nations. The new system 

also better reflects the structure of today’s economy, including the growth of the service sectors and new technologies. 

nominal values: dollar values expressed in current dollars. 

operating margin: measure of the relationship between input costs and the value of production, as an indicator of financial 

performance and condition. Everything else being equal, industries and firms with lower operating margins will generally have 

less flexibility to absorb the costs associated with a regulation than those with higher operating margins. Operating margins 

were calculated in this profile by subtracting the cost of materials and total payroll from the value of shipments. Operating 

margin is only an approximate measure of profitability, since it does not consider capital costs and other costs. It is used to 

examine trends in revenues compared with production costs within an industry; it should not be used for cross-industry 

comparisons of financial performance. 

pre-tax return on assets (PTRA): the ratio of cash operating income (net income plus depreciation) to the book value of 

total assets. This ratio is a measure of facility profitability. 

producer price index (PPI): a family of indexes that measures the average change over time in selling prices received by 

domestic producers of goods and services (Bureau of Labor Statistics, PPI Overview). Used in this profile to convert nominal 

values into real dollar values. 

real values: nominal values normalized using a price index to express values in a single year’s dollars. Removes the effects 

of price inflation when evaluating trends in dollar measures. 

rebuilding/maintenance: unit operations necessary to disassemble used metal products into components, replace the 

components or subassemblies or restore them to original function, and reassemble the metal product. These operations are 

intended to keep metal products in operating condition and can be performed in either a production or a non-production 

environment. 

return on assets: the profit the firm earns from investing in assets. Generally firms in riskier industries have higher returns 

on assets. A risk normalized return on assets (RNRO A) measures the additional profit that firms earn above and beyond what 

their risk level predicts. The RNROA is a measure of industry competitiveness. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

Standard Industrial Classification: classification system used for all establishment-based Federal economic statistics 

classified by industry. Each establishment is assigned a 4-digit SIC code based on its principal product, or service. Last 

revised in 1987 and currently being replaced by the NAICS. 

value added: measure of manufacturing activity, derived by subtracting the cost of purchased inputs (materials, supplies, 

containers, fuel, purchased electricity, contract work, and contract labor) from the value of shipments (products manufactured 

plus receipts for services rendered), and adjusted by the addition of value added by merchandising operations (i.e., the 

difference between the sales value and the cost of merchandise sold without further manufacture, processing, or assembly) plus 

the net change in finished goods and work-in-process between the beginning-and end-of-year inventories. Value added avoids 

the duplication in value of shipments as a measure of economic activity that results from the use of products of some 

establishments as materials by others. Value added is considered to be the best value measure available for comparing the 

relative economic importance of manufacturing among industries and geographic areas. 

value of shipments: net selling values of all products shipped as well as miscellaneous receipts. Includes all items made 

by or for an establishment from materials owned by it, whether sold, transferred to other plants of the same company, or 

shipped on consignment. Value of shipments is a measure of the dollar value of production, and is often used as a proxy for 

revenues. This profile uses value of shipments to indicate the size of a market and how the size differs from year to year, and 

to calculate operating margins. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

ACRONYMS 

NAICS: North American Industry Classification System
 


PPI: producer price index
 


PTRA: pre-tax return on assets
 


ROA: return on assets
 


SIC: Standard Industrial Classification
 


VA: value added
 


VOS: value of shipments
 


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Marketguide.com. 1992. Risk Normalized Return on Assets. http://www.marketguide.com. 

U.S. Bureau of Labor Statistics. 2000. Producer Price Index. 

U.S. Department of Commerce. 1992. Bureau of the Census. Census of Manufacturers, Census of Transportation, Census of 

Wholesale Trade, Census of Retail Trade, Census of Service Industries. 

U.S. Department of Commerce. 1996. Bureau of the Census. Annual Survey of Manufacturers. 

U.S. Department of Commerce. 2000. Bureau of the Census. Foreign Trade Data. 

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Sector Notebook Project. EPA 310-R-95-009. http://es.epa.gov/oeca/sector/index.html 

U.S. Environmental Protection Agency. 1995e. Printed Wiring Board Industry and Use Cluster Profile. Design for the 

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U.S. Environmental Protection Agency. 1997. Profile of the Shipbuilding and Repair Industry. EPA Office of Compliance 

Sector Notebook Project. EPA 310-R-97-008. http://es.epa.gov/oeca/sector/index.html 

U.S. Environmental Protection Agency. 1998. Profile of the Aerospace Industry. EPA Office of Compliance Sector 

Notebook Project. EPA 310-R-98-001. http://es.epa.gov/oeca/sector/index.html 

U.S. Small Business Administration. Statistics of U.S. Businesses. http://www.sba.gov/advo/stats/int_data.html 

Value Line Investment Survey. Industry Reports. Value Line Publishing, Inc: December 2001. 

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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors 

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3-42
 
MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options 

Chapter 4: Regulatory Options
 


INTRODUCTION 

The preamble for the final rule describes the regulatory 

options considered by EPA for the final MP&M effluent 

guidelines. This chapter provides a brief summary of the 

subcategories and the regulatory options. 

4.1 SUBCATEGORIZATION 

EPA may divide a point source category into subcategories 

to address variations in products, raw materials, processes, 

CHAPTER CONTENTS 

4.1 orization ................. ......... 4-1 

4.2 y Options ................. ....... 4-3 

4.3 T Options for Direct Dischargers ........ 4-3 

4.4 or Indirect Dischargers .......... 4-3 

4.5 NS Options for New Sources ....... 4-4 

4.6 Summary of the Final Rule and Regulatory 

Alternatives ................. ............. 4-4 

Glossary ................. ................. ... 4-5 

Acronyms ................. ................. .. 4-6 

Subcateg 

Technolog 

BPT/BA 

PSES Options f 

NSPS and PS 

and other factors that result in distinctly different effluent characteristics. Defining subcategories makes it possible to 

establish effluent limitations that take into account technological achievability and economic impacts unique to each 

subcategory. EPA considered the following factors in defining MP&M subcategories: 

� unit operation, � nature of the waste generated,
 

� activity, � econo mic impacts,
 

� raw materials, � treatment costs,
 

� products, � total energy requirem ents,
 

� size of site, � air pollution control method s,
 

� location, � solid waste generation and disposal, and
 

� age, � publicly-owned treatment work (POTW)
 

burd en. 

In a way similar to the proposed rule, EPA established subcategories for the final MP &M rule based on unit operations 

performed. The subcategories are defined in part based on the type of wastewater that facilities discharge, including: 

facilities that discharge wastewaters with high metals content, with or without oil and grease (O&G); and 

� facilities that discharge wastewaters containing mainly O&G, with limited metals and other associated organic 

constituents. 

The subcategories identified by EPA in each group are: 

Metal-bearing (with or without O&G): 

� General Metals, 

� Metal Finishing Job Shops, 

� Non-Chromium Anodizing, 

� Printed Wiring Board, 

� Steel Forming & Finishing; and 

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MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options 

Oil-bearing only: 

� Oily Wastes, 

� Railroad Line Maintenance, and 

� Shipbuilding Dry Docks. 

For the final rule, EPA is establishing limitations and standards only for direct dischargers in the Oily Wastes subcategory. 

The other subcategories were considered at proposal and for some of the alternative regulatory options but are not further 

regulated under the final rule. Section VI.B of the preamble accompanying the final rule describes the basis for defining these 

subcategories. The following are brief summaries of each subcategory: 

General Metals: The General Metals subcategory includes facilities that perform operations that generate metal-bearing 

wastewater.1 At a minimum, wastewater at these sites requires metals removal and may also require the preliminary treatment 

steps of oil/water separation, chromium reduction, and cyanide destruction. For example, wastewater generated from most 

manufacturing operations and heavy rebuilding operations (e.g., aircraft/aerospace, bus/truck, railroad, ship, industrial 

equipment) would be regulated under the General Metals subcategory as well as sites performing surface finishing operations 

at a captive shop (i.e., not a metal finishing job shop) including continuous electroplating as defined in today’s rule. 

Metal Finishing Job Shops: These facilities must perform one or more of the six operations regulated by the existing Metal 

Finishing (40 CFR 433) and Electroplating (40 CFR 413) effluent guidelines, and must meet the definition of a job shop. 

The six metal finishing operations are electroplating, electroless plating, anodizing, coating, chemical etching and milling, and 

printed circuit board manufacture. A job shop is a facility that owns no more than 50 percent of the materials undergoing 

metal finishing. EPA proposes to regulate Printed W iring Board facilities that are job shops under this subcategory, but is 

seeking comment on this proposal. 

Non Chromium Anodizing: This subcategory includes facilities that perform aluminum anodizing without the use of chromic 

acid or dichromate sealants. The wastewater generated at these facilities contains very low levels of metals (except for 

aluminum) and toxic organic pollutants. 

Printed Wiring Board: These facilities manufacture, maintain, and repair printed wiring boards (i.e., circuit boards), not 

including job shops. They perform some unique operations, including applying, developing, and stripping of photoresist; 

lead/tin soldering; and wave soldering. 

Steel Forming & Finishing: This subcategory applies to facilities that perform MP&M operations or cold forming operations 

on steel wire, rod, bar, pipe, or tube. Other operations on steel, including any hot forming operations for steel, or cold 

forming, electroplating, or continuous hot dip coating of other steel products, will be regulated under the revisions to the 

existing Iron and Steel Manufacturing effluent guidelines (40 CFR 420). 

Oily Wastes: The Oily Wastes subcategory is a “catch-all” for sites that discharge wastewater exclusively from oily 

operations and are not otherwise covered by the Railroad Line Maintenance or Shipbuilding Dry Dock subcategory. Oily 

operations for the this subcategory are defined in today’s final rule as: alkaline cleaning for oil removal, aqueous or solvent 

degreasing, corrosion preventative coating (as specified in § 438.21(b)); floor cleaning; grinding; heat treating; deformation 

by impact or pressure; machining; painting (spray or brush); steam cleaning; and testing (such as hydrostatic, dye penetrant, 

ultrasonic, magnetic flux); iron phosphate conversion coating; abrasive blasting, alkaline treatment without cyanide; 

assembly/disassembly; tumbling/barrel finishing/mass finishing/vibratory finishing; burnishing; electrical discharge 

machining; polishing, thermal cutting; washing of final products; welding; wet air pollution control for organic constituents; 

adhesive bonding; and calibration. 

Railroad Line Maintenance: This is one of two specific subcategories that discharge only oil-bearing wastewaters (as 

defined above for the Oily Wastes subcategory). The Railroad Line Maintenance subcategory includes facilities that 

discharge from performing routine cleaning and light maintenance on railroad engines, cars, car-wheel trucks, or similar parts 

or machines. Facilities engaged in the manufacture, overhaul or heavy maintenance of railroad engines, cars, car-wheel 

1 These sites may also perform operations that generate oil-bearing wastewater. 

4-2 
MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options 

trucks, or similar parts or machines are not covered by this subcategory and depending on the operations performed may be 

covered by either the General Metals or Oily Wastes subcategory. 

Shipbuilding Dry Docks: This is the second of two specific subcategories that discharge only oil-bearing wastewaters (as 

defined above for the Oily Wastes subcategory). The Shipbuilding Dry Dock subcategory applies to discharges of process 

wastewater generated in or on dry docks and similar structures, such as graving docks, building ways, marine railways and lift 

barges at shipbuilding facilities (or shipyards). When generated by operations from within a dry dock or similar structure, this 

subcategory covers process wastewater generated inside and outside the vessel (including bilge water) and wastewater 

generated from barnacle removal conducted as preparation for ship maintenance, rebuilding or repair. Wastewaters generated 

from other operations at shipyards are not included in this subcategory. 

4.2 TECHNOLOGY OPTIONS 

EPA defined specific effluent limitations guidelines and standards for consideration in developing the regulation based on a 

statistical analysis of the performance of several wastewater treatment technology options. This analysis is described in 

Section 9 of the Technical Development Document and the Statistical Support Document. 

EPA is establishing BPT pH limitations and daily maximum limitations for two pollutants, oil and grease as hexane 

extractable material (O&G (as HEM)) and total suspended solids (TSS), for direct dischargers in the Oily Wastes subcategory 

based on the proposed technology option (Option 6). The technology requirements include the following treatment measures: 

(1) in-process flow control and pollution prevention; and (2) oil-water separation by chemical emulsion breaking and 

skimming (see Section 9 of the TDD). This technology is available technology readily applicable to all facilities in the Oily 

Wastes subcategory. Approximately 42% of the direct discharging facilities in the Oily Wastes subcategory currently employ 

this technology already. 

4.3 BPT/BAT OPTIONS FOR DIRECT DISCHARGERS 

EPA selected the Best Practicable Control Technology Currently Available (BPT) for direct dischargers in each 

subcategory based on the average of the best performances within the industry from operations of various ages, sizes, 

processes, and other characteristics. The Agency considered the cost of these treatment technologies relative to the effluent 

reductions achieved to assess the cost-reasonableness of these limitations. EPA then considered application of the Best 

Available Technology Economically Achievable (BAT) for priority and nonconventional pollutants and Best 

Conventional Pollutant Control Technology (BCT) for conventional pollutants. EPA is promulgating BCT equivalent 

to BPT for facilities in the Oily Wastes subcategory and has decided not to establish BAT limitations. 

Table 4.1 shows the technology basis for the selected option for BPT, BCT and BAT for the Oily Wastes subcategory. 

Table 4.1: Selected Options For Direct Dischargers: BPT, BCT and BAT 

Subcategory BPT Option BCT/BAT 

For oil-bearing wastes 

Oily Wastes 6 BCT = 6 

BAT not promulgated 

Source: U.S. EPA analysis. 

4.4 PSES OPTIONS FOR INDIRECT DISCHARGERS 

EPA considered Pretreatment Standards for Existing Sources (PSES) options for regulating existing indirect 

dischargers under today’s final rule. EPA has selected no further regulation for indirect dischargers in all of the defined 

subcategories. 

4-3 
MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options 

Wastewater discharges to POTWs from facilities in all subcategories will continue to be regulated by local limits, general 

pretreatment standards, and 40 CFR 413 or 433, as appropriate. 

4.5 NSPS AND PSNS OPTIONS FOR NEW SOURCES 

EPA is promulgating New Source Performance Standards (NSPS) for new direct dischargers in the Oily Wastes 

subcategory at the BPT and BCT levels. New facilities have the opportunity to incorporate the best available demonstrated 

technologies, including process changes, in-plant controls, and end-of-pipe treatment technologies, without the cost of 

retrofitting. EPA considered the same technologies discussed previously for BPT/BAT and PSES as the basis for new source 

technology. In addition, because new sites may be able to install pollution prevention and pollution control technologies more 

cost-effectively then existing sources, the Agency strongly considered more advanced treatment options. EPA is not 

promulgating Pretreatment Standards for New Sources (PSNS) for new indirect dischargers. 

Table 4.2 lists the technology options and exclusions for new direct and indirect dischargers. 

Table 4.2: Options For New Direct Dischargers (NSPS) and 

Indirect Dischargers (PSNS) 

Subcategory 

NSPS 

Technology 

Option 

PSNS Technology 

Option 

For oil-bearing wastes 

Oily Wastes 6 No further regulation 

Source: U.S. EPA analysis. 

4.6 SUMMARY OF THE FINAL RULE AND REGULATORY ALTERNATIVES 

The following describes the final rule and the three alternative regulatory options considered by EPA: 

� Final Rule: technology Option 6 applied only to direct dischargers in the Oily Wastes subcategory; 

�	 	 NODA/Proposal Option: applies limitations and standards for direct dischargers in all eight MP&M subcategories 

and pretreatment standards for all indirect dischargers in three subcategories (i.e., Metal Finishing Job Shops, Printed 

Wiring Board, and Steel Forming & Finishing); pretreatment standards for facilities above a certain wastewater flow 

volume in two subcategories (i.e., General Metals and Oily Wastes); and no national pretreatment standards for 

facilities in three subcategories (i.e., Non-Chromium Anodizing, Railroad Line Maintenance, and Shipbuilding Dry 

Docks); 

�	 	 Direct Dischargers + 413 to 433 Upgrade Option: applies the same technology requirements for direct dischargers 

as the final rule and includes new requirements for indirect dischargers in the General Metals, Printed Wiring Board, 

and Metal Finishing Job Shops subcategories currently regulated under the Electroplating regulations (40 CFR 413); 

and 

�	 	 Direct Dischargers + 413 plus 50% Local Limits Upgrade Option: applies the same technology requirements for 

direct dischargers as the final rule and includes new requirements for indirect dischargers in the General Metals, 

Printed Wiring Board, and Metal Finishing Job Shops subcategories currently regulated under the Electroplating 

regulations (40 CFR 413) and also includes new requirements for indirect dischargers in the General Metals 

subcategory that are currently regulated by local limits or general pretreatment standards. 

4-4 
MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options 

GLOSSARY 

Best Practicable Control Technology Currently Available (BPT): effluent limitations for direct discharging 

facilities, addressing conventional, toxic, and nonconventional pollutants. In specifying BPT, EPA considers the cost of 

achieving effluent reductions in relation to the effluent reduction benefits. The Agency also considers the age of the 

equipment and facilities, the processes employed and any required process changes, engineering aspects of the control 

technologies, non-water quality environmental impacts (including energy requirements), and such other factors as the Agency 

deems appropriate. Limitations are traditionally based on the average of the best performances of facilities within the industry 

of various ages, sizes, processes, or other common characteristics. Where existing performance is uniformly inadequate, EPA 

may require higher levels of control than currently in place in an industrial category if the Agency determines that the 

technology can be practically applied. 

Best Available Technology Economically Achievable (BAT): effluent limitations for direct dischargers, addressing 

priority and nonconventional pollutants. BAT is based on the best existing economically achievable performance of plants in 

the industrial subcategory or category. Factors considered in assessing BAT include the cost of achieving BAT effluent 

reductions, the age of equipment and facilities involved, the processes employed, engineering aspects of the control 

technology, potential process changes, non-water quality environmental impacts (including energy requirements), economic 

achievability, and such factors as the Administrator deems appropriate. The Agency may base BAT limitations upon effluent 

reductions attainable through changes in a facility's processes and operations. Where existing performance is uniformly 

inadequate, EPA may base BAT upon technology transferred from a different subcategory within an industry or from another 

industrial category. 

Best Conventional Pollutant Control Technology (BCT): effluent limitations for direct discharging facilities, 

addressing conventional pollutants. Conventional pollutants include biochemical oxygen demand (BOD5), total suspended 

solids (TSS), fecal coliform, pH, and O&G. BCT is the equivalent of Best Available Technology (BAT) for control of 

conventional pollutants. EPA evaluates the reasonableness of BCT candidate technologies by applying a two-part cost test: 

(1) the POTW test, and (2) the industry cost-effectiveness test. In the POTW test, EPA calculates the cost per pound of 

conventional pollutant removed by industrial dischargers to upgrade from BPT to a BCT candidate technology, and then 

compares this cost to the POTW cost per pound for similar pollutant load reductions. The upgrade cost to industry must be 

less than the POTW benchmark of $0.25 per pound (in 1976 dollars). In the industry cost-effectiveness test, the ratio of the 

incremental BPT to BCT cost divided by the BPT cost for the industry must be less than 1.29 (i.e., the cost increase must be 

less than 29 percent). 

Job Shop: Facilities with more than 50 percent of their revenues coming from work on products not owned by the site. 

While there are SIC codes associated with some Metal Finishing Job Shops, they sell to a variety of markets and are not a 

market in and of themselves. 

New Source Performance Standards (NSPS): effluent limitations for new direct dischargers based on the best 

available demonstrated control technology. NSPS represents the greatest degree of effluent reduction attainable through the 

application of the best available demonstrated control technology for all pollutants (i.e., conventional, nonconventional, and 

priority pollutants). In establishing NSPS, EPA considers the cost of achieving the effluent reduction and any non-water 

quality environmental impacts and energy requirements. 

Pretreatment Standards for Existing Sources (PSES): categorical pretreatment standards for existing indirect 

dischargers, designed to prevent the discharge of pollutants that pass through, interfere with, or are otherwise incompatible 

with the operation of POTWs. Standards are technology-based and analogous to BAT effluent limitations guidelines. 

Pretreatment Standards for New Sources (PSNS): pretreatment standards for new indirect dischargers, designed to 

prevent discharges of pollutants that pass through, interfere with, or are otherwise incompatible with the operation of POTWs. 

Addresses all pollutants (i.e., conventional, nonconventional, and priority pollutants). Based on the same factors as are 

considered in promulgating NSPS. 

4-5 
MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options 

ACRONYMS 

BAT: Best Available Technology Economically Achievable
 


BCT: Best Conventional Pollutant Control Technology
 


BPT: Best Practicable Control Technology Currently Available
 


NSPS: New Source Performance Standards
 


O&G: oil and grease
 


POTW: publicly-owned treatment works
 


PSES: Pretreatment Standards for Existing Sources
 


PSNS: Pretreatment Standards for New Sources
 


4-6 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Chapter 5: Facility Impact Analysis


INTRODUCTION 

The facility impact analysis assesses whether the MP&M 

effluent guidelines are likely to impose severe or moderate 

economic and financial impacts on MP&M facilities. EPA 

undertook the facility impact analysis to aid in assessing the 

rule's economic achievability. Severe im pacts are facility 

closures and the associated losses in jobs, earnings, and output 

at facilities that close due to the rule. EPA also assessed 

moderate economic impacts to support its evaluation of 

regulatory options and to understand better the rule's 

economic impacts. Moderate impacts are adverse changes 

in a facility's financial position that are not threatening to its 

short-term viability. 

The options considered for regulation would have affected 

three major categories of MP&M facilities: privately-owned, 

railroad line maintenance, and government-owned facilities. 

EPA developed separate analytic methodologies to assess 

economic and financial impacts for each type of facility: 

1.	 Private MP&M facilities: This group includes all 

privately-owned facilities that do not perform 

railroad line maintenance. This major category of 

facilities operates in various subcategories and 

includes private businesses in a wide range of sectors 

CHAPTER CONTENTS 

5.1 ................. .............. 5-2 

5.2 odology ................. .............. 5-2 

5.2.1 ng Engineering Compliance Costs and 

Survey Financial Data to Current Year Dollar 

Values ................. ............... 5-3 

5.2.2 Market-level Impacts and Cost Pass-Through 

Analysis ................. ............. 5-4 

5.2.3 ...... 5-5 

5.2.4 

Maintenance Facilities .............. 5-12 

5.2.5 t Measures for Government-Owned 

Facilities ................. ............ 5-12 

5.3 ................. ................. . 5-14 

5.3.1 ................. ... 5-14 

5.3.2 ................. ...... 5-15 

5.3.3 iew of Impacts ................. .. 5-16 

5.3.4 for Indirect Dischargers .......... 5-18 

5.3.5 for Direct Dischargers ........... 5-19 

5.3.6 ............ 5-20 

5.3.7 Government-Owned Facilities . . 5-21 

Glossary ................. ................. ... 5-25 

Acronyms ................. ................. .. 5-26 

References ................. ................. . 5-27 

Data Sources 

Meth

Converti

Impact Measures for Private Facilities 

Impact Measures for Railroad Line 

Impac

Results 

Baseline Closures 

Price Increases 

Overv

Results

Results

Results for Private Facilities 

Results for 

or industries, including facilities that manufacture and rebuild railroad equipment. Only facilities that repair railroad 

track and equipment along the railroad line are excluded. There are 39,248 private MP&M facilities other than 

railroad line maintenance facilities nationally that may be affected by the rule, representing 89.5 percent of the 

43,858 facilities that discharge process wastewater from MP&M activities. 

2.	 Railroad line maintenance facilities: Railroad line maintenance facilities maintain and repair railroad track and 

vehicles. EPA administered a separate economic and financial survey to these facilities and applied a different 

impact analysis methodology than that used for other private facilities. This methodology used the same impact tests 

as used for other private facilities but applied these tests to the aggregate of maintenance facilities owned by a single 

railroad company instead of to individual facilities. There are 826 railroad line maintenance facilities in the analysis, 

representing 1.9 percent of all facilities in the analysis. 

3.	 Government-owned facilities: Government-owned facilities include MP&M facilities operated by 

municipalities, state agencies and other public sector entities such as state universities. Many of these facilities 

repair, rebuild, and maintain buses, trucks, cars, utility vehicles (e.g., snow plows and street cleaners), and light 

machinery. Government-owned facilities operate in two major subcategories: General Metals and Oily Waste. 

There are 3,785 government-owned facilities in the analysis, representing 8.6 percent of the total. 

The specific methodology used to assess impacts differs for each of the three types of MP&M facilities. In each case, EPA 

established thresholds for measures of financial performance and compared performance before and after compliance with 

each regulatory option to these thresholds. 

This chapter describes the methodology used to assess facility-level economic impacts for the three types of facilities, and 

then presents the results of the analyses. 

5-1 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

5.1 DATA SOURCES 

The economic impact analyses rely on data provided in the financial portion of the detailed questionnaires distributed to


MP&M facilities by EPA under the authority of Section 308 of the Clean Water Act. The surveys were conducted in two


phases, covering different MP& M industry sectors in each phase. The Phase I survey covered seven industry sectors and


reported data for respondent’s fiscal years 1987 to 1989. The Phase II survey covered an additional ten industry sectors (all


remaining MP& M sectors except Iron and Steel, which was the subject of a separate survey) and reported data for fiscal years


1994 to 1996.1 EPA administered each survey to a random stratified sample of facilities and assigned each facility a sample


weight based on the stratification process and the number of facilities surveyed, so that sample-weighted results would


represent all potentially-affected MP&M facilities at the national level. The results of the impact analyses for the sample


facilities were extrapolated to the national level using these facility sample weights.


The survey financial data for private businesses included three years of facility and parent firm income statements and balance


sheets, and the composition of revenues by MP&M business sector to which the facility's goods and services are sold.


Data for railroad line maintenance facilities came from a modified version of the Phase II survey administered to railroad


operating companies. The questionnaire was modified because railroad operating companies generally do not monitor


financial performance or collect financial data at the facility level for their numerous line maintenance facilities. The railroad


operating companies reported the number of line maintenance facilities in each operating unit, and provided both operating


company and parent firm financial data. They also provided technical data for each line maintenance facility.


Data for facilities in the Iron and Steel sector came from a 1997 Section 308 survey of iron and steel facilities. This survey


requested financial data generally similar to that collected by the MP&M surveys, including income statements and balance


sheets for fiscal years 1995-1997 for the facility and the parent firm.


Government-owned MP&M facilities provided data in the Phase II Section 308 survey of municipal and other government


agency facilities. This survey requested information on fiscal year 1996 sources and amounts of revenue and debt levels for


both the government entity and their MP&M facilities, and demographic data for the population served by the government


entity.


In addition to the survey data, a number of secondary sources were used to characterize economic and financial conditions in


the industries subject to the MP&M effluent guidelines. Secondary sources used in the analyses include:


� Department of Commerce economic census and survey data, including the Censuses of Manufactures, Annual 

Surveys of Manufactures, and international trade data; 

� the Benchmark Input-Output Tables of the United States, published by the U.S. Department of Commerce's Bureau 

of Economic Analysis; 

� price index series from the Bureau of Labor Statistics, Department of Labor; 

� U.S. Industry and Trade Outlook, published by McGraw-Hill and the U.S. Department of Commerce; and 

� industry trade publications. 

5.2 METHODOLOGY 

The facility impact analysis starts with compliance cost estimates from the EPA engineering analysis and then calculates how 

these compliance costs would affect the financial condition of MP&M facilities.2 EPA first eliminated from the analysis those 

facilities showing materially inadequate financial performance in the baseline, that is, in the absence of the rule. EPA judged 

these facilities, which are referred to as baseline closures, to be at substantial risk of financial failure regardless of any 

1 Appendix A provides a detailed description of the surveys and describes how EPA combined data from different surveys. 

2 EPA made several changes in the facility impact methodology between proposal and final regulation. These changes, which to a 

large degree address comments on the proposal impact methodology, are documented in the Notice of Data Availability (reference). 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

financial burdens that may result from the MP&M rule. Second, for the remaining facilities, EPA evaluated how compliance 

costs would likely affect facility financial health. In this analysis of compliance cost impact, EPA accounted for potential 

price increases that may help facilities recover compliance costs. EPA’s estimate of potential price increases was based on a 

cost pass-through analysis, which used historical input and output price changes for the years 1982 through 1991 to 

estimate how prices might change in response to regulation-induced production cost increases. A facility is identified as a 

regulatory closure if it would have operated under baseline conditions but would fall below an acceptable financial 

performance level when subject to the new regulatory requirements. 

EPA also identified private MP& M facilities that would likely incur moderate impacts from the rule but that are not expected 

to close as a result of the rule. The test of moderate impacts examined two financial ratios pre-tax return on assets and 

interest coverage ratio calculated on a baseline and post-compliance basis. Incremental moderate impacts are attributed to 

the rule if both financial ratios exceeded threshold values in the baseline (i.e., no moderate impacts in the baseline), but at 

least one financial ratio fell below the threshold value in the post-compliance case. 

5.2.1 Converting Engineering Compliance Costs and Survey Financial Data to Current 

Year Dollar Values 

The facility survey data underlying the facility financial impact analysis are based on the periods 1987-1989 (Phase I) and 

1994-1996 (Phase II). The estimates of costs for complying with the MP&M regulation were developed, however, in dollars 

of the year 1996, the baseline year of the MP&M regulatory analysis.3 To ensure consistent impact analyses, EPA aligned 

facility financial data and compliance cost estimates in dollars of the same year. In addition, for understanding the 

significance of the rule’s potential costs in today’s economy, EPA further brought all dollar values forward to 2001. EPA 

used the following procedures to perform these adjustments. 

EPA used the Construction Cost Index (CCI) to convert compliance cost estimates into 2001 constant dollar equivalents. 

The CCI is a price index that engineers often use to estimate costs associated with building, installing, and operating waste 

treatment equipment and facilities. The CCI includes the costs of labor and building materials in 20 major cities. Table 5.1 

shows CCI values from 1996 to 2001. 

Table 5.1: nstruction Cost Index 

Year Value % Change 

1996 5620 

1997 5825 3.6% 

1998 5920 1.6% 

1999 6060 2.4% 

2000 6221 2.7% 

2001 6342 1.9% 

Co

Source: Engineering News-Record 

EPA used the Producer Price Index (PPI) to bring MP&M survey financial data to the current year. The PPI measures 

average changes in selling prices that domestic producers receive for their output.4 EPA used sector-specific PPI averages to 

3 The engineering cost estimates are described in the Technical Development Document accompanying this rule. 

4 EPA used the PPI to bring all financial statement values forward to 2001. EPA understands that the PPI is an output price index 

and that operating statement costs and balance sheet values may not change over time in the same way as output prices (and revenue). 

However, in adjusting financial statement values from the original survey data years to the current year, EPA’s purpose is to bring the 

statement values forward to the present while preserving the cost and financial structure relationships as reflected in the original income 

statements and balance sheets. Accordingly, use of a single index is appropriate for this adjustment and EPA judged the industry-specific 

PPI values as a better basis for this adjustment than other non-industry specific measures of inflation. 

5-3 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

update financial data from Phase I and Phase II survey respondents to 1996, the base year of the analysis. EPA applied an 

aggregate PPI to update from 1996 to 2001 dollars. 

Table 5.2 shows aggregate PPI values for all finished goods. Prices increased by 6.6 (135.7/127.3) percent from 1996 to 

2001, and by 32.3 percent from 1987 to 2001 (135.7/102.6). 

Table 5.2: Producer Price Index 

Industrial Commodities 

Year Value % Change 

1987 102.6 

1988 106.3 3.6% 

1989 111.6 5.0% 

1990 115.8 3.8% 

1991 116.5 0.6% 

1992 117.4 0.8% 

1993 119.0 1.4% 

1994 120.7 1.4% 

1995 125.5 4.0% 

1996 127.3 1.4% 

1997 127.7 0.3% 

1998 124.8 -2.3% 

1999 126.5 1.4% 

2000 134.8 6.6% 

2001 135.7 0.7% 

Source: Bureau of Labor Statistics 

5.2.2 Market-level Impacts and Cost Pass-Through Analysis 

Increased costs from the regulation can be expected to affect industry-level prices and output. Changes in prices and output in 

turn determine the distribution of economic impacts among directly- and indirectly-affected industries and their customers and 

suppliers. The facilities and industries directly affected by the final rule might ultimately experience little adverse impact, for 

example, if they are able to recover most or all of their added costs by raising prices to their customers or by lowering the 

prices paid to their suppliers and without a material reduction on the production quantity sold. Some regulated facilities and 

companies could even be better off financially as a result of the rule, if they benefit from industry-wide product price 

increases and incur no or relatively-low compliance costs (e.g., if they already have treatment in place). Understanding 

impacts at the industry level is therefore important to understanding who bears the impacts of the rule. 

The MP&M effluent guidelines affect facilities in a wide range of industries, and some of those industries produce a diverse 

slate of products that are sold in multiple industrial sectors. Detailed partial equilibrium modeling of product-level market 

dynamics in each of the affected industries was therefore not feasible. EPA instead used a combination of quantitative and 

qualitative methods to estimate a proportion of compliance costs that might be recovered through price increases in each 

MP&M sector. This cost pass-through analysis provided sector-specific coefficients that were applied to total compliance 

5-4 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

costs in each sector to estimate percentage changes in prices and revenues. EPA then evaluated facility-level impacts 5 assuming that all analyzed facilities in each sector benefit from the same percentage increase in prices and revenues. 

The estimated cost pass-through potential for each sector reflects an econometric analysis of historical pricing and cost trends 

in each MP&M industry sector, coupled with a qualitative market structure analysis. The market structure factors include: 

� market power based on horizontal and vertical integration; 

� extent of competition from foreign suppliers (in both domestic and export markets); 

� barriers to competition, as indicated by above-normal, risk-adjusted profitability; and 

� the long-term growth trend in the industry. 

EPA developed cost pass-through coefficients that indicate the percentage of compliance costs that EPA expects firms subject 6 to regulation to recover from customers through increased revenues. This approach may either overstate or understate the 

true changes in revenue for any one particular facility, depending on the diversity of products produced by the facility and the 

percentage of competitors in each product market that incur compliance costs. 

This approach to estimating market-level adjustments is a simplification because it does not simultaneously estimate changes 

in prices and output. Instead, EPA estimated price changes and then estimated changes in output based on predicted closures, 

taking into account the effect of the predicted price increases on facilities' financial performance. It is difficult to assess how 

this simplified approach might affect the estimated economic impacts of the rule. However, EPA does not believe that the 

overall impact analysis results are highly sensitive to the potential biases introduced by this approach. 

5.2.3 Impact Measures for Private Facilities 

a. Test of severe impacts


The analysis of severe impacts estimates the number of facilities that could potentially close due to the regulation. EPA


predicted that a facility will close if compliance costs cause the facility's overall financial performance and resulting implied


financial value to fall below a specified threshold level. Compliance costs are determined by the type and number of


processes that a facility performs, the characteristics of its wastewaters, and the level of treatment performed in the baseline.


EPA took the number and type of processes and pollutants produced into account when subcategorizing the industry. 


However, EPA was not able to link estimated compliance costs to specific products. Nor was EPA able to link facility 

financial performance to specific products. It was therefore not possible to conduct an impacts analysis at the product level. 

In particular, the analysis does not consider output reductions short of closure for example, closing one of several 

production lines/processes or continuing to produce the same products at a reduced level. It is quite possible that a facility 

with no or relatively low compliance costs for most processes could choose to out-source products made using a process that 

had significant compliance costs associated with it, instead of performing the process in-house. This is particularly true if it is 

a process that is performed infrequently. It is also possible that firms with multiple facilities could consolidate similar 

processes at individual facilities to reduce their compliance costs. These situations are not considered in this economic 

impact analysis. Numerous compliance responses are available to firms and facilities that EPA is unable to model. In 

addition, the analysis of severe impacts does not attempt to forecast future business circumstances for a facility and thus does 

not account for potential improvements in business outlook that might strengthen a facility’s ability to afford compliance 

outlays and thus prevent a potential closure decision. Because of these unknowns, estimated severe impacts are worst case 

and are likely to be overstated. In addition, the relationship between the compliance costs associated with the specific 

processes performed, specific products made from these processes, and the multiple industrial sectors to which these products 

are sold, is unknown and can not be accounted for in this analysis. 

5 EPA also performed an analysis in which complying facilities are assumed to pass none of their compliance costs through to 

consumers (zero-cost pass-through analysis). The results of this analysis are in the in the Record to the final rule (see Section 25.3.2, DCN 

37070). 

6 Appendix B provides a detailed description of the cost pass-through analysis. 

5-5 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

The assessment of severe impacts for MP&M private facilities7

 is based on the change in the facility’s estimated business


value, as determined from a discounted present value analysis of baseline cash flow and the change in cash flow resulting


from regulatory compliance. If the estimated discounted cash flow value of the facility is positive before considering the


effects of regulatory compliance but becomes negative as a result of compliance outlays, then the facility is considered a


regulatory closure. In this impact test, the estimated ongoing business value of the facility is compared with a threshold value


of zero for the closure decision: as long as the discounted cash flow value of the facility is greater than zero, the business is


earning its cost of invested capital and continuation of the business is warranted. If the discounted cash flow value of the


facility is less than zero in the baseline or becomes less than zero as a result of compliance outlays, then the business will not


earn its cost of invested capital and the business owners will be better off financially by terminating the business. As noted in


earlier discussion, facilities for which EPA estimated a negative baseline value were considered baseline closures and were


not tested for additional adverse impacts from regulatory compliance.


In an alternative, theoretically more accurate, formulation of this concept, business owners would compare the discounted


cash flow value of the facility with the value that the facility’s assets would bring in liquidation. In this case, the estimated


ongoing business value would be compared with a value that may be different from zero: liquidation value could be


positive or negative. When liquidation value is positive, business owners might benefit financially by terminating a business


and seeking its liquidation value even when the ongoing business value is positive but less than the estimated liquidation


value. With negative liquidation value which generally would result from business termination liabilities (e.g., site clean-


up) the opposite result could occur: business owners may find it financially advantageous to remain in business even though


the business earns less than its cost of invested capital if the liquidation value of the business is “more negative”, and thus


less in value, than the ongoing business based on the discounted cash flow analysis. EPA attempted to implement this


alternative impact test formulation. However, liquidation values were unavailable for over 75 percent of sample facilities. 


Moreover, EPA judges that the liquidation value estimates are substantially speculative and subject to considerable error. For


these reasons, EPA decided against using liquidation value for comparison with ongoing business value in the closure test.


The cash flow concept used in calculating ongoing business value for the closure analysis is free cash flow available to all


capital. Free cash flow is the cash available to the providers of capital both equity owners and creditors on an after-tax


basis from business operations, and takes into account the cash required for ongoing replacement of the facility’s capital


equipment. Free cash flow is discounted at an estimated after-tax total cost of capital to yield the estimated business value


of the facility. Details of the calculation of free cash flow and the discounting of free cash flow to yield the facility’s


estimated value are explained in the following sections.


� Calculation of Baseline Free Cash Flow and Performance of Baseline Closure Test


Calculation of baseline free cash flow and performance of the baseline closure test involved the following steps:


1.	 Average survey income statement data over response years and convert to 2001 dollars: EPA averaged income 

statement data over the years for which survey respondents reported data. For example, if a facility reported income 

statement data for 1995, 1996, and 1997, then a simple average was calculated for the three reported years. Reported 

values were brought forward from the initial reporting period to 1996 using MP&M sector-specific PPI adjustment 

factors and then from 1996 to 2001 using an aggregate PPI value as described above. 

2.	 Calculate after-tax income excluding the effects of financial structure: The questionnaire responses include a calculation 

of after-tax income in accord with conventional accounting principles. However, this calculation reflects the financial 

structure of the business, which may include debt financing and thus interest charges against income. Because the cash 

flow concept to be discounted in the business value analysis is cash flow available to all capital, it is necessary to restate 

after-tax income to exclude the effects of debt financing, or on a before-interest basis. This restatement involves: (1) 

increasing after-tax income by the amount of interest charges and (2) increasing taxes (and thereby reducing after-tax 

income) by the amount of tax reduction provided by interest deductibility. This adjustment amounts to adding taxadjusted interest expense to after-tax income and yields an estimate of after-tax income independent of capital structure 

or financing effects. In calculating the tax adjustment for interest, EPA used a combined federal/state corporate income 

tax rate of 39 percent, which reflects a combination of an approximate average state rate of 7.5 percent and a federal rate 

of 34 percent with state taxes deductible from federal income tax liability. After-tax income, before interest, was 

calculated as follows: 

7 As opposed to non-business, government entities. 

5-6 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

ATI-BI = ATI + I - �I or (5.1) 

ATI-BI = ATI + (1 - �)I 

where: 

ATI-BI = after-tax income before interest; 

ATI = after-tax income from baseline financial statement; 

I = interest charge from baseline financial statement; and 

� = estimated combined federal-state tax rate of 39 percent. 

3.	 Calculate after-tax cash flow from operations, before interest, by adjusting income for non-cash charges: The 

calculation of after-tax income may include a non-cash charge for depreciation (and potentially amortization). To 

calculate after-tax cash flow (ATCF) from operations, it is therefore necessary to add back any depreciation charge to 

the calculation of after-tax income, before interest. Cash flow, before interest, was calculated as follows: 

ATCF-BI = ATI-BI +D (5.2a) 

where: 

ATCF-BI = after-tax cash flow before interest; 

ATI-BI = after-tax income before interest; and 

D = baseline depreciation. 

4.	 Calculate free cash flow by adjusting after-tax cash flow from operations for ongoing capital equipment outlays: The 

measure of after-tax cash flow from the previous step, cash flow from operations, reflects the cash receipts and outlays 

from ordinary business operations and includes no allowance for replacement of the facility’s existing capital equipment. 

However, to sustain ongoing operations, a business must expend cash for capital replacement. Accordingly, to 

understand the true cash flow of a business and thus provide a conceptually valid cash flow measure for business 

valuation, it is necessary to reduce cash flow from operations by an allowance for capital replacement. For the 

calculation of free cash flow, EPA estimated baseline capital outlays from a regression analysis of capital expenditures by 

public firms in the MP&M business sectors over a 10-year period (details of this analysis and estimation framework are 

contained in Appendix D). Free cash flow is calculated as follows: 

FCF = ATCF-BI - CAPEX 

where: 

FCF = free cash flow 

ATCF-BI = after-tax cash flow before interest; and 

CAPEX = estimated baseline capital outlays. 

Or on a more detailed accounting statement basis: 

FCF = REV - TC - T - �I - CAPEX 

where: 

FCF = free cash flow 

REV = revenue 

TC = total operating costs, excluding interest, depreciation, and taxes 

T = baseline income tax 

� = estimated combined federal-state tax rate of 39 percent; 

I = interest charge from baseline financial statement; and 

CAPEX = estimated annual baseline capital outlays. 

(5.2b) 

(5.2c) 

This calculation of free cash flow is based on a static representation of a facility’s business. Revenue and expenses are 

not projected forward and the analysis of the business assumes, in effect, that the facility’s business will continue in the 

future absent the effects of regulation exactly as reflected in the baseline financial statements provided in the survey 

questionnaire. Consistent with this framework, the estimation of free cash flow includes no adjustment for changes in 

working capital, which might ordinarily be included in the capital outlay adjustment to operating cash flow. 

5-7 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

5.	 Calculate baseline facility value as the present value of free cash flow over a 15-year analysis horizon: To calculate 

baseline business value, EPA discounted free cash flow over a 15-year period at an estimated real (i.e., excluding the 

effects of inflation), after-tax cost of capital of 7 percent. The use of 15 years as the discounting horizon reflects the 

expected useful life of capital equipment to be installed for MP&M regulation compliance. Facility baseline business 

value is calculated as follows: 

(5.3) 

where: 

VALUE = estimated b aseline busine ss value of the fac ility 

FCF = free cash flow 

CoC = after-tax cost-of-capital; and 

t = year index, t = 0-14 (15-year discounting horizon). 

In the present value c alculation, yearly cash flows accrue at the beginning of the year. result, the first year of cash 

flows is no t discounted i.e., t = 0 for the first yea r of the analysis a nd ca sh flows in the fifteenth and final year of the 

analysis period are discounted over a 1 4-year period i.e., t = 14 in the final year of the analysis. 

As explained above, EP A considered a facility to be a baseline closure if its estimated business value was negative before 

incurring regulatory compliance costs. ine closures were not tested for adverse impact in the post-compliance impact 

analysis. 

� Calcu lation of P ost-Com pliance F ree Ca sh Flow and P erform ance of Po st-Com pliance C losure T est 

For the post-compliance closure analysis, EPA recalculated annual free cash flow accounting for changes in revenue, 

operating costs, and taxes that are estimated to result from compliance-related outlays. post-compliance 

free cash flow value and the estimated compliance capital outlay in the present value framework to calculate business value on 

a post-com pliance ba sis. 

Calculation of post-compliance free cash flow and performance of the post-compliance closure test involved the following 

steps: 

1. Adju st baseline an nua l free cash flow to reflect comp liance reve nue a nd exp ense effects: Compliance-related effects on 

annual free cash flow include compliance operating and m aintenance (O&M) expenses, post-comp liance c hange in 

revenue (from the compliance cost pass-thro ugh an alysis), and change in taxes. The change in taxes includes: (1) 

the tax effect of compliance expense and revenue changes and (2) the tax effect from depreciation of compliance capital 

outlays. calculating the tax effect of depreciation, EPA assumed that compliance capital outlays would be 

depreciated for tax purposes on a 15-year straight-line schedule. compliance free cash flow was calculated as 

follows: 

FCFPC = FCFBL + �RE V - �TC - �(�RE V - �TC - �D) (5.4) 

where: 

FCFPC = post-compliance free cash flow; 

FCFBL = baseline free cash flow, as calculated above; 

�REV = post-com pliance chan ge in revenue, as ca lculated in the cost pass-throu gh analysis; 

�T C = change in total facility operating costs (excluding interest, depreciation and taxes), calculated as operating 

and maintenance costs of compliance; 

� = marginal tax rate for calculating compliance-related tax effects (combined federal-state tax rate of 39 

percent); and 

�D = change in depreciation expense, calculated as compliance capital outlay (CC) divided by 15. 

The operating and m aintenance cost of compliance (�T C, above) is the change in costs estimated to result from operating 

and maintaining pollution controls adopted to comply with effluent guidelines. ting costs include the costs of 

monitoring. 

As a

Basel

EPA combined the 

For

PostOpera

5-8 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

2. Limit tax adjustment to not exceed taxes as reported in baseline financial statement: The tax effec t of com pliance will 

generally be to reduce tax liability. That is, in the prior formulation, the term �(�RE V - �TC - �D), which is the tax 

effect of compliance, will generally be negative as the increase in revenue will be less than compliance-related operating 

expenses a nd co mplia nce equip ment dep reciatio n: �TC + �D) > �REV . , in the free cash flow calculation, 

the tax adjustment will generally increase cash flow and business value and, all else equal, will reduce the likelihood that 

a facility will fail the post-compliance closure test. However, the extent to which a facility will realize this contribution 

to cash flow depends on its tax circumstances. In particular, some businesses may not be paying sufficient taxes in the 

baseline to take full benefit of the implied tax reduction at the facility level unless the unused tax loss can be transferred 

to other, profitable business units in the firm, these businesses will not be able to use fully the implied tax reduction on a 

current basis. Also, the marginal tax rate for businesses with relatively lower pre-tax income may be less than the 

assum ed 39 percent ra te used in the ana lysis. hile businesses may b e able to carry forward tax lo sses to re duce taxes in 

later years, EP A rec ognizes that the implied cash flow benefit from tax red uction may no t be fully rea lized, p articular ly in 

circumstances involving single-facility firms. ive in its analysis, EPA therefore limited the amount of tax 

reduction from compliance outlays to be no greater than the amount of tax paid by facilities in the baseline financial 

statement. he ana lysis effectively a ssume s that facilities will no t be ab le to offse t an imp licit negativ e tax liab ility 

against positive tax liability elsewhere in its operations or to carry forw ard (o r back) the ne gative inc ome and its im plicit 

negative tax liab ility to other p ositive inc ome /positive tax liability opera ting periods. verag e, this ap pro ach w ill 

overstate impacts on facilities, because some MP&M businesses may be able to benefit from tax reductions that exceed 

facility baseline taxes, especially if the facility is owned by a multiple-site firm. gly, EPA constrained the tax 

effect term in the free cash flow calculation, [-�(�RE V - �TC - �D)] as specified above, to be no greater than baseline 

financia l statement ta x liability , T. 

3. Calculate post-compliance facility value, including post-compliance free cash flow and the compliance capital outlay: 

As in the baseline analysis, EPA calculated post-compliance facility value as the present value of free cash flow and 

acco unting fo r the co mplia nce capital o utlay as an undisc ounte d cas h outlay in the first analysis perio d. cility postcomp liance business value w as calculated as follow s: 

(5.5) 

where: 

VALUEPC = estimated p ost-co mplia nce b usiness v alue o f the facility 

FCFPC = estimated post-compliance free cash flow 

CoC = after-tax cost-of-ca pital; 

t = year index, t = 0-14 (15-year discounting horizon); and 

CC = com pliance cap ital outlay. 

EPA considered a facility to be a post-compliance closure if its estimated business value was positive in the baseline but 

became negative after ad justing for com pliance-related cost, rev enue and tax effects. dditio n to tallying c losure impa cts in 

terms o f the num ber o f estimated facility closures, E PA also measured the significanc e of clo sures in terms o f losses in 

employment and output. losses equal the number of employees reported by closure facilities in survey 

responses; output losses equal total revenue reported for regulatory closure facilities. imated national results by 

multiplying facility results by facility sample weights. 

b. 

EPA also conducted an analysis of financial stress short of closure to identify the rule’s moderate impacts. Facilities 

experiencing moderate impacts are not projected to close due to the MP& M effluent guidelines. 

reduce the ir financial performanc e to the point whe re they might have d ifficulty obtaining financing for future investme nts. 

The analysis of mode rate impacts exa mined two financial measures: 

Pre-Tax Return on A ssets (PTRA): re-tax o pera ting inco me earnings before inte rest and taxes (E BIT ) to 

assets. This ratio measures the operating performance and profitability of a business’ assets independent of financial structure 

and tax circumstances. PTRA is a comprehensive measure of a firm's economic and financial performance. If a firm cannot 

sustain a competitive PTRA on a post-compliance basis, it may have difficulty financing its investments, including the outlay 

for co mpliance equipment. 

( As a result

W 

To be conservat

T 

On a 

Accordin

Fa

In a 

Employment

EPA est

Test of moderate impacts 

The rule, however, might 

ratio of p 

5-9

MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Interest Co vera ge Ratio (ICR): atio of pre-tax operating cash flow earnings before interest, taxes, and depreciation 

(EBIT DA) to interest expense. o measures the facility's ability to service its debt on the basis of current, ongoing 

financial performa nce and to borro w for capital investmen ts. itors will be concerne d abo ut a firm whose 

ope rating ca sh flow d oes not comfortably exceed its contractual obligations. he greater the ICR , the grea ter the firm's ability 

to meet interest payments, and, generally speaking, the greater the firm's credit-carrying ability. CR also provides a measure 

of the amo unt of cash flow availab le for equity after interest payments. 

Creditors a nd eq uity investors review the ab ove two m easures as criteria to d eterm ine whe ther an d und er wha t terms the y will 

finance a busin ess. RA and ICR also p rovid e insight into a firm's ability to g enera te funds for co mplia nce inv estme nts 

from internally-generated e quity, i.e., from after-tax cash flow. he measu res are defined as follows: 

Pre-Tax R eturn on A ssets 

(5-6) 

where: 

PTRA = pre-tax return on a ssets, 

EBIT = pre-tax operating income, or earnings before interest and taxes, and 

T A = total assets. 

Or, stated in terms o f MP &M income statem ent accou nts, 

(5.7) 

where: 

PTRA = pre-tax return on a ssets; 

REV = revenue; 

T C = total operating costs (excluding interest, taxes, and depreciation/amortization); 

D = depreciation; and 

TA = total assets. 

Inter est C ove rag e Ratio 

(5.8) 

where: 

ICR = interest coverage ratio; 

EBITDA = pre-tax operating cash flow, or earnings before interest, taxes, and depreciation (and amortization) 

and 

I = interest expense. 

r

This rati

Investors and cred 

T

I

PT 

T 

5-10

MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Or, stated in terms of MP&M income statement accounts, 

(5.9) 

where: 

PTRA = pre-tax return on a ssets; 

REV = revenue; 

T C = total operating costs (excluding interest, taxes, and depreciation/amortization); and 

T A = total assets. 

Includ ing the effects of M P& M com plianc e cos ts, post-comp liance P TR A and ICR are : 

(5.10) 

(5.11) 

where: 

PTRApc = pre-tax return on assets, post-compliance; 

ICRpc = interest coverage ratio, post-compliance; 

�REV = post-com pliance chan ge in revenue, as ca lculated in the cost pass-throu gh analysis; 

�T C = change in total facility operating costs (excluding interest, depreciation and taxes), calculated as operating 

and maintenance costs of compliance; 

�D = change in depreciation expense, calculated as compliance capital outlay (CC) divided by 15; 

CC = com plianc e cap ital outlay (a ssumin g all of the o utlay wo uld b e cap italized and repo rted a s an ad dition to 

assets on the balance sheet); and 

�I = incremental interest expense from financing of compliance capital outlay. As a simplifying, conservative 

assum ption, increm ental interest exp ense is calculated assuming that the co mpliance capital o utlay is fully 

deb t financed at the overall real co st-of-cap ital of 7 p ercen t. The annua l increm ental interest value is 

calculated as the annualized value of interest payments over 15 years, assuming a constant annual payment 

of principal a nd interest. 

For evaluating MP& M facilities according to the moderate impact measures, EPA compared baseline and post-compliance 

PT RA and IC R to M P&M sector-specific thresholds that were developed from data comp iled by Risk Management 

Association, Inc. (RMA). iles and reports financial statement information by industry as provided by member 

commercial lending institutions. hreshold values represent the 25th percentile values of P TR A and ICR for stateme nts 

received by RMA for the eight years from 1994 to 2001 within relevant industries. EPA developed M P&M sector-level 

values by weighting and summing the RMA industry values according to the definition of MP&M sectors (see Appendix C 

for details of moderate impact threshold development and sector-specific threshold values). Thresholds by sector ranged from 

0 to 3.1 percent for PTR A and from 1.4 to 2.9 for ICR. financial statements received by RMA are for businesses 

app lying for credit from me mbe r institutions, the data d on’t rep resent a rand om samp le. In pa rticular, the RM A da ta will 

likely exclude re prese ntation fro m the financially weakest b usinesse s, which a re unlike ly to be se eking c redit. s a result, 

EP A views the thresho ld values as being re latively conservative and likely to ov erstate the occurre nce of mo derate imp acts. 

Both measures are important to financial success and firms’ ability to attract capital. ities failing at least one of the 

moderate impact measures in the baseline were deemed to be already experiencing moderate financial weakness and were not 

tested for additional financial impact in the moderate impact analysis. Facilities that passed both mode rate impact tests in the 

baseline but failed o ne or bo th threshold co mpariso ns, post-com pliance, were co nsidered to incur mod erate financial impacts, 

short of closure, as a result of the MP& M regulation. 

RMA comp 

The t

Because the 

A

Facil

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

5.2.4 mpact Measures for Railroad Line Maintenance Facilities 

The proposed M P&M rule would have applied to some railroad facilities that maintain and repair railroad track and that 

perform similar operations on railroad and other vehicles. Railroad representatives indicated during data collection that the 

industry does not collect or monitor significant financial data at the facility level. ed EP A to administer a 

modified version of the survey to railroad operating units and to perform the primary economic impact analysis at the 

ope rating unit level. 

The analysis of impacts for railroad line maintenance facilities uses the same measures of impact as for other private MP&M 

facilities, but applies these measures for the railroad operating unit as a whole. road are the 

sum o f com plianc e cos ts at each M P& M railroad line m aintena nce fac ility identified b y the op erating com pany. 

5.2.5 mpact Measures for Government-Owned Facilities 

Government-owned MP &M facilities include all facilities owned by government entities that discharge process wastewater 

from MP&M activities. Most government-owned facilities that fall under the MP& M rule provide or support transportation 

services. facilities repair, rebuild, and maintain buses, trucks, cars, utility vehicles (e.g., snow-plows and street 

cleaners), and light machine ry. e M P& M profile describes governme nt-owned fac ilities in detail. 

Each government subject to the MP& M effluent guidelines at its facilities has a number of choices, which include: 

� contracting o ut the serv ice to a private provider or oth er go vernm ental ag ency, 

� discontinuing these services altogether, or 

� paying for com pliance and continuing op erations. 

Th e impact ana lysis does not predict how the gove rnment will respond . analysis ev aluates only whether a com munity 

incurring com pliance co sts and continuing o perations und er the rule would incur a severe b urden. vernmen t may choo se 

a different option and avoid some of the budgetary impacts estimated here. 

EP A evaluated impa cts for go vernm ent-ow ned facilities by using three tests. vernm ent that fails all thr ee tests is like ly to 

suffer severe adverse impacts as a result of the rule. test is applied at the facility level, and the other two tests are 

app lied at the government level. 

a. on site-level cost of service test 

The impacts on site-level cost of service test considers whether a government-owned facility's compliance costs exceed one 

percent or more of its total baseline cost of service. test is similar to the test used to assess impacts on private facilities 

and firms, which com pares costs to post-comp liance revenues. T he facility will likely absorb co mpliance costs within its 

current bud get if those costs d o not excee d one percent of the total. omp liance c osts in this scenario will not significantly 

impa ct the municipal bud get. sts in exce ss of one percent do not, in and o f itself, indicate that a bu dgetary imp act will 

occur, but o nly that ad ditiona l analysis sho uld b e per formed to determine if the re is an im pac t. 

EP A ca lculated the ratio of complia nce costs to c ost of se rvice, RC, for each gove rnment-own ed facility as follows: 

(5.12) 

where: 

RC = ratio of compliance costs to cost of service, 

TACC = total annualized comp liance cost for the facility, and 

CBaseline = total ba seline co st of service at the fac ility. 

A facility whose RC is equal to or greater than on e percent fails this test. 

I

These discussions l

Compliance costs for each rail

I

These 

Th

The

A go 

A go 

The first

Impacts 

This

C

Co 

5-12

MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

b. n taxpayers test 

Th e imp acts on taxpa yers test ev aluates the significa nce o f com plianc e cos ts to the p eop le serve d by the gove rnme nt. A 

government will fail this test if the ratio of total annualized pollution control costs per household to median household income 

exceeds one percent, post-compliance. Post-compliance pollution control costs include all pollution control costs (for 

whatever purpose) reported by the government in the baseline plus the sum of MP&M effluent guideline compliance costs at 

all M P& M facilities owned by the go vernm ent. his test clos ely follow s the me thod olog y deve lope d for E PA 's Wa ter Q uality 

Standards W orkbook (E PA, 199 5). 

Th e surve y requ ests inform ation a bou t curren t munic ipal ex penditures on p ollution contro l. Total annualized 

com pliance co sts (TACC) for each government-owned facility is the sum of costs and an amortized capital cost. 

of TA CC a t all MP &M facilities for each go vernmen t, plus baseline municipa l expend itures on pollution co ntrol, yields a 

post-compliance total annualized pollution control cost. otal annualized pollution control costs by the number 

of households to calculate an average cost per household. or median household income in the 

geographic area serve d by the respo nding gove rnment. 

EP A ca lculated a ratio o f com plianc e cos ts to me dian h ouse hold incom e, RH, for each gove rnment as follows: 

(5.13) 

where: 

RH = ratio of total annualized pollution control cost to median household income, 

CBPC = total baseline municipal expenditures on pollution control, and 

TACCi = total ann ualized com plianc e cos t for governm ent-ow ned facility i, 

M H I = median household income for the government jurisdiction. 

Go vernm ents that inc ur co mplia nce costs that c ause this r atio to e xcee d on e per cent fail this test. G overnme nts that fail this 

test in the baseline as well as post-compliance are not judged to experience major bud getary impacts attributable to the rule. If 

the rule causes an increase in this ratio to above one p ercent, then EPA concludes that the rule might present a burden to the 

taxpayers that supp ort the affected government. he calculation is a con servative estimate of the imp act on taxpa yers because 

it does not take into account the fact that non-residential taxpayers (businesses) will bear some of the tax burden or that some 

costs might be rec overed in fees. 

This test is used in EP A’s Economic Guidance for Water Quality Standards. is guidance is used by States and EPA 

Regions to assess economic factors in setting or revising water quality standards. ng 

mea sure o f econ omic impa ct, average to tal pollution co ntrol co st per house hold divide d by m edian house hold incom e. 

value less than one percent indicates that a community would incur “little economic impact”.8 

c. s on government debt test 

The impacts on government deb t test assesses the governm ent's ability to finance comp liance w ith the rule b y issuing debt. 

gove rnment must b e able to finance cap ital com pliance costs in add ition to m eeting o ngoing com pliance costs. Go vernm ents 

often finance capital compliance costs by issuing debt. This criterion tests each government's capacity to issue debt by 

examining the ratio of post-compliance debt service costs to the government's total revenue. he 

interest coverage ratio for private firms. 

Th e ratio o f deb t service costs to reven ue, RD, for each gove rnment is: 

Impacts o

T 

The sum 

EPA divided t

The questionnaire also asks f

T 

Th

The guidance includes as a screeni

A 

Impact

A 

This measure is analogous to t

8 Source: EPA’s Economic Guidance for Water Quality Standards: Workbook (1995) (Chapter 2 “Evaluating Substantial Impacts: 

Public Sector Entities”). Values between one and two percent indicate potential “mid-range economic impact.” Governments with values 

above one percent are subject to further analysis to determine whether a significant economic impact would in fact occur. 

5-13 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

(5.14) 

where: 

RD = debt-to-revenue ratio; 

DB = baseline municipal debt service costs (principal payments and interest); 

Ck = annualized capital cost of compliance, summed over all government-owned facilities in each government; 

and 

TRB = baseline municipal revenue. 

EPA judged that debt service costs above 25 percent of revenues might impede a government’s ability to issue debt in the 

future and present a burden on the budget. 

This criterion is used in EPA’s MUNIPAY model. This model is used in enforcement cases to assess whether municipalities 

(e.g., towns, villages, cities, counties, and public utilities) can afford to pay a specific level of compliance costs, Superfund 

cleanup contributions, or penalties. The model’s affordability assessment limits the amount of debt that can finance these 

costs, capping the debt service ratio at 25 percent.9 A higher ratio “may reduce the confidence of creditors that the 

municipality can repay its debt on time.” The MUNIPAY manual states that this value slightly exceeds the “warning marks” 

found in the public finance and management literature. 

5.3 RESULTS 

This section presents the results of the facility impacts analyses. The first section presents the results of the baseline closure 

analysis. The subsequent sections report the results of the analyses for the rule and the three other regulatory options that 

EPA analyzed. Section 5.3.2 presents the predicted price increases. Section 5.3.3 presents an overview of impacts for all 

MP &M facilities, and then results are provided for indirect dischargers (Section 5.3.4), direct dischargers (Section 5.3.5), 

private facilities (Section 5.3.6), and government-owned facilities (Section 5.3.7). Section 5.3.8 provides results by 

subcategory. 

5.3.1 Baseline Closures 

Table 5.3 shows the results of the baseline closure analysis by subcategory. EPA estimated that a total of 3,593 facilities have 

a negative business value before incurring regulatory compliance costs. These facilities are projected to close in the baseline 

and are not considered in the analysis of impacts attributable to the regulation. 

Appendix A provides information on typical average closure rates in the MP&M industry sectors. Census data show that 

over 10,000 facilities, or almost eight percent of all facilities in these industries, close annually. The number of baseline 

closures predicted in this analysis is consistent with this typical closure rate. 

9 Source: EPA Office of Compliance and Enforcement Assurance, MUNIPAY User’s Manual, September 1999, p. 4-14. 

5-14 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Table 5.3: Summary of Baseline Closures 

Subcategory Total Number of 

Dischargers 

Number of Baseline 

Closures 

Percent Closing in 

the Baseline 

Number Operating 

in the Baseline 

General Metals 11,364 880 7.7% 10,484 

Metal Finishing Job Shops 1,542 50 3.2% 1,491 

Non-Chromium Anodizing 122 29 23.8% 93 

Oily Wastes 29,185 2,409 8.3% 27,776 

Printed Wiring Boards 848 239 28.2% 609 

Railroad Rebuilders 826 0 0.0% 826 

Shipbuilding Dry Dock 14 0 0.0% 14 

All Subcategoriesa 43,858 3,593 8.2% 40,265 

a The total number of facilities does not sum to the number of facilities by subcategory because some facilities operate in more 

than one subcategory and have an indirect and direct discharging operation within the same facility. 

Source: U.S. EPA analysis 

5.3.2 Price Increases 

The price increases predicted for the final rule and alternative regulatory options are shown in Table 5.4. The percentage 

price increases are small, falling well below one-half of one percent for all sectors under the final rule. 

5-15 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Table 5.4: Cost Pass-Through Analysis: 

Percentage Price Increases by Regulatory Option and Sector 

Sector 

Option I: Selected 

Option (Directs 

Only) 

Option II: 

Proposed/NODA 

Option 

Option III: Directs 

+ 413 to 433 

Upgrade 

Option IV: Directs 

+ All to 433 

Upgrade 

Aerospace 0.00% 0.04% 0.00% 0.00% 

Aircraft 0.00% 0.03% 0.00% 0.01% 

Bus and Truck 0.00% 0.06% 0.00% 0.01% 

Electronic Equipment 0.00% 0.04% 0.00% 0.00% 

Hardware 0.00% 0.08% 0.01% 0.01% 

Household Equipment 0.00% 0.02% 0.00% 0.00% 

Instrument 0.00% 0.08% 0.00% 0.01% 

Iron and Steel 0.00% 0.20% 0.00% 0.00% 

Job Shop 0.00% 0.61% 0.09% 0.09% 

Mobile Industrial Equipment 0.00% 0.16% 0.01% 0.01% 

Motor Vehicle 0.00% 0.07% 0.00% 0.00% 

Office Machine 0.00% 0.00% 0.00% 0.00% 

Ordnance 0.00% 0.12% 0.00% 0.00% 

Other Metal Products 0.00% 0.04% 0.00% 0.01% 

Precious and Non-Precious Metals 0.00% 0.03% 0.00% 0.00% 

Printed Circuit Board 0.00% 0.00% 0.00% 0.00% 

Railroad 0.00% 0.02% 0.00% 0.00% 

Ships and Boats 0.00% 0.03% 0.00% 0.00% 

Stationary Industrial Equipment 0.00% 0.05% 0.01% 0.01% 

Source: U.S. EPA analysis 

5.3.3 Overview of Impacts 

Table 5.5 provides an overview of the numbers of facilities closing and experiencing moderate economic impacts, by 

regulatory option. These national estimates include all types of dischargers (direct and indirect) and types of facilities (private 

MP&M, railroad line maintenance, and government-owned facilities.) 

5-16 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Table 5.5: ulatory Impacts for All Facilities by Option, National Estimates 

Option I: 

Selected Option 

(Directs Only) 

Option II: 

Proposed/NODA 

Optiona 

Option III: 

Directs + 413 to 

433 Upgrade 

Option IV: 

Directs + All to 

433 Upgrade 

Number of facilities operating in the baseline: total 40,265 60,253 40,265 40,265 

private MP&M and railroad line maintenance 36,480 54,526 36,480 36,480 

government-owned 3,785 5,727 3,785 3,785 

Number of facilities below low flow cutoffs 51,502 

Number of facilities with subcategory exclusions 37,883 136 36,820 36,339 

Percent of facilities operating in the baseline 

excluded or below cutoffs 94.1% 85.7% 91.4% 90.3% 

Number of facilities operating subject to regulatory 

requirements 2,382 8,615 3,445 3,926 

Number of regulatory closures 0 785 120 120 

Percent of facilities operating in the baseline that are 

regulatory closures 0.0% 9.1% 3.5% 3.1% 

Number of facilities experiencing moderate impacts 0 257 37 49 

Percent of facilities operating in the baseline that 

experience moderate impacts 0.0% 3.0% 1.1% 1.2% 

Reg

a The total number of facilities reported for the Proposed/NODA Option (Option II) analysis differs from the facility count reported for 

the final rule and Options III and IV. After deciding in July 2002 to not consider the NODA option as the basis for the final rule, EPA 

performed no more analysis on the NODA option, including not updating facility counts and related analyses for the change in 

subcategory and discharge status classifications. These differences in facility counts by regulatory option appear in subsequent tables. 

Source: U.S. EPA analysis. 

Table 5.5 shows that the final rule substantially reduces facility-level impacts as compared to the three alternative regulatory 

options considered by EPA. None of the facilities that continue to operate in the baseline close or experience moderate 

impacts due to the final rule. The large difference in results between the final rule and other options stems largely from the 

exclusion from regulatory requirements of over 94 percent of facilities that continue to operate in the baseline: the final rule 

excludes from regulatory requirements all indirect dischargers and direct dischargers in all subcategories except for Oily 

Wastes. Significantly larger numbers of facilities are projected to close under the Proposed/NODA Option and 433 Upgrade 

Options (785 and 120 facilities, respectively). See Chapter 4 for a discussion of the options and subcategory exclusions. 

Table 5.6 shows the estimated burden on facilities from regulatory compliance by option, discharge status, and subcategory. 

The estimated burden includes annualized compliance costs and any estimated increase in facility revenue as a result of the 

regulation, and, for private facilities, reflects the effects of taxes on compliance costs and revenue. These compliance costs 

therefore represent the total after-tax cash flow impact on regulated facilities. 

Table 5.6: Total Annualized Facilitya

 After-tax Compliance Costs 

by Subcategory, Discharge Status, and Regulatory Option 

(millions, 2001$) 

Subcategory 

Option I: 

Selected Option 

(Directs Only) 

Option II: 

Proposed/NODA 

Option 

Option III: 

Directs + 413 to 433 

Upgrade 

Option IV: 

Directs + All to 433 

Upgrade 

Direct Indirect Direct Indirect Direct Indirect Direct Indirect 

General Metals $0.0 $0.0 $267.6 $476.7 $0.0 $16.5 $0.0 $46.5 

5-17 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Metal Finishing Job Shop $0.0 $0.0 $2.9 $139.9 $0.0 $8.2 $0.0 $8.2 

Non-Chromium Anodizing $0.0 $0.0 $23.1 $0.0 $0.0 $0.0 $0.0 $0.0 

Oily Waste $11.9 $0.0 $29.0 $72.3 $12.0 $0.0 $12.1 $0.0 

Printed Wiring Board $0.0 $0.0 $0.2 $106.4 $0.0 $15.0 $0.0 $15.0 

Railroad Line Maintenance $0.0 $0.0 $0.6 $0.0 $0.0 $0.0 $0.0 $0.0 

Shipbuilding Dry Dock $0.0 $0.0 $2.4 $0.0 $0.0 $0.0 $0.0 $0.0 

Steel Forming & Finishingb $25.5 $16.1 

All Categories: Annual Costs $11.9 $0.0 $351.2 $811.4 $12.0 $39.7 $12.1 $69.7 

All Categories: Number of 

Facilities Operating PostCompliance Subject to 

Requirements 

2,382 0 4,143 3,688 2,382 1,063 2,382 1,544 

Total Costs to Industry by Option, 

Directs + Indirects $11.9 $1,162.5 $51.7 $81.8 

a This table reflects after-tax cash flow impacts to facilities and does not represent the cost of society from regulatory compliance. 

Chapter 11 discusses the social costs of the final rule and the other options. The estimates in this table exclude baseline and 

regulatory closures, and are after-tax. b The Steel Forming & Finishing subcategory was removed from the MP&M universe after deciding not to consider the 

Proposed/NODA Option (Option II) for the final rule. As a result, compliance costs are included in the Steel Forming & 

Finishing subcategory for Option II only. 

Source: U.S. EPA analysis. 

Oily Wastes direct dischargers account for the total compliance costs of $11.9 million under the final rule. Total compliance 

costs incurred by facilities that continue to operate post-compliance are almost 100 times higher under the Proposed/NODA 

Option than under the final rule, over four times higher under the Directs and 413 to 433 Upgrade Option than under the final 

rule, and almost seven times higher under the Directs and All to 433 Upgrade Option than under the final rule. 

5.3.4 Results for Indirect Dischargers 

The sum of facilities individually identified as indirect and direct dischargers exceeds the total of all facilities as identified in 

Table 5.5, above. Some facilities operate in more than one subcategory, and some have both an indirect and direct 

discharging operation in the same facility. Facilities with both indirect and direct discharging operations are reported in the 

tables for both discharge categories: Table 5.7, for indirect dischargers, and Table 5.8, for direct dischargers. 

Table 5.7 summarizes the results of the facility impact analysis for indirect dischargers, including both private businesses and 

government-owned facilities. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Table 5.7: ulatory Impacts for Indirect Dischargers by Option, National Estimates 

Option I: 

Selected Option 

(Directs Only) 

Option II: 

Proposed/ 

NODA Option 

Option III: Directs 

+ 413 to 433 

Upgrade 

Option IV: 

Directs + All to 

433 Upgrade 

Number of facilities operating in the baseline: total 37,652 56,071 37,652 37,652 

private MP&M and railroad line maintenance 34,325 51,066 34,325 34,325 

government-owned 3,327 5,005 3,327 3,327 

Number of facilities below low flow cutoffs 51,502 

Number of facilities with subcategory exclusions 37,652 136 36,589 36,108 

Percent of facilities operating in the baseline 

excluded or below cutoffs 100.0% 92.1% 97.2% 95.9% 

Number of facilities operating in the baseline 

subject to regulatory requirements 0 4,433 1,063 1,544 

Number of regulatory closures 0 746 120 120 

Percent of facilities operating in the baseline and 

subject to regulatory requirements that are 

regulatory closures 

0.0% 16.8% 11.3% 7.8% 

Number of facilities experiencing moderate 

impacts 0 228 37 49 

Percent of facilities operating in the baseline and 

subject to regulatory requirements that experience 

moderate impacts 

0.0% 5.1% 3.5% 3.2% 

Reg

Source: U.S. EPA analysis. 

Indirect discharging facilities account for over 93 percent of water discharging MP&M facilities as a whole. However, 

because all indirect discharging are excluded from regulatory requirements under the final rule, EPA estimates that no 

indirect dischargers will incur impacts under the final rule. 

5.3.5 Results for Direct Dischargers 

Table 5.8 summarizes the facility impact results for direct dischargers, which represent approximately seven percent of all 

facilities that continue to operate in the baseline. In addition, most operating direct dischargers are subject to requirements 

under the final rule: only 10 percent are excluded from requirements as a result of subcategory exclusions. As shown in the 

table, EPA estimates that no direct dischargers will close or incur moderate impacts as a result of the final rule’s requirements. 

Impacts on direct dischargers are the same under the 433 Upgrade Option impacts as under the final rule, since these Options 

apply the same requirements to the same universe of facilities. The Proposed/NODA Option would have yielded more 

regulatory closures and moderate impacts than the final rule and 433 Upgrade Options. 

5-19 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Table 5.8: ulatory Impacts on Direct Dischargers by Option, National Estimates 

Option I: 

Selected Option 

(Directs Only) 

Option II: 

Proposed/NODA 

Option 

Option III: 

Directs + 413 

to 433 Upgrade 

Option IV: 

Directs + All to 

433 Upgrade 

Number of facilities operating in the baseline 2,641 4,182 2,641 2,641 

private MP&M and railroad line maintenance 2,183 3,459 2,183 2,183 

government-owned 458 722 458 458 

Number of facilities with subcategory exclusions 259 0 259 259 

Percent of facilities operating in the baseline with 

subcategory exclusions 9.8% 0.0% 9.8% 9.8% 

Number of facilities operating in the baseline subject 

to regulatory requirements 2,382 4,182 2,382 2,382 

Number of regulatory closures 0 39 0 0 

Percent of facilities operating in the baseline and 

subject to regulatory requirements that are regulatory 

closures 

0.0% 0.9% 0.0% 0.0% 

Number of facilities experiencing moderate impacts 0 28 0 0 

Percent of facilities operating in the baseline that 

experience moderate impacts 0.0% 0.7% 0.0% 0.0% 

Reg

Source: U.S. EPA analysis. 

5.3.6 Results for Private Facilities 

Table 5.9 provides the facility impact analysis results for privately-owned facilities, including Railroad Line Maintenance 

facilities. Because privately-owned facilities account for over 90 percent of all MP&M facilities operating in the baseline, 

these results are similar to the results reported for all MP& M facilities in Table 5.5. Almost 95 percent of facilities operating 

post-compliance are excluded from requirements under the final rule, due to the subcategory exclusions for all indirect 

dischargers and all direct dischargers except for Oily Wastes. 

5-20 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Table 5.9: ulatory Impacts for Private Facilities by Option, National Estimates 

Option I: 

Selected Option 

(Directs Only) 

Option II: 

Proposed/NODA 

Option 

Option III: 

Directs + 413 to 

433 Upgrade 

Option IV: 

Directs + All to 

433 Upgrade 

Number of facilities operating in the baseline 36,480 54,526 36,480 36,480 

Number of facilities below low flow cutoffs 46,582 

Number of facilities with subcategory exclusions 34,556 136 33,123 32,745 

Percent of facilities operating in the baseline 

excluded or below cutoffs 94.7% 0.2% 90.8% 89.8% 

Number of facilities operating in the baseline 

subject to regulatory requirements 1,924 7,808 3,357 3,735 

Number of regulatory closures 0 785 120 120 

Percent of facilities operating in the baseline and 

subject to regulatory requirements that are 

regulatory closures 

0.0% 10.1% 3.6% 3.2% 

Number of facilities experiencing moderate 

impacts 0 257 37 49 

Percent of facilities operating in the baseline and 

subject to regulatory requirements that experience 

moderate impacts 

0.0% 3.3% 1.1% 1.3% 

Reg

Source: U.S. EPA analysis. 

5.3.7 Results for Government-Owned Facilities 

Table 5.10 provides facility impact analysis results for government-owned facilities. The 3,785 government-owned facilities 

that continue to operate in the baseline represent over 9 percent of all MP&M facilities operating in the baseline. As 

discussed above, instead of a closure test, the impact analysis for government-owned facilities assesses whether the rule would 

impose major budgetary impacts on these facilities and the governments that own them. 

Under the final rule, 88 percent of government-owned facilities would be excluded from requirements because they qualify 

for subcategory exclusions. EPA’s analysis indicates that none of the options would impose major budgetary impacts on the 

governments operating the facilities. 

5-21 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Table 5.10: gulatory Impacts for Government-Owned Facilities by Option, National Estimates 

Option I: 

Selected Option 

(Directs Only) 

Option II: 

Proposed/NODA 

Option 

Option III: 

Directs + 413 

to 433 Upgrade 

Option IV: 

Directs + All to 

433 Upgrade 

Number of government-owned facilities operating 

in the baseline & post-regulation 3,785 5,727 3,785 3,785 

Number of facilities below low flow cutoffs 4,920 

Number of facilities with subcategory exclusions 3,327 0 3,327 3,305 

Percent of facilities operating in the baseline 

excluded or below cutoffs 87.9% 85.9% 87.9% 87.3% 

Number of facilities operating subject to regulatory 

requirements 458 807 458 480 

Number of facilities experiencing significant 

budgetary impacts a 0 0 0 

Percent of facilities operating in the baseline that 

experience significant budgetary impacts 0% 0% 0% 0% 

Re

0 

a

 A government is judged to experience major budgetary impacts if (1) any of its facilities incur compliance costs exceeding 1% of 

baseline cost of service and (2) the governmental unit fails both the taxpayers impact and government debt impact tests. 

Source: U.S. EPA analysis. 

Tables 5.11 and 5.12 provide additional detail on the results of the facility impact analysis for government-owned facilities. 

Table 5.11 shows the number of government-owned facilities by type and size of government, and the number that fall below 

relevant flow cutoffs under the final rule. 

5-22 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Table 5.11: Number of Government-Owned Facilities 

by Type and Size of Government Entity 

Municipal 

Government 

State 

Government 

County 

Government 

Regional 

Governmental 

Authority Total 

Large Governments (population> 50,000) 

# 

government entities 

26 129 23 0 178 

# ernment 

entities with exclusions 

592 248 758 46 1,645 

Small Governments (population <= 50,000) 

# 

government entities 

280 0 0 0 280 

# ernment 

entities with exclusions 

1,470 0 212 0 1,682 

All Governments 

# 

government entities 

306 129 23 0 458 

# ernment 

entities with exclusions 

2,062 248 970 46 3,327 

Total 2,368 377 993 46 3,785 

of regulated 

of gov

of regulated 

of gov

of regulated 

of gov

Source: U.S. EPA analysis of Municipal Survey. 

Table 5.12 provides additional information on the results of the three tests performed in the government impact analysis. The 

vast majority of facilities, 95.7 percent, are estimated to incur costs less than one percent of their baseline cost of service. 

EPA assumes that these facilities (and their owning governments) can absorb compliance costs within their current budgets 

with no material burden. The remaining 162 facilities, or 4.3 percent of government-owned facilities, incur costs exceeding 

one percent of their baseline costs of service. Although EPA estimates that these facilities will incur costs exceeding the one 

percent no-impact threshold, whether these costs represent a material burden to the owning government depends on the 

magnitude of costs at the government level. To understand whether this higher facility-level cost would constitute a 

significant burden, EPA estimated the total of compliance costs incurred by a government for all of its affected MP&M 

facilities and assessed the impact of these costs under the two tests outlined above: the taxpayer impact test and the 

government debt service impact test. For the final rule, EPA estimated that none of the governments with facilities incurring 

costs greater than one percent of baseline values would fail either of the two government-level impacts tests. 

5-23 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

Table 5.12: Impacts on Governments of MP&M Facility Compliance Costs by Size of Government 

Owned by Small 

Governments 

Owned by Large 

Governments 

All GovernmentOwned Facilities 

Number of government-owned MP&M 

facilities affected 

1,962 1,823 3,785 

Number Percent Number Percent Number Percent 

Number and percent of governments 

failing all three budgetary impact criteria 

0 0.0% 0 0.0% 0 0.0% 

Individual Test Results: number and percent of failures 

Compliance costs > one percent of 

baseline cost of service test 

140 7.1% 22 1.2% 162 4.3% 

Impacts on taxpayers test 0 0.0% 0 0.0% 0 0.0% 

Impacts on government debt test 0 0.0% 0 0.0% 0 0.0% 

Source: U.S. EPA analysis. 

That no governments incur budgetary impacts at the government level is not surprising. The MP&M activities regulated 

under the final rule typically represent a very small portion of governments’ budgets. Even a significant percentage increase 

in the cost of MP&M activities (as measured by the comparison of post-regulation costs to baseline costs) is unlikely to 

present any serious burden on the budgets of the affected governments. 

Moreover, the costs to government-owned facilities are quite low. The large majority (3,327 or 88 percent) of the 3,785 

government-owned facilities are excluded from the final rule. Of the 458 government facilities remaining under regulation, 

183 facilities incur no costs, and 275 incur annualized costs averaging $32,674. 

5-24 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

GLOSSARY 

after-tax cash flow (ATCF): After-tax cash flow available to equity. 

baseline closures: Facilities showing inadequate financial performance in the baseline, that is, in the absence of the rule. 

These facilities closures would have occurred with or without the rule. 

Construction Cost Index (CCI): Measures how much it cost to purchase a hypothetical package of goods and services 

compared to what is was in the base year. It applies to general construction costs. The CCI can be used where labor costs are 

a high proportion of total costs. The CCI uses 200 hours of common labor, multiplied by the 20-city average rate for wages 

and fringe benefits. (http://www.enr.com/cost/costfaq.asp) 

cost of capital: Costs incurred for a firm to obtain financing from all capital sources including, in particular, equity and 

debt. 

cost pass-through analysis: Calculates the percentage of compliance costs that EPA expects firms subject to regulation 

to recover from customers through increased revenues. 

facility: A contiguous set of buildings or machinery on a piece of land under common ownership. 

free cash flow: Cash flow generated by the company that is available to all providers of the company’s capital, both 

creditors and shareholders. 

government-owned facility: Includes facilities operated by municipalities, state agencies and other public sector entities 

such as state universities. 

interest coverage ratio (ICR): Ratio of cash operating income to interest expenses. This ratio measures the facility's 

ability to service its debt and borrow for capital investments. 

liquidation value: Net amount that could be realized by selling the assets of a firm after paying the debt. 

(http://www.duke.edu/~charvey/Classes/wpg) 

moderate impacts: Adverse changes in a facility's financial position that are not threatening to its short-term viability. 

operating and maintenance (O&M): Costs estimated to result from operating and maintaining pollution controls 

adopted to comply with effluent guidelines. Operating costs include the costs of monitoring. 

pre-tax return on assets (PTRA):Ratio of cash operating income to assets. This ratio measures facility profitability. 

private MP&M facility: Includes all privately-owned facilities that do not perform railroad line maintenance. 

Producer Price Index (PPI): A family of indexes that measures the average change over time in the selling prices 

received by domestic producers of goods and services. PPI's measure price change from the perspective of the seller. This 

contrasts with other measures, such as the Consumer Price Index (CPI), that measure price change from the purchaser's 

perspective. Sellers' and purchasers' prices may differ due to government subsidies, sales and excise taxes, and distribution 

costs. (http://stats.bls.gov/ppifaq.htm#1) 

railroad line maintenance facility: Facilities that maintain and repair railroad track and other vehicles. 

regulatory closure: A facility that is predicted to close because it can not afford the costs of complying with the rule. 

severe impacts: Facility closures and the associated losses in jobs, earnings, and output at facilities that close due to the 

rule. 

total annu alized com pliance co st (TAC C): Sum of annual operating and maintenance costs and the annualized 

equivalent of one-time costs, calculated over 15 years assuming a seven percent discount rate. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

ACRONYMS 

ATCF: after-tax cash flow 

CCI: construction cost index


ICR: interest coverage ratio


O&M: operation and maintenance


PPI: producer price index


PTRA: pre-tax return on assets


TACC: total annualized compliance cost


5-26 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

REFERENCES 

Brealey, Richard A. and Stewart C. Myers. 1996. Principles of Corporate Finance, 5th edition. New York: McGraw-Hill. 

McKinsey & Company, Inc. et al. 2000. Valuation: Measuring and Managing the Value of Companies, 3rd edition. New 

York: John Wiley & Sons, Inc. 

U.S. Bureau of Labor Statistics, Producer Price Index Revision-Current Series. On-line database at 

http://stats.bls.gov/ppihome.htm. 

U.S. Department of Commerce, Bureau of the Census, Annual Survey of Manufactures. On-line database at 

http://www.census.gov/prod/www/abs/industry.html. 

U.S. Environmental Protection Agency (U.S. EPA). 1995. Interim Economic Guidance for Water Quality Standards 

Workbook. Office of Water, Economics and Statistical Analysis Branch. March. 

U.S. Environmental Protection Agency (U.S. EPA). 2000. Technical Development Document for the Proposed Effluent 

Limitations Guidelines and Standards for the Metal Products & Machinery Point Source Category . EPA 821-B-00-005. 

December. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects 

Chapter 6: Employment Effects


INTRODUCTION 

This chapter discusses the employment effects associated 

with the final rule and the alternative regulatory options 

considered by EPA. The MP&M regulation can generate 

both positive and negative impacts on employment. Any 

facility closures induced by the rule would result in reduced 

demand for labor and compliance activities at facilities that 

close, but would also increase employment requirements in 

facilities that remain open and continue to operate. The 

CHAPTER CONTENTS 

6.1 Job Losses Due to Closures ................. 6-2 

6.2 Job Gains Due to Compliance Requirements .... 6-3 

6.3 Net Effects on Employment ................. 6-5 

Glossary ................. ................. .. 6-7 

Acronym ................. ................. .. 6-8 

References ................. ................. 6-9 

regulation could also create a demand for compliance-related equipment and installation, which would also generate new 

employment requirements. 

EPA assumed that any estimated facility closures would result in the loss of full-time equivalents (FTEs). 

The MP&M rule may affect overall employment in three ways. 

Direct labor requirements. Direct labor requirements are job losses from closures and job gains from manufacturing, 

installing, and operating compliance-related equipment. Direct labor requirements also include labor to implement the rule’s 

pollution prevention activities.1 

Indirect labor requirements. Compliance expenditures may increase employment in industries doing business with 

compliance equipment and service providers. Economists refer to these as linked industries. For example, a firm that 

manufactures a treatment system will purchase pumps, pipes, and other intermediate goods and services from other firms and 

sectors of the economy. Employment in these linked industries increases when treatment equipment manufacturers purchase 

goods and services from them. Closures of MP&M facilities can also lead to reduced requirements for inputs to MP&M 

industry products, and therefore indirect job losses in the supplier industries. 

Induced labor requirements. Increased direct and indirect labor employment also increases spending on consumeroriented service and retail businesses. Economists refer to the additional labor demand in the businesses patronized by people 

working in the direct and indirect labor industries as “induced” labor requirements. Conversely, people who are laid off from 

MP &M facilities that close due to the rule may spend less, reducing employment in sectors providing consumer services and 

products. 

EPA is not including a total estimate of indirect and induced job gains and losses, however, because the magnitude of losses 

and gains is very small at the national level and occur across all states. The job gains after the first three years are expected to 

be approximately two jobs per year, without any regulation-related losses. The low magnitude of these gains means that it is 

highly unlikely that there will be any material secondary and induced impacts from the regulation. 

Because EPA estimates that no facility closures will occur under the final rule, EPA expects that the rule will cause no job 

losses. However, EPA estimates that the regulation will increase employment, with the manufacture and installation of 

compliance equipment causing a short-term gain in direct employment of 20 FTEs. In addition, EPA estimates that operation 

and maintenance of compliance equipment will cause a continuing direct requirement for two FTEs per year. The net effect 

on direct employment of the regulation is an estimated increase of 47 FTE-years, a measure that reflects both the number 

and duration of jobs gained. This number represents an average gain of three FTEs per year over the 15 year analysis period. 

Although EPA expects no job losses under the final rule, EPA considered other regulatory options that would likely have 

caused facility closures and job losses. The following sections of this chapter review first the job losses from facility closures 

under the alternative regulatory options, and second the expected job gains from compliance equipment installation and 

1 See the Technical Development Document for more information on compliance costs. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects 

operation for both the final rule and the alternative options. The last section discusses net impacts on employment and the 

expected timing of those effects. 

6.1 JOB LOSSES DUE TO CLOSURES 

As discussed, EPA estimates that the final rule will cause no facility closures and thus no job losses.2 However, EPA 

considered other regulatory options that would likely cause facility closures and job losses. To calculate job losses for these 

options, EPA assumed that all employees working at closing facilities will lose their jobs, and that one-third of the facilities 

estimated to close do so in each of the first three years after promulgation of the option. The §308 surveys provide the number 

of employees at each facility, expressed in FTEs. The job losses attributable to an option are simply the sum of employment 

at the plants estimated to close. EPA did not analyze the job losses that would occur if facilities cut production or ceased 

production of products that required certain processes instead of closing. Table 6.1 shows the total employment and 

estimated job losses by subcategory due to facility closures under the alternative regulatory options and as a percent of the 

total employment in the baseline. 

Table 6.1: Job Losses for the Alternative Regulatory Options by Subcategory; Final Rule 

Subcategory 

Total 

Employment 

in the 

Baseline 

Option I: 

Selected 

Option 

Option II: 

NODA/Proposed 

Option 

Option III: 

413 to 433 Upgrade 

Option 

Option IV: 

All to 433 Upgrade 

Option 

Estimated 

Job Losses 

% of 

Total 

Jobs 

Estimated 

Job Losses 

% of 

Total 

Jobs 

Estimated 

Job Losses 

% of 

Total 

Jobs 

General Metals 3,641,623 n/a 7,895 0.2% 6,087 0.2% 6,087 0.2% 

Metal Finishing Job Shop 63,083 n/a 19,072 30.2% 1,425 2.3% 1,425 2.3% 

Non-Chromium Anodizing 13,464 n/a 0 0.0% 0 0.0% 0 0.0% 

Oily Waste 3,143,544 n/a 104 0.0% 0 0.0% 0 0.0% 

Printed Wiring Board 110,644 n/a 3,998 3.6% 363 0.3% 363 0.3% 

Railroad Rebuildersa n/a n/a n/a n/a n/a n/a n/a n/a 

Shipbuilding Dry Dock 994 n/a 0 0.0% 0 0.0% 0 0.0% 

Steel Forming and Finishingb 21,753 1,660 7.6% 

All Subcategories 6,973,352 n/a 32,729 0.5% 7,874 0.1% 7,874 0.1% 

a Employment is only available at the firm level for the Railraod Rebuilders subcategory. b

 The Steel Forming & Finishing subcategory was removed from the MP&M universe after deciding not to consider the 

Proposed/NODA Option (Option II) for the final rule. As a result, estimated job losses are included in the Steel Forming & Finishing 

subcategory for Option II only. Accordingly, the employment from this subcategory is not included in the total. 

Source: U.S. EPA analysis. 

Job losses under the Proposed/NODA Option equal 0.5 percent of total employment at water discharging MP&M facilities 

and 0.1 percent under the 433 Upgrade Options. The metal finishing job shop subcategory accounts for 19,072 of the job 

losses under the Proposed/NODA Option or over 58 percent of the total 32,729 estimated job losses. The subcategories with 

the highest percent of job losses under the Proposed/NODA Option are the Metal Finishing Job Shops (30.2 percent of total 

employment in the subcategory), Steel Forming and Finishing (7.6 percent) and Printed Wiring Boards (3.6 percent). Job 

2 EPA’s analysis considers employment losses only for facility closures. As discussed in Chapter 5, firms may consider a range of 

responses in structuring a compliance strategy, including consolidation and/or transfer of production among facilities to minimize the 

financial burden of compliance. In some instances, these actions could result in employment losses in some facilities and possible 

increases in others. Because of the complexity of these decisions, EPA’s analysis cannot consider the full range of such compliance 

responses and does not consider the potential employment effects – negative or positive – associated with these compliance options. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects 

losses under the 433 Upgrade Options are estimated in the General Metals, Metal Finishing Job Shops, and Printed Wiring 

Boards subcategories of 6,087, 1,425, and 363 employees, respectively. 

6.2 JOB GAINS DUE TO COMPLIANCE REQUIREMENTS 

Direct labor requirements arise from the employment necessary to manufacture, install, and operate equipment that MP&M 

facilities need to comply with the final rule, as well as pollution prevention activities undertaken to comply with the 

regulation. The following sections discuss labor requirements associated with manufacturing compliance equipment, 

equipment installation, and operation, respectively. This section provides a detailed analysis for the final rule only. A 

summary of the net job gains due to compliance with the alternative regulatory options is presented at the end of the section. 

Some more detail on the compliance costs that went into calculating job gains under the alternative regulatory options is 

available in Appendix E. 

a. 	Direct labor requirements for manufacturing compliance equipment


EPA estimated the direct labor requirements for manufacturing wastewater treatment systems using three steps:


� Calculate the cost of compliance equipment; 

� Estimate the share of the cost of compliance equipment due to labor inputs. This estimate shows how much money 

goes to employees of equipment manufacturers; and 

� Convert the dollars spent on manufacturing employees to a full-time employment equivalent (FTE), based on a 

yearly labor cost. 

� Compliance cost


EPA used the total one-time capital costs estimated by the engineers to calculate the purchase cost paid to manufacturers of


compliance equipment. The estimated one-time direct capital equipment cost is $3.1 million for the final regulatory option.


Appendix E explains in more detail how this value was calculated.


� Labor share


The Bureau of Economic Analysis (BEA) calculates direct requirements coefficients that measure how many dollars of


each input are purchased to produce a dollar of a given output.3 EPA used requirements coefficients for BEA Sector 40, the


“Heating, Plumbing, and Fabricated Structural Metal Products Industry,” for the employment analysis. MP&M project


engineers identified BEA Sector 40 as the industrial sector that most nearly matches the businesses that would make, install,


and operate waste treatment systems for MP&M facilities complying with the rule. The inputs into Sector 40 production


include intermediate goods, materials, and services, as well as labor.


BEA’s direct requirements table shows that every dollar of Sector 40 output delivered to final demand requires $0.30632


expended to compensate Sector 40 employees. Multiplying labor's share of output value (30.63 percent) by the value of


compliance equipment purchases for the regulation ($3.1 million) yields the labor cost of manufacturing treatment system


equipment: $0.9 million.


� FTE jobs


EPA converted the total labor cost to the number of FTE-equivalent jobs by dividing the total labor cost by an estimated


yearly labor cost per FTE employee. EPA used the hourly labor rate used in the engineering cost analysis – $29.67 per hour


in 1996 dollars. The $29.67 per hour rate includes fringe benefits (e.g., holidays, vacation, and various insurances) and


payroll taxes. EPA adjusted this amount to 2001 dollars using the Bureau of Labor Statistics Employment Cost Index for


manufacturing of durable goods, to provide an hourly rate in 2001$ of $34.69. The gross 2001 dollar annual labor cost per


FTE position for a 2,000-hour work year is $69,373. EPA estimated that one-time spending on manufacturing treatment


system equipment would require 14 FTEs (941 (in thousands) / 69.4). EPA assumed that one-third of facilities come into


compliance in each of the first three years, therefore, one-third of these FTEs (5) would be associated with equipment


purchases in each of the first three years after promulgation of the rule.


3 See “Benchmark Input-Output Accounts for the U.S. Economy, 1992,” in Survey of Current Business, July 1997, U.S. Department 

of Commerce, Bureau of Economic Analysis. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects 

b. Direct labor requirements for installing treatment systems 

EPA’s method for estimating the direct labor requirements to install treatment system equipment parallels its method for 

analyzing the labor requirements for equipment manufacturing. 

� Compliance cost


EPA used the total one-time capital costs estimated by the engineers to calculate the cost of installation. The estimated one-


time cost of installation labor is $0.5 million for the final regulatory option. Appendix E explains in more detail how this


value was calculated.


� Labor share


One hundred percent of the installation is a labor cost.


� FTE jobs


EPA used the loaded hourly labor cost of $34.69 per hour and 2,000 hours per year to convert labor costs to numbers of FTE


jobs. Complying facilities will require an estimated 7 FTEs (455 (in thousands) / 69.4) to install the equipment needed to


comply with the regulation. This corresponds to 2 FTEs in each of the first three years after promulgation of the rule.


c. Direct labor requirements for operating and maintaining treatment systems


MP&M project engineers estimated that labor costs represent one percent of total compliance operating and maintenance


(O&M) costs. For the final rule, the labor cost of O&M is $0.1 million per year (2001$), corresponding to 2 FTEs (131 (in


thousands) / 69.4). EPA assumed that one-third of facilities come into compliance in each of the first three years after


promulgation of the rule. Therefore, one-third of these FTEs (1) would have operating maintenance requirements in the first


year, two-thirds of these FTEs (1) would have operating maintenance requirements in the second year, and all of these FTEs


(2) would have operating maintenance requirements in the third year when all facilities reach compliance.


d. Total direct labor requirements


The total direct labor requirement for complying with the MP&M rule is the sum of the direct labor requirements of


manufacturing, installing, and operating treatment systems. Table 6.2 summarizes the direct labor requirements from


compliance expenditures under the regulation. These requirements include total one-time expenditures to manufacture and 

install compliance equipment equal to 20 FTEs, and continuing requirements for operating and maintenance of 2 FTEs per 

year. 

Table 6.2: Total Direct Labor Requirements of the Final Rule, 

National Estimates (millions, 2001$, before tax) 

Total Cost Labor 

Share 

Total 

Labor Cost FTEsa 

One-time compliance cost $3.6 $1.4 20 

Capital equipment manufacturing $3.1 30.6% $0.9 14 

Installation labor $0.5 100.0% $0.5 7 

Annual operating and maintenance cost $13.1 1.0% $0.1 2 

a Number of jobs calculated on the basis of an average annual labor cost of $69,373 which assumes an 

average hourly wage of $34.69 and 2,000 hours per labor-year. 

Source: U.S. EPA analysis, Bureau of Labor Statistics, Bureau of Economic Analysis. 

Table 6.3 summarizes the total direct labor requirements from compliance expenditures under the final rule and alternative 

regulatory options. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects 

Table 6.3: Total Direct Labor Requirements of the Final Rule and Alternative Regulatory Options 

Option 

One-time manufacturing and installation of compliance 

equipment Annual operating 

and maintenance 

Manufacturing Installation labor Total 

Option I: Selected Option 14 7 20 2 

Option II: NODA/Proposed Option 2,467 1,195 3,662 215 

Option III: 413 to 433 Upgrade Option 294 142 436 8 

Option IV: All to 433 Upgrade Option 457 221 678 13 

Source: U.S. EPA analysis, Bureau of Labor Statistics, Bureau of Economic Analysis. 

Requirements under the Proposed/NODA Option include total one-time expenditures to manufacture and install compliance 

equipment equal to 3,662 FTEs, and continuing requirements for operating and maintenance of 215 FTEs per year. EPA 

expects the 413 to 433 Upgrade Option and the All to 433 Upgrade Option to require 436 and 678 one-time FTEs and 8 and 

13 continuing FTEs per year, respectively. 

6.3 NET EFFECTS ON EMPLOYMENT 

The timing and duration of employment changes resulting from the rule or the alternative options depend on the type of 

employment demands and the condition of the economy at the time those demands occur. The increased employment 

resulting from facilities’ purchase and installation of compliance equipment will be short-term and is expected to occur in the 

early years of implementation. However, the increased employment needed to operate and maintain compliance systems will 

persist, presumably for the life of the compliance equipment. For job losses that might accompany the alternative options, the 

duration of unemployment would depend on labor demand in the economy and specifically in the locations at which facilities 

close, and the skill level of those individuals becoming unemployed. 

Table 6.4 reports the estimated level and timing of direct employment impacts of the final rule. The estimates assume that: 

(1) facilities come into compliance or close over a three year period, (2) displaced workers are out of work for one year on 

average, and (3) the requirements to operate and maintain compliance systems continue for 15 years. As shown in Table 6.4, 

the final rule results in a small increase in employment in all years of the analysis period. Summing employment each year 

over the 15 year analysis period indicates that the regulation would increase direct labor requirements by 47 “FTE-years”, or 

an average gain of 3 FTEs per year. The comparable estimates for the alternative options (shown in Table 6.5) include the 

effect of job losses from facility closures. 

The industries in which employment changes are expected to occur also depend on the type of employment demands under 

the rule. Increases in employment for operation and maintenance of compliance equipment are expected to occur in the 

MP&M industries. In addition, because the MP&M industry, itself, is likely to be a manufacturer of compliance equipment, a 

material portion of the increase in employment for producing and installing compliance equipment is likely to occur in the 

MP&M industries. Accordingly, a substantial part of the total employment increase will likely occur in the MP&M 

industries. Still, on balance, the impact on total employment – both in the economy as a whole and in the MP&M industries – 

is expected to be very small. The average net gain of 3 FTEs for the final rule equals a negligible percent of total employment 

in the MP&M facilities potentially subject to the rule and even less compared with total 1996 employment in the industries 

that make up the larger MP&M industry.4 

EPA did not consider the possible effects of excess capacity or underemployment in the equipment manufacturing and 

installation industries, and assumed that all compliance requirements would result in proportional changes in employment. 

4 Total employment in the potentially regulated MP&M facilities is 6,973,352 FTEs, as reported in the Section 308 surveys. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects 

Table 6.4: Estimated Final Rule Net Direct Employment Impacts over 15 Years 

(number of FTEs per year and total FTE-years) 

Year 

One-Time 

Manufacturing 

& Installationa 

Annual O&Ma Closures Net Change in 

Employment 

1 7 

2 7 

3 7 

4 2 

5 2 

6 2 

7 2 

8 2 

9 2 

10 2 2 

11 2 2 

12 2 2 

13 2 2 

14 2 2 

15 2 2 

Total FTEs 

over 15 years 

20 26 0 47 

1 0 7 

1 0 8 

2 0 9 

2 

2 

2 

2 

2 

2 

a Assumes that one-third of facilities come into compliance in each of 3 years. 

Source: U.S. EPA analysis. 

Table 6.5 presents the estimated direct employment impact of the final rule and the alternative options. As discussed earlier, 

the final rule would increase direct labor requirements over the 15 year period by an estimated 47 FTEs; however under each 

of the alternative regulatory options, direct labor requirements would decrease. The total estimated net decrease in direct 

labor requirements under the NODA/Proposed Option of 26,060 FTEs is driven by the 32,729 job losses from estimated 

facility closures under the option. The 7,874 job losses from projected facility closures under the 433 Upgrade Options result 

in a net decrease in direct labor requirements under the 413 to 433 Upgrade Option of 7,319 FTEs and the All to 433 Upgrade 

Option of 7,011 FTEs. 

Table 6.5: Estimated 15 Year Net Employment Effects for the 

Final Rule and Alternative Regulatory Options 

Option Net Change in Employment (FTEs) 

Option I: Selected Option 47 

Option II: NODA/Proposed Option (26,060) 

Option III: 413 to 433 Upgrade Option (7,319) 

Option IV: All to 433 Upgrade Option (7,011) 

Source: U.S. EPA Analysis. 

6-6 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects 

GLOSSARY 

direct labor requirements: employment losses resulting from lost MP&M output caused by the rule and employment 

gains caused by compliance expenditures resulting from the rule in the directly-affected industries. 

full-time equivalent (FTE): hours of employment equivalent to one full-time job. 

FTE-year: one year of full-time employment. 

indirect labor requirements: changes in employment in industries that supply directly affected industries resulting from 

increased purchases or reduced output in the directly affected industries. 

induced labor requirements: changes in employment in industries providing goods and services to people whose 

employment is directly or indirectly affected by the rule. 

linked industries: industries that sell goods and services to or purchase output from a directly-affected industry. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects 

ACRONYM 

FTE: full-time equivalent 

6-8

MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects 

REFERENCES 

Apogee Research, Inc. 1992. A Report on Clean Water Investment and Job Creation, prepared for National Utility 

Contractors Association. 

U.S. Bureau of Labor Statistics. 2000. Employment Cost Index - Historical Listing. July 27. 

http://stats.bls.gov/ecthome.htm. 

U.S. Department of Commerce. 1992 and 1997. Bureau of Economic Analysis, Regional Multipliers: A User Handbook for 

the Regional Input-Output Modeling System (RIMSII), Second Edition and Third Edition. Washington, D.C. 

U.S. Department of Commerce. 1997. Bureau of Economic Analysis, The 1992 Benchmark Input-Output Accounts of the 

United States. 

U.S. Environmental Protection Agency (U.S. EPA), Office of Water. 1993. Job Creation Fact Sheet, internal document. 

February. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Government and Community Impact Analysis 

Chapter 7: Government and


Community Impact Analysis


INTRODUCTION 

In this chapter, EPA examines how the final MP&M rule and 

alternatives for regulation considered by EPA might affect the 

economic welfare of communities, where communities are 

defined as States, counties and metropolitan areas. 

Communities may suffer adverse impacts from a rule in two 

ways. First, local governments may incur costs to comply 

with the rule, if they operate MP&M facilities, or to 

administer the rule. Second, communities may be affected if 

CHAPTER CONTENTS 

7.1 ts on Governments ................. . 7-1 

7.1.1 ts on Governments that Operate 

MP&M Facilities ................. .. 7-1 

7.1.2 dministrative Costs ......... 7-2 

7.2 Facility Closures ..... 7-6 

Glossary ................. .................7-7 

Acronyms ................. ................7-8 

Impac

Impac

POTW A

Community Impacts of 

MP&M facility closures resulting from the rule affect the health of their local economies. 

This analysis was undertaken in part to meet potential requirements of the Unfunded Mandates Reform Act (UMRA). 

However, the final rule does not contain a Federal mandate under UMRA because the rule will not result in expenditures of 

$100 million or more for State, local, and tribal governments, in the aggregate, or the private sector in any one year. Thus, the 

final rule is not subject to the requirements of the UMRA sections 202 and 205. Although the final rule does not contain a 

Federal mandate under UMRA, this chapter summarizes the impacts of the final rule on State and local governments as part of 

its decision-making process. 

7.1 IMPACTS ON GOVERNMENTS 

The analysis considered two effects on governments:1 

� Government-owned MP&M facilities may be subject to the regulation, and therefore incur compliance costs; and 

� Municipalities that own publicly-owned treatment works (POTWs) that receive influent from MP&M facilities 

subject to the rule may incur costs to implement the regulation. These include costs of permitting MP& M facilities 

that have not been previously permitted, and repermitting some MP&M facilities with existing permits earlier than 

would otherwise be required. 

7.1.1 Impacts on Governments that Operate MP&M Facilities 

Chapter 5 presented EPA’s analysis of the final rule’s impacts on government-owned MP&M facilities and on the 

governments that own them. The analysis shows that the final rule imposes only limited costs on government-owned 

facilities, because 3,327 (88 percent) of the 3,785 facilities are not subject to this regulation (121 General Metals facilities and 

3,206 Oily Wastes facilities.) Thus, the final rule applies to 458 government owned facilities. 

An estimated 162 government-owned facilities (4.3 percent of the total) would incur costs under the final rule exceeding one 

percent of their baseline cost of service. Therefore, 96.3 percent of the government-owned facilities either incur no costs or 

are likely to be able to absorb the added costs within their existing budgets. None of the affected governments incur costs that 

1 A third potential cost would be implementation cost for direct dischargers. However, all direct dischargers regulated under the final 

rule (and any alternative options considered) must already have NPDES permits in the baseline. EPA therefore does not expect 

governments to incur incremental administrative costs as a result of this rule for direct dischargers, because governments will incorporate 

the new standards into existing NPDES permits. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Government and Community Impact Analysis 

cause them to exceed the thresholds for impacts on taxpayers or for government debt burden. EPA therefore has concluded 

that the final rule will not impose budgetary burdens on any of the governments that own MP&M facilities. 

7.1.2 POTW Administrative Costs 

The selected option excludes all indirect dischargers from MP&M regulation. Therefore, there are no POTW administrative


costs associated with the final rule. However, under some of the alternative regulatory options considered, State and local


governments would incur implementation costs for indirect dischargers. This section describes the administrative activities


involved and presents estimates of their costs.


EPA is able to estimate total costs to POTW s, but is not able to estimate the costs to any one POTW, since it is not possible to


determine which POTWs receive discharges from the regulated MP&M facilities. EPA is also not able to assess budgetary


impacts on community-owned POTWs, since available data do not provide estimates of financial characteristics for the


specific POTW s receiving effluent affected by this rule. The relatively low POT W permitting costs per facility and the


potential cost savings estimated in this section, however, suggest that impacts on individual POTWs, if any, would be minor.


a. Permitting activities


The General Pretreatment Regulations (40 CFR Part 403) establish procedures, responsibilities, and requirements for EPA,


States, local governments, and industry to control pollutant discharges to POTWs. Under the Pretreatment Regulations, 

POTWs or approved States implement categorical pretreatment standards (i.e., PSES and PSNS). 

2 Discharges from an MP&M facility to a POTW may already be permitted in the baseline. For example, industrial users 

subject to another Categorical Pretreatment Standard would have a discharge permit. Other significant industrial users (SIU) 

that are typically permitted by POTWs include industrial users that: 

� discharge an average of 25,000 gallons per day or more of process wastewater to a POTW, 

� contribute a process waste stream which makes up 5 percent or more of the average dry weather hydraulic or organic 

capacity of the POT W treatment plant, or 

� have a reasonable potential for adversely affecting the POTW ’s operation or for violating any pretreatment standard. 

Since all indirect dischargers have been excluded from the final rule, EPA expects no POTW administrative costs to be


associated with the rule. Under the alternative options, which include indirect dischargers, EPA expects no increase in


permitting costs for facilities that already hold a permit in the baseline. However, governments will incur additional


permitting costs for unpermitted facilities (under the NODA/Proposal option only) and to accelerate repermitting for some


indirect dischargers that currently hold permits. On the other hand, some administrative costs might decrease. For example,


control authorities would no longer have to repermit facilities that are estimated to close as the result of the regulatory options


considered. Communities that own POTW s that must issue permits might therefore experience a change in costs as a result of


some of the alternative regulatory options considered.


b. Data sources


EPA collected information from POTWs to support development of the MP&M effluent guideline. Of 150 surveys mailed,


EPA received responses to 147, for a 98 percent response rate. The POTW Survey asked respondents to provide information


on administrative permitting costs for indirect dischargers, sewage sludge use and disposal costs and practices, and general


information (including number of permitted users and number of known MP&M dischargers). The administrative cost


information included the number of hours required to complete specific permitting and repermitting, inspection, monitoring,


and enforcement activities. Respondents were also asked to provide an average labor cost for all staff involved in permitting


activities. EPA used the survey responses on administrative costs to estimate a range of costs incurred by POTWs to permit a 

single MP&M facility. 

EPA also used the data provided in the Association of Metropolitan Sewerage Agencies (AM SA) survey to verify and, in 

some cases, supplement its own analyses of POTW administrative costs of the final MP&M rule. AM SA provided EPA with 

2 Under the General Pretreatment Program, a facility's discharges may be controlled through a "permit, order or similar means". For 

simplicity, this report refers to the control mechanism as a permit. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Government and Community Impact Analysis 

comments on the proposed MP&M rule and supplemented these comments with a spreadsheet database. The database 

contains data from an AM SA formulated survey and covers responses from 176 PO TW s, representing 66 pretreatment 

programs. The AM SA survey was conducted to verify data from EPA's survey of POTWs and therefore included similar, 

although fewer, variables compared to EPA's survey. Elements EPA verified using the AMSA survey include: (1) the 

estimated number of indirect dischargers and (2) the unit costs of certain permitting activities, including permit 

implementation, sampling, and sample analysis. Elements EPA added to its analysis using the AMSA data include: (1) 

screening costs for POTWs that do not currently operate under a pretreatment program and (2) management oversight costs 

associated with implementing the MP& M regulation. 

c. Methodology 

EPA	 estimated the annualized costs of permitting indirect dischargers under the different regulatory options using the 

following steps: 

� Determine the number and characteristics of indirect dischargers that will be permitted under each 

regulatory option. Only the NODA/Proposal option includes costs for permitting an MP&M facility for the first 

time. The final rule does not cover indirect dischargers while the other regulatory options only regulate those 

indirect dischargers that already hold permits in the baseline. For the NODA/Proposal option, EPA determined how 

many new permits would be issued. The NODA/Proposal option only requires concentration-based permits, no 

mass-based permits. In addition, EPA determined the number of facilities that currently hold a permit and that 

would have to be repermitted sooner than would otherwise be the case. 

� Use the data from the POTW Survey to determine a high, middle, and low hourly burden for permitting a 

single facility. EPA defined the low and high estimates of hours such that 90% of the POTW responses fell above 

the low value and 90% of responses fell below the high value. The median value is used to define the middle hourly 

burden. 

� Use the data from the POTW Survey to determine the average frequency of performing certain 

administrative functions. For administrative functions that are not performed at all facilities, survey data were used 

to calculate the portion of facilities requiring these functions. For example, the survey data show that on average 

38.5% of facilities submit a non-compliance report. 

� Multiply the per-facility burden estimate by the average hourly wage. EPA determined a high, middle, and low 

dollar cost of administering the rule for a single facility by multiplying the per-facility hour burden by the average 

hourly wage. The POTW Survey reported an average hourly labor rate of $39.33 ($2001) for staff involved in 

permitting. This is a fully-loaded cost, including salaries and fringe benefits. 

� Calculate the annualized cost of administering the rule. The number of facilities, hourly burden estimate, 

frequency estimates, and hourly wage estimates are all combined to determine the total cost of administering the rule. 

The type of administrative activities required varies over time and the total administrative cost is calculated over a 15 

year time period. EPA calculated the present value of total costs using a seven percent discount rate, and then 

annualized the present value using the same seven percent discount rate. 

d. Unit costs of permitting activities 

EPA estimated unit costs for the following permitting activities: 

� Permit application and issuance: developing and issuing concentration-based permits at previously unpermitted 

facilities; providing technical guidance; and conducting public and evidentiary hearings; 

� Inspection: inspecting facilities both for the initial permit development and to assess subsequent compliance; 

� Monitoring: sampling and analyzing permittee’s effluent; reviewing and recording permittee’s compliance selfmonitoring reports; receiving, processing, and acting on a permittee’s non-compliance reports; and reviewing a 

permittee’s compliance schedule report for permittees in compliance and permittees not in compliance; 

� Enforcement: issuing administrative orders and administrative fines; and 

� Repermitting. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Government and Community Impact Analysis 

EPA believes that these functions constitute the bulk of the required administrative activities. To these costs, EPA added a 

provision for managerial oversight of 25 percent.3 There are other relatively minor or infrequent administrative functions 

(e.g., providing technical guidance to permittees in years other than the first year of the permit, or repermitting a facility in 

significant non-compliance), but their costs are likely to be insignificant compared to the estimated costs for the five major 

categories outlined above. EPA also added a cost for identifying facilities to be permitted for POTWs that do not currently 

operate under a Pretreatment Program. EPA estimates this cost to be approximately $0.8 million. This cost only applies to 

the NODA/Proposal Option since facilities subject to the upgrade options already hold permits. 

Table 7.1 provides a summary of the estimated unit costs for each permitting activity. Appendix F provides a detailed 

discussion of these unit costs. 

Table 7.1: Government Administrative Activities for Indirect Dischargers: Per Facility Hours and Costs 

Administrative Activity Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical hours and costs (2001$) 

Low Median High 

Develop and issue a 

concentration-based permit at a 

previously unpermitted facility 

100% of unpermitted facilities 

(applicable to NODA/Proposal option only) 

One time 4.0 hours; 

$122 

10.0 

hours; 

$304 

40.0 hours; 

$1,217 

Develop and issue a mass-based 

permit at a previously unpermitted 

facility 

100% of MP&M facilities being issued a 

new mass-based permit 

(estimates used for the proposed rule) 

One time 4.0 hours; 

$122 

13.0 

hours; 

$396 

40.0 hours; 

$1,217 

Develop and issue a mass-based 

permit at a facility holding a 

concentration-based permit 

100% of MP&M facilities with permit 

conversion 

(estimates used for the proposed rule) 

One time 2.0 hours; 

$61 

8.0 hours; 

$243 

20.0 hours; 

$608 year 

Provide technical guidance to a 

permittee on permit compliance 

100% of MP&M facilities being issued a 

new concentration-based permit 

(applicable to NODA/Proposal option only) 

One time 1.5 hour; 

$46 

4.0 hours; 

$122 

12.0 hours; 

$365 

100% of MP&M facilities being issued a 

new mass-based permit 

(estimates used for the proposed rule) 

One time 2.0 hours; 

$61 

4.0 hours; 

$122 

12.0 hours; 

$365 

Conduct a public or evidentiary 

hearing 

3.2% of MP&M facilities being issued a 

new mass-based or concentration-based 

permit 

(applicable to NODA/Proposal option only) 

One time 2.0 hours; 

$61 

8.0 hours; 

$243 

40.0 hours; 

$1,217 

Inspect facility for permit 

development 

100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

One Time 2.2 hours; 

$66 

5.0 hours; 

$152 

12.0 hours; 

$365 

Inspect facility for compliance 

assessment 

100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

Annual 2.0 hours; 

$61 

3.3 hours; 

$101 

10.0 hours; 

$304 

Sample and analyze permittee’s 

effluent 

100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

Annual 1.0 hour; 

$30 

3.0 hours; 

$91 

17.7 hours; 

$537 

3 The 25 percent oversight cost provision is based on comments and data received from the Association of Metropolitan Sewerage 

Agencies (AMSA). 

7-4 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Government and Community Impact Analysis 

Table 7.1: Government Administrative Activities for Indirect Dischargers: Per Facility Hours and Costs 

Administrative Activity Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical hours and costs (2001$) 

Low Median High 

Review and enter data from 

permittee’s compliance selfmonitoring reports 

100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

2 reports per 

year 

0.5 hours; 

$15 

1.0 hour; 

$30 

4.0 hours; 

$122 

Receive, process and act on a 

permittee’s non-compliance 

reports 

38.5% of all indirect dischargers receiving a 

new permit 

(applicable to NODA/Proposal option only) 

5 times per 

year 

1.0 hour; 

$30 

2.0 hours; 

$61 

6.0 hours; 

$183 

Review a compliance schedule 

report 

Meeting milestones: 16.0% of all facilities 

issued a new permit – 94% of the 17% who 

have compliance milestones 

(applicable to NODA/Proposal option only) 

2 reports per 

year 

0.5 hours; 

$15 

1.0 hour; 

$30 

2.7 hours; 

$81 

Not meeting milestones: 1% of all facilities 

issued a new permit – 6% of the 17% who 

have compliance milestones 

(applicable to NODA/Proposal option only) 

2 reports per 

year 

1.0 hours; 

$30 

2.0 hours; 

$61 

6.0 hours; 

$183 

Minor enforcement action e.g., 

issue an administrative order 

7% of MP&M facilities being issued a new 

permit 

(applicable to NODA/Proposal option only) 

Annual 1.0 hour; 

$30 

3.7 hours; 

$112 

12.0 hours; 

$365 

Minor enforcement action, e.g., 

impose an administrative fine 

7% of MP&M facilities being issued a new 

permit 

(applicable to NODA/Proposal option only) 

Annual 1.0 hour; 

$30 

5.0 hours; 

$152 

24.0 hours; 

$730 

Repermit 100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

Every 5 years 1.0 hour; 

$30 

4.0 hours; 

$122 

20.0 hours; 

$608 

Source: U.S. EPA analysis of POTW Survey responses. 

e. Results 

Table 7.2 summarizes the number of facilities permitted and the estimated POTW permitting costs for the final rule and the 

alternative options considered. Appendix F presents detailed calculations of permitting costs for these regulatory options. 

The results presented in Table 7.2 reflect three effects of the regulatory options on the cost of permitting indirect dischargers: 

(1) incremental costs from permitting currently unpermitted facilities that require a new permit for the first time 

(NODA/Proposal option only); (2) incremental costs from repermitting some facilities that currently hold a permit earlier than 

would otherwise be the case (within three years rather than within five years); and (3) cost savings from facilities that close as 

a result of the regulation and no longer require repermitting. 

The first part of the table shows the incremental number of facilities requiring a new permit, requiring early repermitting, or 

estimated to close as a result of the rule. The second part of the table presents the resulting change in permitting costs. Costs 

are calculated by multiplying the incremental number of facilities in each year by the unit hours and cost per facility for those 

activities. All facilities are assumed to receive a permit within a three-year compliance period. Some facilities with existing 

permits are repermitted sooner than they otherwise would be on the normal five-year permitting cycle. The cost analysis 

calculates incremental costs by subtracting the costs of repermitting these facilities on a five-year schedule from the costs of 

repermitting all such facilities within three years. EPA assumes that the required initial permitting activities will be equally 

divided over the three-year period. The analysis also calculates the net change in the number of facilities requiring permitting 

by subtracting the number of facilities that close due to the rule from the number of facilities that will require new permits 

under each regulatory option. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Government and Community Impact Analysis 

Table 7.2: POTW Permitting Costs by Regulatory Option 

I: Selected 

Option 

II: NODA/Proposal 

Option 

III: Directs + 413 to 

433 Upgrade 

IV: Directs + 

413+50%LL Upgrade 

Number of facilities permitted: 

New concentration-based permit n/a 103 0 0 

New mass-based permita n/a 0 0 0 

Conversion of existing 

concentration-based to a massbased permita 

n/a 0 0 0 

Repermitted within 3 rather than 

5 years 

n/a 1,434 382 566 

Regulatory closures (no longer 

requiring permits)b 

n/a 722 120 120 

POTW permitting costs over 15 years (2001$): 

Net present value 

Low n/a ($422,000) ($238,000) ($236,000) 

Medium ($1,802,000) ($509,000) ($501,000) 

High ($9,357,000) ($1,982,000) ($1,940,000) 

Annualized (at 7%) 

Low n/a ($46,000) ($26,000) ($26,000) 

Medium ($198,000) ($56,000) ($55,000) 

High ($1,027,000) ($218,000) ($213,000) 

Maximum costs in any one year 

Low n/a $1,023,000 ($6,000) ($3,000) 

Medium $1,022,000 ($4,000) $6,000 

High $991,000 $1,000 $48,000 

a EPA does not require mass-based permits under any of the option considered for the final rule. 

b Some facilities with existing permits will no longer require permitting due to regulatory closures. 

Source: U.S. EPA analysis. 

Because indirect dischargers were excluded from the final regulation, EPA expects no additional POTW administrative costs 

from the final rule. Each of the three alternative regulatory options considered would result in reduced POT W regulatory 

costs. These cost savings result from regulatory closures (i.e., facilities that currently hold a permit and would have required 

repermitting in the baseline, but that will no longer require repermitting under the regulatory options). The cost savings as a 

result of regulatory closures outweigh the additional costs associated with issuing new permits (under the NODA/Proposal 

option only) and repermitting on an accelerated, three-year schedule. Estimated annualized cost savings to POTWs for the 

three alternative regulatory options range between $0.04 and $1.0 million under the NODA/Proposal option, and between 

$0.03 and $0.2 million under the Directs + 413 to 433 Upgrade option and the Directs + 413+50%LL Upgrade option (all 

costs in ($2001). 

7.2 COMMUNITY IMPACTS OF FACILITY CLOSURES 

EPA considered the potential for adverse impact of regulation-induced changes in employment on communities where


MP&M facilities are located. Because EPA anticipates no facility closures and associated employment losses from the final


regulation, EPA expects no employment-related impacts on communities in which MP&M facilities operate. See Chapter 6


for further discussion of potential employment effects.


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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Government and Community Impact Analysis 

GLOSSARY 

publicly-owned treatment works (POTW): a treatment works as defined by section 212 of the Clean Water Act, which


is owned by a State or municipality. This definition includes any devices or systems used in the storage, treatment, recycling,


and reclamation of municipal sewage or industrial wastes of a liquid nature.


(http://www.epa.gov/owm/permits/pretreat/final99.pdf)


Unfunded Mandates Reform Act (UMRA): Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public


Law 104-4, establishes requirements for Federal agencies to assess the effects of their regulatory actions on State, local, and


Tribal governments and the private sector. Under §202 of the UMRA, EPA generally must prepare a written statement,


including a cost-benefit analysis, for proposed and final rules with "Federal mandates" that may result in expenditures to


State, local, and Tribal governments, in the aggregate, or to the private sector, of $100 million or more in any one year.


Before promulgating an EPA rule for which a written statement is needed, §205 of the UMRA generally requires EPA to


identify and consider a reasonable number of regulatory alternatives and adopt the least costly, most cost-effective or least


burdensome alternative that achieves the objectives of the rule.


7-7 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Government and Community Impact Analysis 

ACRONYMS


POTW: publicly-owned treatment works


UMRA: Unfunded Mandates Reform Act


7-8 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 8: Foreign Trade Impacts 

Chapter 8: Foreign Trade Impacts


INTRODUCTION 

EPA assessed the likely impacts on foreign trade as a result of 

the final rule and the alternatives considered for regulation as 

part of the analysis of the rule’s effect on the national 

economy. Changes in the balance of trade have the potential 

to affect currency exchange rates, money supply, interest 

rates, inflation, capital flows and labor migration. The 

CHAPTER CONTENTS 

8.1 Data Sources ................. .............. 8-1 

8.2 Methodology ................. ............. 8-2 

8.3 Results ................. ................. . 8-3 

References ................. ................. .. 8-5 

MP&M industry sectors include a substantial portion of the nation’s economy, and significant impacts on the balance of trade 

in these industries could affect the overall economy. 

As part of the facility impact analysis in Chapter 5, EPA assessed potential price increases and output losses that may result 

from the rule. EPA assessed the impact of these market-level changes on the U.S. balance of trade using information 

provided by MP&M private facility surveys on the source of competition in domestic and foreign markets. The trade analysis 

allocates the value of changes in output for each facility that is projected to close due to the rule to exports, imports or 

domestic sales, based on the predominant source of competition in each market reported in the surveys. 

EPA’s analysis predicts no foreign trade impacts as a result of the final rule because no facility closures are expected. This 

analysis does not account for factors such as price increases from the rule or the response of foreign producers to the rule, but 

EPA believes that these factors will have a negligible effect on the U.S. balance of trade. This chapter analyzes the impact on 

foreign trade of the alternative regulatory options for which closures are predicted. 

8.1 DATA SOURCES 

The assessment of foreign trade impacts is based on the facility closure analysis in Chapter 5. The revenue from any closing 

facilities is assumed to be lost output attributable to the rule. 

The analysis uses survey responses to determine whether a closed facility’s revenues are more likely to be replaced by either 

domestic or foreign producers. Question 5 in the Phase I §308 survey asked respondents to identify their “major source of 

competition” in each of three markets: local/regional, national, and international. Question 8 in the Phase II survey asked 

respondents to identify their “most significant source of competition” in domestic and international markets. Respondents 

selected one of the following possible responses: 

� domestic firms, 

� foreign firms, 

� no competition in this market, and 

� do not operate in this market. 

During the process of clarifying survey answers with respondents, EPA found that most respondents who did not select any of 

the sources of competition said that they did not participate in the relevant market. Therefore, if a respondent did not answer 

the question regarding the most important source of competition in the domestic or international markets, EPA classified the 

facility as not operating in the respective market (domestic or foreign). 

The analysis also uses survey responses to determine revenues from exports. The Phase I §308 survey reported the 

percentage of revenues earned from domestic customers and from overseas markets. EPA used export share and total 

revenues for each facility to calculate export and domestic revenues. The Phase II survey asked respondents to report 

8-1 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 8: Foreign Trade Impacts 

revenues from MP&M exports. EPA then calculated domestic sales by subtracting export revenues from total revenues for 

each facility. 

The Iron & Steel survey did not report comparable information on the source of competition in domestic and foreign markets. 

EPA relied on published trade statistics for the products produced by facilities in the Steel Forming & Finishing subcategory 

to assess potential impacts on trade for these facilities. 

EPA obtained 1996 import and export data from the Bureau of the Census, Foreign Trade Division for those commodities 

determined to be MP&M-related. The data included imports and exports by all facilities in relevant industries, including both 

dischargers and non-dischargers. 

8.2 METHODOLOGY 

The effect of an increase in domestic production costs on the foreign trade balance is influenced by a variety of factors, 

including: 

� the extent to which domestic producers attempt to raise prices to recover costs, 

� the price elasticity of demand in both domestic and export markets, 

� the likely pricing and supply response of foreign producers, and 

� trends in currency exchange rates. 

EPA did not attempt to simultaneously model changes in prices, output, and sales in domestic and foreign markets for the 

products and services produced by the MP&M industry sectors. As in the facility impact analysis described in Chapter 5, the 

trade analysis relies on a sequential analysis that assesses price increases and then predicts output adjustments based on 

closures. EPA used facilities’ own assessments of their competitive status relative to foreign producers, as reported in the 

survey, to assess impacts of these output adjustments on the balance of trade. 

EPA expects that foreign firms would replace some but not all of the output from any closing facilities. Domestic firms that 

remain open or enter the market might also win customers that used to buy from the closing facility. Revenues lost by closing 

facilities are assigned to domestic or foreign producers as follows: 

� Lost exports: If a closing facility stated that most of its international competition came from foreign firms, then EPA 

assigned the facility’s export revenues to foreign firms. U.S. exports would therefore decline by the amount of the 

closing facility’s exports. If the facility identified domestic businesses as its greatest source of competition in foreign 

markets, then EPA assigned the closing facility’s export revenues to other domestic firms. Closures of these 

facilities, which reported relatively low foreign competition for exports, will have no impact on U.S. exports under 

the expected scenario. 

� Increased imports: If a closing facility identified foreign producers as the main source of domestic sector 

competition, then EPA assigned the facility’s lost domestic revenues to foreign firms. Imports would increase by the 

same amount. If other domestic businesses posed the strongest competition, then EPA assigned the closing facility’s 

domestic sales to other U.S. producers, and imports would remain constant. 

The survey data collected for the Steel Forming and Finishing facilities did not provide export data. EPA assumed that the 

ratio of exports to value of shipments for any closing facilities in the analysis was the same as the ratio for the industry as a 

whole. 

From the estimated changes in exports and imports, EPA calculated the net trade impact (reduction in exports plus increase in 

imports) and compared this value to baseline trade levels for (1) all commodities and (2) MP&M sector commodities, only. 

8-2 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 8: Foreign Trade Impacts 

8.3 RESULTS 

Chapter 3 provides an overview of exports, imports and the balance of trade in the MP&M industry sectors. U.S. MP&M 

producers as a group exported products with a value of $345 billion in 1996. Imports to the U.S. of the same products in 

1996 totaled $421 billion, resulting in an overall net MP&M commodity trade deficit of $76 billion. Some MP&M sectors 

contribute to a positive commodity trade balance (e.g. aircraft, with a $27 billion positive balance in 1996). In other sectors, 

substantially more products are imported than exported (e.g. motor vehicles, with a net negative balance of $63 billion.) 

Table 8.1, below, summarizes the estimated impact of the final rule and alternative options on the U.S. balance of trade for all 

commodities. Because EPA’s analysis indicates that the final rule will cause no facility closures, EPA expects that the final 

regulation will not affect the balance of trade. As shown in the table, the other regulatory options would have a negligible 

impact on U.S. imports, exports, and the national trade balance. Option II (NODA option) results in the most closures and 

thus the largest trade impacts. However, even in this option, projected imports increase by only $85 million, or slightly more 

than one hundredth of one percent of baseline imports, and exports decrease by only $55 million, less than one hundredth of 

one percent of baseline exports. The net result for the NODA option is an insignificant 0.08 percent decline in the national 

balance of trade. 

Table 8.1: 

(millions, 2001$) 

1996 Exports 1996 Imports Trade Balancea 

Baseline $666,321 $847,767 ($181,446) 

Option I: Selected Option 

Change due to the ruleb n/a n/a n/a 

Option II: Proposed/NODA Option 

Change due to the rule ($55) $85 ($141) 

Post-compliance $666,266 $847,852 ($181,587) 

% Change from baseline -0.008% 0.010% 0.078% 

Option III: 413 to 433 Upgrade Option 

Change due to the rule $0 $22 ($22) 

Post-compliance $666,321 $847,789 ($181,468) 

% Change from baseline 0% 0.0026% 0.012% 

Option IV: All to 433 Upgrade Option 

Change due to the rule $0 $22 ($22) 

Post-compliance $666,321 $847,789 ($181,468) 

% Change from baseline 0% 0.0026% 0.012% 

Estimated National Impacts on Total U.S. Foreign Trade 

a

 Trade balance is equal to exports minus imports. 

b

 There were no regulatory closures in the selected option, and so this analysis predicts no 

foreign trade impacts. 

Source: Bureau of Census and U.S. EPA analysis. 

Table 8.2 shows regulatory impacts on foreign trade in MP&M industry commodities. As noted above, EPA estimates that 

the final rule will cause no closures and thus have no foreign trade impacts. In the other options, the projected changes in 

exports and imports represent only an insignificant percentage of commodity trade in the MP&M industry sectors. The 

8-3 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 8: Foreign Trade Impacts 

largest impacts occur in Option II (NODA Option), but even these impacts result in only a 0.2 percent decline in the net trade 

balance in these industries. 

Table 8.2: Estimated National Impacts on MP&M-Related Foreign Trade 

(millions, 2001$) 

1996 Exports 1996 Imports Trade Balancea 

Baseline $345,274 $421,015 ($75,741) 

Option I: Selected Option 

Change due to the ruleb n/a n/a n/a 

Option II: Proposed/NODA Option 

Change due to the rule ($55) $85 ($141) 

Post-compliance $345,219 $421,100 ($75,882) 

% Change from baseline -0.016% 0.020% 0.186% 

Option III: 413 to 433 Upgrade Option 

Change due to the rule $0 $22 ($22) 

Post-compliance $345,274 $421,037 ($75,763) 

% Change from baseline 0% 0.005% 0.030% 

Option IV: All to 433 Upgrade Option 

Change due to the rule $0 $22 ($22) 

Post-compliance $345,274 $421,037 ($75,763) 

% Change from baseline 0% 0.005% 0.030% 

a

 Trade balance is equal to exports minus imports. 

b

 There were no regulatory closures in the selected option, and so this analysis predicts no 

foreign trade impacts. 

Source: Bureau of Census and U.S. EPA analysis. 

The analysis of trade impacts does not explicitly account for responses to price increases caused by the rule, as noted 

previously. However, EPA expects little change in exports and imports as a result of the minimal price increases predicted for 

the final rule. The estimated price increases are less than one half of one percent in all sectors (see Table 5.4 in Chapter 5). 

Annual rates of inflation for the United States’ major trading partners are generally well above the projected increases in 

MP&M prices, and price increases in the projected range are not likely to materially affect the terms of U.S. trade in MP&M 

products.1 

1 The following are 1990-98 annual inflation rates, as measured by the GDP implicit deflator, for nine of the U.S.’ s top ten trading 

partners: Canada 1.4%, Mexico 19.5%, Japan 0.2%, China 9.7%, Germany 2.2%, United Kingdom 3.0%, Republic of Korea 6.4%, France 

1.7%, and Singapore 2.1%. The annual change in the U.S. GDP deflator over the same period is 1.9% (Data were not reported for 

Taiwan.) World Bank, 2000 World Development Indicators, Table 4.16. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 8: Foreign Trade Impacts 

REFERENCES 

U.S. Department of Commerce, Bureau of the Census. 1997. Current Industrial Reports: Steel Mill Products. 

U.S. Department of Commerce, Bureau of the Census, Foreign Trade Division. 1996. FT900. 

http://www.census.gov/foreign-trade/Press-Release/96_press_releases/Final_Revisions_1996/. 

World Bank, 2000 World Development Indicators. 

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8-6

MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

Chapter 9: Firm Level, New Source,


and Industry Impacts


INTRODUCTION 

The previous chapters assessed impacts on MP&M 

facilities, on governments and communities, and on the U.S. 

balance of trade. This chapter considers impacts on private 

businesses in more detail, by addressing three categories of 

impacts. First, the analysis of impacts on firms builds on 

the facility impact analysis to assess whether firms that own 

multiple facilities are likely to incur more significant 

impacts than indicated by the facility impact analysis. 

Second, the new source facility impact analysis considers 

whether the final rule might impose disproportionate 

burdens on new sources relative to existing sources, and 

thereby pose a barrier to new entry. Third, this chapter 

discusses potential industry-level impacts of the final rule. 

9.1 FIRM LEVEL IMPACTS 

CHAPTER CONTENTS 

9.1 evel Impacts ................. .........9-1 

9.1.1 ................. ............. 9-1 

9.1.2 Methodology ................. ........9-2 

9.1.3 ................. ............. 9-2 

9.2 ource Impacts ................. ........9-3 

9.2.2 Methodology ................. ........9-4 

9.2.3 ................. ............. 9-5 

9.3 Level Impacts ................. ...... 9-7 

Glossary ................. ................. .... 9-9 

Acronyms ................. ................. .. 9-10 

References ................. ................. . 9-11 

Firm L

Sources 

Results 

New S

Results 

Industry

EPA analyzed economic impacts on firms for the following reasons: 

� Impacts may be more significant at the firm level than at the facility level if a firm owns a number of facilities that 

incur significant costs. To the extent allowed by the available data, the analysis therefore looks at the combined 

effect of the facility compliance costs for all facilities owned by a given firm. 

� A firm-level analysis is needed to assess impacts on small businesses, as required by the Regulatory Flexibility Act 

and SBREFA. Certain findings from the firm-level analysis are used in the small business impact analysis presented 

in the following Chapter 10. 

9.1.1 Sources 

The firm-level analysis begins from the results of the facility-level analysis presented in Chapter 5, supplemented by firmlevel information provided by the MP&M facility surveys and publically available information. 

EPA was not able to conduct a rigorous national analysis of firm-level impacts because the sample frame used to provide 

national estimates from surveyed facilities reflects the population of facilities rather than firms. EPA therefore analyzed 

impacts for a hybrid dataset of MP&M firms that includes both national estimates (for single-facility firms) and sample firms 

(for multiple-facility firms), The Agency believes that the analysis of firm-level impacts presented in this chapter provides a 

useful indication of national firm-level impacts, however, for two reasons: 

� Most MP&M facilities are single-facility firms. The survey facility sample weights can be used to extrapolate to the 

national number of firms for these single-site firms. 

� EPA requested voluntary information in the Phase II detailed questionnaires on other MP&M facilities owned by the 

firms responding to the survey for a sampled facility. EPA aggregated multiple-facility compliance costs to the firmlevel by including costs for all surveyed facilities and, for the Phase II survey, facilities identified in voluntary 

responses. 

9-1 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

It is unlikely that firm-level impacts would be material among all MP&M firms in the nation, if this partial analysis does not 

indicate significant impacts among the firms identified in this analysis. 

9.1.2 Methodology 

The various surveys asked respondents to provide firm-level revenues for the parent firm. For single-facility firms, firm 

revenue and compliance costs are identical to those for the facility. For firms that own more than one sample facility, 

compliance costs are the sum of costs for all facilities reported on in the survey. 

In Part V of the detailed Phase II questionnaire, respondents had the option to submit additional voluntary data for other 

MP&M facilities owned by the same parent firm. EPA did not perform a detailed engineering analysis to develop detailed 

estimates of compliance costs for these facilities; however, EPA used the detailed estimates of compliance costs to estimate 

costs for these additional facilities. EPA assumed that these additional facilities would have the same average compliance 

costs as facilities in the same subcategory, flow range, and discharge type for which detailed cost estimates were developed. 

EPA then grouped together all facilities with a common parent firm from the Phase I, Phase II and Iron and Steel surveys. 

For each firm in the analysis, firm-level compliance cost is: 

(9.1) 

where: 

CCfirm = firm-level compliance cost


CCi = compliance cost for surveyed facility i owned by the firm


Firm-level compliance costs were compared to firm revenues. EPA judged that firms with compliance costs less than one 

percent of revenues would not be materially affected by the regulation. EPA identified firms as subject to potentially more 

serious impacts if their compliance costs exceeded three percent of revenues. 

All firm-level data were inflated to 2001 dollars using the Producer Price Index (PPI), as described in Chapter 5. 

9.1.3 Results 

As noted in the introduction, the Agency was not able to estimate the national numbers of firms that own MP&M facilities 

precisely, because the sample weights based on the survey design represent numbers of facilities rather than firms. EPA 

assumed that the national facilities that are represented by the 307 sample single-site firms that remain open in the baseline are 

also all single-site firms. Based on this assumption, EPA estimated that 26,472 of 36,480 (or 73 percent) of private MP&M 

facilities nationwide are single-facility firms. 

In addition, from the survey responses, EPA identified 389 sample facilities that are owned by 276 multi-facility firms. It is 

not known how many multi-facility firms exist at the national level, so EPA included these 276 firms in the firm-level analysis 

without extrapolation to the national level. 

The combined set of 26,748 firms (26,472 national-level single-facility firms plus 276 sample multi-facility firms) provided 

the basis for the firm-level analysis. This total does not represent a valid national total for the number of affected MP&M 

firms. Nonetheless, this analysis provides a reasonable indication of likely firm-level impacts, given the large number of 

single-facility firms and the use of Part V facility data to supplement the sample facility data for multi-facility firms. 

Table 9.1 presents the number of firms in the firm-level analysis. Of the 26,472 facilities that are single-facility firms, 25,297 

are owned by potentially small firms. Of the 276 firms that own more than one sample facility, 85 are potentially small firms. 

9-2 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

Table 9.1: Number of Privately Owned Facilities and Firms by Firm Type and Sizea 

Total Firms Owned by a small firm Owned by a large firm 

National number of single-facility 

firms (304 unique sample firms) 26,472 25,297 1,175 

Sample multi-facility firms 276 85 191 

Number of firms in the firm-level 

analysis 26,748 25,382 1,366 

a

 Excludes firms whose only facilities close in the baseline. 

Source: U.S. EPA analysis. 

Table 9.2 presents estimated firm-level impacts of the MP&M rule. None of the firms in the analysis incur after-tax 

compliance costs greater than 1 percent of annual revenues. Of the 1,027 firms that incur any costs at all, none close or incur 

moderate impacts as a result of the rule. 

Table 9.2: Firm-level After-Tax Annual Compliance Costs 

as a Percent of Annual Revenues 

Firm Type 

Number of 

Firms in the 

Analysis a 

Number and Percent with After-Tax Annual Compliance Costs/Annual 

Revenues Equal to: 

0% (no costs) Between 0% and 1% >1% 

Number % Number % Number % 

Single-site 26,472 25,453 96.2% 1,019 3.8% 0 0.0% 

Multi-site 276 269 97.5% 8 2.9% 0 0.0% 

Total 26,748 25,722 96.2% 1,027 3.8% 0 0.0% 

a

 Single-site firms whose only MP&M facilities close in the baseline are excluded. To be conservative, EPA 

included compliance costs for facilities that are owned by multi-site firms but predicted to be baseline 

closures in the facility impact analysis. 

Source: U.S. EPA analysis. 

This analysis is likely to overstate costs at the firm level because it does not consider the actions a multi-facility firm might 

take to reduce its compliance costs under the final rule. These include transferring functions among facilities to consolidate 

wet processes and take advantage of scale economies in wastewater treatment. 

9.2 NEW SOURCE IMPACTS 

This section assesses the impacts of New Source Performance Standards (NSPS) and Pretreatment Standards for 

New Sources (PSNS) limitations on new direct and indirect MP&M dischargers. EPA examines the impact of these 

regulations on new dischargers to determine whether new source limitations may pose sufficient financial burden on new 

facilities to constitute a material barrier to entry of new establishments into the MP&M industry sectors. The first section 

summarizes the framework for assessing new source impacts and the second section reviews the findings from our analysis. 

Disproportionate regulatory burdens for new sources could cause adverse industry-level outcomes in the long-run in several 

ways: 

� Imposing more significant costs on new facilities can make existing sources more competitive than new sources, 

causing barriers to new entry. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

� Barriers to entry may increase the market power of existing firms and could discourage competition over time, with 

resulting losses in market efficiencies. 

� Creating a competitive advantage for existing facilities may hinder technological innovation, with resulting losses in 

productivity. 

9.2.1 Methodology 

EPA used the existing facility database, sample-weighted, as the basis for the new source analysis. This assumes that future 

entrants to the industry will look the same as indicated by the sample of facilities in the existing facility database. 

To assess the potential impact of new source limitations, EPA assessed compliance costs for two cases: (1) the capital and 

operating cost of compliance systems for a new facility built in compliance with existing new source discharge limits (“current 

limits”), and (2) the capital and operating cost of compliance systems for a new facility built in compliance with discharge 

limits under consideration (“revised limits”), which would be more stringent than the current new source limits. The 

estimated capital costs for these cases account for the lower cost of a new-construction installation compared to retrofit 

construction at existing facilities. These compliance cost estimates are described in detail in the Technical Development 

Document. For analyzing the additional cost burden of meeting new limits, EPA calculated the incremental cost of 

compliance as the cost of meeting the revised limits less the cost of meeting current limits. 

As noted above, EPA based its analysis of new source limits on the economic and financial information for the sample of 

facilities in the existing facility database. The new source analysis excludes sample facilities that are projected to close or to 

experience moderate economic impacts in the baseline, since the economic characteristics of these financially-weak facilities 

are unlikely to be representative of new facilities. In addition, EPA excluded some sample facilities from the analysis because 

of issues in the engineering estimation of compliance costs. 

The analysis assumes that new sources would benefit from price increases resulting from the final rule for existing sources in 

the same way as existing sources. EPA therefore increased the average baseline revenue for new facilities by the average 

percentage price increase estimated for existing facilities in each subcategory/discharge category, to calculate post-regulation 

revenues for new sources. This effect of this adjustment on new facility revenue is minor. 

To test of financial burden of revised limits and whether this burden might pose a material barrier to entry for new 

establishments, EPA compared the incremental total annualized cost, after-tax, with facility revenue (cost-to-revenue ratio). 

EPA classified the results in ranges as follows, fraction of sample-weighted facilities with cost-to-revenue ratio of less than 

one percent, one to three percent, three to five percent, and greater than five percent. 

Table 9.3 shows the total number of privately owned MP&M facilities in the survey sample, the number of existing facilities 

excluded from the new source analysis, and the number of existing facilities used in this analysis. 

9-4 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

Table 9.3: Number of Existing Facilities Used in New Source Analysis 

Subcategory Discharger 

Type 

Total Number of 

Private MP&M 

Facilitiesa 

Number of Existing 

Facilities Excluded 

from New Source 

Analysis b 

Number of Existing 

Facilities Included in 

New Source Analysis 

General Metals 

Direct 888 181 707 

Indirect 10,419 1,824 8,594 

MF Job Shop 

Direct 12 0 12 

Indirect 1,530 165 1,365 

Non-Chromium Anodizer Directc 122 29 93 

Oily Wastes 

Direct 2,108 936 1,172 

Indirect 23,292 6,148 17,144 

Printed Wiring Boards 

Direct 8 0 8 

Indirect 840 288 552 

Railroad Rebuilders Direct 6 0 6 

Shipbuilding Dry Dock Direct 6 0 6 

All Subcategories 39,230 9,571 29,659 

a EPA did not estimate new source impacts for municipal operations because “barrier to entry" is not a relevant consideration. b EPA excluded an existing facility from the new source analysis either because it was financially weak in the baseline or 

because the engineers were unable to accurately estimate compliance costs. c For the analysis of new source limit impacts on the direct discharge Non-Chromium Anodizer category, EPA used sample 

facility information for indirect dischargers. The final sample facility database contained no observations for direct 

dischargers. 

Source: U.S. EPA analysis. 

9.2.2 Results 

Table 9.4 summarizes (1) the currently applicable discharge limit or technology option for new sources in each subcategory 

and discharge status, and (2) the alternative discharge limits or technology option that EPA considered in assessing whether 

revised new source discharge limits would constitute a barrier to entry. See Preamble Section VI and the Technical 

Development Document for discussion of the specific discharge limits and technology options that EPA considered for 

revised new source discharge limits. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

Table 9.4: Current New Source Requirements and Potential Revised New Source Requirements 

Subcategory Discharge 

Type 

Current New Source 

Requirements 

Revised New Source 

Requirements 

General Metals 

Direct 40 CFR 433 “Modified” Option 2, 

(Two-Stage Precipitation) 

Indirect 40 CFR 433 Option 2 

MF Job Shops 

Direct 40 CFR 433 “Modified” Option 2, 

(Two-Stage Precipitation) 

Indirect 40 CFR 433 Option 2 

Non-Chromium Anodizer Direct 40 CFR 433 Option 2 

Oily Waste 

Direct Estimated existing baseline Option 6 

Indirect Estimated existing baseline Option 6 

Printed Wiring Boards 

Direct 40 CFR 433 Option 2 

Indirect 40 CFR 433 Option 2 

Railroad Rebuilders Direct Option 6 Option 10 

Shipbuilding Dry Dock Direct Option 10 Option 8 

Source: U.S. EPA analysis. 

Table 9.5 reports the estimated percentages of new facilities incurring cost-to-revenue impacts of: (1) less than one percent, 

(2) one to three percent, (3) three to five percent, and (4) greater than five percent. As discussed earlier, these estimates are 

based on estimated incremental new source compliance costs compared to revenues for existing facilities in the MP&M 

survey universe. 

From this analysis, EPA found that revised new source limits would create a barrier to entry for direct discharging facilities in 

the General Metals, Metal Finishing Job Shops, and Non-Chromium Anodizer subcategories and indirect discharging 

facilities in the General Metals, Metal Finishing Job Shops, Printed Wiring Board, and Oily Wastes subcategories. On the 

basis of this finding, EPA decided against issuing revised new source discharge limits for these subcategories. The new 

source analysis indicated that revised new source limits would not create a barrier to entry for direct discharging facilities in 

the Oily Wastes, Printed W iring Board, and Railroad Rebuilders subcategories. This finding supported EPA’s decision to 

promulgate new source limits for the Oily Wastes direct discharger subcategory. Although the economic analysis did not 

indicate a barrier to entry for the Printed Wiring Board and Railroad Rebuilders direct dischargers subcategories, EPA 

decided against issuing new source limits for these subcategories based on other technical considerations as discussed in 

Preamble Section VI. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

Table 9.5: Estimated Percentages of New Facilities by Cost-to-Revenue Impact Ranges 

Subcategory Discharger 

Type 

After-Tax Compliance Costs as a Percent of Revenue 

< 1% 1-3% 3-5% >5% 

General Metals 

Direct 62% 14% 22% 2% 

Indirect 65% 14% 20% 1% 

MF Job Shop 

Direct 0% 0% 0% 100% 

Indirect 80% 9% 5% 6% 

Non-Chromium Anodizer Direct 25% 0% 26% 49% 

Oily Wastes 

Direct 97% 3% 0% 0% 

Indirect 95% 1% 5% 0% 

Printed Wiring Boards 

Direct 100% 0% 0% 0% 

Indirect 92% 3% 0% 5% 

Railroad Rebuilders Direct 100% 0% 0% 0% 

Shipbuilding Dry Dock Direct 100% 0% 0% 0% 

Source: U.S. EPA analysis. 

9.3 INDUSTRY LEVEL IMPACTS 

Potential industry-level impacts include price increases, reduced competitiveness within the domestic industry and in world 

markets, and reduced rates of innovation. EPA did not perform a sector-specific analysis for several reasons: 

� Sector-level impacts are complicated by the large number of product and service markets included in the MP&M 

category (e.g., over 200 SICs and three activities – manufacturing, rebuilding, and repair). 

� Revenue and cost information is not available on a product by product basis, so it is impossible to link price 

increases to individual products. and 

� Many MP&M facilities derive revenue from multiple industry sectors. 

EPA’s analysis of facility- and firm-level impacts suggests, however, that material industry-level impacts are unlikely in any 

of the affected sectors. 

The Agency does not expect any industry level impacts from the MP&M regulation because of: (1) the low number of 

facilities that will have costs, (2) the absence of regulatory closures, and (3) the absence of moderate impacts. Of the 

estimated 89,000 facilities performing MP&M activities, slightly over half, or about 45,000, do not discharge water and thus 

will not be affected by the rule. An additional 3,593 discharge water but are expected to close in the baseline. Of the 

remaining 40,265 facilities that do discharge water and remain open in the baseline, EPA estimates that only 1,380 will incur 

costs under the final rule. That so few MP &M industry facilities incur costs results from the rule’s subcategory exclusions 

and low-flow cutoffs. 

As discussed in Chapter 5, EPA estimates that no facilities will close or incur moderate impacts as a result of the final 

regulation. Given no regulatory closures or moderate impacts, EPA concludes that the final rule is unlikely to impose 

significant costs on a substantial number of facilities in the MP&M industry as a whole or at the subcategory level. 

Chapter 5 also presented information on the prices increases predicted to occur in each industry sector due to the final rule. 

Table 5.4 in Chapter 5 presented EPA’s estimates of price increases by sector. Projected price increases are less than one half 

9-7 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

of one percent for all sectors. Price increases of these magnitudes are unlikely to impose burdens on customers of the 

regulated facilities or substantially affect MP&M producers’ position relative to competitive products (e.g., products made 

with plastics) or foreign producers. Price increases may affect only some components of a product. In these cases, prices to 

end-users would rise even less than the amounts detailed in Chapter 5. 

EPA does not expect the final rule to affect the rate of technological innovation in the MP&M industry. Innovation impacts 

could result if the rule discouraged new entry, contributed to increased concentration in the affected industries, or specified 

the use of particular technologies. The following factors suggest that these conditions do not apply for the final rule: 

� EPA’s analysis of new source impacts presented in the previous section suggests that the final rule will not affect 

entry of new businesses in the regulated sectors. The final rule will increase the investment required to build a new 

facility somewhat. However, the increased capital costs are generally small relative to the overall financial resources 

of the MP&M facilities, as indicated by the results of the facility impact analysis. In addition, the low flow cutoffs 

applicable to a large number of MP&M facilities reduce the potential impacts of large capital requirements on small 

facilities. 

� Given the small fraction of facilities regulated in each sector, and absence of closures of moderate impacts for the 

final regulation, EPA does not expect the rule to increase concentration in any of the MP&M sectors. 

� The rule does not require the use of specific production or pollution control processes or technologies. Rather, it 

specifies a performance standard, based on levels of pollutants in wastewaters that have been shown to be achievable 

by available technologies. Facilities have the flexibility to achieve these limitations using a variety of approaches, 

which is likely to encourage rather than discourage innovation in production and pollution control processes. 

The final rule may affect the relative competitive position of different firms and facilities in those sectors that incur costs. 

Facilities that may benefit from the rule include those that: (1) do not discharge wastewater, (2) are eligible for the 

subcategory exclusions and low-flow cutoffs, (3) already have treatment in place, or (4) can more easily make process 

changes to reduce pollutant loads. 

Facilities that have little or no treatment in place and that discharge substantial pollutant loads may become less competitive. 

The final rule may level the competitive playing field for facilities that have taken steps to reduce their environmental 

impacts, relative to facilities that have avoided investments to reduce or eliminate pollutant discharges. EPA views these 

effects as beneficial, given that the final regulation does not have significant impacts on the industry as a whole, and as long 

as the rule does not disproportionately impact small entities as a group (impacts on small entities are addressed in the next 

chapter). 

9-8 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

GLOSSARY 

new source: Any building, structure, facility, or installation from which there is or may be a discharge of pollutants, the 

construction of which commenced after promulgation of standards of performance under Section 306 of the Clean Water Act 

which are applicable to such source; and which (1) is constructed at a site at which no other source is located; (2) totally 

replaces the process or production equipment that causes the discharge of pollutants at an existing source; or (3) consists of 

processes that are substantially independent of an existing source at the same site. 

New Source Performance Standards (NSPS): effluent limitations for new direct dischargers based on the best 

available demonstrated control technology. NSPS represents the greatest degree of effluent reduction attainable through the 

application of the best available demonstrated control technology for all pollutants (i.e., conventional, nonconventional, and 

priority pollutants). In establishing NSPS, EPA considers the cost of achieving the effluent reduction and any non-water 

quality environmental impacts and energy requirements. 

Pretreatment Standards for New Sources (PSNS): pretreatment standards for new indirect dischargers, designed to 

prevent discharges of pollutants that pass through, interfere with, or are otherwise incompatible with the operation of POTWs. 

Addresses all pollutants (i.e., conventional, nonconventional, and priority pollutants). Based on the same factors as are 

considered in promulgating NSPS. 

Producer Price Index (PPI): a family of indexes that measures the average change over time in the selling prices received 

by domestic producers of goods and services. PPI's measure price change from the perspective of the seller. This contrasts 

with other measures, such as the Consumer Price Index (CPI), that measure price change from the purchaser's perspective. 

Sellers' and purchasers' prices may differ due to government subsidies, sales and excise taxes, and distribution costs. 

(http://stats.bls.gov/ppifaq.htm#1) 

9-9 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

ACRONYMS 

NSPS: New Source Performance Standards


PPI: Producer Price Index


PSNS: Pretreatment Standards for New Sources


9-10 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

REFERENCES 

U.S. Bureau of Labor Statistics, Producer Price Index Revision-Current Series. On-line database at 

http://stats.bls.gov/ppihome.htm. 

U.S. Environmental Protection Agency (U.S. EPA). 2003. Technical Development Document for the Final Effluent 

Limitations Guidelines and Standards for the Metal Products & Machinery Point Source Category. February. 

9-11 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts 

THIS PAGE INTENTIONALLY LEFT BLANK 

9-12

MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

Chapter 10: Small Entity Impact


Assessment


INTRODUCTION 

The Regulatory Flexibility Act (RFA), as amended by the 

Small Business Regulatory Enforcement Fairness Act 

(SBREFA), requires EPA to consider the economic impacts 

a rule will have on small entities. RFA/SBREFA requires 

an agency to prepare a Regulatory Flexibility Analysis 

for any rule subject to notice and comment rulemaking 

requirements, unless the Agency certifies that the rule will not 

have a significant economic impact on a substantial number 

of small entities (Small Business Regulation Enforcement 

Fairness Act of 1996, P.L. 104-121, Section 243). 

The economic analysis prepared for the 1995 MP&M Phase I 

proposal indicated that large numbers of small facilities could 

CHAPTER CONTENTS 

10.1 ................. .... 10-2 

10.2 thodology ................. ............ 10-4 

10.3 ................. ................. 10-4 

10.3.1 all Entities ....... 10-4 

10.3.2 ts on Facilities Owned by Small 

Entities ................. ............. 10-5 

10.3.3 Impacts on Small Firms ............... 10-6 

10.4 tion of Small Entity Impacts in 

Developing of the Final Rule ............. 10-7 

Glossary ................. ................. ... 10-8 

Acronyms ................. ................. .. 10-9 

References ................. ................. 10-10 

Defining Small Entities 

Me

Results 

Number of Affected Sm

Impac

Considera

be impacted by the rule and that a significant number of publically-owned treatment works (POTWs) would also be affected 

by the rule. 

EPA addressed this issue by crafting the final rule to exclude as many small facilities as possible while still covering as much 

of the pollutant discharge as possible. With this in mind, EPA sought, from the beginning, to design a combined phase 

regulation that would not unreasonably burden small entities. 

To ensure that all small entities were considered in developing the MP&M regulation, EPA developed, administered, and 

analyzed questionnaires for all entities that could potentially be affected, including: privately- and government-owned 

facilities that would have to comply with the regulation, and POTWs that receive MP&M discharges. The Agency balanced 

several factors when defining the final rule, including: 

� the predominance of small entities in the MP&M industry, 

� the pounds of pollutants discharged by large and small facilities, 

� the toxicity of the pollutants discharged by large and small facilities, 

� the need for additional reduction in effluent discharges from the MP&M industry, 

� the need to achieve these reductions without imposing unreasonable burdens on small entities, and 

� the need to minimize burden on POTWs. 

Given the large number of small entities that could be affected by the final rule, EPA undertook detailed analyses of potential 

small entity impacts and carefully considered the findings from this analysis in defining the final rule. From these assessments 

and based on the coverage and requirements of the final rule, EPA concluded that the final rule will not have a significant 

economic impact on a substantial number of small entities. EPA has therefore not prepared a Regulatory Flexibility Analysis. 

The following sections of this chapter describe the methodology and results of EPA’s small entity impact assessment, and 

discuss EPA's consideration of small entity impacts in designing the rule. 

10-1 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

10.1 DEFINING SMALL ENTITIES 

EPA identified small entities using Small Business Administration (SBA) size threshold guidelines.1 These thresholds define 

the minimum firm-level employment or revenue size, by industry (four-digit SIC codes), below which a business qualifies as a 

small business under SBA guidelines. The SBA guidelines also set a threshold for small public sector entities. A small 

government is one that serves a population of 50,000 or less. MP&M facilities were determined to be owned by a small 

entity if the parent firm or government fell below the SBA threshold. 

The SBA guidelines for businesses use either employment or revenue to measure size, depending on the specific four-digit 

SIC industry. Manufacturing industries generally have employment size thresholds, while non-manufacturing industries 

typically have revenue size thresholds. EPA used employment-based thresholds for the manufacturing portion of each 

MP&M sector, and separate non-manufacturing thresholds for sectors that include non-manufacturing activities (e.g., 

maintenance and repair). 

EPA selected the SBA threshold occurring most frequently among each sector’s four-digit SIC codes as the sector threshold.2 

Table 10.1 presents the resulting employment size thresholds for manufacturers. 

Table 10.1: 

Thresholds for Manufacturers 

MP&M Sector Employees 

Aerospace 1,000 

Aircraft 1,000 

Bus and Truck 500 

Electronic Equipment 750 

Hardware 500 

Household Equipment 500 

Instrument 500 

Job Shop 500 

Mobile Industrial Equipment 500 

Motor Vehicle 500 

Office Machine 1,000 

Ordnance 1,000 

Other Metal Products 500 

Precious and Non.Precious Metals 500 

Printed Circuit Board 500 

Railroad 1,000 

Ship and Boat 1,000 

Stationary Industrial Equipment 500 

Steel Forming & Finishing 1,000 

Small Business MP&M Sector 

Source: SBA and U.S. EPA analysis. 

1 The SBA website provides the most recent size thresholds at http://www.sba.gov/regulations/siccodes. 

2 The SBA thresholds for four-digit SICs were not used directly because the Phase II §308 survey reports revenues by MP&M sector 

but does not report facility SIC codes. 

10-2 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

Table 10.2 presents the employment size thresholds for non-manufacturers, which are based on revenue except for the 

railroad sector. Some sectors do not have non-manufacturing industries and do not appear in this table. 

Table 10.2: 

Thresholds for Non-Manufacturers 

MP&M Sector Revenue 

Aircraft $5,000,000 

Bus and Truck $5,000,000 

Household Equipment $5,000,000 

Instrument $5,000,000 

Motor Vehiclea $5,000,000 

Office Machine $18,000,000 

Other Metal Products $5,000,000 

Precious and Non-Precious Metals $5,000,000 

Railroad 1,500b 

Ship and Boatc $5,000,000 

Stationary Industrial Equipment $5,000,000 

a Also has a threshold of 100 employees. 

b Employees. 

c Also has a threshold of 500 employees. 

Source: lysis. 

EPA classified facilities as manufacturing or non-manufacturing and selected an MP&M sector threshold based on the sector 

from which they rec eived the mo st revenue, as repo rted in the §30 8 surveys.3 EPA then compared the firm-level employment 

or revenue for the firm owning each facility to the appropriate manufacturing or non-manufacturing threshold for that sector. 

The Phase II surv ey asked ea ch respo ndent to pro vide firm-level emp loyment and revenue d ata. hase I survey also 

asked for firm-lev el reve nue b ut not for firm em ploym ent. his om ission did not matter in the case of single facility 

businesses, where the facility’s reported employment is the firm-level employment. multiple-facility firms in the Phase I 

survey, EPA estimated firm-level employment by assuming that the number of employees per revenue dollar for the firm was 

the same as the em ployees pe r dollar at the facility. 

(10.1) 

where: 

Efirm = firm-level emp loyme nt, 

Efacility = facility-level em ploym ent, 

Rfirm = firm-level revenue, and 

Rfacility = facility-level revenue. 

EP A ide ntified facilities ope rated by go vernm ents that se rve a p opu lation o f 50,0 00 o r fewer a s being ope rated by sma ll 

government entities. survey responses provided population data in most cases, which EPA supplemented 

using the Bureau o f the Ce nsus online 1990 Po pulatio n Ce nsus d ataba se (B ureau of the C ensus.) 

Small Business MP&M Sector 

SBA and U.S. EPA ana

The P 

T

For

Thus, 

The §308 municipal

3
 The §308 MP&M surveys did not collect firm-level revenues by sector and therefore cannot be used to assign a unique sector to 

each firm. The assignment of a threshold was therefore based on the facility-level revenues by sector. 

10-3

MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

10.2 METHODOLOGY 

EPA used several impact measures for its small entity impact analysis. First, EPA reviewed the results of the facility impact 

analyses described in Chapter 5 according to business size to determine whether facilities owned by small entities are 

disproportionately subject to moderate impacts at the facility level. Second, EPA calculated the ratio of annualized 

compliance costs to facility revenues and examined the distribution of this ratio for facilities owned by small versus large 

firms. 

The analysis excluded facilities that the facility impact analysis identifies as baseline failures (see Chapter 5). 

10.3 RESULTS 

10.3.1 Number of Affected Small Entities 

There are an estimated 40,265 MP&M facilities nationwide (excluding baseline closures). A large number of these facilities 

are owned by small entities, based on SBA thresholds. Table 10.3 shows the total number of facilities operating in the 

baseline and the number owned by small entities. Overall, 73 percent of all MP&M facilities are owned by small entities. 

Table 10.3: Number and Percent of MP&M Facilities Owned by Small Entities 

Type of Facility 

Number of Facilities of 

all Sizes Operating 

in the Baseline 

Number of Facilities 

Owned by Small 

Entities 

Percent of Facilities 

Owned by Small 

Entities 

Owned by small business 36,480 27,418 75% 

Owned by small government 3,785 1,962 52% 

Total owned by small 

entitiesa 40,265 29,380 73% 

a Excludes baseline closures. 

Source: U.S. EPA analysis. 

EPA has limited the scope of the final rule to MP&M facilities performing oily operations. Table 10.4 shows that only a 

small percentage (five percent) of small entities are potentially subject to regulation. The final rule excludes a large 

percentage (95 percent) of small entity-owned MP& M facilities from regulation. 

Table 10.4: 

Type of Facility 

Number of Facilities 

Operating in the Baseline 

Number of Facilities Not 

Subject to the Final Rule 

Percentage of Facilities Not 

Subject to the Final Rule 

Owned by small business 27,418 26,368 96% 

Owned by small government 1,962 1,682 86% 

Total owned by small entities 29,380 28,050 95% 

Percent of Facilities Owned by Small Entities Excluded under the Final Option 

Source: U.S. EPA analysis. 

10-4 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

10.3.2 Impacts on Facilities Owned by Small Entities 

The facility impact analysis findings provide the first measure EPA used to assess impacts on facilities owned by small 

entities. No facilities, small or large, are projected to close or experience moderate impacts as a result of the final rule. A 

second approach to assessing small entity impacts – based on a comparison of compliance costs to post-compliance 

revenues – indicates that no facilities will incur costs exceeding 1 percent of revenues, and only 1,019 facilities owned by 

small private businesses will incur any costs at all. This corresponds to 3.7 percent of the facilities owned by small private 

businesses that operate in the baseline. 

Table 10.5 summarizes the results of the facility impact analysis for facilities owned by small entities for the final rule and the 

options considered by EPA. 

Table 10.5: Closures and Moderate Impacts for Facilities Owned by Small Entities 

Final Option Option II Option III Option IV 

Number of facilities operating in the baseline 29,380 29,380 29,380 29,380 

Number of facilities excluded from option 28,050 23,893 27,118 26,907 

Percent excluded 95.5% 81.3% 92.3% 91.6% 

Number of facilities with closures 0 813 109 109 

Facilities with closures as a percent of facilities operating in the 

baseline 

0.0% 2.8% 0.4% 0.4% 

Facilities with closures as a percent of regulated facilities 0.0% 14.8% 4.8% 4.4% 

Number of facilities with moderate impacts 0 0 37 37 

Facilities with moderate impacts as a percent of facilities operating 

in the baseline 

0.0% 0.0% 0.1% 0.1% 

Facilities with moderate impacts as a percent of regulated facilities 0.0% 0.0% 1.6% 1.5% 

Source: U.S. EPA analysis. 

In summary, no facilities owned by small entities that operate in the baseline are expected to close or experience moderate 

impacts under the final rule. 

Table 10.6 shows the results of the second approach to assessing small entity impacts, based on a comparison of compliance 

costs with facility revenues. EPA conducted this analysis only for MP&M facilities owned by private entities (i.e., businesses, 

but not governments), because of the low level of impacts on all sizes of governments. 

10-5 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

Table 10.6: After-Tax Annual Compliance Costs as a Percent of Annual revenues under the Final Option for 

Facilities Owned by Private Small Businessesa 

Discharge 

Status 

Number of Facilities 

Owned by Small 

Private Businesses 

Operating in the 

Baseline 

Number and 

Percent of Facilities 

Owned by Small 

Businesses that are 

Not Regulated 

Number and Percent of Facilities Owned by Small Businesses with 

After-Tax Annual Compliance Costs/Annual Revenues Equal to: 

No Cost More than 0% and 

less than 1% Over 1% 

Number % Number % Number % Number % 

Direct 1,168 119 13.9% 31 2.5% 1,019 83.6% 0 0.0% 

Indirect 26,253 26,253 100.0% 0 0.0% 0 0.0% 0 0.0% 

Totalb 27,418 26,368 96.2% 31 0.1% 1,019 3.7% 0 0.0% 

a Includes only facilities that remain open in the baseline. b

 The sum of the number of direct and indirect dischargers does not add up to the total because some facilities are both indirect and 

direct dischargers. 

Source: U.S. EPA analysis. 

Of the facilities owned by small entities that operate in the baseline, 96.2 percent are not regulated under the final rule. 

Another 0.1 percent are regulated but do not incur costs. The remaining 3.7 percent incur compliance costs but none incur 

after-tax annualized costs exceeding 1 percent of annual revenue. These results are consistent with the finding that no 

facilities owned by small business will close or experience moderate financial impacts. 

10.3.3 Impacts on Small Firms 

EPA also performed a firm-level analysis in which it compared compliance costs with revenue at the firm level as a measure 

of compliance cost burden. EPA applied this analysis only for facilities owned by private entities (i.e., businesses, but not 

governments). Table 10.7 shows the results of this comparison. The Agency was not able to estimate national numbers of 

firms that own MP&M facilities precisely, because the sample weights based on the survey design represent numbers of 

facilities rather than firms. Most of the facilities owned by small firms (25,297 of 27,578, or 92 percent) are single-facility 

firms, however. These single-facility firms can be analyzed using sample weights. In addition, 85 small multi-facility firms 

own at least one sample facility. These firms are included in the analysis but with a sample weight of one, since it is not 

known how many sample firms these 85 small firms represent. The results shown in Table 10.7 therefore represent a total of 

25,382 small MP&M firms (25,297 + 85). 

Table 10.7: Firm Level Before-Tax Annual Compliance Costs as a Percent of 

Annual Revenues for Private Small Businesses 

Number of 

Small Firms in 

the Analysisa 

Number and Percent with Before-Tax Annual Compliance Costs/Annual 

Revenues Equal to: 

0% (no costs) >0% and <1% Over 1% 

Number % Number % Number % 

25,382 24,363 95.99% 1,019 4.01% 0 0% 

a Firms whose only MP&M facilities close in the baseline are excluded. 

Source: U.S. EPA analysis. 

The vast majority, 96 percent, of the small businesses in the analysis incur no costs due to the rule. The remaining 4 percent, 

equal to 1,019 firms, incur before-tax compliance costs of less than 1% of their after-tax revenues. Of these 1,019 small 

firms, none were reported in the facility impact analysis to experience moderate impacts due to the final rule. 

10-6 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

10.4 CONSIDERATION OF SMALL ENTITY IMPACTS IN DEVELOPING THE FINAL RULE 

EPA gave special consideration to impacts on small entities in defining the final regulation. In particular, EPA attempted to 

minimize impacts on small entities while at the same time meeting Clean Water Act objectives of reducing pollutant 

discharges to the nation’s waterways. The final rule minimizes impacts on small entities primarily by excluding all indirect 

dischargers and direct dischargers in all subcategories except Oily Wastes. 

Table 10.8 shows the number and percentage of facilities owned by small versus large entities that are projected to close or 

experience moderate impacts under the final and alternative regulatory options analyzed by EPA in developing the final 

regulation. 

Table 10.8: Percent of Facilities Estimated to Close or Experience Moderate Impacts by Owning Entity 

Size Class and by Regulatory Option 

Regulatory Option 

and Ty pe of Facility 

Number of Facilities 

Subject to 

Regulation 

Projected to 

Close 

Percent 

Closing 

Experiencing 

Moderate Impacts 

Percent with 

Moderate Impacts 

Final Regulatory Option 

Owned by Small Entities 1,330 0 0.0% 0 0.0% 

Owned by Large Entities 1,052 0 0.0% 0 0.0% 

Total 2,382 0 0.0% 0 0.0% 

Option II 

Owned by Small Entities 5,487 813 14.8% 0 0.0% 

Owned by Large Entities 2,863 0 0.0% 0 0.0% 

Total 8,350 813 9.7% 0 0.0% 

Option III 

Owned by Small Entities 2,262 109 4.8% 37 1.6% 

Owned by Large Entities 1,182 0 0.0% 0 0.0% 

Total 3,444 109 0.5% 37 1.1% 

Option IV 

Owned by Small Entities 2,473 109 4.4% 37 1.5% 

Owned by Large Entities 1,453 0 0.0% 12 0.8% 

Total 3,926 109 2.8% 49 1.2% 

Source: U.S. EPA analysis. 

