United States 

Environmental Protection 

Agency 

Environmental Impact and Benefits 

Assessment for Final Effluent 

Guidelines and Standards for the 

Construction and Development Category 

November 2009 

Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 iii 

U.S. Environmental Protection Agency 

Office of Water (4303T) 

1200 Pennsylvania Avenue, NW 

Washington, DC 20460 

EPA-821-R-09-012 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 iv 

ACKNOWLEDGMENTS AND DISCLAIMER 

This document was prepared by U.S. Environmental Protection Agency Office of Water staff. The 

following contractor provided assistance in performing the analyses supporting the conclusions 

detailed in this document. 

Abt Associates, Inc. 

Office of Water staff have reviewed and approved this document for publication. Neither the 

United States Government nor any of its employees, contractors, subcontractors, or their 

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 document, or represents that its use by such a party would 

not infringe on privately owned rights. References to proprietary technologies are not intended to 

be an endorsement by the U.S Environmental Protection Agency.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 v 

Table of Contents 

Table of Contents ........................................................................................................................................ v

List of Tables .............................................................................................................................................. xi

List of Figures..........................................................................................................................................xvii

Acronyms and Abbreviations ................................................................................................................. xix

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

2 Overview of Environmental Impacts from Construction Site Sediment Discharges ..............2-1

2.1 Sediment and Turbidity Discharge to Surface Waters ....................................................................2-2

2.2 Sediment and Turbidity Behavior in Surface Waters......................................................................2-5

2.2.1 Sediment Characteristics Affecting Surface Water Transport ..............................................2-6

2.2.2 Sediment and Turbidity Behavior in Specific Waterbody Types .........................................2-7

2.3 Aquatic Life Impacts of Sediment and Turbidity..........................................................................2-11

2.3.1 Primary Producers...............................................................................................................2-13

2.3.2 Invertebrates........................................................................................................................2-15

2.3.3 Fish......................................................................................................................................2-18

2.3.4 Other Wildlife Dependent on Aquatic Ecosystems.............................................................2-22

2.3.5 Threatened and Endangered Species...................................................................................2-22

2.4 Sediment and Turbidity Impacts on Human Use of Aquatic Resources .......................................2-25

2.4.1 Navigation on Surface Waters ............................................................................................2-25

2.4.2 Reservoir Water Storage Capacity ......................................................................................2-26

2.4.3 Municipal Water Use ..........................................................................................................2-26

2.4.4 Industrial Water Use ...........................................................................................................2-27

2.4.5 Agricultural Water Use .......................................................................................................2-27

2.4.6 Stormwater Management and Flood Control......................................................................2-28

2.4.7 Recreational Uses and Aesthetic Value ..............................................................................2-28

2.4.8 Recreational and Commercial Fishing ................................................................................2-29

2.5 Sediment and Turbidity Criteria....................................................................................................2-29

2.5.1 Federal Sediment Criteria ...................................................................................................2-30

2.5.2 State Sediment Criteria .......................................................................................................2-30

2.6 Surface Water Quality Impairment from Sediment and Turbidity................................................2-36

2.6.1 Current Total Suspended Solid Concentrations in U.S. Surface Waters.............................2-38

3 Other Pollutants in Construction Site Discharges......................................................................3-1

3.1 Introduction .....................................................................................................................................3-1
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 vi 

3.1.1 Pollution Discharge and Transport Pathways .......................................................................3-1

3.1.2 National Scale and Cumulative Impact Concerns.................................................................3-2

3.2 Construction Site Sources of Pollutants Other Than Sediment and Turbidity ................................3-3

3.2.1 Construction Materials and Equipment.................................................................................3-3

3.2.2 Historic Site Contamination..................................................................................................3-4

3.2.3 Natural Site Constituents ......................................................................................................3-5

3.2.4 Altered Stormwater Discharge..............................................................................................3-6

3.3 Other Pollutants from Construction Activity...................................................................................3-7

3.3.1 Nitrogen and Phosphorus ......................................................................................................3-7

3.3.2 Organic Compounds and Materials.....................................................................................3-12

3.3.3 Metals..................................................................................................................................3-16

3.3.4 Dissolved Inorganic Ions ....................................................................................................3-19

3.3.5 pH Level..............................................................................................................................3-20

3.3.6 Pathogens ............................................................................................................................3-21

4 Summary of Literature on Construction Site Discharges to Surface Waters..........................4-1

4.1 Overview of Impacts in Literature...................................................................................................4-1

4.2 Individual Study Summaries .........................................................................................................4-17

4.3 State Reports of Construction Discharge Impacts.........................................................................4-30

5 Overview of Benefits from Regulation.........................................................................................5-1

5.1 Conceptual Framework for Valuation of Environmental Services..................................................5-1

5.1.1 Applicable Benefit Categories ..............................................................................................5-3

5.1.2 Market Benefits.....................................................................................................................5-4

5.1.3 Nonmarket Benefits ..............................................................................................................5-6

5.2 Summary of Effects.........................................................................................................................5-9

6 Water Quality Modeling ...............................................................................................................6-1

6.1 SPARROW Model Documentation.................................................................................................6-1

6.1.1 General Overview .................................................................................................................6-1

6.1.2 Modeling Concept.................................................................................................................6-2

6.1.3 Model Infrastructure .............................................................................................................6-3

6.1.4 Model Specification ..............................................................................................................6-4

6.1.5 Model Estimation..................................................................................................................6-6

6.2 SPARROW Sediment Model ..........................................................................................................6-6

6.2.1 Sediment Model Data Sources..............................................................................................6-6

6.2.2 Model Estimation Results .....................................................................................................6-8

6.2.3 Model Simulation..................................................................................................................6-9

6.3 Extension of SPARROW to Estuaries and Coastal Waters...........................................................6-10
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 vii 

6.4 Modeling Changes in Nutrient Concentrations .............................................................................6-12

6.4.1 Modeling Approach Based on SPARROW Nutrient Models .............................................6-13

6.5 Pollutant Load Modeling...............................................................................................................6-15

6.6 Water Quality Modeling Results ...................................................................................................6-17

6.6.1 Current Water Quality Impacts to Surface Water from Construction Site Discharges.......6-26

6.6.2 Estimated Changes in TSS Concentrations in RF1 Watersheds with Most Developed 

Acres (1992-2001) Receiving Sediment Loading from Construction Sites........................6-31

6.6.3 Estimated Changes in TSS, TN, and TP Concentrations for all RF1 Watersheds 

Receiving Loading from Construction Sites .......................................................................6-35

6.6.4 Impacts on Reservoir Sedimentation ..................................................................................6-44

6.6.5 Estimated Changes in Nutrient Concentrations for all RF1 Watersheds Receiving 

Loading from Construction Sites ........................................................................................6-44

7 Benefits to Navigation ...................................................................................................................7-1

7.1 Data Sources....................................................................................................................................7-1

7.2 Identifying Waterways That Are Dredged, the Frequency of Dredging, and the Quantity of 

Sediment Dredged ...........................................................................................................................7-3

7.2.1 Determining Dredging Job Locations ...................................................................................7-3

7.2.2 Identifying the Baseline Frequency of Dredging ..................................................................7-3

7.2.3 Identifying the Amount of Sediment Dredged......................................................................7-5

7.3 Estimating the Navigational Maintenance Cost per Cubic Yard of Sediment Removed ................7-5

7.4 Estimating the Total Cost of Navigable Waterway Maintenance Under Different Policy 

Scenarios .........................................................................................................................................7-6

7.4.1 Estimating the Reduction in Sediment Dredging in Navigable Waterways Due to the 

Reduction in Discharge from Construction Sites..................................................................7-6

7.4.2 Estimating the Total Cost of Navigable Waterway Maintenance Under the Baseline and 

Post-Compliance Scenarios...................................................................................................7-6

7.4.3 Sensitivity Analysis...............................................................................................................7-7

7.4.4 Annualizing Future Dredging Costs......................................................................................7-8

7.5 Estimating the Avoided Costs from Decreased Dredging of Navigable Waterways ....................7-10

7.6 Sources of Uncertainty and Limitations ........................................................................................7-13

8 Benefits to Water Storage .............................................................................................................8-1

8.1 Review of Literature on Reservoir Sedimentation ..........................................................................8-1

8.2 Data Sources....................................................................................................................................8-3

8.3 Estimating the Unit Cost of Sediment Removal from Reservoirs...................................................8-4

8.4 Estimating the Total Cost of Reservoir Dredging Under Different Policy Options........................8-4

8.4.1 Estimating Sediment Accumulation in Reservoirs and the Amount of Sediment 

Expected To Be Dredged ......................................................................................................8-4
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 viii 

8.4.2 Estimating the Total Cost of Reservoir Dredging Under the Baseline Scenario and 

Policy Options.......................................................................................................................8-5

8.5 Estimating the Avoided costs from Reduced Reservoir Dredging..................................................8-6

8.6 Sources of Uncertainty and Limitations ..........................................................................................8-8

9 Benefits to Drinking Water Treatment .......................................................................................9-1

9.1 Construction Discharge Effects on Drinking Water Treatment Costs.............................................9-1

9.1.1 Sediment................................................................................................................................9-1

9.1.2 Nutrients................................................................................................................................9-1

9.1.3 Increase in Primary Productivity and Associated Water Quality Impacts from Nutrient 

Discharges (Eutrophication)..................................................................................................9-2

9.2 Avoided Costs of Drinking Water Treatment from Reducing Sediment Discharge from 

Construction Sites............................................................................................................................9-4

9.3 Data Sources....................................................................................................................................9-6

9.4 Modeling Sediment Concentrations and Reductions.......................................................................9-6

9.5 Identifying Reaches with Drinking Water Intakes and Their Intake Volumes................................9-7

9.6 Estimating the Cost of Chemical Turbidity Treatment....................................................................9-8

9.7 Estimating the Cost of Sludge Disposal ........................................................................................9-10

9.7.1 Estimating the Amount of Sludge Generated......................................................................9-10

9.7.2 Calculating the Probability That the Sludge Will Require Off-Site Disposal.....................9-11

9.7.3 Estimating the Distance to the Disposal Location ..............................................................9-11

9.7.4 Calculating the Cost of Sludge Disposal.............................................................................9-11

9.8 Sensitivity Analysis.......................................................................................................................9-12

9.9 Estimating the Total Costs of Drinking Water Treatment Under Different Policy Scenarios.......9-13

9.10 Estimating Avoided costs from Lower Sediment and Turbidity in Drinking Water Influent .......9-13

9.11 Sources of Uncertainty/Limitations...............................................................................................9-16

10 Nonmarket Benefits from Water Quality Improvements........................................................10-1

10.1 Water Quality Index ......................................................................................................................10-1

10.1.1Freshwater WQI for Rivers and Streams ............................................................................10-3

10.1.2WQI Methodology for Estuaries.........................................................................................10-5

10.1.3Relation between WQI and Suitability for Human Uses ....................................................10-6

10.1.4Sources of Data on Ambient Water Quality........................................................................10-7

10.1.5Estimated Changes in Water Quality (ΔWQI) from the Regulation...................................10-9

10.2 Willingness to Pay for Water Quality Improvements..................................................................10-16

10.3 Estimating Total WTP for Water Quality Improvements............................................................10-22

10.4 Uncertainty and Limitations ........................................................................................................10-25
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 ix 

11 Total Estimated Benefits.............................................................................................................11-1

11.1 Summary of the Estimated Benefits ..............................................................................................11-1

11.2 Sources of Uncertainty and Limitations ........................................................................................11-5

11.2.1Water Quality Model Limitations .......................................................................................11-6

11.2.2Focus on Selected Pollutants of Concern (Sediment and Nutrients)...................................11-7

11.2.3Omission of Several Benefit Categories from the Analysis of Monetized Benefits ...........11-7

11.2.4Limitations Inherent in the Estimate of Nonmarket Benefits .............................................11-8

12 References ....................................................................................................................................12-1

Appendix A – Status of Available Data on Waterbody Assessments .................................................A-1

Appendix B – Threatened and Endangered Aquatic Species ............................................................. B-1

Appendix C – SPARROW Model Documentation...............................................................................C-1

C.1 General Overview .............................................................................................................................. C-1

C.2 Modeling Concept .............................................................................................................................. C-2

C.2.1 Objectives of SPARROW Modeling .................................................................................... C-2

C.2.2 SPARROW Mass Balance Approach ...................................................................................C-4

C.2.3 Time and Space Scales of the Model.................................................................................... C-4

C.2.4 Accuracy and Complexity of SPARROW Models............................................................... C-6

C.2.5 Comparison of SPARROW with Other Watershed Models ................................................. C-7

C.2.6 Model Infrastructure ............................................................................................................. C-8

C.2.7 Use of Monitoring Data ...................................................................................................... C-10

C.2.8 Model Specification for Monitoring Station Flux Estimation ............................................C-11

C.2.9 Tools for Flux Estimation ................................................................................................... C-11

C.2.10 Guidance for Specifying Monitoring Station Flux Models ..............................................C-12

C.2.11 Stream Network Topology................................................................................................ C-13

C.2.12 Watershed Sources and Explanatory Variables ................................................................C-15

C.3 Model Specification ......................................................................................................................... C-17

C.3.1 Model Equation and Specification of Terms ...................................................................... C-17

C.3.2 Contaminant Sources .......................................................................................................... C-19

C.3.3 Landscape Variables ........................................................................................................... C-20

C.3.4 Stream Transport................................................................................................................. C-21

C.3.5 Reservoir and Lake Transport............................................................................................. C-21

C.4 Model Estimation ............................................................................................................................. C-22

C.4.1 Evaluation of Model Parameters.........................................................................................C-22

C.5 Model Prediction.............................................................................................................................. C-23

Appendix D – A Preliminary SPARROW Model of Suspended Sediment for the Coterminous 

United States (Schwarz 2008a) ....................................................................................................D-1
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 x 

Appendix E – Estuary TSS Concentration Calculations Using Dissolved Concentration 

Potentials ....................................................................................................................................... E-1

Appendix F – TSS Subindex Curve Parameters .................................................................................. F-1

Appendix G – Meta-Analysis Results....................................................................................................G-1

G.1 Literature Review of Water Resource Valuation Studies ..................................................................G-1

G.1.1 Identifying Water Resource Valuation Studies ....................................................................G-2

G.1.2 Description of Studies Selected for Total WTP Meta-Analysis ...........................................G-4

G.2 Total WTP Meta-Analysis Regression Model and Results................................................................G-7

G.2.1 Metadata Total WTP Regression Model ..............................................................................G-8

G.2.2 Total WTP Regression Model and Results.........................................................................G-12

G.2.3 Interpretation of Total WTP Regression Analysis Results.................................................G-14

G.2.4 Model Selection..................................................................................................................G-18

G.3 Model Limitations............................................................................................................................G-19

Appendix H – Water Quality Index Tables ..........................................................................................H-1
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xi 

List of Tables 

Table 2-1: Sediment and Turbidity Terminology ......................................................................................2-2

Table 2-2: Sediment Grade Scale...............................................................................................................2-6

Table 2-3: Suspended Sediment and Turbidity Criteria for Surface Water Quality by State ..................2-32

Table 2-4: Surface Waters Impaired by “Sediment,” by EPA Region.....................................................2-37

Table 2-5: Surface Waters Impaired by “Turbidity” and “Suspended Solids,” by EPA Region .............2-37

Table 2-6: SPARROW Distribution of TSS Concentrations in RF1 Reaches1........................................2-39

Table 3-1: Nutrient-Related Impairment in 305(b)-Assessed Waters, by EPA Region...........................3-11

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts.....4-4

Table 4-2: Summary of Suspended Sediment Concentrations from Selected Studies.............................4-13

Table 4-3: Summary of Sediment Yield Data from Selected Studies......................................................4-15

Table 4-4: Summary of Turbidity Findings from Selected Studies .........................................................4-16

Table 4-5: Construction Impairment in 305(b)-Assessed Waters............................................................4-31

Table 5-1: Summary of Benefits from Reducing Sediment and Other Pollutant Discharges from 

Construction Sites..........................................................................................................................5-11

Table 6-1: Estimation Results for the SPARROW Suspended Sediment Model ......................................6-9

Table 6-2: Nutrient-to-Sediment Ratios...................................................................................................6-14

Table 6-3: Literature Values of Nitrogen and Phosphorus in Soil and Sediment ....................................6-14

Table 6-4: Baseline Construction Sediment Loading Summary and Post-Compliance Reductions........6-15

Table 6-5: Construction Sediment Loading Summary and Reductions by Option and EPA Region ......6-16

Table 6-6: Distribution of TSS Concentrations based on SPARROW Output for 31,927 RF1 Reaches 

Receiving Construction Sediment Discharges ..............................................................................6-26

Table 6-7: Summary of Baseline TSS Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-27

Table 6-8: Summary of TSS Concentrations Under the Hypothetical No Discharge Scenario, by EPA 

Region ...........................................................................................................................................6-27

Table 6-9: Water Quality Impacts: Improvements in TSS Concentrations Under the Hypothetical No 

Discharge Scenario, by EPA Region.............................................................................................6-28

Table 6-10: Distribution of TN Concentrations based on SPARROW Output for 31,927 RF1 Reaches 

Receiving Construction Sediment Discharges ..............................................................................6-29

Table 6-11: Summary of Baseline TN Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-29

Table 6-12: Summary of TN Concentrations Under the Hypothetical No Discharge Scenario, by EPA 

Region ...........................................................................................................................................6-29
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xii 

Table 6-13: Distribution of TP Concentrations based on SPARROW Output for 31,927 RF1 Reaches 

Receiving Construction Sediment Discharges ..............................................................................6-30

Table 6-14: Summary of Baseline TP Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-30

Table 6-15: Summary of TP Concentrations Under the Hypothetical No Discharge Scenario, by EPA 

Region ...........................................................................................................................................6-30

Table 6-16: Reaches with Largest Increase in Developed Land Area (1992-2001) ................................6-32

Table 6-17: Distribution of RF1 Watersheds that Receive Direct Loadings by Increase in Developed 

Land Area (1992-2001), by EPA Region ......................................................................................6-32

Table 6-18: Estimated Changes in RF1 Reach TSS Concentration by Policy Option: Top 1% of RF1 

Watersheds Receiving Direct Construction Loadings by Increase in Developed Land Area 

(1992-2001) ...................................................................................................................................6-33

Table 6-19: Estimated Changes in RF1 Reach TSS Concentration by Policy Option: Top 10% of RF1 

Watersheds Receiving Direct Construction Loadings by Increase in Developed Land Area 

(1992-2001) ...................................................................................................................................6-33

Table 6-20: Estimated Changes in RF1 Reach TSS Concentration by Policy Option: Top 25% of RF1 

Watersheds by Increase in Developed Land Area (1992-2001)....................................................6-34

Table 6-21: Distribution of TSS Concentration based on SPARROW Output for 31,927 RF1 Reaches 

Receiving Construction Sediment Discharges ..............................................................................6-35

Table 6-22: Summary of Baseline TSS Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-36

Table 6-23: Summary of Option 1 TSS Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-36

Table 6-24: Summary of Option 2 TSS Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-37

Table 6-25: Summary of Option 3 TSS Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-37

Table 6-26: Summary of Option 4 TSS Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-38

Table 6-27: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS 

Concentrations...............................................................................................................................6-40

Table 6-28: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS 

Concentrations from Option 1, by EPA Region ............................................................................6-40

Table 6-29: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS 

Concentrations from Option 2, by EPA Region ............................................................................6-41

Table 6-30: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS 

Concentrations from Option 3, by EPA Region ............................................................................6-42
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xiii 

Table 6-31: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS 

Concentrations from Option 4, by EPA Region ............................................................................6-43

Table 6-32: Sediment Accumulation in Reservoirs by Policy Option and EPA Region .........................6-44

Table 6-33: Distribution of TN Concentration Based on SPARROW Output for 31,927 RF1 Reaches 

Receiving Construction Sediment Discharges ..............................................................................6-45

Table 6-34: Distribution of TP Concentration Based on SPARROW Output for 31,927 RF1 Reaches 

Receiving Construction Sediment Discharges ..............................................................................6-45

Table 6-35: Summary of Baseline TN Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-46

Table 6-36: Summary Option 1 TN Concentration in RF1 Reaches Receiving Construction Sediment 

Discharges, by EPA Region ..........................................................................................................6-46

Table 6-37: Summary of Option 2 TN Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-47

Table 6-38: Summary of Option 3 TN Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-47

Table 6-39: Summary of Option 4 TN Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-48

Table 6-40: Summary of Baseline TP Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-48

Table 6-41: Summary of Option 1 TP Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-49

Table 6-42: Summary of Option 2 TP Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-49

Table 6-43: Summary of Option 3 TP Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-50

Table 6-44: Summary of Option 4 TP Concentration in RF1 Reaches Receiving Construction 

Sediment Discharges, by EPA Region ..........................................................................................6-50

Table 7-1: Dredging in U.S. Navigable Waterways, 1995–2008 ..............................................................7-2

Table 7-2: Dredging of Navigable Waterways Performed or Contracted by USACE, 1995–2008...........7-3

Table 7-3: Dredging Jobs and Recurrence Intervals, 1995- 2008..............................................................7-4

Table 7-4: Costs of Recurring Dredging, 1995-2006.................................................................................7-5

Table 7-5: Annualized Dredging Costs Under the Baseline Scenario (millions of 2008$) .......................7-9

Table 7-6: Reductions in Dredging and Annualized Avoided Costs Under Option 1 .............................7-11

Table 7-7: Reductions in Dredging and Annualized Avoided Costs Under Option 2 .............................7-11

Table 7-8: Reductions in Dredging and Annualized Avoided Costs Under Option 3 .............................7-12

Table 7-9: Reductions in Dredging and Annualized Avoided Costs Under Option 4 .............................7-12
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xiv 

Table 8-1: Average Annual Sedimentation Deposition Recorded by RESIS ............................................8-3

Table 8-2: Estimated Cost of Reservoir Dredging Under the Baseline Scenario ......................................8-6

Table 8-3: Reduction in Reservoir Dredging and Avoided Costs Under Option 1....................................8-7

Table 8-4: Reduction in Reservoir Dredging and Avoided Costs Under Option 2....................................8-7

Table 8-5: Reduction in Reservoir Dredging and Avoided Costs Under Option 3....................................8-8

Table 8-6: Reduction in Reservoir Dredging and Avoided Costs Under Option 4....................................8-8

Table 9-1: TN Concentrations Predicted by SPARROW for Reaches Serving as Drinking Water 

Sources1 ...........................................................................................................................................9-2

Table 9-2: Public Water Intakes and Estimated Average Daily Flow by EPA Region .............................9-8