As reported in the table, the final rule avoids entirely the more material impacts on small entities that likely would have 

occurred under the alternative regulatory options. 

In addition to avoiding impacts in the regulated community, the final rule, by excluding indirect discharging facilities from 

revised limits, also eliminated the potential additional burden to POTWs, including small POTW s, from issuance of new and 

revised permits. Chapter 11 and Appendix F discuss POTW administrative activities and costs under the four regulatory 

options. 

10-7 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

GLOSSARY 

Regulatory Flexibility Analysis: an evaluation of the impact of a rule and alternative regulatory options on small entities. 

small entity: a business, government or non-profit organization defined as small for EPA’s RFA/SBREFA evaluation. 

small business: a business with employment or revenue below the threshold specified by the Small Business 

Administration for each 4-digit SIC. 

small government: a government that serves a population of 50,000 or less, as defined by the Small Business 

Administration. 

10-8 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

ACRONYMS 

POTW: Publicly-owned treatment works


RFA: Regulatory Flexibility Act


SBA: Small Business Administration


SBREFA: Small Business Regulatory Enforcement Fairness Act


10-9

MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment 

REFERENCES 

U.S. Department of Commerce, Bureau of the Census. Statistics of U.S. Businesses. 

U.S. Small Business Administration. http://www.sba.gov/regulations/siccodes. 

10-10

MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

Chapter 11: Social Costs


INTRODUCTION 

This chapter presents EPA’s estimates of the regulation’s 

costs to society. Previous chapters described the economic 

impacts of the final rule in terms of facility closures and 

moderate financial impacts, employment losses, community 

impacts, international trade effects, financial impacts on firms 

owning MP&M facilities, and impacts on small entities. The 

economic impact analyses were based on the estimated costs 

to MP&M facilities of complying with the regulation. These 

costs of labor, equipment, material, and other economic 

resources needed for regulatory compliance are also the major 

component of the cost to society of the regulation. Other 

components of social costs include costs to governments 

administering the regulation, and the social costs associated 

with unemployment resulting from facility closures. 

CHAPTER CONTENTS 

11.1 s of Social Costs ............... 11-1 

11.2 ............. 11-2 

11.3 dministration Costs ............... 11-4 

11.4 ts of Unemployment ............. 11-5 

11.4.1 ost of Worker Dislocation .... 11-5 

11.4.2 Administering Unemployment 

Benefits Programs ................. ... 11-6 

11.4.3 ost of Unemployment ......... 11-6 

11.5 ial Costs ................. ...... 11-7 

Glossary ................. ................. . 11-8 

References ................. ................ 11-9 

Component

Resource Costs of Compliance 

POTW A

Social Cos

Social C

Cost of

Total C

Total Soc

Section 11.1 provides an overview of the three components of social cost analyzed for this regulation: the cost of society’s 

economic resources used to comply with the rule; the cost to governments of administering the rule; and the social costs of 

unemployment resulting from the rule. The next three sections discuss each of these three components of social cost in more 

detail. The last section, Section 11.5, summarizes the estimated total social costs. 

11.1 COMPONENTS OF SOCIAL COSTS 

The social costs of regulatory actions are the opportunity costs to society of employing scarce resources in pollution 

prevention and pollution control activities. The social costs of regulation include both monetary and non-monetary outlays 

made by society. Monetary outlays include the resource costs of compliance, government administrative costs, and other 

adjustment costs, such as the cost of relocating displaced workers. Non-monetary outlays, some of which can be assigned 

monetary values, include losses in consumers’ and producers’ surplus in affected product markets, the adverse effects of 

involuntary unemployment, possible loss of time (e.g., delays in investment decisions), and possible adverse impacts on the 

rate of innovation. 

To assess the MP&M regulation’s social costs, EPA relied first on the estimated costs to MP&M facilities for the labor, 

equipment, material, and other economic resources needed to comply with the regulation. The compliance costs used to 

estimate total social costs differ from those used to assess facility- and firm-level economic impacts in their consideration of 

taxes and revenue effects. In the facility and firm impact analysis, compliance costs are measured as they affect the financial 

performance of regulated facilities and firms. The analyses therefore explicitly consider the tax deductibility of compliance 

expenditures.1 In the analysis of costs to society, however, these compliance costs are considered on a before-tax basis. In 

general, because tax deductibility reduces the burden of compliance expenditures to private firms, the estimated compliance 

costs are greater from the perspective of society than from the perspective of private industry. In addition, the analysis of the 

regulation’s impact on regulated facilities and firms accounted for potential recovery of compliance costs through output price 

increases. The assessment of social cost ignores these potential cost offsets because, like taxes, they represent only a transfer 

of compliance costs from the complying entity and not a true reduction in compliance cost. 

Social costs also include lost producers’ and consumers’ surplus that result from reduction in the quantity of goods and 

services produced. Lost producers’ surplus is measured as the difference between revenues earned and the cost of 

production for the lost production. Lost consumers’ surplus is the difference between the price paid by consumers for the 

lost production and the maximum amount they would have been willing to pay for those goods and services. 

1 Costs incurred by government facilities are not adjusted for taxes, since these facilities are not subject to income taxes. 

11-1 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

Accurate calculation of lost producers’ and consumers’ surplus requires knowledge of market supply and demand 

characteristics for each affected industry. EPA was not able to conduct an industry-specific partial equilibrium analysis of 

changes in market prices and output, both because of the very large number of markets involved and because it was not 

possible to link compliance costs to specific products at multi-sector facilities. 

EPA’s assessment of social cost includes two additional cost elements: the cost to governments of administering permitting 

and compliance monitoring activities under the regulation, and the social costs associated with unemployment that may result 

from facility closures. The unemployment-related costs include the cost of administering unemployment programs for 

workers who are projected to lose employment (but not the cost of unemployment benefits, which are a transfer payment 

within society); and an estimate of the amount that workers would be willing to pay to avoid involuntary unemployment. 

11.2 RESOURCE COSTS OF COMPLIANCE 

This section reviews the resource costs of compliance for the final rule and the costs for the alternative regulatory options


considered by EPA. The resource costs of compliance are the value of society’s productive resources – including labor,


equipment, and materials – expended to achieve the reductions in effluent discharges required by the regulation. The social


costs of these resources are higher than the financial burden borne by facilities because facilities are able to deduct the costs


from their taxable income and may be able to recover some of the costs through price increases to customers. The costs to


society, however, are the full value of the resources used, whether paid for by the regulated facilities, by taxpayers in the form


of lost tax revenues, or by customers through increased prices. EPA included no costs for facilities assessed as baseline


closures.


EPA estimated after-tax annualized compliance costs of $11.9 million for the final regulation (see Chapter 5: Facility Impact


Analysis, Table 5.6). The estimated social value of these compliance costs, however, is $13.8 million, as shown in


Table 11-1. This amount represents the value to society of the resources that would be used to comply with the rule.


For the alternative regulatory options, EPA’s estimates included compliance costs both for facilities estimated to close


because of the rule and for facilities estimated to continue operating under the regulation. This approach results in an upper-


bound estimate of the social costs of compliance, since the lost value incurred by closing facilities is presumably less than the
 2 estimated cost of compliance. 

Under the Proposed/NODA Option, annual compliance costs amount to $1,111.4 million for indirect dischargers and $508.9 

million for direct dischargers (2001$). The total annualized compliance costs are $1,620.3 million, or approximately 117 

times the compliance costs under the final rule. This cost increase results from including additional subcategories under the 

Proposed/NODA Option. General Metals indirect dischargers, which are excluded from the final regulation, account for 

approximately 40 percent of the total compliance costs under the Proposed/NO DA O ption. 

Under the Directs + 413 to 433 Upgrade Option, annual compliance costs amount to $83.0 million for indirect dischargers 

and $13.8 million for direct dischargers (2001$). The total annualized compliance costs are $96.8 million, or approximately 

7 times the final rule’s compliance costs. This cost increase results from requiring indirect dischargers that currently comply 

with the standards of 413 to upgrade to 433 standards. General Metals facilities, which are excluded from the final regulation, 

account for approximately 44 percent of the total compliance costs under this option. 

Under the Directs + All to 433 Upgrade Option, annual compliance costs amount to $124.4 million for indirect dischargers 

and $13.8 million for direct dischargers (2001$). The total annualized compliance costs are $138.2 million, or approximately 

10 times the compliance costs under the final rule. This cost increase results from requiring general metals facilities that 

currently comply local limit standards to upgrade to 433 standards. General Metals facilities, which are excluded from the 

final regulation, account for approximately 61 percent of the total compliance costs under this option. 

2 Including costs for regulatory closures yields an estimate of social costs assuming that every facility continued to operate postregulation. Calculating costs as if all facilities continue operating will overstate social costs if some facilities find it more economical to 

close than comply with the regulation. 

11-2 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

Table 11.1: Resource Value of Compliance Costs 

(millions, 2001$) 

Subcategory Indirect Direct Total 

Option I: Selected Option (Directs Only) 

General Metals $0.0 $0.0 $0.0 

MF Job Shop $0.0 $0.0 $0.0 

Non Chromium Anodizing $0.0 $0.0 $0.0 

Oily Wastes $0.0 $13.8 $13.8 

Printed Wiring Boards $0.0 $0.0 $0.0 

Railroad Rebuilders $0.0 $0.0 $0.0 

Shipbuilding Dry Docks $0.0 $0.0 $0.0 

Total $0.0 $13.8 $13.8 

Option II: Proposed/NODA Option 

General Metals $652.9 $396.1 $1,049.0 

MF Job Shop $185.2 $4.6 $189.8 

Non Chromium Anodizing $0 $38.0 $38.0 

Oily Wastes $92.8 $35.9 $128.7 

Printed Wiring Boards $157.9 $0.3 $158.2 

Railroad Rebuilders $0.0 $0.7 $0.7 

Shipbuilding Dry Docks $0.0 $3.2 $3.2 

Steel Forming & Finishing $22.6 $30.1 $52.7 

Total $1,111.4 $508.9 $1,620.3 

Option III: Directs + 413 to 433 Upgrade Option 

General Metals $42.4 $0.0 $42.4 

MF Job Shop $17.1 $0.0 $17.1 

Non Chromium Anodizing $0.0 $0.0 $0.0 

Oily Wastes $0.0 $13.8 $13.8 

Printed Wiring Boards $23.5 $0.0 $23.5 

Railroad Rebuilders $0.0 $0.0 $0.0 

Shipbuilding Dry Docks $0.0 $0.0 $0.0 

Total $83.0 $13.8 $96.8 

Option IV: Directs + All to 433 Upgrade Option 

General Metals $83.8 $0.0 $83.8 

MF Job Shop $17.1 $0.0 $17.1 

Non Chromium Anodizing $0.0 $0.0 $0.0 

Oily Wastes $0.0 $13.8 $13.8 

Printed Wiring Boards $23.5 $0.0 $23.5 

Railroad Rebuilders $0.0 $0.0 $0.0 

Shipbuilding Dry Docks $0.0 $0.0 $0.0 

Total $124.4 $13.8 $138.2 

Source: U.S. EPA analysis. 

11-3

MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

11.3 POTW ADMINISTRATION COSTS 

This section discusses the POTW administrative costs of the final rule and the costs of the alternatives considered by EPA. 

EPA estimates that the final rule will not increase POTW administrative costs. EPA expects no increase in permitting costs 

for facilities that already hold a permit in the baseline. However, governments will incur additional permitting costs from (1) 

permitting of unpermitted facilities (under the NODA/Proposal option only) and (2) acceleration of repermitting for some 

indirect dischargers that currently hold permits. The alternative regulatory options may also cause some administrative costs 

to decrease. For example, control authorities will no longer have to repermit facilities that are estimated to close as a result of 

the MP&M rule. 

Table 11.2 shows the number of facilities requiring a new permit under the four options considered for the final rule. Only 

the NODA/Proposal option would require POTWs to issue new concentration-based permits for the first time. None of the 

options considered would require a new mass-based permit or a conversion from a concentration-based to a mass-based 

permit. The table also shows the number of facilities that will require early repermitting (within three years rather than within 

five years), the number of estimated regulatory closures, and the total number of facilities that are expected to require permits 

under the different regulatory options. 

Table 11.2: Permitting Requirements for Regulatory Alternatives 

(number of indirect discharging facilities) 

Permitting required: Option I: 

Selected Option 

Option II: 

NODA/Proposal 

Option 

Option III: 

Directs + 413 to 

433 Upgrade 

Option IV: 

Directs + All to 

433 Upgrade 

New concentration-based permit n/a 103 0 0 

New mass-based permita n/a 0 0 0 

Convert from existing concentration-based 

to mass-baseda 

n/a 0 0 0 

Repermit within 3 rather than 5 years n/a 1,434 382 566 

Regulatory closures (no longer requiring 

permits)b 

n/a 722 120 120 

Number of facilities operating 

post-regulation requiring a permit 

n/a 3,687 954 1,414 

a EPA does not require mass-based permits under any of the option considered for the final rule. 

b Some facilities with existing permits will no longer require permitting due to regulatory closures. 

Source: U.S. EPA analysis. 

Table 11.3 below presents the estimated permitting costs to governments of administering the final rule and alternative 

options. Chapter 7: Government and Community Impact Analysis describes the methodology used to estimate these 

administrative costs. 

Because the final regulation excludes from coverage all indirect dischargers, EPA estimates that the final rule will not 

increase POTW administrative costs. Each of the three alternative regulatory options considered would result in reduced 

POT W regulatory costs. These cost savings result from regulatory closures (i.e., facilities that currently hold a permit and 

would have required repermitting in the baseline, but that will no longer require repermitting under the regulatory options). 

The cost savings from regulatory closures outweigh the additional costs for issuing new permits (under the NODA/Proposal 

option only) and repermitting on an accelerated, three-year schedule. Estimated annualized cost savings to POTWs for the 

three alternative regulatory options range between $0.05 and $1.0 million under the NODA/Proposal option, and between 

$0.03 and $0.2 million under the Directs + 413 to 433 Upgrade option and the Directs + 413+50%LL Upgrade option (all 

costs in ($2001). 

11-4 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

Table 11.3: Annualized Government Administrative Costs by Regulatory Option 

($2001) 

Option Low Medium High 

Option I: Selected Option (Directs Only) n/a n/a n/a 

Option II: Proposed/NODA Option ($46,000) ($198,000) ($1,027,000) 

Option III: Directs + 413 to 433 Upgrade Option ($26,000) ($56,000) ($218,000) 

Option IV: Directs + All to 433 Upgrade Option ($26,000) ($55,000) ($213,000) 

Source: U.S. EPA analysis. 

11.4 SOCIAL COSTS OF UNEMPLOYMENT 

This section discusses the social costs of unemployment associated with the final rule and the alternatives EPA considered. 

The loss of jobs from facility closures would represent a social cost of the regulation. However, from its facility impact 

analysis, EPA estimates that no facilities will close as a result of the regulation. EPA did not recognize possible savings in 

unemployment-related costs from jobs created by the rule as a negative cost (benefit) of the regulation. Accordingly, EPA 

estimates a zero cost of unemployment for the final rule. 

Chapter 6: Employment Effects discusses the effects of the alternative regulatory options on employment, including the jobs 

potentially lost due to facility closures and the jobs potentially created by expenditures to comply. This section estimates the 

social cost of the estimated changes in employment. EPA considered two components of the social cost of unemployment: 

� The cost of worker dislocation (exclusive of cash benefits) to unemployed individuals, as measured by their 

willingness to pay to avoid unemployment; and 

� The additional cost to governments to administer unemployment benefits programs. 

11.4.1 Social Cost of Worker Dislocation 

EPA calculated the cost of worker dislocation based on an estimate of the value that workers would pay to avoid involuntary 

job losses. The amount that workers would pay to avoid a job loss was derived from hedonic studies of the compensation 

premium required by workers to accept jobs with a higher probability of unemployment. This framework has been used in the 

past to impute a trade-off between wages and job security (Topel, 1984; Adams, 1985; Anderson and Chandran, 1987). 

Specifically, this estimate approximates a one-time willingness-to-pay to avoid an involuntary episode of unemployment and 

reflects all monetary and non-monetary impacts of involuntary unemployment incurred by the worker. It does not include any 

offsets to the cost of unemployment, such as unemployment compensation or the value of increased leisure time. 

Studies by Topel (1984) and Adams (1985) suggest that the compensation premium for accepting a one percent increase in 

the annual probability of unemployment is in the range of 2.5 percent to 3.3 percent of the base compensation value. To 

illustrate this finding, assume that a worker is presented with a choice between two employment opportunities: one with 

compensation of $30,000 per year and an annual unemployment probability of zero, and a second otherwise equivalent 

opportunity but with an annual unemployment probability of one percent. For the worker to accept the second opportunity, his 

or her compensation must be at least 2.5 to 3.3 percent greater than the $30,000 offered for the first opportunity, or at least 

$30,750 to $30,990 (depending on the percentage premium used). In this case, the dollar premium required to accept the 

additional one percent annual probability of unemployment is $750 to $990. 

For analyzing the unemployment-related costs of the MP&M regulation, the hypothetical choice is assumed to be between an 

employment opportunity with a zero percent annual probability of unemployment and a second opportunity with a 100 

percent annual probability of unemployment. In this case, the one-time premium for accepting the employment opportunity 

with the 100 percent probability of employment is assumed to be 250 to 330 percent of the compensation for the otherwise 

11-5 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

comparable employment opportunity with the assumed zero probability of employment.3 To estimate the premium for an 

increase in the probability of unemployment requires an estimate of the average compensation to workers in the MP&M 

industry. EPA calculated an average annual compensation for MP&M industry production workers of $38,309 (2001$)4 

Accordingly, the annual compensation premium for a one percentage point increase in the annual probability of 

unemployment would be $958 to $1,264 and the cost of a 100 percent probability event would be $95,772 to $126,420 

(2001$). This calculation assumes that the cost of a certainty unemployment event is directly proportional to the increase in 

probability from the low probability event (i.e., one percent) on which the calculation is based. 

Chapter 6: Employment Effects presents EPA’s estimate that as many as 32,729 jobs might be lost due to facility closures 

under the Proposed/NODA Option. Multiplying these 32,729 job losses by the estimated range of willingness-to-pay values 

for avoiding unemployment results in a total cost of unemployment for the Proposed/NODA Option of $3.1 billion to $4.1 

billion (2001$). EPA annualized these values over a 15-year period at a 7 percent rate, yielding an annualized cost of $344 to 

$454 million. These values are the annualized amounts over a 15-year period that workers would be willing to pay to avoid 

the job losses projected to result from compliance with the Proposed/NODA Option. 

EPA estimates that as many as 7,874 jobs might be lost due to facility closures under the Directs + 413 to 433 Upgrade 

Option and the Directs + All to 433 Upgrade Option. Multiplying these 7,874 job losses by the estimated range of 

willingness-to-pay values for avoiding unemployment results in a total cost of unemployment for both of the 433 Upgrade 

Options of $754 million to $995 million (2001$). EPA annualized these values over a 15-year period at a 7 percent rate, 

yielding an annualized cost of $83 to $109 million. 

11.4.2 Cost of Administering Unemployment Benefits Programs 

Unemployment as the result of regulation also imposes costs on society through the additional administrative burdens placed 

on the unemployment system. The cost of unemployment benefits per se is not a social cost but instead a transfer payment 

within society from taxpayers to the unemployed. Administrative costs include the cost of processing unemployment claims, 

retraining workers, and placing workers in new jobs. Data obtained from the Interstate Conference of Employment Security 

Agencies indicated that the cost of administering an initial unemployment claim over the period 1991-1993 averaged $93.25 

(1991$-1993$). These costs included total Federal and State funding for administering unemployment benefit programs but 

not the cost of the benefits themselves. Inflating this estimate to 2001 dollars using the BLS Employment Cost Index for and 5 Local Government workers yields a value of $122 per claim. Based on this estimate, EPA assumed that the cost of 

administering unemployment programs would amount to approximately $122 per job loss. Multiplying this figure by the 

estimated loss of 32,729 jobs under the Proposed/NODA Option yields an additional $4.0 million in social costs. EPA 

annualized this value over the 15-year analysis period at a 7 percent rate to yield an annual cost of approximately $438,027 

(2001$). Multiplying the per job loss estimate of the cost of administering unemployment by the estimated loss of 7,874 jobs 

under the 433 Upgrade Options yields almost an additional $960,000 in social costs. EPA annualized these values over the 

15-year analysis period at a 7 percent rate to yield an annual cost of $105,000 under the 433 Upgrade Options. 

11.4.3 Total Cost of Unemployment 

As mentioned above, EPA did not estimate a cost of unemployment for the final rule because no job loss is expected. As 

shown in Table 11.4 below, the 32,729 estimated job losses at facility closures under the Proposed/NOD A Option have an 

estimated social cost of $345 million to $455 million (2001$). The 7,874 estimated job losses at facility closures under the 

433 Upgrade Options have an estimated social cost of $83 million to $109 million (2001$). 

3 This analysis has a considerable artificiality in that a worker would not realistically be presented with this choice. The artificiality of 

the choice in turn underscores the very strong assumption in the analysis. That is, that the cost of an unemployment event can be estimated 

by linearly extrapolating the premium estimated for small percentage differences in the probability of unemployment to a circumstance in 

which the probability of unemployment is 100 percent. An investigation of literature on unemployment failed to find an alternative method 

for estimating unemployment costs. This analytic issue warrants further research. 

4 Calculated the total payroll ($407.7 billion) / total employment (12.2 million) in MP&M SIC codes based on data obtained from the 

1997 Economic Censuses which is $33,508. Inflated this estimate to 2001 dollars using the BLS Seasonally Adjusted Employment Cost 

Index (ECI) for Private Industry Manufacturing - 1997 (4th Qtr): 135.4, 2001 (4th Qtr): 154.8. 

5 BLS, 2000. Table 1a: Employment Cost Index (Compensation), State and Local Government: 1992 (December): 118.5, 1999 

(December): 144.2. 

11-6 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

Table 11.4: Total Annual Social Costs of Unemployment 

(millions, 2001$) 

Social Cost of Unemployment Categories 

Option I: 

Selected Option 

(Directs Only) 

Option II: 

Proposed/NODA 

Option 

Option III: 

Directs + 413 to 

433 Upgrade 

Option IV: 

Directs + All to 

433 Upgrade 

Employment Loss in Closing Facilities n/a 32,729 7,874 7,874 

Annualized Worker Dislocation Cost 

Low Unit Cost 

(based on 2.5 percent premium) n/a $344.16 $82.80 $82.80 

High Unit Cost 

(based on 3.3 percent premium) n/a $454.29 $109.30 $109.30 

Annualized Unemployment Administration 

Cost (million 2001$) n/a $0.44 $0.11 $0.11 

Sum, Worker Dislocation and Unemployment Administration Costs (based on employment loss in closing facilities) 

Low Value n/a $344.60 $82.91 $82.91 

High Value n/a $454.73 $109.40 $109.40 

Source: U.S. EPA analysis. 

11.5 TOTAL SOCIAL COSTS 

Summing across the final rule’s social cost components results in a total social cost estimate of $13.8 million annually 

(2001$), as shown in Table 11.5. The total social costs of the Proposed/NODA Option range between $2.0 billion and $2.1 

billion. The total social costs for the Directs + 413 to 433 Upgrade Option range between $180 million and $206 million. 

The total social costs for the Directs + All to 433 Upgrade Option range between $221 million and $247 million. 

Table 11.5: Total Social Cost (millions, 2001$) 

Social Cost Categories 

Option I: 

Selected Option 

(Directs Only) 

Option II: 

Proposed/NODA 

Option 

Option III: 

Directs + 413 to 

433 Upgrade 

Option IV: 

Directs + All to 

433 Upgrade 

Low High Low High Low High 

Resource cost of compliance expenditures $13.8 $1,620.3 $96.8 $138.2 

Costs to POTWs of administering the rule $0.0 ($0.05) ($1.0) ($0.03) ($0.2) ($0.03) ($0.2) 

Social costs of unemployment $0.0 $344.6 $454.7 $82.9 $109.4 $82.9 $109.4 

Total Social Cost $13.8 $1,964.8 $2,074.0 $179.7 $206.0 $221.1 $247.4 

Source: U.S. EPA analysis. 

11-7 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

GLOSSARY 

consumers’ surplus: the value that consumers derive from goods and services above the price they have to pay to obtain 

the goods and services. 

opportunity cost: the lost value of alternative uses of resources (capital, labor and raw materials) used in pollution control 

activ ities. 

producers’ surplus: the difference between what producers’ earn on their output and the economic costs of producing that 

output, including a normal return on capital. 

social costs: the costs incurred by society as a whole as a result of the final rule; does not include costs that are simply 

transfers among parties but that do not represent a net cost overall. 

11-8 
MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

REFERENCES 

Adams, James D. 1985. Permanent differences in unemployment and permanent wage differentials. Quarterly Journal of 

Econometrics 100, no. 1, 29-56. 

Anderson, Donald W. and Ram V. Chandran. 1987. Market estimates of worker dislocation costs. Economics Letters 24, 

381-384. 

Interstate Conference of Employment Security Agencies. Employment Security Funding Survey 1991-1993. Presented in 

Christopher Van Atten, “Cost of Unemployment,” memorandum to the MP&M Record, March 16, 1995. 

Topel, Robert H. 1984. Equilibrium earnings, turnover, and unemployment: new evidence. Journal of Labor Economics 2 

no. 4: 500-522. 

U.S. Bureau of Labor Statistics. 2000. Employment Cost Index, Historical Listing (June 1989=100). 

http://stats.bls.gov/ecthome.htm. July 27. 

U.S. Department of Labor. 1995. State Unemployment Claims 1989-1993. Presented in Christopher Van Atten, “Cost of 

Unemployment”, memorandum to the MP&M Record, March 16. 

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MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs 

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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

Chapter 12: Benefit Overview


INTRODUCTION 

Part III of the EEBA assesses the benefits to society from the 

reduced effluent discharges that will result from the MP&M 

industry regulations. EPA expects that benefits will accrue 

to society in several broad categories, including reduced 

health risks, enhanced environmental quality, and increased 

productivity in economic activities that are adversely 

affected by MP&M industry discharges. 

This chapter provides a discussion of the pollutants of 

concern (POCs), their effect on human health, their 

environmental effects, a framework for understanding the 

benefits likely to be achieved by the MP &M regulation, and 

a qualitative discussion of those benefits. The following 

chapters quantify and estimate the economic value of these 

benefit categories. Appendices I and H provide further 

information on environmental effects of MP&M pollutants 

and water quality models used to assess these effects. 

CHAPTER CONTENTS 

12.1 ................. ........ 12-2 

12.1.1 teristics of MP&M Pollutants ..... 12-2 

12.1.2 ects of MP&M Pollutants on Human 

Health ................. .............. 12-3 

12.1.3 ental Effects of MP&M 

Pollutants ................. ........... 12-7 

12.1.4 ects of MP&M Pollutants on Economic 

Productivity ................. ......... 12-8 

12.2 to Beneficial Outcomes . . 12-9 

12.3 ive and Quantitative Benefits Assessment 12-11 

12.3.1 ........ 12-11 

12.3.2 n Health Benefits .............. 12-13 

12.3.3 ................. 12-13 

12.3.4 c Productivity Benefits ........ 12-14 

12.3.5 or Valuing Benefit Events .... 12-14 

Glossary ................. ................. .. 12-16 

Acronyms ................. ................. . 12-19 

References ................. ................. 12-20 

MP&M Pollutants 

Charac

Eff

Environm

Eff

Linking the Regulation 

Qualitat

Overview of Benefit Categories 

Huma

Ecological Benefits 

Economi

Methods f

EPA estimated national benefits expected to accrue from the regulation on the basis of sample facility data. The Agency 

extrapolated findings from the sample facility analyses to the national level using two alternative extrapolation methods: (1) 

traditional extrapolation and (2) post-stratification extrapolation. The traditional extrapolation approach relies on sample 

facility weights that were developed based on information about the economic and technical characteristics of the regulated 

community. This extrapolation approach does not incorporate information that could significantly affect the occurrence and 

distribution of regulatory benefits, such as characteristics of the receiving water body and the size of the population that may 

benefit from reduced pollutant discharges. EPA recognizes that using a traditional extrapolation method to estimate national 

level benefits may lead to a large degree of uncertainty in benefits estimates. Thus, EPA also used an alternative set of 

sampling weights, based on a post-sampling stratification method, to calculate alternative national estimates of benefits. EPA 

adjusted the original sample weights using two variables that are likely to affect the occurrence and size of benefits associated 

with reduced discharges from sample MP&M facilities: receiving water body type and size, and the size of the population 

residing in the vicinity of the sample facility. The following chapters present two sets of estimates of benefits expected to 

accrue from the MP&M regulation based on both traditional and post-stratification extrapolation approaches. Appendix G of 

this report provides detailed information on extrapolation methods. 

In addition, the Agency used the Ohio case study results to develop a third estimate of the monetary value of national 

benefits.1 EPA extrapolated the Ohio case study results to the national level based on three key factors that affect the 

occurrence and magnitude of benefits: (1) the estimated change in the MP&M pollutant loadings, (2) the level of recreational 

activities on the reaches affected by MP&M discharges, and (3) state level income. The Agency recognizes that this method is 

not rigorous for extrapolation to the national level. Therefore, EPA used this method only as a sensitivity analysis (see 

Appendix G of this report for detail). 

EPA notes that effluent limitations guidelines for the MP&M industry are technology-based. EPA is not required to 

demonstrate environmental benefits of its technology-based rules. It is well established that EPA is not required to consider 

receiving water quality in setting technology-based effluent limitations guidelines and standards. Weyerhaeuser v. Costle, 

590 F. 2nd 1011, 1043 (D.C. Cir. 1978) ("The Senate Committee declared that '[t]he use of any river, lake, stream or ocean as 

a waste treatment system is unacceptable' regardless of the measurable impact of the waste on the body of water in question. 

Legislative History at 1425 (Senate Report). The Conference Report states that the Act 'specifically bans pollution dilution as 

1 See Chapter 21 for a detailed discussion the Ohio case study. 

12-1 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

an alternative to treatment.' " Id. at 284). In establishing effluent limitations and standards, EPA considers benefits as one of 

the factors that the Agency evaluates. 

12.1 MP&M POLLUTANTS 

EPA defines three general categories of pollutants: priority or toxic pollutants; nonconventional pollutants; and conventional 

pollutants. Priority pollutants (PPs) are defined as any of 126 named pollutants.2 Conventional pollutants include 

biological oxygen demand (BOD), total suspended solids (TSS), oil and grease (O&G), pH, and anything else 

the Administrator defines as a conventional pollutant. Nonconventionals are a catch-all category that includes everything that 

is not in the two previously described categories. The naming system is somewhat confusing in that some nonconventional 

pollutants may be as "toxic" as, or more " toxic" than some of the PPs. 

MP&M effluents contain a variety of priority, nonconventional, and conventional pollutants. The release of these pollutants 

to our nation's surface water degrades aquatic environments, alters aquatic habitats, and affects the diversity and abundance of 

aquatic life. It also increases the health risks to humans who ingest contaminated surface waters or eat contaminated fish and 

shellfish (U.S. EPA, 1997). A number of the pollutants commonly found in MP&M effluents also inhibit biological 

wastewater treatment systems or accumulate in sewage sludge or sediment. 

Metals are a particular concern because of their prevalence in MP&M effluents. Metals are inorganic compounds, generally 

non-volatile (with the notable exception of mercury), and cannot be broken down by biodegradation processes. Metals can 

accumulate in biological tissues, sequester into sewage sludge in publicly-owned treatment works (POTWs), and 

contaminate soils and sediments when released to the environment. Sediments contaminated with metals become resuspended 

by dredging, boat propellers, water currents or wave action, and storm events, releasing metals back into the water column. 

Metals can also become biologically available and enter terrestrial food chains once the sludge is applied on land. Sludges 

with high concentrations of metals are therefore unsuitable for land application. Some metals are quite toxic even when 

present at relatively low levels. 

Some of the inorganic POCs found in MP&M effluents are also natural constituents of water, including potassium, calcium, 

magnesium, iron, chlorine, fluoride, sulfate, phosphates, silica, and a number of trace metals such as copper and zinc. 

Human and ecological exposure and risk from environmental releases of MP&M pollutants depend on chemical-specific 

properties, the mechanism and medium of release, and site-specific environmental conditions. Chemical-specific properties 

include toxicological effects on living organisms, hydrophobicity/lipophilicity, reactivity and persisistence. These 

properties are described in sections 12.1.1 through 12.1.4. 

12.1.1 Characteristics of MP&M Pollutants 

EPA sampled MP&M facilities nationwide to assess the concentrations of pollutants in MP&M effluents. The Agency 

collected samples of raw wastewater from MP&M facilities and applied standard water analysis protocols to identify and 

quantify the pollutant levels in each sample. EPA used these analytical data, along with selection criteria, to identify 132 

contaminants of potential concern.3 

EPA then evaluated the potential environmental fate and transport of these pollutants and their toxicity to humans and aquatic 

receptors. Fate of the MP&M pollutants was estimated based on the propensity of those pollutants to volatilize, adsorb onto 

sediments, bioconcentrate, and biodegrade. Table I.1 in Appendix I lists MP&M pollutants and provides data on human 

health concerns, and fate and effects. 

EPA used various data sources to evaluate pollutant-specific fate and toxicity. To evaluate potential human health effects, the 

Agency relied on reference doses (RfDs) and cancer potency slope factors (SFs), human health-based water 

2 The Agency originally had 129 PPs, but 3 have been dropped from the list bringing the number of PPs to 126. 

3 EPA originally identified 150 MP&M POCs. Of these 150 POCs, the Agency estimated loadings for 132 pollutants for the phase 2 

proposal and NODA. The benefits analysis presented in this chapter and the following chapters was based on 132 pollutants for which 

loadings are available. The final regulation covers only the Oily Wastes subcategory and benefit reductions were estimated for 122 

pollutants. 

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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

quality criteria (WQC), maximum contaminant levels (MCLs) for drinking water protection and other drinking water 

related criteria, and hazardous air pollutant (HAP) and PP lists. Appendix I.1.2 provides short descriptions and 

definitions for each of these measures of human health effects. 

To evaluate potential fate and effects in aquatic environments, the Agency relied on measures of acute and chronic 

toxicity to aquatic species, bioconcentration factors for aquatic species, Henry's Law constants (to estimate volatility), 

adsorption coefficients (Koc) (to estimate association with bottom sediments), and biodegradation half-lives (to 

estimate the removal of chemicals via microbial metabolism). 

The data sources used in the assessment include EPA ambient water quality criteria (AWQC) documents and updates, 

EPA's ASsessment Tools for the Evaluation of Risk (ASTER), the AQUatic Information REtrieval System 

(AQUIRE), and the Environmental Research Laboratory-Duluth fathead minnow database, EPA's Integrated 

Risk Information System (IRIS), EPA's Health Effects Assessment Summary Tables (HEAST), EPA's 1991 and 

1993 Superfund Chemical Data Matrix (SCDM), Syracuse Research Corporation's CHEMFATE and BIODEG 

databases, EPA and other government reports, scientific literature, and other primary and secondary data sources. 

To ensure that the assessment is as comprehensive as possible, EPA also obtained data on chemicals for which 

physical-chemical properties and/or toxicity data were not available from the sources listed above. To the extent possible, 

EPA estimated values for the chemicals using the quantitative structure-activity relationship (QSAR) model 

incorporated in ASTER, and for some physical-chemical properties, used published linear regression correlation equations. 

12.1.2 Effects of MP&M Pollutants on Human Health 

Individuals are potentially exposed to MP&M pollutants released to the aquatic environment via consumption of 

contaminated fish. Populations served by drinking water utilities located downstream of effluent discharges from MP&M 

facilities are also exposed to M P&M pollutants via contaminated drinking water. Many of these pollutants may increase risks 

to human health. 

Based on the available human health toxicity data for the 132 POCs presented in Table I.1 (Appendix I), EPA found that:4 

� 76 pollutants are human system ic toxicants; 

� 13 pollutants with published SFs are classified as known, probable, or possible human carcinogens when ingested via 

drinking water or food. Lead is also classified as a possible human carcinogen in IRIS but EPA has not developed a 

SF for it (U.S. EPA, 1998/99d); 

� 36 pollutants have drinking water criteria (27 with enforceable health-based MCLs, 7 with secondary MCLs for 

taste or aesthetics, and 2 with action levels for treatment); 

� 35 pollutants are designated as HAP s in wastewater; 

� 43 pollutants are identified as PPs; and 

� 76 pollutants have human health-based water quality criteria (WQC) to protect against the ingestion of water and 

organisms or organisms only (see Chapter 13, Table 13.3). 

The carcinogens identified by EPA in MP&M effluent samples include known (A), probable (B1 and B2) and possible (C) 

human carcinogens. These pollutants are associated with the development of cancers in the spleen, liver, kidney, lung, 

bladder, and skin, among others. These pollutants and target organs are shown in Table 12.1. 

4

 Facilities in the Oily Wastes subcategory discharge: 75 of the 76 systemic toxicants; all 13 human carcinogens; all 36 pollutants with 

drinking water criteria; all 35 pollutants designated as HAPs; 41 of the 43 priority pollutants; and 75 of the 76 pollutants that have human 

health-based water quality criteria. Of the 132 POCs evaluated, facilities in the Oily Wastes subcategory do not discharge the following 10 

pollutants: amenable cyanide, boron, cadmium, cyanide, phosphate, sodium, sulfide, total dissolved solids, weak-acid dissociable cyanide, 

and ziram/cymate. 

12-3 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

Table 12.1: Human Carcinogens Evaluated, Weight-of-Evidence Classifications, and Target Organs 

CAS Number Carcinogen Weight-of-Evidence 

Classification Target Organs 

62533 Aniline B2 Spleen 

7440382 Arsenic A Liver, kidneys, lungs, bladder, 

117817 Bis(2-ethylhexyl) phthalate B2 Liver 

75003 Chloroethane a 

75092 Dichloromethane B2 Liver, lungs 

75354 Dichloroethene, 1,1- C Inconclusive b 

123911 Dioxane, 1,4- B2 Liver, nasal cavity, gall bladder 

78591 Isophorone C Preputial gland 

62759 Nitrosodimethylamine, N- B2 Liver, lungs, skin, seminal vesicle, 

lymphatic/hematopoetic system 

86306 Nitrosodiphenylamine, N- B2 Bladder tumors, reticulum cell 

sarcomas 

127184 Tetrachloroethene B2 Liver 

79016 Trichloroethene a 

67663 Trichloromethane B2 Kidneys 

skin 

A = Human Carcinogen


B1 = Probable Human Carcinogen (limited human data)


B2 = Probable Human Carcinogen (animal data only)


C = Possible Human Carcinogen
 a Pollutant has been withdrawn from the IRIS database for additional study. b

 There is equivocal evidence for the oral route of exposure. This chemical is likely a systemic carcinogen via inhalation. 

Target organs include: kidney, pancreas, skin, mammary gland, and blood forming elements (lymphoma and leukemia). 

Source: U.S. Environmental Protection Agency verified (IRIS) or provisional (HEAST) (U.S. EPA (1998/99d), U.S. EPA 

(1997)). 

Non-carcinogenic hazards associated with pollutants in MP&M effluent include systemic effects (e.g., impairment or loss of 

neurological, respiratory, reproductive, circulatory, or immunological functions), organ-specific toxicity (e.g., kidney, small 

intestines, blood, testes, liver, stomach, thyroid), fetal effects (e.g., increased fetal mortality, decreased birth weight), other 

effects (e.g., lethargy, cataracts, weight loss, hyperactivity), and mortality. These effects are listed by pollutant in Table 12.2. 

12-4 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

Table 12.2: MP&M Pollutants Exhibiting Systemic and Other Non-Cancer Human Health Effectsa 

CAS Number Toxicant RfD Target Organ and Effects 

83329 Acenaphthene Liver, hepatotoxicity 

67641 Acetone Increased liver and kidney weights, nephrotoxicity 

98862 Acetophenone General toxicity 

107028 Acrolein Cardiovascular toxicityc 

7429905 Aluminum Renal failure, intestinal contraction interference, adverse neurological effectsd 

120127 Anthracene General toxicity 

7440360 Antimony Longevity, blood glucose, cholesterol 

7440382 Arsenic Hyperpigmentation, keratosis and possible vascular complications 

7440393 Barium Increased kidney weight 

65850 Benzoic acid General toxicity 

100516 Benzyl alcohol Forestomach, epithelial hyperplasia 

7440417 Beryllium Small intestinal lesions 

92524 Biphenyl Kidney damage 

117817 Bis(2-et hylhexy l ) 

phthalate 

Increased relative liver weight 

7440428 Boron Testicular atrophy, spermatogenic arrest 

85687 Butyl benzyl phthalate Significantly increased liver-to-body and liver-to-brain weight 

7440439 Cadmium Significant proteinuria (protein in urine) 

75150 Carbon disulfide Fetal toxicity, malformations 

108907 Chlorobenzene Histopathologic changes in liver 

75003 Chloroethane General toxicity 

7440473 Chromium Renal tubular necrosis (kidney tissue decay)d 

18540299 Chromium-hexavalent Reduced water consumption 

7440484 Cobalt Heart effectsd 

7440508 Copper Gastrointestinal effects, liver necrosisd 

95487 Cresol, o- Decreased body weight and neurotoxicity 

106445 Cresol, p- Central nervous system hypoactivity and respiratory system distress 

57125 Cyanide Weight loss, thyroid effects and myelin degeneration 

75354 Dichloroethene, 1,1- Toxic effects on kidneys, spleen, lungsd

; hepatic lesions 

75092 Dichloromethane Liver toxicity 

68122 Dimethylformamide, 

N,NLiver and gastrointestinal system effects 

105679 Dimethylphenol, 2,4- Clinical signs (lethargy, prostration, and ataxia) and hematological changes 

84742 Di-n-butyl phthalate Increased mortality 

51285 Dinitrophenol, 2,4- Cataract formation 

606202 Dinitrotoluene, 2,6- Mortality, central nervous system neurotoxicity, blood heinz bodies and 

methemoglobinemia, bile duct hyperplasia, kidney histopathology 

117840 Di-n-octyl phthalate Kidney and liver increased weights, increased liver enzymes 

122394 Diphenylamine Decreased body weight, and increased liver and kidney weights 

100414 Ethylbenzene Liver and kidney toxicity 

206440 Fluoranthene Nephropathy, increased liver weights, hematological alterations, clinical effects 

86737 Fluorene Decreased red blood cell count, packed cell volume and hemoglobin 

16984488 Fluoride Objectionable dental fluorosis (soft, mottled teeth) 

591786 Hexanone, 2- Hepatotoxicity and nephrotoxcity c 

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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

Table 12.2: MP&M Pollutants Exhibiting Systemic and Other Non-Cancer Human Health Effectsa 

CAS Number Toxicant RfD Target Organ and Effects 

7439896 Iron Liver pathology, diabetes mellitus, endocrine disturbance, and cardiovascular effects c 

78831 Isobutyl alcohol Hypoactivity and ataxia 

78591 Isophorone Kidney pathology 

7439965 Manganese Central nervous system effects 

78933 Methyl ethyl ketone Decreased fetal birth weight 

108101 Methyl isobutyl ketone Lethargy, increased liver and kidney weights and urinary protein 

80626 Methyl methacrylate Increased kidney to body weight ratio 

91576 Methylnaphthalene, 2-

7439987 Molybdenum Increased uric acid 

91203 Naphthalene Decreased body weight 

7440020 Nickel Decreased body and organ weights 

100027 Nitrophenol, 4-

59507 Parachlorometacresol 

108952 Phenol Reduced fetal body weight 

129000 Pyrene Kidney effects (renal tubular pathology, decreased kidney weights) 

110861 Pyridine Increased liver weight 

7782492 Selenium Clinical selenosis (hair or nail loss) 

7440224 Silver Argyria (skin discoloration) 

100425 Styrene Red blood cell and liver effects 

127184 Tetrachloroethene Liver toxicity, weight gain 

7440280 Thallium Liver toxicity, gastroenteritis, degeneration of peripheral and central nervous systemc 

7440315 Tin Kidney and liver lesions 

7440326 Titanium Considered to be physiologically inertc 

108883 Toluene Changes in liver and kidney weights 

79016 Trichloroethene Bone marrow, central nervous system, liver, kidneys 4 

75694 Trichlorofluoromethane Histopathology and mortality 

67663 Trichloromethane Fatty cyst formation in liver 

7440622 Vanadium Kidney and central nervous system effectsb 

108383 Xylene, m- Central nervous system hyperactivity, decreased body weight 

179601231 Xylene, m- & p- (c) 

95476 Xylene, o- Central nervous system hyperactivity, decreased body weight 

136777612 Xylene, o- & p- (c) 

7440666 Zinc 47% decrease in erythrocyte superoxide dismutase (ESOD) concentration in adult human 

females after 10 weeks of zinc exposure 

137304 Ziram \ Cymate 

a Chemicals with EPA verified (IRIS) or provisional (HEAST, or other Agency document)) human health-based RfDs, referred to as 

“systemic toxicants” (U.S. EPA (1998/99d), U.S. EPA (1997)). b RfD based on a no-observed-adverse-effect level (NOAEL). Health effects summarized from Amdur, M.O., Doul, J., and Klaassen, C.D., 

eds. Cassarett and Doul’s Toxicology, 4th edition, 1991. c Target organ and effects summarized from Amdur, M.O., Doul, J., and Klaassen, C.D., eds. Cassarett and Doul’s Toxicology, 5th edition, 

1996. 

d Target organ and effects summarized from Wexler, P., ed. Encyclopedia of Toxicology, Volumes 1-3, 1998. 

Source: U.S. EPA analysis. 

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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

12.1.3 Environmental Effects of MP&M Pollutants 

Ecological impacts of MP&M pollutants include acute and chronic toxicity to aquatic receptors by dozens of pollutants 

present in MP&M effluents, uptake of certain pollutants into aquatic food webs, sub-lethal effects on metabolic and 

reproductive functions, habitat degradation from turbidity, eutrophication, dissolved oxygen depletion, and loss of prey 

organisms. Metals are of particular concern to this regulation because they (1) do not volatilize, (2) do not biodegrade, (3) 

can be toxic to plants, invertebrates and fish, (4) adsorb to sediments and (5) bioconcentrate in biological tissues. 

EPA obtained the environmental fate and toxicity information for the 132 MP&M POCs. Table I.1 in Appendix I shows the 

environmental fate and toxicity of each MP&M pollutant.5 EPA found that: 

� 56 pollutants are not volatile or are only slightly volatile (all metals were assumed to be non-volatile except for 

mercury); 

� 57 pollutants have moderate to high adsorption potentials (all metals were assumed to have high adsorption potential 

except for nickel); 

� 42 pollutants have moderate to high bioconcentration factors; 

� 62 pollutants biodegrade slowly or are resistant to biodegradation altogether (all metals were assumed to be resistant 

to biodegradation); 

� For freshwater environments, 32 pollutants have acute toxicities to aquatic life that range from moderate to high, and 

33 pollutants have chronic toxicities that range from moderate to high; 

� For saltwater environments, 20 pollutants have acute toxicities to aquatic life that range from moderate to high, and 

23 pollutants have chronic toxicities that range from moderate to high. 

The available information shows that dozens of the MP&M POCs have the potential to pose significant hazards to the aquatic 

environment when released to receiving waters. A number of pollutants are of particular concern because of their combined 

toxicity and fate. These include several polyaromatic hydrocarbons (acenaphthene, anthracene, 3,6-dimethyl-phenanthrene, 

fluoranthene, phenanthrene, and pyrene), several metals (aluminum, cadmium, copper, mercury, and selenium) and several 

phthalates (di-n-octyl phthalate, butyl benzyl phthalate, and di-n-butyl phthalate). Other pollutants are of concern chiefly 

because of their toxicity (arsenic, cyanide, chromium, lead, nickel, silver, and zinc) or their fate (bis(2-ethylhexyl)phthalate, 

bromo-2-chlorobenzene, bromo-3-chlorobenzene, dibenzofuran, dibenzothiophene, diphenylamine, long-chained petroleum 

hydrocarbons, 1-methylfluorene, N-nitrosodiphenylamine, and several metals). 

The available fate and toxicity data indicate that many MP&M pollutants tend (1) to be "toxic", (2) to not readily volatilize 

from the water column, (3) to adsorb to sediments, (4) to bioconcentrate in aquatic organisms, and (5) do not biodegrade. 

Such pollutants accumulate in sediments and reach concentrations which can impair benthic communities. Pollutants that 

have accumulated in sediments can be released back into the water column because sediments act as long-term sinks. The 

pollutants can also enter soils and reach high levels over time if present in sewage sludge that is applied to land. The 

tendency of these pollutants to resist biodegradation and to bioconcentrate in biological tissue also causes them to be taken up 

into aquatic food chains where they can affect predators or humans who consume fish and shellfish (U.S. EPA, 1998). 

The toxicity data also indicate that a sizable number of the POCs in MP&M effluents have toxicities that result in lethal or 

sub-lethal responses in aquatic receptors, including algae, vascular plants, invertebrates, fish, and amphibians. Responses 

include death, which may occur within a matter of hours to days, or longer-term sub-lethal responses (such as reproductive 

failure or growth impairment) that manifest themselves over weeks, months, or even years. The effects of toxic chemicals are 

not shared equally among exposed species: sensitive species are typically more affected than species that are more resistant. 

Hence, toxic conditions could selectively remove sensitive species from receiving waters. Such a pattern is of particular 

concern to threatened and endangered (T&E) species, which may already be close to extinction. Aquatic receptors are 

exposed to many different toxicants at the same time, which may have additive effects. The EPA assessment is based on a 

5 Note that EPA was unable to obtain fate or toxicity data for a substantial number of POCs. 

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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

chemical-by-chemical approach and therefore does not consider additive effects. This approach may understate the benefits 

of the rule. 

EPA also did not evaluate the potential fate and effects of the four conventional pollutants (BOD, pH, O&G, TSS) and several 

other pollutants, including Total Petroleum Hydrocarbon (TPH), Total Kjeldahl Nitrogen (TKN), phosphorus, and 

chemical oxygen demand (COD), which may nonetheless adversely affect aquatic environments.6,7 

Effluents with high levels of BOD or COD consume large amounts of dissolved oxygen in a short time, causing surface waters 

to become oxygen-depleted, thereby killing or excluding aquatic life (U.S. EPA, 1986). At current discharge levels, MP&M 

facilities discharge 1.1 million pounds of BOD per year. 

Low pH (high acidity) water can be lethal to aquatic organisms; sensitive species of fish and invertebrates are eliminated from 

surface waters at pH's between 6.0 and 6.5 (U.S. EPA, 1999). 

O&G and TPH can have lethal effects on fish by coating gill surfaces and causing asphyxia, depleting dissolved oxygen levels 

due to excessive BOD, and impairing stream re-aeration due to the presence of surface films. Compounds present in O&G or 

TPH can also be detrimental to waterfowl by affecting the buoyancy and insulating capacity of their feathers (U.S. EPA, 

1998). At current discharge levels, MP&M facilities discharge 553,481 pounds per year of O&G, including 67,427 pounds a 

year of TPH. 

TSS increases the turbidity of surface water and impairs underwater visibility and transparency, thereby inhibiting 

photosynthesis by diminishing the amount of sunlight that reaches algae or submerged aquatic plants. TSS also causes a 

general degradation of aquatic habitats by increasing the rate of sedimentation, which smothers eggs, covers aquatic plants, 

and affects benthic invertebrates (U.S. EPA, 1998). 

High input of nitrogen in estuarine and marine systems or phosphorus in freshwater systems can increase primary productivity 

and result in eutrophication. Such a process overloads surface waters with algae and reduces the transparency of the water 

column. The excess algae sink to the bottom and decompose at the end of their life cycle. This process consumes large 

amounts of dissolved oxygen and can turn surface waters anoxic (U.S. EPA, 1998; U.S. EPA, 1995). 

12.1.4 Effects of MP&M Pollutants on Economic Productivity 

Most MP&M pollutants associated with adverse health effects are subject to drinking water criteria. Thus, MP&M discharges 

to surface water can increase the cost of municipal water treatment by requiring investment in chemical treatment and 

filtration. Public water treatment systems must comply with drinking water criteria MCLs and secondary standards. 

Compliance may require treatment to reduce the levels of regulated pollutants below their MCLs. Capital investment and 

operating and maintenance (O&M ) costs associated with treatment technologies can be substantial. To the extent that the 

MP&M regulation reduces the concentration of MP&M pollutants in source waters to values that are below pollutant-specific 

drinking water criteria, public drinking water systems will accrue benefits in the form of reduced water treatment costs. 

Releases of MP&M pollutants to surface waters may also increase treatment costs of irrigation water and industrial water. 

Releases of large quantities or high concentrations of toxic pollutants in MP&M effluents may interfere with POTW processes 

(e.g., inhibiting microbial degradation), reduce the treatment efficiency or capacity of POTW s, and reduce disposal options 

for the sludge. In addition, toxic pollutants present in the effluent discharges may pass through a POTW and adversely affect 

receiving water quality, or may contaminate sludges generated during primary or secondary wastewater treatment. EPA 

expects no changes in the current status of POTW processes or disposal options for the sludge at POTWs receiving effluent 

discharges from MP& M facilities associated with the MP& M rule since all indirect dischargers have been excluded from the 

final option. EPA, however, analyzed changes in interferences of POTW operations and contamination of sewage sludge at 

6 TKN is defined as the total of organic and ammonia nitrogen. It is determined in the same manner as organic nitrogen, except that 

the ammonia is not driven off before the digestion step. 

7 EPA, however, considered environmental effects of TKN in the Ohio case study. EPA evaluated the impact of in-stream TKN 

concentrations on recreational value of fishing, boating, swimming, and wildlife viewing sites. For detail see Chapter 21 of this report. 

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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

POTWs receiving effluent discharges from MP&M facilities for the alternative regulatory options which include indirect 

dischargers. 

12.2 LINKING THE REGULATION TO BENEFICIAL OUTCOMES 

This section describes the linkages between promulgation of a regulation and the expected benefits to society. As indicated in 

Figure 12.1, the benefits of the MP&M regulation occur from a chain of events. These events include: (1) Agency publication 

of the regulation, (2) industry changes in production processes and/or treatment systems, (3) reductions in pollutant 

discharges, (4) changes in water quality, (5) changes in ecosystem attributes and sewage sludge quality, (6) changes in human 

responses, and (7) changes in human health and ecological risk. The first two events reflect the institutional and technical 

aspects of the regulation. The benefit analysis begins with the third event, the changes in the pollutant content of effluent 

discharges. 

12-9 
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

Figure 12.1: Chain of Events in a Benefits Analysis 

1. 	EPA Publication of 

Regulation 

2. ges in 

Production Processes 

and/or Treatment 

Chan

3. ctions in 

Pollutant Discharges 

Redu

4. ges in 

Ambient Water 

Quality 

(Pollutant 

Concentrations & 

Aquatic Habitat) 

Chan

5. ge in Aquatic 

Ecosystem 

(e.g., Increased Fish 

Populations & 

Diversity & Reduced 

Bioaccumulation) 

Chan

6. 

Demand & Value of 

Fishery 

(e.g., Recreational & 

Other Benefit 

Categories) 

Change in Level of 

7. tial Change in 

Health Risk 

(e.g., from 

Consumption of Fish 

Caught) 

Poten

Source: U.S. EPA analysis. 

In event four, changes in pollutant discharges translate into improvements in water and sludge quality. In event five, these 

improvements in turn affect in-stream and near-stream biota (e.g., increased diversity of aquatic species and size of species 

populations) and sludge disposal options. Finally, human effects and the related valuation of benefits occur in events six and 

seven. For example, improvements to recreational fisheries and enhanced enjoyment by recreational anglers is connected to 

12-10 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

improved water quality and the value of reduced risk to human health. These linkages are the basis of the benefits analysis 

presented in this and the following chapters. 

12.3 QUALITATIVE AND QUANTITATIVE BENEFITS ASSESSMENT 

A benefit assessment defines and quantifies the types of improvements to human health and ecological receptors that can be 

expected from reducing the amount of MP&M pollutants released to the environment. The following sections provide an 

overview of the concepts and analytic approaches involved in the benefits assessment. The first section describes the general 

categories of benefits expected to result from the regulation and the level of analysis undertaken for them. The following 

three sections review, within the broad categories of benefits likely to be achieved by the MP&M regulation, the specific 

benefits that are evaluated in this analysis. Finally, Section 12.3.5 summarizes methods for attaching values to some of the 

benefit measures. Chapters 13 through 16 present the quantitative assessment of benefits. 

12.3.1 Overview of Benefit Categories 

The benefits of reduced MP &M discharges may be classified in three broad categories: human health, ecological, and 

economic productivity benefits. Table 12.3 summarizes the different types of benefits that fall in each of these categories. 

Each category is comprised of a number of more narrowly defined benefit categories. EPA expects that the MP&M 

regulation will provide benefits to society in all of these categories. EPA was not able to bring the same depth of analysis to 

all of these categories, however, because of imperfect understanding of the link between discharge reductions and benefit 

categories, and how society values some of the benefit events. EPA was able to quantify and monetize some benefits, 

quantify but not monetize other benefits, and assess still other benefits only qualitatively. 

In addition to the national-level benefits analysis, the Agency conducted a case study in the state of Ohio to provide in-depth 

analysis of the regulation's expected benefits. The Ohio case study improves on the national analysis in two ways. First, the 

analysis uses improved data and methods to address co-occurrence of M P&M facility benefits and other-source contributions 

of MP&M pollutants in the same locations. Second, the analysis of recreational benefits is based on original travel cost 

models of resource valuation in a random utility framework. The analysis values changes in the value of water resources for 

four recreational activities -- fishing, boating, swimming, and near-water recreation. Due to data limitations, only three of 

these four activities were valued at the national-level benefits analysis. 

To provide perspective on the extent to which this regulatory impact assessment was able to comprehensively analyze the 

benefits, Table 12.3 summarizes the specific benefits within each of the three broad benefit categories that are expected to 

accrue from the MP&M regulation and the level of analysis applied to each category. As shown in Table 12.3, only a few of 

the relevant benefit categories can be both quantified and monetized. 

12-11 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

Table 12.3: Level of Analysis Performed for Specific Benefit Categories 

Benefit Category 

Quantified 

and 

Monetized 

Quantified 

but Not Monetized Qualitative 

Human Health Benefits 

Reduced cancer risk due to ingestion of chemically-contaminated 

fish and unregulated pollutants in drinking water 

X 

Reduced non-cancer adverse health effects (e.g. 

immunological, neurological, circulatory, or respiratory toxicity) 

due to ingestion of chemically-contaminated fish and unregulated 

pollutants in drinking water 

X 

Reduced non-cancer adverse health effects from exposure to lead 

from consumption of chemically-contaminated fish 

X 

Reduced cancer risk and non-cancer adverse health effects from 

exposure to unregulated pollutants in chemically-contaminated 

sewage sludgea 

X 

Reduced health hazards from exposure to contaminants in waters 

used recreationally (e.g., swimming) 

X 

Ecological Benefits 

Reduced risk to aquatic life X 

Enhanced water-based recreation including fishing, boating, and 

near-water (wildlife viewing) activities 

X 

Other enhanced water-based recreation such as swimming, 

waterskiing, and white water rafting 

X 

Increased aesthetic benefits such as enhancement of adjoining site 

amenities (e.g. , working, traveling, and owning property 

near the water) 

X 

Nonuser value (i.e., existence, option, and bequest value) X 

Reduced contamination of sediments X 

Reduced non-point source nitrogen contamination of water if 

sewage sludge is used as a substitute for chemical fertilizer on 

agricultural landa 

X 

Satisfaction of a public preference for beneficial use of sewage 

sludgea 

X 

Economic Productivity Benefits 

Reduced sewage sludge disposal costsa X 

Reduced management practice and record-keeping costs of sewage 

sludge that meets exceptional quality criteriaa 

X 

Reduced interference with POTW operations a X 

Benefits to tourism industries from increased participation in waterbased recreation 

X 

Improved commercial fisheries yields X 

Improved crop yield (the organic matter in land-applied sewage 

sludge increases soil’s water retention)a 

X 

Avoidance of costly siting processes for more controversial sewage 

sludge disposal methods (e.g., incinerators) because of greater use 

of land applicationa 

X 

Reduced water treatment costs for municipal drinking water, 

irrigation water, and industrial process and cooling water 

X 

reproductive, 

residing

a

 These benefit categories are not applicable to the final rule since all indirect dischargers have been excluded from the selected 

option. EPA, however, analyzed these benefit categories for the alternative regulatory options which include indirect dischargers. 

Source: U.S. EPA analysis. 

Each category of benefits and the level of analysis applied to this category are discussed in greater detail below. 

12-12

MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

12.3.2 Human Health Benefits 

Reduced pollutant discharges to the nation’s waterways will generate human health benefits by several mechanisms. The 

most important and readily analyzed benefits stem from reduced risk of illness associated with the consumption of water, fish, 

shellfish, and other aquatic organisms that is taken from waterways affected by MP&M discharges. Human health benefits 

are typically analyzed by estimating the change in the expected number of adverse human health events in the exposed 

population resulting from a reduction in effluent discharges. While some health effects such as cancer are relatively well 

understood and thus may be quantified in a benefits analysis, others are less well characterized and cannot be assessed with 

the same rigor or at all. 

EPA analyzed the following direct measures of change in risk to human health: incidence of cancer from fish and water 

consumption; reduced risk of non-cancer toxic effects from fish and water consumption; and lead-related health effects to 

children and adults. EPA was able to monetize only two of the three measures (cancer-related and lead-related health risks). 

Incidence of cancer was translated into an expected number of avoided mortality events and, on that basis, monetized. Lead 

impacts to children were evaluated in terms of potential intellectual impairment as measured by estimated changes in IQ. 

Changes in adverse health effects to adults from lead exposure were measured in terms of reduced risk of hypertension, nonfatal coronary heart disease, non-fatal strokes, and mortality. 

EPA also quantified but did not monetize the expected reduction of pollutant concentrations in excess of health-based AWQC 

limits. This benefit measure was obtained by comparing in-waterway pollutant concentrations to toxic effect levels. 

In concept, the value of these health effects to society is the monetary value that society is willing to pay to avoid the health 

effects, or the amount that society would need to be compensated to accept increases in the number of adverse health events. 

“Willingness-to-pay” (WTP) values are generally considered to provide a fairly comprehensive measure of society’s 

valuation of the human and financial costs of illness associated with the costs of health care, losses in income, and pain and 

suffering of affected individuals and of their family and friends. 

In some cases, available economic research provides little empirical data for society's WTP to avoid certain health effects. 

One component of the cost of an illness estimates the direct medical costs of treating a health condition (e.g., hypertension), 

and can be used to value changes in health risk from reduced exposure to toxic pollutants such as lead. These estimates 

represent only one component of society's WT P to avoid adverse health effects and therefore produce a partial measure of the 

value of reduced exposure to M P&M pollutants. Employed alone, these monetized effects will significantly underestimate 

society's WTP. 

12.3.3 Ecological Benefits 

EPA expects that the ecological benefits from the regulation will include protection of fresh- and saltwater plants, 

invertebrates, fish, and amphibians, as well as terrestrial wildlife and birds that prey on aquatic organisms exposed to MP&M 

pollutants. The regulation will reduce the presence and discharge of various pollutants and will enhance or protect aquatic 

ecosystems currently under stress. The drop in pollutant loading is expected to reestablish productive ecosystems in damaged 

waterways and to protect resident species, including T&E species. EPA also expects that the regulation will enhance the 

general health of fish and invertebrate populations, increase their propagation to waters currently impaired, and expand 

fisheries for both commercial and recreational purposes. Improvements in water quality will also favor increased recreational 

activities such as swimming, boating, fishing, and water skiing. Finally, the Agency expects that the regulation will augment 

nonuse values (e.g., option, existence, and bequest values) of the affected water resources. 

It is frequently difficult to quantify and attach economic values to ecological benefits. The difficulty results from imperfect 

understanding of the relationship between changes in effluent discharges and the specific ecological changes, lack of water 

quality monitoring data for most locations, and time lags between water quality changes and changes in species population 

and composition. In addition, it is difficult to attach monetary values to these ecological changes because they often do not 

occur in markets in which prices or costs are readily observed. As such, ecological benefits may be loosely classified as 

nonmarket benefits. This classification can be further divided into nonmarket use benefits and nonmarket nonuse benefits. 

Nonmarket use benefits stem from improvements in ecosystems and habitats, which in turn lead to enhanced human use and 

enjoyment of these areas. For example, reduced discharges may lead to increased recreational use and enjoyment of affected 

waterways in such activities as fishing, swimming, boating, hunting or near-water activities such as bird watching. In some 

12-13 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

cases, it may be possible to quantify and attach partial economic values to ecological benefits using market values (e.g., an 

increase in tourism or boat rentals associated with improved recreational fishing opportunities); in this case, these benefit 

events might better be classified as economic productivity related events, which are discussed below. Economic markets, 

however, do not provide enough information to fully capture the value of these benefits. Such markets capture only related 

expenditures made by recreationists (e.g., food and lodging) and do not capture the value placed on the experience itself. A 

variety of nonmarket valuation techniques can be used to capture the value placed on the resource in question. These 

techniques include hedonic valuation (wage-risk studies) and travel cost methods (TCM), stated preferences methods (i.e., 

contingent valuation (CV), contingent rating (CR), contingent activity (CA), benefits transfer, and averting 

behavior models. 

Nonmarket nonuse benefits are not associated with current use of the affected ecosystem or habitat, but rather arise from (1) 

the realization of the improvement in the affected ecosystem or habitat resulting from reduced effluent discharges and (2) the 

value that individuals place on the potential for use sometime in the future. Nonmarket nonuse benefits may also be 

manifested by other valuation mechanisms, such as cultural valuation, philanthropy, and bequest valuation. It is often 

extremely difficult to quantify the relationship between changes in discharges and the improvements in societal well-being 

associated with such valuation mechanisms. That these valuation mechanisms exist, however, is indisputable, as evidenced, 

for example, by society’s willingness to contribute to organizations whose mission is to purchase and preserve lands or 

habitats to avert development. 

12.3.4 Economic Productivity Benefits 

Reduced pollutant discharges may also benefit economic productivity. First, economic productivity benefits may accrue from 

reduced treatment costs of drinking water, irrigation water, and industrial use water. Reduced pollutant concentrations in 

public water systems source water to levels at or below MCLs or secondary standards could reduce ongoing treatment costs 

and avoid the need to invest in treatment technologies in the future. Reduced pollutant discharges may also reduce sediment 

dredging costs. Contaminated sediments may contribute substantially to contamination of aquatic biota and to human 

exposure of human health toxicants. Controlling point source discharges of toxic pollutants can prevent sediment 

contamination and eliminate the need for future remediation (i.e., dredging) of contaminated sediments. 

Other economic productivity gains may result from improved tourism opportunities in areas affected by MP&M discharges. 

Improved aquatic species survival may contribute to increased commercial fishing yield. When such economic productivity 

effects can be identified and quantified, they are generally straightforward to value because they involve market commodities 

for which prices or unit costs are readily available. 

Economic productivity gains may also occur through reduced costs to public sewage systems (POTWs) for managing and 

disposing of the sludge (i.e., biosolids) from treating effluent discharges. For example, higher quality sludge may be applied 

to agricultural land or otherwise beneficially used rather than being incinerated or disposed of in landfills. POTWs may also 

incur lower costs because of lower record keeping requirements. Under the final regulatory option, EPA expects no POTW 

productivity gains since all indirect dischargers have been excluded from the final regulatory option. 

12.3.5 Methods for Valuing Benefit Events 

Some of the benefits expected from the MP&M regulation will manifest themselves in economic markets through changes in 

price, cost, or quantity of market-valued activities. For benefits endpoints traded in markets, such as increased yields from 

commercial fisheries, benefits can be measured by market prices or market-based factor pricing. Competitive prices can be 

used also to measure avoided cost type of benefits. For example, reduced pollutant loadings to public water supplies may 

lower costs of drinking treatment. Market prices can be used also to value direct medical costs of illnesses associated with 

exposure to pollutants. For this analysis, EPA used medical costs associated with treating hypertension, coronary heart 

disease, and stroke to estimate benefits from reduced exposure to lead (see Chapter 14). The estimated values can be used as 

minimum measures of the benefits associated with reduced cases of these illnesses. 

In other cases, benefits involve activities or sources of value that either do not involve economic markets or involve them only 

indirectly. Methods used to value such benefits are described briefly below: 

12-14 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

a. Wage-risk approach.


The wage-risk approach uses regression estimates of the wage premium associated with greater risks of death on the job to


estimate the amount that persons are willing to pay to avoid death. Benefit values based on this approach are used as part of


the basis for valuing reduced cancer cases due to fish consumption in Chapter 13.


b. Travel cost method


The T CM uses information on costs incurred by people in traveling to a site and in using the site to estimate a demand curve


for that site. The demand curve is then used to estimate the “consumer surplus” associated with the use of the site, that is, the


value that consumers receive from the site over and above the costs that they incur in using it. Consumer surplus is an


estimate of the net benefits of the resource to the people using that resource. For example, if the resource is a recreational


fishing site, the TCM can be used to value the recreational fishing experience. The Agency used an original travel cost study


to value benefits from enhanced water-based recreation in Ohio (see Part V: Chapter 21). The analysis of recreational


benefits in Chapter 15 uses a meta-analysis of water-based recreation studies (including TCM studies) to derive the baseline


and post-compliance values of water-based recreation activities (including fishing, boating, and wildlife viewing) and to


estimate benefits to consumers of water-based recreation from improved water quality resulting from reduced MP&M 

dischargers. 

c. Contingent valuation


In the CV method, surveys are conducted to elicit individuals’ WTP for a particular good, such as a fishery, or clean water.


CV is more broadly applicable than TCM. Like TCM, CV can be used to estimate the consumer surplus associated with


recreational fisheries. CV can also be used to estimate less tangible values, such as how much people care about a clean


environment. Values from both the CV approach and the wage-risk approach support the estimated value of avoided death


that is used to monetize reduced cancer cases from consumption of contaminated fish (Chapter 13). Similarly to the TCM


studies, CV studies are used in a meta-analysis to derive the baseline and post-compliance values of water-based recreation


activities (including fishing, boating, and wildlife viewing) and to estimate benefits from improved opportunities for waterbased recreation from reduced MP&M dischargers (Chapter 15). 

d. Benefits transfer


When time and resource constraints preclude primary research, benefit assessment based on benefits transfer from existing


studies is used. This approach involves extrapolating benefit findings for one analytic situation to another. The relevant


study situations are defined by type of environmental resource (e.g., fishery), policy variable(s), and the characteristics of user


populations. The benefits transfer approach is used to monetize several benefit categories, including changes in the incidence 

of cancer cases (Chapter 13) and the national-level benefits from enhanced water-based recreation (Chapter 15). 

The techniques described above form the basis of the benefits methodologies described in Chapters 13,14, and 15. 

12-15 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

GLOSSARY 

acute toxicity: the ability of a substance to cause severe biological harm or death soon after a single exposure or dose. 

Also, any poisonous effect resulting from a single short-term exposure to a toxic substance. (See: chronic toxicity, toxicity.) 

(http://www.epa.gov/OCEPAterms/aterms.html) 

adsorption coefficients (Koc): represents the ratio of the target chemical absorbed per unit weight of organic carbon in 

the soil or sediment to the concentration of that same chemical in solution at equilibrium. 

ambient water quality criteria (AWQC): AWQC present scientific data and guidance of the environmental effects of 

pollutants which can be useful to derive regulatory requirements based on considerations of water quality impacts; these 

criteria are not rules and do not have regulatory impact (U.S. EPA. 1986. Quality Criteria for Water 1986. U.S. 

Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC. EPA 440/5-86-001). 

AQUatic Information REtrieval System (AQUIRE): a web-based ecotoxicity database maintained by EPA's 

Mid-Continent Ecology Division (MED) which summarizes ecotoxicity data retrieved from the literature. 

(http://www.epa.gov/med/databases/databases.html#aquire) (U.S. EPA, 1998/99b) 

ASsessment Tools for the Evaluation of Risk (ASTER): an ecological risk assessment tool developed by EPA's 

Mid-Continent Ecology Division (MED); ASTER integrates information from the AQUIRE toxic effects database and the 

QSAR system (a structure activity-based expert system) to estimate ecotoxicity, chemical properties, biodegradation and 

environmental partitioning. (http://www.epa.gov/med/databases/aster.html) (U.S. EPA, 1998/99c) 

avoided cost: costs that are likely to be incurred in the future if current conditions still prevail at the time, but which will 

be avoided if particular actions are taken now to change the status quo. 

benthic: relating to the bottom of a body of water; living on, or near, the bottom of a water body. 

BIODEG: a web-based biodegradation database developed by Syracuse Research Corporation. 

(http://esc.syrres.com/efdb/BIODGSUM .HTM) (Syracuse Research Corporation, 1999) 

biodegradation half-lives: represents the number of days a compound takes to be degraded to half of its starting 

concentration under prescribed laboratory conditions. 

biological oxygen dem and (BOD): the amount of dissolved oxygen consumed by microorganisms as they decompose 

organic material in an aquatic environment. 

cancer potency slope factor (SF): a plausible upper-bound estimate of the probability of a response per unit intake of a 

chemical over a lifetime. The slope factor is used to estimate an upper-bound probability of an individual developing cancer 

as a result of a lifetime of exposure to a particular level of a potential carcinogen. 

CHEMFATE: a web-based chemical fate database developed by Syracuse Research Corporation. 

(http://esc.syrres.com/efdb/Chemfate.htm) (Syracuse Research Corporation, 1999) 

chemical oxygen dem and (COD): a measure of the oxygen required to oxidize all compounds, both organic and 

inorganic, in water. (http://www.epa.gov/OCEPAterms/cterms.html) 

chronic toxicity: the capacity of a substance to cause long-term poisonous health effects in humans, animals, fish, and 

other organisms. (http://www.epa.gov/OCEPAterms/cterms.html) 

contingent activity: one of the stated preference methods (see: contingent valuation and contingent activity). Survey 

respondents are asked how their behavior would change in response to a proposed change in one or more attributes of an 

activity (e.g., cost of the activity, site accessibility, or site attractiveness). Given responses to this type of question, and given 

information about incremental travel costs and value of time, a revealed preference method can be used to estimate the value 

of change. 

12-16 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

contingent rating: one of the stated preference methods (see: contingent valuation and contingent activity). Survey


respondents are asked to rate several alternatives on an ad hoc utility scale (e.g., 1 to 10). The choice set of alternatives


usually includes the environmental effect to be valued, substitutes for the effect, and a good with a monetary price to act as a


threshold. Based on the respondent's rating of the environmental effect and the threshold good, and the monetary price of the


threshold good, the value of the environmental effect can be determined.


contingent valuation (CV): a method used to determine a value for a particular event, where people are asked what they


are willing to pay for a benefit and/or are willing to receive in compensation for tolerating a cost. Personal valuations for


increases or decreases in the quantity of some good are obtained contingent upon a hypothetical market. The aim is to elicit


valuations or bids that are close to what would be revealed if an actual market existed.


(http://www.damagevaluation.com/glossary.htm)


Environmental Research Laboratory-Duluth fathead minnow database: a database developed by EPA's


Mid-Continent Ecology Division (MED) which provides data on the acute toxicity of hundreds of industrial organic


compounds to the fathead minnow. (http://www.eoa.gov/med/databases/fathead_minnow.html) (U.S. EPA, 1998/99a)


hazardous air pollutant (HAP): compounds that EPA believes may represent an unacceptable risk to human health if


present in the air.


Health Effects Assessment Summary Tables (HEAST): a comprehensive listing of provisional human health risk


assessment data relative to oral and inhalation routes for chemicals of interest to EPA. Unlike data in IRIS, HEAST entries


have received insufficient review to be recognized as high quality, Agency-wide consensus information. (U.S. EPA. 1997.


Health Effects Assessment Table; FY 1997 Update. EPA-540-R-97-036)


Henry's Law constant: a numeric value which relates the equilibrium partial pressure of a gaseous substance in the


atmosphere above a liquid solution to the concentration of the same substance in the liquid solution.


human health-based water quality criteria (WQC): human health-based criteria are based on specific levels of


pollutants that would make the water harmful if used for drinking, swimming, farming, fish production, or industrial processes


(see ambient water quality criteria (AWQC)). (http://www.epa.gov/OCEPAterms/wterms.html).


hydrophobicity: having a strong aversion to water. (http://www.epa.gov/OCEPAterms/hterms.html)


Integrated Risk Information System (IRIS): IRIS is an electronic database with information on human health effects of


various chemicals. IRIS provides consistent information on chemical substances for use in risk assessments, decision-making


and regulatory activities.


lipophilicity: having a strong attraction to oils


maximum contaminant levels (MCLs): the maximum permissible level of a contaminant in water delivered to any user


of a public system. MCLs are enforceable standards.


(http://www.epa.gov/OCEPAterms/mterms.html)


metals: inorganic compounds, generally non-volatile, and which cannot be broken down by biodegradation processes. They


are a particular concern because of their prevalence in MP&M effluents. Metals can accumulate in biological tissues,


sequester into sewage sludge in POTW s, and contaminate soils and sediments when released to the environment. Some metals


are quite toxic even when present at relatively low levels. 


microbial metabolism: biochemical reactions occurring in living microorganisms such as bacteria, algae, diatoms,


plankton, and fungi. POTWs make use of bacterial metabolism for wastewater treatment purposes. This process is inhibited


by the presence of toxins such as metals and cyanide because these pollutants kill bacteria.


oil and grease (O&G): organic substances that may include hydrocarbons, fats, oils, waxes, and high-molecular fatty


acids. Oil and grease may produce sludge solids that are difficult to process. (http://www.epa.gov/owmitnet/reg.htm)


pH: an expression of the intensity of the basic or acid condition of a liquid; natural waters usually have a pH between 6.5


and 8.5. (http://www.epa.gov/OCEPAterms/pterms.html)


12-17 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

pollutants of concern (POCs): are the 150 contaminants identified by EPA as being of potential concern for this rule and


which are currently being discharged by MP&M facilities.


priority pollutant (PP): 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,


sediment, groundwater, or soil samples.


publicly-owned treatment works (POTWs): a treatment works, as defined by section 212 of the Act, that is owned by a


State or municipality. This definition includes any devices or systems used in the storage, treatment, recycling, and


reclamation of municipal sewage or industrial wastes of a liquid nature. It also includes sewers, pipes, or other conveyances


only if they convey wastewater to a POTW Treatment Plant. (http://www.epa.gov/owm/permits/pretreat/final99.pdf)


quantitative structure-activity relationship (QSAR) model: an expert system which uses a large database of


measured physicochemical properties such as melting point, vapor pressure, and water solubility to estimate the fate and


effect of a specific chemical based on its molecular structure. (http://www.epa.gov/med/databases/aster.html) (U.S. EPA,


1998/99)


reference doses (RfDs): chemical concentrations expressed in mg of pollutant/kg body weight/day, that, if not exceeded,


are expected to protect an exposed population, including sensitive groups such as young children or pregnant women.


secondary MCLs: human health-based drinking water criteria to assess the health hazards associated with the presence of


certain toxic chemicals in drinking water. SMCLs are established for taste or aesthetic effects.


Superfund Chemical Data Matrix (SCDM): a source for factor values and benchmark values applied when evaluating


potential National Priorities List (NPL) sites using the Hazard Ranking System (HRS).


(http://www.epa.gov/superfund/resources/scdm/index.htm).


suspended solids: small particles of solid pollutants that float on the surface of, or are suspended in, water bodies.


(http://www.epa.gov/OCEPAterms/sterms.html)


systemic toxicants: chemicals that EPA believes can cause significant non-carcinogenic health effects when present in the


human body above chemical-specific toxicity thresholds.


threatened and endangered (T&E): animals, birds, fish, plants, or other living organisms threatened with extinction by


anthropogenic (man-caused) or other natural changes in their environment. Requirements for declaring a species endangered


are contained in the Endangered Species Act.


Total Petroleum Hydrocarbon (TPH): a general measure of the amount of crude oil or petroleum product present in an


environmental media (e.g., soil, water, or sediments). While it provides a measure of the overall concentration of petroleum


hydrocarbons present, TPH does not distinguish between different types of petroleum hydrocarbons.


Total Kjeldahl Nitrogen (TKN): the total of organic and ammonia nitrogen. TKN is determined in the same manner as


organic nitrogen, except that the ammonia is not driven off before the digestion step.


total suspended solids (TSS): a measure of the suspended solids in wastewater, effluent, or water bodies, determined by


tests for "total suspended non-filterable solids." (See: suspended solids.) 


(http://www.epa.gov/OCEPAterms/tterms.html)


travel cost method (TCM): method to determine the value of an event by evaluating expenditures of recreators. Travel


costs are used as a proxy for price in deriving demand curves for the recreation site. 


(http://www.damagevaluation.com/glossary.htm)


uptake: the movement of one or more chemicals into an organism via ingestion, inhalation, and/or through the skin.


vascular plants: plants that are composed of, or provided with, vessels or ducts that convey fluids. (www.infoplease.com)


willingness-to-pay (WTP): maximum amount of money one would give up to buy some good.


(http://www.damagevaluation.com/glossary.htm)


12-18 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

ACRONYMS 

AQUIRE: AQUatic Information REtrieval System


ASTER: ASsessment Tools for the Evaluation of Risk


AWQC: ambient water quality criteria


BIODEG: biodegradation


BOD: biological oxygen demand


CA: contingent activity


CHEMFATE: chemical fate


CR: contingent rating


CV: contingent valuation


COD: chemical oxygen demand


HAP: hazardous air pollutant


HEAST: Health Effects Assessment Summary Tables


IRIS: Integrated Risk Information System


Koc: adsorption coefficient


MCL: maximum contaminant level


O&G: oil and grease


POC: pollutant of concern


POTW: publicly-owned treatment work


PP: priority pollutant


QSAR: quantitative structure-activity relationship


RfD: reference dose


SCDM: Superfund Chemical Data Matrix


SF: cancer potency slope factor


T&E: threatened and endangered


TCM: travel cost method


TKN: Total Kjeldahl Nitrogen


TPH: Total Petroleum Hydrocarbon


TSS: total suspended solids


WQC: human health-based water quality criteria


WTP: willingness-to-pay


12-19 
MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview 

REFERENCES 

Amdur, M. O., J. Doul, and C. D. Klaassen, eds. 1991. Cassarett and Doul's: Toxicology, the Basic Science of Poisons. 4th 

ed. New York, NY: McGraw-Hill Inc. 

Amdur, M. O., J. Doul, and C. D. Klaassen, eds. 1996. Cassarett and Doul's: Toxicology, the Basic Science of Poisons. 5th 

ed. New York, NY: McGraw-Hill Inc. 

Syracuse Research Corporation (BIODEG, CHEMFATE). 1999. Syracuse Research Corporation's Environmental Fate 

Databases. Syracuse, NY: Syracuse Research Corporation. http://esc.syrres.com/efdb/BIODGSUM.HTM and 

http://esc.syrres.com/efdb/Chemfate.htm. 

U.S. Environmental Protection Agency. (U.S. EPA). 1980. Ambient water quality criteria documents. Washington, DC: 

Office of Water, U.S. EPA. EPA 440/5-80 Series. Also refers to any update of criteria documents (EPA 440/5-85 and EPA 

440/5-87 Series) or any Federal Register notices of proposed criteria or criteria corrections, and EPA 822-Z-99-001. The 

most recent National Recommended Water Quality Criteria used in this report were published in the Federal Register on 

December 10, 1998. 

U.S. Environmental Protection Agency. (U.S. EPA). 1986. Ambient Water Quality Criteria for Dissolved Oxygen. EPA 

440/5-86-003. 

U.S. Environmental Protection Agency. (U.S. EPA). 1995. Proceedings of the First Gulf of Mexico Hypoxia Management 

Conference. EPA-55-R-97-001. 

U.S. Environmental Protection Agency. (U.S. EPA). 1997. Health Effects Assessment Summary Tables (HEAST). 

Washington, DC: Office of Research and Development and Office of Emergency and Remedial Response, U.S. EPA. 

U.S. Environmental Protection Agency. (U.S. EPA). 1998. National Water Quality Inventory. 1996 Report to Congress. 

EPA 841-R-97-008. 

U.S. Environmental Protection Agency. (U.S. EPA). 1998/99a. QSAR. Duluth, MN: Environmental Research Laboratory, 

U.S. EPA. 

U.S. Environmental Protection Agency. (U.S. EPA). 1998/99b. Aquatic Toxicity Information Retrieval (AQUIRE) 

Database. Mid-Continent Ecology Division (MED), Duluth, MN. U.S. Environmental Protection Agency. Database 

retrieval @ http://www.epa.gov/ecotox/. 

U.S. Environmental Protection Agency. (U.S. EPA). 1998/99c. Assessment Tools for Evaluation of Risk (ASTER) 

Database. Duluth, MN: Environmental Research Laboratory, U.S. EPA. 1998 Database retrieval. 

U.S. Environmental Protection Agency. (U.S. EPA). 1998/99d. Integrated Risk Information System (IRIS). Washington, 

DC: U.S. EPA. 1998 Database retrieval @ http://www.epa.gov/iris/. 

U.S. Environmental Protection Agency. (U.S. EPA). 1999. Progress Report on the EPA Acid Rain Program. U.S. EPA 

Office of Air and Radiation. EPA 430-R-99-011. 

Wexler, P., ed. 1998. Encyclopedia of Toxicology, Volumes 1-3. 

12-20 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Chapter 13: Human Health Benefits


INTRODUCTION 

EPA expects that the final MP&M regulation will yield a 

range of human health benefits by reducing effluent 

discharges to waterways used for fishing or drinking water. 

This chapter analyzes four categories of expected human 

health benefits. The first two categories involve reductions in 

cancer cases from two exposure pathways: consumption of 

contaminated fish tissue and ingestion of contaminated 

drinking water for the exposed population. EPA evaluated 

the expected annual reduction in cancer cases in the exposed 

population and the associated monetary value of avoiding 

those cancer cases. 

EPA quantified, but did not monetize, two additional 

measures of human health-related benefits. The first is the 

changes in fish consumption and drinking water exposures to 

non-cancer causing pollutants measured against non-cancer 

health effect reference doses (RfDs), an indicator of noncancer health risk. The second benefit measure is the change 

in occurrence of pollutant concentrations that are estimated to 

exceed human health-based ambient water quality 

criteria (AWQC). 

EPA also quantified and monetized changes in health risk to 

adults and children from reduced exposure to lead. This 

analysis is presented in Chapter 14. 

The health-related measures were estimated for the baseline 

and for the final option for all of the benefit categories 

CHAPTER CONTENTS 

13.1 ............... 13-2 

13.1.1 from Fish Consumption ........ 13-2 

13.1.2 from Drinking Water Consumption 13-8 

13.1.3 s above Non-cancer Health 

Thresholds ................. ........ 13-10 

13.1.4 .............. 13-14 

13.2 ................. ............... 13-17 

13.2.1 ption Cancer Results ..... 13-17 

13.2.2 Water Consumption Cancer 

Results ................. ........... 13-19 

13.2.3 er Health Threshold Results . . 13-19 

13.2.4 n Health AWQC Results ........ 13-21 

13.3 .............. 13-22 

13.3.1 e Design & Analysis of Benefits by 

Location of Occurrence ............... 13-22 

13.3.2 

Pollutants ................. ......... 13-23 

13.3.3 fects of Pollutants ............ 13-23 

13.3.4 s of MP&M 

Pollutants ................. ......... 13-23 

13.3.5 ream Effects ................ 13-24 

13.3.6 ishing Population .......... 13-24 

13.3.7 Treatment of Cancer Latency .......... 13-25 

13.3.8 Treatment of Cessation Lag ........... 13-25 

13.3.9 Use of Mean Individual Exposure 

Parameters ................. ........ 13-26 

13.3.10 Cancer Potency Factors ............. 13-26 

Glossary ................. ................. . 13-27 

Acronyms ................. ................. 13-28 

References ................. ................ 13-29 

Methodology & Data Sources 

Cancer

Cancer

Exposure

Human Health AWQC 

Results 

Fish Consum

Drinking

Non-canc

Huma

Limitations and Uncertainties 

Sampl

In-Waterway Concentrations of MP&M 

Joint Ef

Background Concentration

Downst

Exposed F

analyzed. In addition, EPA estimated health benefits for alternative options which EPA considered for the MP&M regulation. 

The reduction in the health-related measures (i.e., number of annual cancer cases) from baseline to the post-compliance case 

is the estimated benefit of the MP&M regulation. As discussed in Chapter 12, EPA estimated national benefits for the 

regulation based on sample facility data. The Agency extrapolated findings from the sample facility analyses to the national 

level using two alternative extrapolation methods: (1) traditional extrapolation and (2) post-stratification extrapolation. 

Appendix G provides detailed information on the extrapolation approaches used in this analysis. 

EPA estimated that, for combined recreational and subsistence angler populations, the final option would lead to a marginal 

reduction in cancer cases. The total monetized human health benefits from reduced cancer cases from both the fish 

consumption and drinking water pathways are essentially negligible (i.e., $90 per year based on the traditional extrapolation 

and $134 per year based on the post-stratification extrapolation (2001$)). 

Benefits will also be realized in the form of reductions in non-cancer human health effects (e.g., systemic effects, reproductive 

toxicity, and developmental toxicity) from reduced contamination of fish tissue and drinking water sources. For this analysis, 

EPA estimates the numbers of individuals in the exposed populations who might be expected to realize reduced risk of 

non-cancer health effects in the post-compliance scenario. To evaluate the potential benefits of reducing the in-stream 

concentrations of 76 pollutants that cause non-cancer health effects, EPA estimated target organ-specific hazard indices (HI) 

for drinking water and fish ingestion exposures in both the baseline and post-compliance scenarios. HI values below one are 

generally considered to suggest that exposures are not likely to result in appreciable risk of adverse health effects during a 

lifetime, and values above one are generally cause for concern, although an HI greater than one does not necessarily suggest a 

likelihood of adverse effects. 

13-1 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

The results of EPA's analysis suggest that the incremental risk of non-cancer effects from pollutants discharged by MP&M 

facilities alone is quite low. This analysis found that HIs for the entire population associated with sample facilities is less than 

one in the baseline. The results of EPA’s analysis of the post-compliance scenario indicate that hazard indices for individuals 

in the exposed population may decrease after facilities comply with the MP&M regulation. Increases in the percentage of 

exposed populations that would be exposed to no risk of non-cancer adverse human health effects due to the MP&M 

discharges occur in both the fish and drinking water analyses. Whether the incremental shifts in HIs are significant in reducing 

absolute risks of non-cancer adverse human health effects is uncertain and will depend on the magnitude of contaminant 

exposures for a given population from risk sources not accounted for in this analysis. 

Finally, EPA analyzed the effect of the final regulation on occurrence of pollutant concentrations resulting from MP&M 

discharges that exceed human health-based AWQC. EPA estimated that, as the result of baseline MP&M pollutant 

discharges, in-stream concentrations exceed human health-based AWQC in 78 and 112 receiving reaches nationwide based on 

the traditional extrapolation and post-stratification extrapolation, respectively. EPA estimated that none of these exceedances 

will be eliminated under the final option. 

13.1 METHODOLOGY & DATA SOURCES 

Individuals are potentially exposed to pollutants from MP&M facilities via consumption of contaminated fish tissue and 

drinking water. Potential human health effects include cancer and non-cancer health effects. Risks such as skin, lung, liver, 

kidney, and bladder cancer and leukemia are associated with exposure to 13 MP&M pollutants (see Table 13.1). Non-cancer 

health effects are associated with exposure to 76 MP&M pollutants. These effects include increased blood pressure, 

gastrointestinal effects, liver and kidney toxicity, cardiovascular and central nervous system effects, and decreased birth 

weight (see Table 13.2). 

This section summarizes the methodology for estimating national benefits for three benefit categories: 

1. reduced incidence of cancer from consumption of fish taken from waterways affected by MP&M industry discharges, 

2. reduced incidence of cancer from ingestion of water taken from waterways affected by MP&M industry discharges, 

and 

3. reduced occurrence of pollutant concentrations resulting from MP&M discharges that exceed human health-based 

AWQC. 

This analysis does not include all possible human health benefits and does not provide a comprehensive estimate of the total 

human health benefits associated with the final MP&M rule. Analyses of health benefits are not possible for a significant 

number of the pollutants whose discharges will be reduced under the post-compliance scenario due to the lack of data on a 

quantitative relationship between ingestion rate and the potential health effects associated with these chemicals. 

Beyond these important limitations, the methodologies used to assess the human health benefits involve significant 

simplifications and uncertainties. Elements of the analysis involving significant simplifications and uncertainties include the 

following: sample design and analysis of benefits by location of occurrence; estimation of in-waterway concentrations of 

MP&M pollutants; consideration of the joint effects of pollutants; consideration of background concentrations of MP&M 

pollutants; consideration of downstream effects; and estimation of the exposed fishing population. Section 13.3 provides 

more detail on limitations and uncertainties associated with the human health benefits analyses. Whether these simplifications 

and uncertainties, taken together, are likely to lead to an understatement or overstatement of the estimated economic values 

for the human health benefits that were analyzed is not known. 

13.1.1 Cancer from Fish Consumption 

The analysis of reduced annual occurrence of cancer in exposed populations via the fish consumption pathway involves three 

analytic steps: 

� estimating the reduced annual risk of incurring cancer per exposed individual; 

� estimating the population that would be expected to benefit from reduced contamination of fish; and 

13-2 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

� calculating the c hange in the nu mbe r of can cer ev ents in the expo sed p opu lation. 

Each step is discussed in detail below. 

a. g change in individual cancer risk 

The estimated increm ental risk to an individual of de veloping ca ncer is based on four factors:1 

� the quantity of carcinoge nic chemica ls that MP &M facilities discharge to waterways, 

� the rate at which the discharged chemicals accumulate in fish tissue, 

� the cancer effect of the chemicals, and 

� the rate of personal consumption of contaminated fish. 

For each sample M P&M facility and the waterway to which it discharges, EPA calculated the incremental cancer risk to four 

population classes with different fish consumption rates: children in families that participate in recreational angling, children 

in families tha t particip ate in sub sistence angling, adults in families that particip ate in rec reational ang ling, and adults in 

families that participate in subsistence angling. sk values for baseline (i.e., before 

regula tion) p ollutant d ischarg es and for po st-com plianc e disch arges base d on the po licy options co nsidered in th e final rule 

analysis. iscussion summ arizes the increme ntal cancer risk calculations. 

EPA calculated the in-waterway pollutant concentrations for each reach receiving discharges from an M P&M facility using a 

simplified dilution model for all chemicals for which a quantitative relationship between ingestion rate and the annual 

probability of developing cancer has been estimated. reach” is a specific length of river, lake shoreline, or marine 

coastline, and an “MP&M reach” is one to which a n M P& M facility discharges.2

 This analysis considered only the discharge 

reach and did not estimate concentrations below the initial MP& M reach. T he water quality model used for calculating inwaterway po llutant concentrations acc ounts for the dilution chara cteristics of different water body types (i.e., streams, 

estuaries, and lakes). It does not account for other fate processes, such as chemical degradation or photolysis. The estimated 

pollutant concentrations reflect the average pollutant concentrations in the reach to which a facility discharges. For additional 

details on the calculation of waterway concentrations, see Appendix I. 

The incremental cancer risk associated with each pollutant was calculated based on the estimated concentration of the 

pollutant in the affected waterway, the assumed uptake of the pollutant into fish flesh, the daily rate of fish ingestion, and the 

cancer risk factor for each pollutant. a for calculating the risk to an individual from consumption of a given 

chemical is as follows: 

(13.1) 

where: 

Risk = incremental risk of incurring cancer from fish consumption (change in probability); 

C = pollutant concentrations in surface water (�g/l); 

CF1 = conversion factor, micrograms to milligrams (0.001 mg/�g); 

BCF = bioconcentration factor of pollutant in fish (l/kg); 

CR = human consumption rate of fish (kg/day); 

EF = exposure frequency (365 days/year); 

ED = exposure duration (years); 

BW = human body weight (70 kg for adults and 30 kg for children under 18); 

Estimatin

EPA calculated the incremental cancer ri

The following d 

A “

The formul

1 The risk value is referred to as the incremental risk because it is the incremental lifetime probability that an individual will develop 

cancer above and beyond the baseline probability posed by all other extant factors that contribute to a risk of developing cancer. 

2 A reach is a length of river, shoreline, or coastline with relatively uniform water flow characteristics. Thus, it is reasonable to 

assume that pollutant dischargers have a relatively uniform effect on concentrations within a reach. 

13-3 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

LT = human lifetime (years);


CF2 = conversion factor, years to days (365 days/year); and


SF = pollutant cancer potency factor (mg/kg/day)-1.


The pollutants analyzed and their cancer potency factors are presented in Table 13.1. EPA used the relationship outlined 

above to estimate lifetime risk values for individuals in subsistence and recreational fishing households. The risks to 

recreational and subsistence households are estimated over two lifetime segments. Specifically, children living in recreational 

fishing households are assumed to consume 7.27 grams per day (0.007 kg/day) of freshwater/estuarine fish over an 18-year 

period (ages 0 to 18). Adults are assumed to consume 17.5 grams per day (0.018 kg/day) of freshwater/estuarine fish over a 

52-year period (ages 18 to 70). Risks for individuals living in recreational and subsistence fishing households differ in the 

assumed consumption rates. Children living in subsistence fishing households are assumed to consume 60.58 grams per day 

(0.061 kg/day) of freshwater/estuarine fish over an 18-year period (ages 0-18). Adults in subsistence households are assumed 

to consume 142.4 grams per day (0.142 kg/day) of freshwater/estuarine fish over a 52-year period (ages 18 to 70). The total 

lifetime incremental risk for these households is calculated by summing the risks for both lifetime segments. 

Fish consumption rates for adults are taken from the Methodology for Deriving Ambient Water Quality Criteria for the 

Protection of Human Health (EPA, 2000a). Both these rates, 142.4 g/day for adult subsistence anglers and 17.5g/day for 

adult recreational anglers, are used for the specific sub-population that they represent. EPA was not able to break the data 

supporting these rates down by gender or age group for use in this analysis. 

EPA has determined that the fish consumption rate of 142.4 g/day for adult subsistence anglers falls within the range of the 

arithmetic mean of adult subsistence angler studies representative of the United States (EPA, 1998). The value represents the 

average consumption rate for this population of anglers. It represents uncooked, fresh and estuarine finfish and shellfish. 

This rate is reported on an uncooked basis because pollutant concentration data is reported on an uncooked weight basis. 

Similarly, the fish consumption rate of 17.5 g/day falls within the average consumption rate for adult recreational anglers. 

This rate also represents uncooked, fresh and estuarine finfish and shellfish.3 

Fish consumption rates for children in recreational angling households are based on W est et al. (1989) in the Exposure 

Factors Handbook (EPA 1997c). This study has the most specific data for this population group and cites an intake of 7.27 

grams/day of freshwater and estuarine fish for children in recreational angling households. For children in subsistence 

angling households, the consumption rate was extrapolated from the 7.27 grams/day rate for children in recreational angling 

households using the proportional relationship between consumption rates for adult subsistence and recreational anglers 

(142.4 grams/day divided by 17.5 grams/day). The consumption rate for children in subsistence angling households is 

calculated to be 60.58 grams/day. 

Currently, data on marine fish consumption rates for recreational anglers and subsistence anglers are not readily available. 

Given that there are few marine reaches affected by the MP&M effluent guideline, EPA decided to use the fresh and 

estuarine fish consumption rates in lieu of marine fish consumption rates. This may result in underestimation of benefits, 

however, it may also be argued that few subsistence fishers eat fresh/estuarine fish and marine fish at the same rate. 

3 For detail see memorandum Fish Consumption Rates by Lynn Zipf (EPA, 2002). 

13-4 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Table 13.1: Cancer Potency Factors for MP&M Pollutants 

CAS Number Regulated Pollutant Cancer Potency Factor 

(mg/kg/day)a 

Drinking Water 

Criterion? 

62533 Aniline 0.0057 

62759 Nitrosodimethylamine, N- 51 

67663 Trichloromethane 0.0061 Yes 

75003 Chloroethane 0.0029 

75092 Dichloromethane 0.0075 Yes 

75354 Dichloroethene, 1,1- 0.6 Yes 

78591 Isophorone 0.00095 

79016 Trichloroethene 0.011 Yes 

86306 Nitrosodiphenylamine, N- 0.0049 

117817 Bis(2-ethylhexyl) phthalate 0.014 Yes 

123911 Dioxane, 1,4- 0.011 

127184 Tetrachloroethene 0.052 Yes 

7440382 Arsenic 1.5 Yes 

a The cancer potency factor is the incremental probability of developing cancer over a lifetime resulting from 

ingestion of the indicated chemical at the rate of one milligram per day per kilogram of body mass. For the 

incremental rates of exposure in this analysis and assuming reasonable background chemical exposures, the 

potency factor may be reasonably assumed to be a linear constant. 

Source: U.S. EPA (1998/99); U.S. EPA (1997a). 

The pollutant-specific risks to recreational and subsistence anglers from MP&M facility discharges were then summed across 

pollutants for each type of angler, to obtain incremental risks for each population group from each facility’s discharge. EPA 

developed separate estimates of cancer risk for each combination of angler type and facility discharging at least one pollutant 

with a cancer risk factor. The total change in probability of developing cancer from exposure to more than one MP&M 

pollutant is assumed to be the sum of the incremental risk effects from each pollutant: that is, the effects of the individual 

pollutants are assumed to be linearly additive.4

 The annual increased risk of cancer was estimated by dividing the increased 

lifetime risk values by 70 (an estimate of lifetime). 

b. Estimating the affected population 

The population exposed to contaminated fish and thus expected to benefit from reduced discharges includes recreational and 

subsistence anglers who fish the affected reaches, as well as members of such anglers' households. The geographic area from 

which anglers would travel to fish a reach is assumed to include only those counties that abut a given reach.5 This assumption 

is based on the finding in the 1991 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation that 65 percent 

of anglers travel less than 50 miles to fish (U.S. Department of the Interior, 1993). The average diameter of the counties 

abutting the reaches receiving discharges from the sample MP&M facilities is approximately 20 miles. Given that counties 

may have different shapes and that the road distance to the fishing site is likely to be greater than a straight line, the MP&M 

approach is likely to account for the majority of anglers that are likely to fish the affected reach. It is, however, likely to 

4 Note that the assumption of linear additivity of cancer risk effects applies not only to the combination of pollutants from a single 

facility but also to the combined effects of multiple facility discharges. When more than one MP&M facility discharges to the same 

affected waterway a circumstance found to occur with some frequency in the sample facility data the combination of the multiple 

facility discharges may be accounted for by simply analyzing the effects of each facility independently. The cancer effects from multiple 

facilities can be aggregated to estimate cancer cases in the exposed population. 

5 The exposed, and thus potentially benefiting, population would also include a category of “all other individuals” who consume 

freshwater and estuarine fish. Although these individuals are expected to have a much lower average daily consumption rate than anglers 

in the adjacent counties, they nevertheless would likely receive some benefit from reduced exposure to pollutants through fish 

consumption. This analysis omits this consumption category and the associated benefit estimate. 

13-5 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

introduce a downward bias into the estimate of the affected population. Given that anglers tend to travel farther to visit sites of 

very good or exceptional quality, the magnitude of this bias will depend on the fishing quality of the affected sites. 

Estimating the number of persons fishing a reach involved the following steps: 

� estimating the licensed fishing population in counties abutting MP&M reaches; 

� estimating the population of subsistence fishermen in counties abutting MP&M reaches; 

� estimating the fraction of the total fishing population in counties abutting an MP&M reach that fish the MP&M reach 

and, from that fraction, the size of population expected to fish each MP&M reach; 

� adjusting the calculated fishing populations for the presence of fish advisories; and 

� including family members in the exposed population estimates. 

� Estimating the licensed fishing population in counties abutting MP&M reaches


The number of fishing licenses sold in counties abutting MP&M reaches is assumed to approximate the number of anglers


residing in the abutting counties. EPA excluded the nonresident, one-day, and three-day license categories from the total


number of licenses used in this analysis. Data on fishing licenses are not available for every state in which MP&M facilities


are located. EPA used state-level data to estimate the number of fishing licenses per county for those states for which county-


level data were not assembled. Total state licenses were apportioned to counties based on the ratio of total population in the


county abutting a discharge reach to total state population. Where an M P&M reach spans more than one county, fishing


licenses were summed across all counties abutting the discharge reach. Where a reach lies in more than one state, EPA


separately calculated the number of licenses for the abutting county(ies) based on the fishing license and county population


data for the respective states.


EPA’s analysis does not account for recreational anglers who do not purchase licenses as required by law. This may result in a


significant underestimate of the fishing population at risk from exposure to MP&M pollutants. For example, the 1996


National Survey of Fishing, Hunting, and Wildlife-Associated Recreation found that 34 percent of the anglers (16 years of


age and older) did not have licenses (U.S. Department of the Interior, 1996).


� Estimating the population of subsistence fishermen in counties abutting MP&M reaches


Although fishing licenses may be sold to subsistence fishermen, many of these individuals do not purchase fishing licenses.


The extent of subsistence fishing in the U.S. or in individual states is not generally known. For this analysis, EPA assumed


that the number of subsistence fishermen would be an additional 5 percent of the licensed fishing population.6 That is, after


estimating the licensed fishing population in counties abutting MP&M reaches, EPA added 5 percent to this value as the


estimated number of subsistence fishermen.7


� Estimating the population fishing an MP&M reach


EPA assumed that fishing activity among anglers residing within counties abutting a discharge reach is distributed evenly


among all reach miles within those counties. Thus, the number of anglers who fish an MP&M reach was estimated by


computing the length of the reach as a percentage of total reach miles within corresponding counties and multiplying the


estimated ratio by the total fishing population in counties abutting the reach.


� Adjusting for fish advisories


For MP&M reaches where fish advisories are in place (typically due to non-MP&M regulated pollutants such as dioxin and


mercury), EPA assumed that some proportion of anglers would adhere to the advisory and not fish those reaches (U.S. EPA,


1999a). Past studies suggest that anglers have a high, although not complete, level of awareness of fish advisories. These


studies further suggest that while anglers may change their behavior in response to fish consumption advisories, they do not


necessarily refrain from fishing in these reaches or consuming fish taken from reaches under an advisory. For example,


6 It is important to estimate recreational and subsistence populations separately because fish consumption rates for subsistence anglers 

are considerably higher than those for recreational anglers. 

7 The environmental justice analysis presented in Chapter 17 of this report shows that the percent of residents living below the 

poverty level in the counties affected by MP&M discharges ranges from 7.4 to 25.2. Thus, the assumption that subsistence anglers are an 

additional 5% of the licensed fishing population is likely to provide a reasonable estimate of the subsistence anglers population. 

13-6 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

studies conducted by Belton et al (1986), Knuth and Velicer (1990), Silverman (1990), W est et al. (1989), Connelly, Knuth, 

and Biso gni (19 92), and C onne lly and K nuth (1 993 ) indica te that 50 to 87 percent of an glers surveyed were a ware of state 

fish advisories on water bodies where they fish. 

These studies also indicate that only 10 to 34 percent of anglers who were aware of advisories modified their fishing behavior 

in response by no longe r fishing a p articular locatio n, chan ging the locatio n in whic h they fish, o r taking fe wer fishing trips. 

Ho wever, 13 to 68 p ercen t of anglers who were a ware of advisories changed their consumption or preparation h abits in 

response to advisories. udy by Knuth and Velicer (1990) also found some confusion among anglers regarding which 

waters were under advisory: 37 percent of fishermen actually fishing in waters under advisory reported that they were fishing 

in uncontam inated waters. 

On the basis of these data, EPA assumed that recreational fishing activity would be 20 percent less on reaches subject to an 

advisory than wo uld otherwise b e estimated. A also assum ed that fish advisories do not affect fishing participation by 

subsistence anglers; thus, no adjustment was made to the estimates of the subsistence fishing population based on the presence 

of fish advisories. 

The assumed 20 pe rcent decrease in recreationa l fishing could lead to either an o verestimate or un derestimate o f the risk 

assoc iated with consump tion of contam inated fish. ne thing, anglers who c hange loca tions may simp ly be switching to 

other locatio ns where advisories are in p lace and therefore maintain or increase their current risk. lso, those who continue to 

fish contaminated w aters may chang e their consum ption and prepa ration habits to minimize the risks. Data on the sp ecific fish 

advisories was pulled from EP A’s on-line Listing of Fish and Wildlife Advisories (U.S. EPA , 1999a). 

� Including family mem bers in the exposed population estimates 

EPA assumed that, in addition to anglers themselves, families of anglers would also consume fish taken from waters affected 

by MP &M facility discharges. Therefore, for each MP& M reach, EP A multiplied the estimated numbers of recreational and 

subsistence anglers fishing the affected reaches by 2.65, the size of the average U.S. household in 1996 based on C urrent 

Population Reports, (U.S. Bureau of the Census, 1997). These calculations yielded the household populations of recreational 

and subsistence anglers who are estimated to consume fish from the reach to which the MP&M facility discharges, either 

directly or indirectly through a POT W . EPA expe cts that family members will benefit from reduced M P&M industry 

discharges by consuming fish that has lower levels of pollutant contamination. 

c. 

EPA calculated the number of cancer cases associated with the pollutant discharges (baseline and post-compliance) from each 

facility by multiplying the incremental cancer risk value for the two population classes times the estimated sizes of the 

population classes living near the facility. of the incremental risk value and the population size yields the number 

of annual can cer ev ents in the given p opu lation cla ss estima ted to result from consump tion of fish ta ken fro m wa terways 

affected by MP&M pollutant discharges. the values for the recreational and subsistence fishing household classes 

yields the total number of cancer cases associated with the sample facility discharges. Because the number of cancer cases 

apply to sample facilities, EP A extrapo lated the samp le results to the total M P& M pop ulation b y multiplying the resu lt 

obta ined fo r each samp le facility by its sam ple we ight and summ ing the sa mple -weighte d facility resu lts. formu la 

follows: 

(13.2) 

where: 

TCCf c = total national estimate of annual cancer cases associated with consumption of contaminated fish tissue 

(baseline or post-compliance); 

Wti = facility samp le weigh t i (i = 1 to N facilities, where N is the number of facilities in the sample); 

POPi,sprt = exposed pop ulation in recreational fishing househo lds for the reac h to which facility i discha rges (with 

adjustments as indicated for the presence of fish consumption advisories); 

POPi,sbst = exposed pop ulation in subsistence fishing households for the reac h to which facility i discharges; 

The st

EP 

For o 

A 

Calculating the change in the number of cancer events in the exposed population 

The product

Summing 

The

13-7

MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Riski,sprt = incremental cancer risk from fish consumption in the recreational fishing household population 

assoc iated with MP& M pollutant discharges from facility i; and 

Riski sbst = incremental cancer risk from fish consumption in the subsistence fishing household population 

assoc iated with MP& M pollutant discharges from facility i. 

These values were calculated for the baseline and post-compliance discharge cases. difference is the number of cancer 

cases estimated to be avoided annually through the fish consumption pathway as a result of the final regulation. 

13.1.2 Cancer from Drinking Water Consumption 

Th e analysis of red uced cancer incid ence via the d rinking w ater pathway involve s three analytical step s that are largely 

parallel to those performed for the fish co nsumption pathw ay: 

� estimating cancer risk to an exposed individual from consumption of contaminated drinking water, 

� estimating the population that would benefit, and 

� calculating the change in the number of cancer events in the exposed population. 

The majo r differences in the analysis for the drinking water pathway involve the identification of the exposed po pulation and 

the analysis of pollutant discharge effects in both the reach to which a facility discharges and reaches downstream of the 

discharge p oint. 

a. water consumption 

Estimating cancer risk from consumption of drinking water affected by MP&M discharges requires calculating in-waterway 

pollutant concentrations in locatio ns whe re drin king wa ter treatm ent system s draw water fo r pub lic con sump tion. his 

analysis involves three eleme nts: 

� estimating in-waterway pollutant concentrations for each pollutant in the reach to which a facility directly or 

indirec tly discharges. he me thod and fo rmulas for this calculation are ide ntical to tho se describe d for the analysis 

of cancer effec ts for the fish consumptio n pathway. 

� estimating the pollutant concentrations over a distance of 500 kilometers downstream from each facility’s discharge 

reach, using an exponential decay model in which pollution concentrations diminish below the initial point of 

discharge (e.g., dilution, adso rption, partitioning, vola tilization, an d hydrolysis). ethod s used to calculate 

downstream pollutant concentrations are described in more detail in Appendix H. 

� identifying the location of any drinking water intakes in the initial and downstream reaches where pollutant 

concentrations were calculated and assigning pollutant concentration values to each relevant intake point. The 

EPA's Safe Drinking Water Information System (SDWIS) file in the Risk Screening Environmental Indicator 

(RS EI) model provided information on drinking water intakes (U.S. EPA , 1999b). 

Estimated pollutant concentrations at each drinking water intake determines cancer risk. nking water 

treatment systems will reduce concentrations to below adverse effect thresholds for all chemicals for which EPA has 

pub lished a drinking water criterion . refore , pollutants exam ined in the M P& M drinkin g water analysis inc lude o nly six 

carcinogens for which current drinking water criteria are not available. See Table 13.1 for a list of the pollutants, their cancer 

potency factors, and drinking water criteria. 

The formula for calculating the incremental risk to an individual resulting from the discharge of a given pollutant from a given 

facility at reaches with a known pu blic drinking water intake is as follows: 

(13.3) 

where: 

Risk = incremental risk of incurring cancer from drinking water consumption (change in probability), calculated at 

each drinkin g water intake w ithin 50 0 km of the initial d ischarg e po int; 

The 

Estimating cancer risk from drinking

T 

T 

M 

EPA assumed dri

The 

13-8

MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

C = pollutant concentration in surface water in the reach with an intake (�g/l); 

CF1 = conversion factor, micrograms to milligrams (0.001 mg/�g); 

CR = human consumption rate of water (1.24 l/day); 

EF = exposure frequency (350 days/year); 

ED = exposure duration (70 years); 

BW = human body weight (70 kg); 

LT = human lifetime (70 years); 

CF2 = conversion factor (365 days/year); and 

SF = pollutant can cer p otenc y factor (m g/kg/day)-1 . 

The consumption rate of 1.24 liters per day used in this analysis to represent the average daily consumption of drinking water 

by a person in the United States is taken from Estimated Per Capita Water Ingestion in the United States (EPA, 2000b). 

reco mmended in the Exp osure Facto rs Ha ndb ook (1997c ), EP A use s an exposure freq uenc y of 35 0 da ys per ye ar to es timate 

the increased risk of cancer from consuming drinking water supplied by drinking water systems with intakes on local surface 

water b odie s. 

The incremental individual risk from each facility’s pollutants are then summed over pollutants at each drinking water intake 

to calculate the incremental risk at each intake resulting from pollutant discharges by each upstream facility. The findings 

carried forward to the next step include the incremental cancer risk for each combination of facility and associated drinking 

water intake(s). 

To estimate the annual increa sed risk of cance r in consume rs served by d rinking water intakes affected b y MP &M discharges, 

the lifetime risk values were then divided by 70 years (an estimate of lifetime). These values were calculated for both the 

baseline and post-com pliance discha rge cases. 

b. 

The exposed population for each combination of discharging facility and drinking water intake is the general population 

served by the drinking water system for which the drinking water intake was identified. fe Drinking Water Information 

System (SDW IS) file in the Risk Screening Environmental Indicator (RSEI) model provided information on drinking water 

intakes. 

c. 

EPA calculated the number of cancer cases for baseline and post-compliance pollutant discharges for each combination of 

facility and affected drinking water intake by multiplying the incremental cancer risk value times the population served by the 

water system drawing water at the drinking water intake. 

The total number of cancer cases associated with the facility discharges is the sum of cancer cases over all drinking water 

intakes. PA extrap olated the sam ple results to the to tal M P& M pop ulation b y multiplying the resu lt for eac h sam ple fac ility 

by its sample weight and summing the sample-weighted facility results. Because incremental cancer effects are assumed to be 

linearly ad ditive, ca ncer-risk effects are aggre gated over facilities and drinkin g water intakes b y simple add ition of the effects 

calculated sep arately for each co mbination o f facility and drinking wa ter intake. ula follows: 

(13.4) 

where: 

TCCdw = total national estimate of cancer cases associated with consumption of chemically-contaminated drinking water 

(baseline or post-compliance); 

Wti = facility samp le weigh t i (i = 1 to N facilities); 

POPi,j = population exposed to discharges by facility i at drinking water intake j (j = 1 to M water supply intakes); and 

Riski,j = increm ental ca ncer risk for d ischarg es by fac ility i at drinking water intake j. 

EPA calculated these values for the baseline and post-compliance discharge cases. ference in the values is the number 

of drinking water asso ciated canc er cases estimated to be avo ided ann ually by reduced MP &M industry discharges. 

As 

Estimating the benefiting population 

Sa

Calculating the changes in the number of cancer events 

E 

The form 

The dif

13-9

MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

13.1.3 Exposures above Non-cancer Health Thresholds 

Exposed populations are also at risk of developing non-cancer health problems (including systemic, reproductive, 

immunological, neurological, or circulatory problems) from fish ingestion and water consumption. The common approach for 

assessing the risk of non-cancer health effects from the ingestion of a pollutant is to calculate a hazard quotient by dividing an 

individual's oral exposure to the pollutant, expressed as a pollutant dose in milligrams per kilogram body weight per day 

(mg/kg/day), by the pollutant's oral reference dose (RfD). An RfD is defined as an estimate (with uncertainty spanning 

perhaps an order of magnitude) of a daily oral exposure that likely would not result in the occurrence of adverse health effects 

in humans, including sensitive individuals, during a lifetime. Toxicologists typically establish an RfD by applying uncertainty 

factors to the lowest- or no observed adverse effect level (NOAEL) for the critical toxic effect of a pollutant. A hazard 

quotient less than one means that the pollutant dose to which an individual is exposed is less than the RfD, and, therefore, 

presumed to be without appreciable risk of adverse human health effects. A hazard quotient greater than one means that the 

pollutant dose is greater than the RfD. RfDs are available for 77 of the 132 MP&M pollutants of concern. The pollutants 

analyzed and their RfDs are listed in Table 13.2. 

Table 13.2: RfDs for MP&M Pollutants 

CAS 

Number Regulated Pollutant RfD 

(mg/kg/day) 

Drinking Water 

Criterion? a Target Organ and Effects 

83329 Acenaphthene 0.060 No Liver toxicity 

67641 Acetone 0.100 No Increased liver and kidney weights; nephrotoxicity 

98862 Acetophenone 0.100 No General toxicity 

107028 Acrolein 0.020 No Cardiovascular toxicityb 

7429905 Aluminum 1.000 Yes 

Renal failure, intestinal contraction interference, adverse 

neurological effectsc 

120127 Anthracene 0.300 No 

7440360 Antimony 0.000 Yes Longevity, blood glucose, cholesterol 

7440382 Arsenic 0.000 Yes 

Hyperpigmentation, keratosis and possible vascular 

complications 

7440393 Barium 0.070 Yes Increased kidney weight 

65850 Benzoic acid 4.000 No 

100516 Benzyl alcohol 0.300 No Forestomach, epithelial hyperplasia 

7440417 Beryllium 0.002 Yes Small intestinal lesions 

92524 Biphenyl 0.050 No Kidney damage 

117817 

Bis(2-ethylhexyl) 

phthalate 0.020 Yes Increased relative liver weight 

7440428 Boron 0.090 No Testicular atrophy, spermatogenic arrest 

85687 Butyl benzyl phthalate 0.200 No 

Significantly increased liver-to-body weight and liver-to-brain 

weight ratios 

7440439 Cadmium 0.001 Yes Significant proteinuria (protein in urine) 

75150 Carbon disulfide 0.100 No Fetal toxicity, malformations 

108907 Chlorobenzene 0.020 No Histopathologic changes in liver 

75003 Chloroethane 0.400 No 

7440473 Chromium 1.500 Yes Renal tubular necrosis (kidney tissue decay)c 

18540299 Chromium hexavalent 0.003 Yes Reduced water consumption 

7440484 Cobalt 0.060 No Heart effectsc 

7440508 Copper 0.040 Yes Gastrointestinal effects, liver necrosisc 

95487 Cresol, o- 0.050 No Decreased body weights and neurotoxicity. 

13-10 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Table 13.2: RfDs for MP&M Pollutants 

CAS 

Number Regulated Pollutant RfD 

(mg/kg/day) 

Drinking Water 

Criterion? a Target Organ and Effects 

106445 Cresol, p- 0.005 No 

Central nervous system hypoactivity and respiratory system 

distress 

57125 Cyanide 0.020 Yes Weight loss, thyroid effects and myelin degeneration 

75354 Dichloroethene, 1,1- 0.009 Yes Toxic effects on kidneys, spleen, lungsc

; hepatic lesions 

75092 Dichloromethane 0.060 Yes Liver toxicity 

60297 Diethyl ether 0.200 No Depressed body weights 

68122 

Dimethylformamide, 

N,N- 0.100 No Liver and gastrointestinal system effects 

105679 Dimethylphenol, 2,4- 0.020 No 

Clinical signs (lethargy, prostration, and ataxia) and 

hematological changes 

84742 Di-n-butyl phthalate 0.100 No Increased mortality 

51285 Dinitrophenol, 2,4- 0.002 No Cataract formation 

606202 Dinitrotoluene, 2,6- 0.001 No 

Mortality, central nervous system neurotoxicity, blood heinz 

bodies and methemoglobinemia, bile duct hyperplasia, kidney 

histopathology 

117840 Di-n-octyl phthalate 0.020 No 

Kidney and liver increased weights, liver increased SGOT and 

SGPT activity 

122394 Diphenylamine 0.025 No Decreased body weight, and increased liver and kidney weights 

100414 Ethylbenzene 0.100 Yes Liver and kidney toxicity 

206440 Fluoranthene 0.040 No 

Nephropathy, increased liver weights, hematological 

alterations, clinical effects 

86737 Fluorene 0.040 No 

Decreased red blood cell count, packed cell volume and 

hemoglobin 

16984488 Fluoride 0.060 Yes Objectionable dental fluorosis (soft, mottled teeth) 

591786 Hexanone, 2- 0.040 No Hypatotoxicity and nephrotoxcity d 

7439896 Iron 0.300 Yes 

Liver, diabetes mellitus, endocrine disturbance, and 

cardiovascular effectsd 

78831 Isobutyl alcohol 0.300 No Hypoactivity and ataxia 

78591 Isophorone 0.200 No Kidney pathology 

7439965 Manganese 0.140 Yes Central nervous system effects 

78933 Methyl ethyl ketone 0.600 No Decreased fetal birth weight 

108101 Methyl isobutyl ketone 0.080 No 

Lethargy, increased liver and kidney weights and urinary 

protein 

80626 Methyl methacrylate 1.400 No Increased kidney to body weight ratio 

91576 Methylnaphthalene, 2- 0.020 No 

7439987 Molybdenum 0.005 No Increased uric acid 

91203 Naphthalene 0.020 No Decreased body weight 

7440020 Nickel 0.020 Yes Decreased body and organ weights 

100027 Nitrophenol, 4- 0.008 No 

59507 Parachlorometacresol 2.000 No 

108952 Phenol 0.600 No Reduced fetal body weight in rats 

7723140 Phosphorus (elemental) 0.000 No Parturition mortality; forelimb hair loss 

13-11

MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Table 13.2: RfDs for MP&M Pollutants 

CAS 

Number Regulated Pollutant RfD 

(mg/kg/day) 

Drinking Water 

Criterion? a Target Organ and Effects 

129000 Pyrene 0.030 No 

Kidney effects (renal tubular pathology, decreased kidney 

weights) 

110861 Pyridine 0.001 No Increased liver weight 

7782492 Selenium 0.005 Yes Clinical selenosis (hair or nail loss) 

7440224 Silver 0.005 Yes Argyria (skin discoloration) 

100425 Styrene 0.200 Yes Red blood cell and liver effects 

127184 Tetrachloroethene 0.010 Yes Liver toxicity, weight gain 

7440280 Thallium 0.000 Yes 

Liver toxicity, gastroenteritis, degeneration of peripheral and 

central nervous systemb 

7440315 Tin 0.600 No Kidney and liver lesions 

7440326 Titanium 4.000 No 

108883 Toluene 0.200 Yes Changes in liver and kidney weights 

79016 Trichloroethene 0.006 Yes Bone marrow, central nervous system, liver, kidneys d 

75694 Trichlorofluoromethane 0.300 No Survival and histopathology 

67663 Trichloromethane 0.010 Yes Fatty cyst formation in liver 

7440622 Vanadium 0.007 No Kidney and central nervous system effects b 

108383 Xylene, m- 2.000 Yes Central nervous system hyperactivity, decreased body weight 

179601231 Xylene, m- & p-* 2.000 Yes 

95476 Xylene, o- 2.000 Yes Central nervous system hyperactivity, decreased body weight 

136777612 Xylene, o- & p-* 2.000 Yes 

7440666 Zinc 0.300 Yes 

47% decrease in erythrocyte superoxide dismutase (ESOD) 

concentration in adult human females after 10 weeks of zinc 

exposure 

137304 Ziram \ Cymate 0.020 No 

a “Yes”= there is a published drinking water criterion for a given chemical. b Reference dose based on a no observed adverse effect level (NOAEL). Health effects summarized from Amdur, M.O.; Doul, J.; and 

Klaassen, C.D.,eds. 1991. Cassarett and Doul’s Toxicology, 4th edition. 

Target organ and effects summarized from Wexler, P., ed. 1998. Encyclopedia of Toxicology, Volumes 1-3. d Target organ and effects summarized from Amdur, M.O.; Doul, J.; and Klaassen, C.D.,eds. 1996. Cassarett and Doul’s Toxicology, 

5th edition. 

Source: U.S. EPA (1998/99); U.S. EPA (1997a). 

EPA guidance for assessing exposures to mixtures of pollutants recommends calculating a hazard index (HI) by summing the 

individual hazard quotients for those pollutants in the mixture that affect the same target organ or system (e.g., the kidneys, 

the respiratory system). For example, for three liver toxicants discharged from an MP &M facility (pollutant A with a hazard 

index of 0.10, pollutant B with a hazard index of 0.05, and pollutant C with a hazard index of 0.15), the combined hazard 

index is 0.30. HI values are interpreted similarly to hazard quotients; values below one are generally considered to suggest 

that exposures are not likely to result in appreciable risk of adverse health effects during a lifetime, and values above one are 

generally cause for concern, although an HI greater than one does not necessarily suggest a likelihood of adverse effects. 

To evaluate the potential benefits of reducing the in-stream concentrations of 76 pollutants that cause non-cancer health 

effects, EPA estimated target organ-specific HIs for drinking water and fish ingestion exposures in both the baseline and 

post-compliance scenarios. HI is calculated for each discharge reach associated with one or more MP&M sample facilities by 

dividing the estimated ingestion rate of each pollutant by the RfD value for the pollutant. The formula follows: 

13-12 

c
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

(13.5) 

where: 

HI = haza rd ind ex for the po llutants disc harge d from a facility and ingested by a specific co nsumption pathw ay; 

DCRk = estimated d aily consump tion rate per kilogram of bo dy ma ss for pollutant k via a specific consumption 

pathway (mg/kg/day); 

RfD k = reference d ose fo r pollutant k (mg/kg/day); and 

K = number of pollutants affecting a given organ or system. 

Daily consumption rate (DCR) per kilogram of body mass for pollutant k is estimated as follows: 

(13.6) 

where: 

DCRk = estimated daily consump tion rate per kilogram of bo dy ma ss for pollutant k via a specific consumption 

pathway (mg/kg/day); 

C = pollutant concentration in surface water in the MP&M reach (�g/l); 

CF1 = conversion factor, micrograms to milligrams (0.001 mg/�g); 

CR = human consumption rate of water (mg/day); 

BCF = bioc oncentratio n factor for po llutant k; 

B W = human body weight (kg). 

These HIs are calculated separately for the fish and water consumption pathways. The fish consumption pathway was further 

divided into recreational and subsistence fish consumption rates. s and formulas for estimating the in-waterway 

concentrations and ingestion of pollutants by exposed pop ulations are the same as those used for the fish consumption and 

drinking water cancer analyses. s that the analysis of non-cancer health pathways was performed for the 

discharge reach only and not for reac hes downstream , due to time and reso urce c onstraints. sult, this analysis 

unde restimates populatio ns exp osed to non -cance r risks via d rinking w ater pathways 

EP A then com bined estimate s of the nu mbe rs of ind ividua ls in the exp osed pop ulations with the H Is for the pop ulations to 

determine how many individuals might be expected to realize reduced risk of non-cancer health effects in the post-compliance 

scenario. The basis for identifying exposed populations is the same as that described for the analysis of reduced incidence of 

cancer via the fish con sumption a nd drinking w ater consum ption pathw ays. 8 The shift in populations from a higher to a 

lower HI value fro m the b aseline to po st-com plianc e case s is the qu antitative m easure of benefits fro m this an alysis. T his 

analysis was limited in two primary w ays: 

� First, haz ard ind ices estim ated in this analysis m ay und erstate the actual p otentia l for ad verse health e ffects be cause this 

analysis consid ers co ntributio ns to no n-canc er risk resulting on ly from M P& M facility discharges, and d oes not take into 

account other sources of exposure to MP& M p ollutants or other chemicals that may contribute to an aggregate noncancer risk. result is that the analysis understates the numerical value estimated for HIs, but the incremental 

change in HIs between the baseline and the final option would remain the same. EPA therefore evaluated potential 

incremental changes in non-cancer health risks over the entire range of hazard indices, including hazard indices below 

one. 

� Sec ond , EPA used me an individual expo sure param eters and not the distribution o f expo sure param eters to estimate 

hazard ind ices for the pop ulations affected by M P& M discharges. 

Th e results fro m the non-cancer health risk analysis ap ply to sample discha rge loc ations o nly. Analytic tractability issues 

prevented this analysis from being cond ucted on a sample-we ighted national ba sis. EPA d id not mo netize these bene fits. 

The procedure

The only exception i

As a re 

The net

8
 The exposed populations for the drinking water consumption pathway are those associated with drinking water intakes only in a 

facility’s discharge reach. 

13-13

MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

13.1.4 Human Health AWQC 

EPA used another approach to quantify reductions in health risk from the final MP&M regulation, based on the extent to 

which reduced MP& M discharges would decrease the occurrence of pollutant concentrations in affected waterways that 

exceed human health-based AWQC. This analysis provides a measure of the change in cancer and non-cancer health risk by 

comparing the number of discharge reaches exceeding health-based AWQC for regulated pollutants due to MP&M activities 

in the baseline to the number exceeding AWQC under the final option. 

AWQC are set at levels to protect human health through ingestion of aquatic organisms and ingestion of water and aquatic 

organisms. Accordingly, reducing the frequency at which human health-based AW QC are exceeded should translate into 

reduced risk to human health. This measure should be viewed as an indirect indicator of reduced risk to human health, 

9 because it does not reflect the size of the exposed population and is not tied to changes in human health risk per se. 

EPA estimated the baseline concentrations of all MP&M pollutants for each reach to which one or more MP&M facilities 

discharge. The calculation of concentrations used the same in-waterway dilution and mixing model described in the analysis 

of cancer risk for the fish consumption pathway. The baseline concentrations were compared with human health-based 

AWQC values. (See Table 13.3 for a list of MP&M pollutants with AWQC values.) Reaches in which concentrations of one 

or more pollutants were estimated to exceed an AWQC value were identified as exceeding AWQC limits in the baseline. 

This analysis was repeated using the post-compliance discharge values for the final option. Reaches estimated to have 

concentrations in excess of AWQC in the baseline but not in the post-compliance case were assessed as having substantial 

water quality improvements relative to human health-based criteria as a result of regulation. EPA deems such water quality 

improvements to be indicative of reduced risk to human health. Although not explicitly accounted for in this analysis, human 

health risk reductions are also likely to occur wherever in-waterway concentrations are reduced, regardless of whether or not 

they are reduced to levels below AWQC. 

Table 13.3: MP&M Pollutants with Human Health-Based AWQC 

CAS 

Number Pollutant 

Human Health-Based 

AWQC (ug/l) 

Target Organ and Effectsa 

Organisms 

Only 

Water & 

Organisms 

83329 Acenaphthene 2700 1200 Liver, hepatotoxicity 

67641 Acetone 2800000 3500 Increased liver and kidney weights; nephrotoxicity 

98862 Acetophenone 98000 3400 General toxicity 

107028 Acrolein 1000 410 Cardiovascular toxicityc 

7429905 Aluminum 47000 20000 Renal failure, intestinal contraction interference, adverse 

neurological effectsd 

62533 Aniline 95 5.8 Spleen and body cavity 

120127 Anthracene 6800 4100 No observed effects 

7440360 Antimony 4300 14 Longevity, blood glucose, cholesterol 

7440382 Arsenic 0.16 0.02 Liver, kidneys, lungs, bladder, and skin 

7440393 Barium 1000 Increased kidney weight 

65850 Benzoic acid 2900000 130000 No observed adverse effects 

100516 Benzyl alcohol 810000 10000 Forestomach, epithelial hyperplasia 

7440417 Beryllium 1100 66 Small intestinal lesions 

92524 Biphenyl 1200 720 Kidney damage 

9 The following chapter uses this same information in part as a direct indicator of improved water quality. 

13-14 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Table 13.3: MP&M Pollutants with Human Health-Based AWQC 

CAS 

Number Pollutant 

Human Health-Based 

AWQC (ug/l) 

Target Organ and Effectsa 

Organisms 

Only 

Water & 

Organisms 

117817 Bis(2-ethylhexyl) 

phthalate 

5.9 1.8 Liver 

85687 Butyl benzyl phthalate 5200 3000 Significantly increased liver-to-body weight and liver-to-brain 

weight ratios 

7440439 Cadmium 84 14 Significant proteinuria (protein in urine) 

75150 Carbon disulfide 94000 3400 Fetal toxicity, malformations 

108907 Chlorobenzene 21000 680 Histopathologic changes in liver 

75003 Chloroethane 520 12 

1854029 

9 

Chromium hexavalent 2000 100 Reduced water consumption 

7440473 Chromium 1000000 50000 Renal tubular necrosis (kidney tissue decay)d 

7440508 Copper 1200 650 Gastrointestinal effects, liver necrosisd 

106445 Cresol, p- 3100 170 Central nervous system hypoactivity and respiratory system 

distress 

95487 Cresol, o- 30000 1700 Decreased body weights and neurotoxicity. 

57125 Cyanide 220000 700 Weight loss, thyroid effects and myelin degeneration 

117840 Di-n-octyl phthalate 39 37 Kidney and liver increased weights, liver increased SGOT and 

SGPT activity 

84742 Di-n-butyl phthalate 12000 2700 Increased mortality 

75354 Dichloroethene, 1,1- 3.2 0.057 Inconclusive 

75092 Dichloromethane 1600 4.7 Liver, lungs 

60297 Diethyl ether 770000 6900 Depressed body weights 

131113 Dimethyl phthalate 2900000 310000 

68122 Dimethylformamide, 

N,N220000000 3500 Liver and gastrointestinal system effects 

105679 Dimethylphenol, 2,4- 2300 540 Clinical signs (lethargy, prostration, and ataxia) and 

hematological changes 

51285 Dinitrophenol, 2,4- 14000 70 Cataract formation 

606202 Dinitrotoluene, 2,6- 900 34 Mortality, central nervous system neurotoxicity, blood heinz 

bodies and methemoglobinemia, bile duct hyperplasia, kidney 

histopathology 

123911 Dioxane, 1,4- 2400 3.2 Liver, nasal cavity, gall bladder 

122394 Diphenylamine 1000 470 Decreased body weight gain, and increased liver and kidney 

weights 

100414 Ethylbenzene 29000 3100 Liver and kidney toxicity 

206440 Fluoranthene 370 300 Nephropathy, increased liver weights, hematological alterations, 

clinical effects 

86737 Fluorene 14000 1300 Decreased red blood cell count, packed cell volume and 

hemoglobin 

591786 Hexanone, 2- 65000 1400 Hypatotoxicity and nephrotoxcity b 

7439896 Iron 300 Liver, diabetes mellitus, endocrine disturbance, and 

cardiovascular effectsc 

13-15

MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Table 13.3: MP&M Pollutants with Human Health-Based AWQC 

CAS 

Number Pollutant 

Human Health-Based 

AWQC (ug/l) 

Target Organ and Effectsa 

Organisms 

Only 

Water & 

Organisms 

78831 Isobutyl alcohol 1500000 10000 Hypoactivity and ataxia 

78591 Isophorone 2600 36 Preputial gland 

7439965 Manganese 100 50 Central nervous system effects 

7439976 Mercury 0.051 0.05 

80626 Methyl methacrylate 2300000 48000 Increased kidney to body weight ratio 

78933 Methyl ethyl ketone 6500000 21000 Decreased fetal birth weight 

108101 Methyl isobutyl ketone 360000 2800 Lethargy, increased liver and kidney weights and urinary protein 

91576 Methylnaphthalene, 2- 84 75 

91203 Naphthalene 21000 680 Decreased body weight 

7440020 Nickel 4600 610 Decreased body and organ weights 

100027 Nitrophenol, 4- 1100 220 

62759 Nitrosodimethylamine, 

N8.1 0.00069 Tumors observed at multiple sites 

86306 Nitrosodiphenylamine, 

N16 5 Bladder tumors, reticulum cell sarcomas 

59507 Parachlorometacresol 270000 56000 

108952 Phenol 4600000 21000 Reduced fetal body weight in rats 

7723140 Phosphorus (elemental) 2.2 0.53 Parturition mortality; forelimb hair loss 

129000 Pyrene 290 230 Kidney effects (renal tubular pathology, decreased kidney 

weights) 

110861 Pyridine 5400 35 Increased liver weight 

7782492 Selenium 11000 170 Clinical selenosis (hair or nail loss) 

7440224 Silver 110000 170 Argyria (skin discoloration) 

100425 Styrene 160000 6700 Red blood cell and liver effects 

127184 Tetrachloroethene 3500 320 Liver toxicity, weight gain 

7440280 Thallium 6.5 1.8 Liver toxicity, gastroenteritis, degeneration of peripheral and 

central nervous system 

108883 Toluene 200000 6800 Changes in liver and kidney weights 

79016 Trichloroethene 92 3.1 

75694 Trichlorofluoromethane 66000 9100 Survival and histopathology 

67663 Trichloromethane 470 5.7 Kidneys 

108383 Xylene, m- 100000 42000 Central nervous system hyperactivity, decreased body weight 

1367776 

12 

Xylene, o- & p- (c) 100000 42000 

95476 Xylene, o- 100000 42000 Central nervous system hyperactivity, decreased body weight 

1796012 

31 

Xylene, m- & p- (c) 100000 42000 

7440666 Zinc 69000 9100 47% decrease in erythrocyte superoxide dismutase (ESOD) 

concentration in adult human females after 10 weeks of zinc 

exposure 

13-16

MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Table 13.3: MP&M Pollutants with Human Health-Based AWQC 

CAS 

Number Pollutant 

Human Health-Based 

AWQC (ug/l) 

Target Organ and Effectsa 

Organisms 

Only 

Water & 

Organisms 

137304 Ziram \ Cymate 220000000 700 

a Information on target organs are not available for some pollutants. b Reference dose based on a NOAEL. Health effects summarized from Amdur, M.O.; Doul, J.; and Klaassen, C.D.,eds. 1991. 

Cassarett and Doul’s Toxicology, 4th edition/ c Target organ and effects summarized from Amdur, M.O.; Doul, J.; and Klaassen, C.D.,eds., C.D., ed. 1996. Cassarett and Doul’s 

Toxicology, 5th edition. d Target organ and effects summarized from Wexler, P., ed. 1998. Encyclopedia of Toxicology, Volumes 1-3. 

Source: U.S. EPA (1980); U.S. EPA (1997a); U.S. EPA (1998/99). 

13.2 RESULTS 

EPA estimated the monetary value to society associated with reduced cancer risk from consumption of fish and drinking water 

affected by MP&M pollutant discharges. Little information is available about dose-response relationships for non-cancer 

health outcomes or about the monetary value of avoiding such health outcomes. As a result, EPA was unable to assign 

monetary values to the estimated reductions in non-cancer health risks. Such non-cancer health risks include systemic, 

reproductive, immunological, neurological, and circulatory problems. Although EPA was unable to assign monetary values to 

the latter two benefit measures for this regulation, the quantitative analyses of these events provide additional insight into the 

human health-related benefits likely to result from the final regulation. 

The following sections present the findings from the analysis of each of the benefit measures. 

13.2.1 Fish Consumption Cancer Results 

Table 13.4 shows the estimated changes in incidence of cancer cases from consumption of MP&M pollutants in fish tissue 

and drinking water from regulatory compliance by option. The national-level analysis finds that the final regulation and the 

433 Upgrade Options would lead to a marginal reduction in cancer cases resulting from consumption of contaminated fish 

tissue; correspondingly, monetary benefits estimated from reduced consumption of contaminated fish are negligible under all 

of these three regulatory alternatives. In contrast, the estimated reductions in carcinogen loadings under the Proposed/NODA 

Option would result in $3.68 million (2001$) in benefits to recreational and subsistence anglers. 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Table 13.4: Estimated Avoided Cancer Cases and 

Value of Annual Benefits for the Final Option and Regulatory Alternativesa,b 

Option 

Fish Consumption Drinking Waterc 

Avoided 

Cancer 

Cases per 

Year 

Mean Value 

of Benefit 

(2001$)c 

Avoided 

Cancer 

Cases per 

Year 

Mean Value 

of Benefit 

(2001$)d 

Final Option: Traditional Extrapolation 1.38E-05 $90 0 $0 

Final Option: Post-Stratification Extrapolation 2.05E-05 $134 0 $0 

Proposed/NODA Optione 0.57 $3,684,973 0.001 $6,536 

Directs + 413 to 433 Upgrade Option 1.38E-05 $90 0 $0 

Directs + All to 433 Upgrade Option 2.6E-05 $169 0 $0 

a

 In this analysis, EPA did not consider reductions in discharges of one carcinogen n-nitrosodimethylanine (NDMA) 

due to the low number of detected values for that pollutant. b

 Regulatory alternatives are based on the Traditional Extrapolation. c

 Avoided cancer cases via the drinking water consumption pathway were not included for pollutants with drinking 

water criteria. EPA has published a drinking water criterion for seven of the 13 carcinogens and it is assumed that 

drinking water treatment systems will reduce concentrations of these chemicals to below adverse effect thresholds. d Estimated value of one avoided cancer case (2001$): $6.5 million. e

 The estimated benefits of the Proposed/NODA Option are not directly comparable to the final option alternatives. 

The total number of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported 

for the final rule and the two upgrade options. After deciding in July 2002 not to consider the NODA option as the 

basis for the final rule, EPA performed no more analysis on the NODA option, including not updating facility counts 

and related analyses for the change in subcategory and discharge status classifications. 

Source: U.S. EPA analysis. 

The valuation of benefits is based on estimates of society’s willingness-to-pay to avoid the risk of cancer-related premature


mortality. Although it is not certain that all cancer cases will result in death, avoided cancer cases are valued on the basis of


avoided mortality to provide a conservative estimate of benefits.


In this analysis, EPA used the $6.5 million estimate of the value of a statistical life saved (VSL) recommended in the


Guidelines for Preparing Economic Analysis (EPA, 2000c). EPA based this value on its review and analysis of 26 policy-


relevant value of life studies (EPA, 1997b). The reviewed studies used hedonic wage and contingent valuation analyses in


labor markets to estimate the amounts that individuals would either be willing to pay to avoid slight increases in the risk of


mortality, or would need to be compensated to accept a slight increase in risk of mortality.10 EPA associated the


willingness-to-pay (WTP) values estimated in these studies with small changes in the probability of mortality. To estimate


a WTP value for avoiding certain or high probability mortality events, EPA extrapolated the smaller value to that for a 100


percent probability event.11 The Agency used the resulting estimates of the value of a “statistical life saved” in regulatory


analyses to value regulatory effects that are expected to reduce the incidence of mortality.


The monetary value of a statistical life saved used in this analysis corresponds to the value of unforeseen instant death with no 

significant period of morbidity. Because a long period of morbidity usually precedes death from cancer, the value of an 

avoided cancer case may be underestimated. Therefore, the estimated value of the human health benefit of the final regulation 

may be understated. 

10 The question analyzed in these studies is: How much more must a worker be paid to accept an occupation with a slightly higher 

risk of mortality? 

11 These estimates, however, do not represent the willingness-to-pay to avoid the certainty of death. 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

13.2.2 Drinking Water Consumption Cancer Results 

Table 13.4 also shows the number of cancer cases estimated to be avoided for each pollutant analyzed for drinking water 

populations. The national-level analysis finds that the final regulation and the 433 Upgrade Options would lead to a marginal 

reduction in cancer cases resulting from consumption of contaminated drinking water; correspondingly, monetary benefits 

estimated from reduced consumption of contaminated drinking water are essentially zero under all of these three regulatory 

alternatives. As shown in Table 13.4, the Proposed/NODA Option would eliminate approximately 0.001 cancer cases per 

year. Annual monetary benefits from reduced cancer risk for the Proposed/NODA Option are estimated at $6,536 (2001$). 

As noted in the preceding sections, EPA has established drinking water criteria for seven carcinogens. EPA assumes that 

public drinking water treatment systems will reduce these seven pollutants in the public water supply to levels that are 

protective of human health. To the extent that the final regulation reduces the concentration of MP&M pollutants to values 

that are below pollutant-specific drinking water criteria, public drinking water systems will accrue benefits in the form of 

reduced water treatment costs. EPA was not able to quantify such cost savings at the national level, however. 

Public drinking water supply systems that currently employ various treatment technologies may also reduce concentrations of 

the six unregulated pollutants to the levels that are protective of human health. However, the Agency does not have 

information on specific treatment technologies used by the drinking water systems affected by MP&M discharges. It is not 

feasible to assess whether the technologies employed by the affected drinking water systems reduce concentrations of MP&M 

pollutants that don’t have the published drinking water criteria without collecting detailed information on the affected 

drinking water systems. Thus, this analysis conservatively assumes that public water supply systems do not monitor pollutants 

that don’t have published drinking water criteria and, as result, these pollutants may be passed through the affected drinking 

water supply systems. 

13.2.3 Non-cancer Health Threshold Results 

Table 13.5 summarizes baseline and post-compliance distributions of non-cancer health hazard indices and associated 

population estimates for each exposed population group for the final option. The shift in populations from higher to lower 

hazard score values between the baseline and post-compliance cases is the measure of benefit from reduced non-cancer health 

hazards. 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Table 13.5: Change in Risk of Non-cancer Health Hazards from Reduced Exposure to MP&M Pollutants: 

Distribution of Hazard Indicesa 

Range of 

Ratios 

Fish Consumption Drinking Water Consumption 

Baseline Post-Compliance Baseline Post-Compliance 

Population Percent Population Percent Population Percent Population Percent 

Final Option 

Ratio = 0.00 0 0% 122,865 12.05% 39,822,464 97.48% 40,723,280 99.69% 

0.00 - 10-6 121,814 11.95% 103,103 10.12% 1,029,333 2.52% 128,517 0.31% 

10-6 - 10-3 680,301 66.73% 578,122 56.72% 0 0% 0 0% 

10-3 - 1.00 217,201 21.31% 215,226 21.11% 0 0% 0 0% 

Score > 1.00 0 0% 0 0% 0 0% 0 0% 

Totals 1,019,316 100% 1,019,316 100% 40,851,797 100 40,851,797 100% 

Proposed/NODA Optionb 

Ratio = 0.00 0 0% 342,040 8.17% 0 0% 4,308,352 10.95% 

0.00 - 10-6 872,003 20.82% 796,003 19.01% 36,552,343 92.93% 33,667,164 85.59% 

10-6 - 10-3 2,221,724 53.04% 2,310,376 55.16% 2,783,100 7.07% 1,359,927 3.46% 

10-3 - 1.00 1,054,627 25.18% 737,312 17.60% 0 0% 0 0% 

Score > 1.00 40,630 0.97% 3,253 0.08% 0 0% 0 0% 

Totals 4,188,984 100% 4,188,984 100% 39,335,442 100% 39,335,442 100% 

Directs + 413 to 433 Upgrade Option 

Ratio = 0.00 0 0.0% 169,106 16.59% 39,822,464 97.48% 40,723,280 99.69% 

0.00 - 10-6 121,814 11.95% 91,255 8.96% 1,029,333 2.52% 128,517 0.31% 

10-6 - 10-3 680,301 66.73% 559,690 54.91% 0 0% 0 0% 

10-3 - 1.00 217,201 21.31% 199,265 19.54% 0 0% 0 0% 

Score > 1.00 0 0% 0 0% 0 0% 0 0% 

Totals 1,019,316 100% 1,019,316 100% 40,851,797 100% 40,851,797 100% 

Directs + All to 433 Upgrade Option 

Ratio = 0.00 0 0.0% 169,106 16.59% 39,822,464 97.48% 40,723,280 99.69% 

0.00 - 10-6 121,814 11.95% 91,255 8.96% 1,029,333 2.52% 128,517 0.31% 

10-6 - 10-3 680,301 66.73% 563,526 55.28% 0 0% 0 0% 

10-3 - 1.00 217,201 21.31% 195,429 19.17% 0 0% 0 0% 

Score > 1.00 0 0% 0 0% 0 0% 0 0% 

Totals 1,019,316 100% 1,019,316 100% 40,851,797 100% 40,851,797 100% 

a This analysis addresses only 76 of 132 chemicals of concern, excludes background exposures, and is based only on sample facility 

discharges and associated populations. The exposed population values are not national estimates of the populations that would benefit 

by reduced risk of non-cancer health hazards. 

b

 The estimated benefits of the Proposed/NODA Option are not directly comparable to the final option alternatives. The total number 

of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported for the final rule and the two 

upgrade options. After deciding in July 2002 not to consider the NODA option as the basis for the final rule, EPA performed no more 

analysis on the NODA option, including not updating facility counts and related analyses for the change in subcategory and discharge 

status classifications. 

Source: U.S. EPA analysis. 

For each discharge reach, EPA selected the maximum of the target organ-specific hazard index values calculated for a given 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

discharge reach to characterize the potential for adverse non-cancer health affects from exposure to MP&M pollutants among 

exposed individuals. The results of EPA's analysis suggest that HIs for individuals in the exposed populations may decrease 

after facilities comply with the final rule (see Table 13.5 for detail). Increases in the percentage of exposed populations that 

would be exposed to no risk of non-cancer adverse human health effects due to the MP&M discharges occur in both the fish 

and drinking water analyses. The shift to lower hazard indices should be considered in conjunction with the finding that the 

hazard indices for incremental exposures to pollutants discharged by MP&M facilities (for which reference doses are 

available) are less than one in the baseline analysis for the entire population associated with sample facilities. Whether the 

incremental shifts in hazard indices are significant in reducing absolute risks of non-cancer adverse human health effects is 

uncertain and will depend on the magnitude of contaminant exposures for a given population from risk sources not accounted 

for in this analysis. 

Table 13.5 shows that the Proposed/NODA Option and the 433 Upgrade Options would result in similar shifts in the exposed 

populations from higher to low hazard index values. All of these three alternative regulatory options would increase the 

population with a zero incremental risk of non-cancer health effects from exposure to MP&M pollutants. 

Although EPA was unable to associate an economic value with changes in the number of individuals exposed to pollutant 

levels likely to result in non-cancer health effects, the reductions in health risk indicated by this benefit measure further 

indicate that the final regulation can be expected to yield human health benefits. 

13.2.4 Human Health AWQC Results 

The final human health benefit category is the reduced occurrence of pollutant concentrations that are estimated to exceed 

human health-based AW QC. This analysis provides an alternative measure of the expected reduction in risk to human health. 

EPA estimates that in-stream concentrations of 4 pollutants (i.e., arsenic, iron, manganese, and n-nitrosodimethylamine) will 

exceed human health criteria for consumption of water and organisms in 78 receiving reaches nationwide as the result of 

baseline MP&M pollutant discharges. EPA estimates that there are human health AWQC exceedances caused by 

n-nitrosodimethylamine (NDMA). EPA did not consider NDMA pollutant reductions in its benefits analyses because of low 

number of detected values for that pollutant. EPA estimates that the final rule will not eliminate the occurrence of 

concentrations in excess of human health criteria for consumption of water and organisms and for consumption of organisms 

on any of the reaches on which baseline discharges are estimated to cause concentrations in excess of AWQC values. 

EPA’s analysis of the 433 Upgrade Options yields similar results. However, the Directs +All to 433 option would reduce the 

number of pollutants causing in-stream concentrations to exceed the human health-based AW QC values from 4 to 2 (i.e., 

exceedances from iron and manganese are eliminated). As shown in Table 13.6, the Proposed/NODA Option would not result 

in a significant reduction in the number of reaches that are estimated to exceed human health-based AWQC for consumption 

of water and organisms under the baseline discharge level. The Proposed/NODA option, however, eliminates human healthbased AWQC for consumption of organisms only on 69 (35 percent) of the 197 reaches, in which in-stream pollutant 

concentrations exceeded the relevant criteria in the baseline. The Agency points out that analytic results corresponding to the 

Proposed/NODA Option are not directly comparable to the analytic results corresponding to the final rule alternatives due to 

the inconsistent baseline conditions (see Chapter 5 of this report for detail). 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

Table 13.6: MP&M Discharge Reaches with Pollutant Concentrations Exceeding Human 

Health-Based AWQC Limits and Reductions Achieveda 

Category 

Human Health Water and 

Organisms Human Health Organisms Only 

Number of 

Reaches 

Number of 

Pollutants 

Number of 

Reaches 

Number of 

Pollutants 

Final Option: Traditional Extrapolation 

Baseline 78 4 21 1 

Post-Compliance 78 4 21 1 

Percent Reduction 0.0% 0.0% 

Final Option: Post-Stratification Extrapolation 

Baseline 112 4 21 1 

Post-Compliance 112 4 21 1 

Percent Reduction 0.0% 0.0% 

Proposed/NODA Optionb 

Baseline 5,852 26 197 12 

Post-Compliance 5,789 21 128 9 

Percent Reduction 1.1% 34.6% 

413 to 433 Upgrade Option 

Baseline 78 4 21 1 

Post-Compliance 78 4 21 1 

Percent Reduction 0.0% 0.0% 

Directs + All to 433 Upgrade Option 

Baseline 78 4 21 1 

Post-Compliance 78 2 0 

Percent Reduction 0.0% 100.0% 

0 

a

 Regulatory alternatives are based on the Traditional Extrapolation. b

 The estimated benefits of the Proposed/NODA Option are not directly comparable to the final option 

alternatives. The total number of facilities reported for the Proposed/NODA Option analysis differs from the 

facility count reported for the final rule and the two upgrade options. After deciding in July 2002 not to consider 

the NODA option as the basis for the final rule, EPA performed no more analysis on the NODA option, including 

not updating facility counts and related analyses for the change in subcategory and discharge status classifications. 

Source: U.S. EPA analysis. 

13.3 LIMITATIONS AND UNCERTAINTIES 

This section discusses limitations and uncertainties in the human health benefits analysis. The analysis does not include all 

possible human health benefits, and therefore does not provide a comprehensive estimate of the total human health benefits 

associated with the final rule. Quantification of changes in human health risk described in this chapter are not possible for all 

pollutants whose discharges will be reduced by the final regulation. Due to current research limitations, cancer potency 

factors, reference doses, and AWQC are not available for 6 metals, 27 organics, 8 nonconventional pollutants, and 3 

conventional pollutants. The methodologies used also involve significant simplifications and uncertainties, as described 

below. Whether these simplifications and uncertainties, taken together, are likely to lead to an understatement or 

overstatement of the estimated economic values for the human health benefits that were analyzed is not known. 

13.3.1 Sample Design & Analysis of Benefits by Location of Occurrence 

The M P&M industries are estimated to include over 43,867 facilities nationwide that generate wastewater while processing 

metal parts, metal products, and machinery. Many of these facilities are quite small and, individually, discharge relatively 

small quantities of pollutants. Most individual facilities are not likely to have a significant adverse impact on human health at 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

any one MP &M reach. The industry discharges a significant quantity of pollutants in the aggregate, however, because of the 

large number of facilities. Thus, the combined effect of discharges from several facilities at a given reach may well result in 

appreciable risks to human health. Multiple dischargers affecting a single reach were found to be common, based on the 

sample facility data. 

The sample of MP&M facilities on which this analysis is based (910 facilities) represents only approximately 2 percent of 

MP&M facilities nationwide. This sample was based on basic industry characteristics rather than geographic location. As a 

result, the sample does not accurately reflect the likelihood of co-occurrence of MP&M facilities on a reach and, therefore, 

the contribution to in-waterway pollutant concentrations made by multiple facilities. For example, the sample may include 

three MP&M facilities, all discharging to the same reach. In reality, however, five MP&M facilities might discharge to this 

reach. 

The omission of co-occurrence of discharges from additional facilities does not create a problem in the analysis of 

incremental cancer risk, because each facility’s contribution to total risk can be estimated separately and is assumed be 

linearly additive. The cancer effects associated with individual facility discharges can be summed over facilities to estimate 

occurrence of cancer events in the total population. Therefore, the application of sample weights in the cancer analysis 

accounts for pollutant contributions from facilities co-occurring on MP&M reaches that are not present in the sample of 

facilities. 

This omission does present a problem, however, when analyzing changes in hazard indices and changes in in-waterway 

pollutant concentrations relative to human health-based AWQC for reaches to which more than one facility discharge. For 

these reaches, changes in hazard indices and in-waterway pollutant concentrations from reduced pollutant discharges should 

account for the total discharge of pollutants over the several facilities whose discharges may affect the reach. When facilities 

whose discharges to the reach have unequal sample weights, however, results from the sample facility analysis cannot be 

extrapolated to the population simply by multiplying estimated benefit values by the sum of the sample weights of the 

individual facilities. See Appendix G for an explanation of the sample weighting methodology devised to partially address 

this problem. 

While this weighting methodology does recognize the contributions of facilities with different sample facility weights to 

aggregate results, it still does not account for the contributions made by co-occurring facilities not included in the sample. 

The omission of the frequency of true multiple discharger effects on aggregate instream concentrations and pollutant 

exposures understate the benefits. 

13.3.2 In-Waterway Concentrations of MP&M Pollutants 

Human health benefits are based on the estimated changes in in-waterway concentrations of MP&M pollutants. A variety of 

factors affect in-waterway concentrations, including flow rates under average and low flow conditions, flow depth, chemistry 

of the waterway, mixing processes, longitudinal dispersion, flow geometry, suspension of solids, and reaction rates. This 

analysis takes into account only site-specific variations in flow rates and flow depth. Standard values are used for other inputs 

to the water quality model, due to lack of data on the reaches affected by sample facility discharges. These standard values 

may not be accurate for all the sample facility reaches. In addition, the flow characteristics of the sample facility reaches may 

not be representative of the national distribution of those characteristics. Extrapolating the sample facility benefits to 

national results based on sample facility weights may therefore introduce distortions. The net effect of these assumptions and 

extrapolations on the aggregate benefits estimates is uncertain. 

13.3.3 Joint Effects of Pollutants 

The analyses of human health benefits ignore the potential for joint effects of more than one pollutant. Each pollutant is dealt 

with in isolation; the individually estimated effects are then added together. As such, the analyses do not account for the 

possibility that several pollutants may combine to yield more or less adverse effects to human health than indicated by the 

simple sum of the individual effects. The impact of this limitation on the results of this analysis is unknown. 

13.3.4 Background Concentrations of MP&M Pollutants 

Background concentrations of MP&M pollutants are not considered in the benefits analysis. Rather, the analysis assumes that 

MP&M facilities are the only source of each of the regulated pollutants in the waterway. Background contributions, either 

from other upstream sources or contaminated sediments from previous discharges, are not considered. Even if discharges of 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

these contaminants are reduced or eliminated, sediment contamination and subsequent accumulation of the regulated 

pollutants in aquatic organisms may continue for years. 

Excluding background contributions to in-waterway pollutant concentrations affects the results for non-cancer risk and 

changes in human health-based AWQC exceedances. In the non-cancer risk analysis, hazard indices are likely to be 

systematically biased downwards by the omission of exposures to these chemicals from other water-related and non-water- 12 related sources. The net result is understated absolute risks of non-cancer health hazards. Similarly, reductions in human 

health-based AWQ C exceedances calculated for a given MP& M reach are likely to be systematically biased downwards. The 

analysis is therefore likely to understate the frequency with which in-waterway pollutant concentrations move from values 

exceeding pollutant specific AWQC to values less than pollutant specific AWQC as a result of the regulation. 

13.3.5 Downstream Effects 

The analysis of cancer effects from drinking water consumption considered exposures from intakes downstream of the


MP&M discharges. EPA, however, did not evaluate cancer risk to recreational and subsistence fishermen fishing downstream


reaches, because of resource constraints. In addition, due to differential weighting of sample facility results, it was not


possible to evaluate hazard indices indicating non-cancer health hazards or human health-based AW QC excursions in


downstream reaches. By omitting these downstream effects, this analysis potentially understates baseline risks that would be


reduced by the final option:


� cancer cases (from fish consumption), 

� populations exposed to non-cancer risks, and 

� waterways with pollutant concentrations exceeding human health-based AW QC. 

13.3.6 Exposed Fishing Population 

Estimating the exposed fishing populations for specific MP&M reaches requires statistics on county fishing licences. EPA 

collects these data for every state where the MP&M facilities are located where the state collects these data at the county 

level. Where fishing license data were not available at the county level, EPA estimated the exposed fishing population based 

on state fishing license statistics and census data. This approach is likely to understate actual fishing populations. As noted in 

Section 13.1.1, the 1996 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation found that 34 percent of 

the anglers (16 years of age and older) did not have licenses (U.S. Department of the Interior, 1996). In addition, data 

limitations hamper the estimate of the number of anglers who actually fish a given MP&M reach. Estimating the number of 

anglers fishing MP&M reaches based on the ratio of MP&M reach length to the total number of MP&M reach miles in the 

county recognizes the effect of the quantity of competing fishing opportunities on the likelihood of fishing a given reach, but 

it does not account for the differential quality of fishing opportunities. If water quality in substitute sites is distinctly worse or 

better, the estimates of the exposed populations are likely to be overstated or understated. 

In addition, the number of subsistence anglers was assumed to equal 5 percent of the recreational fishing population. The 

magnitude of subsistence fishing in the United States or in individual states is not known. As a result, this estimate may 

understate or overstate the actual number of subsistence anglers. 

Finally, to account for the effect of a fish advisory on fishing activity, and therefore on the exposed fishing population, EPA 

reduced the fishing population at an MP&M reach under a fish advisory by 20 percent. This could either overestimate or 

underestimate the risk associated with consumption of contaminated fish, because (1) anglers who change locations may 

simply be switching to other locations where advisories are in place and therefore maintain or increase their current risk, and 

(2) anglers who continue to fish contaminated waters may change their consumption and preparation habits to reduce the risks 

from the contaminated fish. 

12 Ideally, the analysis would include not only background concentration and exposure effects from water-related exposures but 

would also account for exposures to chemicals by other routes including, air exposures including dust inhalation, and food contamination. 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

13.3.7 Treatment of Cancer Latency 

Cancer latency refers to the time between the initial event that leads to cancer (e.g., chemical damage to DNA) and the onset 

of cancer. Ideally, cancer would be detected at a very early stage, when very few cells are involved. In reality, cancer latency 

is a very complex issue, and the time to detection varies considerably. 

� Latency is related to health, age, immune status, genetics and other characteristics of the individual. 

� Latency is also related to the specific carcinogen, the route of exposure, the type of cancer, the technology used for 

cancer detection, and numerous other factors. 

� Environmentally induced cancers may not follow a typical progression pattern; their latency may be unusually 

shortened. 

� Cancers may begin long before they are detected. The exact progress and time of recognition/detection of cancer 

cannot be predicted, because of the numerous factors involved. 

� Variations in timing of cancer detection are partially attributable to the type of cancer involved, the individuals 

affected, and differences in the medical technology used. 

� The fundamental issue is when the damage related to cancer actually begins in an individual and when the continued 

cell damage stops. Damage to the individual begins when cancer is induced. Once cellular changes begin, the 

immune system and other body resources are diverted to limiting the carcinogenic process and organ system damage 

is occurring. 

EPA assumed that benefits of avoiding cancer begin to accrue when the initial events leading to cancer cease, even though the 

benefits may not be clinically measurable until some point in the future. In making this assumption, the Agency considered 

two factors: 

� uncertainty as to how and when exposure changes translate into reduced cancer risk, and 

� economic uncertainty associated with the value of avoiding cancer and the timing at which a value of cancer 

avoidance is recognized. 

The monetary valuation of mortality risk from cancer in EPA benefit-cost analyses is based on the VSL. This is derived from 

a number of revealed-preference studies that estimate the value of avoided premature mortality. The estimates correspond to 

the value of unforeseen instant death with no significant period of morbidity. The value of an avoided cancer case used in this 

analysis may therefore be understated, and ultimately the estimated value of the human health benefit of the final regulation 

may be understated. 

13.3.8 Treatment of Cessation Lag 

In August 2001, EPA's Science Advisory Board (SAB) recommended that EPA should not assume that a reduction in cancer 

cases immediately follows a reduction in exposure (U.S.EPA, 2001). The SAB explained that, in fact, there is a lag between 

the time when exposures are reduced and the time when a reduction in risk occurs, and that "...if the lag between reduction in 

exposure and reduction in risk is long, there will be fewer cancer fatalities avoided in years immediately following the policy 

than if the lag were shorter." However, the Agency points out the published studies that attempted to address cessation lag 

found that after cessation of exposure, cancer risk begins to decline quickly (U.S. EPA, 2001). 

The analysis of cancer benefits presented did not account for a cessation lag because the relevant information was not 

available for all but one (arsenic) MP&M pollutants. Not accounting for cessation lag results in an upper bound estimate of 

cancer-related benefits (U.S. EPA, 2001). 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

13.3.9 Use of Mean Individual Exposure Parameters 

EPA used mean individual exposure parameters and not the distribution of exposure parameters to estimate hazard indices,


cancer risk, and adverse human health effects associated with exposure to lead for the populations affected by MP&M


discharges. Because individuals associated with high-end exposure parameter estimates would have higher health risks, EPA's


approach is likely to result in underestimation of human health risk reduction from the final MP&M regulation.


13.3.10 Cancer Potency Factors 

EPA's estimates of cancer cases were calculated using cancer potency factors that are upper bound estimates of cancer 

potency, potentially leading to overestimation of cancer risk. 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

GLOSSARY 

ambient water quality criteria (AWQC): AWQC present scientific data and guidance of the environmental effects of 

pollutants which can be useful to derive regulatory requirements based on considerations of water quality impacts; these 

criteria are not rules and do not have regulatory impact (U.S. EPA. 1986. Quality Criteria for Water 1986. U.S. 

Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC. EPA 440/5-86-001). 

marine reach: a specific length of marine coastline. 

MP&M reach: a reach to which an MP&M facility discharges. 

no observed adverse effect level (NOAEL): exposure level at which there are no statistically or biologically significant 

differences in the frequency or severity of any effect in the exposed or control populations. 

reach: a specific length of river, lake shoreline, or marine coastline. 

reference dose (RfD): an estimate of the maximum daily ingestion in that is likely to be without an appreciable risk of 

deleterious effects during a lifetime. 

value of a statistical life saved (VSL): a monetary value of fatalities. A statistical life is saved when the mortality rate of 

a group of people is reduced sufficiently that one less person will die than would otherwise be the case. One must distinguish 

between statistical and actual lives. An actual life is saved when the identity of the beneficiary is known before the lifesaving 

expenditure is made. 

waterway: streams, lakes, bays, and estuaries. 

willingness-to-pay (WTP): maximum amount of money one would give up to buy some good. 

(http://www.damagevaluation.com/glossary.htm) 

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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

ACRONYMS 

AWQC: ambient water quality criteria


NOAEL: no observed adverse effect level


RfD: reference dose


RSEI: Risk Screening Environmental Indicator Model


SDWIS: Safe Drinking Water Information System


VSL: value of a statistical life saved


WTP: willingness-to-pay


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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

REFERENCES 

Amdur, M. O., J. Doul, and C. D. Klaassen, eds. 1991. Cassarett and Doul's: Toxicology, the Basic Science of Poisons. 4th 

ed. New York, NY: McGraw-Hill Inc. 

Amdur, M. O., J. Doul, and C. D. Klaassen, eds. 1996. Cassarett and Doul's: Toxicology, the Basic Science of Poisons. 5th 

ed. New York, NY: McGraw-Hill Inc. 

Belton, T., R. Roundy, and N. Weinstein. 1986. “Urban Fishermen: Managing the Risks of Toxic Exposure.” Environment 

Vol. 28. No. 9, November. 

Connelly, N. and B. Knuth. 1993. Great Lakes Fish Consumption Health Advisories: Angler Response to Advisories and 

Evaluation of Communication Techniques, Human Dimensions Research Unit, Dept. of Natural Resources, NY State College 

of Agriculture and Life Sciences, Cornell University, HDRU Series No 93-3, February. 

Connelly, N., B. Knuth, and C. Bisogni. 1992. Effects of the Health Advisory and Advisory Changes on Fishing Habits and 

Fish Consumption in New York Sport Fisheries, Human Dimensions Research Unit, Dept. of Natural Resources, NY State 

College of Agriculture and Life Sciences, Cornell University, HDRU Series No 92-9, September. 

Knuth, B. and C. Velicer. 1990. Receiver-Centered Risk Communication for Sportfisheries: Lessons from New York 

Licensed Anglers. Paper presented at the American Fisheries Society Annual Meeting, Pittsburgh, Penn, August. 

Silverman, W. 1990. Michigan’s Sport Fish Consumption Advisory: A Study in Risk Communication. Thesis submitted in 

partial fulfillment of the requirements for the degree of Master of Science (Natural Resources) at the University of Michigan, 

May. 

U.S. Department of Commerce, Bureau of the Census. 1997. http://www.census.gov. 

U.S. Department of the Interior. 1993. 1991 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, DOI, 

March. 

U.S. Department of the Interior, U.S. Fish and Wildlife Service and U.S. Department of Commerce, Bureau of the Census. 

1996. 1996 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, DOI. 

U.S. Environmental Protection Agency. (U.S. EPA). 1980. Ambient water quality criteria documents. Washington, DC: 

Office of Water, U.S. EPA. EPA 440/5-80 Series. Also refers to any update of criteria documents (EPA 440/85 and EPA 

440/5-87 Series) or any Federal Register notices of proposed criteria or criteria corrections. 

U.S. Environmental Protection Agency. (U.S. EPA). 1997a. Health Effects Assessment Summary Tables (HEAST). Office of 

Research and Development and Office of Emergency and Remedial Response, Washington, DC: U.S. EPA. OERR 9200/6-

303 (92-1). 

U.S. Environmental Protection Agency. (U.S. EPA). 1997b. The Benefits and Costs of the Clean Air Act, 1970 to 1990. 

Washington, DC: Office of Air and Radiation, U.S. EPA. EPA 410-R-97-002, October. 

U.S. Environmental Protection Agency. (U.S. EPA). 1997c. Exposure Factors Handbook. Volumes I, II, and III. 

Washington, DC: National Center for Environmental Assessment, Office of Research and Development. EPA-600-P-95-

002Fa,b,c. August. 

U.S. Environmental Protection Agency. (U.S. EPA). 1998. Ambient Water Quality Criteria Derivation Methodology: 

Human Health. Technical Support Document. EPA-822-B-98-005. July. 

U.S. Environmental Protection Agency. (U.S. EPA). 1998/99. Integrated Risk Information System (IRIS) Retrieval. 

Washington, DC: U.S. EPA. 

U.S. Environmental Protection Agency. (U.S. EPA). 1999a. National Listing of Fish and Wildlife Consumption Advisories. 

Washington, DC: Office of Water, U.S. EPA. 

13-29 
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits 

U.S. Environmental Protection Agency. (U.S. EPA). 1999b. Risk-Screening Environmental Indicators Model: Version 1.0.


Washington, DC: Office of Pollution Prevention and Toxics, U.S. EPA. July 6.


http://www.epa.gov/opptintr/env_ind/index.html.


U.S. Environmental Protection Agency. (U.S. EPA). 2000a. Methodology for Deriving Ambient Water Quality Criteria for 

the Protection of Human Health . EPA 822-B-00-004. October. 

U.S. Environmental Protection Agency. (U.S. EPA). 2000b. Estimated Per Capita Water Ingestion in the United States. 

EPA-822-R-00-008. April. 

U.S. Environmental Protection Agency. (U.S. EPA). 2000c. Guidelines for Preparing Economic Analyses. Washington, 

DC: EPA 240-R-00-003. September. 

U.S. Environmental Protection Agency. (U.S. EPA). 2001. Arsenic Rule Benefits Analysis: An SAB Review . Washington, 

DC: EPA-SAB-EC-01-008. August. 

West, P., R. Marans, F. Larkin, and M. Fly. 1989. Michigan Sport Anglers Fish Consumption Survey: A Report to the 

Michigan Toxic Substances Control Commission, University of Michigan School of Natural Resources, Natural Resources 

Sociology Research Lab, Technical Report #1, May. 

Wexler, P., ed. 1998. Encyclopedia of Toxicology, Vol. 1-3. 

13-30 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Chapter 14: Lead-Related Benefits 


INTRODUCTION 

The human health benefits analysis presented in the previous 

chapter examined both cancer and non-cancer health 

risks from exposure to MP&M pollutants. EPA performed a 

separate analysis of benefits from reduced exposure to lead. 

The analysis of health effects from exposure to lead is based 

on dose-response functions tied to specific health 

endpoints to which monetary values can be applied. In this 

way it differs from the analysis of non-cancer health risk from 

exposure to other MP&M pollutants. This analysis assessed 

benefits of reduced lead exposure from consumption of 

contaminated fish tissue to three population groups: (1) 

preschool age children, (2) pregnant women, and (3) adult 

men and women. These lead-related benefits were estimated 

for the final MP&M regulation, the 433 Upgrade Options, and 

the Proposed/NODA option. 

EPA estimated benefits to preschool children based on a 

dose-response relationship for intelligence quotient (IQ) 

decrements. The Agency calculated monetary values for 

avoided neurological and cognitive damages based on the 

impact of an additional IQ point on an individual’s future 

earnings and the cost of compensatory education for children 

with learning disabilities. EPA also assessed the incidence of 

neonatal mortality due to changes in maternal blood lead 

(PbB) levels during pregnancy based on willingness-topay (WTP) values for avoiding death. EPA estimated that 

the final regulation will not yield any benefits to children from 

reduced exposure to lead. 

The health effects in adults that EPA was able to quantify all 

relate to lead’s effect on blood pressure (BP). Quantified 

CHAPTER CONTENTS 

14.1 iew of Lead-Related Health Effects ...... 14-2 

14.1.1 nder Age One .............. 14-3 

14.1.2 etween the Ages of One and 

Seven ................. .............. 14-3 

14.1.3 ................. ............ 14-4 

14.2 ................. . 14-4 

14.2.1 ution of Exposed Children .... 14-5 

14.2.2 

and IQ ................. ............. 14-12 

14.2.3 ........ 14-12 

14.2.4 Additional Educational 

Resources ................. .......... 14-14 

14.2.5 in Neonatal Mortality ......... 14-17 

14.3 alth Benefits ................. .... 14-17 

14.3.1 

Distribution Levels ................. ... 14-20 

14.3.2 alth Benefits ................ 14-22 

14.3.3 le Health Benefits .............. 14-26 

14.4 elated Benefit Results ............... 14-28 

14.4.1 ge Children Lead-Related 

Benefit Results ................. ...... 14-28 

14.4.2 ead-Related Benefit Results ..... 14-29 

14.5 ............... 14-31 

14.5.1 ............ 14-31 

14.5.2 nsatory Education Costs ........ 14-32 

14.5.3 sponse Relationships ......... 14-32 

14.5.4 on Function for Ingested 

Lead in Fish Tissue ................. ... 14-32 

14.5.5 c Valuation ................. 14-33 

Glossary ................. ................. .. 14-35 

Acronyms ................. ................. . 14-38 

References ................. ................. 14-39 

Overv 

Children U 

Children B 

Adults 

Health Benefits to Children 

PbB Distrib 

Relationship Between PbB Levels 

Value of Children's Intelligence 

Value of 

Changes 

Adult He 

Estimating Changes in Adult PbB 

Male He 

Fema 

Lead-R 

Preschool A 

Adult L 

Limitations and Uncertainties 

Excluding Older Children 

Compe 

Dose-Re 

Absorpti 

Economi 

health effects include incidence of hypertension in adult men, initial non-fatal coronary heart disease (CHD), non-fatal 

strokes (cerebrovascular accidents (CBA) and atherothrombotic brain infarctions (BI)), and premature mortality. 

EPA used cost of illness (COI) estimates (i.e., medical costs and lost work time) to estimate monetary values of reduced 

incidence of hypertension, initial CHD, and strokes. EPA used COI estimates to estimate monetary values for reduced 

incidence of hypertension, initial CHD, and strokes. This analysis uses the $6.5 million estimate of the value of a statistical 

life saved recommended in the Guidelines for Preparing Economic Analysis (EPA, 2000a) to estimate monetary value of 

reduced incidence of premature mortality. EPA estimated that the final rule will achieve no lead-related health benefits 

among adults. 

14-1 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

14.1 OVERVIEW OF LEAD-RELATED HEALTH EFFECTS 

The MP&M regulation will reduce lead exposure by reducing the amount of lead discharged to water bodies from MP&M 

facilities, thereby reducing health and ecological risks. This section provides a brief summary of the human health effects 

from exposure to lead. Data for this analysis are taken from the Agency for Toxic Substance and Disease Registry’s 

(ATSDR) Draft Toxicological Profile for Lead (1997) unless otherwise noted. The discussion provided in this section is 

qualitative and was not used to generate risk estimates. 

Lead and lead compounds are toxic and pose threats to human health and well being. The health effects of very high levels of 

PbB include convulsions, coma, and death from lead toxicity. These effects have been understood for many years. The 

effects of lower doses of lead are not fully understood, however, and continue to be the subject of intensive scientific 

investigation (CDC,1991b). 

Lead accumulates in the body and is stored in various organ systems. While high level exposures are of immediate concern 

due to acute toxicity, exposure to small amounts can accumulate over time to harmful levels. Accumulated lead is very 

persistent, with a half-life in bone of approximately 27 years.1 Known or strongly suspected health effects include kidney, 

stomach, and respiratory cancer, nervous system disorders, hypertension, anemia and blood disorders, gastrointestinal 

disorders, renal damage, and other effects (ATSDR, 1997; CARB, 1996). Increased mortality from these effects has been 

observed in studies (ATSDR , 1997). 

Many lead-associated adverse health effects are both chronic in nature and relatively common. These effects include but are 

not limited to hypertension, coronary artery disease, and impaired cognitive function. Specific cases of these conditions are 

difficult to link to lead exposure because the same adverse health effects or endpoints can arise from a variety of causes. 

Despite numerous studies conducted by EPA and other institutions, dose-response functions are available only for a handful 

of health endpoints associated with elevated PbB levels.2 The available research does not always allow complete economic 

evaluation, even for quantifiable health effects. 

Lead is harmful to any exposed individual, and the effects of lead on children are of particular concern. Children’s rapid 

development rate makes them more susceptible to neurobehavioral deficits resulting from lead exposure. U.S. EPA 

identifies three sensitive populations: children under age one, children between the ages one and seven, and adult men and 

women (U.S. EPA, 1990). New research suggests that children older than seven may also be a hypersensitive population. 

Recent research on brain development among 10- to 18-year-old children shows unanticipated and substantial growth in brain 

development, mainly in the early teenage years (Giedd et al., 1999). This analysis does not, however, include this group due 

to data limitations. Table 14.1 summarizes the quantifiable health effects on children under seven and adult men and women, 

along with other important, non-quantified, known health effects on these populations. 

1 A half-life of 27 years means that it takes 27 years for the levels measured in bone to decrease by 50 percent. 

2 In a pioneering study, Schwartz et al. quantified a number of health benefits that would result from reducing the lead content of 

gasoline (U.S. EPA, 1985). EPA extended this work by analyzing lead in drinking water (U.S. EPA, 1986a) and by funding the study of 

lead in the air (U.S. EPA, 1987). 

14-2 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Table 14.1: Quantified and Unquantified Health Effects of Lead 

Population Group Quantified Health Effect Unquantified Health Effect 

Children ages 0-7 Neonatal mortality due to decreased gestational 

age and low birth weight caused by maternal 

exposure to lead 

Nervous system effects in children younger than 7 

years - IQ decrements, cases of IQ less than 

70, PbB levels greater than 20 �g/dL 

Fetal effects from maternal exposure (including 

diminished IQ and reduced birth weight) 

Low IQ (70 <IQ< 84) 

Permanent brain structure changes 

Slowed/delayed growth 

Delinquent and anti-social behavior 

Metabolic effects, impaired heme synthesis, 

anemia 

Impaired hearing 

Possible cancer - stomach, kidney, respiratory tract 

Lead effects in children over 7 years 

Adult Female 

ages 45-74 

Ages 45-74 

Non-fatal CHD 

Non-fatal stroke 

Mortality 

Non-fatal CHD, non-fatal strokes and mortality for 

women in other age ranges 

Other cardiovascular diseases 

Hypertension 

Hypertension in pregnant women 

Reproductive effects - reduced fertility 

Neurobehavioral function 

Gastrointestinal effects - nausea, constipation, loss of 

appetite 

Renal effects - chronic nephropathy, gout 

Possible cancer - stomach, kidney, respiratory tract 

Adult Male 

ages 20 - 74 

For men in specified age ranges: 

Ages 20-74 

Hypertension 

Ages 40-75 

Non-fatal CHD 

Mortality 

Ages 45-74 

Non-fatal stroke 

Non-fatal CHD, non-fatal strokes and mortality for 

men in other age ranges 

Other cardiovascular diseases 

Reproductive - men: sperm abnormalities 

Neurobehavioral function 

Gastrointestinal effects - nausea, constipation, loss of 

appetite 

Renal effects - chronic nephropathy, gout 

Possible cancer - stomach, kidney, respiratory tract 

Source: U.S. EPA analysis. 

14.1.1 Children Under Age One 

Fetal exposure to lead in utero from maternal lead intake may result in several adverse health effects, including decreased 

gestational age, body weight, head circumference, body length, late fetal death, and increased infant mortality (Moore et al., 

1982; McMichael et al., 1986; Ward et al., 1987; Dietrich et al., 1987; Bornschein et al., 1989; Bellinger et al., 1991). The 

Centers for Disease Control (CDC) estimated that the risk of infant mortality increases by 10-4 for each 1 �g/dL increase in 

maternal PbB level during pregnancy (CDC, 1991b). Neurobehavioral deficits in infants can result from both pre-natal and 

early post-natal exposure. The metabolic effects described for children in the section below have also been identified in 

infants. These effects can be quantified based on the dose-response relationship between PbB levels and intelligence quotient 

(IQ) decrements (Schwartz, 1994). 

14.1.2 Children Between the Ages of One and Seven 

Elevated PbB levels in children may result in metabolic effects such as impaired heme synthesis, anemia, slowed growth, and 

cancer (U.S. EPA, 1990). Severe lead poisoning may result in seizures, impaired coordination, recurrent vomiting, coma, and 

acute lead encephalopathy, a potentially fatal condition (Piomelli et al., 1984). Elevated lead exposure may also induce a 

number of effects on the human nervous system. These effects include hyperactivity, behavioral and attentional difficulties, 

delayed mental development, and motor and perceptual skill deficits. The neurobehavioral effects on children can be 

quantified based on the dose-response relationship for IQ decrements (Shwartz,1993). 

14-3 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

14.1.3 Adults 

EPA has classified lead as a probable human carcinogen (Group 2b) based on animal toxicological evidence (IRIS, 2002a; 

see file titled Lead and Compounds (inorganic)). Lead also has been strongly suggested as the causative agent in numerous 

studies of kidney, stomach, and respiratory cancer in humans. The cancers observed in human studies are usually lethal. A 

cancer potency factor for lead has not been published by U.S. EPA, however, due to uncertainties associated with human 

studies. The California Environmental Protection Agency (CEPA) has also classified lead as a carcinogen and estimated a 

cancer potency factor of 8.5 × 10-3 per mg/kg/day for exposure to lead and lead compounds (California Air Resource Board 

[CARB], 1996).3 Reduced cancer risk associated with reduced exposure to lead can be estimated based on cancer cases 

avoided (see Section 13.2.1). The Agency did not incorporate cancer effects from exposure to lead in the final rule analysis 

because these effects appeared very small compared to other adverse health effects from exposure to lead (e.g., neurological 

damages to children). 

Elevated PbB has been linked to elevated BP in adults, especially in men aged 40 to 59 (Pirkle et al., 1985). Elevated BP, 

itself a health hazard, is also a risk factor for heart attack, stroke (Shurtleff, 1974; McGee and Gordon, 1976; Pooling Project 

Research Group [PPRG], 1978), and premature death. Since heart disease and its related diseases are the primary cause of 

death in the United States, avoiding their exacerbation by minimizing lead exposure can be assumed to have considerable 

benefits for the affected population. Although elevated BP in women results in the same effects as for men, the general 

relationships between BP and these health effects differ somewhat across gender (Shurtleff, 1974). 

Other known or strongly suspected health endpoints include nervous system disorders in adults, anemia and blood disorders, 

gastrointestinal disorders, and renal damage (Roels et al., 1976; Factor-Litvak et al., 1993; 1998; and 1999). Finally, data 

suggest that lead is genotoxic and may cause chromosomal damage in humans leading to birth defects (Anwar, 1994; 

Apostoli et al., 2000; Sallmen et al, 2000). Lead may also cause other adverse reproductive effects in women, including 

increased miscarriage and stillbirth (U.S. EPA 1990). A study of National Health and Nutrition Examination Surveys 

(NHANES) II data by Silbergeld et al. suggests that accumulated lead is stored in women’s bone tissues and is mobilized 

back into the blood during the bone demineralization associated with pregnancy, lactation, and osteoporosis (Silbergeld et al., 

1988). Many of these effects cannot be quantified due to a lack of information on the dose-effect relationship. 

14.2 HEALTH BENEFITS TO CHILDREN 

The following analysis assesses benefits to children from reduced lead exposure, via reduced consumption of contaminated 4 fish tissue. This analysis uses PbB concentrations as a biomarker of lead exposure.5 EPA estimated PbB levels in the 

population of exposed children to obtain both baseline and post-compliance readings. Changes in those readings yielded 

estimated benefits from reduced lead exposure in the form of avoided damages. Avoided neurological and cognitive damages 

are expressed as changes in overall IQ levels, including reduced incidence of extremely low IQ scores (<70, or two standard 

deviations below the mean), and reduced incidence of PbB levels above 20 �g/dL. The neurological and cognitive damages 

avoided are then quantified using the value of compensatory education that an individual would otherwise need, and the 

impact on that individual’s future earnings. This analysis does not quantify additional benefit categories, such as the costs of 

PbB screening and medical treatment. The reduced loss in IQ points, reduced cases of IQ levels below 70 points, and reduced 

special education costs associated with various PbB levels are likely to be the largest benefit categories. This analysis does 

not estimate the cost of group homes and other special care facilities. 

The analysis of health benefits to children involves the following steps: 

� estimate the baseline and post-compliance lead discharges from MP&M facilities; 

3 The cancer potency factors for lead acetate and lead subacetate are 28×10-1 and 3.8×10-2, respectively. 

4 This analysis does not consider the beneficial effects due to reduced drinking water exposure. EPA has issued drinking water 

criteria for lead. This analysis assumes drinking water treatment has already reduced lead content below threshold levels. 

5 PbB concentration is the most common measure of body-lead burden. Other measures of body-lead burden include lead in bones, 

teeth, and hair. 

14-4 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

�	 estimate lead concentrations in receiving water bodies before and after final effluent guidelines based on lead 

discharge estimates, effluent flow, characteristics of the receiving POTW s, and characteristics of receiving water 

bodies; 

� estimate the baseline and post-compliance dietary lead intake of children via fish consumption; 

�	 estimate PbB levels of exposed children before and after the final regulation, based on in-stream lead concentrations, 

bioconcentration factors, and fish consumption rates for children; 

�	 assess changes in health impacts to children from reduced lead exposure, including changes in IQ loss, changes in 

incidence of IQ<70, and changes in neonatal mortality; 

� estimate monetary benefits resulting from reduced adverse health impacts to children; and 

� estimate benefits from changes in neonatal mortality from reduced maternal exposure to lead. 

Figure 14.1 depicts the above steps. 

The following sections summarize the relevant dose-response relationships for children, and discuss data sources used for the 

dose-response relationships. Each section also includes the methods used to value the changes in health effects based upon 

dose-response relationships. 

14.2.1 PbB Distribution of Exposed Children 

This section describes the estimation of changes in PbB distribution of exposed children. 


a. Estimating lead concentrations in the receiving water bodies 


Estimating health risks associated with lead exposure from fish consumption requires calculating in-waterway lead 


concentrations. The method and formulas for this calculation were identical to those described for the analysis of cancer 

effects for the fish consumption pathway (see Chapter 13 on Human Health Benefits and the Environmental Assessment in 

Appendix I for details.)6 

b. Estimating PbB levels in exposed children 


This analysis considers children that are born today and live in recreational and subsistence fishermen households. The 


analysis considers a continuous exposure pattern for children from birth through the seventh birthday. Exposure, health 


effects, and benefits are calculated separately for children living in recreational and subsistence fishing households. This 

analysis relies on EPA’s Integrated Exposure, Uptake, and Biokinetics (IEUBK) Model for Lead in Children (IEUBK 

version 0.99d, March 8, 1994). 

� Description of the IEUBK model 


The IEU BK model uses exposure, uptake, and biokinetic response information to estimate the PbB level distribution for a 


population of children receiving similar exposures. The estimated distribution may be used to predict the probability of 


elevated PbB levels in children exposed to a specific combination of environmental-lead levels. The model addresses four 


components of environmental risk assessment: 


� the multimedia nature of exposure to lead; 

� the differential bioavailability of various sources of lead; 

� the pharmacokinetics of internal distribution of lead to bone, blood, and other tissues; and 

� inter-individual variability in PbB levels. 

6 The water quality model used for the Ohio case study is discussed in Appendix H. 

14-5 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Figure 14.1 Assessing Benefits to Children from Reduced Lead Discharges from MP&M Facilities 

Source: U.S. EPA analysis. 

14-6 

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

The model uses estimated or measured lead concentrations in fish tissues and other media, such as soil, dust, air, and water to 

estimate a continuous exposure pattern for children from birth through the seventh birthday (U.S. EPA, 1995). The model 

then estimates a distribution of PbB levels for a population of children receiving similar exposures by predicting its 

geometric mean (GM). The inter-individual and biological variability in PbB levels of children exposed to similar 

environmental lead levels is represented by the geometric standard deviation (GSD). This analysis uses an empirical 

estimate of the variability in PbB concentrations, a GSD of 1.6, estimated from residential community PbB studies (U.S. EPA, 

1995). This estimate is applied for predictions of the national distribution of PbB concentrations. 

The model has three distinct functional components that work together in a series: 

� exposure, 

� uptake, and 

� biokinetics response. 

Each model component is a set of complex equations and parameters. The Technical Support Document (U.S. EPA, 1995)
 

provides the scientific basis of the parameters and equations used in the model, while the Guidance Manual (U.S. EPA, 1994)
 

includes a detailed description of the exposure pathways, absorption mechanism, biokinetic compartments, and associated
 

comparted transfers of lead.
 

� Inputs to the IEUBK model 

The IEUBK model uses three sets of parameters:
 

� exposure parameters estimate the amount of environmental lead taken into the body, through breathing or ingestion; 

� uptake parameters estimate the amount of lead absorbed from environmental sources; 

�	 biokinetic parameters characterize the transfer of lead between compartments of the body (e.g., between blood and 

bone) and elimination of lead from the body. 

The IEUBK model allows the user to input values for most exposure and uptake parameters. The biokinetic parameter values 

cannot be altered. When exposure and uptake values are not specified, the IEUBK model provides default values. Table 14.2 

summarizes the key parameter values used in this analysis and indicates whether a value is an IEUBK default value or has 7 been specified by EPA. 

1. Exposure parameters include exposure rates and exposure concentrations: 

�	 Exposure rates: Children in recreational fishing households are assumed to consume 6.03 grams of fish per day. 

Children living in subsistence households are assumed to consume 30.33 grams of fish per day. These fish 

consumption rates are based on uncooked fish weights. The fish consumption rate for children in recreational fishing 

households is calculated as a weighted average based on West et al. (U.S. EPA, 1997a) for children ages 1-5 (5.63 

grams of fish per day) and children ages 6-10 (7.94 grams of fish per day). For children of subsistence fishing 

households, the fish consumption rate is calculated as a weighted average based on Columbia River Intertribal Fish 

Commission (CRITFC, 1994) estimates for children under age 5 (19.6 grams of fish per day) and the Continuing 

Survey of Foods by Individuals (U.S. EPA, 2002b) for children ages 3-5 (40.31 grams of fish per day) and ages 6-10 

(61.49 grams of fish per day). 

�	 Exposure concentrations: EPA used estimated in-stream concentrations of lead to calculate lead concentration of the 

fish consumption exposure pathway. The Agency used 1996 monitoring data (U.S. EPA, 1996b) on lead 

concentrations in air and the Housing and Urban Development National Survey (HUD , 1995) for data on lead 8 concentrations in dust and soil to characterize lead exposure concentrations for these exposure pathways. This 

7 A complete list of IEUBK default parameters is presented in Appendix L. 

8 EPA found that the typical PbB level distribution predicted in the IEUBK Model for Lead in Children based on the default values 

for air, dust, soil, and drinking water lead concentrations did not correspond to the most recent national population PbB distribution 

(NHANES III, Phase 2, 1994). Therefore, the Agency used more recent data to characterize the background exposure to environmental 

14-7 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

analysis uses median concentration values for these three pathways as inputs to the IEUBK to characterize 

background exposure to environmental lead. EPA used the IEUBK default value for lead concentration in drinking 

water that takes into account contributions of lead from plumbing. Because of past use of lead in plumbing, lead 

concentrations in tap water are likely to be above the current water quality standard for lead in drinking water. 

2.	 Uptake of ingested lead: Lead bioavailability varies across the chemical forms in which lead can exist. Many factors 

complicate the estimation of bioavailability, including nutritional status and timing of meals relative to lead intake. The 

Agency used the default media-specific bioavailabilities in the IEUBK model for this analysis. 

3.	 Biokinetic parameters: The data on which these parameter values are based originate from a variety of sources, including 

available clinical data (U.S. EPA, 1995). These parameters cannot be changed by the user. 

lead. Median values from recent monitoring data allowed the Agency to match the IEUBK-predicted PbB distribution to the NHANESderived distribution. 

14-8 
MP&M EEBA Part III: Benefits 

Table 14.2: Selected List of Parameters Used in the IEUBK Model 

Variable Value IEUBK Default Data Source 

Exposure Rates Fish: 

Recreational 

6.03 g/day No The fish consumption rate for children in recreational 

weighted average based on West et al. (U.S. EPA, 19

g/day) and children ages 6-10 (7.94 g/day). ish 

subsistence fishing households is calculated as a weig

River Intertribal Fish Commission (CRITFC, 1994) e(19.6 g/day) and the Continuing Survey of Foods by I

children ages 3-5 (40.31 g/day) and ages 6-10 (61.49 

Fish: Subsistence 30.33 g/day No 

Fresh Fruit 38.481 g/day 0-11 months 

169.000 g/day 12-23 months 

63.166 g/day 24-35 months 61.672 g/day 36-47 months 61.848 g/day 48-59 months 67.907 g/day 60-71 months 80.024 g/day 72-84 months 

Yes Values taken from Pennington, J. A. T. (1983) Revisi

diets. Journal of American Dietetic Association 82(2)

Fresh Vegetables 56.84 g/day 0-11 months 

106.50 g/day 12-23 months 155.75 g/day 24-35 months 157.34 g/day 36-47 months 158.93 g/day 48-59 months 172.50 g/day 60-71 months 199.65 g/day 72-84 months 

Yes Values taken from Pennington, J. A. T. (1983) Revisi

diets. Journal of American Dietetic Association 82(2)

Meat (Including 

fish and game) 

29.551 g/day 0-11 months 87.477 g/day 12-23 months 95.700 g/day 24-35 months 101.570 g/day 36-47 months 

107.441 g/day 48-59 months 

111.948 g/day 60-71 months 

120.961 g/day 72-84 months 

Yes Values taken from Pennington, J. A. T. (1983) Revisi

diets. Journal of American Dietetic Association 82(2)

Air (Time spent outdoors) 

1 hrs/day 0-11 months 2 hrs/day 12-23 months 

3 hrs/day 24-35 months 

4 hrs/day 36-47 months 

4 hrs/day 48-59 months 

4 hrs/day 60-71 months 

4 hrs/day 72-84 months 

Yes Based on values reported in (1) U.S. Environmental P

Review of the National Ambient Air Quality Standard

and Technical Information. OAQPS Staff Paper, Air 

Research Triangle Park, NC (EPA 1989c), and (2) Re

Advisory Committee on Its Review of the OAQPS LeaEPA-SAB-CASAC-90-002 (EPA 1990a) 

The f
MP&M EEBA Part III: Benefits Chapt

Table 14.2: Selected List of Parameters Used in the IEUBK Model 

Variable Value IEUBK Default Data Source 

Water (Daily 

amount of water consumed) 

0.20 L/day 0-11 months 

0.50 L/day 12-23 months 

0.52 L/day 24-35 months 

0.53 L/day 36-47 months 

0.55 L/day 48-59 months 

0.58 L/day 60-71 months 

0.59 L/day 72-84 months 

Yes Exposure Factors Handbook. U.S. EPA Office of Health and E

Washington, DC. EPA/600/8-89/043 (1989b) 

Soil (Combined soil and dust consumption) 

0.085 g/day 0-11months 

0.135 g/day 12-23 months 

0.135 g/day 24-35 months 

0.135 g/day 36-47 months 

0.100 g/day 48-59 months 

0.090 g/day 60-71 months 

0.085 g/day 72-84 months 

Yes Based on value reported in Review of the National Ambient Air 

Lead: Assessment of Scientific and Technical Information. OAQ

Quality Management Division, Research Triangle Park, NC (19

Exposure Concentrations 

Fish Tissue site-specific No Estimated based on predicted lead concentration in receiving re

factor for lead (49 L/Kg) 

Outdoor Air 0.03 �g/m3 No Median value for 1996 from EPA’s AIRS (Aerometric Informa

monitoring data (U.S. EPA, 1996b) 

Indoor Air 30% of Outdoor Air Yes Based on value reported in Review of the National Ambient Air 

Lead: Assessment of Scientific and Technical Information. OAQ

Quality Management Division, Research Triangle Park, NC (19

Water 4.0 �g/L Yes Analysis of data from American Water Works Service Co. in M

Distribution of lead in tap water. Parts I and II. Report to the U

Water/Office of Toxic Substances, from Battelle Memorial Inst

0115. 

Soil 61.78 �g/g No Median values from the Housing and Urban Development Nati

Department of Housing and Urban Development, 1995) 

Dust 187.11 �g/g No 

Food Lead Intake Fresh Fruit 0.039 �g/day 0-11 months 

0.196 �g/day 12-23 months 0.175 �g/day 24-35 months 0.175 �g/day 36-47 months 0.179 �g/day 48-59 months 0.203 �g/day 60-71 months 0.251 �g/day 72-84 months 

Yes Based on data provided by FDA in Air Quality Criteria for Lea

Environmental Criteria and Assessment Office, Research Trian

600/8-83-028a-d (1986b) 

14-10 

MP&M EEBA Part III: Benefits 

Table 14.2: Selected List of Parameters Used in the IEUBK Model 

Variable Value IEUBK Default Data Source 

Fresh Vegetables 0.148 �g/day 0-11 months 

0.269 �g/day 12-23 months 

0.475 �g/day 24-35 months 

0.466 �g/day 36-47 months 

0.456 �g/day 48-59 months 

0.492 �g/day 60-71 months 

0.563 �g/day 72-84 months 

Yes Based on data provided by FDA in Air Quality Criter

Environmental Criteria and Assessment Office, Resea

600/8-83-028a-d (1986b) 

Meat (No fish or 

game meat) 

0.226 �g/day 0-11 months 

0.630 �g/day 12-23 months 

0.811 �g/day 24-35 months 

0.871 �g/day 36-47 months 

0.931 �g/day 48-59 months 

1.008 �g/day 60-71 months 

1.161 �g/day 72-84 months 

Yes Based on data provided by FDA in Air Quality Criter

Environmental Criteria and Assessment Office, Resea

600/8-83-028a-d (1986b) 

Other Foods (No fish or game 

meat) 

3.578 �g/day 0-11 months 

3.506 �g/day 12-23 months 

3.990 �g/day 24-35 months 

3.765 �g/day 36-47 months 

3.545 �g/day 48-59 months 

3.784 �g/day 60-71 months 

4.215 �g/day 72-84 months 

Yes Based on data provided by FDA in Air Quality Criter

Environmental Criteria and Assessment Office, Resea

600/8-83-028a-d (1986b) 

Lead Absorption 

Factor 

Food 0.5 Yes Based on values reported in the Review of the Nation

Lead: Exposure Analysis Methodology and ValidatioU.S. EPA Office of Air Quality Planning and Standar

(1989d) 

Air 32% Yes 

Water 0.5 Yes 

Soil 0.3 Yes 

Dust 0.3 Yes 

Biokinetic Parameters IEUBK default values and equations were used for all biokinetic parameters (these cannot be changed b

IEUBK biokinetic parameters is listed in Appendix L and in the Technical Support Document: Parame

IEUBK Model for Lead in Children. U.S. EPA, EPA 540-R-94-040, (1995) 

Age Fish Introduced in Infant Diet 9 months N/A Literature on dietary guidelines for children from var

including the National Network for Child Care 

Source: U.S. EPA analysis. 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

c. &M discharges 

EPA used the IEUBK model in this analysis to estimate the effect of lead-contaminated fish consumption on children’s PbB 

concentrations. The Agency first calculated lead concentration in fish tissue corresponding to each reach affected by MP&M 

discharges to pro vide inputs to the IEUB K m ode l. mod el uses the specified fish tissue conc entratio ns in conjunc tion with 

fish ingestion rates and bioava ilability factors to de termine the dose o f lead absorbed by the b ody. his do se is then u sed to 

predict the GM PbB concentration for children associated with each reach affected by lead discharges from the MP&M 

facilities. 

EP A used the IEU BK mod el to predict the ba seline and po st-compliance PbB distributions for children that con sume fish 

from reach es affecte d by lead discharges from MP& M facilities. he difference betwe en the estimated baseline and postcom plianc e Pb B d istribution is the basis for the analysis of b enefits to c hildren from the M P& M regulation. 

14.2.2 Relationship Between PbB Levels and IQ 

A dose-response relationship between PbB and IQ decrements determined by Schwartz (1994) suggests that a decrease of 

0.25 IQ points can be expected for every 1 �g/dL increase in PbB (Schwartz, 1994). p-value (< 0.0001) indicates that 

this relationship is highly significant. 

EPA multiplied the 0.25 IQ po ints lost per �g/dL increase in PbB by the average increase in PbB level for children and by the 

number of exposed children to obtain the total change in number of IQ points for the population. PbB level 

modeled in this analysis is a GM, not the arithmetic mean used by Schwartz (1993). To adjust for this difference, equation 

14.1 uses a ratio between the arithmetic mean and the GM of a logno rm ally-d istributed random variable. ratio 

between the expe cted v alue (m ean) o f the distribution and the GM is 1.11 7 for the assum ed G SD of children's Pb B lev els 

(1.6). 

The total avoided loss of IQ p oints for each gro up is estimated as: 

(14.1) 

where: 

(Pop) k = the number of children (up to age seven) in anglers’ families in the vicinity of a given MP &M reach; and 

GM k = the GM of the PbB distribution in the population of children. 

As shown in equation 14.1, the population of children up to age seven is divided by seven to avoid doub le-counting. The 

IEUB K mo del calculates the GM of the PbB distribution in the population of children born today, assuming a continuous 

exposure pattern for children from birth through the seventh birthday. suming that children are evenly distributed by age, 

this division adjusts this equation to apply only to children age 0-1. ng by seven undercounts overall benefits. dren 

from age 1 to 7 are not accounted for in the base year of the analysis, although they are presumably affected by the lead 

exp osure , beca use the IEU BK mod el assum es a co ntinuous exp osure pattern for child ren from birth throug h the sev enth 

birthd ay. 

14.2.3 e 

Available economic research provid es little emp irical data on so ciety's overall W TP to avo id a decrea se in an infant's IQ. 

This analysis uses research that monetizes a subset of effects associated with decreased IQ. These effects represent only some 

compo nents of society's W TP to avoid IQ decreases, and underestimate society’s WT P when emp loyed alone. 

purpo se of this analysis, these effects are the only ones available at this time to app roximate the W TP to avoid IQ decrem ents. 

Rec ent stud ies pro vide c oncrete ev idenc e of lon g-term e ffects from childhood lead e xpo sure (S chwa rtz, 19 94) . nalysis 

assumes a permanent loss of IQ points based on PbB levels estimated for children up to age seven, and considers two 

con sequ ence s of this IQ decr eme nt: 

Estimating changes in the PbB level in exposed children from reduced MP

Value of Children's Intelligenc

� the decreased present value of the infant’s expected lifetime earnings, and 

�	 the increased educational resources expended for an infant who becomes mentally handicapped or needs 

compensatory education as a consequence of lead exposure. 

The

T 

T

The 

The average

The

As

Dividi Chil

For the 

This a 

14-12 

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

a. Estimating the effect of IQ on earnings 

Reduced IQ has direct and indirect effects on earnings. This analysis models the overall impact from a one-point reduction in 

IQ as the sum of these direct and indirect effects on lifetime earnings. EPA used the most recent estimates of the effects of IQ 9 on earnings based on Salkever (1995). Salkever provided updated estimates of the direct and indirect effects of IQ loss on 

earnings, using the most recent available data set, the National Longitudinal Survey of Youth (NLSY). Salkever used 

regression analysis techniques to estimate direct and indirect effects of IQ on earnings. Three different relationships are 

estimated separately for male and female respondents: 

� a least-squares regression of highest grade on IQ test scores; 

� a probit regression of a 0-1 indicator of positive earnings on highest grade and IQ test scores; 

�	 a least-squares regression, for persons with positive earnings, of the logarithm of earnings on highest grade and IQ 

test scores. 

Other variables were included in each regression to control for effects of family background (parents’ education and income), 

the age of the respondent, ethnic group, and residence location (urban U.S., non-urban U.S., south versus non-south). 

Based on the regression results, Salkever estimated the effects of IQ on earnings as the sum of direct and indirect effects: 

�	 The direct effect is the sum of effects of IQ test scores on employment and earnings for employed persons, holding 

the years of schooling constant. 

�	 The indirect effect is the sum of effects of IQ test scores on years of schooling attained, and the subsequent effect of 

years of schooling on the probability of employment and on earnings for employed persons. 

The analysis found that percentage effects of lead exposure are greater for females than for males. The total estimated effect 

of the loss of an IQ point on earnings, based on the Salkever study, is an earnings reduction of 1.93 percent for men and 3.22 

percent for women. The total effect of the loss of an IQ point on earnings also includes non-IQ effects on schooling (e.g., 

behavioral problems). 

b. Valuing foregone earnings 

EPA monetized IQ loss effects by combining the percent earnings loss estimate with an estimate of the present value of 

expected lifetime earnings. EPA used the 1992 data on money income for the U.S. population (U.S. Department of 

Commerce, 1993) to calculate the mean present value of lifetime earnings of a person born today. The data included earnings 

for employed persons and employment rates as a function of educational attainment, age, and gender. The following 

assumptions were used to calculate the mean present value of lifetime earnings of a person born today: 

� The distribution of earnings for employed persons and labor force participation rates remains constant over time. 

� A person earns income from age 18 through age 67. 

� Real wages grow one percent per year. 

� Future earnings are discounted at a three percent annual rate. 

The money income data (U.S. Department of Commerce, 1993) form the best available basis for projecting lifetime earnings, 

but involve some uncertainties. Labor force participation rates of women, the elderly, and other groups will likely continue to 

change. Currently, men tend to earn more than women due to higher wage rates and higher labor force participation. 

Expected lifetime earnings increase with education for both men and women. Real earnings of women will probably continue 

to rise relative to real earnings of men. Educational attainment has risen over time and may continue to rise. Unpredictable 

9 EPA did not incorporate earlier studies of the effects of IQ on earnings in this analysis because the Salkever study is more complete 

in capturing the various pathways through which IQ affects earnings, such as the indirect effect of IQ on earnings via its effect on 

educational attainment. Also, other studies are much older. The IQ/earning effect is likely to be higher during the high tech boom in the 

last decade. 

14-13 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

fluctuations in the economy's growth rate will probably affect labor force participation rates and real wage growth for all 

groups. Medical advances that increase life expectancy will probably increase lifetime earnings. 

Although earnings data alone form an incomplete measure of an individual's value to society, this analysis does not account
 

for those individuals who do not participate in the labor force at all throughout their working years and whose productive
 

services are not measured by wage rates. The largest group in this population are those who remain at home doing housework
 

and child rearing. Volunteer work also contributes significantly to social welfare, and volunteerism rates tend to increase with
 

educational attainment and income. Assuming that the opportunity cost of non-wage-compensated work equals the
 

average wage earned by persons of the same sex, age, and education, the average lifetime earnings estimates would be
 

significantly higher. Recalculating the tables using full employment rates for all age, sex, and education groups would
 

provide higher lifetime earnings estimates. To be conservative, this analysis considered only the value of lost wages and does
 

not include the opportunity cost of non-wage-compensated work.
 

The adjusted value of expected lifetime earnings equals the present value for an individual entering the labor force at age 18
 

and working until age 67. Given a three percent social discount rate, the other assumptions mentioned, and current survival
 10 probabilities, the present value of lifetime earnings of a person born today in the U.S. would be $448,957 (2001$).
 

c. Valuing costs of education 

The increase in lifetime earnings from additional education equals the gross return on education. The cost of education is
 

subtracted from the gross return to obtain the net increase in earnings from additional education. The cost of education has
 

two components: the direct cost of the education, and the opportunity cost of lost income during the education. The
 

marginal cost of education used in this analysis was assumed to be $8,898 (2001$) per year. This figure was derived from
 

the U.S. Department of Education's reported ($6,961) average per-student annual expenditure (current plus capital
 11 expenditures) in public primary and secondary schools in 1995-96 (U.S. Department of Education, 1998). EPA adjusted 

this value to 2001 dollars based on CPI for education. 

Salkever’s study found the estimated effect of IQ on educational attainment to be 0.1007 years per IQ point. The estimated 

cost of an additional 0.1007 years of education per IQ point is $896 (i.e., 0.1007 × $8,898). This marginal cost was 

discounted to the time the exposure and damage is modeled to occur (age zero) because this cost is incurred after the 

completion of formal education. The average level of educational attainment in the population over age 25 is 12.9 years (U.S. 

Department of Education, 1993). The marginal educational cost was therefore assumed to occur at age 19, resulting in a 

discounted present value cost of $511 (2001$). 

The other component of the cost of education is the opportunity cost of lost income while in school. Income loss is frequently 

cited as a major factor in the decision to terminate education, and must be subtracted from the gross returns to education. An 

estimate of the lost income was derived assuming that people in school are employed part-time but that people out of school 

are employed full-time. The opportunity cost of lost income is the difference between full-time and part-time earnings. The 

value of lost income associated with being in school an additional 0.1007 years is $746 (2001$) discounted to age zero. 

d. Estimating the total effect of IQ on earnings 

Combining the value of lifetime earnings ($448,957) with the estimate of percent wage loss per IQ point yielded $10,675 per 

IQ point. Subtracting the education and opportunity costs reduced this value to $9,419 per IQ point (2001$). 

14.2.4 Value of Additional Educational Resources 

Children with IQs less than 70 and whose PbB is greater than 20 �g/dL will require additional educational resources including 

an educational program tailored to the mentally handicapped. Some children whose PbB is greater than 20 �g/dL will need 

additional instruction while attending school later in life. The following sections describe approaches used to quantify the 

number of children with IQs less than 70 and to estimate increased educational costs resulting from lead exposure. 

10 Assuming a seven percent social discount rate, the present value of lifetime earnings of a person born today in the U.S. would be 

$101,247 (2001$). Appendix M presents a sensitivity analysis with respect to the value of an IQ point. 

11 In comparison, the average annual cost of tuition, fees, room, and board for a four-year public undergraduate institution was $8,655 

(2001$) for the year 2000-2001 (U.S. Department of Education, 2001). 

14-14 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

a. Children with IQs less than 70 

� Quantifying the number of children with IQs less than 70 

Increases in the mean PbB levels of children results in an increased incidence of children with very low IQ scores. 

are normalized to have a mean of 100 and a standard d eviation of 15. andard deviations below the 

mean, and is generally regarded as the point below which children require significant special compensatory education tailored 

to the mentally handicapped. 

The relationship presented here for estimating changes in the incidence of IQs less than 70 used the most current IQ point 

decrement function provided by Schwartz (1993). It assumed that, for a baseline children’s PbB distribution (defined by GM 

and GSD ), the population also has a normalized IQ point distribution with a mean of 100 and a standard deviation of 15. 

proportion of the population expected to have IQs less than 70 was determined from the standard normal distribution 

function for this baseline condition: 

(14.2) 

where: 

P(IQ <70) = probability of IQ scores less than 70 

z = stand ard normal variate (i.e., the numbe r of stand ard d eviations); compu ted for an IQ score of 70 , with 

mean IQ score of 10 0 and stand ard de viation of 15 as: 

(14.3) 

�(z) = standard normal distribution function: 

(14.4) 

The integral in the standard normal distribution function does not have a closed form solution. �(z) are usually 

obtained using software with basic statistical functions or from tables typically provided in statistics texts. olution for 

�(z) where z = -2 is 0.02275. is, for the normalized IQ score distribution with a mean of 100 and standard deviation of 

15, approximately 2.3 percent of children are expected to have IQ scores below 70. 

EP A made two ke y assum ptions to relate chang es in the p roportion of child ren with I Q sc ores b elow 70 to chang es in 

pop ulation mean P bB levels: 

1. The mean IQ score will change a s a result of changes in the me an Pb B level as: 

� Mea n IQ = -0.25 x � Mean PbB 

(14.5) 

where: 

� Mea n IQ = the change in the mean IQ score between the baseline and post-compliance scenarios, and 

� Mean PbB = the change in the m ean Pb B level b etween the two sc enarios. 

This relationship relies on Schwartz’ estimate (1993) of a decrease of 0.25 IQ po ints for each �g/dL increase in PbB . 

The mean PbB level referred to here is the arithmetic mean (or expected value) for the distribution, obtained as described 

previously from the GM and GSD. 

IQ scores 

An IQ score of 70 is two st

The 

Values for 

The s

That

2. The standard deviation for the IQ distribution is 15 for both the baseline and the post-compliance scenario. 

14-15 

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Using these assumptions, EPA determined the change in the probability of children having IQ less than 70 for a given change 

in mean PbB from: 

(14.6) 

where: 

�(zBl) = baseline standard normal distribution function, and 

�(zPc) = post-compliance standard normal distribution function. 

(14.7) 

EPA then converted a given change in the mean PbB level between the baseline and post-compliance scenarios into a measure 

of IQ . procedure ab ove yielded an estim ate of the perc ent of the pop ulation w ith IQs less than 7 0. A m ultiplied this 

percent by the population of exposed children to estimate the increased incidence of children with low IQs. he IQ point 

loss eq uation , EP A ap plied the results of this functio n to ch ildren a ge 0-7 and d ivided by seve n to av oid d oub le cou nting. 

(See discussion un der e quation 14 .1.) 

This procedure quantified only the change in the number of children who pass below the 70 point IQ threshold. EPA 

quantified other changes in children's IQ using the IQ point loss function (Equation 14.1) described previously. 

these two endpoints additively does not result in double counting, because the value associated with the IQ point loss function 

is the change in individual lifetime earnings, while the value associated with IQs less than 70 is the increased educational 

costs for the individual, as discussed below. 

� Valuing educational costs 

EPA estimated the number of avoided cases of children with IQs less than 70. ensatory education expenses will no 

lo ng er be incu rr ed fo r th es e c as es . K ak alik et al. (1 98 1) , usin g d ata fro m a stu dy pr ep ar ed fo r th e D ep ar tm en t o f E du ca tio n's 

Office of Special Education Programs, estimated part-time special education costs for children who remained in regular 

classrooms at $3,064 extra per child per year in 1978. Adjusting for changes in the GDP price deflator yielded an estimate 

of $6 ,959 per child in 2001 dollars. A used the inc reme ntal estimate of the cost of part-time special education to estima te 

the annual cost per child needing special education as a result of lead impacts on mental development. EPA assumed that 

com pensatory education b egins at age seven and continues thro ugh ag e 18 (grad es one throu gh twelv e). Discounting future 

expenses a t a rate o f three p ercen t yielded an exp ected prese nt value cost o f app roxim ately $5 8,01 2 pe r child (2 001 $). his 

discounting underestimates the cost because Kakalik et al. measured the increased cost to educate children attending regular 

school rather than a special education program. The costs of attending a special education program are likely to be much 

higher than those associated with regular schooling. ddition, some compensatory education programs begin earlier than at 

age seven. For example, some states, such as Connecticut and Rhode Island, offer Head Start programs to disadvantaged 

children beginning at age three. 

b. Children with PbB levels greater than 20 �g/dL

� Quantifying the number of children with PbB levels greater than 20 �g/dL 

EPA obtained the percentage of children with PbB levels greater than 20 �g/dL directly from the estimated distribution of 

PbB levels for a given location (IEUBK ). s percentage by the number of exposed children in the 

vicinity of a given MP&M reach to estimate the number of children with PbB levels greater than 20 �g/dL.12 

� Estimating and valuing compensatory education for children with PbB levels greater than 20 �g/dL 

EPA assumed that 20 percent of the children with PbB levels greater than 20 �g/dL would require and receive compensatory 

education for three years. tional expend itures are incurred b y those children. hese 

The EP 

As in t

Treating 

Comp 

EP 

T 

In a

EPA then multiplied thi

After this time, no further educa T 

12 See Section 13.1.1 for detail on estimating the affected population. The percentage of children in the affected population is 

estimated based on the Census data. 

14-16 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

assumptions are conservative. Many studies show adverse cognitive effects of PbB levels at 15 �g/dL (CDC, 1991b). Some 

studies of the persistence of cognitive effects indicate that the effects often last longer than three years. 

The Kakalik et al. (1981) estimate of part-time special education costs for children who remained in regular classrooms can 

be used to estimate the cost of compensatory education for children suffering low-level cognitive damage. As indicated 

above, the part-time special education cost per child is $6,959 per year in 2001 dollars. The Agency assumes that 

compensatory education starts at age 7 and continues for 3 years. Discounting future costs at a rate of 3 percent annually to 

account for the age at which costs are incurred (i.e., age 7 through 9) yields a present value estimate of $16,485 in 2001 

dollars. 

14.2.5 Changes in Neonatal Mortality

a. Quantifying the relationship between maternal PbB levels and neonatal mortality

U.S. EPA (1990) cites a number of studies linking fetal exposure to lead (via in utero exposure from maternal lead intake) to


several adverse health effects. These effects include decreased gestational age (i.e., premature birth), reduced birth weight,


late fetal death, and increases in infant mortality.


The CDC (CDC, 1991a) developed a method to estimate changes in infant mortality due to changes in maternal PbB levels


during pregnancy. The analysis linked the following two relationships:


� gestational age as a function of maternal PbB (Dietrich et al., 1987), and 

� infant mortality as a function of gestational age. This is performed using data from the Linked Birth and Infant 

Death Record Project from the National Center for Health Statistics (CDC, 1991a). 

Combining the two relationships provided a decreased risk of infant mortality of 10-4 (or 0.0001) for each 1 �g/dL decrease in 

maternal PbB level during pregnancy. EPA used this relationship for its analysis of maternal PbB levels and neonatal 

mortality. 

b. Valuing changes in neonatal mortality

This analysis used the estimated WTP for avoiding a mortality event to estimate the monetary benefit associated with 

reducing risks of neonatal mortality. This analysis uses the $6.5 million (2001$) estimate of the value of a statistical life 

saved recommended in the Guidelines for Preparing Economic Analysis (EPA, 2000a). For detail on valuing reduced 

mortality risks see Section 13.2.1. 

14.3 ADULT HEALTH BENEFITS

Lead exposure has been shown to have adverse effects on the health of adults as well as children. The quantified adult health 

effects included in the benefits analysis all relate to lead’s effects on BP.13 The estimated relationships between these health 

effects and lead exposure differ between men and women. Quantified health effects include increased incidence of 

hypertension (estimated for males only), initial CHD, strokes (initial CBA and BI), and premature mortality. This analysis 

does not include other health effects associated with elevated BP, and other adult health effects of lead including 

neurobehavioral and possible cancer effects. 

13 Citing laboratory studies with rodents, U.S. EPA (1990) also presents evidence of the genotoxicity and/or carcinogenicity of lead 

compounds. The animal toxicological evidence suggests that human cancer effects are possible, but dose-response relationships are not 

currently available. 

14-17 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Estimating adult health benefits from reduced exposure to lead requires analytic steps similar to those used in estimating 

children’s health benefits. These steps are: 

� estimate in-stream lead concentrations in the reaches affected by MP&M discharges; 

� estimate baseline and post-compliance adult dietary lead intake via fish consumption. The analysis of adult health 

benefits from reduced exposure to lead via contaminated fish uses the results from water quality modeling efforts 

described in Appendix I; 

� estimate changes in the PbB level distribution in the affected adult population; 

� estimate changes in health status in the affected population of adult men, and the monetary value of health benefits 

from reduced lead discharges from MP &M facilities; and 

� estimate changes in health status in the affected population of adult women, and the monetary value of health 

benefits from reduced lead discharges from MP&M facilities. 

Figure 14.2 depicts the above steps. Table 14.3 summarizes per-case costs of lead-related illnesses. 

Table 14.3: Per-Case Costs of Lead-Related Illnesses

Illness Gender Cost per 

Case (2001$) Cost Description 

Hypertensiona Male $1,141 The cost estimates were derived by taking Krupnick et al.’s (1989) average annual 

per-person costs of hypertension. $ using the CPI for Medical 

Care. Female $1,141 

CHDa, b Male $76,347 The costs were estimated (Wittels et al., 1990) for three CHDs (acute myocardial 

infarction, uncomplicated angina pectoris, and unstable angina pectoris) for 5 

years post-diagnosis using a three percent discount rate. 

service was multiplied by the estimated price of the service and the average cost for 

the three CHD types. he effect of elevated PbB on CHD incidence rates is 

beyond the scope of this analysis, weighting factors were not used to account for the 

different probabilities of contracting the three types of CHD. adjusted to 2001$ 

using the CPI for Medical Care. 

Female $76,347 

Strokea Male $335,135 The cost estimates (Taylor et al., 1996) represent the expected lifetime cost of a stroke 

for males and females age 45-74, including the present discounted value of the stream 

of medical expenditures and the stream of lost earnings. t the study used a 

five percent discount rate. did not adjust this value to reflect a 3 percent 

discount rate used elsewhere in this analysis. lues adjusted to 2001$ using the CPI 

for Medical Care. 

Female $251,351 

Low Birth 

Weightc Female $89,503 

The cost estimate was extrapolated from direct costs for LBW taken from Lewitt et al., 

using a three percent discount rate (Lewitt et al., 1995). alue includes medical, 

special education, and grade repetition costs. Value adjusted to 2001$ using the CPI 

for Medical Care. 

Death -- Any 

Illnessd 

Male $6.5 Value taken from U.S. EPA’s Guidelines for Preparing Economic Analysis (2000a). 

The value is the central estimate recommended in the document based on a range of 

estimates available from studies measuring the value of a statistical life. 

adjusted to 2001$ using the CPI for All Items. 

Female $6.5 

Value adjusted to 2001

The probability of medical 

Since t

Value

Note tha

EPA

Va

The v

Million 

Million Value 

a

 Costs were taken from U.S. EPA, 1997b. b

 Extends methodology in U.S. EPA, 1997b to discount medical costs over a 5 year period. c Note that this analysis does not estimate occurrence of low birth weight cases, due to data limitations. Cost was taken from U.S. 

EPA, 1999. 

d

 Value taken from U.S. EPA, 2000a. 

Source: U.S. EPA analysis.; U.S. EPA 1997b; U.S. EPA 1999, U.S. EPA 2000a. 

14-18 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Figure 14.2 Assessing Benefits to Adults from Reduced Lead Discharges from MP&M Facilities

Source: U.S. EPA analysis. 

14-19

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

14.3.1 Estimating Changes in Adult PbB Distribution Levels

a. Estimating values of PbB concentrations in exposed adults

EPA adapted the methodology described in the Interim Approach to Assessing Risks Associated with Adult Exposure to Lead


in Soil (hereafter, Interim Guidance) to estimate changes in the distribution of PbB levels in exposed adults from reduced


MP&M discharges (U.S. EPA, 1996a). The methodology presented in the Interim Guidance used a simplified representation


of lead biokinetics to predict quasi-steady state PbB concentrations among adults who have relatively steady patterns of


exposures to lead. This methodology is recommended by the Technical Review Workgroup (TRW) to assess the effects


of ingesting lead-contaminated soil on PbB levels of women of childbearing age, to derive risk-based remediation goals

(RBRG) protective of the developing fetus in exposed adult women.14 The Interim Guidance describes the basic algorithms


to be used in the analysis and provides a set of default parameters that can be used in cases where site-specific data are not


available. The TRW points out that this methodology is an interim approach recommended for use pending further


development and evaluation of integrated exposure biokinetic models for adults.


The dose-response relationship recommended in the Interim Guidance for exposures to lead-contaminated soil can be 

modified to analyze PbB levels in recreational and subsistence anglers exposed to lead-contaminated fish tissue. In both 

cases, the exposure pathways involve ingestion. The Interim Guidance differs from this analysis mainly in the medium 

containing lead (soil versus fish tissue). Substituting ingestion of lead in fish for ingestion of lead in soil yields the following 

equation: 

(14.8) 

where: 

PbB adult, central = central tendency estimate of PbB concentrations (�g/dL) in adults exposed to lead in fish at a 

concentration of PbW; 

PbB adult, 0 = typical PbB concentration (�g/dL) in adults in the absence of exposures via fish consumption; 

PbW = in-stream lead co ncentrations (�g/L); 

BCF = bioconcentration factor of lead in fish tissue (L/kg); 

INF = average daily fish consumption (g/day); 

AFF = absolute gastrointestinal absorption fraction for ingested lead in fish tissue (dimensionless); 

BKSF = biok inetic slop e facto r relating ( quasi-steady sta te) incre ases in typ ical ad ult Pb B c oncentratio ns to 

average daily lead uptake (�g/dL PbB increase per mg/day lead uptake); 

EF = exposure frequency for ingestion of contaminated fish (days of exposure during the averaging period); 

may be taken as days per year for continuing, long-term exposure; 

CF = conversion factor (10�3

 kg/g); and 

AT = averaging time, the total period during which fish consumption may occur; 365 days/year for 

continuing long-term exposure. 

Equation 14.8 is recommended for females aged 17 to 45 (U.S. EPA, 1996a). Studies of adult males, however, provided 

many of the parameters used in the Interim Guidance. For example, the biokinetic slope factor (BKSF) relating increase in 

typical adult blood concentrations to average daily lead uptake was developed on data reported by Pocock et al (1983). These 

data characterize the relationship between tap water lead concentrations and blood lead concentrations for a sample of adult 

males.15 Thus, EPA judged that this model can be applicable to all adults. Table 14.4 summarizes values for the model 

parameters. 

14 EPA’s TRW for lead began considering methodologies to evaluate nonresidential adult exposure to lead in 1994. A TRW 

committee on adult lead risk assessment formed in January 1996 to develop a generic methodology that could be adapted for use in sitespecific assessments of adult health risks. 

15 For detail, see p.A-10, Recommendations of the Technical Review Workgroup for Lead to Assessing Risks Associated with Adult 

Exposure to Lead in Soil (U.S. EPA, 1996a). 

14-20 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Table 14.4: Summary of Parameter Values for Estimating PbB Levels in Adults

Parameter Unit Value Comment a 

PbBadult,0 �g/dL 4.55-3.45 Male adult PbB levels based on NHANES III Phase 2 (U.S. EPA, 1991-

1994). le adult PbB levels based on NHANES III Phase 2 (U.S. 

EPA, 1996a). 

BKSF �g/dL per 

�g/day 

0.4 Based on analysis of Pocock et al. (1983) and Sherlock et al. (1984) data. 

INF g/day 17.5 142 .4 Daily fish consumption; lower value (on left) for recreational anglers and 

higher value (on right) for subsistence anglers. Fish consumption rates for 

adults are taken from the Methodology for Deriving Ambient Water Quality 

Criteria for the Protection of Human Health (EPA, 2000b). Both these 

rates, 142.4 g/day for adult subsistence anglers and 17.5g/day for adult 

recreational anglers, are used for the specific sub-population that they 

represent. EPA was not able to break these rates down by gender or age 

group for use in this analysis. 

EF day/yr 365 Days per year for continual long-term exposure. 

BCF L/kg 49 Bioconcentration factor of lead in fish tissue. 

AFF dimensionless 

0.03 Absolute gastrointestinal absorption fraction for ingested lead in fish 

tissue. sed on Maddaloni (1998). 

Fema

Ba

a

 For detailed information on the sources of the parameters and uncertainties associated with their use, see U.S. EPA, 1996a. 

Source: U.S. EPA analysis. 

� Typical adult PbB concentrations at baseline


Previous research suggests males have a higher background PbB level (U.S. EPA, 1996a). This analysis uses population-


specific typical concentrations to account for differences in background lead exposure between genders and between two


socioeconomic subgroups considered in the analysis (i.e., recreational and subsistence fishermen). EPA used data for adult


males and females from NHANES III to characterize the baseline distribution of PbB concentrations in the relevant sub-


populations for each MP&M reach and affected population (NHANESIII, 1991-1994). The baseline PbB distribution


scenario reflects site-specific population characteristics because baseline PbB levels differ across ethnic, income, and urban


status groups.


� Bioavailability of lead from fish tissue


To identify lead bioavailability in fish tissue, EPA reviewed lead absorption data from various materials reported in the lead


toxicity summary document: Draft Toxicological Profile for Lead (ATSDR, 1997). EPA also reviewed Measurement of


Soil-Borne Lead Bioavailability in Human Adults, and Its Application to Biokinetic Modeling (Maddaloni, 1998) and


consulted with the study author (March, 2000). Numerous studies have found that lead ingested with food is absorbed at a


significantly lower rate than lead ingested after fasting. The Interim Approach reports this dynamic and notes that "the


bioavailability of ingested soluble lead in adults varies from less than 10 percent when ingested with a meal to between 60 and


80 percent when ingested after a fast" (U.S. EPA, 1996a). TRW uses a 20 percent lead bioavailability factor for soil. This


factor is based on lead consumption interspersed with and between meals throughout the day, and is therefore likely to


overestimate PbB levels in adults exposed to lead-contaminated fish. In the absence of data on lead incorporated into food,


however, EPA considered this to be the most appropriate data to use in estimating absorption.


In the most recent study reviewed for this analysis (Maddaloni, 1998), non-fasted subjects showed a mean percent absorption


of 2.52 with a range of 0.2 to 5.2 percent and a confidence value of 0.66. The male and female study subjects had normal


clinical chemistry parameters and were between 21 and 40 years of age. The study used soil as the dosing vehicle. Other


studies have used water as the dosing vehicle, but soil is considered to be more similar to fish consumption.


EPA selected an absorption value of 3 percent for lead ingested in fish tissue, based on Maddaloni’s results. The value of 3


percent provides a reasonable estimate for most adults. This analysis does not address individuals who may have higher lead


absorption, or are at elevated risk due to lead exposure. These individuals include pregnant women, who have higher calcium


requirements (and are therefore more likely to be calcium-deficient), people with poor nutritional status (including iron and


calcium deficiencies), and individuals with other metabolic disorders. By evaluating subsistence and recreational anglers at


proposal and for final rule options with lead benefits, the analysis is already focusing on sub-populations at higher risk than


14-21 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

the average population. To maintain an approach that represents likely exposures, intakes, and risks, EPA chose not to 

consider individuals at unusually high risk within an already-high risk sub-population. 

14.3.2 Male Health Benefits

This section describes the health effects of reduced lead exposure that this analysis has quantified for men; the next section 

prese nts a similar discussion for wom en. 

a. Hypertension

� Quantifying the relationship between PbB levels and hypertension 

Studies have linked elevated PbB to elevated BP in adult males, especially men aged 40 to 59 (Pirkle et al., 1985). Further 

studies have d emo nstrated a do se-resp onse relation ship for hype rtensio n (de fined a s diasto lic BP abo ve 90 mm H g for this 

mod el) in males aged 20 to 7 4 (Schw artz, 1988 ). 

(14.9) 

where: 

�Pr(HYP) = the change in the probability of hypertension, 

e = base of the natural logarithm (2.76) 

PbB1 = PbB level in the baseline scenario, and 

PbB2 = PbB level in the post-compliance scenario. 

� Valuing reductions in hypertension 

Th e best measure o f the social costs o f hyperte nsion, so ciety's W TP to avo id the cond ition, can not b e qua ntified witho ut basic 

research that is well beyo nd the scop e of this p roject. lly, the mea sure wo uld include a ll the med ical co sts assoc iated w ith 

treating hypertension, the individual's WT P to avoid the worry that hypertension could lead to a stroke or CH D, and the 

individual's WTP to avoid the behavioral changes required to reduce the probability that hypertension leads to a stroke or 

CHD. 

Th is analysis use d recent research results to q uantify two bene fit catego ry com pon ents: me dical costs and lost work tim e. 

Krupnick and Cro pper (1989 ) estimated the medical costs of hypertension, using data from the National Medical Care 

Expenditure Survey. or physician care, drugs, and hospitalization. ion, 

hypertensives have more bed disability days and work-loss days than non-hypertensives of comparable age and sex. 

and Cropp er estimated the increase in work-loss days at 0.8 per year. Valuing this estimate at the estimated mean daily wage 

rate and adjusting the costs to 2001 dollars yields an estimate of the annual cost of each case of hypertension of $1,141. 

The benefits estimate in this analysis likely underestimates the true social benefit of avoiding a case of hypertension for 

several reaso ns: 

� It doe s not include a measure of the value of pain, suffering, and stress associated with hype rtension. 

� It does no t value the direct costs (out-of-po cket expe nses) of diet and b ehavior m odification (e.g., salt-free diets, 

etc.). hese costs, whic h are typ ical for severe m odifica tions, are likely to be significant. 

� This ana lysis does not add ress the loss of satisfaction associated with the d iet and beha vior mod ifications. 

� This analysis does not include the value of avoiding side effects associated with the medication for hypertension, 

which include drowsiness, nausea, vomiting, anemia, impotence, cancer, and depression. 

� The analysis does not includ e the effects of the disease on fam ily members. 

This relationship is: 

Idea 

Medical costs include expenditures f In addit

Krupnick 

T 

14-22

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

b. Changes in CHD

� Quantifying the relationship between PbB and BP 

EPA quantified the effect of changes in PbB levels on changes in BP to predict the probability of both hypertension and other 

cardiova scular illnesses, such as CH D, strokes, and prema ture mortality. iovascular illnesses include P bB as a 

risk factor (Shurtleff, 1974; McGee and Gordon, 1976; PPR G, 1978). esults of a meta-analysis of several 

studies, Schwartz (1992) estimated a relationship between a change in BP associated with a decrease in PbB from 10 �g/dL to 

5 �g/dL. the coefficient reported by Schwartz to relate BP to Pb B for men: 

(14.10) 

where: 

�DBPmen = the change in men's diastolic BP expected from a change in PbB; 

PbB1 = PbB level in the baseline scenario (in �g/dL); and 

PbB2 = PbB level in the post-compliance scenario (in �g/dL). 

EPA used this PbB to BP relationship to estimate the incidence of initial CHD, strokes (BI and initial CBA), and prem ature 

mortality in men. 

� Quantif ying th e re la tionsh ip be tw ee n BP and CH D 

This analysis used estimated BP changes to predict the increased probability of initial CHD and stroke occurrence (U.S. EPA, 

198 7). BP also increases the pr obab ility of CHD a nd stroke rec urrence, but E PA d id not quan tify these 

relationships in this analysis. An equation with different coefficients for each of three age groups can predict first-time CHD 

events in men. A 1978 study by the PPRG supplied information for men between ages 40 and 59. d a 

mu ltivariate model (controlling for smoking and serum cholesterol) relating the probability of CHD to BP. 

data from five different epidemiological studies. The equation for the change in 10-year probability of a first-time occurrence 

of CH D related to an increase in B P is: 

(14.11) 

where: 

�Pr(CH D40-59) = the change in 10-year probability of an occurrence of a CHD event for men between ages 40 and 

59; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP 

for subsistence and recreational fishermen aged 40 to 59 is 81.8 and 80.0, respectively; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

Inform ation p resented in Shurtleff (1974 ) helpe d de fine the relationship between BP and first-time CH D in o lder m en. his 

study used data from the Framingham Study (McGee and Gordo n, 1976) to estimate univariate relationships between BP and 

a variety of health effects, by se x and for three age ranges: 4 5 to 5 4, 55 to 64 , and 6 5 to 7 4 years. e study p erform ed sing le 

compo site analyses for ages 45 to 74 for each sex. For every equation, t-statistics on the BP variable are significant at the 

99th percent confidence interval. icted first-time CHD related to an increase in BP for men aged 60 to 64 from the 

following equation: 

(14.12) 

where: 

�Pr(CHD60-64) = the change in 2-year probability of occurrence of a CHD event for men aged 60 to 64; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP 

for subsistence and recreational fishermen aged 60 to 64 is 79.5 and 77.8, respectively; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

Several card 

Based on the r

The following equation uses 

Increased

PPRG use

The model used 

T 

Th

EPA pred 

14-23

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

The following equation uses data from Shurtleff (1974) to predict the probability of first-time CHD related to an increase in 

BP for men aged 65 to 74: 

(14.13) 

where: 

�Pr(CHD65-74) = the change in 2-year probability of occurrence of a CHD event for men aged 65 to 74; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP 

for subsistence and recreational fishermen aged 65 to 74 is 79.5 and 76.4, respectively; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

EPA used the above equations to estimate the number of CHD events avoided in a given year due to water quality


improvements from reduced MP&M lead discharges. The resulting CHD incidence estimates include both fatal and non-fatal


events. Only the non-fatal CHD events are considered here because mortality benefits are estimated independently in this


analysis (see Section 14.3.2.d, below). Shurtleff (1974) reported that two-thirds of all CHD events were non-fatal. This


factor was therefore applied to the estimate of avoided CHD events due to reductions in PbB and BP for each age category.


� Valuing reductions in CHD events


EPA first estimated the number of CHD events avoided each year by multiplying the number of exposed recreational and


subsistence anglers in the relevant age group by the change in annual probability of a CHD event. Changes in annual


probability of CHD events for different age groups are calculated by dividing the change in probability over ten- and two-year


periods by the relevant number of years.


EPA then used the central tendency estimate of the COI associated with pollution-related CHD to estimate the benefits of


avoiding an initial CHD event. The cost estimates (Wittels et al., 1990) represent the weighted medical costs of three separate


CHD s (acute myocardial infarction, uncomplicated angina pectoris, unstable angina pectoris), experienced within five years


of diagnosis. EPA estimated the costs by multiplying the probability of a medical test or treatment (within five years of the


initial CHD event) by the estimated price of the test or treatment.16 The estimated cost for acute myocardial infarction was


then reduced by 23%, which represents the proportion of cases that go unrecognized by the patient and therefore do not result


in any medical costs (based on Hartunian et al., 1981). EPA used a three percent discount rate to calculate the present value of


these costs. EPA then calculated the final cost estimate by taking the simple average of the three CHD types. The central


tendency estimate of the COI associated with a case of pollution-related CHD is about $76,347 (2001$).


This estimate likely underestimates the full COI because it does not include lost earnings. It likely underestimates total WTP


to avoid CHD to an even greater extent because it does not include WTP to avoid the pain and suffering associated with the


CHD event.


This analysis combined the value of reducing CHD events with the value of reducing hypertension, even though these


conditions often occur together. The two values represent different costs associated with the conditions. The valuation for


hypertension includes hypertension-associated work day loss and medical costs. CHD valuation is based on the medical costs


for treatment associated with the CHD itself. EPA estimated these two values separately and added them together.


c. Changes in initial CBA and initial BI

� Quantifying the relationship between BP and first-time stroke


Strokes include two types of health events: initial CBA and initial BI. The risk of CBA has been quantified for the male


population between 45 and 74 years old (Shurtleff, 1974). For initial CBA, the equation is:


16 EPA obtained costs from Appendix G of the Benefits and Costs of the Clean Air Act: 1970 to 1990, prepared for U.S. Congress by 

U.S. EPA, Office of Air and Radiation and Office of Policy, Planning, and Evaluation, 1997b. 

14-24 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

(14.14) 

where: 

�Pr(CBAmen) = the change in 2-year probability of CBA in men; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP for 

subsistence and recreational fishermen aged 45 to 74 is 81.1 and 78.8, respectively; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

For initial BI, the equation is (Pirkle et al., 1985): 

(14.15) 

where: 

�Pr(BImen) = the change in 2-year probability of brain infarction in men; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP for 

subsistence and recreational fishermen aged 45 to 74 is 81.1 and 78.8, respectively; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

Similar ly to CH D events, this analysis estim ates on ly non-fata l strokes to avo id do uble-c ountin g with pr ema ture m ortality. 

Shurtleff repo rted tha t 70 percen t of strokes were non -fatal. A ap plied this factor to the estimates o f both C BA and B I to 

ensure that the estimate of avoided CBA and B I events included only non-fatal events (Shurtleff, 1974). 

� Valuing reductions in strokes 

Similarly to CH D eve nts, EPA first calculates the number of avo ided stroke s per year and then uses the estimated lifetime cost 

of a stroke to value reductions in stro kes. aylor et al. estimated the lifetim e cost of stroke, includ ing the p resent value (in 

1990 d ollars) of the stream of medical expenditures and the present discounted value of the stream of lost earnings, using a 

five percent discount rate (Taylor et al., 1996). The estimated expected lifetime cost of a non-fatal stroke for males aged 45 

to 74 is 335,135 (20 01$).17 

d. Changes in premature mortality

� Qu antifying the relatio nship betw een BP and prem ature m ortality 

It is well established that elevated BP increases the probability of premature death. There are, however, several underlying 

conditions that cause elevated BP (e.g., cholesterol level). U.S. EPA (1987) used po pulation mean values for serum 

cholesterol and smoking to reduce results from a 12-year follow-up of men aged 40 to 54 in the Framingham Study (McGee 

and Go rdon, 1976) to an equation with one explanatory variable (DBP): 

(14.16) 

where: 

�Pr(MORT40-54) = the change in 12-year probability of death for men aged 40 to 54; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP 

for subsistence and recreational fishermen aged 40 to 54 is 81.9 and 79.9, respectively; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

EP 

T 

17
 EPA obtained cost from Appendix G of the Benefits and Costs of the Clean Air Act: 1970 to 1990, prepared for U.S. Congress by 

U.S. EPA, Office of Air and Radiation and Office of Policy, Planning, and Evaluation, 1997b. 

14-25

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

This analysis used information from Shurtleff (1974) to estimate the probability of premature death in men older than 54


years. The present analysis estimates a two-year probability based on the Shurtleff study’s two-year follow-up period. EPA


predicted mortality for men aged 55 to 64 years old using the following equation:


(14.17) 

where: 

�Pr(MORT55-64) = the change in two-year probability of death in men aged 55 to 64; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP 

for subsistence and recreational fishermen aged 55 to 64 is 80.6 and 79.0, respectively; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

Using data from Shurtleff (1974), EP A predicted premature mortality for men aged 65 to 74 using the following equation: 

(14.18) 

where: 

�Pr(MORT65-74) = the change in two-year probability of death in men aged 65 to 74; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP 

for subsistence and recreational fishermen aged 65 to 74 is 79.5 and 76.4, respectively; and 

DBP2 = mea n diasto lic BP in the po st-com plianc e scen ario. 

� Valuing redu ctions in prem ature mortality 

Similarly to health outcomes discussed in the preceding sections, EPA first estimated changes in annual probability of 

premature mortality for men in different age groups. The Agency then calculated avoided premature death cases by 

multiplying the estimated change in annual probability of premature mortality by the relevant population. s analysis uses 

the $6.5 million (2001$) estimate of the value of a statistical life saved recommended in the Guidelines for Preparing 

Eco nom ic An alysis (EPA, 20 00a). P to avoid the risk of death. 

The values of avoiding CHD, BA, and BI events are all based on COI estimates associated with a non-fatal health event. On 

the other hand, the va lue of the chang e in pre mature mo rtality is based on the value of avoiding a hea lth event that does end in 

death. Thus, these two endpoints are additive. 

14.3.3 Female Health Benefits

Rec ently exp anded an alysis of data from NH AN ES II by Sc hwartz indicates a significa nt association betwe en P bB and B P in 

women (Schwartz, 1990). her study, by Rabinowitz et al. (1987), found a small but demonstrable association between 

maternal PbB , pregnancy hyper tension , and B P at tim e of delivery. 

a. Relationship between BP and PbB

Altho ugh wo men are at risk for lead -induced hyp ertensio n, no d ose-re sponse func tion for h yperte nsion in wom en is ava ilable 

at this time. gency did no t quantify changes in risk for lead-induc ed hyper tension in wom en for this analysis. 

This analysis used an adjusted dose-response function for a change in BP associated with a decrease in PbB in men (Equation 

14.10) to estimate lead-induced changes in blood pressure in women. is used to provide input values for the 

analyse s discussed in the following sections. 

A review of ten published studies examined the effect of lead exposure on the BP of women, relative to the effect on men 

(Schwartz, 1992). All of the reviewed studies included data for men; some included data for women. Schwartz used a 

conco rdance p roced ure that comb ined data from each study to p redict the decre ase in diastolic BP associated with a d ecrease 

Thi

This value is based on WT 

Anot

Therefore, the A 

Equation 14.19

14-26

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

from 10 �g/dL to 5 �g/dL PbB (Schwartz, 1992). The results suggest that when PbB is decreased, women experience a BP 

change that is 60 percent of the change seen in men. Equation (14.10) can be rewritten for women as: 

(14.19) 

where: 

�DBPwomen = the change in women's diastolic BP expected from a change in PbB; 

PbB1 = PbB level in the baseline scenario; and 

PbB2 = PbB level in the post-compliance scenario. 

b. Changes in CHD

� Quantif ying th e re la tionsh ip be tw ee n BP and CH D 

Elevated BP in women results in the same effects as for men (CHD, two types of stroke, and premature death). However, the 

general relatio nships betwe en B P an d these health e ffects are n ot identical to the dose -respo nse func tions estim ated fo r men . 

All relatio nships prese nted h ere ha ve be en estim ated fo r wom en age d 45 to 74 years o ld using inform ation fro m Sh urtleff 

(1974). A estimated first-time CHD related to an increase in BP in women using the following equation: 

(14.20) 

where: 

�Pr(CHDwomen) = change in 2-year probability of occurrence of CHD event for women aged 45-74; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP 

for wo men in subsiste nce and re creatio nal ho useho lds age d 45 to 74 is 76.5 and 7 4.8, re spec tively; 

and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

EPA estimated non-fatal CHD events by assuming that two-thirds of all estimated CH D events are not fatal (Shurtleff, 1974). 

� Valuing redu ctions in CH D events 

The Agency first calculated the number of avoided CHD events for women using Equation 14.20. EPA assumed that values 

of red ucing C HD events fo r wom en eq ual those calc ulated for me n (ab ove): $76 ,347 (2001$ ) per C HD event. 

c. Changes in BI and initial CBA

� Quantifying the relationship between BP and first-time stroke 

EPA predicted the relationship between BP and initial CBA for women using the following equation: 

(14.21) 

where: 

�Pr(CBAwomen) = change in two-year probability of cerebrovascular accident in women aged 45 to 74; 

DBP1 = mean diastolic BP in the baseline scenario; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

The following equation illustrates the relationship between BI and initial BI in women: 

(14.22) 

EP 

14-27

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

where: 

�Pr(BIwomen) = change in 2-year probability of brain infarction in women aged 45 to 74; 

DBP1 = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP for 

women in subsistence and recreational households aged 45 to 74 is 76.5 and 74.8, respectively; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

EP A multiplied the pre dicted incidences o f avoid ed B I and CB A by 7 0 pe rcent to estimate only no n-fatal strok es (Shurtleff, 

1974). 

� Valuing reductions in strokes 

EPA calculated the value of avoiding an initial CBA or an initial BI for women in the same way as for men (see above). EPA 

predicted lead-related stroke for women in the United States between the ages of 45 and 74, of whom 38.2 percent are aged 

45 to 54 and the remaining 61.8 percent are aged 55 -74. ic values in Taylor et al. (1996), 

EPA estimated the average value of avoiding a stroke among women aged 45 to 74 to be about $25 1,351 (200 1$). 

d. Changes in premature mortality

� Qu antify ing th e relatio nship betw een BP and prem ature m ortality 

The following equation estimates the risk of premature mortality in women (Shurtleff, 1974): 

(14.23) 

where: 

�Pr(MORTwomen) = the change in two-year probability of death for women aged 45 to 74; 

DBP1 = mea n diasto lic BP in the ba seline scenario ; based on the Pha se 2 N HA NE S III, m ean d iastolic 

BP for women in subsistence and recreational households aged 45 to 74 is 76.5 and 74.8, 

respectively; and 

DBP2 = mean diastolic BP in the post-compliance scenario. 

� Valuing redu ctions in pre m ature m ortality 

EPA predicted changes in lead-related premature mortality for women in the same way as for men (see above). 

the value of reducing premature mortality in women to be equal to that estimated for all premature mortality, $6.5 million 

(2001$ ) per incident (see Section 13.2.1). 

14.4 LEAD-RELATED BENEFIT RESULTS

This section describes the estimated benefits of reduced lead exposure from consumption of fish in three populations: (1) 

preschool age children, (2) pregnant women, and (3) adult men and women. Benefit estimates for pregnant women appear 

with those for preschool age children, because the beneficiaries in this category are children under the age of one who suffer 

in utero fetal lead exposure from maternal lead intake during pregnancy. inal regulation will yield no 

benefits to children or adults from reduced exp osure to lead. ive regulatory options considered by EPA w ere 

estimated to yield be nefits from reduce d expo sure to lead. he following discussion reviews the estimated b enefits from these 

alternative op tions. 

14.4.1 Preschool Age Children Lead-Related Benefit Results

EP A analyzed the mone tary value of health bene fits to children from reduc ed lead e xposure in four categories: 

� redu ced neo-n atal mo rtality, 

� avoided IQ lo ss, 

� reduced incidence of IQ below 70, and 

� reduced incidence of PbB levels above 20 �g/dL. 

Using the gender- and age-specif

EPA assumed 

EPA estimated that the f

Alternat

T 

14-28

MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

From this analysis, EPA estimated that the final rule will yield no lead-related benefits to children. 

Other regulatory options considered by EPA were found to yield lead-related benefits to children. Table 14.5 summarizes 

lead-related benefits estimated for the 433 Upgrade Options. EPA estimated that the Directs + 413 to 433 Upgrade Option 

and the Directs + All to 433 Upgrade Option would reduce 0.15 and 0.17 cases of neonatal mortality, and avoid the loss of 32 

and 36 IQ points, respectively. The Directs + 413 to 433 Upgrade Option and the Directs + All to 433 Upgrade Option would 

result in $1.3 and $1.5 million (2001$) in annual lead-related benefits for children, respectively. 

Table 14.5: National Annual Benefits from Reduced Lead in Children (2001$) — 433 Upgrade Optionsa

Category 

Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade 

Reduced Cases 

or IQ Points 

Benefit Value 

(2001$) 

Reduced Cases 

or IQ Points 

Benefit Value 

(2001$) 

Neonatal mortality 0.15 $995,630 0.17 $1,109,294 

Avoided IQ Loss 31.99 $301,323 36.19 $340,845 

Reduced IQ < 70 0.11 $6,637 0.13 $7,501 

Reduced PbB > 20 �g/L 0.00 $0 0.00 $0 

Total Benefits $1,303,590 $1,457,640 

a

 Based on the Traditional Extrapolation. 

Source: U.S. EPA analysis. 

Table 14.6 summarizes lead-related benefits estimated for the Proposed/NODA Option. EPA estimated that the 

Proposed/NODA Option would reduce 1.60 cases of neonatal mortality and avoid the loss of 1,078 IQ points. Annual leadrelated benefits for children equal $20.8 million (2001$) under the Proposed/NODA Option, which substantially exceeds 

estimated lead-related benefits for children under the two 433 Upgrade Options. 

Table 14.6: National Annual Benefits from Reduced Lead in Children (2001$) — Proposed/NODA Optiona

Category Reduced Cases or IQ Points Benefit Value (2001$) 

Neonatal mortality 1.60 $10,417,781 

Avoided IQ Loss 1,078.38 $10,157,286 

Reduced IQ < 70 3.72 $216,007 

Reduced PbB > 20 �g/L 0.00 $0 

Total Benefits $20,791,073 

a

 Based on the Traditional Extrapolation. 

Source: U.S. EPA analysis. 

The results from the estimated lead-related benefits for children are conservative, because this analysis omits other leadrelated impacts, such as the cost of group homes and other special care facilities. Table 14.1 presents other omitted benefits 

categories. Section 14.5 discusses uncertainty and limitations inherent in this analysis. 

14.4.2 Adult Lead-Related Benefit Results

As discussed previously, EPA quantified only the lead-related health effects in adults that relate to lead’s effect on BP. These 

health effects include increased incidence of hypertension, initial non-fatal CHD, non-fatal stokes (CBA and BI), and 

premature mortality. EPA used COI estimates (i.e., medical costs and lost work time) to estimate monetary values for 

14-29 
MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

reduced incidence of hypertension, initial CHD , and strokes. EPA based monetary values for changes in risk of premature 

mortality on estimates of the value of a statistical life saved. The results are conservative estimates, because this analysis does 

not include other health effects associated with elevated BP or with lead. Other effects of lead in adults can include nervous 

system disorders, anemia, and possible cancer effects. 

From this analysis, EPA estimated that the final rule will yield no lead-related health benefits to adults. 

Other regulatory options considered by EPA were found to yield lead-related benefits to adults. Table 14.7 summarizes leadrelated benefits estimated for the 433 Upgrade Options. EPA estimated that the Directs + 413 to 433 Upgrade Option and the 

Directs + All to 433 Upgrade Option respectively would reduce hypertension among males by 53 and 60 cases annually. Both 

the 433 Upgrade Options would also reduce the annual incidence of premature mortality among men and women by 

approximately 0.1 cases. EPA estimated annual lead-related benefits for adults under the Directs + 413 to 433 Upgrade 

Option at $0.70 million (2001$) and under the Directs + All to 433 Upgrade Option at $0.79 million (2001$). 

Table 14.7: National Adult Lead Annual Benefits (2001$) — 433 Upgrade Optionsa,b

Category 

Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade 

Reduced Cases Mean Value of 

Benefits Reduced Cases Mean Value of 

Benefits 

Men Hypertension 53.47 $61,004 59.58 $67,982 

CHD 0.05 $4,155 0.06 $4,631 

CBA 0.02 $5,698 0.02 $6,350 

BI 0.01 $3,226 0.01 $3,596 

Mortality 0.07 $474,735 0.08 $529,125 

Women CHD 0.02 $1,662 0.02 $1,853 

CBA 0.01 $2,417 0.01 $2,694 

BI 0.01 $1,487 0.01 $1,658 

Mortality 0.02 $150,190 0.03 $167,417 

Total Benefits $704,574 $785,304 

a

 Based on the Traditional Extrapolation. b National Level Exposed Population: 

(1) Directs + 413 to 433 Upgrade 

Hypertension: 139,745 men ages 20 to 74; 

CHD, CBA, BI, and mortality: 56,564 men and 62,666 women ages 45-74. 

(2) Directs + 413 + 50% LL Upgrade 

Hypertension: 139,745 men ages 20 to 74; 

CHD, CBA, BI, and mortality: 56,564 men and 62,666 women ages 45-74. 

Source: U.S. EPA analysis. 

Table 14-8 summarizes lead-related benefits estimated for the Proposed/NODA Option. EPA estimated that this option 

would reduce hypertension among males by approximately 545 cases and the incidence of premature mortality among men 

and women by 0.96 cases annually. Lead-related benefits for adults under the Proposed/NODA Option would be $7.05 

million annually, which substantially exceeds estimated benefits under the two 433 Upgrade Options. 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Table 14.8: National Adult Lead Annual Benefits (2001$) —

Proposed/NODA Optiona,b

Category Reduced Cases Mean Value of Benefits 

Men Hypertension 545.25 $622,126 

CHD 0.54 $41,564 

CBA 0.17 $56,907 

BI 0.10 $32,197 

Mortality 0.73 $4,750,132 

Women CHD 0.22 $16,472 

CBA 0.10 $23,928 

BI 0.06 $14,714 

Mortality 0.23 $1,489,984 

Total Benefits $7,048,025 

a

 Based on the Traditional Extrapolation. b National Level Exposed Population: 

Hypertension: 539,142 men ages 20 to 74; 

CHD, CBA, BI, and mortality: 218,226 men and 241,768 women ages 45-74. 

Source: U.S. EPA analysis. 

14.5 LIMITATIONS AND UNCERTAINTIES

This section discusses limitations and uncertainties in the lead-related benefits analysis. Developing dose-response functions 

depends on relating lead exposure to PbB levels, then evaluating PbB levels in relation to specific health outcomes. 

Quantitative dose-response functions for most health effects associated with lead exposure currently do not exist. For this 

reason, the analysis does not provide a comprehensive estimate of health benefits from reduced lead discharges from MP&M 

facilities. 

Table 14.1 summarizes quantified and non-quantified health effects. Economic research does not always yield a complete 

evaluation, even for those effects that can be quantified. This uncertainly is likely to bias the estimate of lead-related benefits 

of the MP&M regulation downward. The analysis methodologies used here also involve significant simplifications and 

uncertainties. Section 13.3 discusses similar limitations and uncertainties associated with the assessment of risk associated 

with non-lead-related human health hazards and the possible direction of bias associated with sample design and benefits 

analysis by: 

� occurrence location, 

� estimated in-waterway concentrations of MP&M pollutants, and 

� estimated exposed fishing population. 

The next five sections discuss other omissions, biases, and uncertainties in the lead-benefit analysis. Table 14.9 provides a 

summary of this discussion. 

14.5.1 Excluding Older Children

Recent research on brain development among 10- to 18-year-old children shows unanticipated and substantial growth in brain 

development, mainly in the early teenage years (Giedd et al., 1999). This growth appears to be a second “burst” of cell 

development in some brain areas, in addition to the previously recognized period of rapid growth during early childhood. 

One of lead’s fundamental effects is to disrupt the protective coating (myelin) on nerve cells. This disruption can lead to 

permanent impairment if it occurs during development. New research suggests that older children may be a hypersensitive 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

sub-population, along with children aged 0 to 7. Excluding this sub-population from the analysis may significantly 

underestimate benefits from reduced lead discharges. 

14.5.2 Compensatory Education Costs

This analysis assumes that compensatory education is required only for children with IQs less than 70, and that part-time 

special education costs are assumed to be incurred only from grades 1 through 12 (Section 14.2.4). This assumption 

underestimates compensatory education costs for the following reasons: 

� Children with IQ scores between 70 and 85 will likely be assigned to special education or “slow” classes that will 

likely be smaller than regular classes and require more teacher attention. Children in this IQ range may frequently 

require more than 12 years to graduate and are more likely to drop out of school. Such children therefore require 

additional education costs. 

� Compensatory education may begin before grade one. Some states (e.g., Connecticut) offer compensatory education 

programs for disadvantaged children beginning at age three. 

This analysis is based on a study that measured the increased cost to educate children with low IQs attending a regular school, 

not a special education program (Kakalik et al., 1981). The cost to attend a special education program is generally much 

higher than that for regular schooling. 

Some overlap may exist between estimates of the avoided costs of compensatory education due to reduced incidence of 

children with IQ below 70 and PbB levels above 20 �g/dL because children with PbB levels may also have low IQ scores. 

Estimating the magnitude of this overlap is, however, not feasible due to data paucity. In addition, the estimated avoided cost 

of compensatory education due to reduced incidence of children with PbB levels above 20 �g/dL is negligible compared to 

other benefits from reduced exposure to lead. Thus, this overlap does not introduce a significant bias in the estimate of total 

benefits from reduced exposure to lead to children. 

14.5.3 Dose-Response Relationships

The dose-response functions described for each health outcome considered above generally quantify the adverse health effects 

expected from increased lead exposure. For children, these effects are defined in terms of changes in PbB. For adults, these 

effects are estimated in terms of changes in BP, which are in turn related to changes in PbB levels. Uncertainty is inherent in 

the dose-response functions, which are typically expressed in terms of the standard deviations of the dose-response 

coefficients used in the analysis. Any uncertainty affecting the dose-response coefficients will also indirectly affect the 

accuracy of this analysis. 

14.5.4 Absorption Function for Ingested Lead in Fish Tissue

Numerous research groups have evaluated lead absorption under a variety of conditions. ATSDR reports a range of three 

percent to 45 percent in the studies they present, which consider lead intake with and without food (ATSDR, 1997). 

Absorption appears to be affected by total lead intake, with some studies showing a higher absorption proportion with higher 

doses. Animal studies show a saturation effect, which modifies absorption. 

Lead’s chemical form also determines its absorption rate. For example, lead sulfide has approximately 10 percent of the 

bioavailability of lead acetate (ATSDR, 1997). Particle size and solubility are also important absorption factors. EPA could 

not obtain data to describe lead’s precise chemical form, particle size, and other physical parameters in fish tissue, which 

would allow more refined absorption estimates. These characteristics vary because MP&M facilities produce lead using 

different processes and release it in different forms. 

An individual’s nutritional status also affects lead absorption rates. People who are malnourished, particularly with respect to 

calcium and iron, have high absorption rates (ATSDR, 1997). EPA assumed that anglers were not malnourished, and made 

no adjustment for their nutritional status. See the section on lead absorption in Maddaloni (1998) for a discussion of factors 

influencing absorption. In the absence of data on lead incorporated into food, EPA considered data from studies of lead 

absorption during meals to be the most appropriate data to use in estimating absorption. 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

14.5.5 Economic Valuation

This analysis used IQ differentials to represent cognitive damage to children resulting from lead exposure. The economic 

analysis relates IQ level to annual earnings, which serve as the basis for valuing benefits from reduced lead exposure. IQ 

differentials are used rather than WTP, the preferred measure to use, because WTP values to avoid cognitive damage are not 

available. This analysis likely underestimates the value of an IQ point because special education and lost wages form only a 

portion of the costs associated with lost cognitive functioning. A simple IQ change analysis does not capture all the ways in 

which a child, family, and society are affected by the effects of lead-induced cognitive damage. 

Dollar values associated with most of the adult health and welfare endpoints represent only some components of society’s 

WTP to avoid these health effects. EPA used COI estimates to value reductions in CHD events, strokes, and hypertension. 

These values are likely to be downward-biased because the value of pain and suffering avoided is not included. Employed 

alone, these monetized effects will underestimate society's WTP. 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Table 14.9: Key Omissions, Biases, and Uncertainties in the Lead-Benefit Analysis

Omissions/Biases/ 

Uncertainties 

Directional 

Impact on 

Benefits 

Estimates 

Comments 

Excluding older children downward New research suggests that older children may be a hypersensitive subpopulation, as children aged 0 to 7 are now considered. Excluding this subpopulation from the analysis may significantly underestimate benefits from 

reduced lead discharges. 

Compensatory education 

costs 

uncertain Assuming that compensatory education is required only for children with IQs 

less than 70 and that part-time special education costs are incurred from grades 1 

through 12 underestimates the special education costs because: 

� Children with IQ scores between 70 and 85 will likely be assigned to special 

education or “slow” classes, requiring more teacher attention, and taking 

longer to graduate or dropping out altogether. 

� Compensatory education may begin before grade one. 

� The cost to attend a special education program is generally much higher 

than that for regular schooling. 

A potential overlap exists between estimates of the avoided costs of 

compensatory education due to reduced incidence of children with IQ below 70 

and PbB levels above 20 g/dL because children with PbB levels may also have 

low IQ scores. This overlap may introduce an upward bias in the estimate of the 

lead-related benefits to children. This bias is, however, negligible due to the 

magnitude of the avoided compensatory education cost estimates. 

Dose-response relationship uncertain Uncertainty is inherent in the dose-response functions (expressed in changes in 

PbB for children, changes in BP for adults). 

response coefficients will also indirectly affect the accuracy of this analysis. 

Absorption factor for lead in 

fish tissue 

uncertain Absorption rate appears to be affected by: 

� total lead intake, with some studies showing a higher absorption proportion 

with higher doses; 

� lead’s chemical form. Because MP&M facilities produce lead using 

different processes and release it in different forms, EPA could not obtain 

data to describe lead’s precise chemical form, particle size, and other 

physical parameters in fish tissue, which would allow more refined 

absorption estimates; 

� an individual’s nutritional status; and 

� time of lead ingestion. absence of data on lead incorporated into 

food, EPA considered data from studies of lead absorption during meals to 

be the most appropriate data to use in estimating absorption. 

Economic valuation downward The values associated with cognitive damage to children and adult health effects 

are likely to be downward-biased. children, a simple IQ change analysis 

does not capture all effects of lead-induced IQ loss on a child, family, and 

society. luation of adults’ health effects from lead exposure do not 

include the value of avoided pain and suffering. 

monetized effects will underestimate society's WTP. 

Overall impact downward 

Any uncertainty affecting the doseIn the 

For

The va

Employed alone, these 

Source: U.S. EPA analysis. 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

GLOSSARY

absolute gastrointestinal absorption fraction: the fraction of lead in food ingested daily that is absorbed from the


gastrointestinal tract.


acute toxicity: the ability of a substance to cause severe biological harm or death soon after a single exposure or dose.


Also, any poisonous effect resulting from a single short-term exposure to a toxic substance.


(http://www.epa.gov/OCEPAterms/aterms.html)


angina pectoris: a syndrome characterized by paroxysmal, constricting pain below the sternum, most easily precipitated


by exertion or excitement and caused by ischemia of the heart muscle, usually due to a coronary artery disease, as


arteriosclerosis. (www.infoplease.com)


arithmetic mean: the mean obtained by adding several quantities together and dividing the sum by the number of


quantities. (www.infoplease.com)


atherothrombotic brain infarctions (BI): scientific name for a stroke. 

bioavailability: degree of ability to be absorbed and ready to interact in organism metabolism. 

(http://www.epa.gov/OCEPAterms/bterms.html) 

biokinetics: the study of movements of or within organisms. (www.infoplease.com) 

biomarker: a physical, functional, or biochemical indicator of a certain process or event. It is commonly used to measure 

the progress of a disease, the effects of treatment, or the status of a condition. 

blood lead (PbB): concentration level of lead in blood stream; usually expressed in �g/dL. 

blood pressure: the pressure of the blood against the inner walls of the blood vessels, varying in different parts of the body 

during different phases of contraction of the heart and under different conditions of health, exertion, etc. 

(www.infoplease.com) 

central tendency estimate: major trend in group of data. 

cerebrovascular accident (CBA): stroke. 

coronary heart disease (CHD): disorder that restricts blood supply to the heart; occurs when coronary arteries become 

narrowed or clogged due to the build up of cholesterol and fat on the inside walls and are unable supply enough blood to the 

heart. 

diastolic: pertaining to or produced by diastole, or (of blood pressure) indicating the arterial pressure during the interval 

between heartbeats. (www.infoplease.com) 

discounting: degree to which future dollars are discounted relative to current dollars. Economic analysis generally 

assumes that a given unit of benefit or cost matters more if it is experienced now than if it occurs in the future. The present is 

more important due to impatience, uncertainty, and the productivity of capital. This analysis uses a three percent discount rate 

to discount future benefits. (http://www.damagevaluation.com/glossary) 

dose response: shifts in toxicological responses of an individual (such as alterations in severity) or populations (such as 

alterations in incidence) that are related to changes in the dose of any given substance. 

dose-response assessment: 1. Estimating the potency of a chemical. 2. In exposure assessment, the process of 

determining the relationship between the dose of a stressor and a specific biological response. 3. Evaluating the quantitative 

relationship between dose and toxicological responses. 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

dose-response curve: graphical representation of the relationship between the dose of a stressor and the biological 

response thereto. 

dose-response functions: see dose-response relationship. 

dose-response relationship: the quantitative relationship between the amount of exposure to a substance and the extent 

of toxic injury or disease produced. (http://www.epa.gov/OCEPAterms/dterms.html) 

encephalopathy: any brain disease. (www.infoplease.com) 

GDP price deflator: measure of the percentage increase in the average price of products in GDP over a certain base year 

published by the Commerce Department. (http://www.damagevaluation.com/glossary.htm) 

genotoxic: may cause chromosomal damage in humans leading to birth defects. 

geometric mean (GM): for a set of n numbers {x1, x2, x3, ..., xn} it is the n-th root of their product: (x1 * x2* x3 ... xn) 1/n. 

geometric standard deviation (GSD): a measure of the inter-individual variability in blood lead concentrations in a 

population whose members are exposed to the same environmental lead levels. For a lognormal distribution, GSD is the 

exponential of the standard deviation of the associated normal distribution. 

half-life: time required for a living tissue, organ, or organism to eliminate one-half of a substance which has been introduced 

into it. 

health endpoints: an observable or measurable biological event or chemical concentration (e.g., metabolite concentration 

in a target tissue) used as an index of an effect of a chemical exposure. 

heme synthesis: creation of heme; an iron compound of protoporphyrin which constitutes the pigment portion or proteinfree part of the hemoglobin molecule and is responsible for its oxygen-carrying properties. 

Integrated Exposure, Uptake, and Biokinetics (IEUBK): the IEUBK model is an exposure-response model that uses 

children’s environmental lead exposure to estimate risk of elevated blood lead (typically> 10 �g/dL) through estimation of 

lead body burdens in mass balance framework. 

least-squares regression: a tool of regression analysis that computes a best-fit line to represent the relationship between 

two (or more) variables based on the principle that the squared deviations of the observed points from that line are minimized 

(see also: regression analysis). 

lognormal distribution: a distribution of a random variable for which the logarithm of the variable has a normal 

distribution. (www.infoplease.com) 

lognormally-distributed random variable: same as lognormal distribution. 

marginal cost: the increase in total costs as one more unit is produced. (http://www.damagevaluation.com/glossary.htm) 

multivariate: (of a combined distribution) having more than one variate or variable. (www.infoplease.com) 

nephropathy: any kidney disease. (www.infoplease.com) 

neurobehavioral deficits: neurologic effects as assessed by observation of behavior. These effects may include 

behavioral and attentional difficulties, delayed mental development, lack of motor and perceptual skills, and hyperactivity. 

neurobehavioral function: see neurobehavioral deficits. 

non-cancer health risks: include systemic effects, reproductive toxicity, and developmental toxicity. 

normal distribution: a random variable X is normally distributed if its density is given by f x (x) = f (x; �, �), where � and 

� are the mean and the variance of the distribution. 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

opportunity cost: the highest-valued sacrifice needed to get a good or service. 

(http://www.damagevaluation.com/glossary.htm) 

p-value: the probability of obtaining a given outcome due to chance alone. For example, a study result with a significance 

level of p<0.05 implies that 5 times out of 100 the result could have occurred by chance. 

(http://www.teleport.com/~celinec/glossary.htm) 

pharmacokinetics: the study of the way drugs move through the body after they are swallowed or injected. 

(http://www.epa.gov/OCEPAterms/pterms.html) 

probability distribution: a distribution of all possible values of a random variable together with an indication of their 

probabilities. (www.infoplease.com) 

probit regression: a regression model, where the dependent variable is set up as a 0-1 dummy variable and regressed on 

the explanatory variables. The predicted value of the dependent variable could be interpreted as the probability that a certain 

event will take place (e.g., an individual will buy a car, visit a particular location, or get a specific disease). 

quasi-steady state: almost not changing state. 

regression analysis: a procedure for determining a relationship between a dependent variable, such as predicted success 

in college, and an independent variable, such as a score on a scholastic aptitude test, for a given population. The relationship 

is expressed as an equation for a line. (www.infoplease.com) 

risk-based remediation goals (RBRG): target human health and environmental risk levels to be achieved via remedial 

actions at Superfund sites. 

Technical Review Workgroup (TRW): a workgroup formed in 1994 to evaluate methodologies for adult lead risk 

assessment. 

�g/L: microgram per liter 

�g/dL: microgram per decaliter 

willingness-to-pay (WTP): maximum amount of money one would give up to buy some good. 

(http://www.damagevaluation.com/glossary.htm) 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

ACRONYMS 

ATSDR: Agency for Toxic Substances and Disease Registry


BI: atherothrombotic brain infarction


BP: blood pressure


CARB: California Air Resources Board


CBA: cerebrovascular accidents


CDC: Centers for Disease Control


CEPA: California Environmental Protection Agency


CHD: coronary heart disease


COI: cost of illness


GM: geometric mean


GSD: geometric standard deviation


IEUBK: Integrated Exposure, Uptake, and Biokinetics


NHANES: National Health and Nutrition Examination Surveys


NLSY: National Longitudinal Survey of Youth


PbB: blood lead


PPRG: Pooling Project Research Group


RBRG: risk-based remediation goals


TRW: Technical Review Workgroup


WTP: willingness-to-pay


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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

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Sallmen, M., M. L. Lindbohm, A. Anttila, H. Taskinen, and K. Hamminki. 2000. “Time to Pregnancy among the Wives of 

Men Occupationally Exposed to Lead.” Epidemiology 11(2), March: 141-7. 

Salkever, D. S. 1995. “Updated Estimates of Earnings Benefits from Reduced Exposure of Children to Environmental 

Lead.” Environmental Research 70: 1-6. 

Schwartz, J. 1988. “The Relationship Between Blood Lead and Blood Pressure in the NHAN ES II Survey.” Environmental 

Health Perspectives 78: 15-22. 

Schwartz, J. 1990. “Lead, Blood Pressure, and Cardiovascular Disease in Men and Women.” Environmental Health 

Perspectives, in press. 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Schwartz, J. 1992. “Chapter 13: Lead, Blood Pressure and Cardiovascular Disease.” In: H. L. Needleman, ed., Human 

Lead Exposure. CRC Press. 

Schwartz, J. 1993. “Beyond LOEL's, p Values, and Vote Counting: Methods for Looking at the Shapes and Strengths of 

Associations.” Neurotoxicology. 14(2/3), October. 

Schwartz, J. 1994. “Low-level Lead Exposure and Children’s IQ: A Meta-analysis and Search for a Threshold.” 

Environmental Research 65: 42-55. 

Sherlock, J. C., D. Ashby, H. T. Delves, G. I. Forbes, M. R. Moore, W. J. Patterson, S. J. Pocock, M. J. Quinn, W. N. 

Richards and T. S. Wilson. 1984. “Reduction in Exposure to Lead from Drinking Water and Its Effect on Blood Lead 

Concentrations.” Human Toxicology 3: 183-392. 

Shurtleff, D. 1974. “Some Characteristics Related to the Incidence of Cardiovascular Disease and Death.” The Framingham 

Study: An Epidemiological Investigation of Cardiovascular Disease. February: Section 30. 

Silbergeld, E. K., J. Schwartz, and K. Mahaffey. 1988. “Lead and Osteoporosis: Mobilization of Lead from Bone in 

Postmenopausal Women.” Environmental Research 47: 79-94. 

Taylor, T. N., P. H. Davis, J. C. Torner, J. Holmes, J. W. Meyer, and M. F. Jacobson. 1996. “Lifetime Cost of Stroke in the 

United States.” Stroke 27(9): 1459-1466. 

U.S. Department of Commerce, Bureau of the Census. 1993. “Money Income of Households, Families, and Persons in the 

United States: 1992.” Washington, DC: Current Population Reports, Consumer Income, Series P60-184, Washington, D.C. 

U.S. Department of Education. 1993. Digest of Education Statistics. National Center for Education Statistics, Office of 

Educational Research and Improvement, U.S. Department of Education. 

U.S. Department of Education. 1998. Digest of Education Statistics. National Center for Education Statistics, Office of 

Educational Research and Improvement, U.S. Department of Education. 

U.S. Department of Education. 2001. Digest of Education Statistics. National Center for Education Statistics, Office of 

Educational Research and Improvement, U.S. Department of Education. 

U.S. Department of Housing and Urban Development. 1995. “The Relation of Lead Contaminated House Dust and Blood 

Lead Levels among Urban Children.” Vol. I and II. Final Report to the U.S. Department of Housing and Urban 

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Regulatory Impact Analysis. Prepared by U.S. Environmental Protection Agency, Office of Policy Analysis, Economic 

Analysis Division. February. 

U.S. Environmental Protection Agency (U.S. EPA). 1986a. Reducing Lead in Drinking Water: A Benefit Analysis. Prepared 

by U.S. Environmental Protection Agency, Office of Policy Planning and Evaluation, Draft Final Report. December. 

U.S. Environmental Protection Agency (U.S. EPA). 1986b. Air Quality Criteria for Lead: Volume III. Environmental 

Criteria and Assessment Office, Research Triangle Park, NC. EPA-600/8-83/028cF. June. 

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Standards, Ambient Standards Branch, Contract No. 68-02-4323. 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

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Management Division. December.


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Model for Lead in Children. EPA 540-R-93-081, PB 93-963510. February.


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the IEUBK M odel for Lead in Children.” EPA 540-R-94-040.


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Office of Research and Development. Washington, DC: EPA-600-P-95-002Fa,b,c. August. 

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the Protection of Human Health . EPA 822-B-00-004. October. 

U.S. Environmental Protection Agency (U.S. EPA). 2002a. Integrated Risk Information System (IRIS) Retrieval. 

Washington, DC: U.S. EPA. 

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EPA-821-C-02-003. August. 

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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits 

Ward N., R. Watson, and D. Bryce-Smith. 1987. "Placental Element Levels in Relation to Fetal Development for


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American Journal of Cardiology 65: 432-440.


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14-44

MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Chapter 15: Recreational Benefits


INTRODUCTION 

The final Metal Product and Machinery (MP&M) 

regulation is expected to provide ecological benefits through 

improvements in the habitats or ecosystems (aquatic and 

terrestrial) that are affected by the MP& M industry 

discharges. Society is expected to value such ecological 

improvements by a number of mechanisms, including 

increased frequency and value of use of the improved habitat 

for recreational activities. In addition, individuals may also 

value the protection of habitats and species that are adversely 

affected by effluent dischargers even when they do not use or 

anticipate future use of the affected waterways for 

recreational or other purposes. 

This chapter presents EPA’s analysis of ecological benefits 

from reduced effluent discharges to the nation’s waterways 

as a result of the final MP&M regulation, the 433 Upgrade 

Options, and the Proposed/NODA option. EPA assessed 

ecological benefits in terms of reduced occurrence of 

pollutant concentrations in excess of AWQC protective of 

CHAPTER CONTENTS 

15.1 al Improvements from the 

MP&M Regulation ................. ..... 15-3 

15.1.1 iew of Ecological Improvements ..... 15-3 

15.1.2 ication of Ecological Improvements . . 15-3 

15.1.3 ................. ... 15-4 

15.1.4 of MP&M Reaches15-6 

15.2 onomic Recreational Benefits ........ 15-6 

15.2.1 ferring Values from Surface Water 

Valuation Studies ................. ...... 15-6 

15.2.2 ational Fishing ................. .. 15-9 

15.2.3 e Viewing ................. .... 15-13 

15.2.4 ational Boating ................. . 15-17 

15.2.5 nefits ................. ..... 15-20 

15.3 ary of Recreational Benefits ............ 15-20 

15.4 itations and Uncertainties Associated with 

Estimating Recreational Benefits .......... 15-22 

Glossary ................. ................. ... 15-26 

Acronyms ................. ................. .. 15-28 

References ................. ................. . 15-29 

Ecologic

Overv

Quantif

Benefiting Reaches 

Geographic Characteristics 

Valuing Ec

Trans

Recre

Wildlif

Recre

Nonuse Be

Summ

Lim

aquatic life and human health. For this analysis, EPA estimated the in-waterway pollutant concentrations of MP&M facility 

discharges for the baseline and the final rule and identified those reaches in which MP&M facility discharges would cause one 

or more pollutant concentrations to exceed ambient water quality criteria (AWQC) for aquatic species and human 

health.1, 2 The change in the number of reaches with concentrations in excess of AWQC from the baseline to post-compliance 

scenarios provides a quantitative measure of the improvement in aquatic species habitat expected to result from the final 

regulation. 

As discussed in Chapter 12, EPA performed all benefits analysis on a basis of the sample facility data. The Agency then 

extrapolated findings from the sample facility analyses to the national level using two alternative extrapolation methods: (1) 

traditional extrapolation and (2) post-stratification extrapolation. EPA also used the differential extrapolation technique in 

addition to both traditional and post-stratification approaches when a sample reach was estimated to receive discharges from 

multiple facilities. Appendix G provides detailed information on the extrapolation approaches used in this analysis. 

Reducing concentrations of MP&M pollutants to below AWQC limits for protection of aquatic species and human health will 

generate benefits to users of water resources for recreation, including anglers, boaters, and viewers. These benefits include: 

� increased value of the recreational trip or day, and 

� increased number of days that consumers of water-based recreation choose to visit the cleaner waterways. 

1 For this analysis, a reach is a length of river, shoreline, or coastline on which a pollutant discharge may be expected to have a 

relatively uniform effect on concentrations. The typical length of a reach in this analysis was five to ten kilometers, although some were 

considerably longer. 

2 AWQC set limits on pollutant concentrations that are assumed to be protective of aquatic life. Pollutant concentrations that exceed 

AWQC can harm organisms that live in or consume water. MP&M pollutants can also harm other organisms that consume these 

organisms. These organisms at risk include humans who may recreate in contaminated waters or consume aquatic organisms living in 

them. 

15-1 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

EPA estimated national annual recreational use benefits for three water-based recreation activities (i.e., recreational fishing, 

boating, and viewing) and nonuse benefits, but did not estimate national swimming benefits due to data limitations.3 EPA 

estimated the following recreational use benefits of the final MP&M rule (2001$): 

� recreational fishing benefits range from $287,220 to $923,988 and from $187,123 to $601,976, based on the 

traditional and post-stratification extrapolation, respectively; 

� near-water recreation (viewing) benefits range from $185,172 to $334,315 and from $120,639 to $217,805, based on 

the traditional and post-stratification extrapolation, respectively; and 

� boating benefits range from $114,111 to $316,078 and from $74,343 to $205,924, based on the traditional and poststratification extrapolation, respectively. 

EPA also estimated nonuse benefits from improved water quality in the nation’s surface water resulting from the final rule. 


Empirical estimates from surface water valuation studies indicate that nonuse values for water resources may be substantial


because people who do not use or expect to use affected waterways for recreational or other purposes may still value


protecting habitats and species impacted by effluent discharges (Harpman, et al., 1993; Fisher and Raucher, 1984; Brown,


1993). The Agency estimated that nonuse benefits will range from $293,252 to $787,190 and from $191,053 to $512,852,


based on the traditional and post-stratification extrapolation, respectively.


EPA calculated the total value of enhanced water-based recreation opportunities by summing over the three recreation


categories and nonuser value. Since recreational trips corresponding to fishing, boating, and wildlife viewing considered in


this analysis are stochastically independent (i.e., only the primary activity is counted on each trip occasion), benefits from


improved recreational opportunities corresponding to these activities are additive. The total annual recreational benefit based


on the traditional extrapolation is estimated at $879,755 to $2,361,570 (2001$), with a midpoint estimate of $1,499,756


(2001$). Likewise, total annual recreational benefit based on the post-stratification extrapolation is estimated at $573,158 to


$1,538,557 (2001$), with a midpoint estimate of $977,087 (2001$).


The analysis of recreational benefits presented in this chapter uses the National Demand Study (NDS) data to estimate the


number of participants in wildlife viewing and boating in the counties affected by MP&M discharges.4 To estimate the


number of recreational fishermen, EPA used fishing license data. The NDS survey asked respondents to report the number of


recreational trips taken annually for the primary purpose of boating and wildlife viewing. The Agency used these data to


estimate the number of participants and the number of recreational trips taken annually by state and activity type.


Appendix N summarizes this information.


EPA chose to use fish license data rather than the NDS data to estimate the number of recreational anglers fishing the MP&M 

reaches because these data are often available at the county level and therefore provide location-specific information. 

Although the use of the NDS and fish license data yields similar estimates of the number of recreational anglers at the state 

level (see Chapter 21) fish license data are likely to be more accurate at the county level. The use of the fish license data in 

the recreational fishing benefit analysis also provides consistency with other parts of the benefits analysis (see Chapters 13 

and 14 for detail). 

Benefit categories examined in this chapter are different from and generally do not overlap with benefits associated with 

reduced risk to human health discussed in Chapter 13. Nevertheless, there is some likelihood that the valuation of ecological 

benefits based on enhanced recreational fishing overlaps to a degree with the valuation of human health benefits from reduced 

cancer risk via fish consumption. 

3 Fewer water bodies are designated for primary contact recreation, such as swimming, than for secondary contact recreation, such as 

boating and fishing. Assessing recreational swimming benefits requires first obtaining information on designated uses of the sample 

MP&M reaches from the 305(b) database. This analysis was not feasible due to resource and time constraints. 

4 Additional information on the NDS survey can be found in Chapter 21. 

15-2 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

15.1 ECOLOGICAL IMPROVEMENTS FROM THE MP&M REGULATION 

15.1.1 Overview of Ecological Improvements 

Many M P&M pollutants can adversely affect the survival, growth, and reproduction of aquatic organisms. Such effects are 

ecologically significant when they affect the size, structure, or function of populations: 

� MP&M pollutants can affect population size by reducing prey, and by affecting development or reproduction in 

sensitive life stages of target species; 

� MP &M pollutants can alter population structure by impairing sensitive age groups or affecting the development or 

maturation rates of target species; and 

� MP&M pollutants can impact population function by decreasing genetic diversity and changing interactions among 

different populations in the affected areas. 

MP&M pollutants may also contaminate fish tissue and therefore decrease the value of fishery resources. Thus, the final 

MP&M regulation may generate a broad range of ecological effects by reducing MP&M pollutant discharges. Ecological 

effects associated with reductions in MP& M discharges may include: 

� recovery of populations of aquatic species that are particularly sensitive to MP&M pollutants; 

� decreases in noxious algae, which affect the taste and odor of the receiving waters; 

� increases in the concentrations of dissolved oxygen (DO) in the water column; 

� improvements in the natural assimilative capacity of the affected waterways; 

� decreases in fish tissue contamination; and 

� terrestrial life benefits. 

Improvements in aquatic species habitat are expected to improve the quality and value of water-based recreation and nonuse 

values of the affected resources. Recent studies valuing recreational fishing showed that the value of water resources for 

recreational fishing increases as the level of toxic contamination in fish tissue decreases (Lyke, 1993; Phaneuf et al., 1998; 

and Jakus et al., 1997). Thus, knowing that the water is cleaner and does not contain any or contains fewer pollutants that 

harm humans and aquatic life, increases individuals’ enjoyment of their recreational experience. The value of a recreational 

fishery also increases from increased number, size, diversity, and health of recreational fish species. 

Participants in other water-based recreation, such as boating and wildlife viewing, will also benefit from improved abundance 

and diversity of aquatic and terrestrial species. For example, wildlife viewers may benefit from improved abundance of 

piscivorous birds (e.g., osprey and cormorants) whose population is likely to increase due to an increase in the forage fish 

populations. Boaters may benefit from enhanced opportunities for companion activities, such as fishing and wildlife viewing 

(e.g., piscivorous birds) and from improved water clarity and smell. Reducing conventional pollutant loadings will also 

improve visual aesthetics, thereby enhancing all water-based recreation experiences. 

15.1.2 Quantification of Ecological Improvements 

EPA evaluated potential impacts to aquatic life from the final MP&M regulation by estimating in-waterway concentrations of 

pollutants discharged by MP&M facilities and comparing those concentrations within AWQC limits for protection of aquatic 

species. Pollutant concentrations in excess of AW QC limits indicate a significant detriment to the aquatic species habitat. 

EPA expects that eliminating these exceedances as the result of the MP&M regulation will significantly improve aquatic 

species habitat and thus provide a quantitative measure of ecological benefit for this regulatory analysis. 

15-3 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

For this analysis, EPA estimated in-waterway concentrations for all MP&M pollutants for which AW QC limits are available. 

Of the 132 MP&M pollutants of concern, AWQC values are available for 114 pollutants.5 Table I.3 in Appendix I lists the 

pollutants evaluated in this analysis and their acute and chronic aquatic life AWQC. The acute value is the maximum 

allowable one-hour average concentration at any time at which aquatic life can survive. The chronic value is the average 

concentration of a toxic pollutant over a four-day period at which aquatic life is not unacceptably affected. The endpoints of 

concern are one or more sub-lethal responses, such as changes in reproduction or growth in the affected organisms. The 

chronic levels should not be exceeded more than once every three years. 

EPA used the mixing and dilution methods outlined in Appendix I to estimate the in-waterway concentrations resulting from 

MP&M facility discharges. Acute and chronic exposure concentrations for each pollutant are calculated on the basis of 7Q10 

and 1Q10 stream flow rates, where 7Q10 is the lowest consecutive seven-day average flow with a recurrence interval of ten 

years, and 1Q10 is the lowest one-day average flow with a recurrence interval of ten years. For reaches to which more than 

one sample MP&M facility discharge, EPA summed the discharge values by pollutant for all known sample facilities 

discharging to the reach. 

EPA first identified the MP&M discharge reaches in which MP&M discharges alone caused one or more pollutant 

concentrations to exceed AWQC limits for aquatic species under the baseline discharge level. If concentrations of all MP&M 

pollutants exceeding the limits in the baseline fell below AWQC limits as a result of the final rule, then aquatic species habitat 

conditions on that discharge reach would likely improve significantly as a result of the final regulation. The final regulation 

would result in partial aquatic habitat improvements if concentrations of some, but not all, MP&M pollutants fell below their 

AW QC limits. Although not explicitly accounted for in this analysis, species habitat conditions are likely to improve 

whenever in-waterway concentrations are reduced, regardless of whether or not they fall to levels below aquatic AWQC. 

EPA’s analysis based on the traditional extrapolation method indicates that pollutant concentrations at current industry 

discharge levels exceed acute exposure criteria for protection of aquatic species on 18 receiving reaches, and exceed chronic 

exposure criteria for protection of aquatic species on 353 receiving reaches.6 EPA estimates that the final rule would 

eliminate concentrations in excess of the acute aquatic life exposure criteria on nine reaches, and would eliminate 

concentrations in excess of the chronic aquatic life exposure criteria on nine reaches. 

Similarly, EPA’s analysis based on the post-stratification extrapolation method indicates that baseline pollutant concentrations 

at current industry discharge levels exceed acute exposure criteria for protection of aquatic species on 15 reaches, and exceed 

chronic exposure criteria for protection of aquatic species on 350 reaches. EPA estimates that the final rule would eliminate 

concentrations in excess of the acute aquatic life exposure criteria on six reaches, and would eliminate concentrations in 

excess of the chronic aquatic life exposure criteria on six reaches. Table 15.1 summarizes these results. 

15.1.3 Benefiting Reaches 

As a first step in estimating the monetary value of improvements in the aquatic habitats affected by MP&M discharges from 

the final MP&M rule, EPA identified reaches that are likely to experience significant water quality improvements from 

reduced MP&M discharges due to the final MP&M rule (hereafter, benefiting reaches). A reach is considered to benefit 

from the MP&M rule if at least one AWQC exceedance is eliminated due to reduced MP&M discharges. This approach 

differs from some past approaches where EPA took credit for pollution reductions only in cases where all AWQC 

exceedances are eliminated. EPA believes that the latter approach significantly underestimates benefits from reduced 

pollutant discharges. 

This analysis combines two AWQC calculation procedures: 

� analysis of in-waterway concentrations relative to human health AWQC limits described in Chapter 13,7

 and 

5 Facilities in the Oily Wastes subcategory discharge 122 of the 132 POCs evaluated. See Chapter 12 for detail. 

6 This analysis used baseline pollutant loads for direct and indirect dischargers belonging to all subcategories considered for 

regulation. 

7 Although EPA estimated the value of reduced cancer risk from consumption of contaminated fish tissue, the Agency was unable to 

estimate the value of reduced systemic risk from consumption of fish caught in the reaches affected by MP&M discharges (see Chapter 13). 

The recreational benefits analysis presented in the following sections assumes that some of the value of reduced systemic health risk is 

implicitly captured in the increased value of water resources from reduced occurrence of human health-based AWQC exceedances. For 

15-4 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

� analysis of in-waterway concentrations relative to aquatic life AWQC limits described in the preceding section of 

this chapter. 

Table 15.1 summarizes the number of reaches with estimated baseline concentrations that exceed AWQC limits for either 

human health or aquatic species, and the number of those reaches where the regulation is estimated to eliminate or reduce 

exceedances. Based on the traditional extrapolation, the combined analysis over all AWQC limit categories (i.e., acute and 

chronic aquatic life and human health) indicates that MP&M pollutant concentrations would exceed at least one AWQC limit 

on 395 reaches as the result of baseline MP&M discharges. The expected discharge reductions from the final rule eliminate 

exceedances on nine of these discharge reaches, leaving 386 reaches with concentrations of one or more pollutants that 

exceed AW QC limits. 

Likewise, based on the post-stratification extrapolation, the combined analysis indicates that MP&M pollutant concentrations 

would exceed at least one AWQC limit on 426 reaches as the result of baseline MP&M discharges. The expected discharge 

reductions from the final rule eliminate exceedances on six of these discharge reaches, leaving 420 reaches with 

concentrations of one or more pollutants that exceed AWQC limits. 

EPA assigned full benefits in situations where the rule eliminates all AWQC exceedances and partial benefits where the rule 

eliminates one or more, but not all, AWQC exceedances. EPA calculates partial benefits as the ratio of the AWQC 

exceedances removed by reducing MP&M discharges to the total number of AWQC exceedances caused by MP&M facilities 

in the baseline. For example, if the MP&M rule removes seven out of a total ten baseline AWQC exceedances on a benefiting 

reach, the Agency attributes a 70 percent benefit to the MP&M regulation, where 100 percent would represent an “AWQC 

exceedance-free” level. 

Table 15.1: Estimated MP&M Discharge Reaches with MP&M Pollutant Concentrations in Excess of AWQC 

Limits for Protection of Aquatic Species or Human Health 

Regulatory 

Status 

Number of Reaches with Concentrations 

Exceeding AWQC Limits Total Number of 

Reaches with 

Concentrations 

Exceeding 

AWQC Limits 

Number of Benefiting Reaches 

AWQC Limits for 

Aquatic Species 

AWQC Limits for 

Human Health All AWQC 

Exceedances 

Eliminated 

Reaches with 

Some 

Exceedances 

Acute Chronic Eliminated

H20 and 

Organisms 

Organisms 

Only 

Selected Option: Traditional Extrapolation 

Baseline 18 353 78 21 395 N/A N/A 

Final Option 9 344 78 21 386 9 0 

Selected Option: Post-Stratification Extrapolation 

Baseline 15 350 112 21 426 N/A N/A 

Final Option 9 344 112 21 420 6 0 

Note: In the baseline, the total number of reaches with concentrations exceeding AWQC limits does not equal the sum of the numbers in 

the separate analysis categories because some reaches were estimated to have concentrations in excess of AWQC limits for more than one 

analysis category. 

Source: U.S. EPA analysis 

Surface water valuation studies show that benefits from partial improvements are likely to be considerable. For example, 

Carson and M itchell (1993) found that almost nine out of ten individuals indicated that “halfway” improvements are worth the 

same as a complete improvement in water quality. The remaining one out of ten individuals were willing to pay a reduced 

amount for partial improvements in water quality. 

example, some studies showed that anglers place a much higher value on fishery resources that are safe for consumption (Lyke, 1993 and 

Phaneuf, 1997). 

15-5 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

The effects of partially removing AW QC exceedances, however, are difficult to generalize. The overall improvement in 

surface water quality from reduced toxic loadings will depend on the amount and duration of exceedances, together with the 

kinds of chemical(s) that are removed from the mixture by regulatory action. AWQC are developed on a 

chemical-by-chemical basis; they are not designed to assess the toxicity of multiple chemicals. In most cases, the toxicities of 

chemicals in a mixture are considered additive (i.e., the total toxicity is the sum of the toxicities of the individual chemicals). 

Total toxicity decreases by the amount of a chemical removed from the mixture. Benefits to sensitive aquatic species (i.e., 

amphibians, fish, benthic invertebrates, zooplankton) could occur if the concentration of one chemical fell below its AWQC 

even when two or more other chemicals still were at or exceeding their respective AW QC. The reason is that the total toxic 

pressure in the receiving water decreases so that a smaller fraction of the most sensitive species remain affected. For 

example, consider a case in which three chemicals exceeding their chronic AWQC adversely affect 7 percent of all aquatic 

species in a receiving water. If certain species are particularly sensitive to one of the three chemicals, then eliminating the 

AWQC exceedance for this chemical would lower the percentage of sensitive species being adversely affected. 

15.1.4 Geographic Characteristics of MP&M Reaches 

EPA cannot identify all of the specific reaches affected by MP&M facilities that reduce discharges under the final rule 

because location is known only for the facilities included in the random stratified sample. EPA assumes that facilities 

represented by the sample facility have the same environmental and geographic characteristics that affect benefits from the 

final rule. These characteristics include water body type and physical characteristics (e.g., stream flow conditions), 

populations residing near the water body, and the number of potential recreational users affected. 

The analysis of the sample reach locations indicates that sample MP&M reaches tend to be located in heavily populated areas. 

For example, approximately 35 percent of sample reaches receiving discharges from sample MP&M direct dischargers are 

located adjacent to counties with populations of at least 500 thousand residents. These reaches have a greater number of 

potential recreational users than do reaches in less populated areas. 

15.2 VALUING ECONOMIC RECREATIONAL BENEFITS 

The final MP&M rule will improve aquatic habitats by reducing concentrations of priority (i.e., toxic), nonconventional, 

and conventional pollutants in water. In turn, these improvements will enhance the quality and value of water-based 

recreation, such as fishing, wildlife viewing, camping, waterfowl hunting, and boating. The Agency used the estimated 

increase in the monetary value of recreational opportunities for fishing, boating, and wildlife viewing as a partial measure of 

the economic benefit to society from the improvements to aquatic species habitat expected to result from the final MP&M 

regulation. The Agency also estimated nonuse benefits from improvements in aquatic habitats and ecosystems that are 

affected by the MP&M industry discharges. 

This analysis uses a benefits transfer approach to monetize changes in water resource recreational values for reaches 

affected by MP&M discharges.8 This approach builds upon an analysis of applicable surface water valuation literature to 

estimate the total WTP value (including both use and nonuse values) for improvements in surface water quality. 

15.2.1 Transferring Values from Surface Water Valuation Studies 

EPA identified several surface water evaluation studies that quantified the effects of water quality improvements on various 

water-based recreational activities. The Agency used the following technical criteria for evaluating study transferability 

(Boyle and Bergstrom, 1990): 

� The environmental change valued at the study site must be the same as the environmental quality change caused by 

the rule (e.g., changes in toxic contamination vs changes in turbidity); 

� The populations affected at the study site and at the policy site must be the same (e.g., recreational users vs 

nonusers); 

8 Benefits transfer involves the application of value estimates, functions, and/or models developed in one context to address a similar 

resource valuation question in another context. 

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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

� The assignment of property rights at both sites must lead to the same theoretically appropriate welfare measure (e.g., 

willingness-to-pay vs willingness to accept compensation). 

In addition to the above criteria, the Agency considered authors' recommendations regarding robustness and theoretical 

soundness of various estimates. 

Existing studies are unlikely to meet all of the above criteria. Boyle and Bergstrom (1990) reported that most researchers will 

likely encounter problems with at least one criterion. This analysis is no exception. The major limitation in performing the 

national analysis is the comparability of the water quality changes considered in the original studies with the water quality 

changes considered in this analysis. These comparisons are discussed below. 

The Agency used eight of the most comparable studies and calculated the changes in recreation values resulting from water 

quality improvements (as a percentage of the baseline value) implied by those studies. EPA took a simple mean of upper- and 

lower-bound estimates from these studies to derive a range of percentage changes in the water resource values due to water 

quality improvements. The studies used for benefits transfer in the MP&M regulatory analysis included Lyke (1993), Jakus et 

al. (1997), Montgomery and Needelman (1997), Phaneuf et al. (1998), Desvousges et al. (1987), Lant and Roberts (1990), 

Farber and Griner (2000), and Tudor et al. (2002). Appendix K presents WTP values for various water quality improvements 

and summarizes EPA’s reasoning for selecting specific WTP estimates for benefits transfer. Each of the eight studies and the 

WTP values selected for benefits transfer are discussed briefly below. 

Lyke’s (1993) study of the Wisconsin Great Lakes open water sport fishery showed that anglers may place a significantly 

higher value on a contaminant-free fishery than on one with some level of contamination. Lyke estimated the value of the 

fishery to Great Lakes trout and salmon anglers if it were improved enough to be "completely free of contaminants that may 

threaten human health," and found that this value would add between 11 and 31 percent of the fishery’s current value. 

Jakus et al. (1997) used a repeated discrete choice travel cost (TC) model to examine the impacts of sport-fishing 

consumption advisories in eastern Tennessee. The model controlled for anglers’ knowledge of advisories, the type of angler 

(i.e., fish consumption vs. catch and release), and catch rate. The estimated welfare gain (as a percentage of baseline) from 

cleaning up six reservoirs and removing these advisories ranges from six to 8 percent. These estimates are below Lyke’s 

estimated 11 to 31 percent range, due to the difference in methodology used. The TC method captures use values only, while 

the combined TC and stated preferences method used in Lyke captures both the use and nonuse components of the resource 

value to users. Differences in the fisheries and user populations may also affect the estimated percentage changes in the 

resource value. 

Montgomery and Needelman (1997) estimated benefits from removing “toxic” contamination from lakes and ponds in New 

York State. They used a binary variable as their primary water quality measure, which indicates whether the New York 

Department of Environmental Conservation considers water quality in a given lake to be impaired by toxic pollutants. The 

model controls for major causes of impairments other than “toxic” pollutants to separate the effects of various pollution 

problems that affect the fishing experience. The estimates from Montgomery and Needelman imply that removing “toxic” 

impairments in all New York lakes and ponds would increase recreational fishing value by 13.7 percent. 

Phaneuf et al. (1998) studied angling in the Wisconsin Great Lakes. They estimated changes in recreational fishing values 

resulting from a 20 percent reduction of toxin levels in lake trout flesh. The study uses a TC model to value water quality 

improvements when corner solutions are present in the data. Corner solutions arise when consumers visit only a subset of the 

available recreation sites, setting their demand to zero for the remaining sites. Phaneuf et al. found that improved industrial 

and municipal waste management results in general water quality improvement. This improvement leads in turn to a 20 

percent decrease in fish tissue toxin levels, yielding a welfare gain of $166.21 (2001$) per angler per year.9 This estimate 

implies that recreational fishing values would increase by approximately 27.5 to 34.3 percent from reduced toxin levels. This 

analysis estimates use values only. 

Desvousges et al. (1987) used findings from a contingent valuation (CV) survey to estimate WTP for improved 

recreational fishing from enhanced water quality in the Pennsylvania portion of the Monongahela River. In a hypothetical 

market, each survey respondent was asked to provide an option price for different water quality changes, including “raising 

9 The study used the 1989 survey data on recreational angling in Wisconsin’s Great Lakes. Therefore, this analysis assumes that all 

estimates in the original study are in 1989 dollars. 

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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

the water quality from suitable for boating (hereafter, “boatable” water) to a level where gamefish would survive (hereafter, 

“fishable” water).” 

In applying Desvousges et al. for the MP&M analysis, EPA assumed that reaches with AWQC exceedences under the 

baseline conditions are likely to support rough fishing but may not be clean enough to support gamefishing. Removing 

AWQC exceedences is therefore comparable to shifting water quality from "boatable" to "fishable." This is a relatively 

conservative assumption. Desvousges et al. found that improving water quality from “boatable” to “fishable” would yield a 

5.9 to 7.9 percent increase in water resource value to recreational anglers. 

Lant and Roberts (1990) used a CV study to estimate the recreational and nonuse benefits of improved water quality in 

selected Iowa and Illinois river basins. River quality was defined by means of an interval scale of “poor,” “fair,” “good,” and 

“excellent.” The authors defined “fair” water quality as adequate for boating and rough fishing and “good” water quality as 

adequate for gamefishing. 

For the MP&M analysis, EPA assumes that eliminating AWQC exceedences is roughly equivalent to shifting water quality 

from "fair" to "good." The estimates from this study imply an increase of 9.7 to 13.1 percent in recreational fishing value 

from improving water quality from “fair” to “good.” 

Farber and Griner (2000) used a CV study to estimate changes in water resource values to users from various improvements 

in water quality in Pennsylvania. The study defines water quality as “polluted,” “moderately polluted,” and “unpolluted” 

based on a water quality scale developed by EPA Region III: “Polluted” streams are unable to support aquatic life; 

“moderately polluted” streams are somewhat unable to support aquatic life; and “unpolluted” streams adequately support 

aquatic life. Streams unable to support aquatic life (i.e., "polluted") are likely to be affected by environmental stressors 

unrelated to MP&M discharges, such as acidity or severe oxygen depletion. 

The MP&M analysis assumes that most streams affected by MP&M facility discharges are moderately polluted; i.e., these 

streams support aquatic life, but sensitive species may be adversely affected by MP&M pollutants that exceed AWQC values 

protective of aquatic life. Removing all AWQC exceedences would make such streams unpolluted. The estimates from this 

study imply that improving water quality from “moderately polluted” to “unpolluted’ would yield an increase in recreation 

fishery value ranging from 3.9 to 9 percent. 

Tudor et al. (2002) used a TC model to estimate changes in water resource recreation values resulting from eliminating 

MP&M pollut 10 ant concentrations in excess of AWQC limits at recreation sites in Ohio. The study involves four recreation 

activities -- fishing, boating, near-water recreation, and swimming -- and covers most recreationally-important water bodies in 

all Ohio counties. The study considers two types of water quality effects from MP&M pollutants on consumers’ decisions to 

visit a particular water body: 

(1) visible or otherwise perceivable effects (e.g., turbidity and odor); and 

(2) "toxic" effects that are not directly perceivable by consumers. 

Because priority and nonconventional pollutants at high enough concentrations may adversely affect aquatic species, “toxic” 

effects may be indirectly observable via species abundance and diversity. The study uses a dummy variable to account for 

effects of "toxic" MP&M pollutants, identifying recreation sites at which estimated concentrations of one or more MP&M 

pollutants exceed AWQC for protection of aquatic life. The study estimated that eliminating AWQC exceedances and 

reducing TKN concentrations would yield per trip benefits of $1.34, $1.78, $.60, and $0.33 (2001$) from improved fishing, 

boating, wildlife viewing, and swimming opportunities, respectively. The estimated changes in the recreational use value of 

Ohio water resources, are 0.77, 1.67, and 0.77 percent for fishing, boating, and wildlife viewing, respectively. This analysis 

estimates use values only. 

With the exception of the Tudor et al. (2002) study, the types of water quality changes assessed in these studies are only 

roughly comparable to those studied in the MP&M analysis. Whereas the analysis of the final MP&M regulation and Tudor 

et al. (2002) assessed the impact of eliminating AWQC exceedances, the other studies used other measures of water quality 

improvement. EPA addressed the differences in measurement between the other studies and the MP&M analysis by linking 

10 Preliminary results of this study were presented at the annual American Agricultural Economic Association meeting (Tudor et al., 

1999a) and at the annual Northeastern Agricultural and Resource Economic Association Meeting (Tudor et al., 1999b). EPA subjected this 

study to a formal peer review by experts in the natural resource valuation field. The peer review concluded that EPA had done a competent 

job, especially given the available data. This study can be found in Chapter 21. The peer review report is in the docket for the rule. 

15-8 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

water quality changes expected from the MP&M regulation to the type of water quality changes assessed in the other studies. 

EPA assumed that eliminating AWQC exceedances is roughly comparable to the following discrete water quality changes:11 

� “achieving a contaminant free fishery;” 

� reducing the level of toxins in fish tissue; 

� removing fish consumption advisories (FCA); and 

� improving water quality from “boatable” to “fishable,” from “fair” to “good,” and from “moderately polluted” to 

“unpolluted.” 

The MP&M analysis uses the estimates derived from the eight surface water evaluation studies described above to calculate a 

range of national WTP values. The following sections present the methodology and relevant values used to estimate the value 

of improved fishing, wildlife viewing, and boating opportunities resulting from the MP&M regulation. 

15.2.2 Recreational Fishing

The MP&M rule will improve the recreational angling experience by reducing concentrations of priority, nonconventional, 

and conventional contaminants in water. EPA estimated the benefits of these reductions by estimating: 

� the number of recreational fishing days on benefiting reaches; 

� the baseline fishery value of each benefiting reach; and 

� changes in recreational fishery value, using values from the available surface water valuation studies. 

a. Number of recreational fishing days

EPA calculated the annual number of person-days of recreational fishing for each benefiting reach using a two-step approach:


� Participating population


The geographic area from which anglers would travel to fish a reach is assumed to include only those counties that abut a


given reach. As noted in Chapter 13, this assumption is based on the finding in the 1991 N ational Survey of Fishing, Hunting,


and W ildlife-Associated Recreation that 65 percent of anglers travel less than 50 miles to fish (U.S. Department of the


Interior, 1993). ND S data showed that recreational anglers travel from 20 to 66 miles to their destination, with an average


one-way travel distance of 30 miles.12,13


EPA estimated the population participating in recreational fishing using the number of licensed fishermen in counties


bordering MP&M discharge reaches using the following steps:


� assume that fishing activity among these anglers is distributed evenly among all reach miles within those counties; 

� compute the length of the MP&M reach as a percentage of total reach miles within corresponding counties; 

� multiply the estimated ratio by the total fishing population in counties abutting the reach to estimate the number of 

anglers who may fish an MP &M reach; and 

11 Section 15.1.3 discusses a method used for estimating partial water quality improvements. 

12 See Chapter 21 for detail on the NDS data. 

13 These estimates exclude outliers. 

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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

� reduce the number of anglers by 20 percent in reaches where MP&M and other pollutants have required a fish 

consumption advisory. This reduction is an estimate of angler response to the presence of a fish consumption 

advisory.14 

� Average number of fishing days


Anglers generally participate in recreational fishing several times a year. The U.S. Fish and Wildlife Service (FWS)


provides estimates of the average number of fishing days per angler in each state. The FW S estimates range from 10.5 days


per angler in Arizona to 21.1 days per angler in Alabama for freshwater fishing, and 7.3 days per angler in Louisiana to 18.7


days per angler in Virginia for saltwater fishing.15


EPA calculated the total number of angler days by multiplying the number of recreational anglers for each benefiting reach by 

the average number of fishing days for the reach (based on the state in which the reach is located). 

b. Baseline fishery value

The net value of a recreational fishing day is the total value of the fishing day exclusive of any fishing-related costs (e.g., 

license fees, travel costs, bait, tackle, charter boats, etc.) incurred by the angler. 

EPA used two recreational fishing valuation studies (Bergstrom and Cordell (1991) and Walsh et al. (1992)) to calculate the 

net economic value per recreational fishing day under the baseline conditions. Both studies used a meta-analysis of 

recreational fishery valuation studies to estimate per-day values of the three types of recreational fishing: warmwater, 

coldwater, and anadromous. Based on the two studies, EPA developed an average per-day value for each type of recreational 

fishing. This analysis uses low and high average benefit values for fishing days of $28.11 and $60.43 (2001$) to estimate a 

range of the baseline fishery values. 

Table 15.2: Baseline Values of Fishing

Fishery Type 

Per-day Value (2001$)a 

Average Per-day Value 

Bergstrom (2001$) and Cordell 

(1991)b 

Walsh et al. 

(1992)c 

Warmwater $19.52 $36.70 $28.11 

Coldwater $27.77 $47.71 $37.74 

Anadromous $36.73 $84.15 $60.43 

Range of above $28.11 - $60.43 

a Original study values were adjusted to 2001 dollars based on the relative change in CPI from 1987 to 2001. b Study location: various U.S. locations. Estimating approach: meta-analysis of TC studies.


Study location: various U.S. locations. Estimating approach: meta-analysis of CV and TC studies.


Source: U.S. EPA analysis 

EPA calculated the total baseline value for each fishery located on a benefiting reach by multiplying the estimated net value of 

a recreational fishing day by the total number of fishing days calculated in subsection (a) above. Applying facility weights 

and summing over all benefiting reaches provides a total baseline recreational fishing value for MP&M reaches expected to 

benefit from the elimination of pollutant concentrations in excess of AWQC limits. 

14 See Belton et al. (1986), Knuth and Velicer (1990), Silverman (1990), West (1989), Connelly et al. (1992), and Connelly and 

Knuth (1993) for more information on angler response to fish advisories. 

15 These averages reflect participation levels in the 48 contiguous states. No sample facility is located in Hawaii or Alaska. 

15-10 

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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

c. Changes in recreational fishery value

Expected benefits from the final MP&M regulation include an increase in the quality of an angler’s recreational opportunities 

and/or the number of days an angler chooses to fish each season. 

EPA assumes that the expected welfare gain for recreational anglers is a function of changes in the overall quality of all 

recreational opportunities available to each angler. Recreational anglers residing in the counties abutting MP&M reaches will 

therefore benefit from improved recreational opportunities whether or not they actually visit an MP&M reach. 

EPA used the eight studies discussed above to calculate the changes in recreation values from water quality improvements (as 

a percentage of baseline) implied by those studies. Table 15.3 compiles information on the baseline values, values of changes 

in water quality, and percentage changes in values reported or implied by these studies. 

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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Table 15.3: Studies Estimating Changes in Value of a Recreational Fishery

Study Type of Water Quality 

Change Valued 

Baseline Value of 

Recreational 

Angling (2001$) 

Value of Water 

Quality Change 

(2001$) 

Value of Change 

as % of Baseline 

Type of Benefits 

Included 

Lyke (1993) Fish tissue is completely 

free of toxic 

contaminants that may 

threaten human health 

$95.0-$119.0 

million per year a 

$10.5-$37.1 

million per year a 

11% - 31%a Use and nonuse values 

for recreational 

anglers 

Jakus et al. 

(1997) 

Lifting FCAs $26.0-$52.6 

per trip 

$2.0-$3.2 

per trip 

6.0% - 8.0% Use values for 

recreational anglers 

Montgomery 

and 

Needelman 

(1997) 

Elimination of toxic 

impairment 

$656.6 per angler 

per year b 

$90.3 per 

angler per year 

13.7% Use values for 

recreational anglers 

Phaneuf at al. 

(1998) 

20% reduction of toxic 

contamination in trout 

flesh 

$484.5 - $605.8 per 

angler per year a 

$166.2 per angler 

per year 

27.5% - 34.3% Use values for 

recreational anglers 

Desvousges 

et al. (1987) 

Improvement from 

“boatable” to “fishable” 

$28.11- $37.73 per 

trip c 

$2.21 per trip d 5.9% - 7.9% Recreational and 

nonuse values to users 

Lant and 

Roberts 

(1990) 

Improvement from 

“fair” to “good” 

$28.11- $37.73 per 

trip c 

$3.67 per trip e 9.7% - 13.1% Recreational and 

nonuse values to users 

Farber and 

Griner (2000) 

Improvement from 

“moderately polluted” 

to “unpolluted” 

$28.11- $37.73 per 

trip c 

$1.49-$2.55 per 

trip f 

3.9% - 9.0% Recreational use 

values to users and 

nonusers 

Tudor 

et al. (2002) g 

Elimination of AWQC 

exceedances 

$173.34 per trip $1.34 per trip 0.77% Use values for 

recreational anglers 

Average percentage change in recreational fishery value (based on above studies) h 9.8% -14.7 % Recreational and 

nonuse values to users 

a

 The baseline fishery value for the study site location is based on the baseline fishery value reported in Lyke (1993). The study used 

data from two mail surveys conducted in 1989 at the University of Wisconsin-Madison. These surveys were originally used by Lyke 

(1993). 

b Based on the average value for a coldwater fishing day of $37.74 (see Table 15.2), multiplied by the average number of freshwater 

(non-Great Lakes) angling days per year in New York State (17.4 days, USFWS, 1996). 

Range based on the range of values for a fishing day used in this analysis (see Table 15.2); d Based on the value of water quality improvement of $36.79 per year (updated from 1987 dollars reported in Desvousges et al., 1987). 

divided by the average number of freshwater angling days per year in Pennsylvania (16.6 days, USFWS, 1996). e Based on the value of water quality improvement of $57.81 per year (updated from 1990 dollars reported in Lant and Roberts) 

divided by the average number of freshwater angling days per year in Iowa and Illinois (16.6 and 15.5 days, USFWS, 1996). f Based on the values of water quality improvements ranging from $24.55 to $41.93 per year reported in Farber and Griner (2000), 

divided by the average number of freshwater angling days per year in Pennsylvania (16.6 days, USFWS, 1996). g See Chapter 21 of this report for detail. The baseline value of recreational fishery is based on the estimated mean value of water 

resources for recreational anglers reported by Tudor et al. (2002). The estimated median value of recreational fishing is $175.48. These 

values were derived from a September 23, 2002 analysis. h EPA took a simple mean of lower- and upper-bound estimates from the eight studies to calculate a range of percentage changes in the 

recreational fishery value from improved water quality conditions. When only one value is available from the study (i.e., Tudor et al., 

2002), EPA used this value in calculating both the lower- and upper-bound estimates. 

Source: U.S. EPA analysis 

EPA used the percentage change in the fishery value implied by the eight studies to estimate increased recreational fishing 

values for all MP&M reaches in which the regulation eliminates AWQC exceedances of one or more MP&M pollutants. That 

is, the Agency estimated benefits for all MP&M discharge reaches where at least one AWQC exceedance is eliminated due to 

reduced MP&M discharges. As noted above, EPA took a simple mean of lower- and upper-bound estimates from the eight 

studies described above to calculate a range of percentage changes in the recreational fishery value from reduced MP&M 

discharges. These studies yielded estimates of increased value ranging from 9.8 to 14.7 percent. Multiplying these 

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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

percentages by the baseline value of fisheries located on benefiting reaches yielded a range of benefits from eliminating 

pollutant concentrations in excess of AWQC limits. 

Table 15.4 below summarizes the results of EPA’s recreational fishing benefits analysis. 

Table 15.4: Summary of Recreational Fishing Benefits (2001$)

Number of 

Benefiting 

Reaches 

Participating 

Population 

(millions) 

Average 

Number of 

Fishing Days 

Total Angler 

Days 

(millions) 

Baseline 

Fishery 

Value/ Rec. 

Day 

Baseline 

Fishery Value 

($ millions) 

% Change in 

Fishery Value 

MP&M 

Benefits 

Selected Option: Traditional Extrapolation 

Low 

Estimate 9 0.98 17.3 16.98 $28.11 $477 9.8% $287,220 

High 

Estimate 9 0.98 17.3 16.98 $60.43 $1,026 14.7% $923,988 

Selected Option: Post-Stratification Extrapolation 

Low 

Estimate 6 1.08 17.2 18.61 $28.11 $523 9.8% $187,123 

High 

Estimate 6 1.08 17.2 18.61 $60.43 $1,125 14.7% $601,976 

Source: U.S. EPA analysis 

15.2.3 Wildlife Viewing

EPA expects that water quality improvements from the MP&M regulation will decrease the uptake of pollutants through 

aquatic food chains. These changes are expected to increase the health and reproductive success of sensitive wildlife species 

that feed on fish and other aquatic organisms. In particular, Piscivorous (i.e., fish-eating) bird species such as the osprey 

(Pandion haliaetus), bald eagle (Haliaeetus leucocephalus), great blue heron (Ardeidae herodias), mergansers (Merginae 

sp.), and cormorants (Phalacrocorax sp.) will benefit from increased numbers, size, and health of forage fish. Increased 

food and lower pollutant levels in fish flesh will improve reproduction in these birds, leading to healthier and larger bird 

populations. Reducing conventional pollutant loadings will also improve visual aesthetics, thereby enhancing wildlife 

viewing and other near-water-based recreation experiences, such as photography, camping, picnicking, and waterfowl hunting 

(hereafter, this discussion refers to all of these activities as “wildlife viewing”). 

As with the recreational fishing analysis, EPA assumes that the expected welfare gain for consumers of viewing activities is a 

function of changes in the overall quality of all recreational opportunities available to each consumer. Consumers of waterbased recreation residing in the counties abutting MP&M reaches are therefore likely to benefit from improved recreational 

opportunities whether or not they actually visit an MP&M reach. 

EPA estimated wildlife viewing benefits using an approach similar to that used in estimating recreational fishing benefits. 

EPA estimated: 

� the number of wildlife viewing days on benefiting reaches; 

� the baseline value of wildlife viewing for each benefiting reach; and 

� changes in wildlife viewing value, using values from the available surface water valuation studies. 

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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

a. Number of wildlife viewing days

EPA calculated the annual number of person-days of wildlife viewing for each benefiting reach using a two-step approach: 

� Participating population

The analysis of the NDS data showed that participants in viewing activities travel from 16 to 117 miles to their destination,


with an average one-way travel distance of 34 miles.16 EPA therefore assumes that improvements in recreational


opportunities will benefit only recreational users residing within the counties abutting MP&M reaches. EPA estimated the


population participating in viewing activities using the number of water-based recreation consumers residing in the counties


traversed by benefiting reaches using the following steps:


� estimate resident populations in the counties traversed by the benefiting reaches using Census data; 

� calculate the number of wildlife viewing participants based on the percent of the population engaged in wildlife 

viewing activities; 

� estimate the percentage of individuals that participate in wildlife viewing in each state using NDS data. The total 

state population participating in wildlife viewing ranges from 8.6 percent in New Mexico to 44.4 percent in Maine; 

and 

� adjust the number of wildlife viewing participants within the affected county based on the ratio of the affected reach 

length to the number of total reach miles in the affected county to calculate the population potentially benefiting from 

the rule.17,18 

� Average number of viewing days

Recreators generally participate in wildlife viewing several times a year. The Agency used NDS data on the number of


wildlife viewing trips to estimate the average number of user days in each state. The NDS data show that the number of


wildlife viewing trips in the 48 states range from 1.8 days per user in South Dakota to 24.2 days per user in Mississippi.19


EPA multiplied the number of wildlife viewing consumers by estimates of the average number of days per user in each state to


estimate the annual number of user days for each benefiting MP&M reach.


b. Baseline value of wildlife viewing

EPA estimated the baseline value of wildlife viewing for the benefiting reaches based on the estimated annual person-days 

calculated in subsection (a) above and the estimated value per person-day of wildlife viewing. 

EPA used two recreational activity valuation studies (Bergstrom and Cordell (1991) and Walsh et al. (1992)) to calculate the 

net economic values per wildlife viewing day. These studies estimate net benefit values for four recreational activities: 

wildlife viewing, waterfowl hunting, camping, and picnicking. Based on the two studies, EPA developed an average per-day 

value for three of the four activities.20 EPA’s MP&M benefits analysis uses the lowest average benefit value, $22.73, for the 

low estimate of wildlife viewing benefits and the highest average value, $28.73, for the high estimate. Table 15.5 presents 

information on the relevant values reported in these studies. 

Using facility sample weights and summing over all benefiting reaches provides the total baseline value of wildlife viewing 

for MP&M reaches that EPA expects to benefit by eliminating pollutant concentrations in excess of AWQC limits. 

16 These estimates exclude outliers. 

17 Information in EPA's Reach File 1 indicates that the ratio of affected reach length to the total number of reach miles within a 

county ranges from 0.02 to 0.39. 

18 This analysis assumes that recreation activities among residents of the counties affected by MP&M discharges are distributed 

evenly across all reach miles within those counties. 

19 See Chapter 21 for details on the NDS data. 

20 EPA excluded the per-day value of waterfowl hunting ($55.53) from the activities included in this analysis, because this activity is 

limited to designated hunting areas only. 

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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Table 15.5: Baseline Values of Wildlife Viewing

Recreational Activity 

Per-day Value (2001$)a 

Average Per-day Value 

Bergstrom (2001$) and Cordell 

(1991)b 

Walsh et al. 

(1992)c 

Camping $27.10 $30.38 $28.73 

Picnicking $18.46 $27.00 $22.73 

Near-water Activities $20.07 $34.59 $27.33 

Range of above $22.73 - $28.73 

a Original study values were adjusted to 2001 dollars based on the relative change in CPI from 1987 to 2001. b Study location: various U.S. locations. Estimating approach: meta-analysis of TC studies.


Study location: various U.S. locations. Estimating approach: meta-analysis of contingent valuation (CV) and TC studies.


Source: U.S. EPA analysis 

c. Changes in wildlife viewing value

EPA selected a subset of the candidate benefits transfer studies discussed in Section 15.2.1 to estimate changes in water 

resource value to wildlife viewers due to the MP&M rule. The four selected studies include Tudor et al. (2002), Desvousges 

et al. (1987), Lant and Roberts (1990), and Farber and Griner (2000)21. Table 15.6 compiles information on the baseline 

values of wildlife viewing, values of changes in water quality, and percentage change in values reported or implied by these 

studies. 

21 The remaining four studies value changes in the value recreational fishing only. 

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c
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Table 15.6: Studies Estimating Changes in Value of Wildlife Viewing

Study Water Quality 

Change Valued 

Baseline Value of 

Wildlife Viewing 

(2001$) 

Value of Water 

Quality Change 

(2001$) 

Value of Change 

as % of Baseline 

Type of Benefits 

Included 

Desvousges 

et al. (1987) 

Improvement from 

“boatable” to 

“fishable” 

$22.8 - $28.7 

per trip a 

$5.00 per trip b 17.4% - 22.0% Recreational and 

nonuse values to users 

Lant and 

Roberts 

(1990) 

Improvement from 

“fair” to “good” 

$22.8 - $28.7 

per trip a 

$8.60 per trip c 29.9% - 37.8% Recreational and 

nonuse values to users 

Farber and 

Griner 

(2000) 

Improvement from 

“moderately polluted” 

to “unpolluted” 

$22.8 - $28.7 

per trip a 

$3.33 - $5.69 per 

trip d 

11.6% - 25.0% Recreational and 

nonuse values to users 

Tudor 

et al. (2002) 

Elimination of AWQC 

exceedances 

$77.99 per trip e $0.60 per trip 0.77% Recreational use 

values to users 

Average percentage change (based on the above studies) f 14.9% - 21.3% 

a Based on the range of median values for a near-water recreation day (updated to 2001 dollars) reported in Walsh et al. (1992) and 

Bergstrom and Cordell (1991) (see Table15.5). b Based on the value of water quality improvement of $36.79 per person per year (updated from 1987 dollars reported in Desvousges et 

al.) divided by the average number of near-water recreation days per year in Pennsylvania (7.37 days, NDS, 1993). 

Based on the value of water quality improvement of $57.79 per year (updated from 1990 dollars) reported in Lant and Roberts 

divided by the average number of near-water recreation days per year in Iowa and Illinois (9.58 and 5.04 days, NDS, 1993). d Based on the value of water quality improvements ranging from $24.55 to $41.93 per person per year reported in Farber and Griner 

(2000) divided by the average number of near-water recreation days per year in Pennsylvania (7.37 days, NDS, 1993). e

 The baseline value of viewing is based on the estimated mean value of water resources for wildlife viewers reported by Tudor et al. 

(2002). The estimated median value of recreational fishing is $82.77. These values were derived from a September 23, 2002 analysis. f EPA took a simple mean of lower- and upper-bound estimates from the four studies to calculate a range of percentage changes in the 

wildlife viewing value from improved water quality conditions. When only one value is available from the study (i.e., Tudor et al., 

2002), EPA used this value in calculating both the lower- and upper-bound estimates. 

Source: U.S. EPA analysis 

This analysis uses the change of 14.9 percent for the low benefits estimate and 21.3 percent for the high benefits estimate to 

calculate benefits from reduced M P&M facility discharges to users of water-based recreation. These values represent the 

average of the low and high values, respectively, estimated in the four studies. 

Table 15.7 below summarizes the results of EPA’s wildlife viewing benefits analysis. 

15-16 

c
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Table 15.7: Summary of Wildlife Viewing Benefits (2001$)

Number of 

Benefiting 

Reaches 

Participating 

Population 

(millions) 

Ave. 

Number of 

Viewing 

Days 

Total 

Viewing 

Days 

(millions) 

Baseline 

Value/ Rec. 

Day 

Total 

Baseline 

Value 

($ millions) 

% Change 

in Value 

Benefit 

from 

MP&M 

Selected Option: Traditional Extrapolation 

Low 

Estimate 9 3.12 7.5 23.52 $22.73 $535 14.9% $185,172 

High 

Estimate 9 3.12 7.5 23.52 $28.73 $676 21.3% $334,315 

Selected Option: Post-Stratification Extrapolation 

Low 

Estimate 6 3.17 7.5 23.91 $22.73 $544 14.9% $120,639 

High 

Estimate 6 3.17 7.5 23.91 $28.73 $687 21.3% $217,805 

Source: U.S. EPA analysis. 

15.2.4 Recreational Boating

Improvements in water quality from the final MP&M rule may enhance recreational boating by (1) providing more 

opportunities for companion activities (e.g., fishing and wildlife viewing) and (2) improving visual aesthetics. EPA assumes 

that the expected welfare gain for boaters is a function of changes in the overall quality of all recreational opportunities 

available to each boater on a given day. 

This analysis estimates recreational boating benefits the same way as recreational fishing and wildlife viewing benefits. The 

analysis estimates: 

� the number of recreational boating days on benefiting reaches, 

� the baseline value of boating for each benefiting reach, and 

� changes in recreational boating value. 

a. Number of recreational boating days

EPA calculated the annual number of recreational boating days for each benefiting reach using two steps:


� Participating population


The analysis of the NDS data showed that boaters travel from 10 to 108 miles to their destination, with an average one-way


travel distance of 32 miles.22 This analysis therefore considers only boaters residing in the counties abutting MP&M reaches.


EPA estimated the number of boaters residing in the counties traversed by benefiting reaches by combining information from


Census data and NDS data on the proportion of individuals participating in boating in each state. The percent of the total


state population in the 48 states participating in boating ranges from 8.0 percent in Colorado to 28.7 percent in W ashington.


EPA further adjusted the number of boaters likely to use MP& M reaches within the affected county based on the ratio of the


affected reach length to the number of total reach miles in the affected county.23


22 These estimates exclude outliers. 

23 See section 13.1.1 for detail. 

15-17 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

� Average number of boating days


People using benefiting reaches for boating generally participate in this activity several times per year. The NDS data show


the number of boating trips in the 48 states ranging from 3.2 days per user in New Hampshire to 14.6 days per user in


Colorado.


EPA estimated the annual number of user days for recreational boating activities by multiplying the number of boaters by the


average number of boating days per user in each state.


b. Baseline value of boating

EPA estimated the baseline value of boating on benefiting reaches using the estimated annual person-days of boating per 

reach and estimated values per person-day of various types of boating. EPA calculated a range of net economic values per 

recreation day of boating based on studies by Bergstrom and Cordell (1991) and Walsh et al. (1992). Mean net benefit values 

for motorized and non-motorized boating are $37.30 to $59.26 in 2001 dollars. Table 15.8 compiles information on the 

relevant values reported in these studies. 

Table 15.8: Baseline Values of a Boating Day

Recreational Activity 

Per-day Value (2001$)a 

Average Per-day Value 

Bergstrom (2001$) and Cordell 

(1991)b Walsh et al. (1992)c 

Motorized $25.43 $49.18 $37.30 

Non-motorized $42.67 $75.85 $59.26 

Boating (any type) $37.30 - $59.26 

a Original study values were adjusted to 2001 dollars based on the relative change in CPI from 1987 to 2001. b Study location: various U.S. locations. Estimating approach: meta-analysis of TC studies.


Study location: various U.S. locations. Estimating approach: meta-analysis of CV and TC studies.


Source: U.S. EPA analysis 

Weighting by facility sample weights and summing over all benefiting reaches provides a total baseline value of boating for 

MP&M reaches expected to benefit by eliminating pollutant concentrations in excess of AWQC limits. 

c. Changes in recreational boating values

The Agency used the same four studies discussed in Section 15.2.3 to calculate the change in per-day boating value as a result 

of water quality improvements. EPA expressed this change as a percentage of the baseline value. Table 15.9 compiles 

information on the baseline values of boating, values of changes in water quality, and percentage change in boating values 

reported or implied by these studies. 

15-18 

c
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Table 15.9: Studies Estimating Changes in Value of Recreational Boating

Study Water Quality 

Change Valued 

Baseline Value of 

Boating (2001$) 

Value of Water 

Quality Change 

(2001$) 

Value of Change as 

% of Baseline 

Type of Benefits 

Included 

Desvousges 

et al. (1987) 

Improvement from 

“boatable” to 

“fishable” 

$37.30 - $59.26 

per trip a 

$3.92 per trip b 6.6% -10.5% Recreational and 

nonuse values to 

users 

Lant and 

Roberts 

(1990) 

Improvement from 

“fair” to “good” 

$37.30 - $59.26 

per trip a 

$7.91 per 

trip c 

13.3% -21.2% Recreational use 

values to users and 

nonusers 

Farber and 

Griner (2000) 

Improvement from 

“moderately 

polluted” to 

“unpolluted” 

$37.30 - $59.26 

per trip a 

$2.62 - $4.48 per 

trip d 

4.4%-12.0% Recreational and 

nonuse values to 

users 

Tudor et al. 

(2002) 

Elimination of 

AWQC exceedances 

$106.60 per trip e $1.78 pr trip 1.67% Recreational values 

for users 

Average percentage change (based on the above studies) f 6.5% - 11.4% 

a Based on the average value for a boating day (updated to 2001 dollars) reported in Walsh et al. (1992) and Bergstrom and Cordell 

(1991). 

b Based on the value of water quality improvement of $36.79 per person per year (updated from 1987 dollars) reported in Desvousges 

et al. divided by the average number of boating days per year in Pennsylvania (9.37 days, NDS, 1993). 

Based on the value of water quality improvement of $57.79 per person per year (updated from 1990 dollars) reported in Lant and 

Roberts divided by the average number of boating days per year in Iowa and Illinois (9.58 and 5.04 days, NDS, 1993). d Based on the value of water quality improvements ranging from $24.55 to $41.93 per person per year reported in Farber and Griner 

(2000) divided by the average number of boating days per year in Pennsylvania (9.37 days, NDS, 1993). e

 The baseline value of boating is based on the estimated mean value of water resources for boaters reported by Tudor et al. (2002). 

The estimated median value of recreational boating is $112.55. These values were derived from a September 23, 2002 analysis. f

 EPA took a simple mean of lower- and upper-bound estimates from the four studies described to calculate a range of percentage 

changes in the recreational boating value from improved water quality. When only one value is available from the study (i.e., Tudor et 

al., 2002), EPA used this value in calculating both the lower- and upper-bound estimates. 

Source: U.S. EPA analysis 

This analysis uses the change of 6.5 percent for the low benefits estimate and 11.4 percent for the high benefits estimate to 

calculate benefits to boaters from reduced M P&M facility discharges. These values represent the average of the low and high 

values, respectively, estimated in the four studies. 

Table 15.10 summarizes the results of EPA’s recreational boating benefits analysis. 

15-19 

c
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Table 15.10: Summary of Recreational Boating Benefits (2001$)

Number of 

Benefiting 

Reaches 

Participating 

Population 

(millions) 

Ave. 

Number of 

Boating 

Days 

Total 

Boating 

Days 

(millions) 

Baseline 

Value/ Rec. 

Day 

Total 

Baseline 

Value 

($ millions) 

% Change 

in Value 

MP&M 

Benefits 

Selected Option: Traditional Extrapolation 

Low 

Estimate 9 2.53 8.3 21.06 $37.30 $786 6.5% $114,111 

High 

Estimate 9 2.53 8.3 21.06 $59.26 $1,249 11.4% $316,078 

Selected Option: Post-Stratification Extrapolation 

Low 

Estimate 6 2.57 8.4 21.47 $37.30 $801 6.5% $74,343 

High 

Estimate 6 2.57 8.4 21.47 $59.26 $1,272 11.4% $205,924 

Source: U.S. EPA analysis. 

15.2.5 Nonuse Benefits

EPA estimated changes in nonuse values for this analysis because nonuse value is a sizeable portion of the total value of water 

resources. Individuals who never visit or otherwise use a natural resource may still be affected by changes in its status or 

quality. Empirical estimates indicate that such "nonuse values" may be substantial for some resources (Harpman et al., 1993; 

Fisher and Raucher, 1984; Brown, 1993). Most studies have found that nonuse values exceed use values. Brown reviewed 

31 CV studies in which both use and nonuse values were estimated, and calculated the ratio of nonuse values to use values 

(Brown, 1993). The goal of Brown’s study was to assess consistency of ratios of use to nonuse value and to develop a basis 

for obtaining a rough estimate of nonuse value, and therefore total values, for the many studies that measured only use values. 

His 31 estimated ratios range from 0.1 to 10, with the median ratio of 1.92. The ratios of nonuse to use values reported by 

Brown for the studies that valued environmental improvements in water resources range for users of those resources from 

0.85 to 2.56. The estimated average ratio is 1.57. That is, for every dollar of annual use-benefit value to users of the subject 

environmental resource, the annual nonuse value to resource users for the subject environmental resource is $1.57. 

Carson and Mitchell suggested that nonuse benefits account for 19 to 39 percent of total WTP values for water quality 

improvements depending on the definition of nonuse values (Carson and Mitchell, 1993). The ratio of nonuse to use value 

ranges from one-fourth to two-thirds based on the Carson and Mitchell study (1993). Fisher and Raucher (1984) found that 

nonuse benefits comprise one-half of recreational use benefits. 

EPA used findings from the Fisher and Raucher (1984) study in which nonuse values are estimated to be equal to 50 percent 

of use values to estimate nonuse benefits from the final MP&M regulation. The method has long been used by EPA as a 

pragmatic alternative to omitting nonuse values entirely. EPA acknowledges that this method is crude and nonuse values 

estimated by the 50 percent of use value approach are quite low given the applicable literature discussed above. 

The Agency estimates that nonuse benefits from the final MP&M rule will range from $293,252 to $787,190 and from 

$191,053 to $512,852, based on the traditional and post-stratification extrapolation, respectively. 

15.3 SUMMARY OF RECREATIONAL BENEFITS

EPA assumes that eliminating concentrations of MP&M pollutants in excess of AWQC limits will achieve water quality 

protective of aquatic life and human health. This improved water quality then generates benefits for both users and nonusers 

of water-based recreation. These benefits can be seen as an increase in the value of each day spent on or near the waterway, 

as well as an increase in the number of days spent on or near the waterway. EPA estimated the monetary value of improved 

water-based recreational opportunity for the 9 discharge reaches based on the traditional extrapolation (6 reaches based on the 

post-stratification extrapolation) for which concentrations in excess of AWQC limits would be eliminated. 

15-20 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

EPA first estimated the number of recreational days on benefiting reaches for each water-based activity. The Agency then 

calculated the baseline value of these activities and then calculated the percentage changes in this value stemming from water 

quality improvements. 

EPA calculated partial benefits for reaches with reduced numbers of AWQC exceedances by adjusting the percentage increase 

in the recreational value of these reaches. EPA made these adjustments based on the ratio of the number of AWQC 

exceedances eliminated post-compliance to the number of AWQC exceedances occurring at baseline. 

Table 15.11 summarizes benefit estimates by recreational category for the final rule based on the traditional and poststratification extrapolation methods. The activities considered in this analysis are stochastically independent; EPA calculated 

the total value of enhanced water-based recreation opportunities by summing over the three recreation categories. EPA also 

estimated the changes in nonuse value resulting from reduced MP&M discharges based on the ratio of use to nonuse values 

implied by the Fisher and Raucher study (Fisher and Raucher, 1984). Based on the traditional extrapolation, the estimated 

increase in nonuse value ranges from $0.29 to $0.79 million (2001$), with a midpoint value of $0.50 million (2001$). The 

resulting increased value of recreational activities to consumers (users and nonusers) of water-based recreation ranges from an 

estimated $0.59 to $1.57 million (2001$) annually. The estimated mean value of recreational benefits is $1.00 million 

(2001$) annually. Likewise, based on the post-stratification extrapolation, the estimated increase in nonuse value ranges from 

$0.19 to $0.51 million (2001$), with a midpoint value of $0.33 million (2001$). The resulting increased value of recreational 

activities to consumers (users and nonusers) of water-based recreation ranges from an estimated $0.38 to $1.03 million 

(2001$) annually. The estimated mean value of recreational benefits is $0.65 million (2001$) annually. 

Tables 15.12 and 15.13 summarize benefit estimates for the 433 Upgrade Options and Proposed/NODA Option, respectively. 

Recreational use and nonuse benefits are almost 200 times higher under the two 433 Upgrade Options, and over 430 times 

higher under the Proposed/NO DA O ption. 

Table 15.11: Estimated Recreational Benefits from Reduced MP&M Discharges (Thousands, 2001$)

Recreational Activity 

Traditional Extrapolation Post-Stratification Extrapolation 

Low Value Midpoint 

Value High Value Low Value Midpoint 

Value High Value 

Fishing $287 $537 $924 $187 $350 $602 

Boating $114 $203 $316 $74 $132 $206 

Viewing and near-water activities $185 $260 $334 $121 $169 $218 

Total Recreational Use Benefits $587 $1,000 $1,574 $382 $651 $1,026 

Nonuse Benefits (1⁄2 of the 

Recreational Use Benefits) $293 $500 $787 $191 $326 $513 

Total Recreational Benefits $880 $1,500 $2,362 $573 $977 $1,539 

Source: U.S. EPA analysis. 

15-21 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Table 15.12: Estimated Recreational Benefits from Reduced MP&M Discharges (Thousands, 2001$)a

Recreational Activity 

Directs + 413 to 433 Upgrade Directs +All to 433 Upgrade 

Low Value Midpoint 

Value High Value Low Value Midpoint 

Value High Value 

Fishing $28,713 $53,703 $92,369 $29,052 $54,337 $93,460 

Boating $36,511 $64,854 $101,134 $36,652 $65,103 $101,523 

Viewing and near-water activities $56,584 $79,434 $102,158 $56,657 $79,536 $102,290 

Total Recreational Use Benefits $121,808 $197,990 $295,661 $122,360 $198,976 $297,272 

Nonuse Benefits (1⁄2 of the 

Recreational Use Benefits) $60,904 $98,995 $147,831 $61,180 $99,488 $148,636 

Total Recreational Benefits $182,712 $296,986 $443,492 $183,541 $298,464 $445,908 

a

 Based on the Traditional Extrapolation. 

Source: U.S. EPA analysis. 

Table 15.13: Estimated Recreational Benefits from Reduced MP&M Discharges (Thousands, 2001$)a

Recreational Activity 

Proposed/NOD A Optionb 

Low Value Midpoint Value High Value 

Fishing $53,897 $100,805 $173,386 

Boating $75,847 $134,724 $210,089 

Viewing and near-water activities $140,623 $197,410 $253,884 

Total Recreational Use Benefits $270,366 $432,939 $637,360 

Nonuse Benefits (1⁄2 of the 

Recreational Use Benefits) $135,183 $216,469 $318,680 

Total Recreational Benefits $405,550 $649,408 $956,040 

a

 Based on the Traditional Extrapolation. b

 The estimated recreational benefits of the Proposed/NODA Option are not directly comparable to the final option alternatives. The 

total number of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported for the final rule and 

the two upgrade options. After deciding in July 2002 not to consider the NODA option as the basis for the final rule, EPA performed 

no more analysis on the NODA option, including not updating facility counts and related analyses for the change in subcategory and 

discharge status classifications. 

Source: U.S. EPA analysis. 

15.4 LIMITATIONS AND UNCERTAINTIES ASSOCIATED WITH ESTIMATING

RECREATIONAL BENEFITS

EPA assessed recreational benefits in terms of reduced occurrence of pollutant concentrations exceeding acute and chronic 

toxic effect levels for aquatic species. EPA also attached a monetary value to ecological improvements expected to result 

from the MP&M regulation, in the form of the increased value of three water-based recreation activities recreational fishing, 

wildlife viewing, and boating plus the increase in nonuse value. The estimated increase in value detailed in this chapter 

constitutes only a partial measure of the value to society of improving aquatic habitats and aquatic life. This benefits analysis 

is limited because it ignores improvements to recreational activities other than fishing, boating, and wildlife viewing (e.g., 

swimming), as well as non-recreational benefits, such as increased assimilative capacity and improvements in the taste and 

odor of the affected waters. 

15-22 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

The methodologies used to assess ecological benefits also involved significant simplifications and uncertainties, whose 

combined effect on the estimated benefits is not known. Estimated economic values may be under- or overestimated. Some 

of these simplifications and uncertainties also apply to the human health benefits analysis, and have been discussed at length 

in the previous chapter, including those associated with: 

� developing the sample of MP&M facilities analyzed in the EEBA, 

� estimating in-waterway concentrations of MP&M pollutants, 

� considering background concentrations of MP&M pollutants, and 

� considering downstream effects. 

Table 15.14 summarizes the additional elements of uncertainty that are specific to the recreational benefits analysis. 

Table 15.14: Key Omissions, Biases, and Uncertainties in the Analysis

for Improved Recreational and Nonuse Benefits

Assumption/Limitation Direction of Impact on Benefit Estimates 

Scope of Recreational Benefits Analysis 

Only the receiving reach itself is 

estimated to provide benefits. 

(-) 

Water quality in reaches downstream of the reaches affected by MP&M discharges may also 

improve, generating additional benefits to society. luding these benefits from the analysis 

biases benefits estimates downward. 

Only recreational users living in the 

counties abutting MP&M reaches are 

assumed to benefit from water quality 

improvements due to the MP&M 

rule. 

(-) 

The analysis underestimates the total value of benefits from the MP&M regulation because it 

does not account for people’s WTP for water quality improvements to distant water bodies. 

For example, economic values for improving nationally-significant water bodies (e.g., Great 

Lakes, Chesapeake Bay, Long Island Sound) are likely to be substantial at a regional level or 

even nationwide. 

The analysis of recreational fishing 

ignores effects that occur in 

secondary industries. 

(-) 

The analysis of recreational benefits ignores potential economic effects on tourism industries 

stemming from improved recreational opportunities. a 

positive effect on industries supplying bait, tackle, charter boats, etc. r 

demand for boating may have positive effects on industries such as boat construction, sales, 

rentals, boating equipment, marinas, racing activities, etc. ovements in wildlife viewing 

and near-water recreation opportunities may benefit industries involved in providing other 

recreational opportunities, such as tours, books, binoculars, etc. 

The analysis of recreational benefits 

ignores changes in the value of 

water-based recreational activities 

other than fishing, wildlife viewing, 

and boating (e.g., swimming or 

waterskiing). 

(-) 

The estimate of recreational benefits is incomplete because it includes only a subset of 

recreational activities (i.e., fishing, wildlife viewing, and boating) which society may value 

improved aquatic habitat. 

activities, such as swimming or waterskiing. 

changes in the affected reaches, such as improved taste and odor. 

Exc

Improved recreational fishing may have 

An increase in consume

Impr

for 

It ignores changes in value for other water-based recreational 

In addition, the analysis did not consider other 

15-23 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Table 15.14: Key Omissions, Biases, and Uncertainties in the Analysis

for Improved Recreational and Nonuse Benefits

Assumption/Limitation Direction of Impact on Benefit Estimates 

Extrapolating from sample facility 

results to national results is based on 

the sample facility weights 

(?) 

This extrapolation technique is not ideal and introduces uncertainty into the analysis. ility 

sample weights are based on facility size and type of industry. se weights do not 

necessarily account for the frequency benefit pathway characteristics in the MP&M facility 

universe. Therefore benefit estimates may suffer from uncertainties associated with the 

extrapolation method. For example, a sample facility may have a significant impact on benefit 

estimates if it is more likely to be located in a densely populated area, such as a facility located 

in Cleveland, Ohio, or a facility discharging in Long Island Sound, than the facilities it 

represents. 

To improve accuracy of the national benefit estimates, EPA used an alternative extrapolation 

method (i.e., post-stratification extrapolation). 

weights that account for the distribution of benefit pathway characteristics, including water 

body type and population size, in the MP&M facility universe. ppendix G summarizes this 

extrapolation approach. 

Congestion Externalities (+) 

Recreational benefits associated with water quality improvements can be eroded by congestion 

if policies greatly increase the number of participants. can be particularly problematic 

when policies affect geographically scattered sites, so that there is considerable switching to 

the improved site from substitute sites. be a lesser problem for national 

regulations that might affect the total number of recreation days and the overall value of 

recreational opportunities, but are less likely to have a large effect on industrial sites relative to 

its substitutes. 

Benefits Transfer 

The waters assessed by local-level 

studies are not necessarily nationally 

representative. 

(?) 

The studies selected came from the Midwest and the Northeast. 

valued, as well as respondent preferences, may not be representative of the rest of the country. 

Types of water quality changes 

expected from the MP&M rule may 

differ from the water quality changes 

considered in the original studies. 

(?) 

The types of water quality changes expected from the MP&M regulation are only roughly 

comparable with the majority of water quality changes considered in the original studies 

(Tudor et al. is the only exception). paucity of available studies, the Agency made 

simplifying assumptions to “map” the water quality changes valued in the original studies onto 

those expected from the rule. lthough these assumptions are likely to increase uncertainty 

associated with recreational benefits estimates, the direction of bias is not known. 

Compatibility of time periods 

considered in the original studies and 

in the analysis of MP&M costs and 

benefits. 

(+) 

Most studies considered in the benefits transfer analysis did not specify payment periods. The 

scenario in the Farber and Griner (2000) paper asked for payments for the next five years. 

This scenario implies that five years of pollution control will result in permanent water quality 

changes. the MP&M regulation assumes that pollution control continues over 

15 years and that water quality improvements depend on continued operation of the water 

pollution controls. 

opposed to the total value paid over five years, annualized over the 15 years considered in the 

cost analysis. assumption may result in an overestimation of the regulation’s benefit. 