Table 9-3: Drinking Water Turbidity Treatment Costs Under the Baseline Scenario .............................9-13

Table 9-4: Reduction in Drinking Water Treatment Costs Under Option 1 ............................................9-15

Table 9-5: Reduction in Drinking Water Treatment Costs Under Option 2 ............................................9-15

Table 9-6: Reduction in Drinking Water Treatment Costs Under Option 3 ............................................9-16

Table 9-7: Reduction in Drinking Water Treatment Costs Under Option 4 ............................................9-16

Table 10-1: Freshwater Water Quality Subindices ..................................................................................10-4

Table 10-2: Original and Revised Weights for Freshwater WQI Parameters..........................................10-4

Table 10-3: Estuarine Water Quality Subindices.....................................................................................10-5

Table 10-4: Weights for Estuarine WQI Parameters ...............................................................................10-6

Table 10-5: Water Quality Classifications...............................................................................................10-6

Table 10-6: Percentage of Reach Miles in Coterminous 48 States by WQI Classification for EPA 

Regions: Baseline Scenario ...........................................................................................................10-9

Table 10-7: Estimated Water Quality Improvements Under Option 11.................................................10-12

Table 10-8: Estimated Water Quality Improvements Under Option 21.................................................10-13

Table 10-9: Estimated Water Quality Improvements Under Option 31.................................................10-14

Table 10-10: Estimated Water Quality Improvements Under Option 41...............................................10-15

Table 10-11: Independent Variable Assignments..................................................................................10-18

Table 10-12: Estimates of Annual Household Willingness to Pay for Water Quality Improvement by 

Region and Policy Option (2008$)..............................................................................................10-21

Table 10-13 :Regional Willingness to Pay for Water Quality Improvement (Millions 2008$).............10-24

Table 11-1: Annual Total National Benefits by Benefits Category (millions of 2008$).........................11-3

Table 11-2: Annual Total National Benefits Under Option 1 (millions of 2008$)..................................11-4

Table 11-3: Annual Total National Benefits Under Option 2 (millions of 2008$)..................................11-4

Table 11-4: Annual Total National Benefits Under Option 3 (millions of 2008$)..................................11-5
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xv 

Table 11-5: Annual Total National Benefits Under Option 4 (millions of 2008$)..................................11-5

Table A-1: Year of Available Waterbody Assessment Data, as of September 17, 2009..........................A-1

Table B-1: List of Federal Threatened and Endangered Aquatic Species Potentially Impacted by 

Sediment.........................................................................................................................................B-1

Table E-1: TSS Concentrations (mg/L) Estimated by DCP, Southeastern Estuaries ............................... E-1

Table E-2: TSS Concentrations (mg/L) Estimated by DCP, Gulf of Mexico Estuaries ........................... E-1

Table E-3: TSS Concentrations (mg/L) Estimated by DCP, Northeastern Estuaries ............................... E-2

Table E-4: TSS Concentrations (mg/L) Estimated by DCP, West Coast Estuaries.................................. E-3

Table E-5: Total Number Reaches in the Analysis located in Southeastern Estuaries that Use DCP to 

Calculate TSS by Estuary............................................................................................................... E-3

Table E-6: Total Number Reaches in the Analysis located in Gulf of Mexico Estuaries that Use DCP 

to Calculate TSS by Estuary........................................................................................................... E-4

Table E-7: Total Number Reaches in the Analysis located in Northeastern Estuaries that Use DCP to 

Calculate TSS by Estuary............................................................................................................... E-4

Table E-8: Total Number Reaches in the Analysis located in West Coast Estuaries that Use DCP to 

Calculate TSS by Estuary............................................................................................................... E-4

Table F-1: TSS Subindex Curve Parameters, by Ecoregion..................................................................... F-1

Table G-1: Selected Summary Information for Studies............................................................................G-5

Table G-2: Variables and Descriptive Statistics for the Total WTP Regression Model.........................G-11

Table G-3: Estimated Multilevel Model Results for the Trans-log and Semi-log Total WTP 

Regression Models: WTP for Aquatic Habitat Improvements.....................................................G-14

Table G-4: Comparison of WTP for Different Changes in WQI Based on Semi-log and 

Trans-log Models..........................................................................................................................G-19

Table H-1: Estimated Water Quality Improvements Under Option 1 ......................................................H-2

Table H-2: Estimated Water Quality Improvements Under Option 2 ......................................................H-5

Table H-3: Estimated Water Quality Improvements Under Option 3 ......................................................H-8

Table H-4: Estimated Water Quality Improvements Under Option 4 ....................................................H-11

Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xvii 

List of Figures 

Figure 2-1: EPA Regions.........................................................................................................................2-37

Figure 2-2: Extent of Stressors and Their Relative Risk to the Biological Condition of the Nation's 

Streams ..........................................................................................................................................2-38

Figure 2-3: TSS Concentrations by Total RF1 Reach Miles as Predicted by SPARROW for Current 

Conditions .....................................................................................................................................2-39

Figure 3-1: Extent of Stressors and Their Relative Risk to the Biological Condition of the Nation’s 

Streams ..........................................................................................................................................3-12

Figure 5-1: Calculation of Monetized Benefits from the Regulation.......................................................5-12

Figure 6-1: Schematic of the Major SPARROW Model Components ......................................................6-3

Figure 6-2: Schematic Illustrating a Vector Stream Reach Network with Node Topology and 

Water/Contaminant Reach-Node Routing Table.............................................................................6-4

Figure 6-3: Location of 1,828 Water Quality Monitoring Stations Used in the SPARROW Sediment 

Model, in Relation to the Reach File 1 (RF1) Reach Network .......................................................6-8

Figure 6-4: Plot of Construction Acres by RF1 Watershed Percentile Group (1992-2001) ....................6-18

Figure 6-5: Cumulative Plot of Construction Acres by RF1 Watershed (1992-2001).............................6-19

Figure 6-6: EPA Region 1: Percent Urban Change 1992–2001 by RF1 Watershed................................6-20

Figure 6-7: EPA Region 2: Percent Urban Change 1992–2001 by RF1 Watershed................................6-20

Figure 6-8: EPA Region 3: Percent Urban Change 1992–2001 by RF1 Watershed................................6-21

Figure 6-9: EPA Region 4: Percent Urban Change 1992–2001 by RF1 Watershed................................6-21

Figure 6-10: EPA Region 5: Percent Urban Change 1992–2001 by RF1 Watershed..............................6-22

Figure 6-11: EPA Region 6: Percent Urban Change 1992–2001 by RF1 Watershed..............................6-22

Figure 6-12: EPA Region 7: Percent Urban Change 1992–2001 by RF1 Watershed..............................6-23

Figure 6-13: EPA Region 8: Percent Urban Change 1992–2001 by RF1 Watershed..............................6-23

Figure 6-14: EPA Region 9: Percent Urban Change 1992–2001 by RF1 Watershed..............................6-24

Figure 6-15: EPA Region 10: Percent Urban Change 1992–2001 by RF1 Watershed............................6-24

Figure 9-1: Steps in Calculating the Cost of Treating Turbidity in Drinking Water .................................9-5

Figure 12-1: A Simple Continuum of Model Types Based on the Level of Statistical and Mechanistic 

Descriptions of Contaminant Sources and Biogeochemical Processes ..........................................C-7

Figure 12-2: Schematic of the Major SPARROW Model Components ...................................................C-9

Figure 12-3: Location of 1,828 Water Quality Monitoring Stations Used in the SPARROW Sediment 

Model, in Relation to the Reach File 1 (RF1) Reach Network ....................................................C-10
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xviii 

Figure 12-4: Schematic Illustrating a Vector Stream Reach Network with Node Topology and 

Water/Contaminant Reach-Node Routing Table..........................................................................C-13

Figure 12-5: Schematic Illustrating Digital Overlay of Stream Reach Drainage Area and Polygonal 

Areas Associated with Diffuse Sources........................................................................................C-15

Figure 12-6: Schematic Illustrating Digital Overlay of Stream Reach Drainage Area and Polygonal 

Areas Associated with Landscape Properties...............................................................................C-16

Figure 12-7: Conceptual Illustration of a Reach Network for Five Incremental Watersheds. Model 

Equation C-4 Describes the Supply and Transport of Load within an Individual Reach and its 

Incremental Watershed.................................................................................................................C-17
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xix 

 Acronyms and Abbreviations 

AGNPS – Agricultural Nonpoint Source Model 

ASCE – American Society of Civil Engineers 

ATS – Active Treatment Systems 

ATTAINS – Assessment TMDL Tracking and Implementation System 

AWWA – American Water Works Association 

BMP – Best Management Practices 

BOD – Biological Oxygen Demand 

BUVD – Benefits Use Valuation Database 

C&D – Construction and Development 

cfs – cubic feet per second 

ChA – Chlorophyll-a 

cm - centimeter 

CPI – Consumer Price Index 

CS – Compensating Surplus 

CWA – Clean Water Act 

DAF – Dissolved Air Flotation 

DCPs – Dissolved Concentration Potentials 

DEMs – Digital Elevation Models 

DO – Dissolved Oxygen 

EDA – Estuarine Drainage Area 

EMAP-NCA – Environmental Monitoring & Assessment Program, National Coastal Assessment 

Monitoring Data 

EPA – Environmental Protection Agency 

EPT – Ephemeroptera, Plectoptera, and Trichoptera 

ERF – Enhanced Reach File 

EVRI – Environmental Valuation Resource Inventory 

FC – Fecal Coliform 

ft/s – feet per second 

FTUs – Formazin Turbidity Units 

g - Gram 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xx 

GIS – Geographic Information System 

HAB – Harmful Algal Bloom 

HARC – The Houston Area Research Center 

HSPF – Hydrologic Simulation Program-Fortran 

HUC – Hydrologic Unit Code 

JTU – Jackson Turbidity Units 

kg/ha – Kilograms per hectare 

LC – Lethal Concentration 

LID – Low Impact Development 

m - Meter 

MCL – Maximum Contamination Limit 

mgd – Millions of gallons per day 

μg/L – Micrograms per liter 

mg/L – Milligrams per liter 

MIB – 2-methylisoborneol 

mL/L – Milliliters per liter 

mm – Millimeter 

MRLC – Multi-Resolution Land Characteristics Consortium 

NAWQA – National Water-Quality Assessment 

NBER – National Bureau of Economic Research 

NCEE – National Center for Environmental Economics 

NEIWPCC – New England Interstate Water Pollution Control Commission 

NERRS – The National Estuarine Research Reserve System 

NHD – National Hydrogeography Dataset 

NID – National Inventory of Dams 

NLCD – National Land Cover Database 

NMFS – National Marine Fisheries Service 

NOAA – National Oceanic and Atmospheric Administration 

NPDES – National Pollutant Discharge Elimination System 

NRC – National Research Council 

NRCS – Natural Resource Conservation Service 

NRI – National Resources Inventory 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xxi 

NSQAN – National Stream Quality Accounting Network 

NTUs – Nephelometric Turbidity Units 

NWIS – National Water Information System 

NWLS – Nonlinear Weighted Least Squares 

PAC – Powdered Activated Carbon 

PAHs – Polycyclic Aromatic Hydrocarbons 

POTWs – Publicly Owned Treatment Works 

PPI – Producer Price Index 

PTS – Passive Treatment Systems 

RESIS – Reservoir Sedimentation Survey Information System 

RF1 – Reach File Version 1.0 

RFF – Resources for the Future 

RMSE – Root Mean Squared Error 

RR – River Reach 

RUSLE – Revised Universal Soil Loss Equation 

RUSLE k-factor – Soil Erodibility 

RUSLE r-factor – Precipitation 

SABS - Suspended and Bedded Sediments 

SAS – Statistical Analysis System 

SAS IML – Statistical Analysis System Interactive Matrix Language 

SAV – Submerged Aquatic Vegetation 

SCDHEC – South Carolina Department of Health and Environmental Control 

SCDNR – South Carolina Department of Natural Resources 

SDWIS – Safe Drinking Water Information System 

SPARROW – Spatially Referenced Regressions on Watershed Attributes 

SSC – Suspended Sediment Concentration 

STATSGO - State Soil Geographic database 

STORET – Storage and Retrieval data warehouse 

SWAT – Soil Water Assessment Tool 

T&E – Threatened and Endangered 

TDS – Total Dissolved Solids 

TESS – Threatened and Endangered Species System database 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 xxii 

TMDL – Total Maximum Daily Load 

TM/ETM – Thematic Mapper/Embedded Trace Macrocells 

TN – Total Nitrogen 

TP – Total Phosphorus 

TSS – Total Suspended Solids 

U.S. OMB – United States Office of Management and Budget 

USACE – United States Army Corps of Engineers 

USDA – United Stated Department of Agriculture 

EPA – United States Environmental Protection Agency 

USFWS – United States Fish and Wildlife Service 

USGS – United States Geological Survey 

USLE – Universal Soil Loss Equation 

WATERS – Watershed Assessment Tracking & Environmental Results 

WHO – World Health Organization 

WQI – Water Quality Index 

WQL – Water Quality Ladder 

WSUT – Weak Structural Utility Theoretic 

WTP – Willingness To Pay 

WWF – World Wildlife Fund 

WY – Water Year 

yd3

 – cubic yard
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 1-1 

1 Introduction 

This document presents information on environmental impacts associated with construction site 

discharges to surface waters and benefits associated with their reduction. Additional information on the 

construction industry and construction site discharges to surface waters is provided in the U.S. 

Environmental Protection Agency’s (EPA’s) Development Document for Final Effluent Guidelines and 

Standards for the Construction and Development Category (USEPA 2009b) and Economic Analysis for 

Final Effluent Guidelines and Standards for the Construction and Development Category (USEPA 

2009c). 

Construction takes place on approximately 853,000 acres in the coterminous United States and discharge 

more than 5 billion pounds of sediment each year (USEPA 2009b). All major surface water types receive 

construction discharges, including streams, rivers, wetlands, lakes, estuaries, and other coastal waters. In 

any given year, some level of construction activity occurs in the majority of U.S. watersheds. However, 

most construction acreage is concentrated in a relatively limited number of watersheds. Between 1992 

and 2001, more than half of all construction activity took place in less than 5 percent of U.S. watersheds.1

A common development pattern is for new construction to concentrate in rural and suburban watersheds 

adjacent to more densely developed urban areas. Because construction is a temporary activity, the 

locations of the most highly impacted watersheds in the United States shift over time. 

Construction activities significantly change the surface of the land. Typical activities include clearing 

vegetation and excavating, moving, and compacting earth and rock. Consequences from these activities 

include reduced stormwater infiltration, increased runoff volume and intensity, and higher soil erosion 

rates. 

Construction sites have been documented to increase pollutant discharges to surface waters. The most 

thoroughly documented pollutants are sediment and turbidity. Nitrogen and phosphorus are common soil 

constituents that can also pollute receiving waters. Other pollutants can derive from a wide variety of 

construction equipment and materials or from historic contamination of construction sites. These 

pollutants include metals, trash and debris, nutrients, organic matter, pesticides, petroleum hydrocarbons, 

polycyclic aromatic hydrocarbons (PAHs), and other toxic organics. Construction activity can also impact 

receiving waters by increasing the volume and intensity of stormwater runoff from a site. These flows can 

erode receiving water banks and beds, particularly when pipes, ditches, or other stormwater conveyances 

concentrate discharges. Surface water erosion can alter waterbody morphology and elevate sediment and 

turbidity levels downstream. 

Most pollutants enter surface waters when precipitation erodes soil and carries particulate matter to 

receiving waters. A number of pollutants bind to soil particles and travel with them as they erode. Other 

pollutants dissolve in precipitation and are carried to surface waters in solution. Construction site 

pollutants can also enter surface waters during dry weather due to activities such as excavation 

dewatering, construction equipment washout, wind erosion, and equipment operation in or near surface 

waters. 

 

1

 Watersheds are those associated with the more than 62,000 surface water reaches in the coterminous United States in the 

Reach File Version 1 (RF1) surface water network (USEPA 2007f). See Chapter 4 for additional information. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 1-2 

Increases in pollutant and stormwater discharges from construction sites and subsequent increases in 

waterbody pollutant levels can have adverse impacts on aquatic ecosystem function and human aquatic 

resource use. Construction is a temporary activity in any given location, but impacts can range from 

temporary to long-term. Impacts include both physical and chemical impacts on waterbodies and 

biological impacts on aquatic organisms and communities. Impacts to human aquatic resource uses can 

include impaired drinking water supplies, recreation, navigation, fishing, water storage, aesthetics, 

property value, irrigation, industrial water supplies, and stormwater (including flood) management. 

Though some surface waters can eventually recover from construction discharge impacts, they may 

continue to be degraded by excess stormwater and pollutants from buildings, roads, and other structures 

put in place by construction activity. This document, however, focuses solely on the impacts associated 

with active construction sites. Chapters 2 and 3 provide additional information on the pollutants and 

environmental impacts associated with construction site discharge to surface waters, with Chapter 2 

focusing on sediment and turbidity and Chapter 3 presenting all other pollutants associated with 

construction site stormwater discharges. Chapter 4 presents a review of literature documenting impacts of 

construction site stormwater discharges. 

EPA has established effluent limitations guidelines (ELGs) and new source performance standards 

(NSPS) for stormwater discharges from the construction and development industry. These guidelines and 

standards require discharges from certain construction sites to meet a numeric turbidity limit. The 

guidelines and standards also require all construction sites currently required to obtain a National 

Pollutant Discharge Elimination System (NPDES) permit to implement a variety of best management 

practices (BMPs) designed to limit erosion and control sediment discharges from construction sites. EPA 

evaluated four options in developing the final rule. These options are described below: 

3⁄4 Option 1 establishes minimum requirements for implementing a variety of erosion and 

sediment controls and pollution prevention measures on all construction sites that are 

required to obtain a permit. 

3⁄4 Option 2 contains the same requirements as Option 1. In addition, construction sites of 30 or 

more disturbed acres would be required to meet a numeric turbidity limit in stormwater 

discharges from the site. The technology basis for the numeric limit is Active Treatment 

Systems (ATS). The numeric turbidity standard would be applicable to stormwater discharges 

for all storm events up to the local 2-year, 24-hour event. 

3⁄4 Option 3 contains the same requirements as Option 1. Option 3 also requires all sites with 10 

or more acres of disturbed land to meet a numeric turbidity standard based on the application 

of ATS. The turbidity standard would apply to all stormwater discharges for all storm events 

up to the local 2-year, 24-hour event. 

3⁄4 Option 4 contains the same requirements as Option 1. Option 4 also requires all sites with 10 

or more acres of disturbed land to meet a numeric turbidity standard of 280 NTU based on the 

application of Passive Treatment Systems (PTS). The turbidity standard would apply to all 

stormwater discharges for all storm events up to the local 2-year, 24-hour event, although 

only certain types of discharges would require monitoring. 

EPA expects that reduced discharges of pollutants from construction sites will enhance environmental 

services provided by the affected water bodies and, as a result, human welfare. Chapter 5 provides an 

overview of EPA’s approach to assessing and quantifying the benefits of reducing construction site 

discharges to surface waters. Chapter 6 describes the methodology EPA used to quantify improvements 

in surface water quality associated with each of the evaluated regulatory options. EPA used the Spatially 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 1-3 

Referenced Regressions on Watershed Attributes (SPARROW) method, the Dissolved Concentration 

Potential (DCP) method, and information on relationships between sediment and nutrient levels in surface 

waters to quantify changes in surface water levels of total suspended solids (TSS), sedimentation, total 

phosphorus (TP), and total nitrogen (TN) under each regulatory option. Chapter 6 also presents the results 

of this analysis. EPA found available data on the location and magnitude of other types of construction 

site pollutant discharges to be insufficient for inclusion in the water quality modeling analysis. 

In analyzing benefits of the regulation, EPA quantified and monetized economic benefits from reduced 

dredging of navigable waterways, reduced dredging of water storage facilities (reservoirs), and reduced 

drinking water treatment costs. Chapters 7, 8, and 9 describe the methods EPA used to analyze these 

benefit categories and present the results of EPA’s analysis. Other benefit categories, including reduced 

flood risk, increases in property values, industrial water use, agricultural water use, stormwater 

management system management, and commercial fishing, are discussed qualitatively in Chapters 2 and 

5. EPA estimates that Options 1, 2, 3, and 4 will reduce expenditures on navigable waterway dredging by 

approximately $1.3, $2.6, $3.3, and $2.9 million per year respectively. Expenditures on reservoir 

dredging are expected to decrease by $1.4 million per year under Option 1, $2.9 million per year under 

Option 2, $3.6 million per year under Option 3, and $3.2 million per year under Option 4. Reductions in 

drinking water treatment costs are estimated to amount to $1.2, $1.8 million, $2.1 million, and $1.8 

million annually under Options 1, 2, 3 and 4, respectively. Overall, EPA expects this regulation to save 

governments and private entities between $3.8 and $8.9 million in these three areas each year, depending 

on the policy option. 

EPA also expects that reductions in pollutant discharges to surface waters resulting due to regulation will 

enhance or protect aquatic ecosystems. The drop in pollutant discharges is expected to improve the 

protection of resident species; enhance the general health of fish, invertebrate, plant and other aquatic 

organism populations; increase their propagation in waters currently impaired; and expand fisheries for 

both commercial and recreational purposes. Improvements in water quality such as decreased turbidity 

will also favor increased recreational activities such as swimming, boating, nature observation, fishing, 

camping and other outings, as well as overall aesthetic enjoyment. Improvements associated with reduced 

nitrogen and phosphorus discharges include reduced eutrophication of surface waters, fewer beach 

closings, greater fisheries productivity, and greater enjoyment of water resources. Finally, EPA expects 

that the regulation will augment nonuse values (values that do not depend on use by humans, see Chapter 

5 for details) of the affected water resources. EPA used a meta-analysis of surface water valuation studies 

to estimate the total value of nonmarket benefits (values that arise outside of market transactions, such as 

recreation at publicly accessible sites) stemming from the regulation. EPA estimates that mean total 

willingness to pay (WTP) for water quality improvements resulting from regulation ranges from $210 

million to $413 million, depending on the regulatory option under consideration. Chapter 10 of this report 

provides details on EPA’s analysis of nonmarket benefits. EPA expects that nonmarket benefits resulting 

from Options 1, 2, 3, and 4 will be approximately $210, $353, $413, and $361 million per year, 

respectively. Combining these nonmarket benefits estimates with the avoided cost estimate produces total 

benefits estimates of $214 million per year for Option 1, $360 million per year for Option 2, $422 million 

per year for Option 3, and $369 million per year for Option 4. 

Sufficient data were available to monetize benefits for only a subset of: 

3⁄4 pollutants discharging from construction sites 

3⁄4 impacted surface waters 

3⁄4 ecological services from surface waters. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 1-4 

The monetized benefits estimates therefore represent only a portion of the total benefits of each of the 

regulatory options. The scope of the monetized benefits analysis is discussed in more detail in Chapter 

11.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-1 

2 Overview of Environmental Impacts from Construction Site 

Sediment and Turbidity Discharges 

This chapter summarizes information available from the literature and other sources on the pollutants and 

environmental impacts associated with construction site discharges of sediment. 

Construction sites have been documented to increase pollutant discharges to surface waters in a number 

of studies. Sediment and turbidity are the most thoroughly documented pollutants. This chapter discusses 

the process of sediment and turbidity discharge from construction sites, their behavior in surface waters, 

and their potential impacts on aquatic organisms and human use of aquatic resources. Documentation of 

these discharges and impacts can be found in the literature reviewed and summarized in Chapter 4. A 

number of other pollutants can also discharge from construction sites including nitrogen, phosphorus, 

metals, toxic organic compounds, and others. These are discussed in Chapter 3. 

Suspended and bedded sediments (collectively referred to as “SABS” or “sediment” in this document) 

and turbidity are natural components of many aquatic ecosystems. Sediments contribute to the physical 

structure of surface waters and help to transport nutrients and organic matter. Natural turbidity can 

modulate levels of aquatic photosynthetic activity and predator–prey relationships. Undisturbed 

ecosystems contain species adapted to the sediment and turbidity levels naturally associated with those 

ecosystems. 

At excessive levels, however, sediment and turbidity become pollutants. Modifications to the physical and 

chemical composition of sediment and turbidity discharges can also transform them into pollutants. 

Sediment and turbidity are the most commonly documented pollutants in construction site discharges and 

impacted surface waters. In a number of documented cases, sites have discharged sediment and turbidity 

at very high levels (see Chapter 4). 

Although suspended sediment, bedded sediment, and turbidity are distinct and separate water quality 

properties, all describe impacts associated with eroded soil discharge to surface waters. For this reason, 

many studies discuss these properties concurrently, as does this document. Soil and sediment are 

composed of a variety of components including organic matter, phosphorus, nitrogen, metals, and other 

compounds, both natural and anthropogenic. Many of these components travel with soil as it erodes and 

discharges to surface waters. These components are discussed in more detail in Section 2.2.

There are multiple terms available to describe levels of sediment and turbidity in water. Table 2-1

presents terms and definitions used in this document. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-2 

Table 2-1: Sediment and Turbidity Terminology 

Sediment Metric Description 

Bedded sediment A general term for sediment that, at any given time, settles from the water column onto surfaces 

within a surface water (e.g., channel bed or aquatic plant leaves) in a process commonly known as 

sedimentation or siltation. Measures of bedded sediments include depth of deposition within a given 

time period, percent fines, geometric mean diameter, Fredle number (a permeability index) (Berry et 

al. 2003), and others. 

Settleable solids A measure of the solids that will settle to the bottom of a cone-shaped container (called an Imhoff 

cone) in a 60-minute period. Settleable solids are primarily a measure of particles that can be 

removed from water by sedimentation. Expressed as milliliters per liter (ml/L). 

Suspended sediment A general term for sediment that, at any given time, is either maintained in suspension by a surface 

water’s turbulent currents or that exists in suspension as a colloid. 

Suspended sediment 

concentration (SSC) 

The velocity-weighted concentration of suspended sediment in the sampled zone in a surface water 

defined as extending from the water’s surface to a point approximately 0.3 feet above the bed. It is 

determined by measuring the dry weight of all sediment from a known volume of sample. Expressed 

as milligrams of dry sediment per liter of water-sediment mixture (mg/L). 

Total suspended 

solids (TSS) 

A dry weight measure of suspended inorganic and organic material in the water column. It is 

measured by filtering a subsample of water and measuring the weight of the dried solids. Expressed 

in milligrams of solids per liter of water-solids mixture (mg/L). 

Turbidity A measure of the scattering and absorption of light when it enters a water sample. The quantity of 

suspended particles in water helps to determine turbidity levels as do particle shape, size, and color 

distributions. Suspended particles can include clay, silt, colloids, finely divided organic and 

inorganic matter, soluble colored organic compounds, plankton, and other microscopic organisms. In 

this document, turbidity levels are typically expressed in nephelometric turbidity units (NTUs). 

Higher NTU levels indicate more turbid water. 

Surface water turbidity levels are controlled by the quantity and nature of particulate matter suspended in 

the water column. This particulate matter can consist of mineral particles (sediment), algae and other 

organisms, and organic detritus. Particle shape, size, and color distributions influence total turbidity levels 

as well. Suspended sediment contributes to surface water turbidity. However, because several factors 

beyond the mass of suspended solids in the water column control surface water turbidity, the quantitative 

relationship between suspended particle concentrations and turbidity levels has been found to vary among 

watersheds, surface waters, and precipitation events. 

The sections below provide additional information on the nature of construction site sediment and 

turbidity discharges to surface waters (Section 2.1), their behavior and transport in surface waters (Section 

2.2), their impacts on aquatic ecosystems (Section 2.3), their impacts on human use of aquatic resources 

(Section 2.4), appropriate levels in surface waters as delineated by water quality criteria (Section 2.5), and 

the extent to which they currently impair surface waters in the United States (Section 2.6). 

2.1 Sediment and Turbidity Discharge to Surface Waters 

Construction activities increase soil vulnerability to erosion. Typical construction site activities include 

clearing vegetation and excavating, moving, and compacting earth and rock. Vegetation removal and 

surface work loosens soil, removes protective root structures, and exposes soil directly to the erosive 

powers of precipitation and stormwater runoff. Soil compaction reduces precipitation infiltration and 

increases overland water flow, thereby increasing the quantity of stormwater runoff available to erode 

soil. In addition, stockpiled construction materials such as stripped topsoil, fill material, and soil from 

foundation excavation are often placed in steep, uncovered piles vulnerable to erosion. Construction 

vehicles track soil onto roadways from which it can easily wash into storm sewer drainage systems and 

subsequently to surface waters. Susceptibility to erosion remains high at construction sites until soildisturbing activities are complete and the land surface is revegetated or otherwise stabilized. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-3 

Precipitation events are the primary cause of construction site sediment and turbidity discharges to surface 

waters. Raindrop impact energy and overland water flow detach soil particles from the land surface, 

suspend them in surface flow, and transport them to other locations on the construction site or to 

discharge points. Suspended soil particles in the stormwater flow create turbidity. 

Sediment erosion rates are highly variable among sites and depend on a number of factors including site 

topography (slope length, steepness, and shape), precipitation intensity and quantity (i.e., rainfall 

erosivity), soil type (particle size, erodibility, land use (vegetation cover, erosion control practices), and 

nature of the construction activity. Some phases of construction activity disturb soil more than others. For 

a more detailed discussion of sediment and turbidity discharges from construction sites, see EPA’s 

Development Document for Final Effluent Guidelines and Standards for the Construction and 

Development Category (USEPA 2009b). 

Mobilization of soil particles is dependent on many factors including soil particle size, soil cohesiveness, 

and rainfall energy and duration. As flows grow larger and more powerful, they are able to transport 

larger particles. Sand-sized and larger particles are more easily detached from the soil surface because 

they are generally not cohesive. However, once mobilized, they more easily settle from stormwater runoff 

because of their greater mass. Smaller particles, such as clays, are generally harder to mobilize because 

they are more likely to be cohesive. Once mobilized, however, individual smaller particles are more likely 

to stay suspended in stormwater flow and be transported off-site because of their lesser mass (Reed 1980). 

The soil fraction composed of smaller particles tends to contain a disproportionate quantity of the organic 

matter and adsorbed materials and contaminants (e.g., metals, nutrients, pesticides, and other organic 

compounds) found in soils. Smaller particles also contribute disproportionately to turbidity levels. 

Suspended sediment and turbidity levels in construction site stormwater flows can be very high. 

Suspended sediment concentrations up to 160,000 mg/L in construction site stormwater have been 

documented (Daniel et al. 1979; Horner et al. 1990; Warner and Collins-Camargo 2001; Hedrick et al. 

2006; USEPA 2009b). Turbidity levels up to 30,000 NTU have been documented (Lubliner and Golding 

2005; Bhardwaj and McLaughlin 2008). These figures reflect suspended sediment and turbidity levels 

prior to treatment on-site and/or prior to discharge to and dilution in surface waters. 

In addition to elevated stormwater sediment and turbidity concentrations, stormwater runoff volume can 

also increase on construction sites (Yorke and Davis 1972; Selbig et al. 2004; Clausen 2007; Line and 

White 2007; Selbig and Bannerman 2008; Montgomery County DEP 2009) (see Chapter 4). 

The path stormwater travels varies among construction sites. Stormwater can infiltrate soil, flow over 

land, or travel through underground storm sewer systems. Vegetated areas can provide opportunities for 

stormwater and pollutant capture on the land surface whereas paved surfaces tend to efficiently transport 

stormwater downslope. Storm sewer systems can also very efficiently move water away from 

construction sites (and into surface waters). Storm sewer systems are often installed early in site 

development when large areas of earth are still disturbed and highly prone to erosion (NRC 2008). 

Distance to surface water also varies among construction sites. Some sites are far from surface waters, 

whereas others are adjacent to or, in some cases, directly in water (e.g., bridge construction, stream 

channelization). Longer travel paths prior to surface water discharge can provide more opportunities for 

stormwater and pollutant capture (Yorke and Herb 1978). 

The combination of elevated sediment concentrations and elevated runoff volume results in higher total 

export of sediment from a site. Erosion is a natural process; however, sediment yields from construction 

sites can be many times higher than those from agricultural, forested, and mature developed sites. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-4 

Forested watersheds in the United States are estimated to yield 0.02 to 1 tons/acre/year of sediment. 

Cropland in the United States is estimated to yield 0.65 to 15 tons/acre/year (Vice et al. 1969; Yorke and 

Herb 1978; Osterkamp et al. 1998; Faucette et al. 2007). Paul and Meyer (2001) state that construction 

activity generally increases sediment yields 100 to 10,000 times over those from forested areas, with 

larger increases possible in basins with steep topography. Studies summarized in Table 4-2 report 

sediment yields of 0.009 to 219 tons/acre/year from catchments containing construction. All studies of 

construction site sediment yield report that construction activity increases sediment discharges from sites. 

Because of the importance of precipitation and its subsequent flow over land to the transport of soil, most 

sediment and turbidity discharges take place during or shortly after precipitation events. Once a 

precipitation event ceases, discharges from construction sites generally cease within a relatively short 

time period unless site terrain or stormwater management systems, such as stormwater ponds, attenuate 

discharge flows. This dynamic creates an intermittent discharge of pollutants from most construction 

sites. 

In addition to being an episodic discharger of pollutants, construction is a temporary activity at any one 

site that typically lasts several months to several years. Individual construction sites are therefore transient 

sources of pollutant discharges to surface waters. Sediment discharges from the land surface to surface 

waters due to active construction at an individual site are greatly reduced once active construction ceases 

and site soils have been stabilized (e.g., through revegetation or paving). 

Surface water impacts such as elevated turbidity and suspended sediment levels generally cease 

immediately downstream of construction sites soon after site discharges cease. Other impacts, however, 

persist beyond the lifespan of individual precipitation events, individual construction sites, or even the 

presence of construction activity in a watershed. This is due to the persistence of sediments and some 

associated pollutants in surface waters as well as longer-term impacts on aquatic organism communities. 

In addition, most construction activities in the United States are concentrated in a relatively small number 

of watersheds during any single decade (see Chapter 6). Within these watersheds, the location of 

individual construction sites changes from year to year, but the watershed’s total annual construction 

acreage remains elevated for a number of years until the watershed is “built out.” Surface waters draining 

these watersheds therefore experience a persistent, elevated level of impact from construction activity for 

several years, despite the transient nature of individual sites in the watershed. 

Sediment and turbidity discharges can also take place during dry weather at some construction sites. 

Dewatering of site depressions and foundation excavations, slope failure, attenuated drainage from 

stormwater basins, construction equipment operation or other activity directly in or near surface waters 

(e.g., channelization, pipeline crossing, culvert emplacement, bridge construction), vehicle and other 

construction equipment washing, 6ndscape irrigation, and overland flow from groundwater seeps can 

create discharges during dry weather. Erosion of bare ground due to snowmelt in the spring can be high 

(NRC 2008). Construction activity can also destabilize slopes under both dry and wet conditions. If 

situated sufficiently close to a waterbody, slope failure can result in mass loading of sediment and 

associated turbidity. 

Requirements for construction site erosion and sediment control have gradually increased since the 1950s. 

EPA, the U.S. Federal Highway Administration, the U.S. Federal Energy and Regulatory Commission, 

the U.S. Department of Agriculture’s Soil Conservation Service, states, counties, municipalities and other 

entities have issued a variety of regulations and guidance. EPA has previously issued requirements for 

construction sites under the National Pollutant Discharge Elimination System (NPDES) stormwater 

program. Phase I of the stormwater program was promulgated in 1990 and addresses large construction 
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November 2009 2-5 

activities disturbing 5 or more acres. Phase II of the stormwater program was promulgated in 1999 and 

addresses construction sites disturbing 1 to 5 acres. EPA’s Development Document for Final Effluent 

Guidelines and Standards for the Construction and Development Category (USEPA 2009b) provides 

additional information on the history of construction site erosion and sediment control requirements. 

Under current practice, many construction site discharges are moderated to some degree by a variety of 

sediment erosion and stormwater discharge control practices. Practices include the use of sedimentation 

basins, silt fences, vegetative filter strips, grass swales, check dams, erosion control blankets, straw bale 

barriers, gravel bag berms, sand bag barriers, straw mulch, hydraulic mulch, wood mulch, soil binders, 

geotextiles and mats, rock filters, earth dikes and drainage swales, velocity dissipation devices, 

sedimentation traps, and other methods. These practices and others are discussed in detail in EPA’s 

Development Document for Final Effluent Guidelines and Standards for the Construction and 

Development Category (USEPA 2009b).

2.2 Sediment and Turbidity Behavior in Surface Waters 

Sediment and turbidity movement within surface waters is highly variable. Factors influencing sediment 

and turbidity movement and persistence in surface waters include the intensity, quantity, and composition 

of sediment and turbidity discharges and the nature of the receiving waters. Important waterbody 

characteristics include type, size, and flow rate. 

Sediment discharges from construction sites typically contain a large proportion of inorganic material that 

can persist in surface waters for long periods of time. Under appropriate conditions (e.g., anaerobic), 

organic material in construction site discharges can also persist. For these reasons, impacts from 

construction site discharges can last beyond the life span of a single precipitation event, an individual 

construction site, and of the presence of construction activity in a watershed (Yorke and Herb 1978). 

Several researchers have stated that long-term monitoring beyond the cessation of a construction project 

is necessary in some cases to fully document its impacts (Barton 1977; Taylor and Roff 1986; Chen et al. 

2009; Lee et al. 2009). 

While in the surface water network, sediments can cycle many times between suspension in the water 

column, where they contribute to turbidity levels, and deposition on waterbody beds. This process 

depends on surface water currents and other disturbances. Larger sediment particles are more likely to 

settle on surface beds, and smaller particles are more likely to remain suspended in the water column and 

contribute to surface water turbidity. Because of this dynamic, larger particles tend to take longer to move 

through surface water systems than smaller particles. 

Construction sites discharge sediment and turbidity to all major surface water categories: wetlands; 

streams and rivers; lakes, reservoirs, ponds, and other impoundments; and estuaries, bays, and other 

coastal waters. Some waterbodies have flow regimes and physical structures that allow them to purge or 

absorb excess sediment and turbidity inputs within relatively short time frames. Other surface waters 

allow excess sediment and turbidity to persist for long periods of time. The ability of individual surface 

waters to transport sediment varies as precipitation and other factors modify the nature of their flow. 

Surface waters also vary widely in their size relative to construction site discharges. Larger surface waters 

generally have more capacity for dilution of pollutant discharges. Sediment transport in several surface 

water types are discussed in more detail in Section 2.2. 

Sediments and turbidity migrate with surface water flow downstream and therefore affect waters beyond 

the initial receiving water. Migration of construction sediment typically benefits the initial receiving water 
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November 2009 2-6 

but impacts downstream waters. Several studies have documented improvements in stream conditions 

once construction site sediment and turbidity discharges cease and accumulated sediments migrate 

downstream from the initial point of entry. The sediments, however, remain within the surface water 

network and are redistributed to new waterbodies downstream. Because of sediment’s ability to persist 

and migrate downstream, surface waters downstream of a watershed containing a consistent level of 

construction activity from year to year can experience a relatively consistent level of impact from 

construction sediment and turbidity discharges, even though the timing of individual precipitation events 

and the locations of individual construction sites within the watershed change from year to year. 

2.2.1 Sediment Characteristics Affecting Surface Water Transport 

Important sediment characteristics that influence transport and fate include the size, shape, density, fall 

velocity, and concentration of sediment particles (Shen and Julien 1993). Multiple size scales exist. Table 

2-2 presents the broad size categories of one scale. 

Table 2-2: Sediment Grade Scale 

Category Boulders, 

Cobbles Gravel Sand Silt Clay 

Size range (mm) 64 – 4,000 2 – 64 0.062 – 2 0.004 – 0.062 0.00024 – 0.004 

Source: Julien (1995). 

Sediment size is also loosely discussed in terms of “fine sediment” and “coarse sediment.” Fine sediment 

consists primarily of particles smaller than 0.85 mm. Coarse sediment consists primarily of particles from 

0.85 to 9.5 mm (USEPA 2006b). Particles smaller than 1 micron (0.001 millimeter) in diameter are 

sometimes referred to as colloids. 

Sediment samples rarely contain particles of a single size. Particle size distributions describe the 

percentage by weight of sediment materials in each size category for a given sediment sample. 

Particle size helps to determine how quickly a particle will settle out of suspension in the water column 

onto the bed of a waterbody. The fall or settling velocity of sediment particles can be estimated using a 

form of Stokes’ law (Julien 1995; Chapra 1997): 

2

18 s

m

WS d g ⋅ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ − ⎟⋅

⎠

⎞ ⎜

⎝

⎛ = μ

ρρ αω (Eq. 2-1) 

Where: 

ω = settling velocity (cmys

-1) 

α = dimensionless form factor accounting for geometry (α=1 for sphere) 

g = gravitational acceleration (g=981 cmys

-2) 

ρs = density of sediment particle (gycm-3) 

ρw = density of water (gycm-3) 
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November 2009 2-7 

μm = dynamic viscosity (gycm-1ys

-1) 

ds = particle diameter (cm) 

This formula indicates that settling velocity increases as particle size increases, assuming all other factors 

are equal. Stokes-type settling is most directly applicable to the behavior of sediment in detention basins 

and some reservoirs and lakes, in which flow velocities are low and turbulent energy in the water column 

is minimal. In streams and rivers, settling velocity operates in conjunction with turbulent energy affecting 

particle suspension to determine sediment settling dynamics. Since turbulent energy is required to offset 

the force of gravity (settling) in order to keep a particle in suspension, a greater amount of turbulent 

energy is required to suspend larger, heavier particles than for smaller particles. 

The degree to which a particle remains suspended helps to determine the degree to which it will be able to 

move with water flow within a surface water or whether it will be deposited on a surface water bed. It 

also determines whether or not a sediment particle will contribute to surface water turbidity, since only 

suspended particles increase turbidity levels. Particles smaller than 0.063 mm (silt and clay) tend to 

remain suspended in flowing freshwater systems and are the primary contributors to water turbidity 

(USEPA 2006b). However, small sediments can also be transported in the aquatic environment in the 

form of aggregates bound together by living (e.g., bacteria and algae) and nonliving (e.g., organic detritus, 

extra-cellular polymeric substances) material (Bilotta and Brazier 2008). These aggregates have transport 

behaviors closer to particles coarser than the particles composing the aggregate. Coarser particles are 

more likely to settle from the water column, with the coarsest particles settling closest to their discharge 

point to surface water. 

The sections below provide additional information on transport dynamics associated with several surface 

water types. 

2.2.2 Sediment and Turbidity Behavior in Specific Waterbody Types 

2.2.2.1 Sediment and Turbidity in Streams and Rivers 

A distinguishing characteristic of streams and rivers is their strong, primarily unidirectional flow. Their 

transport of sediment is predominantly controlled by stream transport capacity and sediment 

physiochemical characteristics and supply rate. Larger sediments generally experience more episodic 

movement over longer time scales through watersheds. Smaller sediments generally move more 

continuously and within a shorter time scale. This difference is due to the fact that larger sediments rely 

on larger, more powerful flows for transport, which occur episodically and less frequently than flows able 

to move smaller particles. 

Sediments transported by river and stream channels are typically described as bed load and suspended 

load. The boundary separating these categories changes with flow conditions. Sediments not in transport 

at a given time are often referred to as bedded sediments. If flow conditions change, these sediments can 

become bed load or suspended load. Sediment particle size is the primary distinguishing factor among 

these categories, with the largest particles deposited as bedded sediments, larger particles moving as bed 

load, and finer particles moving as suspended load. Total sediment load consists of the sum of bed load 

and suspended load. 

Bed load consists of the movement of particles along the streambed by rolling, sliding, and a hopping 

process known as saltation which involves the brief suspension of sediment particles by turbulent flow. 

Particles moving as bed load often consist of sand and coarser size fractions. Bed load movement is 
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November 2009 2-8 

intermittent in most settings, and bed load particles may not move for long periods between high flow 

events. 

Suspended sediment load is the movement of sediment particles supported by turbulent motion in stream 

or river flow and often consists primarily of clay and silt size particles. Suspended sediment (and the 

turbidity it creates) move more quickly downstream with streamflow than bed load from a given 

discharge point. If the suspended sediment input to the stream or river is a single pulse, rather than 

continuous, the stream or river can often move it downstream within a relatively short time period. 

Several hydraulic and geomorphologic factors determine stream transport capacity including channel 

width, flow depth and cross-sectional geometry, bed slope and roughness, and discharge velocity and 

volume. In general, the more turbulent energy available for suspension and mobilization of sediment, the 

greater the sediment transport capacity per unit of stream width and the larger the size of sediment 

particles that can be moved. Several empirical formulas have been developed to estimate sediment 

transport capacity on the basis of flow- and channel- related variables. One example, developed by 

Simons et al. (1981), takes the form of a power law: 

2 3

1

c c

s ⋅⋅= Vhcq (Eq. 2-2) 

Where: 

qs = sediment transport per unit stream area (ft2

/s) 

h = depth of flow in ft 

V = flow velocity in ft/s 

c1, c2, c3 = empirical coefficients calibrated to reflect channel slope (gradient) and median 

particle size diameter. 