The magnitude of this error is unlikely to be significant because this study is used in 

combination with other surface water valuation studies. 

Baseline Value of Fishery 

Converting annual WTP values to 

per-trip values 

(+) 

EPA converted annual WTP values reported in the three CV studies used in this analysis to 

per-trip values by dividing seasonal welfare gain per user reported in each CV study by the 

average number of fishing, boating, or viewing days in a given state. his calculation implies 

that every individual participates in only one activity, which may not be the case. This 

implication may result in an overestimation of the per-trip welfare gain, and, consequently, an 

overestimation of total recreational benefits from the final rule. 

Fac

The

The opposite may also be true. 

This method relies on adjusted sample facilities 

A

This 

Congestion may

As a result, the resources 

Due to the

A

The analysis of 

EPA therefore chose the annual WTP values presented in the paper, as 

This

T

15-24

MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Table 15.14: Key Omissions, Biases, and Uncertainties in the Analysis

for Improved Recreational and Nonuse Benefits

Assumption/Limitation Direction of Impact on Benefit Estimates 

This analysis estimates the baseline 

value of the fisheries at locations 

across the country using a range of 

values for all types of fisheries. 

(?) 

Site-specific fisheries may have higher or lower baseline values, and thus, higher or lower 

benefits from reduced MP&M discharges. 

The total number of recreational 

person-days in the counties abutting 

MP&M reaches is evenly distributed 

across all reach miles in these 

counties. 

(+) 

This method for estimating the number of recreational users potentially affected by water 

quality improvements from the final regulation accounts for the quantity but not quality of 

potential recreational opportunities available to recreational users. re may be important 

substitute sites in or outside the counties abutting MP&M reaches. recreationally 

important substitute sites may result in overestimation of benefits from the final regulation. 

Ideally the analysis would consider recreational importance of both sites affected by MP&M 

discharges and substitute sites. 

Nonuse Values 

Nonuse values are estimated as onehalf of recreational use benefits. (?) 

It is unknown what bias estimating nonuse values based on recreational use values has on 

benefits. 

Overall Impact on Benefits 

Estimates 

(?) 

The

Ignoring

+ Potential overestimate. 

? Uncertain impact. 

- Potential underestimate. 

Source: U.S. EPA analysis. 

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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

GLOSSARY

1Q10: the lowest one-day average flow with a recurrence interval of ten years. 

7Q10: the lowest consecutive seven-day average flow with a recurrence interval of ten years. 

ambient water quality criteria (AWQC): published and periodically updated by the EPA under the Clean Water Act. 

The criteria reflect the latest scientific knowledge on the effects of specific pollutants on public health and welfare, aquatic 

life, and recreation. The criteria do not reflect consideration of economic impacts or the technological feasibility of reducing 

chemical concentrations in ambient water. The criteria serve as guides to states, territories, and authorized tribes in 

developing water quality standards and ultimately provide a basis for controlling discharges or releases of pollutants into our 

nation’s waterways. AWQC are developed for two exposure pathways: ingestion of the pollutant via contaminated aquatic 

organisms only, and ingestion of the pollutant via both water and contaminated aquatic organisms. 

benefiting reaches: reaches where the MP&M rule is expected to eliminate existing AWQC exceedences. These 

receiving waters are likely to experience significant water quality improvements as a result of the reduced MP&M discharges. 

A reach is considered to benefit if at least one AWQC exceedance is eliminated due to reduced MP&M discharges. 

benefits transfer: involves the application of value estimates, functions, and/or models developed in one context to 

address a similar resource valuation question in another context. Often a meta-analysis is undertaken where benefits estimates 

based on existing studies are used to develop new estimates which are applicable to the scenario under consideration. This 

process accounts for relevant differences in study characteristics, such as the quality of environmental resource, the 

environmental change considered, and the user population being investigated. 

contingent valuation (CV): directly asks people what they are willing to pay for a benefit and/or willing to receive in 

compensation for tolerating a cost through a survey or questionnaire. Personal valuations for increases or decreases in the 

quantity of some good are obtained contingent upon a hypothetical market. The aim is to elicit valuations or bids that are 

close to what would be revealed if an actual market existed. 

conventional pollutants: biological oxygen demand (BOD), total suspended solids (TSS), oil and grease (O&G), pH, and 

anything else the Administrator defines as a conventional pollutant. 

dissolved oxygen (DO): oxygen freely available in water, vital to fish and other aquatic life and for the prevention of 

odors. DO levels are considered a most important indicator of a water body's ability to support desirable aquatic life. 

Secondary and advanced waste treatment are generally designed to ensure adequate DO in waste-receiving waters. 

(http://www.epa.gov/OCEPAterms/dterms.html) 

Metal Products and Machinery (MP&M): industry includes facilities that manufacture, rebuild, and maintain metal parts, 

products, or machines. 

National Demand Study (NDS): U.S. EPA and the National Forest Service conducted the National Demand Survey for 

Water-Based Recreation in 1993. The survey collected data on demographic characteristics and water-based recreation 

behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over. 

nonconventional pollutant: catch-all category that includes everything that is not classified as a priority pollutant or a 

conventional pollutant. 

piscivorous: feeding preferably on fish. 

priority pollutant (PP): 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water, 

sediment, groundwater, or soil samples. 

toxic pollutants: EPA’s Office of Water narrowly defines a toxic pollutant as one of 126 priority pollutants. This 

definition is not completely synonymous with pollutants that have a “toxic” effect. Many nonconventional pollutants may 

also be hazardous to aquatic life and human health. 

“toxic” pollutant: any pollutant that has an adverse effect on aquatic life or human health. 

15-26 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

travel cost (TC) model: derives values by evaluating expenditures of recreators. Travel costs are used as a proxy for price 

in deriving demand curves for the recreation site. (http://www.damagevaluation.com/glossary.htm) 

U.S. Fish and Wildlife Service (FWS): the principal federal agency responsible for conserving, protecting, and 

enhancing fish, wildlife, and plants and their habitats for the continuing benefit of the American people. 

(http://www.fws.gov/r9extaff/pafaq/fwsfaq.html) 

willingness-to-pay (WTP): maximum amount of money one would give to buy some good. 

(http://www.damagevaluation.com/glossary.htm) 

15-27 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

ACRONYMS

1Q10: the lowest 1-day average flow with a recurrence interval of 10 years 

7Q10: the lowest 7-day average flow with a recurrence interval of 10 years 

AWQC: ambient water quality criteria 

CV: contingent valuation


DO: dissolved oxygen


FWS: U.S. Fish and Wildlife Service


MP&M: Metal Products and Machinery


NDS: National Demand Study


TC: travel cost


WTP: willingness-to-pay


15-28 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

REFERENCES

Belton, T., R. Roundy and N. Weinstein. 1986. Urban Fishermen: Managing the Risks of Toxic Exposure. Environment,


Vol. 28. No. 9, November.


Brown, T. 1993. “Measuring Nonuse Value: A Comparison of Recent Contingent Valuation Studies.” In: Bergstrom, J. C.,


Benefits and Costs Transfer in Natural Resource Planning. Sixth Interim Report. Athens, GA: University of Georgia,


Department of Agriculture and Applied Economics.


Bergstrom, J. C. and K. Boyle. 1990. “Using Benefits Transfer to Value Groundwater Benefits: Conceptual Issues.”


Presented at the joint AAEA/AERE meeting in Orlando, FL. August 1993.


Bergstrom, J. C. and H. K. Cordell. 1991. “An analysis of the Demand for and Value of Outdoor Recreation in the United


States.” Journal of Leisure Research, Vol. 23, No. 1: 67-86.


Carson, R. T., and R. C. Mitchell. 1993. “The Value of Clean Water: The Public’s Willingness to Pay for Boatable,


Fishable, and Swimmable Quality Water.” Water Resources Research 29(7): 2445-2454.


Connelly, N. and B. Knuth. 1993. Great Lakes Fish Consumption Health Advisories: Angler Response to Advisories and


Evaluation of Communication Techniques. Human Dimensions Research Unit, Dept. of Natural Resources, NY State College


of Agriculture and Life Sciences, Cornell University, HDRU Series No 93-3, February.


Connelly, N., B. Knuth, and C. Bisogni. 1992. Effects of the Health Advisory and Advisory Changes on Fishing Habits and


Fish Consumption in New York Sport Fisheries. Human Dimensions Research Unit, Dept. of Natural Resources, NY State


College of Agriculture and Life Sciences, Cornell University, HDRU Series No 92-9, September.


Desvousges, W. H. et al. 1987. “Option Price Estimates for Water Quality Improvements: A Contingent Valuation Study for


the Monongahela River.” Journal of Environmental Economics and Management, 14: 248-267.


Farber, S. and B. Griner. 2000. Valuing Watershed Quality Improvements Using Conjoint Analysis. University of 

Pittsburgh, PA. 

Fisher, A. and R. Raucher. 1984. “Intrinsic Benefits of Improved Water Quality: Conceptual and Empirical Perspectives.” 

In: Advances in Applied Microeconomics, Vol. 3, V.K. Smith, editor, JAI Press. 

Harpman, D. A., M. P. Welsh, and R.C. Bishop. 1993. “Nonuse Economic Value: Emerging Policy Analysis Tool.” Rivers, 

Vol. No.4. 

Jakus, P. M., M. Downing, M. S. Bevelhimer, and J. M. Fly. 1997. “Do Sportfish Consumption Advisories Affect Reservoir 

Anglers’ Site Choice?” Agricultural and Resource Economic Review, 26(2). 

Knuth, B. and C. Velicer. 1990. Receiver-Centered Risk Communication for Sportfisheries: Lessons from New York 

Licensed Anglers. Paper presented at the American Fisheries Society Annual Meeting, Pittsburgh, Penn, August. 

Lant, C. L. and R. S. Roberts. 1990. “Greenbelts in the Cornbelt: Riparian Wetlands, Intrinsic Values, and Market Failure.” 

Environment and Planning A, Vol. 22: 1375-1388. 

Lyke, A. J. 1993. “Discrete Choice Models to Value Changes in Environmental Quality: A Great Lakes Case Study.” PhD 

dissertation. Madison, WI: University of Wisconsin, Department of Agricultural Economics. 

Montgomery, M. and M. Needelman. 1997. “The Welfare Effects of Toxic Contamination in Freshwater Fish.” Land 

Economics 73(2): 211-223. 

Phaneuf, D. J., C. L. Kling, and J. A. Herriges. 1998. “Valuing Water quality Improvements Using Revealed Preference 

Methods When Corner Solutions are Present.” American Journal of Agricultural Economics 80: 1025-1031. 

15-29 
MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits 

Silverman, W. 1990. Michigan’s Sport Fish Consumption Advisory: A Study in Risk Communication. Thesis submitted in 

partial fulfillment of the requirements for the degree of Master of Science (Natural Resources) at the University of Michigan, 

May. 

Tudor, L., E. Besedin, M. Fisher, and S. Smith. 1999a. “Economic Analysis of Environmental Regulations: Application of 

the Random Utility Model to Recreational Benefit Assessment for the MP&M Effluent Guideline.” Presented at the annual 

American Agricultural Economics Association meeting, Nashville TN. 

Tudor, L., E. Besedin, S. Smith, and L. Snyder. 1999b. “What Pollutants Matter for Consumers of Water-Based 

Recreation?” Presented at the annual Northeastern Agricultural and Resource Economics Association meeting, Morgantown, 

WV, June. 

Tudor, L., et al. 2002. Economic Analysis of Environmental Regulations: Using a Random Utility Model to Assess 

Recreational Benefits for Effluent Guidelines. Final Report. Washington, DC: U.S. EPA. January. 

U.S. Department of Commerce, Bureau of the Census. 1996. Projections of Household by Type 1995-2010 (series 1). 

http://www.census.gov/population/projections/nation/hh-fam/table1n.txt. 

U.S. Department of the Interior. 1993. 1991 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, DOI, 

March. 

U.S. Environmental Protection Agency (U.S. EPA). 1986. Quality Criteria for Water. EPA 440/5-86-001. 

U.S. Fish and Wildlife Service (USFWS). 1996. 1996 National Survey of Fishing, Hunting, and Wildlife-Associated 

Recreation. U.S. Fish and Wildlife Service and Bureau of the Census. 

Walsh, R. G., D. M. Johnson, and J. R. McKean. 1992. “Benefit transfer of Outdoor Recreation Demand Studies, 1968-

1988.” Water Resource Research, Vol. 28, No. 3: 707-713. 

West, P., R. Marans, F. Larkin, and M. Fly. 1989. Michigan Sport Anglers Fish Consumption Survey: A Report to the 

Michigan Toxic Substances Control Commission, University of Michigan School of Natural Resources, Natural Resources 

Sociology Research Lab, Technical Report #1, May. 

15-30 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

Chapter 16: POTW Benefits


INTRODUCTION 

The final rule only regulates direct dischargers. Therefore, 

the selected option does not affect POTW operations. For the 

alternative policy options that consider both direct and 

indirect dischargers, EPA evaluated two categories of 

productivity benefits for publicly-owned treatment 

works (POTWs): 

� reduced interference with the operations of 

POTW s, and 

� reduced contamination of sewage sludge (i.e., 

biosolids) at POTWs that receive discharges from 

MP&M facilities. 

Interference with POTW processes occurs when high levels 

of toxics, such as metals or cyanide, kill bacteria required for 

CHAPTER CONTENTS 

16.1 nterference with POTW Operations . . . 16-2 

16.2 ng Benefits from Reduced Sludge 

Contamination ................. ....... 16-2 

16.2.1 ................. ....... 16-2 

16.2.2 

Practices ................. ............ 16-4 

16.2.3 

Quality Benefits ................. ...... 16-7 

16.2.4 and Practices . . 16-8 

16.2.5 ying Sludge Benefits ........... 16-10 

16.3 ated Savings in Sludge Use/Disposal Costs 16-15 

16.4 Limitations ................. . 16-16 

Glossary ................. ................. .. 16-18 

Acronyms ................. ................. . 16-19 

References ................. ................. 16-20 

Reduced I

Assessi

Data Sources 

Sludge Generation, Treatment, and Disposal 

Overview of Improved Sludge 

Sludge Use/Disposal Costs 

Quantif

Estim

Methodology

wastewater treatment processes. The removal of these pollutants would eliminate the need for extra labor and materials to 

maintain POTW operations. 

Toxic priority and nonconventional pollutants may also pass through a POTW and contaminate sludge generated during 

primary and secondary wastewater treatment.1 POTW treatment of wastewater with reduced pollutant concentrations 

translates into cleaner sludge, which can be disposed of using less expensive and more environmentally benign methods. In 

some cases, cleaner sludge may have agricultural applications, which would generate additional resource conservation 

benefits. 

Some MP&M pollutants that pass through a POTW and contaminate sludge are not currently subject to sewage sludge 

pollutant concentration limits. The alternative policy options would reduce concentrations of these pollutants in sewage 

sludge as well, which may translate into reduced environmental and human health risks. EPA did not estimate the reduced 

risk attributable to the reduction of these pollutants. 

Wastewater from MP&M facilities also contains hazardous air pollutants (HAPs). These pollutants may represent 

unacceptable health risks to POTW workers if released into the air at high enough concentrations during the wastewater 

treatment cycle. This reduction in pollutants may translate into health benefits to POTW workers and those living near 

POTWs. 

The remaining sections of this chapter present methodology for estimating benefits to the receiving POTW s from reducing 

pollutants in the wastewater of indirect MP&M dischargers. As noted above, the final option does not affect POTW 

operations since it regulates direct dischargers only. For the alternative options that consider both direct and indirect 

dischargers, EPA evaluated two benefits measures associated with MP&M pollutants: (1) the reduction in pollutant 

interference at POTWs; and (2) pass-through of pollutants into the sludge, which limits options for POTW disposal of sewage 

sludge. 

1 The term sewage sludge, also called biosolids, is often shortened to sludge throughout this chapter for simplicity. 

16-1 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

16.1 REDUCED INTERFERENCE WITH POTW OPERATIONS 

High levels of some MP& M pollutants (such as metals, chlorobenzene, polyaromatic hydrocarbons, and oil and grease) can 

kill bacteria that are required for the wastewater treatment process (U.S. EPA, 1987). POTWs affected by such "inhibition 

problems" may incur extra labor and materials costs to maintain system operations. As a partial measure of the economic 

benefits resulting from the alternative regulatory options, EPA estimated the extent to which reduced MP&M discharges 

would decrease pollutant concentrations to below POTW pollutant inhibition values, using the following steps: 

� estimate the baseline and post-compliance influent concentrations for each POTW receiving discharges from 

MP&M facilities, based on annual pollutant loadings from the MP&M facility, the number of POTW operating days 

per year, and the gross volume of influent; 

� compare baseline and post-compliance influent concentrations with available inhibition levels (see Table I.5 in 

Appendix I); and 

� estimate the change in the number of POTWs in which influent concentrations of MP&M pollutants exceed POTW 

inhibition values. 

Adverse effects on POTW operations, including inhibition of microbial degradation, are likely when influent


concentrations of one or more pollutants exceed an inhibition value. EPA estimated influent concentrations in excess of


POTW inhibition values for the sample facilities for the baseline and the alternative regulatory options. Results of this


analysis are presented in Appendix I of this report. Eliminating the exceedances will result in operating cost savings to


POTWs. EPA has not estimated a monetary value for this benefit, however, due to data limitations.


The final rule only regulates direct dischargers. Therefore, the selected option does not affect POTW operation. For the


alternative policy options that consider both direct and indirect dischargers, EPA estimated that 51 PO TW s had influent


concentrations in excess of biological inhibition values for one or more pollutants under the baseline conditions


corresponding to the 433 Upgrade Options. This represents 0.3% of the over 16,000 POTWs operating nationwide. (Table


I.12 in Appendix I provides detailed information on pollutants exceeding POTW inhibition criteria.) Both upgrade options


would eliminate exceedances of POTW inhibition criteria in 21 POTWs.


EPA’s analysis finds that influent concentrations in 293 POTWs exceed biological inhibition values for one or more


pollutants under the Proposed/NODA Option. The Proposed/NODA Option would eliminate inhibition criteria exceedances


in 156 of the affected POTW.2


POTWs may impose local limits to prevent inhibitions. If local limits are in place, the estimated reduction in potential


inhibition problems at the affected POTWs may be overstated. In this case, however, the estimated social cost of the MP&M


regulation is also overstated.


16.2 ASSESSING BENEFITS FROM REDUCED SLUDGE CONTAMINATION 

16.2.1 Data Sources 

The analysis of POTW benefits from improved sludge quality draws on several data sources. The §308 POTW Surveys 

provide most of the required information. EPA collected information from 147 POTWs representing a 98 percent response 

rate to the 150 surveys that were mailed. EPA also used the §308 survey of MP&M facilities. The two data collection efforts 

were not designed to provide a match between the MP&M sample facilities and the POTWs to which they discharge. EPA 

obtained a significant amount of information from the POTW Surveys, but had substantially less information on the POTWs 

that receive discharges from the MP&M facilities. To address this data limitation, EPA used the POTW Survey data to infer 

2

 The total number of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported for the final 

rule and the upgrade options (Directs + 413 to 433 Upgrade Option, Directs + All to 433 Upgrade Option). After deciding in July 2002 not 

to consider the NODA option as the basis for the final rule, EPA did not perform any more analyses on the NODA option -- including not 

updating facility counts and related analyses for the change in subcategory and discharge status classifications. 

16-2 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

information on the key factors that are likely to influence choice of sewage sludge use and disposal practices for the POTWs 

receiving discharges from the MP&M facilities. 

The POTW Survey contains three sections. Section 1 provides general information on POTW location and size. Section 2 

provides data on the cost of administering pre-treatment programs (see Appendix F). Section 3 contains data on the cost of 

treating and disposing of sewage sludge and provides new and more consistent data for analyzing the effect of reduced 

pollutant loadings on sewage sludge management costs. 

The POTW Survey asked for the following information: 

� current sludge disposal practices; 

� sludge disposal costs for one or more disposal methods; 

� reasons for not using a less expensive disposal method; 

� number of MP&M facilities discharging to the POTW, by flow size (less than 1 million gal/year; 1-6.25 million 

gal/year; greater than 6.25 million gal/year); 

� total metal loadings discharged to the POTW from all sources; and 

� percentage of total metal loadings attributable to MP&M facilities. 

The POTW Survey was intended to address data limitations encountered in the Phase 1 analysis, particularly the inadequacy 

of information about POTWs that receive discharges from the MP&M sample facilities. The only information available for 

the Phase I analysis was POTW geographic location, influent volume, and the metals content of the discharge received from 

the sampled MP&M facilities. Discharges to the POTW by non-sampled MP&M facilities and by non-MP&M facilities were 

not known. These discharges may significantly affect sewage sludge quality, however, resulting in a discrepancy between 

predicted and actual pollutant concentrations in sewage sludge and the corresponding disposal practices. In addition, lack of 

information on the factors that may influence a POTW's decisions about sludge management practices introduced additional 

uncertainty in the analysis. 

EPA used the POTW Survey to calculate the following parameters: 

� baseline percentage of the total metal loadings to POTWs by POTW flow category attributable to MP&M facilities; 

� post-compliance loading reductions for non-sampled MP&M facilities discharging to the receiving POTWs; 

� costs of sewage sludge disposal practices; and 

� percentage of qualifying sludge that is not beneficially used for any of the following reasons: lack of land; lower cost 

alternative; inability to meet vector or pathogen requirements; poor weather; stricter state standards; and other 

reasons. 

EPA also used the data provided by the Association of Metropolitan Sewerage Agencies (AMSA) to refine its analysis of


POTW benefits for the final rule. AMSA provided EPA with comments on the proposed MP&M rule and supplemented these


comments with a spreadsheet database (AMSA, 2000). The database contains data from an AMSA formulated survey and


covers responses from 176 POTW s, representing 66 pretreatment programs. The AMSA survey was conducted to verify data


from EPA's survey of POTW s and therefore included similar, although fewer, variables compared to EPA's survey.


EPA used the results of the AMSA survey to supplement information from the MP&M POTW Survey on percentage of metal


loadings contributed by MP&M facilities and the number of MP&M facilities served by POTWs. Based on the results of the


joint analysis of the EPA and AMSA surveys, EPA revised the following elements of the POTW benefits methodology: (1)


the number of MP&M facilities served by small, medium, and large POTW s, (2) percentage of metal loadings contributed by


MP&M facilities, and (3) percentage of qualifying sludge that is not land-applied.


Finally, EPA used other data sources in this analysis, including Handbook for Estimating Sludge Management Costs (EPA, 

1985) and Regulatory Impact Analysis of the Part 503 Sludge Regulation (EPA, 1993b). 

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MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

16.2.2 Sludge Generation, Treatment, and Disposal Practices 

a. Sludge generation


POTW s generally treat wastewater from industrial indirect dischargers along with domestic wastewater. Sludge results from


primary, secondary, and advanced wastewater treatment. The extent and type of wastewater treatment determine the chemical


and physical character of the sludge. Sludge may be conditioned, thickened, stabilized, and dewatered to reduce its volume. 

Sludge contains five classes of components: organic matter, pathogens, nutrients, inorganic chemicals, and organic chemicals. 

The mix and levels of these components ultimately determine the human health and environmental impact of sludge 

use/disposal, and so may also dictate the most appropriate uses and disposal practices (EPA, 1993b). 

Organic matter (the primary constituent of sludge) comes from human waste, kitchen waste, and stormwater runoff. Organic 

and inorganic chemicals in sludge come from industrial processes that discharge to municipal sewers. The concentration of 

inorganic pollutants in sludge, including metals, depends upon the volume and type of industrial wastes discharged to the 

POTW , as well as the extent and character of stormwater runoff. 

b. Sludge use/disposal practices 

After treatment, sludge can be used in the following ways: 

� Land Application: Spraying or spreading on the land surface, injection below the surface, or incorporation into the 

soil, for soil conditioning or fertilization of crops or vegetation. Agricultural lands (pasture, range land, crops), 

forest lands (silviculture), and drastically disturbed lands (land reclamation sites) may all receive sludge; 

� Bagged Application: Collection of sludge in containers for application to land (i.e., distribution and marketing); 

� Surface Disposal: Disposal on land specifically set aside for this use, including surface impoundments (also called 

lagoons), sludge monofills (i.e., sludge-only landfills), and dedicated sites (i.e., land on which sludge is spread solely 

for final disposal); 

� Co-disposal: Disposal in a municipal solid waste landfill (MSWL) or hazardous waste landfill; and 

� Incineration: Combustion of organic and inorganic matter at high temperatures in an enclosed device. 

Land application and bagged application are beneficial uses of sludge. Both methods can be categorized as being "high" or 

"low," depending on pollutant concentrations in sewage sludge. "High" applications meet stringent limits on the total 

concentration of a given pollutant at a given application site. "High" sludge is exempt from meeting pollutant loading rate 

limits and certain record-keeping requirements. "Low" applications meet less stringent "ceiling" limits for pollutants. Ceiling 

limits govern whether a sewage sludge can be applied to land at all. "Low" applications require more record-keeping because 

POTWs must track total (cumulative) loadings applied to each given site, in addition to tracking the concentration of sludge 

applied at any given time. 

Many POTWs use more than one use/disposal practice, which helps to maintain flexibility and avoid the capacity limitations 

of a single practice. The practice chosen depends on several factors, including: 

� cost to prepare sludge for use/disposal; 

� pollutant concentrations; 

� market demand for sludge; 

� cost to transport sludge to use/disposal sites; 

� availability of suitable sites for land application, landfilling, or surface disposal; 

� weather and other local conditions; 

� allowance of a safety factor to account for unplanned or unforseen conditions; 

16-4 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

� state environmental regulations; and 

� public acceptance (EPA, 1993b). 

The choice of use/disposal method is restricted by the quality of the sludge generated by the POTW . Sludge for beneficial 

uses must meet more stringent standards for pollutant concentrations than sludge used or disposed of in other ways. Similarly, 

sludge that is surface-disposed in an unlined unit generally must meet more stringent standards than sludge surface-disposed 

in a lined unit, disposed in an MSWL, or incinerated. Sludge disposed in a MSWL must meet more stringent standards than 

incinerated sludge. 

Table 16.1 summarizes sludge use/disposal methods according to the number and percent of dry metric tons (DMT), based on 

information provided in Section 3 of the §308 POTW Survey. The information presented in this table takes into account data 

provided by AM SA on POTW characteristics such as POTW flow and the total amount of sludge generated by each POTW . 

Because the AM SA data was collected five years after the EPA POT W Survey was administered and it does not correspond 

to the base year of the analysis (1996), EPA did not use AMSA data to adjust the allocation of sludge to each use/disposal 

method category. 

Table 16.1: Sludge Use/Disposal (1996) by POTWs Discharging > 2 Million Gallons/Day a 

Use/Disposal Sub-Class Thousand DMT Percent of DMT 

Total Beneficial Use 2,641.2 39.9% 

Land Application-High 1,017.4 15.4% 

Bag Application-High 339.9 5.1% 

Land Application-Low 1,283.9 19.4% 

Bagged Application-Low 0 0% 

Total Surface Disposal 528.2 8.0% 

Surface Disposal: Unlined Unit 347.2 5.3% 

Surface Disposal: Lined Unit 181.0 2.7% 

Co-Disposal: Municipal Landfill 1,768.8 26.8% 

Incineration 1,129.9 17.1% 

Unknown: Other 543.2 8.2% 

All 6,611.2 100.0% 

a The §308 POTW Survey did not collect information from POTWs discharging < 2 million gallons per day. 

Source: U.S. EPA, POTW Survey and AMSA Survey (2000) on Proposed MP&M Effluent Guidelines. 

As Table 16.1 shows, 39.9 percent of total sludge tons reported by respondents is used beneficially (land application and


bagged application). Co-disposal in a municipal landfill is the second most frequently used disposal method, accounting for


26.8 percent of all sludge disposed in the U.S. Surface disposal in unlined and lined units, incineration, and "other" disposal


methods account for 5.3 percent, 2.7 percent, 17.1 percent, and 8.2 percent of all sludge tons, respectively. No sludge was


sent to a hazardous waste landfill by the POTW Survey respondents.


c. Pollutant limits and disposal options


Section 405(d) of the Clean Water Act, as amended, requires EPA to specify acceptable management practices and numerical


limits for certain pollutants in sludge. The Agency published Standards for the Use/Disposal of Sludge (40 CFR Part 503,


February 1993) to protect public health and the environment from reasonably anticipated adverse effects of pollutants in


sludge (U.S. EPA, 1993a). The standards include general requirements, pollutant limits, management practices, operational 

standards, monitoring frequency, record-keeping, and reporting for the final use and disposal of sludge in four circumstances: 

� sludge co-disposed with household waste in a MSWL; 

16-5 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

� sludge land-applied for beneficial purposes (including bagged sludge); 

� sludge disposed on land or on surface disposal sites; and 

� incinerated sludge. 

With the exception of MSWLs, the standards for each practice include numerical limits on sludge pollutant concentrations. 

Part 503 sets limits on pollutant concentrations for land application at two levels: 

� Land Application-Low limits, which govern whether sludge can be applied to land at all; and 

� more stringent Land Application-High limits which define, in part, sludge that is exempt from meeting certain 

record-keeping requirements. 

For sludge meeting only the Land Application-Low limits, Part 503 contains pollutant loading rate limits. These determine 

the amount of sludge and associated pollutant content that may be applied to a particular site. 

EPA did not establish pollutant-specific, numerical criteria for toxic pollutants of concern in the sludge disposed in MSWLs, 

because the design standards applicable to MSWLs are considered adequate to protect human health and the environment. 

Also, MSWL sludge is co-disposed with household waste, making precise numerical criteria infeasible. The Solid Waste 

Disposal Facility Criteria (40 CFR Part 258, Federal Register 50978, October 9, 1991) specify that POTW s using an MSWL 

must ensure that their sewage is non-hazardous and passes the Paint Filter Liquid Test. 

The pollutant limits for sludge land application, surface disposal, and incineration constrain a POTW's choice of sludge 

use/disposal practice. Table 16.2 presents numerical limits for the three sludge use/disposal practices for eight MP&M 

pollutants. The land application pollutant limits place restrictions on concentrations of metals in sludge; the surface disposal 

criteria cover a subset of the metals regulated for land application. The MP&M effluent limitations guideline covers five 

metals and causes incidental removal of the remaining three metals regulated under the Part 503 sludge regulation. The 

alternative policy options would improve the quality of sewage sludge generated by POTWs receiving discharges from 

MP&M facilities and, as a result, would increase sludge use/disposal options for the affected POTWs. 

Table 16.2: Sludge Use/Disposal Pollutant Limits 

Pollutant 

Application Limits 

Surface Disposal Limits 

(mg/kg dry weight)a 

MP&M Pollutants of 

Concern Low Limits (Low) 

(mg/kg dry weight) 

High Limits (High) 

(mg/kg dry weight) 

Arsenic 75 41 73 �

Cadmium 85 39 �

Copper 4,300 1,500 �

Lead 840 300 �

Mercury 57 17 �

Nickel 420 420 420 �

Selenium 100 100 �

Zinc 7,500 2,800 �

a Pollutant limits for active sludge unit whose boundary is greater than 150 meters from the surface disposal site property line. 

Source: Standards for the Use or Disposal of Sludge; Final Rules. 40 CFR Part 257 et al. Federal Register February 19, 

1993a. 

16-6 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

d. Reasons for not land-applying qualifying sludge


POTW characteristics including location, state regulations, and community concerns also affect use/disposal methods for


sludge. The POTW Survey provided information on the percentage of sludge that qualified for beneficial use but was not


beneficially used. Survey data indicate that 57 percent of qualifying sludge was not land-applied, for the following reasons: 

� land application is more expensive than another method; 

� land is not available for sludge application; 

� the cumulative pollutant loads at the land application site used had been exceeded; 

� the vector or pathogen requirements to land apply could not be met at an acceptable cost; and 

� inclement weather, concern over liability, stakeholder complaints, stricter state standards, desire to diversify 

practices, or technical problems. 

Of the 57 percent of sludge that was not land-applied, only 11 percent of qualifying sludge was otherwise beneficially used 

(i.e., sold in bags). Therefore, only 50 percent of the total qualifying sludge is beneficially used.3 In addition, POTW Survey 

data indicate that, on average, 7.5 percent of all sludge that qualifies for surface disposal is not surface disposed. 

16.2.3 Overview of Improved Sludge Quality Benefits 

This section discusses potential economic productivity benefits resulting from cleaner sludge, describes the methodology used 

to estimate benefits to POTWs directly affected by the regulation, and presents the results of the analysis. 

EPA expected that the alternative regulatory options would reduce MP&M facility discharges of eight metals with Part 503 

limits. The influent pollutant reductions to the receiving POTWs translate into sludge with reduced pollutant concentrations, 

allowing the sludge to meet the criteria for lower-cost use/disposal methods. The reduction in pollutants will then provide 

many POTWs with greater flexibility in the disposal of their sludge, and for some the opportunity to use less expensive 

methods of sludge use/disposal. In some cases, wastewater treatment systems may be able to use the cleaner sludge in 

agricultural applications, generating additional agricultural productivity benefits. Numerous benefits will result from reduced 

contamination of sludge, including the following: 

� POTW s may have less expensive options for use/disposal of sludge. Methods involving stricter criteria are generally 

less expensive than the alternatives. In particular, land application usually costs substantially less than incineration 

or landfilling. As a result, under the alternative policy options sludge from some PO TW s may meet more stringent 

criteria for less expensive use/disposal methods. 

� Some sludge currently meeting only Land Application-Low concentration limits and pollutant loading rate limits 

would meet the more stringent Land Application-High concentration limits. Users applying sludge meeting Land 

Application-High pollutant limits would be exempt from meeting pollutant loading rate limits. They would have 

fewer record-keeping requirements than users of sludge meeting only Land Application-Low concentration and 

loading rate limits. 

� By land-applying sludge, POTW s may avoid costly siting negotiations for more contentious sewage sludge use or 

disposal practices, such as incineration. 

� POTW sludge provides supplemental nitrogen, which enhances soil productivity when land-applied. Sludge applied 

to agricultural land, golf courses, sod farms, forests, or residential gardens is a valuable source of nitrogen fertilizer. 

� Non-point source nitrogen contamination of water may be reduced if sludge is used as a substitute for chemical 

fertilizers on agricultural land. Compared to nitrogen in most chemical fertilizers, nitrogen in sludge is relatively 

insoluble in water. The release of nitrogen from sludge occurs largely through continuous microbial activity, 

resulting in greater plant uptake and less nitrogen runoff than from conventional chemical fertilizers. 

3 Percent of Qualifying Sludge Beneficially Used = (100% � 57%) +[(57% × 11%)/100%]=50% 

16-7 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

� The organic matter in land-applied sludge can improve crop yields by increasing the ability of soil to retain water. 

� Reduced concentrations of sludge pollutants not currently regulated may reduce human health and environmental 

risks. Human health risks from exposure to these unregulated sludge pollutants may occur from particulate 

inhalation, dermal exposure, ingestion of food grown in sludge-amended soils, ingestion of surface water containing 

sludge runoff, ingestion of fish from surface water containing sludge runoff, or ingestion of contaminated ground 

water. 

� Land application of sludge satisfies an apparent public preference for this practice of sludge disposal, apart from 

considerations of costs and risk. 

This analysis assumes that POTW s will choose the least expensive sludge use/disposal practice for which their sludge meets 

pollutant limits. POTW s with sludge pollutant concentrations exceeding the Land Application-High, Land Application-Low, 

or surface disposal pollutant limits in the baseline may be able to reduce sludge use/disposal costs after MP&M facilities have 

complied with the effluent limitations considered under alternative regulatory options. 

As public entities, POTW s are not forced by the market to act as profit-maximizing or cost-minimizing agents, but rather are 

assumed to optimize their jurisdictional welfare function. POTW s take factors other than cost into consideration when 

determining their sludge use/disposal methods. These factors may include the desire to be perceived by the public as using 

sludge in an environmentally friendly way, or the desire to enhance relationships with clients by providing no-cost or low-cost 

fertilizer. Greater flexibility in disposal practices may therefore provide benefits beyond cost savings. 

16.2.4 Sludge Use/Disposal Costs and Practices 

This section summarizes the estimated cost differences of various use and disposal methods, based on the POTW Survey. 

Alternative sludge use/disposal practices costs vary considerably among POTWs, based on several factors, the most important 

being the availability of local agricultural land or land suitable for surface disposal of sludge. Table 16.3 lists and ranks the 

use/disposal methods from least expensive to most expensive, according to the average qualitative ranking of each method in 

the POTW Survey. 

Table 16.3: National Estimate of Qualitative Ranking of 

Use/Disposal Methods 

Mean Rankings 

Least Expensive Land Application-High 

� Land Application-Low 

MSWL 

� Bagged Application-High 

Surface Disposal in Unlined Unit 

� Bagged Application-Low 

Surface Disposal in Lined Unit 

� Incineration 

Most Expensive Hazardous Waste Landfill 

Source: U.S. EPA, §308 POTW Survey. 

Land Application-Low and Land Application-High were ranked as the two cheapest sewage sludge disposal options, 

supporting the assumption that beneficial use of sludge offers cost savings. The third least expensive option co-disposal in 

an MSWL costs less on average than either bagging sludge or surface disposing in an unlined unit. 

16-8 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

EPA used the POTW Survey data as the primary source for estimating an average difference in costs among certain 

combinations of use/disposal practices (e.g., the cost savings achieved by switching from incineration to land application). 

Table 16.4 compares the cost savings realized by switching to sludge land application and surface disposal practices from less 

stringently regulated sludge use/disposal practices. While on average the estimates provided in Table 16.4 are expected to 

hold, the cost savings will vary for individual POTWs. POTWs whose sludge qualifies for beneficial use post-compliance but 

did not qualify for such use in the baseline may achieve cost savings in some, but not all, circumstances. For example, a 

POTW may not achieve cost savings from agricultural application due to sludge transportation costs or because there are less 

expensive alternatives for that particular facility. Switching from sewage sludge co-disposal in a MSW L to surface disposal 

offers no savings to a POTW. 

Table 16.4: Cost Savings for Shifts in Sludge Use/Disposal Practices (2001$/DMT) 

Switch From 

Switch To: 

Land 

Applicationa 

(High) 

Land 

Applicationa 

(Low) 

Sold in a Bag for 

Land Application 

Surface Disposal 

on Unlined Unit 

Surface Disposal 

on Lined Unit 

Incineration $103.82 $103.82 $95.91 $103.08 No Saving 

Surface Disposal on 

Lined Unit 

$126.39 $126.39 $71.89 

Surface Disposal on 

Unlined Unit 

$6.44 $6.44 $0.59 

Co-disposal: 

MSWL 

$100.44 $100.44 $69.96 No Saving No Saving 

Land ApplicationLow 

$0.54-1.09 

a EPA assumes that the costs of land application to forests, public contact sites, and reclaimed land are similar to the costs of 

agricultural application. 

Source: U.S. EPA analysis of the §308 POTW Survey data. 

The cost section of the POTW Survey did not distinguish between low and high land application or low and high bagged 

application. Therefore, costs provided in the survey reflect the cost of both methods. To estimate the cost savings of 

avoiding these requirements by meeting Land Application-High limits, EPA used the compliance requirements for meeting 

Land Application-Low limits for bulk sludge (U.S. EPA, 1997). These cost savings provide a partial measure of the monetary 

benefit of improved sludge quality. 

EPA estimates that the incremental record-keeping associated with the cumulative Land Application-Low limits requires two 

to four hours per application. Materials costs for meeting these requirements should be negligible. EPA estimated the recordkeeping costs avoided from upgrading sludge quality from Land Application-Low to Land Application-High standards, using 

the following assumptions: 

� a 40-acre site is a typical site size for land application (approximately 16 hectares) (US EPA, 1997); 

� the typical application rate for land application is 7 DMT per hectare per application (US EPA, 1997); and 

4 � labor at POTW s costs an average of $30.42 per hour (2001$), based on the §308 POTW Survey. 

Based on these assumptions, EPA estimated that $0.54 to $1.09 would be saved per DMT of sludge upgraded from Land 

Application-Low to Land Application-High.5 

4 See Appendix F for detail. 

5 Savings per DMT are calculated by dividing the estimated labor cost per application ($30.42 per Hour * Hours per Application) by 

the total amount of sludge disposed of per one application (16 Hectares * 7 DMT per hectare). 

16-9 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

16.2.5 Quantifying Sludge Benefits 

EPA estimated the number of POTWs receiving MP&M discharges and the associated quantity of sludge that would not meet 

Land Application-High pollutant limits, Land Application-Low pollutant limits, or surface disposal pollutant limits under both 

the baseline and regulatory options. EPA then assumed that, as a result of compliance with the MP& M effluent limitations 

guideline, a POTW meeting all pollutant limits for a less costly sludge use/disposal method would benefit from the reduced 

cost of that particular method. EPA estimated the reduction in sludge use/disposal costs using the steps described below: 

1.	 Estimate total industrial baseline and post-compliance loadings of Part 503 regulated metals for each POTW with 

MP&M sample facility discharges; 

2.	 Calculate the baseline and post-compliance sludge pollutant concentrations for all MP&M wastewater discharged to 

the POTW; 

3. Compare POTW sludge pollutant concentrations with sludge pollutant limits for surface disposal and land 

application; 

4. Estimate baseline and post-compliance sludge use/disposal practices based on the estimated pollutant concentrations 

in sewage sludge; 

5.	 Identify POTW s that upgrade their sewage sludge disposal practices under the alternative policy options; calculate 

the economic POTW benefits by multiplying the cost savings for the shift in practices by the quantity of newly 

qualified sludge; adjust the estimate of benefits for the percentage of POTWs that cannot land apply sewage sludge 

due to transportation costs or other reasons, such as cold temperature; and 

6. Estimate national benefits using MP&M sample facility weights. 

a. Step 1: Estimate total industrial baseline and post-compliance loadings of Part 503 

regulated metals 

EPA estimated the quantities of Part 503 metals discharged to POTWs receiving wastewater from MP&M sample facilities 

and facilities operating in other metal discharging industries.6 EPA used POTW Survey data to estimate the total metal 

loadings and percent of total loadings discharged to POTWs by MP&M facilities. 

The POTW Survey provides the following information: 

� number of known MP&M facilities discharging to the POTW, 

� total loadings of each regulated metal received by the POTW, and 

� percent of the total metal loadings attributable to MP&M industries. 

6 EPA did not include metals from residential wastewater due to lack of data. The effect on the analysis of omitting residential metal 

loadings is not known. 

16-10 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

Table 16.5 summarizes this information by POTW flow volume. 

Table 16.5: MP&M Contribution 

to Total Industrial Loadings Received by POTWs 

MP&M Contribution 

POTW size (million gallons per day) 

2-10 11-50 >50 

MP&M facilities Average number of MP&M facilities per POTW 

small (<1 MG/year) 32.8 72.1 147.7 

medium (1-6.25 MG/year) 2.5 8.0 24.5 

large (>6.25 MG/year) 1.2 2.7 10.4 

Chemicals MP&M percentage of total loadings by weight 

Arsenic 7.4 14.0 7.0 

Cadmium 16.1 23.4 12.8 

Copper 18.9 21.6 10.9 

Lead 13.8 19.8 10.3 

Mercury 7.9 20.8 6.0 

Nickel 25.1 24.4 15.8 

Selenium 7.2 8.5 3.3 

Zinc 20.2 16.0 8.2 

Source: U.S. EPA, §308 POTW Survey. 

EP A estimated to tal baseline metal load ings from all M P& M sources, as follows: 

(16.1) 

where: 

PLM k,i = base line load ings of p ollutant k to PO TW ; from all MP &M sources (�g/year); 

LMPsmall,k,i = loadings of p ollutant k from sm all (< 1 MG/y ear) sample MP&M f ac ilitie s, dis ch argin g to POTW i 

(�g/year); 

AvgNumSm = the average number of small MP&M fa cilitie s dis charging to P OTW i; EPA estimated the average 

numb er of M P& M facilities of a g iven size (small, medium , large) tha t discharge to P OT W s in 

given flow categories, based on the §308 PO TW Survey (see Table 16.5);7,8 

SampleSm = number of MP&M sm all (< 1 MG/year) sa mp le fa cilitie s dis charging to P OTW; 

LMPmedium,k,i = loadings of p ollutant k from m edium (1-6.2 5 M G/year) sam ple M P& M facilities, disch arging to 

PO TW s (�g/year); 

AvgNumM ed = the a verage nu mb er of med iu m MP&M fa cilities d ischarging to PO TW i (based on th e POTW 

flow category (see Table 16 .5)); 

7
 EPA classified MP&M facilities as small, medium, and large flow in the POTW Survey, based on their discharge volume. 

8
 This analysis considers the following POTW flow categories: (1) from 2 MG/day to10 MG/day; (2) from 11 to 50 MG/day; and 

(3) greater than 50 MG/day. 

16-11

MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

SampleMed = n um b er of MP&M medium (1-6 .2 5 MG/year) sa mp le fa cilities d ischar ging to PO TW i; 

LMPlarge,k,i = load ings of p ollutant k fr om large (>6.25 M G/year) samp le MP&M f acilities d isch argin g to PO TW 

i (�g/year); 

AvgNumLg = the average number of large MP&M fa cilitie s dis charging to P OTW i (based on the POTW flow 

category (see Table 16.5)); and 

SampleLg = number of MP&M la rg e (>6.25 MG/year) sa mp le fa cilitie s dis charging to P OTW i. 

EP A estimated to tal baseline metal load ings from all industrial sources using d ata from the P OT W Survey, as follows: 

(16.2) 

where: 

PLk,i = total ba seline loading s of po llutant k fr om all in du strial sources to POTW i (�g/year), 

PLM k,i = base line load ings of p ollutant k to POTW i from all M P& M sources (�g/year), 

100% = the total re ported P OT W transfers o f pollutant k from all industrial sources, and 

%MPk = the percentage of total repo rted P OT W transfers o f pollutant k fr om MP&M fa cilities in a g iv en POT W 

flow category (see Table 16 .5). 

Post-compliance pollutant loadings to POT W s are calculated by subtracting the reduction in MP& M load ings due to the 

regulation from the e stimated total baseline loa dings. 

b. ge quality 

First, for each metal with limits under the Part 503 regulation, EPA calculated POT W influent concentrations based on the 

pollutant loading a nd PO TW flow rates, as follows: 

(16.3) 

where : 

ICk,i = PO TW influent co ncentration o f polluta nt k (�g/liter) for POTW i; 

PLk,i = total lo ad in g of pollu ta nt k to POTW i (�g/year) ; 

Fli = OT W i flow (liters/day); and 

ODi = OT W i operation days (365 days/year). 

Second , EP A ca lculated sludge polluta nt con centra tions for each polluta nt: 

(16.4) 

where : 

PCk,i = concentra tion of p ollutant k in POTW i sludge (mg/kg or ppm), 

ICk,i = OT W i influent co ncentration o f polluta nt k (�g/liter or ppb), 

TREk = treatment rem oval efficiency fo r pollutant k (unitless), 

PFk = sludge partition factor fo r pollutant k (unitless), and 

SG = sludge gener ation fac tor ((L -mg)/(�g-kg) or ppm/ppb). 

Th e par tition facto r represents the fraction of the pollutant lo ad ex pected to partition to sludg e during wastewater treatment. 

This factor is chemical-specific. EPA uses a sludge generation factor of 5.96 (mg of chemical/kg sludge)/( g chemical/L of 

wastewater). The value of 5.96 is based on the "normal quantity of sludge produced " by a PO TW with primary 

sedimentation/activated sludge/digestion/dewatering as reported in Wastewater Engineering (Metcalf & Eddy, 197 2). The 

estimated sludge generation factor indicates that concentration in sludge is 5.96 ppb dry weight for every 1 ppb of pollutant 

removed and partitioned to sludge. 

Step 2: Calculate baseline and post-compliance slud

P 

P 

P 

16-12

MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

c. Step 3: Compare sludge pollutant concentrations at each POTW with limits for surface 

disposal and land application 

EPA next compared sludge baseline and post-compliance pollutant concentrations to pollutant limits for land application and 

surface disposal using the following formula: 

(16.5) 

where : 

SE p = sludge exceeds c oncentratio n limits for d isposal or use pra ctice, p; 

PCk = sludge polluta nt, k, concentration; and 

CRk,p = sludge polluta nt, k, criterion for disp osal o r use p ractice , p. 

If any sludge pollutant concentration at a POTW exceeds the pollutant limit for a sludge use/disposal practice in the baseline 

(i.e., PC/CR >1), then EPA assumed that the POTW cannot use that sludge use/disposal practice. , as a result of compliance 

with the MP&M regulation, a POTW meets all pollutant limits for a sludge use/disposal practice (i.e., PC/CR � 1), that 

PO TW is assumed to b enefit from an increase in sludge use/dispo sal options. 

d. imate baseline sludge use/disposal practices at POTWs that can meet land 

application or surface disposal pollutant limits post-compliance 

Benefits from changes in sludge use/disposal practices depend on the baseline practices employed. hat 

PO TW s choose the least exp ensive sludge use/disp osal p ractice for whic h their slud ge me ets po llutant limits. OT W s with 

sludge qualifying for land application in the baseline are assumed to dispose of their sludge by land application; likewise, 

PO TW s with sludge me eting surfa ce disp osal p ollutant lim its (but no t land applic ation p ollutant lim its) are ass ume d to 

dispose o f their sludge on surface d isposal sites. 

EPA assumed that the mix of surface disposal practices employed by POT W s in the baseline (e.g., surface disposal in a lined 

unit and su rface disposal in an un lined un it) ma tc he s th at of natio na l surface disposal practic es as ca lc ulated fr om th e POTW 

Survey (see Table 16.1). 

POTW Survey data indicate that 25 percent of total sludge meeting Land Application-High standards is sold in bags and 75 

percent is land-applied. sold in bags. W meeting 

Land App lication-H igh stand ards in the po st-com plianc e scen ario is assumed to sell 25 p ercen t of its sludge in bags and to 

land-apply the remainder. 

Th e PO TW Surve y shows that 34 percent of total surface dispo sed sludge is dispo sed o f in lined un its and 6 6 pe rcent in 

unlined units. face disposal practices may not match the actual sludge disposal surface practices of any 

individual POTW . however, the assumed surface disposal practices are consistent with actual POT W sludge 

surface disposal practices. rvey data also showed that, on average, 7.5 percent of all sludge that qualifies for surface 

disposal was not surface disposed. 

PO TW s generating slud ge exc eeding land app lication a nd surface d isposal pollutant limits in the baseline are assum ed to 

either incinerate sludge or place sludge in a MSWL. ey indicates that 39 percent of sludge not land-applied or 

deposited in surface disposal sites is incinerated and 61 percent is placed in MW SLs. exceeding surface 

disposal and land application limits in the baseline is assumed to incinerate 39 percent of its sludge and co-dispose of the 

rema inder. , this mix of sludge use/disp osal p ractice s may not ma tch the a ctual slud ge disp osal p ractice s of any sin gle 

PO TW ; in aggregate, howe ver, the assumed distribution corresp onds to a ctual practices. 

Using the sludge disposal cost differentials from Ta ble 16.4, EPA estimated savings for shifts into land application and 

surface disposal from the assumed mix of baseline use/disposal practices (see Table 16.6). As previously discussed, EPA 

assum ed that 50 p ercen t of sludge cou ld not be use d be neficially (lan d-ap plied or so ld in ba gs) and dispo sed les s exp ensive ly 

through agricultural application of sludge due to transportation costs, land availability, or weather constraints. 

did not estimate benefits for this percentage of the sludge newly qualified for land application. 

If

Step 4: Est

EPA assumes t

P 

None of the sludge meeting Land Application-Low standards is Each POT 

This mix of sur

In aggregate,

Su

The surv

Each POTW

Again

The Agency 

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MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

e. Step 5: Calculate economic benefits for POTWs receiving wastewater from sample MP&M 

facilities 

Table 16.6 shows the cost savings for shifts from composite baseline sludge use/disposal practices to land application or 

surface disposal. Reductions in sludge use/disposal costs are calculated for each POTW receiving wastewater from an 

MP&M facility, using the following formula: 

(16.6) 

where : 

SCRi = e stim ated sludge use/disposal cost reductio ns re su ltin g fr om th e regula tion fo r POTW i (2001$ ); 

FLi = OT W i wastewater flow (million gallons/year); 

S = sludge to wastewater ratio, assumed to be 1,127 lbs. (dry weight) per million gallons of water (lbs./million 

gallons) and divided by 2,200 to convert pounds to metric tons; and 

CDi = e stim ated cost diffe re ntia l betw ee n le as t costly composite ba se line use/disposal meth od fo r whic h POTW i 

qualifie s and le as t costly use/disposal meth od fo r whic h POTW i qualifies post-compliance (2001$/D MT ). 

P 

Table 16.6: Cost Savings from Shifts in Sludge Use/Disposal Practices from 

Composite Baseline Disposal Practices (2001$/DMT) 

Baseline POTW Mix of Sludge 

Use/Disposal Practices 

Post-Compliance POTW Sludge Use/disposal Practice 

Agricultural 

Application-High (75% 

of sludge meeting Land 

Application-High 

pollutant limits) 

Bagged Sludge 

(25% of sludge 

meeting Land 

Application-High 

pollutant limits) 

Agricultural 

ApplicationLow 

Surface Disposala 

(Meet surface pollutant 

limits; do not meet land 

application pollutant 

limits) 

Meets Land Application-Low 

pollutant limits, but not Land 

Application-High limits $0.54-1.09 N/Ab N/A N/A 

Meets surface disposal pollutant 

limits, but not Land ApplicationLow limits 

Assumed disposal mix: 

34% lined unit 

66% unlined unit 

$126.39 

$6.44 

$71.89 

$0.59 

$126.39 

$6.44 N.A. 

Does not meet land application 

pollutant limits or surface disposal 

pollutant limits 

Assumed disposal mix: 

39% incineration, 

61% co-disposal 

$103.82 

$100.44 

$95.91 

$69.96 

$103.82 

$100.44 

$0-$103.08 

N/A 

a Surface disposal includes monofills, surface impoundments, and dedicated sites. 

b Not applicable (i.e., there is no cost savings). 

Source: U.S. EPA, §308 POTW Survey. 

EPA assumed that only 50 percent of the sludge qualified for land application is beneficially used (i.e., land-applied or sold in 

bags). The remaining 50 percent of the sludge newly qualified for land application will be disposed of by other methods; 

therefore, EPA assumed that no cost savings will be associated with 50 percent of the sludge qualified for land application. 

To ensure that these benefits are not overstated, this analysis includes an adjustment to the estimate of national sludge 

use/disposal cost benefits for POTWs that may be located at some distance from agricultural sites. This adjustment does not 

apply to benefits from shifts into surface disposal. 

16-14 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

f. Step 6: Estimate national sludge benefits 

EPA scaled the sludge use/disposal cost reductions to the national level as follows: 

(16.7) 

where: 

NSCR = national estimated sludge use/disposal cost reductions resulting from the regulation (2001$); 

n = numbe r of PO TW s estimated to shift into meeting surface d isposal or land application p ollutant limits as a 

result of MP &M effluent limitations; 

FWi = facility sample weights for facility or fa cilities d isch arging to PO TW i; and 

SCRi = e stim ated sludge use/disposal cost reductio ns re su ltin g fr om th e regula tion fo r POTW i (2001$ ). 

16.3 ESTIMATED SAVINGS IN SLUDGE USE/DISPOSAL COSTS 

Of the POTWs receiving discharge wastewater from MP&M facilities, 1,020 POTWs exceed the Land Application-High 

pollutant limits and 856 exceed the Land Application-Low pollutant limits at baseline discharge levels under the alternative 

options considered for the final rule. This represents approximately 6 percent of the over 16,000 operating POTWs 

nationwide. The number of POTWs exceeding Land Application-High and Land Application-Low pollutant limits under the 

Proposed/NODA Option at baseline conditions is equal to 5,328 and 3,728, respectively.9 

The final rule only regulates direct dischargers and, as a result, sewage sludge quality will not be affected by the selected 

option. EPA, however, did estimate savings in sludge disposal costs for the alternative options which consider both direct and 

indirect dischargers. EPA used the estimated sludge use/disposal cost differentials presented in Table 16.6 to calculate cost 

savings for the POTWs expected to upgrade their sludge disposal practices under alternative policy options. These results are 

presented in Table 16.7 below. The benefits are estimated at $11,319 to $22,539 (2001$) annually for both upgrade options. 

The Proposed/NODA Option would result in more substantial cost savings (i.e., $22.8 million (2001$)) to POTWs. However, 

the Proposed/NODA Option is not directly comparable to the two upgrade options due to inconsistent baselines. 

9 The total number of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported for the final 

rule and the upgrade options (Directs + 413 to 433 Upgrade Option, Directs + All to 433 Upgrade Option). After deciding in July 2002 not 

to consider the NODA option as the basis for the final rule, EPA did not perform any more analyses on the NODA option -- including not 

updating facility counts and related analyses for the change in subcategory and discharge status classifications. 

16-15 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

Table 16.7: ional Estimate of Cost Savings from Shifts in Sludge Use/Disposal 

Under the Alternative Policy Optionsa 

Shift Category/Number 

of POTWs 

Associated Sludge 

Quantity (DMT/Year) 

Estimated Benefits 

(2001$) 

Directs + 413 to 433 Upgrade 

Upgrade from minimum Land Application-Low limits to 

Land Application-High pollutant limits 

15 16,548 $11,319 to $22,539 

Upgrade from not meeting land application or surface 

disposal limits to Land Application-High pollutant limits 

0 $0 

Upgrade from not meeting land application or surface 

disposal limits to Land Application-Low pollutant limits 

0 $0 

Total 15 16,548 $11,319 to $22,539 

Directs + All to 433 Upgrade 

Upgrade from minimum Land Application-Low limits to 

Land Application-High pollutant limits 

15 16,548 $11,319 to $22,539 

Upgrade from not meeting land application or surface 

disposal limits to Land Application-High pollutant limits 

0 $0 

Upgrade from not meeting land application or surface 

disposal limits to Land Application-Low pollutant limits 

0 $0 

Total 15 16,548 $11,319 to $22,539 

Proposed/NODA Option 

Upgrade from minimum Land Application-Low limits to 

Land Application-High pollutant limits 

45 88,389 $60,458 to $120,386 

Upgrade from not meeting land application or surface 

disposal limits to Land Application-High pollutant limits 

24 140,460 $6,725,273 

Upgrade from not meeting land application or surface 

disposal limits to Land Application-Low pollutant limits 

25 316,565 $16,009,889 

Total 93 545,414 $22,795,620 to 

$22,855,548 

Nat

0 

0 

0 

0 

a

 Based on the Traditional Extrapolation. 

Source: U.S. EPA analysis. 

16.4 Methodology Limitations 

EPA used the POTW Survey to develop estimates of the cost-saving differentials for the various sludge use/disposal 

practices. Sludge use/disposal costs vary by POTW. The POTWs affected by the MP&M regulation may face costs that 

differ from those estimated. As a result, the analysis may over- or under-estimate the cost differentials. 

POTW Survey data were also used to estimate metal loadings to POTW s in the baseline analysis. There are two major 

limitations associated with this approach: 

� The baseline metal loadings from individual MP&M facilities of interest may differ from this estimate. The effect of 

using the §308 survey data to characterize the POTWs that receive MP&M discharges is therefore not known. 

� The total share of metals coming from MP&M facilities is likely to be underestimated because lower flow MP&M 

facilities are not always known by the POTW . During the pretest of the MP&M POTW questionnaire, POTW s told 

EPA that they were not aware of many of the lower flow facilities that were discharging to them. The POTW would 

have to use the phone book in order to find and permit these facilities. EPA consequently considered exempting low 

flow facilities in the general metals and only oily wastes indirect discharge categories under some of the alternative 

regulatory options. 

16-16 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

This analysis assumes that the mix of disposal practices estimated for a specific POTW may not match the actual sludge 

disposal practices used by that POTW . We know that the mix in the aggregate, as confirmed by the POTW Survey, is correct. 

The practices used in this analysis are therefore consistent with actual POTW sludge surface disposal practices. Because 

accurate assumptions for specific POTWs could not be made, the analysis may over- or underestimate the cost differentials. 

EPA quantified, but did not monetize economic benefits from reducing interference with POTW operations for the alternative 

regulatory options. EPA did not estimate cost reductions that occur at POTW s with sludge inhibition problems caused by 

MP &M discharges. These omissions thereby underestimate the benefits of the regulation. 

16-17 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

GLOSSARY

hazardous air pollutants (HAPs): air pollutants that are not covered by ambient air quality standards but which, as


defined in the Clean Air Act, may present a threat of adverse human health effects or adverse environmental effects. Such


pollutants include asbestos, beryllium, mercury, benzene, coke oven emissions, radionuclides, and vinyl chloride. MP&M


pollutants include but are not limited to: chlorobenzene, dioxin,1,4-isophorone, and pyrene.


(http://www.epa.gov/OCEPAterms/hterms.html)


hazardous waste landfill: an excavated or engineered site where hazardous waste is deposited and covered.


(http://www.epa.gov/OCEPAterms/hterms.html)


influent concentrations: measure of a pollutant's concentration in wastewater being received by a POTW for treatment


(see also: pollutant inhibition values).


interference: the obstruction of a routine treatment process of POTWs that is caused by the presence of high levels of


toxics, such as metals and cyanide in wastewater discharges. These toxic pollutants kill bacteria used for microbial


degradation during wastewater treatment (see: microbial degradation).


microbial degradation: the breakdown of organic molecules via biochemical reactions occurring in living


microorganisms such as bacteria, algae, diatoms, plankton, and fungi. POTWs make use of microbial degradation for


wastewater treatment purposes. This process is inhibited by the presence of toxics such as metals and cyanide because these


pollutants kill microorganisms.


municipal solid waste landfill (MSWL): common garbage or trash generated by industries, businesses, institutions, and


homes. Also known as municipal solid waste. (http://www.epa.gov/OCEPAterms/mterms.html)


pathogens: microorganisms (e.g., bacteria, viruses, or parasites) that can cause disease in humans, animals and plants.


(http://www.epa.gov/OCEPAterms/pterms.html)


pollutant inhibition values: determined threshold concentration for a pollutant, which when exceeded by the pollutant's


influent concentration in wastewater received for treatment will have adverse effects on POTW operations, such as inhibition


of microbial degradation (see: microbial degradation).


publicly-owned treatment works (POTWs): a treatment works as defined by Section 212 of the Act, which is owned by


a state or municipality. This definition includes any devices or systems used in the storage, treatment, recycling, and


reclamation of municipal sewage or industrial wastes of a liquid nature.


(http://www.epa.gov/owm/permits/pretreat/final99.pdf)


silviculture: management of forest land for timber. (http://www.epa.gov/OCEPAterms/sterms.html)


vector: 1. An organism, often an insect or rodent, that carries disease. 2. Plasmids, viruses, or bacteria used to transport


genes into a host cell. A gene is placed in the vector; the vector then "infects" the bacterium.


(http://www.epa.gov/OCEPAterms/vterms.html)


16-18 
MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

ACRONYMS

DMT: dry metric tons 

HAPs: hazardous air pollutants 

MSWL: municipal solid waste landfill 

POTWs: publicly-owned treatment works 

16-19

MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits 

REFERENCES

Association of Metropolitan Sewage Agencies (AMSA). 2000. Survey on Proposed MP&M Effluent Guidelines. 

Metcalf and Eddy. 1972. Wastewater Engineering. New York, NY: McGraw-Hill, Inc. 

Solid Waste Disposal Facility Criteria. 40 CFR Part 258, Federal Register 50978, October 9, 1991) 

Standards for the Use/Disposal of Sludge. 40 CFR Part 503, February 1993. 

U.S. Environmental Protection Agency (U.S. EPA). 1985. Handbook for Estimating Sludge Management Costs 

U.S. Environmental Protection Agency (U.S. EPA). 1987. Guidance for Preventing Interference with POTW Operations. 

U.S. Environmental Protection Agency (U.S. EPA). 1993a. Standards for the Use and Disposal of Sludge; Final Rules. 40 

CFR Part 257 et al., Federal Register, February 19. 

U.S. Environmental Protection Agency (U.S. EPA). 1993b. Regulatory Impact Analysis of the Part 503 Sludge Regulation. 

Final. Office of Water. EPA 821-12-93-006. March. 

U.S. Environmental Protection Agency (U.S. EPA). 1997. Economic Assessment for Proposed Pretreatment Standards for 

Existing and New Sources for the Industrial Laundry Point Source Category. Office of Water. EPA 821-R-97-008 (pp 10-51 

- 10-54). 

16-20 
MP&M EEBA Part III: Benefits Chapter 17: Environmental Justice & Protection of Children 

Chapter 17: Environmental Justice &


Protection of Children


INTRODUCTION 

Executive Order 12898 requires that, to the greatest extent


practicable and permitted by law, each federal agency must


make achieving environmental justice part of its mission. 


Therefore, EPA examined whether the final regulation will


promote environmental justice in areas affected by MP&M


discharges.


CHAPTER CONTENTS 

17.1 raphic Characteristics of Populations Living 

in the Counties Near MP&M Facilities ..... 17-1 

17.2 Children from Environmental Health 

and Safety Risks ................. ...... 17-3 

Glossary ................. ................. ... 17-4 

Reference ................. ................. .. 17-5 

Demog

Protection of

EPA concludes that discharges from MP&M facilities regulated


under the final rule do not have a disproportional environmental impact on minority populations, based on the demographic


characteristics of the populations residing in the counties affected by MP&M discharges.


The final rule is not subject to Executive Order 13045, “Protection of Children from Environmental Health Risks and Safety


Risks" (62 FR 19885, April 23, 1997), because it is based on technology performance and not on health or safety risks. 


However, EPA analyzed the reduction of children's health impacts associated with the MP&M regulation, and determined that


reductions in the baseline lead exposure are minimal.


The following section assesses whether MP&M discharges have a disproportionally high impact on minority populations.


17.1 DEMOGRAPHIC CHARACTERISTICS OF POPULATIONS LIVING IN THE COUNTIES 

NEAR MP&M FACILITIES 

EPA assessed whether adverse environmental, human health, or economic effects associated with MP&M facility discharges 

are more likely to affect minorities and low-income populations. This analysis uses data on the race, national origin, and 

income level of populations residing in counties traversed by reaches receiving discharges from the 32 sample MP&M 

facilities considered in the final rule analysis. The 32 sample facilities are located in 46 counties in 12 states. The MP&M 

survey was designed to provide a representative coverage of various types of MP&M facilities, but not of their geographical 

location. EPA is therefore able to analyze only the location characteristics of the sample facilities, and not all 43,901 MP&M 

dischargers.1 

EPA compared demographic data from the 1990 Population Census for the counties traversed by sample MP&M reaches 

with the corresponding state level indicators (U.S. Census Bureau, 1990). EPA considered several demographic 

characteristics to assess the environmental justice of the final regulation, including the relative proportions of African 

Americans, Native Americans, and Asian or Pacific Islanders, median income, the proportion of the population below the 

poverty level, unemployment percentage, and the proportion of the population that are children. Table 17.1 presents the 

results of this analysis, which show that the demographic characteristics of MP&M counties generally reflect state averages. 

EPA calculated median income for the group of counties in each state receiving MP&M discharges as a weighted average of 2 each county's median household income. County’s populations are used as weights in this analysis. EPA calculated this 

summary variable in place of the true median household income for MP&M counties because appropriate census data are not 

1 This estimate of MP&M facilities includes baseline closures. 2 Average median income in MP&M counties = 

�i

 Median Income (i) × Number of Households (i)/� Number of Households (i), where (i) is a sample MP&M county. 

17-1 
MP&M EEBA Part III: Benefits Chapter 17: Environmental Justice & Protection of Children 

available. The Agency notes that comparing this weighted average median income to the state-level median income may 

introduce uncertainty in the analysis. 

Income data, as well as other characteristics examined to determine whether minority and/or low-income populations are 

subject to disproportionally high environmental impacts, show that the socioeconomic characteristics of populations residing 

in counties affected by MP&M discharges reflect corresponding state averages. Based on these findings, EPA expects that 

environmental benefits resulting from the MP&M rule will not accrue to populations disproportionally based on race or 

national origin and therefore will promote environmental justice. 

Table 17.1: County Level Comparison of Demographic Data: Counties with Sample MP&M Facilities Versus 

Entire State 

State Counties % 

White 

% 

AfricanAmerican 

% Native 

Am., 

Eskimo, 

or Aleut 

% Asian 

or Pacific 

Islander 

Median 

Income 

% 

Below 

Poverty 

Level 

% 

Unemployed 

% 

Children 

California 

MP&M Only 3 58.64% 11.82% 0.53% 11.20% $36,100 13.98% 7.04% 25.83% 

Entire State 58 69.07% 7.39% 0.84% 9.57% $35,798 12.51% 6.65% 26.01% 

Indiana 

MP&M Only 3 95.38% 3.76% 0.23% 0.41% $24,785 14.31% 7.42% 23.38% 

Entire State 93 90.59% 7.75% 0.26% 0.66% $28,797 10.68% 5.74% 26.29% 

Kentucky 

MP&M Only 1 98.44% 0.54% 0.12% 0.74% $34,485 7.40% 3.64% 29.38% 

Entire State 120 92.06% 7.11% 0.19% 0.47% $22,534 19.03% 7.37% 25.93% 

Maryland 

MP&M Only 1 94.61% 4.47% 0.35% 0.34% $36,019 7.50% 4.56% 26.86% 

Entire State 24 71.03% 24.87% 0.30% 2.88% $39,386 8.27% 4.30% 24.31% 

Mississippi 

MP&M Only 3 56.88% 42.46% 0.09% 0.47% $26,342 19.31% 6.93% 28.00% 

Entire State 82 63.46% 35.59% 0.34% 0.49% $20,136 25.21% 8.43% 29.04% 

Missouri 

MP&M Only 1 99.45% 0.04% 0.44% 0.04% $17,594 18.87% 4.00% 24.21% 

Entire State 115 87.68% 10.69% 0.44% 0.77% $26,362 13.34% 6.16% 25.71% 

New York 

MP&M Only 2 92.64% 4.90% 0.34% 0.78% $25,864 12.13% 10.57% 28.09% 

Entire State 63 74.47% 15.90% 0.33% 3.83% $32,965 13.03% 6.88% 23.66% 

North Carolina 

MP&M Only 3 88.47% 10.71% 0.23% 0.33% $26,189 10.75% 3.94% 24.10% 

Entire State 100 75.60% 21.96% 1.25% 0.76% $26,647 12.97% 4.79% 24.27% 

Ohio 

MP&M Only 2 89.17% 9.69% 0.24% 0.74% $28,527 11.70% 6.82% 24.76% 

Entire State 89 87.81% 10.62% 0.21% 0.82% $28,706 12.54% 6.60% 25.85% 

Oklahoma 

MP&M Only 4 82.68% 8.48% 6.99% 1.06% $26,456 13.63% 5.86% 26.20% 

Entire State 77 82.26% 7.38% 8.03% 1.04% $23,577 16.71% 6.87% 26.60% 

17-2 
MP&M EEBA Part III: Benefits Chapter 17: Environmental Justice & Protection of Children 

Table 17.1: County Level Comparison of Demographic Data: Counties with Sample MP&M Facilities Versus 

Entire State 

State Counties % 

White 

% 

AfricanAmerican 

% Native 

Am., 

Eskimo, 

or Aleut 

% Asian 

or Pacific 

Islander 

Median 

Income 

% 

Below 

Poverty 

Level 

% 

Unemployed 

% 

Children 

Pennsylvania 

MP&M Only 22 92.89% 6.12% 0.12% 0.64% $27,851 11.56% 6.46% 22.88% 

Entire State 68 88.57% 9.15% 0.13% 1.14% $29,069 11.13% 5.97% 23.54% 

Washington 

MP&M Only 1 84.94% 4.97% 1.18% 7.90% $36,179 7.96% 4.15% 22.56% 

Entire State 40 88.64% 3.03% 1.71% 4.34% $31,183 10.92% 5.72% 25.86% 

Source: U.S. EPA analysis of 1990 Census Data (U.S. Bureau of Census 1990). 

17.2 PROTECTION OF CHILDREN FROM ENVIRONMENTAL HEALTH AND SAFETY RISKS 

EPA assessed whether the final regulation will benefit children, including reducing health risk from exposure to MP&M


pollutants from consumption of contaminated fish tissue and drinking water and improving recreational opportunities. EPA


was able to quantify only one category of benefits specific to children: avoided health damages to pre-school age children


from reduced exposure to lead. This analysis considered several measures of children’s health benefits associated with lead


exposure for children up to age six. Avoided neurological and cognitive damages were expressed as changes in three metrics:


(1) overall IQ levels; (2) the incidence of low IQ scores (<70); and (3) the incidence of blood lead levels above 20 mg/dL.


EPA also assessed changes in the incidence of neonatal mortality from reduced maternal exposure to lead. EPA’s


methodology for assessing lead-related benefits to children is presented in Chapter 14 of this report.


The Ohio case study analysis showed that the final rule is expected to yield $422,000 (2001$) in annual benefits to children in


the state of Ohio from reduced neurological and cognitive damages and reduced incidence of neonatal mortality. On the other


hand, the national-level analysis shows that benefits to children from reduced lead discharges are negligible nationwide. As


noted in Chapter 18, different findings from these two analyses are likely to be due to insufficient data and a more simplistic


approach used in the national-level analysis.


Children over age seven are also likely to benefit from reduced neurological and cognitive damages from reduced exposure to


lead. Giedd et al. (1999) studied brain development among 10 to 18 year-old children and found substantial growth in brain


development, mainly in the early teenage years. This research suggests that older children may be hypersensitive to lead


exposure, as are children aged 0 to 7.


Additional benefits to children from reduced exposure to lead not quantified in this analysis may include prevention of the


following adverse health effects: slowed or delayed growth, delinquent and anti-social behavior, metabolic effects, impaired


heme synthesis, anemia, impaired hearing, and cancer (see Chapter 14 of this report for details).


17-3 
MP&M EEBA Part III: Benefits Chapter 17: Environmental Justice & Protection of Children 

GLOSSARY


MP&M reach: a reach to which an MP&M facility discharges.


17-4

MP&M EEBA Part III: Benefits Chapter 17: Environmental Justice & Protection of Children 

REFERENCES 

U.S Bureau of Census. 1990. 1990 Census of Population Data. http://www.census.gov/.


Giedd, J.N., L. Blumenthal, N.O. Jeffries, F.X. Castellanes, Hong Liu, A. Zijdenbos, Tomas Paus, Alan C. Evans, and J. 

Rapoport. 1999. “Brain Development During Childhood and Adolescence: A Longitudinal MRI Study.” Nature 

Neuroscience, Vol. 2, No. 10. October: 861-863. 

17-5

MP&M EEBA Part III: Benefits Chapter 17: Environmental Justice & Protection of Children 

THIS PAGE INTENTIONALLY LEFT BLANK 

17-6

MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 18: MP&M Benefit / Cost Comparison 

Chapter 18: MP&M Benefit / Cost


Comparison


INTRODUCTION 

The preceding Chapters 12 through 16 provided quantitative


and qualitative assessments of the expected benefits to


society from reduced M P&M effluent discharges under the


final regulation. Chapter 11 assessed the regulation’s


expected social costs. This chapter sums the estimated


values for the benefit categories that EPA was able to


monetize, and compares the aggregate benefits estimate with the estimate of social costs.


CHAPTER CONTENTS 

18.1 ating National Level Benefits and Costs .... 18-1 

18.2 ts ................. .............. 18-2 

18.3 its ................. ................. 18-2 

18.4 ing Monetized Benefits and Costs ....... 18-2 

Estim

Social Cos

Benef

Compar

18.1 ESTIMATING NATIONAL LEVEL BENEFITS AND COSTS 

EPA traditionally estimates national level costs and benefits by extrapolating analytic results from sample facilities to the 

national level using sample facility weights. EPA's traditional sampling approach relies on information about the economic 

and technical characteristics of the regulated community. Although important for understanding the technical requirements 

and costs of a regulation, this sampling approach does not incorporate information that could significantly affect the 

occurrence and distribution of regulatory benefits, such as characteristics of the receiving water body and the size of 

population that may benefit from reduced pollutant discharges. As a result, the traditional sampling approach may yield 

benefit estimates that are less accurate than those that could be obtained by using a sampling framework that accounts for such 

benefit-receptor characteristics. 

EPA recognizes that using a traditional extrapolation method to estimate national-level benefits may lead to a large degree of 

uncertainty in benefits estimates. Therefore, in addition to the traditional extrapolation method used in the proposed rule, 

EPA also estimated national-level benefits for the final rule using an alternative extrapolation method.1 

Under this method, EPA used an alternative set of sampling weights, based on a post-sampling stratification method, to 

calculate alternative national estimates of benefits. EPA adjusted the original sample weights using two variables that are 

likely to affect the occurrence and size of benefits associated with reduced discharges from sample MP&M facilities: 

receiving water body type and size, and the size of the population residing in the vicinity of the sample facility. The Agency 

used a commonly used post-stratification method calling "raking" to adjust original sample weights to reflect these benefit 

pathway characteristics. EPA used data from three data sources – EPA's Permit Compliance System database (PCS), EPA's 

Reach File 1, and Census Data – to develop the adjusted weights. Because of data limitations, EPA restricted the 

re-weighting effort only to direct dischargers and excluded indirect dischargers that are not considered in the final MP&M 

rule. EPA therefore performed this alternative analysis for only the selected option. Appendix G details the post-sampling 

stratification method used to adjust the original sample weights. 

EPA uses the post-stratification extrapolation benefit estimates to validate general conclusions that the Agency draws from its 

main analysis based on the traditional extrapolation method. 

1 EPA also conducted a sensitivity analysis of national benefits for the final MP&M regulation by extrapolating the results of the 

Ohio case study to the national level. The results of this analysis are presented in Appendix G. 

18-1 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 18: MP&M Benefit / Cost Comparison 

18.2 SOCIAL COSTS 

As discussed in Chapter 11, EPA estimated the cost to society from compliance with the final regulation. The components of 

social costs include the resource cost of compliance (e.g., labor, equipment, material, and other economic resources needed to 

comply with the rule), costs to governments administering the regulation, and the social costs of unemployment resulting from 

facility closures. EPA estimated that the final rule will cause no unemployment and thus impose no unemployment-related 

costs to society. EPA also estimated that governments will incur no additional costs from administering the regulation. EPA 

estimated the final rule’s annual cost to society at $13.82 million (2001$). This value is based only on the estimated resource 

cost of compliance. 

18.3 BENEFITS 

EPA developed a partial monetary estimate of the final rule’s expected benefits based on three benefit categories: human 

health, water-based recreation (including nonuse value), and economic productivity benefits (avoided sewage sludge disposal 

costs). The Agency estimated the total monetized benefits by summing the monetary values reported in the preceding 

chapters across all categories of benefits. As noted in Chapter 12, these benefits estimates are incomplete because they omit 

numerous mechanisms by which society is likely to benefit from reduced effluent discharges from the MP&M industry. 

Examples of benefit categories not reflected in these monetized estimates include: 

� non-lead and non-cancer related health benefits, 

� improved aesthetic quality of waters near discharge outfalls, 

� benefits from improved wildlife habitat, including habitat for threatened or endangered species, 

� tourism benefits, and 

� reduced costs of drinking water treatment. 

The Agency estimated the total national benefits based on three extrapolation approaches. Table 18.1 summarizes the 

monetary value of benefits to society from the final rule. Traditional extrapolation yields total benefit values of $0.88 to 

$2.36 million (2001$) annually, with a midpoint estimate of $1.45 million (2001$). Benefits estimates based on the poststratification extrapolation method range from $0.57 to $1.54 million (2001$), with a midpoint estimate of $0.98 million. 

The ranges of national benefit estimates from the two extrapolation methods substantially overlap, with each method 

confirming the value estimated by the other method. This finding provides confidence in the reasonableness of the estimates 

from the separate extrapolation methods, given the limitations of data and coverage of benefit categories underlying the 

analysis for both methods. 

18.4 COMPARING MONETIZED BENEFITS AND COSTS 

EPA cannot perform a complete cost-benefit comparison because not all of the benefits resulting from the final regulatory 

option can be valued in dollar terms. As reported in Table 18.1, combining the national estimates of benefits and costs yields 

the following value of net monetizable benefits under the traditional and post-stratification extrapolation methods: 

� Under the traditional extrapolation technique, the estimated net monetizable benefits range from negative $11.5 

million to negative $12.9 million annually (2001$). Comparing the midpoint estimate of social costs with the 

midpoint estimate of monetized benefits results in a net benefit of negative $12.3 million (2001$). 

� The post-stratification extrapolation method, which does not affect the estimated costs of the rule, results in total net 

monetizable benefits ranging from negative $12.3 to negative $13.3 million (2001$), with a midpoint estimate of 

negative $12.8 million (2001$). 

18-2 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 18: MP&M Benefit / Cost Comparison 

The lack of a comprehensive benefits valuation limits the assessment of the relationship between costs and benefits of the 

final rule. EPA believes that the benefits of regulation, even in the low-estimate case (post-stratification extrapolation), would 

be comparable to the social costs if all of the benefits of regulation could be quantified and monetized. 

18-3

MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 18: MP&M Benefit / Cost Comparison 

Table 18.1: Comparison of National Annual Monetizable Benefits to Social Costs: Final Rule (2001$) 

Benefit and Cost Categories Low Mid High 

Final Option -- Traditional Extrapolation 

Benefit Categories 

Reduced cancer risk from fish consumption $90 $90 $90 

Reduced cancer risk from water consumption $0 $0 $0 

Reduced risk from exposure to lead $0 $0 $0 

Enhanced water-based recreation $586,503 $999,838 $1,574,380 

Nonuse benefits $293,252 $499,919 $787,190 

Avoided sewage sludge disposal costs N/A N/A N/A 

Total Monetized Benefitsa $879,845 $1,499,846 $2,361,660 

Cost Categories 

Resource costs of compliance $13,824,563 $13,824,563 $13,824,563 

Costs of administering the final regulation $0 $0 $0 

Social costs of unemployment $0 $0 $0 

Total Monetized Costs $13,824,563 $13,824,563 $13,824,563 

Net Monetized Benefits (Benefits Minus Costs)b ($12,944,718) ($12,324,717) ($11,462,903) 

Final Option -- Post-Stratification Extrapolation 

Benefit Categories 

Reduced cancer risk from fish consumption $134 $134 $134 

Reduced cancer risk from water consumption $0 $0 $0 

Reduced risk from exposure to lead $0 $0 $0 

Enhanced water-based recreation $382,105 $651,392 $1,025,705 

Nonuse benefits $191,053 $325,696 $512,852 

Avoided sewage sludge disposal costs N/A N/A N/A 

Total Monetized Benefitsa $573,292 $977,221 $1,538,691 

Cost Categories 

Resource costs of compliance $13,824,563 $13,824,563 $13,824,563 

Costs of administering the final regulation $0 $0 $0 

Social costs of unemployment $0 $0 $0 

Total Monetized Costs $13,824,563 $13,824,563 $13,824,563 

Net Monetized Benefits (Benefits Minus Costs)b ($13,251,271) ($12,847,342) ($12,285,872) 

a EPA did not estimate low and high benefits estimates for reduced cancer risk or lead exposure because it used a single estimate for the 

value of a statistical life (VSL) to estimate mortality benefits in these categories. EPA calculated low and high estimates of total monetized 

benefits by adding midpoint benefits estimates for cancer risk and lead exposure to respective low and high estimates of recreation and 

nonuse benefits. 

b EPA’s estimate of social cost is based only on the estimated resource cost of compliance and was calculated as only a single value instead 

of a range. Low, mid, and high net benefit values were calculated by subtracting the total monetized cost estimate from low, mid, and high 

estimates of total monetized benefits. 

Source: U.S. EPA analysis. 

18-4 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

Chapter 19: Social Costs and


Benefits of Regulatory Alternatives


INTRODUCTION 

EPA considered three regulatory options as alternatives to the 

selected MP&M rule. These options (the Proposed/NODA 

Option, Directs + 413 to 433 Upgrade Option, Directs + All 

to 433 Upgrade Option) are described in Chapter 4. EPA 

estimated the social costs and benefits of these three options, 

using the same methods applied in the analyses of the final 

rule. This chapter summarizes the results of these benefit and 

cost analyses. The total number of facilities reported for the 

Proposed/NODA Option (Option II) analysis differs from the 

facility count reported for the final rule and Options III and 

IV. After deciding in July 2002 not to consider the NODA 

option as the basis for the final rule, EPA performed no more 

analysis on the NODA option, including not updating facility 

counts and related analyses for the change in subcategory and 

discharge status classifications. 

19.1 ESTIMATED SOCIAL COSTS 

CHAPTER CONTENTS 

19.1 ated Social Costs ................. .... 19-1 

19.1.1 MP&M Facilities . . 19-1 

19.1.2 rnment Administrative Costs ....... 19-2 

19.1.3 Unemployment................ 19-2 

19.1.4 ocial Costs ................. ... 19-3 

19.2 ated Benefits ................. ........ 19-4 

19.2.1 n Health Benefits ............... 19-4 

19.2.2 ational Benefits ................. 19-5 

19.2.3 d Sewage Sludge Disposal or 

Use Costs ................. ........... 19-6 

19.2.4 onetized Benefits .............. 19-7 

19.3 ison of Estimated Benefits and Costs .... 19-7 

Glossary ................. ................. .. 19-10 

Acronym ................. ................. .. 19-11 

Estim

Compliance Costs for 

Gove

Cost of

Total S

Estim

Huma

Recre

Avoide

Total M

Compar

EPA estimated social costs for the final rule and alternative options in Chapter 11: Social Costs. This section provides a 

summary of those results. 

19.1.1 Compliance Costs for MP&M Facilities 

Table 19.1 presents the estimated resource value of compliance costs by discharge status under the final option and alternative 

regulatory options. These compliance costs are not adjusted for the effect of taxes or pass-through of compliance costs to 

customers, and therefore represent the social value of resources used for compliance. EPA annualized compliance costs using 

a 7 percent discount rate over a 15-year analysis period. A more detailed description as well as the results presented by 

subcategory can be found in Chapter 11: Social Costs. The total resource compliance costs of the final rule are equal to 

$13.8 million (2001$). The total annualized compliance costs under the Proposed/NODA Option are $1,620.3 million, or 

117 times the final rule’s compliance costs. The total annualized compliance costs under the Directs + 413 to 433 Upgrade 

Option are $96.8 million, or 7 times the final rule’s costs. The total annualized compliance costs under the Directs + All to 

433 Upgrade Option are $138.2 million, or 10 times the final rule’s costs. 

Table 19.1: Resource Value of Compliance Costs under Different Options (millions, 2001$) 

Option Indirect Direct Total 

Option I: Selected Option (Directs Only) $0.0 $13.8 $13.8 

Option II: Proposed/NODA Option $1,111.4 $508.9 $1,620.3 

Option III: Directs + 413 to 433 Upgrade Option $83.0 $13.8 $96.8 

Option IV: Directs + All to 433 Upgrade Option $124.4 $13.8 $138.2 

Source: U.S. EPA analysis. 

19-1 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

19.1.2 Government Administrative Costs 

The final rule excludes all indirect dischargers from coverage. EPA therefore expects no POTW administrative costs for the 

final rule. Under the alternative options, which include indirect dischargers, EPA expects no increase in permitting costs for 

facilities that already hold a permit in the baseline. However, governments will incur additional permitting costs for 

unpermitted facilities (under the Proposed/NODA option only) and to accelerate repermitting for some indirect dischargers 

that currently hold permits. The alternative regulatory options may also cause some administrative costs to decrease. For 

example, control authorities will no longer have to repermit facilities that are estimated to close as a result of the MP&M rule. 

EPA estimates that each of the three alternative options considered would result in reduced POTW regulatory costs. These 

cost savings result from regulatory closures (i.e., facilities that currently hold a permit and would have required repermitting 

in the baseline, but that will no longer require repermitting under the regulatory options). The cost savings as a result of 

regulatory closures outweigh the additional costs of issuing new permits (under the Proposed/NODA option only) and 

repermitting on an accelerated, three-year schedule. 

Table 19.2 below presents the estimated permitting costs to governments of administering the final rule and alternative 

options. Chapter 7: Government and Community Impact Analysis describes the methodology used to estimate these 

administrative costs. Estimated annualized cost savings to POTW s for the three alternative regulatory options range between 

$0.05 and $1.0 million under the Proposed/NODA option, and between $0.03 and $0.2 million under the Directs + 413 to 433 

Upgrade Option and the Directs + All to 433 Upgrade Option (all costs in (2001$). 

Table 19.2: Annualized Government Administrative Costs by Regulatory Option 

(2001$) 

Option Low Medium High 

I: Selected Option n/a n/a n/a 

II: Proposed/NODA Option (46,000) (198,000) (1,027,000) 

III: Directs + 413 to 433 Upgrade (26,000) (56,000) (218,000) 

IV: Directs + 433 to All Upgrade (26,000) (55,000) (213,000) 

Source: U.S. EPA analysis. 

19.1.3 Cost of Unemployment 

The loss of jobs associated with any facility closures would represent a social cost of the regulation. However, from its 

facility impact analysis, EPA estimates that no facilities will close as a result of the final rule. Accordingly, EPA estimates a 

zero cost of unemployment for the final regulation. 

Table 19.3 presents the estimated social costs of unemployment for the alternative regulatory options, for which EPA 

estimated closures. These estimates include the estimated willingness-to-pay to avoid cases of involuntary unemployment, 

and the cost of administering the unemployment compensation system for unemployed workers. EPA annualized costs using 

a 7 percent discount rate over a 15-year analysis period. 

The Agency based lower-bound estimates of the number of net job losses expected from compliance. Net job losses are 

estimated at 26,060 jobs under the Proposed/NODA Option, 7,319 under the 413 Upgrade Option, and 7,011 under the Local 

Limits Option. The gross estimate for lost employment, which does not consider increased employment from compliance 

activities and thus provides a conservative upper-bound of potential unemployment effects, is 32,729 jobs under the 

Proposed/NODA Option and 7,874 under both 433 Upgrade Options. From these estimates for lost employment, social costs 

of unemployment under the Proposed/NODA Option range from $344 million to $454 million (2001$). Social costs of 

unemployment under the 433 Upgrade Options range from $83 million to $109 million (2001$). 

19-2 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

Table 19.3: Social Costs of Unemployment for Final Rule and Alternative Options 

(millions, 2001$) 

Unemployment/ 

Cost Category 

Option I: 

Selected Option 

(Directs Only) 

Option II: 

Proposed/NODA Option 

Option III: 

Directs + 413 to 433 

Upgrade Option 

Option IV: 

Directs + All to 433 

Upgrade Option 

Low Mid High Low Mid High Low Mid High 

Net Unemployment 

(FTE-years)a n/a 26,060 7,319 7,011 

Gross Unemployment 

(FTE-years)a n/a 32,729 7,874 7,874 

Annualized Cost of 

Unemployment n/a $344.16 $399.22 $454.29 $82.80 $96.05 $109.30 $82.80 $96.05 $109.30 

Annualized 

Administrative Cost n/a $0.44 $0.44 $0.44 $0.11 $0.11 $0.11 $0.11 $0.11 $0.11 

Total Social Cost of 

Unemployment n/a $344.60 $399.66 $454.73 $82.91 $96.16 $109.40 $82.91 $96.16 $109.40 

a Number of FTE positions multiplied by the duration of employment/unemployment. EPA assumed that workers losing jobs due to 

regulatory closures would be unemployed for one year. The timing and duration of employment gains due to compliance expenditures 

differ for employment associated with manufacturing and installing equipment (in the first year) and operating and maintaining 

equipment (all 15 years of the analysis period). 

Source: U.S. EPA analysis. 

19.1.4 Total Social Costs 

EPA estimated that the final rule will not result in social costs of unemployment and that governments will not incur 

additional costs in administering the regulation. EPA estimates the total social cost of the final rule at $13.8 million (2001$). 

This cost results entirely from the estimated resource costs of compliance. 

For the Proposed/NODA Option, EPA estimated social costs to range from $1.96 billion to $2.07 billion (2001$) annually 

based on the cost estimates presented above. The midpoint estimate, $2.02 billion is almost 150 times greater than the final 

rule’s social cost. This increase results from the more stringent technology requirements for most subcategories under the 

Proposed/NODA Option compared to those under the final rule. In addition, this alternative option includes additional 

subcategories not covered by the regulation. 

For the Directs + 413 to 433 Upgrade Option, EPA estimated social costs to range from $180 million to $206 million (2001$) 

annually. The midpoint estimate, $193 million, is 14 times greater than the final rule’s social cost. This increase results from 

requiring facilities currently regulated under the Electroplating regulations (40 CFR 413) to comply with the Metal Finishing 

regulations (40 CFR 433). 

For the Directs + All to 433 Upgrade Option, EPA estimated social costs to range from $221 million to $247 million (2001$) 

annually. The midpoint estimate, $234 million, is 17 times greater than the final rule’s social cost. This increase results from 

requiring facilities currently regulated by local limits or general pretreatment standards to meet with the Metal Finishing 

regulations (40 CFR 433). 

19-3 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

19.2 ESTIMATED BENEFITS 

EPA estimated the benefits for the alternative options based on the methodologies described in Chapters 12 through 16. 

19.2.1 Human Health Benefits 

EPA used the methodology described in Chapter 13 to assess human health benefits from reduced incidence of cancer from 

consumption of contaminated fish tissue and drinking water under the three alternative options. 

EPA estimated that the final rule, as well as both upgrade options, would reduce incidence of cancer from consumption of 

contaminated fish by 1.4E-5 cancer cases per year. The Proposed/NODA Option would eliminate an estimated 0.57 cancer 

cases per year from the baseline level. The estimated monetary value of reduced incidence of cancer from consumption of 

contaminated fish is $3.68 million under the Proposed/NODA Option, $90 (2001$) under the final rule and Directs + 413 to 

433 Upgrade Option, and $169 (2001$) under the Directs + All to 433 Upgrade Option. 

Under the final rule, as well as both upgrade options, EPA expects no reductions in cancer cases from consumption of 

contaminated drinking water. Under the Proposed/NODA Option, 0.001 fewer cancer cases are expected annually from the 

baseline level. Estimated annual monetary benefits resulting from fewer cancer cases caused by the consumption of 

contaminated drinking water are $6,536 (2001$) for the Proposed/NODA Option. 

EPA used the methodology described in Chapter 14 to assess benefits to children and adults from reduced exposure to lead 

under the alternative options. EPA estimated that the final rule will yield no lead-related benefits to children from reduced 

consumption of contaminated fish. Annual lead-related benefits for children of $20.8 million (2001$) are expected for the 

Proposed/NODA Option. The Directs + 413 to 433 Upgrade Option and the Directs + All to 433 Upgrade Option would 

result in $1.3 and $1.5 million (2001$) in lead-related benefits for children, respectively. 

EPA estimated that the Proposed/NODA Option would reduce neonatal mortality by 1.60 cases, and avoid an estimated loss 

of 1,078 IQ points. The Directs + 413 to 433 Upgrade Option and the Directs + All to 433 Upgrade Option would reduce 

cases of neonatal mortality by 0.15 and 0.17, and avoid the loss of 32 and 36 IQ points, respectively. EPA estimated leadrelated benefits for adults at $7.0 million under the Proposed/NODA Option, and approximately $0.7 million (2001$) for both 

upgrade options. Combined lead-related benefits for children and adults total $27.8 million for the Proposed/NODA Option, 

and between $2.0 and $2.2 million (2001$) for both upgrade options. Table 19.4 summarizes all health-related benefits. 

19-4 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

Table 19.4: Annual Human Health Benefits for the Alternative Options (2001$) 

Regulatory Option 

Reduced Cancer Risk 

from Fish Consumption 

Reduced Cancer Risk 

from Water 

Consumption 

Lead-Related Benefits Total 

Monetized 

Human 

Health 

Benefits 

Number 

of Cancer 

Cases 

Monetary 

Value 

Number 

of Cancer 

Cases 

Monetary 

Value Children Adult 

Proposed/NODA Option 

Baseline 0.920 3.117 

Proposed/NODA Option 0.353 $3,684,973 3.116 $6,536 $20,791,073 $7,048,025 $31,530,607 

Final Option Alternatives 

Baseline 0.033 5.3E-07 

Selected Option 0.033 $90 5.3E-07 $0 $0 $0 $90 

Directs + 413 to 433 

Upgrade 

0.033 $90 5.3E-07 $0 $1,303,590 $704,574 $2,008,254 

Directs + 413 + 50% LL 

Upgrade 

0.033 $169 5.3E-07 $0 $1,457,640 $785,304 $2,243,113 

Source: U.S. EPA analysis. 

19.2.2 Recreational Benefits 

EPA used the methodology described in Chapter 15 to assess improvements in recreational benefits under the alternative 

options. The Agency found that the final option will reduce the occurrence of pollutant concentrations in excess of ambient 

water quality criteria (AWQC) limits by 2 percent (9 of 395 baseline occurrences) (see Table 19.5). EPA found that the 

Proposed/NODA Option would reduce pollutant concentrations in excess of AWQC limits by 2.6 percent (154 of 5,999 

baseline occurrences), while both upgrade options would reduce such occurrences by 72 percent (285 of 395 baseline 

occurrences) from the baseline level. 

EPA estimated the range of recreational value increases (including both use and nonuse value) for these reaches resulting 

from habitat improvements for each option. EPA expects recreational value of improved reaches to increase by $0.9 million 

to $2.4 million annually under the final rule, $406 million to $956 million annually under the Proposed/NODA Option, 

$182.7 million to $443.5 million under the Directs + 413 to 433 Upgrade Option, and by $183.5 million to $445.9 million 

under the Directs + All to 433 Upgrade Option (2001$) (see Table 19.7). The midpoint estimates of combined annual 

recreational and nonuse benefits under these options are $1.5 million, $649 million, $297.0 million, and $298.5 million 

(2001$). The midpoint estimates of recreational and nonuse benefits are approximately 200 times greater under the upgrade 

options than under the final rule. 

19-5 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

Table 19.5: Number of MP&M Discharge Reaches with MP&M Pollutant Concentrations Exceeding AWQC Limits 

Regulatory Status 

Number of Reaches with Concentrations Exceeding Number of Reaches 

with Concentrations 

Exceeding AWQC 

Limitsa 

AWQC Acute 

Exposure Limits for 

Aquatic Species 

AWQC Chronic 

Exposure Limits for 

Aquatic Species 

AWQC Limits for 

Human Health 

Proposed/NODA Option 

Baseline 330 928 5,865 5,999 

Proposed/NODA 

Option 

86 539 5,803 5,845 

Final Option Alternatives 

Baseline 18 353 78 395 

Selected Option 9 344 78 386 

Directs + 413 to 433 

Upgrade 

0 53 78 109 

Directs + 413 + 50% LL 

Upgrade 

0 31 78 109 

a All reaches exceeding aquatic acute exposure limits also exceed chronic exposure limits. In order not to double count the number of 

reaches expected to benefit from the regulation, the total number of reaches exceeding AWQC limits is the sum of the number of 

reaches that exceed human health criteria and the number exceeding aquatic chronic criteria, which do not also exceed AWQC limits for 

human health. 

Source: U.S. EPA analysis. 

19.2.3 Avoided Sewage Sludge Disposal or Use Costs 

The final rule will not regulate indirect dischargers and therefore will not reduce metals discharges to POTW s or the number 

of POTW s that exceed land application standards for sewage sludge disposal. However, reduced metals discharges to 

POTWs resulting from the Proposed/NODA Option would enable 48 additional POTWs to dispose of sewage sludge by land 

application, resulting in $22.8 million (2001$) in cost savings (see Table 19.6). The Directs + 413 to 433 Upgrade Option 

and the Directs + All to 433 Upgrade Option would not reduce the number of POTWs that exceed land application standards. 

However, under both upgrade options 15 POTWs would be able to improve their sludge quality from meeting the land 

application low standard to meeting the land application high standard, resulting in approximately $16,929 (2001$) in cost 

savings to POTWs. 

Table 19.6: Cost Savings from Land Application 

Regulatory Option # of POTWs Exceeding Land Application 

(High) Standards 

Cost Savings from Upgrading Sewage 

Sludge Disposal Methods (2001$) 

Proposed/NODA Option 

Baseline 5,328 

Proposed/NODA Option 5,259 $22,825,584 

Final Option Alternatives 

Baseline 856 

Selected Option 856 $0 

Directs + 413 to 433 Upgrade 856 $16,929 

Directs + All to 433 856 $16,929 

Source: U.S. EPA analysis. 

19-6 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

19.2.4 Total Monetized Benefits 

EPA estimates total monetized benefits under the final option ranging from $879,845 to $2,361,660 (2001$), with a midpoint 

estimate of $1,499,846. Total monetized benefits for the Proposed/NODA Option range from $460 million to $1,010 million, 

with a midpoint estimate of $704 million (2001$). Total monetized benefits estimates for the Directs + 413 to 433 Upgrade 

Option and the Directs + All to 433 Upgrade Option are similar, with respective ranges of $185 million to $446 million, and 

$186 million to $448 million (2001$). Midpoint estimates of total monetized benefits for these options are $299 million and 

$301 million (2001$), respectively. Midpoint estimates for monetized benefits for the upgrade options are approximately 200 

percent higher than the midpoint estimate of benefits for the final rule. 

19.3 COMPARISON OF ESTIMATED BENEFITS AND COSTS 

Combining the estimates of social benefits and social costs under the final option yields net monetized benefits ranging from 

negative $11.5 million to negative $12.9 million (2001$), with a midpoint estimate of negative $12.3 million (see Table 19.7). 

Under the Proposed/NODA Option, net monetized benefits range from negative $1,505 million to negative $1,064 million 

(2001$) per year, with a midpoint estimate of negative $1,316 million. Annual net monetized benefits under the Directs + 

413 to 433 Upgrade Option and the Directs + All to 433 Upgrade Option range from $5 million to $240 million, and negative 

$35 million to positive $201 million (2001$) per year, respectively. Midpoint estimates of net benefits for these options are 

$106 million and $66 million (2001$), respectively (see Table 19.7). As discussed in Chapter 12, the benefits assessment of 

regulatory options is necessarily incomplete due to the omission of numerous mechanisms by which society is likely to benefit 

from reduced effluent discharges. 

19-7 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

Table 19.7: Comparison of Social Benefits and Costs of Alternative Options (2001$) 

Benefit and Cost Categoriesd Low Mid High 

Selected Option 

Benefit Categories 

Reduced Cancer Risk from Fish Consumption $90 $90 $90 

Reduced Cancer Risk from Water Consumption $0 $0 $0 

Reduced Risk from Lead Exposure $0 $0 $0 

Enhanced Water-Based Recreation $586,503 $999,838 $1,574,380 

Nonuse Benefits $293,252 $499,919 $787,190 

Avoided Sewage Sludge Disposal Costs N/A N/A N/A 

Total Monetized Benefitsa $879,845 $1,499,846 $2,361,660 

Cost Categories 

Resource Costs of Compliance $13,824,563 $13,824,563 $13,824,563 

Administration Costs to POTWs $0 $0 $0 

Social Costs of Unemployment $0 $0 $0 

Total Monetized Costs $13,824,563 $13,824,563 $13,824,563 

Net Monetized Benefits (Benefits Minus Costs)b ($12,944,718) ($12,324,717) ($11,462,903) 

Proposed/NODA Option 

Benefit Categories 

Reduced Cancer Risk from Fish Consumption $3,684,973 $3,684,973 $3,684,973 

Reduced Cancer Risk from Water Consumption $6,536 $6,536 $6,536 

Reduced Risk from Lead Exposure $27,839,098 $27,839,098 $27,839,098 

Enhanced Water-Based Recreation $270,366,433 $432,938,869 $637,360,014 

Nonuse Benefits $135,183,216 $216,469,435 $318,680,007 

Avoided Sewage Sludge Disposal Costs $22,795,620 $22,825,584 $22,855,548 

Total Monetized Benefitsa $459,875,876 $703,764,495 $1,010,426,176 

Cost Categories 

Resource Costs of Compliance $1,620,252,136 $1,620,252,136 $1,620,252,136 

Administration Costs to POTWs ($46,000) ($198,000) ($1,027,000) 

Social Costs of Unemployment $344,597,370 $399,662,865 $454,728,360 

Total Monetized Costs $1,964,803,507 $2,019,717,002 $2,073,953,497 

Net Monetized Benefits (Benefits Minus Costs)c ($1,504,927,631) ($1,315,952,507) ($1,063,527,321) 

19-8

MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

Table 19.7: Comparison of Social Benefits and Costs of Alternative Options (2001$) 

Benefit and Cost Categoriesd Low Mid High 

Directs + 413 to 433 Upgrade 

Benefit Categories 

Reduced Cancer Risk from Fish Consumption $90 $90 $90 

Reduced Cancer Risk from Water Consumption $0 $0 $0 

Reduced Risk from Lead Exposure $2,008,254 $2,008,254 $2,008,254 

Enhanced Water-Based Recreation $121,808,075 $197,990,383 $295,661,071 

Nonuse Benefits $60,904,038 $98,995,192 $147,830,535 

Avoided Sewage Sludge Disposal Costs $11,319 $16,929 $22,539 

Total Monetized Benefitsa $184,731,776 $299,010,848 $445,522,489 

Cost Categories 

Resource Costs of Compliance $96,779,134 $96,779,134 $96,779,134 

Administration Costs to POTWs ($26,000) ($56,000) ($218,000) 

Social Costs of Unemployment $82,907,075 $96,155,345 $109,403,616 

Total Monetized Costs $179,660,209 $192,878,479 $205,964,750 

Net Monetized Benefits (Benefits Minus Costs)c $5,071,567 $106,132,369 $239,557,739 

Directs + All to 433 Upgrade 

Benefit Categories 

Reduced Cancer Risk from Fish Consumption $169 $169 $169 

Reduced Cancer Risk from Water Consumption $0 $0 $0 

Reduced Risk from Lead Exposure $2,243,113 $2,243,113 $2,243,113 

Enhanced Water-Based Recreation $122,360,444 $198,976,248 $297,272,287 

Nonuse Benefits $61,180,222 $99,488,124 $148,636,143 

Avoided Sewage Sludge Disposal Costs $11,319 $16,929 $22,539 

Total Monetized Benefitsa $185,795,267 $300,724,583 $448,174,251 

Cost Categories 

Resource Costs of Compliance $138,237,664 $138,237,664 $138,237,664 

Administration Costs to POTWs ($26,000) ($55,000) ($213,000) 

Social Costs of Unemployment $82,907,075 $96,155,345 $109,403,616 

Total Monetized Costs $221,118,739 $234,338,009 $247,428,280 

Net Monetized Benefits (Benefits Minus Costs)c ($35,323,472) $66,386,574 $200,745,971 

a EPA did not estimate low and high benefits estimates for reduced cancer risk or lead exposure because a single estimate for the value of 

a statistical life (VSL) was used to estimate mortality benefits in these categories. EPA calculated low and high estimates of total monetized 

benefits by adding midpoint benefits estimates for cancer risk and lead exposure to respective low and high estimates of recreation and 

nonuse benefits. 

b EPA’s estimate of social costs for the final regulation is based only on the estimated resource costs of compliance and is a single value 

instead of a range. EPA calculated low, mid, and high net benefit values by subtracting the total monetized cost estimate from low, mid, 

and high estimates of total monetized benefits. c EPA calculated the low net benefit value by subtracting the high value of costs from the low value of benefits, and calculated the high net 

benefit value by subtracting the low value of costs from the high value of benefits. The mid net benefit value is the mean value of benefits 

less the midpoint of costs. 

d Category values may not sum to reported totals due to rounding of individual estimates for presentation purposes. 

Source: U.S. EPA analysis. 

19-9 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

GLOSSARY 

ambient water quality criteria (AWQC): published and periodically updated by the EPA under the Clean Water Act. 

The criteria reflect the latest scientific knowledge on the effects of specific pollutants on public health and welfare, aquatic 

life, and recreation. The criteria do not reflect consideration of economic impacts or the technological feasibility of reducing 

chemical concentrations in ambient water. The criteria serve as guides to states, territories, and authorized tribes in 

developing water quality standards and ultimately provide a basis for controlling discharges or releases of pollutants into our 

nation’s waterways. AWQC are developed for two exposure pathways: ingestion of the pollutant via contaminated aquatic 

organisms only, and ingestion of the pollutant via both water and contaminated aquatic organisms. 

19-10 
MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

ACRONYM 

AWQC: ambient water quality criteria 

19-11

MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives 

THIS PAGE INTENTIONALLY LEFT BLANK 

19-12

MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Chapter 20: Baseline Conditions in


Ohio


INTRODUCTION 

Section IV of this EEBA focuses on the state of Ohio as a 

case study of the MP &M regulation’s expected benefits and 

costs. Ohio has a diverse water resource base, a relatively 

large number of MP&M industry facilities, and a more 

extensive water quality ecological database than many other 

states. EPA gathered extensive data on MP&M facilities and 

on Ohio’s baseline water quality conditions and water-based 

recreation activities to support the case study analysis. 

These data characterize current water quality conditions, 

water quality changes expected from the regulation, and the 

expected welfare changes from water quality improvements 

at water bodies affected by MP&M discharges. 

The case study analysis supplements the national-level 

analysis performed for the MP&M regulation in two 

important ways. First, the case study used improved data 

and methods to determine MP&M pollutant discharges from 

both MP&M facilities and other sources. In particular, EPA 

administered 1,600 screener questionnaires in the state of 

Ohio to augment information on Ohio MP&M facilities. The 

Agency also used information from the sampled MP&M 

facilities to assign discharge characteristics to non-sampled 

MP&M facilities1

. Second, the analysis used an original 

travel cost study to value four recreational uses of water 

resources affected by the regulation: swimming, fishing, 

boating, and near-water activities. The added detail provides 

a more complete and reliable analysis of water quality 

changes from reduced MP&M discharges. The case study 

CHAPTER CONTENTS 

20.1 iew of Ohio’s Geography, Population, 

and Economy ................. ........ 20-2 

20.2 MP&M Facilities in Ohio ........... 20-3 

20.3 ................. ... 20-6 

20.3.1 ................. .... 20-8 

20.3.2 r Recreation in Ohio ............. 20-11 

20.3.3 ercial Fishing in Ohio .......... 20-12 

20.3.4 e Water Withdrawals ........... 20-12 

20.4 e Water Quality in Ohio .............. 20-12 

20.4.1 

in Ohio ................. ............ 20-13 

20.4.2 

Attainment ................. ......... 20-13 

20.4.3 

Non-Attainment in Ohio ................ 20-14 

20.5 Impairments on Water 

Resource Services ................. .... 20-15 

20.5.1 ity Impairment on Life 

Support for Animals and Plants .......... 20-15 

20.5.2 ect of Water Quality Impairment on 

Recreational Services ................. . 20-17 

20.6 and Distribution of Endangered and 

Threatened Species in Ohio ............. 20-18 

20.6.1 ................. ........ 20-19 

20.6.2 sks ................. .... 20-19 

20.6.3 ........... 20-20 

Glossary ................. ................. .. 20-24 

Acronyms ................. ................. . 20-27 

References ................. ................. 20-28 

Overv

Profile of

Ohio’s Water Resources 

Aquatic Life Use 

Wate

Comm

Surfac

Surfac

Use Attainment in Streams and Rivers 

Lake Erie and Other Lakes Use 

Causes and Sources of Use 

Effects of Water Quality

Effect of Water Qual

Eff

Presence

E&T Fish 

E&T Mollu

Other Aquatic E&T Species 

analysis therefore provides more complete estimates of changes in human welfare resulting from reduced health risk, 

enhanced recreational opportunities, and improved economic productivity. 

The statewide case study of recreational benefits from the MP&M regulation combines water quality modeling with a 

random utility model (RUM) to assess how changes in water quality from the regulation will affect consumer valuation of 

water resources. The study addresses a wide range of pollutant types and effects, including water quality measures not often 

addressed in past recreational benefits studies. The estimated model supports a more complete analysis of recreational 

benefits from reductions in nutrients and “toxic” pollutants.2 

1 Appendix H provides a detailed discussion on the approach used to estimate discharge characteristics for non-sampled MP&M 

facilities. 

2 The term “toxic” used here refers to the 126 priority or toxic pollutants specifically defined as such by EPA, as well as 

nonconventional pollutants that have a toxic effect on human health or aquatic organisms. 

20-1 
MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

This and the next two chapters present the Ohio case study. This chapter provides background information on the state of 

Ohio, the following chapter presents the results from the recreational benefits analysis, and the last chapter summarizes social 

costs and benefits of the final regulation for the state of Ohio. 

20.1 OVERVIEW OF OHIO’S GEOGRAPHY, POPULATION, AND ECONOMY 

Table 20.1 summarizes general information on Ohio. Ohio is large, heavily-industrialized, and densely-populated. The state 

covers a total surface area of 44,828 sq. mi. (106,607 sq. km.), of which water represents 3,875 sq. mi. (10,036 sq. km.). 

About 90 percent of the water surface area consists of Lake Erie; the remainder includes inland waters, such as lakes, 

reservoirs, and rivers (including the Ohio River). The state housed 11,353,140 people in 2000. The three largest 

metropolitan areas are located on Lake Erie (Toledo and Cleveland) and the Ohio River (Cincinnati). 

20-2 
MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Table 20.1: Facts about the State of Ohio 

Geography 

Location Midwest United States, northeast part: 

south of Lake Erie 

east of Indiana 

north of the Ohio River 

Total land area 40,953 sq. (106,607 sq. .) 

Of the 26,451,000 acres of terrestrial surface area in Ohio: 

97 percent is non-federal land (National Resources Inventory (NRI)) 

3,558,000 acres, representing 13.5 percent of the total area of Ohio, are developed 

The remaining non-federal lands are rural land, classified mostly as crop land, forest, and pasture 

lands. 

Total water surface area 3,875 sq. (10,036 sq. .) 

Approximately 90 percent is represented by Lake Erie, and 10 percent are inland waters including 

rivers, lakes, and reservoirs.a 

Total area 44,828 sq. (116,104 sq. .) 

Demographics 

Population 11,353,140 in 2000, approximately 4 percent of total U.S. population (U.S. Census Bureau) 

Population increase: 4.7 percent from 1990 to 2000, compared to a 13.1 percent increase in the U.S. 

population overall. 

Most densely populated part of the state: northeastern Ohio, both urban and rural areas. 

Largest cities: Cleveland, Cincinnati, and Toledo. 

Economics 

Ohio Midwest U.S. 

Per capita income (1996$) $23,537 

Rank in per capital income in 

the U.S.: 21 

$24,166 $24,231 

Percent of population below 

the poverty level (1995 

Current Population Survey 

data, DOC 1996) 

11.5% N/A 13.8% 

Ohio per capita income increased by 16 percent from 1986 to 1996. 

Income growth is consistent with other midwestern states and is 2 percent greater than overall U.S. per 

capita income growth. 

Gross State Product (GSP) $303,569,000,000 (1996$), representing 4 percent of Gross Domestic Product (GDP) for the U.S. in 

1996. 

Percent increase in GSP/GDP 

from 1986 to 1996 (in 

adjusted 1996$) 

Ohio GSP U.S. GDP 

25% 29% 

mi. km

(USDA, 1992a) 

mi. km

mi. km

a Total water surface areas are estimated by the USDA’s National Resources Inventory (NRI) (USDA 1992b). 

(http://www.ftw.nrcs.usda.gov/nri_data.html) 

Source: U.S. EPA analysis. 

20.2 PROFILE OF MP&M FACILITIES IN OHIO 

EPA selected Ohio as the case study state because MP&M industries account for a large share of the state's economy (see 

Table 20.2). Data from the 1997 Economic Censuses show that industries containing MP&M facilities employ 19.8 percent 

of Ohio's total industrial workers and produce 21.2 percent of industrial worker output by value. MP&M industries also 

account for 22.1 percent of payroll payments, indicating that jobs in MP&M industries are more highly paid than industrial 

20-3 
MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

jobs on average in Ohio. The discussion below explains the sources and methodology EPA used, and then presents detailed 

results and caveats. 

Table 20.2: &M Share of Industrial Output and Employment in Ohio, 1997 

Total Employment Payroll Value of Output 

MP&M 827,507 $23,233,857,000 $132,117,226,000 

Total 4,087,393 $112,777,104,000 $677,978,137,000 

MP&M Share 19.8% 22.1% 21.2% 

MP

Source: Department of Commerce 1992 Economic Censuses. 

EPA obtained employment, payroll, and output data from the 1997 Economic Census CD-ROM, drawing from the eight 

economic censuses in Table 20.3. Employment and payroll numbers include all employees (i.e., production plus nonproduction workers). The measure of output differs according to the source, but in each case the output measures shown in 

Table 20.2 correspond conceptually to total revenue. EPA extracted the EMPLOYEE, PAYROLL, and VALUE fields for 

each 4-digit SIC industry in the MP&M category and for the entire state of Ohio. Industries include both in-scope and out-ofscope facilities. 

Table 20.3: e Economic Censuses 

Source Measure of 

Output 

Census of Retail Trade Value of sales 

Census of Wholesale Trade Value of sales 

Census of Service Industriesa Value of receipts 

Census of Transportation, 

Communications, and Utilities Value of revenue 

Financial, Insurance, and Real Estate 

Industries Value of receipts 

Census of Manufacturers Value of shipments 

Census of Mineral Industries Value of shipments 

Census of Construction Industries Value of 

construction work 

Th

a Includes both taxable and non-taxable establishments. 

Source: Department of Commerce 1997 Economic Censuses. 

The MP&M industries include facilities to which the MP&M rule may not apply. For example, MP&M industries include 

non-dischargers, but census data do not distinguish between in-scope and out-of-scope facilities. In addition, EPA 

substantially revised the scope of the final regulation by excluding from the final regulation all indirect dischargers and direct 

dischargers in all subcategories except for Oily Wastes. Definition of MP&M subcategories is provided in Section 4.1 of this 

report. The final rule applies to an estimated 172 direct discharging facilities in Ohio. 

Also, the analysis examines only the industrial sectors for which the Department of Commerce compiles statistics in the 

Econom ic Censuses. Published industrial employment and output measures often exclude military and other government 

personnel and farm output and employment, whether those exclusions are noted or not. The analysis excludes $4.7 billion in 

value of agricultural products sold in 1997 by farms in Ohio, according to the U.S. Department of Agriculture's 1997 Census 

of Agriculture. The Ohio analysis also excludes the government sector, which employed approximately 760,000 people in 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Ohio in 1997, according to the U.S. Bureau of Labor Statistics.3 These exclusions are normal when economists compare the 

size of industrial groups. 

If total employment in Ohio includes the government sector, then MP&M industries account for only 16.7 percent, rather than 

19.8, percent of employment. If total industrial manufacturing and non-manufacturing output in Ohio includes the agricultural 

sector, then MP&M industries account for only 21.0, rather than 21.1, percent of output. This said, data from the Bureau of 

Labor Statistics and USDA are not completely consistent with the Economic Census data. 

EPA augmented information on MP&M facilities available from published data sources and the Section 308 survey by 

oversampling the state of Ohio with 1,600 screeners. The Agency used information from the Section 308 survey and the 

1,600 screeners to characterize discharges from MP&M facilities in Ohio and to assess the economic impact of the final 

regulation at the state level. Figure 20.1 depicts locations of the Ohio facilities included in the case study analysis. 

The map of facility locations shows that the additional information from 1,600 screeners enabled EPA to perform the benefits 

assessment with a greater level of detail than is possible at the national level. The added detail results in a more complete and 

reliable analysis of changes in human welfare resulting from reduced health risk and improved recreational opportunities. 

3 U.S. Bureau of the Census, Statistical Abstract of the United States, 1993, Washington, D.C., 1993. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Figure 20.1: Location of Sample MP&M Facilities in Ohio 

Source: U.S. EPA analysis. 

20.3 OHIO’S WATER RESOURCES 

The benefits of enhanced water quality stem directly from enhancing water quality and/or quantity of services provided by 

water resources. To aid in understanding the analysis of benefits from the final rule in Ohio, this section summarizes 

environmental services provided by Ohio’s water resources. 

Ohio is a water-rich state: 

� 24,000+ miles of named and designated rivers and streams; 

� 451-mile border on the Ohio River; 

� 200,000 acres among 450 lakes, ponds, rivers, and reservoirs; and 

� 230+ miles of Lake Erie shoreline. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

These water resources provide three broad categories of services: in-stream, withdrawal, and existence services.


Water resources provide in-stream services prior to the withdrawal of water from the water body. Major in-stream services


include life support for animals and plants, water-based recreation, commercial fishing and navigation, water storage, and


aesthetics. Withdrawal services include uses of water resources after the water is withdrawn from the water body. These uses


include drinking water supply, irrigation, production and processing services, and sanitary services. Existence services are


not linked to current uses of water bodies, and arise from knowing that species diversity or the natural beauty of a given water


body is preserved.


The Ohio Environmental Protection Agency (Ohio EPA) assesses surface waters in their Ohio Water Resource Inventory


(OWRI) report based on water resource services provided by the assessed water body. The main focus of this assessment is


on beneficial uses associated with Ohio’s water resources, including aquatic life use, recreation, and public water supply.


Table 20.4 shows how Ohio surface waters fall into these use designations.


Table 20.4: Summary of Designated Life Uses for Ohio Surface Waters (1996) 

Use Designation Stream/River 

(Miles)a 

Lakes / 

Reservoir 

(Acres)a 

Lake Erie 

(Shore 

Miles)a 

Total 43,917 200,000 236 

Aquatic Life Usea 

Exceptional Warmwater Habitat (EWH) 

Warmwater Habitat (WWH) 

Other 

24,067 

3,217 

18,318 

2,532 

193,903 

193,903 

236 

236 

Recreation 

Primary Contact (PCR)b 

Secondary Contact (SCR) 

224,96 

1,188 

200,000 236 

Public Water Supply 118,801 

a Total river/stream miles are based on Ohio EPA estimates. U.S. EPA estimates 61,532 total river 

miles and 29,113 total perennial miles based on RF3, which includes many smaller undesignated 

streams. 

b Note that some water bodies have more than one designated use (e.g., aquatic life and primary 

recreation). 

Source: Ohio EPA, OWRI, 1996. 

The aquatic life use category is further subdivided into seven categories. The most widely-applied aquatic use designation in 

Ohio is Warmwater Habitat (WWH), accounting for 18,318 (76 percent) stream and river miles (Ohio EPA, OWRI, 1996). 

The second most widely applied designation is Exceptional Warmwater Habitat (EWH), accounting for 3, 217 stream 

and river miles (13 percent), 236 Lake Erie shore miles (100 percent), and 193,903 acres of inland lakes (100 percent). Other 

aquatic life categories include: 

� Modified Warmwater Habitat (MWH), 

� Limited Resource Waters (LRW), 

� Limited Warmwater Habitat (LWH), 

� Seasonal Salmonid Habitat (SSH), and 

� Coldwater Habitat (CWH). 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Recreational uses are subdivided into Primary Contact Recreation (PCR) and Secondary Contact Recreation 

(SCR): 

� Primary Contact Recreation (PCR) rivers and streams deep enough for full human body immersion activities, 

such as swimming. 

� Secondary Contact Recreation (SCR) only deep enough to permit wading and incidental contact, such as boating. 

Approximately half of the designated stream miles, all inland lakes, and all of the Lake Erie shore miles are designated for 

PCR (see Table 20.4). In addition, three percent of the designated stream miles (1,188 miles) are suitable for SCR. 

The following sections detail each category of water resource use. 

20.3.1 Aquatic Life Use 

The Ohio water resources support hundreds of aquatic species and plants. Ohio water resources are also home to a number of 

endangered and threatened species. Suitable stream and lake habitat are essential for both resident and transient animal 

populations, including imperiled aquatic species. Habitats include specific biotic components (e.g., assemblages of plant and 

animal species) and physical (e.g., dissolved oxygen (DO) content and temperature range) components. Water quality 

impairments associated with siltation, excess nutrients, or low DO can adversely affect habitats that support important 

activities, such as reproduction, foraging, migration, and overwintering. 

The following sections briefly introduce water-dependent biological resources in Ohio. Water quality effects on life support


for animals and plants are discussed in Section 20.5


a. Ohio fish species


Fish are found throughout Ohio in almost every inland surface water body and Lake Erie. Many fish species serve important


recreational or commercial functions, while others are important forage for birds, other fish, and land-based species.


Ecosystem well-being therefore depends on the health of fish and other aquatic species populations. The Ohio EPA monitors


biological data, especially those on sensitive aquatic species, to determine the aquatic life use attainment of surface waters. 

The state gives high priority to healthy aquatic ecosystem maintenance. 

Ohio’s rivers and lakes offer a variety of man-made and natural habitats that offer excellent fishing opportunities for 

numerous gamefish species. The state of Ohio spends significant resources on fishery management, trout stocking, and 

recreational area maintenance to enhance these fish populations. Table 20.5 below provides brief summaries of the habitat 

and diet of major recreational and commercial fish species in Ohio (Ohio DNR , 1999). 

Table 20.5: Recreationally or Commercially Valuable Fish Species in Ohio 

Fish Native or 

introduced? Habitat Spawning 

season Diet 

Bass Most native bass 

(e.g., largemouth, 

smallmouth, 

spotted, and sock) 

Ponds, lakes, rivers, and streams in 

every county; Lake Erie 

Mid-April to 

mid-June 

Frogs, crayfish, insects, and other 

fish 

Bullhead Native Throughout Ohio; concentrations in 

northern and west central Ohio 

Mid-May to 

June 

Insect larvae, crayfish, snails, 

dead animals 

Burbot Native Lakes and rivers; prefer deep waters, but 

move inshore to spawn 

Winter Minnows and the young of other 

fish species 

Carp Introduced Warm lakes, streams, and ponds with 

abundant organic matter, in every 

county 

Late April to 

June 

Insect larvae, mollusks, fish, 

crustaceans 

Catfish 

(channel, 

flathead) 

Native Throughout Ohio's rivers and lakes; 

tolerate a wide range of conditions 

When waters 

reach 70° F in 

temperature 

Bottom feeders with a diet of 

insect larvae, mollusks, and fish 

both dead and alive 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Table 20.5: Recreationally or Commercially Valuable Fish Species in Ohio 

Fish Native or 

introduced? Habitat Spawning 

season Diet 

Crappie, 

white 

Larger ponds, reservoirs, and rivers, 

including near-shore habitats of Lake 

Erie, in most areas of Ohio 

May and June Insects and small fish 

Crappie, 

black 

Same general habitat as white crappie, 

slightly less widely distributed May and June Insects and small fish 

Drum Native Lake Erie; drums support a commercial 

fishery 

Spring into 

late summer 

Mollusks, crayfish, fish, insects 

Lamprey Lake Erie and tributaries; Ohio River 

and larger tributaries 

Some species parasitize other fish 

by attaching themselves to a 

larger host's flank and feeding on 

its flesh 

Muskellunge 

(Muskie) 

Native Historically found in Lake Erie bays and 

tributaries and streams of Ohio River 

drainage; now also found in several 

impoundments 

April and early 

May, when 

temperatures 

reach low- to 

mid-50s 

Suckers, gizzard shard, and other 

soft-rayed fish 

Perch, white Introduced Lake Erie and tributaries April and May 

Insects, crustaceans, other fish Perch, yellow Native Lakes, impoundments, ponds, slowmoving rivers April and May 

Pike Native Historically abundant in Lake Erie and 

tributaries; today distributed in a small 

portion of Lake Erie, Sandusky Bay, 

Maumee Bay, and their tributary streams 

in marshes, bays, and pools with 

abundant vegetation 

As ice breaks 

in late 

February and 

early March 

Pike is a 

popular icefishing species 

Mostly fish, but are opportunistic 

feeders; will occasionally eat 

frogs, muskrats, small ducks 

Salmon 

(chinook and 

coho) 

Introduced Stocked in Lake Erie for both 

recreational and commercial fishing 

purposes 

Sauger Native Lake Erie and its tributaries; Ohio River Spring, when 

water 

temperatures 

reach high 40s 

Insects, crayfish, other small fish 

during low light (dawn and dusk) 

Saugeye 

(cross 

between 

sauger and 

walleye) 

Introduced Stocked into many Ohio impoundments 

Sucker, white Native Every county; Lake Erie April to May Bottom feeders, consuming 

various plant and animal species 

Sunfish Bluegill, 

pumpkinseed, 

green, warmouth, 

and longear 

sunfish are native; 

redear sunfish are 

introduced 

Rivers, streams, and lakes throughout 

Ohio, and Lake Erie 

Between May 

and August 

Adults feed mostly on smaller 

fish, insects, crustaceans 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Table 20.5: Recreationally or Commercially Valuable Fish Species in Ohio 

Fish Native or 

introduced? Habitat Spawning 

season Diet 

Trout Lake and brook 

trout are native; 

rainbow and 

brown trout are 

introduced and 

maintained by 

stocking 

Lake trout populations are stocked in 

Pennsylvania and New York and are not 

highly prevalent in Ohio and Lake Erie 

waters; Brook trout are stocked in 

several locations throughout Ohio 

Walleye Native Historically found in Lake Erie, but has 

been stocked in the Ohio River and 

reservoirs throughout the state 

April Shiners, gizzard shad, alewives, 

rainbow smelt 

Whitefish Native Shallow bays of Lake Erie's western 

basin 

Bottom feeders with a diet of 

mollusks and insect larvae 

Source: U.S. EPA analysis. 

b. Other species dependent on aquatic resources 

Resident and migratory bird species make extensive use of Ohio waters. Areas along the banks or shorelines of rivers, 

streams, lakes, ponds, and reservoirs provide high quality nesting areas; the waters themselves are an abundant source of food. 

Ohio waters also serve as important staging areas for birds migrating to or from points north or south. Wading or aquatic 

birds are generally unaffected by water quality impairments directly. They are affected indirectly, however, through feeding 

on fish or invertebrates whose populations may be affected by point and non-point pollution sources. The regulations aimed 

at protecting aquatic species will therefore benefit wading and aquatic bird species indirectly. More than 130 aquatic bird 

species rely on Lake Erie and its tributaries. Many species are also found near inland surface waters. Major classifications of 

birds in Ohio include (Ohio DNR , 1999): 

� Waterfowl, residing year-round in Ohio waters, especially Lake Erie. Large groups of migrating and breeding birds 

are also found elsewhere in the state. More than 30 species are associated with the Great Lakes area alone. All 

species depend on fish and crustaceans or aquatic plants for feeding. Waterfowl include loons, grebes, swans, ducks 

and geese. The trumpeter swan is of particular interest to Ohio, which became one of several states involved in 

efforts to restore these birds to the Midwest beginning in 1996 (Ohio DNR, 1999). 

� Wading birds, including bitterns, herons, and egrets. These species both reside in Ohio waters and use them as 

breeding grounds. They use “stand-and-wait” methods to catch fish or other aquatic organisms in shallow waters. 

Many wading birds, such as the great egret, black-crowned night heron, and American bittern, frequent Lake Erie 

and surrounding areas. 

� Marsh birds, including rails, moorhens, coots, and gallinules. They may feed on insects, crustaceans, mollusks, 

frogs, invertebrates, and small fish. These bird populations suffer from excessive development and habitat 

destruction. Ohio surface waters, especially those around Lake Erie, can serve as important breeding grounds for 

these and other bird species. 

� Shore birds, including 42 species of plovers, sandpipers, gulls, and terns, in the Lake Erie and other Ohio areas. 

Many of them feed on aquatic organisms from Lake Erie. 

� Raptors, including the bald eagle and osprey. These birds of prey rely on fishing for a large part of their diet. Bald 

eagles are also a nationally-listed threatened species. 

� The belted kingfisher, which relies on fish in Ohio waters as a main source of food. 

Ohio’s biological resources also includes reptiles. Several species of lizards, snakes, and turtles depend on aquatic habitats 

for food and breeding. These reptiles include: 

� Lizards - The five-fined skink, reported in areas along Lake Erie, can be found throughout Ohio. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

� Snakes - The eastern fox snake, Eastern massasasuga, eastern ribbon snake, copperbelly water snake, Lake Erie 

water snake, and northern water snake feed within aquatic habitats. 

� Turtles - The midland smooth softshell turtle and eastern spiny softshell turtle, found in the Ohio River and 

tributaries, are among Ohio turtles requiring aquatic habitats. 

20.3.2 Water Recreation in Ohio 

EPA used the 1994 Survey of National Demand for Water-based Recreation (NDS) (U.S. EPA, 1994) to 

characterize recreational uses of Ohio’s water resources. The 1994 survey collected data on demographic characteristics and 

water-based recreation behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over. 

Respondents reported on water-based recreation trips taken within the previous 12 months, including the primary purpose of 

their trips (e.g., fishing, boating, swimming, and viewing), total number of trips, trip length, distance to the recreation site(s), 

and number of participants. EPA estimated recreational water use in Ohio by taking the following steps: 

� estimate the percentage of survey respondents that visited Ohio, by state; 

� apply this percentage to the total number of state residents aged 16 and over, to yield the total number of participants 

from each state; 

� estimate the total number of recreation trips during the 12-month period for in-state and out-of-state participants; 

� estimate the total number of recreation trips for out-of-state participants by multiplying an average number of trips 

per Ohio water body visitor by the total number of participants from each state; 

� estimate the average number of annual trips per out-of-state visitor based on the number of times the respondents 

visited the site of their last recreational trip (i.e., Ohio water body).4 EPA assumed that Ohio residents whose last 

recreation trip was in-state used Ohio water bodies for all of their recreation trips during the 12-month period; and 

� estimate the total number of in-state trips, summing the weighted number of recreation trips over all Ohio 

respondents. 

EPA found that: 

� An estimated one million individuals made about 6.3 million boating trips to Ohio waters in 1993. In-state residents 

made 90 percent of the boating trips. 

� Approximately one million people visited Ohio water bodies for recreational fishing.5 These visitors accounted for 

about 15.6 million fishing trips to the area. Recreational fishermen from Ohio were the most frequent users of the 

state water resources, representing approximately 97 percent of all visitors. 

� Approximately 972,000 and 896,000 visitors used the Ohio water bodies for near-water viewing and swimming, 

respectively, in 1993. These visitors account for approximately 9.4 and 7.8 million viewing and swimming trips to 

the area. Ohio residents account for 89 percent of viewers and 93 percent of swimmers. 

� Most out-of-state recreational users came from the states surrounding Ohio, such as Indiana, Michigan, and 

Pennsylvania. 

4 NDS collected information only on the last site visited. Its numbers do not reflect people whose last visit was to a different area, but 

who may have also visited an Ohio water body on a previous trip during the year. See Section 21.3 for detail on the NDS data. 

5 EPA compared the estimated number of participants with total fishing licenses issued by Ohio in 1996. Ohio issued a total of 

895,770 licenses for resident and nonresident fishing. The NDS data therefore provide relatively accurate information on participation 

rates. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

20.3.3 Commercial Fishing in Ohio 

Commercial fishing is a minor activity in Lake Erie: 12 license holders share a total of 19 licenses (LECBA 2003). 

Commercial catch data compiled by the Great Lakes Fishery Commission are summarized in Table 20.6 (Baldwin et al. 

2002). 

Table 20.6: Commercial Catches for Ohio 

Lake Erie Waters (1990) 

Fish Catch (1990 lbs) 

Yellow perch 1,559,000 

Carp 1,190,000 

White perch 786,000 

Sheepshead 640,000 

White bass 392,000 

ChannelcCatfish 365,000 

Quillback 134,000 

Buffalo 132,000 

Bullheads 59,000 

Suckers 41,000 

Goldfish 31,000 

Gizzard shad 19,000 

Lake whitefish 10,000 

Rock bass 1000 

Source: Baldwin et al. (2002) 

Yellow perch represents about half of the dockside value for the entire commercial fishery in the Ohio waters of Lake Erie. 

The value of this fishery ranged from $1.3 million to $2.5 million between 1993 and 1998. Overfishing and pollution have 

decreased the yellow perch population throughout Lake Erie dramatically over the past 30+ years. Annual catches averaged 

around 20 million pounds during the 1960s and 70s. The Lake Erie Committee set the 1998 lakewide total allowable 

catch (TAC) quota for this species at 7.44 million pounds. The yellow perch fishery rebounded somewhat over the past 

couple of years, due to strong annual recruitment, strict commercial catch restrictions, and a strict creel limit of 30 fish per 

day for the sport angler (LECB A 2003 ). 

20.3.4 Surface Water Withdrawals 

Water resources provide a wide range of services upon being withdrawn (removed) from the water body. Once used, water 

can be returned to its original sources, returned to another water body, or consumed (e.g., for human drinking water). Water 

withdrawals from surface water averaged 9,615 mgd in 1995 (USGS 1995). The majority of this water is used in power 

generation, accounting for 85 percent of all surface water withdrawals. Public water supply accounts for ten percent of all 

withdrawals. Industrial and commercial water use account for one and four percent of the total, respectively. Water quality 

and quantity impairments can have substantial impacts on the key withdrawal services that water provides to a wide range of 

economic entities. 

20.4 SURFACE WATER QUALITY IN OHIO 

This section describes current water quality conditions in Ohio and the effects of water quality impairments on beneficial uses 

of Ohio’s water resources. Ohio EPA assessed designated use attainment in approximately 42 percent of Ohio streams and 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

rivers; approximately 64 percent of lakes, ponds, and reservoirs; and all of the Lake Erie shoreline (Ohio EPA, OWRI, 1996). 

The OWRI report summarizes the results of this assessment. This report provides information on designated use support by 

water type and use designation, identifies major pollutant/stressors that affect the quality of surface water bodies and prevent 

designated use attainment, and lists major sources of impairment. The following three sections summarize findings from the 

1996 OWRI report. 

20.4.1 Use Attainment in Streams and Rivers in Ohio 

Most water bodies are designated for several uses and more than one use can be impaired at a time. The most commonly 

occurring sole impairment in fresh water bodies is to aquatic life support. The Ohio EPA used an ecosystem approach that 

relies on various tools to determine aquatic life use attainment. Water chemistry, physical and habitat assessment, and direct 

sampling of biota all contribute to determine whether a water body meets an attainment status. Field data yield biological 

indices that eventually determine a final attainment score. 

Ohio EPA assessed 6,560 perennial river miles for aquatic life use attainment. Of the 6,560 river miles assessed for aquatic 

life use: 

� 38.5 percent (2,536 miles) are in full attainment (i.e., all water quality indicators meet criteria for specific water 

bodies); 

� 10.8 percent (708 miles) are in full attainment, but are threatened by pollution and other sources; 

� 23.3 percent (1,528 miles ) are in partial attainment (i.e., one of two, or two water quality indicators do not meet 

criteria); and 

� 27.4 percent (1,797 miles) are in non-attainment (i.e., no criteria are met or the river experiences a severe toxic 

impact). 

Fecal coliform bacteria counts determine recreational use attainment. Such counts are less stringent for Secondary Contact 

Recreation than for Primary Contact Recreation. Ohio EPA has assessed 2,402 river miles for recreation use since 1988 

(Ohio EPA, OW RI, 1996). Of the 2,402 river miles assessed for recreation use: 

� 57 percent (1,370.3 miles) of the sampled rivers and streams are in full attainment (i.e., a water body meets all 

chemical criteria for recreational use and human contact); 

� 19.7 percent (474.1 miles) are in partial attainment (i.e., a water body only partially meets human contact criteria); 

and 

� 23.2 percent (557.4 miles) are in non-attainment (i.e., a water body fails to meet human contact criteria). 

20.4.2 Lake Erie and Other Lakes Use Attainment 

Lake Erie, which has a history of pollution problems, currently has fish consumption advisories for carp and channel catfish 

(Ohio DN R, 1999). Ohio E PA assesses Lake Erie as having partial use attainment for aquatic life and fish consumption, and 

full attainment for recreation.6 Ohio EPA used parameters specified by the Ohio EPA Lake Condition Index (LCI) to 

develop use attainment for other lakes. Only approximately two percent of all lakes are in full use attainment for aquatic life, 

recreation, and fish consumption. Approximately 82, 50, and 53 percent are in full attainment for aquatic life, recreation, and 

fish consumption, respectively, but are threatened by pollution for these categories. High percentages of lake acres are in 

partial attainment for recreation (38.8 percent) and public supply (43.8 percent) use designations. Table 20.7 shows use 

attainment for Lake Erie and other lakes, ponds, and reservoirs. 

6 Further methodologies to better assess use attainment in Lake Erie are still under development by the Ohio EPA. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Table 20.7: Use Attainment Summary for Lake Erie ane Other Lakes 

Use Category % of Total 

Units Assessed Full Attainment Full Attainment, 

Threatened 

Partial 

Attainment Non-Attainment 

% Units % Units % Units % Units % 

Lake Erie (Unit: Shore Miles)a 

Aquatic Life (EWH) 100 236 100 

Recreation 100 231 98 5 2 

Fish Consumption 100 236 100 

Lakes, Ponds, & Reservoirs (Unit: Acres) 

Aquatic Life (EWH) 64.7 1,651 2.2 63,174 82.2 10,686 13.9 1,302 1.7 

Recreation (PCR) 64.4 1,392 1.8 38,499 50.3 29,793 38.9 6,582 9.0 

Public Water Supply 64.1 1,301 1.7 40,846 53.6 33,365 43.8 673 0.9 

a

 Assessments are based on unit of measure presented in parentheses. 

Source: Ohio EPA, OWRI 1996. 

20.4.3 Causes and Sources of Use Non-Attainment in Ohio 

Ohio EPA assessed the causes and sources of impairment to Ohio surface waters and examined trends in major causes and 

sources from previous assessment cycles. The following discussion summarizes findings from the 1996 OWRI report (Ohio 

EPA, 1996). 

a. Causes 

Causes are the agents responsible for damage and threats to aquatic life. The major causes of impairment in Ohio surface 

waters include: 

� organic enrichment/low DO, 

� habitat modifications, 

� siltation, 

� flow alteration, 

� nutrients, and 

� metals. 

Ohio EPA examined trends in these major causes from previous assessment cycles through 1996. They found that point 

source-related causes declined, while non-point sources became major contributors. Ohio EPA concluded that this trend 

“reflects the relative effectiveness of the programs to control point sources compared to general lack of measures to control 

many [non-point sources]” (Ohio EPA, OWRI, 1996). 

Organic enrichment, which alters DO levels and affects aquatic communities, is the main cause of impairment in Ohio’s rivers 

and streams. Inadequate wastewater treatment from municipal and industrial point sources account for most of this 

impairment. Metals are a major cause of impairment to approximately 226 river miles, a moderate cause of impairment to 

179 river miles, and a minor cause of impairment or threat to 165 river miles. 

Nutrients, resulting mostly from agricultural non-point sources, are the main cause of impairment in lakes. Metals are a major 

cause for impairment in approximately 250 acres of Ohio’s lakes, ponds, and reservoirs, and form the main cause of 

impairment in Lake Erie, the major water resource in Ohio (90 percent of the surface water volume). Highly developed areas 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

bordering the lake contribute urban runoff, along with discharges from industrial and municipal sources. Other causes of 

impairment in Lake Erie include priority organics, DO, and nutrients.7 

b. Sources 

Sources are the origins of the agents responsible for damage and threats to water resources. The major sources of impairment 

to Ohio surface waters include: 

� municipal and industrial discharges, 

� hydromodification, 

� agricultural runoff, 

� urban runoff, and 

� mining. 

Point source-caused impairment has declined over time, while that from non-point sources, such as agricultural and urban 

runoff, has increased. Point sources remain a major source of impairment in almost 900 miles, or 25 percent, of Ohio’s 

affected rivers and streams. Point sources are the major source of impairment for Lake Erie. They form a major source of 

impairment for 24 shore miles, and a moderate source of impairment for an additional 281 shore miles of Lake Erie. In 

addition, point sources adversely affect 1,678 lake acres. 

Non-point sources related to agricultural and urban runoff form the major source of impairment for some 9,000 acres, or twothirds of Ohio’s lakes, ponds, and reservoirs. In addition, 46 Lake Erie shore miles list non-point sources as their major 

impairment source. 

20.5 EFFECTS OF WATER QUALITY IMPAIRMENTS ON WATER RESOURCE SERVICES 

Water resource services are negatively affected by pollutants that impair the aquatic ecosystems. Certain pollutants can 

adversely affect aquatic species directly by increasing species morbidity and/or impairing reproductive success, or indirectly 

by adversely altering food chain interactions. These direct and indirect impacts can change quantity and type of fish and other 

species in the aquatic ecosystem. In the worst case scenario, an impaired ecosystem no longer supports any aquatic life. High 

pathogen counts or excessive eutrophication in water bodies that are suitable for swimming may force swimmers to go 

elsewhere or forego swimming altogether. Any aesthetic degradation decreases the value of each individual’s recreational 

experience. In severe cases, the affected water bodies become unsuitable for recreation. Water quality impairments also 

increase the cost of treating water to meet drinking water standards. 

This section details the effects of water quality impairments on in-stream services provided by Ohio’s water resources. 

20.5.1 Effect of Water Quality Impairment on Life Support for Animals and Plants 

Deficiencies in water quantity and quality can impair the health of aquatic ecosystems. In worst case scenarios, the ecosystem 

may no longer support aquatic life at all. The major causes of water quality impairment in Ohio include high biological 

oxygen demand (BOD) from organic enrichment, habitat and flow alterations, nutrients, siltation and turbidity, 

metals, pH, ammonia, and priority organics. Habitat, flow alterations, and thermal discharges are unrelated to MP&M 

effluents and are not discussed here. MP&M effluents contribute to the remaining major causes of water quality impairment, 

with the ecological effects outlined below. 

a. BOD/COD 

BOD and chemical oxygen demand (COD) are two methods to determine the oxygen requirements of pollutants in 

wastewater. Low oxygen level is the primary cause of impairment in Ohio’s rivers and streams and a major source of 

7 Major, moderate, and minor impacts refer to the high, moderate, and slight magnitude codes specified by the U.S. EPA for the 

301(b) report. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

impairments in Ohio’s lakes. When bacteria decompose excess organic matter, they consume DO in surface waters. Oxygen


is needed to chemically (abiotically) oxidize the pollutants present in wastewater. When too much oxygen is needed to


oxidize pollutants, hypoxic (oxygen deficient) or anoxic (oxygen depleted) conditions result. Sources of high oxygen demand


include effluents from municipal treatment plants and certain industries, and runoff from feedlots or farms. Another source is


eutrophication caused by excessive nutrient input. The nutrients stimulate algal blooms. Bacteria consume the algae when


they die, decreasing DO in the water column. DO is a critical variable for fish and invertebrate survival. If oxygen


concentrations drop below a minimum level, organisms suffocate and either move out or die (EPA, 1986). This effect can


drastically reduce the amount of useable aquatic habitat.


b. Nutrients


Nutrients are the leading causes of impairment in Ohio lakes and comprise one of the major causes of impairment in rivers,


streams, and Lake Erie. The overabundance of nitrogen and phosphorus is one of the most documented forms of aquatic


ecosystem pollution. Although both compounds are essential nutrients for phytoplankton (free-floating algae) and periphyton


(attached algae), which form the base of the aquatic food web, too much nutrient input overstimulates primary productivity


and results in eutrophication. The impact of these compounds has contributed significantly to water quality decline in the


United States (EPA, 1992). Phosphorus is a limiting nutrient in most freshwater systems (W etzel, 1983), whereas nitrogen is 

typically limited in estuarine and marine systems. 

In freshwater, excess phosphate (PO4) has been linked to eutrophication and nuisance growth of algae and aquatic weeds


(Wetzel, 1983), even though direct toxicity to fish and other aquatic species is not a major concern. DO in the water column


decreases, however, when algae and other aquatic plants die off, and certain toxins may be produced, both of which can


contribute to fish kills.


c. Siltation and turbidity


Siltation and turbidity are the third leading causes of impairments in Ohio rivers and lakes, except Lake Erie. Siltation is the


most important factor in surface water degradation in the U.S. (EPA, 1992). Major sources include urban and stormwater


runoff, mining and logging activities, and runoff from plowed fields (EPA, 1992). All these inputs create cloudy water with


increased turbidity and decreased visibility and light penetration. High primary productivity by phytoplankton following


excessive nutrient input can also increase turbidity. Excess suspended matter decreases the amount of light penetrating the


water column, which can reduce primary productivity. This turbidity can eliminate or displace fish species requiring clear 

water to live, feed, or reproduce. 

d. Metals


Metals are the leading cause of impairment in Lake Erie and comprise one of the major causes of impairment in inland lakes


and rivers. Metals are naturally-occurring inorganic constituents of the earth’s crust. Priority pollutant metals commonly


found in the aquatic environment include antimony, arsenic, cadmium, chromium, copper, lead, mercury, nickel, selenium,


silver, thallium and zinc (EPA, 1998a). These compounds enter the aquatic environment via urban stormwater runoff, 

industrial and municipal effluents, and atmospheric deposition. As a group, metals can be highly toxic: water quality 

criteria (WQC) for acute toxicity range from around 1,100 �g/l (chromium VI in saltwater) to around 1 �g/l (mercury in 

freshwater); WQC for chronic toxicity range from 120 �g/l (zinc in freshwater) to <1.0 �g/l (mercury in salt- and freshwater) 

and are therefore an order of magnitude lower (EPA, 1998a). 

Once metals reach the aquatic environment, they tend to associate with organic and inorganic particulates in the water 

column. Sediments become long-term sinks for metals, which accumulate in the bottom. Metals can enter the food chain 

when ingested by benthic invertebrates or other burrowing organisms. Most metals have bioconcentration factors 

(BCFs) ranging from 100 to 10,000 and can therefore bioaccumulate in aquatic organisms. A few, including selenium, lead, 

and mercury, may reach hazardous levels in fish or wildlife receptors and result in avian developmental or neurological 

abnormalities. 

e. Organic chemicals


Priority organics are the second most frequent cause of impairment in Lake Erie and comprise one of the major causes of


impairment in rivers and streams. Thousands of different compounds exist as organic chemicals, including petroleum


hydrocarbons and myriad industrial chemicals. They enter the aquatic environments via municipal and industrial effluents,


stormwater runoff, contaminated groundwater, atmospheric deposition, illegal dumping, or accidental releases. Aquatic


toxicities vary by orders of magnitude depending on the compound. Factors influencing toxicity and long-term ecological 

effects include water solubility, volatility, biodegradation potential, and bioaccumulation potential. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Excessive amounts of organic chemicals degrade surface water quality by causing acute or (more typically) chronic toxicity.


This toxicity impairs growth, development, and/or reproductive success in fish and aquatic invertebrates. Persistent and low


water-soluble organic chemicals accumulate in sediments and are taken up into local aquatic food chains. They can reach


dangerous concentrations in fish and avian receptors, resulting in reproductive failures or other avian health effects.


f. pH


Approximately 180 river miles are pH-impaired in Ohio. pH is a measure of acidity. Acid reaches surface waters via


atmospheric deposition (“acid rain”), industrial effluents, and leachates from mine overburdens or spoils. Acidity by itself


is a key variable shaping aquatic communities: it is a toxicant in its own right but also controls metal solubility, and the 

toxicity of several metals and ammonia. 

Aquatic species vary widely in their sensitivity to pH: the most sensitive vertebrate and invertebrate species die off when


average pH ranges between 6.0 and 6.5. Most fish species are eliminated when pH reaches 5.0. Only a few can survive at pH


4.5 (U.S. EPA, 1999). Macro invertebrates exhibit the same pattern, except that hardy species can survive down to a pH of


about 3.5.


g. Ammonia


Large amounts of ammonia enter lakes and rivers via wastewater treatment plants and industrial effluents, atmospheric


deposition, and non-point source surface runoff. Approximately 150 river miles in Ohio are ammonia-impaired. This


compound, unique among regulated pollutants, is also produced naturally inside fish as a metabolic waste product. Excess


ammonia usually diffuses rapidly out of the blood stream and into the surrounding water via the gills. High concentrations of


external un-ionized ammonia (NH3) reduce or reverse this diffusive gradient and allow ammonia to build up to toxic levels 

inside the organism (EPA, 1998c). 

Ammonia in surface water exists in two major forms: un-ionized ammonia (NH3), which is highly toxic to fish or 

invertebrates, and ammonium ion (NH4

+

), which is much less toxic. Which form prevails depends mainly upon the pH level; 

temperature and ionic composition play a smaller role. EPA calculated a WQ C that becomes more severe with decreasing 

acidity. For example, the acute criteria for surface waters containing salmonids equals 36.7 mg/l at pH=6.0 but only 2.14 

mg/l at pH=8.5. For surface waters without salmon, the acute criteria for the same pH equal 55.0 mg/l and 3.2 mg/l, 

respectively (EPA, 1998c). 

20.5.2 Effect of Water Quality Impairment on Recreational Services 

Healthy surface waters are essential to support a diversity of recreational uses, including viewing and other near-water 

activities. Industrial or other human activities impair surface water quality. Certain metals and chlorinated compounds can 

bioaccumulate in aquatic food chains and reach unhealthy levels in carnivorous fish or shellfish. Health advisories to limit or 

avoid their consumption may result. High concentrations of toxic compounds can also lead to human contact advisories. The 

release of untreated or poorly treated sewage can cause high levels of pathogenic bacteria in water and result in swimming 

advisories or beach closures. All of these actions limit the full use of surface waters and can have significant local economic 

impacts. 

a. Fish consumption advisories 

In 1997, the Ohio Department of Health (ODH) issued a statewide fish consumption advisory to protect women of 

childbearing age and children six years or younger against mercury’s neurological and developmental effects. The advisory, 

which applies only to these two population groups, recommended that these women and children eat no more than one meal 

per week of any fish caught in Ohio waters. The advisory covers all state waters because most of the mercury measured in 

fish tissues originates from region-wide fossil fuel combustion processes. The mercury reaches surface waters via 

atmospheric deposition on the surrounding landscape (Ohio DNR, 1999). 

Since 1983, the ODH has developed numerous water body-specific fish consumption advisories for approximately 174 water 

body segments (rivers and lakes) and Lake Erie. These water bodies represent a relatively small fraction of Ohio’s 5,000 

discrete water body segments, as determined by Ohio EPA. The contaminants of greatest concern include polychlorinated 

biphenyls (PCBs), mercury, polycyclic aromatic hydrocarbons (PAHs), lead, organometallics, Mirex, phthalate 

esters, Chlordane, and hexachlorobenzene. Of these, four mercury, PAHs, lead, and phthalates are included on the 

MP&M list of pollutants of concern (POCs). As a group, these contaminants are generally characterized as lipophilic 

(i.e., fat loving), resistant to biological degradation or cellular metabolism, and toxic. Once they reach surface water, they 

20-17 
MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

concentrate in sediments and bioaccumulate or biomagnify through aquatic food chains. These compounds can linger for


decades in aquatic systems.


The kind of sports or recreational fish species affected by the consumption advisories varies by water body segment. More


than 23 different species are covered by advisories, including walleye, common carp, sauger, saugeye, white crappie,


freshwater drum, and various species of bass, perch, catfish, salmon, trout, suckers, and sunfish. Restrictions vary depending


on the pollutant, the fish species concerned, and the concentrations measured in edible tissues. The ODH developed


maximum recommended rates of fish consumption that include outright consumption bans, one meal every two months, one


meal a month, or one meal a week. The same water body segments can commonly have different advisories for different fish


species (Ohio DNR, 1999).


b. Contact advisories


The ODH also issued human contact advisories for nine water body segments in Ohio located on the Black River, Little


Scioto River, Mahoning River, the middle fork of the Little Beaver Creek, and the Ottawa River. Swimming or wading is


prohibited due to the presence of high levels of PAHs, PCB s, Mirex, phthalate esters, and/or Chlordane. Of these, PAHs and


phthalates are included on the list of MP&M POCs. Fish consumption advisories also cover all of these segments (Ohio 

DNR, 1999). 

c. Beach closures


Beach closures typically occur during the summer months when high levels of fecal coliform bacteria or other disease-causing


organisms (e.g., Escherichia coli) proliferate in surface waters. Such waters can become contaminated from several sources,


including: agricultural runoff, sewer overflows, boating wastes, and poor hygienic practices by some bathers. Excessive


levels of indicator pathogens in surface waters can indicate a serious threat to human health and may cause health departments


to post warnings, restrict access, or forbid swimming altogether. The MP&M regulation is not expected to reduce beach 

closures during summer months. 

Numerous public bathing beaches dot Ohio’s 262-mile shoreline along Lake Erie. The ODH has developed a composite 

metric based on E. coli counts in surface waters at 11 selected beaches along Ohio’s north coast. The metric tracks the 

average number of days that swimming advisories are posted at the 11 beaches for a 15 week period beginning around 

Memorial Day and continuing through Labor Day. The most recent data available show that the 11 beaches were under 

advisement an average of 21 days during the summer months (minimum of 0 days and maximum of 49 days) in 1996. 

The ODH developed a 4-tiered scale to score and track the average number of days that the 11 public beaches are under 

advisement from one year to the next. Between 1990 and 1996, the average (based on a five-year running average) number of 

beach advisories scored in the “fair” category consistently, meaning that the beaches were under advisement between 20 and 

30 days in the summer (State of Ohio, 1998). 

Ohio’s lakes, ponds, and reservoirs (excluding Lake Erie) yielded no quantitative data on beach closures. The 1996 Ohio 

Water Resource Inventory of Public Lakes, Ponds and Reservoirs provides a breakdown of the portion of Ohio’s 446 public 

lakes that are threatened or impaired as a result of high levels of fecal coliform bacteria. 

20.6 PRESENCE AND DISTRIBUTION OF ENDANGERED AND THREATENED SPECIES IN 

OHIO 

Many factors can affect the survival of endangered and threatened (E&T) species. Some factors are species-specific; 

others result from one or more anthropogenic stressors. Inherent vulnerability factors include narrow geographic distribution, 

slow reproductive rates, or requirements for large areas. Major anthropogenic stressors include intentional taking (e.g., 

fishing), incidental taking, physically altering habitat (e.g., converting wetlands into agricultural land), water pollution, and 

introducing alien species. A single stressor or a set of stressors can contribute to a species' decline or extinction. Previous 

studies reported that more than 40 percent of endangered aquatic species were affected by five or more environmental 

stressors, and only seven percent of federally-listed species had a single threat to their survival. Although stressors seldom act 

alone, water pollution is one of the major hazards to E&T aquatic species, cited as responsible for the decline of 19 (54 

percent) out of 35 E&T fish species in Ohio (Ohio DN R, 1998). 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

The following sections provide an overview of E&T species found in Ohio, their distribution, and the major hazards 

threatening their survival. Species discussed below include those listed under both the federal Endangered Species Act 

(50 CFR Part 17) and the Ohio Department of Natural Resources’ (DNR) Division of Natural Areas and Preserves. The 

MP&M regulation concentrates on water-related benefits; these sections therefore describe only those species associated with 

aquatic environments.8 The DNR list includes 90 E&T species with a total of 1,227 observations throughout Ohio. 

“Observations” refers to locations where species were observed; most species have multiple observations. This analysis 

includes observations spanning the years 1980 to 1988. 

20.6.1 E&T Fish 

E&T fish inhabit almost every major water body in Ohio, including Lake Erie and the Ohio River and its tributaries. The 

Ohio DNR lists 35 total state-listed E&T fish species, of which 13 are threatened and 22 endangered. The list includes only 

one federally-listed species, the scioto madtom. 

Of the total E&T fish, approximately 12 species use Lake Erie as a possible habitat and nine use the Ohio River. Most of the 

species listed live in riverine habitats. Approximately 28 species were identified in a river system in Ohio, including the 

Ohio, Scioto, Muskingham, Miami, Walhondig, and Maumee River systems. MP&M facilities are found on all these major 

river systems. 

The DNR lists 384 observations of E&T fish in Ohio, of which 240 observations of 30 different species have been reported 

since 1980. Figure 20.2 maps the observations of E&T fish in Ohio and shows the extent to which these observations were 

reported in the state. Multiple observations can occur for a single species. In southern Ohio, most observations come from 

the Muskingham and Scioto River systems and the Ohio River. Most observations in northern Ohio came from Lake Erie 

tributaries or the lake itself. 

In addition to water pollution, cited above as major hazard to E&T aquatic species, other major hazards to E&T fish include 

siltation and impoundments. Approximately two-thirds of E&T fish species are threatened by siltation, and 17 percent are 

threatened by impoundments or dams. MP &M regulations can improve affected ecosystems or habitats by reducing 

discharges from MP&M facilities. These improvements can then help reduce siltation and restore some of the E&T fish 

populations. 

Many obscure E&T fish species have a pure existence value. Some E&T species, like brook trout and lake sturgeon, have 

high potential for consumptive uses. Restoring their populations and those of other commercial and recreational fish species 

may enhance recreational fishing opportunities. Table 20.8 lists E&T fish in Ohio, their habitat locations, and the cause for 

their E&T listing. The table lists species alphabetically by scientific name. 

20.6.2 E&T Mollusks 

Mollusks yield the largest number of reported observations of aquatic E&T species in Ohio, representing 48 percent of the 

total 1,227 observations. The Ohio DNR lists 29 E&T mollusk species, four threatened and 25 endangered. Of these, five 

mollusk species are on the federal endangered species list: catspaw, clubshell, fanshell, white catspaw, and pink mucket. 

Ohio’s E&T mollusks concentrate in five major areas: Lake Erie and the Grand River tributary, Scioto River and Big Arby 

tributary, Muskingham River, Little Miami River, and the Ohio River. E&T mollusk populations reside mostly along the 

mainstems of large rivers and in Lake Erie, but are also found in the St. Joseph, Sandusky, and Cuyahoga Rivers. 

8 “Aquatic species” were identified by the Ohio Department of Natural Resources, Division of Natural Areas and Preserves. These 

species include any species that are “closely associated with aquatic habitats through their breeding or feeding requirements.” 

20-19 
MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Figure 20.2: E&T Fish Observances in Ohioa 

(1980-1997) 

a Each $ represents an observance. 

Source: U.S. EPA analysis. 

20.6.3 Other Aquatic E&T Species 

Improved water quality resulting from the MP &M regulation may also benefit other aquatic E&T species. Unlike fish and 

mollusks, whose primary habitat is a surface water body at all times, these species may use surface water-related habitats only 

for breeding or feeding. Improved water quality may benefit these populations indirectly by enhancing the quality and 

quantity of aquatic biological resources. 

Other aquatic-associated E&T species of Ohio include: 

� Birds ten state-listed species, one threatened and nine endangered, include one federally-listed threatened species, 

the bald eagle. The state-listed species include: American and least bitterns, common and black terns, yellow- and 

black-Crowned night-herons, king rail, osprey, and snowy egret. These species are observed mostly along the Lake 

Erie coast. The bald eagle is observed mostly in Ohio’s northeast corner. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

� Amphibians three state-listed endangered species: blue-spotted salamander, observed in the very northwest 

section of the state along small streams and near the Maumee River; eastern spadefoot, found near the Ohio and 

Muskingham Rivers; and eastern hellbender, observed along the Muskingham and Scioto River systems and 

tributaries of the Ohio River. 

� Reptiles two species: the copperbelly water snake, a state-listed endangered and federally-listed threatened 

species found in lakes and ponds in the northwest corner of Ohio; and the Lake Erie water snake, state-listed as 

threatened and a proposed threatened species for the federal list, found only along the edges of the Lake Erie islands. 

� Mammals the river otter is state-listed as endangered. Sparse observations of the animal come from various small 

creeks and lakes in the eastern part of Ohio. 

� Crustaceans the state-listed endangered Sloan’s crayfish has been observed in several small tributaries of the 

Great Miami River system. 

� Insects nine state-listed species, one threatened and eight endangered, are reported throughout the state. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20

Table 20.8: Endangered and Threatened Fish Species of Ohio 

Common Name Scientific Name Number of Observations Last Observed Federal Status State Status Habitat 

Lake sturgeon Acipenser fulvescens 3 1979 E Lake Erie, spawning in larger rivers such 

as Maumee and Auglaize 

Po

Longnose sucker Catostomus catostomus 1 1950 E Lake Erie Po

ox

Rosyside dace Clinostomus 

funduloides 

53 1997 T Small, upland streams of Teays and 

Little Scioto River systems 

R

Cisco Coregonus artedi 1 1976 E Lake Erie Po

Blue sucker Cycleptus elongatus 2 1985 E Ohio River and lower reaches of large 

tributaries 

Potu

Lake chubsucker Erimyzon sucetta 28 1994 T Lakes (not Erie) and larger streams In

si

Bluebreast darter Etheostoma camurum 19 1995 T Scioto and Muskingham River systems, 

large streams 

Po

Spotted darter Etheostoma maculatum 8 1992 E Large streams of Muskingham and 

Scioto systems 

Po

Tippecanoe darter Etheostoma tippecanoe 11 1994 T Muskingham and Scioto River systems 

Tonguetied minnow Exoglossum laurae 16 1996 T Great Miami River system U

po

Western banded killifish 

Fundulus diaphanus menona 

9 1994 E Lake Erie and larger tributaries Si

Goldeye Hiodon alosoides 16 1989 E Ohio River and lower reaches of large 

tributaries 

Po

Mississippi silvery 

minnow 

Hybognathus nuchalis 1 1983 E Ohio River and tributaries Si

Ohio lamprey Ichthyomyzon bdellium 4 1992 E Ohio River and lower reaches of large 

tributaries 

Po

Northern brook lamprey 

Ichthyomyzon fossor 25 1992 E Small streams, tributaries of Grand and 

Scioto rivers 

Po

Mountain brook lamprey 

Ichthyomyzon greeleyi 6 1993 E Mahoning River and tributaries Po

Silver lamprey Ichthyomyzon unicuspis 40 1993 T Lake Erie and larger tributaries Po

Blue catfish Ictalurus furcatus 1 1987 E Scioto River 

Spotted gar Lepisosteus oculatus 1 1978 E Lake Erie Si

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MP&M EEBA Part V: Ohio Case Study C

Table 20.8: Endangered and Threatened Fish Species of Ohio 

Common Name Scientific Name Number of Observations Last Observed Federal Status State Status Habitat 

Shortnose gar Lepisosteus 

platostomus 

9 1981 E Scioto River and tributaries 

Speckled chub Macrhybopsis 

aestivalis 

1 1990 E Ohio and Muskingham rivers, large 

rivers 

Greater redhorse Moxostoma 

valenciennesi 

12 1989 T Maumee river system, large streams 

Popeye shiner Notropis ariommus 4 1993 E Extirpated from Ohio, creeks and sm

rivers of Maumee system 

Bigeye shiner Notropis boops 22 1995 T Great Miami River and Ohio River 

systems, upland streams 

Bigmouth shiner Notropis dorsalis 16 1994 T Black and Rocky River systems, broo

and small streams 

Blackchin shiner Notropis heterodon 2 1983 E Lake Erie and other lakes 

Blacknose shiner Notropis heterolepis 7 1983 E Lake Erie and other lakes 

Mountain madtom Noturus eleutherus 11 1991 E Ohio River tributaries, larger streams 

rivers 

Northern madtom Noturus stigmosus 10 1989 E Muskingham, Little Miami, Walhond

Rivers 

Scioto madtom Noturus trautmani 1 1957 E E Big Darby Creek, tributary of Scioto 

Pugnose minnow Opsopoeodus emiliae 6 1982 E Lakes, canals, streams, and Lake Eri

Channel darter Percina copelandi 18 1991 T Lake Erie and Ohio River River darter Percina shumardi 8 1989 T Lake Erie and larger tributaries of Oh

River 

Paddlefish Polyodon spathula 11 1996 T Ohio River tributaries, larger streams 

rivers 

Brook trout Salvelinus fontinalis 1 1997 T Tributaries of Lake Erie 

Source: Division of Natural Areas and Preserves, Ohio Department of Natural Resources, Natural Heritage Program 1998. 
MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

GLOSSARY 

ammonia: a compound of nitrogen and hydrogen (NH3). It is a colorless, pungent gas. 

biological oxygen dem and (BOD): the amount of dissolved oxygen consumed by microorganisms as they decompose 

organic material in polluted water. 

bioconcentration factors (BCFs): indicators of the potential for chemicals dissolved in the water column to be taken up 

by aquatic biota across external surface membranes, usually gills. 

biotic: pertaining to the characteristics of a naturally occurring assemblage of plants and animals that live in the same 

environment and are mutually sustaining and interdependent. 

chemical oxygen dem and (COD): the amount of oxygen consumed in the complete chemical oxidation of matter, both 

organic and inorganic, present in polluted water. 

Coldwater Habitat (CWH): a designation assigned to a water body based on the potential aquatic assemblage. 

dissolved oxygen (DO): oxygen freely available in water, vital to fish and other aquatic life and for the prevention of 

odors. DO levels are considered a most important indicator of a water body's ability to support desirable aquatic life. 

Secondary and advanced waste treatment are generally designed to ensure adequate DO in waste-receiving waters. 

(http://www.epa.gov/OCEPAterms/dterms.html) 

endangered and threatened (E&T): animals, birds, fish, plants, or other living organisms threatened with extinction by 

anthropogenic (i.e., man-caused) or other natural changes in their environment. The Endangered Species Act contains 

requirements for declaring a species endangered. 

Endangered Species Act: federal legislation enacted in 1973 that protects animals, birds, fish, plants, or other living 

organisms threatened with extinction by anthropogenic or other natural changes in their environment. For a species to be 

protected under this act it must be "listed" as either an "endangered" or "threatened" species. 

eutrophication: process by which bodies of water receive increased amounts of dissolved nutrients, such as nitrogen and 

phosphorus, that encourage excessive plant growth and result in oxygen depletion. 

Exceptional Warmwater Habitat (EWH): the aquatic life use designed to protect aquatic communities of exceptional 

diversity and biotic integrity. Such communities typically have a high species richness; often include strong populations of 

rare, endangered, threatened, and declining species; and/or are exceptional sport fisheries. 

existence services: services that are not linked to current uses of water bodies. They arise from the knowledge that 

species diversity or the natural beauty of a given water body is being preserved. 

in-stream services: water use taking place within the stream channel for purposes such as life support for animals and 

plants, water-based recreation, hydroelectric power generation, navigation, commercial fishing, water storage, and aesthetics. 

Limited Resource Waters (LRW): an aquatic life use assigned to streams with very limited aquatic life potential, usually 

restricted to highly acidic mine drainage streams, or highly modified small streams (<3 sq. mi. drainage area) in urban or 

agricultural areas with little or no water during the summer months. 

Limited Warmwater Habitat (LWH): see limited resource waters. 

metals: inorganic compounds, generally non-volatile (with the notable exception of mercury), that cannot be broken down 

by biodegradation processes. They are of particular concern due to their prevalence in MP&M effluents. Metals can 

accumulate in biological tissues, sequester into sewage sludge in POTW s, and contaminate soils and sediments when released 

into the environment. Some metals are quite toxic even when present at relatively low levels. 

�g/l: micrograms per liter. 

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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

Modified Warmwater Habitat (MWH): aquatic life use assigned to streams that have irretrievable, extensive, man-


induced modifications that preclude attainment of the Warmwater Habitat use, but which harbor the semblance of an aquatic


community. Such waters are characterized by poor chemical quality (low and fluctuating dissolved oxygen), degraded habitat


conditions (siltation, habitat simplification), and species that are tolerant of these effects.


nonconventional pollutants: a catch-all category that includes everything not classified as either a priority or


conventional pollutant.


nutrients: any substance, assimilated by living things, that promotes growth. The term is generally applied to nitrogen and


phosphorus in wastewater, but is also applied to other essential and trace elements.


(http://www.epa.gov/OCEPAterms/nterms.html)


Ohio EPA Lake Condition Index (LCI): an ecologically-based index that aggregates results across ten ecological


metrics.


Ohio Water Resource Inventory (OWRI): a biennial report to U.S. EPA and Congress required by Section 305(b) of the


Clean Water Act. The report is composed of four major sections: (1) inland rivers and streams, wetlands, Lake Erie, and water


program description; (2) fish tissue contaminants; (3) inland lakes, ponds, and reservoirs; and (4) groundwater.


overburdens: rock and soil cleared away before mining.


(http://www.epa.gov/OCEPAterms/oterms.html)


pH: an expression of the intensity of the basic or acid condition of a liquid. Natural waters usually have a pH between 6.5


and 8.5. (http://www.epa.gov/OCEPAterms/pterms.html)


pollutants of concern (POCs): the 131 contaminants identified by EPA as being of potential concern for this rule and


that are currently being discharged by MP&M facilities. EPA used fate and toxicity data, in conjunction with various


modeling techniques, to identify these pollutants and assess their potential environmental impacts on receiving water bodies


and POTWs. MP&M pollutants of concern include 43 priority pollutants, 3 conventional pollutants, and 86 nonconventional


pollutants.


polychlorinated biphenyls (PCBs): a group of toxic, persistent chemicals that are mixtures of chlorinated biphenyl


compounds having various percentages of chlorine. PCBs are industrial chemicals formerly used in electrical transformers


and capacitors for insulating purposes, and in gas pipeline systems as a lubricant.


polycyclic aromatic hydrocarbons (PAHs): a class of organic compounds with a fused-ring aromatic structure. PAHs


result from incomplete combustion of organic carbon (including wood), municipal solid waste, and fossil fuels, as well as


from natural or anthropogenic introduction of uncombusted coal and oil. PAHs include benzo(a)pyrene, fluoranthene, and


pyrene.


Primary Contact Recreation (PCR): water recreation activities requiring full human body immersion, such as


swimming, diving, water skiing, and surfing.


priority organics: prority pollutants that are organic chemicals.


priority pollutants: 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,


sediment, groundwater, or soil samples.


random utility model (RUM): a model of consumer behavior. The model contains observable determinants of consumer


behavior and a random element.


Secondary Contact Recreation (SCR): water recreation activities requiring some direct contact with water but where


swallowing of water is unlikely, such as paddling, wading, and boating.


siltation: deposition of finely divided soil and rock particles on the bottom of stream and river beds and in reservoirs.


Survey of National Demand for Water-based Recreation (NDS): a U.S. EPA survey of recreational behavior. The


1993 survey collected data on socioeconomic characteristics and water-based recreation behavior using a nationwide stratified


random sample of 13,059 individuals aged 16 and over. (http://www.epa.gov/opei)


20-25 
MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

total allowable catch (TAC): amount of fish permitted to be removed under a fishery management regime in which the 

total catch allowed of a certain species for a fishing season has been fixed in advance. 

“toxic” pollutants: refers to the 126 priority or toxic pollutants specifically defined as such by EPA, as well as 

nonconventional pollutants that have a toxic effect on human health or aquatic organisms. 

turbidity: cloudy condition in water that interferes with the passage of light through the water column. It is caused by the 

presence of suspended silt or organic matter in the water body. 

un-ionized: neutral form of an ionizable compound. With reference to ammonia, it is the neutral form of ammonia-nitrogen 

in water, usually occurring as NH4OH. Un-ionized ammonia is the principal form of ammonia that is toxic to aquatic life. The 

relative proportion of un-ionized to ionized ammonia (NH4+) is controlled by water temperature and pH. 

Warmwater Habitat (WWH): a designation assigned to a water body based on the potential aquatic assemblage. 

water quality criteria (WQC): specific levels of water quality that, if reached, are expected to render a body of water 

suitable for certain designated uses. 

withdrawal services: services associate with water removed from the ground or diverted from a surface-water source for 

uses such as drinking water supply, irrigation, production and processing services, and sanitary services. 