The unit transport capacity is strongly related to flow velocity (V). Higher velocity flows are able to move 

more sediment. Channels with lower stream gradients have, for a given volume of stream flow, lower 

stream water velocities which allow more time for settlement of sediment in that channel. Many, if not 

most, of the hydraulic factors controlling sediment transport capacity, including channel geometry, depth, 

slope, and velocity of flow, vary in time and space within a given river system. 

Many North American rivers and streams possess a strong seasonal discharge cycle with spring discharge 

volumes typically many times larger than those of late summer and autumn flows. Intense or prolonged 

rainfall events can also generate flood pulses of hourly to daily duration, which often have significant 

turbulent energy. In addition, as Equation 2-2 indicates, sediment transport capacity is a nonlinear 

function of flow-related variables, so large flows have significantly greater transport energy. The 

movement of sediments, both as bed load and as suspended load, is thus highly nonuniform in time for 

most river systems. The majority of annual sediment flux, particularly the movement of coarse or highly 

cohesive sediment particles, may occur over a relatively short period of time during a single flood event. 

Between such events, sediments are typically stored within the stream or river channel. Episodic 

movement and deposition in stream channels between periods of movement has been documented for 

sediments in watersheds whose total sediment load has been substantially increased by construction 

activity. Sediment inputs from construction sites can outpace the immediate transport capacity of the 

stream and may not migrate downstream until a major flow event occurs (Lee et al. 2009). 
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Erosion and deposition of sediments within a river course also exhibit spatial patterns strongly related to 

stream morphology. River reaches with smaller cross-sectional flow area, steeper slopes, and higher flow 

velocities discourage the deposition of sediments. These traits tend to be characteristic of smaller streams 

and rivers in upper elevation catchments, often at the headwaters of larger watersheds. These highergradient streams may decrease their construction-associated sedimentation levels more quickly than other 

aquatic ecosystems due to the occurrence of high flows, particularly in spring or after large storms, that 

can resuspend and transport finer sediments downstream. Assuming cessation of additional sediment 

input upstream, these stream dynamics may eventually restore a naturally coarse-grained channel bed 

(Barton 1977; Berry et al. 2003), though some sediment may continue to persist in low-energy areas of 

the stream (e.g., shallow side pools). By contrast, wider channels with lower bed slopes and flow 

velocities act as regions of relative sediment deposition. Channel bottoms may be covered with finer 

sediments, in contrast to the exposed rocks, boulders, and gravels seen in the channel beds of higherenergy streams and rivers. Natural sediment deposition is more characteristic of channels at lower 

elevations in a watershed. 

Stream and river hydraulic and geomorphologic variables provide one set of controls on sediment 

transport capacity. Sediment transport is also be regulated by the rate and quality of sediment supply 

(Julien 1995). Sediment supply can outpace, match, or fall below the ability of a channel to transport it. 

Within a particular reach, sediment fluxes can originate from land surface erosion, streambank erosion, 

upstream reach sediment input, or remobilization of sediments previously deposited within the reach. 

Channels whose sediment supplies outpace their transport capacity will accumulate sediments. The size of 

a channel can decrease as sediments accumulate, increasing the likelihood of flooding and other overbank 

flow events. Channels with sediment supplies falling below transport capacity will work to mobilize 

additional material from channel beds and banks. 

In all streams, sediments are preferentially deposited in regions of low-energy flow, including pools and 

the inside of bends (Chapra 1997). If sufficient quantities of sediment are deposited, the deposition 

features can alter channel morphology and flow patterns, obstruct flow, and exacerbate flood events. 

Increased sediment supply during construction activity has converted some naturally meandering streams 

to braided or straighter, more channelized forms (Paul and Meyer 2001). Fine sediments deposited on 

stream and river beds may also impede water exchange with groundwater sources (both recharge and 

discharge) (USFWS 1998). Sediment deposits can also provide substrate for the growth of plants in 

channels in locations where they would normally not occur. King and Ball (1964) and Taylor and Roff 

(1986) documented this effect downstream of highway construction sites. 

Individual sediment deposits are often not permanent features of streams and rivers, since they can be 

scoured and moved downstream during major flow events. Streams and rivers can also flow outside their 

normal channels during major flow events and deposit sediments on low-lying areas adjacent to the 

channel such as banks, floodplains, and terraces. These sediments may, at a later time, be remobilized 

during an even larger flow event. 

Lee et al. (2009) documented that watersheds undergoing construction activity may take many years or 

even decades to move sediments discharged from construction sites fully through watersheds. Sediment 

yields from larger watersheds may therefore remain elevated for some time after the implementation of 

enhanced sediment and erosion control measures and after the completion of most construction in the 

watershed. 

Within the time scale of a single precipitation event, a certain amount of time is also necessary to move 

suspended sediment and turbidity from its original point of entry into the surface water network to 
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November 2009 2-10 

downstream waters. If construction is occurring primarily in the headwaters of a watershed, turbidity and 

suspended sediment levels immediately downstream of construction sites will often decline within 

relatively short periods of time following the cessation of precipitation events (assuming discharge from 

the site is primarily precipitation-driven and not significantly attenuated). Turbidity and suspended 

sediment levels further downstream, however, can remain elevated even after flow levels return to normal 

because of the time required for their transport downstream. 

An additional dynamic influencing short-term observation of impacts from upstream construction activity 

is that levels of turbidity and sediment deriving from construction activity tend to decrease as they move 

downstream from a construction site through the surface water network. This decrease is due to sediment 

deposition during transport and dilution by waters containing lower levels of sediment and turbidity than 

construction site discharges (Wolman and Schick 1967; Lee et al. 2009). 

2.2.2.2 Sediment and Turbidity in Lakes, Reservoirs, and Ponds 

A number of lakes, ponds, and reservoirs have a unidirectional flow component and therefore have some 

of the basic dynamics described for rivers and streams. However, most lakes, ponds, and reservoirs have 

lower flow velocities, greater depth of flow, and longer water residence times than streams and rivers and 

therefore act as deposition zones (sinks) for sediments. Longer flow residence times also mean that 

influxes of turbid water can linger for longer periods of time than they would in a stream or river. 

Residence times vary widely among lakes, ponds, and reservoirs. 

The lake and reservoir flow environment more closely approximates the still-water conditions under 

which Stokes’ law (Section 2.2.1) applies to sediment particles to predict settling velocity. The 

assumption of Stokes-type settling is often used to predict the rate of sedimentation in lakes and 

reservoirs by comparing the time required for a particle to settle with the particle’s transit time through 

the waterbody. Several surface water characteristics influence settling and transit times, including size, 

shape, depth, and regulation of outflow (Chapra, 1997). These characteristics vary widely among lakes, 

ponds, and reservoirs. 

Particle transit time also varies with flow. Higher flows generally create shorter transit times as higher 

volumes of water move more quickly through a surface water. Higher flows often transport both coarser 

particles, which are more likely to settle in a lake, pond, or reservoir, and large quantities of fine 

sediments, which may not have sufficient time to settle during a higher flow period. Sediments unable to 

settle in lakes, ponds, and reservoirs due to insufficient settling time or turbulence from water flow, wind, 

or human activity can continue their transport downstream. 

Given sufficient residence time for incoming flows, however, these waters can remove significant 

quantities of sediment and turbidity from incoming waters (Barton 1977). The deposition process 

decreases sediment and turbidity loads to streams and rivers and other surface waters downstream 

(Renwick et al. 2005; Lee et al. 2009), though it increases sedimentation in the lake, reservoir, or pond. 

Over time, sediment accumulation reduces a waterbody’s volume and decreases its capacity for sediment 

removal. 

Sediment deposition in lakes and reservoirs often begins with the formation of deltas at the point of water 

inflow, where incoming stream flow decelerates and the heaviest particles settle (Julien 1995). 

Turbulence from wind, currents, and human activity can keep finer sediments in suspension, creating a 

deposition pattern called focusing in which coarser particles are deposited in shallower waters and finer 

particles in deeper waters, often in the centers of lakes (Chapra 1997). Given sufficient sediment 
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deposition, ponds and shallow areas in lakes and reservoirs may be transformed into wetland 

environments. 

2.2.2.3 Sediment and Turbidity in Wetlands 

Wetlands are natural sediment deposition zones. Water flow volumes and velocities are smaller than in 

most other surface water types, allowing time for settling of sediment from the water column. Because of 

lower flow-through volumes and speeds, influxes of turbid and sediment-laden water typically remain for 

a longer period of time in a wetland than they do in a stream or river channel. 

Wetland ecosystem structure is heavily influenced by the type of vegetation a wetland supports, which in 

turn is influenced by water and sediment distribution in the wetland. Elevated sedimentation levels can 

change the type of vegetation able to persist in a given wetland, with consequences for the organisms it no 

longer supports. In severe sedimentation cases, excess sediment may partially or completely fill in a 

wetland, creating dry land conditions. 

2.2.2.4 Sediment and Turbidity in Estuaries 

Estuaries are like lakes and reservoirs in that they vary widely in benthic geometries, residence times, 

flushing rates, vertical mixing, stratification, wave exposure, and other factors that govern sediment 

transport and deposition. Estuarine flows transition first from unidirectional turbulent channel flow 

controlled primarily by topographic gradient and discharge rate to a tidal river reach zone in which 

downstream flow is influenced by the tidal cycle. The flow regime transitions again in the estuary proper, 

in which water discharge and tidal forces offset each other. Flow regime changes again in the bay or open 

ocean where tidal, wave, or a combination of these forces dominate. 

As turbidity and sediment-laden freshwater decelerates and encounters tidal cycles in the estuary, a null 

zone is formed in which channel discharge and tidal action largely cancel each other, favoring the 

deposition of suspended sediment (Chapra 1997). In addition, increasing salinity levels favor flocculation 

of fine sediments. Large deltas may form where rivers deposit sediments in coastal waters. Sediment 

deposited in estuaries can be disturbed and redistributed due to natural events (e.g., floods, high winds, 

tidal action) or by human activity (e.g., dredging). 

2.3 Aquatic Life Impacts of Sediment and Turbidity 

Numerous aquatic ecosystem impacts from construction site discharges have been documented (see 

Chapter 4).This section summarizes studies of aquatic organism and ecosystem impacts associated with 

elevated sediment and turbidity levels in surface waters. EPA has reviewed available literature on the 

biological effects of suspended and bedded sediments (SABS) and turbidity. This review is not an 

exhaustive summary of the available literature, which is extensive, but instead provides an overview of 

more commonly noted impacts. 

A variety of organisms, including aquatic plants, invertebrates, amphibians, and fish, are affected by 

elevated sediment and turbidity levels. High levels of sediment and turbidity affect aquatic ecosystems by 

reducing photosynthetic activity, reducing food availability, burying habitat, and directly harming 

organisms. Organisms may relocate, sicken, or die. Organism loss can alter the composition of the aquatic 

community. 

Both the magnitude and duration of sediment and turbidity exposure are important. Organisms vary in 

their ability to tolerate and recover from exposure. The proportion of organisms able to tolerate (or 
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November 2009 2-12 

escape) periods of elevated sediment and turbidity levels can increase in impacted surface waters, while 

the proportion of sensitive species declines. 

The appropriate level of sediment or turbidity varies from waterbody to waterbody. Some aquatic 

ecosystems contain organisms adapted to higher levels of sediment or turbidity (e.g., some large coastal 

floodplain rivers), whereas other ecosystems contain organisms adapted to lower levels of sediment or 

turbidity (e.g., alpine lakes, headwater streams). Smaller streams tend to have naturally clearer water, 

particularly those in forested watersheds. Short-term increases in suspended sediment and turbidity levels 

can naturally occur during spring thaws, storms, and other high flow events. However, even organisms 

adapted to sediment or turbidity influx (whether episodic or constant) can be harmed if input levels rise 

excessively and/or if their resiliency is taxed by other stressors. 

A construction project can change natural sediment and turbidity dynamics by elevating sediment and 

turbidity levels significantly beyond those associated with natural events, for longer periods of time, and 

at times when an aquatic ecosystem and its organisms are unaccustomed to receiving such inflows (e.g., 

late summer low flow periods). Some waterbody types are better able to flush excess suspended sediment, 

turbidity, and deposited sediment (e.g., high energy streams), whereas others may retain accumulated 

sediments for years (e.g., lakes and wetlands) (see Section 2.2). 

Sediment impacts have been researched through both laboratory dose-response and in-stream field 

studies. Much research has been done on the effects of sediment on fish (salmonids, in particular, because 

of their economic importance and sensitivity). More studies have been conducted on suspended sediment 

versus bedded sediment effects on organisms. Stream and coral reef habitats have been studied more 

intensively than river, lake, and estuarine habitat, though available data indicate that biota sensitive to 

elevated sediment levels exist in all of these environments. Many habitats with moderate and variable 

levels of sediment also need additional research (Berry et al. 2003). 

Though not addressed in detail in this discussion, sediments and turbidity can also affect chemical, fungal, 

and microbial decomposition processes in aquatic ecosystems. Depending on the source and nature of the 

sediments, sedimentation can change the organic content of surface water sediment, which can alter 

fungal and microbial community activity. Rapid sediment deposition over an organic sediment layer can 

also cause a shift to anaerobic conditions in the isolated strata and an accompanying shift in microbial 

metabolism, methane generation, and nutrient dynamics (Tornblom and Bostrom 1995). In wetlands, 

depressed levels of algal and microbial biomass and density can indicate chronically elevated 

sedimentation levels (USEPA 1995). 

The studies cited in this section attribute observed impacts to sediment and turbidity discharges in 

general, rather than to the specific source materials from which the sediments and turbidity derived. The 

discussion in this section provides a qualitative summary of the types of organism impacts associated with 

elevated sediment and turbidity levels. Berry et al. (2003) discuss the possibilities, given available data, 

for determining quantitative relationships (e.g., dose-response models) between sediment levels and 

organism responses. For most organisms, there is insufficient information currently available to create 

complete dose-response models (Berry et al. 2003). A model for clear water fish is discussed in Section 

2.3.3. 

The examples below discuss effects of elevated sediment and turbidity levels acting on organisms in 

isolation. However, many construction sites have been documented to discharge other pollutants in 

addition to sediment and turbidity such as nitrogen, phosphorus, metals, and other organic and inorganic 

compounds (see Chapter 3). A number of these other pollutants travel with sediment as it erodes and 

moves through the surface water network. In addition, many construction sites discharge to surface waters 
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November 2009 2-13 

already impacted by stressors including pollutant discharges from other sources, invasive species, and 

flow modification. Interactions among sediment, turbidity, and other stressors and their cumulative effects 

on aquatic organisms have not been fully characterized (Berry et al. 2003). 

Documented effects and relevant studies are organized in the discussion below by the type of aquatic 

organism studied (primary producer, invertebrate, fish, and other wildlife) and by the type of organism 

reaction. A section specific to threatened and endangered species follows this discussion (Section 2.3.5). 

2.3.1 Primary Producers 

Primary producers, including algae, phytoplankton, submerged aquatic vegetation, and other macrophytes 

(plants with large leaves), are found in most aquatic ecosystems. Aquatic ecosystems vary in the degree to 

which they derive energy and resources from primary producers. Some systems (e.g., small streams in 

forested watersheds) derive a large proportion of their energy from external sources (e.g., leaf debris) 

(Bilotta and Brazier 2008). Other systems are heavily dependent on the productivity of primary producers 

(e.g., sea grass beds). 

Primary producers can grow in a variety of forms: rooted to surface water substrate, free-floating, or 

attached to rocks, aquatic plants, or other structures. The term periphyton refers to the algae, small plants, 

bacteria, microbes, and detritus attached to submerged surfaces in aquatic ecosystems. 

Primary producers transform sunlight into energy and provide food for other aquatic organisms. Aquatic 

macrophytes and algae influence water column chemistry, including dissolved oxygen, pH, and nutrient 

levels; provide important habitat for many organisms; and reduce waterbody flow velocity. Loss of algae 

and macrophytes due to elevated sediment and turbidity levels can compound these pollutants’ effects 

because fewer plant structures are available to slow water flow and facilitate settling of suspended 

sediment from the water column. This dynamic can allow elevated suspended sediment and turbidity 

levels to persist further downstream (Wood and Armitage 1997). 

Turbidity is an important parameter for aquatic ecosystems because of its influence on the compensation 

point (the depth at which carbon production from photosynthesis equals consumption through respiration 

in aquatic plants). A decrease in a surface water’s compensation point can translate into a decline in an 

aquatic ecosystem’s primary producer community or a shift in its species composition. These changes can 

affect the ecosystem’s overall species composition, productivity, and health. 

Impacts to primary producers have been noted in some studies of construction site impacts to surface 

waters. Construction sites documented in these studies were discharging elevated levels of sediment and 

turbidity. King and Ball (1964) documented a decline in primary productivity levels. Cline et al. (1982) 

noted an increase in periphyton sediment content and a decline in periphyton abundance and algal 

diversity. 

Several ways in which turbidity and sediment affect primary producers are described below. 

2.3.1.1 Light Reduction 

The growth and distribution of macrophytes, algae, phytoplankton, and coral zooxanthellae can be limited 

by excessive turbidity (Berry et al. 2003). Macrophytes with leaves that emerge above the water’s surface 

are much less affected by reduced light penetration than submerged species. Reduced primary production 

due to light limitation from elevated turbidity is a common impact that has been documented by 

LaPerriere et al. (1983), Rivier and Seguier (1985), and Lloyd (1987) (all as cited in Waters 1995), Meyer 

and Heritage (1941) and Edwards (1969) (both as cited in Kerr 1995). 
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LaPerriere et al. (1983) found that moderate turbidity levels of approximately 170 NTU reduced oxygen 

production in a stream by 0.08–0.64 g/m2

 per day relative to clear water conditions. Above 1,000 NTU, 

primary production was undetectable. Lloyd (1987) developed a model relating turbidity and primary 

production. The model indicated that in a clear, shallow stream, a turbidity increase of 5 NTU decreased 

primary production 3 to 13 percent. A 25 NTU increase reduced primary production up to 50 percent. The 

study documented stronger effects from turbidity in lakes due to greater water depths and the greater 

importance of the phytoplankton community. Quinn et al. (1992, as cited in Bilotta and Brazier 2008) 

found a 40 percent reduction in phytoplankton biomass exposed to suspended sediment concentrations of 

10 mg/L for 56 days. 

Guenther and Bozelli (2004) investigated whether lower light levels or increased sinking of 

phytoplankton due to binding to sediment particles was the cause of their reduced photosynthetic activity 

in turbid waters. The authors identified lower light levels as the primary cause. 

Stony corals typically live in clear, oligotrophic waters where their symbiotic association with 

photosynthetic zooxanthellae provides more than 100 percent of the coral’s daily metabolic carbon 

requirements (Muscatine et al. 1981; Grottoli et al. 2006). Corals exposed to elevated turbidity levels have 

reduced photosynthesis rates and must compensate for this loss of resources (Philipp and Fabricius 2003). 

Sediments can also reduce photosynthetic activity by settling on and coating macrophytes and periphyton. 

Even a thin layer of sediment can block enough light to inhibit the growth of algae attached to surface 

water substrates (Welsh and Ollivier 1998). Lower primary production by macrophytes and periphyton 

can result in their decline and replacement with suspended algal communities. A shift in primary producer 

community composition can alter a surface water’s invertebrate and fish community composition. 

2.3.1.2 Direct Physical Damage 

Algae and aquatic macrophytes can suffer physical damage from elevated suspended sediment levels 

(Waters 1995). Lewis (1973, as cited in Kerr 1995) conducted an experiment in which suspended coal 

particle concentrations greater than 100 mg/L caused severe abrasive damage to the leaves of the aquatic 

moss Eurhynchium riparioides over a period of 3 weeks. The damage substantially lowered the moss’s 

ability to produce chlorophyll and photosynthesize. Periphyton is also susceptible to being scoured from 

stream substrate by suspended sediment particles (Welsh and Ollivier 1998). Francouer and Biggs (2006) 

investigated the interaction of flow velocity and suspended solids in benthic algal communities and found 

that high suspended sediment concentrations further increased algae removal above that due to flow 

alone. Some taxa were more susceptible to removal than others. The results indicated that suspended 

sediment scour may be an important mechanism for algae removal during flood events and that some 

variability in biomass removal among flood events and benthic algal composition may be the result of 

differences in suspended sediment load. Birkett et al. (2007, as cited in Bilotta and Brazier 2008) found 

that suspended sediment levels of 100 mg/L stimulated periphyton growth and filament length under low 

flow conditions, but levels of 200 mg/L significantly reduced biomass and filament length. 

Macrophytes, algae, and periphyton can also be buried and damaged or killed by excessive sedimentation, 

with different species having varying abilities to cope (Berry et al. 2003). Some organisms can outgrow 

sediment damage, though this growth may consume additional organism resources. A reduction or 

complete elimination of periphytic material and a reduction in component diversity due to elevated 

sediment levels is described by Van Nieuwenhuyse and LaPerriere (1986), Pain (1987) (both as cited in 

Waters (1995), and Samsel (1973, as cited in Kerr 1995). 
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2.3.1.3 Substrate Modification 

Sediment deposition in surface waters can change the types of algae (Wood and Armitage 1997) and 

macrophytes able to grow in a waterbody by modifying nutrient availability and substrate chemistry, 

texture, stability, and, depth. In many waterbodies, the locations of macrophyte beds are highly correlated 

with the spatial distribution of fine-grained sediments since these often provide better rooting substrate. 

Sediment deltas that form rapidly as a result of sediment runoff to a waterbody are often susceptible to 

colonization by nuisance invasive macrophyte species. 

Some established plants respond to elevated sedimentation levels by extending rhizome growth upwards, 

but such growth patterns are abnormal for some species and consume plant energy (USFWS 1998). In 

some cases, plants may not be able to grow quickly enough to adjust to increased sedimentation. Over 

time, substrate may change to the extent that it no longer supports the original primary producer 

community. Wetlands are particularly vulnerable to changes in sedimentation rate. 

2.3.2 Invertebrates 

Aquatic invertebrates, including zooplankton, insects, crustaceans, and bivalves (e.g., mussels and clams), 

are widely distributed among aquatic ecosystems and can be found in even the smallest surface waters. 

They provide an important link in the aquatic food chain between primary producers and larger organisms 

such as amphibians, reptiles, fish, birds, and mammals (Waters 1995). 

Because many benthic macroinvertebrates are relatively stationary and have limited powers to evade 

polluted waters, their abundance, diversity, and species composition is widely used as an indicator for 

overall aquatic ecosystem health (USEPA 2006d). The effects of elevated sediment and turbidity levels 

on invertebrates, particularly on benthic macroinvertebrates, have been the focus of a large number of 

studies over the past several decades. 

Elevated sediment and turbidity levels impact invertebrates by reducing their health, disease and 

parasitism resistance, and abundance, as documented in literature reviews conducted by Kerr (1995), 

Waters (1995), and Henley et al. (2000). The density, diversity, and structure of aquatic invertebrate 

communities can change as a consequence, to contain a higher proportion of sediment-tolerant species. 

Gammon (1970, as cited in Kerr 1995) finds that TSS concentrations between 40 and 120 mg/L reduced 

macroinvertebrate density by 25 percent, and greater TSS concentrations reduced density by 60 percent. 

Organisms intolerant of turbid waters include mayflies and other Ephemeroptera, stoneflies (Plecoptera), 

caddisflies (Trichoptera), clam, and bryozoan species (Cooper 1987, as cited in Kerr 1995). 

The severity of sediment and turbidity impacts on invertebrate populations is related to the intensity and 

duration of exposure (Anderson et al. 1996, as cited in USEPA 2004a). Some populations are adapted to 

the short-term increases in suspended sediment and turbidity levels that can occur during spring thaws, 

storms, and other natural high flow events. However, high or sustained elevated levels of sediment and 

turbidity can alter long-term community structure by degrading habitat, food sources, organism health, 

and increasing “drift” (i.e., voluntary/involuntary release of substrate by aquatic macroinvertebrates with 

subsequent transport downstream) 

If disturbance to a macroinvertebrate community is intense enough, it may impact the ability of the 

community to recover its former condition, particularly if sensitive organisms are eliminated from 

surrounding areas, as well, or habitat has been modified to the point where it is no longer able to support 

the former community (Montgomery County DEP 2009). Healthy invertebrate populations in unaffected 
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November 2009 2-16 

parts of a surface water system can provide organisms to recolonize disturbed areas once construction 

activity has ceased. 

Multiple studies have documented impacts to invertebrates from construction site sediment and turbidity 

discharges (King and Ball 1964; Peterson and Nyquist 1972; Barton 1977; Reed 1977; Chisholm and 

Downs 1978; Extence 1978; Lenat et al. 1981; Tsui and McCart 1981; Cline et al. 1982; Taylor and Roff 

1986; Young and Mackie 1991; Ohio EPA 1996a, 1997f, 1998d, 1999d, 2003a, 2006b; Reid and 

Anderson 1999; Fossati et al. 2001; Levine et al. 2003, 2005; Hedrick et al. 2007; Chen et al. 2009; 

Montgomery County DEP 2009). 

Several categories of impact identified by Kerr (1995) and Wood and Armitage (1997) are described 

below. 

2.3.2.1 Loss of Habitat 

Increased sediment and turbidity levels in waterbodies can degrade aquatic invertebrate habitat, including 

pools and interstitial spaces in waterbody substrate and wetland areas. Habitat alteration due to changes in 

substrate composition can modify the distribution and density of different invertebrate species and 

therefore the structure of an invertebrate community (Waters 1995; Berry et al. 2003). Sediment settles 

from the water column onto the substrate to which sedentary organisms attach and also fills small crevices 

in waterbody beds (interstitial spaces) where a variety of species shelter, reproduce, feed, and seek 

protection from predators, high flow velocities, and low flow conditions. Large accumulations of 

sediment are also anoxic at depth. When interstitial spaces fill with sediment, the microhabitat diversity of 

the substrate significantly declines, as does the diversity of organisms it can support (USEPA 2006d). 

Embeddedness refers to the extent to which rocks or organic debris in a surface water are covered or 

sunken into fine bottom sediments and is an important physical component of stream or river habitat 

(USEPA 1999). Increased embeddedness due to sedimentation indicates habitat degradation. Studies have 

found high correlations between benthic invertebrate response to depth and degree of embeddedness 

(Berry et al. 2003). Correlations have also been found between benthic invertebrate abundance and 

substrate particle size (Waters 1995). Even small increases in sedimentation levels can impact caddisfly 

pupa survival (Berry et al. 2003). Ryan (1991, as cited in Henley et al. 2000), concludes that as little as a 

12 to 17 percent increase in interstitial fine sediment could result in a 16 to 40 percent decrease in 

macroinvertebrate abundance. 

Siltation of previously coarse substrates is also implicated in the reduction of mussel populations by Ellis 

(1936) and Marking and Bills (1980) (both as cited in Waters 1995). Very thin veneers of sediment can 

decrease the settlement and recruitment of some bivalve larvae (Berry et al. 2003). One study postulates 

that many of the more than 30 extinct species of North American mussels became extinct due to a loss of 

habitat, most likely from sedimentation of surface water substrate (Henley et al. 2000). In coral reef 

systems, most coral larvae seek firm substrates and will not settle and establish themselves on shifting 

sediments (Berry et al. 2003). 

Invertebrates can also be indirectly affected by impacts to primary producers in an aquatic community. A 

shift in primary producers can impact the structure of invertebrate community by changing food and 

shelter availability and quality. As discussed in the previous section, increased turbidity and sediment 

levels can adversely affect the primary producer community and change its abundance and composition. 

As turbidity increases, the contribution of primary production from aquatic macrophytes may decline, 

while that associated with phytoplankton comes to dominate the system. Sediments and turbidity can 

adversely affect the growth, abundance, and species composition of the periphytic algal community, 
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November 2009 2-17 

reducing food availability for invertebrate grazers that feed predominantly on periphyton. A shift in 

primary producers can impact the structure of the invertebrate community by changing food and shelter 

availability and quality. This can lead to shifts in the relative proportion of various feeding guilds and 

habitat preferences in the resulting aquatic invertebrate community (Vannote et al. 1980; Merritt and 

Cummins 1996). 

2.3.2.2 Altered Movement and Increased Drifting 

Movement upstream or downstream (“drift”) of benthic macroinvertebrates has been found to increase 

with elevated suspended sediment and turbidity concentrations (Kerr 1995; Berry et al. 2003). Increases 

in invertebrate drift have been shown to be specifically related to the turbidity caused by suspended 

sediments (Waters 1995). One study found that suspended sediment levels of 120 mg/L were sufficient to 

cause drift (Berry et al. 2003). Rosenberg and Wiens (1978, as cited in Bilotta and Brazier 2008) found 

increased benthic macroinvertebrate drift after 2.5 hours of exposure to 8 mg/L suspended sediment. 

Increased drift leads to increased predation on the displaced benthic invertebrates (Waters 1995) and 

causes a decline in invertebrate abundance in the impacted surface water (White and Gammon 1977, as 

cited in Waters 1995). Benthic macroinvertebrate drifting downstream of construction sites for pipeline 

stream crossings has been documented in several studies (Tsui and McCart 1981; Reid and Anderson 

1999).

2.3.2.3 Physiological Changes 

The physical effects of elevated sediment levels on invertebrate species can be severe. Sediment particles 

can clog an organism’s filter feeding apparatus, filtration system, respiratory system, and digestive system 

(Kerr 1995), resulting in compromised health or death of the organism. Affected organisms include taxa 

with fragile or rudimentary gill structures, such as stoneflies (Merritt and Cummins 1996) and organisms 

that primarily graze in the water column (e.g., zooplankton). Alabaster and Lloyd (1982, as cited in 

Bilotta and Brazier 2008) found that Cladocera and Copepoda gills and guts were clogged after 72 hours 

of exposure to suspended sediment levels of 300 to 500 mg/L. 

Most species of coral are highly intolerant of sedimentation and will expend energy to flush excess 

sediments. If a coral is unable to compensate for excess sedimentation, tissues can smother and die. 

Sediments can also reduce zooxanthellae photosynthesis, reduce growth rates, cause temporary bleaching, 

and eventually death. These impacts can affect other parts of the coral reef food web. Consequently, 

increasing sedimentation levels are associated with reductions in coral reef diversity (Rogers 1990), 

alterations in community composition by growth morphology (Rogers 1990, Stafford-Smith and Ormond 

1992), decreased skeletal extension rates (Dodge et al. 1974, Miller and Cruise 1995, Heiss 1996), and 

reduced coral recruitment (Rogers 1990, McClanahan et al. 2002, Fabricius et al. 2003). Other coral reef 

organisms, such as sponges, are thought to be sensitive to sedimentation as well (Berry et al. 2003). 

2.3.2.4 Impaired Feeding Activity 

Another effect of elevated suspended sediment and turbidity levels on aquatic invertebrates is a reduction 

in their feeding activity or the efficiency of their feeding. Kerr (1995) noted that this impact is 

predominant in filter feeding species such as mussels and clams and cited Ellis (1936), who found that 

under turbid conditions, mussels and clams reject silt-laden food and, under highly turbid conditions, 

close their shells completely. Waters (1995) also discussed sediment’s impact on feeding, citing Ellis 

(1936) and Aldridge et al. (1987), who found that sediment additions impaired feeding in three species of 

bivalves. Reactions to elevated sediment levels by freshwater mussels may be species specific. Mussels 

compensate for increased suspended sediment levels by increasing filtration rates, increasing the 
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November 2009 2-18 

proportion of filtered material that is rejected, and increasing the selection efficiency for organic matter. 

This process increases the metabolic cost of feeding per unit of food and decreases resources available for 

growth and reproduction. 

Species that have evolved in turbid environments may be better able to select between organic and 

inorganic particles during feeding. For example, one study found that a mussel from rocky coastal 

environments (Mytilus californianus) was more sensitive to elevated suspended sediment levels than a 

mussel of the same genus from a bay environment (Mytilus edulis), which is typically more turbid (Berry 

et al., 2003). Many endangered freshwater mussel species evolved in fast flowing streams with 

historically low levels of suspended sediment. Compared to other species that evolved in more turbid 

environments, such rocky intertidal habitat species may not be able to differentiate between organic and 

inorganic particles as well. 

Light attenuation due to elevated turbidity can reduce feeding efficiency of invertebrates. Copepods and 

daphnids have been found to reduce feeding activity in response to elevated suspended sediment levels 

(Berry et al. 2003). Sediment coatings on macrophytes and periphyton can reduce their quality as food 

sources because herbivores must consume elevated levels of sediment with the plant material (Ryan 

1991). 

Kerr (1995) cited three studies (Paffenhofer 1972; Appleby and Scarratt 1989; Kirk 1992) that found 

reduced growth rates in invertebrates subjected to elevated sediment levels due, potentially, to reduced 

feeding activity and/or reduced food value. Graham (1990, as cited in Waters 1995) finds that a decrease 

in the percentage of organic matter in periphytic material may result from additional sediment particles 

binding to the periphyton. 

2.3.2.5 Direct Mortality 

Sediment can directly or indirectly increase invertebrate mortality levels. Forbes et al. (1981, as cited in 

Kerr 1995) found that elevated suspended sediment levels increased amphipod (shrimp-like crustacean) 

mortality. Henley et al. (2000) concluded from a literature review that increased sediment concentrations 

have a negative effect on the survival rates of freshwater mussels with the magnitude of the impact being 

species specific. Robertson (1957, as cited in Bilotta and Brazier 2008) found the survival and 

reproduction of Cladocera to be harmed by 72 hours of exposure to suspended sediment concentrations of 

82 to 392 mg/L. 

Organisms can also be buried and smothered by heavy sedimentation. Large sediment accumulations 

become anoxic at depth. Species vary in their ability to evade burial under moderate sedimentation 

deposits. Sediment grain size, depth, and bulk density and species motility, living position, and tolerance 

of anoxic conditions during burial affect this ability (Berry et al. 2003). 

Sediment organic matter is an important food source for many benthic macroinvertebrates. A number of 

toxic pollutants bind to and travel with sediment and can be ingested by invertebrates with the organic 

matter (Paul and Meyer 2001). The aquatic organism effects associated with these toxic pollutants are 

discussed in Chapter 3. 

2.3.3 Fish 

A substantial amount of research has been conducted to identify and quantify the effects of elevated 

sediment and turbidity levels on a variety of fish species, more so than other taxonomic groups (Berry et 

al. 2003). Salmonids, in particular, have been well studied because of their commercial and recreational 
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November 2009 2-19 

importance and because of the concerns and well-documented impacts arising from logging in the Pacific 

Northwest and elsewhere (Waters 1995). 

The level of impact from suspended sediments and turbidity is a function of the interaction of many 

factors, including natural sediment and turbidity levels for the area in question; pollutant concentration; 

exposure duration; particle size; ambient temperature; physical and chemical properties of the sediments; 

associated toxins; co-occurring stressors; and fish acclimatization, life history stage, migration, and 

breeding season (Anderson et al. 1996, as cited in USEPA 2004a). In general, longer duration and higher 

level exposures produce greater effects. Milder, primarily behavioral effects are observed at lower 

magnitude and duration exposure levels. As exposure and duration levels increase, effects become 

sublethal and lethal (Berry et al. 2003). Several researchers have sought to construct models linking 

varying levels and durations of sediment exposure to fish responses, but responses vary widely among 

different species, and data are generally insufficient at this time to fully characterize many of them (Berry 

et al. 2003). 

Newcombe (2003) has created a model for clear water fishes that relates magnitude and duration of 

exposure to turbid water to a variety of adverse effects. The model indicates that fish exposure to turbidity 

levels of 7 to 1,100 NTU for 1 to 48 hours can create effects ranging from small behavioral impacts to 

death of a portion of the exposed population. This model does not describe fish that typically inhabit more 

turbid surface waters and that exhibit a different set of reactions to a given range of turbidity levels and 

exposures. 

Short-term increases in suspended sediment and turbidity levels can naturally occur during spring thaws, 

storms, and other natural events. Aquatic ecosystems also vary in their natural sediment and turbidity 

levels. Studies have found that, in general, fish typically found in environments with naturally high 

turbidity preferred more turbid environments in the laboratory (Berry et al. 2003). Other fish species are 

very sensitive to elevated sediment levels (e.g., entire salmonid fisheries have been destroyed by elevated 

sediment levels) (Berry et al. 2003). 

A construction project may produce sediment and turbidity levels significantly higher those associated 

with natural events, for longer periods of time, and at times when an aquatic ecosystem is unaccustomed 

to receiving such sediment inputs. Many fish species have seasonal migration and breeding behaviors 

which can be disrupted by elevated sediment levels. Younger fish are more vulnerable to sediment 

impacts and are more prevalent within a population at certain times of the year (Berry et al. 2003). 

Observed impacts from elevated sediment and turbidity levels fall into several broad categories, discussed 

below. The potential cumulative effect of these impacts includes reduced disease and parasite resistance, 

reduced growth, and degraded health of individual organisms in the fish community. These impacts may 

decrease fish population levels in affected areas. Reductions can take place both through direct mortality 

in the short term and reduced reproductive success in the long term. Newcombe (1994) and Newcombe 

and Jensen (1996) (both as cited in Henley et al. 2000) found that elevated sediment concentrations are 

associated with increased mortality in at least 14 species of fish. EPA (USEPA 2004a) states that 

suspended sediment concentrations between 500 and 6,000 mg/L can result in significant (greater than 50 

percent) mortality. Berkman and Rabeni (1987, as cited in Wood and Armitage 1997) noticed a decline in 

the overall abundance of fish stocks as sediment levels increased in a river in northeastern Missouri. The 

overall impact of these effects can be a change in fish community composition to one composed 

predominantly of species that are tolerant of sediment and turbidity, primarily bottom-feeders (Berry et al. 

2003), or an overall reduction in fish abundance. 
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Impacts to fish communities downstream of construction sites discharging elevated levels of sediment and 

turbidity have been documented in multiple studies (King and Ball 1964, Graves and Burns 1970, Barton 

1977, Garton 1977, Reed 1977, Werner 1983, Taylor and Roff 1986, Ohio EPA 1997f, Ohio EPA 1999c, 

Reid and Anderson 1999, Hunt and Grow 2001, California Department of Fish and Game 2004, 

Montgomery County DEP 2009). 

2.3.3.1 Avoidance and Other Behavioral Responses 

The severity of behavioral responses is associated with the timing of disturbance, the level of stress, fish 

energy reserves, phagocytes, metabolic depletion, seasonal variation, and habitat alteration (USEPA 

2006b). 

Because fish are generally more mobile than aquatic plants, plankton, and invertebrates, some species are 

capable of actively avoiding waters with high levels of suspended sediment or turbidity if migration 

routes to alternative habitats are available (mobility may be limited by culverts, small dams, and other 

obstacles). While larger, nonlarval fish are generally capable of avoidance behavior (Berry et al. 2003), 

smaller and younger fish are generally less mobile and therefore more vulnerable to elevated sediment 

and turbidity levels. Avoidance behaviors may reduce or eliminate fish populations in stream reaches with 

sustained elevated sediment levels. Barton (1977) noted a relocation of fish populations away from areas 

with elevated TSS levels due to construction near a small stream. Servizi and Martens (1987, as cited in 

Reid et al. 2003) determined that turbidity levels in excess of 37 NTU elicit avoidance behavior among 

fish populations. Berg (1982, as cited in Bash et al. 2001) documented that juvenile coho salmon exposed 

to a short-term pulse of 60 NTU water left the water column and congregated at the bottom of the test 

tank, returning to the water column once turbidity was reduced to 20 NTU. Boubee et al. (1997, as cited 

in Newcombe 2003) found inhibition of upstream migration of one species at turbidity levels of 15 to 20 

NTU. Because of avoidance behaviors, some fish may be excluded from otherwise desirable habitat due 

to increased turbidity (Berry et al. 2003). 

Other fish behavioral responses to elevated sediment and turbidity levels include increased frequency of 

the cough reflex and temporary disruption of territoriality (USEPA 2006b). 

2.3.3.2 Feeding and Hunting 

Elevated suspended sediment and turbidity levels have been shown to reduce feeding rates in several 

species of fish (Kerr 1995). Elevated turbidity reduces the prey reaction distance for trout and therefore 

reduces foraging success (Hedrick et al. 2006). This effect is generally believed to be a result of decreased 

visibility caused by turbidity because many fish use vision to locate food. De Robertis et al. (2003) found 

that turbidity level increases affected piscivorous fish feeding more than planktivorous fish feeding. 

Piscivorous fish visually locate food sources at much longer distances than planktivorous fish do. EPA 

(USEPA 2004a) concluded that turbidities greater than 25 NTU or TSS levels of 2,000–3,000 mg/L or 

greater are sufficient to reduce fish predation abilities. Sweka and Hartman (2001) observed that brook 

trout prey reaction distance decreased curvilinearly as turbidities levels increased from 0 to 43 NTU. 

Although most fish species’ predation abilities are reduced by increased turbidity levels, some fish 

species have been documented to hunt better as turbidity increases because of increased contrast between 

the prey and surrounding water. This advantage deteriorates as turbidity levels further increase, however 

(Berry et al. 2003). Some larval fish species (e.g., striped bass - Morone saxatilis) appear to be able to 

feed under extremely turbid conditions (Berry et al. 2003). 

Reduction in predation can benefit prey species able to use turbid water to hide from predators (Rowe et 

al. 2003). However, a paper by the Canadian DFO (2000) suggests that as turbidity levels continue to 
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November 2009 2-21 

increase, the advantage of reduced predation is outweighed by the prey species’ own increased difficulty 

in locating food. 

An additional effect on feeding and hunting from elevated sediment and turbidity levels is decreased food 

availability and quality due to invertebrate community impacts, decreased primary productivity, and 

sediment coatings on macrophytes and periphyton, which increase the quantity of nonnutritive sediment 

herbivores must consume (Waters 1995; Ryan 1991). Bottom-feeding fish can be adversely impacted if 

sediment organic content and associated microbial and macroinvertebrate communities are degraded by 

sedimentation. 

2.3.3.3 Breeding and Egg Survival 

Elevated sedimentation levels can reduce spawning habitat for multiple fish species, particularly benthic 

spawners. Many fish species use well-aerated interstitial spaces in surface water beds to lay eggs. 

Sedimentation impact levels vary with differences in species sensitivity, life stage impacted, sediment 

particle size, and sedimentation rates and total magnitude. Eggs and larvae can be buried too deeply by 

sediment to survive (Berry et al. 2003). Sediments that settle onto surface water beds, particularly finersized particles, can reduce the level of dissolved oxygen available to eggs deposited in the substrate (Kerr 

1995). Even thin layers of sediment only a few millimeters thick can disrupt the normal exchange of gases 

and metabolic wastes between eggs and the water column (Berry et al. 2003). Bjornn et al. (1977), as 

cited in Kerr (1995), found that successful emergence of fish fry from eggs began to decline when fine 

sediment levels in the substrate were in excess of 20 to 30 percent. Sediment deposition can result in 

surface water substrate armoring, trapping larvae as they attempt to emerge after hatching (Berry et al. 

2003). Emergence success of cutthroat trout was reduced from 76 percent to 4 percent when fine sediment 

was added to spawning gravel redds (Weaver and Fraley, 1993). 

Correlations have also been found between elevated suspended sediment and turbidity levels and reduced 

breeding and hatching success. Saunders and Smith (1965, as cited in Kerr 1995) found that increased 

suspended sediment levels reduced fish spawning activity. Other studies document lower survival rates 

for eggs spawned in surface waters with elevated suspended sediment and turbidity levels (Chapman 

1988, as cited in Canadian DFO 2000). Reynolds (1989, as cited in Henley et al. 2000) found that 

increases in turbidity levels increased sac fry mortality in arctic grayling (Thymallus arcticus). 

2.3.3.4 Habitat Loss 

Like invertebrates, fish use crevices in waterbody beds for feeding, shelter from predators and high flow 

events, and reproduction. Loss of these interstitial spaces degrades fish habitat (USEPA 2006b). Sediment 

can also decrease the depth of or eliminate other important habitats such as stream pools and wetlands 

adjacent to surface waters. Riparian wetlands are particularly important for fish spawning. Fish are also 

affected by loss or decline in aquatic macrophytes, which are important sources of shelter and food for a 

number of fish species. Coral reef bleaching and decline severely impact fish habitat in coral reef 

ecosystems. 

2.3.3.5 Juvenile Growth and Survival 

High sediment concentrations can impact juvenile fish more severely than adult fish. Smallmouth bass 

reduced growth after a 24-hour exposure to suspended sediment levels as low as 11.4 mg/L (Berry et al. 

2003). Reynolds et al. (1989, as cited in Bilotta and Brazier 2008) found 6 to 15 percent mortality of 

arctic grayling sac fry after 24 hours of exposure to suspended sediment levels of 25 to 65 mg/L. 