20-26 
MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

ACRONYMS 

BCFs: bioconcentration factors


BOD: biological oxygen demand


COD: chemical oxygen demand


CWH: Coldwater Habitat


DO: dissolved oxygen


E&T: endangered and threatened


EWH: Exceptional Warmwater Habitat


LRW: Limited Resource Waters


LWH: Limited Warmwater Habitat


MWH: Modified Warmwater Habitat


ODH: Ohio Department of Health


DNR: Ohio Department of Natural Resources


LCI: Ohio EPA Lake Condition Index


OWRI: Ohio Water Resource Inventory


POCs: pollutants of concern


PCBs: polychlorinated biphenyls


PAHs: polycyclic aromatic hydrocarbons


PCR: Primary Contact Recreation


RUM: random utility model


SSH: Seasonal Salmonid Habitat


SCR: Secondary Contact Recreation


NDS: Survey of National Demand for W ater-based Recreation


TAC: total allowable catch


WWH: Warmwater Habitat


WQC: water quality criteria


20-27 
MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 

REFERENCES 

Baldwin, N. A., R. W. Saalfeld, M. R. Dochoda, H. J. Buettner, and R.L. Eshenroder. August 2002. Commercial Fish 

Production in the Great Lakes 1867-2000. http://www.glfc.org/databases/commercial/commerc.asp 

Lake Erie Charter Boat Association (LECB A). 2003. www.lecba.org. 

Ohio Department of Natural Resources, Ohio Division of Wildlife. 1999. Fish Consumption Advisories. 

http://www.dnr.state.oh.us/odnr/wildlife/index.html. 

Ohio Department of Natural Resources, Division of Natural Areas and Preserves. 1998. Database File of Aquatic and 

Associated Aquatic Endangered & Threatened Animals. 

Ohio Environmental Protection Agency. 1998. State of the Lake Report (www.epa.ohio.gov/oleo/leqi/leqi.html) 

Ohio Environmental Protection Agency. 1996. Ohio Water Resource Inventory. Volume 1: Summary, Status , and Trends 

and 3: Ohio’s Public Lakes, Ponds, and Reservoirs. www.chagrin.epa.state.oh.us/document_index. 

U.S. Department of Agriculture (USDA). 1992a. Agricultural Waste Management Field Handbook. National Engineering 

Handbook Series, Part 651. 210-AWM FH, 4/92. 

U.S. Department of Agriculture. 1992b. National Resources Inventory. National Resources Conservation Services. 

http://www.ftw.nrcs.usda.gov/nri_data.html. 

U.S. Department of Commerce, Bureau of the Census. 1992. Census of Manufactures, Census of Transportation, Census of 

Wholesale Trade, Census of Retail Trade, Census of Service Industries. 

U.S. Department of Commerce, Bureau of the Census. 1999. Ohio Population, Demographic, and Housing Statistics. 

http://www.census.gov/cgi-bin/datamap/state?39. 

United States Geological Survey (USGS). 1995. Water Use in the United States. http://water.usgs.gov/watuse. 

U.S. Environmental Protection Agency (U.S. EPA). 1986. Ambient Water Quality Criteria for Dissolved Oxygen. EPA 

440/5-86-003. 

U.S. Environmental Protection Agency (U.S. EPA). 1992. Managing Non-point Source Pollution: Final Report to 

Congress. EPA-506/9-90. 

U.S. Environmental Protection Agency (U.S. EPA). 1994. National Demand for Water-Based Recreation Survey. 

Washington, D.C.: Office of Policy Evaluation and Information. 

U.S. Environmental Protection Agency (U.S. EPA). 1998a. National Recommended Water Quality Criteria; Notice; 

Republication. 63(237:68354-68364). 

U.S. Environmental Protection Agency (U.S. EPA). 1998b. Condition of the Mid-Atlantic Estuaries. EPA 600-R-98-147. 

U.S. Environmental Protection Agency (U.S. EPA). 1998c. 1988 Update of Ambient Water Quality Criteria for Ammonia. 

EPA 822-R-98-008. 

U.S. Environmental Protection Agency (U.S. EPA). 1999. Progress Report on the EPA Acid Rain Program. U.S. EPA 

Office of Air and radiation. EPA 430-R-99-011. 

Wetzel, R.G. 1983. Limnology, 2nd ed. Saunders College Publishing. 

20-28 
MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Chapter 21: Modeling Recreational


Benefits in Ohio with a RUM Model


INTRODUCTION 

The recreational benefits analysis outlined in this chapter 

focuses on Ohio as a case study of the MP&M regulation's 

expected benefits. EPA combined water quality modeling 

and a random utility model of consumer behavior (RUM) 

to assess how changes in water quality from the MP&M 

regulation will affect consumer valuation of water resources 

for recreational uses. The RUM analysis provides a 

framework for estimating the effect of ambient water quality 

and other site characteristics on the total number of trips 

taken for different water-based recreation activities and the 

allocation of these trips among particular sites. 

The Agency used this case study to address limitations 

inherent in the benefits transfer method used in the analysis 

of recreational benefits at the national level (see Chapter 15 

for detail). The RUM model assesses water quality 

characteristics directly affected by the MP&M regulation, 

such as presence of ambient water quality criteria 

(AWQC) exceedances and nonconventional nutrient Total 

Kjeldahl Nitrogen (TKN) concentrations and their effect 

on recreation behavior. The direct link between the water 

quality measures included in the RUM model and the water 

quality measures affected by the regulation, as well as the 

site specific nature of the analysis reduce uncertainty in 

benefit estimates. In general, RUM models are well-regarded 

in the economic literature and when these models are 

appropriately applied, the results are thought to be quite 

reliable. 

CHAPTER CONTENTS 

21.1 Methodology ................. ........... 21-2 

21.1.1 ................. ......... 21-2 

21.1.2 he Site Choice Decision ...... 21-3 

21.1.3 rip Participation ............ 21-6 

21.1.4 ting Welfare Changes from Water 

Quality Improvements ................. . 21-9 

21.1.5 the State Level . . 21-10 

21.2 ................. ................. . 21-10 

21.2.1 ................. .... 21-11 

21.2.2 Visits to Sites . . . 21-14 

21.2.3 ................. 21-14 

21.3 ................ 21-17 

21.3.1 l ................. .... 21-18 

21.3.2 ................. .... 21-19 

21.3.3 ................. .. 21-20 

21.3.4 Activity) Model . . . 21-20 

21.4 el ................. .. 21-20 

21.5 ating Benefits from Reduced MP&M 

Discharges in Ohio ................. ... 21-23 

21.5.1 iting Reaches in Ohio ........... 21-23 

21.5.2 ating creational Benefits in Ohio 21-24 

21.6 itations and Uncertainty ................ 21-25 

21.6.1 ................. 21-25 

21.6.2 One-Day Trips Only ......... 21-26 

21.6.3 nefits ................. ... 21-26 

21.6.4 ial Sources of Survey Bias ....... 21-26 

Glossary ................. ................. .. 21-28 

Acronyms ................. ................. . 21-30 

References ................. ................. 21-31 

Overview 

Modeling t

Modeling T

Calcula

Extrapolating Results to 

Data 

The Ohio Data 

Estimating the Price of 

Site Characteristics 

Site Choice Model Estimates 

Fishing Mode

Boating Model 

Swimming Model 

Viewing (Near-water 

Trip Participation Mod

Estim

Benef

Estim Re

Lim

One-State Approach 

Including 

Nonuse Be

Potent

Benefits transfer results are subject to uncertainty because water quality changes evaluated in available recreation demand 

studies are only roughly comparable with water quality measures considered in regulatory development. This case study 

analysis improves upon previous recreation demand studies that focused mainly on directly observable water quality effects, 

e.g., designated use support (i.e., whether a water body supports fishing), the presence of fish advisories, an oil sheen, or 

eutrophication. The Ohio case study includes unobservable water quality effects as well. The MP&M regulation affects a 

broad range of pollutants, many of which are toxic to human and aquatic life but are not directly observable (i.e., priority and 

nonconventional pollutants. These unobservable toxic pollutants degrade aquatic habitats, decrease the size and 

abundance of fish and other aquatic species, increase fish deformities, and change watershed species composition. Water 

quality changes (i.e., changes in toxic pollutant concentrations) affect consumers’ water resource valuation for recreation, 

even if consumers are unaware of changes in ambient pollutant concentrations. 

This study allows for a more complete estimate of recreational benefits from reduced discharges of MP&M pollutants. In 

addition to estimates of recreational benefits from reduced frequency of AWQC exceedances, the Ohio case study evaluated 

changes in the water resource values from reduced discharges of TKN . The analysis also values additional recreational uses 

not addressed in the national analysis, such as swimming. 

21-1 
MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

The study used data from the National Demand Survey for Water-Based Recreation (NDS), conducted by U.S. EPA 

and the National Forest Service, to examine the effects of in-stream pollutant concentrations on consumer decisions to visit a 

particular water body (U.S. EPA, 1994). 

21.1 METHODOLOGY 

21.1.1 Overview 

The Ohio study combines direct simulation and inferential analyses to assess how changes in water quality will affect 

consumers’ valuation of water resources. 

The direct simulation analysis component estimates baseline and post-compliance water quality at recreation sites actually 

visited by the surveyed consumers and all other sites within the consumers’ choice set, visited or not. 

The inferential analysis component, a RUM analysis of consumer behavior, estimates the effect of ambient water quality and 

other site characteristics on the total number of trips taken for different water-based recreation activities and the allocation of 

these trips among particular recreational sites. The RUM analysis is a travel cost model (TCM), in which the cost to travel 

to a particular recreational site represents the “price” of a visit. 

The main advantage of the RUM model is inclusion of the effect of substitute sites on site values. For any particular site, 

assuming that it is not totally unique in nature, the availability of substitutes makes the value for that site lower than it would 

be without available substitutes. 

EPA modeled two consumer decisions: 

� how many water-based recreational trips to take during the recreational season (the trip participation model); and 

� conditional on the first decision, which recreation site to choose (the site choice model). 

The econometric estimation proceeded in two steps, each corresponding to the above decisions. The Agency estimated these 

decisions in reverse order (i.e., EPA modeled the second decision, site choice, first). 

� Modeling the Site Choice Decision. Assuming that a consumer decides to take a water-based recreation trip, EPA 

estimated the likelihood that the consumer will choose a particular site as a function of site characteristics, the price 

paid per site visit, and household income. A consumer weighs the attributes for various "choice set" sites against the 

travel costs to each site. These travel costs include both the cost of operating a vehicle and the opportunity costs of 

time spent traveling. The consumer then weighs the value given to the site's attributes against the cost of getting to 

the site when making a site selection. The site choice model estimates how recreational users value access to specific 

sites, and estimates per trip economic values for changes in water quality at recreational sites in the study area. 

EPA estimated the site choice model using a two-level nested multinomial logit (NMNL) model, which groups 

sites with similar characteristics. The nested logit model assumes that individuals first choose the group of sites and 

then a site within that group. This study assumes that individuals first choose a water body type (Lake Erie, rivers, or 

small lakes) and then a specific site. EPA used the estimated site-choice model coefficients to estimate the value to 

the consumer of being able to choose among Ohio recreation sites on a given day. This measure is referred to as the 

“inclusive value.” 

� Modeling Trip Frequency. The site choice models estimated in the previous step treat the total number of 

recreational trips taken each season as exogenous to the site selection. The Agency estimated the expected 

number of trips taken during the recreation season using a Negative Binomial Poisson model (Hausman et al., 

1995; Feather et al., 1995; and Creel and Loomis, 1992), which treats trip frequency as a pre-season decision 

regarding total participation in a given recreation activity. 

EPA estimated the total number of trips during the recreation season as a function of the expected maximum 

utility (inclusive value) from recreational activity participation on a trip, and socioeconomic characteristics affecting 

demand for recreation trips (e.g., number of children in the household). The coefficient of the individual’s expected 

21-2 
MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

maximum utility of taking a trip) provid ed a m eans o f estimating the seas onal welfare effect of water qua lity 

improvements, beca use ch anges in water quality ch ange the value of av ailable recre ation sites . 

Estimating the site choice and total trip p articipa tion mode ls jointly is theo retically possible, but co mpu tational requirements 

make an integrated utility-theoretic model infeasible. EPA estimated separate site choice and trip frequency models for the 

four recreational activities: boating, swimming, fishing, and near-water recreation (e.g., viewing wildlife).1 

The Agency used estimated coefficients of the indirect utility function with estima ted ch anges in water quality to calcula te 

per-trip chang es in co nsum er welfare from impro ved water q uality at rec reation sites within e ach c onsu mer c hoice set. Trip 

frequency per season increases if site water quality changes are substantial. A sample consumer’s expected seasonal welfare 

gain is therefore a function of both welfare gain per trip and the estimated change in number of trips per season. 

Combin ing the trip freque ncy mo del’s p redic tion of trip s unde r the ba seline an d po st-com plianc e and the site cho ice mo del’s 

corresponding per-trip welfare measure yields the total se aso nal w elfare measure. 

EP A ca lculated each individ ual’s seasona l welfare g ain for e ach re creatio n activity fro m po st-com plianc e wate r qua lity 

changes, and then used Census population data to aggregate the estimated welfare change to the state level. The sum of 

estimated welfare changes over the four recreation activities yielded estimates of total welfare gain. 

To analyze water quality improveme nt benefits in the RUM framewo rk, EPA used available discharge, ambient concentration, 

and other relevant data to measure baseline and post-compliance water quality at the impact sites. H provides 

detail on water q uality modeling used in this analysis. 

21.1.2 

EPA used the RUM framework to estimate the probability of a consumer visiting a recreation site. ramework is based 

on the assumption that a consumer derives utility from the recreational activity at each recreation site. Each visit decision 

involves cho osing one site and excluding o thers. 

The consumer’s decision involves comparing each site and choosing the site that produces the maximum utility. An observer 

cannot measure all potential determinants of consumer utility, so the indirect utility function will have a non-random element 

(V) and a random error term (�), such tha t the actua l determ inants of consumer utility V� = V + �. e pro bab ility (�jn) that 

site j will be visited by an individual n is defined as: 

(21.1) 

where: 

Vjn + �jn = utility of visiting site j, and 

Vsn + �sn = utility of visiting a substitute site. 

Estimating the m ode l requires spe cifying the fun ctiona l form o f the indire ct utility function , V, in wh ich site ch oice is 

mod eled a s a functio n of site ch aracte ristics and the “pric e” to v isit particular sites. r exam ple, a se t of con ditiona l utility 

functions (one for each site alternative j in the choice set) can b e determ ined as follows: 

(21.2) 

where: 

Vjn = the utility realized from a conventional budget-constrained, utility maximization model conditional on choice of 

site j by consumer n; 

�M = marginal utility of income; 

M jn = the income of individual n availab le to visit site j; 

Appendix

Modeling the Site Choice Decision 

This f

Th 

Fo 

1
 The Agency also attempted a model structure that allows for interaction among the choice of recreational activities. In this model, a 

person first chooses a recreational activity and then chooses a site. This model did not perform very well because less than ten percent of 

recreational users included in the dataset participate in all four activities. 

21-3

MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Pjn = a composite measure of travel and time costs for consumer n on site alternative j; 

� = a vector of coefficients representing the marginal utility of a specified site characteristic to be estimated along 

with �M (e.g., size of the water body, presence of boating ramps); and 

Xjn = a vector of site characteristics for site alternative j as perceived by consumer n. These characteristics include the 

actual monitored and/or modeled water quality parameters that are hypothesized to be determinants of consumer 

valuation of water-based recreation resources, and that may also be affected by the MP& M regulation. 

The magnitude of the coefficients in Equation 21.2 reflects the relative importance of site characteristics when consumers 

decide which site to visit. The coefficients (�) of water quality characteristics of recreation sites are expected to be positive; 

that is, all else being equal, consumers of water-based recreation would prefer "cleaner" recreation sites. The coefficient on 

travel cost is expected to be negative, i.e., consumers prefer lower travel costs. 

To estimate the site choice probabilities, EPA specified and estimated a nested multinomial logit model (NM NL) for fishing, 

boating, and swimming activities. The nested structure explicitly groups similar alternatives, which allows for a richer pattern 

of substitution among alternative sites. The NMNL is based on the assumption that an individual chooses first between 

groups of alternatives and then, within the chosen group, between individual alternatives. For this analysis, EPA grouped all 

recreational sites in Ohio by water body type based on site similarities. EPA tested various alternative site groupings, but the 

models presented here were most successful at explaining the probability of selecting a site. The best model used the 

following activity-specific site groupings:2 

� Fishing model: 

� Group 1: Lake Erie sites; 

� Group 2: river sites; 

� Group 3: small lakes and reservoirs; 

� Boating model: 

� Group 1: Lake Erie sites; 

� Group 2: inland sites, including rivers, small lakes, and reservoirs; 

� Swimming model: 

� Group 1: Lake Erie sites; 

� Group 2: inland sites, including rivers, small lakes, and reservoirs; 

� Viewing model: EPA used a non-nested model in which an individual compares all sites and chooses the one 

offering the highest utility level for each trip occasion. 

First, the Agency attempted to estimate a nested model based on the three water body types lakes, rivers, and Lake Erie 

for all four recreational activities included in the analysis. This structure, however, performed well only for fishing. A 

two-nested model that included inland and Lake Erie sites seemed to perform better for the boating and swimming models. 

None of the nested structures performed well for participants in near-water/wildlife viewing activities. 

This finding is not surprising because sites are grouped based on their similarities within a given nest. It is reasonable to 

assume that inland lakes, rivers, and Lake Erie sites are dissimilar from an angler's point of view, because each of the three 

water body types is likely to support different fish species. Lake sites may therefore not be close substitutes for rivers sites. 

For other activities, differences in fishery resources across water body types are unlikely to be important. Water body size 

and the presence of recreational amenities are likely to play a more important role than differences in fish species and the type 

of aquatic habitat. Lake and river sites may therefore be regarded as substitutes for each other by boaters and swimmers. 

Lake Erie, on the other hand, is a unique water resource that differs from inland water bodies because of its physical 

characteristics (e.g., size and water temperature); river and lake sites are therefore not likely to be considered substitutes for 

Lake Erie sites. Finally, participants in near-water recreation use water resources indirectly and are therefore more likely to 

regard recreational sites located on different water body types as close substitutes to each other. For this reason, the viewing 

model is a simple logit model without a nested structure. 

2 Three of the four models (fishing, boating, and swimming) passed specification tests for appropriateness of a nested structure (see 

Section 21.3 for detail). Test results showed that only two site groups are appropriate for the boating and swimming models inland sites 

(rivers, small lakes, and reservoirs) and Lake Erie sites in Ohio. The fourth activity, wildlife viewing, did not pass specification tests for a 

nested structure and was estimated as a flat multinomial logit (MNL) model. 

21-4 
MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

The models assume that an individual first decides to visit a specific water body grouping (hereafter, region), then decides 

which site within that group to visit. An individual probability of visiting site j, given the choice of region R, is a simple 

multinomial logit. If the random terms �nj for individual n at site j are independently and identically distributed and have an 

extreme value Weilbull distribution, then � jn takes the form (McFadden, 1981): 

(21.3) 

where: 

π jn|r = probab ility of selecting site j in region r; 

= the consumer’s utility from visiting site j; 

r = regions -- “Lake Erie,” “rivers,” etc. as specified above for a given activity; and 

= the sum of the consumer’s utility at each site j for all sites in the opportunity set for region r. 

Estimated parameters of the indirect utility function are then used to estimate the inclusive value. n, the 

inclusive value measures the overall quality of recreational opportunities for each water-based activity and represents the 

expected maximum utility of taking a trip. although EPA used a rando m draw from the oppo rtunity set for the 

purp ose o f estimating the mo del param eters, the Agency calculated the inclusive value (i.e., the ex pected m aximu m utility) 

using all recreation sites in the consumer's opportunity set in a given region. 

The inclusive value is calculated as the log of the denominator in Equation 21.2 (M cFadden, 198 1). 

(21.4) 

where: 

Ir = inclusive value for sites associated with region R; 

= individual n’s utility from visiting site j; and 

W = a vector o f baseline water qua lity characteristics. 

The prob ability of choosing a p articular region is: 

(21.5) 

where: 

π r = probability of selecting region r; 

Ir = the inclusive values for a given region; 

γr = the coefficient on the inclusive value for a given region; 

r = activity-specific regions (e.g., “Lake Erie,” “rivers,” and “small lakes” for fishing). 

To estimate the mo del d escrib ed b y Eq uation s 21.2 and 2 1.5, E PA used a stand ard sta tistical softwa re pa ckag e, LIMDEP. 

For consumer 

Note that,

and 

21-5

MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

21.1.3 Modeling Trip Participation 

After modeling the site choice decision, the next step modeled the determinants of the number of water-based recreation trips 

a consumer takes during a season. To link the quality of available recreation sites with consumer demand for recreation trips, 

EPA modeled the number of recreation trips taken during the recreation season as a function of the inclusive value estimated 

in the previous step and socioeconomic characteristics affecting demand for recreation activities. The dependent variable, the 

number of recreation trips taken by an individual during the recreation season, is an integer value greater than or equal to 

zero. To account for the non-negative property of the dependent variable, EPA used count data models based on probability 

densities that have the non-negative integers as their domain. 

One of the simplest count data models is a Poisson estimation process, which is commonly used with count data, such 

as number of recreation trips taken during the recreation season. Inherent in the model specification is the assumption that 

each observation of a number of trips is drawn from a Poisson distribution. Such a distribution favors a large number of 

observations with small values (e.g., two trips, four trips) or zeros, resulting in its being skewed toward the lower end. Due to 

the nature of the observed number of trips, it is quite reasonable to assume that the underlying distribution can be 

characterized as a Poisson distribution. Figure 21.1 shows the number of recreation trips taken per year and the number of 

respondents who reported taking that number of trips. 

21-6 
MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreation

Figure 21.1: Number of Trips Per Year By Activity Type

Source: U.S. EPA analysis of NDS data (U.S. EPA, 1994)
MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Estimating the Poisson model is similar to estimating a nonlinear regression. The single parameter of the Poisson distribution 

is �, which is both the mean and variance of yn. The probability that the actual number of trips taken is equal to the estimated 

number of trips is estimated as follows (Green, 1993): 

(21.6) 

where: 

Yn = the actual number of trips taken by an individual in the sample; 

yn = the estimated number of trips taken by an individual in the sample; 

n = 1, 2,..., N, the number of individuals in the sample; and 

�n = ��X, expected numb er of trips for an individual in the sample, where X is a vector of variables affecting the 

demand for recreational trips (e.g., inclusive values and socioeconomic characteristics) and β is the vector of 

estimated co efficients. 

From Equation 21.6, the expected number of water-based recreation trips per recreation activity season taken by an individual 

is given b y: 

(21.7) 

where: 

E[yn|xn] = the exp ected numb er of trips, yn, given xn; 

Var[yn|xn] = the variance of the numbe r trips, yn, given xn; 

� = a vector of coefficients on x; and 

x = a matrix of socioec ono mic va riables and inclusive values. 

An empirical drawback of the Poisson model is that the variance of the number of trips taken must be equal to the mean 

numb er of trips, and this equa lity is not always supp orted by actual data. ticular, the ND S survey data exhibit 

overdispersion, a condition where variance exceeds the mean. The estimated variance-to-mean ratios of the number of 

trips in the NDS d ata sample are 31, 2 7.9, 3 5.6, and 10.5 fo r fishing, swim ming, viewing, and boating trips, resp ectively. 

Overdisp ersion is therefo re pre sent in the data se t. 

To address the problem of overdispersion, EPA used the negative binom ial regression mo del, an extension of the 

Poisson regression model, which allows the variance of the number of trips to differ from the mean. In the negative binomial 

mod el, � is respecified so that (Green, 1993): 

(21.8) 

where the error term (�) has a gamm a distribution, E[exp(�i

)] is equal to 1.0, and the variance of � is �. 

Th e resulting probab ility distributio n is: 

(21.9) 

where: 

yn = 0,1,2 ... numb er of trips taken b y individual n in the sample; 

n = 1,2,..., N number of individuals in the sample; and 

�n = expected number of trips for an individual in the sample. 

In par

21-8

MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Integrating � from E quation 21 .9 pro duces the un conditiona l distribution of yn. ive binomial model has an 

add itional p aram eter, α, which is the over dispe rsion p aram eter, suc h that: 

(21.10) 

The overd ispersion rate is then given by the following equation: 

(21.11) 

EPA used the negative binomial model to predict the seasonal number of recreation trips for each recreation activity based on 

the inclusive value, individ ual soc ioeco nom ic characteristics, and the ove rdispersion parameter, �. the inclusive value has 

the anticipated positive sign, then increases in the inclusive value stemming from improved ambient water quality at 

recreation sites will lead to an incre ase in the numb er of trips. e com bined MNL mod el site cho ice and coun t data trip 

participation models allowed the Agency to account for changes in per-trip welfare values, and for increased trip participation 

in response to improve d amb ient water quality at recreation sites. 

21.1.4 

EPA estimated the welfare change associated with water quality improvements from the baseline to post-compliance 

conditions as a compensating variation (CV), which equates the expected value of realized utility under the baseline and 

post-compliance conditions. d seasonal change in welfare attributed to the quality improvements for an individual 

n in the sample co nsists of two comp onents: 

� per trip welfare gain, and 

� increased numb er of trips unde r the po st-compliance water quality cond ition. 

The Age ncy first calculated the welfare gain from water quality improvement for each consumer on a given day by using a 

CV m easure for consumer n (Kling and Thom pson, 1996): 

(21.12) 

where: 

CVn = the compe nsating variation for individual n at site j on a given d ay; 

r = “Lake Erie,” “inland,” etc. 

j = 1,...Jr

 represents a set of alternative sites for a given recreational activity in region r; 

= the inclusive value index (I); 

W 0 = a vector of info rmatio n describing base line wate r qua lity; 

W 1 = a vector of information describing post-compliance water quality; and 

�M = the implicit coefficient on income that influences recreation behavior. 

In deriving E quation 21 .12, E PA assumed that the marginal utility of inc ome , � M , is constant across alternatives (as well as 

across quality change s). ption doe s not ap ply, the derivatio n of E q. 21 .12 is m ore c omp licated (Ha usma n et al., 

1995). 

The negat

If

Th

Calculating Welfare Changes from Water Quality Improvements 

The expecte

If this assum 

21-9

MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

EP A then estimated the lo w and high values of the seasonal welfare gain for individual n in the sam ple as fo llows: 3 

(21.13) 

(21.14) 

where: 

W low, n = lower bound estimate o f the seaso nal welfare gain for individual n; 

W high, n = upper bound estimate of the seasonal welfare gain for individual n; 

I

1 = the post-policy inclusive value; 

Y1 = the estim ated n umb er of trip s after wa ter qua lity impro vement; 

I

0 = the baseline inclusive value; 

Y0 = the estimated number of trips in the baseline; and 

�� = the implicit coefficient on income that influences recreation behavior. 

These estimates are per individual in the population for those individuals meeting qualifications for inclusion in the NDS 

respo nse set (i.e., respo ndents who se home state is Oh io and respo ndents from the neighbo ring states whos e last trip w as to 

Ohio’s sites).4 EPA extrapolated the estimates of value per individual to the Ohio state level based on Census data (U.S. 

Bure au of the Cen sus, 2000 ). section details the extrapo lation method used in the analysis. 

21.1.5 Extrapolating Results to the State Level 

EPA used a simplified extrapolation technique to estimate the state-level benefits. EPA first estimated the number of 

participants in fishing, swimming, boating, and wildlife viewing in Ohio, based on the estimated percentage of the NDS 

survey respo ndents residing in O hio who pa rticipate in a given activity and the state adult po pulatio n. e 20 00 C ensus d ata 

provide information on the number of Ohio residents aged 16 and older. EPA then multiplied the estimated average seasonal 

welfare gain per participant in a given recreational activity by the corresponding number of recreational users. The total 

welfare gain to the users of water-based recreation in Ohio is the sum of fishing, swimming, boating, and wildlife viewing 

benefits. 

21.2 DATA 

This section describes the data and supporting analyses required to implement the RUM analysis. ollowing general 

categories of data and supporting analyses are required: 

� information on the consumers of water-based recreation responding to the NDS in Ohio; 

� recreation sites identified for the water quality and RUM analyses, including the sites visited by consumers of waterbased recreational activity and sup plemen tal sites in their choice sets; 

� estimated price of visiting the sites. isit price” is estimated as a function of travel distance (and travel time) 

between each consumer’s hometown and each site in the choice set; and 

� information on site characteristics likely to be important determinants of consumer behavior. Of particular 

importan ce to this analysis are the water qua lity and related characteristics of sites in the choice set, and h ow those 

characteristics may be expected to change as a result of regulation. 

The following

Th 

The f

The “v

3
 EPA selected this approach for calculating seasonal welfare gain per individual based on Dr. Parsons’ recommendation (G.R. 

Parsons, 1999). 

4
 Section 21.2.1 provides a detailed description of the data sample used in the analysis. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

The following sections discuss each category of data and/or supporting analysis below. 

21.2.1 The Ohio Data 

EPA obtained information on survey respondent socioeconomic characteristics and recreation behavior, including last trip 

profile and the annual number of trips associated with each water-based activity, from the NDS (U.S. EPA, 1994). The 1994 

survey collected data on demographic characteristics and water-based recreation behavior using a nationwide stratified 

random sample of 13,059 individuals aged 16 and over. Respondents reported on water-based recreation trips taken within 

the past 12 months, including the primary purpose of their trips (e.g., fishing, boating, swimming, and viewing), total number 

of trips, trip length, distance to the recreation site(s), and number of participants. Where fishing was the primary purpose of a 

trip, respondents were also asked to state the number of fish caught. Table 21.1 shows the number of trips taken per year by 

primary recreation activity, as reported in the NDS. 

EPA selected case study observations for Ohio residents who took trips within or outside of the state. Trips to Ohio 

recreation sites by residents of neighboring states were also included in the site choice models, but not in the trip participation 

models.5 All four activity models included single-day trips only. EPA included only activity participants with valid 

hometown ZIP co des, whose destination site was uniquely identified. The Agency used data on both Ohio participants and 

Ohio non-participants to estimate total seasonal trips, but included only Ohio participants and several residents of nearby 

states in the site choice models. Although they could not be used in the site choice model, participant observations from Ohio 

with missing location information were used to analyze the number of trips. Tables 21.1 and 21.2 list valid observations by 

activity, residence, and model type. Figure 21.2 illustrates the distribution of the sample observations in relation to the 

location of MP&M facilities affected by the rule in Ohio. 

Table 21.1: lassification of Sample Observations for Estimation of the Site Choice Models 

Total 

Ohio 

Residents 

Ohio Residents 

with Last Trip 

In-State 

Valid Ohio 

Residents with 

Last Trip InState 

Valid Ohio 

Residents with 

Last Trip 

Outside State 

Valid 

Nonresidents 

with Last 

Trip in Ohio 

Valid for 

Site Choice 

Model 

Participants (Total) 609 408 237 35 11 297 

Fishing 122 103 66 9 0 84 

Swimming 147 100 58 14 2 76 

Viewing 231 126 64 2 7 73 

Boating 109 79 49 10 2 64 

C

Source: U.S. EPA analysis. 

5 These additional observations total 11 across the four activities and thus represent only a small fraction of total observations. 

Including only Ohio respondents in the trip participation models underestimates the benefits associated with water quality improvements, 

because the welfare gains to recreators from neighboring states are ignored. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Table 21.2: Classification of Sample Observations for Estimation of the Trip Participation Models 

Ohio Residents Total Residents with Last 

Trip In-State 

Residents with Last 

Trip Outside State 

Valid for Trip 

Participation Model 

Non-Participants 300 291 

Participants (Total) 609 408 34 322 

Fishing 122 103 4 84 

Swimming 147 100 9 78 

Viewing 231 126 7 75 

Boating 109 79 14 85 

Total Observations 909 408 34 613 

Source: U.S. EPA analysis. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Figure 21.2: Location of MP&M Facilities in Relation to the Visited Sites 

Source: U.S. EPA analysis. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

21.2.2 Estimating the Price of Visits to Sites 

EPA estimated trip “price” for each consumer of water-based recreation as the sum of travel costs plus the opportunity cost of 

time, following the procedure described in Haab et al. (2000). and Kealy (1992), this study assumed that 

time sp ent “on -site” is constant ac ross sites a nd ca n be ignore d in the p rice ca lculation . 

To estimate consumers’ travel costs, EPA first used ZipFip software to calculate the one-way distance to each site for each 

participant.6

 The average estimated one-way distance to the site visited is 37.5 6 miles. EP A then multiplied round-trip 

distance by average motor vehicle cost per mile ($0.29, 1993 do llars).7,8 The model adds the opportunity cost of travel time, 

measured in terms of wages lost, to the travel cost for those who would have lost income by taking the recreation trip. For 

these consumers the dummy variable LOSEINC equals one. times equal the round-trip distance divided by a travel 

speed of 40 mph and multiplied by the individual’s hourly wage as calculated below. 

The travel cost variable in the mo del was calculated as follows: 

(21.15) 

Individuals not losing income (e.g., individuals taking vacation or a weekend trip or individuals whose work schedule is not 

flexible) do not face lost wages as a result of the trip and inclusion of the opportunity cost of time would be inappropriate. 

These consumers still have an opportunity cost for their travel time, which could otherwise be spent doing something else, 

like fishing. istance traveled allows for a longer time spe nt at the recreation site. 

consum ers, the analysis included an ad ditional round -trip travel time variable calculated a s: 

(21.16) 

The average o ne-way estimated trave l time to the visited site is 56.34 minutes.9 

21.2.3 Site Characteristics 

EPA identified 1,954 recreation sites on 1,631 reaches in the universal opportunity set. hese, 580 observations are known 

recreational sites (e.g. parks); 1,366 observations are Reach File 1 (RF1) reaches without a known recreational site; and 

eight observ ations a re neithe r located in R F1 nor ide ntified as known recreation sites but w ere visited by an N DS respo ndent. 

Based on Parsons

Travel

In other words, a shorter d For these 

Of t

6 The program was created by Daniel Hellerstein and is available through the USDA at 

http://usda.maunlib.cornell.edu/datasets/general/93014. 

7 Note that all expenditures are in 1993 dollars because the NDS trip choices and the associated expenditure occurred in 1993. 

8 The estimate of motor vehicle cost per mile was based on estimates compiled by the Insurance Information Institute. 

9 The average travel time to the visited site was fairly uniform across the activities. Average one-way time to the visited site was 51.38 

minutes, 71.64 minutes, 43.76 minutes, and 58.57 minutes for fishing, boating, swimming, and viewing, respectively. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Each consumer choice set theoretically includes hundreds of substitutable recreation sites in Ohio and in the neighboring 

states. To prevent the recreation site analysis from becoming unmanageable, EPA analyzed a sample of recreation sites for 

each consumer observation. The Agency then created a randomly-drawn reduced choice set for each recreational activity as 

follows:10 

� Fishing. The reduced choice set consists of 20 Lake Erie sites, 20 river sites, and 20 small lakes/reservoirs. Thus, a 

total individual choice set consists of 60 alternatives (including the chosen site); 

� Boating. The reduced choice set consists of 20 Lake Erie sites and 20 inland recreation sites (including rivers and 

lakes/reservoirs). A total individual choice set consists of 40 alternative sites (including the chosen site); 

� Swimming. Similar to boating, the reduced choice set consists of 20 Lake Erie sites and 20 inland recreation sites 

(including rivers and lakes/reservoirs). A total individual choice set consists of 40 alternative sites (including the 

chosen site); 

� Wildlife Viewing. The reduced choice set consists of 40 sites, including Lake Erie, river, and small lake/reservoir 

sites. 

Each participant choice set, by definition, includes the site actually visited by the respondent. For each consumer, EPA drew 

the additional sites from a geographic area defined by a distance constraint (and the water body types listed above). The 

Agency used a 120-mile distance limit for inland recreation sites (Ohio rivers, small lakes, or reservoirs). All Lake Erie sites 

are eligible for inclusion in the choice sets for all models. EPA assumed that consumers of water-based recreation would be 

willing to travel farther to visit Lake Erie sites, because this water resource presents unique recreational opportunities.11 EPA 

used the resulting aggregate choice set of sites for all individuals participating in a given recreation activity to model 

consumer decisions regarding trip allocation across recreation sites. 

The Agency used two classes of characteristics to estimate site choice: 

� those unaffected by the MP&M regulation, but likely to determine valuation of water-based recreational resources; 

and 

� those affected by the regulation and hypothesized to be significant in explaining recreation behavior and resource 

valuation. 

Regulation-independent site characteristics include water body type and size, location characteristics, and the presence of site


amenities (e.g., boat ramps, swimming beaches, picnic areas). Regulation-dependent site characteristics include regulation-


affected water quality variables.


a. Regulation-independent site characteristics


Site characteristics that are likely to be important determinants of consumer valuation of water-based recreational resources


but that are independent of the MP& M regulation include general site descriptors. These descriptors include the type and size


of the water body and location characteristics, and the presence of site amenities. EPA obtained data on regulationindependent site characteristics from two main sources, RF1 and the Ohio Department of Natural Resources (ODNR). 

RF1 provided water body type (i.e., lake, river, or reservoir) and physical dimension (i.e., length, width, and depth). The 

dummy variables, LAKE ERIE, RIVER, and LAKE characterize water body types. If a site is located on Lake Erie, LAKE 

ERIE takes the value of 1; 0 otherwise. If a site is located on river, RIVER takes the value of 1; 0 otherwise. Finally, if a site 

is located on a small lake or reservoir, LAKE takes the value 1; 0 otherwise. Water body size was determined by the length of 

the reach segment in miles for rivers and Lake Erie sites. For small lakes and reservoirs, the appropriate water body size is the 

water body area in acres. The site choice models use the logarithm of water body size as a measure of site importance, 

10 McFadden (1981) has shown that estimating a model using random draws can give unbiased estimates of the model with the full set 

of alternatives. 

11 Travel distance from respondent’s hometown to the Lake Erie sites did not exceed 250 miles. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

because people are more likely to be aware of large water bodies.12 Water body size data for sites not located in RF1 came


from the ODNR.


ODN R, supplemented by the Ohio Atlas and Gazetteer, provided data on recreational amenities and site setting (e.g.,


presence/absence of boat ramps, swimming beaches, or picnic areas; public accessibility; and size of land available for


recreation). EPA used land available for recreation, LN(LAND), (e.g., acreage of state park, fishing, hunting, and other


recreation areas) to approximate site setting and attractiveness. Dummy variables represent the presence of three recreational


amenities: BEACH is a swimming beach; RAMP is a boating ramp; and PARK indicates a park.


b. Regulation-dependent site characteristics


Selecting regulation-dependent site characteristic variables that are both policy-relevant and significant in explaining


recreation behavior proved challenging. MP&M facilities discharge many pollutants, most of them unlikely to have visible


indicators of degraded water quality (e.g., odor, reduced turbidity, etc). EPA hypothesized that pollutant loadings can,


nonetheless, reduce the likelihood of selecting a recreation site. Reduced pollutant discharges improve water quality and


aquatic habitat, thereby increasing fish populations and enhancing the recreational fishing experience. In addition, in-stream


nutrient concentrations are good predictors of eutrophication, which causes aesthetic losses and may thus affect the utility of a 

water resource for all four recreational uses. 

The connection between the policy variables (i.e., the change in concentrations of MP&M pollutants) and the effects 

perceived by consumers (e.g., increased catch rate, increased size of fish, greater diversity of species, or improved aesthetic 

qualities of the water body) are not modeled directly, but are captured implicitly in the differential valuation of water 

resources as reflected in the RUM analyses. 

EPA considered two types of pollutant effects in defining water quality variables for model inclusion: 

� visible or otherwise directly perceivable effects (e.g., water turbidity); and 

� unobservable toxic effects likely to impact aquatic habitat and species adversely. 

The Agency accounted for directly observable effects using the ambient concentrations of nutrients (e.g., TKN ) as an


explanatory variable.


Rather than include the concentrations of all toxic pollutants separately, EPA constructed a variable to reflect the adverse


impact potential of toxic pollutants on aquatic habitat. EPA identified recreation sites at which estimated concentrations of


one or more MP&M pollutants exceeds AWQC limits for aquatic life protection, to assess the likely adverse impacts on


aquatic organisms. A dummy variable, AWQC_EX, takes the value of 1 if in-stream concentrations of at least one MP&M


pollutant exceed AWQC limits for aquatic life protection, 0 otherwise. This approach accounts for the fact that adverse


effects on aquatic habitat are not likely to occur below a certain threshold level.


c. Biological factors


Numerous biological parameters (e.g., abundance of sport fish) that are a function of the availability and quality of suitable


habitat for breeding and feeding are also likely to affect recreation behavior. To account for biological parameters affecting 

the demand for water-based recreation, EPA used relative fish abundance (Biomass) obtained from the Ohio Water 

Resource Inventory (OWRI) database (OH EPA, 1996). Relative fish abundance is measured as the total fish weight (in 

kg) per 300 meters. Because this variable reflects presence of both tolerant and intolerant fish species, it is less correlated 

with the two regulation-dependent water quality variables (i.e., TKN and AW QC) included in the analysis compared to the 

index of well-being (IWB2) used in the proposed rule analysis. 

Chemical properties of the waters (e.g., pollutant concentrations) are likely to affect the diversity and abundance of the fishery 

resources. Biological parameters may also be affected by numerous anthropogenic stressors unrelated to water quality, such 

as over-fishing, physical alteration of habitat, invasion of exotic species, etc. Although EPA used the baseline values of 

relative fish abundance to estimate the site choice models, the Agency did not estimate changes in biological parameters 

caused by the regulation analysis due to data limitations and the challenges posed by modeling population impacts of a broad 

spectrum of pollutants at hundreds of recreation sites. 

12 EPA uses the logarithm of water body size because it expects the effect of water body size on utility to diminish as that size 

increases. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

d. Presence of fish advisories


Another important factor that may affect a recreational consumer’s decision to visit a particular site is presence of fish


consumption or contact advisories (FCAs). EPA obtained information on fish consumption advisories and contact


advisories at reaches in Ohio from the ODNR (Ohio DNR, 1999). Fish consumption advisories and contact advisories were 

listed by the name of the stream or river with the consumption advisory. An advisory that applied to only part of the river 

included the names of cities, towns, or highways to identify the stretch of the reach for which the advisory was relevant. The 

name of the river and the other geographic identifiers were used to assign reach numbers from RF1 to the consumption 

advisories. EPA created a dummy variable for each type of advisory (i.e., fish advisories and contact advisories). The 

variable takes the value of 1 if the relevant advisories are present; 0 otherwise. 

21.3 SITE CHOICE MODEL ESTIMATES 

EPA estimated four separate models of recreational demand: fishing, boating, swimming, and viewing. The Agency classified 

trips by the primary activity listed by the respondent. All four activity models cover single-day trips. EPA estimated the site 

choice model using the site actually visited and randomly-drawn sites from the choice set for each recreation activity as 

described in Section 21.2-3 above. 

EPA estimated activity models for five alternative choice sets (i.e., five random draws from the universal choice set), 

producing five sets of estimated coefficients. Mean estimates from the five alternative draws represent EPA’s best estimate of 

actual coefficient values. Table 21.3 lists the variables used as arguments in the utility function and presents the mean 

estimation results for the four models. In estimating site choice models for fishing, boating, swimming, and viewing, the 

Agency restricted the coefficient on travel cost to be equal across all four models to ensure a constant marginal utility of 

income across all four activities. 

The following sections provide a short description of the results of the site choice model corresponding to each recreation 

activity. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Table 21.3: Site Choice Model Estimation Results 

(Mean parameter estimates from five random draws)a 

Variable 

Activity 

Fishing Boating Swimming Viewing 

TRCOST b -0.044 (-22.704) -0.044 (-22.704) -0.044 (-22.704) -0.044 (-22.704) 

TIME c -1.474 (-7.482) -0.362 (-4.27) -0.436 (-7.007) -0.719 (-12.647) 

RAMP d 0.878 (7.509) N/A N/A N/A 

LN(LAND) e N/A N/A 0.058 (2.431) 0.162 (7.471) 

PARK f N/A N/A 0.753 (3.79) 0.787 (4.638) 

BEACH g N/A N/A 0.491 (2.96) N/A 

LN(SIZE) h 

All N/A 0.502 (5.777) -0.273 (-6.083) N/A 

Lake Erie 0.908 (6.639) N/A N/A 0.665 (10.474) 

River 0.171(1.993) N/A N/A -0.261 (-4.937) 

Lake 0.050 (-0.348) N/A N/A -0.429 (-4.329) 

Biomass i Lake Erie N/A -0.130 (-1.777) N/A N/A 

River 0.068 (2.328) 0.017 (0.4432) N/A N/A 

TKN j -0.584 (-3.763) -1.187 (-6.863) -0.660 (-4.631) -0.711 (-4.401) 

AWQC k -0.573 (-3.698) -0.172 (-1.179) N/A N/A 

Inclusive Values 

ERIE 0.811(9.895) 0.296 (6.098) 0.730 (7.466) N/A 

Inland N/A 0.088 (2.525) 0.275 (6.302) N/A 

RIVER 0.591 (6.945) N/A N/A N/A 

LAKE 0.429 (2.629) N/A N/A N/A 

Adj. R2 0.467 0.280 0.408 

a EPA performed this analysis based on five alternative draws to assess sensitivity of the estimated coefficients with 

respect to random draws. b Travel Cost is calculated as 0.29 * round-trip distance. c Travel Time is (round-trip distance / 40)*Wage. d

 1 if a boating ramp is present, and 0 otherwise. 

e Log of the number of land acres. 

f 1 if the site is a park, and 0 otherwise. g

 1 if a swimming beach is present, and 0 otherwise. h

 Log of the size of the water body. For rivers and Lake Erie, this is the log of the reach segment length or Lake Erie 

shore segment in miles. For lakes, this is log of the lake circumference. i

 Biomass is measured as the total fish weight (in kg) per 300 meters. 

j In-stream concentrations of TKN (mg/l). k

 1 for any reach if in-stream concentrations of at least one MP&M pollutant exceeds the AWQC limits for protection of 

aquatic life, and 0 otherwise. 

Note: T-statistic for test that the estimated coefficient equals 0 is given in parentheses beside the coefficient estimates. 

N/A indicates that the variable was not included in the estimation for this activity. 

Source: U.S. EPA analysis. 

21.3.1 Fishing Model 

The estimated fishing model includes travel cost (TRCOST), time (TIME) spent traveling, and site characteristics. The 

Agency included the following site characteristics in the fishing model: boat ramp (RAMP), water body size (LN(SIZE)), 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

relative fish abundance (Biomass), TKN concentrations, and presence of AWQC exceedances. Table 21.3 shows that most 

coefficients have the expected sign and are significantly different from zero at the 95th percentile. Travel cost and travel time 

have a negative effect on the probability of selecting a site, indicating that anglers prefer to visit sites closer to their homes 

(other things being equal). 

Anglers who fish from a boat are likely to view the presence of a boat ramp as an important factor that may affect their site 

choice. However, the presence of a boat ramp is unlikely to be important for anglers who fish from shore. Thus, the Agency 

used an interaction variable (RAMP x USE_BOAT) such that the ramp variable was turned on only if the angler reported 

using a boat on his last fishing trip. A positive sign on the boat ramp indicates that anglers owning a boat are more likely to 

choose sites with a boat ramp. 

The water body size has a different effect on the probability of selecting a site in the Lake Erie, river, and small lake/reservoir 

groups. The larger the river or the Lake Erie shore segment, the more likely that anglers visited the reach. The size of inland 

lakes and reservoirs does not have a significant effect on the probability of visiting the site. 

The Agency used the square root of the fish weight per 300 meters as a measure of fish abundance (Biomass). The probability 

of a river site visit increases as the relative fish abundance at the site increases. However, inclusion of this variable in the 

Lake Erie nest was not significant, which indicates that relative fish abundance does not have a significant effect on choosing 

a Lake Erie site. This finding is counterintuitive and is likely to be due to the lack of variation in the relative fish abundance 

variable for the Lake Erie sites. This variable was excluded from the Lake Erie nest in the final model presented here. Data on 

relative fish abundance were not available for lakes. 

Finally, higher ambient concentrations of TKN, which indicate potential eutrophication problems, and presence of AWQC 

exceedances negatively affect the probability of site selection. In other words, anglers prefer cleaner sites, all else being 

equal. 

Estimated inclusive values on Lake Erie sites, rivers, and small lakes lie within a unit interval [0,1] and are significantly 13 different from 0, indicating that the nested choice structure is appropriate. 

EPA found other variables, tested as explanatory variables, to be insignificant, including the presence of FCAs. It might be 

expected, a priori, that the presence of an FCA decreases a site’s likelihood as a fishing choice. In fact, the existence of 

FCAs did not significantly affect a site’s probability of being chosen; 59 percent of the sites actually chosen by NDS 

respondents had an FCA in place. Creel surveys provided by ODNR indicated that, on average, anglers released 70 percent 

of their catch (ODNR, 1997). This finding suggests that recreational anglers are aware of FCAs, and catch but do not 

consume fish in the affected areas. 

21.3.2 Boating Model 

The estimated boating model includes travel cost (TRCOST), time (TIME) spent traveling, and site characteristics. The 

Agency included the following site characteristics in the boating model: water body size (LN(SIZE)), relative fish abundance 

(Biomass), TKN concentrations, and presence of AWQC exceedances. Table 21.3 shows that most coefficients have the 

expected sign and are significantly different from zero at the 95th percentile. 

Travel cost and travel time have a negative effect on the probability of selecting a site, indicating that boaters prefer to visit 

sites closer to their homes (other things being equal). However, the magnitude of the travel time coefficient indicates that 

boaters are willing to travel farther than participants in other recreational activities. This is not surprising, since motorboating and sailing are restricted to the sites where these activities are allowed. The positive coefficient on the water body 

size variable (LN(SIZE)) indicates that the larger the water body the more likely the boaters visited it. 

The coefficients on water quality variables (TKN and AW QC) are negative, indicating that boaters prefer to visit cleaner 

sites. The Biomass coefficient is positive, but insignificant for inland sites, and negative for Lake Erie sites. The negative 

coefficient on this variable is likely to be due to the fact that 88 percent of the sample trips used in this model were 

motorboating trips. Motorboating itself is likely to be a significant environmental stressor for biological communities due to 

noise and turbidity associated with this activity. Thus, lower fish abundance at popular boating sites may indicate that 

intensive motorboating may adversely affect species abundance. As was the case with the fishing model, the estimated 

13 Inclusive values equal to 1 cause the model to collapse to a flat multinomial logit. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

inclusive value is significantly different from zero and lies within a unit interval [0.1], supporting the nested model 

framework. 

21.3.3 Swimming Model 

EPA included the travel cost and time variables (TRCOST, TIME), physical characteristics of the site, and ambient TKN 

concentrations in the swimming model. This model also includes the presence of recreational amenities that are likely to be 

important to swimmers: presence of a beach, the designation of the site as a park, and the natural log of the land acres. All 

estimated coefficients have the expected sign and are significantly different from zero at the 95th percentile. 

Price, travel time, the presence of a park with a beach, and the size of the land area around the site all increase the probability 

of a particular site being chosen for swimming. Swimmers are less likely to visit large sites (referring to the size of the water 

body) or sites with visible water quality effects as indicated by higher in-stream concentrations of TKN. As for the fishing 

and boating models, the estimated inclusive value is significantly different from zero and lies within a unit interval [0,1] 

supporting the nested model framework. 

Again, some variables expected to be significant, such the presence of contact advisories, are not. This variable’s 

insignificance probably stems from its scarcity. Of 1,954 sites included in the universal opportunity set, contact advisories are 

in place for only 13. (None of the sites actually visited had contact advisories in place.) The probability that a chosen site has 

contact advisories in place is very small, because individual choice sets are randomly selected. 

The fish Biomass variable representing biological characteristics of a water body also did not have a significant influence on 

consumer decisions to visit a particular site and was dropped from the model. This outcome is not surprising, since abundant 

aquatic life may, in fact, interfere with swimming activities. 

21.3.4 Viewing (Near-water Activity) Model 

EPA included the travel cost and time variables (TRCOST, TIME), physical site characteristics, and ambient TKN 

concentrations in the viewing model. In addition, the Agency included the natural log of the land acres and the designation 

of the site as a park. All estimated coefficients have the expected sign and are significantly different from zero at the 95th 

percentile. 

The probability of choosing a site for near-water activities is most significantly related to visit price, travel time, land size, 

and in-stream concentrations of TKN. Similarly to the fishing model, the water body size has a different effect on the 

probability of selecting a site in the Lake Erie, river, and small lake/reservoir groups. The larger the Lake Erie shore segment, 

the more likely that viewers visited the site. The negative coefficients on river and inland lake size indicate that consumers 

prefer smaller inland water bodies for near-water and wildlife viewing activities. 

21.4 TRIP PARTICIPATION MODEL 

EPA estimated the determinants of individual choice concerning how many trips to take during a recreation season with a 

separate model for each of the four activities. These participation models rely on socioeconomic data, and on estimates of 

individual utility (the inclusive value) derived from the site choice models. Variables of importance include age, ethnicity, 

gender, education, and the presence of young or older children in the household. Whether or not the individual owns a boat is 

particularly important in boating participation, and is included in the model for that activity only. Variable definitions for the 

trip participation model are: 

� IVBASE: inclusive value, estimated using the coefficients obtained from the site choice models; 

� #TRIPS: number of trips taken by the individual; 

� AGE: individual’s age. If the individual did not report age, their age is set to the sample mean; 

� MALE: equals 1 if the individual is a male, 0 otherwise; 

� NOH S: equals 1 if the individual did not complete high school, 0 otherwise; 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

� COLLEGE: equals 1 if the individual completed college, 0 otherwise;


� AFAM: equals 1 if the individual is African American, 0 otherwise;


� YNG KIDS: equals 1 if there are kids 6 years or younger, 0 otherwise;


� OLDKIDS: equals 1 if there are kids 7 years or older, 0 otherwise;


� OW NBT : equals 1 if individual owns a boat, 0 otherwise;


� Constant: a constant term representing each individual’s utility associated with not taking a trip; and


� � (alpha): overdispersion parameter estimated by the Negative Binomial Model.


Table 21.4 presents explanatory variables and a mean value for each. 

Table 21.4: Mean Values for Explanatory Variables Used in the Participation Models 

Variables 

(Mean) 

Non-Participant 

(N=291) 

Boating 

(N=85) 

Fishing 

(N=84) 

Swimming 

(N=78) 

Viewing 

(N=75) 

# TRIPS 0.00 7.71 10.07 9.46 9.59 

AGE 43.99 39.06 38.53 34.76 36.91 

MALE 0.33 0.49 0.65 0.47 0.47 

NOHS 0.17 0.09 0.14 0.13 0.13 

COLLEGE 0.15 0.32 0.20 0.32 0.35 

AFAM 0.11 0.02 0.05 0.03 0.12 

YNGKIDS 0.18 0.26 0.24 0.24 0.27 

OLDKIDS 0.38 0.48 0.58 0.56 0.48 

OWNBT N/A 0.53 N/A N/A N/A 

Source: U.S. EPA analysis. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Table 21.5 presents the results for the participation models of the four recreation activities. 

Table 21.5: Trip Participation Negative Binomial Model Estimates 

Variables/ Statistics Boating Fishing Swimming Viewing 

IVBASE 0.12 0.82 0.72 0.47 

(0.71) (2.86 ) ( 4.57) ( 3.66) 

AGE -0.07 -0.04 -0.06 -0.05 

(-4.73) ( -2.06) ( -2.24) ( -2.77) 

MALE 1.23 2.22 1.15 0.91 

(2.75) ( 3.25) ( 1.52) ( 2.00) 

NOHS 1.29 -1.09 -0.92 0.1 

(2.37) (-1.56 ) ( -0.96) ( 0.17) 

COLLEGE -0.19 -0.40 0.53 1.22 

(-0.29) (-0.721 ) ( 0.71) ( 2.05) 

AFAM -3.74 -1.44 -4.07 -1.16 

(-1.81) (-1.53 ) ( -2.68) ( -1.34) 

YNGKIDS 1.51 -0.95 0.35 -0.17 

(2.96) ( -1.26) (0.42 ) ( -0.38) 

OLDKIDS -1.67 1.11 0.4 0.8 

(-3.58) ( 2.78) ( 0.65) ( 1.81) 

OWNBT 3.82 

N/A N/A N/A 

(5.26) 

Constant 0.20 -5.74 -0.1 -1.98 

(0.11) ( -3.01) ( -0.06) ( -1.6) 

Alpha � 5.77 9.03 8.92 8.17 

(5.85) ( 7.16) ( 6.78) ( 6.03) 

Note: T-statistic for test that coefficient equals 0 is given in parentheses below the coefficient estimates. 

N/A indicates that the variable was not included in the estimation for this activity. 

Source: U.S. EPA analysis. 

Parameter estimates of the inclusive value index (IVBASE) in the swimming, fishing, and viewing models are positive and 

differ significantly from zero at the 95th percentile, indicating that water quality improvements have a positive effect on the 

number of trips taken during a recreation season. 

The estimated coefficient on IVBASE in the boating model, while positive, was not statistically significant. Taking a boating 

trip often requires more preparation (e.g., taking a boat to the water body) than taking other trips. Therefore, although water 

quality improvements increase the value of a boating day, factors other than water quality are likely to have a stronger impact 

on the number of boating trips per season. 

The AG E variable is negative and significant for all four recreation activities: younger people are likely to take more 

recreation trips. Ethnicity and gender (the AFAM and MALE variables) also have a significant impact on whether an 

individual participates in water-based recreation. African Americans living in Ohio are less likely to participate in any of the 

four recreation activities than representatives of other ethnic groups. Males are more likely than females to participate in any 

of the recreation activities. 

Education also influences trip frequency significantly. People who did not complete high school (NOHS=1) tend to take 

fewer fishing or swimming trips. Those with a college degree (COLLEGE=1) are more likely to participate in swimming and 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

viewing. Respondents who attended college are less likely, however, to participate in fishing and boating than those who 

completed only a high school education. For the boating model, the COLLEGE variable is not significantly different from 

zero. 

The presence of older children (OLDKIDS) in the household is associated with greater participation in swimming, viewing 

(near-water recreation), and fishing activities, but is not a significant determinant in decisions to participate in boating. 

Younger children in the household (YNGKIDS) tends to lead to greater participation in boating and swimming, but leads to 

fewer fishing or viewing trips. 

21.5 ESTIMATING BENEFITS FROM REDUCED MP&M DISCHARGES IN OHIO 

21.5.1 Benefiting Reaches in Ohio 

EPA identified reaches where it expects the MP&M rule to eliminate or reduce the number of existing AWQC exceedances 

(hereafter, benefiting reaches). The Agency first identified the reaches in which baseline discharges from industrial sources, 

including both MP&M and non-MP&M facilities, caused one or more pollutant concentrations to exceed AWQC limits for 

aquatic species. A reach is considered to benefit from the MP&M rule if at least one AWQC exceedance is eliminated due to 

reduced M P&M discharges. Although the method for identifying benefiting reaches is similar to the method used in the 

national analysis (see Chapter 15 for detail), there are three notable differences: 

� Unlike the national analysis, the Ohio case study incorporates information on all industrial and municipal point 

source discharges and non-point sources to assess in-stream concentrations of toxic and nonconventional pollutants 

in the baseline and post-compliance. Appendix H provides information on the data sources and methods used to 

assess ambient water quality conditions in Ohio. 

� The water quality model used in this analysis estimates ambient pollutant concentrations in the reaches receiving 

discharges from MP& M facilities and reaches below the initial discharge reach. Appendix H provides detail on the 

water quality model used in this analysis. 

� The analysis of recreational benefits accounts for changes in TKN concentrations. 

EPA's analysis indicates that pollutant concentrations at the baseline discharge levels from all industrial sources (including all 

MP&M facilities) exceed acute exposure criteria for aquatic life on 15 reaches, and exceed chronic exposure criteria for 

protection of aquatic species on 21 reaches. EPA estimates that reducing pollutant discharges from oily waste facilities 

directly discharging to the receiving streams would not eliminate all concentrations in excess of the acute aquatic life 

exposure criteria or the chronic exposure criteria on any reach under the final rule; it would reduce the number of acute and 

chronic exceedances on one reach. 

In addition, baseline pollutant concentrations exceed human health-based AWQC for consumption of water and organisms on 

three reaches and exceed AQW C for consumption of organisms only on two reaches. EPA estimates that reducing pollutant 

discharges from oily waste facilities directly discharging to the receiving streams would reduce the number of pollutants 

exceeding the human health-based AWQC on one reach under the final rule; it would not eliminate all human health-based 

AQWC exceedances on any reach in Ohio. Table 21.6 summarizes these results. In addition, the final regulation is 

estimated to reduce in-stream concentrations of TKN in the affected reaches. The estimated average reductions are 0.54 

percent in lakes and 0.45 percent in rivers and streams. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Table 21.6.: Estimated MP&M Discharge Reaches with MP&M Pollutant Concentrations in Excess of AWQC 

Limits for the Oily Wastes Subcategory for Protection of Aquatic Species or Human Health 

Regulatory Status 

Number of Reaches with 

Concentrations 

Exceeding AWQC 

Limits for Human 

Health 

Number of Reaches 

with Concentrations 

Exceeding AWQC 

Limits for Aquatic 

Species 

Number of Benefiting Reaches 

All AWQC 

Exceedances 

Eliminated 

Reaches with Some AWQC 

Exceedances Eliminated H20 and 

Organisms 

Org. 

Only Acute Chronic 

Baseline 3 2 15 21 - -

Final Regulation 3 2 15 21 0 1 

Source: U.S. EPA analysis. 

21.5.2 Estimating Recreational Benefits in Ohio 

To estimate peoples’ willingness-to-pay for water quality improvements, the Agency first calculated per-person seasonal 

welfare gain corresponding to the final regulation. Table 21.7 presents, for each recreation activity, the compensating 

variation per trip (the median value over all individuals in the sample) associated with the reduced MP&M discharges. 

Because the trip choices and the associated expenditures occurred in 1993, the welfare gain was calculated in 1993 dollars 

and then adjusted to 2001 dollars based on the Consumer Price Index (CPI). 

The model indicates that the reductions in MP&M discharges from the final regulation result in a modest increase in per-trip 

values for three of the four recreation activities (fishing, viewing, and swimming). There is no welfare gain to boaters from 

improved water quality under the post-compliance scenario.14 Table 21.7 provides the mean estimates of welfare gain per 

recreational user in Ohio. 

Table 21.7: Welfare Gain per Recreational User in Ohio (2001$) 

Per Trip Welfare 

Gain 

Average Number of Trips 

per Person per Year 

Mean Seasonal 

Welfare Gain 

Fishing $0.02 13.6 $0.17 

Boating $0.00 6.22 $0.00 

Viewing $0.01 9.26 $0.11 

Swimming $0.01 8.72 $0.01 

Source: U.S. EPA analysis. 

Table 21.7 also reports seasonal compensating variation per individual. The reported seasonal welfare gain includes both the 

increase in the utility from better water quality at the available recreation sites receiving MP&M discharges and the increase 

in utility from greater recreational trip participation. 

As noted above, the Ohio case study evaluated changes in the water resource values from both reduced discharges of TKN 

and reduced frequency of AWQ C exceedances. Changes in TKN concentration in the Ohio water bodies resulting from 

reduced MP&M discharges from the Oily Wastes subcategory account for approximately 96 percent of the monetary value of 

benefits resulting from the final rule. 

14 The choice set of recreational sites available to boaters was restricted to the sites where motorboating and sailing is permitted 

because the majority of Ohio boaters included in this analysis used either motor or sail boats. Water quality improvements at the sites 

where boating is not allowed does not result in welfare gain to boaters. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

Both the per-trip and seasonal welfare estimates are much lower than values reported in the existing studies of water-based 

recreation. This is not surprising, since the water quality changes expected from the final rule are very modest. 

To calculate state-level recreational benefits from the final rule, EPA first calculated seasonal welfare gain from water quality 

improvements per individual in the sample. The Agency then multiplied the average welfare gain per individual by the 

corresponding number of participants in a given activity (see Section 21.1.5 above for detail). The resulting product is the 

annual benefit from the final MP&M rule to consumers of a given water-based recreation activity in Ohio. Table 21.8 

summarizes state-level results. 

Table 21.8: Estimated Recreational and Nonuse Benefits from Reduced MP&M Discharges 

from the Oily Wastes Subcategory in Ohio 

Activity 

Percentage of Ohio Residents 

Participating in Single-Day 

Trips (from NDS) 

Number of 

Participants Aged 

16 and oldera 

Total Annual 

Welfare Gain to 

Recreational 

Users in Ohio 

Fishing 10.2% 892,283 $153,102 

Boating 7.7% 676,026 $0 

Viewing 9.1% 798,220 $88,047 

Swimming 9.1% 798,220 $9,783 

Total Recreational Use Benefit $250,933 

Nonuse Benefits $125,466 

Total Recreational Benefits (Use 

+ Nonuse) $375,859 

a EPA estimated the number of participants in each recreation activity by multiplying the percent of NDS 

survey respondents from Ohio participating in a single day trip for each activity by the total adult population 

aged 16 an older (8,790,969). This analysis uses the 2000 Census data to estimate current population in Ohio. 

Source: U.S. EPA analysis. 

Under the final regulation, the extrapolation from the sample to the adult population in Ohio yields mean annual benefits 

estimates of $153,102, $9,783, $88,047, and $0 (2001$) for fishing, swimming, viewing, and boating, respectively. The total 

mean recreational use benefit is $250,932 (2001$). The Agency used the same approach as in the national analysis to 

estimate nonuse benefits (see Section 15.2.3, Nonuse Benefits, for detail). EPA estimated nonuse benefits as one-half of 

recreational use benefits for low, mid, and high estimates, respectively. The estimated mean nonuse benefit is $125,466 

(2001$). 

21.6 LIMITATIONS AND UNCERTAINTY 

21.6.1 One-State Approach 

Some benefits are likely to be missed by a state-level case study. For example, residents from neighboring states undoubtedly 

recreate in Ohio waters, and residents of Ohio undoubtedly recreate in neighboring states. A state-by-state approach that 

restricts its analysis to recreation activities within the state misses these categories of benefits.15 This omission is likely to be 

more significant for unique locations of high quality (e.g., Lake Erie), where participants travel significant distances, and for 

sites very close to state boundaries. 

15 Note that EPA used a few observation on visitors from neighboring states to estimate site choice models. The analysis does not 

include these observations in calculating state-level benefits from water quality improvements. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

21.6.2 Including One-Day Trips Only 

Use of day-trips only tends to understate recreational benefits for swimming, fishing, boating, and viewing, since recreation as 

part of multi-day trips is excluded. Inclusion of multi-day trips, however, can be problematic. Multi-day trips are frequently 

multi-activity trips. An individual might travel a substantial distance, participate in several recreation activities and go 

shopping and sightseeing, all as part of one trip. Recreational benefits from improved recreational opportunities for the 

primary activity are overstated if all travel costs are treated as though they are associated with the one recreational activity of 

interest. The total benefits per trip from water quality improvements are not overstated, however, if individuals participated 

only in several water-based activities. 

21.6.3 Nonuse Benefits 

Estimating nonuse benefits using the 50 percent rule is less precise than using a more sophisticated benefits transfer approach. 

However, limiting the benefits of water quality improvements only to recreational benefits would significantly underestimate 

the benefits of the rule. The effects of using the simpler approach, e.g. either overestimation or underestimation of benefits, is 

unknown. Other benefits include aesthetic benefits for residents living near water bodies, habitat values for a variety of 

species (in addition to recreational fish), and nonuse values. To correct for this limitation of using only a travel cost model, 

EPA quantified nonuse values in proportion to recreation values. This approach provides only a rough approximation of the 

value of water resources to nonusers. For example, some natural resources have high use values but small or negligible 

nonuse values (e.g., cows), while other species have very high nonuse values but small or negligible use values (e.g., blue 

whales). 

21.6.4 Potential Sources of Survey Bias 

The survey results could suffer from bias, such as recall bias (e.g., Westat, 1989), nonresponse bias, and sampling effects.


a. Recall bias


Recall bias can occur when respondents are asked the number of days in which they recreated over the previous season, such


as in the NDS survey. Some researchers believe that recall bias tends to lead to an overstatement of the number of recreation 

days, particularly for more avid participants. Avid participants tend to overstate the number of recreation days, since they 

count days in a "typical" week and then multiply them by the number of weeks in the recreation season.16 They often neglect 

to consider days missed due to bad weather, illness, travel, or when fulfilling "atypical" obligations. Some studies also found


that the more salient the activity, the more "optimistic" the respondent tends to be in estimating number of recreation days.


Individuals also have a tendency to overstate the number of days they participate in activities that they enjoy and value.


Taken together, these sources of recall bias may result in an overstatement of the actual number of recreation days.


b. Nonresponse bias


A problem with sampling bias may arise when extrapolating sample means to population means. This could happen, for


example, when avid recreation participants are more likely to respond to a survey than those who are not interested in the


forms of recreation, are unable to participate, assume that the survey is not meant for them, or consider the survey not worth 

their time. 

c. Sampling effects 

Recreational demand studies frequently face two types of observations that do not fit general recreation patterns: 

non-participants and avid participants: 

Non-participants are those individuals who would not participate in the recreation activity under any conditions. This analysis


assumes that an individual is a non-participant in a particular activity if he or she did not participate in that activity at any site.


This assumption tends to understate benefits, since some individuals may not have participated during the sampling period


simply by chance, or because price/quality conditions were unfavorable during the sampling period.


Avid participants can also be problematic because they claim to participate in an activity an inordinate number of times. This


16 Westat (1989) uses ten or more activity-days per year as an indicator of an "avid" user. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

reported level of activity is sometimes correct, but often overstated, perhaps due to recall bias (see Westat, 1989). Even 

where the reports are correct, these observations tend to be overly influential. EPA dropped observations of participants who 

reported more than 100 trips per year when estimating trip participation models, to correct for potential bias caused by these 

observations. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

GLOSSARY

ambient water quality criteria (AWQC): levels of water quality expected to render a body of water suitable for its 

designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking, 

swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html) 

compensating variation (CV): the amount of money a person would need to pay or receive in order to leave that person 

as well off as they were before a change. 

consumer choice set: the set of alternatives (e.g., alternative recreation sites) from which a consumer may choose. 

exogenous: external to the inner workings of a system or model; variables are exogenous to the extent that they are "given" 

and not the result of the operation of the system or anything going on in the model itself. 

expected maximum utility: see “inclusive value.” 

fish biomass (Biomass): measure of biological factors in the water body represented by the total fish weight in 

kilograms per 300 meters. 

fish consumption advisories (FCAs): an official notification to the public about specific areas where fish tissue 

samples have been found to be contaminated by toxic chemicals which exceed FDA action limits or other accepted guidelines. 

Advisories may be species specific or community wide. 

inclusive value: the value to the consumer of being able to choose among X alternatives (e.g., among a number of 

recreational sites) on a given trip occasion. 

indirect utility function: gives the maximum value of utility for any given prices and money income. The indirect utility 

function is obtained when the quantity of goods that maximizes consumer utility subject to a budget constraint are substituted 

into a utility function. 

inferential analyses: based on interpretation. 

multinomial logit (MNL): a utility maximization model. In this model, an individual is assumed to have preferences 

defined over a set of alternatives (e.g., recreation sites). The choice model takes the form of comparing utilities from different 

alternatives and choosing the one that produces the maximum utility. In this framework, observed data consist of attributes of 

the choices (e.g., available recreational amenities at different sites) and the choice actually made. Usually no characteristics of 

the individuals are observed beyond their actual choice. 

National Demand Survey for Water-Based Recreation (NDS): a U.S. EPA survey of recreational behavior. The 

1994 survey collected data on socioeconomic characteristics and water-based recreation behavior using a nationwide stratified 

random sample of 13,059 individuals aged 16 and over. (http://www.epa.gov/opei) 

negative binomial regression model: an extension of the Poisson regression model that allows the variance of the 

process to differ from the mean (see also Poisson distribution and Poisson estimation process). 

Negative Binomial Poisson model: (see negative binomial regression model). 

nested multinomial logit model (NMNL): an extension of MNL (see above). In this model, an individual is assumed to 

choose among different groups of alternatives first (i.e., Great Lakes or inland recreation sites) and then to choose specific 

alternatives (e.g., a particular river reach, lake, or Great Lakes site) in the choice set for each group. 

nonconventional pollutants: a catch-all category that includes all pollutants that are not classified as priority pollutants 

or conventional pollutants. 

Ohio Water Resource Inventory (OWRI): a biennial report to U.S. EPA and Congress required by Section 305(b) of the 

Clean Water Act. The report is composed of four major sections: (1) inland rivers and streams, wetlands, Lake Erie, and water 

program description; (2) fish tissue contaminants; (3) inland lakes, ponds, and reservoirs; and (4) groundwater. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

overdispersion: condition for a distribution where the variance exceeds the mean. It usually signifies a nonrandom 

dispersion, for example the case where a small minority of the population is responsible for the majority of recreational trips 

taken. 

Poisson distribution: a random variable X is defined to have a Poisson distribution if the probability density of X is given -� by fx(X)= fx (X;�) = e �x

 / x! for x = 0,1,2.., and 0 otherwise. In this model, � is both the mean and variance of X. 

Poisson estimation process: used to model discrete random variables. Typically, a Poisson random variable is a count 

of the number of events that occur in a certain time interval or spatial area, for example, the number of recreational trips taken 

during a recreational season. 

priority pollutants: 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water, 

sediment, groundwater, or soil samples. 

random utility model (RUM): a model of consumer behavior. The model contains observable determinants of consumer 

behavior and a random element. 

Reach File 1 (RF1): a database of approximately 700,000 miles of streams and open waters in the conterminous United 

States. The database contains information on stream flow, time travel velocity, reach length, width, depth, and other stream 

attributes. 

site choice model: used to determine which recreational site is chosen by the consumer. EPA estimated the likelihood that 

the consumer will choose a particular site as a function of site characteristics, the price paid per site visit, and household 

income. 

Total Kjeldahl Nitrogen (TKN): the total of organic and ammonia nitrogen. TKN is determined in the same manner as 

organic nitrogen, except that the ammonia is not driven off before the digestion step. 

travel cost model (TCM): method to determine the value of an event by evaluating expenditures by participants. Travel 

costs are used as a proxy for price in deriving demand curves for recreation sites. 

(http://www.damagevaluation.com/glossary.htm) 

total seasonal welfare: see “welfare effect.” 

trip participation model: used to estimate the number of water-based recreational trips taken during the recreation season. 

EPA estimated the total number of trips during the recreation season as a function of the expected maximum utility (inclusive 

value) from recreational activity participation on a trip and socioeconomic characteristics affecting demand for recreation 

trips (e.g., number of children in the household). 

utility-theoretic: consistent with the behavioral postulate that individuals act to maximize their welfare (utility) that 

underlines the structure of models of consumer behavior. 

welfare effect: gain or loss of welfare to the group of individuals (e.g., fishermen) as a whole. 

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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

ACRONYMS 

AWQC: ambient water quality criteria 

CV: compensating variation


FCAs: fish consumption advisories


IWB2: index of well-being


LIMDEP: Limited Dependent Variable


MNL: multinomial logit


NDS: National Demand Survey for Water-Based Recreation


NMNL: nested multinomial logit model


ODNR: Ohio Department of Natural Resources


OWRI: Ohio W ater Resource Inventory


RUM: random utility model


RF1: Reach File 1


TKN: Total Kjeldahl Nitrogen


TCM: travel cost model


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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 

REFERENCES 

Bockstael, N. E., I. E. Strand, and W. M. Hanemann. 1987. Time and the Recreation Demand Model. American Journal of 

Agricultural Economics 69: 213-32. 

Creel, M. and J. Loomis. 1992. “Recreation Value of Water to W etlands in the San Joaquin Valley: Linked Multinomial Logit 

and Count Data Trip Frequency Models.” Water Resource Research. Vol 28. No 10, October: 2597-2606. 

Feather, Peter M., Daniel Hellerstein, and Theodore Tomasi. 1995. “A Discrete-Count Model of Recreation Demand.” 

Journal of Environmental Economics and Management, 29:214-227. 

Green, W. H. 1993. Econometric Analysis. New York, NY: Macmillan Publishing Company. 

Hausman, J., G. Leonard, and D. McFadden. 1995. “A Utility-Consistent, Combined Discrete Choice and Count Data 

Model: Assessing Recreational Use Losses Due to Natural Resource Damage.” J. of Public Economics No 56 pp.1-30. 

Jones, C. A. and Y. D. Sung. 1993. Valuation of Environmental Quality at Michigan Recreational Fishing Sites: 

Methodological Issues and Policy Application. Final Report. EPA Contract No. CR-816247-01-2. September. 

Kling, C. L. and C. J. Thomson. 1996. “The Implication of Model Specification for Welfare Estimation in Nested Logit 

Models.” American Journal of Agricultural Economics Association, No. 78, February: 103-114. 

McFadden, D. 1981. “Econometric Models of Probabilistic Choice.” In: C. F. Manski and D. L. McFadden, eds., Structural 

Analysis of Discrete Data. Cambridge, MA: MIT Press. 

Ohio Atlas and Gazetteer, The. 1995. Freeport, ME: Delorme. 

Ohio Department of Natural Resources, Division of Wildlife. Creel Survey Summaries from 1992 to 1997. 

Ohio Department of Natural Resources, Division of Wildlife. 1999. Fish Consumption Advisories. 

Ohio Environmental Protection Agency. 1996. Ohio Waste Resource Inventory Volume 1: Summary Status, and Trends; and 

Volume 3: Ohio Public Lakes, Ponds, and Reservoirs. www.Chagrin.EPA.State.OH.US/document_index. 

Parsons, G. and M. J. Kealy. 1992. “Randomly Drawn Opportunity Sets in a Random Utility Model of Lake Recreation.” 

Land Economics 68 No. 4: 418-33. 

Parsons, G. R. 1999. Comments on Assessing the Recreational Benefits of the MP&M Regulation: A State-level Case Study 

Based on the Random Utility Model Approach. Memo to Abt Associates Inc., August. 

U.S. Department of Commerce, Bureau of the Census. 2000. http://www.state.oh.us/odhs/octf/stats/gjcs/ohio.pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 1994. National Demand for Water-Based Recreation Survey. 

Washington, D.C.: Office of Policy Evaluation and Information. 

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MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

Chapter 22: MP&M Benefit-Cost


Analysis in Ohio


INTRODUCTION 

This chapter presents estimated benefits and costs of the final 

MP&M regulation in Ohio. The preceding chapter 

summarized the methodology and results of the case study of 

the expected recreational benefits from water quality 

improvements in Ohio. This chapter first presents estimates 

of the remaining three benefit categories, including: 

� reduced human health risk from exposure to 

carcinogens and systemic health toxicants, 

� changes in health risk from exposure to lead for 

adults and children, and 

� publicly-owned treatment works (POTW) 

benefits.1 

CHAPTER CONTENTS 

22.1 its of the Final Regulation .............. 22-1 

22.1.1 n Health Benefits (Other than Lead) . 22-2 

22.1.2 Related Benefits ................ 22-3 

22.1.3 c Productivity Benefits ......... 22-4 

22.1.4 onetized Benefits .............. 22-4 

22.2 ts of the Final Regulation ........... 22-5 

22.2.1 mpliance Closures . . 22-5 

22.2.2 MP&M Facilities . . 22-6 

22.2.3 ocial Costs ................. ... 22-7 

22.3 

in Ohio ................. ............. 22-7 

Glossary ................. ................. ... 22-8 

Acronyms ................. ................. .. 22-9 

Benef

Huma

LeadEconomi

Total M

Social Cos

Baseline and Post-Co

Compliance Costs for 

Total S

Comparison of Monetized Benefits and Costs 

The chapter then presents the social costs of the final regulation for the state of Ohio and compares the aggregate benefits and 

social costs estimates. From this analysis, EPA estimates that the final regulation will have net monetizable benefits in Ohio 

of $868 thousand annually (2001$). 

EPA estimated MP&M costs and benefits in Ohio using methodologies similar to those used for the national-level analysis but 

with greater detail and coverage of information. In addition to the RUM study of recreational benefits discussed in the 

previous chapter, other analytical improvements included the following: 

� the use of more detailed data on MP&M facilities. EPA oversampled the state of Ohio with 1,600 screeners to 

obtain information on co-occurrence of MP&M discharges; 

� the use of data on non-MP&M discharges to estimate current baseline conditions in the state; and 

� the use of a first-order decay model to estimate in-stream concentrations in the Ohio water bodies. This model 

allows the assessment of the environmental effects of MP&M discharges on the reaches receiving MP&M discharges 

and downstream reaches. 

Appendix H describes the water quality model used in this analysis and the approach and data sources used to estimate total 

pollutant loadings from all industrial and municipal sources to Ohio’s water bodies. The Agency believes that the added level 

of detail results in more robust benefit-cost estimates. 

22.1 BENEFITS OF THE FINAL REGULATION 

EPA estimates that MP&M facilities in all subcategories in Ohio discharge approximately 127.6 million pounds of pollutants 

per year to POTWs, and approximately 83.6 million pounds of pollutants directly to surface water. EPA estimates that the 

final regulation will reduce direct discharges by approximately 0.5 million pounds of pollutants annually. 

1 The final rule regulates only direct dischargers. Therefore, the selected option does not affect POTW operations. 

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MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

22.1.1 Human Health Benefits (Other than Lead) 

EPA estimates total monetized human health benefits from the final regulation of $14,504 (2001$). Chapter 13 details the 

methodologies used to estimate human health benefits from reduced exposure to carcinogens and systemic health toxicants 

other than lead. 

a. 	Reduced incidence of cancer cases from consumption of contaminated fish and drinking 

water 

Table 22.1 shows the number of cancer cases avoided by the final regulation for both the drinking water and fish consumption 

pathways. EPA estimates that improved water quality resulting from the final regulation will reduce the incidence of cancer 

cases via the drinking water and fish consumption pathways from 0.11 cases in the baseline to 0.10 cases under the final 

regulation, with a total annual value of $14,504. Essentially all of the cancer avoidance occurs via the fish consumption 

pathway, which yields annual cancer avoidance benefits of $14,503. Monetized cancer avoidance benefits from reduced 

drinking water contamination are negligible. 

Table 22.1: imated Annual Benefits from Avoided 

Cancer Cases from Fish and Drinking Water Consumption 

Cancer Cases Benefits (2001$) 

Baselinea 

Drinking Water 0.1026421 

Fish Consumption 0.00331 

Total 0.11 

Final Regulation 

Drinking Water 0.1026420 negligible b 

Fish Consumption 0.00108 $14,503 

Total 0.10 $14,504 

Est

a

 The baseline includes baseline loadings from dischargers in all


subcategories.
 b

 Monetized cancer avoidance benefits from reduced drinking


water contamination are approximately $1.


Source: U.S. EPA analysis. 

b. Systemic health effects


EPA’s analysis of in-waterway pollutant concentrations indicates that baseline hazard ratios, for both the fish consumption


and drinking water pathways, for the population associated with sample facilities only, are less than one on all reaches but


one. For those reaches with a baseline hazard ratio of less than one, EPA’s analysis finds shifts in populations from higher


(but less than 1.0) to lower hazard ratio value between the baseline and post-compliance cases. For the single reach with a 

baseline hazard ratio greater than one, the hazard ratio declined but did not fall below one. 

c. Reduced frequency of human health-based AWQC exceedances in Ohio’s water bodies 

Baseline in-waterway concentrations of MP&M pollutants exceed human health-based ambient water quality criteria 

(AWQC) limits for consumption of water or organisms in three reaches. Two reaches exceeded human health-based AWQC 

for consumption of organisms only. EPA estimates that the final regulation will not eliminate these exceedences of human 

health AWQ C on any reach but will reduce the number of exceedences on one reach. 

22-2 
MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

22.1.2 Lead-Related Benefits 

Total monetized lead-related benefits in Ohio for children and adults under the final regulation are $422,113 (2001$).


Chapter 14 of this report describes the methodology used to estimate these benefits.


a. Estimated benefits to Ohio’s children


Table 22.2 presents lead-related benefits from the final regulation for preschool age children and pregnant women in Ohio.


EPA estimates that the final regulation will reduce neonatal mortality by 0.024 cases annually, with an annual monetary value 

of $162,094 (2001$). 

EPA estimates that the final regulation will avoid the loss of an estimated 26.96 IQ points among preschool children in Ohio, 

with an annual value of $253,934 (2001$). The annual avoided costs of compensatory education from reduced incidence of 

children with IQ below 70 and blood lead levels above 20 �g/dL amount to approximately $6,085. In total, the final 

regulation results in lead-related benefits for Ohio children of $422,113 annually (2001$). 

Table 22.2: Ohio Child Lead Annual Benefits (2001$): Final 

Regulation 

Category Reduced Cases 

or IQ Points 

Monetary Value of 

Benefits 

Neonatal mortality 0.024 $162,094 

Avoided IQ loss 26.96 $253,934 

Reduced IQ < 70 0.09 $5,345 

Reduced PbB > 20 �g/dL 0.04 $740 

Total Benefits $422,113 

Source: U.S. EPA analysis. 

b. Adult benefits


Table 22.3 presents benefit estimates for reduced lead-related health effects in adults. These health effects include increased


incidence of hypertension, initial non-fatal coronary heart disease (CHD), non-fatal stokes (cerebrovascular


accidents [CBA] and brain infarction [BI]), and premature mortality. The final regulation would reduce hypertension in 

Ohio by an estimated 9.4 cases annually among males, with annual benefits of approximately $10,670 (2001$). Reducing the 

incidence of initial CHD, strokes, and premature mortality among adult males and females in Ohio would result in estimated 

benefits of $963, $2,115, and $103,645, respectively. Overall, adult lead-related benefits total $117,393. This analysis does 

not include other lead-related health effects from elevated blood pressure (BP) or from effects such as nervous system 

disorders, anemia, and possible cancer effects. 

22-3 
MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

Table 22.3: Ohio Adult Lead Benefits (2001$): 

Final Regulation 

Category 

Final Regulation 

Reduced Cases Monetary Value of Benefits 

Men Hypertension 8.697 $10,670 

CHD 0.011 $693 

CBA 0.005 $947 

BI 0.003 $535 

Mortality 0.015 $79,178 

Women CHD 0.003 $270 

CBA 0.002 $392 

BI 0.001 $241 

Mortality 0.004 $24,467 

Total Benefits $117,393 

Source: U.S. EPA analysis. 

22.1.3 Economic Productivity Benefits 

The selected option does not affect POTW operations because the final rule regulates only direct dischargers. For the 

alternative policy options that consider both direct and indirect dischargers, EPA evaluated two categories of productivity 

benefits for POTWs: 

� reduced interference with the operations of POTWs, and 

� reduced contamination of sewage sludge (i.e., biosolids) at POTWs that receive discharges from MP&M facilities. 

Chapter 16 presents the methodology for evaluating POTW benefits. EPA’s analysis found that the alternative policy options 

did not yield POTW productivity benefits in Ohio. 

22.1.4 Total Monetized Benefits 

Summing the monetary values over all benefit categories (Chapters 21 and 22) yields total monetized benefits in Ohio of 

$930,408 (2001$) annually for the final regulation (see Table 22.4). As noted in Chapter 12, this benefit estimate is 

necessarily incomplete because it omits some mechanisms by which society is likely to benefit from reduced effluent 

discharges from the MP&M industry. Examples of benefit categories excluded from this estimate include: non-lead-related, 

non-cancer health benefits; improved aesthetic value of waters near discharge outfalls; benefits from improved habitat for 

wildlife, including threatened or endangered species; tourism benefits; and reduced costs for drinking water treatment. 

22-4 
MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

Table 22.4: Estimated Annual Benefits in Ohio from Reduced MP&M Discharges under the Final 

Regulation 2001$) 

Benefit Category Low Mid High 

1. d Cancer Risk: 

Fish Consumption 

Water Consumptiona 

$14,503 

n/a 

$14,503 

n/a 

$14,503 

n/a 

2. isk from Exposure to Lead: 

Children 

Adults 

$422,113 

$117,393 

$422,113 

$117,393 

$422,113 

$117,393 

3. ater-Based Recreation $250,932 $250,932 $250,932 

4. fits $125,466 $125,466 $125,466 

5. d Sewage Sludge Disposal Costs $0 $0 $0 

Total Monetized Benefits $930,408 $930,408 $930,408 

Reduce

Reduced R

Enhanced W

Nonuse bene

Avoide

a

 The monetized cancer avoidance benefits from reduced drinking water contamination are negligible. 

Source: U.S. EPA analysis. 

22.2 SOCIAL COSTS OF THE FINAL REGULATION 

22.2.1 Baseline and Post-Compliance Closures 

The methodology used to assess baseline and post-compliance closures differed from the methodology used for the national 

analysis presented in Chapter 5. The screener data collected for Ohio facilities did not provide financial data to perform an 

after-tax cash flow or net present value test. EPA therefore used data from the national analysis to estimate the percentage of 

facilities that close in the baseline and post-compliance. EPA assumed that the frequency of Ohio facility closures would be 

the same as that found in the national analysis for facilities with the same discharge status, subcategory, and flow category. 

For example, 2 percent of Oily Wastes facilities discharging less than one million gallons per year close in the baseline in the 

national analysis, and this same percentage is assumed for Ohio screener direct dischargers in that flow size category. 

Table 22.5 summarizes the numbers of facilities in Ohio closing or excluded from the final regulation by discharge status. All 

indirect dischargers operating post-regulation are excluded from requirements by subcategory exclusions. Of the 198 direct 

dischargers operating post-regulation, 85 (or 43 percent) are excluded from requirements by subcategory exclusions. A total 

of 113 direct discharging facilities in the Oily Wastes subcategory are therefore subject to requirements under the final 

regulation. 

22-5 
MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

Table 22.5: Regulatory Impacts for Ohio MP&M Facilities by Discharge Type 

Indirect Direct Total 

Number of MP&M facilities operating in the baseline 1,682 198 1,880 

Number of MP&M facilities with subcategory exclusions 1,682 85 1,767 

Number of MP&M facilities operating in the baseline estimated subject to 

regulatory requirements 

0 113 113 

Number of regulatory closures 0 0 0 

Percent of MP&M facilities operating in the baseline and subject to 

regulatory requirements that are regulatory closures 

0.0% 0.0% 0.0% 

Source: U.S. EPA analysis. 

22.2.2 Compliance Costs for MP&M Facilities 

The calculation of annualized compliance costs in Ohio uses the methodology presented in Chapter 11. These compliance 

costs are not adjusted for the effect of taxes or for recovery of costs through price increases, and therefore represent the social 

value of resources used for compliance. EPA annualized compliance costs using a social discount rate of seven percent over 

an estimated 15-year useful life of compliance equipment. 

In calculating compliance costs for Ohio facilities, EPA combined the compliance cost estimates developed for the “detailed 

questionnaire” Ohio facilities included the national analysis with compliance costs estimated for the additional “screener 

questionnaire” facilities included in the Ohio analysis. The Agency estimated compliance costs for each Ohio screener 

facility and then calculated an annualized compliance cost by subcategory, flow range, and discharge status for the Ohio 

facilities. These costs included facilities that might be assessed as baseline closures and thus would overstate expected 

compliance costs to the extent that some facilities are expected to close and not incur compliance costs. Because EPA 

estimated closures among Ohio screener facilities based on the closure rates from the national analysis, it was not possible to 

identify specific Ohio screener facilities as baseline or post-regulation closures and to remove their compliance costs from the 

total compliance cost estimates on a facility-specific basis. Instead, EPA reduced the total compliance costs, by facility 

category, by the estimated fraction of facilities assessed as baseline closures from the national analysis. EPA added these 

costs for the “screener questionnaire” facilities to the estimated compliance costs for the “detailed questionnaire” facilities to 

calculate total compliance costs for Ohio MP&M facilities. 

Table 22.6 reports the estimated resource value of compliance costs by discharge status and subcategory. The total estimated 

annualized compliance costs are $62 thousand. 

Table 22.6: Resource Value of Compliance Costs in Ohio (2001$) 

Subcategory Indirect Direct Total 

General Metals $0 $0 $0 

MF Job Shop $0 $0 $0 

Non Chromium Anodizer $0 $0 $0 

Oily Wastes $0 $62,232 $62,232 

Printed Wiring Boards $0 $0 $0 

Railroad Line Maintenance $0 $0 $0 

Steel Forming & Finishing $0 $0 $0 

Total $0 $62,232 $62,232 

Source: U.S. EPA analysis. 

22-6 
MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

22.2.3 Total Social Costs 

As discussed in Chapter 11, the regulation’s social costs include the resource cost of compliance (e.g., labor, equipment, 

material, and other economic resources needed to comply with the rule), costs to governments administering the regulation, 

and the social costs associated with unemployment resulting from facility closure. EPA estimated that the final rule will not 

result in social costs of unemployment and that governments will not incur additional costs in administering the regulation. 

Accordingly, as shown in Table 22.7, EPA’s estimate of the final rule’s social costs in Ohio is the same as that reported for 

the resource cost of compliance, $62 thousand (2001$) annually. 

Table 22.7: Annual Social Costs for the Final Regulation in Ohio 

(2001$, costs annualized at 7%) 

Component of Social Costs Final Rule 

Resource value of compliance costs $62,232.0 

Government administrative costs $0.0 

Social cost of unemployment $0.0 

Total Social Cost $62,232.0 

Source: U.S. EPA analysis. 

22.3 COMPARISON OF MONETIZED BENEFITS AND COSTS IN OHIO 

EPA cannot perform a complete cost-benefit comparison because not all of the benefits resulting from the final rule can be 

valued in dollar terms. As reported above, for Ohio, EPA estimated the final rule’s social cost at $62 thousand annually 

(2001$) and estimated monetizable benefits of $930 thousand annually (2001$). Subtracting the social costs from social 

benefits yields a net monetizable benefit of $868 thousand annually (2001$). 

In contrast to the national estimates of costs and benefits for the final regulation, the Ohio case study shows substantial net 

positive benefits even for the lower-bound benefits estimate. This difference results mainly from the more complete 

assessment of benefits from reduced MP&M pollutant discharges and more detailed water quality modeling. In addition to 

estimating recreational benefits from reduced frequency of AWQC exceedences, the Ohio case study estimated changes in 

water resource values from reduced discharges of TKN. Changes in TKN concentration in Ohio water bodies account for 

approximately 96 percent of the monetary value of recreational and nonuse benefits from the final rule. EPA also included an 

additional recreational benefit category in the Ohio analysis: swimming. Although the estimated per-trip welfare gain to 

swimmers is less than the gain for participants in other water-based recreational activities, this benefit category accounts for a 

sizable portion of the state-level benefits. Other factors that affect the Ohio benefit-cost comparison include: the presence of 

unique water resources such as Lake Erie; use of a more sophisticated water quality model, which estimates water quality 

changes in reaches downstream from the discharge reach; and a more accurate account of baseline water quality conditions. 

The presence of unique water resources, such as Lake Erie, and other numerous recreational opportunities (e.g., inland lakes, 

rivers, and reservoirs), suggest that the estimated benefits for Ohio are likely to be higher than the average of benefits for 

other states. 

22-7 
MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

GLOSSARY 

ambient water quality criteria (AWQC): levels of water quality expected to render a body of water suitable for its 

designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking, 

swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html) 

blood pressure (BP): the pressure of the blood on the walls of the arteries. 

brain infarction (BI): stroke. 

cerebrovascular accidents (CBA): stroke. 

coronary heart disease (CHD): disorder that restricts blood supply to the heart; occurs when coronary arteries become


narrowed or clogged due to the build up of cholesterol and fat on the inside walls and are unable supply enough blood to the


heart.


interference: the obstruction of a routine treatment process of POTWs that is caused by the presence of high levels of


toxics, such as metals and cyanide in wastewater discharges. These toxic pollutants kill bacteria used for microbial


degradation during wastewater treatment (see: microbial degradation).


publicly-owned treatment works (POTW): a treatment works as defined by Section 212 of the Act, which is owned by


a state or municipality. This definition includes any devices or systems used in the storage, treatment, recycling, and


reclamation of municipal sewage or industrial wastes of a liquid nature.


(http://www.epa.gov/owm/permits/pretreat/final99.pdf)


22-8 
MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

ACRONYMS 

AWQC: ambient water quality criteria 

BI: brain infarction 

BP: blood pressure 

CBA: cerebrovascular accidents 

CHD: coronary heart disease 

POTW: publicly-owned treatment works 

22-9 
MP&M EEBA Part V: Ohio Case Study Chapter 22: MP&M Benefit-Cost Analysis in Ohio 

THIS PAGE INTENTIONALLY LEFT BLANK 

22-10

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Appendix A: Detailed Economic


Impact Analysis Information


INTRODUCTION 

This appendix provides information to support the economic 

analyses of MP&M industries evaluated for the final rule and 

presented in Chapter 3 through Chapter 11 of the EEBA. 

The first section below provides the SIC and NAICS codes 

that define the MP&M industrial sectors. The second section 

presents information on the annual turnover of 

establishments (“births” and “deaths”) in the industrial 

sectors. The third section provides a description of the 

MP &M surveys that supported the economic impact and 

benefits analyses presented in the EEBA (see Section 3 of 

the TDD ). 

A.1 MP&M SIC AND NAICS CODES 

Standard Industrial Classification (SIC) codes and North 

CHAPTER CONTENTS 

A.1 ................ A-1 

A.1.1 SIC Codes by Sector ................. ... A-1 

A.1.2 ..... A-7 

A.2 l Establishment “Births” and “Deaths” in 

MP&M Industries ................. ..... A-26 

A.3 escription of MP&M Surveys ............... A-28 

A.3.1 reener Surveys ................. .... A-28 

A.3.2 io Screener Surveys ................ A-28 

A.3.3 ailed MP&M Industry Surveys ....... A-28 

A.3.4 ................. A-29 

A.3.5 ................. . A-29 

A.3.6 ................ A-29 

A.3.7 ................. ...... A-29 

References ................. ................. . A-31 

MP&M SIC and NAICS Codes 

Bridge Between NAICS and SIC codes 

Annua

D

Sc

Oh

Det

Iron and Steel Survey 

Municipality Survey 

Federal Facility Survey 

POTW Survey 

American Industrial Classification System (NAICS) codes are hierarchical systems that allow for detailed classification of 

industries using numerical codes. This section lists and describes the SIC codes that make up the MP&M industry sectors. It 

also describes the process by which data organized by NAICS code was converted to SIC code format. 

A.1.1 SIC Codes by Sector 

Table A.1 lists and describes the 4-digit SIC codes that make up the MP&M industry sectors. These codes were used until 

recently to define industries for reporting of Federal Census data, and are the framework for the part of the industry profile 

(Chapter 3) based on publicly available material. 

Table A.1: MP&M Sectors and SIC Codes Evaluated for the Final Rulea 

SIC Code Standard Industrial Classification Groups 

Aerospace 

3761 Guided Missiles and Space Vehicles 

3764 Guided Missile and Space Vehicle Propulsion 

3769 Other Space Vehicle and Missile Parts 

Aircraft 

3721 Aircraft 

3724 Aircraft Engines and Engine Parts 

3728 Aircraft Parts and Auxiliary Equipment 

4581 Airports, Flying Fields, Airport Terminal Services 

Bus And Truck 

3713 Truck and Bus Bodies 

3715 Truck Trailers 

A-1 
MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.1: MP&M Sectors and SIC Codes Evaluated for the Final Rulea 

SIC Code Standard Industrial Classification Groups 

4111 Local And Suburban Transit 

4119 Local Passenger Transit, N.E.C. 

4131 Intercity And Rural Bus Transportation 

4141 Local Bus Charter Service 

4142 Bus Charter Service, Except Local 

4173 Bus Terminal And Service Facilities 

4212 Local Trucking without Storage 

4213 Trucking, Except Local 

4214 Local Trucking with Storage 

4215 Courier Services, Except by Air 

4231 Trucking Terminal Facilities 

Electronic Equipment 

3661 Telephone and Telegraph Apparatus 

3663 Radio and Television Broadcast and Communications Equipment 

3669 Communications Equipment, N.E.C. 

3671 Electron Tubes 

3675 Electronic Capacitors 

3677 Electronic Coils and Transformers 

3678 Connectors for Electronic Applications 

3679 Electronic Components, N.E.C. 

3699 Electrical Machinery, Equipment, And Supplies, N.E.C. 

Hardware 

2796 Platemaking and Related Services 

3398 Metal Heat Treating 

3412 Metal Shipping Barrels, Drums, Kegs, Pails 

3421 Cutlery 

3423 Hand And Edge Tools, Except Machine Tools and Handsaws 

3425 Hand Saws and Saw Blades 

3429 Hardware, N.E.C. 

3433 Heating Equipment, Except Electric and Warm Air Furnace 

3441 Fabricated Structural Metal 

3443 Fabricated Plate Work (Boiler Shops) 

3444 Sheet Metal Work 

3446 Architectural and Ornamental Metal Work 

3448 Prefabricated Metal Buildings And Components 

3449 Miscellaneous Metal Work 

3451 Screw Machine Products 

3452 Bolts, Nuts, Screws, Rivets, and Washers 

3462 Iron and Steel Forgings 

3466 Crowns and Closures 

3469 Metal Stamping, N.E.C. 

3492 Fluid Power Valves and Hose Fittings 

3493 Steel Springs 

3494 Valves And Pipe Fittings, Except Brass 

A-2

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.1: MP&M Sectors and SIC Codes Evaluated for the Final Rulea 

SIC Code Standard Industrial Classification Groups 

3495 Wire Springs 

3496 Miscellaneous Fabricated Wire Products 

3498 Fabricated Pipe and Fabricated Pipe Fitting 

3499 Fabricated Metal Products, N.E.C. 

3541 Machine Tools, Metal Cutting Types 

3542 Machine Tools, Metal Forming Types 

3544 Special Dies and Tools, Die Sets, Jigs and Fixtures, and Industrial Molds 

3545 Machine Tool Access and Measuring Devices 

3546 Power Driven Hand Tools 

3965 Fasteners, Buttons, Needles, Pins 

Household Equipment 

2514 Metal Household Furniture 

2522 Office Furniture, Except Wood 

2531 Public Building and Related Furniture 

2542 Partitions and Fixtures, Except Wood 

2591 Drapery Hardware and Window Blinds/shades 

2599 Furniture and Fixtures, N.E.C. 

3431 Metal Sanitary Ware 

3432 Plumbing Fittings and Brass Goods 

3442 Metal Doors, Sash, and Trim 

3631 Household Cooking Equipment 

3632 Household Refrigerators and Home and Farm and Freezers 

3633 Household Laundry Equipment 

3634 Electric Housewares and Fans 

3635 Household Vacuum Cleaners 

3639 Household Appliances, N.E.C. 

3641 Electric Lamps 

3643 Current-carrying Wiring Devices 

3644 Noncurrent-carrying Wiring Devices 

3645 Residential Electrical Lighting Fixtures 

3646 Commercial, Industrial, and Institutional 

3648 Lighting Equipment, N.E.C. 

3651 Radio/television Sets Except Communication Types 

7623 Refrigeration and Air-conditioning Service and Repair Shops 

Instruments 

3812 Search, Detection, Navigation, Guidance, Aeronautical, Nautical Systems and Instruments 

3821 Laboratory Apparatus and Furniture 

3822 Automatic Environmental Controls 

3823 Process Control Instruments 

3824 Fluid Meters and Counting Devices 

3825 Instruments to Measure Electricity 

3826 Laboratory Analytical Instruments 

3827 Optical Instruments and Lenses 

3829 Measuring and Controlling Devices, N.E.C. 

A-3

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.1: MP&M Sectors and SIC Codes Evaluated for the Final Rulea 

SIC Code Standard Industrial Classification Groups 

3841 Surgical and Medical Instruments and Apparatus 

3842 Orthopedic, Prosthetic and Surgical Suppl. 

3843 Dental Equipment and Supplies 

3844 X-ray Apparatus and Tubes 

3845 Electromedical Equipment 

3851 Ophthalmic Goods 

7629 Electric Repair Shop 

Iron and Steel 

3315 Steel Wiredrawing and Steel Nails and Spikes 

3316 Cold-Rolled Steel Sheet, Strip, and Bars 

3317 Steel Pipe and Tubes 

Job Shop 

3471 Plating and Polishing 

3479 Metal Coating and Allied Services 

Mobile Industrial Equipment 

3523 Farm Machinery and Equipment 

3524 Garden Tractors and Lawn and Garden Equipment 

3531 Construction Machinery and Equipment 

3532 Mining Machinery and Equipment, Except Oil Field 

3536 Hoists, Industrial Cranes and Monorails 

3537 Industrial Trucks, Tractors, Trailers 

3795 Tanks and Tank Components 

Motor Vehicle 

3465 Automotive Stampings 

3592 Carburetors, Piston Rings, Valves 

3647 Vehicular Lighting Equipment 

3694 Electrical Equipment for Motor Vehicles 

3711 Motor Vehicle and Automobile Bodies 

3714 Motor Vehicle Parts and Accessories 

3716 Mobile Homes 

3751 Motorcycles 

3792 Travel Trailers and Campers 

3799 Miscellaneous Transportation Equipment 

4121 Taxicabs 

5013 Motor Vehicle Supplies and New Parts 

5511 Motor Vehicle Dealers (New and Used) 

5521 Motor Vehicle Dealers (Used Only) 

5561 Recreational Vehicle Dealers 

5571 Motorcycle Dealers 

5599 Automotive Dealers, N.E.C. 

7514 Passenger Car Rental 

7515 Passenger Car Lease 

7519 Utility Trailer and Recreational Vehicle Rental 

7532 Top, Body, and Upholstery Repair and Paint Shops 

A-4

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.1: MP&M Sectors and SIC Codes Evaluated for the Final Rulea 

SIC Code Standard Industrial Classification Groups 

7533 Auto Exhaust Systems 

7537 Auto Transmission Repair 

7538 General Automotive Repair 

7539 Auto Repair Shop, N.E.C. 

7549 Auto Services, Except Repair and Carwashes 

Office Machine 

3571 Electronic Computers 

3572 Typewriters 

3575 Computer Terminals 

3577 Computer Peripheral Equipment, N.E.C. 

3578 Calculating, Accounting Machines Except Computers 

3579 Office Machines, N.E.C. 

7378 Computer Maintenance and Repairs 

7379 Computer Related Services, N.E.C. 

Ordnance 

3482 Small Arms Ammunition 

3483 Ammunition, Except for Small Arms 

3484 Small Arms 

3489 Ordnance and Accessories, N.E.C. 

Miscellaneous Metal Products 

3497 Metal Foil and Leaf 

3861 Photographic Equipment and Supplies 

3931 Musical Instruments 

3944 Games, Toys, Children's Vehicles 

3949 Sporting and Athletic Goods, N.E.C. 

3951 Pens and Mechanical Pencils 

3953 Marking Devices 

3993 Signs and Advertising Displays 

3995 Burial Caskets 

3999 Manufacturing Industries, N.E.C. 

7692 Welding Repair 

7699 Repair Shop, Related Service 

Precious Metals and Jewelry 

3873 Watches, Clocks, and Watchcases 

3911 Jewelry, Precious Metal 

3914 Silverware, Plated Ware and Stainless 

3915 Jewelers' Materials and Lapidary Work 

3961 Costume Jewelry 

7631 Watch, Clock, Jewelry Repair 

Printed Circuit Boards 

3672 Printed Circuit Boards 

Railroad 

3743 Railcars, Railway Systems 

4011 Railroad Transportation 

A-5

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.1: MP&M Sectors and SIC Codes Evaluated for the Final Rulea 

SIC Code Standard Industrial Classification Groups 

4013 Railroad Transportation 

Ships and Boats 

3731 Ship Building and Repairing 

3732 Boat Building and Repairing 

4412 Deep Sea Foreign Transportation 

4424 Deep Sea Domestic Transportation 

4432 Freight Transportation Great Lakes 

4449 Water Transportation of Freight, N.E.C. 

4481 Deep Sea Passenger Transportation 

4482 Ferries 

4489 Water Passenger Transportation, N.E.C. 

4491 Marine Cargo Handling 

4492 Towing and Tugboat Service 

4493 Marinas 

4499 Water Transportation Services, N.E.C. 

Stationary Industrial Equipment 

3511 Steam, Gas, Hydraulic Turbines, Generating Units 

3519 Internal Combustion Engines, N.E.C. 

3533 Oil Field Machinery and Equipment 

3534 Elevators and Moving Stairways 

3535 Conveyors and Conveying Equipment 

3543 Industrial Patterns 

3547 Rolling Mill Machinery and Equipment 

3548 Electric and Gas Welding and Soldering 

3549 Metal Working Machinery, N.E.C. 

3552 Textile Machinery 

3553 Woodworking Machinery 

3554 Paper Industries Machinery 

3555 Printing Trades Machinery and Equipment 

3556 Food Products Machinery 

3559 Special Industry Machinery, N.E.C. 

3561 Pumps and Pumping Equipment 

3562 Ball and Roller Bearings 

3563 Air and Gas Compressors 

3564 Blowers and Exhaust and Ventilation Fans 

3565 Industrial Patterns 

3566 Speed Changers, High Speed Drivers and Gears 

3567 Industrial Process Furnaces and Ovens 

3568 Mechanical Power Transmission Equipment, N.E.C. 

3569 General Industrial Machinery, N.E.C. 

3581 Automatic Merchandising Machines 

3582 Commercial Laundry Equipment 

3585 Refrigeration and Air and Heating Equipment 

3586 Measuring and Dispensing Pumps 

A-6

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.1: MP&M Sectors and SIC Codes Evaluated for the Final Rulea 

SIC Code Standard Industrial Classification Groups 

3589 Service Industry Machines, N.E.C. 

3593 Fluid Power Cylinders and Actuators 

3594 Fluid Power Pumps and Motors 

3596 Scales and Balances, Except Laboratory 

3599 Machinery, Except Electrical, N.E.C. 

3612 Transformers 

3613 Switchgear and Switchboard Apparatus 

3621 Motors and Generators 

3629 Electric Industrial Apparatus, N.E.C. 

7353 Heavy Construction Equip Rental, Leasing 

7359 Equipment Rental, Leasing, N.E.C. 

a

 EPA evaluated options for these industrial sectors but did not regulate them all under the final rule. 

N.E.C. = Not Elsewhere Classified 

Source: Executive Office of the President, Office of Management and Budget, Standard Industrial Classification Manual 

1987. 

A.1.2 Bridge Between NAICS and SIC codes 

In 1997, the Census Bureau switched from using SIC codes to using NAICS codes. NAICS codes allow for greater 

comparability with the International Standard Industrial Classification System (ISIC), which is developed and maintained by 

the United Nations. NAICS codes also better reflect the structure of today’s economy, including the growth of the service 

sectors and new technologies, than do the decades-old SIC codes. Because EPA chose to create regulatory subgroups for the 

MP&M industries based on aggregated four-digit SIC codes, it was necessary for EPA to convert some data based on NAICS 

codes into SIC code format. 

The SIC-NAICS conversion is not always straightforward because NAICS and SIC codes often don’t map on a one-to-one 

basis. Specific industries that were grouped together in one SIC code sometimes map to several NAICS codes, and 

sometimes several SIC codes were aggregated together in one NAICS code. 

To address this conversion problem, EPA created a “bridge” that converts the NAICS classification structure to the SIC 

structure using share values computed from Economic Census data. This bridge is based on data from the 1997 Census, 

which reported the share of number of establishments and value of output that each SIC code that contributed to each NAICS 

code, and vice versa. 

The first step in creating the bridge was to obtain a table that listed the value of shipments (VOS) that each NAICS code 

contributed to each SIC code. Since the total VOS for each NAICS code was known, EPA computed share values for each 

NAICS, which were equal to the percent of total VOS in that NAICS code that was classified in a certain SIC code. The 

equation is: 

Share of NAICSx going to SICy = (VOS that NAICSx contributed to SICy) / (total VOS for NAICSx) (A-1) 

Using these share values, EPA converted data classified by NAICS to SIC format, simply by multiplying VOS for each 

NAICS by its share value, for each SIC, and then summing up the totals for each SIC. For example, if NAICS codes 333121, 

332456, and 332457 all contributed a portion of their output to SIC 3322, then: 

VOS for SIC3322 = (share of NAICS333121 going to SIC3322) * (VOS for NAICS333121) 

+ (share of NAICS332456 going to SIC3322) * (VOS for NAICS332456) (A-2) 

+ (share of NAICS332457 going to SIC3322) * (VOS for NAICS332457) 

A-7 
MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Occasionally it was not possible to compute share values because the Census Bureau withheld some 1997 VOS data because 1 of disclosure issues . In those cases, EPA estimated 1997 VOS based on 1992 Census data and then used those estimates to 

compute share values. First, EPA calculated the average VOS per establishment in 1992 for each relevant SIC code: 

VOS per establishment for SICy = (A-3) (VOS for SICy in 1992) / (number of establishments for SICy in 1992) 

EPA then multiplied this average VOS per establishment for a certain SIC by the number of establishments that each NAICS 

contributed to that SIC in 1997: 

Estimated VOS that NAICSx contributed to SICy in 1997 = (A-4) (VOS per establishment for SICy) * (number of establishments NAICSx contributed to SICy in 1997) 

EPA used this estimated VOS to compute an estimated share value. 

To gain a rough measure of how accurately the NAICS codes could be broken into sectors, EPA calculated, by sector: (1) the 

percentage of NAICS codes that matched “one-to-one” with an SIC code, (2) the percentage that did not match one-to-one but 

were contained in a single sector, and (3) the percentage that didn’t match one to one and were contained in multiple sectors 

(Figure A.1, Table A.2). 

Figure A.1: Percentage of VOS 1997 to 1999 Attributable to One-to-One NAICS-SIC Match, 

Not One-to-One but in the Same Sector, and Not One-to-One but in Different Sectors 

Sectors: 1 Hardware; 2 Aircraft; 3 Electronic Equipment; 4 Stationary Industrial Equipment; 5 Ordnance; 6 Aerospace; 7 Mobile 

Industrial Equipment; 8 Instruments; 9 Precious Metals and Jewelry; 10 Ships and Boats; 11 Household Equipment; 12 Railroad; 13 

Motor Vehicle; 14 Bus and Truck; 15 Office Machine; 16 Printed Circuit Boards; 17 Job Shop; 18 Miscellaneous Metal Products; 19 

Iron and Steel 

Source: Department of Commerce, Bureau of the Census, Manufacturing Industry Series; U.S. EPA analysis. 

1 The Bureau of the Census does not release any data that could reveal data about a specific firm. In cases when a NAICS or SIC 

code is so specific that it includes only a few firms, information about VOS is not released. However, the number of establishments in a 

specific industry is not considered private information. 

A-8 
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MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.2: Percentage of Input One-to-One, Not One-to-One but in the Same Sector, 

and Not One-to-One and in Different Sectors 

Sector VOS 

One-to-One 

Employment 

One-to-One 

VOS 

Same Sector 

Employment 

Same Sector 

VOS 

Different Sectors 

Employment 

Different Sectors 

YEAR: 1997 

62.5% 64.3% 18.2% 16.5% 19.3% 19.2% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

46.7% 47.2% 47.2% 43.2% 6.2% 9.7% 

63.3% 68.1% 3.9% 4.4% 32.8% 27.6% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

91.8% 88.1% 5.5% 7.8% 2.7% 4.1% 

30.4% 30.2% 14.4% 14.4% 55.2% 55.4% 

10.2% 8.3% 0.0% 0.0% 89.8% 91.7% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

67.5% 60.6% 6.3% 4.5% 26.3% 34.9% 

0.0% 0.0% 0.0% 0.0% 100.0% 100.0% 

85.3% 69.5% 1.1% 3.1% 13.6% 27.4% 

39.1% 42.8% 0.0% 0.0% 60.9% 57.2% 

73.1% 59.9% 26.4% 38.6% 0.5% 1.5% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

99.9% 99.9% 0.0% 0.0% 0.1% 0.1% 

83.1% 76.5% 12.2% 17.8% 4.6% 5.7% 

98.1% 95.3% 0.0% 0.0% 1.9% 4.7% 

YEAR: 1998 

62.8% 64.9% 17.9% 16.3% 19.3% 18.8% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

47.6% 47.3% 46.0% 42.7% 6.4% 10.0% 

62.0% 68.3% 3.8% 4.4% 34.2% 27.3% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

91.8% 88.0% 5.5% 8.0% 2.7% 4.1% 

29.4% 29.3% 15.1% 14.7% 55.5% 55.9% 

8.4% 8.7% 0.0% 0.0% 91.6% 91.3% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

66.2% 60.0% 6.9% 4.8% 26.9% 35.2% 

0.0% 0.0% 0.0% 0.0% 100.0% 100.0% 

84.2% 68.0% 1.3% 3.4% 14.6% 28.6% 

40.7% 43.4% 0.0% 0.0% 59.3% 56.6% 

73.5% 58.9% 26.0% 39.7% 0.5% 1.4% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

99.9% 99.9% 0.0% 0.0% 0.1% 0.1% 

82.1% 76.1% 12.9% 18.2% 4.9% 5.8% 

97.9% 95.3% 0.0% 0.0% 2.1% 4.7% 

A-9

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MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.2: Percentage of Input One-to-One, Not One-to-One but in the Same Sector, 

and Not One-to-One and in Different Sectors 

Sector VOS 

One-to-One 

Employment 

One-to-One 

VOS 

Same Sector 

Employment 

Same Sector 

VOS 

Different Sectors 

Employment 

Different Sectors 

YEAR: 1999 

62.3% 64.3% 18.3% 16.4% 19.4% 19.3% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

48.7% 47.9% 45.4% 42.3% 5.9% 9.8% 

61.6% 67.8% 3.6% 4.3% 34.7% 27.9% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

89.6% 87.0% 7.4% 8.8% 3.0% 4.2% 

29.9% 29.8% 15.2% 15.2% 54.9% 55.1% 

7.1% 7.5% 0.0% 0.0% 92.9% 92.5% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

65.5% 57.8% 7.7% 5.4% 26.9% 36.8% 

0.0% 0.0% 0.0% 0.0% 100.0% 100.0% 

84.6% 68.3% 1.3% 3.9% 14.1% 27.9% 

40.5% 45.8% 0.0% 0.0% 59.5% 54.2% 

75.6% 56.6% 23.8% 41.9% 0.6% 1.6% 

100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

99.9% 99.9% 0.0% 0.0% 0.1% 0.1% 

82.0% 76.8% 13.0% 17.1% 5.0% 6.1% 

97.7% 95.0% 0.0% 0.0% 2.3% 5.0% 

Sectors: 1 Hardware; 2 Aircraft; 3 Electronic Equipment; 4 Stationary Industrial Equipment; 5 Ordnance; 6 Aerospace; 7 Mobile 

Industrial Equipment; 8 Instruments; 9 Precious Metals and Jewelry; 10 Ships and Boats; 11 Household Equipment; 12 Railroad; 13 

Motor Vehicle; 14 Bus and Truck; 15 Office Machine; 16 Printed Circuit Boards; 17 Job Shop; 18 Miscellaneous Metal Products; 19 

Iron and Steel 

Source: Department of Commerce, Bureau of the Census, Manufacturing Industry Series; U.S. EPA analysis. 

Table A.3 presents the data that was used to calculate the relationship between NAICS and SIC codes. The table lists the


MP&M sector to which each SIC code belongs, gives a short description of each SIC, and lists NAICS codes that encompass


similar industries. The table also lists the number of establishments, the value of shipments, and the number of employees


that are contributed to each SIC by each NAICS, as well as the share values, i.e. the portion of its total value of shipments that


a given NAICS code contributes to a given SIC code.


A-10 
MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

Aerospace 

3761 Guided Missiles and Space 

Vehicles 336414 Guided Missile and Space Vehicle 

Manufacturing 22 14,791,466 100.0% 

3764 Guided Missile and Space 

Vehicle Propulsion 336415 

Guided Missile and Space Vehicle Propulsion 

Unit and Propulsion Unit Parts 

Manufacturing 

28 3,239,033 100.0% 

3769 Other Space Vehicle and 

Missile Parts 336419 

Other Guided Missile and Space Vehicle 

Parts and Auxiliary Equipment 

Manufacturing 

49 898,758 100.0% 

Aircraft 

3721 Aircraft 336411 Aircraft Manufacturing 204 56,273,651 100.0% 

3724 Aircraft Engines and Engine 

Parts 336412 Aircraft Engine and Engine Parts 

Manufacturing 369 22,617,284 100.0% 

3728 Aircraft Parts and Auxiliary 

Equipment 336413 Other Aircraft Parts and Auxiliary Equipment 

Manufacturing 1,138 20,073,061 100.0% 

4581 Airports, Flying Fields, 

Airport Terminal Services 

488111 Air Traffic Control 114 43,450 100.0% 

488119 Other Airport Operations 1,699 3,243,149 99.8% 

488190 Other Support Activities for Air 

Transportation 2,400 5,859,631 100.0% 

561720 Janitorial Services 127 203,918 1.0% 

Bus & Truck 

3713 Truck and Bus Bodies 336211 Motor Vehicle Body Manufacturing 715 8,719,326 96.2% 

3715 Truck Trailers 336212 Truck Trailer Manufacturing 390 5,507,768 100.0% 

4111 Local And Suburban Transit 

485111 Mixed Mode Transit Systems 28 51,567 100.0% 

485113 Bus and Other Motor Vehicle Transit 

Systems 542 1,152,525 100.0% 

485999 All Other Transit and Ground Passenger 

Transportation 534 601,988 89.9% 

4119 Local Passenger Transit, 

N.E.C. 

485320 Limousine Service 3,234 1,873,924 100.0% 

485410 School and Employee Bus Transportation 158 158,947 3.6% 

485991 Special Needs Transportation 1,789 1,141,413 100.0% 

485999 All Other Transit and Ground Passenger 

Transportation 232 67,395 10.1% 

487110 Scenic and Sightseeing Transportation, Land 307 462,186 82.9% 

621910 Ambulance Services 3,275 4,443,174 88.4% 

4131 Intercity And Rural Bus 

Transportation 485210 Interurban and Rural Bus Transportation 407 1,147,432 100.0% 

4141 Local Bus Charter Service 485510 Charter Bus Industry 482 459,953 26.0% 

4142 Bus Charter Service, Except 

Local 485510 Charter Bus Industry 1,049 1,308,246 74.0% 

4173 Bus Terminal And Service 

Facilities 488490 Other Support Activities for Road 

Transportation 26 15,253 3.9% 

A-11

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

4212 Local Trucking without 

Storage 

484110 General Freight Trucking, Local 14,545 11,108,345 90.5% 

484210 Used Household and Office Goods Moving 3,259 1,198,983 9.5% 

484220 Specialized Freight (except Used Goods) 

Trucking, Local 34,935 18,932,851 96.0% 

562111 Solid Waste Collection 7,083 18,211,495 100.0% 

562112 Hazardous Waste Collection 414 1,095,553 100.0% 

562119 Other Waste Collection 827 837,625 100.0% 

4213 Trucking, Except Local 

484121 General Freight Trucking, Long-Distance, 

Truckload 23,111 51,142,148 100.0% 

484122 General Freight Trucking, Long-Distance, 

Less Than Truckload 6,210 25,010,091 100.0% 

484210 Used Household and Office Goods Moving 3,555 9,111,477 72.4% 

484230 Specialized Freight (except Used Goods) 

Trucking, Long-Distance 14,439 20,500,392 100.0% 

4214 Local Trucking with Storage 

484110 General Freight Trucking, Local 915 1,164,931 9.5% 

484210 Used Household and Office Goods Moving 2,286 2,273,241 18.1% 

484220 Specialized Freight (except Used Goods) 

Trucking, Local 543 782,939 4.0% 

4215 Courier Services, Except by 

Air 

492110 Couriers 2,362 19,289,602 53.1% 

492210 Local Messengers and Local Delivery 5,384 3,519,100 100.0% 

4231 Trucking Terminal Facilities 488490 Other Support Activities for Road 

Transportation 14 12,989 3.3% 

Electronic Equipment 

3661 Telephone and Telegraph 

Apparatus 

334210 Telephone Apparatus Manufacturing 598 38,300,044 100.0% 

334416 Electronic Coil, Transformer, and Other 

Inductor Manufacturing 7 8,904 0.6% 

334418 Printed Circuit Assembly (Electronic 

Assembly) Manufacturing 20 1,364,671 5.2% 

3663 Radio and Television 

Broadcast and Comm Eq 334220 

Radio and Television Broadcasting and 

Wireless Communications Equipment 

Manufacturing 

1,091 37,042,241 94.2% 

3669 Communications Eq, N.E.C. 334290 Other Communications Equipment 

Manufacturing 497 4,233,288 100.0% 

3671 Electron Tubes 334411 Electron Tube Manufacturing 159 3,858,499 100.0% 

3675 Electronic Capacitors 334414 Electronic Capacitor Manufacturing 129 2,482,163 100.0% 

3677 Electronic Coils and 

Transformers 334416 Electronic Coil, Transformer, and Other 

Inductor Manufacturing 426 1,512,232 97.9% 

3678 Connectors for Electronic 

Applications 334417 Electronic Connector Manufacturing 347 5,598,906 100.0% 

A-12

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

3679 Electronic Components 

N.E.C. 

334220 

Radio and Television Broadcasting and 

Wireless Communications Equipment 

Manufacturing 

126 2,265,873 5.8% 

334418 Printed Circuit Assembly (Electronic 

Assembly) Manufacturing 695 24,704,154 94.8% 

334419 Other Electronic Component Manufacturing 1,851 10,547,090 100.0% 

336322 Other Motor Vehicle Electrical and 

Electronic Equipment Manufacturing 253 1,420,996 8.4% 

3699 Electronic Mach., 

Equipment, & Suppl. N.E.C. 

332212 Hand and Edge Tool Manufacturing 4 140,811 2.1% 

333293 Printing Machinery and Equipment 

Manufacturing 5 0 0.9%b 

333314 Optical Instrument and Lens Manufacturing 5 7,320 0.2% 

333319 Other Commercial and Service Industry 

Machinery Manufacturing 

57 934,728 10.0% 

333512 Machine Tool (Metal Cutting Types) 

Manufacturing 8 151,363 2.8% 

333618 Other Engine Equipment Manufacturing 2 0 0.7%b 

333992 Welding and Soldering Equipment 

Manufacturing 6 11,101 0.2% 

334510 Electromedical and Electrotherapeutic 

Apparatus Manufacturing 11 52,855 0.5% 

334511 

Search, Detection, Navigation, Guidance, 

Aeronautical, and Nautical System and 

Instrument Manufacturing 

7 77,832 0.2% 

334516 Analytical Laboratory Instrument 

Manufacturing 10 36,473 0.5% 

334519 Other Measuring and Controlling Device 

Manufacturing 5 6,174 0.1% 

335129 Other Lighting Equipment Manufacturing 4 859 0.0% 

335999 All Other Miscellaneous Electrical 

Equipment and Component Manufacturing 567 4,051,267 58.8% 

Hardware 

2796 Platemaking and Related 

Services 323122 Prepress Services 1,276 2,663,020 53.2% 

3398 Metal Heat Treating 332811 Metal Heat Treating 808 3,485,459 100.0% 

3412 Metal Shipping Barrels, 

Drums, Kegs, Pails 332439 Other Metal Container Manufacturing 151 1,310,595 57.8% 

3421 Cutlery 332211 Cutlery and Flatware (except Precious) 

Manufacturing 164 2,198,365 99.6% 

3423 Hand & Edge Tools, Except 

Mach. Tools, Saws 332212 Hand and Edge Tool Manufacturing 1,069 5,677,903 86.0% 

3425 Hand Saws and Saw Blades 332213 Saw Blade and Handsaw Manufacturing 176 1,452,540 100.0% 

A-13

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

3429 Hardware N.E.C. 

332439 Other Metal Container Manufacturing 117 402,378 17.7% 

332510 Hardware Manufacturing 952 10,359,952 96.0% 

332919 Other Metal Valve and Pipe Fitting 

Manufacturing 16 0 3.9%b 

3433 Heatg. Equip. Except Elec. & 

Warm Air Frnc. 333414 Heating Equipment (except Warm Air 

Furnaces) Manufacturing 453 3,387,391 91.1% 

3441 Fabricated Structural Metal 332312 Fabricated Structural Metal Manufacturing 2,900 14,200,270 86.8% 

3443 Fabricated Plate Work 

(Boiler Shops) 

332313 Plate Work Manufacturing 1,035 2,806,913 100.0% 

332410 Power Boiler and Heat Exchanger 

Manufacturing 472 3,849,100 100.0% 

332420 Metal Tank (Heavy Gauge) Manufacturing 614 4,764,118 100.0% 

333415 

Air-Conditioning and Warm Air Heating 

Equipment and Commercial and Industrial 

Refrigeration Equipment Manufacturing 

9 43,264 0.2% 

3444 Sheet Metal Work 

332322 Sheet Metal Work Manufacturing 4,479 15,957,992 100.0% 

332439 Other Metal Container Manufacturing 126 275,440 12.1% 

3446 Architectural and Ornamental 

Metal Work 332323 Ornamental and Architectural Metal Work 

Manufacturing 1,744 3,536,413 88.2% 

3448 Prefabricated Metal 

Buildings & Components 332311 Prefabricated Metal Building and Component 

Manufacturing 604 4,199,550 100.0% 

3449 Miscellaneous Metal Work 

332114 Custom Roll Forming 401 3,074,662 100.0% 

332312 Fabricated Structural Metal Manufacturing 152 2,166,021 13.2% 

332321 Metal Window and Door Manufacturing 33 364,564 3.6% 

332323 Ornamental and Architectural Metal Work 

Manufacturing 6 91,939 2.3% 

3451 Screw Machine Products 332721 Precision Turned Product Manufacturing 2,745 8,326,077 100.0% 

3452 Bolts, Nuts, Screws, Rivets, 

and Washers 332722 Bolt, Nut, Screw, Rivet, and Washer 

Manufacturing 1,040 8,134,661 100.0% 

3462 Iron and Steel Forgings 332111 Iron and Steel Forging 421 4,924,426 100.0% 

3466 Crowns and Closures 332115 Crown and Closure Manufacturing 67 969,982 100.0% 

3469 Metal Stamping N.E.C. 

332116 Metal Stamping 2,166 12,041,638 100.0% 

332214 Kitchen Utensil, Pot, and Pan Manufacturing 77 1,369,914 100.0% 

3492 Fluid Power Valves and Hose 

Fittings 

332912 Fluid Power Valve and Hose Fitting 

Manufacturing 424 6,602,909 100.0% 

3493 Steel Springs 332611 Spring (Heavy Gauge) Manufacturing 129 761,711 100.0% 

3494 Valves & Pipe Fittings, 

Except Brass 

332919 Other Metal Valve and Pipe Fitting 

Manufacturing 222 2,753,397 94.4% 

332999 All Other Miscellaneous Fabricated Metal 

Product Manufacturing 23 73,983 0.7% 

3495 Wire Springs 

332612 Spring (Light Gauge) Manufacturing 394 2,481,151 100.0% 

334518 Watch, Clock, and Part Manufacturing 2 0 2.5%b 

A-14

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

3496 Miscellaneous Fabricated 

Wire Products 332618 Other Fabricated Wire Product 

Manufacturing 1,253 4,587,656 87.3% 

3498 Fabricated Pipe and 

Fabricated Pipe Fitting 332996 Fabricated Pipe and Pipe Fitting 

Manufacturing 856 4,024,999 100.0% 

3499 Fabricated Metal Products 

N.E.C. 

332117 Powder Metallurgy Part Manufacturing 128 1,317,301 100.0% 

332439 Other Metal Container Manufacturing 98 273,541 12.1% 

332510 Hardware Manufacturing 58 435,815 4.0% 

332919 Other Metal Valve and Pipe Fitting 

Manufacturing 7 0 1.7%b 

332999 All Other Miscellaneous Fabricated Metal 

Product Manufacturing 2,592 7,558,137 71.9% 

337215 Showcase, Partition, Shelving, and Locker 

Manufacturing 78 123,057 1.5% 

339914 Costume Jewelry and Novelty Manufacturing 82 49,953 3.9% 

3541 Machine Tools, Metal 

Cutting Types 333512 Machine Tool (Metal Cutting Types) 

Manufacturing 393 5,183,521 97.2% 

3542 Machine Tools, Metal 

Forming Types 333513 Machine Tool (Metal Forming Types) Manufacturing 225 2,255,011 100.0% 

3544 Special Dies & Tools, Die 

Sets, Jigs, Etc. 

333511 Industrial Mold Manufacturing 2,529 5,116,635 100.0% 

333514 Special Die and Tool, Die Set, Jig, and 

Fixture Manufacturing 4,746 8,244,855 100.0% 

3545 Machine Tool Access & 

Measuring Devices 

332212 Hand and Edge Tool Manufacturing 185 714,277 10.8% 

333515 Cutting Tool and Machine Tool Accessory 

Manufacturing 1,920 5,347,173 100.0% 

3546 Power Driven Hand Tools 333991 Power-Driven Handtool Manufacturing 217 3,609,779 100.0% 

3965 Fasteners, Buttons, Needles, 

Pins 339993 Fastener, Button, Needle, and Pin 

Manufacturing 249 0 99.2%b 

Household Equipment 

2514 Metal Household Furniture 337124 Metal Household Furniture Manufacturing 420 2,422,853 100.0% 

2522 Office Furniture, Except 

Wood 337214 Office Furniture (except Wood) 

Manufacturing 359 8,230,935 100.0% 

2531 Public Buildng & Relatd 

Furniture 

336360 Motor Vehicle Seating and Interior Trim 

Manufacturing 184 6,060,320 57.1% 

337127 Institutional Furniture Manufacturing 267 1,697,870 41.9% 

339942 Lead Pencil and Art Good Manufacturing 17 110,985 9.0% 

2542 Partitions & Fixtures, Exc 

Wood 337215 Showcase, Partition, Shelving, and Locker 

Manufacturing 926 5,249,474 65.6% 

2591 Drapery Hardware and 

Window Blinds/Shades 337920 Blind and Shade Manufacturing 488 2,393,564 100.0% 

2599 Furniture and Fixtures, 

N.E.C. 

337127 Institutional Furniture Manufacturing 727 2,305,770 57.0% 

339113 Surgical Appliance and Supplies 

Manufacturing 16 645,688 4.2% 

A-15

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

3431 Metal Sanitary Ware 332998 Enameled Iron and Metal Sanitary Ware 

Manufacturing 88 1,575,505 100.0% 

3432 Plumbing Fittings and Brass 

Goods 

332913 Plumbing Fixture Fitting and Trim 

Manufacturing 116 3,590,128 100.0% 

332999 All Other Miscellaneous Fabricated Metal 

Product Manufacturing 5 118,059 1.1% 

3442 Metal Doors, Sash, and Trim 332321 Metal Window and Door Manufacturing 1,384 9,876,049 96.4% 

3631 Household Cooking 

Equipment 335221 Household Cooking Appliance 

Manufacturing 84 3,543,231 100.0% 

3632 Household Refrig. & Home 

& Farm & Freezers 335222 Household Refrigerator and Home Freezer 

Manufacturing 27 4,887,364 100.0% 

3633 Household Laundry 

Equipment 335224 Household Laundry Equipment 

Manufacturing 17 3,723,375 100.0% 

3634 Electric Housewares and 

Fans 

333414 Heating Equipment (except Warm Air 

Furnaces) Manufacturing 16 329,270 8.9% 

335211 Electric Housewares and Household Fan 

Manufacturing 138 3,488,251 100.0% 

3635 Household Vacuum Cleaners 335212 Household Vacuum Cleaner Manufacturing 34 2,399,206 100.0% 

3639 Household Appliances 

N.E.C. 

333298 All Other Industrial Machinery 

Manufacturing 4 0 0.2%b 

335228 Other Major Household Appliance 

Manufacturing 36 3,300,662 100.0% 

3641 Electric Lamps 335110 Electric Lamp Bulb and Part Manufacturing 82 3,306,009 100.0% 

3643 Current-Carrying Wiring 

Devices 335931 Current-Carrying Wiring Device 

Manufacturing 519 5,877,522 100.0% 

3644 Noncurrent-Carrying Wiring 

Devices 335932 Noncurrent-Carrying Wiring Device 

Manufacturing 219 4,451,186 100.0% 

3645 Residential Electrical 

Lighting Fixtures 335121 Residential Electric Lighting Fixture 

Manufacturing 497 2,177,355 96.6% 

3646 Commercial, Industrial, and 

Institutional 335122 Commercial, Industrial, and Institutional 

Electric Lighting Fixture Manufacturing 356 4,047,437 100.0% 

3648 Lighting Equipment N.E.C. 335129 Other Lighting Equipment Manufacturing 327 3,054,806 100.0% 

3651 Radio/Television Sets Except 

Commun. Types 334310 Audio and Video Equipment Manufacturing 554 8,454,194 100.0% 

7623 Refrig, air condition 811310 

Commercial and Industrial Machinery and 

Equipment (except Automotive and 

Electronic) Repair and Maintenance 

2,343 1,890,237 10.8% 

811412 Appliance Repair and Maintenance 1,671 789,622 19.9% 

Instruments 

3812 Search, Det, Nav, Ggnc, 

Aero, Naut Sys/Inst 334511 

Search, Detection, Navigation, Guidance, 

Aeronautical, and Nautical System and 

Instrument Manufacturing 

680 32,497,776 99.8% 

3821 Laboratory Apparatus and 

Furniture 339111 Laboratory Apparatus and Furniture 

Manufacturing 385 2,471,153 100.0% 

A-16

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

3822 Automatic Environmental 

Controls 334512 

Automatic Environmental Control 

Manufacturing for Residential, Commercial, 

and Appliance Use 

317 2,935,692 100.0% 

3823 Process Control Instruments 334513 

Instruments and Related Products 

Manufacturing for Measuring, Displaying, 

and Controlling Industrial Process Variables 

1,002 7,890,923 100.0% 

3824 Fluid Meters and Counting 

Devices 334514 Totalizing Fluid Meter and Counting Device 

Manufacturing 222 3,765,769 100.0% 

3825 Instruments to Measure 

Electricity 

334416 Electronic Coil, Transformer, and Other 

Inductor Manufacturing 17 24,303 1.6% 

334515 Instrument Manufacturing for Measuring and 

Testing Electricity and Electrical Signals 826 13,852,897 100.0% 

3826 Laboratory Analytical 

Instruments 334516 Analytical Laboratory Instrument 

Manufacturing 664 7,157,038 99.5% 

3827 Optical Instruments and 

Lenses 333314 Optical Instrument and Lens Manufacturing 495 3,174,652 99.8% 

3829 Measuring and Controlling 

Devices N.E.C. 

334519 Other Measuring and Controlling Device 

Manufacturing 853 5,114,547 99.9% 

339112 Surgical and Medical Instrument 

Manufacturing 6 62,148 0.3% 

3841 Surgical & Medical 

Instruments & Apparatus 339112 Surgical and Medical Instrument 

Manufacturing 1,598 18,450,024 99.7% 

3842 Orthopedic, Prosthetic & 

Surgical Suppl. 

322121 Paper (except Newsprint) Mills 2 0 1.4%b 

322291 Sanitary Paper Product Manufacturing 16 651,398 6.7% 

334510 Electromedical and Electrotherapeutic 

Apparatus Manufacturing 74 807,427 7.1% 

339113 Surgical Appliance and Supplies 

Manufacturing 1,636 14,743,779 95.8% 

3843 Dental Equipment and 

Supplies 339114 Dental Equipment and Supplies 

Manufacturing 877 2,699,867 100.0% 

3844 X-Ray Apparatus and Tubes 334517 Irradiation Apparatus Manufacturing 155 3,942,256 100.0% 

3845 Electromedical Equipment 334510 Electromedical and Electrotherapeutic 

Apparatus Manufacturing 460 10,567,566 92.5% 

3851 Ophthalmic Goods 339115 Ophthalmic Goods Manufacturing 575 3,607,813 100.0% 

7629 Electric repair shop 

811212 Computer and Office Machine Repair and 

Maintenance 1,538 913,258 10.7% 

811213 Communication Equipment Repair and 

Maintenance 201 231,458 14.4% 

811219 Other Electronic and Precision Equipment 

Repair and Maintenance 2,033 2,509,452 86.1% 

811411 Home and Garden Equipment Repair and 

Maintenance 579 185,507 18.5% 

811412 Appliance Repair and Maintenance 4,327 3,125,853 78.6% 

A-17

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

Iron and Steel 

3315 Steel Wiredrawing and Steel 

Nails and Spikes 

331222 Steel Wire Drawing 273 4,920,798 100.0% 

332618 Other Fabricated Wire Product 

Manufacturing 31 370,492 7.0% 

3316 Cold-Rolled Steel Sheet, 

Strip, and Bars 331221 Rolled Steel Shape Manufacturing 186 6,343,466 100.0% 

3317 Steel Pipe and Tubes 331210 Iron and Steel Pipe and Tube Manufacturing 

from Purchased Steel 235 7,565,377 100.0% 

Job Shop 

3471 Plating and Polishing 332813 Electroplating, Plating, Polishing, Anodizing, 

and Coloring 3,404 5,979,405 100.0% 

3479 Metal Coating & Allied 

Services 

332812 

Metal Coating, Engraving (except Jewelry 

and Silverware), and Allied Services to 

Manufacturers 

2,156 8,460,896 100.0% 

339911 Jewelry (except Costume) Manufacturing 22 5,798 0.1% 

339914 Costume Jewelry and Novelty Manufacturing 16 2,257 0.2% 

339912 Silverware and Hollowware Manufacturing 12 6,296 0.7% 

Mobile Industrial Equipment 

3523 Farm Machinery and 

Equipment 

332212 Hand and Edge Tool Manufacturing 1 0 0.1%b 

332323 Ornamental and Architectural Metal Work 

Manufacturing 140 380,152 9.5% 

333111 Farm Machinery and Equipment 

Manufacturing 1,339 15,921,455 100.0% 

333922 Conveyor and Conveying Equipment 

Manufacturing 28 33,377 0.5% 

3524 Garden Tractors & Lawn & 

Garden Equipment 

332212 Hand and Edge Tool Manufacturing 3 0 0.3%b 

333112 Lawn and Garden Tractor and Home Lawn 

and Garden Equipment Manufacturing 145 7,454,511 100.0% 

3531 Constr Mach and Eq 

333120 Construction Machinery Manufacturing 785 21,965,455 100.0% 

333923 Overhead Traveling Crane, Hoist, and 

Monorail System Manufacturing 87 1,805,198 57.4% 

336510 Railroad Rolling Stock Manufacturing 25 346,760 4.2% 

3532 Mining Mach. & Equip., 

Except Oil Field 333131 Mining Machinery and Equipment 

Manufacturing 292 2,710,923 100.0% 

3536 Hoists, Industrial Cranes & 

Monorails 333923 Overhead Traveling Crane, Hoist, and 

Monorail System Manufacturing 220 1,340,561 42.6% 

3537 Industrial Trucks, Tractors, 

Trailers 

332439 Other Metal Container Manufacturing 4 6,775 0.3% 

332999 All Other Miscellaneous Fabricated Metal 

Product Manufacturing 19 27,488 0.3% 

333924 Industrial Truck, Tractor, Trailer, and Stacker 

Machinery Manufacturing 

461 5,538,326 100.0% 

3795 Tanks and Tank Components 336992 Military Armored Vehicle, Tank, and Tank 

Component Manufacturing 37 0 86.0%b 

A-18

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

Motor Vehicle 

3465 Automotive Stampings 336370 Motor Vehicle Metal Stamping 810 23,668,110 100.0% 

3592 Carburetors, Piston Rings, 

Valves 336311 Carburetor, Piston, Piston Ring, and Valve 

Manufacturing 141 2,755,311 100.0% 

3647 Vehicular Lighting 

Equipment 336321 Vehicular Lighting Equipment Manufacturing 106 3,282,824 100.0% 

3694 Electrical Equipment for 

Motor Vehicles 336322 Other Motor Vehicle Electrical and 

Electronic Equipment Manufacturing 569 9,074,335 53.6% 

3711 Motor Vehicle and 

Automobile Bodies 

336111 Automobile Manufacturing 194 95,385,563 100.0% 

336112 Light Truck and Utility Vehicle 

Manufacturing 112 110,400,169 100.0% 

336120 Heavy Duty Truck Manufacturing 84 14,490,344 100.0% 

336211 Motor Vehicle Body Manufacturing 76 82,633 0.9% 

336992 Military Armored Vehicle, Tank, and Tank 

Component Manufacturing 6 0 14.0%b 

3714 Motor Vehicle Parts and 

Accessories 

336211 Motor Vehicle Body Manufacturing 23 265,552 2.9% 

336312 Gasoline Engine and Engine Parts 

Manufacturing 881 25,974,369 100.0% 

336322 Other Motor Vehicle Electrical and 

Electronic Equipment Manufacturing 193 6,446,681 38.1% 

336330 Motor Vehicle Steering and Suspension 

Components (except Spring) Manufacturing 212 10,750,312 100.0% 

336340 Motor Vehicle Brake System Manufacturing 269 10,033,288 100.0% 

336350 Motor Vehicle Transmission and Power Train 

Parts Manufacturing 523 33,288,093 100.0% 

336399 All Other Motor Vehicle Parts Manufacturing 1,508 34,193,298 99.6% 

3716 Mobile Homes 336213 Motor Home Manufacturing 88 3,943,709 100.0% 

3751 Motorcycles 336991 Motorcycle, Bicycle, and Parts 

Manufacturing 385 0 99.0%b 

3792 Travel Trailers and Campers 336214 Travel Trailer and Camper Manufacturing 315 3,076,049 67.4% 

3799 Miscellaneous Transportation 

Equipment 

332212 Hand and Edge Tool Manufacturing 1 0 0.1%b 

336214 Travel Trailer and Camper Manufacturing 498 1,485,367 32.6% 

336999 All Other Transportation Equipment 

Manufacturing 378 4,557,989 100.0% 

4121 Taxicabs 485310 Taxi Service 3,184 1,280,597 100.0% 

5013 Motor Vehicle Supplies and 

New Parts 

421120 Motor Vehicle Supplies and New Parts 

Wholesalers 12,620 83,214,728 100.0% 

441310 Automotive Parts and Accessories Stores 16,253 22,093,428 51.2% 

5511 Motor Vehicle Dealers (New 

and Used) 441110 New Car Dealers 25,897 518,971,824 100.0% 

5521 Motor Vehicle Dealers (Used 

Only) 441120 Used Car Dealers 23,340 34,680,468 100.0% 

5561 Recreational Vehicle Dealers 441210 Recreational Vehicle Dealers 3,014 10,069,749 100.0% 

A-19

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

5571 Motorcycle Dealers 441221 Motorcycle Dealers 3,635 7,369,260 100.0% 

5599 Automotive Dealers, N.E.C. 441229 All Other Motor Vehicle Dealers 1,678 2,517,267 100.0% 

7514 Passenger Car Rental 532111 Passenger Car Rental 4,367 14,783,704 100.0% 

7515 Passenger Car Lease 532112 Passenger Car Leasing 879 3,800,424 100.0% 

7519 Utility Trailer and 

Recreational Vehicle Rental 532120 Truck, Utility Trailer, and RV (Recreational 

Vehicle) Rental and Leasing 360 256,119 2.5% 

7532 Top, Body, and Upholstery 

Repair and Paint Shops 811121 Automotive Body, Paint, and Interior Repair 

and Maintenance 35,569 17,755,296 100.0% 

7533 Auto Exhaust Systems 811112 Automotive Exhaust System Repair 5,251 1,985,377 100.0% 

7537 Auto Transmission Repair 811113 Automotive Transmission Repair 6,768 2,431,584 100.0% 

7538 Gen Automotive Repair 811111 General Automotive Repair 77,751 25,598,455 100.0% 

7539 Auto Repair Shop, N.E.C. 811118 Other Automotive Mechanical and Electrical 

Repair and Maintenance 9,674 3,494,643 100.0% 

7549 Auto Services, Except Repair 

and Carwashes 

488410 Motor Vehicle Towing 5,893 2,295,188 100.0% 

811191 Automotive Oil Change and Lubrication 

Shops 7,413 2,787,318 100.0% 

811198 All Other Automotive Repair and 

Maintenance 1,646 798,626 73.5% 

Office Machine 

3571 Electronic Computers 334111 Electronic Computer Manufacturing 563 66,331,909 100.0% 

3572 Typewriters 334112 Computer Storage Device Manufacturing 211 13,907,367 100.0% 

3575 Computer Terminals 334113 Computer Terminal Manufacturing 142 1,483,460 100.0% 

3577 Computer Peripheral Eq 

N.E.C. 334119 Other Computer Peripheral Equipment 

Manufacturing 1,006 25,130,308 93.1% 

3578 Calculating, Accounting 

Machines Except Computers 

333313 Office Machinery Manufacturing 35 144,380 4.5% 

334119 Other Computer Peripheral Equipment 

Manufacturing 61 1,870,426 6.9% 

3579 Office Machines, N.E.C. 

333313 Office Machinery Manufacturing 134 3,047,549 95.5% 

334518 Watch, Clock, and Part Manufacturing 16 0 19.6%b 

339942 Lead Pencil and Art Good Manufacturing 13 257,020 20.8% 

7378 Computer Maintenance and 

Repairs 811212 Computer and Office Machine Repair and 

Maintenance 6,087 7,565,169 89.0% 

7379 Computer Related Services, 

N.E.C. 

334611 Software Reproducing 124 1,258,435 100.0% 

541512 Computer Systems Design Services 20,233 15,942,861 31.1% 

541519 Other Computer Related Services 8,405 4,339,989 100.0% 

Ordnance 

3482 Small Arms Ammunition 332992 Small Arms Ammunition Manufacturing 113 938,818 100.0% 

3483 Ammunition, Except for 

Small Arms 332993 Ammunition (except Small Arms) 

Manufacturing 53 1,497,045 100.0% 

3484 Small Arms 332994 Small Arms Manufacturing 198 1,251,792 100.0% 

A-20

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

3489 Ordnance and Accessories, 

N.E.C. 332995 Other Ordnance and Accessories 

Manufacturing 70 1,750,485 100.0% 

Miscellaneous Metal Products 

3497 Metal Foil and Leaf 

322225 Laminated Aluminum Foil Manufacturing for 

Flexible Packaging Uses 43 1,546,143 100.0% 

332999 All Other Miscellaneous Fabricated Metal 

Product Manufacturing 64 1,711,600 16.3% 

3861 Photographic Equipment & 

Supplies 

325992 Photographic Film, Paper, Plate, and 

Chemical Manufacturing 

311 12,895,637 100.0% 

333315 Photographic and Photocopying Equipment 

Manufacturing 428 8,410,124 100.0% 

3931 Musical Instruments 339992 Musical Instrument Manufacturing 576 1,356,651 100.0% 

3944 Games, Toys, Children's 

Vehicles 

336991 Motorcycle, Bicycle, and Parts 

Manufacturing 4 0 1.0%b 

339932 Game, Toy, and Children's Vehicle 

Manufacturing 785 4,534,497 100.0% 

3949 Sporting and Athletic Goods, 

N.E.C. 339920 Sporting and Athletic Goods Manufacturing 2,571 10,591,160 100.0% 

3951 Pens and Mechanical Pencils 339941 Pen and Mechanical Pencil Manufacturing 112 1,590,770 100.0% 

3953 Marking Devices 339943 Marking Device Manufacturing 634 643,007 100.0% 

3993 Signs and Advertising 

Displays 

339950 Sign Manufacturing 5,709 7,910,809 100.0% 

3995 Burial Caskets 339995 Burial Casket Manufacturing 177 1,271,184 100.0% 

3999 Manufacturing Industries, 

N.E.C. 

314999 All Other Miscellaneous Textile Product 

Mills 52 173,353 2.8% 

316110 Leather and Hide Tanning and Finishing 26 24,625 0.7% 

325998 All Other Miscellaneous Chemical Product 

and Preparation Manufacturing 9 80,624 0.6% 

326199 All Other Plastics Product Manufacturing 140 319,241 0.5% 

332212 Hand and Edge Tool Manufacturing 7 0 0.6%b 

332999 All Other Miscellaneous Fabricated Metal 

Product Manufacturing 185 285,362 2.7% 

335121 Residential Electric Lighting Fixture 

Manufacturing 53 69,864 3.1% 

337127 Institutional Furniture Manufacturing 5 28,296 0.7% 

339999 All Other Miscellaneous Manufacturing 2,284 7,183,815 85.4% 

7692 Welding Repair 811490 Other Personal and Household Goods Repair 

and Maintenance 4,840 1,640,808 36.8% 

A-21

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

7699 Repair Shop, Related Service 

488390 Other Support Activities for Water 

Transportation 12 4,737 0.7% 

561622 Locksmiths 3,799 1,081,317 100.0% 

561790 Other Services to Buildings and Dwellings 1,254 0 22.4%b 

562991 Septic Tank and Related Services 2,538 0 81.8%b 

811212 Computer and Office Machine Repair and 

Maintenance 104 23,844 0.3% 

811219 Other Electronic and Precision Equipment 

Repair and Maintenance 838 404,627 13.9% 

811310 

Commercial and Industrial Machinery and 

Equipment (except Automotive and 

Electronic) Repair and Maintenance 

16,404 13,600,413 77.7% 

811411 Home and Garden Equipment Repair and 

Maintenance 3,032 816,008 81.5% 

811412 Appliance Repair and Maintenance 181 59,338 1.5% 

811430 Footwear and Leather Goods Repair 82 18,294 7.0% 

811490 Other Personal and Household Goods Repair 

and Maintenance 3,946 1,362,271 30.6% 

3873 Watches, Clocks, and 

Watchcases 334518 Watch, Clock, and Part Manufacturing 128 718,191 77.9% 

Precious Metals and Jewelry 

3911 Jewelry, Precious Metal 339911 Jewelry (except Costume) Manufacturing 2,272 5,416,836 99.9% 

3914 Silverware, Plated Ware & 

Stainless 

332211 Cutlery and Flatware (except Precious) 

Manufacturing 11 8,032 0.4% 

339912 Silverware and Hollowware Manufacturing 151 899,684 99.3% 

3915 Jewelers' Materials & 

Lapidary Work 339913 Jewelers' Material and Lapidary Work 

Manufacturing 394 919,066 100.0% 

3961 Costume Jewelry 339914 Costume Jewelry and Novelty Manufacturing 826 1,223,475 95.9% 

7631 Watch, Clock, Jewelry Repair 811490 Other Personal and Household Goods Repair 

and Maintenance 1,716 345,774 7.8% 

Printed Circuit Boards 

3672 Printed Circuit Boards 334412 Bare Printed Circuit Board Manufacturing 1,401 9,787,576 100.0% 

Railroad 

3743 Railcars, Railway Systems 336510 Railroad Rolling Stock Manufacturing 207 7,916,635 95.8% 

Ships and Boats 

3731 Ship Building and Repairing 336611 Ship Building and Repairing 700 10,571,810 100.0% 

3732 Boat Building and Repairing 

336612 Boat Building 1,043 5,622,040 100.0% 

811490 Other Personal and Household Goods Repair 

and Maintenance 1,739 821,273 18.4% 

4412 Deep Sea Foreign 

Transportation 483111 Deep Sea Freight Transportation 487 11,570,718 100.0% 

4424 Deep Sea Domestic 

Transportation 483113 Coastal and Great Lakes Freight 

Transportation 292 3,114,639 66.6% 

A-22

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

4432 Freight Transportation Great 

Lakes 483113 Coastal and Great Lakes Freight 

Transportation 32 519,863 11.1% 

4449 Water Transportation of 

Freight, N.E.C. 483211 Inland Water Freight Transportation 222 2,821,121 83.3% 

4481 Deep Sea Passenger 

Transportation 

483112 Deep Sea Passenger Transportation 80 3,908,143 100.0% 

483114 Coastal and Great Lakes Passenger 

Transportation 64 89,597 49.2% 

4482 Ferries 

483114 Coastal and Great Lakes Passenger 

Transportation 61 92,493 50.8% 

483212 Inland Water Passenger Transportation 76 121,992 41.6% 

4489 Water Passenger 

Transportation, N.E.C. 

483212 Inland Water Passenger Transportation 154 171,135 58.4% 

487210 Scenic and Sightseeing Transportation, Water 654 861,001 76.3% 

4491 Marine Cargo Handling 

488310 Port and Harbor Operations 168 889,125 100.0% 

488320 Marine Cargo Handling 623 4,456,243 100.0% 

4492 Towing & Tugboat Service 

483113 Coastal and Great Lakes Freight 

Transportation 292 1,043,440 22.3% 

483211 Inland Water Freight Transportation 161 566,027 16.7% 

488330 Navigational Services to Shipping 361 1,014,026 67.0% 

4493 Marinas 713930 Marinas 4,217 2,541,481 100.0% 

4499 Water Transporation 

Services, N.E.C. 

488330 Navigational Services to Shipping 504 499,176 33.0% 

488390 Other Support Activities for Water 

Transportation 640 444,499 67.7% 

532411 

Commercial Air, Rail, and Water 

Transportation Equipment Rental and 

Leasing 

126 454,392 7.1% 

Stationary Industrial Equipment 

3511 Steam, Gas, Hydraulic 

Turbines, Generator Units 333611 Turbine and Turbine Generator Set Units 

Manufacturing 86 5,783,057 100.0% 

3519 Internal Combustion Engines, 

N.E.C. 

333618 Other Engine Equipment Manufacturing 297 0 99.3%b 

336399 All Other Motor Vehicle Parts Manufacturing 7 123,954 0.4% 

3533 Oil Field Machinery and 

Equipment 333132 Oil and Gas Field Machinery and Equipment 

Manufacturing 563 6,240,079 100.0% 

3534 Elevators and Moving 

Stairways 

333921 Elevator and Moving Stairway Manufacturing 196 1,607,066 100.0% 

3535 Conveyors and Conveying 

Equipment 333922 Conveyor and Conveying Equipment 

Manufacturing 871 6,346,525 99.5% 

3543 Industrial Patterns 332997 Industrial Pattern Manufacturing 673 623,927 100.0% 

3547 Rolling Mill Machinery and 

Equipment 333516 Rolling Mill Machinery and Equipment 

Manufacturing 100 700,084 100.0% 

3548 Electric and Gas Welding and 

Soldering 333992 Welding and Soldering Equipment 

Manufacturing 244 4,433,877 99.8% 

3549 Metal Working Machinery, 

N.E.C. 333518 Other Metalworking Machinery 

Manufacturing 474 3,463,811 100.0% 

A-23

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

3552 Textile Machinery 333292 Textile Machinery Manufacturing 478 1,779,034 100.0% 

3553 Woodworking Machinery 333210 Sawmill and Woodworking Machinery 

Manufacturing 327 1,321,752 100.0% 

3554 Paper Industries Machinery 333291 Paper Industry Machinery Manufacturing 366 3,438,235 100.0% 

3555 Printing Trades Machinery 

and Equipment 333293 Printing Machinery and Equipment 

Manufacturing 546 0 99.1%b 

3556 Food Products Mach 333294 Food Product Machinery Manufacturing 597 2,877,841 100.0% 

3559 Special Industry Machinery, 

N.E.C. 

333220 Plastics and Rubber Industry Machinery 

Manufacturing 455 3,584,992 100.0% 

333295 Semiconductor Machinery Manufacturing 257 11,158,627 100.0% 

333298 All Other Industrial Machinery 

Manufacturing 1,677 0 99.8%b 

333319 Other Commercial and Service Industry 

Machinery Manufacturing 

78 644,019 6.9% 

3561 Pumps and Pumping 

Equipment 333911 Pump and Pumping Equipment 

Manufacturing 489 6,826,043 100.0% 

3562 Ball and Roller Bearings 332991 Ball and Roller Bearing Manufacturing 185 6,120,940 100.0% 

3563 Air and Gas Compressors 333912 Air and Gas Compressor Manufacturing 314 5,633,008 100.0% 

3564 Blowers and Exhaust and 

Ventilation Fans 

333411 Air Purification Equipment Manufacturing 370 2,174,729 100.0% 

333412 Industrial and Commercial Fan and Blower 

Manufacturing 204 1,901,196 100.0% 

3565 Industrial Patterns 333993 Packaging Machinery Manufacturing 689 4,858,270 100.0% 

3566 Speed Changers, High Speed 

Drivers & Gears 333612 Speed Changer, Industrial High-Speed Drive, 

and Gear Manufacturing 268 2,402,392 100.0% 

3567 Industrial Process Furnaces 

and Ovens 333994 Industrial Process Furnace and Oven 

Manufacturing 404 2,871,475 100.0% 

3568 

Mechanical Power 

Transmission Equipment, 

N.E.C. 

333613 Mechanical Power Transmission Equipment 

Manufacturing 299 3,301,091 100.0% 

3569 General Industrial 

Machinery, N.E.C. 333999 All Other Miscellaneous General Purpose 

Machinery Manufacturing 1,257 7,991,746 87.5% 

3581 Automatic Merchandising 

Machines 333311 Automatic Vending Machine Manufacturing 121 1,325,960 100.0% 

3582 Commercial Laundry 

Equipment 333312 Commercial Laundry, Drycleaning, and 

Pressing Machine Manufacturing 68 604,966 100.0% 

3585 Refrigeration & Air and 

Heating Equipment 

333415 

Air-Conditioning and Warm Air Heating 

Equipment and Commercial and Industrial 

Refrigeration Equipment Manufacturing 

792 22,846,865 99.8% 

336391 Motor Vehicle Air-Conditioning 

Manufacturing 60 5,626,596 100.0% 

3586 Measuring and Dispensing 

Pumps 333913 Measuring and Dispensing Pump 

Manufacturing 71 1,316,899 100.0% 

3589 Service Industry Machines, 

N.E.C. 333319 Other Commercial and Service Industry 

Machinery Manufacturing 1,165 7,596,253 81.3% 

A-24

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M 

Industries Evaluated for the Final Rulea 

(thousands, 1997$) 

SIC SIC Industry NAICS 

Code 1997 NAICS Industry Number of 

Establishments 

Sales, 

Shipments 

or Receipts 

Share 

Value 

3593 Fluid Power Cylinders and 

Actuators 333995 Fluid Power Cylinder and Actuator 

Manufacturing 320 3,528,906 100.0% 

3594 Fluid Power Pumps and 

Motors 333996 Fluid Power Pump and Motor Manufacturing 170 2,712,058 100.0% 

3596 Scales and Balances, except 

Laboratory 

333997 Scale and Balance (except Laboratory) 

Manufacturing 122 682,940 100.0% 

3599 Machinery, Except Electrical, 

N.E.C. 

332710 Machine Shops 23,619 27,143,131 100.0% 

332999 All Other Miscellaneous Fabricated Metal 

Product Manufacturing 132 506,611 4.8% 

333319 Other Commercial and Service Industry 

Machinery Manufacturing 

50 172,536 1.8% 

333999 All Other Miscellaneous General Purpose 

Machinery Manufacturing 

836 1,146,348 12.5% 

3612 Transformers 335311 Power, Distribution, and Specialty 

Transformer Manufacturing 

318 4,716,162 100.0% 

3613 Switchgear and Switchboard 

Apparatus 335313 Switchgear and Switchboard Apparatus 

Manufacturing 583 7,609,164 100.0% 

3621 Motors and Generators 335312 Motor and Generator Manufacturing 528 11,788,281 96.3% 

3629 Electric Industrial Apparatus, 

N.E.C. 335999 All Other Miscellaneous Electrical 

Equipment and Component Manufacturing 413 2,838,366 41.2% 

7353 Heavy Construction Equip 

Rental, Leasing 

234990 All Other Heavy Construction 2,295 2,734,732 8.7% 

532412 

Construction, Mining, and Forestry 

Machinery and Equipment Rental and 

Leasing 

3,286 5,339,163 77.4% 

7359 Equip Rental, Leasing, 

N.E.C. 

532210 Consumer Electronics and Appliances Rental 3,011 1,790,890 100.0% 

532299 All Other Consumer Goods Rental 3,133 2,133,450 99.1% 

532310 General Rental Centers 6,509 3,910,618 100.0% 

532411 

Commercial Air, Rail, and Water 

Transportation Equipment Rental and 

Leasing 

498 0 74.3%b 

532412 

Construction, Mining, and Forestry 

Machinery and Equipment Rental and 

Leasing 

671 1,555,089 22.6% 

532420 Office Machinery and Equipment Rental and 

Leasing 400 436,178 7.1% 

532490 Other Commercial and Industrial Machinery 

and Equipment Rental and Leasing 3,408 6,775,140 69.7% 

562991 Septic Tank and Related Services 563 0 18.2%b 

a

 EPA evaluated options for these industrial sectors but did not regulate them all under the final rule. 

Share values were calculated using estimated value of shipments data. 

N.E.C. = Not Elsewhere Classified 

b

Source: Department of Commerce, Bureau of the Census, 1997 Economic Census, Bridge Between NAICS and SIC; and EPA analysis. 

A-25 
MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

A.2 ANNUAL ESTABLISHMENT “BIRTHS” AND “DEATHS” IN MP&M INDUSTRIES 

EVALUATED FOR THE FINAL RULE 

EPA used the Statistics of U.S. Businesses (SUSB) dynamic data to estimate the rate at which MP&M facilities evaluated for 

the final rule enter and leave the industry each year. The SUSB dynamic data report numbers of facilities starting up, closing, 

expanding employment and contracting employment each year from 1989 through 1997 (the latest currently available.) 

Table A.4 shows the average number of facilities (establishments) operating at the beginning of each year for the period 1989 

through 1997, the number of facility “births” and “deaths”, and the average “birth rate” and “death rate” for each of the major 

3-digit manufacturing SIC codes that include MP&M 4-digit SIC codes evaluated for the final rule.2 This table shows that, 

over the period 1989-1997, annual closure rates ranged from 6 to over 12 percent in the different industries, with an overall 

average of almost 8 percent. 

Table A.4: Annual Births and Deaths for MP&M Establishments Evaluated for the Final Rule by 3 Digit SIC 

Codes (1989-1997) 

SIC SIC Description 

Average # 

Establishments at the 

Beginning of the Year 

Average 

Establishment 

Births 

Average 

Establishment 

Deaths 

% Births % Deaths 

3410 Metal Cans And Shipping 

Containers 464 22 35 4.7% 7.5% 

3420 Cutlery, Handtools, And Hardware 2,294 143 139 6.2% 6.1% 

3430 Plumbing And Heating, Except 

Electric 687 45 53 6.6% 7.8% 

3440 Fabricated Structural Metal Products 12,268 853 908 7.0% 7.4% 

3450 Screw Machine Products, Bolts, Etc. 2,436 84 111 3.4% 4.6% 

3460 Metal Forgings And Stamping 3,812 199 226 5.2% 5.9% 

3470 Metal Services, N.E.C. 5,028 341 340 6.8% 6.8% 

3480 Ordnance & Accessories, N.E.C. 390 39 40 10.0% 10.2% 

3490 Misc. Fabricated Metal Products 7,084 606 531 8.6% 7.5% 

3510 Engines And Turbines 346 26 24 7.5% 6.8% 

3520 Farm And Garden Machinery 1,711 133 129 7.8% 7.5% 

3530 Construction And Related 

Machinery 3,165 217 230 6.9% 7.3% 

3540 Metalworking Machinery 11,072 672 660 6.1% 6.0% 

3550 Special Industry Machinery 4,427 307 317 6.9% 7.1% 

3560 General Industrial Machinery 3,961 243 225 6.1% 5.7% 

3570 Computer And Office Equipment 2,025 262 246 12.9% 12.1% 

3580 Refrigeration And Service 

Machinery 2,104 154 165 7.3% 7.9% 

3590 Industrial Machinery, N.E.C. 21,972 1,996 1,659 9.1% 7.5% 

3610 Electric Distribution Equipment 764 53 51 6.9% 6.6% 

3620 Electrical Industrial Apparatus 2,024 117 130 5.8% 6.4% 

3630 Household Appliances 461 44 41 9.5% 8.9% 

2 The data are disaggregated only to the 3-digit SIC level, and EPA therefore was unable to calculate closure rates for the specific 

4-digit SICs that comprise the MP&M industries evaluated for the final rule. The analysis does not include 3-digit SICs that may include 

large numbers of non-metal products producers, for example SIC 241 (furniture, both wood and metal.) 

A-26 
MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

Table A.4: Annual Births and Deaths for MP&M Establishments Evaluated for the Final Rule by 3 Digit SIC 

Codes (1989-1997) 

SIC SIC Description 

Average # 

Establishments at the 

Beginning of the Year 

Average 

Establishment 

Births 

Average 

Establishment 

Deaths 

% Births % Deaths 

3640 Electric Lighting And Wiring 

Equipment 1,905 123 143 6.5% 7.5% 

3650 Household Audio & Video Equip 766 96 87 12.5% 11.4% 

3660 Communications Equipment 1,794 169 159 9.4% 8.9% 

3670 Electronic Components And 

Accessories 6,068 614 522 10.1% 8.6% 

3690 Misc. Electrical Equipment & 

Supplies 1,890 136 157 7.2% 8.3% 

3710 Motor Vehicles And Equipment 4,477 387 372 8.6% 8.3% 

3720 Aircraft And Parts 1,633 122 127 7.5% 7.8% 

3730 Ship And Boat Building And 

Repairing 2,669 343 339 12.9% 12.7% 

3740 Railroad Equipment 189 15 15 7.9% 7.7% 

3750 Motorcycles, Bicycles, & Parts 256 38 25 14.8% 9.7% 

3760 Guided Missiles, Space Vehicles, 

Parts 127 7 11 5.5% 8.4% 

3790 Miscellaneous Transportation 

Equipment 962 106 109 11.0% 11.3% 

3810 Search & Navigation Equipment 758 34 60 4.5% 7.9% 

3820 Measuring And Controlling Devices 4,209 275 295 6.5% 7.0% 

3840 Medical Instruments And Supplies 3,770 334 289 8.9% 7.7% 

3850 Ophthalmic Goods 536 40 48 7.5% 8.9% 

3860 Photographic Equip & Supplies 784 71 72 9.1% 9.1% 

3870 Watches, Clocks, Watchcases & 

Parts 159 12 20 7.5% 12.7% 

3910 Jewelry, Silverware, And Plated 

Ware 2,606 246 275 9.4% 10.6% 

3930 Musical Instruments 434 46 35 10.6% 8.0% 

3940 Toys And Sporting Goods 2,843 384 345 13.5% 12.1% 

3950 Pens, Pencils, Office, & Art Supplies 975 62 70 6.4% 7.2% 

3960 Costume Jewelry And Notions 1,010 105 128 10.4% 12.7% 

3990 Miscellaneous Manufactures 7,338 784 740 10.7% 10.1% 

TOTAL 136,653 11,103 10,698 8.1% 7.8% 

N.E.C. = Not Elsewhere Classified 

Source: Small Business Administration, Statistics of U.S. Businesses. 

A-27

MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

A.3 DESCRIPTION OF MP&M SURVEYS 

EPA used two screener and seven detailed questionnaires (surveys) issued between 1989 and 1996 to collect financial and 

technical data from a sample of facilities that were evaluated for regulation under the final MP&M rule (see Section 3 of the 

TDD). The responses to these surveys provided the basic financial and economic information used in the facility and firm 

impact analyses. In addition, the POT W Survey provided information on facility permitting costs associated with regulatory 

options considered by EPA. The various surveys are described below as they relate to the financial and economic analyses. 

The M P&M rulemaking docket provides copies of the survey instruments and detailed information on the conduct of the 

surveys. 

A.3.1 Screener Surveys 

In 1990, EPA distributed 8,342 screener surveys to sites believed to be engaged in the original seven Phase I MP&M sectors. 

In 1996, EPA distributed 5,325 screener surveys to sites believed to be engaged in the eleven Phase II MP&M sectors. The 

screener surveys helped EPA to identify sites to receive the more detailed follow-up surveys and to make a preliminary 

assessment of the MP &M industry evaluated for the final rule. EPA identified the SIC codes applicable to the respective 

MP&M sectors evaluated for the final rule and randomly selected names and addresses in those SICs to receive the screener 

surveys based on Dun & Bradstreet databases. 

A.3.2 Ohio Screener Surveys 

EPA also sent the 1996 screener survey to 1,600 randomly selected sites in Ohio to support the Ohio case study. 

A.3.3 Detailed MP&M Industry Surveys 

Based on responses to the screener surveys, EPA sent a more detailed survey to a selected group of water-using MP&M 

facilities evaluated for the final rule. EPA collected financial and technical data from sample facilities in two phases. 

Based on responses to the 1990 screener, EPA sent the Phase I detailed survey to a select group of water-using facilities. The 

Agency designed this survey to collect detailed technical and financial information. EPA selected 1,020 detailed survey 

recipients from water-discharging screener respondents, water-using screener respondents that did not discharge process 

water, and a non-randomly selected group of known water-discharging facilities that did not receive the screener. 

EPA used information from the first two groups of survey recipients to develop pollutant loadings and reductions and to 

develop compliance cost estimates. Because EPA did not randomly select the third group of recipients, EPA did not use the 

data to develop national estimates. 

To reduce burden on survey recipients for Phase II of the data collection effort, EPA developed two similar detailed surveys. 

Based on the development of the 1995 MP&M proposal, EPA chose to collect more detailed information from sites with 

annual process wastewater discharges greater than one million gallons per year (1 MG Y). EPA sent the “long” detailed 

survey to all 353 1996 screener respondents evaluated for the final rule who indicated they discharged one million or more 

gallons of process wastewater annually and performed MP&M operations. The Agency sent the “short” detailed survey to 

101 randomly selected 1996 screener respondents evaluated for the final rule who indicated they discharged less than one 

million gallons of process wastewater annually and performed MP&M operations. 

The detailed survey responses provide financial, economic, and employment information about the site or the company 

owning the facility. In addition, the 1996 long detailed questionnaire included a section that requested supplemental 

information on other facilities owned by the company. EPA included this voluntary section to measure the impact of the final 

MP&M effluent guidelines on companies with multiple facilities that discharge process wastewater. This section requested 

the same information collected in the 1996 MP&M screener survey. Responses to questions in this section provided 

information on the size, industrial sector, revenue, unit operations, and water usage of the company’s other facilities. 

The 1996 short survey included the identical general facility and economic information collected in the long detailed survey, 

with one exception. Short survey recipients were not asked to provide information on the liquidation value of their plant. 

A-28 
MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

A.3.4 Iron and Steel Survey 

EPA also developed a detailed survey, under a separate rulemaking effort, to collect detailed information from facilities 

covered by the Iron and Steel Manufacturing effluent guidelines (40 CFR Part 420). Following field sampling of iron and 

steel sites and review of the completed industry surveys, EPA decided at proposal that some iron and steel operations would 

be more appropriately covered by the MP&M rule because they were more like MP&M operations. EPA relied on the Iron & 

Steel survey for financial and economic information on 47 iron and steel facilities. Commenters on the proposed rule stated 

that these operations and resulting wastewaters are comparable to those at facilities subject to the Iron and Steel 

Manufacturing effluent guidelines and that these discharges should remain subject to Part 420 rather than the final MP&M 

rule. Also at NODA, EPA considered including in the Steel Forming and Finishing subcategory wastewater discharges 

resulting from continuous electroplating of flat steel products (e.g., strip, sheet, and plate). EPA also relied on the Iron & 

Steel survey for financial and economic information on these 24 iron and steel facilities. EPA re-examined its database for 

facilities that perform continuous steel electroplating, and found that, contrary to its initial finding, continuous electroplaters 

do not perform operations similar to other facilities in this subcategory (i.e., steel forming and finishing facilities performing 

cold forming on steel wire, rod, bar, pipe, and tube). Thus, EPA included continuous electroplaters performing electroplating 

and coating operations in the General Metals subcategory for analyses supporting the final rule. As described in Section VI of 

the preamble to the final rule, EPA is not revising limitations or standards for any of these facilities. Such facilities will 

continue to be regulated by the General Pretreatment Standards (Part 403), local limits, permit limits, and Iron & Steel 

effluent limitations guidelines (Part 420), as applicable. 