Mortality of 41 percent was documented after 72 hours of exposure to suspended sediment levels of 185 

mg/L. Juvenile coho and Chinook salmon exhibited abnormal surfacing behavior under elevated 
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November 2009 2-22 

suspended sediment concentrations, rendering them more vulnerable to avian predators (Canadian DFO 

2000). This study also notes that excessive sediment levels can stress juvenile fish and consequently 

increase their predation risk. These types of effects can reduce the strength of a year class (Berry et al. 

2003). 

2.3.3.6 Physical Damage 

At high levels, suspended sediment, particularly more angular particles, are capable of causing physical 

damage to fish by clogging gills (which impairs breathing) and by skin abrasion (Bell 1973, as cited in 

Waters 1995). Kerr (1995) notes that fish can withstand short episodes of high sediment concentrations by 

producing mucus to protect skin and gills, but this reaction stresses fish. Excessive gill mucus can cause 

gill tissue traumatization and asphyxiation. The severity of damage appears to be related to sediment 

dose, exposure duration, and particle size and angularity. Fish can release stress hormones (i.e., cortisol 

and epinephrine) in response to decreased gill function. Reid et al. (2003) finds that elevated sediment 

levels reduces the ability of rainbow trout to withstand hypoxic conditions and thereby reduces their 

average life span. 

2.3.4 Other Wildlife Dependent on Aquatic Ecosystems 

Birds, mammals, reptiles, and amphibians that consume aquatic plants, invertebrates, fish, and other 

aquatic organisms or otherwise utilize aquatic habitats for shelter and reproduction can also be affected by 

elevated sediment and turbidity levels in surface waters. Some species are sufficiently mobile that they 

can avoid impacted aquatic communities and seek substitutes, if available and accessible (Berry et al. 

2003). 

Welsh and Ollivier (1998) documented lower densities of two salamander species and one frog species in 

streams impacted by sedimentation from a construction site. The authors postulated that sedimentation of 

interstitial spaces in the stream’s substrate was the cause because of the organisms’ dependence on these 

spaces. Interstitial spaces provide amphibians with shelter from predators and high flows and habitat for 

food (e.g., diatoms and periphyton) production. Concerns have been raised about the potential impact of 

sedimentation on the endangered Barton Springs salamander (Eurycea sosorum), which depends on a 

coarse substrate and healthy aquatic macrophyte population (USFWS 2005). 

Sedimentation can also affect or eliminate riparian wetlands, important habitats for the laying of 

amphibian egg masses (USEPA 2006b). 

There are few studies available on the effects of elevated sediment and turbidity levels in aquatic 

ecosystems on fundamentally terrestrial wildlife that utilize aquatic ecosystems. Most available studies 

examine effects on birds. Loons appear to need clear water for hunting fish and may avoid turbid waters 

for nesting. Other studies documented birds modifying their fish hunting behaviors and distribution on a 

surface water in order to avoid more turbid areas, probably because of increased foraging difficulty (Berry 

et al. 2003). 

2.3.5 Threatened and Endangered Species 

Threatened and endangered (T&E) and other special status species can be adversely affected by elevated 

turbidity and sediment levels. The multiple ways in which elevated sediment and turbidity levels impact a 

variety of organisms are discussed above. These impacts can reduce organism growth, health, survival, 

and reproduction, thereby leading to further decline in an impacted T&E species population. The potential 
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November 2009 2-23 

further decline of listed species or critical habitat for these species from construction activity is 

particularly important because these species are already rare and at risk of irreversible decline. 

The federal trustees for T&E species are the Department of the Interior’s U.S. Fish and Wildlife Service 

(USFWS) and the Department of Commerce’s National Marine Fisheries Service (NMFS). USFWS is 

responsible for terrestrial and freshwater species (including plants) and migratory birds, and NMFS deals 

with marine species and anadromous fish (USFWS 2008). Various state agencies and departments have 

state-level jurisdiction over T&E species and habitats of concern. 

A species is federally listed as “endangered” when it is likely to become extinct within the foreseeable 

future throughout all or part of its range if no immediate action is taken to protect it. A species is listed as 

“threatened” if it is likely to become endangered within the foreseeable future throughout all or most of 

its range if no action is taken to protect it. The 1973 Endangered Species Act outlines detailed procedures 

used by the Services to list a species, including listing criteria, public comment periods, hearings, 

notifications, time limits for final action, and other related issues (USFWS 1996). 

A species is considered to be federally threatened or endangered if one or more of the following listing 

criteria apply (USFWS 2007): 

3⁄4 The species’ habitat or range is currently undergoing or is jeopardized by destruction, 

modification, or curtailment. 

3⁄4 The species is overused for commercial, recreational, scientific, or educational purposes. 

3⁄4 The species’ existence is vulnerable because of predation or disease. 

3⁄4 Current regulatory mechanisms do not provide adequate protection. 

3⁄4 The continued existence of a species is affected by other natural or manmade factors. 

States and the federal government have also included species of “special concern” on their lists. These 

species have been selected because they are (1) rare or endemic, (2) in the process of being listed, 

(3) being considered for listing in the future, (4) found in isolated and fragmented habitats, or 

(5) considered a unique or irreplaceable state resource. 

The federal government and individual states develop and maintain lists of species that are considered 

endangered, threatened, or of special concern. The federal and state lists are not identical: a state does not 

list a species that is on the federal list if it is extirpated in the state. States may also list a species that is 

not on the federal list if the species is considered threatened or endangered at the state, but not federal, 

level. 

Information on federally listed T&E species is available from the USFWS Threatened and Endangered 

Species System (TESS) database (USFWS 2009), available at 

http://www.fws.gov/endangered/wildlife.html. Information on both federal and state listed species is 

available online in the NatureServe database (NatureServe 2009) at http://www.natureserve.org/explorer/. 

Additional information on state-listed species is available from state T&E species coordinators. 

Numerous physical and biological stressors have resulted in the listing of multiple aquatic species as 

threatened or endangered. Major factors include habitat destruction or modification, displacement of 

populations by exotic species, dam building and impoundments, various point and nonpoint sources of 

pollution, poaching, and accidental deaths due to human activity. 

Because construction activity occurs in every state and in or near a variety of waterbody types, there are 

many potentially affected T&E aquatic species. The current USFWS list of T&E species includes 255 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-24 

aquatic species in five species groups: fish, amphibians, mollusks, crustaceans, and corals (USFWS 

2009). For a complete list of these species, see Appendix B. This list is only a partial representation of 

potentially affected species because it does not include aquatic or aquatic ecosystem-dependent plants, 

insects, arachnids, reptiles, birds, and mammals. Although the USFWS list of T&E species does not 

provide information on causes of endangerment for each species, a review of the ecological literature 

suggests that elevated sedimentation, turbidity, and suspended sediment levels may have adverse impacts 

on species that are already in peril either directly or indirectly through impacts on supporting food chains 

(Cloud 2004; NatureServe 2009; USFWS 2009). 

Several T&E species are thought to be particularly susceptible to excessive sedimentation, turbidity, and 

suspended sediment levels. One such example is Amblema neislerii, an endangered freshwater mussel 

commonly known as the fat threeridge. As the NatureServe Explorer database notes, “...the species and 

its habitats continue to be impacted by excessive sediment bed loads of smaller sediment particles, 

changes in turbidity, [and] increased suspended solids...” (NatureServe 2009). Other similarly sedimentsusceptible species of mollusks include the oyster mussel (Epioblasma capsaeformis), purple bankclimber 

(Elliptoideus sloatianus), Louisiana Pearlshell (Margaritifera hembeli), Alabama Heelsplitter (Potamilus 

inflatus), and Ouachita rock pocketbook (Arkansia wheeleri) (USFWS 2009). 

Many fish species are also vulnerable to sedimentation. Sedimentation is a risk factor for a number of 

shiner species, including the Arkansas river shiner (Notropis girardi), Pecos bluntnose shiner (Notropis 

simus pecosensis), and blue shiner (Cyprinella caerulea) (NatureServe 2009). The NatureServe Explorer 

notes that the biggest protection need for the threatened blue shiner (Cyprinella caerulea), native to the 

southeastern United States, is “...prevent[ing] siltation of habitat, especially during the spawning 

period...” (NatureServe 2009). Sedimentation is also listed as a threat factor to a number of minnow and 

darter species, such as the loach minnow (Tiaroga cobitis), spikedace (Meda fulgida), fountain darter 

(Etheostoma fonticola), leopard darter (Percina pantherina), watercress darter (Etheostoma nuchale), and 

vermilion darter (Etheostoma chermocki) (USFWS 1998, 2009; Drennen 2003; Cloud 2004; NatureServe 

2009). 

Sedimentation has also contributed to the threatened status of some populations of rainbow trout and 

several salmon species in the Pacific Northwest (NatureServe 2009). For example, California coho 

salmon and steelhead trout are listed as threatened and endangered by the state of California. The state has 

expressed concern about the impact of sediment discharges from construction sites on habitat throughout 

these species’ range (McEwan and Jackson 1996; California Department of Fish and Game 2004). The 

state noted, for example, that discharges from the construction of Interstate 5 in California depleted 

spawning gravels for coho salmon (California Department of Fish and Game 2004). 

Construction activities have been noted as a source of stress for the endangered Barton Springs 

salamander (Eurycea sosorum) (USFWS 2005). The U.S. Fish and Wildlife Service (2004) also issued a 

biological opinion addressing potential harm to the Arkansas river shiner (Notropis girardi) from a bridge 

construction project in Texas. While USFWS concluded that the project would not jeopardize the 

continued existence of the species, it did note that the river’s fish community would be adversely affected 

by temporary loss of habitat, seining and handling of individual fish necessary to remove them from the 

immediate vicinity of the construction site, increased turbidity in the river, and harassment due to 

construction activity (e.g., equipment usage, materials storage, foot and vehicle traffic, installation of 

sediment and erosion controls, incidental deposition of debris in river). USFWS stated that these impacts 

might affect habitat access and seasonal movement of the fish. 
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November 2009 2-25 

Texas has identified the threatened and endangered aquatic plants Texas wild-rice (Zizania texana) and 

puzzle sunflower (Helianthus paradoxus) as vulnerable to siltation. Texas wild-rice also grows best in 

water with little or no turbidity. Little Aguja pondweed (Potamogeton clystocarpus) was identified as 

vulnerable to substrate texture modification (USFWS 1998). 

2.4 Sediment and Turbidity Impacts on Human Use of Aquatic Resources 

As sediment eroded from construction sites settles in surface waters or elevates sediment concentrations 

and turbidity in the water column, human uses of surface waters and human-built elements of the 

environment can be damaged. Damage from discharge of sediment and turbidity to surface waters has 

been recognized in the literature for several decades (Wolman and Schick 1967). Impacts to several types 

of human use of aquatic resources are described below. 

2.4.1 Navigation on Surface Waters 

Navigable waterways, including rivers, lakes, bays, shipping channels, and harbors, are an integral part of 

the United States’ industrial transportation network. Navigable channels are prone to reduced 

functionality due to sediment build-up, which can reduce the navigable depth and width of the waterway 

(Clark et al. 1985). Increased sedimentation can lead to increased navigational difficulties and 

inefficiencies such as groundings and delays (Osterkamp et al. 1998). Shipping companies may switch to 

lighter loads or smaller vessels, which are generally less efficient. Removing sediment to keep navigable 

waterways passable requires dredging, which can be costly. The U.S. Army Corps of Engineers (USACE) 

spends an average of more than $572 million (2008$) every year to dredge waterways and keep them 

passable (USACE 2009). 

Dredging is itself an environmentally disruptive activity because it significantly disturbs the physical 

structure of a surface water’s bed and because dredged material may contain significant quantities of toxic 

substances and heavy metals. Dredging can disturb contaminants that have settled to the bottom of the 

waterway, increasing the potential for their uptake by fish and other aquatic organisms. Dredged sediment 

may be disposed of in another section of the waterbody or watershed, relocating the problem rather than 

removing it. Additionally, if the sediment removed from a site is contaminated, it can add to pollution at 

the disposal site (Clark et al. 1985). 

In addition, unless there is overdredging to compensate or sedimentation is monitored so that dredging 

activity may be timed optimally, waterbodies will be on average be less navigable than if sedimentation 

rates were reduced. 

In areas with significant sediment contamination, dredging may not be a feasible option because of high 

disposal costs for the dredged sediment. This can lead to damage of shipping vessels or shipping 

inefficiencies. An example is the Great Lakes Harbor and Indiana Harbor Ship Canal, where navigational 

dredging has not been conducted since 1972 because of the difficulty of contaminated dredge spoil 

disposal. Ships have trouble navigating the harbor and canal and must enter the harbor while loaded at 

less than optimum vessel drafts. There is also restricted use of some docks, requiring unloading at 

alternative docks and double handling of bulk commodities to the preferred dock. In 1995, the increased 

transportation costs associated with the lack of dredging were estimated to be $12.4 million annually 

(USEPA 2004c). 

Chapter 7 of this report describes the methodology for and presents the results of EPA’s analysis of 

benefits to navigation from several alternative regulatory options. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-26 

2.4.2 Reservoir Water Storage Capacity 

Reservoirs are water impoundments, often manmade, that serve many functions, including providing 

drinking water, flood control, hydropower supply, and recreational opportunities. The National Inventory 

of Dams includes more than 75,000 dams (Renwick et al. 2005). Renwick et al. (2005) estimated the 

presence of 375,000–750,000 additional manmade impoundments in the United States, of which the 

majority are smaller than 1 acre in area. These smaller impoundments are used for recreation, livestock 

water supply, sediment trapping and erosion control, and other purposes. Sediment in streams can be 

carried into reservoirs and smaller impoundments, where it can settle and build up layers of silt over time. 

Historically, the United States Geological Survey (USGS) has recorded an average of 2.6 billion pounds 

of sediment deposition settles in reservoirs each year (USGS 2007c). An increase in sedimentation rates 

will reduce reservoir capacity and utility. To replace this capacity, sediment must be dredged from 

reservoirs, or new reservoirs must be constructed (Clark et al. 1985). Both dredging and new reservoir 

construction can have a variety of environmental impacts. Crowder (1987) estimated that the United 

States was losing about 0.22 percent of its reservoir capacity each year due to sedimentation. Clark et al. 

(1985) noted that total U.S. reservoir capacity is filling up slowly and has enough excess capacity 

dedicated to hold sediment build-up over hundreds of years. However, the study went on to conclude that 

while total reservoir sedimentation is manageable, sedimentation is far from uniform and that, in 

approximately 15 percent of U.S. reservoirs, sedimentation rates exceeded 3 percent of capacity annually 

and, in the more extreme cases, 10 percent per year (Clark et al. 1985). 

Sediment can also affect reservoir evaporation rates. Turbid waters tend to be sharply stratified, with a 

warm layer at the surface and a cooler layer below. Because warm water evaporates faster, turbidity can 

cause higher rates of water loss from reservoirs. However, turbidity may also increase the reflectivity of 

water, which makes water absorb solar heat more slowly than it otherwise would and reduces evaporation 

rates (Clark et al. 1985). The overall effect from temperature stratification in an individual reservoir could 

be positive or negative. 

Wolman and Schick (1967) describe the loss of the use of a Massachusetts reservoir because of sediment 

discharged from airport construction. Significant resources were expended over two successive years for 

supply and pumping of water from alternative sources. 

Chapter 8 of this report describes the methodology for and presents the results of EPA’s analysis of 

benefits from reduced sedimentation of larger reservoirs under several alternative regulatory options. 

2.4.3 Municipal Water Use 

Discharges from construction sites can affect the quality and cost of providing drinking water. Sediment, 

turbidity, and other discharged pollutants negatively affect water quality and require increased spending 

on treatment measures such as settlement ponds, filtration, and chemical treatment (Osterkamp et al. 

1998). There is an additional cost associated with removing sludge that is created during the treatment 

process (USEPA 2007b). The presence of pollutants or dissolved minerals in drinking water may also 

affect the flavor and odor of water. 

Surface water sedimentation can impede water flow into drinking water treatment facility intakes. 

Facilities may need to expend resources to unblock intakes or rebuild them in a different part of the 

drinking water supply surface water. 
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November 2009 2-27 

Construction site discharge impacts on municipal water supplies have been documented in the literature. 

Wolman and Schick (1967) noted that erosion and deposition of sediment had compromised use of 

surface waters as municipal water supplies in Maryland. They stated that where storage facilities were 

lacking and water was pumped directly from a river, sediment concentration spikes following rain events 

could force facilities to temporarily cease water withdrawals. This practice was practicable for only a very 

short period of time. Highway officials in the state had compensated municipalities for the construction of 

additional water storage facilities and for changing water intake locations to cope with sediment 

discharges from highway construction sites. Tan and Thirumurthi (1978) documented increases in 

turbidity, suspended solids, nitrogen, and conductivity levels in water supply lakes due to highway 

construction in Nova Scotia, Canada. Buckner (2002) described contamination of a municipal water 

supply due to runoff from an upstream highway construction site. 

Chapter 9 of this report describes the methodology for and presents the results of EPA’s analysis of 

benefits from reduced impacts to municipal water supplies under several alternative regulatory options. 

2.4.4 Industrial Water Use 

Elevated sediment and turbidity levels may have negative effects on industrial water users. Suspended 

sediment increases the rate at which hydraulic equipment, pumps, and other equipment wear out, causing 

accelerated depreciation of capital equipment. Sediment can also clog water intake systems at power 

plants and other industrial facilities and possibly require their relocation to another part and/or depth level 

of the surface water. Some industrial facilities treat water before use, and elevated sediment and turbidity 

levels may require additional treatment (Osterkamp et al. 1998) or make a surface water source unusable. 

Wolman and Schick (1967) noted that erosion and deposition of sediment had led to turbid waters 

unsuitable for industrial uses in the state of Maryland. They stated that even small amounts of sediment 

can cause problems for industrial operations such as vegetable processing or cloth manufacture. They also 

noted that sediment had led to failure of pumping equipment. Excess sediment levels may require the use 

of filters to improve water quality. 

At least one positive effect from elevated turbidity levels is also possible, however. Since turbidity may 

reduce the rate at which waterbodies absorb solar heat, more turbid waterbodies may supply cooler water, 

which in turn could improve the efficiency of cooling water systems at power plants and other industrial 

facilities (Clark et al. 1985). 

2.4.5 Agricultural Water Use 

Irrigation water that contains sediment or other pollutants from construction sites can harm crops and 

reduce agricultural productivity. Suspended sediment can form a crust over a field, reducing water 

absorption, inhibiting soil aeration, and preventing emergence of seedlings. Sediment can also coat the 

leaves of plants, inhibiting plant growth and reducing crop value and marketability. Furthermore, 

irrigation water that contains dissolved salts or pollutants can harm crops and damage soil quality (Clark 

et al. 1985). 

Wolman and Schick (1967) noted that erosion and deposition of sediment in surface waters in the state of 

Maryland had led to failure of pumping equipment. Pumps are often used for the movement of irrigation 

water. Excess sediment levels may require the use of filters to improve water quality. 

Surface water sedimentation can block irrigation water intake structures or require their relocation to 

another part of the surface water. Sedimentation can also cause sediment build-up in irrigation water 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-28 

canals and other transport systems (Osterkamp et al. 1998), reducing their efficiency. Sediment can also 

accumulate in livestock water supply impoundments and reduce their capacity (see Section 2.4.2). 

2.4.6 Stormwater Management and Flood Control 

Sediment in discharges from construction sites can clog or fill ditches, culverts, storm sewer pipes and 

basins, stormwater detention ponds, infiltration basins, and other stormwater management structures 

(Osterkamp et al. 1998). Sedimentation also decreases the capacity of natural stream and river channels, 

ponds, lakes, and reservoirs, increasing the likelihood of overbank flow events and their magnitude (Paul 

and Meyer 2001). Wolman and Schick (1967) noted that erosion and deposition of sediment in the state of 

Maryland had led to sediment deposition in streams and overflow and clogging of storm drains. 

If left in place, sedimentation can increase flooding. The U.S. Army Corps of Engineers (2007) stated that 

channel sedimentation due to construction discharges contributed to flooding of a Virginia residential 

neighborhood. Preventing flood damages from excessive sedimentation may require increased 

maintenance efforts such as dredging (Clark et al. 1985), vacuuming, and other types of sediment removal 

from stormwater management structures and surface waters. If sedimentation removal is performed in 

surface waters or in structures directly adjacent to surface waters, it can create additional environmental 

impacts such as resuspension of sediments and associated turbidity and contaminants in the water column 

and disturbance of the physical structure of the surface water bed and the associated benthic aquatic 

community. 

Surface water and stormwater management system sedimentation can increase the severity of property 

damages to bridges, roads, farmland, and other private and public property from flooding. Additionally, 

sediments carried by flood waters can damage property and can be expensive to remove once deposited, 

particularly in developed areas (Clark et al. 1985; Osterkamp et al. 1998). Clark et al. (1985) estimated 

flooding damages attributable to all sediment discharges to be $1.5 billion (2008$), annually. 

The quantity of sediment captured in stormwater control structures is unknown or could be very variable. 

Nelson and Booth (2002) did not observe significant long-term sediment accumulation in the stormwater 

retention/detention facilities (including the ditches and pipes connecting them to surface waters) of newer 

residential developments in Issaquah, Washington. They did note, however, that a significant amount of 

sediment was removed every year from catch-basins in the city of Issaquah, though a large fraction of this 

sand may have derived from winter road sanding. 

2.4.7 Recreational Uses and Aesthetic Value 

Polluted water greatly reduces the aesthetic appeal of a variety of recreational activities that take place in 

or near surface waters, including boating; swimming; hunting; and outings to hike, jog, picnic, camp, and 

view wildlife. Turbidity and suspended sediments are highly visible pollutants. Murky and visually 

unpleasant water and odors can detract from the enjoyment gained through outdoor activities. 

Sedimentation of streams, reservoirs, lakes, and bottomlands can reduce their depth and thus their 

capacity for boating and swimming (Osterkamp et al. 1998). 

Turbidity caused by sediment discharges may affect the safety of boating. Turbidity may obscure 

underwater obstacles, making collisions more likely. Increased sedimentation levels may create sandbars, 

increasing the chances of running aground. Clark et al. (1985) estimated that turbidity (from all sources) 

may be responsible for as many as 200 boating fatalities and many more injuries each year. Turbidity may 

also create safety hazards for swimmers by reducing the ability to see underwater hazards, increasing the 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-29 

frequency of diving accidents by impairing the ability to gauge water depth, and making location of 

swimmers in danger of drowning more difficult. 

Wolman and Schick (1967) noted that erosion and deposition of sediment in the state of Maryland had 

damaged recreation areas. Concern about potential highway construction impacts to aesthetic and 

recreational values of a small lake was sufficient to motivate residents of an adjoining neighborhood to 

initiate monitoring of lake water quality (Line 2009). 

Aesthetic degradation of land and water resources resulting from sediment and turbidity discharges can 

also reduce the market value of property near surface waters and thus affect the financial status of 

property owners. For example, a hedonic price study by Bejranonda et al. (1999) found that “the rate of 

sediment inflow entering the lakes has a negative influence on lakeside property rent.” Sediment 

discharges also have a significant impact on stream morphology. For example, higher coarse-sediment 

load leads to an increase in width of the river bed and, as a result, bank erosion (Wheeler et al. 2003). A 

1993 study of Lake Erie’s housing market found that “erosion-prone lakeshore property will be 

discounted” (Kriesel et al. 1993). Stabilization of stream banks leads to an increase in the value of 

surrounding property (Streiner and Loomis 1996). 

2.4.8 Recreational and Commercial Fishing 

Pollutants can negatively affect local flora and fauna, negatively impacting aesthetic appeal, wildlife 

viewing, and hunting for game (Osterkamp et al. 1998). By harming fish and shellfish communities, 

sediment can reduce fish and shellfish numbers or cause more desirable fish and shellfish to be replaced 

by less desirable fish and shellfish in a given location. 

As discussed in Section 2.3, sediment, turbidity, and other discharges from construction sites can reduce 

fish and shellfish populations by inhibiting their reproduction, growth, and survival. In some areas, 

desirable sediment-sensitive fish may be replaced by less-desirable, sediment-tolerant fish and shellfish. 

These population changes and reductions would reduce the size of commercial and recreational harvest by 

lowering both the total abundance of organisms and their individual size. These changes negatively affect 

recreational anglers, subsistence anglers, commercial anglers, fish and shellfish sellers, and consumers of 

fish and shellfish products. 

In addition, sediments, turbidity, and other pollutants reduce the aesthetics of a waterbody, which can 

reduce anglers’ enjoyment of their fishing experience and their choices of how often and where to fish. 

Sediment and turbidity may also affect recreational anglers by reducing the distance over which fish can 

see lures, resulting in lower catch rates (Clark et al. 1985). 

2.5 Sediment and Turbidity Criteria 

Natural levels of sediment and turbidity play important roles in aquatic ecosystems, providing habitat for 

benthic species, protection from predators, nutrient transport, and other functions. Appropriate levels of 

sediment are waterbody specific, as sediment levels vary naturally among different types of waterbodies 

according to geology, topography, stream gradient, waterbody morphology, vegetative land cover, 

climate, soil erodibility, and other landscape characteristics of the contributing watershed (USEPA 

2006b). When sediment and turbidity enter surface water at elevated levels, they can raise TSS and 

turbidity levels in the water column, increase deposition of sediment on the waterbody’s bed, and degrade 

overall water quality (USEPA 2006b). Elevated sediment and turbidity levels also negatively impact 
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November 2009 2-30 

human use of aquatic resources for recreation, drinking water supply, agricultural use, industrial use, and 

navigation and can impair stormwater management system function and surface water aesthetics. 

EPA and many states have issued criteria that describe appropriate sediment and turbidity levels for 

surface waters. Many of these criteria are narrative and provide a qualitative description of appropriate 

levels. Other criteria are numeric. 

2.5.1 Federal Sediment Criteria 

In 1987, EPA published the following guidance for developing numeric water quality criteria for 

sediment and turbidity: 

Solids (Suspended, Settleable) and Turbidity – Freshwater fish and other aquatic life: Settleable and suspended solids 

should not reduce the depth of the compensation point for photosynthetic activity by more than 10 percent from the 

seasonally established norm for aquatic life (USEPA 1987). 

This criterion has not been widely adopted by states. Guidance addressing primarily aesthetic properties, 

however, has been adopted by several states. 

Aesthetic Qualities – All waters shall be free from substances attributable to wastewater or other discharges that: settle 

to form objectionable deposits; float as debris, scum, oil, or other matter to form nuisances; produce objectionable 

color, odor, taste, or turbidity; injure or are toxic or produce adverse physiological response in humans, animals, or 

plants; [or] produce undesirable or nuisance aquatic life (USEPA 1987). 

EPA has not issued new sediment or turbidity criteria since 1987. However, EPA has published a 

document providing guidance on setting sediment criteria: Framework for Developing Suspended and 

Bedded Sediments (SABS) Water Quality Criteria (USEPA 2006b). 

2.5.2 State Sediment Criteria 

Many states have developed their own criteria to designate appropriate sediment levels for waters within 

the state. Some criteria vary by waterbody type. Streams with hard substrates (e.g., gravel, cobble, 

bedrock) or cold water fisheries typically have more stringent criteria than streams with soft substrates 

(e.g., sand, silt, clay) or warm water fisheries. Hawaii has separate criteria for reefs (Berry et al. 2003). In 

addition, Total Maximum Daily Loads (TMDLs) addressing sediment and turbidity have been developed 

for some surface waters and contain information on acceptable levels specific to those waterbodies. 

Many criteria are narrative statements describing the general nature of healthy sediment or turbidity levels 

without attempting to provide numeric guidelines. Narrative criteria most frequently address turbidity or 

surface water appearance (e.g., “free of substances that change color or turbidity”). Other criteria refer to 

undesirable biological effects (e.g., “no adverse effects” or “no actions which will impair or alter the 

communities”) (Berry et al. 2003). EPA (USEPA 2006b) provides a summary of state narrative and 

numeric criteria (though some criteria may have been modified since this information was compiled). 

A number of states have also set numeric criteria for TSS or turbidity levels. Most of these numeric 

criteria are for turbidity. Turbidity criteria exist as absolute values or ranges of values or as a permitted 

exceedance beyond background turbidity levels. Some states have issued numeric criteria for suspended 

sediments. 

A number of states have criteria based on sedimentation levels over a time period or during a storm event. 

Values are typically 5 mm during an individual event (e.g., during the 24 hours following a heavy 

rainstorm) for streams with hard substrate bottoms and 10 mm for streams with soft bottoms. Hawaii’s 

reef criterion is 2 mm of deposited sediment after an event (Berry et al. 2003). 
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November 2009 2-31 

Table 2-3 summarizes state suspended sediment and turbidity criteria for surface water quality. States 

using narrative criteria are noted with a “Yes” in the “Narrative TSS” or the “Narrative Turbidity” 

column. Values for numeric criteria and some details on their implementation are provided in the 

“Numeric TSS” and “Numeric Turbidity” columns. This table is intended to provide an overview of state 

numeric suspended sediment and turbidity water quality requirements and does not summarize all details 

relevant to their applicability. In addition, states periodically update their water quality criteria. Readers 

should consult individual state water quality criteria publications in order to obtain the most complete and 

current requirements. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 2-3: Suspended Sediment and Turbidity Criteria for Surface Water Quality by State 

State 

Numeric TSS 

(mg/L) 

Narrative 

TSS Numeric Turbidity Criteria Notes on Numeric Turbidity Crite

Alabama - - <50 NTU increase Source: Alabama DEM (2009). Alaska - - <5 NTU increase2 <10% increase (max. 25 NTU)3 Source: Alaska DEC (2003) Arizona - - 10-50 NTU4

Human Contact: 50 NTU in rivers, 25 NTU in lCold Water Fishery: 10 NTU Warm Water Fishery: 50 NTU in rivers, 25 NT

Arkansas - - 10-75 NTU 4 for baseflow values 

“Non-point source runoff shall not result in the 

exceedance of the in stream all flows valuethan 20% of the ADEQ ambient monitorinsamples taken in not less than 24 monthly 

Source: Arkansas Pollution Control and Ecology Commission (2007). 

California - - <20% increase2 <10% increase3 20% increase applies to Central Coast Region a

measured in JTU 10% increase applies to San Francisco Region 

Source: California EPA 2009a, b

Colorado - Yes - Connecticut - - <5 NTU increase Under ambient conditions. Class AA criteria Source: Connecticut DEP (2002). Delaware - - <10 NTU increase For all fresh waters and mixing zones District of Columbia - - - Florida - - <29 NTU increase Georgia - Yes - Hawaii 10-557 Yes 2-25 NTU for streams 0.1-15 NTU for coastal/marine waters 

Turbidity levels not to exceed NTU as specified 

Water type Geometric More than 

mean 10% of the time 2% 

Stream (wet 

season) 

5 15 

Stream (dry 

season) 

2 5.5 

All Estuaries 1.5 3.0 

Pearl Harbor 4.0 8.0 

Embayment 0.4 1.0 

Open Coastal 

(wet season) 

0.5 1.25 

Open Coastal 

(dry season) 

0.02 0.05 

Oceanic 0.03 0.1 

Marine 0.1 
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November 2009 

Table 2-3: Suspended Sediment and Turbidity Criteria for Surface Water Quality by State 

State 

Numeric TSS 

(mg/L) 

Narrative 

TSS Numeric Turbidity Criteria Notes on Numeric Turbidity Crite

Idaho - Yes 

<50 NTU increase at any time, <25 

NTU increase over 10 consecutive 

days 

For applicable mixing zones set by the departmSource: Idaho DEQ (2008). 

Illinois - - Indiana - Yes - Iowa - Yes 25 NTU Kansas - Yes - Kentucky - Yes - Louisiana - Yes 25, 50, 150 NTU, or <10% increase4

25 NTU: freshwater lakes, reservoirs, and oxbo

designated scenic streams and outstanding resource waters 

50 NTU: Amite, Pearl, Ouachita, Sabine, Calca

Tangipahoa, Tickfaw, and Tchefuncte Riveestuarine lakes, bays, bayous, and canals 

150 NTU: Atchafalaya, Mississippi, and Vermi

Rivers and Bayou Teche 

10% Increase: All other waters 

Maine - - - Maryland - - 150 NTU at any time 50 NTU monthly average Massachusetts - Yes - Michigan - - - Minnesota - - 5, 10, or 25 NTU4

Domestic Consumption: Class A & B 5 NTU, C

NTU 

Fisheries & Recreation: Class A 10 NTU, Clas

25 NTU 

Industrial Consumption: Class A 5 NTU 

Mississippi - Yes 50 NTU Applicable to waters outside the limits of a 750

mixing zone 

Missouri - Yes - Montana - Yes - Nebraska - - - Nevada Yes Waterbody specific New Hampshire - Yes 0-10 NTU increase4 Class A waters: No turbidity other than natural Class B & C: 10 NTU increase over natural New Jersey 25 - 10, 30, or 50 NTU4

Freshwater: 50 NTU any time, 15 NTU 30-day Saline water: 30 NTU any time, 10 NTU 30-daCoastal saline: 10 NTU 

New Mexico - Yes <10 NTU increase2 or <20% increase3 GA waterbody Turbidity shall not exceed 5 NT

Source: New Mexico (2005) 
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November 2009 

Table 2-3: Suspended Sediment and Turbidity Criteria for Surface Water Quality by State 

State 

Numeric TSS 

(mg/L) 

Narrative 

TSS Numeric Turbidity Criteria Notes on Numeric Turbidity Crite

New York - Yes Waterbody specific North Carolina 5009 Yes 10, 25, or 50 NTU 4

10 NTU in trout waters 25 NTU in other lakes and reservoirs 50 NTU in other streams If background exceeds these levels, no increase 

North Dakota 30 - - Ohio - - Oklahoma - - 10 or 50 NTU4

Cold water aquatic communities/trout fisheries

Lakes: 25 NTU; Other Surface Waters: 50 

If background exceeds these levels, point sourc

cause increases above ambient levels 

Oregon - Yes <10% Increase 

Exception for temporary increases to do “legitim

activities” including dredging and construcproviding all practicable turbidity control tare applied. 

Pennsylvania - Yes 40-100 NTU Specific limits only apply to Neshaminy Basin Puerto Rico - - Specific to waterbody class 

Class SB shall not exceed 10 NTU, except by n

causes; Class SC shall not exceed 10 NTUshall not exceed 50 NTU, except when due phenomena 

Source: Puerto Rico EQB (2003). 

Rhode Island - - 5 NTU or <10 NTU increase4 Class A: 5 NTU Class B & C: 10 NTU increase South Carolina Yes 10 NTU or <10% increase Providing existing uses are maintained Specific to freshwaters suitable for supporting tSouth Dakota 

53-263 (at any time);

30-150 (monthly 

average)4

Yes - 

Tennessee - Yes - Texas - Yes - Utah 35-904 Yes 10 or 15 NTU4 10 NTU for cold and warm water game fish 15 NTU for nongame fish, waterfowl, and otheVermont - - 10 or 25 NTU4

10 NTU for Class A(1) ecological waters and C

cold water fish habitats 

25 NTU for Class B warm water fish habitats 

Virginia - Yes - 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 2-3: Suspended Sediment and Turbidity Criteria for Surface Water Quality by State 

State 

Numeric TSS 

(mg/L) 

Narrative 

TSS Numeric Turbidity Criteria Notes on Numeric Turbidity Crite

Washington - - 5 or 10 NTU2 or <10% or <20% increase3 4

Class AA &A: 5 NTU2 or 10% increase3

Class B & C: 10 NTU2 or 20% increase3 

Lakes: 5 NTU increase Also includes a flow-based criteria based on flo

construction site. Flows ranging from 10 to 100 CFS have turbidity measured at 100 to from downstream activity 

Source: Washington (1997) 

West Virginia - - 10 NTU2 or <10% increase3 Wisconsin - Yes - Wyoming - Yes <10 or 15 NTU increase4

<10 NTU increase for Class 1 and 2 waters that 

water fisheries 

<15 NTU increase for Class 1 and 2 waters that 

water fisheries and all Class 3 waters 

1 “There shall be no turbidity of other than natural origin that will cause substantial visible contrast with the natural appearance of waters or interfere with any benefic2 If naturally less than 50 NTU. 3 If naturally greater than 50 NTU. 4 Varies based on waterbody classification. 5 May not “produce objectionable odor, color, taste, or turbidity.” 6 Applies during dry season only. Geometric mean of 10 mg/L, not exceeding 30 mg/L 10% of the time and 55 mg/L 2% of the time. 7 “To be aesthetically acceptable, waters shall be free from human-induced pollution which causes: 1) noxious odors; 2) floating, suspended, colloidal, or settleable m

 objectionable films, colors, turbidity, or deposits.” 

8 Waterbody types AA, A, B, C, D, SA, SB, SC, SD, I: no increase may cause substantial visible contrast from natural conditions. 9 Standard is for total dissolved solids (TDS). 10 For mixing zones. Source: USEPA (2006b), unless otherwise noted. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-36 

2.6 Surface Water Quality Impairment from Sediment and Turbidity 

Section 305(b) of the Clean Water Act (CWA) requires states, territories, and other jurisdictions of the 

United States to submit reports to EPA on the quality of their surface waters every two years. These 

entities have determined the appropriate uses of each waterbody within their jurisdiction. Uses can 

include recreation, drinking water source, navigation, cold water fishery, and wildlife habitat, among 

others. States and other entities determine appropriate narrative and/or numeric water quality criteria for 

each of the designated uses. The criteria describe the physical, chemical, and biological characteristics of 

a surface water able to fulfill its designated uses. The sediment criteria described in the section above are 

examples of such criteria. Typically, a surface water has criteria for multiple water quality parameters. If 

a waterbody fails to meet any one of its designated uses, CWA Section 303(d) requires a state or other 

entity to list the waterbody as “impaired.” If a waterbody meets its designated uses but is in danger of 

failing to do so in the future, the state or other entity must list the waterbody as “threatened” (USEPA 

2005a). 

The Assessment TMDL Tracking and Implementation System (ATTAINS) provides information on water 

quality conditions reported by the states to EPA under Sections 305(b) and 303(d) of the Clean Water 

Act. The information available in ATTAINS is updated as data are processed and are used to generate the 

biennial National Water Quality Inventory Report to Congress. This information reflects only the status of 

those waters that have been assessed. Appendix A provides information on the state water report year for 

which data were available for populating the tables below as of September 17, 2009. 

According to ATTAINS, 49 percent of assessed reach miles (or 458,209 miles) have been identified as 

impaired; sediment contributes to impairment in 107,231 miles, and turbidity contributes to impairment in 

26,278 miles. For lakes and reservoirs, 66 percent of assessed lake acres (or 11,545,337 acres) have been 

identified as impaired; sediment contributes to impairment in 715,558 lake acres, and turbidity contributes 

to impairment in 1,008,276 acres. For bays and estuaries, 63 percent of assessed square miles (or 11,222 

square miles) have been identified as impaired; sediment contributes to impairment in 209 square miles, 

and turbidity contributes to impairment in 240 square miles. It should be noted that individual waters may 

be impaired by more than one pollutant. Although states tend to target their monitoring efforts to those 

surface waters they believe to be impaired, the total area of impaired surface waters due to sediment and 

turbidity is probably underestimated due to the low percentage of surface waters that were assessed. As of 

September 17, 2009, states had assessed only 26 percent of the nation’s reach miles, 42 percent of its lake 

acres, and 20 percent of its bay and estuary square miles. 

Table 2-4 and Table 2-5 present information on “sediment” and “turbidity and suspended solids” 

impairment by EPA Region as reported by the states in ATTAINS. This information is presented by EPA 

Region. Figure 2-1 provides a map of the EPA Regions. 
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November 2009 2-37 

Table 2-4: Surface Waters Impaired by “Sediment,” by EPA Region 

EPA Region Reach (miles) Lake/Pond/Reservoir (acres) Bay/Estuary (sq. miles) 

1 236 443 4 

2 1,123 39,277 0 

3 8,188 193 12 

4 11,126 23,333 – 

5 34,410 57,965 – 

6 3,567 153,478 193 

7 9,659 144,125 – 

8 9,597 147,592 – 

9 198 – – 

10 29,127 149,152 – 

Nation 107,231 715,558 209 

Source: EPA ATTAINS database (USEPA 2009a) as of 9/17/09. 

Table 2-5: Surface Waters Impaired by “Turbidity” and “Suspended Solids,” by EPA 

Region 

EPA Region Stream/ River (miles) Lake/Pond/Reservoir (acres) Bay/Estuary (sq. miles) 

1 339 43,735 5 

2 3,539 1,539 8 

3 229 – – 

4 732 13,814 4 

5 9,278 134,427 – 

6 6,880 626,642 195 

7 84 38,173 – 

8 2,304 3,168 – 

9 904 136,980 28 

10 2,439 9,798 – 

Nation 26,728 1,008,276 240 

Source: EPA ATTAINS database (USEPA 2009a) as of 9/17/09. 

Figure 2-1: EPA Regions 
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November 2009 2-38 

Water quality in the United States has also been assessed through a series of national, probability-based 

surveys known as the National Aquatic Resource Surveys. These surveys use randomized sampling 

designs, core indicators, and consistent monitoring methods and laboratory protocols to provide 

statistically defensible assessments of water quality at the national scale. The Wadeable Streams 

Assessment (USEPA 2006d) is a statistical survey of the smaller perennial streams and rivers that, 

according to the report, comprise 90 percent of all perennial stream miles in the United States. Excess 

streambed sedimentation is ranked as one of the most widespread stressors examined in the survey. (The 

survey did not analyze turbidity or suspended sediment levels.) According to the survey, 25 percent of 

streams have “poor” streambed sediment condition, and 20 percent are in “fair” condition relative to 

reference streams. The survey also examined the association between stressors and biological condition, 

and found that high levels of sediments more than double the risk for poor biological condition (see 

Figure 2-2). 

Figure 2-2: Extent of Stressors and Their Relative Risk to the Biological Condition of the 

Nation's Streams 

Source: USEPA (2006c). 

Another National Aquatic Resource Survey, the National Coastal Condition Report III (USEPA 2008b), 

assesses coastal aquatic habitat and found water clarity (related to turbidity) to be poor in 17 percent of 

coastal waters. Data on excessive sedimentation was not collected for the report. 

2.6.1 Current Total Suspended Solid Concentrations in U.S. Surface Waters 

TSS concentrations vary from waterbody to waterbody due to differences in their contributing watersheds 

created by natural conditions and human activity. EPA used the SPARROW model to estimate current 

TSS concentrations in the United States’ Reach File Version 1 (RF1) surface water network (see Chapter 

6 for more information). For this analysis, the RF1 network consists of approximately 650,000 miles of 

the largest rivers and streams in the coterminous United States and associated lakes, reservoirs, and 

estuarine waters. Due to data limitations, EPA did not model surface water turbidity levels. Table 2-6

summarizes the distribution of TSS concentrations found in individual RF1 reaches as estimated by 

EPA’s analysis. The median TSS concentration, weighted by reach length, is 302.9 mg/L. The range of 

TSS concentrations falls between 26.7 mg/L and 6,154.5 mg/L at the 5th and 95th percentiles, respectively. 

The average TSS concentration is 1,068.6 mg/L, indicating that concentrations in the 90th to 95th
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 2-39 

percentile range are high enough to shift the average concentration significantly higher than the median 

concentration. 

Table 2-6: SPARROW Distribution of TSS Concentrations in RF1 Reaches1

Distribution of TSS Concentrations in RF1 Reaches1

RF1 

Reach 

Count 

RF1 

Reach 

Miles 

Average 

TSS 

(mg/L)1

5th 

Percentile 

25th 

Percentile 

50th 

Percentile 

75th 

Percentile 

95th 

Percentile 

62,370 650,043 1,068.6 26.7 102.9 302.9 1,079.6 6,154.5 1

 Concentrations weighted by reach length. Concentration estimates reflect replacement of potential outliers (defined as values above the 95th

percentile) with the 95th percentile value.

Figure 2-3 shows total RF1 reach miles and their current TSS concentrations as predicted by SPARROW 

(not weighted by reach length). As shown below, the majority of waters have TSS concentrations below 

1,000 mg/L, though substantially higher average concentrations up to and exceeding 6,000 mg/L can be 

found in some waters. 