A.3.5 Municipality Survey 

EPA distributed surveys in 1996 to city and county facilities that might operate facilities engaged in MP&M operations 

evaluated for the final rule. The Agency designed this survey to measure the rule’s impact on municipalities and other 

government entities that perform maintenance and rebuilding operations on MP&M products (e.g., bus and truck, 

automobiles). The Agency sent the municipality survey to 150 city and county facilities randomly selected from the 

Municipality Year Book-1995 based on population and geographic location. EPA allocated sixty percent of the sample to 

municipalities and 40 percent to counties. The 60/40 distribution was approximately proportional to their aggregate 

populations in the frame. EPA divided the municipality sample and the county sample into three size groupings as measured 

by population. The surveys collected information on costs of service and on the financial and economic characteristics of the 

governments operating these facilities. 

A.3.6 Federal Facility Survey 

EPA designed this survey to assess the impact of the MP& M effluent limitations guidelines and standards on federal agencies 

that operate MP&M facilities. EPA distributed the survey to federal agencies likely to perform industrial operations on metal 

products or machines. The Agency requested that the representatives of the seven chosen federal agencies voluntarily 

distribute copies of the survey to sites they believed performed MP&M operations. The information collected in the 1996 

federal survey was identical to the long survey. After engineering review and coding, EPA entered data from 44 federal 

surveys into the database. Because EPA did not randomly select the survey recipients, data from these questionnaires were 

not used to develop national estimates. 

A.3.7 POTW Survey 

EPA distributed the Publicly-Owned Treatment Works (POTW ) survey in November 1997. The Agency designed this survey 

to estimate possible costs and burden that POTWs might incur in administering MP&M permits or other control instruments 

and to estimate benefits from implementation of the options considered for the final rule. The Agency sent the POTW Survey 

to 150 POTWs with flow rates greater than 0.50 million gallons per day. EPA randomly selected the recipients from the 1992 

Needs Survey Review, Update, and Query System Database (RUQus), and divided the POTW sample into two strata by daily 

flow rates: 0.50 to 2.50 million gallons, and 2.50 million gallons or more. 

In addition to the total volume of wastewater treated at the site, the POTW Survey requested the number of industrial permits 

written, the cost to write the permits, the permitting fee structure, the percentage of industrial dischargers covered by National 

Categorical Standards (i.e., effluent guidelines), and the percentage of permits requiring specific administrative activities. 

EPA used this information to estimate administrative burden and costs. In addition, EPA requested information on the use or 

disposal of sewage sludge generated by the POTW. The Agency only required POTWs that received discharges from an 

MP&M facility to complete those questions. The POTW Survey requested the following sewage sludge information: amount 

A-29 
MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

generated, use or disposal method, metal levels, use or disposal costs, and the percentage of metal loadings from MP&M 

facilities. The Agency used this information to assess the potential changes in sludge handling resulting from the MP&M rule 

and to estimate economic benefits of these options to the POTW. 

A-30 
MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

REFERENCES 

U.S. Department of Commerce. 2000. U.S. Bureau of the Census. The Bridge Between NAICS and SIC Report. March. 

http://www.census.gov/epcd/www/naicensu.html 

U.S. Department of Commerce. 2001. U.S. Bureau of the Census. Manufacturing Industry Series: Industry Stats on NAICS 

Basis with Distribution Among 1987 SIC-Based Industries. ECON97S Report Series CD-Rom. 

Small Business Administration. Statistics of U.S. Businesses. http://www.sba.gov/advo/stats/int_data.html 

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MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information 

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A-32

MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

Appendix B: Cost Pass-Through


Analysis


INTRODUCTION 

This appendix presents the methodology and results from 

the analysis of cost pass-through (CPT) potential for 

19 MP&M sectors.1 This analysis consists of two parts: 

1. an econometric analysis of the historical 

relationship of output prices to changes in 

input costs, and 

2. an analysis of market structure 

characteristics. 

These two analyses together provide a numerical estimate 

of how much of compliance-related cost increases a sector 

can be expected to pass on to its consumers. 

The rest of this appendix is organized into the following 

six sections: 

� B.1: Rationale for developing sector-specific 

CPT coefficients as opposed to firm-specific 

CPT coefficients; 

� B.2: Econometric analysis of CPT potential, 

based on the historical changes in output 

prices relative to changes in input costs; 

APPENDIX CONTENTS 

B.1 hoice of Sector-Specific CPT Coefficients ........ B-1 

B.2 etric Analysis ................. ............ B-2 

B.2.1 ework ................. ............... B-3 

B.2.2 Used to Estimate the Regression 

Equation ................. ................. . B-4 

B.2.3 ession Results ................. ......... B-6 

B.3 t Structure Analysis ................. ........ B-9 

B.3.1 es Descriptions ................. ...... B-9 

B.3.2 s ................. ................. B-13 

B.4 dation of Econometrically-Estimated CPT 

Coefficients ................. ................. . B-16 

B.4.1 etal Products ................. ...... B-17 

B.4.2 ................. ............... B-17 

B.4.3 le ................. ........... B-18 

B.4.4 raft ................. ................ B-18 

B.4.5 trial Equipment ................ B-18 

B.4.6 ................. .............. B-18 

B.5 ..... B-18 

Attachment B.A: Selected Review of CPT Literature ....... B-20 

B.A.1 

Firm-Specific Cost Pass-Through Rate.” ......... B-20 

B.A.2 ge Rate Pass-Through ................ B-20 

B.A.3 ax Pass-Through ................. ....... B-20 

B.A.4 ................. ............ B-20 

Acronyms ................. ................. ....... B-22 

The C

Econom

Fram

Data 

Regr

Marke

Measur

Result

Vali

Other M

Job Shops 

Motor Vehic

Airc

Mobile Indus

Aerospace 

Adjusting Estimates of Compliance CPT Potential 

Ashenfelter et al. (1998), “Identifying the 

Exchan

T

Studies Cited 

� B.3: Analysis of the market structure factors expected to affect cost recovery; 

� B.4: Validation of econometric estimates of the CPT coefficients; 

� B.5: Adjustment of estimated CPT coefficients to reflect the portion of an MP&M sector that will incur 

compliance costs; and 

� B.6: Attachment: Findings from a review of the CPT literature. 

B.1 THE CHOICE OF SECTOR-SPECIFIC CPT COEFFICIENTS 

EPA believes the use of sector-specific CPT coefficients instead of firm-specific CPT coefficients in the impact analysis is an 

appropriate and practical way of analyzing compliance CPT. The sector-wide rate provides an estimate of the change in each 

facility’s output prices as a function of the regulation-induced increase in its production costs, assuming that the same cost 

increase is experienced by all establishments competing with the facilities in question. For MP&M sectors in which a large 

fraction of establishments will be affected by the regulation, it is reasonable to assume that the M P&M compliance cost acts 

1 The analysis of cost pass-through potential presented here refines in several places the methodology developed for the Phase I 

MP&M analysis. These refinements are highlighted at the appropriate stages of the discussion that follows. 

B-1 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

like an industry-wide cost shock. As noted below in section five, EPA applies an additional adjustment to the estimated CPT 

rate to reflect the fraction of total sector output that is estimated to incur regulation-induced production cost increases. 

In contrast to the concept of a sector-specific CPT adjustment, a firm-specific CPT rate relates a change in the prices charged 

by a specific firm to a change in its production costs, assuming no change in the production cost for rival producers of that 

product. Not surprisingly, previous studies have found that the CPT rate for changes on an individual firm’s costs differs 

from the rate at which a firm would pass through cost changes that are common to all, or a substantial fraction of, firms in an 

industry (e.g., Ashenfelter et al., 1998). It is true, however, that firms in an industry will have differing CPT among each 

other to some extent for reasons such as, differentiated products (e.g., products of different firms are not commodities and are 

not perfectly substitutable); imperfectly competitive markets (e.g., markets in which individual firms possess different degrees 

of market power); and segmented markets (e.g., geographically segmented markets). In the presence of such imperfections, 

individual firms will very likely respond differently in their ability to pass on cost increases in higher output prices even when 

the production cost increase applies to all, or a substantial fraction, of an industry’s production. Nonetheless, estimating the 

CPT ability of individual firms or sub-sector groups of firms within the MP&M sectors would require a detailed analysis of 

market segments and substitutability of MP&M products. While this effort may be theoretically possible, it would be highly 

expensive and an overall daunting challenge given the breadth of the MP&M industry sectors. 

Therefore, this analysis of CPT potential in the MP& M industry is undertaken at the sector-specific level under the 

assumption of perfect competition in these sectors -- including product homogeneity (i.e., products produced by one firm are 

perfect substitutes for products produced by other firms), and homogeneity of production technology and cost across firms 

(i.e., pricing is at marginal cost).2 Under these conditions, the price response to a general industry-wide change in production 

costs is likely to be industry-wide and similar across all firms. 

B.2 ECONOMETRIC ANALYSIS 

EPA performed an econometric analysis of input costs and output prices to estimate historical CPT elasticities for 18 of the 19 

Phase I and Phase II MP&M Sectors. EPA could not estimate historical CPT coefficients for Aerospace due to data 

limitations. These elasticities indicate the changes in output prices by sector that have occurred historically in relation to 

changes in the cost of production inputs. Two factors determine the share of a cost increase that a facility can pass through to 

its customers: the elasticity of demand and the elasticity of supply in the facility's market. Both factors are difficult to 

measure accurately; among other reasons, observed changes in price are due to simultaneous changes in demand and supply. 

In view of this difficulty, this pass-through analysis does not decompose cost pass-through into the separate effects stemming 

from elasticity of demand and elasticity of supply. 

An additional analytic challenge involves joint consideration of quantity and price effects. Specifically, the amount of cost 

increase that a firm may recover through a revenue increase may generally be decomposed into a change in price and a change 

in quantity sold. In most markets, increased prices (in response to increased costs) translate into reduced quantity of sales. 

The interaction of supply and demand elasticities determines whether or not total revenue increases. 

For practical reasons, this analysis focused on the change in equilibrium price due to a change in input costs and furthere 

assumes that the sale quantities of businesses complying with the regulation do not change. The analysis determined changes 

in market quantities from closures rather than by estimating output changes in non-closing facilities. The analysis assumed 

that the quantity of shipments or sales does not vary with the increase in fixed and average costs unless the facility closes. 

The following grounds support this restriction: 

� The cost model for the individual facility reflects a constant marginal cost relationship. The change in 

quantity of output at a facility is a function of the change in equilibrium price and the marginal cost relationship 

at the facility. For instance, in the case in which marginal cost increases with output, an upward shift in the 

marginal cost relationship due to compliance costs will generally cause a facility to reduce its production 

quantity. The extent of changes in production quantity will vary across facilities based on the shift in marginal 

cost and the rate at which marginal cost changes with production. Engineering analysis of facilities provides no 

information, however, about any change in the marginal cost relationship for a given facility, providing only 

lump-sum costs. In lieu of this information, the analysis uses constant marginal costs, which in turn means that 

2 These assumptions likely approximate the real world for those MP&M sectors that consist of a large number of small, highly 

competitive firms such as Job Shops or Printed Wiring Boards. 

B-2 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

profit-seeking facilities will tend not to change their output quantities in response to added costs resulting from 

regulation. As a result, the only quantity-related decision that can be meaningfully analyzed at the facility level 

is whethe r to term inate production com pletely. 

� An estimate of quantity response would be based on the aggregate industry response and would not be 

logically ap plicable to the facility-level analysis. An an alysis can estimate quan tity elasticity resp onse to 

changes in input costs, but this value would represent the aggregate quantity response in the particular MP&M 

sector. The aggregate response encompasses a diversity of responses across facilities: a few facilities may 

eliminate production entirely while others may reduce, keep the same, or even increase output. Applying the 

aggregate quantity response to individual facilities while simultaneously allowing for terminated production 

would exaggerate the likely facility-level quantity response and the likelihood of facility closures. The current 

analysis simulates the aggregate response from a micro-analytic perspective: exiting facilities that found 

compliance to be an uneconomic proposition affect the industry-wide quantity response. 

B.2.1 

The analysis measured the sensitivity of equilibrium prices to changes in input costs. t elasticity of price,” denoted 

Ep, measured the percentag e change in o utput price pe r percent cha nge in unit input costs.3 EPA estim ated the cost elasticity 

of price by regressing annual output price indices on annual input price indices. The methodology’s direct estimation 

measured actual changes in output price with respect to changes in input costs. ook into account the full range 

of possible mechanisms by which input costs affect output prices, including technical changes, substitution, non-competitive 

pricing mechanisms, imperfect information phenome na, and any other shifts or irregularities in the supply and demand 

functions. 

Th e 19 M P& M industry se ctors e ncompa ss 224 industrial 4-d igit SIC cod es. A, ho weve r, could estim ate the c ost elastic ity 

of price based on historical data for only 170 manufacturing SIC codes. EPA could not estimate the cost elasticity of price for 

Aerospace and non-manufacturing industries due to data limitations, but assigned a CPT coefficient to the aerospace sector 

based on the market structure analysis (see Section 2 for details, below).4 EPA assumed zero CP T for non-manufacturing 

industries because these industries tend to be very competitive. 

For each MP&M sector, EPA estimated a relationship for the k = 1 to10 yearly observations (from 1987 to 1996) by 

least-squares linear regression, as follow s: 

(B-1) 

where : 

Pout,k = price index for the bundle of goods produced by the MP &M sector, year k; 

Ep = elasticity of output price with respect to input costs for a given MP& M sector; 

Pin,k-1 = price index of inputs (labor and non-labor) to a given sector, year k-1; 

b = elasticity of output price with respect to em ploymen t costs; 

� = error term; and 

ln(x) = natural log of x 

Specifying the key regression variables as logarithms permitted EPA to estimate the elasticities of output prices with respect 

to the ind ependent variab les directly. at is: 

Framework 

The “cos

This practice t

EP 

Th 

3 The elasticity measure also applies to revenue because quantity of production is assumed constant. 

4 Output Price Index data for the Aerospace sector were unavailable. EPA attempted to use proxy data for missile manufacturing,, a 

component of the defense sector, to estimate a CPT coefficient for the Aerospace sector. This analysis did not produce meaningful results. 

The missile manufacturing industry witnessed a sharp decline in producer prices during the 1987-1996 time period, therefore yielding a 

negative CPT coefficient for the Aerospace sector. Since the Aerospace sector and the missile manufacturing industry are sufficiently 

different from each other, EPA decided not to use the estimated CPT coefficient and instead derive a coefficient for the Aerospace sector 

based solely on the market structure analysis. 

B-3 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

(B-2) 

which is the elasticity of output price with respect to input cost changes in the previous year. 

EP A’s use of the logarithm ic transfo rmatio ns also eliminated an y linear tren d ov er time; in effect, the ind ividua l yearly 

observatio ns becom e cross-sectional variables. he mo del therefore required no specific time-serie s structure . 

EPA considered additional independent variables that might aid in explaining output price changes. or example, EPA 

included some measures of aggregate income, but these measures did not contribute significantly to the estimated 

relationships. 

The coefficients Ep from this regression are the estimated cost-elasticities of price for each MP& M sector. imated 

coefficients address the question: over the period of analysis, by how much did output prices change as input costs increased? 

Th e value of Ep for each secto r, linked with other information o n market structure, yielded a comp osite measure of cost 

pass-through potential by M P& M sector. ussed below, EP A used the re sults of the m arket stru cture analysis to 

valida te the estim ated v alues o f Ep, which represent the expected CPT potential for the different MP& M sectors. 

validated Ep values are the CP T co efficients ultimately assigned to sec tors for the econo mic/financial impact ana lysis. 

B.2.2 ata Used to Estimate the Regression Equation 

Estimating Ep required a measure of the change over time in input costs and a measure of the change in output price for each 

MP &M sector. akes time to respond to price changes (i.e., input 

prices from 1988 would pred ict output prices in 1989). udies found the lags 

associate with price pass-through can extend from 5 to 8 quarters (J. Menon, 1995). a on changes of annual 

output price indices from 1987 to 1996 and input price indices from 1986 to 1995. The final data set contains ten years of 

data for each of the 18 industrial sectors of concern. ysis estimated the relationship between change in output price 

index (dependent variable) and change in input cost index. nput cost index (independent variables) combines a wide 

range of no n-labor co st values, including energy, with emp loyment cost value s. 

a. 

The dependent variable is the output price index. The Producer Price Index (PPI), an appropriate measure of output price, 

me as ur es ch an ge s in the p rice that the p ro du ce r rec eive s at th e p lant ga te an d is th er efor e the releva nt pr ice for the p ro du ce r's 

production decisions. ducts are often intermediate goods whose market prices are producer prices. A 

estimated the dep endent variable a s the weighted avera ge of P PIs fo r the go ods produced b y the indu stries in each sector. 

EP A calculated the output price ind ex for the sectors as follow s: 

(B-3) 

where : 

Pout,k = average output price index value for a given MP &M sector in year k; 

qi,k = value o f shipments for S IC ind ustry i, year k; and 

PPIi,k = Pro ducer Price Ind ex for the outp ut of SIC industry i, year k. 

EPA used the following information to fill in data gaps for all output prices when the PPI series had missing data: 

� Inform ation at the 3-digit SIC cod e level if data were unavailable at the 4-digit SIC cod e level; 

� Th e per centage cha nge in p rice at the 3-digit lev el, app lied to the 4-digit level to calculate missing values, if da ta 

at the 4-digit level were available for several years; and 

T 

F

The est

As disc

The 

D

EPA lagged output prices by a year because the market t

For example, exchange rate pass-through st

EPA used dat

The anal

The i

Dependent variable 

MP&M pro EP

B-4

MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

� A best-fit line to extrapolate data for years with missing data when at least five years worth of data were 

available. 

b. 

The independ ent variable is the input cost index. The input cost index averages the producer price index values for 

commo dity inputs to the sector in question, weighted by the share of each input to sector output. The weighted average 

calculation involves two steps: (1) estimating input cost indice s at the 4-digit SIC level and (2) deve loping the input co st 

index at the MP&M sector level. eps are discussed in detail below. 

� Estimating Input Cost Indices at the 4-digit SIC level 

EPA first identified the composition of production inputs required to produce output from a given industry by obtaining direct 

requirement coefficients from the 1992 Benchm ark Input-Output Tables of the United States. 

5 The direct requirement 

coefficients are defined as fo llows: for each dollar of ou tput from industry i, the dire ct requirem ents co efficient rj indicates 

the value of inp ut j required to achiev e one dollar of ou tput from industry i. The sum of all requiremen ts coefficients rj for 

industry i equals one. Note that the direct requirements coefficients from the input-output table include information on the 

purchase of capital goods. Changes in the cost of capital goods are therefore reflected in the PPI series for the associated 

industrie s. cause only on e set of d irect req uirements co efficients we re ava ilable for and are use d in the a nalysis, this 

analysis assumes that the input mix rem ains constant ove r the ten-year period considere d in the analysis. 

EPA then used yearly PPI values and the Employm ent Cost Index (EC I) from the Bureau of Lab or Sta tistics to estim ate 

changes in the labor and non-labor com ponents of production cost over time. 

to estimate changes in labor cost for all sectors except for aircraft manufacturing, for which a sector-specific ECI is available. 

EPA calculated the input cost index for a 4-digit SIC group as a weighted average of prices for (a) all non-labor inputs for 

which the PPI series data were available and (b) labor input. The percentage of inputs accounted for in our regression model 

range s from 39 p ercen t to 10 0 pe rcent, w ith an average of 66 perc ent. 

To summ arize, E PA calcula ted the input co st index as follow s. ach 4 -digit SIC industry, i, that uses non-lab or inp uts, j, 

the average input price for the year k is: 

(B-4) 

where : 

Pi,k = avera ge inp ut price index for SIC industry i, year k; 

rj = direc t requireme nts coe fficient for inp ut com mod ity j by indu stry i; and 

PPIj,k = Pro ducer Price Ind ex, co mmodity j, year k. 

rl = direct requirements coe fficient for wages and salaries by industry i; and 

ECIk = Employement Co st Index in year k. 

� Developing the input cost index at the MP& M sector level 

EPA developed the input cost index at the MP& M sector level by weighting the individual 4-digit SIC group cost index 

values by 4-digit SIC value of shipments from the Census of Manufactures and various Annual Surveys of Manufactures for 

the co rresp ond ing years. is analysis assume s that weig hts by p rod uction value a re co nstants o ver time . 

The resulting values provided an aggregate measure of input costs over the ten-year period 1986-1995 for each MP&M 

sector. &M industry sector, containing N 4-digit SIC industries, the average input price in each year k is: 

Independent variables 

These st

Be

The Agency used ECI for private manufacturers 

For e 

Th

For each MP 

5 The Bureau of Economic Analysis' Input-Output Table uses its own industry classification system, which is similar to the Standard 

Industrial Classification (SIC) used in the Economic Censuses. This discussion refers to that classification system as the BEA 

classification. Although the BEA classification has more categories than the SIC system, EPA grouped and mapped the BEA classification 

codes to the more aggregate SIC codes that form the MP&M sectors. EPA calculated an average price when one BEA input classification 

code corresponded to more than one SIC code. 

B-5 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

(B-5) 

where: 

Pin,k = average input price index value for a given MP&M sector in year k; 

Pi,k = input price index value for SIC industry i, year k ; and 

qi,k = value of shipments for SIC industry i, year k. 

B.2.3 Regression Results 

Table B.1 below gives the estimated parameter values (corrected for autocorrelation) and t-statistics for each of the sectors. 

Most of the estimated parameters have the expected sign and are statistically significant at 95th percentile. The estimated 

parameters show that 16 of the 18 MP&M sectors have been able to increase prices, at the margin, between 42 percent and 

121 percent for every one percent increase in non-labor input costs. The estimated input cost coefficients are negative for 

two industrial sectors: Printed Circuit Boards and Office Machines. This finding suggest that additional market factors such 

strong domestic and global competition drive output prices down. 

Figure B.1 below depicts output price and input cost trends from 1987 to 1996 for these two industries. It shows that in both 

sectors, output prices decreased faster than input costs. This difference indicates that significant competition in these sectors 

drives output prices down, undoubtedly through rapid technology innovation. An inverse relationship between labor cost and 

output prices also indicates presence of strong competition in these two sectors. Based on these findings, it is reasonable to 

assume that the printed circuit board and office machine sectors have zero CPT ability. 

B-6 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

Table B.1: CPT Regression Results By Sector 

MP&M Sector 

Regression Coefficients (t-statistics in parenthesis) 

Phase 1 Proposed Rule 

(1982 to 1991) 

Phase 2 Model 

(1987 to 1996) 

Non-Labor 

Input Costs 

Labor Input 

Costs Intercept Total Input Costs 

(Labor+Non-Labor) 

Aerospace .774 

(12.73) 

.001 

(4.21) N/A N/A 

Aircraft .924 

(37.22) 

.003 

(3.32) 

-0.9280 

(-1.45) 

1.20 

(8.90) 

Bus & Truck .930 

(30.91) 

.003 

(2.46) 

0.629 

(1.00) 

0.864 

(6.52) 

Electronic Equipment .899 

(25.28) 

.005 

(3.46) 

2.79 

(4.06) 

0.395 

(2.72) 

Hardware .889 

(27.02) 

.005 

(3.68) 

1.06 

(1.80) 

0.772 

(6.22) 

Household Equipment .921 

(43.03) 

.003 

(4.16) 

1.69 

(2.91) 

0.636 

(5.22) 

Instruments .923 

(46.44) 

.003 

(4.34) 

1.06 

(1.79) 

0.771 

(6.18) 

Iron and Steel N/A N/A 1.12 

(1.57) 

0.767 

(5.14) 

Job Shop N/A N/A 1.97 

(3.33) 

0.575 

(4.61) 

Mobile Industrial Equipment .901 

(23.94) 

.004 

(2.68) 

0.546 

(0.92) 

0.884 

(7.05) 

Motor Vehicle .898 

(27.85) 

.004 

(3.36) 

0.833 

(1.03) 

0.820 

(4.76) 

Office Machines .920 

(35.05) 

.004 

(3.52) 

47.5 

(17.2) 

-9.33 

(-15.6) 

Ordnance .907 

(29.05) 

.004 

(3.18) 

1.89 

(3.63) 

0.591 

(5.41) 

Other Metal Products N/A N/A 1.71 

(3.04) 

0.631 

(5.34) 

Precious Metals & Jewelry .938 

(24.82) 

.002 

(1.68) 

1.69 

(2.47) 

0.640 

(4.42) 

Printed Circuit Boards n/a n/a 6.23 

(9.07) 

-0.337 

(-2.31) 

Railroad .911 

(30.52) 

.004 

(3.23) 

0.548 

(0.914) 

0.881 

(6.98) 

Ships and Boats .970 

(34.68) 

.001 

(0.93) 

0.817 

(1.53) 

0.823 

(7.32) 

Stationary Industrial Equipment .909 

(28.09) 

.004 

(3.06) 

0.973 

(1.78) 

0.791 

(6.88) 

N/A = Not available from the Phase I analysis. 

Source: U.S. EPA analysis 

B-7

MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

Table B.2: Output Prices and Unit Input Cost Trends in the Printed Circuit Board and Office Machine Sectors 

Source: EPA Analysis. 

Table B.1 also presents Phase 1 results for comparison. Note the following differences in the Phase 1 and Phase 2 analyses: 

1. Time period: 

� Phase 1 analysis covers 1982 to 1991; 

� Phase 2 analysis covers 1987 to 1996. 

2. Explanatory variables: 

� Phase 1 analysis included non-labor and labor cost variables separately. The model has no intercept term. Note 

that EPA then used only the non-labor input cost coefficient to estimate a CPT potential for a given sector; 

� Phase 2 analysis combines labor and non-labor input costs because compliance costs are associated with both. 

The intercept term captures additional market trends (e.g., increased import penetration) not reflected in the 

input cost indices. 

3. Industrial sectors: 

� Phase 1 analysis included 15 industrial sectors. It excluded iron and steel, job shops, other metal products, and 

printed circuit boards industries; 

� Phase 2 analysis includes 18 of the 19 industrial sectors and excludes the aerospace industry. The Phase 1 

analysis included aerospace, but EPA used proxies from the aircraft industries to estimate output price indices 

for the aerospace-related 4-digit SICs. EPA now estimates the CPT potential for this sector based on the market 

structure analysis alone. 

EPA assigned MP&M sectors to low, average, and high CPT categories based on the natural breaks in the estimated 

parameter values. The estimated parameter values exhibit two distinct breaks in their distribution, between Precious Metals 

and Jewelry (65.89 percent) and Hardware (78.17 percent) and between Motor Vehicle (82.45 percent) and Railroad (88.49 

percent). EPA added the Aerospace sector to the high CPT category based on results from the market structure analysis. 

Table B.3 summarizes results from this analysis. 

B-8 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

Table B.3: 

Low CPT Average CPT High CPT

Office Machine Hardware Railroad 

Printed Circuit Boards Instruments Mobile Industrial Equipment 

Electronic Equipment Iron & Steel Bus & Truck 

Job Shop Stationary Industrial Equipment Aircraft 

Ordnance Ships & Boats Aerospacea 

Other Metal Products Motor Vehicle 

Household Equipment 

Precious Metals & Jewelry 

Classification of MP&M Sectors by CPT Ability 

a Aerospace assigned to High category based on results from the market structure analysis (discussed in the next section). 

Source: U.S. EPA analysis 

B.3 MARKET STRUCTURE ANALYSIS 

The second part of the analysis of cost pass-through potential is based on an analysis of the current market structure of the 

MP &M industry sectors. Information on the competitive structure and market characteristics of an industry provide insight 

into the likely ranges of supply and demand elasticities and the sensitivity of output prices to input costs. For example, when 

input costs increase, the profit-maximizing firm attempts to maintain its profits by increasing output prices accordingly. The 

amount of the cost increase that the firm can pass on as higher prices depends on the relative market power of the firm and its 

customers. The market structure analysis described in this section attempts to measure the relative market power enjoyed by 

firms in each MP &M sector and provides ordinal rankings used to validate the CPT coefficients estimated by the 

econometric analysis. The analysis represents the current market structure and CPT ability of firms in the MP&M sectors and 

in no way attempts to forecast the future market structure of these sectors. 

B.3.1 Measures Descriptions 

The following discussion describes five indicators of market power used to assess cost pass-through potential for the 19


MP&M sectors. Only manufacturing firms have been considered; non-manufacturing firms have been excluded due to data


limitations. As noted above, EPA assigned zero CPT ability to non-manufacturing firms. The five indicators of market


power analyzed are : the eight-firm concentration ratio, import competition, export competition, long term growth, and


competition barriers. Each of these factors are discussed in detail below.


a. Concentration


The extent of concentration among a group of market participants is an important determinant of that group's market power.


A group of many small firms typically has less market power than a group of a few large firms, because the latter are in a 

more advantageous position to collude with each other. All else being equal, highly-concentrated industries are therefore 6 expected to pass-through a higher proportion of the compliance costs that will result from this regulation. 

This analysis uses the eight-firm concentration ratio, which measures the percentage of the value of shipments concentrated in 

the top eight firms in each four-digit SIC category, as an indicator of market concentration. The analysis estimates sector 

concentration ratios as the weighted averages of component industry concentration ratios, weighted by SIC value of 

shipments.7 An increase in the sector concentration ratio makes firms in an industry better able to pass on larger portions of 

their input cost increases without adversely affecting quantities sold to a significant extent. 

6 A substantial body of empirical research exists that has addressed the relationship between industry concentration and market 

power. Eg., see Waldman & Jensen, 1997. 

7 The eight-firm concentration ratio and value of shipments data used are for the year 1992. 

B-9 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

This analysis is potentially limited by the necessity to aggregate component industries into sectors. The accuracy of any


analysis to characterize market power originating from industry concentration depends to a great extent on defining the


relevant market. A well-defined market requires including all competitors and excluding all non-competitors. Defining the


relevant market too narrowly overstates market power, while defining the market too broadly would underestimate it. 


Aggregating concentration ratios for the four-digit SIC categories into a sector concentration ratio results in a sector average


that may overstate market power for some portions of the sector and understate market power for other portions. This


analysis would likely estimate concentration ratios for markets that in general are too broadly-defined.8 Even so, the sectoral


concentration ratios estimated should provide meaningful information that will assist in determining relative market power for


each sector, because firms producing similar or related products are still classified within the same sector and each sector


produces a distinctly different family of products (e.g., motor vehicles, aircrafts, ships and boats).


Another important determinant of the relevant market is its geographical extent. Given the nature of the MP&M industry,


however, this factor is not important because it pertains more to industries dealing with perishable commodities and those


with high transportation costs.


b. Import competition


Theory suggests that imports as a percent of domestic sales are negatively associated with market power because competition


from foreign firms limits domestic firms’ ability to exercise such power. Firms belonging to sectors in which imports make


up a relatively large proportion of domestic sales will therefore be at a relative disadvantage in their ability to pass-through


costs compared to firms belonging to sectors with lower levels of import penetration, a measure of import competition.


Import penetration, the ratio of imports in a sector to the total value of domestic consumption in that sector, is particularly 

significant because foreign producers will not incur costs as a result of this regulation. 

In the market structure analysis, higher import penetration generally means that firms are exposed to greater competition from


foreign producers and will thus possess less market power to increase prices in response to regulation-induced increases in


production costs. The Census Bureau provides import data at the four-digit SIC level. EPA estimated sector import


penetration ratios as the ratio of the sum of component industry imports divided by the sum of component industry value of


domestic consumption9

.


c. Export competition


The MP&M regulation will not increase the production costs of foreign producers with whom domestic firms must compete in


export markets. As a result, sectors that rely to a greater extent on export sales will have less latitude in increasing prices to


recover cost increases resulting from regulation-induced increases in production costs. They will therefore have a lower CPT 

potential, all else being equal. 

This analysis uses export dependence, defined as the percentage of shipments from a sector that is exported, to measure the 

degree to which a sector is exposed to competitive pressures abroad in export sales. EPA used export data at the four-digit 

SIC level and derived sector export dependence ratios: the sum of component industry exports divided by the sum of 

component industry value of shipments. 

That domestic producers export a substantial share of their product does not necessarily imply that they are subject to greater 

competitive pressures abroad compared to what they face in domestic markets. Such would be the case in sectors where U.S. 

producers are the dominant suppliers worldwide. To account for this possibility, EPA analyzed in more detail those sectors 

showing high export dependence to see if domestic firms in those sectors appear to dominate the world market.10 Based on 

information presented in the profile of MP&M industry profile, EPA determined that firms in all four of these sectors (i.e., 

precious metals and jewelry, ordnance, office machine, and aircraft) operate in highly competitive international markets. The 

conventional theory that higher export dependence results in relatively lower market power is therefore assumed to hold true 

for all MP&M sectors. 

8 The four-digit SIC category, while not a perfect delineation, is most often used by industrial organization economists in their studies 

because, among publicly available data sources, these industries appear to correspond most closely to economic markets (Waldman & 

Jensen, 1997). 

9 Census data on imports, exports, and value of shipments for the year 1996 were used for estimating this and the next market 

structure indicator. 

10 EPA considered sectors with export dependence exceeding 30 percent for this part of the analysis. 

B-10 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

A substantial body of literature studies the link between environmental regulation and competitiveness in international trade. 

Overall, little empirical evidence seems to support the hypothesis that environmental regulations have had a significant 

adverse effects on the international competitiveness of domestic firms (Jaffe et al., 1995). Nonetheless, export dependence as 

an important independent factor in assessing the validity of the estimated CPT coefficients. If historical changes in input 

costs have affected both domestic and foreign firms more or less uniformly, then the econometrically estimated Ep would not 

address situations in which only domestic firms face higher costs. Determining the exact extent to which changes in input 

costs have affected both domestic and foreign producers uniformly is beyond the scope of this analysis. Such changes, 

however, can affect a significant proportion of cost changes related to the non-environmental aspect of inputs, such as those 

for energy, imported raw materials, and imported manufactured inputs. 

Given the above, European and other developed countries have also implemented strict environmental regulations comparable 

to U.S. regulations; even changes in environmental costs have therefore often been relatively uniform across domestic and 

foreign firms. This uniformity may account for the fact that past studies do not show substantial impacts of U.S. 

environmental regulation on the balance of trade. 

Because this regulation will affect only domestic firms, and the analysis assumes that no similar regulatory response is 

expected in foreign countries at least in the short term, domestic firms will face relatively higher production costs compared to 

their international competitors as a result of regulation. To study the impact of this regulation on the change in MP&M 

industry competitiveness in international markets, the market structure analysis must therefore include measures that assess 

the effect of each sector’s dependence on export markets on its ability to pass through costs. 

d. Long-term industry growth


An industry’s competitiveness and the ability of firms to engage in price competition are likely to differ between declining


and growing industries. Most studies have found that recent growth in revenue is positively related to profitability (Waldman


& Jensen, 1997), which suggests a greater ability to recover costs fully.


Based on Census Bureau data, EPA estimated the average growth rate in the value of shipments between 1988 and 1996 for


each sector, with the value of shipments for each component industry also serving as the weights for deriving average sector


growth rates. EPA expects firms in sectors with higher growth rates to be better positioned to pass through compliance costs


rather than being forced to absorb such cost increases in order to retain market share and revenues.


e. Competition barriers


Barriers to entry and exit help a concentrated industry exert market power by deterring potential competitors from entering the


market. Without these barriers, a firm that tries to pass through compliance costs by raising its prices risks losing its market 

share to new firms that see an opportunity to compete at higher prices. 

� Entry barriers are the fixed costs of beginning business in an industry. Entry barriers include high capital 

costs, brand name reputations that require a large advertising expense to overcome, a long learning curve, and 

any other factors that make the costs for new entrants higher than the costs of existing firms. 

� Exit barriers are the fixed costs that cannot be salvaged upon leaving the industry. They are sometimes called 

sunk costs and are measured as the difference between the replacement value of a facility's capital and its 

liquidation value. Exit barriers include factors that make it difficult for a firm to liquidate its assets, such as 

specialized machinery that cannot be sold or converted to alternative uses, brand names that cannot transfer well 

to other products, or substantial shutdown liabilities that would offset the value of assets in liquidation. The 

capital valuations are typically needed to measure exit barriers. 

An analysis measuring entry and exit barriers can avoid problems of data availability by identifying directly the presence of 

above-normal profits that such barriers would permit. This analysis uses a sector’s risk-normalized return on assets 

(ROA) as an indicator of profit rates and the likely presence of entry and exit barriers. A popular measure used by managers 

for measuring firm performance, the ROA is used an indicator of firm profitability. This analysis estimates an ROA before 

interest payments and taxes to compare firms with different capital structures. Using the pre-tax ROA results in the adding 

back of the interest tax shield and permits comparing ROAs among firms assumed to be entirely equity-financed. The 

analysis measures firm riskiness by the Asset Beta, which is the firms’ Equity Beta (i.e., measure of the firm’s riskiness as an 

investment relative to the market for equity investments as a whole), adjusted to remove their financing decision from the beta 

calculation. With this adjustment, the analysis can compare firms with different capital structures because the Asset Beta 

represents the beta of common stock had the firm been entirely equity-financed. 

B-11 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

The Capital Asset Pricing Model (CAPM) states that the expected risk premium on an investment (return earned over and 

above the risk-free rate) reflect investment’s riskiness relative to the market (beta). The Treynor Ratio, a commonly used 

performance measure that uses betas as a measure of risk, embodies this principle of the CAPM: 

Treynor Ratio = (Return from Investment - Risk Free Interest Rate) / (Beta of Investment) 

For this analysis, however, the Treynor Ratio, or any other performance measure requiring estimation of the risk premium on 

an investment, could not be used. More than 60 percent of the firms in the analysis had five year, pre-tax ROAs that were 

lower than the risk-free interest rate of 5.21 percent (return on the three-month U.S. Treasury Bill for the five-year period 

1996-2000). The analysis using the Treynor Ratio yielded results that did not permit a meaningful comparison of risknormalized ROAs among sectors. This analysis therefore used a modified form of the Treynor Ratio that adjusts the total 

return and not just the risk premium by the riskiness of an investment. Applying this modification, the analysis estimated the 

risk-normalized ROAs as follows: 

Risk-Normalized ROA = ROA / Asset Beta 

The analysis estimated risk-normalized ROAs for sectors using firm level data as opposed to data at the 4-digit SIC level, and 

identified firms belonging to each MP&M sector using a two step process: 

� First, EPA assigned facilities (and their parent firms) responding to the MP&M facilities survey to the sector 

from which they received the largest portion of their revenues. 

� Second, EPA identified additional facilities belonging to each sector using a financial information Web site 

(marketguide.com), which provides a classification of publicly-traded firms by the 4-digit SIC code of their 

largest business segment based on revenues. 

EPA estimated ROA and Beta values for a five-year time period, and estimated sector risk-normalized ROAs by weighting 11 each firm’s risk-normalized ROA by its market capitalization. 

The use of the risk-normalized ROA measure only assigns MP&M sectors relative rankings and does not imply that they face 

high or low barriers to competition in absolute terms. The analysis assumes that higher risk-adjusted profits in general 

indicate potential entry and exit barriers and above average market power. 

11 EPA further studied the business activities of firms belonging in the MP&M facilities survey that were identified as conglomerates 

or found to own multiple facilities belonging to more than one MP&M sector, and of firms in the broader sample having a market 

capitalization exceeding $25 billion. This additional step ensured that the market capitalization weight used in the analysis represented only 

the fraction of revenues that the firm receives from its business activities in the MP&M sector(s) of interest. 

B-12 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

B.3.2 Results 

EPA used these five indicators to assign each sector a cost pass-through score. Higher numerical values indicate greater CPT 

potential for some indicators (e.g., industry concentration) and lesser CPT potential for others (e.g., import competition). 

Table B.4 summarizes the specific ranking definitions for each indicator. 

Table B.4: Summary of Ranking Rules for Assessing Relative 

Pass-Through Potential Based on Market Structure Considerationsa 

Variable Indicates Greater PassThrough Potential (High Rank) Variable Indicates Lesser PassThrough Potential (Low Rank) 

8-Firm Concentration Ratio Greater than median Lesser than median 

Ratio of Imports to Shipments Lesser than median Greater than median 

Ratio of Exports to Shipments Lesser than median Greater than median 

Average Growth Rate of Shipments Greater than median Lesser than median 

Risk-Normalized Pre-Tax Return on Assets Greater than median Lesser than median 

a All assessments of pass-through potential are relative among the 19 MP&M Sectors. 

Source: U.S. EPA analysis. 

For each of the five indicators, EPA ranked sectors from 1 to 19, with 1 assigned to the sector assessed to have the lowest 

CPT potential and 19 assigned to the sector assessed to have the highest CPT potential.12 Based on this scoring system, the 

possible score for a sector when all five of its ranks are summed ranges from 5 to 95. Table B.5 presents a summary of the 

results for the market structure analysis. 

12 This ranking scale differs from the scale used to assign scores in the market structure analysis undertaken for the Phase I MP&M 

analysis. In the Phase I analysis, depending on the variable under consideration, a sector received a value of +1 if it indicated a greater 

CPT potential relative to the median and a value of -1 if it indicated a lesser CPT potential relative to the median . The sector at the 

median received a value of 0. The use of the median value as the threshold for determining relatively higher or lower (+1 or -1) market 

power was somewhat arbitrary, especially for values closely centered around the median. The new scale, since it considers individual 

sector ranks, is superior because it explicitly recognizes that extreme values are more likely to be indicative of high or low market power, 

and accordingly assigns them a higher or lower score. For example, the old scale would assign a sector with industry concentration just 

above the median (e.g., other metal products) the same score of +1 as a very highly-concentrated industry, such as aerospace. The new 

scale, however, recognizes the difference in industry concentration between the two sectors and therefore assigns the first sector a rank 

close to 10 and aerospace a rank close to 19. 

B-13 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

Table B.5: Results of the Market Structure Analysisa 

Overall 

Rank Sector 

8-firm 

Concentration 

Ratio 

Import 

Penetration 

(%) 

Export 

Dependence 

(%) 

Avg. Annual 

Growth Rate 

(%) 

RiskNormalized 

ROA (%) Aggregate 

Score 

Value Rank Value Rank Value Rank Value Rank Value Rank 

1 Precious Metals and 

Jewelry 

35.0 4 77.36 1 49.85 2 -1.9 3 14.43 10 20 

2 Printed Circuit 

Boards 35.0 3 21.99 8 17.07 10 1.5 8 7.50 2 31 

3 Ordnance 76.90 16 18.92 10 50.17 1 -7.3 2 12.30 6 35 

4 Household 

Equipment 54.22 10 33.18 3 17.02 11 1.5 9 12.02 5 38 

4 Office Machine 61.38 14 51.85 2 43.41 4 3.1 15 9.58 3 38 

6 Electronic 

Equipment 47.27 9 24.55 6 24.04 6 5.1 18 7.21 1 40 

7 Aircraft 85.3 18 22.74 7 46.43 3 -1.7 4 16.15 13 45 

8 Iron and Steel 41.87 6 4.54 16 1.32 17 0.4 6 11.38 4 49 

9 Other Metal 

Products 54.27 11 32.40 4 17.57 9 1.1 7 26.60 19 50 

10 

Stationary 

Industrial 

Equipment 

41.16 5 17.71 11 23.64 7 3.7 16 16.78 14 53 

11 Hardware 24.52 2 14.31 14 11.37 13 2.1 11 17.18 15 55 

12 Instruments 44.2 8 15.33 12 23.07 8 1.8 10 19.64 18 56 

13 Mobile Industrial 

Equipment 58.56 13 21.42 9 29.62 5 2.8 13 18.13 17 57 

14 Ships and Boats 58.20 12 6.49 15 6.48 15 -1.5 5 16.11 12 59 

15 Job Shop 19.26 1 0.00 19 0.00 19 3.1 14 13.44 9 62 

15 Motor Vehicle 77.30 17 27.56 5 15.74 12 2.6 12 18.10 16 62 

17 Aerospace 92.29 19 0.75 18 0.75 18 -7.6 1 13.19 8 64 

17 Bus & Truck 42.51 7 2.86 17 3.04 16 4.8 17 12.31 7 64 

19 Railroad 71.00 15 15.16 13 10.26 14 7.6 19 14.62 11 72 

a Shaded values are the medians for each market structure indicator. 

Source: U.S. EPA analysis 

This rank scoring system has some important limitations: 

1. This grading scale implicitly assigns equal weights to each of the five market structure indicators. Clearly, the impact 

of each of these five indicators on market power will vary from sector to sector, and some indicators are likely to 

dominate others within each sector. 

2. Although the ranking scale distinguishes between sectors with extreme values and those that are close to the median, 

it does not permit an accurate judgement about how significant a particular value may be in determining market 

power. For each indicator, sectors are simply ranked from 1 to 19 based on the lowest to highest market power 

potential. The change in market power expected as one moves from sector 1 to sector 5 is not likely to be equal, 

however, to the change in market power expected as one moves from sector 6 to sector 10. 

In general, the market structure analysis revealed that a discernable gap exists in the estimated parameters around rank 4/5 and 

around rank 14/15 for most indicators (see Table B.6). For each indicator, two small groups, each containing about four to 

B-14 
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

five sectors, therefore seem to have relatively low and high market power. A much larger group of about nine to ten sectors 

exhibit average market power. 

Table B.6: istribution of Estimated Parameters for Market Structure Variables 

Rank 

8-firm 

Concentration 

Ratio 

Import 

Penetration 

Export 

Dependence 

Average Annual 

Growth Rate 

Risk-Normalized 

ROA 

19.26 77.36% 50.17% -7.6% 7.21 

24.52 51.85% 49.85% -7.3% 7.50 

35.00 33.18% 46.43% -1.9% 9.58 

35.07 32.40% 43.41% -1.7% 11.38 

a 41.16 27.56% 29.62% -1.5% 12.02 

41.87 24.55% 24.04% 0.4% 12.30 

42.51 22.74% 23.64% 1.1% 12.31 

44.22 21.99% 23.07% 1.5% 13.19 

47.27 21.42% 17.57% 1.5% 13.44 

a 54.22 18.92% 17.07% 1.8% 14.43 

54.27 17.71% 17.02% 2.1% 14.62 

58.20 15.33% 15.74% 2.6% 16.11 

58.56 15.16% 11.37% 2.8% 16.15 

61.38 14.31% 10.26% 3.1% 16.78 

a 71.00 6.49% 6.48% 3.1% 17.18 

76.90 4.54% 3.04% 3.7% 18.10 

77.30 2.86% 1.32% 4.8% 18.13 

85.32 0.75% 0.75% 5.1% 19.64 

92.29 0.00% 0.00% 7.6% 26.60 

D

a

 Highlighted rows mark the natural gaps in the various indicators. 

Source: U.S. EPA analysis 

The aggregate market structure scores for all sectors range from a low of 19 to a high of 71. Apart from the lowest score 

(precious metals and jewelry) and the highest score (railroad), all the other scores are uniformly distributed with no clear 

breaks in their distribution that can be used for classifying sectors by their CPT potential (see Table B.5). EPA therefore used 

an alternative classification system for the market structure analysis. Based on the average aggregate score of 50 (average 

rank of 10), EPA assigned sectors with an aggregate score of 40 or below (average rank of 8 or less) to the low CPT category, 

and assigned sectors with an aggregate score of 60 or above (average rank of 12 or more) to the high CPT category. EPA 

assigned sectors with aggregate scores between these cutoffs to the average CPT category. Table B.7 shows the 

categorization of all 19 sectors by their CPT potential based on this classification system. In total, EPA classified six, eight, 

and five sectors in the low, average, and high CPT categories, respectively. The classification cutoffs, though somewhat 

arbitrary, result in a sector classification similar to the trends witnessed for most individual indicators, such that about five 

sectors are classified in the low and high CPT categories and the remaining sectors are classified as having average CPT 

potential. 

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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

Table B.7: 

Low CPT Average CPT High CPT 

Precious Metals & Jewelry Aircraft Job Shop 

Printed Circuit Boards Iron & Steel Motor Vehicle 

Ordnance Other Metal Products Aerospace 

Household Equipment Stationary Industrial Equipment Bus & Truck 

Office Machine Hardware Railroad 

Electronic Equipment Instruments 

Mobile Industrial Equipment 

Ships & Boats 

Classification of MP&M Sectors by CPT Ability 

Source: U.S. EPA analysis 

Although recognizing the limitations of the ranking scale, EPA believes that it is useful for presenting the results succinctly 

and provides a basis for validating the estimated CPT coefficients. Analyzing the relative importance of each indicator for 

each of the sectors is beyond the scope of this analysis. 

B.4 VALIDATION OF ECONOMETRICALLY-ESTIMATED CPT COEFFICIENTS 

The econometric analysis provides a quantitative assessment of what the cost pass-through ability of each sector appears to 

be. The market structure analysis yields a judgment of what the pass-through ability of each sector ought to be. In this 

section the two analyses are brought together, with the results of the market structure analysis used to validate the CPT 

coefficients estimated by the econometric analysis. 

Table B.8 shows a comparison of each sector’s CPT classification based on the econometric analysis and the market structure 

analysis. The two analyses classify 13 of the 19 sectors in the same CPT category. For these sectors, the market structure 

analysis appears to validate the CPT coefficient derived using the econometric analysis. No econometric estimate is available 

for one sector (aerospace); for this sector, EPA used only the market structure analysis. For the remaining five sectors, 

however, the two analyses assign sectors to different CPT categories. EPA undertook a more detailed analysis of these 

sectors’ market structure to validate their CPT coefficient. Specifically, EPA examined the following two factors affecting 

firm’s market power in a given industrial sector: 

� Whether any (i.e., one or more) of the five structural indicators may be extremely important or irrelevant for a 

particular sector, and therefore whether its effect on market power is being under-weighted or over-weighted, 

respectively. 

� Whether other factors affecting market power for these sectors have not been included in the market structure 

analysis, but which possibly have substantial effects on market power/CPT ability in particular sectors. 

The discussion below summarizes EPA’s review and conclusions for each of these six sectors. 

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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

Table B.8: Comparison of Sectoral Classification Based on Econometric and Market 

Structure Analysis 

Sector Econometric Analysis Market Structure Analysis 

CPT Categorization Matches 

Electronic Equipment Low Low 

Household Equipment Low Low 

Office Machine Low Low 

Ordnance Low Low 

Precious Metals and Jewelry Low Low 

Printed Circuit Boards Low Low 

Hardware Average Average 

Instruments Average Average 

Iron and Steel Average Average 

Ships and Boats Average Average 

Stationary Industrial Equipment Average Average 

Bus & Truck High High 

Railroad High High 

CPT Categorization Does Not Match 

Other Metal Products Low Average 

Job Shop Low High 

Motor Vehicle Average High 

Aircraft High Average 

Mobile Industrial Equipment High Average 

CPT Comparison Not Possible 

Aerospace N/A High 

Source: U.S. EPA analysis. 

B.4.1 Other Metal Products 

This sector is assigned to the low category by the econometric analysis and the average category by the market structure 

analysis. EPA believes that the estimated CPT coefficient for this sector is accurate and that the market structure score for 

this sector is somewhat misleading because of the exceptionally high risk-normalized ROA derived for it. A priori, there 

appears to be no reason why firms in this sector should be able to earn significantly higher returns than in other sectors, and 

the high risk-normalized ROA estimated is likely an artifact of the small sample of firms for which financial data were 

available to estimate risk-normalized returns for this sector. The other four indicators of market power suggest below-average 

CPT for this sector, which agrees with the CPT coefficient estimated from the econometric analysis. 

B.4.2 Job Shops 

EPA assigned this sector to the low category by the econometric analysis and the high category by the market structure 

analysis. EPA believes that the market structure analysis may be misleading due to the high CPT ranks assigned to the Import 

Penetration and Export Dependence indicators of market power for this sector. These two indicators of market power are not 

relevant for this sector, however, because the sector is not trade-oriented. EPA expects the level of domestic competition 

among job shops to be the single most important factor that determines market power and the ability of firms to pass through 

costs in the sector. The Job Shop sector has the lowest concentration ratio among all the sectors, suggesting that the sector is 

characterized by a substantial number small firms (see Table 3.8 in the MP&M Industry Profile) that are most likely engaged 

B-17 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

in intense competition among each other. The estimated, low, CPT coefficient for this sector therefore appears to be 

appropriate. 

B.4.3 Motor Vehicle 

This sector is assigned to the average category by the regression analysis and the high category by the market structure 

analysis. EPA believes that this sector is characterized by average cost pass-through potential due to the extremely 

competitive nature of the motor vehicle industry both domestically and in international markets. In recent years, in a bid to 

remain or become more competitive, the trend in this industry has been towards the continual consolidation of firms into 

globalized manufacturers. In fact, motor vehicle manufacturers are no longer constrained within national boundaries, as 

mergers and joint ventures include some of the largest firms from different countries. In addition, manufacturers have 

increasingly standardized the design of motor vehicles and their parts, changes that have resulted in much less product 

differentiation (but greater product quality) among manufacturers. The increasing intensity of global competition and the 

move towards decreasing product differentiation are likely to limit the ability of domestic producers to pass-through 

significant portions of their cost increases associated with this regulation. Therefore, the finding of an average cost passthrough coefficient appears to be justified. 

B.4.4 Aircraft 

This sector is assigned to the high category by the econometric analysis and the average category by the market structure 

analysis. Based on the unique nature of the global aircraft industry, EPA believes that the estimated CPT coefficient for this 

sector is appropriate. Not only is the industry concentrated domestically (concentration ratio of 85.3), but this is also true of 

the global aircraft manufacturing industry. In recent years, the industry has witnessed substantial restructuring through 

mergers and consolidation, both nationally and internationally (see section 3.2.2 in the MP&M Industry Profile). The highly 

concentrated nature of the industry, combined with the sizeable share of the domestic market that is controlled by domestic 

aircraft manufacturers, suggests that firms in this sector have the ability to pass through a significant portion of their cost 

increases. 

B.4.5 Mobile Industrial Equipment 

EPA assigned this sector to the high category by the econometric analysis and the average category by the market structure 

analysis. EPA believes that this sector is more appropriately characterized by average CPT because the sector has witnessed 

certain trends in recent years that suggest that firms in this sector do not have a high ability to pass through cost increases. 

Specifically, growth rates in the construction and the farm and machinery equipment industries started to level off or even 

declined in recent years after a sustained period of growth (see section 3.2.10 in the MP&M Industry Profile). These 

declining trends are not fully represented in the regression analysis because the last year of analysis is 1996. EPA therefore 

revised the CPT coefficient for this sector to equal the average CPT value for all sectors classified in the average category 

based on the regression analysis. 

B.4.6 Aerospace 

Since the market structure analysis categorizes the Aerospace sector in the high CPT category, EPA estimated the CPT 

coefficient for this sector as the average CPT value for all sectors classified in the high category based on the regression 

analysis (excluding Mobile Industrial Equipment whose CPT coefficient was revised based on the market structure analysis). 

B.5 ADJUSTING ESTIMATES OF COMPLIANCE CPT POTENTIAL 

The CPT values estimated above reflect sector level CPT potential. The methodology must consider that ability to pass on 

cost increases through price increases will differ at the industry level versus the facility level. Cost increases that affect all 

facilities in an industry are more likely to be recovered through industry-wide price increases, whereas cases where only a few 

facilities in an industry incur cost increases are less likely to result in price increases. This analysis must therefore take into 

account the proportion of an industry that will experience cost increases when applying industry-level cost pass-through 

coefficients. 

B-18 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

For the final MP&M rule, EPA will use the method used in the Phase I analysis where EPA adjusted the industry-level cost 

pass-through coefficient downward in proportion to the percentage of sector output bearing compliance cost. The ratio of the 

revenues in water-discharging facilities affected by the rule divided by total revenues in the MP&M sector provided a 

measure of the fraction of production in the MP&M sector likely to be affected by cost increase. That is, a cost pass-through 

percentage of 90 percent would be reduced to 72 percent if 80 percent of the sector output was subject to the regulation (.80 × 

.90 = .72). EPA applied this adjusted pass-through percentage to the percentage cost increase experienced by the regulated 

facilities only (i.e., sum of compliance costs divided by the sum of baseline costs for the facilities subject to the rule). Table 

B.9 presents the adjusted CPT coefficients estimated for each sector. 

Table B.9: Adjusted Estimates of Compliance Cost Pass-Through Potential by MP&M Sector 

Sector Unadjusted Cost PassThrough Potential 

Estimated Fraction of 

Sector’s Revenue Subject to 

Regulation (%) 

Adjusted Cost Pass-Through 

Potential 

Aerospacea 0.98 100.00 1.00 

Aircraftb 1.20 100.00 1.00 

Bus & Truck 0.86 100.00 0.96 

Electronic Equipment 0.39 100.00 0.42 

Hardware 0.77 33.50 0.26 

Household Equipment 0.64 100.00 0.64 

Instruments 0.77 100.00 0.77 

Iron and Steel 0.77 100.00 0.77 

Job Shop 0.57 43.70 0.25 

Mobile Industrial Equipmentc 0.79 100.00 0.79 

Motor Vehicle 0.82 44.10 0.36 

Office Machinesd (9.33) 34.50 0.00 

Ordnance 0.59 100.00 0.59 

Other Metal Products 0.63 100.00 0.63 

Precious Metals & Jewelry 0.64 42.90 0.27 

Printed Circuit Boards (0.34) 53.60 0.00 

Railroad 0.88 100.00 0.88 

Ships and Boats 0.82 100.00 0.82 

Stationary Industrial Equipment 0.79 32.20 0.25 

a CPT coefficient for the Aerospace sector estimated based on the market structure analysis. b For the Aircraft sector, the cost-pass through potential is capped at 100%.


CPT coefficient for the Mobile Industrial Equipment sector revised based on the market structure analysis.


d For the Office Machine and Printed Circuit Boards sectors, the cost-pass through coefficients are set to zero based on both the 

estimated negative regression coefficient and the results of the market structure analysis. 

Source: U.S. EPA analysis 

B-19 

c
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

ATTACHMENT B.A: SELECTED REVIEW OF CPT LITERATURE 

To support the CPT analysis, EPA undertook a selected review of previous CPT analyses. The two most studied areas in the 

literature deal with exchange rate pass-through and tax pass-through. Unfortunately, neither of these study types is useful in 

assessing the reliability of the MP&M CPT results. Sections B.A.2 and B.A.3 provide a brief summary of this studies. One 

study (Ashenfelter et al,1998) estimates the pass-through rate for cost changes faced by an individual firm and compares it 

with passes-through of cost changes common to all firms in an industry. This appears to be the most relevant to the analysis 

of compliance costs pass through. Section B.A.1 provides a brief summary of findings from this study. 

B.A.1 Ashenfelter et al. (1998), “Identifying the Firm-Specific Cost Pass-Through 

Rate.” 

As noted above, Ashenfelter et al. (1998) examines the pass-through rate for cost changes faced by only an individual firm 

(Staples, an office superstore chain), and distinguishes that rate from the rate at which a firm passes through cost changes 

common to all firms in an industry. Based on their analysis, they find the combined firm-specific and industry-wide passthrough rate (i.e., with no distinction between cost changes specific to the individual firm and those applicable to the entire 

industry) to be 57 percent. Conversely, the pass-through rate estimated for only firm-specific cost changes is about 15 percent 

and the pass-through rate for only industry-wide cost changes is close to 85 percent. The finding of a high CPT rate for 

industry-wide cost changes lends support to EPA’s finding of similarly high historical CPT rates for many of the MP&M 

sectors. 

B.A.2 Exchange Rate Pass-Through 

The exchange rate pass-through literature examines the response of local currency import prices to variation in the exchange 

rate between exporting and importing countries. Based on seven studies covering the period 1970 to the mid-1980s, Menon 

(1995) finds that the estimated aggregate pass-through of exchange rate changes to import prices ranges from a low of 48.7 

percent to a high of 91 percent. The mean value for pass-through for the sample of studies he considered is 69.9 percent. In 

contrast, Feinberg (1989) considers the impacts of exchange rate movements on U.S. domestic prices and finds an average 

pass-through of 16 percent in real terms. The pass-through is close to complete for industries that are heavily reliant on 

imported inputs and producing goods highly substitutable for imports. Pass-through rates are much lower for capital-intensive 

and concentrated industries and those protected by barriers to entry. The exchange rate pass-through scenario, however, is 

not comparable to the nature of compliance cost changes expected under the MP&M regulation and the resultant pass-through 

responses from domestic producers because the studies focus primarily on the impact of exchange rate changes on prices of 

imported goods and not on prices of domestically produced goods. Feinberg’s study appears to be more relevant, but he does 

not present pass-through rates for individual industries, and does not explain why pass-through rates are much lower for 

capital-intensive and concentrated industries and those protected by barriers to entry. 

B.A.3 Tax Pass-Through 

The literature on tax pass-through examines the impact of excise tax changes on prices. Of the several studies that addressed 

the issue of tax pass-through, the majority report pass-through rates slightly in excess of a 100 percent (Ashenfelter et al., 

1998). This literature is not entirely relevant to the CPT scenario being analyzed for this rule because most of these studies 

analyze changes in excise tax rates in the cigarette industry. In addition, excise tax changes on final goods do not affect 

manufacturing costs, and they have a uniform impact on the entire industry. Excise taxes do affect domestic producers, 

however, by altering final demand and therefore revenues received. 

B.A.4 Studies Cited 

Ashenfelter, Orley, et al. (1998), “Identifying the Firm-Specific Cost Pass-Through Rate,” FTC Working Paper No. 217, 

January. 

Feinberg, Robert M (1989), “The Effects of Foreign Exchange Movements on U.S. Domestic Prices,”The Review of 

Economics and Statistics. 

Jaffe, A. et al., (1995), “Environmental Regulation and the Competitiveness of U.S. Manufacturing: What does the Evidence 

Tell Us?” Journal of Economic Literature, XXXIII (March): 132-63. 

B-20 
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

Menon, Jayant (1995), “Exchange Rate Pass-Through,” Journal of Economic Surveys, 9(2).


Waldman, Don E. and Elizabeth J. Jensen (1997), Industrial Organization: Theory and Practice. Addison-Wesley.


B-21

MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis 

ACRONYMS 

CAPM: Capital Asset Pricing Model 

CPT: cost pass-through 

ECI: Employment Cost Index 

PPI: Producer Price Index 

ROA: risk-normalized return on assets 

B-22 
MP&M EEBA: Appendices Appendix C: Summary of Mod erate Impact Threshold Values by Sector 

Appendix C: Summary of Moderate


Impact Threshold Values by Sector


INTRODUCTION 

Facilities subject to moderate impacts from the rule are 

expected to experience financial stress short of closure. 

This analysis uses two financial indicators: (1) Pre-Tax 

Return on Assets (PTRA) and (2) Interest Coverage Ratio 

(ICR). These threshold values were compared to pre- and 

post-compliance PTRA and ICR values for sample 

facilities to determine if facilities choosing to remain in 

business after promulgation of effluent guidelines would 

APPENDIX CONTENTS 

C.1 Developing Threshold Values for Pre-Tax Return on Assets 

(PTRA) ................. ................. .. C-1 

C.2 Developing Threshold Values for t Coverage Ratio 

(ICR) ................. ................. .... C-2 

C.3 ary of Results ................. ............. C-4 

References ................. ................. ....... C-5 

Interes

Summ

experience moderate impacts on their ability to attract and finance new capital. The remainder of this appendix describes the


sources and methodology used to derive sector-specific moderate impact threshold values.


EPA calculated the thresholds using income and financial structure information by 4-digit SIC code from the Risk


Management Association (RMA) Annual Statement Studies for eight years 1994-2001 (RMA, 2001; RMA 1998). This


source provides quartile values derived from statements of commercial bank borrowers and loan applicants for firms having


less than $250 million in total assets. These criteria may introduce bias, since firms with particularly poor financial


statements might be less likely to apply to banks for loans, and some types of firms may be more likely to use bank financing


than others. However, the RM A data offers the advantage of being available by 4-digit SIC codes and for quartile ranges.


RMA did not provide data for all 4-digit SIC codes associated with an MP&M sector. Out of 174 manufacturing SIC codes


and 50 non-manufacturing SIC codes, 52 manufacturing SIC codes (30 percent) and 13 non-manufacturing SIC codes (26


percent), had no years of data available. RMA did not compile data for any SIC codes in two manufacturing sectors,


Ordnance and Aerospace and one non-manufacturing sector, Precious Metals and Jewelry. When data were not available for


any SIC codes within the sector, EPA calculated an average manufacturing or non-manufacturing threshold to use as a proxy.


The 4-digit SIC code data were consolidated into weighted sector averages, weighted by 1997 value of shipments from the


Economic Censuses (U.S. DOC, 1997). For each sector and impact measure, a separate threshold was calculated for


manufacturing and non-manufacturing SIC codes. The use of the RMA data for calculating the threshold values for pre-tax


return on assets and interest coverage ratio is outlined below.


C.1 DEVELOPING THRESHOLD VALUES FOR PRE-TAX RETURN ON ASSETS (PTRA) 

Pre-tax return on total assets measures the effectiveness of management in employing the resources available to it. A low 

ratio may indicate that a borrower would have difficulty financing treatment investments and continuing to attract investment. 

The following data from Risk Management Association Annual Statement Studies were used to calculate PTRA: 

� % Profit Before Taxes / Total Assets 25th	 Ratio of profit before taxes divided by total assets and multiplied by 

100 for the lowest quartile of values in each 4-digit SIC code. 

� Operating Profit Gross profit minus operating expenses. 

� Profit Before Taxes Operating profit minus all other expenses (net). 

RMA provides a measure of pre-tax return on assets that approximates the measure that EPA defined for the moderate impact 

analysis. As defined by RMA, this measure is the ratio of pre-tax income to assets, designated ROARMA: 

ROARMA = Pre-Tax Income (EBT) / ASSETS25th 

C-1 
MP&M EEBA: Appendices Appendix C: Summary of Mod erate Impact Threshold Values by Sector 

However, as defined by EPA for its analysis, the numerator of the PTRA measure requires the use of earnings before interest 

and taxes (EBIT) instead of pre-tax income (EBT). Defined as EBIT, the PTRA numerator will capture all return from assets, 

whether going to debt or equity. To derive a pre-tax, total return value, EPA adjusted RMA’s measure of PTRA using the 

median percentage values of EBIT and EBT available from RMA. This adjustment yields the PTRA measure that EPA used 

in the moderate impact analysis, designated ROAMP&M: 

ROAMP&M = ROARMA * EBIT / EBT 

Negative values are included in the weighted-sector PTRA averages but a different method is used to adjust the ROA values 

reported in RMA to the value used in the moderate impact analysis. Specifically, using only those observations (i.e., 4-digit 

SIC code and year combinations) with positive values for % Profit Before Taxes / Total Assets, Operating Profit, and Profit 

Before Taxes, EPA calculated an adjustment factor by subtracting the difference between ROAMP&M and ROARMA as follows: 

ROAMP&M-ROARMA = adjustment factor. 

Those values were consolidated into sector-specific adjustment factors, weighted by 1997 value of shipments from the 

Economic Censuses (U.S. DOC, 1997). Each negative PTRA observation from RMA was adjusted by its sector specific 

adjustment factor to approximate the measure used in the moderate impact analysis: 

ROARMA + sector-specific adjustment factor = ROAMP&M 

The sector-specific adjustment factors average 0.47 for manufacturing sectors and range from 0.13 for the Office Machines 

sector to 0.60 for the Aircraft and Motor Vehicle sectors. The sector-specific adjustment factors average 0.22 for nonmanufacturing sectors and range from 0.15 for the Motor V ehicle sector to 0.74 for the Railroad sector. 

C.2 DEVELOPING THRESHOLD VALUES FOR INTEREST COVERAGE RATIO (ICR) 

Interest coverage ratio is a measure of a firm's ability to meet current interest payments and, on a pro-forma basis, to meet the 

additional interest payments under a new loan. A high ratio may indicate that a borrower would have little difficulty in 

meeting the interest obligations of a loan. This ratio also serves as an indicator of a firm's capacity to take on additional debt. 

The following data from Risk Management Association Annual Statement Studies were used to calculate ICR: 

� EB IT/Interest25th Ratio of earnings (profit) before annual interest expense and taxes 

(EBIT) divided by annual interest expense for the lowest quartile of 

values in each 4-digit SIC code. 

� % Dep r., Dep., Amort./Salesmed Median ratio of annual depreciation, amortization and depletion 

expenses divided by net sales and multiplied by 100. 

� Op erating Profit Gro ss profit minus ope rating expense s. 

RMA provides a measure of interest coverage that approximates the measure that EPA defined for the moderate impact 

analysis. As defined by RMA, this measure is the ratio of earnings before interest and taxes to interest, designated ICRRMA: 

ICRRMA = EBIT / INTEREST25th 

However, as defined by EPA for its analysis, the numerator of the ICR measure requires the use of earnings before interest, 

taxes, depreciation, and amortization (EBITDA) instead of earnings before interest and taxes (EBIT). Defined this way, the 

ICR numerator will include all operating cash flow that could be used for interest payments. To derive the desired ICR value 

(designated ICRMP&M), EPA adjusted the RMA value as outlined below: 

ICRMP&M = EBITDA / INTEREST 

Therefore, ICRMP&M = ICRRMA * (EBIT + DA) / EBIT 

or ICRMP&M = ICRRMA * {1+ [(DA / SALES) / (EBIT / SALES)]}

C-2 
MP&M EEBA: Appendices Appendix C: Summary of Mod erate Impact Threshold Values by Sector 

For consistency of calculation, EPA used the median values available from RMA for the adjusting both the numerator 

(DA / SALES) and denominator (EBIT / SALES) terms.1 

EPA used the same method as described above to adjust the negative ICR values reported in RMA to the value used in the 

moderate impact analysis. Including only those observations with positive values for EBIT/Interest, % Depr., Dep., 

Amort./Sales, and Operating Profit, an adjustment factor was calculated by subtracting the difference between ICRMP&M and 

ICRRMA as follows: 

ICRMP&M-ICRRMA = adjustment factor. 

A sector-specific adjustment factor was calculated for ICR values similar to the PTRA. Each negative ICR observation from 

RMA was adjusted by its sector specific adjustment factor to approximate the measure used in the moderate impact analysis: 

ICRRMA + sector-specific adjustment factor = ICRMP&M 

The sector-specific adjustment factors average 0.59 for manufacturing sectors and range from 0.28 for the Precious Metals 

and Jewelry sector to 0.79 for the Printed Circuit Board sector. The sector-specific adjustment factors average 0.50 for nonmanufacturing sectors and range from 0.24 for the Office Machines sector to 1.85 for the Aircraft sector. 

1 Numerator (% Depr., Dep., Amort./Sales) is available for quartile values; denominator (Operating Profit) only for 

median values. 

C-3 
MP&M EEBA: Appendices Appendix C: Summary of Mod erate Impact Threshold Values by Sector 

C.3 SUMMARY OF RESULTS 

Table C.1 shows the resulting threshold values for PTRA and ICR by sector. The PTRA values for manufacturers range from 

zero percent for the Office Machine sector to 2.8 percent for the Aircraft and Household Equipment sectors and for the nonmanufacturers the values range from 0.3 percent for the Office Machine sector to 3.1 percent for the Railroad sector. The 

ICR values for manufacturers range from 1.4 for the Office Machine and Railroad sectors to 2.3 for the Hardware, Household 

Equipment, and Printed Circuit Board sectors and for the non-manufacturers the values range from 1.2 for the Office Machine 

sector to 2.9 for the Aircraft sector. 

In assessing moderate impacts, EPA used the non-manufacturing threshold for facilities that reported 100 percent of their 

revenues came from rebuilding and maintenance; otherwise, EPA used the manufacturing threshold. 

Table C.1: Summary of Moderate Impact Thresholds by Sector 

Sector 

Pre-Tax Return on Assets (PTRA) Interest Coverage Ratio (ICR) 

Manufacturing NonManufacturing Manufacturing NonManufacturing 

Hardware b 2.6% 1.6% 2.3 1.9 

Aircraft 2.8% 0.4% 2.2 2.9 

Electronic Equipmentb 2.1% 1.6% 2.2 1.9 

Stationary Industrial Equipment 2.1% 2.5% 2.1 2.8 

Ordnancea 2.2% 1.6% 2.1 1.9 

Aerospacea 2.2% 1.6% 2.1 1.9 

Mobile Industrialb 2.6% 1.6% 2.1 1.9 

Instrument 2.2% 2.0% 2.1 2.0 

Precious and Non-Preciousa 1.8% 1.6% 1.7 1.9 

Ships and Boats 1.7% 1.0% 1.6 2.0 

Household Equipment 2.8% 2.6% 2.3 2.0 

Railroadb 1.1% 3.1% 1.4 2.7 

Motor Vehicle 2.4% 1.5% 2.0 1.7 

Bus and Truck 2.3% 1.7% 2.0 2.8 

Office Machine 0.0% 0.3% 1.4 1.2 

Printed Circuit Boardb 2.5% 1.6% 2.3 1.9 

Job Shopb 2.3% 1.6% 2.2 1.9 

Other Metal Products 1.0% 1.7% 1.6 1.8 

Iron and Steel 2.4% N/A 2.2 N/A 

Unknown Sectora 2.2% 1.6% 2.1 1.9 

a When data were not available for any SIC codes within the sector, EPA calculated an average manufacturing or nonmanufacturing threshold to use as a proxy. b There are no non-manufacturing SIC codes in several sectors, but in these sectors there are some facilities who reported 

that all of their revenue came from rebuilding and maintenance. In these cases, EPA used the average non-manufacturing 

thresholds in that sector as a proxy for the non-manufacturing threshold. 

Source: RMA, 2001; RMA, 1998; U.S. Economics Census, 1997; U.S. EPA Analysis, 2002. 

C-4 
MP&M EEBA: Appendices Appendix C: Summary of Mod erate Impact Threshold Values by Sector 

REFERENCES 

U.S. Department of Commerce. 1997. Bureau of the Census. Census of Manufacturers, Census of Transportation, Census of Wholesale 

Trade, Census of Retail Trade, Census of Service Industries. 

Risk Management Association (RMA). 1997-1998. Annual Statement Studies. 

Risk Management Association (RMA). 2000-2001. Annual Statement Studies. 

C-5 
MP&M EEBA: Appendices Appendix C: Summary of Mod erate Impact Threshold Values by Sector 

THIS PAGE INTENTIONALLY LEFT BLANK 

C-6

MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

Appendix D: Estimating Capital


Outlays for MP&M Discounted Cash


Flow Analyses


INTRODUCTION 

The economic impact analysis for the Metal Products & 

Machinery Industry (MP&M) final regulation involved 

calculation of the business value of sample facilities on the 

basis of a discounted cash flow (DCF) analysis of 

operating cash flow as reported in facility questionnaires. 

Business value is calculated on a pre- and post-compliance 

basis and the change in this value serves as an important 

factor in estimating regulatory impacts in terms of 

potential facility closures. For proposal, the business 

value calculation was based only on cash flow from 

operations and did not recognize cash outlays for capital 

acquisition as a component of cash flow. EPA Office of 

Water (OW) previously identified that the omission of 

capital acquisition cash outlays from the DCF analysis 

APPENDIX CONTENTS 

D.1 ncepts Underlying Analysis of 

Capital Outlays ................. .............D-2 

D.2 ying Variables for the Analysis ................ D-4 

D.3 et .............D-7 

D.4 ................. D-8 

D.4.1 r Model Specification ................. .. D-9 

D.4.2 Linear Model Specification .............. D-10 

D.4.3 vity Analysis ................. ....... D-12 

D.5 tion ................. .............. D-12 

Attachment D.A: Bibliography of Literature Reviewed 

for this Analysis ................. ........... D-17 

Attachment D.B: istorical Variables Contained in the Value 

Line Investment Survey Dataset ................ D-18 

Analytic Co

Specif

Selecting the Regression Analysis Datas

Specification of Models to be Tested 

Linea

LogSensiti

Model Valida

H

may lead to overstatement of the business value of sample facilities and, as a consequence, understatement of regulatory 

impacts in terms of estimated facility closures. 

In response to this omission, the Office of Management and Budget suggested the adoption of depreciation as a surrogate for 

cash outlays for capital replacement and additions. However, for several reasons EPA believes depreciation is a poor 

surrogate. First, depreciation is meant to capture the consumption/use of previously acquired assets, not the cost of replacing, 

or adding to, the existing capital base. Therefore, depreciation is fundamentally the wrong concept to use as a surrogate for 

capital outlays for capital replacement and additions. Second, depreciation is estimated based on the historical asset cost, 

which may understate or overstate the real replacement cost of assets. Third, both book and tax depreciation schedules 

generally understate the assets’ useful life. Thus, reported depreciation will overstate real depreciation value for recently 

acquired assets that are still in the depreciable asset base, and conversely, understate the real depreciation value of assets that 

have expired from the depreciable asset base but still remain in valuable use. Finally, depreciation does not capture the 

important variations in capital outlays that result from differences in revenue growth and financial performance among firms. 

Businesses with real growth in revenues will need to expand both their fixed and working capital assets to support business 

growth, and all else being equal, growing businesses will have higher ongoing outlays for fixed and working capital assets. 

Similarly, the ability of businesses to renew and expand their asset base depends on the financial productivity of the deployed 

capital as indicated by measures such as return on assets or return on invested capital. As a result, businesses with “strong” 

asset productivity will attract capital for renewal and expansion of their asset base, while businesses with “weak” asset 

productivity will have difficulty attracting the capital for renewal and expansion of the business’ asset base. All else being 

equal, businesses with strong asset productivity will have higher ongoing outlays for capital assets; businesses with weak asset 

productivity will have lower ongoing outlays for capital assets. 

As an approach to addressing the omission of capital acquisition cash outlays from the DCF analysis, EPA undertook to 

estimate a regression model of capital outlays using capital expenditure and relevant explanatory financial and business 

environment information for public-reporting firms in the MP&M industry sectors. The estimated model was then used to 

estimate capital outlays for facilities in the MP&M sample dataset. The estimated capital outlay values were used in the DCF 

analyses to calculate business value of sample facilities and estimate regulatory impacts in terms of facility closures. 

D-1 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

This appendix reports the results of this effort, including: an overview of the analytic concepts underlying the analysis of 

capital outlays; specific variables included in the regression analysis; summary of data selection and preparation; general 

specification of regression models to be tested; and the findings from the regression analyses. 

D.1 ANALYTIC CONCEPTS UNDERLYING ANALYSIS OF CAPITAL OUTLAYS 

On the basis of general economic and financial concepts of investment behavior, EPA began its analysis by outlining a 

framework relating the level of a firm’s capital outlays to explanatory factors that: 

� can be observed for public-reporting firms either as firm-specific information or general business environment 

information and thus be included in a regression analysis; and 

� for firm-specific information, are also available from the MP&M sample facility dataset. 

To aid in identifying the explanatory concepts and variables that might be used in the analysis and as well in specifying the 

models for analysis, EPA reviewed recent studies of the determinants of capital outlays. EPA’s review of this literature 

generally confirmed the overall approach in seeking to estimate capital outlays and helped to identify additional specific 

variables that other analysts found to contribute important information in the analysis of capital outlays (e.g., the decision to 

test capacity utilization as an explanatory variable, see below, resulted from the literature review). Articles reviewed are 

listed in Attachment D.A to this appendix. 

Table D.1 beginning below and continuing the following two pages summarizes the conceptual relationships between a firm’s 

capital outlays and explanatory factors that EPA sought to capture in this analysis. In the table, EPA outlines the concept of 

influence on capital outlays, the general explanatory variable(s) that EPA identified to capture the concept in a regression 

analysis, and the hypothesized mathematical relationship (sign of estimated coefficients) between the concept and capital 

outlays. Table D.2 identifies the specific variables included in the analysis, including any needed manipulations and the 

correspondence of the variables to MP&M survey information. 

Table D.1: Summary of Factors Influencing Capital Outlays 

Explanatory Factor/Concept To Be 

Captured in Analysis Translation of Concept to Explanatory Variable(s) Expected 

Relationship 

Availability of attractive opportunities for 

additional capital investment. rm’s 

owners, or management acting on behalf of 

owners, should expend cash for capital 

outlays only to the extent that the expected 

return on the capital outlays whether for 

replacement of, or additions to, existing 

capital stock are sufficient to compensate 

providers of capital for the expected return 

on alternative, competing investment 

opportunities, taking into account the risk of 

investment opportunities. 

Historical Return On Assets of establishment as a indicator of 

investment opportunities and management effectiveness, and, hence, 

of desirability to expand capital stock and ability to attract capital 

investment. plicitly assumes past 

performance is indicative of future expectations. 

Positive 

Business growth and outlook as a 

determinant of need for capital expansion 

and attractiveness of investment 

opportunities. se equal, a firm is more 

likely to have attractive investment 

opportunities and need to expand its capital 

base if the business is growing and the 

outlook for business performance is 

favorable. 

Revenue Growth, from the prior time period(s) to the present, 

provides a historical measure of business growth and is a potential 

indicator of need for capital expansion. se of a historical variable 

implicitly assumes past performance is indicative of future 

expectations. 

Positive 

Clearly, the theoretical preference is for a forward-looking indicator 

of business growth and need for capital expansion. Options EPA 

identified include Index of Leading Indicators and current Capacity 

Utilization, by industry. her current Capacity Utilization may 

presage need for capital expansion. 

Positive 

A fi

Use of a historical variable im

All el

U

Hig

D-2 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

Table D.1: Summary of Factors Influencing Capital Outlays 

Explanatory Factor/Concept To Be 

Captured in Analysis Translation of Concept to Explanatory Variable(s) Expected 

Relationship 

Importance in capital in business 

production. 

intensive the production activities of a 

business, the greater will be the need for 

capital outlay to replenish, and add to, the 

existing capital stock. More capital intensive 

businesses will spend more in capital outlays 

to sustain a given level of revenue over time. 

The Capital Intensity of production as measured by the production 

capital required to produce a dollar of revenue provides an indicator 

of the level of capital outlay needed to sustain and grow production. 

As an alternative to a firm-specific concept such as Capital Intensity 

of production, differences in business characteristics might be 

captured by an Industry Classification variable. 

Positive 

Life of capital equipment in the business. 

All else equal, the shorter the useful life of 

the capital equipment in a business, the 

greater will be the need for capital outlay to 

replenish, and add to, the existing capital 

stock. 

No information is available on the actual useful life of capital 

equipment by business or industry classification. ever, the 

Capital Turnover Rate, as calculated by the ratio of book 

depreciation to net capital assets, provides an indicator of the rate at 

which capital is depleted, according to book accounting principles: 

the higher the turnover rate, the shorter the life of the capital 

equipment. However, the measure is imperfect for reasons of both 

the inaccuracies of book reporting as a measure of useful life, and as 

well the confounding effects of growth in the asset base due to 

business expansion which will tend to lower the indicated turnover 

rate, all else equal, without a real reduction in life of capital equipment. 

As above, an alternative to a firm-specific concept, differences in 

business characteristics might be captured by an Industry 

Classification variable. 

Positive, 

generally, but 

with 

recognition of 

the potential 

for countertrend effects 

The cost of financial capital. 

which capital both debt and equity is 

made available to a firm will determine 

which investment opportunities can be 

expected to generate sufficient return to 

warrant use of the financial capital for 

equipment purchases. se equal, the 

higher the cost of financial capital, the fewer 

the investment/capital outlay opportunities 

that would be expected to be profitable and 

the lower the level of outlays for replacement 

of, or additions to, capital stock. 

Preferably, measures of cost-of-capital would be developed 

separately for debt and equity. 

The Cost of Debt Capital, as measured by an appropriate benchmark 

interest rate, provides an indication of the terms of debt availability 

and how those terms are changing over time. Preferably, the debt 

cost/terms would reflect the credit condition of the firm, which could 

be based on a credit safety rating (e.g., S&P Debt Rating). While 

such information would be available for public firms, EPA judged 

that developing a comparable concept for MP&M sample facilities 

would not be possible within the scope of this analysis. 

Negative 

The cost of equity capital is more problematic than the cost of debt 

capital since it is not directly observable for either public-reporting 

firms or, in particular, private firms in the MP&M dataset. 

However, a readily available surrogate such as Market-to-Book 

Ratio provides insight into the terms at which capital markets are 

providing equity capital to public-reporting firms: the higher the 

Market-to-Book Ratio, the more favorable the terms of equity 

availability. lthough such information would not be available for 

private firms in the MP&M sample, EPA judged that it would be 

possible to develop a industry-level value for use with the MP&M 

facility analysis. 

Negative 

All else equal, the more capital 

How

The cost at 

All el

A

D-3

MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

Table D.1: Summary of Factors Influencing Capital Outlays 

Explanatory Factor/Concept To Be 

Captured in Analysis Translation of Concept to Explanatory Variable(s) Expected 

Relationship 

The price of capital equipment. 

of capital equipment in particular, how 

capital equipment prices are changing over 

time will influence the expected return 

from capital outlays. ll else equal, when 

capital equipment prices are increasing, the 

expected return from incremental capital 

outlays will decline and vice versa. 

However, although the generally expected 

effect of higher capital equipment prices is to 

remove certain investment opportunities 

from consideration, the potential effect on 

total capital outlay may be mixed. 

expected returns are such that the demand to 

invest in capital projects is relatively 

inelastic, the effect of higher prices for 

capital equipment may be to raise, instead of 

lower, the total capital outlay for a firm. 

Index provides an indicator of the change in capital equipment 

prices. 

Negative, 

generally, but 

with 

recognition of 

the potential 

for countertrend effects 

The price 

A

If 

Source: U.S. EPA analysis. 

D.2 SPECIFYING VARIABLES FOR THE ANALYSIS 

Working from the general concepts of explanatory variables outlined above, EPA defined the specific explanatory variables to 

be included in the analysis. A key requirement of the regression analysis is that the firm-specific explanatory variables 

included in the regression analysis later be able to be used as the basis for estimating capital expenditures for facilities in the 

MP&M dataset. As a result, in defining the firm-specific variables, it was necessary to ensure that the definition of variables 

selected for the regression analysis using data on public-reporting firms be consistent with the data items available for 

facilities in the MP&M dataset. 

Also, EPA’s selection of firm-specific variables was further constrained by an earlier decision to use the Value Line 

Investment Survey (VL) as the source of firm-specific information for the regression analysis. The decision to use VL as the 

source of firm-specific data for the analysis was driven by several considerations: 

� Considerably lower price than alternatives. VL data were available at a price of $95 for a one-time data 

purchase; the price for other data sources such as Bloomberg and Standard & Poor’s ranged from $7,000 to 

$11,000. 

� Reasonable breadth of public-reporting firm coverage. The VL dataset includes 7,500 firms. 

� Reasonable breadth of temporal coverage. VL provides data for the most recent 10 years i.e., 1991-2000. 

Although ideally EPA would have preferred a longer time series to include more years not in the “boom” 

investment period of the mid- to late-1990s. 

� Timeliness of access. The VL data are provided as a standard package and thus could be available within a 

week of ordering while other data sources (e.g., Bloomberg) would have required more time because data would 

have provided as a custom purchase. 

� Reasonable coverage of concepts/data needed for analysis. The VL data includes a wide range of financial data 

that are applicable to the analysis (VL provides 37 data items over the 10 reporting years; see Attachment DB). 

However, because of the pre-packaged nature of the VL data, it was not possible to customize any data items to 

support more precise definition of variables in the analysis. In particular, EPA found that certain balance sheet 

items were not reported to the level of specificity preferred for the analysis. Overall, though, EPA expects the 

consequence of using more aggregate, less-refined concepts should be minor. 

D-4 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

The decision to use VL data for the analysis constrained, in some instances, EPA’s choice of variables for the analysis. 

Table D.2 reports the specific definitions of variables included in the analysis (both the dependent variable and explanatory 

variables), including any needed manipulations, the data source, the MP &M estimation analysis equivalent (either the 

corresponding variable(s) in the MP&M questionnaire or other source outside the questionnaire), and any issues in variable 

definition. 

Table D.2: Variables For Capital Expenditure Modeling Analysis 

Variables for Regression Analysis 

MP&M Analysis 

Variable Source Calculation Equivalent Comment / Issue

Dependent Variable 

Gross 

expenditures 

on fixed 

assets: 

CAPEX, 

includes 

outlays to 

replace, and 

add to, 

existing 

capital stock 

Value Line Obtained from VL as Capital 

Spending per Share. 

CAPEX calculated by 

multiplying by Average 

Shares Outstanding. 

None: to be estimated 

based on estimated 

coefficients. 

This value and all other dollar values 

in the regression analysis were deflated 

to 1996 (base year for MP&M 

regulatory analysis) using 2-digit SIC 

PPI values. 

Explanatory Variables 

Firm-Specific Variables 

On Assets: 

ROA 

Value Line ROA = Operating Income / 

Total Assets. 

Operating Income, defined 

as Revenue less Operating 

Expenses (CoGS+SG&A), 

and Total Assets were 

obtained directly from VL. 

From Survey: Revenue 

less Total Operating 

Expenses (Material & 

Product Costs + 

Production Labor + 

Cost of Contract 

Work + Fixed 

Overhead + R&D + 

Other Costs & 

Expenses) 

Would have preferred a post-tax 

concept in numerator and a deployed 

production capital concept in 

denominator. 

no tax value per se and would require 

calculation of tax using an estimated tax 

rate, which could introduce error. 

neither VL nor MP&M survey data 

provide sufficient information to get at 

deployed production capital. 

Both 

However, VL provides 

Also 

D-5 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

Table D.2: Variables For Capital Expenditure Modeling Analysis 

Variables for Regression Analysis 

MP&M Analysis 

Variable Source Calculation Equivalent Comment / Issue

Revenue 

Growth: 

RVGR 

Value Line Primary formulation tested 

for linear models was 

percentage change in revenue 

over two years prior to 

current year: RVGR = (REVt 

– REVt-2) / REVt-2. 

provides 10 years of financial 

statement values 1991-

2000, including Revenue by 

year. 

For log-linear models, the 

growth concept was dropped 

and REV was used as the 

explanatory variable (see 

below and also see later 

discussion under model 

specification). 

No equivalent needed. 

Analysis proposed to set 

this value to zero in 

estimating capital outlay 

values for MP&M 

facilities. The use of a 

zero growth value is 

consistent with 

estimating the 

replacement capital 

expenditures in a nogrowth steady-state. 

Using a revenue growth term in the 

analysis defined over the prior two 

years requires three years of revenue 

data (e.g., current year plus trailing two 

years) and effectively eliminates two 

observation years from the analysis 

(1991 and 1992). iven that these data 

years occurred at the end of a recession 

period and before the mid- to late-90s 

economic boom period, EPA was very 

concerned about the potential loss of 

these years from the analysis dataset. 

In the end, the use of a log-linear model 

eliminated the need to construct the 

lagged difference variables and thus 

mooted the concern over loss of early 

year observations. se of the loglinear model, however, also eliminated 

the potential to set the growth term to 

zero in estimating baseline capital 

outlays for MP&M facilities. 

Revenue: 

REV 

Value Line In the linear models, REV 

included as a scale variable 

together with REVGR, as 

outlined above. or loglinear models, retained only 

REV as the explanatory 

variable. 

REV, captures the percent 

change/growth concept in the 

log-linear formulation. 

From Survey: Revenue Using REV only and not REVGR in 

the log-linear model restored the two 

data years at the beginning of the 

analysis period (1991 and 1992) to the 

analysis dataset. 

including data for the first two 

observation period years is important 

for the generality of the analysis. 

Also tested Total Assets as a scale 

variable, which provided good, but not 

as strong, an explanation, as REV. 

Capital 

Turnover 

Rate: CAPT 

Value Line CAPT = Depreciation / 

Total Assets. Depreciation 

and Total Assets directly 

available from VL. 

From Survey: 

Depreciation / Total 

Assets 

Would have preferred denominator of 

net fixed assets instead of total assets. 

However, VL provides detailed balance 

sheet information for only the four most 

recent years. possible to separate 

current assets and intangibles from total 

assets. 

Capital 

Intensity: 

CAPI 

Value Line CAPI = Total Assets / 

Revenue. l Assets and 

Revenue directly available 

from VL 

From Survey: 

Total Assets / Revenue 

As above, would have preferred net 

fixed assets instead of total assets, but 

needed data are not available from VL 

for the full analysis period. 

Market-toBook Ratio: 

MV/B 

Value Line MV/B = average market price 

of common equity (Price) 

divided by book value of 

common equity (Book Value 

per Share). Price and Book 

Value per Share directly 

available from VL. 

Use average of MV/B 

for firms by MP&M 

industry group in 

regression analysis 

dataset; calculated at 

time of MP&M industry 

survey. 

Ultimately found MV/B highly 

correlated with other, more important 

explanatory variables, which makes 

sense, given that equity terms would be 

derived from more fundamental factors, 

such as ROA. MV/B from 

the analysis eliminated the need to 

define an approach to use this variable 

with MP&M survey data. 

VL 

G

The u

F

The simple variable, 

EPA believes 

Not

Tota

Omitting 

D-6

MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

Table D.2: Variables For Capital Expenditure Modeling Analysis 

Variables for Regression Analysis 

MP&M Analysis 

Variable Source Calculation Equivalent Comment / Issue

General Business Environment Variables 

Interest on 

10-year, Arated 

industrial 

debt: 

DEBTCST 

Bloomberg 

Financial 

Services 

DEBTCST = annual average 

of rates for each data year 

Use average of 

DEBTCST rates at time 

of MP&M industry 

survey. 

10-year maturity, industry debt selected 

as reasonable benchmark for industry 

debt costs. 10 years became “standard” 

maturity for industrial debt during 

1990s. 

Index of 

Leading 

Indicators: 

ILI 

Conference 

Board 

Monthly index series 

available from Conference Board. 

For linear models, ILI = 

percent change from 

beginning to end of current 

year. 

For log-linear models, ILI = 

geometric mean of current 

year values. 

For linear formulation, 

use average of year-toyear percent change in 

ILI at time of MP&M 

industry survey. 

For log-linear 

formulation, use average 

of ILI values at time of 

MP&M industry survey. 

Capacity 

Utilization 

by Industry: 

CAPUTIL 

Federal 

Reserve 

Board 

(Dallas 

Federal 

Reserve) 

Monthly index series 

available from Federal 

Reserve. 

For linear models, 

CAPUTIL = percent change 

in annual average values from 

prior year to current year. 

For log-linear models, 

CAPUTIL = current year 

average value. 

For linear formulation, 

use average of year-toyear percent change in 

CAPUTIL at time of 

MP&M industry survey. 

For log-linear 

formulation, use average 

of CAPUTIL values at 

time of MP&M industry 

survey. 

Producer 

Price Index 

series for 

capital 

equipment: 

CAPPRC 

Bureau of 

Labor 

Statistics 

Annual average values 

available from BLS. 

For linear models, 

CAPPRC = percent change 

from prior year to current 

year. 

For log-linear models, 

CAPPRC = current year 

average value as reported by 

BLS. 

For linear formulation, 

use average of year-toyear percent change in 

CAPPRC at time of 

MP&M industry survey. 

For log-linear 

formulation, use average 

of CAPPRC values at 

time of MP&M industry 

survey. 

BLS reports PPI series for capital 

equipment based on “consumption 

bundles” defined for manufacturing and 

non-manufacturing industries. or this 

analysis, EPA used the PPI series based 

on the manufacturing industry bundle. 

F

Source: U.S. EPA analysis. 

D.3 SELECTING THE REGRESSION ANALYSIS DATASET 

In addition to specifying the variables to be used in the regression analysis, EPA also needed to select the public firm dataset 

on which the analysis would be performed. 

As noted above, EPA used the Value Line Investment Survey as the source for public firm data. VL includes over 7,500 

publicly traded firms and identifies firms’ principal business both by a broad industry classification (e.g., Electrical 

Equipment, Machinery) and by an SIC code assignment. In most instances, the SIC codes assignment is only at the 2-digit 

level. To build the public firm dataset corresponding to the MP&M industry sectors, EPA initially selected all firms included 

in the following 2-digit SIC code families: 

D-7 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

� 2500: Furniture and fixtures,


� 3300: Primary metal industries,


� 3400: Fabricated metal products,


� 3500: Industrial machinery and equipment,


� 3600: Electrical and electronic equipment,


� 3700: Transportation equipment, and


� 3800: Instruments and related products.


From manual inspection, EPA deleted firms in four-digit SIC code 3579, which, in the VL classification, was comprised only 

of software manufacturers. In addition, in SIC code group 3300, EPA included firms only in the ferrous metal processing 

sectors: SIC codes 3311, 3312, 3315, 3316, 3317, and 3398.1 

As a result of this selection, EPA developed an initial dataset of 1,015 firms. On inspection, EPA found that a substantial 

number of firms did not have data for the full 10 years of the analysis period. The general reason for the omission of some 

years of data is that the firms did not become publicly listed in their current operating structure whether through an initial 

public offering, spin-off, divestiture of business assets, or other significant corporate restructuring that renders earlier year 

data inconsistent with more recent data until after the beginning of the 10-year data period.2 As a result, the omission of 

observation years for a firm always starts at the beginning of the data analysis period. This systematic front-end truncation of 

firm observations in the dataset could be expected to bias the analysis in favor of the capital expenditure behavior nearer the 

end of the 1990s decade. To avoid this problem, EPA removed all firm observations that have fewer than 10 years of data. 

As a result, the dataset used in the analysis has a total of 3,900 yearly data observations and represents 390 firms. 

Table D.3 presents the number of firms by industry classifications. 

Table D.3: 

SIC Industry Classification Number of Firms 

2500: Furniture and fixtures 13 

3300: Primary metal industries 27 

3400: Fabricated metal products 24 

3500: Industrial machinery and equipment 119 

3600: Electrical and electronic equipment 101 

3700: Transportation equipment 65 

3800: Instruments and related products 41 

Number of Firms by Industry Classifications 

D.4 SPECIFICATION OF MODELS TO BE TESTED 

On the basis of the variables listed above and their hypothesized relationship to capital outlays, EPA specified a time-series, 

cross sectional model to be tested in the regression analysis. EPA’s dataset consisted of 390 cross sections observed at 10 

years (1991 through 2000). The general structure of this model was as follows: 

CAPEXi,t = f(ROAi,t, REVi,t, CAPTi,t, CAPIi,t, DEBTCST i,t, CAPPRCt

, CAPU TILj,t) 

1 These 4-digit SIC codes include all MP&M sectors in SIC 2-digit code 33 plus 4-digit SIC code 3311, to capture information for 

the steel manufacturing industry. 

2 When VL adds a firm to its dataset, it fills in the public-reported data history for the firm for the lesser of 10 years or the length of 

time that the firm has been publicly listed and thus subject to SEC public reporting requirements. 

D-8 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

Where:


CAPEXi,t = capital expenditures of firm i, in time period t;

1


t = year (year = 1991, . . . , 2000);


i = firm i (i=1, . . . , 390);


j = industry classification j


ROAi,t = return on total assets for firm i in year t;


REVi,t = revenue ($ millions) for firm i in year t;


CAPTi,t = capital turnover rate for firm i in year t;


CAPIi,t = capital intensity for firm i in year t;


DEBTCSTt = financial cost of capital in year t;


CAPPRCt = price of capital goods in year t;


CAPUTILj,t = the Federal Reserve Board’s Index of Capacity utilization for a given industry j in year t.


EPA tested both linear and log-linear model specifications. Both models fit quit well, achieving overall correlation (R2

) in the


upper 80 percent/low 90 percent range. However, the pattern of coefficient significance was better in the log-linear model. In


addition, the log-linear model offered advantages in terms of retention of early time period observations and variable


specification, as discussed below. Therefore, EPA selected a log-linear specification as the final model. The following


paragraphs briefly discuss testing of both linear and log-linear forms of the model. Parameter estimates are presented for the


final log-linear model only because this specification appeared to be superior to a linear model.


D.4.1 Linear Model Specification 

EPA first tested linear models of CAPEX as a function of the proposed explanatory variables. In testing linear models of 

CAPEX, EPA tested a number of structural modifications within the overall hypothesized framework of explanatory 

variables. These included: 

� Testing the influence of industry classification on the estimation of the coefficients for certain of the explanatory 

variables: e.g., using the product of an industry classification dummy variable and CAPPRC to test whether 

certain industries in particular, “high-tech” vs. “traditional” industries responded differently to change in 

price of capital equipment over time. 

� Testing contemporary vs. lagged specification of certain explanatory variables: e.g., using prior, instead of 

current, period revenue, REV, as an explanatory variable. 

� Testing scale-normalized specification of the dependent variable: e.g., using CAPEX/REV as the dependent 

variable instead of simple CAPEX. 

� Testing flexible functional forms that included quadratic terms. 

� Testing additional explanatory variables including the index of 10 leading economic indicators (ILI) and marketto-book ratio (MV/B). 

EPA also tested the data for autocorrelation, heteroscedasticity, and multicollinearity. 

Cross-sectional, time-series datasets typically exhibit both autocorrelation and group-wise heteroscedasticity characteristics. 

Autocorrelation is frequently present in economic time series data as the data display a “memory” with the variation not being 

independent from one period to the next. Heteroscedasticity usually occurs in cross-sectional data where the scale of the 

dependent variable and the explanatory power of the model vary across observations. Not surprisingly, the dataset used in 

this analysis had both characteristics. 

The collinearity diagnostic showed that several independent variables are collinear. In particular, EPA found that the index of 

leading economic indicators (ILI) and the price of capital equipment (CAPPRC) variables are highly correlated. EPA further 

found that the market-to-book ratio variable (MV/B) was highly correlated with both capital turnover (CAPT) and return-onassets (ROA) variables. To address the multicollinearity issue, EPA substituted capacity utilization (CAPUT IL) for the index 

of leading economic indicators (ILI) and dropped the market-to-book ratio (M V/B) variable in the final model. 

1 All dollar values were deflated to 1996 (base year for MP&M regulatory analysis) using 2-digit SIC PPI values. 

D-9 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

D.4.2 Log-Linear Model Specification 

The main advantage of the log-linear model is that it incorporates directly the concept of percent change in the explanatory 

variables. Specifying the key regression variables as logarithms permitted us to estimate directly as the coefficients of the 

model, the elasticities of capital expenditures with respect to firm financial characteristics and general business environment 

factors. In addition, by eliminating the need to use percent change variables, EPA was able to avoid losing early year 

observations in the analysis dataset. Finally, the logarithmic transformations helped to reduce outlier effects in the model. 

EPA specified a log-linear model, as follows: 

ln(CAPEXi,t) = � + �[�x ln(Xi,t)] + �[�y ln(Yt

)] + �

W here: 

CAPEXi,t = capital expenditures of firm i, year t; 

�x = elasticity of capital expenditures with respect to firm characteristic X; 

Xi,t, = a vector of financial characteristics of firm i, year t; 

�y = elasticity of capital expenditures with respect to economic indicator Y; 

Yt = a vector of economic indicators, year t; for CAPU TIL, Y is also differentiated by industry 

classification 

� = an error term; and 

ln(x) = natural log of x 

Based on this model, the elasticity of capital expenditures with respect to an explanatory variable, for example, return on 

assets is calculated as follows: 

Because the log-linear specification incorporates directly the concept of percent change in the explanatory variables, EPA 

dropped the “change” specification variables i.e., revenue growth (REVGR), year-to-year change in the Index of Leading 

Indicators (ILIGR), and year-to-year change in the Capital Equipment Price Index (CAPPRC) from the analysis. For these 

variables, EPA used the logarithm of the simple, unadjusted values in the log-linear specification. 

One disadvantage of the specified log-linear model is that the logarithmic transformation is not feasible for negative and zero 

values. This means that negative and zero values require linear transformation to be included in the analysis. The following 

variables in the sample required transformation: 

� CAPEX: four firms in the sample reported zero capital expenditures at least in one time period. EPA set these 

expenditures to $1. 

� REVENUE: one firm reported negative revenue (-$1,018) in one time period. Because this is likely due to 

accounting adjustments from prior period reporting, EPA set the firm’s revenue in the current time period to $1. 

� ROA: the values for return on assets in the public firm sample range from -1.1 to 0.6. Approximately 25 percent 

of the firms in the dataset reported negative ROAs in at least one year. To address this issue while reducing 

potential effects of data transformation on the modeling results, EPA used the following data transformation 

approach: 

R EPA excluded 12 firms with any annual ROA values below the 99th percentile of the ROA distribution (i.e., 

ROA � - 0.31). 

R EPA used an additive data transformation to ensure that remaining negative ROA values were positive in 

the logarithm transformation. The additive transformation was performed by adding 0.31 to all ROA 

values. 

D-10 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

The analysis tested several specifications of a log-linear model, including models with slope dummies for different industrial 

sectors and models with the intercept suppressed. The model presented below was most successful at explaining firms’ 

investment behavior. 

EPA estimated the specified model using the generalized least squares procedure. This procedure involves the following two 

steps: 

� First, EPA estimated the model using simple OLS, ignoring autocorrelation for the purpose of obtaining a 

consistent estimator of the autocorrelation coefficient (�); 

� Second, EPA used the generalized least squares procedure, where the analysis is applied to transformed data. 

The resulting autocorrelation adjustment is as follows: 

Zi,t = Zi,t - �Zi,t-1 

where Zit is either dependent or independent variables. 

EPA was unable to correct the estimated model for group-wise heteroscedasticity due to computational difficulties. The 

statistical software used in the analysis (LIMDEP) failed to correct the covariance matrix due to the very large number of 

groups (i.e., 390 firms) included in the dataset. Application of other techniques to correct for group-wise heteroscedasticity 

was not feasible due to time constraints. The estimated coefficients remain unbiased; however, they are not minimum 

variance estimators. 

Table D.4 presents model results. The model has a fairly good fit, with adjusted R2

 of 0.89. All coefficients have the 

expected sign and all but two (constant and capital price) are significantly different from zero at the 95th percentile. 

Table D.4: ime Series, Cross-Sectional Model 

Results 

Variable Coefficient t-Statistics 

Constant -2.077 -0.97 

Ln(ROA) 0.618 9.353 

Ln(REV) 1.025 113.867 

Ln(CAPT) 0.6 20.285 

Ln(CAPI) 0.976 27.342 

Ln(DEBTCST) -0.205 -2.653 

Ln(CAPPRC) -0.478 -0.939 

Ln(CAPUTIL) 0.904 3.176 

Autocorrelation Coefficient 

r 0.413 27.842 

T

The empirical results show that the output variable (REV) is a dominant determinant of firms’ investment spending. A 

positive coefficient on this variable means that larger firms invest more, all else equal, which is clearly a simple expected 

result. Very important for the MP&M analysis, as expected, firms with higher financial performance and better investment 

opportunities (ROA) invest more, all else equal: for each one percent increase in ROA, a firm is expected to increase its 

capital outlays by 0.62 percent. Other firm-specific characteristics were also found important and will aid in differentiating 

the expected capital outlay for MP&M facilities according to firm-specific characteristics. Firms that require more capital to 

produce a given level of business activity (i.e., firms that have high capital intensity, CAPI) tend to invest more: a one percent 

increase in capital intensity leads to a 0.98 increase in capital spending. Higher capital turnover/shorter capital life (CAPT) 

also has a positive effect on investment decisions: a one percent increase in capital turnover rate translates to a 0.60 percent in 

capital outlays. 

The model also shows that current business environment conditions play an important role in firms’ decision to invest. The 

D-11 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

most influential factor is capacity utilization in manufacturing facilities. A one percent increase in the Federal Reserve Index 

of Capacity Utilization for the relevant industrial sector (CAPUTIL) leads to a 0.90 percent increase in capital investment. 

Negative signs on the debt cost (DEBTCST) and capital price (CAPPRC) variables match expectations, indicating that less 

costly credit and falling (either relatively or absolutely) capital equipment prices are likely to have a positive effect on firms’ 

capital expenditures. That these systematic variables are significant in the regression analysis means that EPA will be able to 

control for economy- and industry-wide conditions in estimating capital outlays for MP&M facilities. 

D.4.3 Sensitivity Analysis 

To examine the degree to which the estimated model was affected by transformation of ROA values and inclusion/exclusion 

of firms with the lowest ROA values, EPA ran two additional models. First, EPA estimated a model based on a subset of data 

that includes only firms with positive ROA values. Second, EPA estimated a model based on a complete dataset that includes 

the 12 firms with the lowest ROA values. Although all three models produced compatible results, the first model shows some 

notable differences in the estimated coefficients compared to the model presented in the preceding section. EPA found that 

when firms with the lowest negative ROAs are excluded from the analysis: 

� The magnitude of the ROA effect on capital expenditures decreases; 

� The magnitude of the debt cost effect on capital expenditures decreases slightly; 

� The coefficient on the capital price term becomes significant. 

These differences can be expected since firms with negative ROAs are weak performers and therefore are less likely to have 

large capital outlays. Not surprisingly, general economic indicators that affect firms’ decisions to invest can be less or more 

important if a firm’s financial performance/asset productivity is weak. For financially weaker firms, the financial cost of 

capital is a more important factor compared to firms that are strong financially. This finding indicates a strong “threshold of 

adequate financial performance” effect: capital outlays fall off severely at the lowest financial performance levels but the 

marginal effect of financial performance becomes more moderate as asset productivity moves into a more acceptable i.e., 

positive return range. Price of capital goods appears to be an insignificant factor in firms’ decision to invest when weak 

firms are included in the analysis. At first, this finding seems to be counterintuitive: previous studies of investment behavior 

found a strong capital price effect on firms’ decision to invest in high tech equipment. However, because financially weak 

firms are less likely to invest in general, it is reasonable to assume that they will not respond as strongly to changes in capital 

equipment prices. Thus, their investment decisions were relatively less affected by falling high-tech equipment prices in the 

last decade. 

D.5 MODEL VALIDATION 

To validate the results of the regression analysis, EPA used the estimated regression equation to calculate capital expenditures 

and then compared the resulting estimate of capital expenditures with actual data. EPA used two methods to validate its 

results: 

� EPA used median values for explanatory variable from the Value Line data as input to estimate capital 

expenditures and then compared the estimated value to the median reported capital expenditures, and 

� EPA used MP&M survey data to estimate capital expenditures and then compared the estimated values to 

depreciation reported in the survey. 

First, EPA estimated capital expenditures for a hypothetical firm based on the median values of the four dependent variables 

from the Value Line data and the relevant values of the three economic indicators. The estimated capital expenditures for this 

hypothetical firm are $10.9 million. EPA then compared this estimate to the median value of capital expenditures from the 

Value Line data. The median capital expenditure value in the dataset is $11.3 million, which provides a very close match to 

the estimated value. This is not surprising since the same dataset was used to estimate the regression model and to calculate 

the median values used in this analysis. 

EPA also used MP&M survey data to confirm that the estimated capital expenditures seem reasonable. Because the MP&M 

survey does not provide information on capital expenditures, EPA compared the capital expenditure estimates to the 

D-12 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

depreciation values reported in the survey. Depreciation had been proposed as a possible surrogate for cash outlays for 

capital replacements and additions. However, depreciation does not capture important variations in capital outlays that result 

from differences in firms’ financial performance. 

For this analysis, EPA chose a representative facility from each of the nineteen MP&M sectors for model validation. The 

selected facility for each sector corresponds as closely as possible to the hypothetical median facility in the sector based on 

the distribution of facility revenues and facility return on assets. For each of the nineteen facilities, EPA estimated capital 

expenditures using the estimated regression equation and facility financial data. Table D.5 shows the estimated regression 

coefficients, financial averages for the nineteen MP&M sectors, estimated facility capital expenditures, reported facility 

depreciation, and the comparison of capital expenditures and depreciation. 

As shown in Table D.5, the estimated model provides reasonable estimates of capital expenditures. A facility’s size, as 

indicated by revenue, is a principal determinant of the general range of value for capital expenditures, all else equal (i.e., 

greater revenues correspond to greater predicted capital expenditures). However, the size of capital expenditures relative to 

the depreciation allowance depends substantially on a facility’s return on assets. Facilities with lower return on assets tend to 

invest less than indicated by depreciation while facilities with higher return on assets tend to invest more than depreciation. 

This finding is consistent with the expectation that businesses with higher financial performance will have relatively more 

attractive investment opportunities and are more likely to attract the capital to undertake those investments. To highlight this 

relationship between capital expenditure, depreciation allowance, and a facility’s return on assets, EPA presents graphs for the 

Hardware, Iron & Steel, Job Shops, and Printed Circuit Board sectors that plot MP&M survey facilities in these sectors along 

with linear trend lines for each sector’s depreciation and capital expenditures with respect to return on assets.4 

4 For presentation purposes, some outlier facilities were excluded from the graphs. 

D-13 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

Table D.5: ted by Revenue 

and ROA Percentiles 

Sectors 

Pre-Tax 

Return 

on 

Assets 

(ROA) 

Revenue 

Capital 

Turnover 

Rate 

Capital 

Intensity 

Cost 

of 

Debt 

Price of 

Capital 

Goods 

Capacity 

Utilization 

Estimated 

Capital 

Expenditures 

Depreciation 

Difference 

between 

Depreciation 

and Capital 

Expenditures 

Coefficient 

Intercept 

(-2.077) 

0.62 1.03 0.60 0.98 (0.21) (0.48) 0.90 

Aerospace 0.02 90.66 0.02 1.29 7.11 135.4 73.67 2,113,741 1,821,434 -0.14 

Aircraft 0.05 18.39 0.06 0.54 9.8 115.87 80.01 440,385 558,478 0.27 

Bus & 

Truck 0.06 58.09 0.03 0.25 7.11 135.4 73.69 471,199 503,124 0.07 

Electronic 

Equipment 0.05 36.85 0.12 0.4 7.11 135.4 86.37 1,100,627 1,730,023 0.57 

Hardware 0.03 11.99 0.06 0.61 9.8 115.87 81.93 311,085 403,535 0.3 

Household 

Equipment 0.05 18 0.05 0.8 7.11 135.4 84.24 624,804 745,476 0.19 

Instruments 0.15 62.47 0.04 0.47 7.11 135.4 77.21 1,195,144 1,139,873 -0.05 

Iron & 

Steel 0.12 23.17 0.06 0.47 6.4 136.9 90.82 617,740 613,834 -0.01 

Job Shop 0.03 2 0.07 0.26 7.11 135.4 81.92 25,146 37,250 0.48 

Mobile 

Industrial 

Equipment 

0.07 37.6 0.03 0.63 9.8 115.87 79.45 670,447 586,609 -0.13 

Motor 

Vehicle 0.1 104.44 0.06 0.46 7.11 135.4 81.24 2,473,215 2,810,386 0.14 

Office 

Machine 0.1 28.95 0.06 0.43 7.11 135.4 85.02 661,715 748,972 0.13 

Ordnance 0.05 27.08 0.04 0.65 9.8 115.87 79.77 674,446 770,051 0.14 

Other 

Metal 

Products 

0.08 27.78 0.17 0.44 7.11 135.4 80.01 1,100,691 2,034,831 0.85 

Precious 

Metals & 

Jewelry 

0.04 13.5 0.03 0.62 7.11 135.4 77.21 224,438 226,708 0.01 

Estimation of Capital Outlays for MP&M Sample Facilities: Median Facilities Selec

For facilities that responded to the Phase 1 survey, EPA calculated a 3-year average of the non-facility specific information over the 

years in which survey data were collected (1987-1989). Likewise, for facilities that responded to the Phase 2 survey, EPA 

calculated a 3-year average of the non-facility specific information for the years 1994-1996. Since the Iron and Steel sector was 

surveyed in 1997, EPA calculated a 3-year average of the non-facility specific information for the years 1995-1997. 

Source: U.S. EPA analysis 

D-14 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

Figure D.1: Comparison of Estimated Capital Outlays to Reported Depreciation for MP&M Survey Facilities in 

the Hardware Sector 

Source: U.S. EPA analysis. 

Figure D.2: Comparison of Estimated Capital Outlays to Reported Depreciation for MP&M Survey Facilities in 

the Iron & Steel Sector 

Source: U.S. EPA analysis. 

D-15

MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

Figure D.3: Comparison of Estimated Capital Outlays to Reported Depreciation for MP&M Survey Facilities in 

the Job Shop Sector 

Source: U.S. EPA analysis. 

Figure D.4: Comparison of Estimated Capital Outlays to Reported Depreciation for MP&M Survey Facilities in 

the Printed Circuit Board Sector 

Source: U.S. EPA analysis. 

D-16

MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

ATTACHMENT D.A: BIBLIOGRAPHY OF LITERATURE REVIEWED FOR THIS ANALYSIS 

As noted above, EPA relied on previous studies of investment behavior to select critical determinants of firms’ capital 

expenditures. Empirical results from these studies suggest that investment is most sensitive to quantity variables (output or 

sales), return-over-cost, and capital utilization (R. Chirinko). Empirical results from more recent studies further found that 

increasing depreciation rates and capital equipment prices were of first-order importance in the equipment investment 

behavior in the 1990 (T. Tevlin, K. Whelan). Specifically, declining prices of micro-processor based equipment played a 

crucial role in the investment boom in the 1990. 

Chirinko, Robert S. 1993. “Business Fixed Investment Spending: A Critical Survey of Modeling Strategies, Empirical 

Results and Policy Implications.” Journal of Economic Literature 31, no. 4: 1875-1911. 

Goolsbee, Austan. 1997. “The Business Cycle, Financial Performance, and the Retirement of Capital Goods.” University of 

Chicago, Graduate School of Business Working Paper. 

Greenspan, Alan. 2001. “Economic Developments.” Remarks before the Economic Club of New York, New York, May 24. 

Kiyotaki, Nobuhiro and Kenneth D. West. 1996. “Business Fixed Investment And The Recent Business Cycle In Japan.” 

National Bureau of Economic Research Working Paper 5546. 

McCarthy, Jonathan. 2001. “Equipment Expenditures since 1995: The Boom and the Bust.” Current Issues In Economics 

And Finance 7, no. 9: 1-6. 

Opler, Tim and Lee Pinkowitz, Rene Stulz and Rohan Williamson. 1997. “The Determinants and Implications of Corporate 

Cash Holdings.” Working paper, Ohio State University College of Business. 

Tevlin, Stacey and Karl Whelan. 2000. “Explaining the Investment Boom of the 1990s.” Board of Governors of the Federal 

Reserve System Finance and Economics Discussion Paper no. 2000-11 

Uchitelle, Louis. 2001. “Wary Spending by Companies Cools Economy.” New York Times, May 14, p. A1. 

D-17 
MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses 

ATTACHMENT D.B: HISTORICAL VARIABLES CONTAINED IN THE VALUE LINE 

INVESTMENT SURVEY DATASET 

All variables are provided for 10 years (except where a firm has been publicly reported for less than 10 years): 

� Price of Common Stock


� Revenues


� Operating Income


� Operating Margin


� Net Profit Margin


� Depreciation


� Working Capital


� Cash Flow per share


� Dividends Declared per share


� Capital Spending per share


� Revenues per share


� Average Annual Price-Earnings Ratio


� Relative Price-Earnings Ratio


� Average Annual Dividend


� Return Total Capital


� Return Shareholders Equity


� Retained To Common Equity


� All Dividends To Net Worth


� Employees


� Net Profit


� Income Tax Rate


� Earnings Before Extras


� Earnings per share


� Long Term Debt


� Total Loans


� Total Assets


� Preferred Dividends


� Common Dividends


� Book Value


� Book Value per share


� Shareholder Equity


� Preferred Equity


� Common Shares Outstanding


� Average Shares Outstanding


� Beta


� Alpha


� Standard Deviation


D-18 
MP&M EEBA: Appendices Appendix E: Calculation of Capital Cost Components 

Appendix E: Calculation of Capital


Cost Components


INTRODUCTION APPENDIX CONTENTS 

E.1 Calculation of One-Time Capital Cost Estimates ......... E-1 

E.1 CALCULATION OF ONE-TIME 

CAPITAL COST COMPONENTS 

EPA used the engineering estimates of total one-time capital costs to calculate the purchase cost paid to manufacturers of 

compliance equipment, and the costs of shipping, installation, insurance, engineering, and consultants. Two components of 

capital costs were used to estimate job gains due to compliance requirements: (1) the estimated direct capital equipment cost 

and (2) the labor cost of installation. Table E.1 shows the cost components that comprise the total capital costs attributed to 

the regulation. 

Table E.1: Components of Total Installed Capital Costs (millions, 2001$, before tax)a 

Cost Component Option I: 

Selected Option 

Option II: 

Proposed/ 

NODA Option 

Option III: 

413 to 433 

Upgrade Option 

Option IV: 

All to 433 Upgrade 

Option 

(a) Total installed direct capital costs $4,407,590 $802,051,833 $95,552,532 $148,434,303 

(b) Direct capital equipment cost $3,070,680 $558,773,471 $66,569,538 $103,411,210 

(c) Shipping (20% of a) $881,518 $160,410,367 $19,110,506 $29,686,861 

(d) Labor cost of installation (7% of f) $455,392 $82,867,995 $9,872,488 $15,336,232 

(e) Indirect costs: insurance, engineering & 

consultants (47.6% of a) $2,098,013 $381,776,672 $45,483,005 $70,654,728 

(f) Total installed capital costs $6,505,602 $1,183,828,505 $141,035,538 $219,089,032 

a Excludes costs for baseline and regulatory closures. 

Source: U.S. EPA analysis. 

The components of total capital costs for the final rule in Table E.1 are discussed below in reverse order of the table 

presentation. 

� Total installed capital costs: EPA estimated the total one-time capital cost for each facility expected to comply with 

the regulation.1 Compliance costs are discussed in more detail in Chapter 5: Facility-Level Impact Analysis of this 

EEB A. The national estimate of capital costs for the regulation is $6.5 million ($2001).2 

1 See the Technical Development Document for a description of the methods used to estimate capital costs. 

2 The $6.5 million is the sum of one-time outlays for purchasing and installing the capital equipment needed to comply with the final 

rule. This expense is not the annual equivalent of that capital investment. The capital outlay is annualized in the economic impact analysis 

over a 15-year period. The resulting value, which is part of the total annual cost of compliance, is $0.7 million. 

E-1 
MP&M EEBA: Appendices Appendix E: Calculation of Capital Cost Components 

� Indirect Costs: MP&M project engineers estimate that indirect costs such as insurance, engineering, and consulting 

are 47.6% of installed direct capital cost. EPA calculated the total direct and indirect cost using the total capital cost. 

The national estimate of indirect costs for the regulation is $2.1 million. 

� Total Installed Direct Capital Costs: The direct capital costs include the cost of compliance equipment, shipping, 

and the labor cost of installation. The national estimate of direct costs for the regulation is $4.4 million. MP&M 

project engineers estimate that shipping costs might be as much as 20 percent of the total installed direct capital cost. 

The estimated one-time shipping cost is $0.9 million for the final regulatory option. Installation labor costs are 

estimated by the engineers to be seven percent of the total installed capital costs. The estimated one-time cost of 

installation labor is $0.5 million for the final regulatory option. Therefore, the direct capital equipment cost is $3.1 

million, the remainder of the total installed direct capital cost when the cost of shipping and installation are 

subtracted out. 

E-2 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

Appendix F: Administrative Costs


INTRODUCTION 

Effluent guidelines and limitations are implemented by Federal, State, and local government entities through the NPDES 

permit program (for direct dischargers) and the General Pretreatment Regulations (for indirect dischargers). A new effluent 

guideline rule may require that facilities: (1) be permitted for 

the first time; (2) be issued a different form of permit, if they 

already have a permit in the baseline; and (3) be repermitted 

sooner than would otherwise be required. In these cases, the 

permitting authority will incur additional costs to implement 

the effluent guideline rule. 

This appendix provides information on the unit costs of these 

permitting activities and describes the calculation of 

government permitting costs for the final MP&M rule and 

regulatory alternatives. EPA expects no additional costs for 

permitting direct dischargers under the final rule. Costs for 

issuing permits for indirect dischargers are based on 

information reported by publicly-owned treatment works 

(POTWs) in the Metal Products and Machinery (MP&M) 

POT W Survey. EPA also used the data provided in the 

Association of Metropolitan Sewerage Agencies (AMSA) 

survey to supplement information from the MP&M POTW 

Survey. EPA evaluated POTW administrative costs for 

pretreatment options for the final rule. As discussed in 

APPENDIX CONTENTS 

F.1 luent Guidelines Permitting Requirements ...... F-1 

F.1.1 ermit Program .... F-1 

F.1.2 ................. .. F-2 

F.2 TW Administrative Cost Methodology ........ F-2 

F.2.1 ................. ........ F-2 

F.2.2 Overview of Methodology ............... F-3 

F.3 .............. F-4 

F.3.1 .......... F-4 

F.3.2 ion ................. ........... F-7 

F.3.3 ................. .......... F-7 

F.3.4 ement ................. ......... F-9 

F.3.5 itting ................. ........ F-10 

F.4 dministrative Costs by Option ......... F-10 

Appendix F Exhibits ................. ........... F-12 

References ................. ................. . F-25 

Eff

NPDES Basic Industrial P

Pretreatment Program 

PO

Data Sources 

Unit Costs of Permitting Activities 

Permit Application and Issuance 

Inspect

Monitoring 

Enforc

Reperm

POTW A

Section VI of the preamble to the final rule, EPA is not establishing any pretreatment standards in the final rule. 

The remainder of this appendix is organized as follows: Section F.1 provides an overview of permitting requirements under 

the NPDES Permit Program and the General Pretreatment Regulations. Section F.2 describes the MP&M POTW Survey and 

the methods used to develop annualized cost estimates for permitting indirect dischargers. Section F.3 presents the estimates 

of unit costs by permitting activity for indirect dischargers. The final Section F.4 lists the steps involved in applying these 

unit costs to calculate administrative costs for regulatory options evaluated by EPA for the final rule. 

F.1 EFFLUENT GUIDELINES PERMITTING REQUIREMENTS 

Any facility that directly discharges wastewater to surface water is required to have a permit issued under the National 

Pollution Discharge Elimination System (NPDES) permit program. Facilities that discharge indirectly through a POTW are 

regulated by the General Pretreatment Regulations for Existing and New Sources of Pollution (40 CFR Part 403). The major 

portion of government administrative costs associated with implementing an effluent guidelines rule are the costs of managing 

the NPDES and Pretreatment programs. 

F.1.1 NPDES Basic Industrial Permit Program 

Best Practical Technology (BPT), Best Control Technology (BCT), and New Source Performance Standards (NSPS) for 

effluent limitations guidelines are implemented through the NPDES industrial permit program. However, EPA does not 

expect the administrative costs associated with the NPDES industrial permit program to increase as a result of the final rule. 

The Clean Water Act prohibits discharge of any pollutant to a water of the U.S. except as permitted by a NPDES permit. 

Therefore, every facility that discharges wastewater directly to surface water must hold a permit specifying the mass of 

pollutants that can be discharged to waterways. The final rule will affect the terms of the permits but is unlikely to increase 

the administrative costs associated with permitting. 

The final rule may decrease the administrative burden of NPDES permits. The TDD and rulemaking record for the final rule 

F-1 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

provide valuable information to permitting authorities that may reduce the research required to develop Best Professional 

Judgment (BPJ) permits.1 Further, establishing discharge standards may reduce time spent by permitting authorities 

establishing limits and the frequency of evidentiary hearings. The promulgation of limitations may also enable EPA and the 

authorized States to cover more facilities under general permits. General permits are single permits covering a common class 

of dischargers in a specified geographic area. 

F.1.2 Pretreatment Program 

The General Pretreatment Regulations (40 CFR Part 403) establish procedures, responsibilities, and requirements for EPA, 

States, local governments, and industry to control pollutant discharges to POTWs. Under the Pretreatment Regulations, 

POTWs or approved States implement categorical pretreatment standards for existing sources (PSES) and new sources 

(PSNS). 

Discharges from an MP&M facility2

 to a POTW may already be permitted in the baseline.3 For example, industrial users 

subject to another Categorical Pretreatment Standard would have a discharge permit. Other significant industrial users (SIU) 

that are typically permitted by POTWs include industrial users that: 

� discharge an average of 25,000 gallons per day or more of process wastewater to a POTW, 

� contribute a process waste stream that makes up five percent or more of the average dry weather hydraulic or organic 

capacity of the POT W treatment plant, or 

� have a reasonable potential for adversely affecting the POTW ’s operation or for violating any pretreatment standard. 

As discussed in Section VI of the preamble to the final rule, EPA did not establish or revise any pretreatment standards in the 

final rule. Consequently, there are no POTW administrative costs associated with the final rule. Under the options evaluated 

for the final rule, which include options for setting pretreatment standards, EPA expects no increase in permitting costs for 

indirect dischargers that already hold a permit in the baseline. However, governments will incur additional permitting costs 

for unpermitted facilities (under the NODA/Proposal option only) and to accelerate repermitting for some indirect dischargers 

that currently hold permits. The remainder of this appendix estimates these cost increases. As with direct industrial 

dischargers, promulgation of the MP&M rule may cause some administrative costs to decrease. For example, control 

authorities will no longer have to repermit facilities that are estimated to close as a result of some of the options EPA 

evaluated for the final rule. These cost savings are reflected in estimates of total government administrative costs associated 

with the regulatory options considered for the final rule. 

F.2 POTW ADMINISTRATIVE COST METHODOLOGY 

F.2.1 Data Sources 

EPA collected information from POTWs to support development of the MP&M effluent guideline (see Section 3 of the


TDD). Of 150 surveys mailed, EPA received responses to 147, for a 98 percent response rate. The POTW Survey asked


respondents to provide information on administrative permitting costs for indirect dischargers, sewage sludge use and disposal


costs and practices, and general information (including number of permitted users and number of known MP&M dischargers).


The administrative cost information included the number of hours required to complete specific permitting and repermitting,


1 Permits issued to facilities not covered by effluent guidelines or water quality-based standards are developed based on BPJ (see 

NPDES’ permit writers manual). 

2 MP&M facilities are defined on the basis of three considerations: (1) they produce metal parts, products, or machines for use in one 

of the 19 industry sectors evaluated for coverage in the MP&M point source category; (2) they use operations in one of the eight regulatory 

subcategories evaluated for coverage in the MP&M point source category; and (3) they discharge process wastewater, either directly or 

indirectly, to surface waters. In this document, the term “MP&M facilities” refers to all facilities meeting the above definition, regardless 

of whether a facility’s industrial sector, subcategory, or discharger category is covered by the final regulation. 

3 Under the General Pretreatment Program, a facility's discharges may be controlled through a "permit, order or similar means". For 

simplicity, this document refers to the control mechanism as a permit. 

F-2 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

inspection, monitoring, and enforcement activities. Respondents were also asked to provide an average labor cost for all staff 

involved in permitting activities. EPA used the survey responses on administrative costs to estimate a range of costs incurred 

by POTWs to permit a single MP&M facility. 

The Association of Metropolitan Sewerage Agencies (AMSA) also provided data on administrative costs to EPA (see Section 

3 of the TDD). EPA used the data provided in the AMSA survey to verify and, in some cases, supplement its own analyses of 

POTW administrative costs for regulatory options evaluated for the final rule. AMSA provided EPA with comments on the 

proposed MP&M rule and supplemented these comments with a spreadsheet database. The database contains data from an 

AMSA formulated survey and covers responses from 176 POTW s, representing 66 pretreatment programs. The AMSA 

survey was conducted to verify data from EPA's survey of POTW s and therefore included similar, although fewer, variables 

compared to EPA's survey. Elements EPA verified using the AMSA survey include: (1) the estimated number of indirect 

dischargers; and (2) the unit costs of certain permitting activities, including permit implementation, sampling, and sample 

analysis. Elements EPA added to its analysis using the AMSA data include: (1) screening costs for POTW s that do not 

currently operate under a pretreatment program; and (2) oversight costs associated with implementing the MP&M regulation. 

F.2.2 Overview of Methodology 

EPA estimated the annualized costs of permitting indirect dischargers under the different regulatory options evaluated for the 

final rule using the following steps: 

� Determine the number and characteristics of indirect dischargers that will be permitted under each 

regulatory option evaluated for the final rule. Only the NODA option includes costs for permitting an MP&M 

facility for the first time. The final rule does not cover indirect dischargers while the other regulatory options only 

regulate those indirect dischargers that already hold permits in the baseline. For the NODA option, EPA determined 

how many new permits would be issued. The NODA option requires only concentration-based permits, no massbased permits. 

� Use the data from the POTW Survey to determine a high, middle, and low hourly burden for permitting a 

single facility. EPA defined the low and high estimates of hours such that 90% of the POTW responses fell above 

the low value and 90% of responses fell below the high value. The median value is used to define the middle hourly 

burden. 

� Use the data from the POTW Survey to determine the average frequency of performing certain 

administrative functions. For administrative functions that are not performed at all facilities, survey data were used 

to calculate the portion of facilities requiring these functions. For example, the survey data show that on average 

38.5% of facilities submit a non-compliance report. 

� Multiply the per-facility burden estimate by the average hourly wage. EPA determined a high, middle, and low 

dollar cost of administering the rule for a single facility by multiplying the per-facility hour burden by the average 

hourly wage. The POTW Survey reported an average hourly labor rate of $39.33 ($2001) for staff involved in 

permitting. This is a fully-loaded cost, including salaries and fringe benefits. 

� Calculate the annualized cost of administering the rule. The number of facilities, hourly burden estimate, 

frequency estimates, and hourly wage estimates are all combined to determine the total cost of administering the rule. 

The type of administrative activities required varies over time and the total administrative cost is calculated over a 15 

year time period. EPA calculated the present value of total costs using a seven percent discount rate, and then 

annualized the present value using the same seven percent discount rate. 

F.3 UNIT COSTS OF PERMITTING ACTIVITIES 

This section presents unit costs for the following permitting activities: 

� Permit application and issuance: developing and issuing concentration-based permits at previously unpermitted 

facilities; providing technical guidance; and conducting public and evidentiary hearings; 

� Inspection: inspecting facilities both for the initial permit development and to assess subsequent compliance; 

F-3 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

� Monitoring: sampling and analyzing permittee’s effluent; reviewing and recording permittee’s compliance selfmonitoring reports; receiving, processing, and acting on a permittee’s non-compliance reports; and reviewing a 

permittee’s compliance schedule report for permittees in compliance and permittees not in compliance; 

� Enforcement: issuing administrative orders and administrative fines; and 

� Repermitting. 

EPA believes that theses functions constitute the bulk of the required administrative activities. To these costs, EPA added a 

provision for managerial oversight of 25 percent.4 There are other relatively minor or infrequent administrative functions 

(e.g., providing technical guidance to permittees in years other than the first year of the permit, or repermitting a facility in 

significant non-compliance), but their costs are likely to be insignificant compared to the estimated costs for the five major 

categories outlined above. EPA also added a cost for identifying facilities to be permitted for POTWs that do not currently 

operate under a Pretreatment Program. EPA estimates this cost to be approximately $0.8 million. This cost only applies to 

the NODA/Proposal Option since facilities subject to the upgrade options already hold permits. 

For each major administrative function, this section provides below: (1) a description of the activities involved, (2) the 

estimated percentage of facilities that require the administrative function; (3) the frequency with which the function is 

performed, and (4) high, middle, and low estimates of per facility hours and costs. All costs are presented in year 2001 

dollars. 

F.3.1 Permit Application and Issuance 

Before issuing a wastewater discharge permit to a facility, the permit authority typically inspects the facility, monitors the


facility’s wastewater, and completes pollutant limits calculations and permit paperwork. This section discusses the costs of


completing limits calculations and paperwork; subsequent sections address inspection and monitoring costs. This section also


discusses the costs of technical assistance that the control authority may provide facilities to facilitate compliance with new


limits. Finally, this section includes the costs of public and evidentiary hearings that may be required for some permits.


a. Issue a concentration-based permit at a previously unpermitted facility


To issue a concentration-based permit, permit authorities first review permit applications for completeness. If an application


is incomplete, the authorities notify the applicant and request the missing information. Completed applications are assigned to


permit writers, who review the applications in more detail as they develop permit conditions. The effort required to complete 

these activities depends, in part, on the extent to which the permit authority has automated the permitting process. 

EPA assumed that one-third of facilities will be permitted in each of the three years following the rule’s effective date because 

compliance is mandated within three years of the date the standard is effective (40 CFR Section 403.6). EPA further assumed 

that facilities are repermitted in five year cycles. (The administrative costs of repermitting are discussed separately below.) 

The actual number of facilities that are permitted each year is likely to differ somewhat from EPA’s simplifying assumption. 

These minor differences in permit timing are not expected to significantly change the estimated administrative costs. 

4 The 25 percent oversight cost provision is based on comments and data received from the Association of Metropolitan Sewerage 

Agencies (AMSA). 

F-4 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

Table F.1: Administrative Activity: Develop and issue a concentration-based permit at a previously 

unpermitted facility 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

100% of unpermitted MP&M facilities 

(applicable to NODA/Proposal option only) One time 4.0 hours; 

$122 

10.0 hours; 

$304 

40.0 hours; 

$1,217 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

b. Issue a mass-based permit for a previously unpermitted facility5


The same administrative activities required to issue a concentration-based permit are also required for a mass-based permit.


In addition, for mass-based permits issued under the MP&M rule, the permit writer must determine whether the facility


practices pollution prevention and water conservation methods equivalent to those specified as the basis for BPT. If so, the


permitting authority must determine the facility’s historical flow rate. If not, the authority must derive a mass-based limit


based on other factors such as production rates. When a facility matches BPT water conservation practices and provides


historic flow data, development of a mass-based permit is a relatively straight-forward process. However, the task will be


more challenging at a facility practicing only limited water conservation, particularly if the facility has multiple production 

units and generates integrated process and sanitary wastewaters. 

Table F.2: Administrative Activity: Develop and issue a mass-based permit at a previously 

unpermitted facility 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

100% of MP&M facilities being issued a new mass-based 

permit 

(estimates used for the proposed rule) 

One time 4.0 hours; 

$122 

13.0 hours; 

$396 

40.0 hours; 

$1,217 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

c. Issue a mass-based permit for a facility with a concentration-based permit6


Some of the activities described above for issuing a mass-based permit will be simplified in cases where the facility already


holds a concentration-based permit. For example, much of the basic information required in the permitting application will


already be in the permitting authorities’ records. However, the potentially labor-intensive task of determining the flow basis 

for the permit remains. 

5 None of the regulatory options considered for the final rule require issuance of mass-based permits for previously unpermitted 

facilities. However, since these costs were developed for the proposed rule, they are presented in this appendix even though they are not 

used in the administrative costs estimates. 

6 None of the regulatory options considered for the final rule require conversion of a concentration-based to a mass-based permit. 

However, since these costs were developed for the proposed rule, they are presented in this appendix even though they are not used in the 

administrative costs estimates. 

F-5 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

Table F.3: Administrative Activity: Develop and issue a mass-based permit at a facility holding a 

concentration-based permit 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

100% of MP&M facilities with permit conversion 

(estimates used for the proposed rule) One time 2.0 hours; 

$61 

8.0 hours; 

$243 

20.0 hours; 

$608 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

d. Provide technical guidance to a permittee


Technical guidance is frequently provided by permit authorities to permittees concurrent with the issuance of a new permit.


There are no legal requirements that a permit authority provide a permittee with technical guidance. However, such guidance


is generally in the interest of all parties as it can expedite the permitting process, accelerate the permittee’s compliance, and


reduce the compliance burden. The extent of technical guidance provided varies dramatically among permit authorities. In


some cases, a permit authority may hold a one-day workshop to provide information on a new pretreatment standard to


facilities. In other cases, a permit authority may meet extensively with individual permittees to educate them regarding their


responsibilities under pretreatment standards. The range of technical guidance appears to depend on whether the permittee


already has a wastewater permit, whether the permittee is part of a multi-facility company, the resources of the permit 

authority, and the extent to which the permit authority has written or standardized guidance available for dissemination. 

EPA assumed that permit authorities provide technical guidance to all facilities being issued a new mass-based or 

concentration-based permit under the MP&M pretreatment standards. Costs for technical guidance were estimated separately 

for facilities receiving a concentration-based permit and facilities receiving a mass-based permit. EPA assumed that technical 

guidance is provided in the year the initial permit is issued. 

Table F.4: Administrative Activity: Provide technical guidance to permittee on permit compliance 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

100% of MP&M facilities being issued a new concentrationbased permit 

(applicable to NODA/Proposal option only) 

One time 1.5 hours; 

$46 

4.0 hours; 

$122 

12.0 hours; 

$365 

100% of MP&M facilities being issued a new mass-based 

permit 

(estimates used for the proposed rule) 

One time 2.0 hours; 

$61 

4.0 hours; 

$122 

12.0 hours; 

$365 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

e. Conduct a public or evidentiary hearing on a proposed permit


Federal regulations provide for a period during which the public may submit written comments on a proposed permit for


direct dischargers and/or request that a public hearing be held. Permitting authorities for indirect dischargers may have the


same requirements. Thus, proposed permits for indirect dischargers may be subject to public comments and hearings.


Pretreatment public hearings are typically conducted at a scheduled local government (e.g., City Council) meeting. The 

meetings may require substantial preparation. 

Federal regulations also provide for evidentiary hearings following final permit determination for direct dischargers. Again, 

permitting authorities for indirect dischargers may have these requirements as well. Thus, final permit determinations for 

indirect dischargers may be subject to evidentiary hearings. 

Data from the POT W Survey indicated that a public or evidentiary hearing would be required for 3.6% of indirect dischargers 

being issued a new mass-based or concentration-based permit, on average. 

F-6 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

Table F.5: Administrative Activity: Conduct a public or evidentiary hearing 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

3.2% of MP&M facilities being issued a new mass-based or 

concentration-based permit 

(applicable to NODA/Proposal option only) 

One time 2.0 hours; 

$61 

8.0 hours; 

$243 

40.0 hours; 

$1,217 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

F.3.2 Inspection 

Permit authorities may choose to integrate their inspection and monitoring work force or to administer these functions 

separately. This discussion covers inspections only; monitoring is discussed below. Inspections are performed both to assess 

conditions for initial permitting and to evaluate compliance with permit requirements. Inspections involve record reviews, 

visual observations, and evaluations of the treatment facilities, effluents, receiving waters, etc. EPA assumed that the initial 

inspection would occur in the same year a new permit is issued, and that all permitted facilities would be inspected annually to 

assess compliance. 

Table F.6: Administrative Activity: Inspect facility for permit development 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

100% of MP&M facilities being issued a new permit 

(applicable to NODA/Proposal option only) One Time 2.2 hours; 

$66 

5.0 hours; 

$152 

12.0 hours; 

$365 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

Table F.7: Administrative Activity: Inspect facility for compliance assessment 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

100% of MP&M facilities being issued a new permit 

(applicable to NODA/Proposal option only) Annual 2.0 hours; $61 

3.3 hours; 

$101 

10.0 hours; 

$304 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

F.3.3 Monitoring 

Permitting authorities monitor facilities both to gather data needed for permit development and to assess compliance with


permit conditions. Monitoring includes sampling and analysis of the permittee’s effluent, review of the permittee’s


compliance self-monitoring reports, receipt of non-compliance reports, and review of compliance schedule reports. These


activities are discussed below.


a. Sample and analyze permittee’s effluent


As noted above, inspection and monitoring staff may be integrated or distinct. The costs of inspection were presented above.


Federal regulations require that the permit authority “randomly sample and analyze the effluent from industrial users ...


independent of information supplied by industrial users” (40 CFR Part 403.8). The permit authority obtains samples required


by the permit and performs chemical analyses. The results are used to verify the accuracy of the permittee’s self-monitoring


program and reports, determine the quantity and quality of effluents, develop permits, and provide evidence for enforcement 

proceedings where appropriate. 

F-7 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

EPA estimated sampling costs for all facilities issued a new permit under the MP&M rule, and assumed annual monitoring. 

Although EPA requires only annual effluent sampling, some localities sample more frequently. EPA encourages this practice. 

Table F.8: Administrative Activity: Sample and analyze permittee’s effluent 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

100% of MP&M facilities being issued a new 

permit 

(applicable to NODA/Proposal option only) 

Annual 1.0 hour; 

$30 

3.0 hours; 

$91 

17.7 hours; 

$537 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

b. Review and record permittee’s compliance self-monitoring reports


40 CFR Part 403.12 specifies that: “Any Industrial User subject to a categorical pretreatment standard ... shall submit to the


Control authority during the months of June and December ... a report indicating the nature and concentration of pollutants in


the effluent which are limited by such categorical pretreatment standards.” The permit authority briefly reviews these 

submissions and may enter the information into a computerized system and/or file the data. 

EPA estimated the costs of handling annual self-monitoring reports for all facilities being issued a new permit under the 

MP&M rule. 

Table F.9: Administrative Activity: Review and enter data from permittee’s compliance selfmonitoring reports 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

100% of MP&M facilities being issued a new 

permit 

(applicable to NODA/Proposal option only) 

2 reports per year 0.5 hours; 

$15 

1.0 hour; 

$30 

4.0 hours; 

$122 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

c. Receive, process, and act on a permittee’s non-compliance report


Generally, when a permittee violates a permit condition, it must submit a non-compliance report to the permit authority.


Permittees report both unanticipated bypasses or upsets and violations of maximum daily discharge limits. The permit


authority receives and processes both verbal and written non-compliance reports. In some cases, immediate action by the 

permit authority is required to mitigate the problem. 

Data from the POTW Survey indicate that 38.5 percent of all facilities submit at least one non-compliance report annually. 

Of facilities that submit at least one non-compliance report, the median number of reports filed per year is five reports. 

Table F.10: Administrative Activity: Receive, process and act on a permittee’s non-compliance reports 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

38.5% of all indirect dischargers receiving a new permit 

(applicable to NODA/Proposal option only) 5 times per year 1.0 hour; 

$30 

2.0 hours; 

$61 

6.0 hours; 

$183 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

F-8 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

d. Review a permittee’s compliance schedule report 

Permittees submit reports to permit authorities that state whether compliance schedule milestones contained in their permits 

have been met. If the facility is in compliance, the permit authority reviews and files the report. 

Data from the POTW Survey indicate that approximately 17% of all facilities are issued compliance milestones. Of these 

facilities, 94% meet the milestones. Facilities submit an average of two compliance milestone reports per year. The cost of 

handling the report depends on whether the facility is in compliance with the schedule. 

Table F.11: Administrative Activity: Review a compliance schedule report 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

Meeting milestones: 16.0% of all facilities issued a new permit 

– 94% of the 17% who have compliance milestones 

(applicable to NODA/Proposal option only) 

2 reports per 

year 

0.5 hours; 

$15 

1.0 hour; 

$30 

2.7 hours; 

$81 

Not meeting milestones: 1% of all facilities issued a new 

permit – 6% of the 17% who have compliance milestones 

(applicable to NODA/Proposal option only 

2 reports per 

year 

1.0 hours; 

$30 

2.0 hours; 

$61 

6.0 hours; 

$183 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

F.3.4 Enforcement 

When a permitting authority identifies a permit violation, the authority determines and implements an appropriate 

enforcement action. Considerations when determining enforcement response include (1) the severity of the permit violation, 

(2) the degree of economic benefit obtained by the permittee through the violation, (3) previous enforcement actions taken 

against the violator, (4) the deterrent effect of the response on similarly situated permittees, and (5) considerations of fairness 

and equity. EPA estimated administrative costs for two levels of enforcement actions: (1) less severe actions such as issuing 

an administrative order, and (2) more severe activities such as levying an administrative fine. 

EPA estimated that, annually, seven percent of facilities issued a new permit under the MP&M rule will require a minor 

enforcement action, such as issuing an administrative compliance order. In addition, EPA estimated that seven percent of 

facilities receiving a new permit will require more severe enforcement actions such as a fine or penalty. 

Table F.12: Administrative Activity: Minor enforcement action e.g., issue an administrative order 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

7% of MP&M facilities being issued a new permit 

(applicable to NODA/Proposal option only) Annual 1.0 hour; 

$30 

3.7 hours; 

$112 

12.0 hours; 

$365 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

Table F.13: Administrative Activity: Minor enforcement action, e.g., impose an administrative fine 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

7% of MP&M facilities being issued a new permit 

(applicable to NODA/Proposal option only) Annual 1.0 hour; 

$30 

5.0 hours; 

$152 

24.0 hours; 

$730 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

F-9 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

F.3.5 Repermitting 

The duration of permits cannot exceed five years. Renewing a permit for a facility in compliance with an existing permit is 

expected to be a relatively straightforward task. The data submitted in the permit application generally require few changes, 

although pollutant limits may need to be recalculated in some cases. The labor required for repermitting depends, in part, on 

the extent to which the permit authority has automated the paperwork. 

Table F.14: Administrative Activity: Repermit 

Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical costs (2001$) 

Low Median High 

100% of MP&M facilities being issued a new permit 

(applicable to NODA/Proposal option only) every 5 years 1.0 hour; 

$30 

4.0 hours; 

$122 

20.0 hours; 

$608 

Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics. 

In addition to repermitting MP& M facilities being issues a new permit, EPA also considered two other types of cost: (1) the 

costs associated with repermitting facilities that already hold a permit in the baseline sooner than would otherwise be 

required; and (2) cost savings associated with no longer having to permit facilities that already hold a permit in the baseline 

but that are estimated to close as a result of the rule. Both cost components are reflected in the POTW administrative costs 

presented in the next section. 

F.4 POTW ADMINISTRATIVE COSTS BY OPTION 

Exhibits F.1 through F.7 at the end of this appendix present the calculation of POT W permitting costs for the final rule and 

the three regulatory alternatives considered by EPA. 

Exhibit F.1 provides an overview of the permitting activities, the estimated percentage of facilities that require the 

administrative function, the frequency with the function is performed, and per facility hours and costs for each function. 

Exhibit F.2 contains the per facility hour burden and other assumptions described above for each of the three types of 

permitting (new concentration-based permit, new mass-based permit, and converting a concentration-based to a mass-based 

permit.) 

Exhibits F.3 through F.5 show hours by type of permit for the low, medium, and high estimate of per-facility burden, 

respectively. These exhibits also summarize costs and dollars by year and permit type. 

Exhibit F.6 presents the number of facilities requiring different types of permitting, for each of the regulatory options. The 

exhibit shows the total number of facilities that will be subject to requirements, the baseline permit status of those facilities, 

and the number of facilities by expected post-compliance permit status. These estimates are based on facility survey 

information about baseline permit status and the results of the facility impact analysis described in Chapter 5 of the EEBA. 

The exhibit also shows the number of currently-permitted facilities that are projected to close as a result of the rule, and which 

will therefore no longer require re-permitting. 

The final Exhibit F.7 shows the resulting calculation of POTW administrative hours and costs by year for each regulatory 

option. This exhibit also shows the present value of these costs, the annualized cost, and the maximum hours and costs 

incurred in any one year, for each option. These calculations reflect the incremental number of facilities requiring different 

types of permitting, inspection, monitoring, enforcement and repermitting in each year multiplied by the unit hours and cost 

per facility for those activities. All facilities are assumed to receive a permit under the final rule within the three-year 

compliance period. Some facilities with existing permits are repermitted sooner than they otherwise would be on the normal 

five-year permitting cycle. The cost analyses calculates incremental costs by subtracting the costs of repermitting these 

facilities on a five-year schedule from the costs of repermitting all such facilities within three years. EPA assumes that the 

required initial permitting activities will be equally divided over the three-year period. The analysis also calculates the net 

F-10 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

change in the number of facilities requiring permitting by subtracting the number of facilities that close due to the rule from 

the number of facilities that will require new permits under each regulatory option. 

More detailed information on these cost calculations is provided in the docket for the final rule. 

F-11 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

APPENDIX F EXHIBITS 

Exhibit F.1: Government Administrative Activities for Indirect Dischargers: Per Facility Hours and Costs


Exhibit F.2: Per-Facility Hours and Assumptions


Exhibit F.3: Low Estimate of Hours and Costs per Facility


Exhibit F.4: Medium Estimate of Hours and Costs per Facility


Exhibit F.5: High Estimate of Hours and Costs per Facility


Exhibit F.6: Number of Facilities Requiring Additional Permitting


Exhibit F.7: POTW Administrative Costs by Option


F-12 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.1: Government Administrative Activities for Indirect Dischargers: Per Facility Hours and Costs 

Administrative Activity Percent of facilities for which 

activity is required 

Frequency 

of activity 

Typical hours and costs 

Low Median High 

Develop and issue a concentrationbased permit at a previously 

unpermitted facility 

100% of unpermitted facilities 

(applicable to NODA/Proposal option only) One time 4.0 hours; 

$122 

10.0 

hours; 

$304 

40.0 

hours; 

$1,217 

Develop and issue a mass-based permit 

at a previously unpermitted facility 

100% of MP&M facilities being issued a 

new mass-based permit 

(estimates used for the proposed rule) 

One time 4.0 hours; 

$122 

13.0 

hours; 

$396 

40.0 

hours; 

$1,217 

Develop and issue a mass-based permit 

at a facility holding a concentrationbased permit 

100% of MP&M facilities with permit 

conversion 

(estimates used for the proposed rule) 

One time 2.0 hours; 

$61 

8.0 hours; 

$243 

20.0 

hours; 

$608 year 

Provide technical guidance to a 

permittee on permit compliance 

100% of MP&M facilities being issued a 

new concentration-based permit 

(applicable to NODA/Proposal option only) 

One time 1.5 hour; 

$46 

4.0 hours; 

$122 

12.0 

hours; 

$365 

100% of MP&M facilities being issued a 

new mass-based permit 

(estimates used for the proposed rule) 

One time 2.0 hours; 

$61 

4.0 hours; 

$122 

12.0 

hours; 

$365 

Conduct a public or evidentiary hearing 

3.2% of MP&M facilities being issued a 

new mass-based or concentration-based 

permit 

(applicable to NODA/Proposal option only) 

One time 2.0 hours; 

$61 

8.0 hours; 

$243 

40.0 

hours; 

$1,217 

Inspect facility for permit development 

100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

One Time 2.2 hours; 

$66 

5.0 hours; 

$152 

12.0 

hours; 

$365 

Inspect facility for compliance 

assessment 

100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

Annual 2.0 hours; 

$61 

3.3 hours; 

$101 

10.0 

hours; 

$304 

Sample and analyze permittee’s effluent 

100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

Annual 1.0 hour; 

$30 

3.0 hours; 

$91 

17.7 

hours; 

$537 

Review and enter data from permittee’s 

compliance self-monitoring reports 

100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

2 reports 

per year 

0.5 hours; 

$15 

1.0 hour; 

$30 

4.0 hours; 

$122 

Receive, process and act on a 

permittee’s non-compliance reports 

38.5% of all indirect dischargers receiving a 

new permit 

(applicable to NODA/Proposal option only) 

5 times per 

year 

1.0 hour; 

$30 

2.0 hours; 

$61 

6.0 hours; 

$183 

Review a compliance schedule report 

Meeting milestones: 16.0% of all facilities 

issued a new permit – 94% of the 17% who 

have compliance milestones 

(applicable to NODA/Proposal option only) 

2 reports 

per year 

0.5 hours; 

$15 

1.0 hour; 

$30 

2.7 hours; 

$81 

Not meeting milestones: 1% of all facilities 

issued a new permit – 6% of the 17% who 

have compliance milestones 

(applicable to NODA/Proposal option only) 

2 reports 

per year 

1.0 hours; 

$30 

2.0 hours; 

$61 

6.0 hours; 

$183 

Minor enforcement action e.g., issue an 

administrative order 

7% of MP&M facilities being issued a new 

permit 

(applicable to NODA/Proposal option only) 

Annual 1.0 hour; 

$30 

3.7 hours; 

$112 

12.0 

hours; 

$365 

Minor enforcement action, e.g., impose 

an administrative fine 

7% of MP&M facilities being issued a new 

permit 

(applicable to NODA/Proposal option only) 

Annual 1.0 hour; 

$30 

5.0 hours; 

$152 

24.0 

hours; 

$730 

Repermit 

100% of MP&M facilities being issued a 

new permit 

(applicable to NODA/Proposal option only) 

Every 5 

years 

1.0 hour; 

$30 

4.0 hours; 

$122 

20.0 

hours; 

$608 

Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of Labor Statistics 

(Sept. 2002 rate, adjusted to $2001). 

F-13

MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.2: Per-Facility Hours and Assumptions 

Activity Low Medium High % Facil x/yr Notes 

New concentration-based permit 

develop and issue permit 4.0 10.0 40.0 100.0% 1 one-time 

provide technical guidance 1.5 4.0 12.0 100.0% 1 one-time 

conduct public or evidentiary 

hearings 2.0 8.0 40.0 3.2% 1 one-time, 3.2% of facilities 

inspection for permit development 2.2 5.0 12.0 100.0% 1 one-time 

inspection for compliance assessment 2.0 3.3 10.0 100.0% 1 annual 

sample and analyze effluent 1.0 3.0 17.7 100.0% 1 annual 

review & record self-monitoring 

reports 0.5 1.0 4.0 100.0% 2 2x/year 

process & act on non-compliance 

reports 1.0 2.0 6.0 38.5% 5 5x/year, 38.5% of facilities 

review compliance schedule report -

in compliance with schedule 0.5 1.0 2.7 16.0% 2 2x/yr, 17% of facilities with compliance 

milestones, of which 94% in compliance 

review compliance schedule report -

not in compliance with schedule 1.0 2.0 6.0 1.0% 2 2x/yr, 17% of facilities with compliance 

milestones, of which 6% not in compliance 

minor enforcement action (e.g., admin 

order) 1.0 3.7 12.0 7.0% 1 annual, 7% of facilities 

minor enforcement action (e.g., admin 

fine) 1.0 5.0 24.0 7.0% 1 annual, 7% of facilities 

repermit 1.0 4.0 20.0 100.0% 1 every three years 

New mass-based permit 

develop and issue permit 4.0 13.0 40.0 100.0% 1 one-time 

provide technical guidance 2.0 4.0 12.0 100.0% 1 one-time 

conduct public or evidentiary 

hearings 2.0 8.0 40.0 3.2% 1 one-time, 3.2% of facilities 

inspection for permit development 2.2 5.0 12.0 100.0% 1 one-time 

inspection for compliance assessment 2.0 3.3 10.0 100.0% 1 annual 

sample and analyze effluent 1.0 3.0 17.7 100.0% 1 annual 

review & record self-monitoring 

reports 0.5 1.0 4.0 100.0% 2 2x/year 

process & act on non-compliance 

reports 1.0 2.0 6.0 38.5% 5 5x/year, 38.5% of facilities 

review compliance schedule report -

in compliance with schedule 0.5 1.0 2.7 16.0% 2 2x/yr, 17% of facilities with compliance 

milestones, of which 94% in compliance 

review compliance schedule report -

not in compliance with schedule 1.0 2.0 6.0 1.0% 2 2x/yr, 17% of facilities with compliance 

milestones, of which 6% not in compliance 

minor enforcement action (e.g., admin 

order) 1.0 3.7 12.0 7.0% 1 annual, 7% of facilities 

minor enforcement action (e.g., admin 

fine) 1.0 5.0 24.0 7.0% 1 annual, 7% of facilities 

repermit 1.0 4.0 20.0 100.0% 1 every three years 

Converting concentration-based to mass-based 

develop and issue permit 2.0 8.0 20.0 100.0% 1 one-time 

provide technical guidance 0.0 0.0 0.0 0.0% 0 N/A 

F-14

MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.2: Per-Facility Hours and Assumptions 

Activity Low Medium High % Facil x/yr Notes 

conduct public or evidentiary 

hearings 0.0 0.0 0.0 0.0% 0 N/A 

inspection for permit development 0.0 0.0 0.0 0.0% 0 N/A 

inspection for compliance assessment 2.0 3.3 10.0 100.0% 1 annual 

sample and analyze effluent 1.0 3.0 17.7 100.0% 1 annual 

review & record self-monitoring 

reports 0.5 1.0 4.0 100.0% 2 2x/year 

process & act on non-compliance 

reports 1.0 2.0 6.0 38.5% 5 5x/year, 38.5% of facilities 

review compliance schedule report -

in compliance with schedule 0.5 1.0 2.7 16.0% 2 2x/yr, 17% of facilities with compliance 

milestones, of which 94% in compliance 

review compliance schedule report -

not in compliance with schedule 1.0 2.0 6.0 1.0% 2 2x/yr, 17% of facilities with compliance 

milestones, of which 6% not in compliance 

minor enforcement action (e.g., admin 

order) 1.0 3.7 12.0 7.0% 1 annual, 7% of facilities 

minor enforcement action (e.g., admin 

fine) 1.0 5.0 24.0 7.0% 1 annual, 7% of facilities 

repermit 1.0 4.0 20.0 100.0% 1 every three years 

Discount rate: 7%


Average hourly rate: $30.42 ($2001)


Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of Labor Statistics 

(Sept. 2002 rate, adjusted to $2001). 

F-15 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.3: Low Estimate of Hours and Costs per Facility 

(average considering frequency of activity and percent of facilities requiring activity) 

Activity Initial Year Annual 

(non-permitting year) 

Repermit 

Year 

New concentration-based permit 

develop and issue permit 4 

provide technical guidance 2 

conduct public or evidentiary hearings 0 

inspection for permit development 2 

inspection for compliance assessment 2 2 2 

sample and analyze effluent 1 1 1 

review & record self-monitoring reports 1 1 1 

process & act on non-compliance reports 2 2 2 

review compliance schedule report - in compliance with schedule 0 0 0 

review compliance schedule report - not in compliance with schedule 0 0 0 

minor enforcement action (e.g., admin order) 0 0 0 

minor enforcement action (e.g., admin fine) 0 0 0 

repermit 1 

Total Hours by Year 14 6 7 

Total Dollars by Year $425 $190 $220 

New mass-based permit 

develop and issue permit 4 

provide technical guidance 2 

conduct public or evidentiary hearings 0 

inspection for permit development 2 

inspection for compliance assessment 2 2 2 

sample and analyze effluent 1 1 1 

review & record self-monitoring reports 1 1 1 

process & act on non-compliance reports 2 2 2 

review compliance schedule report - in compliance with schedule 0 0 0 

review compliance schedule report - not in compliance with schedule 0 0 0 

minor enforcement action (e.g., admin order) 0 0 0 

minor enforcement action (e.g., admin fine) 0 0 0 

repermit 1 

Total Hours by Year 14 6 7 

Total Dollars by Year $440 $190 $220 

Upgrading from concentration-based to mass-based 

develop and issue permit 2 

provide technical guidance 0 

conduct public or evidentiary hearings 0 

inspection for permit development 0 

inspection for compliance assessment 2 2 2 

sample and analyze effluent 1 1 1 

F-16

MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.3: Low Estimate of Hours and Costs per Facility 

(average considering frequency of activity and percent of facilities requiring activity) 

Activity Initial Year Annual 

(non-permitting year) 

Repermit 

Year 

review & record self-monitoring reports 1 1 1 

process & act on non-compliance reports 2 2 2 

review compliance schedule report - in compliance with schedule 0 0 0 

review compliance schedule report - not in compliance with schedule 0 0 0 

minor enforcement action (e.g., admin order) 0 0 0 

minor enforcement action (e.g., admin fine) 0 0 0 

repermit 1 

Total Hours by Year 8 6 7 

Total Dollars by Year $251 $190 $220 

Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of Labor 

Statistics (Sept. 2002 rate, adjusted to $2001). 

F-17

MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.4: Medium Estimate of Hours and Costs per Facility 

(average considering frequency of activity and percent of facilities requiring activity) 

Activity Initial Year Annual 

(non-permitting year) 

Repermit 

Year 

New concentration-based permit 

develop and issue permit 10 

provide technical guidance 4 

conduct public or evidentiary hearings 0 

inspection for permit development 5 

inspection for compliance assessment 3 3 3 

sample and analyze effluent 3 3 3 

review & record self-monitoring reports 2 2 2 

process & act on non-compliance reports 4 4 4 

review compliance schedule report - in compliance with schedule 0 0 0 

review compliance schedule report - not in compliance with schedule 0 0 0 

minor enforcement action (e.g., admin order) 0 0 0 

minor enforcement action (e.g., admin fine) 0 0 0 

repermit 4 

Total Hours by Year 32 13 17 

Total Dollars by Year $986 $400 $522 

New mass-based permit 

develop and issue permit 13 

provide technical guidance 4 

conduct public or evidentiary hearings 0 

inspection for permit development 5 

inspection for compliance assessment 3 3 3 

sample and analyze effluent 3 3 3 

review & record self-monitoring reports 2 2 2 

process & act on non-compliance reports 4 4 4 

review compliance schedule report - in compliance with schedule 0 0 0 

review compliance schedule report - not in compliance with schedule 0 0 0 

minor enforcement action (e.g., admin order) 0 0 0 

minor enforcement action (e.g., admin fine) 0 0 0 

repermit 4 

Total Hours by Year 35 13 17 

Total Dollars by Year $1,077 $400 $522 

Upgrading from concentration-based to mass-based 

develop and issue permit 8 

provide technical guidance 0 

conduct public or evidentiary hearings 0 

inspection for permit development 0 

inspection for compliance assessment 3 3 3 

F-18

MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.4: Medium Estimate of Hours and Costs per Facility 

(average considering frequency of activity and percent of facilities requiring activity) 

Activity Initial Year Annual 

(non-permitting year) 

Repermit 

Year 

sample and analyze effluent 3 3 3 

review & record self-monitoring reports 2 2 2 

process & act on non-compliance reports 4 4 4 

review compliance schedule report - in compliance with schedule 0 0 0 

review compliance schedule report - not in compliance with schedule 0 0 0 

minor enforcement action (e.g., admin order) 0 0 0 

minor enforcement action (e.g., admin fine) 0 0 0 

repermit 4 

Total Hours by Year 21 13 17 

Total Dollars by Year $643 $400 $522 

Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of Labor 

Statistics (Sept. 2002 rate, adjusted to $2001). 

F-19

MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.5: High Estimate of Hours and Costs per Facility 

(average considering frequency of activity and percent of facilities requiring activity) 

Activity Initial Year Annual 

(non-permitting year) 

Repermit 

Year 

New concentration-based permit 

develop and issue permit 40 

provide technical guidance 12 

conduct public or evidentiary hearings 1 

inspection for permit development 12 

inspection for compliance assessment 10 10 10 

sample and analyze effluent 18 18 18 

review & record self-monitoring reports 8 8 8 

process & act on non-compliance reports 12 12 12 

review compliance schedule report - in compliance with schedule 1 1 1 

review compliance schedule report - not in compliance with schedule 0 0 0 

minor enforcement action (e.g., admin order) 1 1 1 

minor enforcement action (e.g., admin fine) 2 2 2 

repermit 20 

Total Hours by Year 116 51 71 

Total Dollars by Year $3,529 $1,543 $2,151 

New mass-based permit 

develop and issue permit 40 

provide technical guidance 12 

conduct public or evidentiary hearings 1 

inspection for permit development 12 

inspection for compliance assessment 10 10 10 

sample and analyze effluent 18 18 18 

review & record self-monitoring reports 8 8 8 

process & act on non-compliance reports 12 12 12 

review compliance schedule report - in compliance with schedule 1 1 1 

review compliance schedule report - not in compliance with schedule 0 0 0 

minor enforcement action (e.g., admin order) 1 1 1 

minor enforcement action (e.g., admin fine) 2 2 2 

repermit 20 

Total Hours by Year 116 51 71 

Total Dollars by Year $3,529 $1,543 $2,151 

Upgrading from concentration-based to mass-based 

develop and issue permit 20 

provide technical guidance 0 

conduct public or evidentiary hearings 0 

inspection for permit development 0 

inspection for compliance assessment 10 10 10 

F-20

MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.5: High Estimate of Hours and Costs per Facility 

(average considering frequency of activity and percent of facilities requiring activity) 

Activity Initial Year Annual 

(non-permitting year) 

Repermit 

Year 

sample and analyze effluent 18 18 18 

review & record self-monitoring reports 8 8 8 

process & act on non-compliance reports 12 12 12 

review compliance schedule report - in compliance with schedule 1 1 1 

review compliance schedule report - not in compliance with schedule 0 0 0 

minor enforcement action (e.g., admin order) 1 1 1 

minor enforcement action (e.g., admin fine) 2 2 2 

repermit 20 

Total Hours by Year 71 51 71 

Total Dollars by Year $2,151 $1,543 $2,151 

Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of Labor 

Statistics (Sept. 2002 rate, adjusted to $2001). 

F-21

MP&M EEBA: Appendices Appendix F: Administrative Costs 

Exhibit F.6: Number of Facilities Requiring Additional Permitting 

Option II: NODA/Proposal Option 

Number of facilities operating post-regulation requiring a permit 3,687 

Of facilities operating post-regulation: 

existing concentration-based 692 

existing mass-based 2,892 

no permit in baseline 103 

concentration based to be converted to mass-based 0 

new concentration-based 103 

new mass-based 0 

Number of currently permitted facilities closing (no longer requiring a permit) 722 

Of facilities closing due to the rule: 

existing concentration-based 209 

existing mass-based 513 

Option III: Directs + 413 to 433 Upgrade 

Number of facilities operating post-regulation requiring a permit 954 

Of facilities operating post-regulation: 

existing concentration-based 184 

existing mass-based 770 

no permit in baseline 0 

concentration based to be converted to mass-based 0 

new concentration-based 0 

new mass-based 0 

Number of currently permitted facilities closing (no longer requiring a permit) 120 

Of facilities closing due to the rule: 

existing concentration-based 0 

existing mass-based 120 

Option IV: Directs + 413+50%LL Upgrade 

Number of facilities operating post-regulation requiring a permit 1,414 

Of facilities operating post-regulation: 

existing concentration-based 515 

existing mass-based 899 

no permit in baseline 0 

concentration based to be converted to mass-based 0 

new concentration-based 0 

new mass-based 0 

Number of currently permitted facilities closing (no longer requiring a permit) 120 

Of facilities closing due to the rule: 

existing concentration-based 0 

existing mass-based 120 

Source: U.S. EPA analysis. 

F-22

MP&M EEBA: Appendices 

Exhibit F.7: POTW Administrative Costs by Option (@ 7% discount rate) 

Option II: NODA/Proposal Option 

Year Relative to Promulgation of Rule 

1 10 11 

Total Hours High 32,561 -15,017 -28,095 -60,763 -60,763 -30,038 -30,038 -30,038 -60,763 -60,763 -30,038 -3

M edium 33,603 -4,289 -7,680 -14,480 -14,480 -8,335 -8,335 -8,335 -14,480 -14,480 -8,335 -

Low 33,638 -2,472 -4,083 -5,908 -5,908 -4,372 -4,372 -4,372 -5,908 -5,908 -4,372 -

Tota l Cos ts High $990,604 $-456,868 $-854,738 $-1,848,612 $-1,848,612 $-913,859 $-913,859 $-913,859 $-1,848,612 $-1,848,612 $-913,859 $-91

M edium $1,022,297 $-130,480 $-233,655 $-440,526 $-440,526 $-253,575 $-253,575 $-253,575 $-440,526 $-440,526 $-253,575 $-25

Low $1,023,378 $-75,221 $-124,220 $-179,746 $-179,746 $-133,008 $-133,008 $-133,008 $-179,746 $-179,746 $-133,008 $-13

High M edium Low 

N P V $-9,357,000 $-1,802,000 $-422,000 

Annualized Cost $-1,027,000 $-198,000 $-46,000 

M ax O ne Y ear H ours 32,561 33,603 33,638 

M ax O ne Y ear C osts $991,000 $1,022,000 $1,023,000 

Option III: Directs + 413 to 433 Upgrade 

Year Relative to Promulgation of Rule 

1 10 11 

Total Hours High 33 -2,513 -5,059 -13,011 -13,011 -5,059 -5,059 -5,059 -13,011 -13,011 -5,059 -

M edium -144 -805 -1,465 -3,055 -3,055 -1,465 -1,465 -1,465 -3,055 -3,055 -1,465 -

Low -185 -498 -812 -1,209 -1,209 -812 -812 -812 -1,209 -1,209 -812 

Tota l Cos ts High $1,000 $-76,451 $-153,901 $-395,845 $-395,845 $-153,901 $-153,901 $-153,901 $-395,845 $-395,845 $-153,901 $-15

M edium $-4,394 $-24,479 $-44,563 $-92,952 $-92,952 $-44,563 $-44,563 $-44,563 $-92,952 $-92,952 $-44,563 $-4

Low $-5,616 $-15,154 $-24,692 $-36,789 $-36,789 $-24,692 $-24,692 $-24,692 $-36,789 $-36,789 $-24,692 $-2

High M edium Low 

NPV $-1,982,000 $-509,000 $-238,000 

Annualized Cost $-218,000 $-56,000 $-26,000 

M ax O ne Y ear H ours 33 -144 -185 M ax O ne Y ear C osts $1,000 $-4,000 $-6,000 

2 3 4 5 6 7 8 9 

2 3 4 5 6 7 8 9 
MP&M EEBA: Appendices Appe

Exhibit F.7: POTW Administrative Costs by Option (@ 7% discount rate) 

Option IV: Directs + 413+50%LL Upgrade 

Year Relative to Promulgation of Rule 

1 10 11 12 

Total Hours High 1,566 -980 -3,525 -15,311 -15,311 -3,525 -3,525 -3,525 -15,311 -15,311 -3,525 -3,525 

M edium 162 -498 -1,158 -3,515 -3,515 -1,158 -1,158 -1,158 -3,515 -3,515 -1,158 -1,158 

Low -108 -421 -735 -1,324 -1,324 -735 -735 -735 -1,324 -1,324 -735 -735 

Tota l Cos ts High $47,645 $-29,805 $-107,256 $-465,813 $-465,813 $-107,256 $-107,256 $-107,256 $-465,813 $-465,813 $-107,256 $-107,256 $-

M edium $4,935 $-15,150 $-35,234 $-106,945 $-106,945 $-35,234 $-35,234 $-35,234 $-106,945 $-106,945 $-35,234 $-35,234 $

Low $-3,283 $-12,822 $-22,360 $-40,288 $-40,288 $-22,360 $-22,360 $-22,360 $-40,288 $-40,288 $-22,360 $-22,360 $

High M edium Low 

N P V $-1,940,000 $-501,000 $-236,000 

Annualized Cost $-213,000 $-55,000 $-26,000 

M ax O ne Y ear H ours 1,566 162 -108 

M ax O ne Y ear C osts $48,000 $5,000 $-3,000 

2 3 4 5 6 7 8 9 

Sou rce: E stima tes of h ours by a ctivity from th e 19 96 M P&M POTW Surv ey. Avera ge h ourly rate fro m B urea u of Labo r Statistic s (Sep t. 2002 rate, a djuste d to $2 001 ).


F-24

MP&M EEBA: Appendices Appendix F: Administrative Costs 

REFERENCES


Association of Metropolitan Sewage Agencies (AMSA). 2000. Survey on Proposed MP&M Effluent Guidelines.


U.S. Department of Labor, Bureau of Labor Statistics. Average Hourly Rate. 

U.S. Environmental Protection Agency. 1996. MP&M POTW Survey. 

F-25 
MP&M EEBA: Appendices Appendix F: Administrative Costs 

THIS PAGE INTENTIONALLY LEFT BLANK 

F-26

MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

Appendix G: Extrapolation Methods


INTRODUCTION 

EPA estimates both cost and benefits of environmental 

regulations based on a random stratified sample of MP&M 

facilities.1

 EPA then estimates national level costs and 

benefits by extrapolating analytic results from sample 

facilities to the national level using statistically determined 

sample facility weights. 

Sample facility weights used in the benefit cost analysis of 

environmental regulations are based on detailed 

questionnaire stratification. Stratification means dividing the 

population of facilities into a number of non-overlapping 

sub-populations (strata). These strata consist of facilities that 

are homogeneous with respect to facility size (i.e., number of 

employees or revenue) or engineering characteristics such as 

wastewater flow because this information was not available at 

the time the sample frame was developed. The sample 

APPENDIX CONTENTS 

G.1 Raking to Adjust MP&M Facility Sample 

Weights ................. ............. G-2 

G.1.1 ................. ....... G-2 

G.1.2 ing Adjustment ................. .. G-3 

G.2 y for Developing Sample-Weighted 

Estimates for Sites with More Than One 

MP&M Facility ................. ...... G-7 

G.3 

Study Results to the National Level ....... G-13 

G.3.1 e in Pollutant Loads ............. G-14 

G.3.2 ctivities on Reaches 

Affected by MP&M Discharges .......... G-14 

G.3.3 Differences in Household Income ....... G-14 

G.4 ults ................. ................ G-15 

Glossary ................. ................. .. G-17 

Using

Data Sources 

Rak

Methodolog

Methodology for Extrapolation of Ohio Case 

Chang

Level of Recreational A

Res

weights for facilities in the sample are based on the total population in each category and probabilities of selection in each 

stratum. 

EPA traditionally uses a standard linear weighting technique (hereafter, traditional extrapolation) to estimate national 

compliance costs, changes in pollutant removals, and national-level benefits of environmental regulations. However, using 

sample weights that are based only on facility-specific (e.g., engineering) characteristics and various non-facility factors can 

lead to a conditional bias in the estimation of national-level benefits. In particular, this approach omits consideration of 

important non-facility factors that influence the occurrence and size of benefits. 

Non-facility factors that are likely to affect the occurrence and size of benefits from reduced sample facility discharges and 

that are not reflected in the standard stratification and sample-weighting approach include the receiving water body type and 

size and the size of the population residing in the vicinity of a sample facility. Furthermore, co-occurrences of facilities 

discharging to the same reach may also affect the occurrence of benefits. Many of the environmental assessment and benefits 

analyses include comparisons of the estimated baseline and post-compliance pollutant concentrations (e.g., sludge 

concentrations or in-waterway concentrations) with the relevant threshold values. Because the effect of aggregate discharges 

from several facilities is likely to be different from the sum of effects from these facilities considered independently, it is also 

important to account for the likelihood of joint discharges of MP&M facilities to the same reach. 

The Agency used two approaches to address omission of these important non-facility factors (i.e., water body type and size, 

affected population, and co-occurrence of MP&M discharges) in designing the MP&M facilities sample. First, EPA adjusted 

sampling weights through post-stratification using two variables receiving water body type and size and the size of the 

population residing in the vicinity of the sample facility. Section G.1 presents the method of doing this adjustment. Second, 

EPA used a differential sample weighting technique in developing national estimates of environmental effects and 

benefits. This method accounts for the presence of more than one facility with different sample weights discharging directly or 

indirectly (through a POTW ) to reaches affected by multiple MP&M dischargers. Section G.2 of this appendix describes the 

differential sample weighting technique. 

EPA used both the traditional extrapolation-based weights and the sample weights adjusted through post-stratification 

(hereafter, post-stratification extrapolation) to analyze the final MP&M rule’s benefits. The benefit estimates based on the 

post-stratification extrapolation weights are used to validate general conclusions that EPA draws from its main analysis based 

on the traditional extrapolation method. In addition to developing national benefit estimates based on both traditional and 

1 A census of all MP&M facilities was not performed due to the large size of the MP&M industry. 

G-1 
MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

post-stratification extrapolation weights, EPA developed a third estimate of national benefits based on the Ohio case study 

results.2

 Section G.3 of this appendix discusses this method in detail. The Agency recognizes that the extrapolation method 

used for the Ohio case study results is not rigorous. Therefore, this method is used to supplement the main results. 

G.1 USING RAKING TO ADJUST MP&M FACILITY SAMPLE WEIGHTS 

Omitting information that affects the occurrence and size of benefits from the original sample frame’s design may lead to 

conditional bias in MP&M rule benefit estimates. To address this problem, EPA used a post-stratification weight-adjustment 

method called raking to account for two additional variables that were not accounted for in the original sample design and 

that may affect benefit occurrence: 

� physical characteristics of the receiving water body (including type and size); and 

� size of the population residing in the vicinity of the sample facility. 

G.1.1 Data Sources 

EPA first classified the universe of MP&M facilities into different poststrata. The Agency relied on three data sources to 

identify discharge reach characteristics and the population size in the vicinity of the discharge reach: 

1. EPA's Permit Compliance System database (PCS) indicated water bodies to which MP&M facilities discharge; 

2. EPA's Reach File 1 (RF1) provided additional information on the receiving water bodies, including water body type, 

flow characteristics, and counties abutting these water bodies; and 

3. Census data provided information on county populations. 

The PCS database provides information on facilities covered by NPDES permits. The database covers only those facilities 

that discharge directly to surface or ground water. No information is available on the location of MP&M facilities that 

discharge to surface water indirectly or via POTW s. EPA therefore limited post-stratification to direct discharging facilities. 

The Agency used the resulting adjusted sample weights to estimate national-level benefits for only the final regulatory option, 

which covers only direct discharging facilities. Chapters 13 through 19 of this report present benefit estimates in various 

benefit categories considered in this analysis. 

The extent of improvement in estimation accuracy depends on the reliability of the information used for post-stratification. 

Accordingly, it was necessary to understand and account for PCS database limitations in implementing a post-stratification 

approach. The PCS database is designed to provide information on a facility's SIC codes, facility flow, and receiving reach 

characteristics. These characteristics include water body name and type, stream ID, and stream flow. Although these data can 

be used to classify facilities in the identified poststrata, these fields are not always populated in the database. To fill missing 

data, EPA combined data from PCS with supplementary analyses and information from RF1, using the following framework: 

� PCS provided a stream ID and information on the water body type and flow characteristics. EPA obtained stream 

characteristics from PCS and used the stream ID to obtain information on counties abutting the reach from RF1; 

� PCS provided a stream ID, but not the water body type and flow characteristics. EPA used the stream ID to obtain 

information on water body type, flow characteristics, and counties abutting the reach from RF1; 

� PCS provided water body name and type, but not stream ID and flow characteristics. EPA first used facility lat/long 

data to assign the PCS facility to the nearest reach that matches the water body name provided in PCS. The Agency 

then used the identified stream ID from RF1 to obtain information on water body type, flow characteristics, and 

counties abutting the reach from RF1; 

2 See Chapter 21 for a detailed discussion the Ohio case study. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

� PCS provided no receiving stream information on the, but facility lat/long data were available. EPA first used these 

data to assign the PCS facility to the nearest reach. The Agency then used the identified stream ID to obtain 

information on water body type, flow characteristics, and counties abutting the reach; 

� PCS provided neither information on the receiving stream nor facility lat/long data. EPA assumed the distribution of 

the receiving water body characteristics, including the size of the population residing in the counties abutting the 

receiving reaches, to be similar to the distribution of these characteristics across facilities with known characteristics. 

PCS identifies 4,290 direct discharging facilities with MP&M SIC codes that had active NPDES permits 1997. Of these,


EPA classified 3,242 facilities into the poststrata considered in this analysis. Because the total number of PCS facilities with


MP &M SIC codes differs from the sum of sampling weights of direct dischargers considered in the final regulation, the


Agency assumed that the sum of the sampling weights provides the correct estimate of the MP& M facility universe. Thus, the


count of facilities in the benefits analysis matches the number of MP&M facilities. This analysis yielded an adjustment factor


of 2,832/ 3,242= 0.87 Table G.1 lists facility counts from PCS data, adjusted to equal the sample frame total.


Table G.1: Facility Counts from PCS Data 

(Adjusted to Equal the Sample Frame Total) 

First Variable: Water Body Type and 

Size Second Variable: Population Size 

Variable Category 

PCS Facilities 

Count Variable Category PCS Facilities Count 

Bay-Lakes Combined 288 Pop�100,000 934 

Small Streams 543 100,000<Pop�500,000 1155 

Medium Streams 1514 500,000<Pop�1,000,000 403 

Large Streams 487 1,000,000<Pop�2,000,000 276 

2,000,000<Pop�4,000,000 47 

Pop<4,000,000 17 

Total 2,832 2,832 

Source: PCS data. 

G.1.2 Raking Adjustment 

Raking is a post-stratification method that can be used when multiple variables form the poststrata. If the original sampling 

weights need to be adjusted using post-stratification with two variables, then the analysis must create a set of poststrata 

resulting from the cross-classification of the two post-stratification variables. EPA’s analysis used the following steps: 

1. Combine the variables "water body type" (four categories), with "population size residing in the vicinity of the 

sampled facility" (six categories) to yield 24 poststrata. 

2. Classify each sampled facility into one of the 24 poststrata. 

3. Determine how many facilities fall into each poststratum. 

4. Multiply the sampling weight of a facility in a poststratum by the ratio of the number of facilities in the population in 

the poststratum to the sum of the sampling weights of all facilities in that stratum. If the number of facilities in the 

population are known only by each category of the two variables, then the weights can be adjusted through raking. 

This section briefly describes the raking procedure. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

Water body type was one of the two post-stratification variables used for raking. EPA originally used six categories of this 

variable: Bay/Ocean, Great Lakes, Lakes, Lakes, Small Streams, Medium Streams, and Large Streams. However, the number 

of MP&M sample facilities in Bay/Ocean, Great Lake, and Lake categories was too small for some categories to implement 

raking. Therefore, EPA combined categories in which the number of facilities in the sample was either zero or too small to 

create four categories: 

� Bay-Lakes Combined (includes, Bays, Oceans, Great Lakes and Lakes);


� Small Streams;


� Medium Streams; and 


� Large Streams.


Table G.2 shows the number of sampled facilities in each category of water body type, the sum of the sampling weights of the 

sampled facilities, and the known number of facilities in the population in that category. Comparing the sum of the MP&M 

facilities sampling weights and the PCS-based count of facilities for each category of water body type shows that Bay-Lake 

Combined and Small Streams are under-represented in the MP&M sample frame while Medium and Large Streams are overrepresented. 

Table G.2: Facility Distribution by Water Body Type 

Number of Facilities 

in the MP&M 

Sample Frame 

MP&M Sample Frame PCS Facilities 

Number of 

Facilities in the 

Sample 

Sum of the 

Sampling 

Weights 

Number of 

Facilities in the 

Population 

Ratio of 

Number PCS to 

SampleWeighted 

Facilities 

Bay-Combined 7 38.7 288 7.44 

Small Streams 7 231.3 543 2.35 

Medium Streams 43 1,439.4 1514 1.05 

Large Streams 25 1,122.6 487 0.43 

Total 82 2,832.0 2,832.0 1.00 

Source: PCS data. 

Table G.3 shows the six population categories created in the EPA analysis. Comparing the sum of the MP&M facilities’ 

sampling weights and the PCS-based count of facilities corresponding to each category of water body type shows that 

facilities from the population size category of less than 100,000, greater than 4,000,000, and greater than 2,000,000 but less 

than 4,000,000 are over-represented in the sample. Conversely, facilities in the population categories from 100,000 to 

500,000 and from 500,000 to 1,000,000 are under-represented. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

Table G.3: Distribution of Facilities by Population Size 

Population 

MP&M Sample Frame PCS Facilities 

Number of 

Facilities in the 

Sample 

Sum of the 

Sampling 

Weights 

Number of 

Facilities in 

the 

Population 

Ratio of 

SampleWeighted 

to PCS 

Facilities 

Pop�100,000 18 1,30 3.0 934 1.40 

100,000<Pop�500,000 35 1,17 1.8 1,155 1.01 

500,000<Pop�1,000,000 12 136.3 403 0.34 

1,000,000<Pop�2,000,000 12 121.6 276 0.44 

2,000,000<Pop�4,000,000 3 61.8 47 1.31 

>4,000,000 2 37.6 17 2.21 

Total 82 2,832.0 2,832.0 1.00 

Source: PCS data. 

Raking is an iterative process in which adjusted sample weights are synthetically generated to match known characteristics of 

the population along single stratification dimensions and, as a result, should reflect the population characteristics within multidimensional stratification cells. The iterative process works as follows. First, EPA multiplied the sampling weight of each 

facility in each category of water body type by the ratio of the total number of facilities in the population to the sum of the 

sampling weights in that category. For example, using the numbers in Table G.2, EPA multiplied the sampling weights of all 

sampled facilities in the Bay-Combined category by the ratio 288/38.7 = 7.44. The sum of the adjusted weights, 38.72x 

7.44=288.08, is the known population total. Similarly, EPA multiplied all the sampling weights of facilities in the Large 

Streams category by the ratio 487/1122.6 = 0.43, to yield 1,122.6x0.43= 482.7 as the sum of the adjusted weights. EPA 

performed the same calculations for the other categories of water body type. 

These calculations match the sum of the sampling weights to the known control totals for the single stratification dimension of 

water body type. At this first step, however, it is very unlikely that the resulting sums will agree with the known number of 

facilities within categories of the second stratification dimension, population size category. Table G.4 shows the sum of the 

adjusted sampling weights and the PCS population totals by population sizes after Iteration 1. 

Table G.4: Sum of the Sampling Weight by Population Category 

after Iteration 1 

Population 

Sum of the 

Adjusted 

Sampling Weights 

Number of Facilities 

in the Population 

(PCS Based) 

�100,000 1,542.49 934 

100,000<Pop�500,000 728.62 1,155 

500,000<Pop�1,000,000 133.42 403 

1,000,000<Pop�2,000,000 294.18 276 

2,000,000<Pop�4,000,000 58.31 47 

>4,000,000 74.99 17 

Total 2,832.01 2,832 

Source: U.S. EPA analysis. 

To correct for this inconsistency, EPA multiplied each weight by the ratio of the known total to the sum of the adjusted 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

weights for each facility in each population size category. For example, the Agency multiplied each facility in the first 

population category by the ratio 934/1542.49. Now, the resulting sum of the adjusted weights agrees with the category totals 

for the population category, but differs from the category totals for water body type. 

EPA therefore repeated this process of sequentially adjusting sample weights one dimension at a time until the sum of the 

adjusted sampling weights simultaneously agreed with the total population counts of facilities for both water body type and 

population size categories. After seven iterations, the sum of the sampling weights agreed with PCS-based counts for both 

variables except for a difference of less than one. 

Tables G.5 and G.6 show the sum of the sampling weights before and after this iterative process in each cell. Obtaining the 

estimated numbers in each cell of Table G.6 by aggregating the final raked sampling weights may yield better estimates of the 

cell populations than summing the original sampling weights in Table G.5. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

Table G.5: Estimated Number of MP&M Facilities in each Poststratum before Raking 

Population Size 

Water Body Type 

BayCombination 

Small 

Streams 

Medium 

Streams 

Large 

Streams Total 

Pop�100,000 0 151 1,114 38 1,303 

100,000<Pop500,000 11 9 208 944 1,172 

500,000<Pop�1,000,000 1 25 31 79 136 

1,000,000<Pop�2,000,000 27 19 25 51 122 

2,000,000<Pop�4,000,000 0 0 51 11 62 

>4,000,000 0 27 10 0 37 

Total 39 232 1,439 1,122 2,832 

Source: PCS data. 

Table G.6: Estimated Number of MP&M Facilities in Each Poststratum after Raking 

Population 

Water Body Type 

Bay- Total

Combination 

Small 

Streams 

Medium 

Streams 

Large 

Streams 

Pop�100,0000 0 204 726 4 934 

100,000<Pop�500,000 112 50 575 418 1155 

500,000<Pop�1,000,000 16 210 126 51 403 

1,000,000<Pop�2,000,000 161 64 39 13 277 

2,000,000<Pop�4,000,0000 0 45 2 47 

>4,000,0000 0 4 3 0 17 

Total 289 542 1,514 488 2,833 

0 

1

Source: U.S. EPA analysis 

Tables G.5 and G.6 show that sampling weights increase for small stream facilities in the population �100,000 category, while 

sampling weights decrease for medium and large stream facilities in the same population category, due to their overrepresentation in the sample. 

G.2 METHODOLOGY FOR DEVELOPING SAMPLE-WEIGHTED ESTIMATES FOR SITES 

WITH MORE THAN ONE MP&M FACILITY 

The MP&M analysis is based on a random stratified sample of MP&M facilities intended to provide detailed information 

about specific facility characteristics and to provide national estimates with these characteristics. They are not reach-specific 

sample weights designed to estimate the national occurrence of reaches associated with a specific characteristic of MP&M 

discharges. For example, the sum of MP &M sample facility weights discharging to one reach is an accurate estimate of the 

number of national facilities similar to the sample facilities, but is not a valid national estimate of all potential MP&M 

discharges to that reach or the number of reaches similar to that reach. Accordingly, to use the sample weights to estimate 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

the number of similar facilities on similar reaches nationwide requires some adjustments to the standard sample-weight based 

extrapolation process. 

It may not be valid to assume that the co-location of sample facilities is similar to the co-location characteristics of all MP&M 

facilities. This point is illustrated by the case in which two sample facilities with different weights discharge to the same 

reach. Assume that one of these two sample facilities has a sample weight of five and the other has a sample weight of 200. 

The sample weights indicate that there are four additional facilities in the U.S. that are economically and technically similar to 

the facility with the weight of five. It is also correct to estimate that the other four facilities will discharge the same volume of 

the same pollutants as the other four facilities. Now let us assume that there are 199 other facilities nationwide similar to the 

facility with the weight of 200. The more numerous facilities represented by the facility with a weight of 200 could only 

rarely be co-located with one of the four facilities represented by the sample facility with a weight of five. 

EPA developed a method that accounts for joint occurrence on reaches of facilities with different statistical weights to 

estimate the number of reaches affected by MP&M facilities nationwide. EPA created a series of new discharge variables (a 

discharge event) for each reach affected by MP&M sample facilities, and assigned weights for the discharge events that 

provide a national estimate of pollutant discharges across all reaches. The sample discharge events (flows and pollutant 

loadings) are calculated based on the sum of the flows and pollutant loadings for subsets of the MP& M sample facilities that 

discharge to that reach. The weights for the discharge events are developed from the facility weights for those subsets of 

facilities. The calculation includes direct MP&M facility discharges and indirect discharges from POTW s (for options that 

include them) after considering pollutant removals from POTW treatment. 

The number of discharge events on a sample reach equals the number of unique sample weights for the facilities on the reach. 

EPA calculated a sample weight for each discharge event based on the sample weights of the facilities contributing loadings 

and flows to the event. Table G.7 illustrates discharge event calculations and corresponding sample weights. Steps for 

calculating the relevant parameters for discharge events on reaches affected by multiple discharges are as follows: 

� Rank pollutant loadings (or discharge flows) in ascending order of facility sample weight for each pollutant of 

concern discharged by one or more of those facilities. 

� Generate the first discharge event loadings (or flows) as the total loadings (or flows) from all sample facilities on the 

reach. Assign the smallest sample weight to the first discharge event (Wt1 in Table G.7) among the facilities 

discharging to the reach. A smaller sample weight relative to the others means that this facility represents no other 

population facilities that could occur jointly with the other facilities. The weight of the first facility is therefore 

considered as “used up,” and that facility’s loadings (or flows) are not included in subsequent discharge events 

defined for the reach. 

� Generate subsequent discharge events by removing the loadings (or flows) of facilities with the smallest sample 

weight from a running sum of loadings (or flows) of all facilities in the ranking. The weight assigned to each 

subsequent event is the remaining unused weight of the facility with the smallest weight among the facilities 

remaining in the particular discharge event. Calculate this weight as the difference between the weight of the next 

facility in the ranking and the weight of the previous facility (Wt2-Wt1). 

EPA avoids double counting indirect dischargers by including the discharge flow of any given POTW into a reach only once 

in any given discharge event, even when multiple sample facilities discharge indirectly into one POTW. 

This methodology generates a set of discharge events (loadings or flows) for each pollutant discharged to the reach. The 

following steps illustrate application of the differential weighting technique to estimating the national number of reaches on 

which ambient water quality criteria (AWQC) are exceeded: 

� assign a weight to each discharge event based on the weights of the facilities discharging to the reach; 

� combine the effluent flow with the stream flow of the reach; 

� divide the pollutant loading into the stream flow to determine the pollutant concentration caused by the event; 

� compare pollutant concentration to AWQC values to determine whether the concentration exceeds those values; 

� identify an estimated AWQC “exceedance” if the concentration is greater than a criterion; and 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

� give the AWQC exceedance event the weight of the discharge event, to establish national estimates of the number of 

reaches on which an AWQC is exceeded. 

Table G.7: Construction of Discharge Events for Any Pollutant Discharged to Any Reach 

Event Number Loadings and Flows Assigned to 

Event Weight Assigned to Event 

One Wt1 

Two Wt2 - Wt1 

� �

N - 2 LoadN-2 + LoadN-1 + LoadN 

FlowN-2 + FlowN-1 + FlowN 

WtN-2 - WtN-3 

N - 1 LoadN-1 + LoadN 

FlowN-1 + FlowN 

WtN-1 - WtN-2 

N LoadN +FlowN WtN - WtN-1 

�

Notes: N sample facilities discharge to the reach and are ranked in ascending order of sample weight and indexed by i (1 = 

facility with smallest weight, N = facility with largest weight); Loadi

 = Loading from facility i; Flowi

 = Flow from facility i or 

the POTW associated with facility i; Wti

 = Sample weight of facility i; and a POTW’s flow is included only once per event, 

even if multiple facilities in that event discharged through that POTW, to avoid over-counting the POTW’s flow. 

Source: U.S. EPA analysis. 

This weighting method is a relatively simplistic approach to a complex analytic issue, and does not provide a precise estimate 

of the national distribution of in-stream MP&M pollutant concentrations that reflects the true co-location characteristics of 

MP&M facilities. A statistically-valid estimate of that distribution is not possible given the design of the Section 308 surveys. 

However, the differential weighting technique does correct for the significant overstatement of benefits that would result from 

using a simple weighting approach to estimate national reach characteristics. The Agency believes that this method is a 

reasonable approach to addressing this issue, given time and resource constraints. Approaches that are both more 

sophisticated and more expensive might not yield significantly different aggregate findings. 

Figure G.1 provides a graphical example of a hypothetical reach to which three known sample facilities discharge. Table G.8 

provides a numeric example of this calculation for a hypothetical reach to which three known sample facilities discharge. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

Figure G.1a: Estimating MP&M Pollutant Loadings to Receiving Streams When Using a Random Sample of MP&M 

Facilities 

Source: U.S. EPA analysis. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

Figure G.1b: Estimating MP&M Pollutant Loadings to Receiving Streams When Using a Random Sample of MP&M 

Facilities 

Note: The situation may be further complicated by actually having a non-sampled MP&M facility on the same reach. The differential 

weighting method does not address this issue. 

Source: U.S. EPA analysis. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

Figure G.1c: Estimating MP&M Pollutant Loadings to Receiving Streams When Excluding Background 

Concentrations 

Source: U.S. EPA analysis. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

Table G.8: Example of Differential Sample Weighting Technique 

Facility Weight Pollutant A 

lbs/yr 

Flow 

gal/year 

Raw data: 

1 10 5 2,000,000 

2 3 2 4,000,000 

3 1 12 10,000,000 

Total 14 19 16,000,000 

Reach flow (gal/year): 100,000,000 

Calculating flow and pollutant loadings for the reach: 

1. facilities in ascending order of weights 

3 1 12 10,000,000 

2 3 2 4,000,000 

1 10 5 2,000,000 

2. late flow and pollutant loadings for discharge event 1 with weight = 1 

Facility 

Pollutant A 

lbs/yr 

Flow 

gal/year Remaining Weight 

3 12 10,000,000 0 

2 2 4,000,000 2 

1 5 2,000,000 9 

Event 1 19 16,000,000 

3. ity with the lowest weight and calculate flow and pollutant loadings for discharge event 2 with 

weight = 2 (3-1) 

2 2 4,000,000 0 

1 5 2,000,000 7 

Event 2 7 6,000,000 

4. inate the facility with the next lowest weight and calculate and pollutant loadings for discharge event 3 

with weight = 7 (10-3) 

1 5 2,000,000 0 

Event 3 5 2,000,000 

5. tream concentrations based on the flows, loadings, and weights for each discharge event and 

the reach flow 

Discharge 

Event 

Pollutant A 

Loading 

lbs/yr 

Facility 

Flow 

gal/year 

Stream 

Flow 

gal/year 

Total 

Flow 

gal/year 

In-stream 

Concentration 

ppb 

Weight 

1 19 16,000,000 100,000,000 116,000,000 0.0955 1 

2 7 6,000,000 100,000,000 106,000,000 0.0385 2 

2,000,000 100,000,000 102,000,000 0.0286 7 

Total Affected 

Reaches: 10 

Rank

Calcu

Eliminate the facil

Elim

Estimate national in-s

Source: U.S. EPA analysis. 

G.3 METHODOLOGY FOR EXTRAPOLATION OF OHIO CASE STUDY RESULTS TO THE 

NATIONAL LEVEL 

EPA extrapolated the Ohio case study results to the national level based on three key factors that affect the occurrence and 

magnitude of benefits: 

� the estimated change in MP&M pollutant loadings, which reflects the potential for improvements in surface water 

quality; 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

� the level of recreational activities on the reaches affected by MP&M discharges. Recreational level reflects the 

degree to which potentially affected water resources are likely to be in demand by local residents; and 

� the average household income level, which affects the willingness-to-pay (WTP) for water quality improvements. 

G.3.1 Change in Pollutant Loads 

The first step in applying this alternative extrapolation method was to develop a measure of benefits per pound of pollutant 

removed for each category of benefits. EPA developed this measure by simply dividing the state-level benefit estimates by 

the total number of pounds of pollutant removed by the regulation in the state of Ohio ($ per pound of pollutant removed). 

EPA developed three different measures to better represent the relationship between pollutants and benefit categories: 

� Cancer health benefits: EPA divided cancer benefits from the Ohio case study by total carcinogen pounds removed 

in Ohio to estimate cancer health benefit per pound of carcinogen load removed; 

� Lead health benefits: EPA divided lead health benefits from the Ohio case study by total lead pounds removed in 

Ohio to estimate lead health benefit per pound of lead load removed; and 

� Recreational benefits: EPA divided recreational benefits from the Ohio case study by total pounds of pollutants 

removed (i.e., all pollutants except for total dissolved solids and biological oxygen demand) in Ohio to estimate 

recreational benefit per pound of pollutant load removed. 

All of these values are readily available from the Ohio case study. EPA extrapolated the state-level benefits for each of these 

benefit categories to the national level. First, the Agency multiplied the three estimated benefit per pound of pollutant values 

for Ohio by the total number of pounds of pollutant removed in each of the three pollutant categories at the national level. 

Then, EPA summed across the three benefit categories to obtain an initial estimate for total benefits at the national level. 

G.3.2 Level of Recreational Activities on Reaches Affected by MP&M Discharges 

The second step was to adjust for differences between Ohio and the nation in the level of recreational activity on reaches 

affected by MP&M discharges. The level of recreational activity reflects the degree to which water resources likely to be 

affected by MP&M discharges are in demand by local residents. EPA accounted for differences between Ohio and the nation 

in recreational intensity because the total user value of water quality improvements is a function of the number of users 

associated with a particular reach. For this adjustment factor, EPA used the ratio of the number of recreational user days per 

reach mile at the national level to the number of recreational user days per reach mile in Ohio. Due to data limitations 

preventing identification of all reaches affected by MP&M discharges, this analysis used total recreational user days and reach 

miles nationally and in Ohio, rather than only for those reaches affected by MP&M discharges. EPA used the National 

Demand Study (NDS) to estimate the number of user days for each recreation activity. Appendix N of this report provides the 

relevant data by state and recreation activity. To estimate the number of recreational user days, EPA summed the activityspecific values over the four activities considered in this analysis (i.e., recreational fishing, boating, swimming, and wildlife 

viewing). EPA’s Reach File 1 provided information on the total number of reach miles in Ohio and in the 48 contiguous 

states. The Agency then calculated the number of user days per reach mile in the state of Ohio and in the nation by simply 

dividing the total number of user days by the total number of reach miles in the corresponding region. EPA then calculated 

the adjustment factor as follows: 

(G.1) 

G.3.3 Differences in Household Income 

In the third step, EPA adjusted the extrapolated benefits based on the expectation that the WTP for water quality 

improvements will vary with household income level for different parts of the country. The adjustment factor used is the ratio 

G-14 
MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

of the average household income of the nation to the average household income of Ohio. This adjustment factor assumes that 

households around the country are willing to pay the same proportion of their incomes for water quality improvements, 

although the absolute value of this dollar amount will vary due to regional differences in average household income. The 

average household income of the nation is estimated as a weighted average, with the median household income for each state 

weighted by the proportion of MP&M facilities located in that state. The U.S. Census Bureau’s Current Population Surveys 

(March 1999, 2000, and 2001) provide the basis for data on the median household income by state for the year 2000.3 The 4 1992 Economic Census provides information on total MP&M facilities by state. 

(G.2) 

G.4 RESULTS 

Table G .9 presents national benefits based on the extrapolation of Ohio case study results. 

monetary value of benefits from reduced MP&M discharges is $2.5 million (2001$) for the final option. This estimate is 60% 

higher compared to the benefit estimate based on the traditional extrapolation methodology (i.e., $1.5 million (2001$)). As 

noted in the prior disc ussion, this difference is likely to be due to the mor e rigor ous appr oac h used for the O hio ca se stud y. 

The national-level analysis of human health benefits finds negligible health benefits from the final rule. In contrast, the Ohiobased extrapolation of human health benefits yields $10,860 and $295,202 (2001$) in human health benefits at the national 

level fro m red uced incidences o f cance r cases and adve rse hea lth impa cts from lead e xpo sure, respec tively. wn in 

Ta ble G .9, the estimated huma n health bene fits to Oh io resid ents exc eed the natio nal-leve l bene fits based on this 

extrap olation metho d. T his finding is due to the fact tha t the estima ted p ollutant re mov als for lea d and carcin ogens in O hio 

exceed those at the national level. As discussed in Appendix H, EP A administered 1,600 screener questionnaires to augment 

inform ation o n Oh io’s M P& M facilities. T he Agenc y used inform ation fro m the sa mple d M P& M facilities to estim ate 

discharge c harac teristics of non-sam pled M P& M chara cteristics (se e Ap pendix H for detail on estimating sa mple facility 

discharges in Ohio). As a result, the MP &M facilities included in the case study analysis represent a significant portion of the 

MP &M facility universe in Ohio. , the sample facilities used at the national-level analysis represent only 2 percent 

of the MP &M facility universe. Thus, analytic findings from the national-level analysis may have a larger than desired degree 

of uncertainty due to a very small sample size. 

Based on this approach, the 

As sho 

In contrast

3 Source: http://www.census.gov/hhes/income/income00/statemhi.html 

4 Appendix J presents information on distribution of MP&M facilities by state. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

Table G.9: Extrapolation of Ohio Case Study Results to the National Level (2001$) 

Category Ohio Nation 

Pounds removal of carcinogens 52.45 17.86 

Total cancer benefits $31,895.42 $10,860.86 

Total cancer benefits per pound removal of carcinogens $608.11 

Pounds removal of Lead 217.06 118.54 

Total lead benefits $540,549.14 $295,202.69 

Total lead benefits per pound removal of lead $2,490.32 

Pounds removal of total pollutants 483,258.02 5,412,810.88 

Total recreational benefits $250,932.62 $2,810,612.05 

Total recreational benefits per pound removal of total pollutants $0.52 

Nonuse benefits (1⁄2 of total recreational benefits) $125,466.31 $1,405,306.03 

Total benefits prior to application of adjustment factors $948,843.49 $4,521,981.63 

Reach miles 11,927 713,702 

Annual recreation days (millions) 49 1,646 

Annual recreation days per reach mile 4,148 2,306 

Recreational activity adjustment factor 0.5559 

Total benefits prior to application of income adjustment factor $2,513,907.82 

Average household income $43,894 $42,909 

Income Adjustment factor 0.9776 

Total benefits $2,457,494.66 

Source: U.S. EPA analysis. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

GLOSSARY 

ambient water quality criteria (AWQC): levels of water quality expected to render a body of water suitable for its 

designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking, 

swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html) 

differential sample weighting technique: weighting method for all threshold value-based analyses, such as the leadrelated benefits analysis. 

reach: a specific length of river, lake, or marine shoreline 

standard linear weighting technique: weighting method used where the effects being considered (e.g., compliance 

costs) are linearly additive over facilities. 

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MP&M EEBA: Appendices Appendix G: Extrapolation Methods 

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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

Appendix H: Fate and Transport 


Model for DW and Ohio Analyses 


INTRODUCTION 

For the drinking water (DW) and Ohio analyses, EPA 

used a simplified fate and transport model to quantify the 

fate and transport of MP&M pollutant releases to surface 

waters. This model estimates pollutant concentrations at 

the initial point of discharge and below the initial 

discharge reach. 

The national MP&M analysis considered pollutant 

concentrations only at the point of discharge (see 

Appendix I.2.2). The drinking water and Ohio analyses 

account for the in-stream concentrations of pollutants at 

the initial point of discharge and in reaches downstream 

from the initial discharge reach. 

This appendix describes the equations characterizing the 

model, its underlying assumptions, and the data sources 

used in model estimation. EPA combined the equations 

defining the model with geographic information (reach 

flow, velocity, length, etc.) to estimate pollutant 

APPENDIX CONTENTS 

H.1 ions ................. ........... H-1 

H.2 ssumptions ................. ......... H-3 

H.2.1 

Stream or River Reach ................. ... H-3 

H.2.2 tudinal Dispersion of the Pollutant is 

Negligible ................. ............. H-3 

H.2.3 eometry, Suspension of Solids, and 

Reaction Rates Are Constant within a 

River Reach ................. ........... H-4 

H.3 ................. ........ H-4 

H.4 ociating Risk with Exposed Populations ....... H-4 

H.5 ................. ............... H-4 

H.5.1 g Data Used in the 

Drinking Water Risk Analysis .............. H-4 

H.5.2 g Data Used in the Ohio Case 

Study Analysis ................. ......... H-4 

Glossary ................. ................. ..... H-8 

Acronyms ................. ................. .... H-9 

References ................. ................. .. H-10 

Model Equat 

Model A 

Steady Flow Conditions Exist within the 

Longi 

Flow G 

Hydrologic Linkages 

Ass 

Data Sources 

Pollutant Loadin 

Pollutant Loadin 

concentrations at the initial point of discharge and below the initial discharge reach. 

The estimation of pollutant concentrations below the initial discharge reach includes several factors that reduce the in-stream 

pollutant concentrations with the passage of time. These factors include: volatilization, sedimentation, and chemical 

decay from hydrolysis and microbial degradation. EPA adjusted concentrations for changes in stream flow volume in 

downstream reaches. The discussion below outlines the main assumptions of this analysis. Although more advanced models 

are available that account for time-variable flow, sediment transport, channel geometry changes within a reach, and detailed 

simulation of all in-stream processes, these models will not necessarily produce more accurate results without sufficient data 

to support the input parameters. Estimates of the additional input parameters required by these models are subject to a high 

degree of uncertainty when applied on a national scale, and gathering such data is beyond the scope of this study. 

EPA has previously applied the approach used in this analysis. For example, the first-order contaminant degradation 

relationship described below in Equation H.1 is currently being used by the Office of Pollution Prevention and Toxics for 

exposure analysis in the Risk Screening Environmental Indicator (RSEI) model (U.S. EPA, 1999). 

H.1 MODEL EQUATIONS 

The total pollutant concentration in the water columns for each reach included in the analysis is calculated by the following 

equation expressed in generic terms of mass (M), length (L), and time (T): 

(H.1) 

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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

where: 

CT = total tox icant co ncentration in th e water colum n (M /L3), 

W T = mass input rate of toxicant (M/T), 

Q = river flow (L3

/T), 

VT = overall net loss rate of chemical (L/T), 

H = flow depth (L), 

x = distance downstream from the point of release (L), and 

U = flow velocity (L/T). 

In reaches where more than one facility discharges or where pollutant loadings occur from upstream reaches, the mass input 

rate (WT) represents a combined input rate from all relevant industrial facilities affecting the reach. The relevant industrial 

facilities in the drinking water risk analysis are all MP &M sample facilities (see Chapter 13). The relevant industrial facilities 

in the Ohio case study analysis include:1 

� all sample M P& M facilities, 

� non-sample MP&M facilities, and 

� non-M P& M facilities. 

Th e ove rall net loss rate of chem ical (VT) is given b y: 

(H.2) 

where: 

VT = overall net loss rate of chemical (L/T), 

VTd = dissolved chemical loss rate (L/T), 

VTs = loss of chemical due to sediment interaction (L/T), 

kl = volatilization transfer coefficient (L/T), 

Kd = dissolved chemical decay rate (hydrolysis and microbial degradation) (1/T), 

H = flow depth (L), 

fd = dissolved fraction of toxicant (unitless), 

v n = net loss of solids (L/T), and 

f p = particulate fraction of toxicant (unitless). 

The disso lved and particulate fractions of the p ollutant, fd, and f p , respectively, are estimated by: 

(H.3) 

and 

(H.4) 

1
 See Chapter 22 for detail. 

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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

where : 

K p = partition coefficient [L3/M], and 

S = suspended solid s [M /L3

]. 

The disso lved concentration of metals and m ost other po llutants in the w ater co lumn is genera lly consid ered a mo re acc urate 

expression than the total concentrations o f the toxic or bio availab le fractio n. this reaso n, EP A modified Eq uation (H.1 ) to 

express the p ollutant concentration s in terms of dissolved c oncentration. he dissolved fraction of a pollutant is estimated as: 

(H.5) 

Substituting Equation (H.1) for CT yields the dissolved pollutant concentration for downstream distance x from the discharge 

reach: 

(H.6) 

For

T 

H.2 MODEL ASSUMPTIONS 

The following three principal assumptions underlie Equation H.5: 

H.2.1 Steady Flow Conditions Exist within the Stream or River Reach 

This assumption is necessary due to this study’s broad geographical coverage. This assumption significantly reduces the 

computational effort and input parameter requirements and still produces a good first-order fate and transport model of 

pollutants in surface waters. 

The pollutant concentration is completely mixed, both laterally (across the stream) and vertically (with depth) within each 

reach. The approach involves a two-dimensional model in which the concentration is uniform over the entire cross-section of 

the stream reach but varies with the distance of the reach. EPA assumed that the contaminant completely mixes at the point of 

release. This assumption will likely underestimate the concentration of a contaminant release in areas where mixing is 

incomplete (e.g., shore-hugging plume) and overestimate concentrations in areas beyond the point showing incomplete mixing 

(e.g., in areas beyond a shore-hugging plume). 

H.2.2 Longitudinal Dispersion of the Pollutant is Negligible 

The model does not account for mixing outside the plane of discharge along the river reach, although it predicts variation in 

pollutant concentrations over distance due to both pollutant fate and decay and the differing hydrology of downstream 

reaches. In natural streams, longitudinal velocity gradients due to channel irregularities can cause mixing, thereby decreasing 

the peak concentrations as the contaminant moves downstream from the point of release. Under steady-state situations, 

however, the longitudinal dispersion of the pollutant is assumed to be negligible. 

The solution of the dispersion equation approximates a first-order decay function such as the one shown in Equations H.1 and 

H.5 under steady flow conditions and complete lateral and vertical mixing. 

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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

H.2.3 Flow Geometry, Suspension of Solids, and Reaction Rates Are Constant within 

a River Reach 

EPA assumes the data that describe a river reach and that are calculated for a reach to be constant for the full extent of the 

reach. 

H.3 HYDROLOGIC LINKAGES 

EPA modeled pollutant concentrations for a distance of 500 km downstream from the discharge point in the drinking water 

risk analysis. In the Ohio case study analysis, EPA used the lesser of 500 km or the distance to the Ohio border from the 

initial discharge point to identify reaches potentially affected by pollutant discharges from the discharge point. The Agency 

obtained information on the hydrologic linkages between reaches from the RSEI Model (U.S. EPA, 1999). The data file in 

RSEI provided flow (mean flow, 7Q10) and velocity (mean, low) data for each reach. 

EPA used the process equations listed above to estimate both the initial pollutant concentrations at the beginning of each 

reach and the changes in concentrations as pollutants traveled to the end of the reach. The concentration at the end of each 

reach served as the value for the beginning of the next reach. 

H.4 ASSOCIATING RISK WITH EXPOSED POPULATIONS 

The number of individuals served by each drinking water intake is an output of the fate and transport model described in this 

appendix. If a drinking water intake exists on the initial reach or any downstream reach, then the model calculates the instream pollutant concentration at that intake. Data on the population served by the intake is saved with the concentration for 

further analysis (see Chapter 13 for a discussion of the cancer risk assessment). 

H.5 DATA SOURCES 

Data sources used for the fate and transport model are discussed briefly in the section below, by categories of information. 

H.5.1 Pollutant Loading Data Used in the Drinking Water Risk Analysis 

EPA estimated annual pollutant loadings (kg/yr) for the direct and indirect sample MP&M facilities analyzed under the 

various regulatory options.2 The Agency first adjusted pollutant loadings for indirect dischargers to reflect POTW treatment, 

and then divided annual pollutant loadings by the number of days in one year (365) to establish daily pollutant loadings. 

H.5.2 Pollutant Loading Data Used in the Ohio Case Study Analysis 

EPA estimated pollutant discharges from both MP&M and significant non-MP&M sources at the reaches included in the Ohio 

case study analysis. Consumer perception and valuation of enhanced water-based recreational opportunities depend on the 

absolute level of pollutant contamination at recreation sites, and on the change in contamination from the baseline to the 

post-compliance cases. For this reason, capturing the effect of concurrent discharges from all MP&M and other pollutant 

sources is particularly important for the recreational benefits analysis. 

EPA used the Office of Water's BASINS software package to identify all possible point source dischargers contributing to 

ambient pollutant concentrations at a given reach. BASIN S is a GIS-based system that serves as a database management 

system for water quality monitoring, point-source pollutant discharge, and various geo-technical data. Several sources 

provide information on point source discharges to BASINS, including the Permit Compliance System (PCS) and Toxic 

Release Inventory (TRI) databases. Version 2.0 includes data reported through 1996. Preprogrammed queries in BASINS 

2 EPA is not establishing pretreatment standards for indirect dischargers under the final rule. 

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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

generate information on various point source discharge variables at either the state or watershed level. BASINS data on point 

source dischargers include: 

� location information on major industrial dischargers, including PCS facilities and facilities reporting under TRI; 

� SIC codes; 

� flow volume; and 

� discharge characteristics for up to 50 pollutants or parameters for PCS facilities. 

The following sections describe steps used to characterize both MP&M and non-MP&M discharges in Ohio.


a. Characterize MP&M facility discharges


EPA used different approaches to assign discharge characteristics to MP&M facilities in Ohio, based on the level of


information available for each facility. The Agency divided all MP&M facilities into three groups, based on the level of 

information provided by different sources: 

� Facilities covered by the detailed Phase 1 and 2 questionnaire (hereafter, sampled MP&M facilities) 

The detailed surveys contain data on: 

� discharge status; 

� discharge volume; 

� industrial processes used; 

� pollution prevention activities; 

� employment, revenue, and costs. 

EPA engineers estimated loadings of 126 MP&M pollutants using information on facilities' processes and pollution


prevention activities.3 All MP& M facilities in this group therefore have extensive data on their location, size, and discharge


characteristics.


� Facilities covered by the detailed Iron and Steel questionnaire (hereafter sampled I&S facilities)


The detailed I&S survey contained data similar to the detailed MP&M survey. EPA engineers used data on I&S facilities'


processes and pollution prevention activities to estimate pollutant loadings from these facilities.


� Facilities covered by the Phase 2 screener questionnaire or that were covered by the Phase 1 mini-DCP (hereafter, 

MP&M screener facilities). 

The screener surveys contain significantly fewer data on MP&M facilities. The data collected from the screener survey 

recipients include: 

� facility location, which can be used to assign the facilities to receiving waterways or receiving POTWs; 

� SIC codes; 

� discharge status (i.e., whether the facility discharges process wastewater and the approximate amount); 

� employment and revenue data; 

� whether the facility is engaged in manufacturing, maintenance or repairing activities; and 

3 There are 132 pollutants of concern. EPA engineers estimated pollutant loadings for only the pollutants for which EPA is 

considering calculating pollutant removals at each option. For example, pollutant loadings are not provided for sodium, calcium, and TDS. 

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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

� data on MP&M unit operations (including type of MP&M unit operations performed at the site, and whether process 

wastewater is discharged as a result of each operation). 

The project engineers used these data to estimate pollutant loadings for these facilities. Loading estimates for the screener 

facilities, which are based on less comprehensive information, involve greater uncertainty. 

� Facilities that respond to neither the screener nor detailed questionnaires (hereafter referred to as non-sampled 

MP&M facilities) 

To address the problem of omitted discharge information on non-sampled MP&M facilities, EPA used information from the 

1600 screener MP&M facilities and a random draw approach to assign the relevant characteristics for non-sampled MP&M 

facilities. Each screener facility represents n non-sampled facilities, where n is determined by the screener facility sample 

weight. All non-sampled facilities are smaller indirect dischargers because all direct MP&M facility dischargers and large 

indirect discharging facilities in Ohio are covered by the long, short, or screener questionnaire. 

The exact location of non-sampled facilities is unknown. All non-sampled facilities discharge to one of the Ohio POTWs 

because they are indirect dischargers. The Agency assigned n facilities represented by each screener facility to the receiving 

POTW s by drawing a random sample of n POTW s from the universe of POTWs in Ohio.4

 The Agency assigned screener 

facility characteristics (i.e., pollutant loadings) to all n facilities represented by the screener facility. 

EPA used a random draw procedure for all observations from the screener survey that have a sample weight greater than one. 

b. Characterize non-MP&M point source discharges 

EPA used preprogrammed queries in BASINS to obtain information on all non-MP&M point source discharges in Ohio. 

BASINS data on non-MP&M point source dischargers include: 

� location, 

� SIC codes, 

� flow volume, and 

� discharge characteristics for up to 50 pollutants or parameters for PCS facilities. 

The Agency assigned discharge characteristics to all non-MP&M industrial direct discharges based on the information 

provided in BASINS. POTW effluent may contain pollutants from both MP&M and non-MP&M discharges. The Agency 

combined information from BASINS with loading estimates provided by the project engineers to estimate total pollutant 

loadings from a given POTW . This analysis used the following assumptions to estimate total POTW pollutant loadings under 

the baseline discharge levels: 

� If a POTW was not estimated to receive discharges from the MP&M facilities, then the analysis used POTW 

loadings reported in BASINS. 

� If a pollutant or a parameter was not reported in BASINS, then the analysis used aggregate loadings from all MP&M 

facilities discharging to a given POTW to calculate total POTW loadings of a given pollutant. 

� If a POTW was estimated to receives discharges from MP&M facilities and a given pollutant was reported in 

BASINS, then the analysis used the greater of the aggregate loadings from all MP&M facilities or POTW loadings 

reported. 

EPA estimated post-compliance pollutant loadings from each POTW by subtracting the estimated reduction in the MP&M 

facility loadings for a given pollutant from its total baseline loadings for a given POTW. 

c. Characterize non-point source discharges


The water quality analysis in Ohio used empirical data on Total Kjeldahl Nitrogen (TKN) concentrations to characterize


the baseline water quality conditions. Empirical data on in-stream concentrations captured TKN contribution from both point


4 The Agency was unable to validate random assignments because POTWs do not know all of their MP&M dischargers. 

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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

and non-point sources under baseline conditions. EPA estimated changes in TKN concentrations resulting from the final rule 

by using the estimated pollutant loading reductions from MP&M sources and the water quality model described above. The 

Agency assumed that the non-point source contribution of toxic pollutants found in MP& M effluent to ambient concentrations 

of these pollutants in Ohio’s streams and lakes is negligible. 

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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

GLOSSARY

BASINS: a software package that serves as a database management system for water quality monitoring, point source 

pollutant discharge, and various geo-technical data, and also provides an analytic platform for modeling in-stream pollutant 

concentrations over an entire watershed based on multiple sources of pollutants within the watershed. 

(http://www.epa.gov.OST/BASINS) 

hydrolysis: the decomposition of organic compounds by interaction with water. ( http://www.epa.gov/OCEPAterms) 

metals: inorganic compounds, generally nonvolatile, and which cannot be broken down by biodegradation processes. They 

are a particular concern because of their prevalence in MP&M effluents. Metals can accumulate in biological tissues, 

sequester into sewage sludge in POTWs, and contaminate soils and sediments when released to the environment. Some 

metals are quite toxic even when present at relatively low levels. 

microbial degradation: a process whereby organic molecules are broken down by microbial metabolism. 

Permit Compliance System (PCS): a computerized database of information on water discharge permits, designed to 

support the National Pollutant Discharge Elimination System (NPDES). 

(http://www.epa.gov/ceisweb1/ceishome/ceisdocs/pcs/pcs-exec.htm) 

MP&M reach: a reach to which an MP&M facility discharges. 

sedimentation: letting solids settle out of wastewater by gravity. ( http://www.epa.gov/OCEPAterms) 

Total Kjeldahl Nitrogen (TKN): the total of organic and ammonia nitrogen. TKN is determined in the same manner as 

organic nitrogen, except that the ammonia is not driven off before the digestion step. 

Toxic Release Inventory (TRI): database of toxic releases in the United States compiled from SARA Title III Section 313 

reports. ( http://www.epa.gov/OCEPAterms) 

volatilization: a process whereby chemicals dissolved in water escape into the air. 

(http://www.epa.gov/OCEPAterms) 

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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

ACRONYMS

PCS: Permit Compliance System


RSEI: Risk Screening Environmental Indicator model


TKN: Total Kjeldahl Nitrogen


TRI: Toxic Release Inventory


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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses 

REFERENCES

U.S. Environmental Protection Agency (U.S. EPA). 1999. Risk-Screening Environmental Indicators Model: Version 1.0, 

July 6, Washington, DC: Office of Pollution Prevention and Toxics. http://www.epa.gov/opptintr/env_ind/index.html. 

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MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Appendix I: Environmental 


Assessment 


INTRODUCTION 

This Environmental Assessment estimates the environmental 

impact of MP&M discharges on water bodies and POTWs 

under both current conditions and those corresponding to 

four regulatory options: the Final Option, Proposed/NODA 

Option, Directs + 413 to 433 Upgrade Option, and Directs + 

All to 433 Upgrade Option.1 EPA estimates four types of 

environmental impacts: 

�	 the occurrence of pollutant concentrations in excess 

of EPA ambient water quality criteria 

(AWQC) for protection of human health in 

waterways (e.g., streams, lakes, bays, and estuaries) 

receiving discharges from MP&M facilities; 

�	 the occurrence of pollutant concentrations in excess 

of AW QC for protection of aquatic species in 

waterways receiving discharges from MP&M 

facilities; 

�	 the occurrence of POTW inhibition problems 

resulting from MP&M facilities' discharges; and 

�	 barriers to POTW s’ use of preferred sewage sludge 

management or disposal methods (i.e., beneficial 

land application or surface disposal), due to metals 

discharges from MP&M facilities. 

APPENDIX CONTENTS 

I.1 racterization ................. . I-4 

I.1.1 ying MP&M Pollutants ................. I-4 

I.1.2 hysical-Chemical Characteristics and Toxicity 

Data of MP&M Pollutants ................. ..... I-9 

I.1.3 MP&M Pollutants Based on Risk to 

Aquatic Receptors ................. .......... I-21 

I.1.4 umptions and Limitations ................. I-23 

I.2. hodology ................. ................. I-23 

I.2.1 ple Set Data Analysis and National 

Extrapolation ................. .............. I-23 

I.2.2 r Quality Modeling ................. .... I-23 

I.2.3 ct of Indirect Discharging Facilities on 

POTW Operations ................. .......... I-25 

I.2.4 umptions and Limitations ................. I-27 

I.3 ................. ................. I-28 

I.3.1 ty-Specific Data ................. ...... I-28 

I.3.2 r Body-Specific Data ................. .. I-28 

I.3.3 ation Used to Evaluate POTW Operations . . I-29 

I.4 ................. ................. ..... I-33 

I.4.1 n Health Impacts ................. ..... I-34 

I.4.2 ic Life Effects ................. ....... I-37 

I.4.3 Effects ................. ............ I-41 

Glossary ................. ................. ....... I-44 

Acronyms ................. ................. ...... I-48 

References ................. ................. ..... I-49 

MP&M Pollutant Cha

Identif

P

Grouping 

Ass

Met

Sam

Wate

Impa

Ass

Data Sources 

Facili

Wate

Inform

Results 

Huma

Aquat

POTW 

EPA also estimated changes in human health risk from reduced exposure to MP&M pollutants via consumption of 

contaminated fish and drinking water. Chapters 13 and 14 of this EEBA present both the methodology used to estimate 

human health impacts from exposure to MP&M pollutants and the results of this analysis. 

EPA assessed potential environmental impacts of MP&M discharges on the receiving water bodies and POTWs by using 

pollutant fate and toxicity data in conjunction with various modeling techniques. EPA quantified the releases of 132 

pollutants of concern under the final and alternative regulatory options.2 EPA then evaluated potential site-specific aquatic 

life and human health impacts resulting from the baseline and post-regulation pollutant releases. EPA compared projected 

water concentrations for each pollutant to either (a) EPA water quality criteria, or (b) toxic effect levels (i.e., lowest reported 

1 The results of the Proposed/NODA Option are not directly comparable to the final option alternatives. The total number of facilities 

reported for the Proposed/NODA Option analysis differs from the facility count reported for the final rule and the two upgrade options. 

After deciding in July 2002 not to consider the NODA option as the basis for the final rule, EPA performed no more analysis on the NODA 

option, including not updating facility counts and related analyses for the change in subcategory and discharge status classifications. 

2 EPA originally identified 150 MP&M POCs. Of these 150 POCs, the Agency estimated loadings for 132 pollutants for the phase 2 

proposal and NODA. The benefits analysis presented in earlier chapters is based on 132 pollutants for which loadings are available. The 

final regulation covers only the Oily Wastes subcategory and benefit reductions were estimated for 122 pollutants. 

I-1 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

or estimated toxic concentration that causes a problem) in the absence of water quality criteria for a pollutant. Figure I.1 

depicts steps used in the environmental assessment. The following sections detail these analytic steps. 

Figure I.1a: MP&M Environmental Impact Assessment 

Source: U.S. EPA analysis. 

I-2 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Figure I.1b: MP&M Environmental Impact Assessment (Continued) 

Source: U.S. EPA analysis. 

I-3 

MP&M EEBA: Appendices Appendix I: Environmental Assessment 

The remainder of this appendix is organized as follows. Section I.1 provides information on the pollutants found in MP&M 

discharges. Section I.2 describes the methodology used to estimate environmental impacts, including extrapolation of sample 

sets to the national level and estimates of water quality impacts. Section I.3 describes data sources for both MP&M facilities 

and POTWs. Section I.4 presents the environmental assessment results. 

I.1 MP&M POLLUTANT CHARACTERIZATION 

The extent of human and ecological exposure and risk from environmental releases of toxic chemicals depends on 

chemical-specific properties, the mechanism and media of release, and site-specific environmental conditions. 

Chemical-specific properties include toxic effects on living organisms, and the fate of chemicals in the environment. EPA 

estimated the fate of MP&M pollutants based on their propensity to volatilize, adsorb onto sediments, bioconcentrate, and 

biodegrade. EPA characterized the fate and toxicity of MP&M pollutants in three steps: 

� identifying pollutants of concern (POCs) in MP&M discharges, 

� compiling physical-chemical and toxicity data for those pollutants, and 

� grouping pollutants based on their characteristics. 

The pollutant-specific fate and toxicity data were used in various portions of the quantitative benefits assessment. In addition, 

EPA summarized the distribution of MP&M pollutants based on their fate and toxicity properties using the groupings 

developed in the third step. This summary is presented in Chapter 12. 

I.1.1 Identifying MP&M Pollutants 

EPA sampled MP&M facilities nationwide to assess the concentrations of pollutants in MP&M effluents. The Agency 

collected samples of raw wastewater from MP&M facilities and applied standard water analysis protocols to identify and 

quantify the pollutant levels in each sample. EPA used these analytical data, along with selection criteria, to identify 132 

contaminants of potential concern. MP&M POCs include 43 priority pollutants (PP), 3 conventional pollutants, and 86 

nonconventional pollutants. 

EPA then evaluated the potential environmental fate of these pollutants and their toxicity to humans and aquatic receptors. 

EPA was able to assess the potential fate and toxicity of 118 of these pollutants, including 43 priority pollutants (33 priority 

organics, nine priority metals and one inorganic) and 75 nonconventional pollutants (50 nonconventional organics, 18 

nonconventional metals, and seven nonconventional inorganics). Table I.1 presents the potential fate and toxicity, based on 

known characteristics of each chemical, of 132 pollutants of concern. Potential fate and toxicity data are not available for 

four conventional, 2 nonconventional, and eight bulk nonconventional pollutants (also listed in Table I.1) associated with 

adverse water quality impacts, as described in Section 12.1.3 of this report. 

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MP&M EEBA: Appendices A

Table I.1: Potential Fate and Toxicity of Pollutants of Concern

Typea Pollutant CAS

Toxicity to

Aquatic Life

(Freshwater)

Toxicity to

Aquatic Life

(Saltwater)

Volatility Adsorption BCFb Biodegc RfDd S

Acute Chronic Acute Chronic

O Acenaphthene 83329 Moderate Low Moderate Low Moderate Moderate Moderate Low �

O Acetone 67641 Low Low Low Low Moderate Low Insignificant Moderate �

O Acetophenone 98862 Low Low Unknown Unknown Low Low Low Moderate �O Acrolein 107028 High High High High Moderate Nonadsorptive Moderate Low � O Aniline 62533 Moderate High Low Low Low Low Low Moderate 

O Anthracene 120127 High High High Moderate Moderate High Moderate Resistant �

O Benzoic acid 65850 Low Low Unknown Unknown Low Low Low Moderate �

O Benzyl alcohol 100516 Low Low Low Low Low Nonadsorptive Insignificant Moderate �

O Biphenyl 92524 Moderate Low Low Low Moderate Moderate Moderate Moderate �

O Bis(2-ethylhexyl) phthalate 117817 Unknown Unknown Unknown Unknown Nonvolatile High Moderate Moderate � O Bromo-2-chlorobenzene, 1- 694804 Low Low Unknown Unknown Moderate Moderate Moderate LowO Bromo-3-chlorobenzene, 1- 108372 Low Low Unknown Unknown Moderate Moderate Moderate LowO Butyl benzyl phthalate 85687 Moderate Low Moderate Low Low High Moderate Moderate �

O Carbon disulfide 75150 Low High Unknown High High Low Low Unknown �O Chlorobenzene 108907 Low Low Low Low High Low Low Low � 

O Chloroethane 75003 Low Low Unknown Unknown High Low Low Low � 

O Cresol, o- 95487 Low Low Low Low Low Low Low Moderate �

O Cresol, p- 106445 Low Low Unknown Unknown Low Low Low High �O Cyanide 57125 High High High High Unknown Low Insignificant Moderate � O Cymene, p- 99876 Low Low Low Low High Moderate High Low

O Decane, n- 124185 Low Low Low Low Unknown High High Moderate

O Dibenzothiophene 132650 Moderate Low Unknown Unknown Moderate High High Unknown

O Dichloroethene, 1,1- 75354 Low Low Low Low High Low Low Resistant � 

O Dichloromethane 75092 Low Low Low Low High Low Insignificant Low � 

O Dimethyl phthalate 131113 Low Low Low Low Nonvolatile Low Low Moderate 

O Dimethylformamide, N,N- 68122 Low Low Unknown Unknown Nonvolatile Nonadsorptive Insignificant Moderate �O Dimethylphenanthrene, 3,6- 1576676 Moderate Moderate Unknown Unknown Low High High Moderate

O Dimethylphenol, 2,4- 105679 Low Low Unknown Unknown Low Low Moderate Moderate �

O Di-n-butyl phthalate 84742 Moderate Low Moderate High Low Moderate Moderate Moderate � O Dinitrophenol, 2,4- 51285 Low Low Low Low Low Moderate Insignificant Resistant � 

O Dinitrotoluene, 2,6- 606202 Low Moderate Unknown Unknown Low Low Low Resistant �O Di-n-octyl phthalate 117840 Moderate Moderate Unknown Unknown Low Moderate High Low �O Dioxane, 1,4- 123911 Low Low Unknown Unknown Low Low Insignificant Resistant �

O Diphenylamine 122394 Low Low Unknown Unknown Low Moderate Moderate Moderate �O Diphenyl ether 101848 Moderate Low Low Unknown Moderate Moderate Moderate Moderate

O Docosane, n- 629970 Low Low Low Low Unknown High High Moderate

O Dodecane, n- 112403 Low Low Low Low Unknown High High Moderate

O Eicosane, n- 112958 Low Low Low Low Unknown High High Moderate

O Ethylbenzene 100414 Low Low Moderate Moderate High Low Low Moderate � 

O Fluoranthene 206440 High High High Moderate Moderate High High Resistant �

O Fluorene 86737 Moderate High Moderate Moderate Moderate Moderate Low Low �O Hexacosane, n- 630013 Low Low Low Low Unknown Unknown Unknown Moderate

O Hexadecane, n- 544763 Low Low Low Low Unknown High High Moderate

O Hexanoic acid 142621 Low Low Unknown Unknown Moderate Low Low Moderate

O Hexanone, 2- 591786 Low Low Unknown Unknown Moderate Low Low Moderate �

O Isobutyl alcohol 78831 Low Low Low Low Moderate Low Insignificant Moderate �

� 

� 
MP&M EEBA: Appendices Appendix I

Table I.1: Potential Fate and Toxicity of Pollutants of Concern

Typea Pollutant CAS

Toxicity to

Aquatic Life

(Freshwater)

Toxicity to

Aquatic Life

(Saltwater)

Volatility Adsorption BCFb Biodegc RfDd SFe DW

Acute Chronic Acute Chronic

I-6

O Isophorone 78591 Low Low Low Low Low Low Insignificant Low � 

O Isopropylnaphthalene, 2- 2027170 Moderate Moderate Unknown Unknown Moderate High High Unknown

O Methyl ethyl ketone 78933 Low Low Low Low Moderate Nonadsorptive Insignificant Moderate �

O Methyl isobutyl ketone 108101 Low Low Low Low Moderate Low Insignificant Moderate �

O Methyl methacrylate 80626 Low Low Unknown Unknown Moderate Low Low Low �O Methylfluorene, 1- 1730376 Moderate Low Unknown Unknown Moderate High High Unknown

O Methylnaphthalene, 2- 91576 Low Low Moderate Moderate Moderate Moderate High Moderate �

O Methylphenanthrene, 1- 832699 Moderate Moderate Unknown Unknown Low High High Unknown

O Naphthalene 91203 Low Low Low Low Moderate Low Low Moderate � 

O Nitrophenol, 2- 88755 Low Low Low Low Low Low Low Low 

O Nitrophenol, 4- 100027 Low Low Low Low Nonvolatile Low Moderate Moderate � 

O Nitrosodimethylamine, N- 62759 Low Low Low Low Nonvolatile Low Insignificant Resistant � 

O Nitrosodiphenylamine, N- 86306 Low Low Low Low Low Moderate Moderate Low �

O Nitrosopiperidine, N- 100754 Low Low Unknown Unknown Nonvolatile Nonadsorptive Insignificant ResistantO Octacosane, n- 630024 Low Low Low Low Unknown Unknown Unknown ModerateO Octadecane, n- 593453 Low Low Low Low Unknown High High Moderate

O Parachlorometacresol 59507 Low Low Unknown Unknown Low Low Moderate Low �

O Phenanthrene 85018 Moderate Moderate Moderate Moderate Moderate High Moderate Resistant O Phenol 108952 Low Low Low Low Low Low Insignificant High � 

O Pyrene 129000 Moderate Moderate Unknown Unknown Moderate High High Resistant �O Pyridine 110861 Low Low Unknown Unknown Low Nonadsorptive Insignificant Moderate �

O Styrene 100425 Low Low Low Low High Low Low Low � 

O Terpineol, alpha- 98555 Low Low Unknown Unknown Moderate Low Low Moderate

O Tetrachloroethene 127184 Low Low Low Low High Low Low Resistant � 

O Tetracosane, n- 646311 Low Low Low Low Unknown High High ModerateO Tetradecane, n- 629594 Low Low Low Low Unknown High High ModerateO Toluene 108883 Low Low Low Low High Low Low Moderate � 

O Triacontane, n- 638686 Low Low Low Low Unknown Unknown Unknown Moderate

O Trichloroethene 79016 Low Low Low Low High Low Low Resistant � 

O Trichlorofluoromethane 75694 Low Low Unknown Unknown High Low Low Resistant �

O Trichloromethane 67663 Low Low Low Low High Low Insignificant Resistant � T

O Tripropyleneglycolmethylether 20324338 Low Low Unknown Unknown Nonvolatile Low Insignificant Moderate

O Xylene, m- 108383 Low Low Low Low High Low Moderate Low � 

O Xylene, m- & p-* 179601231 Low Low Low Low High Low Moderate Low � 

O Xylene, o- 95476 Low Low Low Low High Low Moderate Low � 

O Xylene, o- & p-* 136777612 Low Low Low Low High Low Moderate Low � 

O Ziram \ Cymate 137304 High High Low Low Nonvolatile Nonadsorptive Insignificant Resistant �

M Aluminum 7429905 Moderate Moderate Unknown Unknown Nonvolatile High Moderate Resistant � 

M Antimony 7440360 Low Low Low Low Nonvolatile High Insignificant Resistant � 

M Barium 7440393 Low Low Unknown Unknown Nonvolatile High Unknown Resistant � 

M Beryllium 7440417 Moderate High Unknown Unknown Nonvolatile High Low Resistant � 

M Cadmium 7440439 High High High High Nonvolatile High Moderate Resistant � M Calcium 7440702 Unknown Low Unknown Unknown Nonvolatile High Unknown ResistantM Chromium 7440473 Moderate Moderate Low Moderate Nonvolatile High Low Resistant � 

M Chromium hexavalent 18540299 High Moderate Low Moderate Nonvolatile High Low Resistant � M Cobalt 7440484 Low Moderate Unknown Moderate Nonvolatile High Unknown Resistant �

�

� 

� 

� 
MP&M EEBA: Appendices A

Table I.1: Potential Fate and Toxicity of Pollutants of Concern

Typea Pollutant CAS

Toxicity to

Aquatic Life

(Freshwater)

Toxicity to

Aquatic Life

(Saltwater)

Volatility Adsorption BCFb Biodegc RfDd S

Acute Chronic Acute Chronic

M Copper 7440508 High High High High Nonvolatile High Moderate Resistant � M Gold 7440575 Unknown Unknown Unknown Unknown Nonvolatile High Unknown Resistant

M Iron 7439896 Unknown Low Low Low Nonvolatile High Unknown Resistant � M Lead 7439921 High High Moderate High Nonvolatile High Low Resistant 

M Magnesium 7439954 Low Low Unknown Unknown Nonvolatile High High Resistant

M Manganese 7439965 Unknown Low Unknown Moderate Nonvolatile High Unknown Resistant � 

M Mercury 7439976 High High High High High High High Resistant 

M Molybdenum 7439987 Unknown Moderate Unknown Unknown Nonvolatile High Unknown Resistant �M Nickel 7440020 Moderate Moderate High High Nonvolatile Low Low Resistant � M Selenium 7782492 High High Moderate Moderate Nonvolatile High Insignificant Resistant � 

M Silver 7440224 High High High High Nonvolatile High Insignificant Resistant � 

M Sodium 7440235 Low Low Unknown Unknown Nonvolatile High Unknown Resistant

M Thallium 7440280 Low Moderate Low Low Nonvolatile High Moderate Resistant � 

M Tin 7440315 Unknown Moderate Unknown Unknown Nonvolatile High Unknown Resistant �M Titanium 7440326 Unknown Low Unknown Unknown Nonvolatile High Unknown Resistant �

M Vanadium 7440622 Low High Unknown Unknown Nonvolatile High Unknown Resistant �M Yttrium 7440655 Unknown Unknown Unknown Unknown Nonvolatile High Unknown Resistant

M Zinc 7440666 Moderate Low High Moderate Nonvolatile High Low Resistant � 

OI Ammonia as N 7664417 Low Low Low Low Moderate Nonadsorptive Unknown Moderate

OI Arsenic 7440382 Moderate Low High Moderate Unknown Unknown Low Unknown � 

OI Boron 7440428 Unknown Moderate Unknown Unknown Unknown Unknown Unknown Unknown �OI Chloride 16887006 Low Low Unknown Unknown Unknown Unknown Unknown Unknown 

OI Fluoride 16984488 Low Low Unknown Unknown Unknown Unknown Unknown Unknown � 

OI Phosphate 14265442 Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown

OI Sulfate 14808798 Unknown Low Unknown Unknown Unknown Unknown Unknown Unknown 

OI Sulfide 18496258 Unknown High Unknown High Unknown Unknown Unknown Unknown

OI Phosphorus (as PO4) Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown

CP BOD 5-day (carbonaceous) C-003CP Oil and Grease

CP Oil and Grease (as Hem) C-036CP Total Suspended Solids (TSS) C-009

BNCP Amenable Cyanide C-025BNCP Chemical Oxygen Demand (COD) C-004BNCP Total Dissolved Solids (TDS) C-010

BNCP Total Kjeldahl Nitrogen C-021BNCP Total Organic Carbon (TOC) C-012
MP&M EEBA: Appendices Appendix I

Table I.1: Potential Fate and Toxicity of Pollutants of Concern 

Typea Pollutant CAS 

Toxicity to 

Aquatic Life 

(Freshwater) 

Toxicity to 

Aquatic Life 

(Saltwater) 

Volatility Adsorption BCFb Biodegc RfDd SFe DW

Acute Chronic Acute Chronic 

BNCP 

Total Petroleum Hydrocarbons (as Sgt -hem) C-037 

BNCP Total Recoverable Phenolics C-020 

BNCP Weak-acid Dissociable Cyanide C-042 

Table Notes: Unless indicated otherwise, all metals are assumed to be nonvolatile, to have high adsorption, 

and to be resistant to biodegradation. 

a Type 

O = Organic 

M = Metal 

OI = Other Inorganic CP = Conventional Pollutant 

BNCP = Bulk Nonconventional Pollutant 

b BCF = Bioconcentration Factor 

Biodeg = Biodegradation Potential 

d RfD = Reference Dose 

Source: U.S. EPA analysis. 

e SF = Slope Factor 

f DWC = Drinking Water Criteria 

g Drinking Water Criteria Codes 

M = Maximum Contaminant Level (MCL) establi

SM = Secondary Maximum Contaminant Level (SM

aesthetic effect 

THM = MCL established for trihalomethanes 

TT = Treatment technology action level establishe

h HAP = Hazardous Air Pollutant i PP = Priority Pollutant 

I-8 

c 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

I.1.2 Physical­Chemical Characteristics and Toxicity Data of MP&M Pollutants 

Pollutants present in MP& M effluents can have significant effects on human health and aquatic receptors. EPA used various 

data sources to evaluate both pollutant-specific fate and toxicity and potential human health effects, including: 

� reference doses (RfDs), 

� cancer potency slope factors (SFs), 

� human health-based water quality criteria (WQC), 

�	 maximum contaminant levels (MCLs) for drinking water protection and other drinking water related criteria, 

and 

� hazardous air pollutant (HAP) and priority pollutant (PP) lists. 

To evaluate potential fate and effects in aquatic environments, the Agency relied on: 

� measures of acute and chronic toxicity to aquatic species, 

� bioconcentration factors for aquatic species, 

� Henry's Law (H) constants (to estimate volatility), 

� adsorption coefficients (to estimate association with bottom sediments), and 

� biodegradation half-lives (to estimate the removal of chemicals via microbial metabolism). 

The data sources used in the assessment include: 

� EPA ambient WQC documents and updates; 

� EPA's ASsessment Tools for the Evaluation of Risk (ASTER); 

�	 the AQUatic Information REtrieval System (AQUIRE) and the Environmental Research Laboratory-Duluth 

fathead minnow database; 

� EPA's Integrated Risk Information System (IRIS); 

� EPA's Health Effects Assessment Summary Tables (HEAST); 

� EPA's 1991 and 1993 Superfund Chemical Data Matrix (SCDM); 

� Syracuse Research Corporation's CHEMFATE and BIODEG databases; and 

� EPA and other government reports, scientific literature, and other primary and secondary data sources. 

EPA also obtained information on chemicals for which the sources listed above did not provide physical-chemical properties 

and/or toxicity data, to ensure that the assessment be as comprehensive as possible. To the extent possible, EPA estimated 

values for the chemicals using the quantitative structure-activity relationship (QSAR) model incorporated in ASTER. 

The Agency also used published linear regression correlation equations to determine some physical-chemical properties. 

a. Human health effects 


EPA used various data sources to determine pollutant-specific toxicity to human health. EPA obtained RfDs and SFs from 


IRIS, HEAST, and EPA’s Region II Risk-Based Concentration (RBC) table. EPA developed drinking water criteria and 


human health-based AWQC values for two exposure routes: (1) ingesting the pollutant via contaminated aquatic organisms 

only (carcinogens and non-carcinogens), and (2) ingesting the pollutant via both water and contaminated aquatic organisms 

I-9 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

(non-carcinogens only). Table I.2 summarizes pollutant toxicity data pertaining to human health. In addition to fate and 

toxicity data, Table I.1 also includes HAP and PP lists. Short descriptions and definitions for each of the measures of human 

health effects are provided below. 

Table I.2: 

CAS Number Pollutant Name 

Human Health AWQC Values 

Ingesting 

Water and 

Organisms 

Ingesting 

Organisms 

Only 

Slope Factor Reference Dose 

Drinking 

Water 

Criteria 

(�g/l) (�g/l) (mg/kg/day) (mg/kg/day) (�g/l) 

51285 Dinitrophenol, 2,4- 70 14000 0.002 

57125 Cyanide 700 220000 0.02 200 

59507 Parachlorometacresol 56000 270000 2 

62533 Aniline 5.8 95 0.0057 

62759 Nitrosodimethylamine, N- 0.00069 8.1 51 

65850 Benzoic acid 130000 2900000 4 

67641 Acetone 3500 2800000 0.1 

67663 Trichloromethane 5.7 470 0.0061 0.01 100 

68122 Dimethylformamide, N,N- 3500 220000000 0.1 

75003 Chloroethane 12 520 0.0029 0.4 

75092 Dichloromethane 4.7 1600 0.0075 0.06 5 

75150 Carbon disulfide 3400 94000 0.1 

75354 Dichloroethene, 1,1- 0.057 3.2 0.6 0.009 7 

75694 Trichlorofluoromethane 9100 66000 0.3 

78591 Isophorone 36 2600 0.00095 0.2 

78831 Isobutyl alcohol 10000 1500000 0.3 

78933 Methyl ethyl ketone 21000 6500000 0.6 

79016 Trichloroethene 3.1 92 0.011 0.006 5 

80626 Methyl methacrylate 48000 2300000 1.4 

83329 Acenaphthene 1200 2700 0.06 

84742 Di-n-butyl phthalate 2700 12000 0.1 

85018 Phenanthrene 

85687 Butyl benzyl phthalate 3000 5200 0.2 

86306 Nitrosodiphenylamine, N- 5 16 0.0049 

86737 Fluorene 720 1500 0.04 

88755 Nitrophenol, 2-

91203 Naphthalene 680 21000 0.02 

91576 Methylnaphthalene, 2- 75 84 0.02 

92524 Biphenyl 720 1200 0.05 

95476 Xylene, o- 42000 100000 2 10000 

95487 Cresol, o- 1700 30000 0.05 

98555 Terpineol, alpha98862 Acetophenone 3400 98000 0.1 

99876 Cymene, p100027 Nitrophenol, 4- 220 1100 0.008 

100414 Ethylbenzene 3100 29000 0.1 700 

100425 Styrene 6700 160000 0.2 100 

100516 Benzyl alcohol 10000 810000 0.3 

100754 Nitrosopiperidine, N101848 Diphenyl Ether 

105679 Dimethylphenol, 2,4- 540 2300 0.02 

106445 Cresol, p- 170 3100 0.005 

107028 Acrolein 410 1000 0.02 

Human Health Data for 132 MP&M Pollutants of Concern 

I-10 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.2: 

CAS Number Pollutant Name 

Human Health AWQC Values 

Ingesting 

Water and 

Organisms 

Ingesting 

Organisms 

Only 

Slope Factor Reference Dose 

Drinking 

Water 

Criteria 

(�g/l) (�g/l) (mg/kg/day) (mg/kg/day) (�g/l) 

108101 Methyl isobutyl ketone 2800 360000 0.08 

108372 Bromo-3-chlorobenzene, 1-

108383 Xylene, m- 42000 100000 2 10000 

108883 Toluene 6800 200000 0.2 1000 

108907 Chlorobenzene 680 21000 0.02 100 

108952 Phenol 21000 4600000 0.6 

110861 Pyridine 35 5400 0.001 

112403 Dodecane, n- (a) 

112958 Eicosane, n- (a) 

117817 Bis(2-ethylhexyl) phthalate 1.8 5.9 0.014 0.02 6 

117840 Di-n-octyl phthalate 37 39 0.02 

120127 Anthracene 4100 6800 0.3 

122394 Diphenylamine 470 1000 0.025 

123911 Dioxane, 1,4- 3.2 2400 0.011 

124185 Decane, n127184 Tetrachloroethene 320 3500 0.052 0.01 5 

129000 Pyrene 230 290 0.03 

131113 Dimethyl phthalate 310000 2900000 

132650 Dibenzothiophene 

137304 Ziram \ Cymate 700 220000000 0.02 

142621 Hexanoic acid 

206440 Fluoranthene 300 370 0.04 

544763 Hexadecane, n- (a) 

591786 Hexanone, 2- 1400 65000 0.04 

593453 Octadecane, n- (a) 

606202 Dinitrotoluene, 2,6- 34 900 0.001 

629594 Tetradecane, n- (a) 

629970 Docosane, n630013 Hexacosane, n- (b) 

630024 Octacosane, n- (b) 

638686 Triacontane, n- (b) 

646311 Tetracosane, n- (b) 

694804 Bromo-2-chlorobenzene, 1-

832699 Methylphenanthrene, 1-

1576676 Dimethylphenanthrene, 3,6-

1730376 Methylfluorene, 1-

2027170 Isopropylnaphthalene, 2-

7429905 Aluminum 20000 47000 1 50 

7439896 Iron 300 0.3 300 

7439921 Lead 15 

7439954 Magnesium 

7439965 Manganese 50 100 0.14 50 

7439976 Mercury 0.05 0.051 2 

7439987 Molybdenum 0.005 

7440020 Nickel 610 4600 0.02 

7440224 Silver 170 110000 0.005 100 

7440235 Sodium 

7440280 Thallium 1.8 6.5 0.00007 2 

Human Health Data for 132 MP&M Pollutants of Concern 

I-11 

MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.2: 

CAS Number Pollutant Name 

Human Health AWQC Values 

Ingesting 

Water and 

Organisms 

Ingesting 

Organisms 

Only 

Slope Factor Reference Dose 

Drinking 

Water 

Criteria 

(�g/l) (�g/l) (mg/kg/day) (mg/kg/day) (�g/l) 

7440315 Tin 0.6 

7440326 Titanium 4 

7440360 Antimony 14 4300 0.0004 6 

7440382 Arsenic 0.02 0.16 1.5 0.0003 50 

7440393 Barium 1000 0.07 2000 

7440417 Beryllium 66 1100 0.002 4 

7440428 Boron 0.09 

7440439 Cadmium 14 84 0.0005 5 

7440473 Chromium 50000 1000000 1.5 100 

7440484 Cobalt 0.06 

7440508 Copper 650 1200 0.04 1300 

7440575 Gold 

7440622 Vanadium 0.007 

7440655 Yttrium 

7440666 Zinc 9100 69000 0.3 5000 

7440702 Calcium 

7664417 Ammonia as N 

7782492 Selenium 170 11000 0.005 50 

14265442 Phosphate 

14808798 Sulfate 250000 

16887006 Chloride 250000 

16984488 Fluoride 0.06 4000 

18496258 Sulfide 100 10000 

18540299 Chromium hexavalent 100 2000 0.003 100 

20324338 

Tripropyleneglycolmethyl 

ether 

136777612 Xylene, o- & p- (c) 42000 100000 2 10000 

179601231 Xylene, m- & p- (c) 42000 100000 2 10000 

C003 BOD 5-day (carbonaceous) 

C004 

Chemical Oxygen Demand 

(COD) 

C009 Total Suspended Solids (TSS) 

C010 Total Dissolved Solids (TDS) 

C012 Total Organic Carbon (TOC) 

C020 Total Recoverable Phenolics 

C021 Total Kjeldahl Nitrogen 

C025 Amenable Cyanide 

C036 Oil And Grease (as Hem) 

C037 

Total Petroleum 

Hydrocarbons (as Sgt-hem) 

C042 

Weak-acid Dissociable 

Cyanide 

Phosphorus (as PO4) 

Oil and Grease 

Human Health Data for 132 MP&M Pollutants of Concern 

Sources: U.S. EPA (1980), U.S. EPA (1984), U.S. EPA (1997), U.S. EPA (1998), U.S. EPA (1998/99), Worthing (1987). 

I-12 

MP&M EEBA: Appendices Appendix I: Environmental Assessment 

� Systemic toxicants 


System ic toxicants are chemicals that EPA believes can cause significant non-carcinogenic health effects when present in 


the human body above chemical-specific toxicity thresholds. These effects may result from acute or chronic chemical 


exposures, and include: 


�	 systemic health effects (i.e., loss of one or more neurological, respiratory, reproductive, immunological, or 

circulatory functions); 

� organ-specific toxicity (e.g., liver and kidney effects); 

� developmental toxicity (e.g., reduced weight in newborns or loss of IQ); and 

� lethality. 

EPA typically relies on animal toxicity data to develop RfDs for systemic toxicants that can enter the human body via 


ingestion. These values represent chemical concentrations expressed in mg of pollutant/kg body weight/day. Certain exposed 


populations are considered to be protected if these chemical concentrations are not exceeded. These populations include 


sensitive groups, such as young children or pregnant women. EPA included all available RfD data for the MP&M pollutants 


of concern (POCs) in the analysis. 


� Carcinogens 


Carcinogens are chemicals that EPA believes can cause or have the potential to cause cellular damage, which can lead to 


tumors or cancers in humans, either directly or indirectly. Unlike systemic toxicants, most carcinogens are not believed to 


have a toxicity threshold. Any amount of a carcinogen therefore has the potential to result in a cancer event, even though such 


a probability can be very small at low concentrations. The Agency has developed SFs, using animal or epidemiological data, 


that express the probability that a chemical will induce tumor or cancer development. EPA included all available SF data for 


the MP&M POCs in the analysis. 


� Drinking water criteria 


EPA developed human health-based drinking water criteria to assess the health hazards associated with the presence of certain 


toxic chemicals in drinking water. The criteria are usually presented as MCLs. MC Ls for non-carcinogens represent 


chemical-specific concentrations (expressed in �g/l) that are not expected to result in adverse health effects in exposed 


populations if not exceeded in drinking water. MCLs for carcinogens represent chemical-specific concentrations (expressed 


in �g/l) that are expected to result in less than one additional cancer case per million lifetime exposures if not exceeded in 


drinking water. The Agency also investigated additional drinking water criteria, including: 


� Secondary Maximum Contaminant Levels (SMCLs) established for taste or aesthetic effects, 

� MCLs established specifically for trihalomethanes, and 

� action levels developed on the basis of treatment technology. 

EPA included all the available primary and secondary drinking water criteria for the MP&M POCs in the analysis. 


� Pollutant uptake via water and/or organisms 


EPA has developed WQC for numerous priority toxic pollutants to protect the health of humans who consume water and 


organisms or only organisms obtained from aquatic habitats contaminated by those PPs. The criteria, expressed in �g/l, 


represent concentrations in surface waters that will cause adverse health effects in humans when exceeded. EPA obtained all 


available human health WQC for the MP&M POCs and included them in the analysis. 


� Priority pollutants (PPs) 


Priority pollutants are 126 individual chemicals, defined by the Agency as toxic, that EPA routinely analyzes when assessing 


contaminated surface water, sediment, groundwater, or soil samples. These chemicals are of particular concern to the Agency 


because of their high toxicity or persistence in the environment. EPA identified all MP&M PPs and included them in the 


analysis. 


I-13 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

� Hazardous air pollutants (HAPs) 


HAPs are compounds that EPA believes may represent an unacceptable risk to human health if present in the air. HAPs, 


expressed in �g/m3

, can be of particular concern to POTW workers if released into the air at high enough concentrations 


during the wastewater treatment cycle. EPA identified all HAPs among the MP&M POCs analyzed. 


b. Aquatic receptor effects  

The potential impact of chemicals on aquatic receptors can be assessed qualitatively based on five effect and fate parameters: 


� aquatic toxicity (acute and chronic), 

� bioconcentration, 

� volatilization, 

� adsorption, and 

� biodegradation. 

Site-specific risks require a measure of exposure and cannot be quantified using this approach. Chemicals can be classified 

and ranked in terms of their impacts on aquatic receptors, however, by using the five parameters discussed below. Table I.3 

summarizes the measured or estimated values of these parameters for the MP&M POCs. Each effect and fate parameter is 

described below. 

Biological oxygen demand (BOD), oil and grease (O&G), pH, and total suspended solids (TSS): These 

fate/effect parameters are relevant only for specific chemicals. These parameters are not available for the conventional 

pollutants or bulk nonconventional pollutants, such as total petroleum hydrocarbons (TPH), alkalinity, total organic 

carbon (TOC), or total Kjeldahl nitrogen (TKN). Most of these pollutants are responsible for significant environmental 

impacts, however. Section 12.2.4 outlines these impacts in greater detail. 

� Aquatic toxicity data 


The Agency addressed two general classes of aquatic toxicity: 


�	 Acute toxicity (AT) assesses the impacts of a pollutant after a relatively short exposure duration, typically 48 and 

96 hours for invertebrates and fish, respectively. The endpoint of concern is mortality, reported as the LC50. This 

value represents the concentration lethal to 50 percent of the test organisms for the duration of the exposure. 

�	 Chronic toxicity (CT) assesses the impact of a pollutant after a longer exposure duration, typically from one week 

to several months. The endpoints of concern are one or more sub-lethal responses, such as changes in reproduction 

or growth in the affected organisms. The results are reported in various ways, including EC1 or EC5 (i.e., the 

concentration at which one percent or five percent of the test organisms show a significant sub-lethal response), 

NOEC (No Observed Effect Concentration), LOEC (Lowest Observed Effect Concentration), or 

MATC (Maximum Allowable Toxicant Concentration). 

� Bioconcentration factor (BCF) data 


The bioconcentration factor (BCF, measured in l/kg) is a good indicator of the potential for a chemical dissolved in the 


water column to be taken up by aquatic biota across external surface membranes, usually fish gills. The BCF is defined as 


follows: 


BCF = equilibrium chemical concentration in target organism (mg/kg, wet weight) 

mean chemical concentration in surrounding water (�g/L) (I.1) 

EPA analyzes POCs with elevated BCF values because these pollutants can bioconcentrate in aquatic organisms and transfer 

up the food chain if they are not metabolized and excreted. This transfer can result in significant exposures to predators 

(including humans) consuming contaminated fish or shellfish. 

I-14 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Although the bioaccumulation factor (BAF) is a better measure of the potential for a chemical dissolved in the water column 


to be taken up by aquatic biota, field measured BAFs are not yet available. EPA recognizes that using bioconcentration 


factors will underestimate the risk to aquatic organisms. 


� Volatilization data 


Volatilization is a process whereby chemicals dissolved in water escape into the air. Chemicals with higher volatilization 


potential are typically of less concern to aquatic receptors because they tend to be removed quickly from the water column. 


These volatile pollutants are a concern to human health when inhaled. For aquatic receptors, however, POCs with higher 


volatilization potential present lower hazards. 


EPA used the air/water partitioning coefficient H to estimate a chemical’s volatilization potential. H represents the ratio of a 


chemical’s aqueous phase concentration to its equilibrium partial pressure in the gas phase (at 25°C); units are typically 


expressed as atm.m3

/mole. Metals do not have measurable partial pressures (with some notable exceptions, including 


several organic mercury compounds), and are therefore considered to be nonvolatile unless otherwise indicated. 


� Adsorption data 


Adsorption is a process whereby chemicals associate preferentially with the organic carbon (OC) found in soils and 


sediments. Highly adsorptive compounds tend to accumulate in sludge or sediments. Such chemicals are also more likely to 


be taken up by benthic invertebrates and to affect local food chains. Both accumulation in sediment and the effect on local 


food chains make these chemicals more likely to impact higher predators, including humans. 


EPA used the adsorption coefficient (Koc) to assess the potential of organic MP&M POCs to associate with organic 

carbon. Koc represents the ratio of the target chemical adsorbed per unit weight of organic carbon in the soil or sediment to 

the concentration of that same chemical in solution at equilibrium. Metals in the aquatic environment typically end up in the 

sediment phase but do not bind to the organic carbon (except for nickel). The Agency assumed that all metals show a high 

affinity for sludge and sediments independent of their negligible Koc values. 

� Biodegradation data 


Biodegradation is a process whereby organic molecules are broken down by microbial metabolism. Biodegradation 


represents an important removal process: compounds that are readily biodegraded generally represent lower intrinsic hazards 


because they can be eliminated rapidly. These compounds are therefore less likely to create long-term toxicity problems or to 


accumulate in sludge or sediments and organisms. Chemicals that biodegrade slowly or not at all can accumulate and linger 


for longer periods of time in sludge or sediments, and represent a higher hazard to aquatic receptors. 


EPA used biodegradation half-life to estimate the potential for an organic chemical to biodegrade in the aquatic 


environment. Biodegradation half-life represents the number of days a compound takes to be degraded to half of its starting 


concentration under prescribed laboratory conditions. Metals do not biodegrade. 


Table I.3 summarizes pollutant toxicity data pertaining to aquatic life. 


I-15 
MP&M EEBA: Appendices Appendix I

Table I.3: 

CAS 

Number Pollutant Name 

Freshwater Aquatic Life Saltwater Aquatic Life 

Bio

 

concentration 

Factor 

Henry's Law 

Constant 

A

C

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Value 

(l/kg) 

Value (atm/ 

m 3-mole) 

51285 Dinitrophenol, 2,4- 1160 790 1500 940 1.51 0.000000443 

57125 Cyanide 22 5.2 1 1 1 59507 Parachlorometacresol 4050 1300 79 0.0000025 

62533 Aniline 250 4 29400 2940 19.9 0.0000019 

62759 Nitrosodimethylamine, N- 280000 4000 4300000 430000 0.026 0.000000263 

65850 Benzoic acid 180000 17178 15 0.00000154 

67641 Acetone 6210000 1866000 5640000 10000 0.39 0.00004 

67663 Trichloromethane 13300 6300 19610 1961 3.75 0.00367 

68122 Dimethylformamide, N,N- 7100000 710000 0.005 0.000000018 

75003 Chloroethane 65614 21069 7.2 0.00882 75092 Dichloromethane 330000 82500 256000 2560 0.91 0.00219 

75150 Carbon disulfide 2100 2 2 11.5 0.0303 

75354 Dichloroethene, 1,1- 11600 5114 224000 22400 5.6 0.0261 

75694 Trichlorofluoromethane 17387 6412 49 0.097 

78591 Isophorone 120000 11000 12900 1290 4.38 0.00000576 

78831 Isobutyl alcohol 949000 4000 600000 60000 2.2 0.0000118 

78933 Methyl ethyl ketone 3220000 233550 1287000 128700 1 0.00006 

79016 Trichloroethene 40700 14850 14000 2000 10.6 0.0103 

80626 Methyl methacrylate 191000 19100 6.6 0.00034 

83329 Acenaphthene 580 208 970 710 242 0.00009 

84742 Di-n-butyl phthalate 850 500 450 3.4 89 0.00000181 

85018 Phenanthrene 180 19 110 11 486 0.00002 

85687 Butyl benzyl phthalate 820 260 510 400 414 0.00000126 

86306 Nitrosodiphenylamine, N- 5800 1000 3300000 33000 136 0.000005 

86737 Fluorene 212 8 1000 100 30 0.00006 

88755 Nitrophenol, 2- 160000 3451 32000 16000 13.5 0.00000947 

91203 Naphthalene 1600 370 1200 120 10.5 0.00048 

91576 Methylnaphthalene, 2- 1133 417 600 60 2566 0.00052 

92524 Biphenyl 360 230 4600 460 436 0.0003 

95476 Xylene, o- 3820 1332 6000 600 208 0.00519 

95487 Cresol, o- 14000 2251 10200 1020 18 0.0000012 

98555 Terpineol, alpha- 12742 4879 48 0.0000544 

Aquatic Life Toxicity Data for 132 MP&M Pollutants of Concern 

I-16 

MP&M EEBA: Appendices A

Table I.3: 

CAS 

Number Pollutant Name 

Freshwater Aquatic Life Saltwater Aquatic Life 

Bio

 

concentration 

Factor 

Henry's L

Constan

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Value 

(l/kg) 

Value (at

m 3-mole

98862 Acetophenone 162000 31094 11 0.0

99876 Cymene, p- 6500 237 4400 440 770 

100027 Nitrophenol, 4- 7680 1300 7170 1900 79 0.00000000

100414 Ethylbenzene 9090 4600 430 43 37.5 0.0

100425 Styrene 4020 402 9100 910 13.5 0.0100516 Benzyl alcohol 10000 1000 15000 1500 4 0.00000

100754 Nitrosopiperidine, N- 1019538 282592 0.00000

101848 Diphenyl Ether 4000 240 930 0.00

105679 Dimethylphenol, 2,4- 2120 1970 94 0.00000

106445 Cresol, p- 7500 2570 17.6 0.00

107028 Acrolein 14 5.8 55 5.5 215 0.0

108101 Methyl isobutyl ketone 505000 50445 812000 81200 2.4 0.0

108372 Bromo-3-chlorobenzene, 1- 1784 682 190 0.0

108383 Xylene, m- 16000 3900 12000 1200 208 0.0108883 Toluene 5500 1000 6300 5000 10.7 0.0

108907 Chlorobenzene 2370 2100 10500 1050 10.3 0.0

108952 Phenol 4200 200 5800 2410 1.4 0.00000

110861 Pyridine 93800 25000 2 0.0000

112403 Dodecane, n- (a) 18000 1300 500000 50000 14500 

112958 Eicosane, n- (a) 18000 1300 500000 50000 100000 117817 Bis(2-ethylhexyl) phthalate 130 0.000

117840 Di-n-octyl phthalate 690 69 5460 0.00000

120127 Anthracene 2.78 2.2 40 16 478 0.0

122394 Diphenylamine 3790 734 269 0.00000

123911 Dioxane, 1,4- 9850000 1457300 0.4 0.000

124185 Decane, n- a 18000 1300 500000 50000 8800 

127184 Tetrachloroethene 4990 510 10200 450 30.6 0

129000 Pyrene 591 61 1110 0.00131113 Dimethyl phthalate 33000 1700 58000 5800 36 0.00000

132650 Dibenzothiophene 420 122 1100 0.0

137304 Ziram \ Cymate 8 1.8 5200 520 0.001 

142621 Hexanoic acid 320000 15170 16 0.000

Aquatic Life Toxicity Data for 132 MP&M Pollutants of Concern 
MP&M EEBA: Appendices Appendix I

Table I.3: 

CAS 

Number Pollutant Name 

Freshwater Aquatic Life Saltwater Aquatic Life 

Bio

 

concentration 

Factor 

Henry's Law 

Constant 

A

C

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Value 

(l/kg) 

Value (atm/ 

m 3-mole) 

206440 Fluoranthene 45 7.1 40 16 1150 0.0000161 

544763 Hexadecane, n- (a) 18000 1300 500000 50000 32300 

591786 Hexanone, 2- 428000 38868 6.6 0.000113 

593453 Octadecane, n- (a) 18000 1300 500000 50000 10100 

606202 Dinitrotoluene, 2,6- 18500 60 12 0.000000747 

629594 Tetradecane, n- (a) 18000 1300 500000 50000 19500 

629970 Docosane, n- b 530000 68000 500000 50000 100000 

630013 Hexacosane, n- (b) 530000 68000 500000 50000 

630024 Octacosane, n- (b) 530000 68000 500000 50000 

638686 Triacontane, n- (b) 530000 68000 500000 50000 

646311 Tetracosane, n- (b) 530000 68000 500000 50000 100000 

694804 Bromo-2-chlorobenzene, 1- 2942 1196 240 0.0006 

832699 Methylphenanthrene, 1- 555 54 4790 0.0000078 

1576676 Dimethylphenanthrene, 3,6- 543 21 33000 0.0000053 

1730376 Methylfluorene, 1- 627 115 3300 0.00008 

2027170 Isopropylnaphthalene, 2- 540 78 3200 0.00063 

7429905 Aluminum 750 87 231 

7439896 Iron 1000 33000 3300 

7439921 Lead 65 2.5 210 8.1 49 

7439954 Magnesium 64700 6470 85215 

7439965 Manganese 388 10 

7439976 Mercury 1.4 0.77 1.8 0.94 5500 0.018 

7439987 Molybdenum 27.8 

7440020 Nickel 470 52 74 8.2 47 

7440224 Silver 3.4 0.34 1.9 0.19 0.5 

7440235 Sodium 1640000 1020000 

7440280 Thallium 1400 40 2130 213 116 

7440315 Tin 18.6 7440326 Titanium 191 

7440360 Antimony 3500 1600 4800 2900 1 

7440382 Arsenic 340 150 69 36 44 

7440393 Barium 410000 2813 

Aquatic Life Toxicity Data for 132 MP&M Pollutants of Concern 

I-18 

MP&M EEBA: Appendices A

Table I.3: 

CAS 

Number Pollutant Name 

Freshwater Aquatic Life Saltwater Aquatic Life 

Bio

 

concentration 

Factor 

Henry's L

Constan

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Value 

(l/kg) 

Value (at

m 3-mole

7440417 Beryllium 130 5.3 19 7440428 Boron 31.6 

7440439 Cadmium 4.3 2.2 42 9.3 64 

7440473 Chromium 570 74 1100 50 16 

7440484 Cobalt 1620 49 10 7440508 Copper 13 9 4.8 3.1 360 

7440575 Gold 

7440622 Vanadium 11200 9 

7440655 Yttrium 

7440666 Zinc 120 120 90 81 47 

7440702 Calcium 200000 

7664417 Ammonia as N 13300 3060 3800 570 0.000

7782492 Selenium 12.83 5 290 71 4.8 

14265442 Phosphate 

14808798 Sulfate 1000000 

16887006 Chloride 860000 230000 

16984488 Fluoride 1600 160 

18496258 Sulfide 2 2 18540299 Chromium hexavalent 16 11 1100 50 16 

20324338 Tripropyleneglycolmethylether 2484600 683870 0.2 0.000000

136777612 Xylene, o- & p- c 2600 1205 6000 600 208 0

179601231 Xylene, m- & p- c 2600 1205 6000 600 208 0

C003 BOD 5-day (carbonaceous) 

C004 Chemical Oxygen Demand (COD) C009 Total Suspended Solids (TSS) C010 Total Dissolved Solids (TDS) C012 Total Organic Carbon (TOC) 

C020 Total Recoverable Phenolics 

C021 Total Kjeldahl Nitrogen 

C025 Amenable Cyanide 

C036 Oil and Grease (as Hem) 

Aquatic Life Toxicity Data for 132 MP&M Pollutants of Concern 
MP&M EEBA: Appendices Appendix I

Table I.3: 

CAS 

Number Pollutant Name 

Freshwater Aquatic Life Saltwater Aquatic Life 

Bio

 

concentration 

Factor 

Henry's Law 

Constant 

A

C

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Acute Value 

(�g/l) 

Chronic Value 

(�g/l) 

Value 

(l/kg) 

Value (atm/ 

m 3-mole) 

C037 

Total Petroleum Hydrocarbons (as Sgt-hem) 

C042 Weak-acid Dissociable Cyanide 

Phosphorus (as PO4) Oil and Grease 

Aquatic Life Toxicity Data for 132 MP&M Pollutants of Concern 

a Aquatic toxicity data for n-decane are reported based on structural similarity 

b Aquatic toxicity data for n-docosane are reported based on structural similarity 

Values for the most stringent isomer (p-Xylene) are assumed 

Sources: Arthur D. Little (1983), Arthur D. Little (1986), Birge et al. (1979), Clay (1986), Holdway and Spraque (1979), ICF, Inc. (1985), Leblanc (1980), Ly

Atomic Energy Commission (1973), U.S. EPA (1972), U.S. EPA (1976), U.S. EPA (1980), U.S. EPA (1993), U.S. EPA (1998/99a), U.S. EPA (1998/99b), Zhan

I-20 

c 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

I.1.3 Grouping MP&M Pollutants Based on Risk to Aquatic Receptors 

The impact assessment for aquatic receptors looks at the six individual fate and effects parameters for each MP&M POC, 

including acute and chronic aquatic toxicities, bioconcentration factors, Henry’s Law constants, adsorption coefficients, and 

biodegradation half-lives. EPA grouped POCs with similar attributes, and assigned qualitative descriptors of potential 

environmental behavior and impact to each group. This grouping was used to describe the range of MP&M pollutant 

characteristics in Chapter 12. The grouping described below focuses specifically on aquatic environments and their biological 

receptors; it does not cover the human health toxicity data discussed in the previous section. 

Table I.4 provides a summary of the categorization scheme for the six fate and effects parameters. 

Table I.4: Summary of Categorization Scheme For Six Fate and Effects Parameters 

Parameter High Hazard Moderate Hazard Low Hazard Insignificant Hazard 

Acute Toxicity (AT) AT < 100�g/l 100 � AT� 1,000�g/l AT > 1,000�g/l 

Chronic Toxicity (CT) CT < 10�g/l 10 � CT � 100�g/l CT > 100�g/l 

Bioconcentration Factor (BCF) BCF > 500 50 � BCF � 500 5 � BCF < 50 BCF < 5 

Henry’s Law Constant (H) H > 10-3 10-5 � H � 10-3 3.0x10-7 � H < 10-5 H < 3.0x10-7 

Adsorption Coefficient (KOC) oc > 10,000 1,000 � K oc � 10,000 10 � Koc < 1,000 K oc < 10 

Biodegradation Half-Life (t1/2) 1/2 < 7 d 7 d � t1/2 < 28 d 28 d �t1/2 < 180 d t1/2 �180 d 

K 

t 

Source: U.S. EPA analysis. 

a. Acute and chronic aquatic toxicity 

EPA used the available AT data to group chemicals according to their relative short-term effects on aquatic organisms, using 

the following categories: 

� AT < 100�g/l High acute toxicity 

� 100�g/l � AT � 1,000�g/l Moderate acute toxicity 

� AT > 1,000 �g/l Low acute toxicity 

These categories reflect the fact that acute toxicity decreases when higher concentrations of a pollutant are required to induce 

short-term mortality in the test organisms. EPA’s Office of Pollution Prevention and Toxics (OPPT) uses this categorization 

as guidance to assess data submitted in Premanufacture Notices (PMN) (EPA, 1996). 

EPA used the available CT data to group chemicals according to their relative long-term effects on aquatic organisms, based 

on the following categories: 

� CT < 10�g/l High chronic toxicity 

� 10�g/l � CT � 100�g/l Moderate chronic toxicity 

� CT > 100 �g/l Low chronic toxicity 

These categories assume that CT occurs at a concentration averaging one tenth of that responsible for acute toxicity. They 

also reflect the fact that chronic toxicity decreases when higher concentrations of a pollutant are required to induce longerterm lethal or sub-lethal responses in the test organisms. 

I-21 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

b. Bioconcentration factor (BCF) 

EPA used the available BCF data to group chemicals according to their potential to bioconcentrate in aquatic organisms, 

based on the following categories: 

� BCF > 500 High potential to bioconcentrate 

� 50�BCF�500 Moderate potential to bioconcentrate 

� 5�BCF<50 Low potential to bioconcentrate 

� BCF<5 No significant potential to bioconcentrate 

These categories reflect the fact that decreased BCF reduces the intrinsic hazard of a chemical to aquatic receptors, because 

the chemical is less likely to accumulate in biological tissues. 

c. Volatilization potential 

EPA used available H data to group organic chemicals according to their potential to volatilize from water into air, based on 

the following categories: 

� H > 10-3 High potential to volatilize 

� 10-5 � H � 10-3 Moderate potential to volatilize 

� 3.0×10-7 � H < 10-5 Low potential to volatilize 

� H < 3.0×10-7 No potential to volatilize 

Increased volatility decreases a chemical’s hazard to aquatic receptors because the chemical is more likely to quickly move 


from the receiving water into the atmosphere. (The opposite is true for human health; hazard to human health increases with 


increased volatility because a volatile chemical is more available for intake by inhalation.) 


d. Adsorption potential 

EPA used the available Koc to group the organic POCs according to their potential to adsorb to sediments, based on the 


following categories: 


� K oc > 10,000 High potential for adsorption 

� 1,000 � Koc � 10,000 Moderate potential for adsorption 

� 10 � K oc < 1,000 Low potential for adsorption 

� Koc < 10 No significant adsorption 

A lower adsorption potential indicates a lower potential for a chemical to be a hazard to aquatic receptors. The lower the 

adsorption potential the less likely a chemical is to accumulate in sediments or to affect benthic invertebrates and to be taken 

up into local food chains. 

e. Biodegradation potential 

EPA used biodegradation half-lives to group organic POCs according to their potential to biodegrade, based on the following 

categories: 

� t1/2 < 7 d Rapid rate of biodegradation 

� 7 d � t1/2 < 28 d Moderate rate of biodegradation 

� 28 d �t1/2 < 180 d Slow rate of biodegradation 

� t1/2 �180 d Resistant to biodegradation 

A faster rate of biodegradation by microbial metabolism decreases an organic chemical’s hazard to aquatic receptors. The 

more rapid the rate of biodegradation, the more quickly a chemical will be removed from the aquatic environment. Most 

metals occur as inorganic compounds (notable exceptions include organic forms of certain metals, such as mercury, lead, or 

selenium), and are not removed by biodegradation. EPA assumes that all metals are resistant to biodegradation for the 

purposes of this assessment. 

I-22 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

I.1.4 Assumptions and Limitations 

The following are the major assumptions and limitations associated with the data compilation and categorization used in the 

MP&M analysis: 

� Some data are estimated, and subject to uncertainty; 

� Data are unavailable for some chemicals and parameters; 

� The POCs considered in this study do not include all the constituents that may be present in MP&M pollutants; 

� Data derived from laboratory tests may not accurately reflect conditions in the field; and 

� Available aquatic toxicity and bioconcentration test data may not represent the most sensitive species. 

I.2 METHODOLOGY 

I.2.1 Sample Set Data Analysis and National Extrapolation 

This analysis uses discharge information from 862 sample MP&M facilities (excluding two sample facilities in Puerto Rico) 

that discharge directly or indirectly to 607 receiving waterways (521 rivers/streams, 62 bays/estuaries, and 24 lakes). The 

in-stream water quality analysis excluded eight of the 62 marine reaches due to data limitations. EPA performed 

environmental assessment on a basis of the sample facility data. The Agency then extrapolated findings from the sample 

facility analyses to the national level using two alternative extrapolation methods: (1) traditional extrapolation and (2) poststratification extrapolation. EPA also used the differential extrapolation technique in addition to both traditional and poststratification approaches when a sample reach was estimated to receive discharges from multiple facilities. Appendix G 

provides detailed information on the extrapolation approaches used in this analysis. Based on the extrapolation methods used 

in this analysis, EPA estimates that approximately 43,901 MP&M facilities discharge to between 29,500 and 40,000 water 

bodies nationwide.3 

EPA evaluated the national-level environmental impacts of reducing pollutant discharges from MP&M facilities to the 

nation's water bodies for the final rule. EPA considered only pollutant loadings from MP&M facilities to particular water 

bodies in the national analysis. With one exception, EPA did not take background loadings from other sources into account. 

For the analysis of sewage sludge quantity, EPA was able to use information from the Phase 2 Section 308 survey of POTWs 

to estimate total metal loadings from all sources to a POTW of a given size (i.e., small, medium, and large). The Agency 

based this estimate on survey estimates of the average number of small, medium, and large MP&M facilities discharging to a 

POTW in each size category and the percent contribution of total metal loadings discharged from MP&M facilities. 

I.2.2 Water Quality Modeling 

EPA used four different equations to model the impacts of MP&M discharges on receiving waterways. EPA used a simple 

stream dilution model for MP&M facilities that discharge into streams or rivers. This model does not account for fate 

processes other than complete immediate mixing.4 EPA derived the facility-specific data (i.e., pollutant loading and facility 

flow) used in this equation from sources described in Sections 3.1 and 5.2 of this report. 

The Agency used one of three receiving stream flow conditions (the lowest one-day average flow with a recurrence interval of 

10 years (1Q10), the lowest consecutive seven-day average flow with a recurrence interval of 10 years (7Q10), and the 

harmonic mean flow), depending on the criterion or toxic effect level being considered. 

3 These estimates include facilities that were assessed to be baseline closures by the MP&M economic analysis. 

4 EPA used an exponential decay model to estimate pollutant concentrations for the analysis of cancer risk from drinking water 

consumption for streams. This model is discussed in detail in Appendix G. 

I-23 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

The 1Q10 and 7Q10 flows are used in comparisons of in-stream concentrations with acute and chronic aquatic life criteria or 

toxic effect levels, respectively, as recommended in the Technical Support Document for Water Quality-based Toxics Control 

(U.S. EPA , 1991). 

The harmo nic mean flow, defined as the inverse mean of reciprocal daily arithmetic mean flow values, is used in comparisons 

of in-stream concentrations with human health criteria or toxic effect levels based on lifetime exposure. mmends the 

long-term harmonic mean flow as the design flow for assessing potential long-term human health impacts. 

flow is preferab le to arithm etic me an flow beca use in-strea m po llutant co ncentration is a function of, and inverse ly 

proportional to, the stream flow downstream of the discharge. 

The event frequency represents the number of times an exposure event occurs during a specified time period. 

event frequency equal to the facility operating days to assess impacts on aquatic life. The calculated in-stream concentration 

is thus the averag e con centra tion on days the facility is discha rging w astewa ter. A set the event frequency at 365 d ays to 

assess lo ng-term huma n health impacts. e calculated in-stream conc entration is thus the a verag e con centra tion on all days 

of the year. This frequency leads to a lower calculated concentration because of the additional dilution from days when the 

facility is not operating, but it is consistent with the conservative assumption that the target population is present to consume 

drinking water every day and contaminated fish throughout an entire lifetime. owing equation calculates in-stream 

concen tration for streams and rivers: 

(I.2) 

where: 

C is = in-stream pollutant concentration (�g/L); 

L = facility pollutant loading (�g/yr); for indirect dischargers, L = L indirect facility * (1-TMT), where TMT is POTW 

treatment removal efficiency (unitless); 

OD = facility or POT W operating days (days/yr); 

FF = MP &M facility flow (L/day); for indirect dischargers, FF = PO TW flow (L/day); 

EF = event frequency (days/yr); and 

SF = receiving stream flow (L/day). 

EP A use d the fo llowing simple steady-sta te mo del for facilities that d ischarge into lakes othe r than the Gre at lakes. his 

model takes into account pollutant degradation and the hydraulic residence time of the lake: 

(I.3) 

where: 

Clake = stead y-state lake conc entratio n of po llutant (�g/L), 

Ci = stead y-state inflow conc entratio n of po llutant (�g/L), 

T w = mean hydraulic residence time (yr), 

k = first-order pollutant decay rate (yr-1), and 

(I.4) 

where: 

V = lake volume (m3

), and 

Q = mean total inflow rate (m3

/yr). 

EPA reco 

Harmonic mean 

EPA set the 

EP 

Th 

The foll 

T 

I-24 

MP&M EEBA: Appendices Appendix I: Environmental Assessment 

EPA used alternative means to predict pollutant concentrations suitable for comparison with ambient criteria or toxic effect 

levels for facilities discharging to hydrologically complex waters, such as bays and estuaries. e, EPA employed 

site-specific critical dilution factors (CDFs) to predict the concentration at the edge of a mixing zone. re CDFs were 

not available, EPA used available estuarine diss olve d co ncentratio n potentials (DCPs). 

EPA obtained site-specific CDFs from a survey of states and regions conducted by EPA's Office of Pollution Prevention and 

To xics (Mixing Z one Dilution F actors for New C hem ical E xpo sure A ssessm ents, U.S. EPA, 1992a). The dilution model for 

estimating estuary concentrations by using a CDF is presented below: 

(I.5) 

where: 

C es = estuary pollutant concentration (�g/L); 

L = facility pollutant loading (�g/yr); for indirect dischargers, L = L indirect facility * (1-TMT), where TMT is POTW 

treatment removal efficiency (unitless); 

EF = event frequency (days/yr); 

FF = facility flow (L/day); for indirect dischargers, FF = PO TW flow (L/day); and 

CDF = critical dilution factor (unitless). 

EP A used a cute CD Fs to evaluate ac ute aquatic life effects and chron ic CD Fs to evaluate ch ronic aqu atic life or adverse 

human health effects. ng water intake and fishing location are at the edge of the chronic mixing 

zone. he event frequency equal to the facility operating days for comparison with aquatic life criteria or toxic effect 

levels, and equ al to 365 days for com parison with hum an health criteria or toxic effect levels. 

The National Oceanic and Atmospheric Administration (NOAA) has developed D CPs to predict pollutant 

concen trations in various salinity zones for each e stuary in NO AA's National Estuarine Inventory (NE I). 

represents the concentration of a nonreactive dissolved substance under well-mixed, steady-state conditions given an annual 

load of 10,000 tons. DCPs account for the effects of flushing by considering the freshwater inflow rate, and dilution by 

considering the total estuarine volume. s reflect the predicted estuary-wide response, and may therefore not be indicative 

of concentrations at the edge of much smaller mixing zones. mating pollutant concentrations 

using DCPs is presented below: 

(I.6) 

where: 

C es = estuary pollutant concentration (�g/L); 

L = facility pollutant loa ding (k g/yr); for ind irect disc harge rs, 

L = L indirect facility *(1-TM T), where TM T is PO TW treatment removal efficiency (unitless); 

DCP = dissolved c oncentratio n po tential (�g/L); 

BL = benchmark load (10,00 0 tons/yr); and 

CF = conversion factor (907.2 kg/ton). 

EPA determined potential water quality impacts by comparing projected waterway pollutant concentrations to EPA water 

quality criteria or toxic effect levels fo r the pr otectio n of aq uatic life an d hum an hea lth. A de termin ed w ater q uality 

exceedances by dividing the projected waterway pollutant concentration by the EPA water quality criteria or toxic effect 

levels for the protection of aquatic life and human health. A value greater than one indicates an exceedance. 

I.2.3 

a. ition 

Inhibition of PO TW operations occurs when high levels of toxics, such as metals or cyanide, kill the bacteria required for the 

wastewater treatment process. on of PO TW operations by comparing calculated POT W influent 

Where possibl 

Whe 

EPA assumed that the drinki 

EPA set t 

A DCP 

DCP 

The dilution model used for esti 

EP 

Impact of Indirect Discharging Facilities on POTW Operations 

Analysis of biological inhib 

EPA analyzed inhibiti 

I-25 

MP&M EEBA: Appendices Appendix I: Environmental Assessment 

concentrations with available inhibition levels. Exceedances are indicated by a value greater than one. POTW influent 

concentrations are estimated as: 

(I.7) 

where: 

Cpi = POT W influent concentration (�g/L), 

L = facility pollutant loading (�g/yr), 

O D = facility operating days (days/yr), and 

PF = POT W flow (L/day). 

b. udge disposal practices 

EPA also analyzed the effects of MP& M discharges on POTW operations by comparing the estimated concentrations of 

metals in sewage slud ge with the pub lished m etals co ncen tration lim its for pre ferab le sewa ge slud ge disp osal o r use p ractice s. 

In particular, E PA exam ined: 

� whether M P& M base line discharge s wou ld pre vent P OT W s from being able to meet the me tals con centra tion limits 

required for more favorable and lower-cost sewage sludge use/disposal practices (i.e., beneficial land application and 

surface disposal); and 

� whether limitations on the selection of management practices would be removed under the final rule. 

EPA estimated the sewage sludge concentrations of eight metals for sample facilities under baseline and post-regulatory 

option discharge levels. EPA compared these concentrations with the relevant metals concentration limits for three sewage 

sludge management options: Land Application-High (Concentration Limits), Land Application-Low (Ceiling Limits), and 

Surface D isposal. etal concentration s in sewage sludge are estimated as: 

(I.8) 

where: 

C sp = sewage sludge pollutant concentration (mg/kg), 

L = facility pollutant loading (�g/yr), 

T M T = POT W treatment removal efficiency (unitless), 

PART = pollutant-specific sludge partition factor (unitless), 

S G F = sludge generation factor (mg/kg per �g/L), 

OD = POT W operating days (days/yr), and 

PF = POT W flow (L/day). 

EPA derived the facility-specific data to evaluate POTW operations from the sources described in Sections 3.1 and 5.2. EPA 

examined multiple MP& M facilities discharging to the same POT W by summing the individual loadings before calculating 

the PO TW influent and sewage slud ge conc entrations. 

The partition factor is a chemical-specific value representing the fraction of the load expected to partition to sewage sludge 

during wastewater treatment. s analysis, EPA used a sludge generation factor of 5.96 mg/kg per �g/L. This factor 

indicated that the resulting concentration in sewage sludge is 5.96 mg/kg dry weight for every 1 �g/L of pollutant removed 

from wastewater and partitioned to sewage sludge. 

Analysis of sl

M 

For thi

I-26

MP&M EEBA: Appendices Appendix I: Environmental Assessment 

I.2.4 Assumptions and Limitations 

The following discussion focuses on major assumptions and limitations associated with these in-stream water quality analyses.


a. Other source contributions


EPA did not account for "other source contributions" of MP&M pollutants to estimate in-stream concentrations of these


pollutants. Accounting for the discharges from other sources is important because assessing benefits from reduced


exceedance of AWQC limits depends on comparing concentrations of pollutants from all sources with applicable thresholds.


Analyses must also identify situations in which threshold criteria are exceeded in the baseline case but met under a regulatory


option. Failing to account for other source contributions has an uncertain effect on estimated benefits. For example, if


non-sample MP&M facilities are major contributors to aggregate pollutant discharges to a receiving stream, then the analysis


will likely understate the extent of aquatic habitat improvements that may be accomplished by reduced MP&M pollutant


discharges. Conversely, if the total MP&M contribution to the aggregate pollutant discharges to a receiving stream is not


significant, then reducing MP&M discharges may reduce but not eliminate AWQC exceedances, and the benefits of the 

MP &M regulation can be overstated. The net effect of the following are unknown: 

� excluding other sources understates the number and extent of baseline exceedances; 

� excluding non-sample MP&M facilities understates the reduction in MP&M pollutant discharges due to the rule; and 

� the number of cases in which estimated baseline exceedances are eliminated may be either over- or understated, 

depending on the contribution of pollutants from non-MP&M sources. 

b. Water body modeling


EPA made four major assumptions concerning all water body modeling, and two major assumptions specific to stream


modeling. These assumptions are summarized below:


� Complete mixing of POTW discharge flow occurs immediately. This mixing results in the calculation of an 

"average" concentration, even though the actual concentration may vary across the width and depth of the water 

body. 

� Pollutant loads to the receiving water body are continuous and representative of long-term facility operations. This 

assumption may overestimate long-term risks to human health and aquatic life, but may underestimate potential 

short-term effects. 

� In the absence of data from EPA's Permit Compliance System (PCS) on specific individual POTW flow, 

POTW daily flow rates were set equal to the simple arithmetic mean flow among minor POTW s reporting flows in 

PCS. The arithmetic mean for minor POTWs was used because all POTW s receiving discharges from the sample 

MP&M facilities for which flow data are not available in the PCS database are classified as minor dischargers in the 

PCS database. 

� EPA used 1Q10 and 7Q10 receiving stream flow rates to estimate aquatic life impacts, and harmonic mean flow rates 

to estimate human health impacts, when modeling stream reaches. EPA estimated 1Q10 low flows by using the 

results of a regression analysis conducted for OPPT of 1Q10 and 7Q10 flows from representative U.S. rivers and 

streams (Versar, 1992). EPA estimated harmonic mean flows from the mean and 7Q10 flows as recommended in the 

Technical Support Document for Water Quality-based Toxics Control (U.S. EPA, 1991). These flows may not be 

the same as those used by specific states to assess impacts. 

� Where data on stream flow parameters were not available, EPA set mean and 7Q10 flow values equal to the 

corresponding mean values associated with reaches located upstream and downstream of the sample reach. 

I-27 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

c. Exposure analyses


MP&M exposure assessment in freshwater locations uses two sets of human health-based AWQC:


� AW QC for the protection of human health from the consumption of organisms and drinking water, and 

� AWQC for the protection of human health from consumption of organisms only. 

MP&M exposure assessments in marine locations use AWQC for the protection of human health from the consumption of


organisms only, because saltwater is not used for drinking water supply.


d. Extrapolation from sample set to national level


Although the sample set should represent a national group of facilities discharging to waterways and POTWs, effluent from an


individual sample facility may have a different potential environmental impact than effluent from the facilities it is assumed to


represent. For example, a facility that discharges to a stream with a very low flow may be similar to the facilities it represents


in all aspects except available dilution in the receiving stream. The sample frame used in the MP&M analysis was not


designed to take receiving water body characteristics into account. Using sample weights to extrapolate environmental 

impacts may either under- or overstate estimated impacts. 

I.3 DATA SOURCES 

The following three sections describe the various data sources used to evaluate water quality and POTW impacts. 

I.3.1 Facility­Specific Data 

Section I.2.1 provides detailed information on sample size and distribution, and on receiving waterways. The names, 

locations, and the flow data for the POTW s to which the MP&M facilities discharge were obtained from the MP&M facility 

surveys and EPA's PCS database. EPA took alternative measures to obtain a complete set of receiving POTWs if these 

sources did not yield information for a given facility. EPA used latitude/longitude coordinates (if available) to locate those 

POTW s that have not been assigned a reach number in PCS. EPA identified the nearest POTW in the case of facilities for 

which the POTW receiving the plant discharge could not be positively identified. EPA based its identification of the closest 

linear distance on the latitude/longitude coordinates of the MP&M facility or the city in which it was located. EPA then 

identified the corresponding reach in PCS, and obtained POTW flow from the Needs Survey or PCS. 

EPA identified reaches to which direct MP&M facilities discharge by identifying the receiving reach in PCS or by identifying 

the nearest reach. EPA based its identification of the closest linear distance on the MP&M facility’s latitude/longitude 

coordinates. 

I.3.2 Water body­Specific Data 

a. Streams and rivers


EPA used 1Q10, 7Q10, and mean flow data for the 521 streams and rivers. EPA obtained 7Q10 and mean flow data from the


W.E. Gates study data or from measured stream flow data, both of which are contained in EPA's GAGE file. The W.E. Gates


study contains calculated average and low flow statistics based on the best available flow data and on drainage areas for 

reaches throughout the United States. The GAGE file also includes average and low flow statistics based on measured data 

from United States Geological Survey (USGS) gaging stations. In the absence of data on stream flow parameters, EPA 

set 7Q10 and mean flow values equal to the corresponding median values associated with the sample reaches. EPA used the 

results of a regression analysis conducted for OPPT of 1Q10 and 7Q10 flows from representative U.S. rivers and streams 

(Versar, 1992) to estimate 1Q10 flows. EPA estimated harmonic mean flows from the mean and 7Q10 flows as 

recommended in the Technical Support Document for Water Quality-based Toxics Control (U.S. EPA, 1991). 

I-28 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

b. Lakes


EPA used data on hydraulic residence time (i.e., the amount of time water remains in a lake) to analyze small lakes, and CDFs


(which describe dilution in a portion of a lake) to analyze the Great lakes.5


The sample MP&M facilities discharged directly to one lake reach and indirectly to 23 lake reaches: 15 to small lakes, 3 to


sections of Lake Erie, 5 to sections of Lake Michigan, and 1 to a section of Lake Ontario. EPA calculated the average


hydraulic residence time for small lakes based on lake surface and drainage areas. EPA obtained data on lake surface and


drainage area from the U.S. Army Corps of Engineers, Major Dams: Map Layer Description File (USCE, 1999). CDFs were


readily available for Lake Michigan, but not for the three sample reaches on Lake Erie. EPA arithmetically averaged the


seven chronic CDFs available for reaches discharging to Lake Erie (1, 1, 4, 4, 10, 10, 4) (U.S. EPA, 1992a, p. A-4) for the


three reaches being modeled.


c. Estuaries and bays


Sixty-two bays and estuaries receive discharges from sample MP&M facilities. Data necessary to support water quality


modeling were not available for eight of the 62 bays/estuaries. A dilution model predicted pollutant concentrations in the


chronic and acute mixing zones, based on site-specific CDFs (U.S. EPA, 1992a and Versar, 1994), to estimate the pollutant 

concentrations in 28 of these complex water bodies. 

Both acute and chronic CDFs were available for 20 of the 62 bays/estuaries. EPA estimated acute and chronic CDFs for New 

York bays/estuaries by arithmetically averaging available values for nearby New Jersey sites discharging to the Arthur Kill 

(acute: 1.5, 4.0, 5.0; chronic: 5; 20; 10) and Upper New York Bay (acute: 8.0; chronic: 22.9). Acute and chronic CDFs for 

Buzzards Bay in Massachusetts were estimated by arithmetically averaging values for nearby Massachusetts and Rhode Island 

sites discharging to the Atlantic Ocean. 

EPA could not identify or approximate chronic CDFs for the remaining 13 sample reaches. Acute CDFs are available for 46 

of the 62 bays/estuaries. EPA extrapolated acute CDFs for two bays/estuaries in Florida by using CDFs for another Florida 

bay. Likewise, EPA extrapolated acute CDFs for four bays/estuaries in California by using CDFs for another California bay. 

EPA obtained DCP values for five of the 13 sample bays/estuaries for which CDFs were not available from the Development 

of Mixing Zone Dilution Factors report (Versar, 1994). EPA then used a dilution model that predicts pollutant concentrations 

in the estuarine environment using a site-specific DCP value. 

I.3.3 Information Used to Evaluate POTW Operations 

Since many M P&M facilities considered in the alternative options are indirect dischargers, the Agency consulted with


POTW s as they would have had to implement the rule. EPA consulted with POTWs individually and through the Association


of Municipal Sewerage Agencies (AMSA). In addition, EPA consulted with pretreatment coordinators and State and local


regulators.


EPA used removal efficiency rates, inhibition values, and sewage sludge regulatory levels to evaluate POTW operations.


EPA obtained POTW removal efficiency rates from several sources. The Agency developed rates from POTW removal data


and pilot-plant studies or used removals for a similar pollutant when data were not available. Use of the selected removal


rates assumes that the evaluated POTWs are well-operated and have at least secondary treatment in place (U.S. EPA, 2000).


EPA obtained inhibition values from the Guidance Manual for Preventing Interference at POTWs (U.S. EPA, 1987a) and


from CERCLA Site Discharges to POTWs: Guidance Manual (U.S. EPA, 1990). EPA used the most conservative values for


activated sludge (i.e., the lowest influent concentrations that would cause inhibition). The Agency used a value based on


compound type (e.g., aromatics) for pollutants with no specific inhibition value.


EPA obtained sewage sludge regulatory levels from the Federal Register 40 CFR Part 257 et al., Standards for the Use or


Disposal of Sewage Sludge; Final Rules (February 19, 1993) and from the Federal Register 59(38):9095-9099 (February 25,


1994) and 60(206):54,764-54,770 (October 25, 1995) for eight metals regulated in sewage sludge. EPA used pollutant limits


established for the final use or disposal of sewage sludge when the sewage sludge is applied to agricultural and


non-agricultural land or is applied to a dedicated surface disposal site.


5 Small lakes are defined as any non-Great lakes, including reservoirs. 

I-29 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Finally, EPA obtained sludge partition factors from the Report to Congress on the Discharge of Hazardous Wastes to 

Publicly-Owned Treatment Works (Domestic Sewage Study) (U.S. EPA, 1986). 

Table I.5 lists POTW treatment removal efficiency rates, inhibition values, sewage sludge partition factors, and sewage sludge 

regulatory levels used in the evaluation of POTW operations. 

Table I.5: 

CAS 

Number Pollutant Name 

POTW 

Inhibition Level 

Value 

(�g/l) 

POTW Sludge 

Partition Factor 

Sludge Criteria 

Value 

(mg/kg) 

POTW Removal 

Efficiency Rate 

(Percentage) 

51285 Dinitrophenol, 2,4- 1000 0.10000000149 77.51 

57125 Cyanide 5000 1 70.44 

59507 Parachlorometacresol 5000 0.07900000364 63 

62533 Aniline 1000 0.1 93.41 

62759 Nitrosodimethylamine, N- 0.1 77.51 

65850 Benzoic acid 10000 0.1 80.5 

67641 Acetone 120000 0.1 83.75 

67663 Trichloromethane 500000 0.015 

68122 Dimethylformamide, N,N- 1000 0.1 87 

75003 Chloroethane 0.0075 77.51 

75092 Dichloromethane 150000 0.1395 54.28 

75150 Carbon disulfide 50000 0.0075 84 

75354 Dichloroethene, 1,1- 150000 77.51 

75694 Trichlorofluoromethane 700 77.32 

78591 Isophorone 120000 0.079 77.51 

78831 Isobutyl alcohol 1000000 0.1 28 

78933 Methyl ethyl ketone 120000 0.1 96.6 

79016 Trichloroethene 20000 0.0578 77.51 

80626 Methyl methacrylate 120000 99.96 

83329 Acenaphthene 500000 0.366 98.29 

84742 Di-n-butyl phthalate 10000 0.216 84.66 

85018 Phenanthrene 500000 0.366 94.89 

85687 Butyl benzyl phthalate 10000 0.452 81.65 

86306 Nitrosodiphenylamine, N- 90.11 

86737 Fluorene 500000 0.366 69.85 

88755 Nitrophenol, 2- 50000 26.83 

91203 Naphthalene 500000 0.275 94.69 

91576 Methylnaphthalene, 2- 5000 0.079 28 

92524 Biphenyl 5000 0.366 96.28 

95476 Xylene, o- 5000 0.149 77.32 

95487 Cresol, o- 90000 0.079 52.5 

98555 Terpineol, alpha- 1000000 0.1 94.4 

98862 Acetophenone 120000 0.1 95.34 

99876 Cymene, p- 5000 0.0075 99.79 

100027 Nitrophenol, 4- 50000 0.1 77.51 

100414 Ethylbenzene 200000 0.06 93.79 

100425 Styrene 500000 0.149 93.65 

100516 Benzyl alcohol 1000000 0.1 78 

100754 Nitrosopiperidine, N- 1000 77.32 

101848 Diphenyl Ether 1000 77.32 

105679 Dimethylphenol, 2,4- 40000 0.079 77.51 

POTW­Related Data for 132 MP&M Pollutants 

I-30 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.5: 

CAS 

Number Pollutant Name 

POTW 

Inhibition Level 

Value 

(�g/l) 

POTW Sludge 

Partition Factor 

Sludge Criteria 

Value 

(mg/kg) 

POTW Removal 

Efficiency Rate 

(Percentage) 

106445 Cresol, p- 90000 0.079 71.67 

107028 Acrolein 50 0.10000000149 77.51 

108101 Methyl isobutyl ketone 120000 0.1 87.87 

108372 Bromo-3-chlorobenzene, 1- 100 77.32 

108383 Xylene, m- 5000 0.149 95.07 

108883 Toluene 200000 0.278 96.18 

108907 Chlorobenzene 140000 0.154 96.37 

108952 Phenol 90000 0.146 95.25 

110861 Pyridine 1000 0.1 95.4 

112403 Dodecane, n- (a) 

112958 Eicosane, n- (a) 

117817 Bis(2-ethylhexyl) phthalate 10000 0.728 59.78 

117840 Di-n-octyl phthalate 10000 0.075 68.43 

120127 Anthracene 500000 0.55 77.51 

122394 Diphenylamine 1000 0.08 77.32 

123911 Dioxane, 1,4- 120000 0.1 45.8 

124185 Decane, n- 9 

127184 Tetrachloroethene 20000 0.034 84.61 

129000 Pyrene 500000 0.366 83.9 

131113 Dimethyl phthalate 0.1 77.51 

132650 Dibenzothiophene 5000 0.366 84.68 

137304 Ziram \ Cymate 50 

142621 Hexanoic acid 10000 84 

206440 Fluoranthene 5000 0.366 42.46 

544763 Hexadecane, n- (a) 

591786 Hexanone, 2- 120000 77.32 

593453 Octadecane, n- (a) 

606202 Dinitrotoluene, 2,6- 5000 0.1 77.51 

629594 Tetradecane, n- (a) 

629970 Docosane, n- 88 

630013 Hexacosane, n- (b) 

630024 Octacosane, n- (b) 

638686 Triacontane, n- (b) 

646311 Tetracosane, n- (b) 

694804 Bromo-2-chlorobenzene, 1- 100 77.32 

832699 Methylphenanthrene, 1- 5000 0.366 84.55 

1576676 Dimethylphenanthrene, 3,6- 500000 0.366 84.55 

1730376 Methylfluorene, 1- 500000 0.366 84.55 

2027170 Isopropylnaphthalene, 2- 500000 0.1 77.32 

7429905 Aluminum 1 91.36 

7439896 Iron 5000 1 81.99 

7439921 Lead 100 1 300 77.45 

7439954 Magnesium 1000000 1 14.14 

7439965 Manganese 10000 1 35.51 

7439976 Mercury 100 1 17 71.66 

7439987 Molybdenum 1 18.93 

7440020 Nickel 5000 1 420 51.44 

POTW­Related Data for 132 MP&M Pollutants 

I-31

MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.5: 

CAS 

Number Pollutant Name 

POTW 

Inhibition Level 

Value 

(�g/l) 

POTW Sludge 

Partition Factor 

Sludge Criteria 

Value 

(mg/kg) 

POTW Removal 

Efficiency Rate 

(Percentage) 

7440224 Silver 30 1 88.28 

7440235 Sodium 3500000 1 2.69 

7440280 Thallium 1 71.66 

7440315 Tin 9000 1 42 

7440326 Titanium 1 91.82 

7440360 Antimony 1 66.78 

7440382 Arsenic 40 1 41 65.77 

7440393 Barium 1 15.98 

7440417 Beryllium 1 71.66 

7440428 Boron 1000 1 30.42 

7440439 Cadmium 500 1 39 90.05 

7440473 Chromium 1000 1 80.33 

7440484 Cobalt 1 6.11 

7440508 Copper 1000 1 1500 84.2 

7440575 Gold 1 32.52 

7440622 Vanadium 20000 1 9.51 

7440655 Yttrium 1 32.52 

7440666 Zinc 5000 1 2800 79.14 

7440702 Calcium 2500000 1 8.54 

7664417 Ammonia as N 480000 38.94 

7782492 Selenium 1 100 34.33 

14265442 Phosphate 57.41 

14808798 Sulfate 84.61 

16887006 Chloride 57.41 

16984488 Fluoride 61.35 

18496258 Sulfide 25000 57.41 

18540299 Chromium hexavalent 1000 1 57.41 

20324338 Tripropyleneglycolmethylether 120000 52.4 

136777612 Xylene, o- & p- (c) 5000 0.149 36832 

179601231 Xylene, m- & p- (c) 

C003 BOD 5-day (carbonaceous) 89.12 

C004 Chemical Oxygen Demand (COD) 81.3 

C009 Total Suspended Solids (TSS) 

C010 Total Dissolved Solids (TDS) 

C012 Total Organic Carbon (TOC) 70.28 

C020 Total Recoverable Phenolics 57.41 

C021 Total Kjeldahl Nitrogen 57.41 

C025 Amenable Cyanide 57.41 

C036 Oil and Grease (as Hem) 86.08 

C037 

Total Petroleum Hydrocarbons (as 

Sgt-hem) 

C042 Weak-acid Dissociable Cyanide 

Phosphorus (as PO4) 

Oil and Grease 

POTW­Related Data for 132 MP&M Pollutants 

Sources: U.S. EPA (1985), U.S. EPA (1987), U.S. EPA (1990). 

I-32

MP&M EEBA: Appendices Appendix I: Environmental Assessment 

In the absence of data on POTW flow rates, EPA set the POTW flow rate equal to the arithmetic mean flow among minor 

POTWs in the PCS database, using the following steps: 

1.	 Calculate arithmetic mean flow among minor POTWs in the PCS database. The estimated arithmetic mean flow for 

minor POTWs in the PCS database is one million gallons per day (MGD). 

2. Set POTW flow rate equal to the relevant arithmetic mean flow. For all POTWS with missing flow data, EPA set 

their flow rates equal to the arithmetic mean flow rate for minor POTWs in the PCS database, one MGD. 

I.4 RESULTS 

EPA assessed the environmental impacts of MP&M dischargers on water bodies and POTWs under the baseline conditions 

and those corresponding to four regulatory options: the Final Option, Proposed/NODA Option, and two 433 Upgrade Options 

on the basis of sample facility data. The Agency extrapolated the findings from the sample facility analyses to the national 

level using facility sample weights, as described in Appendix G. 

MP&M facilities nationwide currently discharge an estimated 53 million pounds of pollutants per year to publicly-owned 

treatment works (POTWs) and approximately 6.2 million pounds of pollutants directly to surface waters. MP&M facility 

effluents contain 42 priority or toxic pollutants, 81 nonconventional pollutants, and three conventional pollutants (BOD, TSS, 

and O& G). 

EPA estimates that the final rule will lead to a modest reduction in pollutant discharges to the waters of the U.S. As shown by 

Table I.6, the regulation will reduce discharges of pollutants with acute and chronic effects on aquatic life by 8,959 and 

12,270 pounds per year, respectively. The final rule does not regulate indirect dischargers and thus will not reduce pollutant 

loads received by POTWs. 

EPA estimates that the Proposed/NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade 

Option would remove 3,299, 91, and 110 thousand pounds per year of eight sewage sludge contaminants, respectively. In 

addition, the Proposed/NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option would 

result in 30,226, 133, and 551 thousand pounds per year reduction in 86 pollutants causing inhibition of POTW operations. 

The Proposed/NODA Option would reduce discharges of pollutants with acute and chronic effects on aquatic life by 97 and 

117 million pounds per year, respectively. The Directs + 413 to 433 Upgrade Option and the Directs + All to 433 Upgrade 

Option would reduce discharges of 132 and 353 thousand pounds of pollutants with acute effects on aquatic life, and 136 and 

576 thousand pounds of pollutants with chronic effects on aquatic life, respectively. 

I-33 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.6: &M Facility Discharges (National Basis)a 

Category 

POTW Impacts Receiving Stream Impacts: Aquatic 

Life Toxicity 

Activated Sludge 

Inhibition 

Biosolids 

Contaminants HAP Acute Chronic 

Selected Option 

# of Pollutants N/A N/A N/A 106 113 

Baseline (1,000 lbs/yr) N/A N/A N/A 868 1,154 

Post-Compliance (1,000 lbs/yr) N/A N/A N/A 859 1,142 

Proposed/NODA Option 

# of Pollutants 85 8 35 105 112 

Baseline (1,000 lbs/yr) 39,594 3,589 408 141,522 187,742 

Post-Compliance (1,000 lbs/yr) 9,369 290 189 44,827 70,428 

Directs + 413 to 433 Upgrade 

# of Pollutants 86 8 35 106 113 

Baseline (1,000 lbs/yr) 1,085 253 3 868 1,154 

Post-Compliance (1,000 lbs/yr) 952 161 3 935 1,018 

Directs + All to 433 Upgrade 

# of Pollutants 86 8 35 106 113 

Baseline (1,000 lbs/yr) 1,085 253 3 868 1,154 

Post-Compliance (1,000 lbs/yr) 534 143 3 514 578 

MP

a Excludes loadings from facilities projected to close in the baseline. See Chapter 5. 

Source: U.S. EPA analysis. 

I.4.1 Human Health Impacts 

Under this human health benefit category EPA assessed the reduced occurrence of pollutant concentrations that are estimated 

to exceed human health-based AWQC. This analysis provides an alternative measure of the expected reduction in risk to 

human health. Table I.7 presents information on baseline and post-compliance exceedances of human health AWQC criteria 

for all the regulatory options. 

EPA estimates that in-stream concentrations of four pollutants (i.e., arsenic, iron, manganese, and n-nitrosodimethylamine) 

will exceed human health criteria for consumption of water and organisms in 78 receiving reaches nationwide as the result of 

baseline MP&M pollutant discharges. EPA estimates that there are human health AWQC exceedances caused by 

n-nitrosodimethylamine (NDM A). EPA did not consider NDM A pollutant reductions in its benefits analyses because of the 

low number of detected values for that pollutant. EPA estimates that the final rule will not eliminate the occurrence of 

concentrations in excess of human health criteria for consumption of water and organisms and for consumption of organisms 

on any of the reaches on which baseline discharges are estimated to cause concentrations in excess of AWQC values. 

The Proposed/NODA Option would eliminate instances of in-stream pollutant concentrations exceeding AWQC limits for 

consumption of water and organisms and consumption of organisms only in 63 and 68 reaches, respectively, nationwide. The 

Directs + 413 to 433 Upgrade O ption would not eliminate any instances of in-stream pollutant concentrations exceeding 

AWQ C limits for consumption of water and organisms and consumption of organisms only. The Directs + All to 433 

Upgrade Option would not eliminate any occurrences of pollutant concentrations in excess of AWQC values for consumption 

of water and organisms, but would eliminate instances of pollutant concentrations in excess of AWQC values for consumption 

of organisms only in 21 reaches nationwide. As noted above the Agency did not estimate reductions in NDM A loadings 

under the post-compliance scenario due to data limitations. 

I-34 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.7: mmary of Estimated AWQC Exceedances for Protection of Human Health (National Basis) 

Category 

Human Health Water and Organisms Human Health Organisms Only 

Streams (No.) Pollutants (No.) Total 

Exceedances Streams (No.) Pollutants (No.) Total 

Exceedances 

Selected Option: Traditional Extrapolation 

Baseline 78 4 121 21 1 21 

Post-Compliance 78 4 121 21 1 21 

Selected Option: Post-Stratification Extrapolation 

Baseline 112 4 154 21 1 21 

Post-Compliance 112 4 154 21 1 21 

Proposed/NODA Option 

Baseline 5,852 26 7,085 197 12 335 

Post-Compliance 5,789 21 6,667 128 9 212 

Directs + 413 to 433 Upgrade 

Baseline 78 4 121 21 1 21 

Post-Compliance 78 4 121 21 1 21 

Directs + All to 433 Upgrade 

Baseline 78 4 121 21 1 21 

Post-Compliance 78 2 78 0 

Su

0 0 

Source: U.S. EPA analysis. 

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MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.8 summarizes pollutants estimated to exceed human health-based AWQC criteria for consumption of water and 

organisms under the baseline and post-compliance conditions. 

Table I.8: Summary of Pollutants Estimated to Exceed Human Health­Based AWQC Criteria for Consumption of 

Water and Organisms (National Basis) 

Pollutant 

Selected Option: 

Traditional 

Extrapolation 

Selected Option: 

Post-Stratification 

Extrapolation 

Proposed/NODA 

Option 

Directs + 413 to 

433 Upgrade 

Directs + All to 

433 Upgrade 

Basea PCb Base PC Base PC Base PC Base PC 

Aniline 0 0 0 0 20 17 0 0 0 0 

Antimony 0 0 0 0 0 0 0 0 0 0 

Arsenic 45 45 45 45 772 557 45 45 45 45 

Bis(2-ethylhexyl) phthalate 0 0 0 0 85 43 0 0 0 0 

Cadmium 0 0 0 0 0 0 0 0 0 0 

Chloroethane 0 0 0 0 17 14 0 0 0 0 

Copper 0 0 0 0 16 0 0 0 0 0 

Cresol, p- 0 0 0 0 9 9 0 0 0 0 

Dibenzofuran 0 0 0 0 12 9 0 0 0 0 

Dichloroethene, 1,1- 0 0 0 0 97 81 0 0 0 0 

Dichloromethane 0 0 0 0 17 17 0 0 0 0 

Dinitrophenol, 2,4- 0 0 0 0 9 9 0 0 0 0 

Dinitrotoluene, 2,6- 0 0 0 0 9 9 0 0 0 0 

Dioxane, 1,4- 0 0 0 0 17 17 0 0 0 0 

Fluoranthene 0 0 0 0 9 9 0 0 0 0 

Iron 21 21 21 21 28 0 21 21 21 0 

Isophorone 0 0 0 0 9 9 0 0 0 0 

Manganese 21 21 21 21 54 0 21 21 21 0 

Mercury 0 0 0 0 0 0 0 0 0 0 

Naphthalene 0 0 0 0 9 9 0 0 0 0 

Nickel 0 0 0 0 16 0 0 0 0 0 

Nitrophenol, 4- 0 0 0 0 9 9 0 0 0 0 

Nitrosodimethylamine, N- 32 32 67 67 5,789 5,789 32 32 32 32 

Nitrosodiphenylamine, N- 0 0 0 0 17 17 0 0 0 0 

Pyrene 0 0 0 0 9 9 0 0 0 0 

Pyridine 0 0 0 0 12 9 0 0 0 0 

Thallium 0 0 0 0 16 0 0 0 0 0 

Trichloroethene 0 0 0 0 21 17 0 0 0 0 

Trichloromethane 0 0 0 0 12 12 0 0 0 0 

Total Exceedances 121 121 154 154 7,085 6,667 121 121 121 77 

a

 Base = Baseline discharge level 

b

 PC = Post-Compliance discharge level 

Source: U.S. EPA analysis. 

I-36 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.9 summarizes pollutants estimated to exceed human health-based AW QC criteria for consumption of organisms only 

under the baseline and post-compliance conditions. 

Table I.9: Summary of Pollutants Estimated to Exceed Human Health­Based AWQC Criteria for Consumption of 

Organisms Only (National Basis) 

Pollutant 

Selected Option: 

Traditional 

Extrapolation 

Selected Option: 

Post-Stratification 

Extrapolation 

Proposed/NODA 

Option 

Directs + 413 to 

433 Upgrade 

Directs + All to 

433 Upgrade 

Basea PCb Base PC Base PC Base PC Base PC 

Aniline 0 0 0 0 12 9 0 0 0 0 

Antimony 0 0 0 0 0 0 0 0 0 0 

Arsenic 0 0 154 111 0 0 

Bis(2-ethylhexyl) phthalate 0 0 0 0 24 12 0 0 0 0 

Cadmium 0 0 0 0 0 0 0 0 0 0 

Chloroethane 0 0 0 0 0 0 0 0 0 0 

Copper 0 0 0 0 16 0 0 0 0 0 

Cresol, p- 0 0 0 0 0 0 0 0 0 0 

Dibenzofuran 0 0 0 0 12 9 0 0 0 0 

Dichloroethene, 1,1- 0 0 0 0 17 17 0 0 0 0 

Dichloromethane 0 0 0 0 0 0 0 0 0 0 

Dinitrophenol, 2,4- 0 0 0 0 0 0 0 0 0 0 

Dinitrotoluene, 2,6- 0 0 0 0 0 0 0 0 0 0 

Dioxane, 1,4- 0 0 0 0 0 0 0 0 0 0 

Fluoranthene 0 0 0 0 9 9 0 0 0 0 

Iron 0 0 0 0 0 0 0 0 0 0 

Isophorone 0 0 0 0 0 0 0 0 0 0 

Manganese 21 21 21 21 32 0 21 21 21 0 

Mercury 0 0 0 0 0 0 

Naphthalene 0 0 0 0 0 0 0 0 0 0 

Nickel 0 0 0 0 16 0 0 0 0 0 

Nitrophenol, 4- 0 0 0 0 0 0 0 0 0 0 

Nitrosodimethylamine, N- 0 0 0 0 27 27 0 0 0 0 

Nitrosodiphenylamine, N- 0 0 0 0 9 9 0 0 0 0 

Pyrene 0 0 0 0 9 9 0 0 0 0 

Pyridine 0 0 0 0 0 0 0 0 0 0 

Thallium 0 0 0 0 0 0 0 0 0 0 

Trichloroethene 0 0 0 0 0 0 0 0 0 0 

Trichloromethane 0 0 0 0 0 0 0 0 0 0 

Total Exceedances 21 21 21 21 335 212 21 21 21 0 

0 0 0 

0 0 0 0 

a

 Base = Baseline discharge level b

 PC = Post-Compliance discharge level 

Source: U.S. EPA analysis. 

I.4.2 Aquatic Life Effects 

EPA evaluated the effects of MP& M facility discharges on aquatic habitats and ecosystem functioning under the baseline 

conditions and the post-compliance scenarios corresponding to the four regulatory alternatives considered for the MP&M 

regulation. This analysis compared the estimated baseline and post-compliance in-stream concentrations of MP&M pollutants 

with AWQC for aquatic species. As noted in the preceding sections, aquatic life AWQCs addressed in this analysis set the 

upper limit on pollutant concentrations assumed to be protective of aquatic life. 

I-37 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.10 presents the number of MP&M discharge reaches on which pollutant concentrations are estimated to exceed 

chronic and acute exposure criteria for protection of aquatic life. EPA estimated that, as the result of baseline MP&M 

pollutant discharges, in-stream concentrations exceed acute exposure criteria for aquatic species in 18 and 15 receiving 

reaches nationwide based on the traditional extrapolation and post-stratification extrapolation, respectively. In addition, 

baseline in-stream concentrations in 353 and 350 receiving reaches exceed chronic AWQC for protection of aquatic life based 

on the traditional extrapolation and post-stratification extrapolation, respectively. 

Table I.10: mmary of Estimated AWQC Exceedances for Protection of Aquatic Life (National Basis) 

Category 

Acute Aquatic Life Chronic Aquatic Life 

Streams (No.) Pollutants 

(No.) 

Total 

Exceedances Streams (No.) Pollutants 

(No.) 

Total 

Exceedances 

Selected Option: Traditional Extrapolation 

Baseline 18 4 35 353 9 423 

Post-Compliance 9 1 9 344 5 362 

Selected Option: Post-Stratification Extrapolation 

Baseline 15 4 26 350 9 402 

Post-Compliance 9 1 9 344 5 362 

Proposed/NODA Option 

Baseline 330 17 631 928 47 2,582 

Post-Compliance 86 12 254 539 39 1,369 

Directs + 413 to 433 Upgrade 

Baseline 18 4 35 353 9 423 

Post-Compliance 0 0 0 53 3 53 

Directs + All to 433 Upgrade 

Baseline 18 4 35 353 9 423 

Post-Compliance 0 0 0 32 2 32 

Su

Source: U.S. EPA analysis. 

Based on the traditional extrapolation, EPA estimates that the final option will eliminate concentrations in excess of acute and 

chronic criteria in nine reaches. Likewise, EPA estimates that the final option will eliminate concentrations in excess of acute 

and chronic criteria in six reaches based on the post-stratification extrapolation. 

The Proposed/NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option would 

eliminate exceedances of chronic AWQC values on 389, 300, and 321 reaches, respectively. These options would also 

eliminate in-stream pollutant concentrations in excess of acute AWQC value on 244, 18, and 18 reaches under the 

Proposed/NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option, respectively. 

Table I.11 presents the number MP&M reaches on which pollutant concentrations are estimated to exceed chronic AWQC for 

protection of aquatic life by pollutant. 

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MP&M EEBA: Appendices Appendix I: Environmental Assessment

I-39

Table I.11: Aquatic Life

 (National Basis)

Pollutant

Selected Option:

Traditional

Extrapolation

Selected Option:

Post-Stratification

Extrapolation

Proposed/NODA

Option

Directs + 413 to

433 Upgrade

Directs + All to

433 Upgrade

Basea PCb Base PC Base PC Base PC Base PC

Acenaphthene 0 0

Acrolein 0 44 33 0 0

Aluminum 0 32 12 0 0

Ammonia as N 0 51 0 0 0

Aniline 0 45 42 0 0

Anthracene 0 64 29 0 0

Biphenyl 0 9 0 0

Butyl benzyl phthalate 0 9 0 0

Cadmium 9 70 21 0

Carbon disulfide 0 38 34 0

Chromium 0 46 12 0

Cobalt 0 12 12 0

Copper 9 344 69 9 0

Cyanide 0 0

Di-n-butyl phthalate 0 0

Di-n-octyl phthalate 0 12 12 0

Dibenzofuran 0 0 0 0 21 12 0

Dibenzothiophene 0 15 12 0

Dimethylphenanthrene, 3,6- 0 24 21 0

Dinitrophenol, 2,4- 0 0

Dinitrotoluene, 2,6- 0 21 21 0

Diphenyl Ether 0 21 21 0

Fluoranthene 0 30 24 0

Fluorene 0 27 21 0

Fluoride 0 54 13 0 0

Iron 0 12 0 0 0

Isopropylnaphthalene, 2- 0 15 12 0 0

Lead 0 244 83 0 0

Magnesium 0 12 12 0 0

Manganese 0 32 0 0 0

Methylfluorene, 1- 0 15 12 0 0

Methylnaphthalene, 2- 0 12 12 0 0

Methylphenanthrene, 1- 0 15 12 0 0

Molybdenum 0 103 39 0 0

Naphthalene 0 9 0 0

Nickel 0 163 16 0 0

Phenanthrene 0 24 21 0 0

Phenol 0 9 0 0

Pyrene 0 21 21 0 0

Selenium 0 78 50 0 0

Silver 9 166 131 0

Styrene 0 0

Sulfide 0 293 283 0 0

Summary of Pollutants Estimated to Exceed Chronic AWQC for Protection of

0 0 0 9 9 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 9 0 0

0 0 0 0 0 0

0 6 0 9 0 9

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

9 9 9 0 9

0 0 0 3 0 0 0 0

0 0 0 9 9 0 0 0

0 0 0 0 0 0

0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 9 9 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 9 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 6 0 9 0 9

0 0 0 9 0 0 0 0

0 0 0 0 0
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.11: Aquatic Life 

(National Basis) 

Pollutant 

Selected Option: 

Traditional 

Extrapolation 

Selected Option: 

Post-Stratification 

Extrapolation 

Proposed/NODA 

Option 

Directs + 413 to 

433 Upgrade 

Directs + All to 

433 Upgrade 

Basea PCb Base PC Base PC Base PC Base PC 

Tin 0 83 21 0 0 

Titanium 0 6 0 0 

Vanadium 0 157 142 0 0 

Zinc 9 85 33 0 

Total Exceedances 35 9 26 9 2,582 1,369 35 0 35 0 

Summary of Pollutants Estimated to Exceed Chronic AWQC for Protection of

0 0 0 0 0 

0 0 0 0 0 0 

0 0 0 0 0 

0 6 0 9 0 9 

a

 Base = Baseline discharge level 

b

 PC = Post-Compliance discharge level 

Source: U.S. EPA analysis. 

Table I.12 presents the number MP&M reaches on which pollutant concentrations are estimated to exceed acute AWQC for 

protection of aquatic life by pollutant. 

Table I.12: mmary of Pollutants Estimated to Exceed Aquatic Life Based Acute AWQC (National Basis) 

Pollutant 

Selected Option: 

Traditional 

Extrapolation 

Selected Option: 

Post-Stratification 

Extrapolation 

Proposed/NODA 

Option 

Directs + 413 to 

433 Upgrade 

Directs + All to 

433 Upgrade 

Basea PCb Base PC Base PC Base PC Base PC 

Acenaphthene 0 0 

Acrolein 0 33 26 0 

Aluminum 9 10 0 

Ammonia as N 0 0 0 0 

Aniline 0 9 0 0 

Anthracene 0 64 29 0 0 

Biphenyl 0 9 0 0 

Butyl benzyl phthalate 0 0 0 0 

Cadmium 9 0 

Carbon disulfide 0 0 

Chromium 0 0 

Cobalt 0 0 

Copper 276 267 273 267 241 69 276 9 276 9 

Cyanide 0 0 

Di-n-butyl phthalate 0 0 

Di-n-octyl phthalate 0 0 

Dibenzofuran 0 0 

Dibenzothiophene 0 0 

Dimethylphenanthrene, 3,6- 0 0 

Dinitrophenol, 2,4- 0 0 

Dinitrotoluene, 2,6- 0 0 

Diphenyl Ether 0 0 

Fluoranthene 0 21 21 0 

Fluorene 0 21 21 0 

Fluoride 0 0 

Iron 0 0 

Isopropylnaphthalene, 2- 0 0 

Su

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 

0 6 0 0 9 0 9 

0 0 0 0 0 0 

0 0 0 0 0 0 

0 0 0 0 0 

0 0 0 0 0 0 

0 0 0 0 0 0 

0 6 0 9 6 9 0 9 

0 0 0 0 0 0 0 0 

0 0 0 7 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 9 9 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 

0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

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MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.12: mmary of Pollutants Estimated to Exceed Aquatic Life Based Acute AWQC (National Basis) 

Pollutant 

Selected Option: 

Traditional 

Extrapolation 

Selected Option: 

Post-Stratification 

Extrapolation 

Proposed/NODA 

Option 

Directs + 413 to 

433 Upgrade 

Directs + All to 

433 Upgrade 

Basea PCb Base PC Base PC Base PC Base PC 

Lead 18 9 15 18 0 18 0 

Magnesium 21 21 21 21 0 0 21 21 21 0 

Manganese 9 0 

Methylfluorene, 1- 0 0 

Methylnaphthalene, 2- 0 0 

Methylphenanthrene, 1- 0 0 0 

Molybdenum 0 0 

Naphthalene 0 0 

Nickel 9 23 0 

Phenanthrene 0 0 

Phenol 0 0 

Pyrene 0 0 

Selenium 0 12 12 0 

Silver 64 56 61 56 60 12 64 23 64 23 

Styrene 0 0 

Sulfide 0 0 

Tin 0 0 

Titanium 0 0 

Vanadium 0 0 

Zinc 9 85 33 0 

Total Exceedances 423 362 402 362 631 254 423 53 423 32 

Su

9 9 6 

0 6 0 0 0 9 0 9 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

9 9 9 0 9 0 9 

0 0 0 9 9 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 

0 6 0 9 0 9 

a

 Base = Baseline discharge level 

b

 PC = Post-Compliance discharge level. 

Source: U.S. EPA analysis. 

I.4.3 POTW Effects 

EPA evaluated the effects of indirect MP&M dischargers on POTW operations for the final and alternative options. 788


sample MP&M facilities discharge 132 pollutants to 572 POTWs. Of these, EPA evaluated 89 pollutants for potential


inhibition of POTW operations and eight pollutants for potential sludge contamination. The 788 indirect sample MP&M


facilities discharge 52.8 million pounds per year of priority and nonconventional pollutants to the receiving POTW s. The final


MP&M rule does not regulate indirect dischargers and thus will not reduce the baseline MP&M loadings to receiving


POTWs.


a. Biological inhibition


EPA estimated inhibition of POTW operations by comparing predicted POTW influent concentrations to available inhibition


levels for 89 pollutants. EPA’s analysis shows that 51 POTW s had influent concentrations that exceed the biological


inhibition values for one of the four following pollutants silver, cadmium, chromium and copper under the baseline


conditions corresponding to the Final Option and the 433 Upgrade Options (see Table I.13). Both of the 433 Upgrade Options


would eliminate influent concentrations in excess of POTW inhibition criteria at 21 POTWs. Under the baseline conditions


corresponding to the Proposed/NODA Option, 293 POTWs had influent concentrations in excess of the biological inhibition


criteria. The Proposed/NODA Option would eliminate influent concentrations in excess of the biological inhibition criteria at 

156 POTWs. 

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MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.13: National Summary of Projected Inhibition and Sludge Contamination Problems 

Category 

Biolo gical Inh ibition (# of P OT W s) Sludg e Co ntam ination (# of PO TW s) 

POTWs (No.) Pollutants 

(No.) 

Total 

Exceedances POTWs (No.) Pollutants 

(No.) 

Total 

Exceedances 

Selected Option: Traditional Extrapolation 

Baseline 51 4 139 1,020 7 2,702 

Post-Compliance 51 4 139 1,020 7 2,702 

Selected Option: Post-Stratification Extrapolation 

Baseline 51 4 139 1,020 7 2,702 

Post-Compliance 51 4 139 1,020 7 2,702 

Proposed/NODA Option 

Baseline 293 12 885 5,328 8 14,493 

Post-Compliance 137 8 410 5,259 8 14,321 

Directs + 413 to 433 Upgrade 

Baseline 51 4 139 1,020 7 2,702 

Post-Compliance 30 4 115 1,005 7 2,626 

Directs + All to 433 Upgrade 

Baseline 5 4 139 1,020 7 2,702 

Post-Compliance 30 4 115 1,005 7 2,562 

Source: U.S. EPA analysis. 

I-42

MP&M EEBA: Appendices Appendix I: Environmental Assessment 

Table I.14 presents MP&M pollutants that are estimated to upset POTW operations and contaminate sewage sludge. 

Table I.14: Summary of Pollutants Estimated to Impact POTW Operations (National basis) 

Pollutant 

Selected Option: 

Traditional 

Extrapolation 

Selected Option: 

Post-Stratification 

Extrapolation 

Proposed/NODA 

Option 

Directs + 413 to 433 

Upgrade 

Directs + All to 433 

Upgrade 

Basea PCb Base PC Base PC Base PC Base PC 

Biological Inhibition (# of POTWs) 

Acrolein 0 0 0 0 77 65 0 0 0 0 

Arsenic 0 0 75 65 0 0 

Benzoic acid 0 0 0 0 68 0 0 0 0 0 

Bromo-2-chlorobenzene, 1- 0 0 0 0 48 48 0 0 0 0 

Bromo-3-chlorobenzene, 1- 0 0 0 0 48 48 0 0 0 0 

Chromium 30 30 30 30 81 7 30 27 30 27 

Copper 27 27 27 27 142 0 27 27 27 27 

Iron 0 0 0 0 65 32 0 0 0 0 

Lead 39 39 39 39 150 81 39 30 39 30 

Nickel 0 0 0 0 50 0 0 0 0 0 

Silver 42 42 42 42 65 65 42 30 42 30 

Zinc 0 0 0 0 16 0 0 0 0 0 

Total Exceedances 139 139 139 139 885 410 139 115 139 115 

Sludge Contamination (# of POTWs) 

Lead 234 234 234 234 2,829 2,790 234 234 234 234 

Mercury 0 0 0 0 118 118 0 0 0 0 

Nickel 763 763 763 763 2,371 2,325 763 751 763 687 

Arsenic 84 84 84 84 1,686 1,683 84 84 84 84 

Cadmium 754 754 754 754 1,877 1,871 754 739 754 739 

Copper 534 534 534 534 1,874 1,835 534 500 534 500 

Zinc 224 224 224 224 2,132 2,132 224 209 224 209 

Selenium 109 109 109 109 1,567 1,567 109 109 109 109 

0 0 0 0 

a

 Base = Baseline discharge level 

b

 PC = Post-Compliance discharge level. 

Source: U.S. EPA analysis. 

b. Sewage sludge 

EPA estimated that baseline concentrations of seven metals at the national level fail to meet Land Application-High limits for 

sludge disposal at 1,020 POTWs under the final regulatory alternatives. These concentrations were compared with the 

relevant metals concentration limits for the following sewage sludge management options: Land Application-High 

(Concentration Limits), Land Application-Low (Ceiling Limits), and Surface Disposal. 

The Agency estimates that the final regulation will not eliminate metal concentrations in excess of sludge contamination 

criteria at any of these 1,020 POTW s, since indirect dischargers are exempted from the final rule. EPA estimated that 15 

POTWs would be able to upgrade their sewage sludge disposal practices by meeting Land Application-High sludge 

concentration limits under the 433 Upgrade Options. Under the Proposed/NODA Option, 69 POTWs would be able to 

upgrade their sewage sludge disposal practices to Land Application-High. 

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MP&M EEBA: Appendices Appendix I: Environmental Assessment 

GLOSSARY 

action levels: the existence of a contaminant concentration in the environment high enough to warrant implementation of 

drinking water treatment technology. 

acute toxicity (AT): the ability of a substance to cause severe biological harm or death soon after a single exposure or 

dose. Also, any poisonous effect resulting from a single short-term exposure to a toxic substance (See also: chronic toxicity). 

(http://www.epa.gov/OCEPAterms/aterms.html) 

adsorption: removal of a pollutant from air or water by collecting the pollutant on the surface of a solid material; an 

advanced method of treating waste in which activated carbon removes organic matter from wastewater. 

(http://www.epa.gov/OCEPAterms/aterms.html) 

adsorption coefficient (Koc): represents the ratio of the target chemical adsorbed per unit weight of organic carbon in the 

soil or sediment to the concentration of that same chemical in solution at equilibrium. 

alkalinity: the capacity of bases to neutralize acids (e.g., adding lime to lakes to decrease acidity). 

(http://www.epa.gov/OCEPAterms/aterms.html) 

ambient water quality criteria (AWQC): levels of water quality expected to render a body of water suitable for its 

designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking, 

swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html) 

atm/m3

-mole: atmosphere per cubic meter mole (see also: mole). 

benthic: relating to the bottom of a body of water; living on, or near, the bottom of a water body. 

(http://www.ucmp.berkeley.edu/glossary/gloss5ecol.html) 

bioconcentration factor (BCF): indicator of the potential for a chemical dissolved in the water column to be taken up by 

aquatic biota across external surface membranes, usually gills. 

BIODEG: a web-based biodegradation database developed by Syracuse Research Corporation. 

(http://esc.syrres.com/efdb/biodgsum.htm) 

biodegradation: a process whereby organic molecules are broken down by microbial metabolism. 

biodegradation half-life: represents the number of days a compound takes to be degraded to half of its starting 

concentration under prescribed laboratory conditions. 

biological oxygen dem and (BOD): the amount of dissolved oxygen consumed by microorganisms as they decompose 

organic material in an aquatic environment. 

cancer potency slope factors (SFs): a plausible upper-bound estimate of the probability of a response per unit intake of 

a chemical over a lifetime. The slope factor is used to estimate an upper-bound probability of an individual developing 

cancer as a result of a lifetime of exposure to a particular level of a potential carcinogen. 

carcinogens: chemicals that EPA believes can cause or have the potential to cause tumors or cancers in humans, either 

directly or indirectly. 

CHEMFATE: a web-based chemical fate database developed by Syracuse Research Corporation. 

(http://esc.syrres.com/efdb/Chemfate.htm) 

chronic toxicity (CT): the capacity of a substance to cause long-term toxic or poisonous health effects in humans, animals, 

fish, and other organisms (see also: acute toxicity). (http://www.epa.gov/OCEPAterms/cterms.html) 

critical dilution factors (CDFs): express the relationship between a point source loading and the resulting concentration 

at the edge of the mixing zone. Typically, this is expressed as a ratio of parts receiving water to one part effluent. 

I-44 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

dissolved concentration potentials (DCPs): represents the concentration of a nonreactive dissolved substance under 

well-mixed, steady-state conditions given an annual load of 10,000 tons. 

dry metric tons (DMT): dry measure is a system of units for measuring dry commodities. 1 DMT=1,000 kilogram. 

EC1: the concentration at which one percent of the test organisms show a significant sub-lethal response. 

EC5: the concentration at which five percent of the test organisms show a significant sub-lethal response. 

Environmental Research Laboratory-Duluth fathead minnow database: a database developed by EPA's 

Mid-Continent Ecology Division (MED) which provides data on the acute toxicity of hundreds of industrial organic 

compounds to the fathead minnow. (http://www.eoa.gov/med/databases/fathead_minnow.html) 

GAGE: a U.S. Geological Survey stream flow database. The database contains stream flow data and drainage area 

measurement from all U.S. Geological Survey flow gages. 

hazardous air pollutant (HAP): air pollutants that are not covered by ambient air quality standards but which, as defined 

in the Clean Air Act, may present a threat of adverse human health effects or adverse environmental effects (e.g., beryllium, 

mercury, ethylbenzene, chloroethane, and doxane). (http://www.epa.gov/OCEPAterms/hterms.html) 

Health Effects Assessment Summary Tables (HEAST): a comprehensive listing of provisional human health risk 

assessment data relative to oral and inhalation routes for chemicals of interest to EPA. Unlike data in IRIS, HEAST entries 

have received insufficient review to be recognized as high quality, Agency-wide consensus information (U.S. EPA. 1997. 

Health Effects Assessment Table; FY 1997 Update. EPA-540-R-97-036). 

Henry's Law (H): chemical law stating that the amount of a gas that dissolves in a liquid is proportional to the partial 

pressure of the gas over the liquid, provided no chemical reaction takes place between the liquid and the gas. The law is 

named after William Henry (1774 1836), the English chemist who first reported the relationship. (www.infoplease.com) 

human health-based water quality criteria (WQC): levels of water quality expected to render a body of water suitable 

for its designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for 

drinking, swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/wterms.html) 

Integrated Risk Information System (IRIS): IRIS is an electronic database with information on human health effects of 

various chemicals. IRIS provides consistent information on chemical substances for use in risk assessments, decision-making, 

and regulatory activities. 

LC50 (Lethal Concentration): a standard measure of toxicity that tells how much of a substance is needed to kill half of a 

group of experimental organisms in a given time (see also: LD 50). (http://www.epa.gov/OCEPAterms/lterms.html) 

LD50 (Lethal Dose): the dose of a toxicant or microbe that will kill 50 percent of the test organisms within a designated 

period. The lower the LD 50, the more toxic the compound. 

l/kg: liter per kilogram 

Lowest Observed Effect Concentration (LOEC): the lowest level of pollutant concentration that causes statistically 

and biologically significant differences in test samples as compared to other samples subjected to no stressor. 

(http://www.epa.gov/OCEPAterms) 

Maximum Allowable Toxicant Concentration (MATC): for a given ecological effects test, the range (or geometric 

mean) between the No Observable Adverse Effect Level and the Lowest Observable Adverse Effects Level. 

(http://www.epa.gov/OCEPAterms/mterms.html) 

maximum contaminant levels (MCLs): the maximum permissible level of a contaminant in water delivered to any user 

of a public system. MCLs are enforceable standards. (http://www.epa.gov/OCEPAterms/mterms.html) 

mg/kg: milligram per kilogram 

I-45 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

�g/l: microgram per liter 

mole: the amount of substance that contains Avogardo’s number of atoms, molecules or other elementary units. 

National Estuarine Inventory (NEI): The National Estuarine Inventory is a series of inter-related activities that define,


characterize, and assess the nation's estuarine systems. NEI data are compiled in a systematic and consistent manner that


enables the nation's estuaries to be compared and assessed according to their environmental quality, economic values, and


resource uses. A principal feature of the NEI is the determination of the physical dimensions and hydrologic features of


estuarine systems of the United States which are primary determinants of estuarine processes and ultimately affect the ecology


of a system.


National Oceanic and Atmospheric Administration (NOAA): organization within the Bureau of Commerce that


conducts research and gathers data about the global oceans, atmosphere, space, and sun.


No Observed Effect Concentration (NOEC): exposure level at which there are no statistically or biologically significant


differences in the frequency or severity of any effect in the exposed or control populations.


(http://www.epa.gov/OCEPAterms/nterms.html)


oil and grease (O&G): organic substances that may include hydrocarbons, fats, oils, waxes, and high-molecular fatty


acids. Oil and grease may produce sludge solids that are difficult to process. (http://www.epa.gov/owmitnet/reg.htm)


organic carbon (OC): carbon in compounds derived from living organisms.


partition factor: a chemical-specific value representing the fraction of the load expected to partition to sewage sludge


during wastewater treatment.


Permit Compliance System (PCS): a computerized database of information on water discharge permits, designed to


support the National Pollutant Discharge Elimination System (NPDES).


(http://www.epa.gov/ceisweb1/ceishome/ceisdocs/pcs/pcs-exec.htm)


pH: an expression of the intensity of the basic or acid condition of a liquid; natural waters usually have a pH between 6.5 and


8.5. (http://www.epa.gov/OCEPAterms/pterms.html)


pollutants of concern (POCs): the 150 contaminants identified by EPA as being of potential concern for this rule and


which are currently being discharged by MP&M facilities.


Premanufacture Notices (PMN): a notice, required by Section 5 of TSCA, that must be submitted to EPA by anyone


who plans to manufacture or import a new chemical substance for a non-exempt commercial distribution. The notice must be


submitted at least 90 days prior to the manufacture or import of the chemical.


(http://www.epa.gov/oppt/newchems/index.htm)


priority pollutant (PP): 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,


sediment, groundwater, or soil samples. These chemicals are also known as toxic pollutants.


quantitative structure-activity relationship (QSAR): an expert system that uses a large database of measured


physicochemical properties, such as melting point, vapor pressure, and water solubility, to estimate the fate and effect of a


specific chemical based on its molecular structure. (http://www.epa.gov/med/databases/aster.html)


reference doses (RfDs): RfDs represent chemical concentrations - expressed in mg of pollutant/kg body weight/day -


which, if not exceeded, are expected to protect an exposed population, including sensitive groups such as young children or


pregnant women.


Secondary Maximum Contaminant Levels (SMCLs): non-enforceable water treatment levels applying to public


water systems and specifying the maximum contamination levels that, in the judgment of EPA, are required to protect the


public welfare. These treatment levels apply to any contaminants that may adversely affect the odor or appearance of such


water and consequently may cause people served by the system to discontinue its use. 


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MP&M EEBA: Appendices Appendix I: Environmental Assessment 

suspended solids: small particles of solid pollutants that float on the surface of, or are suspended in, sewage or other 

liquids. They resist removal by conventional means. 

Superfund Chemical Data Matrix (SCDM): a source for factor values and benchmark values applied when evaluating 

potential National Priorities List (NPL) sites using the Hazard Ranking System (HRS). 

(http://www.epa.gov/superfund/resources/scdm/index.htm) 

systemic toxicants: chemicals that EPA believes can cause significant non-carcinogenic health effects when present in the 

human body above chemical-specific toxicity thresholds. 

total Kjeldahl nitrogen (TKN): TKN is defined as the total of organic and ammonia nitrate. It is determined in the same 

manner as organic nitrogen, except that the ammonia is not driven off before the digestion step. 

total organic carbon (TOC): a measure of the suspended solids in wastewater, effluent, or water bodies, determined by 

tests for "total suspended non-filterable solids" (see also: suspended solids). 

total petroleum hydrocarbons (TPH): a general measure of the amount of crude oil or petroleum product present in an 

environmental media (e.g. soil, water, or sediments). While it provides a measure of the overall concentration of petroleum 

hydrocarbons present, TPH does not distinguish between different types of petroleum hydrocarbons. 

total suspended particles (TSP): method of monitoring airborne particulate matter by total weight. 

(http://www.epa.gov/OCEPAterms/tterms.html) 

total suspended solids (TSS): a measure of the suspended solids in wastewater, effluent, or water bodies, determined by 

tests for "total suspended non-filterable solids" (see also: suspended solids). 

United States Geological Survey (USGS): a governmental organization that provides reliable scientific information to: 

describe and understand the Earth; minimize loss of life and property from natural disasters; manage water, biological, 

energy, and mineral resources; and enhance and protect our quality of life. (www.noaa.gov) 

volatilization: a process whereby chemicals dissolved in water escape into the air. 

I-47 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

ACRONYMS

AQUIRE: AQUatic Information REtrieval System


ASTER: ASsessment Tools for the Evaluation of Risk


AT: acute toxicity


AWQC: ambient water quality criteria


BCF: bioconcentration factor


BOD: biological oxygen demand


CDF: critical dilution factor


CT: chronic toxicity


DCP: dissolved concentration potential


DMT: dry metric tons


H: Henry's Law


HAP: hazardous air pollutant


HEAST: Health Effects Assessment Summary Tables


IRIS: Integrated Risk Information System


Koc: adsorption coefficient


LOEC: Lowest Observed Effect Concentration


MATC: Maximum Allowable Toxicant Concentration


MCL: maximum contaminant level


NEI: National Estuarine Inventory


NOAA: National Oceanic and Atmospheric Administration


NOEC: No Observed Effect Concentration


O&G: oil and grease


OC: organic carbon


PCS: Permit Compliance System


PMN: Premanufacture Notices


POC: pollutant of concern


PP: priority pollutant


QSAR: quantitative structure-activity relationship


RBC: EPA’s Region III Risk-Based Concentration Table


RfD: reference dose


SCDM: Superfund Chemical Data Matrix


SF: cancer potency slope factor


SMCL: Secondary Maximum Contaminant Level


TKN: total Kjeldahl nitrogen


TOC: total organic carbon


TPH: total petroleum hydrocarbons


TSP: total suspended particulates


TSS: total suspended solids


USGS: United States Geological Survey


WQC: water quality criteria


I-48 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

REFERENCES

Arthur D. Little. 1983. Evaluation of waterborne exposure pathways to paragraph 4(c) pollutants. Draft Report, April 28. 

Also: Paragraph 4(c) list of detected chemicals. 

Arthur D. Little. 1986. Bioaccumulation Study. 

Birge, W. J. et al. 1979. Aquatic toxicity tests on inorganic elements occurring in oil shale. Oil Shale Symposium -

Sampling, Analysis and Quality Assurance, March. EPA-600/9-80-022. 

Clay, D. R. 1986. Office of Toxic Substances, U.S. Environmental Protection Agency. Memorandum to J.M. Conlon, 

OWRS, U.S. Environmental Protection Agency. 

Holdway, D. A. and J. B. Spraque. 1979. “Chronic toxicity of vanadium to flagfish.” Water Research 13:905-910. 

Howard, P. H., ed. 1991. Handbook of Environmental Degradation Rates. Chelsea. MI: Lewis Publishers, Inc. 

ICF, Inc. 1985. Superfund Public Health Evaluation Manual - Draft. 

Leblanc, G. A. 1980. “Acute toxicity of priority pollutants to water flea (Daphnia magna).” Bull Environmental


Contamination Toxicology 24: 684-691.


Lyman, W. J.; W. F. Reehl, and D. H. Rosenblatt. 1981. Handbook of Chemical Property Estimation Methods -


Environmental Behavior of Organic Compounds. New York, NY: McGraw-Hill, Inc. (reported or estimated using methods


outlined).


Syracuse Research Corporation. 1997. CHEMFATE Datafile within the Environmental Fate Database. Syracuse, NY:


Syracuse Research Corporation.


U.S. Atomic Energy Commission. 1973. Toxicity of power plant chemicals to aquatic life. Washington, DC: U.S. Atomic


Energy Commission.


U.S. Environmental Protection Agency (U.S. EPA). 1972. "Blue Book”, NAS-NAE (Water Quality Criteria - 1972).


Washington, DC: U.S. EPA. EPA-R3-73-033. If the code @ AA is provided with this reference, the value is an estimate


based on application factors described in the "Blue Book."


U.S. Environmental Protection Agency (U.S. EPA). 1976. “Red Book” (Quality Criteria For Water). Washington, DC: U.S.


EPA.


U.S. Environmental Protection Agency (U.S. EPA). 1980. Ambient water quality criteria documents. Washington, DC:


Office of Water, U.S. EPA. EPA 440/5-80 Series. Also refers to any update of criteria documents (EPA 440/5-85 and EPA


440/5-87 Series) or any Federal Register notices of proposed criteria or criteria corrections. The most recent National


Recommended Water Quality Criteria used in this report were published in the Federal Register on December 10, 1998.


U.S. Environmental Protection Agency (U.S. EPA). 1984. Summary of current oral Acceptable Daily Intakes (ADIs) for


systemic toxicants. Cincinnati, Ohio: Environmental Criteria and Assessment Office, U.S. EPA, May.


U.S. Environmental Protection Agency (U.S. EPA). 1985. Report to Congress on the Discharge of Hazardous Wastes to 

Publicly-Owned Treatment Works (Domestic Sewage Study). Office of Water Regulations and Standards. Washington, DC: 

U.S. EPA. 

U.S. Environmental Protection Agency (U.S. EPA). 1987. Guidance Manual for Preventing Interference at POTWs. 

Washington, DC: U.S. EPA 

U.S. Environmental Protection Agency (U.S. EPA). 1990. CERCLA Site Discharges to POTWs: Guidance Manual. 

Washington, DC: Office of Emergency and Remedial Response EPA/540/G-90/005. 

I-49 
MP&M EEBA: Appendices Appendix I: Environmental Assessment 

U.S. Environmental Protection Agency (U.S. EPA). 1997. Health Effects Assessment Summary Tables (HEAST).


Washington, DC: Office of Research and Development and Office of Emergency and Remedial Response. U.S. EPA.


U.S. Environmental Protection Agency (U.S. EPA). 1998. Risk-Based Concentration Table, October. Philadelphia, PA:


Region III, U.S. EPA.


U.S. Environmental Protection Agency (U.S. EPA). 1998/99. QSAR. Duluth, MN: Environmental Research Laboratory,


U.S. EPA.


U.S. Environmental Protection Agency (U.S. EPA). 1998/99a. Aquatic Toxicity Information Retrieval (AQUIRE) Data


Base. Duluth, MN: Environmental Research Laboratory, U.S. Environmental Protection Agency. 1998 Database retrieval.


U.S. Environmental Protection Agency (U.S. EPA). 1998/99b. Assessment Tolls for Evaluation of Risk (ASTER) Data


Base. Duluth, MN: Environmental Research Laboratory, U.S. Environmental Protection Agency. 1998 Database retrieval.


U.S. Environmental Protection Agency (U.S. EPA). 1998/99c. Integrated Risk Information System (IRIS) Retrieval.


Washington, DC: U.S. EPA. 1998 Database retrieval.


U.S. Environmental Protection Agency (U.S. EPA). 2000. Development Document for the Proposed Effluent Limitations 

Guidelines and Standards for the Metal Products and Machinery Point Source Category. Washington, DC: U.S. EPA. 

Versar. 1992. Upgrade of Flow Statistics Used to Estimate Surface Water Chemical Concentrations for Aquatic and Human 

Exposure Assessment. Prepared for the Office of Pollution Prevention and Toxics, U.S. EPA. 

Versar. 1994. Development of Mixing Zone Dilution Factors. Preliminary Draft, Progress Report. Prepared for U.S. EPA, 

Office of Pollution Prevention and Toxics, Economics, Exposure, and Technology Division. October 27. EPA Contract No. 

68-D3-0013. 

Worthing, C. R. 1987. The Pesticide Manual - a world compendium, Eighth Edition. Surry, UK: The British Crop Protection 

Council. ISBN 0-948404-42-6. 

Zhang, R. and Zhang, S. 1982. “Toxicity of Fluorides to Fishes.” C.A. Selects - Environm Pollut 24, 97:176354k. 

I-50 
MP&M EEBA: Appendices Appendix J: Spacial Distribution of MP&M Facilities and Recreational User Populations 

Appendix J: Spacial Distribution of


MP&M Facilities and Recreational


User Populations


INTRODUCTION 

This appendix compares the national distribution of all 

MP&M facilities by state and the national distribution of 

recreational participants by state (see Table J.1 and Figure 

J.1). 

EPA based the distribution of MP&M facilities by state on 

APPENDIX CONTENTS 

Table J.1 Distribution of MP&M Facilities and Participants 

of Water-Based Recreation by State ..... J-2 

Figure J.1 Cumulative Distribution of Facilities 

and Participants ................. ... J-4 

Census data on total numbers of facilities in the SICs that make up the MP&M industries, not just water dischargers. This 

comparison assumes that the state distribution of water-discharging MP&M facilities is the same as the overall distribution of 

MP&M facilities. 

EPA based the distribution of recreational participants by state and by type of recreation activity on information provided by 

the National Demand Study data. This comparison suggests that the reaches that benefit from the final rule are also those 

where a very large percentage of all recreational participants reside and recreate. 

J-1 
MP&M EEBA: Appendices Appendix J: Spacial Distribution of MP&M Facilities and R

J-2

Table J.1: Distribution of MP&M Facilities and Participants of Water-based Recreation by State

State

Percent of State Population

Participating by Activity

Average # of Per-Person

Trips per Season by

Activity

Total State

Pop. (1990)

(Millions)

Potential (Extrapolated) # Participants 

Based on State Population

Nat.’l # of

MP&M

Facilities

State % of

National

Facilities

Cum. ST

% of

Facilities

Cu

Boat View Fish Swim Boat View Fish Swim Boat View Fish Swim Bo

CA 11.7% 36.9% 13.6% 20.1% 5.4 14 7.1 11.7 29.8 3,490,513 10,992,849 4,057,154 5,983,736 68,359 11.9% 11.9% 10.

TX 12.8% 16.4% 18.9% 14.5% 7.2 5 10.6 6.5 17.0 2,171,791 2,792,303 3,205,978 2,456,192 38,176 6.6% 18.5% 16.

NY 12.4% 25.6% 11.2% 20.5% 7.9 5.7 9.2 8.7 18.0 2,231,374 4,602,209 2,022,183 3,695,714 36,329 6.3% 24.8% 23.

FL 18.7% 32.6% 20.5% 24.2% 10.1 17.9 17.1 15.4 12.9 2,423,418 4,221,438 2,657,943 3,126,991 30,198 5.2% 30.0% 30.

IL 11.8% 17.2% 14.6% 9.4% 9.6 9 13.7 5.7 11.4 1,349,105 1,962,335 1,667,985 1,079,284 28,343 4.9% 34.9% 34.

OH 11.5% 15.8% 14.2% 14.0% 8 8.2 13.1 8.8 10.8 1,251,590 1,718,851 1,535,284 1,518,596 26,460 4.6% 39.5% 38.

PA 10.5% 14.4% 15.2% 13.7% 9.4 7.4 10.9 8 11.9 1,249,014 1,713,391 1,809,469 1,633,326 26,237 4.6% 44.1% 41.

MI 16.0% 24.8% 18.4% 20.8% 8.6 9.4 12 8.5 9.3 1,484,665 2,307,687 1,710,593 1,936,520 23,662 4.1% 48.2% 46.

NJ 15.9% 32.3% 15.9% 23.9% 10.9 6.4 6.3 7.3 7.7 1,225,246 2,495,046 1,225,246 1,849,008 19,805 3.4% 51.6% 49.

NC 8.8% 17.9% 16.5% 13.5% 7.7 5.2 13.6 7.4 6.6 586,317 1,188,920 1,091,201 895,762 15,158 2.6% 54.3% 51.

IN 14.3% 15.0% 20.3% 16.3% 7.7 9 11.8 5.5 5.5 794,663 831,624 1,127,312 905,546 14,656 2.5% 56.8% 54.

MA 15.7% 30.9% 15.7% 28.9% 8.7 11.6 14.3 9.5 6.0 942,332 1,860,501 942,332 1,739,689 13,915 2.4% 59.2% 56.

WI 15.7% 22.1% 18.1% 19.7% 10 6.1 11.5 6.2 4.9 768,940 1,079,788 883,463 965,266 13,845 2.4% 61.6% 59.

GA 11.5% 13.9% 16.6% 11.5% 11.4 4.1 10.3 7.4 6.5 746,819 903,129 1,076,808 746,819 13,747 2.4% 64.0% 61.

MO 13.0% 12.6% 18.8% 15.2% 5.2 4 5 8 5.1 665,035 646,562 960,606 775,874 13,395 2.3% 66.3% 63.

VA 13.4% 17.0% 16.2% 13.4% 9 4.2 8.4 6.1 6.2 827,102 1,049,783 1,002,066 827,102 12,829 2.2% 68.6% 65.

WA 25.0% 39.2% 18.8% 25.9% 5.8 11.7 18.2 5.8 4.9 1,216,673 1,907,623 916,260 1,261,735 11,991 2.1% 70.6% 69.

MN 17.6% 19.6% 19.6% 17.6% 5.4 16.5 11.5 6.8 4.4 767,875 857,162 857,162 767,875 11,272 2.0% 72.6% 71.

TN 17.9% 13.5% 22.6% 14.5% 7.5 3.7 15.1 6.7 4.9 873,280 659,079 1,103,957 708,510 10,808 1.9% 74.5% 74.

MD 14.8% 18.7% 17.1% 12.1% 8.8 12.1 13.2 8.4 4.8 706,988 893,037 818,617 576,753 8,993 1.6% 76.0% 76.

AL 14.7% 11.9% 20.6% 13.8% 7.5 9.2 18.6 10.6 4.0 593,114 481,905 834,066 556,044 8,825 1.5% 77.6% 77.

CT 16.4% 37.1% 14.5% 27.0% 7.7 6.8 7.7 12.3 3.3 537,516 1,219,747 475,495 888,969 8,593 1.5% 79.1% 79.

LA 16.4% 15.3% 27.0% 13.8% 4 3.4 13.4 4.4 4.2 692,165 647,509 1,138,723 580,525 8,500 1.5% 80.5% 81.

CO 6.6% 13.2% 25.9% 11.3% 17.2 14.8 13.1 5.2 3.3 217,554 435,109 854,678 372,950 8,231 1.4% 82.0% 82.

OR 20.3% 37.8% 24.9% 23.0% 8.8 7.2 13.2 7.4 2.8 576,323 1,074,057 707,306 654,913 7,978 1.4% 83.3% 83.

KY 11.9% 12.3% 22.4% 10.0% 6.5 3 9.4 17.5 3.7 437,524 454,352 824,564 370,212 7,822 1.4% 84.7% 85.

AZ 7.3% 11.2% 11.8% 10.7% 7.2 8 8.3 5.7 3.7 267,685 411,823 432,415 391,232 7,799 1.4% 86.1% 86.

IA 13.5% 16.4% 18.7% 13.5% 5 4.4 13.8 2.7 2.8 373,482 454,673 519,627 373,482 7,661 1.3% 87.4% 87.

OK 11.2% 12.6% 25.2% 14.0% 4.9 3.4 14.6 4.2 3.1 351,954 395,948 791,896 439,942 6,972 1.2% 88.6% 88.
MP&M EEBA: Appendices Appendix J: Spacial Distribution of MP&M Facili

Table J.1: Distribution of MP&M Facilities and Participants of Water-based Recreation by St

State

Percent of State Population

Participating by Activity

Average # of Per-Person

Trips per Season by

Activity

Total State

Pop. (1990)

(Millions)

Potential (Extrapolated) # Participants 

Based on State Population

Nat.’l # of

MP&M

Facilities

State % of

National

Facilities

Cum

% 

Fac

Boat View Fish Swim Boat View Fish Swim Boat View Fish Swim 

SC 13.8% 19.9% 26.0% 15.5% 9.8 8.5 16.2 7.5 3.5 481,589 693,488 905,387 539,380 6,907 1.2% 89

KS 6.7% 17.0% 18.5% 13.3% 17.6 9 12.9 6.2 2.5 165,172 422,105 458,810 330,343 6,370 1.1% 90

AR 14.1% 12.5% 28.1% 18.0% 4.6 10.2 13.3 7.3 2.4 330,571 293,841 661,141 422,396 5,825 1.0% 91

MS 13.6% 12.1% 23.6% 15.7% 6.3 24.2 17.4 12.9 2.6 349,222 312,462 606,544 404,363 5,165 0.9% 92

NE 10.7% 15.5% 10.7% 15.5% 3.9 2.1 13.9 3.9 1.6 169,113 244,274 169,113 244,274 4,424 0.8% 93

UT 8.1% 17.1% 13.5% 12.6% 6.6 3.5 3.6 6.8 1.7 139,691 294,902 232,818 217,296 3,633 0.6% 94

WV 9.5% 10.3% 18.3% 15.9% 6.6 4.6 17.2 6.7 1.8 170,807 185,041 327,381 284,679 3,442 0.6% 94

RI 15.8% 40.4% 19.3% 36.8% 6.9 4.6 8.3 7 1.0 158,442 404,907 193,651 369,697 3,106 0.5% 95

ME 22.2% 44.4% 27.8% 37.5% 7.6 5.7 10.5 10.3 1.2 272,873 545,746 341,091 460,473 2,980 0.5% 95

NH 18.8% 31.2% 14.1% 34.4% 3.3 14.9 13.2 15.7 1.1 207,985 346,641 155,989 381,305 2,960 0.5% 96

NM 6.7% 8.6% 12.4% 9.5% 3.7 5.6 9.8 3.8 1.5 101,005 129,863 187,580 144,292 2,927 0.5% 96

ID 24.1% 25.3% 20.5% 20.5% 5.8 4.3 13.4 9.5 1.0 242,590 254,720 206,202 206,202 2,572 0.4% 97

NV 17.3% 21.3% 13.3% 12.0% 4.8 7.3 15.4 6.3 1.2 208,318 256,391 160,244 144,220 2,406 0.4% 97

MT 14.5% 20.0% 34.5% 29.1% 7.8 15.6 20.7 8.3 0.8 116,228 159,813 276,041 232,455 2,204 0.4% 98

SD 16.7% 21.4% 16.7% 21.4% 2.3 1.8 6 7 0.7 116,001 149,144 116,001 149,144 2,049 0.4% 98

ND 15.0% 15.0% 25.0% 15.0% 3.7 3 4.5 11.5 0.6 95,820 95,820 159,700 95,820 1,749 0.3% 98

HI 16.4% 58.2% 18.2% 47.3% 6.7 33.9 6.6 15.5 1.1 181,347 644,788 201,496 523,890 1,677 0.3% 99

VT 20.6% 17.6% 8.8% 20.6% 7.1 5.5 8.7 10.4 0.6 115,862 99,310 49,655 115,862 1,488 0.3% 99

DE 15.7% 41.2% 15.7% 13.7% 6.4 11 11.5 6.9 0.7 104,497 274,305 104,497 91,435 1,379 0.2% 99

WY 19.4% 16.1% 48.4% 6.5% 6.3 4.6 8.1 8 0.5 87,791 73,159 219,478 29,264 1,309 0.2% 99

AK 34.5% 41.4% 37.9% 6.9% 5.4 7.1 17.4 2 0.6 189,670 227,604 208,637 37,934 1,156 0.2% 10

Source: Information on total MP&M facilities by state is from Census data; information on where recreating people live is from NDS data.
MP&M EEBA: Appendices Appendix J: Spacial Distribution of MP&M Facilities and R

Figure J.1: Cumulative Distribution of Facilities and Participants 

a The numbers refer to states in the order they appear in the above table. Therefore, 1 is California, 2 is Texas, 3 is New York, etc. 

Sources: Information on total MP&M facilities by state is from Census data; information on where recreating people live is from NDS data. 

J-4 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

Appendix K: Selecting WTP Values


for Benefits Transfer


INTRODUCTION 

EPA identified eight surface water evaluation studies that 

quantified the effects of water quality improvements on 

various water-based recreational activities. As noted in 

Chapter 15 of this report, the Agency selected these 

studies based on technical criteria for evaluating study 

transferability (Desvousges et al., 1987; Desvousges et al., 

1992; and B oyle and Bergstrom, 1992), including the 

following: 

� The environmental change valued at the study 

site must be the same as the environmental 

quality change caused by the rule (e.g., changes 

in toxic contamination vs. changes in nutrient 

concentrations); 

� The populations affected at the study site and at 

the policy site must be the same (e.g., 

recreational users vs nonusers); 

� The assignment of property rights at both the 

study and policy sites must lead to the same 

APPENDIX CONTENTS 

K.1 Desvousges et al., 1987. 

for Water Quality Improvements: A Contingent 

Valuation Study for the Monongahela River ...... K-2 

K.2 Farber and Griner, 2000. ng Watershed Quality 

Improvements Using Conjoint Analysis ........... K-3 

K.3 Jakus et al., 1997. portfish Consumption 

Advisories Affect Reservoir Anglers’ Site Choice? . . K-5 

K.4 Lant and Roberts, 1990. reenbelts in the Cornbelt: 

Riparian Wetlands, Intrinsic Values, and 

Market Failure ................. ............. K-6 

K.5 Audrey Lyke, 1993. rete Choice Models to Value 

Changes in Environmental Quality: A Great Lakes 

Case Study ................. ................ K-7 

K.6 Montgomery and Needelman, 1997. re 

Effects of Toxic Contamination in Freshwater Fish . . K-8 

K.7 Phaneuf et al., 1998. Water Quality 

Improvements Using Revealed Preference Methods 

When Corner Solutions are Present” ............. K-8 

Glossary ................. ................. .... K-10 

Acronyms ................. ................. ... K-11 

References ................. ................. .. K-12 

Option Price Estimates 

Valui

Do S

G

Disc

The Welfa

“Valuing

theoretically-appropriate welfare measure (e.g., willingness-to-pay (WTP) vs. willingness-to-accept 

compensation); and 

� The candidate studies should be based on defensible research methods. Six of the eight studies are published in peer 

reviewed journals. One study, Tudor et al. (2002), was presented at the annual American Agricultural Economic 

Association and the Northeastern Resource and Environmental Economic meetings.1 The eighth study, Lyke (1993), 

is an unpublished Ph.D. dissertation. 

In addition to the above criteria, the Agency considered authors' recommendations regarding the robustness and theoretical 

soundness of various estimates in selecting point estimates for benefits transfer. 

The rest of this appendix presents welfare estimates from seven studies used in estimating recreational benefits from the final 

regulation and provides EPA’s reasons for selecting specific values from each study. The study by Tudor et al. (2002) is 

discussed in detail in Chapter 21. All welfare estimates from that study are eligible for use in benefits transfer, because the 

study is based on the policy scenarios specific to the MP&M regulation. 

1 Preliminary results of this study were presented at the annual American Agricultural Economic Association meeting (L. Tudor et al., 

1999a) and at the annual Northeastern Agricultural and Resource Economic Association meeting (L. Tudor et al., 1999b). EPA subjected 

this study to a formal peer review by experts in the natural resource valuation field. The peer review concluded that EPA had done a 

competent job, especially given the available data. This study can be found in Chapter 21. The peer review report is in the docket for the 

rule. 

K-1 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

K.1 DESVOUSGES ET AL., 1987. OPTION PRICE ESTIMATES FOR WATER QUALITY 

IMPROVEMENTS:A CONTINGENT VALUATION STUDY FOR THE MONONGAHELA RIVER 

This study used findings from a contingent valuation (CV) survey to estimate WTP for improved recreational fishing from 

enhanced water quality in the Pennsylvania portion of the Monongahela River. In a hypothetical market, each survey 

respondent was asked to provide an option price for different water quality changes, such as "raising the water quality from 

suitable for boating (hereafter, ‘boatable’ water) to a level where gamefish would survive (hereafter, ‘fishable’ water)." Table 

K.1 lists water quality changes evaluated in the study and the corresponding WTP estimates. The following discussion 

provides justification for selecting the point estimates EPA used in the benefits transfer analysis in Chapter 15. 

Table K.1: Changes in the Resource Value from a Specified Water Quality Improvement from 

Desvousges et al. 

Water Quality Change Valued Adjusted to 2001$ b Original Estimates (1981$) 

User Nonuser Combined User Nonuser Combined 

Iterative Bidding: $25 starting point 

Unsuitable to Boatable $53.4 $57.8 $56.4 $27.4 $29.7 $29.0 

Boatable to Fishablea $36.8 $28.3 $30.9 $18.9 $14.5 $15.9 

Fishable to Swimmable $23.0 $14.0 $16.9 $11.8 $7.2 $8.7 

Boatable to Swimmable $62.5 $42.2 $48.9 $32.1 $21.7 $25.1 

Unsuitable to Swimmable $115.9 $100.0 $105.3 $59.5 $51.4 $54.1 

Iterative Bidding: $125 starting point 

Unsuitable to Boatable $184.4 $75.6 $111.7 $94.7 $38.8 $57.4 

Boatable to Fishable $113.1 $51.2 $71.9 $58.1 $26.3 $36.9 

Fishable to Swimmable $64.4 $22.5 $36.6 $33.1 $11.6 $18.8 

Boatable to Swimmable $194.1 $78.9 $117.3 $99.7 $40.5 $60.2 

Unsuitable to Swimmable $378.5 $154.2 $229.0 $194.4 $79.2 $117.6 

Direct Question: no payment card 

Boatable to Unsuitable $88.2 $27.6 $47.7 $45.3 $14.2 $24.5 

Boatable to Fishable $60.9 $21.0 $34.2 $31.3 $10.8 $17.6 

Fishable to Swimmable $39.3 $16.6 $24.1 $20.2 $8.5 $12.4 

Boatable to Swimmable $103.0 $39.5 $60.7 $52.9 $20.3 $31.2 

Unsuitable to Swimmable $191.2 $67.2 $108.4 $98.2 $34.5 $55.7 

Direct Question: payment card 

Boatable to Unsuitable $91.1 $103.2 $99.3 $46.8 $53.0 $51.0 

Boatable to Fishable $88.2 $42.6 $57.1 $45.3 $21.9 $29.3 

Fishable to Swimmable $44.5 $15.0 $24.3 $22.9 $7.7 $12.5 

Boatable to Swimmable $138.6 $58.3 $83.6 $71.2 $29.9 $42.9 

Unsuitable to Swimmable $229.6 $161.3 $182.8 $117.9 $82.8 $93.9 

(1987) 

Location: Pennsylvania portion of the Monongahela River


Estimating Approach: CV


Survey Population : Recreational Users and Nonusers
 a The value selected for benefits transfer is given in bold. b WTP values from the original study are adjusted to 2001$ based on the Consumer Price Index (CPI). 

Source: Desvousges et al., 1987. 

EPA judged that only one value from this study met the requirements for the quality of research methods and was compatible 

with the environmental changes and population characteristics considered in the analysis of recreational benefits from the 

MP&M rule. EPA selected this value for the following reasons: 

K-2 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

� Environmental quality change. The Desvousges et al. (1987) study derived WTP values for five different changes 

in water quality, as shown in Table K.1 above. EPA judged that only one of these improvements, from “boatable” to 

“fishable,” is compatible with the changes in water quality expected under the MP&M rule. Streams unsuitable for 

recreational activities such as boating are likely to be affected by multiple environmental stressors from many 

sources, including many that are not related to MP&M discharges (e.g., severe oxygen depletion.) In these cases, it 

is reasonable to assume that changes in concentrations of MP&M pollutants would reduce or eliminate one of the 

stressors on the reach, but would be unlikely to change the designation of the reach. 

The analysis in Chapter 15 assumes that reaches with ambient water quality criteria (AWQC) exceedances 

under the baseline conditions are boatable and likely to support rough fishing, but may not be clean enough to 

support gamefishing. AWQC are set at a level below which pollutant concentrations are not expected to cause 

significant harm to human health or aquatic life. Exposure to pollutant concentrations above the AW QC levels are 

expected to have a harmful effect. Therefore, by definition, water with pollutant levels that exceed criteria set to 

protect human health or aquatic life are not suitable waters for sensitive aquatic species or ideal as a sources of fish 

for consumption. 

Removing AWQC exceedances is therefore comparable to shifting water quality from "boatable"to "fishable." The 

Agency did not use the boatable to swimmable designation because a more limited number of reaches are suitable for 

swimming nationally due to reasons not related to MP&M discharges (e.g., amenities, pathogens). Determining 

national level locations affected by MP&M pollutants that are suitable for swimming required more resources than 

were available for the national analysis. 

� Research methods. The authors used four different payment vehicles in their CV study. For the recreational 

benefits analysis, EPA decided to use the WTP estimates derived from the “iterative bidding” (IB) payment 

vehicle, because it is universally preferred to the “direct question/open-ended” format for eliciting option price 

bids. 

Survey respondents in the direct question format are asked to state the most that they would be willing to pay for the 

program or policy. This format confronts respondents with an unfamiliar choice. Studies that use this approach 

usually have high non-response rates. 

Respondents in the IB format are asked whether they would be willing to pay a given amount. If the answer is yes, 

then this amount is raised in pre-set increments until the respondent says that he or she will not pay the last amount 

given. If the answer is no, then the amount is decreased until the respondent indicates WTP the stated amount. 

Some studies found that the respondent’s final WTP amount depends on the initial amount offered. This problem is 

referred to in economic literature as starting point bias. The Agency selected the WTP estimates derived using the 

$25 starting point IB process to avoid upward starting point bias. Table K.1 shows that the selected estimates are the 

most conservative among all the payment vehicles used. 

� Population characteristics. The user population considered in this study matches the user population characteristics 

considered in EPA’s analysis (i.e., recreational anglers, boaters, and wildlife viewers). 

K.2 FARBER AND GRINER, 2000. VALUING WATERSHED QUALITY IMPROVEMENTS 

USING CONJOINT ANALYSIS 

Farber and Griner (2000) used a CV study to estimate changes in water resource values to users from various improvements 

in Pennsylvania’s water quality. The study defines water quality as “polluted,” “moderately polluted,” and “unpolluted” 

based on a water quality scale developed by EPA Region III. “Polluted” streams are unable to support aquatic life, 

“moderately polluted” streams are somewhat unable to support aquatic life, and “unpolluted” streams adequately support 

aquatic life. Farber and Griner developed WTP estimates for water quality improvements for the following three water 

quality changes: 

� from “moderately polluted” to “unpolluted,” 

� from “severely polluted” to “moderately polluted,” and 

K-3 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

� from “severely polluted” to “unpolluted.” 

The authors used six different model variations to estimate the WTP for the three improvements scenarios for various 

population groups (e.g., users, nonusers, and a mix of users and nonusers). Table K.2 presents the estimated WTP values. 

The following discussion provides EPA’s reasons for selecting point estimates for the use in benefits transfer. 

Table K.2: Estimate WTP for Specified Water Quality Improvements from Farber and Griner (2001$) 

Water Quality Change Valued Binary Choice Model Intensity of Preference Model 

User Nonuser Combine User Nonuser Combine 

Basic 

Moderately Polluted to Unpolluted $49 .7 $6.3 $40 .4 $56 .2 $14 .0 $54 .2 

Severely Polluted to Moderately Polluted $66 .9 $5.8 $55 .6 $73 .8 $51 .4 $70 .9 

Severely Polluted to Unpolluted $11 7.3 $44 .9 $95 .7 $12 9.6 $57 .7 $11 6.8 

Interactive 

Moderately Polluted to Unpolluted $48 .2 $3.2 $38 .0 $56 .9 $13 .3 $54 .6 

Severely Polluted to Moderately Polluted $65 .2 $1.5 $52 .7 $75 .1 $50 .6 $71 .9 

Severely Polluted to Unpolluted $11 5.5 $41 .3 $92 .9 $13 3.1 $57 .6 $11 9.5 

Fixed Effects 

Moderately Polluted to Unpolluted a $24 .5 $16 .4 $28 .3 $41 .8 $5.5 $41 .0 

Severely Polluted to Moderately Polluted $42 .4 $10 .6 $38 .2 $63 .4 $30 .3 $59 .0 

Severely Polluted to Unpolluted $86 .6 $48 .4 $80 .4 $11 0.5 $31 .0 $98 .6 

Location: Lower Allegheny Watershed in Western Pennsylvania


Estimating Approach: Conjoint Analysis


Survey Population: Recreational users and nonusers
 a Values selected for the use in benefits transfer are given in bold. b WTP values from the original study are adjusted to 2001$ based on CPI. 

Source: Farber and Griner, 2000. 

The Agency selected only two values from this study based on their compatibility with the environmental changes and 

population characteristics considered in both the original study and the analysis of recreational benefits from the MP&M rule. 

The following discussion summarizes EPA’s reasons used in the selection process: 

� Environmental quality change. EPA judged that only one water quality improvement scenario change from 

“moderately polluted” to “unpolluted” is compatible with the environmental quality change expected from the 

final regulation 

AWQC are set at a level below which pollutant concentrations have not been demonstrated to cause significant harm 

to human health or aquatic life. Exposure to pollutant concentrations above the AWQ C levels are expected to have a 

harmful effect. Therefore, by definition, water with pollutant levels that exceed criteria set to protect human health 

or aquatic life are polluted waters. 

EPA chose the case where the policy variable changed from moderately polluted to unpolluted because this is likely 

to be the most frequently occurring scenario for reaches with MP&M discharges. Streams unable to support any 

aquatic life (i.e., “severely polluted”) are likely to be affected by numerous environmental stressors, in addition to 

MP&M discharges. Eliminating MP&M-related AWQC exceedences would eliminate or reduce one of the stressors, 

but is unlikely to change the quality of the water from severely polluted to unpolluted. It is more realistic to assume 

that most streams affected by MP&M facility discharges are moderately polluted, i.e., these streams support some 

aquatic life; but sensitive species are adversely affected by MP&M pollutants exceeding AWQC values protective of 

aquatic life. Removing all AWQC exceedances would make such streams unpolluted. 

� Research methods. EPA considered only two of the six versions of the benefits transfer model based on the 

authors’ recommendations. The authors appear to prefer the “fixed effects” versions of both the binary choice 

K-4 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

(BC) and intensity of preference (IP) models. Specifically, they note that "A likelihood ratio test, with degrees 

of freedom being the number of individuals in the estimating sample, can be used to test the superiority of the fixed 

effects model. Such a test shows the fixed effects model to be a statistical improvement over either the basic or 

interactive models" (see Table K.2). In addition, they state that, "the purpose of estimating a fixed effects model was 

to account for the possibility that some respondents may approve of all changes, regardless of price and quality. If 

this behavior existed in the sample, not controlling for it would result in overestimates of marginal valuations for 

each type of quality change. This expectation is supported by the fact that the fixed effects valuation estimates are 

lower than the others." 

� Population characteristics. The user population considered in this study matches the user population 

characteristics considered in EPA’s analysis (i.e., recreational anglers, boaters, and wildlife viewers). 

K.3 JAKUS ET AL., 1997. DO SPORTFISH CONSUMPTION ADVISORIES AFFECT 

RESERVOIR ANGLERS’ SITE CHOICE? 

Jakus et al. (1997) used a repeated discrete choice travel cost (TC) model to examine the impacts of fish consumption 

advisories (FCA) in eastern and middle Tennessee. The estimated consumer surplus from recreational fishing in middle 

and east Tennessee is $26.02 and $52.57 per angler per day, respectively, under the baseline water quality conditions. The 

estimated welfare gain from removing FCAs is $2.04 and $3.16 per angler per day , respectively. Table K.3 summarizes the 

study’s estimates. 

Table K.3:Consumer Surplus from Recreational Fishing from Jakus et al. (1997)a 

Water Quality Change Valued Consumer Surplus 

Adjusted to 2001$ 

Consumer Surplus 

($1997) 

Site Choice Model -- multinomial logit 

Average surplus per trip in middle TN (baseline water quality $26.02 $23.60 

Benefit per trip from removing all advisories in middle TN $2.04 $1.85 

Average surplus per trip in East TN (baseline water quality conditions) $52.57 $47.67 

Benefit per trip from removing all advisories in east TN $3.16 $2.86 

Benefit per trip from removing Watts Bar advisory $1.75 $1.59 

Repeated Discrete Choice Model -- repeated nested logit model 

Seasonal benefit from removing all advisories in middle TN $24.22 $21.96 

Seasonal benefit from removing all advisories in east TN $52.27 $47.40 

Seasonal benefit from removing Watts Bar advisory $30.43 $27.60 

Location: Tennessee


Estimating Approach: TC


Survey Population: Tennessee residents; anglers and non-anglers
 a Values selected for the use in benefits transfer are given in bold. b WTP values from the original study are adjusted to 2001$ based on CPI. 

Source: Jakus et al, 1997. 

EPA selected two values from this study for use in benefits transfer, based on their compatibility with the environmental 

quality change and population characteristics at both the original study and policy sites, for the following reason: 

� Environmental quality change. FCAs are usually triggered by the presence of toxic pollutants in fish tissue. EPA 

expects the final regulation to reduce discharges of toxic pollutants, including those linked to FCAs (e.g., mercury 

and lead). The Agency therefore assumed that the removal of FCAs is compatible with water quality improvements 

expected from the final regulation. 

K-5 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

The recreational benefits analysis uses consumer surplus estimates for both regions studied by the authors, because 

MP&M facilities are located in these regions as well as throughout heavily populated regions of the U.S. EPA did 

not include the value corresponding to the Watts Bar lake in the benefits transfer analysis because this lake is 

included in the set of fishing areas for east Tennessee. 

K.4 LANT AND ROBERTS, 1990. GREENBELTS IN THE CORNBELT: RIPARIAN 

WETLANDS, INTRINSIC VALUES, AND MARKET FAILURE 

Lant and Roberts (1990) used a CV study to estimate the recreational and nonuse benefits of improved water quality in 

selected Iowa and Illinois river basins. River quality was defined by means of an interval scale of “poor,” “fair,” “good,” and 

“excellent.” The authors defined the four water quality intervals as follows: 

� “poor” water quality is inadequate to support any recreation activity, 

� “fair” water quality is adequate for boating and rough fishing, 

� “good” water quality is adequate for gamefishing, and 

� “excellent” is adequate to support swimming and exceptional fishing. 

Table K.4 summarizes WTP values for specified water quality improvements from this study. 

Table K.4: WTP Values for a Specified Water Quality Improvement from Lant and Roberts (1990) 

Water Quality Change Valued 

Adjusted to 2001$ Original Study Values 1987$ 

Use Value Nonuse Value Use Value Nonuse Value 

Poor to fair $47.5 $58.6 $30.50 $37.61 

Fair to good a $57.8 $73.5 $37.10 $47.16 

Good to excellent $64.7 $67.3 $41.51 $43.22 

Location: Selected Iowa and Illinois river basins


Estimating Approach: CV


Survey Population: Recreational users and nonusers
 a The values given in bold were selected for the use in benefits transfer. b WTP values from the original study are adjusted to 2001$ based on CPI. 

Source: Lant and Roberts, 1990. 

The Agency judged that only one value from this study is compatible with the environmental changes and population 

characteristics considered in the analysis of recreational benefits from the MP&M rule, for the following reasons: 

� Environmental quality change. The Agency judged that only one of the three possible water quality changes 

considered in this study “fair” to “good” was compatible with the water quality change expected under the 

MP&M rule. EPA assumed in its analysis of recreational benefits expected from the MP&M rule that reaches with 

AWQC exceedances under the baseline conditions may support rough fishing, but may not be clean enough to 

support more sensitive species such as those desired for game fishing. Removing AWQC exceedances will shift 

water quality from “fair“ to “good.” 

� Population characteristics. The user population considered in this study matches the population characteristics 

considered in EPA’s analysis (i.e., recreational anglers, boaters, and wildlife viewers). 

K-6 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

K.5 AUDREY LYKE, 1993. DISCRETE CHOICE MODELS TO VALUE CHANGES IN 

ENVIRONMENTAL QUALITY:A GREAT LAKES CASE STUDY 

Lyke’s (1993) study of the Wisconsin Great Lakes open water sport fishery showed that anglers may place a significantly 

higher value on a contaminant-free fishery than on one with some level of contamination. Lyke estimated the value of the 

fishery to Great Lakes trout and salmon anglers if it was improved enough to be "completely free of contaminants that may 

threaten human health.” The author also estimated various policy scenarios that affect the value of recreational fishing in the 

Wisconsin Great Lakes, including reducing the daily bag limit for lake trout and restoring naturally reproducing populations 

of lake trout. Table K.5 presents welfare estimates from this study. 

Table K.5: WTP Estimates for Specified Water Quality Improvements from Lyke (1993)a 

Water Quality Change Valued 

Adjusted to 2001$ b Original Study Value 

Value of WI 

Fishing 

Change in 

Value 

Value of WI 

Fishing 

Change in 

Value 

CV -- linear logit model 

1990 fishing conditions remain the same as 1989 $95,062,744 $66,600,000 

WI daily bag limit for lake trout reduced to one a day $43,962,951 ($51,099,793 $30,800,000 ($35,800,000 

Great Lakes fish are free of pollutants affecting human health $105,625,27 $10,562,527 $74,000,000 $7,400,000 

Restoring naturally reproducing populations of lake trout $17,271,159 $17,271,159 $12,100,000 $12,100,000 

WI inland fishing conditions remain the same as 1989 $964,330,17 $675,600,00 

Restoring naturally reproducing populations of lake trout in WI 

waters of Great Lakes (inland anglers only) $0 $0 $0 $0 

CV -- constant elasticity of substitution model (mean) 

1990 fishing conditions remain the same as 1989 $118,899,79 $83,300,000 

Great Lakes fish are free of pollutants affecting human health $156,011,38 $37,111,581 $109,300,00 $26,000,000 

CV -- constant elasticity of substitution model (median) 

1990 fishing conditions remain the same as 1989 $26,834,528 $18,800,000 

Great Lakes fish are free of pollutants that affect human health $40,537,266 $13,702,738 $28,400,000 $9,600,000 

Location: Wisconsin


Estimating Approach: TC and CV


Survey Population: Wisconsin Great Lakes and inland anglers
 a The values selected for the use in benefits transfer are given in bold. b WTP values from the original study are adjusted to 2001$ based on CPI. 

Source: Lyke, 1993. 

EPA selected two WTP values from this study for use in benefits transfer for the following reasons: 

� Environmental quality change. EPA judged that only one policy scenario Great Lakes fish that are free from 

contaminants harmful to human health is compatible with water quality improvements associated with removal of 

all AWQ C exceedances. Other scenarios, such as reducing daily bag limit for lake trout to one per day and restoring 

naturally reproducing populations of lake trout, are irrelevant to the MP& M regulation. The Agency used estimates 

from the “1990 fishing conditions remain the same as 1989 conditions" scenario as an estimate of the baseline value 

of recreational fishing in Wisconsin. 

� Research methods. The Agency did not consider estimates from the TC model because the author noted that “the 

nested logit travel cost model results seem too high.” 

K-7 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

K.6 MONTGOMERY AND NEEDELMAN, 1997. THE WELFARE EFFECTS OF TOXIC 

CONTAMINATION IN FRESHWATER FISH 

Montgomery and Needelman (1997) estimated benefits from removing “toxic” contamination from lakes and ponds in New 

York State. They used a binary variable as their primary water quality measure, which indicates whether the New York 

Department of Environmental Conservation considers water quality in a given lake to be impaired by toxic pollutants. Their 

model controls for major causes of impairments other than “toxic” pollutants, to separate the effects of various pollution 

problems that affect the fishing experience. Table K.6 lists environmental quality changes considered in the study and the 

WTP values corresponding to a specified water quality change. 

Table K.6: Welfare Estimates from Montgomery and Needelman (1997) 

Water Quality Change Valued 

Compensating Variation 

per Capita per Season 

(2001$)b 

Compensating Variation 

per Capita per Season 

(1989$) 

Eliminate toxic contamination in all lakes a $90.28 $63.25 

All toxic lakes are closed to fishing $124.31 $87.09 

Raise pH in acidic lakes (none are threatened or impaired) $19.73 $13.82 

Close all acidic lakes to fishing $21.20 $14.85 

Eliminate toxic contamination and raise pH in acidic lakes $113.39 $79.44 

Location: New York State


Estimating Approach: TC -- Repeated discrete choice model


Survey Population: New York State residents; anglers and non-anglers
 a The values selected for the use in benefits transfer are given in bold. b WTP values from the original study are adjusted to 2001$ based on CPI. 

Source: Montgomery and Needelman, 1997. 

The Agency selected only one value from this study for use in the benefits transfer based on its compatibility with 

environmental quality changes at both the original study and the MP&M sites, for the following reason: 

� Environmental quality change. Only one of the five policy scenarios considered eliminate toxic contamination 

in all lakes is directly compatible with the potential changes brought about by the MP&M rule. The MP&M rule 

is unlikely to significantly affect the acidity in lakes and streams affected by MP&M discharges. The last three 

policy scenarios in Table K.6 involve changes in pH levels, and are therefore not included in the benefits transfer. 

The Agency also did not consider the estimate from the second scenario in Table K.6 closing all toxic lakes to 

fishing in benefits transfer, because it does not consider water quality improvement per se. 

K.7 PHANEUF ET AL., 1998. VALUING WATER QUALITY IMPROVEMENTS USING 

REVEALED PREFERENCE METHODS WHEN CORNER SOLUTIONS ARE PRESENT 

Phaneuf et al. (1998) studied angling in Wisconsin Great Lakes. They estimated changes in recreational fishing values 

resulting from a 20 percent reduction of toxin levels in lake trout flesh. The study uses a TC model to value water quality 

improvements when corner solutions are present in the data. Corner solutions arise when consumers visit only a subset of 

the available recreation sites, setting their demand to zero for the remaining sites. Phaneuf et al. found that improved 

industrial and municipal waste management results in general water quality improvement. Table K.7 presents findings from 

this study based on two policy scenarios and four different model specifications. 

K-8 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

Table K.7: Welfare Estimates from Phaneuf et al. (1998) 

Water Quality Change 

Valued 

Adjusted to 2001$a Study Values (1989$) 

RNL RPRN KT System RNL RPRN KT System 

20% reduction in toxins $41.62 $12.53 $166.21 $15.69 $29.16 $8.78 $116.45 $10.99 

Loss of South Lake Michigan $232.19 $140.37 $12,119 $441.36 $162.67 $98.34 $849.09 $309.21 

Location: Wisconsin Great Lakes 

Estimating Approach:	 TC models, including: 

RNL: Repeated Nested Logit model; 

RPRNL: Random Parameters Repeated Nested Logit model; 

KT: Kuhn-Tucker model; and 

System: Systems of Demands model 

Survey Population: Wisconsin anglers; Great Lakes and inland anglers a WTP values from the original study are adjusted to 2001$ based on CPI. 

Source: Phaneuf et al, 1998. 

The Agency selected only one value for use in benefits transfer for the following reasons: 

� Environmental quality change. Only one policy scenario evaluated in this study a 20 percent reduction in the 

toxin levels in fish tissue is compatible with the water quality changes expected from the MP&M regulation (i.e., 

removal of aquatic life-based AWQC exceedances. The second scenario loss of South Lake Michigan fishing 

sites is irrelevant to the final regulation. 

� Research methods. Phaneuf et al. estimated four different models and provided WTP estimates based on each of 

them. The authors indicated, however, that " the KT model comes closest to matching the ideal theoretical model" 

(see authors conclusions, page 1030). Other models either rely on more restrictive assumptions or require additional 

research. The Agency chose the value from the KT model based on the authors’ recommendation, which is one of 

the selection criteria for values used in benefits transfer. 

K-9 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

GLOSSARY 

ambient water quality criteria (AWQC): Levels of water quality expected to render a body of water suitable for its 

designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking, 

swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html) 

binary choice (BC): offers respondents to a contingent valuation survey specific dollars and cents choices, for example, 

“Would you be willing to pay between $10 and $20 per year to improve visibility at the Grand Canyon?” 

conjoint analysis: "any decompositional method that estimates the structure of consumer's preferences given his or her 

overall evaluations of a set of alternatives that are prespecified in terms of levels of different attributes. Price typically is 

included as an attribute." (Green and Srinivasan, 1990). 

contingent valuation (CV): a method used to determine a value for a particular event, where people are asked what they 

are willing to pay for a benefit and/or are willing to receive in compensation for tolerating a cost. Personal valuations for 

increases or decreases in the quantity of some good are obtained contingent upon a hypothetical market. The aim is to elicit 

valuations or bids that are close to what would be revealed if an actual market existed. 

(http://www.damagevaluation.com/glossary.htm) 

corner solutions: a corner solution arises when a consumer who has a choice of two goods, x1 and x2, chooses to consume 

no x1 at the utility maximum. 

direct question/open-ended (OE): in the OE approach, respondents are asked the most they would be willing to pay for 

the program or policy. This approach has a virtue of not providing any hints about what might be a reasonable value. This 

approach, however, confronts respondents with an unfamiliar choice (i.e., placing a price on environmental commodities). 

Studies that use the OE approach have high item non-response rates. 

fish consumption advisory (FCA): an official notification to the public about specific areas where fish tissue samples 

have been found to be contaminated by toxic chemicals which exceed FDA action limits or other accepted guidelines. 

Advisories may be species specific or community wide. 

intensity of preference (IP): an experimental design that allows individuals to state an intensity of preferences for or 

against the alternative to the status quo. For example, the individual designates they would "probably yes" or "definitely yes" 

prefer the alternative to the status quo. 

iterative bidding (IB): with IB, respondents are asked whether they would be WTP a given amount. If the answer is yes, 

this amount is raised in pre-set increments until the respondent says that he or she will not pay the last amount given. If the 

answer is no, then the amount is decreased until the respondent indicates a willingness-to-pay the stated amount. 

starting point bias: when survey interviewers suggest a first bid this can influence the respondent’s answer and cause the 

respondent to agree too readily with bids in the vicinity of the initial bid. (http://www.damagevaluation.com/glossary.htm) 

travel cost (TC): method to determine the value of an event by evaluating expenditures of participants. Travel costs are 

used as a proxy for price in deriving demand curves for a recreation site. (http://www.damagevaluation.com/glossary.htm) 

willingness-to-pay (WTP): maximum amount of money one would be willing to pay or give up to buy some good. 

(http://www.damagevaluation.com/glossary.htm) 

K-10 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

ACRONYMS 

AWQC: ambient water quality criteria 

BC: binary choice 

CV: contingent valuation 

FCA: fish consumption advisory 

IB: iterative bidding” 

IP: intensity of preference 

TC: travel cost 

WTP: willingness-to-pay 

K-11 
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer 

REFERENCES 

Boyle, K. J. and J.C Bergstrom. 1992. “Benefit Transfer Studies: Myths, Pragmatism and Idealism.” Water Resources 

Research, Vol. 28, No. 3, March: 657-663. 

Desvousges, W. H. et al. 1987. “Option Price Estimates for Water Quality Improvements: A Contingent Valuation Study for 

the Monongahela River.” Journal of Environmental Economics and Management, 14: 248-267. 

Desvousges, W. H. et al. 1992. “Benefit Transfer: Conceptual Problems in Estimating Water Quality Benefits Using 

Existing Studies.” Water Resources Research, Vol. 28, No. 3, March: 675-683. 

Farber, S. and B. Griner. 2000. Valuing Watershed Quality Improvements Using Conjoint Analysis. University of 

Pittsburgh, PA. 

Jakus, P.M., M. Downing, M.S. Bevelhimer, and J.M. Fly. 1997. “Do Sportfish Consumption Advisories Affect Reservoir 

Anglers’ Site Choice?” Agricultural and Resource Economic Review, 26(2). 

Lant, C. L. and R.S. Roberts. 1990. “Greenbelts in the Cornbelt: Riparian Wetlands, Intrinsic Values, and Market Failure.” 

Environment and Planning A, Vol. 22: 1375-1388. 

Lyke, A.J. 1993. Discrete Choice Models to Value Changes in Environmental Quality: A Great Lakes Case Study. PhD 

dissertation, Madison, WI: University of Wisconsin, Department of Agricultural Economics. 

Montgomery, M. and M. Needelman. 1997. “The Welfare Effects of Toxic Contamination in Freshwater Fish.” Land 

Economics 73(2): 211-223. 

Phaneuf, D. J., C. L. Kling, and J.A. Herriges. 1998. “Valuing Water Quality Improvements Using Revealed Preference 

Methods When Corner Solutions Are Present.” American Journal of Agricultural Economics, 80: 1025-1031. 

Tudor, L., E. Besedin, M. Fisher, S. Smith, and L. Snyder. 1999. What Pollutants Matter for Consumers of Water-Based 

Recreation? Presented at the annual meeting of the Northeastern Agricultural and Resource Economics Association. 

Morgantown, WV. June 1999. 

Tudor, L., E. Besedin, M. Fisher, and S. Smith. 1999. Economic Analysis of Environmental Regulations: Application of the 

RUM model to Ecological Benefits Assessment for MP&M Effluent Guideline Limitations. Presented at the annual meeting of 

American Agricultural Economics Association. Nashville, TN. August 1999. 

K-12 
MP&M EEBA: Appendices Appendix L: Parameters Used in the IEUBK Model 

Appendix L: Parameters Used in the


IEUBK Model


INTRODUCTION APPENDIX CONTENTS 

Table B.1: Description of Parameters Used In the IEUBK 

Lead Model This appendix contains a comprehensive list of model 

parameters that are used in the IEUBK model for lead in 

children. 

The remainder of this appendix is a reproduction of Appendix B: Description of Parameters In the IEUBK Lead Model, taken 

from the Technical Support Document for the Integrated Exposure Uptake Biokinetic Model for Lead in Children (v0.99d) 

(December 1994). 

L-1 
APPENDIX B: DESCRIPTION OF PARAMETERS


IN THE IEUBK LEAD MODEL 

TABLE B-1. DESCRIPTION OF PARAMETERS IN THE IEUBK LEAD MOD

PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

ABSD 

Total absorption for dust at low saturation 

0.3 0-84 E Based on US EPA (1989a). 

ABSF 

Total absorption for food at low saturation 

0.5 0-84 E Based on US EPA (1989a). 

ABSO 

Total absorption for other ingested lead at low saturation 

0.0 0-84 E Based on the default condition that there is no other source of lead ingestion in the household. 

ABSS 

Total absorption for soil at low saturation 

0.3 0-84 E Based on US EPA (1989a). 

ABSW 

Total absorption for water at low saturation 

0.5 0-84 E Based on US EPA (1989a). 

air_absorb(t) 

Net percentage absorption of air lead 

32 32 32 32 32 32 32 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

E 

Deposition efficiencies of airborne lead particles were estimated by U S EPA (1989a). A respiratory deposition/absorption rate of 25% to 45% is reported for young children living in non-point source areas while a rate o42% is calculated for those living near point sources. An intermediate vaof 32% was chosen. 

air_concentration(t) Outdoor air lead concentration 

0.1 0.1 0.1 0.1 0.1 0.1 0.1 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

E 

Based on the lower end of the range 0.1 - 0.3 μg Pb/m3 that is reported fo

outdoor air lead concentration in U.S. cities without lead point sources (UEPA 1989) 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-2
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

AVF, AVW, AVD, AVO, AVS Bioavailability 1 0-84 I Parameter added for later flexibility in describing the absorption process; has no effect in current algorithm. 

AVINTAKE Available intake U-2 0-84 I The amount of Pb that is available for intake can_fruit(t) 

Lead intake from canned fruit when fruit is consumed only in canned form 

1.811 1.063 1.058 0.999 0.940 0.969 1.027 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I Pb concentration from data provided to EPA by FDA (US EPA (1986). Quantity consumed from Pennington (1983). 

can_veg(t) 

Lead intake from canned vegetables when vegetable is consumed only in canned form 

0.074 0.252 0.284 0.295 0.307 0.291 0.261 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I Pb concentration from data provided to EPA by FDA (US EPA (1986). Quantity consumed from Pennington (1983). 

contrib_percent 

Ratio of indoor dust lead concentration to soil lead concentration 

0.70 0-84 E Analysis of soil and dust data from 1983 East Helena study (US EPA, 19

CONRBC 

Maximum lead concentration capacity of red blood cells 

1200 0-84 I 

Based on Marcus (1983) reanalysis of infant baboon data from Mallon (1983). See Marcus (1985a) for assessment of form of relationship and estimates from data on human adults [data from deSilva (1981a,b), Manand Malloy (1983), and Manton and Cook (1984)] and infant and juvenile baboons (Mallon, 1983). 

constant_soil_conc(t) Soil lead concentration 

200 200 200 200 200 200 200 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

E Air Quality Criteria Document for Lead. (US EPA, 1986) 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-3 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

constant_water_conc Water lead concentration 4.0 0-84 E 

Based on analysis of data from the American Water Works Service Co. (Marcus, 1989) 

CRBONEBL(t) 

Ratio of lead concentration (μg/kg) in bone to blood lead concentration (μg/L) 

B-4c 0-84 I 

Data in Barry (1981) were used. Bone lead concentration was calculated as an arithmetic average of the concentrations in the rib, tibia, and calvaria. The blood lead concentratiowere taken directly from the study. Concentrations in each of the following eight age groups were consideredstillbirths, 0-12 days, 1-11 mos, 1-5 yrs, 6-9 yrs, 11-16 yrs, adult (men), aadult (women). Ages 0 and 40 yrs were assumed for stillbirths and adultsrespectively. 

CRKIDBL(t) 

Ratio of lead concentration (μg/kg) in kidney to blood lead concentration (μgL) 

B-4a 0-84 I 

Data in Barry (1981) were used. Lead concentrations in kidney (combined values for cortex and medulla) and blood were taken directly from the study. Concentrations in each of the following eight age groups were consideredstillbirths, 0-12 days, 1-11 mos, 1-5 yrs, 6-9 yrs, 11-16 yrs, adult (men), aadult (women). Ages 0 and 40 yrs were assumed for stillbirths and adultsrespectively. 

CRLIVBL(t) 

Ratio of lead concentration (μg/kg) in liver to blood lead concentration (μg/l) 

B-4b 0-84 I 

Data in Barry (1981) were used. Lead concentrations in liver and blood were taken directly from the study. Concentrations in each of the following eight age groups were consideredstillbirths, 0-12 days, 1-11 mos, 1-5 yrs, 6-9 yrs, 11-16 yrs, adult (men), aadult (women). Ages 0 and 40 yrs were assumed for stillbirths and adultsrespectively. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-4 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

CROTHBL(t) 

Ratio of lead concentration (μg/kg) in other soft tissue to blood lead concentration (μg/L) 

B-4d 0-84 I 

Data in Barry (1981) were used. Lead concentration ratio for soft tissues was calculated as a weighted arithmetic average of concentration ratios for muscle (53.8%), fat (24.0%skin (9.4%), dense connective tissue (4.4%), brain (2.7%), GI tract (2.3%lung (1.9%), heart (0.7%), spleen (0.3%), pancreas (0.2%), and aorta (0.2%), where the weights applied are given in parentheses. The weight associated with each soft tissue component was equal to the weight of thcomponent (kg) divided by weight of all soft tissues (kg). These weights were estimated from Schroeder and Tipton (1968) and are assumed to apply in the range 0-84 months of age. Concentrations in each of the following eight age groups were consideredstillbirths, 0-12 days, 1-11 mos, 1-5 yrs, 6-9 yrs, 11-16 yrs, adult (men), aadult (women). Ages 0 and 40 yrs were assumed for stillbirths and adultsrespectively. 

DAYCARE(t) Dust lead intake at daycare E-12c 0-84 I Simple combination of the total amount of dust ingested daily, fraction of total dust ingested as daycare dust, and dust lead concentration at dayca

DaycareConc 

Dust lead concentration at daycare 

200 0-84 E Based on the assumption that default daycare dust concentrations are th

same as default residence dust concentrations. 

DaycareFraction 

Fraction of total dust ingested daily as daycare dust 

0 0-84 E Based on the default assumption that the child does not attend daycare. 

diet_intake(t) User-specified diet lead intake 

5.53 5.78 6.49 6.24 6.01 6.34 7.00 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

E Pb concentration from data provided to EPA by FDA (US EPA (1986). Quantity consumed from Pennington (1983). 

DietTotal(t) Total Dietary Intake E-4b 0.84 I Summation of all dietary sources; same as INDIET(t) 

DustTotal(t) Daily amount of dust ingested E-10 0-84 I Simple combination of total amount soil and dust ingested daily and fract

of this combined ingestion that is dust alone. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-5 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

EXAIR(t) Air lead intake E-3 0-84 I Simple combination of average air lead concentration and ventilation ratef_fruit(t) 

Lead intake from fresh fruit if no home-grown fruit is consumed 

0.039 0.196 0.175 0.175 0.179 0.203 0.251 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I Pb concentration from data provided to EPA by FDA (US EPA (1986). Quantity consumed from Pennington (1983). 

f_veg(t) 

Lead intake from fresh vegetables if no home-grown vegetables are consumed 

0.148 0.269 0.475 0.466 0.456 0.492 0.563 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I Pb concentration from data provided to EPA by FDA (US EPA (1986). Quantity consumed from Pennington (1983). 

FirstDrawConc First Draw water lead concentration 4.0 0-84 E Based on analysis of data from the American Water Works Service Co. (Marcus, 1989) FirstDrawFraction 

Fraction of total water consumed daily as first draw 

0.5 0-84 E 

In the absence of appropriate data, a conservative value corresponding to consumption largely after four fours stagnation time was used, e.g. early morning or late afternoon. 

FountainConc Fountain water lead concentration 10 0-84 E Default assumption is that the drinking fountain has a lead-lined reservoir

but that consumption is not always first draw. Therefore, a value was selected from the range of 5-25 g/L. 

FountainFraction 

Fraction of total water consumed daily from fountains 

0.15 0-84 E A default value was based on 4-6 trips to the water fountain at 40-50 ml p

trip. 

fruit_all(t) Daily amount of all frults consumed 

38.481 

169.000 

63.166 61.672 61.848 67.907 80.024 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I Pb concentration from data provided to EPA by FDA (US EPA (1986). Quantity consumed from Pennington (1983). 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-6 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

HomeFlushedConc 

Home flushed water lead concentration 

1.0 0-84 E Based on analysis of data from the American Water Works Service Co. (Marcus, 1989) 

HCT0 Hematocrit at birth 0.45 0 I Data from Silve et al. (1987); also Spector (1956) and Altman and Ditme

(1973) 

InCanFruit(t) Lead intake from canned fruit E-5d 0-84 I Simple combination of the fraction of non-home grown fruits consumed daily, and lead intake from canned fruits when fruits are consumed only in canned form. InCanVeg(t) Lead intake from canned vegetables E-5b 0-84 I Simple combination of the fraction of vegetables consumed daily as non- home grown, and lead intake from canned vegetables when vegetables a

consumed only in canned form. 

INDIET(t) Diet lead intake 

E-4a 

or 

E-4b 

0-84 I 

Two options are provided. Default option - Considers composite diet lead intake. Alternate option - Combines lead intake from several individual componeof diet. 

IndoorConc(t) Indoor air lead concentration E-1 0-84 I Algebraic expression of relationship 

indoorpercent 

Ratio of indoor dust lead concentration to corresponding outdoor concentration 

30 0-84 E 

Based on homes near lead point sources. The default value is reported iOAQPS (USEPA 1989, pp A-1) and is estimated by Cohen and Cohen (1980). 

INDUST(t) Household dust lead intake E-9a or E-9c 0-84 I 

Two options are provided. Default option - Assumes that all dust lead exposure is from the househoAlternate option - Considers dust lead exposure from several alternative sources as well. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-7 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

INDUSTA(t) 

Lead intake from alternate dust sources 

E-9b 

or 

E-9d 

0-84 I 

Two options are provided. Default option - Assumes that lead intake from alternate sources is zero. Alternate option - Combines lead intake from several alternate sources. 

InFish(t) Lead intake from fish E-5h 0-84 I Simple combination of total meat consumed daily, fraction of meat consumed as fish, and lead concentration in fish. InFrFruit(t) 

Lead intake from non-home grown fresh fruits 

E-5e 0-84 I Simple combination of the fraction of fruits consumed daily as non-home grown and lead intake from fresh fruits. 

InFrVeg(t) 

Lead intake from non-home grown fresh vegetables 

E-5c 0-84 I Simple combination of the fraction of vegetables consumed daily as non- home grown and lead intake from fresh vegetables. 

InGame(t) Lead intake from game animal meat E-5i 0-84 I Simple combination of total meat consumed daily, fraction of meat consumed as game animal meat, and lead concentration in game animal meat. 

InHomeFruit(t) Lead intake from home grown fruits E-5f 0-84 I Simple combination of total amount of fruit consumed daily, fraction of fru

consumed as home grown, and lead concentration in home grown fruit. 

InHomeVeg(t) 

Lead intake from home grown vegetables 

E-5g 0-84 I 

Simple combination of total amount of vegetable consumed daily, fraction vegetables consumed as home grown, and lead concentration in home grown vegetables. 

InMeat(t) 

Lead intake from non-game and non-fish meat 

E-5a 0-84 I 

Simple combination of total amount of meat consumed daily, fraction of meat consumed as non-game and non-fish meat, and lead concentration non-game and non-fish meat. 

InOtherDiet(t) 

Combined lead intake from dairy food, juice, nuts, beverage, pasta, bread, sauce, candy, infant and formula food 

3.578 3.506 3.990 3.765 3.545 3.784 4.215 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I 

Sum of the amounts of lead ingested in food items not substituted by the calculation of exposure to lead in home grown fruits and vegetables, wild game or fish. Pb concentration from data provided to EPA by FDA (US E(1986). Quantity consumed from Pennington (1983). 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-8 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

INOTHER(t) 

Combined other sources of ingested lead, such as paint chips, ethnic medicines, etc. 

0 0-84 E Assumes no other sources of ingested lead 

INSOIL(t) Soil lead intake E-8 0-84 I Simple combination of total amount of soil and dust ingested daily, fractio

of this combined ingestion that is soil alone, and lead concentration in so

INWATER(t) Water lead intake 

E-6a 

or 

E-6b 

0-84 I 

Two options are provided. Default option - Simple combination of water consumed daily and a constant water lead concentration. Alternate option - Water lead concentration depends on contribution from several individual sources of water. 

MCORT(t) Mass of lead in cortical bone B-7e and B-9f 0 and 0-84 

I 0 months - Simple combination of an assumed bone to blood lead 

concentration ratio, blood lead concentration, and weight of cortical boneBasis for value of bone to blood lead concentration ratio was human autopsy data (Barry, 1981). 0-84 months - Application of the Backward Euler solution algorithm to the system of differential equations (B-6a-B-6i in Table A-3). Both cases above assume that the cortical bone to blood lead concentratratio is equal to the bone (composite) to blood lead concentration ratio. 

meat_all(t) 

Daily amount of meat (including fish and game) consumed 

29.551 87.477 95.700 

101.570 107.441 111.948 120.961 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I Pb concentration from data provided to EPA by FDA (US EPA (1986). Quantity consumed from Pennington (1983). 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-9 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

meat(t) 

Lead intake from meat if no game meat or fish is consumed 

0.226 0.630 0.811 0.871 0.931 1.008 1.161 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I Pb concentration from data provided to EPA by FDA (US EPA (1986). Quantity consumed from Pennington (1983). 

MKIDNEY(t) Mass of lead in kidney B-7f and B-9c 0 and 0-84 

I 0 months - Simple combination of an assumed kidney to blood lead 

concentration ratio, blood lead concentration, and weight of kidney. Basifor the value of the kidney to blood lead concentration ratio was human autopsy data (Barry, 1981). 0-84 months - Application of the Backward Euler solution algorithm to the system of differential equations (B-6a-B-6i in Table A-3). 

MLIVER(t) Mass of lead in liver B-7g and B-9b 0 and 0-84 

I 0 months - Simple combination of an assumed liver to blood lead 

concentration ratio, blood lead concentration, and weight of the liver. Basfor the value of the liver to blood lead concentration ratio was human autopsy data (Barry, 1981). 0-84 months - Application of the Backward Euler solution algorithm to the system of differential equations (B-6a-B-6i in Table A-3). 

MOTHER(t) Mass of lead in soft tissues B-7h and B-9d 0 and 0-84 

I 0 months - Simple combination of an assumed soft tissue to blood lead 

concentration ratio, blood lead concentration, and weight of the soft tissueat birth. Basis for the value of soft tissue to blood lead concentration ratio was human autopsy data (Barry et al., 1981), using total lead and total weight of other tissue. 0-84 months - Application of the Backward Euler solution algorithm to the system of differential equations (B-6a-B-6i in Table A-3). 

MPLASM(t) Mass of lead in plasma pool B-7d and B-9g 0 and 0-84 

I 0 months - Simple combination of the mass of lead in blood and red bloo

cells. 0-84 months - Based on the assumption that the lead concentration in plasma-ECF is equal to the lead concentration in the plasma. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-10 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

MPLECF(t) 

Mass of lead in plasma-extracellular fluid (plasma-ECF) 

B-7b 

and 

B-8a 

0 

and 

0-84 

I 0 months - Based on two assumptions. 

(1) masses of lead in plasma-ECF and red blood cells are in kinetic qua

equilibrium, and 

(2) lead concentration in the plasma-ECF is equal to lead concentration 

the plasma. 0-84 months - Application of the Backward Euler solution algorithm to the system of differential equations (B-6a-B-6i in Table A-3). 

MRBC(t) Mass of lead in red blood cells B-7c and B-9a 0 and 0-84 

I 0 months - Based on the assumption that the masses of lead in plasmaECF and red blood cells are in kinetic quasi-equilibrium. 

0-84 months - Application of the Backward Euler solution algorithm to the system of differential equations (B-6a-B-6i in Table A-3). 

MTRAB(t) Mass of lead in trabecular bone B-7i and B-9e 0 and 0-84 

I 0 months - Simple combination of an assumed bone to blood lead 

concentration ratio, blood lead concentration, and weight of trabecular bone. Basis for the value of bone to blood lead concentration ratio was human autopsy data (Barry, 1981). 0-84 months - Application of the Backward Euler solution algorithm to the system of differential equations (B-6a-B-6i in Table A-3). Both cases above assume that trabecular bone to blood lead concentratioratio is equal to bone (composite) to blood lead concentration ratio. 

multiply_factor 

Ratio of indoor dust lead concentration to air lead concentration 

100 0-84 E 

Analyses of the 1983 East Helena study in (USEPA 1989, Appendix B-8) suggest about 267 μg/g increment of lead in dust for each μg /m3. lead in 

air. A much smaller factor of 100 μg/g PbD per μg/m3 is assumed for nosmelter community exposure. 

OCCUP(t) 

Dust lead intake from secondary occupation 

E-12a 0-84 I 

Simple combination of amount of dust ingested, fraction of the total dust ingested as secondary occupational dust, and lead concentration in secondary occupational dust 

OccupConc 

Secondary occupational dust lead concentration 

1200 0-84 E Air Quality Criteria Document for Lead. (US EPA, 1986) 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-11 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

OccupFraction 

Fraction of total dust ingested as secondary occupation dust 

0 0-84 E The default condition is that there is no adult in the residence who works 

a lead-related job. 

PAINT(t) 

Dust lead intake from lead based home paint 

E-12e 0-84 I 

Simple combination of amount of dust ingested daily, fraction of the total dust ingested as lead-based home paint, and lead concentration in leadbased home paint. 

PaintConc 

Leadconcentration in housedust containing lead based paint 

1200 0-84 E Air Quality Criteria Document for Lead. (US EPA, 1986) 

PAF 

Fraction of total absorption as passive absorption at low dose 

0.20 0-84 E 

Based on in vitro everted rat intestine data (Aungst and Fung, 1981), reanalyses (Marcus, 1994) of infant baboon data (Mallon, 1983) and infanduplicate diet study (Sherlock and Quinn, 1986) 

PaintFraction 

Fraction of total dust ingested that results from lead based home paint 

0 0-84 E The default is that there is no lead-based paint in the home. 

PBBLDMAT Maternal blood lead concentration 2.5 adult E Based in part on Midvale 1989 study. The default value of 2.5 g/dL has little influence of the early post natal exposure of the child. 

PBBLD0 Lead concen

tration in blood B-7a 0 I Based on 85% of maternal blood lead concentration (US EPA 1989) 

PBBLOODEND(t) Lead concen

tration in blood B-10a 0-84 I Simple combination of the blood lead concentrations determined in each iteration in the solution algorithm between the previous month and that month. 

RATBLPL 

Ratio of lead mass in blood to lead mass in plasmaECF 100 0-84 I Based on the lower end of the 50-500 range for the red cell/plasma lead concentration ratio recommended in Diamond and O'Flaherty (1992a). 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-12 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

RATFECUR 

Ratio of endogenous fecal lead elimination rate to urinary lead elimination rate 

0.75 0-84 I 

Assume child ratio is larger than the adult ratio; values derived from a reanalysis of data from Ziegler et al. (1978) and Rabinowitz and Wetherill (1973). 

RATOUTFEC 

Ratio of elimination rate via soft tissues to endogenous fecal lead elimination rate 

0.75 0-84 I Within the range of values derived from a reanalysis of data from Ziegler 

al. (1978) and Rabinowitz and Wetherill (1973). 

SATINTAKE(t) 

Half saturation absorbable lead intake 

U-3 0-84 I 

Assumed proportional to the weight of body . The coefficient of proportionality is assumed to depend on the estimate of the parameter fo24 month old and the corresponding body weight. 

SATINTAKE24 

Half saturation absorbable lead intake for a 24 month old 

100 0-84 E Extrapolated from reanalysis of human infant data (Sherlock and Quinn, 1986) and infant baboon data (Mallon, 1983) 

SCHOOL(t) Dust lead intake from school E-12b 0-84 I Simple combination of amount of dust ingested daily, the fraction of total dust ingested daily as school dust, and lead concentration in dust at schSchoolConc 

Dust lead concentration at school 

200 0-84 E By default, this dust lead concentration is set to the same as the resident

dust lead concentration. 

SchoolFraction 

Fraction of total dust ingested daily as school dust 

0 0-84 E Based on the default assumption that children are not in school. 

SECHOME(t) Dust lead intake at secondary home E-12d 0-84 I Simple combination of amount of dust ingested daily, fraction of dust ingested daily as secondary home dust, and lead concentration in dust at the secondary home. SecHomeConc 

Secondary home dust lead concentration 

200 0-84 E Based on the assumption that dust lead concentration in a secondary hom

is the same as the default dust lead concentration in the primary home. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-13 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

SecHomeFraction 

Fraction of total dust ingested daily as secondary home dust 

0 0-84 E Based on the default assumption that the child does not spend a significa

amount of time in a secondary home. 

soil_indoor(t) 

Indoor household dust lead concentration 

E-11 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I Under alternate dust sources model, based on assumption that both soil and outdoor air contribute to indoor dust lead. 

soil_ingested(t) 

Soil and dust (combined) consumption 

0.085 0.135 0.135 0.135 0.100 0.090 0.085 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

E 

Based on values reported in OAQPS report (USEPA 1989, pp. A-16). Thvalues reported were estimated for children, ages 12-48 mos, by several authors such as Binder et al. (1986) and Clausing et al. (1987). Sedman (1987) extrapolated these estimates to those for children, ages 0-84 mos

TBLBONE(t) Lead transfer time from blood to bone 1 and B-1e 24 and 0-84 I 

24 months - Initialization is keyed to the two year old child, based in part information from Heard and Chamberlain, (1982) for adults, and O'Flaher(1992). Once the concentration ratios are fixed, the exact value of this parameter, within a wide range of possible values, has little effect on the blood lead value. 0-84 months - Assumed proportional body surface area. The coefficient oproportionality is assumed to depend on an estimate of the parameter for 24 month old and the corresponding body surface area. Also, it is assumed that body surface area varies as 1/3 power of the weight of body 

based on Mordenti (1986). 

TBLFEC(t) 

Lead transfer time from blood to feces 

B-1f 0-84 I 

Simple combination of an assumed ratio of urinary lead elimination rate tendogenous fecal lead elimination rate, and lead transfer time from blood urine (See RATFECUR). The ratio of of elimination rates was estimated for adults using Chamberlet al. (1978), and Chamberlain (1985) and is assumed to apply to ages 0-months. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-14 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

TBLKID(t) 

Lead transfer time from blood to kidney 

10 

and 

B-1d 

24 

and 

0-84 

I 

24 months -on information from Heard and Chamberlain, (1982) for adults, and O'Flaherty (1992). of this parameter, within a wide range of possible values, has little effect othe blood lead value. 0-84 months - Assumed proportional body surface area. proportionality is assumed to depend on an estimate of the parameter for 24 month old and the corresponding body surface area. assumed that body surface area varies as 1/3 power of the weight of body 

based on (Mordenti, 1986). 

TBLLIV(t) Lead transfer time from blood to liver 10 and B-1b 24 and 0-84 I 

24 months -information from Heard and Chamberlain, (1982) for adults, and O'Flaher(1992). parameter, within a wide range of possible values, has little effect on the blood lead value. 0-84 months - Assumed proportional body surface area. proportionality is assumed to depend on an estimate of the parameter for 24 month old and the corresponding body surface area. assumed that body surface area varies as 1/3 power of the weight of body 

based on (Mordenti, 1986). 

TBLOTH(t) 

Lead transfer time from blood to other soft tissue 

10 

and 

B-1c 

24 

and 

0-84 

I 

24 months -on information from Heard and Chamberlain, (1982) for adults, and O'Flaherty (1992). of this parameter, within a wide range of possible values, has little effect othe blood lead value. 0-84 months - Assumed proportional body surface area. proportionality is assumed to depend on an estimate of the parameter for 24 month old and the corresponding body surface area. that body surface area varies as 1/3 power of the weight of body based on 

(Mordenti, 1986). 

TBLOUT(t) 

Lead transfer time from blood to elimination pool via soft tissue 

B-1g 0-84 I 

Simple combination of an assumed ratio of to endogenous fecal lead elimination rate, times the lead transfer time froblood to feces (See RATOUTFEC). 

Initialization is keyed to the two year old child, based in par

Once the concentration ratios are fixed, the exact val

The coefficient o

Also, it is 

Initialization is keyed to the two year old child, based in part 

Once the concentration ratios are fixed, the exact value of this 

The coefficient o

Also, it is 

Initialization is keyed to the two year old child, based in par

Once the concentration ratios are fixed, the exact val

The coefficient o

Also, it is assum

elimintion rate via soft tissue

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-15 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

TBLUR(t) Lead transfer time from blood to urine 20 and B-1a 24 and 0-84 I 

24 months - Assumed proportional to body surface area. The coefficient proportionality is assumed to depend on an adult estimate for the parameand the corresponding body surface area. The adult estimate of 39 days was obtained using Araki et al (1986a, 1986b, 1987), Assenato et al (1986), Campbell et al (1981), Carton et al (1987), Chamberlain et al. (1978), Folashade et al (1991), Heard and Chamberlain (1981), He et al 

(1988), Kawaii et al (1983), Kehoe (1961), Koster et al (1989), Manton anMalloy (1983), Rabinowitz and Wetherill (1973), Rabinowitz et al (1976), and Yokoyama et al (1985). 0-84 months - Assumed proportional body surface area. The coefficient 

proportionality is assumed to depend on an estimate of the parameter for 24 month old and the corresponding body surface area. Both cases above assume that (a) body surface area varies as 1/3 power 

weight of body based on (Mordenti, 1986) and (b) respectively, 70 kg and 12.3 kg are standard adult and 2 year old body weights based on Spector (1956). Since glomerular filtration rate (GFR) is proportional to body surface area for ages 24 months based on (Weil, 1955), surface area scaling is equivalent to scaling by GFR for ages 24 months. 

TBONEBL(t) Lead transfer time from bone to blood B-1h 0-84 I Based on the assumption that masses of lead in bone and blood are in kinetic quasi-equilibrium. TCORTPL(t) 

Lead transfer time from cortical bone to plasma-ECF 

B-2l 0-84 I Based on the assumption that the cortical and trabecular bone pools have similar lead kineticsfor children younger than 84 months. 

time_out(t) Time spent outdoors 

1 2 3 4 4 4 4 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

E 

Values are reported in the OAQPS staff report (USEPA 1989, pp. A-2) anthe TSD (USEPA 1990a). The values have been derived from a literature review (Pope, 1985). 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-16 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

TimeStep 

Length of timestep in solution algorithm 

1/6 0-84 E 

This user-selectable parameter is available mainly for adjusting the moderun time to the speed of the computer. Newer, faster computers can run the model at the shortest TimeStep (15 min) in less than one minute. The default value, 4 hours, is based on a tradeoff between numerical accuracof results and computer run-time. Except in the case of extreme exposurscenarios, there is no difference in the numerical accuracy at any user selectable value for TimeStep. 

TKIDPL(t) 

Lead transfer time from kidney to plasma-ECF 

B-2h 0-84 I Based on the assumption that the lead transfer time from kidney to blood 

equal to the lead transfer time from kidney to plasma-ECF. 

TLIVFEC(t) Lead transfer time 

from liver to feces B-2f 0-84 I Based on the assumption that the masses of lead in liver and blood are i

kinetic quasi-equilibrium. 

TLIVPL(t) 

Lead transfer time from liver to plasma-ECF 

B-2e 0-84 I Based on the assumption that the lead transfer time from liver to blood is equal to the lead transfer time from liver to plasma-ECF. 

TOTHOUT(t) 

Lead transfer time from soft tissues to elimination pool 

B-2o 0-84 I Based on the assumption that the masses of lead in soft tissues and bloo

are in kinetic quasi-equilibrium. 

TOTHPL(t) 

Lead transfer time from soft tissues to 

plasma-ECF 

B-2n 0-84 I Based on the assumption that the lead transfer time from soft tissues to blood is equal to the lead transfer time from soft tissues to plasma-ECF. 

TPLCORT(t) 

Lead transfer time from plasma-ECF to cortical bone 

B-2k 0-84 I 

Based on the following assumptions: The rate at which lead leaves the plasma-ECF to reach the bone is proportional to the rate which lead leaves the blood to reach the same poThe cortical and trabecular bone pools have similar lead kinetics for children younger than 84 months. The cortical bone is 80% of the weight of bone based on Leggett et al. (1982). 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-17 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

TPLKID(t) 

Lead transfer time from plasma-ECF to kidney 

B-2g 0-84 I 

Based on the assumption that the rate at which lead leaves the plasmaECF to reach the kidney is proportional to the rate at which lead leaves thblood to reach the same pool. 

TPLLIV(t) 

Lead transfer time from plasma-ECF to liver 

B-2d 0-84 I 

Based on the assumption that the rate at which lead leaves the plasmaECF to reach the liver is proportional to the rate at which lead leaves the blood to reach the same pool. 

TPLOTH(t) 

Lead transfer time from plasma-ECF to soft tissues 

B-2m 0-84 I 

Based on the assumption that the rate at which lead leaves the plasmaECF to reach the soft tissues is proportional to the rate which lead leaves the blood to reach the same pool. 

TPLRBC 

Lead transfer time from plasmaECF to red blood cells 

0.1 0-84 I 

Initialization value of 0.1 was assigned as plausible nominal value reflectibest professional judgement on appropriate time scale for composite process of transfer of lead through the red blood cell membrane to lead binding components. 

TPLRBC2(t) 

Lead transfer time from plasmaECF to red blood cells constrained by the maximum capacity of red blood cell lead concentration 

B-2.5 0-84 I 

Simple combination of the lead transfer time from plasma-ECF to red blocells, and the ratio of red blood cell lead concentration to the correspondimaximum concentration. Based on Marcus (1985a) and reanalysis of infbaboon data. 

TPLTRAB(t) 

Lead transfer time from plasma-ECF to trabecular bone 

B-2i 0-84 I 

Based on the following assumptions: The rate at which lead leaves the plasma-ECF to reach the bone is proportional to the rate which lead leaves the blood to reach the same poThe cortical and trabecular bone pools have similar lead kinetics. The trabecular bone is 20% of the weight of bone based on Leggett et al. (1982). 

TPLUR(t) 

Lead transfer time from plasma-ECF to urine 

B-2c 0-84 I 

Based on the assumption that the rate at which lead leaves the plasmaextra-cellular fluid to reach the urine pool is proportional to the rate at whilead leaves the blood to reach the same pool. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-18 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

TRBCPL 

Lead transfer time from red blood cells to plasmaECF B-2b 0-84 I Based on the assumption that the transfer time out of RBC is similar at a

ages, since mean red cell value is similar. 

TTRABPL(t) 

Lead transfer time from trabecular bone to plasmaextra-cellular fluid B-2j 0-84 I Based on the assumption that the cortical and trabecular bone pools have similar lead kinetics for children younger than 84 months. 

TWA(t) 

Time weighted average air lead concentration 

E-2 0-84 I Simple combination of outdoor and indoor air lead concentrations and the number of hours spent outdoors. 

UPAIR(t) Air lead uptake U-4 0-84 I Simple combination of media-specific lead intake and the corresponding 

absorption coefficient. 

UPDIET(t) Diet lead uptake U-1a 0-84 I Simple combination of media-specific lead intake and the corresponding 

absorption coefficient. 

UPDUST(t) Dust lead uptake U-1c 0-84 I Simple combination of media-specific lead intake and the corresponding 

absorption coefficient. 

UPDUSTA(t) 

Dust lead uptake rate from alternate sources 

U-1.5c 0-84 I Simple combination of media-specific lead intake and the corresponding 

absorption coefficient. 

UPGUT(t) Total gut uptake U-1f 0-84 I Sum of all gastrointestinal uptake. 

UPOTHER(t) Uptake of other ingested lead U-1d 0-84 I Assumes no other gut lead intake UPSOIL(t) Soil lead uptake U-1e 0-84 I Simple combination of media-specific lead intake and the corresponding 

absorption coefficient. 

UPTAKE(t) Total lead uptake U-5 0-84 I Simple combination of the media-specific daily lead uptake rates, translated to a monthly rate. 

UPWATER(t) Water lead uptake U-1b 0-84 I Simple combination of media-specific lead intake and the corresponding 

absorption coefficient. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-19 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

UserFishConc 

Lead concentration in fish 

0 0-84 E Based on the assumption that only commercially available fish are consumed. 

userFishFraction 

Fraction of total meat consumed as fish 

0 0-84 E Based on the assumption that only commercially available fish are consumed. 

UserFruitConc 

Lead concentration in home grown fruits 

0 0-84 E Based on the assumption that only commercially available fruits are consumed. 

userFruitFraction 

Fraction of total fruits consumed as home grown fruits 

0 0-84 E Based on the assumption that only commercially available fruits are consumed. 

UserGameConc 

Lead concentration in game animal meat 

0 0-84 E Based on the assumption that only commercially available meat is consumed. 

userGameFraction 

Fraction of total meat consumed as game animal meat excluding fish 

0 0-84 E Based on the assumption that only commercially available meat is consumed. 

UserVegConc 

Lead concentration in home grown vegetables 

0 0-84 E Based on the assumption that only commercially available vegetables are consumed. 

userVegFraction 

Fraction of total vegetables consumed as home grown vegetables 

0 0-84 E Based on the assumption that only commercially available vegetables are consumed. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-20 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

veg_all(t) 

Daily amount of all vegetables consumed 

56.84 

106.50 155.75 157.34 158.93 172.50 199.65 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

I Pb concentration from data provided to EPA by FDA (US EPA (1986). Quantity consumed from Pennington (1983). 

vent_rate(t) Ventilation rate 

2 3 5 5 5 7 7 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

E 

Values are reported in the OAQPS report (USEPA 1989, pp. A-3) and the TSD (USEPA 1990a). These estimates are based on body size in combination with smoothed data from Phalen et al., (1985). 

VOLBLOOD(t) Volume of blood B-5a 0-84 I Statistical fitting of data from Silve et al (1987); also Spector (1956) and Altman and Ditmer (1973) VOLECF(t) Volume of extra- cellular fluid (ECF) B-5d 0-84 I 

The volume of extracellular fluid that exchanges rapidly with plasma is estimated 73% of the blood volume based on Rabinowitz (1976). This additional volume of distribution is assumed to be the volume the extracellular fluid pool, which is the difference between the volume of the distribution and the blood volume. 

VOLPLASM(t) Volume of plasma B-5c 0-84 I Statistical fit to VOLBLOOD(t) - VOLRBC(t) VOLRBC(t) Volume of red 

blood cells B-5b 0-84 I Statistical fit to hematocrit × blood volume 

water_consumption(t) Daily amount of 

water consumed 

0.20 0.50 0.52 0.53 0.55 0.58 0.59 

0-11 

12-23 24-35 36-47 48-59 60-71 72-84 

E Exposure Factors Handbook (US EPA, 1989b) 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-21 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

weight_soil 

Percentage of total soil and dust ingestion that is soil 

45 0-84 E Guidance Manual, Section 2.3 (US EPA, 1994) 

WTBLOOD(t) Weight of blood B-5m 0-84 I Based on an blood density of 1.056 kg/l (Spector 1956). WTBODY(t) Weight of body B-5f 0-84 I 

Statistical fitting of data from Silve et al. (1987); also Spector (1956) and Altman and Ditmer (1973). Also, body weight of 24 month old is assumeto be 12.3 kg (Spector 1956). 

WTBONE(t) Weight of bone B-5g 0-84 I 

12-84 months - Based on child skeletal ash data in Harley and Kneip (1984) and the following assumptions. WTBONE = (WTBONEADULT / WTSKEL_ASHADULT) * WTSKEL_ASH 

where WTBONEADULT = 10 kg 

WTSKEL_ASHADULT = 2.91 kg 

0-12 months - Assumed to be 11% of the weight of the body. The ratio oweight of bone to weight of body (11%) is based on the 12-month estimafor WTBONE from the above equation, and an estimate for WTBODY at same age. 

WTCORT(t) Weight of cortical bone B-5i 0-84 I Assumed to be 80% of the weight of the bone based on Leggett et al. (1982). 

WTECF(t) Weight of extra- cellular fluid (ECF) B-5e 0-84 I Based on an assumed ECF density approximately the same as water, of 1.0 kg/L. 

WTKIDNEY(t) Weight of kidney B-5j 0-84 I 

Statistical fitting of data from Silve et al. (1987); also Spector (1956) and Altman and Ditmer (1973). Also, body weight of 24 month old is assumeto be 12.3 kg (Spector 1956). 

WTLIVER(t) Weight of liver B-5k 0-84 I 

Statistical fitting of data from Silve et al. (1987); also Spector (1956) and Altman and Ditmer (1973). Also, body weight of 24 month old is assumeto be 12.3 kg (Spector 1956). 

WTOTHER(t) Weight of soft tissues B-5l 0-84 I Simple combination of the weight of body and the weights of kidney, liver

bone, blood and extra-cellular fluid. 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-22 
PARAMETER NAME DESCRIPTION 

DEFAUL 

T VALUE 

OR 

EQN. 

NO. 

AGE 

RANGE 

(mo) 

I 

or 

E BASIS FOR VALUES/EQUATIONS 

WTTRAB(t) Weight of trabecular bone B-5h 0-84 I Assumed to be 20% of the weight of the bone based on Leggett et al. (1982). 

NOTE: I = interior parameter, E = Exterior, user selectable parameter B-23 
MP&M EEBA: Appendices Appendix M: Sensitivity Analysis of Lead-Related Benefits 

Appendix M: Sensitivity Analysis of


Lead-Related Benefits


INTRODUCTION 

The methodology for estimating lead-related benefits for 

the MP&M regulation is discussed in Chapter 14. In its 

main analysis, EPA uses a three percent discount rate to 

value benefits associated with reductions in exposure to 

lead. OMB, however, frequently recommends the use of a 

seven percent discount rate in benefit-cost analyses for 

government regulations. This appendix therefore presents a 

APPENDIX CONTENTS 

M.1 or Quantified Lead-Related Health Effects ..... M-1 

M.2 Related Benefit Results ................. ..... M-2 

M.2.1 chool Age Children Lead-Related 

Benefits ................. ................. . M-2 

M.2.2 dult Lead-Related Benefits ................. M-3 

Values f

LeadPres

A

sensitivity analysis of the results for lead-related benefits estimated using a seven percent discount rate and compares them 

with estimated lead-related benefits in the main (three percent) analysis. Because EPA found that the final rule will not yield 

any lead-related health benefits to either children or adults, the analysis in this appendix is limited only to the two Upgrade 

Options considered as alternatives to the final rule, and the Proposed/NODA Option. 

M.1 VALUES FOR QUANTIFIED LEAD-RELATED HEALTH EFFECTS 

Table M.1 below compares per-case values for lead-related health effects estimated using a three percent discount rate and a 

seven percent discount rate. Values for some health effect categories do not change for the following two reasons: 

� Discounting is not used in estimating a specific value. For example, the cost of treating hypertension used in this 

analysis is the estimate of annual medical costs and lost work time associated with this condition. 

� The original study did not provide sufficient information for estimating the cost of illness value based on a seven 

percent discount rate. Taylor et al. (1996) used a five percent discount rate to estimate the expected lifetime cost of 

a stroke. The authors do not provide sufficient information to recalculate the value based on a different discount 

rate. Therefore, EPA did not revise this value in the main analysis to reflect discounting at a three percent rate. 

M-1 
MP&M EEBA: Appendices Appendix M: Sensitivity Analysis of Lead-Related Benefits 

Table M.1: Comparison of Per-Case Values for Lead-Related Health Effects (2001 $) 

Health Category 

Value/Cost @ 3% 

Discount Rate 

Value/Cost @ 7% 

Discount Rate 

Lead-Related Health Effects for Children 

Value of an IQ point [A-(B+C)] $9,419 $1,817 

(A) Wage loss per IQ point $10,675 $2,427 

(B) Cost of additional education per IQ point $511 $247 

(C) Opportunity cost of lost income while in school $746 $363 

Additional education cost for children with IQ < 70 $58,012 $36,831 

Additional education cost for children with PbB > 20 μg/dL $16,485 $12,169 

Value of preventing neonatal mortalitya $6,500,000 $6,500,000 

Lead-Related Health Effects for Adults 

Hypertension (male & female)b $1,141 $1,141 

CHD (male & female) $76,347 $74,115 

Stroke (male)c $335,135 $335,135 

Stroke (female)c $251,351 $251,351 

Mortality (male & female)a $6,500,000 $6,500,000 

a Value of a Statistical Life (VSL) is taken from U.S. EPA’s Guidelines for Preparing Economic Analyses. The recommended value was 

not adjusted in the main analysis. 

b

 Annual cost of treatment. No discounting is required. C Values based on Taylor et al. (1996) which uses a five percent discount rate to estimate the expected lifetime cost of a stroke. EPA 

used this value in the main analysis presented in Chapter 14 of this report. 

Source: U.S. EPA analysis. 

M.2 LEAD-RELATED BENEFIT RESULTS 

This section presents lead-related benefits of the alternative regulatory options – the 433 Upgrade Options and the 

Proposed/NODA Option – based on a seven percent discount rate. 

M.2.1 Preschool Age Children Lead-Related Benefits 

Table M.2 summarizes lead-related benefits for children estimated for the 433 Upgrade Options based on a three percent and 

a seven percent discount rate. As shown in Table M .2, using a seven percent discount rate results in a 19 percent reduction in 

the total monetary value of lead-related benefits for preschool children compared to the value of benefits estimated based on a 

three percent discount rate. Changes in the monetary values associated with individual benefit categories range from zero 

percent (neonatal mortality) to 81 percent (avoided IQ loss). 

M-2 
MP&M EEBA: Appendices Appendix M: Sensitivity Analysis of Lead-Related Benefits 

Table M.2: Comparison of the Monetary Value of Lead-Related Benefits to Children (2001$) Based 

on Alterantive Discount Rates – 433 Upgrade Options 

Category 

Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade 

Reduced 

Cases or 

IQ Points 

Mean Benefit Value (2001$) Reduced 

Cases 

or IQ 

Points 

Mean Benefit Value (2001$) 

3% DR 7% DR 

% 

Change 3% DR 7% DR 

% 

Change 

Neonatal mortality 0.15 $995,630 $995,630 0% 0.17 $1,109,294 $1,109,294 0% 

Avoided IQ Loss 31.99 $301,323 $58,128 81% 36.19 $340,845 $65,752 81% 

Reduced IQ < 70 0.11 $6,637 $4,213 37% 0.13 $7,501 $4,762 37% 

Reduced PbB > 20 �g/L 0.00 $0 $0 0% 0.00 $0 $0 0% 

Total Benefits $1,305,590 $1,057,970 19% $1,457,640 $1,179,808 19% 

Source: U.S. EPA analysis. 

Table M.3 summarizes lead-related benefits for children estimated for the Proposed/NODA Option based on a three percent 

and a seven percent discount rate. As shown in Table M .3, using a seven percent discount rate results in a 40 percent 

reduction in the total monetary value of lead-related benefits for preschool children compared to the value of benefits 

estimated based on a three percent discount rate. Changes in the monetary values associated with individual benefit 

categories range from zero percent (neonatal mortality) to 81 percent (avoided IQ loss). 

Table M.3: Comparison of the Monetary Value of Lead-Related Benefits to Children (2001$) 

Based on Alterantive Discount Rates – Proposed/NODA Option 

Category 

Reduced Cases or IQ Points 

Benefit Value (2001$) 

Category 3% DR 7% DR % C hange 

Neonatal Mortality 1.60 $10,417,781 $10,417,781 0% 

Avoided IQ Loss 1,078.38 $10,157,286 $1,959,421 81% 

Reduced IQ < 70 3.72 $216,007 $137,140 37% 

Reduced PbB > 20 �g/L 0.00 $0 $0 0% 

Total Benefits $20,791,073 $12,514,342 40% 

Source: U.S. EPA analysis. 

M.2.2 Adult Lead-Related Benefits 

Table M.4 presents lead-related benefits for adults for the 433 Upgrade Options based on a three percent and a seven percent 

discount rate. Under both 433 Upgrade Options the difference between the total monetary value of benefits to adults estimated 

based on a three percent and a seven percent discount rate is negligible (less than 0.1 percent). The reduction in total benefits 

is marginal between the two discount rate scenarios because the monetary value of only one lead-related benefit category for 

adults (i.e., CHD) is affected by the discount rate. 

M-3 
MP&M EEBA: Appendices Appendix M: Sensitivity Analysis of Lead-Related Benefits 

Table M.4: 

Alterantive Discount Rates – 433 Upgrade Options 

Category 

Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade 

Reduced Cases 

Mean Value of Benefits 

Reduced Cases 

Mean Value of Benefits 

3% DR 7% DR 3% DR 7% DR 

Men Hypertension 53.47 $61,004 $61,004 59.58 $67,982 $67,982 

CHD 0.05 $4,155 $4,033 0.06 $4,631 $4,495 

CBA 0.02 $5,698 $5,698 0.02 $6,350 $6,350 

BI 0.01 $3,226 $3,226 0.01 $3,596 $3,596 

Mortality 0.07 $474,735 $474,735 0.08 $529,125 $529,125 

Women CHD 0.02 $1,662 $1,614 0.02 $1,853 $1,799 

CBA 0.01 $2,417 $2,417 0.01 $2,694 $2,694 

BI 0.01 $1,487 $1,487 0.01 $1,658 $1,658 

Mortality 0.02 $150,190 $150,190 0.03 $167,417 $167,417 

Total Benefits $704,574 $704,404 $785,304 $785,115 

Comparison of the Monetary Value of Lead-Related Benefits to Adults (2001$) Based on 

Source: U.S. EPA analysis. 

Table M.5 summarizes lead-related benefits for adults for the Proposed/NODA Option based on a three percent and a seven 

percent discount rate. For this option, the estimated total monetary values of benefits drop from $7,048,025 under the three 

percent discount rate to $7,046,328 under the seven percent discount rate (i.e., a decrease of less than 0.1 percent). This 

marginal difference in the total value of benefits based the three percent and the seven percent discount rate is due to the fact 

that only one benefit category (i.e., CHD) is affected by the discount rate. 

Table M.5: Comparison of the Monetary Value of Lead-Related Benefits to Adults (2001$) Based on 

Alterantive Discount Rates – Proposed/NODA Option 

Category Reduced Cases 

Mean Value of Benefits 

3% DR 7% DR 

Men Hypertension 545.25 $622,126 $622,126 

CHD 0.54 $41,564 $40,349 

CBA 0.17 $56,907 $56,907 

BI 0.10 $32,197 $32,197 

Mortality 0.73 $4,750,132 $4,750,132 

Women CHD 0.22 $16,472 $15,991 

CBA 0.10 $23,928 $23,928 

BI 0.06 $14,714 $14,714 

Mortality 0.23 $1,489,984 $1,489,984 

Total Benefits $7,048,025 $7,046,328 

Source: U.S. EPA analysis. 

M-4 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-Based Recreation Survey 

Appendix N: Analysis of the National


Demand for Water-Based Recreation


Survey


INTRODUCTION 

This appendix presents EPA’s analysis of the National 

Demand for Water-based Recreation Survey (NDS). The 

objective of this analysis is to determine the number of 

people who participate in water-based recreation and their 

total number of recreation trips, characterize participation 

and number of trips taken by water body type, and provide 

more detailed information on specific recreation activities 

(e.g., fish species targeted on fishing trips) and 

expenditures associated with various activities. 

N.1 BACKGROUND INFORMATION 

APPENDIX CONTENTS 

N.1 ground Information ................. ......... N-1 

N.2 Analysis ................. ................. N-2 

N.3 tion in Water-Based Recreation by Activity Type N-2 

N.4 ation of Trips by Water Body Type ............ N-11 

N.5 el Distance ................. ...... N-16 

N.6 l Expenditures per Trip ................. . N-19 

N.7 bution of Direct Costs for Single-day Trips ...... N-22 

N.8 of Boating Trips ................. ........ N-27 

N.9 of Fishing Trips ................. ........ N-30 

Back

Data 

Participa

Alloc

One-Way Trav

Individua

Distri

Profile 

Profile 

U.S. EPA cooperated with the National Forest Service and several other federal agencies and interested groups to collect data 

on the outdoor recreation activities of Americans. The 1993 NDS collected data on demographic characteristics and waterbased recreation behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over. Respondents 

reported on water-based recreation trips taken within the past 12 months, including the primary purpose of their trips (i.e., 

fishing, boating, swimming, and viewing), and number of trips, trip length, distance to the recreation site(s), number of 

participants, their trip expenditures, and detailed trip allocation information on the last trip taken for each recreation type. For 

example, respondents reported: 

� where fishing was the primary purpose of a trip, the number of fish caught and the species targeted (i.e., coldwater, 

warmwater, anadromous, or marine); 

� the type of water body (e.g., lake, river, ocean, wetland); and 

� where boating was the primary purpose of trip, the type of boating (i.e., motorboating, sailing, canoeing, rowing, 

rafting, and other floating). 

EPA used NDS data to characterize water-based recreation activities nationwide, including: 

� percent of state population participating in water-based recreation by recreation activity and trip length (i.e., singleday vs. multiple-day trips); 

� average number of water-based recreation trips per person by recreation activity and trip length; 

� allocation of single- or multiple-day trips among different water body types by recreation type; 

� mean one-way distance traveled to the site visited on last trip; 

� total expenditures per person for last single-day or multiple-day trip; 

� distribution of total expenditures among various expenditure categories for single- and multiple-day trips (e.g., 

lodging, boat rental, and entrance fee); 

� allocation of fishing trips by target species; and 

� allocation of boating trips by boating type. 

N-1 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-Based Recreation Survey 

N.2 DATA ANALYSIS 

The NDS used a random digit dialed population-based sample (aged 16 and over) of the nation. For simple random sampling, 

estimates of the sample mean and total are consistent estimates of the population mean and total. EPA therefore treats 

sample-based estimates as being representative of the population-based estimates. For example, the percent of survey 

respondents participating in a given water-based recreation activity is theoretically consistent with the percent of the state 

population (aged 16 and over) that participates in that activity. The estimated percentages can be applied to the state 

population (aged 16 and over) to derive the number of participants in various water-based recreation activities in each state. 

The survey database cannot be used to characterize subsistence fishing because subsistence fishermen’s behavior differs 

significantly from recreational fishermen’s behavior. In addition, this population subgroup is likely to be under-represented in 

the survey database due to various factors. First, subsistence fishermen constitute a relatively small portion of the total 

fisherman population. They also tend to have a lower education level. Some of them may lack long-distance telephone 

services and/or have language barriers. These factors are likely to result in inadequate representation of this subgroup in the 

survey data. 

N.3 PARTICIPATION IN WATER-BASED RECREATION BY ACTIVITY TYPE 

This analysis estimates the percent and the number of state residents who participated in water-based recreation by activity 

type and trip length (i.e., single-day vs. multiple-day). Participants in each activity in a given state include state residents who 

took at least one single-day and/or multiple-day trip for each respective activity during the previous 12 months. Because 

some participants took both single-day and multiple-day trips, the percent and the number of state residents participating in all 

trips does not equal the sum of the single-day plus multiple-day percentages or number of participants. The analysis also 

estimates the average number of recreation trips per person per year, by recreational activity, trip length (single-day vs. 

multiple-day trips), and state of residence. Tables N.1, N.2, N.3, and N .4 characterize participation in boating, fishing, 

swimming, and viewing, respectively. 

1.	 Estimating the percent of state population participating in each of the four water-based recreation activities. The 

total percent of state residents participating in each activity equals the total number of respondents who took at least 

one single-day and/or multiple-day trip divided by the state’s sample size. Similarly, the percent participating in 

single-day or multiple-day trips for each respective activity equals the respective number of sample respondents who 

took either single-day or multiple-day trips, respectively, divided by the state’s sample size. 

2.	 Estimating the number of state residents participating in each of the four water-based recreation activities. EPA 

calculated the total number of participants in each state by multiplying the percent of sample respondents who took 

at least one single-day and/or multiple-day trip by each state's actual population 16 years of age and older. Similarly, 

the total number of participants in single-day or multiple-day trips for each respective activity equals the respective 

percent of sample respondents who took either single-day or multiple-day trips, respectively, times the state's 

population 16 years of age and older. 

3.	 Estimating the average number of trips per person per year. EPA estimated the average number of recreation trips 

per person per year by dividing the total number of trips taken for each activity by state residents by the total number 

of participants in this activity. Similarly, dividing the number of single-day trips or multiple-day trips by the 

respective number of participants provided the average number of single-day and multiple-day trips per person, 

respectively. Tables N.1-N.4 also show the mean trip length for the last multiple-day trip. 

For comparison purposes, Tables N.2 and N.4 also present estimates of the total percent and the number of state residents 

participating in recreational fishing and wildlife viewing based on the U.S. Fish and Wildlife Service’s (USFWS) 1996 

National Survey of Fishing Hunting and Wildlife Associated Recreation. The table shows that the two surveys yield similar 

results. NDS estimates, however, are slightly higher than USFWS estimates for some states. NDS fishing and viewing 

participation estimates are higher for 47 and 30 states, respectively. This discrepancy may be due to the difference in the year 

when the respective surveys were conducted. 

N-2 
MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.1: Participation in Boating

State State Pop. 16 and Up NDS Sample Size Sample Weight

Total Participation in Boating Participation in Single-Day Trips Participat

Percent

Population # People

Avg #

Trips

per

Person

per Year

Days

per

Year

Percent

Population

Number of

People

Avg # of

Trips per

Person per

Year

Percent

Population

Nu

P

AK 457,728 29 15,784 48% 219,709 5.1 5.7 45% 205,978 5.1 14% 

AL 3,451,586 218 15,833 18% 621,285 7.9 10.6 16% 552,254 7.1 6% 

AR 2,072,622 128 16,192 20% 414,524 6.4 24.1 16% 331,620 5.0 7% 

AZ 3,907,526 178 21,952 12% 468,903 7.3 10.8 9% 351,677 7.3 6% 

CA 25,599,275 1,313 19,497 20% 5,119,855 5.3 11.3 14% 3,583,898 5.1 11% 2,

CO 3,322,455 212 15,672 13% 431,919 10.6 14.0 8% 265,796 14.6 7% 

CT 2,651,452 159 16,676 20% 530,290 8.7 18.3 18% 477,261 7.9 7% 

DC 468,575 35 13,388 11% 51,543 2.0 2.7 9% 42,172 2.3 3% 

DE 610,269 51 11,966 20% 122,054 10.6 13.3 18% 109,848 10.8 8% 

FL 12,741,821 662 19,247 23% 2,930,619 10.3 16.6 20% 2,548,364 9.8 5% 

GA 6,250,708 373 16,758 18% 1,125,127 10.9 19.0 15% 937,606 10.4 9% 

HI 949,184 55 17,258 20% 189,837 7.6 9.5 18% 170,853 6.6 2% 

IA 2,281,002 171 13,339 19% 433,390 6.6 13.3 17% 387,770 4.7 5% 

ID 969,166 83 11,677 30% 290,750 5.8 8.1 25% 242,292 5.6 8% 

IL 9,530,327 466 20,451 18% 1,715,459 8.3 15.9 13% 1,238,943 9.0 8% 

IN 4,682,392 300 15,608 21% 983,302 9.3 18.3 15% 702,359 7.5 8% 

KS 2,058,489 135 15,248 13% 267,604 13.9 27.1 9% 185,264 14.2 9% 

KY 3,161,283 219 14,435 16% 505,805 6.2 9.0 13% 410,967 6.4 5% 

LA 3,394,854 189 17,962 20% 678,971 4.2 6.2 18% 611,074 4.1 5% 

MA 5,008,007 249 20,112 23% 1,151,842 11.8 11.7 18% 901,441 8.1 8% 4

MD 4,085,342 257 15,896 19% 776,215 9.1 18.2 17% 694,508 8.9 6% 2

ME 1,010,273 72 14,032 33% 333,390 7.1 18.2 26% 262,671 6.7 13% 

MI 7,628,170 576 13,243 24% 1,830,761 9.3 17.4 19% 1,449,352 9.0 11% 8

MN 3,782,817 245 15,440 24% 907,876 5.9 8.8 20% 756,563 5.9 7% 2

MO 4,331,937 277 15,639 22% 953,026 6.0 11.4 16% 693,110 5.3 12% 5

MS 2,160,165 140 15,430 18% 388,830 10.0 14.9 16% 345,626 7.1 6% 

MT 701,423 55 12,753 22% 154,313 5.8 7.9 15% 105,213 7.8 7% 

NC 6,291,182 407 15,457 14% 880,765 7.5 12.1 12% 754,942 7.2 6% 3

ND 502,176 40 12,554 28% 140,609 4.2 13.8 18% 90,392 3.9 13% 

NE 1,314,974 84 15,654 20% 262,995 5.2 13.3 13% 170,947 3.8 10% 

NH 960,593 64 15,009 23% 220,936 3.7 8.4 22% 211,330 3.2 5% 

NJ 6,545,471 347 18,863 18% 1,178,185 10.1 12.2 17% 1,112,730 10.5 3% 1
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

Table N.1: Participation in Boating 

State State Pop. 16 and Up NDS Sample Size Sample Weight 

Total Participation in Boating Participation in Single-Day Trips Participation in Mu

Percent 

Population # People 

Avg # 

Trips 

per 

Person 

per Year 

Days 

per 

Year 

Percent 

Population 

Number of 

People 

Avg # of 

Trips per 

Person per 

Year 

Percent 

Population 

Number of 

People 

P

NM 1,370,134 105 13,049 19% 260,325 3.8 8.9 10% 137,013 3.5 11% 150,715 

NV 1,537,896 75 20,505 23% 353,716 10.1 18.4 21% 322,958 6.8 9% 138,411 

NY 14,797,284 774 19,118 18% 2,663,511 6.5 9.2 13% 1,923,647 7.5 5% 739,864 

OH 8,789,530 650 13,522 17% 1,494,220 7.0 10.6 13% 1,142,639 7.5 7% 615,267 

OK 2,665,966 143 18,643 20% 533,193 4.5 8.3 13% 346,576 4.9 8% 213,277 

OR 2,673,283 217 12,319 26% 695,054 8.4 12.2 22% 588,122 8.9 9% 240,595 

PA 9,693,987 742 13,065 15% 1,454,098 9.2 16.1 12% 1,163,278 9.1 5% 484,699 

RI 827,474 57 14,517 16% 132,396 8.0 N/A 16% 132,396 6.9 2% 16,549 

SC 3,115,130 181 17,211 19% 591,875 9.0 16.5 15% 467,270 8.9 7% 218,059 

SD 577,391 42 13,747 26% 150,122 6.5 24.1 21% 121,252 4.8 10% 57,739 

TN 4,445,987 296 15,020 23% 1,022,577 7.9 10.3 20% 889,197 7.7 6% 266,759 

TX 15,618,097 657 23,772 18% 2,811,257 8.2 14.7 15% 2,342,715 7.4 7% 1,093,267 

UT 1,598,531 111 14,401 20% 319,706 4.5 9.6 11% 175,838 5.6 13% 207,809 

VA 5,529,436 389 14,214 19% 1,050,593 9.9 17.4 15% 829,415 8.6 6% 331,766 

VT 479,265 34 14,096 24% 115,024 7.1 12.4 21% 100,646 7.1 9% 43,134 

WA 4,552,631 324 14,051 35% 1,593,421 6.0 10.4 29% 1,320,263 5.7 14% 637,368 

WI 4,156,609 299 13,902 22% 914,454 10.3 14.7 19% 789,756 10.0 7% 290,963 

WV 1,455,370 126 11,551 13% 189,198 5.1 8.3 10% 145,537 6.6 2% 29,107 

WY 381,882 31 12,319 26% 99,289 5.5 6.2 23% 87,833 5.7 6% 22,913 

N/A - Not Available 

Source: NDS. 

N-4

MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.2: Participation in Recreational Fishing

State

Pop. 16

and Up

NDS

Sample

Size

Sample

Weight

Total Participation in Fishing Single-Day Trips 

Percent

Pop.

(NDSbased)

Percent

Pop.

(USFW

S-based)

Number

of People

Avg # of

Trips per

Person

per Year

Days

per

Year

% Pop. # People

Avg # of

Trips per

Person

per Year

% Po

AK 457,728 29 15,784 59% 41% 270,060 13.8 18.3 55% 251,750 12.8 2

AL 3,451,586 218 15,833 25% 21% 862,896 16.8 19.5 21% 724,833 18.5 

AR 2,072,622 128 16,192 37% 26% 766,870 11.9 16.2 31% 642,513 12.5 1

AZ 3,907,526 178 21,952 20% 14% 781,505 8.1 16.5 15% 586,129 7.0 1

CA 25,599,275 1,313 19,497 22% 12% 5,631,840 6.5 13.1 16% 4,095,884 6.6 1

CO 3,322,455 212 15,672 38% 23% 1,262,533 12.0 19.5 30% 996,736 12.2 1

CT 2,651,452 159 16,676 18% 14% 477,261 6.9 8.0 16% 424,232 7.1 

DC 468,575 35 13,388 N/A N/A N/A N/A N/A N/A N/A N/A N

DE 610,269 51 11,966 20% 19% 122,054 10.3 16.0 18% 109,848 10.8 

FL 12,741,821 662 19,247 25% 18% 3,185,455 15.3 17.4 21% 2,675,782 16.6 

GA 6,250,708 373 16,758 23% 18% 1,437,663 8.8 12.6 19% 1,187,635 9.8 

HI 949,184 55 17,258 20% 14% 189,837 6.1 6.2 18% 170,853 6.6 

IA 2,281,002 171 13,339 24% 23% 547,440 11.9 16.7 19% 433,390 13.4 

ID 969,166 83 11,677 40% 32% 387,666 12.3 19.4 33% 319,825 10.8 2

IL 9,530,327 466 20,451 20% 18% 1,906,065 13.7 27.4 16% 1,524,852 14.2 

IN 4,682,392 300 15,608 26% 19% 1,217,422 11.0 14.6 23% 1,076,950 11.1 

KS 2,058,489 135 15,248 28% 19% 576,377 11.3 18.8 21% 432,283 12.2 1

KY 3,161,283 219 14,435 30% 23% 948,385 9.0 13.9 25% 790,321 9.2 1

LA 3,394,854 189 17,962 34% 26% 1,154,250 15.0 18.9 29% 984,508 15.3 

MA 5,008,007 249 20,112 22% 12% 1,101,762 15.3 24.0 18% 901,441 13.3 

MD 4,085,342 257 15,896 21% 15% 857,922 12.2 15.7 18% 735,362 12.9 

ME 1,010,273 72 14,032 31% 21% 313,185 9.9 11.2 28% 282,876 10.5 

MI 7,628,170 576 13,243 27% 20% 2,059,606 10.5 16.4 20% 1,525,634 11.3 1

MN 3,782,817 245 15,440 34% 31% 1,286,158 10.3 18.7 24% 907,876 10.9 1

MO 4,331,937 277 15,639 26% 23% 1,126,304 5.8 10.5 20% 866,387 5.5 

MS 2,160,165 140 15,430 27% 21% 583,245 17.0 19.3 25% 540,041 16.8 

MT 701,423 55 12,753 42% 24% 294,598 19.5 26.2 36% 252,512 19.7 2

NC 6,291,182 407 15,457 25% 20% 1,572,796 11.2 15.1 20% 1,258,236 12.5 1
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

Table N.2: Participation in Recreational Fishing 

State 

Pop. 16 

and Up 

NDS 

Sample 

Size 

Sample 

Weight 

Total Participation in Fishing Single-Day Trips Mul

Percent 

Pop. 

(NDSbased) 

Percent 

Pop. 

(USFW 

S-based) 

Number 

of People 

Avg # of 

Trips per 

Person 

per Year 

Days 

per 

Year 

% Pop. # People 

Avg # of 

Trips per 

Person 

per Year 

% Pop. # Pe

ND 502,176 40 12,554 33% 24% 165,718 4.5 6.3 30% 150,653 3.9 10% 50

NE 1,314,974 84 15,654 20% 19% 262,995 11.9 24.8 14% 184,096 13.2 12% 157

NH 960,593 64 15,009 16% 18% 153,695 14.9 23.6 14% 134,483 13.2 8% 76

NJ 6,545,471 347 18,863 19% 13% 1,243,639 5.9 7.0 16% 1,047,275 6.1 4% 26

NM 1,370,134 105 13,049 22% 19% 301,429 8.7 12.3 16% 219,221 8.5 12% 164

NV 1,537,896 75 20,505 21% 17% 322,958 11.9 15.2 17% 261,442 13.5 5% 76

NY 14,797,284 774 19,118 15% 11% 2,219,593 8.8 16.2 12% 1,775,674 9.1 5% 739

OH 8,789,530 650 13,522 19% 13% 1,670,011 14.6 23.2 16% 1,406,325 14.0 7% 615

OK 2,665,966 143 18,643 32% 31% 853,109 13.0 12.9 28% 746,470 13.4 1% 26

OR 2,673,283 217 12,319 36% 21% 962,382 11.4 15.2 29% 775,252 11.8 13% 347

PA 9,693,987 742 13,065 21% 15% 2,035,737 10.8 16.1 17% 1,647,978 11.1 8% 775

RI 827,474 57 14,517 21% 14% 173,770 7.8 7.5 19% 157,220 8.3 2% 16

SC 3,115,130 181 17,211 29% 24% 903,388 16.1 20.4 27% 841,085 15.6 7% 218

SD 577,391 42 13,747 26% 31% 150,122 8.2 13.7 24% 138,574 7.7 12% 69

TN 4,445,987 296 15,020 26% 17% 1,155,957 15.1 19.0 24% 1,067,037 14.8 6% 266

TX 15,618,097 657 23,772 29% 18% 4,529,248 10.2 16.6 23% 3,592,162 10.1 13% 2,030

UT 1,598,531 111 14,401 23% 21% 367,662 5.6 17.9 15% 239,780 4.1 11% 175

VA 5,529,436 389 14,214 26% 18% 1,437,653 8.2 13.0 19% 1,050,593 8.7 10% 552

VT 479,265 34 14,096 9% 19% 43,134 12.0 8.7 9% 43,134 8.7 3% 14

WA 4,552,631 324 14,051 27% 22% 1,229,210 14.9 21.3 22% 1,001,579 16.1 12% 546

WI 4,156,609 299 13,902 29% 25% 1,205,417 10.4 18.4 22% 914,454 11.1 14% 58

WV 1,455,370 126 11,551 25% 18% 363,842 17.0 22.4 22% 320,181 16.3 8% 11

WY 381,882 31 12,319 58% 31% 221,492 12.9 46.0 52% 198,579 8.2 32% 122

N/A - Not Available 

Source: U.S. Fish and Wildlife Service’s (USFWS) 1996 National Survey of Fishing Hunting and Wildlife Associated Recreation. 

N-6

MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.3: Participation in Recreational Swimming

State State Pop. 16 and Up NDS Sample Size Sample Weight

Total Participation in Swimming Participation in Single-Day Trips Partic

Percent

Pop.

Number of

People

Avg #

Trips

per

Person

per Year

Days

per

Year

Percent

Pop.

Number of

People

Avg # of

Trips per

Person per

Year

Percent

Pop.

AK 457,728 29 15,784 7% 32,041 2.0 N/A 7% 32,041 2.0 3% 

AL 3,451,586 218 15,833 23% 793,865 7.7 11.5 17% 586,770 9.3 7% 

AR 2,072,622 128 16,192 23% 476,703 8.5 20.1 21% 435,251 6.8 9% 

AZ 3,907,526 178 21,952 19% 742,430 4.5 7.6 13% 507,978 5.3 7% 

CA 25,599,275 1,313 19,497 29% 7,423,790 9.5 13.0 22% 5,631,840 11.0 9% 

CO 3,322,455 212 15,672 17% 564,817 4.0 6.4 12% 398,695 5.0 6% 

CT 2,651,452 159 16,676 41% 1,087,095 11.0 19.8 33% 874,979 11.3 18% 

DC 468,575 35 13,388 17% 79,658 2.5 6.4 9% 42,172 2.0 9% 

DE 610,269 51 11,966 22% 134,259 5.3 8.5 14% 85,438 6.9 6% 

FL 12,741,821 662 19,247 33% 4,204,801 13.3 17.9 26% 3,312,873 14.9 8% 

GA 6,250,708 373 16,758 29% 1,812,705 5.3 11.0 15% 937,606 6.6 13% 

HI 949,184 55 17,258 58% 550,527 19.2 27.6 56% 531,543 16.5 24% 

IA 2,281,002 171 13,339 18% 410,580 2.4 4.0 13% 296,530 2.7 4% 

ID 969,166 83 11,677 25% 242,292 8.9 11.1 23% 222,908 8.8 8% 

IL 9,530,327 466 20,451 21% 2,001,369 4.0 8.0 12% 1,143,639 5.4 8% 

IN 4,682,392 300 15,608 22% 1,030,126 5.0 8.9 17% 796,007 5.4 8% 

KS 2,058,489 135 15,248 19% 391,113 5.5 9.3 14% 288,188 6.0 7% 

KY 3,161,283 219 14,435 17% 537,418 11.0 13.2 11% 347,741 15.7 5% 

LA 3,394,854 189 17,962 24% 814,765 4.3 9.3 16% 543,177 4.7 11% 

MA 5,008,007 249 20,112 41% 2,053,283 9.4 17.8 34% 1,702,722 8.9 14% 

MD 4,085,342 257 15,896 27% 1,103,042 5.6 11.4 14% 571,948 7.8 12% 

ME 1,010,273 72 14,032 46% 464,726 14.5 29.7 40% 404,109 12.8 15% 

MI 7,628,170 576 13,243 30% 2,288,451 8.5 16.3 24% 1,830,761 8.6 10% 

MN 3,782,817 245 15,440 24% 907,876 5.4 6.5 18% 680,907 6.6 5% 

MO 4,331,937 277 15,639 22% 953,026 6.4 10.9 16% 693,110 7.7 9% 

MS 2,160,165 140 15,430 21% 453,635 10.5 12.8 17% 367,228 11.9 6% 

MT 701,423 55 12,753 40% 280,569 6.9 10.1 33% 231,470 7.6 16% 

NC 6,291,182 407 15,457 23% 1,446,972 5.7 10.8 15% 943,677 7.0 10% 

ND 502,176 40 12,554 25% 125,544 8.0 N/A 15% 75,326 11.5 13% 

NE 1,314,974 84 15,654 19% 249,845 3.5 10.7 15% 197,246 3.9 6% 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

Table N.3: Participation in Recreational Swimming 

State State Pop. 16 and Up NDS Sample Size Sample Weight 

Total Participation in Swimming Participation in Single-Day Trips Participation in 

Percent 

Pop. 

Number of 

People 

Avg # 

Trips 

per 

Person 

per Year 

Days 

per 

Year 

Percent 

Pop. 

Number of 

People 

Avg # of 

Trips per 

Person per 

Year 

Percent 

Pop. 

Number o

People 

NH 960,593 64 15,009 42% 403,449 15.8 56.5 38% 365,025 14.5 16% 153,69

NJ 6,545,471 347 18,863 39% 2,552,734 6.2 12.7 28% 1,832,732 6.9 16% 1,047,27

NM 1,370,134 105 13,049 15% 205,520 2.7 4.2 10% 137,013 3.8 5% 68,50

NV 1,537,896 75 20,505 19% 292,200 6.3 13.2 12% 184,548 6.3 9% 138,41

NY 14,797,284 774 19,118 33% 4,883,104 7.6 15.0 25% 3,699,321 8.1 11% 1,627,70

OH 8,789,530 650 13,522 23% 2,021,592 7.3 15.6 15% 1,318,430 8.7 6% 527,37

OK 2,665,966 143 18,643 28% 746,470 3.4 5.7 16% 426,555 4.1 8% 213,27

OR 2,673,283 217 12,319 34% 908,916 6.7 12.7 27% 721,786 7.1 12% 320,79

PA 9,693,987 742 13,065 28% 2,714,316 5.7 10.4 17% 1,647,978 7.5 12% 1,163,27

RI 827,474 57 14,517 40% 330,990 6.9 N/A 37% 306,165 7.0 11% 91,02

SC 3,115,130 181 17,211 22% 685,329 6.0 9.4 17% 529,572 6.9 5% 155,75

SD 577,391 42 13,747 24% 138,574 7.3 9.2 24% 138,574 7.2 7% 40,41

TN 4,445,987 296 15,020 23% 1,022,577 5.8 9.7 17% 755,818 6.7 8% 355,67

TX 15,618,097 657 23,772 24% 3,748,343 5.1 7.7 16% 2,498,896 6.0 9% 1,405,62

UT 1,598,531 111 14,401 20% 319,706 5.9 10.4 15% 239,780 6.2 10% 159,85

VA 5,529,436 389 14,214 28% 1,548,242 4.9 11.1 17% 940,004 5.5 13% 718,82

VT 479,265 34 14,096 26% 124,609 12.3 19.6 24% 115,024 11.6 6% 28,75

WA 4,552,631 324 14,051 35% 1,593,421 5.4 10.7 28% 1,274,737 5.7 14% 637,36

WI 4,156,609 299 13,902 27% 1,122,284 5.5 8.9 22% 914,454 6.1 7% 290,96

WV 1,455,370 126 11,551 25% 363,842 6.5 12.7 18% 261,967 6.4 9% 130,98

WY 381,882 31 12,319 6% 22,913 8.0 N/A 6% 22,913 8.0 3% 11,45

N/A - Not Available 

Source: NDS. 

N-8

MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.4: Participation in Wildlife Viewing (Near-Water Recreation)

State Pop. 16 and Up NDS Sample Size Sample Weight

Total Participation in Near-Water Recreation Single-Day Trips 

Percent

Pop.

(NDSbased)

Percent

Pop.

(USFWSbased)

Number of

People

Avg # of

Trips per

Person per

Year

Days

per

Year

Percent

Pop.

Number of

People

Avg # of

Trips per

Person per

Year

Perce

Pop

AK 457,728 29 15,784 48% 50% 219,709 7.2 8.4 41% 187,668 7.1 

AL 3,451,586 218 15,833 36% 30% 1,242,571 4.4 8.2 12% 414,190 9.2 2

AR 2,072,622 128 16,192 28% 34% 580,334 6.4 15.0 13% 269,441 10.2 

AZ 3,907,526 178 21,952 25% 31% 976,882 4.7 8.7 11% 429,828 8.0 

CA 25,599,275 1,313 19,497 51% 25% 13,055,630 11.2 14.3 37% 9,471,732 14.0 

CO 3,322,455 212 15,672 25% 42% 830,614 8.6 11.7 13% 431,919 14.8 

CT 2,651,452 159 16,676 60% 31% 1,590,871 5.3 8.8 38% 1,007,552 6.7 2

DC 468,575 35 13,388 51% N/A 238,973 3.9 30.7 23% 107,772 2.4 3

DE 610,269 51 11,966 57% 34% 347,853 9.9 16.6 41% 250,210 11.0 2

FL 12,741,821 662 19,247 44% 25% 5,606,401 14.2 18.2 32% 4,077,383 17.9 

GA 6,250,708 373 16,758 36% 29% 2,250,255 3.1 9.4 14% 875,099 4.1 2

HI 949,184 55 17,258 64% 14% 607,478 30.3 30.7 56% 531,543 33.9 

IA 2,281,002 171 13,339 32% 38% 729,921 2.9 7.1 16% 364,960 4.4 

ID 969,166 83 11,677 43% 40% 416,741 3.2 7.0 24% 232,600 4.2 2

IL 9,530,327 466 20,451 31% 35% 2,954,401 5.9 10.6 17% 1,620,156 9.0 IN 4,682,392 300 15,608 31% 35% 1,451,542 5.4 11.2 15% 702,359 9.0 KS 2,058,489 135 15,248 33% 32% 679,301 5.8 12.8 17% 349,943 9.0 

KY 3,161,283 219 14,435 28% 32% 885,159 2.4 9.1 12% 379,354 3.0 

LA 3,394,854 189 17,962 34% 27% 1,154,250 3.2 8.5 15% 509,228 3.4 

MA 5,008,007 249 20,112 50% 35% 2,504,004 9.8 21.0 31% 1,552,482 11.5 2

MD 4,085,342 257 15,896 46% 34% 1,879,257 6.3 12.7 18% 735,362 12.1 2

ME 1,010,273 72 14,032 54% 46% 545,547 5.4 6.6 44% 444,520 5.7 

MI 7,628,170 576 13,243 44% 36% 3,356,395 6.3 10.3 24% 1,830,761 9.4 MN 3,782,817 245 15,440 33% 38% 1,248,330 10.5 15.2 19% 718,735 16.5 

MO 4,331,937 277 15,639 32% 40% 1,386,220 2.7 8.1 13% 563,152 4.0 

MS 2,160,165 140 15,430 29% 23% 626,448 11.3 15.2 12% 259,220 24.2 

MT 701,423 55 12,753 33% 47% 231,470 10.1 12.9 20% 140,285 15.6 

NC 6,291,182 407 15,457 45% 35% 2,831,032 4.1 11.5 18% 1,132,413 5.2 2ND 502,176 40 12,554 25% 23% 125,544 2.6 3.4 15% 75,326 3.0 

NE 1,314,974 84 15,654 25% 35% 328,744 1.8 5.9 14% 184,096 2.1 

NH 960,593 64 15,009 42% 44% 403,449 12.2 21.5 31% 297,784 14.9 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

Table N.4: Participation in Wildlife Viewing (Near-Water Recreation) 

State Pop. 16 and Up NDS Sample Size Sample Weight 

Total Participation in Near-Water Recreation Single-Day Trips Mu

Percent 

Pop. 

(NDSbased) 

Percent 

Pop. 

(USFWSbased) 

Number of 

People 

Avg # of 

Trips per 

Person per 

Year 

Days 

per 

Year 

Percent 

Pop. 

Number of 

People 

Avg # of 

Trips per 

Person per 

Year 

Percent 

Pop. 

Numb

Peo

NJ 6,545,471 347 18,863 54% 26% 3,534,554 5.5 11.8 32% 2,094,551 6.3 23% 1,50NM 1,370,134 105 13,049 29% 29% 397,339 2.6 7.8 9% 123,312 5.6 18% 24

NV 1,537,896 75 20,505 35% 21% 538,264 6.2 11.0 21% 322,958 7.2 23% 35

NY 14,797,284 774 19,118 45% 23% 6,658,778 4.3 9.9 25% 3,699,321 5.7 18% 2,66

OH 8,789,530 650 13,522 36% 33% 3,164,231 4.7 11.1 16% 1,406,325 8.2 19% 1,67OK 2,665,966 143 18,643 34% 35% 906,428 1.9 5.3 12% 319,916 3.4 17% 45

OR 2,673,283 217 12,319 59% 42% 1,577,237 6.4 12.4 38% 1,015,848 7.2 33% 88

PA 9,693,987 742 13,065 39% 37% 3,780,655 3.9 9.4 14% 1,357,158 7.4 24% 2,32

RI 827,474 57 14,517 56% 32% 463,385 4.0 9.2 40% 330,990 4.6 9% 7

SC 3,115,130 181 17,211 45% 29% 1,401,808 5.3 10.9 20% 623,026 8.3 25% 77

SD 577,391 42 13,747 29% 30% 167,443 2.1 7.9 21% 121,252 1.8 5% 2

TN 4,445,987 296 15,020 41% 37% 1,822,855 2.1 6.1 13% 577,978 3.7 25% 1,11

TX 15,618,097 657 23,772 33% 25% 5,153,972 3.6 7.6 16% 2,498,896 5.0 16% 2,49

UT 1,598,531 111 14,401 31% 30% 495,545 2.4 4.6 17% 271,750 3.5 11% 17

VA 5,529,436 389 14,214 41% 37% 2,267,069 3.4 11.4 17% 940,004 4.2 25% 1,38

VT 479,265 34 14,096 47% 48% 225,255 5.6 9.6 18% 86,268 5.5 32% 15

WA 4,552,631 324 14,051 58% 39% 2,640,526 9.2 13.4 40% 1,821,052 11.6 29% 1,32

WI 4,156,609 299 13,902 38% 42% 1,579,511 4.6 8.8 22% 914,454 6.1 16% 66

WV 1,455,370 126 11,551 27% 31% 392,950 4.3 16.1 10% 145,537 4.6 17% 24

WY 381,882 31 12,319 29% 39% 110,746 3.1 4.5 16% 61,101 4.6 13% 4

N/A - Not Available 

Source: U.S. Fish and Wildlife Service’s (USFWS) 1996 National Survey of Fishing Hunting and Wildlife Associated Recreation. 

N-10

MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey 

N.4 ALLOCATION OF TRIPS BY WATER BODY TYPE 

This analysis assesses the allocation of trips by water body type, recreation activity, and state of residence. EPA determined 

the number of trips taken to each water body type based on the water body type visited on the last single- or multiple-day trip 

for each recreation activity. Dividing the total number of trips taken in a state to a given water body type for a given activity 

by the total number of trips taken for that activity in the state provided estimates of the percent taken to the various water 

body types. The NDS distinguishes four general water body types: 

� Lakes: 

— lakes, 

— ponds, and 

— reservoirs; 

� Streams: 

— rivers, 

— streams, and 

— canals; 

� Oceans: 

— oceans, 

— bays, and 

— sounds; and 

� Other: 

— wetlands, and 

— unknown water body types. 

Note that respondents in several states apparently provided inaccurate information. For example, Montana residents are 

unlikely to take single-day trips to the ocean. The data indicate, however, that five, six, and eleven percent of participants 

reported that they took single-day fishing, swimming, and viewing trips to the ocean, respectively. This inconsistency may 

arise due to the following two factors: 

� respondents traveled to other states for multi-purpose multiple-day trips and participated in the given activity on only 

one day per trip; and 

� response errors (e.g., some respondents identified water body types incorrectly). 

Tables N.5 and N.6 show allocation of single- and multiple-day trips by water body type for boating, fishing, swimming, and 

viewing. 

N-11 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

N-12

Table N.5: Allocation of Single-Day Trips by Water Body Type

State

Boating (%) Fishing (%) Swimming (%) Vie

Lake Stream Oceana Otherb Lake Stream Ocean Other Lake Stream Ocean Other Lake Stream 

AK 30% 20% 50% 0% 9% 45% 45% 0% 100% 0% 0% 0% 18% 18

AL 50% 44% 6% 0% 56% 29% 16% 0% 57% 13% 30% 0% 25% 15

AR 78% 22% 0% 0% 78% 22% 0% 0% 78% 17% 0% 4% 38% 38

AZ 100% 0% 0% 0% 76% 19% 5% 0% 63% 32% 5% 0% 50% 1

CA 38% 8% 51% 2% 43% 16% 40% 1% 28% 9% 61% 2% 11% 2

CO 79% 21% 0% 0% 65% 33% 2% 0% 83% 4% 8% 4% 65% 15

CT 38% 27% 35% 0% 35% 22% 43% 0% 33% 5% 60% 2% 20% 9

DC 0% 33% 67% 0% N/A N/A N/A N/A 67% 0% 33% 0% 0% 50

DE 25% 0% 75% 0% 25% 38% 38% 0% 0% 0% 100% 0% 18% 6

FL 15% 27% 56% 1% 24% 24% 52% 1% 13% 9% 79% 0% 4% 5

GA 79% 12% 9% 0% 60% 21% 18% 2% 67% 7% 23% 2% 53% 2

HI 22% 0% 78% 0% 10% 0% 90% 0% 0% 0% 100% 0% 0% 0

IA 43% 52% 0% 4% 59% 38% 0% 3% 70% 17% 9% 4% 60% 28

ID 65% 35% 0% 0% 47% 53% 0% 0% 59% 35% 6% 0% 44% 44

IL 55% 40% 5% 0% 74% 22% 4% 0% 93% 7% 0% 0% 76% 1

IN 88% 12% 0% 0% 82% 13% 5% 0% 92% 4% 2% 2% 78% 15

KS 100% 0% 0% 0% 96% 4% 0% 0% 94% 6% 0% 0% 71% 10

KY 77% 23% 0% 0% 73% 24% 2% 0% 64% 18% 18% 0% 58% 25

LA 45% 45% 10% 0% 39% 43% 14% 4% 38% 27% 27% 8% 32% 16

MA 21% 36% 44% 0% 49% 21% 31% 0% 33% 4% 63% 0% 15% 8

MD 13% 34% 53% 0% 30% 32% 39% 0% 23% 19% 58% 0% 10% 18

ME 63% 19% 19% 0% 65% 15% 20% 0% 67% 4% 30% 0% 19% 6

MI 80% 14% 5% 0% 73% 20% 8% 0% 92% 3% 5% 0% 81% 8

MN 77% 23% 0% 0% 90% 10% 0% 0% 84% 7% 7% 2% 95% 5

MO 53% 42% 3% 3% 73% 21% 4% 2% 52% 36% 7% 5% 60% 30

MS 47% 42% 11% 0% 76% 15% 9% 0% 50% 36% 14% 0% 38% 13

MT 75% 25% 0% 0% 42% 53% 5% 0% 63% 31% 6% 0% 78% 1

NC 61% 19% 19% 0% 52% 24% 24% 0% 36% 15% 42% 7% 22% 14

ND 100% 0% 0% 0% 80% 20% 0% 0% 83% 17% 0% 0% 100% 0

NE 89% 11% 0% 0% 100% 0% 0% 0% 77% 23% 0% 0% 64% 27
MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.5: Allocation of Single-Day Trips by Water Body Type 

State 

Boating (%) Fishing (%) Swimming (%) 

Lake Stream Oceana Otherb Lake Stream Ocean Other Lake Stream Ocean Other Lake 

NH 58% 25% 17% 0% 44% 44% 11% 0% 55% 0% 41% 5% 25% 

NJ 24% 11% 65% 0% 31% 13% 55% 2% 20% 2% 77% 0% 9% 

NM 43% 43% 14% 0% 38% 62% 0% 0% 50% 30% 20% 0% 25% 

NV 92% 0% 8% 0% 60% 40% 0% 0% 100% 0% 0% 0% 85% 

NY 47% 18% 35% 0% 53% 21% 26% 0% 43% 7% 49% 1% 40% 

OH 83% 11% 5% 1% 84% 13% 2% 1% 86% 4% 7% 3% 71% OK 88% 13% 0% 0% 94% 3% 3% 0% 80% 15% 0% 5% 87% 

OR 41% 36% 23% 0% 31% 56% 13% 0% 50% 26% 22% 2% 11% 

PA 46% 32% 19% 3% 54% 27% 18% 2% 53% 19% 26% 2% 37% 

RI 11% 22% 67% 0% 36% 18% 45% 0% 29% 5% 67% 0% 4% 

SC 64% 20% 12% 4% 66% 13% 19% 2% 68% 4% 29% 0% 31% 

SD 100% 0% 0% 0% 57% 43% 0% 0% 89% 11% 0% 0% 75% 

TN 75% 17% 8% 0% 63% 34% 3% 0% 72% 23% 5% 0% 48% 

TX 74% 8% 18% 0% 64% 13% 23% 0% 62% 16% 20% 2% 41% 

UT 78% 0% 22% 0% 87% 13% 0% 0% 64% 36% 0% 0% 89% 

VA 31% 35% 35% 0% 27% 38% 35% 0% 23% 17% 58% 2% 16% 

VT 100% 0% 0% 0% 67% 33% 0% 0% 71% 14% 14% 0% 80% 

WA 38% 27% 33% 1% 36% 30% 34% 0% 63% 25% 12% 0% 21% 

WI 66% 30% 4% 0% 78% 20% 2% 0% 80% 10% 5% 5% 73% 

WV 83% 8% 8% 0% 43% 57% 0% 0% 55% 35% 10% 0% 55% 

WY 83% 0% 17% 0% 73% 27% 0% 0% 100% 0% 0% 0% 100% 

a Note that respondents in several states apparently provided inaccurate information because some states at great distances from the ocean rep

day trips to the ocean. 

b Other includes wetlands and unknown water body types. 

N/A - Not Available 

Source: NDS. 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

N-14

Table N.6: Allocation of Multiple-Day Trips by Water Body Type

State

Boating (%) Fishing (%) Swimming (%) Vie

Lake Stream Ocean Othera Lake Stream Ocean Other Lake Stream Ocean Other Lake Stream 

AK 75% 0% 25% 0% 33% 0% 67% 0% 0% 0% 0% 0% 33% 0

AL 43% 0% 43% 14% 40% 0% 40% 20% 19% 0% 62% 19% 7% 0

AR 71% 14% 0% 14% 45% 36% 0% 18% 0% 17% 67% 17% 30% 0

AZ 63% 13% 25% 0% 73% 13% 7% 7% 7% 7% 50% 36% 7% 11

CA 49% 23% 25% 3% 58% 17% 11% 13% 19% 12% 41% 29% 13% 6

CO 85% 8% 0% 8% 48% 36% 12% 4% 33% 0% 25% 42% 30% 4

CT 33% 33% 33% 0% 60% 0% 40% 0% 14% 5% 73% 9% 10% 0

DC 0% 0% 100% 0% N/A N/A N/A N/A 0% 0% 67% 33% 8% 0

DE 100% 0% 0% 0% 50% 0% 50% 0% 0% 0% 100% 0% 0% 8

FL 11% 25% 43% 21% 21% 11% 36% 32% 9% 2% 52% 38% 7% 3

GA 46% 19% 27% 8% 24% 12% 40% 24% 15% 5% 62% 18% 8% 2

HI 0% 0% 50% 50% 0% 0% 100% 0% 0% 0% 83% 17% 0% 0

IA 78% 0% 11% 11% 78% 22% 0% 0% 14% 0% 57% 29% 21% 7

ID 80% 20% 0% 0% 56% 38% 0% 6% 75% 25% 0% 0% 50% 1

IL 58% 23% 10% 10% 70% 7% 11% 11% 33% 8% 25% 35% 23% 7

IN 68% 16% 5% 11% 69% 6% 13% 13% 35% 0% 24% 41% 17% 4

KS 100% 0% 0% 0% 100% 0% 0% 0% 63% 0% 25% 13% 30% 9

KY 78% 22% 0% 0% 71% 18% 0% 12% 25% 19% 38% 19% 5% 0

LA 57% 0% 14% 29% 31% 15% 31% 23% 11% 0% 79% 11% 8% 

MA 42% 11% 32% 16% 53% 0% 20% 27% 7% 0% 63% 30% 16% 

MD 18% 9% 55% 18% 44% 33% 11% 11% 15% 5% 69% 10% 9% 

ME 63% 13% 13% 13% 50% 50% 0% 0% 50% 0% 17% 33% 25% 

MI 76% 10% 10% 4% 65% 8% 6% 20% 50% 6% 22% 22% 40% 

MN 63% 6% 6% 25% 83% 9% 0% 9% 50% 0% 6% 44% 44% 

MO 75% 17% 4% 4% 52% 29% 5% 14% 30% 10% 25% 35% 20% 

MS 50% 33% 17% 0% 60% 0% 20% 20% 14% 0% 57% 29% 8% 

MT 25% 25% 25% 25% 50% 50% 0% 0% 50% 0% 17% 33% 33% 1

NC 52% 19% 19% 10% 11% 8% 72% 8% 3% 3% 79% 16% 2% 
MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.6: Allocation of Multiple-Day Trips by Water Body Type 

State 

Boating (%) Fishing (%) Swimming (%) 

Lake Stream Ocean Othera Lake Stream Ocean Other Lake Stream Ocean Other Lake 

ND 100% 0% 0% 0% 100% 0% 0% 0% 25% 0% 0% 75% 25% 

NE 75% 25% 0% 0% 75% 13% 13% 0% 33% 0% 33% 33% 40% 

NH 0% 33% 67% 0% 0% 0% 100% 0% 40% 0% 60% 0% 14% 

NJ 0% 0% 67% 33% 18% 18% 18% 45% 4% 0% 83% 13% 9% 

NM 85% 8% 0% 8% 70% 20% 10% 0% 33% 17% 0% 50% 27% 

NV 75% 0% 25% 0% 33% 17% 17% 33% 20% 20% 40% 20% 31% 

NY 41% 17% 22% 20% 50% 17% 20% 13% 22% 0% 62% 16% 12% 

OH 68% 18% 9% 6% 70% 13% 7% 10% 14% 5% 59% 21% 20% 

OK 62% 8% 8% 23% 50% 10% 10% 30% 30% 10% 25% 35% 21% 

OR 38% 31% 15% 15% 42% 33% 8% 17% 9% 17% 30% 43% 8% 

PA 42% 9% 36% 12% 53% 16% 22% 9% 17% 3% 64% 16% 12% 

RI 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 100% 11% 

SC 56% 0% 33% 11% 17% 17% 50% 17% 0% 8% 67% 25% 2% 

SD 75% 0% 0% 25% 25% 75% 0% 0% 100% 0% 0% 0% 25% 

TN 50% 14% 21% 14% 33% 33% 11% 22% 15% 4% 65% 15% 11% 

TX 53% 6% 26% 15% 53% 10% 25% 12% 16% 16% 42% 26% 17% 

UT 85% 0% 0% 15% 70% 10% 0% 20% 50% 25% 0% 25% 25% 

VA 35% 15% 45% 5% 24% 19% 43% 14% 12% 2% 71% 16% 4% 

VT 100% 0% 0% 0% 0% 0% 0% 0% 50% 0% 50% 0% 55

WA 27% 27% 36% 9% 29% 32% 25% 14% 29% 19% 39% 13% 12

WI 78% 11% 11% 0% 73% 15% 3% 9% 43% 9% 30% 17% 34

WV 20% 20% 0% 60% 25% 50% 0% 25% 9% 9% 55% 27% 9

WY 100% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0% 50

a Other includes wetlands and unknown water body types. 

N/A - Not Available 

Source: NDS. 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey 

N.5 ONE-WAY TRAVEL DISTANCE 

This analysis estimates the average one-way distance to sites by trip duration (i.e., single day versus multi-day trips), trip 

length, recreation activity, and state of residence. EPA estimated the mean one-way distance traveled based on the distance 

reported for the last single- or multiple-day trip for each activity. As shown in Table N.7, some respondents indicated 

traveling to the ocean across long distances on single-day trips. These values are likely to be due to the following two factors: 

� respondents traveled long distances for multi-purpose multiple-day trips and participated in the given activity on only 

one day on the trip; and 

� response errors. 

EPA estimated the average travel distance traveled after dropping outliers because these outliers may provide undue influence 

on sample means. 

N-16 
MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.7: Average One-Way Distance 

State 

Miles to Single-Day Site Miles to Multiple-D

Boating Fishing Swimming Viewing Boating Fishing 

AK 41 47 32 39 76 193 

AL 31 29 35 53 93 218 

AR 52 38 19 222 215 246 

AZ 54 45 44 117 205 323 

CA 32 40 26 31 233 316 

CO 41 56 15 69 372 260 

CT 30 41 36 49 168 161 

DC 46 N/A 417 85 1000a N/A 

DE 36 32 50 189 1,625 1,700 

FL 21 23 20 24 317 381 

GA 34 52 42 46 199 283 

HI 37 13 14 13 3 80 

IA 60 25 26 49 314 321 

ID 35 48 101 54 228 146 

IL 52 34 30 29 255 368 

IN 47 29 50 64 295 378 

KS 42 22 52 68 151 177 

KY 55 32 46 106 151 143 

LA 30 27 39 53 132 76 

MA 22 25 30 29 136 154 

MD 36 40 56 38 581 199 

ME 44 24 30 23 436 148 

MI 31 33 25 32 192 249 

MN 45 55 16 16 354 185 

MO 42 40 39 71 195 265 

MS 11 27 36 39 72 122 

MT 43 172 31 102 588 154 

NC 42 49 45 67 153 182 

ND 69 55 35 75 154 130 

NE 70 29 71 56 125 603 

NH 27 24 28 34 186 248 

NJ 49 31 41 41 483 179 

NM 76 50 71 252 161 191 

NV 108 46 43 53 48 401 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

Table N.7: Average One-Way Distance 

State 

Miles to Single-Day Site Miles to Multiple-Day Site 

Boating Fishing Swimming Viewing Boating Fishing Swimmin

NY 32 26 25 37 202 195 

OH 63 38 30 45 265 262 

OK 62 50 46 47 189 244 

OR 31 36 33 51 398 200 

PA 40 38 36 57 296 228 

RI 10 26 18 26 

SC 15 33 40 60 713 132 

SD 43 35 19 46 352 143 

TN 29 27 24 84 61 888 

TX 40 38 38 65 190 187 

UT 43 66 44 68 235 122 

VA 37 30 39 69 407 159 

VT 41 20 33 50 70 

WA 23 28 20 41 154 198 

WI 34 30 30 33 289 303 

WV 86 30 95 158 338 278 

WY 69 46 32 47 73 56 

a Based on one observation only. 

N/A - Not Available 

Source: NDS. 

N-18 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey 

N.6 INDIVIDUAL EXPENDITURES PER TRIP 

This analysis estimates the mean total expenditures per person by trip length, recreation activity, and state of residence. Total 

expenditures for single-day boating, fishing, and viewing trips consist of transportation, entrance fee, and boat rental. Total 

expenditures for multiple-day boating, fishing, and viewing trips include expenses for transportation, entrance fees, boat 

rental, and lodging. Transportation includes expenses for plane, train, bus, or ship only, and do not reflect costs associated 

with operating a car. Expenditures on single-day and multiple-day swimming trips do not include boat rental. Expenditures 

on single-day and multiple-day trips for all activities do not include bait, tackle, recreational clothing and equipment (e.g., 

photographic supply and binoculars), boat ownership, or food. Results of the analysis are presented below in Table N.8. 

N-19 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

Table N.8: Individual Expenditures per Trip 

State 

Average Expenditures per Person on Single-day Trips 

(1993$ per trip) 

Average Expenditures per Person on Multi

(1993$ per trip) 

Boating Fishing Swimming Viewing Boating Fishing Swimming 

AK $16 $10 $0 $12 $66 $98 

AL $14 $15 $2 $5 $23 $421 $

AR $18 $24 $1 $1 $59 $48 $

AZ $16 $5 $7 $3 $41 $84 $

CA $53 $22 $5 $3 $454 $220 $

CO $49 $11 $3 $18 $320 $235 $

CT $19 $12 $35 $7 $387 $114 $

DCa $17 N/A $2 $3 $2,000 N/A $

DE $6 $18 $2 $2 $43 $63 $

FL $22 $22 $2 $4 $376 $852 $

GA $19 $17 $8 $28 $147 $275 $

HI $22 $7 $0 $0 $110 $0 $IA $8 $2 $1 $2 $119 $662 $

ID $21 $0 $1 $1 $54 $29 

IL $37 $9 $2 $1 $342 $333 $

IN $18 $10 $3 $14 $299 $321 $

KS $19 $3 $2 $2 $89 $175 $

KY $18 $2 $35 $18 $340 $180 $

LA $50 $14 $1 $1 $186 $58 $

MA $19 $13 $18 $3 $89 $197 $

MD $49 $51 $2 $36 $116 $178 $

ME $2 $2 $1 $2 $329 $44 $

MI $26 $7 $2 $4 $227 $125 $

MN $10 $6 $2 $1 $198 $160 

MO $26 $11 $2 $9 $164 $122 $

MS $14 $26 $1 $1 $52 $169 $

N-20

MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.8: Individual Expenditures per Trip 

State 

Average Expenditures per Person on Single-day Trips 

(1993$ per trip) 

Average Expenditures per Person 

(1993$ per tr

Boating Fishing Swimming Viewing Boating Fishing S

MT $8 $23 $1 $0 $25 $95 

NC $13 $26 $24 $3 $165 $132 

ND $14 $2 $0 $0 $53 $3 

NE $3 $4 $85 $2 $24 $310 

NH $16 $7 $3 $0 $127 $0 

NJ $32 $44 $13 $7 $360 $168 

NM $15 $3 $22 $32 $73 $78 

NV $25 $1 $1 $4 $104 $25 

NY $25 $29 $5 $8 $242 $76 

OH $26 $15 $8 $22 $403 $239 

OK $11 $22 $2 $3 $173 $314 

OR $15 $5 $22 $1 $429 $51 

PA $23 $21 $19 $9 $275 $310 

RI $23 $16 $3 $2 $0 $0 

SC $27 $12 $2 $7 $576 $201 

SD $5 $6 $0 $2 $248 $54 

TN $26 $4 $0 $14 $458 $49 

TX $152 $12 $2 $3 $151 $138 

UT $25 $2 $4 $13 $164 $10 

VA $10 $23 $1 $243 $175 $116 

VT $6 $1 $6 $2 $100 $0 

WA $11 $19 $13 $1 $266 $170 

WI $10 $5 $3 $8 $425 $135 

WV $46 $2 $14 $3 $250 $275 

WY $5 $5 $0 $22 $85 $17 

a Average boating expenditures in Washington, D.C. are based on a single observation. 

N/A - Not Available 

Source: NDS. 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey 

N.7 DISTRIBUTION OF DIRECT COSTS FOR SINGLE-DAY TRIPS 

This analysis estimates the percent of total expenditures for single-day and multiple-day trips spent on each component of 

total expenditures. Total expenditures for single-day boating, fishing, and viewing trips consist of: 

� transportation, 

� entrance fee, and 

� boat rental. 

Lodging is not included in single-day expenditures. Swimming trip expenditures do not include boat rental. Transportation 

includes expenses for: 

� plane, 

� train, 

� bus, or 

� ship only 

and do not include automobile travel costs. 

EPA determined the percent of total expenditures for each category by dividing the total amount spent on each category by 

the total expenditures in a state for a given activity. 

Tables N.9 and N.10 present results for single- and multiple-day trips, respectively. 

N-22 
MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.9: Distribution of Direct Costs for Single-Day Trips 

State 

Boating 

(% of total expenditures) 

Fishing 

(% of total expenditures) 

Swimminga 

(% of total expenditures) 

(% 

Transb Enter Fee Boat Rental Trans Enter Fee Boat Rental Trans Enter Fee Trans 

AK 0% 3% 97% 0% 5% 95% N/A N/A 0% 

AL 0% 12% 88% 4% 28% 68% 0% 100% 0% 

AR 0% 8% 92% 36% 41% 23% 0% 100% 0% 

AZ 0% 6% 94% 0% 25% 75% 0% 100% 83% 

CA 45% 13% 42% 15% 21% 65% 37% 63% 50% 

CO 0% 9% 91% 57% 17% 25% 0% 100% 84% 

CT 0% 9% 91% 0% 3% 97% 0% 100% 0% 

DC 0% 35% 65% N/A N/A N/A 0% 100% 0% 

DE 0% 30% 70% 0% 52% 48% 0% 100% 0% 

FL 0% 10% 90% 1% 12% 87% 0% 100% 0% 

GA 0% 7% 93% 0% 29% 71% 66% 34% 83% 

HI 62% 0% 38% 0% 18% 82% N/A N/A N/A 

IA 0% 3% 97% 0% 0% 100% 0% 100% 0% 

ID 0% 5% 95% 0% 0% 100% 0% 100% 0% 

IL 4% 13% 82% 0% 13% 87% 0% 100% 6% 

IN 0% 2% 98% 0% 24% 76% 0% 100% 53% 

KS 0% 24% 76% 0% 51% 49% 0% 100% 0% 

KY 0% 2% 98% 0% 27% 73% 96% 4% 82% 

LA 0% 68% 32% 0% 46% 54% 0% 100% 0% 

MA 0% 43% 57% 4% 28% 68% 88% 12% 0% 

MD 31% 17% 52% 0% 2% 98% 0% 100% 17% 

ME 0% 23% 77% 0% 0% 100% 0% 100% 0% 

MI 0% 8% 92% 0% 10% 90% 0% 100% 0% 

MN 0% 17% 83% 0% 65% 35% 0% 100% 0% 

MO 0% 25% 75% 0% 20% 80% 0% 100% 1% 

MS 0% 36% 64% 0% 44% 56% 0% 100% 0% 

MT 0% 8% 92% 96% 4% 0% 0% 100% N/A 

NC 0% 23% 77% 0% 12% 88% 0% 100% 40% 

ND 0% 30% 70% 0% 32% 68% 0% 100% N/A 

NE 0% 0% 100% 0% 0% 100% 0% 100% 0% 

NH 0% 48% 52% 0% 61% 39% 0% 100% 0% 

NJ 15% 10% 74% 8% 48% 44% 26% 74% 35% 

NM 0% 8% 92% 0% 49% 51% 91% 9% 98% 

NV 0% 19% 81% 0% 67% 33% 0% 100% 0% 

NY 23% 29% 48% 5% 36% 58% 44% 56% 5% 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

Table N.9: Distribution of Direct Costs for Single-Day Trips 

State 

Boating 

(% of total expenditures) 

Fishing 

(% of total expenditures) 

Swimminga 

(% of total expenditures) 

Vi

(% of total 

Transb Enter Fee Boat Rental Trans Enter Fee Boat Rental Trans Enter Fee Trans Enter 

OH 28% 6% 66% 7% 14% 79% 0% 100% 43% 

OK 0% 27% 73% 58% 9% 33% 0% 100% 0% 

OR 0% 17% 83% 0% 25% 75% 0% 100% 0% 

PA 0% 11% 89% 0% 22% 78% 94% 6% 49% 

RI 0% 0% 100% 0% 1% 99% 0% 100% 0% 

SC 0% 66% 34% 0% 10% 90% 0% 100% 95% 

SD 0% 19% 81% 0% 39% 61% 0% 100% 0% 

TN 26% 0% 73% 0% 22% 78% 0% 100% 98% 

TX 0% 2% 98% 0% 20% 80% 0% 100% 0% 

UT 0% 42% 59% 0% 60% 40% 0% 100% 0% 

VA 9% 25% 66% 0% 25% 75% 0% 100% 20% 

VT 0% 8% 92% 0% 0% 100% 0% 100% 0% 

WA 0% 18% 82% 45% 16% 39% 0% 100% 17% 

WI 0% 19% 81% 1% 20% 79% 2% 98% 48% 

WV 0% 33% 67% 0% 0% 100% 78% 22% 88% 

WY 0% 47% 53% 0% 53% 48% N/A N/A 0% 

a Swimming expenditures do not include boat rental. 

b Transportation expenses include expenditures on plane, train, bus, or ship taken on the trip only and do not reflect travel costs. 

N/A - Not Available 

Source: U.S. EPA analysis. 

N-24 
MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.10: Distribution of Direct Costs for Multiple-Day Trips

State

Boating

(% of total expenditures)

Fishing

(% of total expenditures)

Swimminga

(% of total expenditures)

(% 

Transb Enter Fee Lodg- ingc Boat Rental Trans Enter Fee Lodging Boat Rental Trans Enter Fee Lodging Trans 

AK 0% 2% 15% 84% 0% 3% 77% 20% N/A N/A N/A 0% 

AL 0% 0% 20% 80% 0% 0% 37% 63% 4% 0% 96% 0% 

AR 0% 1% 51% 48% 0% 7% 73% 20% 0% 0% 100% 8% 

AZ 0% 5% 55% 40% 0% 10% 65% 24% 31% 0% 69% 29% 

CA 28% 6% 53% 13% 12% 16% 58% 14% 23% 2% 76% 20% 

CO 0% 7% 77% 16% 20% 0% 69% 10% 12% 2% 87% 23% 

CT 22% 0% 16% 63% 0% 1% 95% 4% 17% 1% 83% 11% 

DC 100% 0% 0% 0% N/A N/A N/A N/A 0% 0% 100% 22% 

DE 0% 0% 77% 23% 0% 0% 100% 0% 8% 0% 92% 0% 

FL 10% 1% 71% 18% 0% 2% 16% 81% 0% 2% 98% 7% 

GA 14% 7% 51% 28% 8% 4% 69% 19% 0% 0% 99% 9% 

HI 0% 0% 91% 9% N/A N/A N/A N/A 0% 0% 100% 0% 

IA 0% 21% 63% 16% 0% 0% 64% 36% 18% 0% 82% 26% 

ID 0% 1% 92% 7% 0% 8% 78% 15% 0% 3% 97% 20% 

IL 18% 6% 41% 35% 14% 2% 79% 5% 33% 4% 63% 21% 

IN 8% 3% 58% 31% 7% 5% 81% 7% 17% 57% 26% 20% 

KS 0% 2% 30% 68% 0% 1% 74% 25% 24% 0% 76% 16% 

KY 0% 3% 12% 85% 21% 3% 48% 27% 0% 0% 100% 14% 

LA 0% 3% 81% 16% 0% 31% 62% 8% 9% 0% 91% 1% 

MA 0% 1% 38% 61% 0% 14% 78% 9% 19% 0% 81% 21% MD 0% 0% 56% 44% 0% 0% 98% 2% 12% 0% 88% 13% 

ME 0% 72% 22% 6% 0% 15% 80% 6% 0% 4% 96% 0% 

MI 9% 6% 58% 26% 5% 4% 83% 8% 17% 6% 77% 33% 

MN 17% 0% 78% 5% 0% 0% 73% 27% 0% 1% 99% 44% 

MO 0% 3% 74% 23% 0% 6% 64% 30% 30% 0% 70% 24% 

MS 0% 1% 65% 34% 0% 0% 69% 31% 0% 0% 100% 0% 

MT 0% 0% 100% 0% 0% 0% 92% 8% 5% 0% 95% 0% 

NC 12% 8% 55% 25% 18% 4% 69% 9% 8% 0% 92% 7% 

ND 0% 0% 16% 84% 0% 0% 100% 0% N/A N/A N/A N/A 

NE 0% 6% 66% 28% 30% 6% 64% 0% 0% 0% 100% 60% 

NH 0% 13% 0% 87% N/A N/A N/A N/A 9% 0% 91% 0% 

NJ 34% 0% 60% 6% 0% 37% 25% 39% 20% 0% 79% 21% 

NM 0% 7% 52% 41% 0% 5% 83% 12% 0% 18% 82% 17% 

NV 0% 3% 37% 60% 0% 0% 100% 0% 45% 2% 53% 7% 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

Table N.10: Distribution of Direct Costs for Multiple-Day Trips 

State 

Boating 

(% of total expenditures) 

Fishing 

(% of total expenditures) 

Swimminga 

(% of total expenditures) 

Vie

(% of total 

Transb Enter Fee Lodg­ ingc Boat Rental Trans Enter Fee Lodging Boat Rental Trans Enter Fee Lodging Trans Enter Fee 

NY 20% 13% 58% 8% 0% 5% 75% 20% 9% 1% 90% 16% 0% 

OH 8% 2% 49% 41% 5% 1% 79% 15% 6% 24% 70% 17% 0% 

OK 16% 0% 62% 22% 0% 81% 19% 0% 18% 4% 78% 30% 1% 

OR 4% 1% 43% 52% 0% 2% 87% 11% 41% 0% 59% 5% 3% 

PA 37% 4% 36% 23% 1% 1% 61% 37% 19% 1% 81% 9% 0% 

RI N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 20% 0% 

SC 10% 0% 24% 67% 0% 10% 87% 3% 0% 0% 100% 21% 1% 

SD 0% 0% 96% 4% 0% 12% 69% 18% 0% 0% 100% 28% 0% 

TN 0% 0% 53% 46% 0% 0% 38% 63% 12% 0% 88% 7% 0% 

TX 6% 5% 56% 33% 6% 5% 70% 19% 21% 8% 72% 20% 1% 

UT 0% 0% 66% 34% 0% 60% 40% 0% 0% 1% 99% 34% 0% 

VA 54% 3% 30% 14% 0% 10% 60% 31% 2% 1% 97% 8% 2% 

VT 0% 50% 50% 0% N/A N/A N/A N/A 0% 0% 100% 60% 0% 

WA 10% 4% 46% 40% 57% 0% 18% 25% 23% 4% 73% 24% 1% 

WI 9% 4% 35% 52% 26% 21% 48% 6% 13% 0% 87% 18% 1% 

WV 0% 0% 80% 20% 0% 3% 97% 0% 0% 0% 100% 0% 13% WY 0% 0% 71% 29% 0% 4% 96% 0% N/A N/A N/A 0% 10% 

a Swimming expenditures do not include boat rental. 

b Transportation expenses include expenditures on plane, train, bus, or ship taken on the trip only and do not reflect travel costs. 

c Total expenses for multiple-day trips include lodging, while total expenditures for single-day trips do not. 

N/A - Not Available 

Source: NDS; U.S. EPA analysis. 

N-26 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey 

N.8 PROFILE OF BOATING TRIPS 

This analysis provides a profile of sample boater characteristics by state of residence. Table N.11 shows distribution of 

boaters by type of boating in which they participated on their last trip and the source of the boat used on their most recent 

boating trip. 

Boating types include: 

� motorboating;


� sailing;


� white water kayaking and canoeing;


� other kayaking or canoeing;


� rowing, rafting, tubing, or floating;


� wind surfing; and


� other.


Boat sources include: 

� boaters who used their own boat, including those who indicated using either their own boat or one belonging to 

someone in their immediate family on their last trip; 

� boat renters, including those who either rented or chartered a boat on their last trip; and 

� other, including respondents who did not indicate either using their own boat or renting a boat. 

Dividing the number of respondents who participated in each boating type on their last trip by the total sample of boaters 

provided an estimate of the percent participating in each type. 

N-27 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

N-28

Table N.11: Profile of Boating Trips

State

Total Number of

Boaters

Source Boat Used on Last Tripa

(Percent of Boaters)

Type of Boating on Last Tripb

(Percent of Boaters)

NDS

Sample

Sample

Weighted Own Rent Other Motor Sail

White

Water

Kayak

Other

Kayak Row Raft W

AK 14 220,972 36% 36% 29% 71% 0% 21% 7% 0% 0% 

AL 39 617,486 51% 21% 28% 79% 0% 8% 0% 0% 3% 

AR 25 404,809 48% 20% 32% 88% 0% 0% 0% 0% 4% 

AZ 21 461,000 57% 14% 29% 67% 14% 0% 10% 10% 0% 

CA 269 5,244,634 31% 23% 46% 66% 14% 2% 1% 1% 4% 

CO 27 423,143 44% 22% 33% 70% 4% 4% 4% 4% 7% 

CT 32 533,626 41% 34% 25% 50% 22% 6% 9% 0% 6% 

DC 4 53,551 0% 50% 50% 50% 50% 0% 0% 0% 0% 

DE 10 119,661 60% 10% 30% 80% 10% 10% 0% 0% 0% 

FL 152 2,925,614 31% 32% 37% 74% 8% 3% 5% 1% 1% 

GA 69 1,156,297 30% 32% 38% 77% 9% 7% 0% 0% 3% 

HI 11 189,837 9% 36% 55% 36% 36% 9% 9% 0% 0% IA 32 426,854 25% 31% 44% 81% 3% 9% 3% 0% 0% ID 25 291,917 56% 12% 32% 72% 4% 0% 4% 4% 16% 

IL 86 1,758,816 34% 28% 38% 66% 5% 2% 6% 0% 6% 

IN 62 967,694 35% 26% 39% 84% 10% 2% 0% 0% 0% 

KS 18 274,465 44% 22% 33% 83% 0% 0% 6% 11% 0% 

KY 35 505,228 54% 17% 29% 89% 0% 0% 3% 0% 3% 

LA 38 682,563 47% 18% 34% 87% 3% 3% 0% 3% 0% 

MA 58 1,166,524 28% 45% 28% 53% 19% 9% 12% 0% 0% 

MD 49 778,917 20% 47% 33% 65% 10% 4% 0% 2% 8% 

ME 24 336,758 21% 46% 33% 63% 8% 8% 13% 0% 0% 

MI 141 1,867,312 30% 43% 26% 76% 9% 4% 3% 1% 1% 

MN 59 910,964 39% 29% 32% 83% 2% 2% 3% 0% 0% 

MO 60 938,326 38% 28% 33% 75% 3% 5% 12% 0% 0% 

MS 25 385,744 36% 32% 32% 80% 12% 0% 8% 0% 0% 

MT 12 153,038 67% 0% 33% 42% 0% 17% 17% 0% 8% 

NC 57 881,075 30% 30% 40% 70% 9% 7% 2% 2% 5% 

ND 11 138,098 55% 27% 18% 82% 0% 0% 0% 0% 0% 

NE 17 266,126 18% 12% 71% 88% 0% 12% 0% 0% 0% 

NH 15 225,139 13% 53% 33% 80% 7% 13% 0% 0% 0% 

NJ 64 1,207,234 17% 53% 30% 70% 13% 0% 5% 2% 0% 

NM 20 260,978 55% 10% 35% 65% 5% 0% 15% 0% 10% 

NV 17 348,590 41% 12% 47% 82% 0% 6% 0% 6% 0% NY 137 2,619,158 21% 50% 28% 64% 11% 1% 6% 4% 2% 
MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.11: Profile of Boating Trips 

State 

Total Number of 

Boaters 

Source Boat Used on Last Tripa 

(Percent of Boaters) 

Type of Boating on Last Tripb 

(Percent of Boaters) 

NDS 

Sample 

Sample 

Weighted Own Rent Other Motor Sail 

White 

Water 

Kayak 

Other 

Kayak Row R

OH 109 1,473,937 30% 37% 33% 75% 7% 6% 3% 0% 

OK 29 540,650 21% 34% 45% 66% 7% 7% 3% 0% 

OR 57 702,199 49% 21% 30% 70% 9% 0% 4% 2% 

PA 111 1,450,179 28% 40% 32% 72% 6% 1% 4% 2% 

RI 9 130,654 33% 56% 11% 33% 44% 0% 11% 11% 

SC 34 585,163 53% 21% 26% 71% 3% 6% 9% 0% 

SD 11 151,221 18% 36% 45% 82% 0% 9% 0% 0% 

TN 67 1,006,355 45% 25% 30% 84% 6% 0% 0% 1% 

TX 118 2,805,077 41% 20% 39% 80% 5% 1% 3% 2% 

UT 22 316,826 59% 9% 32% 86% 0% 0% 0% 0% 

VA 72 1,023,443 36% 43% 21% 57% 22% 1% 8% 0% 

VT 8 112,768 50% 50% 0% 50% 13% 13% 13% 0% 

WA 114 1,601,852 37% 26% 37% 70% 11% 0% 6% 3% 

WI 65 903,611 42% 38% 20% 68% 8% 5% 15% 2% 

WV 17 196,359 47% 6% 47% 59% 0% 6% 6% 0% 

WY 8 98,550 38% 0% 63% 63% 0% 13% 13% 0% 

a Own includes those who used their own boat or one belonging to someone in their immediate family.


Rent includes those who rented or chartered a boat.


Other includes those who did not indicate either using own boat or renting a boat.


b Kayak includes kayak or canoe; raft includes rafting, tubing, or floating; other includes other or type not indicated. 

N/A - Not Available 

Source: U.S. EPA analysis; NDS. 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey 

N.9 PROFILE OF FISHING TRIPS 

This analysis provides a profile of fishing trips, including angling success rate, average catch, and type of fisheries targeted on 

the last trip by state of residence. The success rate equals the total number of fishermen who report catching at least one fish 

on their last trip divided by the total number of fishermen in each state. The average catch equals the total fish caught by all 

fishermen divided by the total number of fishermen in the state. Average catch therefore includes those who did not indicate 

catching any fish. Similarly, the percent of fishermen who fished from a boat equals the total number of fishermen who 

reported fishing from a boat on their last trip, divided by the total number of fishermen. Finally, the percent of fishermen who 

participated in each type of fishing equals the total number of fishermen who reported fishing in either cold, warm, salt, 

anadromous, or other water divided by the total number of fishermen. Other includes both those who indicated other and 

missing values. Results of the analysis are presented below in Table N.12. 

N-30 
MP&M EEBA: Appendices Appendix N: Analysis of the National Deman

Table N.12: Profile of Fishing Trips 

State 

Sample 

Weighted 

Number of 

Fishermen 

Fish Catch on Last Tripa Fished from a Boat on Last 

Trip 

(% of 

fishermen) 

Type of Water Fished o

Average Number 

of Fish Caught 

Success Rate 

(% of fishermen ) 

Cold 

(% of 

fishermen) 

Warm 

(% of 

fishermen) 

Salt 

(% of 

fishermen) 

AK 268,323 9 65% 65% 41% 0% 53% 

AL 870,813 7 67% 71% 22% 45% 20% 

AR 761,041 7 85% 62% 36% 60% 0% 

AZ 790,286 4 67% 39% 44% 47% 3% 

CA 5,556,583 5 73% 47% 47% 14% 28% 

CO 1,253,757 4 65% 16% 79% 13% 5% 

CT 466,922 3 71% 50% 29% 21% 43% 

DC N/A N/A N/A N/A N/A N/A N/A 

DE 119,661 6 70% 50% 20% 20% 60% 

FL 3,156,584 5 70% 57% 14% 23% 50% 

GA 1,457,940 6 74% 47% 32% 33% 24% 

HI 189,837 21 91% 36% 0% 18% 82% 

IA 546,907 7 76% 37% 51% 41% 0% 

ID 385,331 3 73% 21% 85% 0% 0% 

IL 1,942,878 5 74% 44% 38% 45% 4% 

IN 1,201,814 6 74% 45% 39% 47% 4% 

KS 579,427 6 74% 39% 21% 74% 0% 

KY 952,715 5 76% 39% 26% 65% 2% 

LA 1,149,580 8 80% 56% 19% 47% 25% 

MA 1,086,074 4 72% 43% 39% 19% 30% 

MD 842,502 5 77% 62% 38% 17% 38% 

ME 308,695 3 68% 55% 64% 18% 18% 

MI 2,052,719 6 75% 61% 54% 28% 3% 

MN 1,281,526 5 70% 63% 53% 34% 0% 

MO 1,141,630 4 74% 37% 51% 36% 3% 

MS 586,331 8 82% 55% 24% 58% 13% 

MT 293,322 3 78% 22% 87% 9% 4% 

NC 1,592,117 10 77% 42% 27% 22% 42% 

ND 163,207 4 69% 46% 54% 46% 0% 

NE 266,126 9 88% 41% 41% 53% 0% 

NH 150,093 2 40% 60% 50% 30% 10% 

NJ 1,244,960 5 73% 45% 18% 21% 48% 

NM 300,125 3 61% 22% 74% 17% 4% 
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Wat

Table N.12: Profile of Fishing Trips 

State 

Sample 

Weighted 

Number of 

Fishermen 

Fish Catch on Last Tripa Fished from a Boat on Last 

Trip 

(% of 

fishermen) 

Type of Water Fished on Last Tr

Average Number 

of Fish Caught 

Success Rate 

(% of fishermen ) 

Cold 

(% of 

fishermen) 

Warm 

(% of 

fishermen) 

Salt 

(% of 

fishermen) 

Anadrom

(% o

fisherm

NV 328,084 4 75% 13% 63% 19% 6% 

NY 2,236,799 4 80% 49% 46% 19% 26% 

OH 1,649,727 5 72% 48% 41% 45% 4% 

OK 857,583 6 70% 33% 26% 57% 9% 

OR 960,904 3 51% 40% 62% 8% 13% 

PA 2,064,218 4 61% 44% 51% 25% 16% 

RI 174,205 5 50% 50% 33% 17% 33% 

SC 912,165 8 81% 60% 32% 34% 25% 

SD 151,221 7 64% 45% 73% 27% 0% 

TN 1,141,537 4 76% 46% 36% 51% 5% 

TX 4,564,193 5 66% 55% 23% 45% 24% 

UT 360,030 3 56% 16% 84% 8% 0% 

VA 1,421,449 7 73% 46% 28% 21% 39% 

VT 42,288 5 100% 33% 33% 67% 0% 

WA 1,250,568 2 56% 60% 49% 8% 22% 

WI 1,209,448 9 69% 59% 57% 34% 0% 

WV 358,067 6 58% 16% 68% 26% 0% 

WY 221,738 4 72% 28% 89% 6% 0% 

a Missing values for fish catch were included as zero in both the mean and the median. 

b Other includes both those that indicated other and missing values. 

N/A - Not Available 

Source: NDS; U.S. EPA analysis. 

N-32