Figure 2-3: TSS Concentrations by Total RF1 Reach Miles as Predicted by SPARROW for 

Current Conditions 

0

25000

50000

75000

100000

125000

150000

175000

200000

225000

0

450

900

1,350

1,800

2,250

2,700

3,150

3,600

4,050

4,500

4,950

5,400

5,850

6,300

6,750

7,200

7,650

>=8,100

TSS Concentration (mg/L)

Total River Miles

95th Percentile: 6,154.51 mg/L

Total: 62,370 River Reaches (650,043 River Miles)

An important factor to consider when examining this information is that episodic precipitation events are 

the primary cause of construction site TSS discharges to surface waters. Most TSS discharge, therefore, 

takes place during or shortly after precipitation events. Once a precipitation event ceases, discharges from 

construction sites generally cease within a short time period. The estimates presented here, however, are 

not intended to reflect the higher surface water TSS concentrations associated with individual, episodic 

storm events. They instead represent average concentrations estimated to exist in RF1 reaches over multi-
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November 2009 2-40 

year time periods. EPA uses these long-term estimates because the data and modeling resources currently 

available to EPA do not permit the finer level of time resolution necessary to model individual storm 

events. A study by Wong (2005) documents that long-term averages of TSS concentration sampling data 

provide a different type of perspective on surface water conditions than data on observed 90th and 98th

percentile concentrations, which better reflect the types of concentrations observed after precipitation or 

other erosion events. 

EPA expects that, in general, surface water TSS concentrations during and immediately following storm 

events would be higher than the concentrations presented here. During periods when no precipitation 

event is available to cause a sediment discharge from a construction site, surface water sediment 

concentrations would generally be lower. 

Current levels of sediment erosion and sediment and turbidity discharge to many surface waters in the 

United States are much higher than would be observed under natural, undisturbed conditions due to 

human activity. Sediment and turbidity levels in surface waters reflect these elevated levels of sediment 

input. 

Human activities that have increased sediment erosion and transport in surface waters include agriculture, 

grazing, logging, construction, and mining. In addition, land use change has increased streambed erosion 

in many urbanized areas, mobilizing significant quantities of sediment (Nelson and Booth 2002; Renwick 

et al. 2005; Wilkinson 2005; Wilkinson and McElroy 2007). It is estimated that soil erosion due to human 

activity is approximately 10 times greater than erosion from all natural forces combined (Wilkinson 

2005). Approximately one-third of all eroded sediment in the United States derives from agricultural 

activities (Osterkamp et al. 1998). Construction is considered to be another major source of erosion 

(Wilkinson 2005). Nelson and Booth (2002) documented a 50 percent increase in annual sediment yield 

due to human activity (primarily urbanization) in a western Washington watershed. 

Only a portion of all eroded sediments are transported through the entire surface water system to the 

ocean in any given year (Renwick et al. 2005; Wilkinson 2005; Wilkinson and McElroy 2007). A large 

portion is stored on the land surface and in and adjacent to surface waters. Some surface waters are 

currently mobilizing historical sediments stored on floodplain terraces and in small impoundments (e.g., 

from past farming, mining, and clear-cutting practices). These stored sediments provide a large and 

continuing source of sediment input to the U.S. surface water network (Renwick et al. 2005). 
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November 2009 3-1 

3 Other Pollutants in Construction Site Discharges 

In addition to mobilizing and discharging sediment and turbidity to surface waters, construction activity 

can discharge many other kinds of pollutants. Whereas sediment and turbidity are nearly ubiquitous in 

discharges from construction sites, the presence and quantity of other pollutants varies widely. These 

pollutants can derive from construction materials and equipment, historic site contamination, and natural 

soil and groundwater constituents. They may be carried in stormwater in solution or adsorbed to 

transported sediment particles. These “other pollutants,” though not as well documented as sediment and 

turbidity in the construction context, can also impact water quality and therefore aquatic life and human 

use of water resources. This chapter discusses these pollutants and their potential sources, behavior in 

surface waters, and potential to impact aquatic organisms and human use of aquatic resources. Section 3.1

provides an overview of the importance and concerns associated with these other pollutants. Section 3.2

provides additional information on potential sources of pollutants on construction sites and changes in 

stormwater discharge. Section 3.3 provides additional information on specific pollutants potentially found 

in construction site discharges. Pollutant-specific impacts from laboratory testing and field studies are 

cited as appropriate, but further information is available in Chapter 4, which provides a review and 

summary of studies from the literature that document specific cases of construction site discharges, the 

pollutants they contain, and their impacts on the aquatic environment. 

3.1 Introduction 

In addition to mobilizing and discharging sediment and turbidity to surface waters, construction activity 

can discharge many other kinds of pollutants. These pollutants can derive from construction materials and 

equipment, historic site contamination, and natural soil and groundwater constituents. Construction 

activity can also impact receiving waters by alteration of the hydrologic regime; for example, increasing 

the volume and intensity of stormwater runoff but decreasing a surface water’s dry weather baseflow. 

These alterations may lead to erosion and sediment mobilization further down in the watershed of the 

construction activity. Section 3.2 provides additional information on potential sources of pollutants on 

construction sites and changes in stormwater discharge. 

The pollutants of interest include nutrients (nitrogen, phosphorus), organic compounds and materials 

(organic material, petroleum hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), pesticides), 

metals, dissolved inorganic ions (major anions and cations), substances that can modify surface water pH, 

and pathogens (bacteria, fungi). Studies of construction sites have documented the presence of many of 

these pollutants in construction site stormwater, discharges, and receiving waters. Section 3.3 describes 

their potential sources, their transport behavior, and their potential to impact the aquatic environment and 

resources. 

3.1.1 Pollution Discharge and Transport Pathways 

Most pollutants enter surface waters when precipitation erodes or dissolves materials and carries them to 

receiving waters. Many of the pollutants described in this section adsorb to or are a component of soil 

particles and travel with them as they erode (e.g., organic compounds, metals, nutrients). Stormwater flow 

preferentially removes fine particles from soil because of their lesser mass and tendency to remain 

suspended in flow once mobilized. Many of these pollutants form adsorption complexes that bind them to 

clay minerals, other fine particles, and organic matter. For this reason, many pollutants that associate with 
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November 2009 3-2 

sediments are present in higher levels in the sediments mobilized by stormwater than in the parent soil 

(Novotny and Chesters 1989). The movement of these pollutants is therefore very similar to that 

described for sediment in Section 2.2. 

Other pollutants dissolve in precipitation or disassociate (“desorb”) from sediment and move in a manner 

that mimics that of stormwater flows (e.g., nitrate, chloride). Some pollutants may switch between 

dissolved and particulate forms as conditions change. This may make control of these pollutants more 

difficult since standard practices for limiting sediment discharge may not significantly reduce these 

dissolved constituents. 

Pollutants can also enter surface waters during dry weather due to excavation dewatering, groundwater 

seepage, construction equipment washout, landscape irrigation, and equipment operation in or near 

surface waters. Dewatering, the removal and discharge of water from excavated areas or work areas 

located within surface waters, is necessary at some construction sites to maintain dry work conditions. 

Water can collect in excavated areas due to precipitation and groundwater seepage (e.g., Horner et al. 

1990, Montgomery County DEP 2009). Levels of dewatering activity and groundwater contamination 

vary widely among construction sites. 

Excavation dewatering discharges can contain a variety of naturally occurring or human activity-derived 

pollutants. Any pollutant that can enter groundwater flow can be present in a dewatering discharge. 

Common groundwater contaminants include heavy metals, organic solvents and degreasers, pesticides 

and herbicides, and nitrates. Naturally occurring constituents, such as iron and manganese, can form 

extensive and unaesthetic deposits on surface water beds when groundwater under low reductionoxidation (“redox”) conditions is exposed to ambient air. 

3.1.2 National Scale and Cumulative Impact Concerns 

The importance of the discharge of these other pollutants from construction sites is uncertain, but there 

are strong indications that the cumulative effects of construction activities is of national significance. 

Levels of pollutant discharge other than sediment and turbidity are highly variable among construction 

sites. EPA quantified construction site contributions of nitrogen and phosphorus, both common soil 

constituents, to surface waters. Chapter 6 describes the methodology EPA used for this analysis as well as 

analysis results. Many other pollutants, however, may be less commonly found on construction sites. The 

current literature contains limited information on the frequency and level at which these pollutants appear 

at construction sites across the United States. 

What literature is available indicates that individual construction sites typically do not discharge acutely 

toxic levels of metals and toxic organic compounds unless a material spill occurs or site soils and/or 

groundwater are contaminated with sufficient levels of toxic pollutants. However, discharges of pollutants 

below acutely toxic levels can nevertheless be of concern. 

Many potential construction site pollutants are components of or adsorb to sediments (e.g., metals, toxic 

organic compounds, nutrients). Pollutants with this behavior can accumulate over time in surface water 

sediments to levels many times higher than those found in the adjacent water column. Pollutant levels can 

eventually become high enough to affect aquatic organisms (USFWS 1998). Construction is a widespread 

and frequent activity in the United States. The number of construction projects active each year (greater 

than 80,000) is much larger than the number of active facilities in many other industrial sectors (USEPA 

2009b). The multiplier effect of such a large number of potential discharge points means that the 

construction sector’s cumulative pollutant contributions can be very high, even when contributions from 

individual sites are relatively low. 
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November 2009 3-3 

In addition, construction sites can discharge mixtures of pollutants. While no one pollutant may be 

present at a level sufficient to harm a surface water or its biota, the cumulative or synergistic effect of 

several mixture components may be sufficient to do so, particularly if other environmental stressors are 

present. Construction sites also discharge to many surface waters that are already impaired or in danger of 

impairment. Construction sites contribute to the already elevated cumulative pollutant loads in these 

waterbodies. 

3.2 Construction Site Sources of Pollutants Other Than Sediment and Turbidity

3.2.1 Construction Materials and Equipment 

An enormous variety of materials are used on construction sites. A number of these materials contain 

chemicals or produce debris or residues that can cause harm if they enter surface waters in sufficient 

quantities. These materials can contribute nutrients, metals, and toxic organic compounds to stormwater 

discharges. 

A partial list of common building and equipment materials includes paint; sealants; grouts; solvents; 

adhesives; wood preservatives (e.g., creosote, copper azole, ammoniacal copper zinc arsenate); wood 

shavings, scraps, and sawdust; drywall; packaging material; detergents (e.g., trisodium phosphate); metal 

materials and debris; asphalt-based materials (e.g., roofing and paving materials); Portland cement; 

concrete; concrete truck washout; aggregate; dust suppressants (e.g., calcium chloride); blasting 

explosives; and construction equipment oil, fuel, lubricants, antifreeze, other fluids, and washwaters. 

Pavement materials include asphalt, Portland cement, pavement sealers (e.g., methacrylate sealers, coaltar based sealants) and additives (e.g., plasticizers, air entraining agents, water reducers, strength 

accelerating agents, corrosion inhibitors, colorants, pumping acids, rejuvenators, liquid and fibrous 

polymers, carbon black, sulfur). Many industrial wastes/by-products are used as aggregate or additives in 

asphalt and Portland cement pavement mixtures or as aggregate for unpaved roads. These materials 

include recycled asphalt and concrete pavement, shredded scrap tires, blast furnace slag, plastic waste, 

coal bottom ash, coal fly ash, steel slag, mine tailings and other wastes, municipal sludge, municipal solid 

waste incineration bottom ash, phosphogypsum, wood waste, petroleum refinery residuals, foundry sand, 

bricks, and asphalt shingles (National Research Council 2001; Eldin 2002). 

Additional materials, both newly manufactured and industrial wastes/by-products, are used as soil 

amendments or fill on construction sites. Materials include imported soil and stone, lime (soil 

stabilization), cement (soil stabilization), crushed limestone, asphalt emulsions, and a variety of industrial 

by-products. Industrial by-products used as fill include coal combustion wastes (e.g., fly ash, bottom ash, 

boiler slag), lime kiln dust, gypsum, gypsum wallboard, foundry sand, sandblasting abrasives, cement kiln 

dust, wood ash, glass, and wastewater filter sand (Eldin 2002; USEPA 2005b; Iowa Waste Reduction 

Center 2009). Landscaping materials include topsoil, soil sterilants in treated topsoil, fertilizer, mulch, 

hydroseeding mixtures, lime, straw, and pesticides. 

Pollutants from construction equipment and materials can be carried to surface waters in stormwater (or 

other) flow in solution or by erosion of debris and particulate matter (City of Vancouver 2007). 

Infiltration of construction site stormwater into soil can reduce transport of pollutants to surface water, 

though many pollutants adsorb to soil particles and will travel with them if they are subsequently eroded 

and transported to surface water. Improper operation and maintenance of construction equipment and 

insufficient storage and housekeeping practices (e.g., improper storage of oil, gasoline, and chemical 
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November 2009 3-4 

products) can lead to additional leakage, spillage, or erosion of materials and their transport to surface 

waters. Spills at construction sites have been documented (Garton 1977 (diesel fuel); Ohio 1997f 

(construction debris); Kress 2007). 

Some types of construction activity (e.g., stream channel realignment, culvert installation, bridge support 

emplacement) take place directly in surface waters. In addition, at some sites construction equipment 

must cross surface waters in order to access all parts of a site. These activities can provide easy access for 

contaminants from construction materials and equipment to surface waters, as well as cause direct 

disturbance of surface water sediments, physical structure, and organisms if precautions are not taken. 

Sedimentation ponds can become sources of pollutants if they are allowed to become host to large 

amounts of algae, insect larvae, bacteria, and other organisms or if they are allowed to heat water to high 

temperatures before discharge. 

The type and level of building, equipment, fill, soil amendment, and landscape material pollutants in 

stormwater discharges from a specific construction site depend on the choice of materials and 

construction practices used on the site. Many building, pavement, soil amendment, fill, and landscaping 

materials vary in composition based on source materials and manufacturing method. Pollutants present in 

a material from one source or manufacturer may not be present in the same category of material from 

another source or manufacturer (NRC 2001c). Not every member of every material category will contain 

constituents of concern. 

3.2.2 Historic Site Contamination 

Construction soil and groundwater disturbance can mobilize pollutants already present on a site due to 

prior human activity. A broad range of human activities and land uses can contaminate a site’s soil and 

groundwater. 

Industrial activities can directly deposit a wide range of pollutants in soil and groundwater, including 

metals, nutrients, and toxic organic compounds (Folkes et al. 2001; Seattle and King County Public 

Health 2001; Area Wide Soil Contamination Task Force 2003, Blanco et al. 2009). 

Agricultural lands can contain elevated levels of pesticide residues, nutrients from fertilizers and manure 

(particularly phosphorus), and salts from irrigation waters (New Jersey DEP 1999; Area Wide Soil 

Contamination Task Force 2003; Renshaw et al. 2006; Robinson et al. 2007; Stone and Anderson 2009). 

Approximately 40 percent of current developed land was previously used for agricultural activities (NRC 

2008). 

In urban areas, construction often begins with demolition and removal of existing buildings and other 

structures. Materials in preserved wood (e.g., creosote, chromated copper-arsenate); paint chips and dust 

(including lead-based paints); brick and wood scraps; concrete debris; asbestos-based insulation and other 

materials; polychlorinated biphenyls (PCBs) from old electricity transformers; metal scrap; waste 

solvents; historic fuel spills; and fill material composed of coal ash and clinkers, brick and concrete 

demolition debris, and other industrial by-products may be present (Blanco et al. 2009). Pesticides have 

been widely used in urban areas, as well, and residues may persist in soil and other materials (Folkes et al. 

2001). Erosion and leachate from these materials may elevate pH, metals, and toxic organic compounds 

levels in stormwater runoff. 

Several contaminants can be deposited on the land through atmospheric deposition, including metals (e.g., 

copper, chromium, lead, mercury, and zinc), nutrients (nitrogen, phosphorus), and organic compounds 

(e.g., PAHs, PCBs, and pesticides). Sources of these contaminants include manufacturing facilities (e.g., 
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November 2009 3-5 

smelters), power plants, motor vehicles, and wind-borne dusts (Folkes et al. 2001, NRC 2008). 

Atmospheric deposition from these sources is more likely to occur near urbanized areas and downwind of 

sources. However, atmospheric deposition can be very widespread and may affect areas that are 

fundamentally undeveloped (e.g., undisturbed forest). Over time, atmospherically deposited pollutants 

may accumulate to significant levels (Seattle and King County Public Health 2001, Area Wide Soil 

Contamination Task Force 2003). For example, lead emissions from several decades of leaded fuel use in 

the United States elevated lead levels in many urban soils. 

The frequency and magnitude at which soil contamination is present nationwide is unknown. A task force 

sponsored by the state of Washington estimated that up to 676,550 acres in the state may contain soil 

contaminated with low to moderate levels of lead and/or arsenic (Area Wide Soil Contamination Task 

Force 2003). The state of New Jersey, which convened a similar task force, estimated that soil on up to 5 

percent of the state’s land was contaminated with lead and arsenic residues from the historical use of lead 

arsenate pesticides (New Jersey DEP 1999). At the severe end of the contamination scale, approximately 

300 sites on the Superfund National Priorities List have been identified as having some level of soil 

contamination. At this time, EPA has determined that the soil contamination is severe enough at 

approximately 150 of the sites to require cleanup under the Superfund program (USEPA 2004c). 

The type and level of historical pollutants at a specific construction site depend on the nature of the past 

activities and pollutant release and varies widely among sites. Not all sites contain constituents of concern 

from historic contamination. 

3.2.3 Natural Site Constituents 

Naturally occurring soil, rock, and groundwater composition and constituents vary widely among sites. 

Soil is a complex mixture of inorganic and organic materials deriving from weathered rock, biological 

activity, precipitation, and atmospheric deposition. Impacts from suspended solids, turbidity, and 

sedimentation from eroded soil are discussed in Chapter 2. Soil constituents also commonly include 

nitrogen, phosphorus, partially and fully decomposed organic matter, bacteria, fungi, insects, metals (e.g., 

iron, aluminum, magnesium, manganese), and other inorganic materials (e.g., compounds containing 

silicon, potassium, calcium, sodium, sulfur, inorganic carbon, and chlorides). Some constituents, such as 

zinc, copper, arsenic, chromium, nickel, barium, and lead, can be natural soil components but can be 

highly uneven in their distribution. Type and level of soil and rock constituents at specific construction 

sites depend on the nature of the soil and rock present on the construction sites. For example, Hawaiian 

soils have naturally higher levels of copper, zinc, and aluminum (Wong 2005). 

Rock and soil constituents are natural components of sediments entering aquatic ecosystems. At elevated 

levels, however, they can become pollutants. Construction activity modifies natural erosion processes by 

pulverizing and exposing formerly sequestered rock and soil materials to erosion and leaching by 

precipitation, making transport of their constituents to surface waters much more likely. 

Some sites also have natural groundwater seeps that may be further exposed or captured in site drainage 

by construction activity. These seeps, particularly those with naturally acidic waters or highly sodic 

waters, may contain elevated levels of certain constituents, including metals. Kalainesan (2007) 

documented elevated levels of dissolved solids and metals in a construction site groundwater seep 

(aluminum, manganese, iron, magnesium, and sulfate). 

Elevated levels of pollutants in stormwater and receiving surface waters due to natural rock, soil, and 

groundwater constituents at construction sites has been documented in the literature (Huckabee et al. 
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November 2009 3-6 

1975; Burton et al. 1976, Chisolm and Downs 1978; Extence 1978; Tan and Thirumurthi 1978; Daniel et 

al. 1979; Shields and Sanders 1986; Kucken et al. 1994; Kalainesan 2007; USEPA 2009b). 

Naturally occurring organisms such as bacteria and fungi can also function as pathogen pollutants. For 

example, Aspergillus sydowii, a common terrestrial fungus, can enter marine environments through soil 

erosion and discharge to surface waters and is a widespread sea fan pathogen in the Caribbean (Lafferty et 

al. 2004). 

3.2.4 Altered Stormwater Discharge 

An additional impact from construction activity is that vegetation removal, soil compaction, modified 

surface topography, and installation of impervious surfaces (e.g., roads, parking lots, and roofs) can 

increase the volume and intensity of water runoff discharging from a construction site during precipitation 

events but decrease a surface water’s dry weather baseflow. 

The increased flow can cause changes in the hydrograph of a receiving stream, causing it to exhibit more 

frequent and larger peak flows, followed by periods of reduced base flow and possibly desiccation. The 

stream may incise or widen to accommodate periodic elevated flow volumes (Wheeler et al. 2003). The 

changes in streambed morphology can increase sediment erosion, destabilize stream banks, elevate 

sediment and turbidity levels in downstream waters, and cause long-term changes in aquatic and riparian 

habitat. Elevated flows can also damage aquatic communities by scouring and washing downstream 

aquatic plants, algae, invertebrates, and other organisms. These changes can degrade or destroy 

previously existing aquatic communities. 

Groundwater recharge is reduced by increased intensity and volume of stormwater runoff and reduced 

soil infiltration. Reduced groundwater recharge results in less water available for surface water dry 

weather baseflow. Reductions in dry weather baseflow can cause extended dry periods in streams and 

stress aquatic organisms. 

The type and level of runoff from a specific construction site depends on the nature of the construction 

site, though many sites, particularly those that have been cleared of vegetation, have elevated surface 

runoff. Surface runoff changes are predictable through the use of standard hydrologic models (e.g., Soil 

Conservation Service TR-55, USDA 1986). 

Line and White (2007) documented similarly elevated levels of stormwater runoff during all stages of a 

residential construction project in North Carolina. The authors postulated that soil compaction during the 

clearing and grading stages of the project were sufficient to mimic the impervious surfaces in place during 

later stages of the project. The authors also documented an increase in peak discharge rate and elimination 

of stream baseflow in the affected area. Selbig and Bannerman (2008) found that a stormwater basin 

serving a community under construction using Low Impact Development (LID) techniques discharged 

more water than the original undeveloped landscape but significantly less than a stormwater basin serving 

a neighborhood developed with conventional techniques. Clausen (2007) found that while runoff was 

elevated from a traditional construction site, it actually declined at a site using LID practices. Other 

studies documenting increases in surface runoff include Yorke and Davis (1972), Selbig et al. (2004), and 

Montgomery County DEP (2009). Burton et al. (1976) and Chen et al. (2009) did not detect a change in 

site runoff levels. 

Construction sites can also concentrate previously diffuse surface runoff with ditches, pipes, and other 

structures. When a high volume of runoff enters a surface water, particularly at too high a velocity, it can 
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November 2009 3-7 

erode the receiving water’s banks and bed if not properly diffused. This erosion can modify local 

waterbody morphology, transport sediment, and elevate turbidity levels in downstream waters. 

3.3 Other Pollutants from Construction Activity 

This section provides additional information on specific pollutants potentially found in construction site 

discharges. The discussion describes potential sources of other pollutants, their transport behavior, and 

their potential to impact the aquatic environment and resources. 

3.3.1 Nitrogen and Phosphorus 

Phosphorus and nitrogen compounds are natural components of most aquatic ecosystems. At excessive 

levels, however, they become pollutants. Nitrogen and phosphorus are present at some level in nearly all 

construction site soils. Information on levels of nitrogen and phosphorus compounds typically found in 

soils and sediments is provided in Section 6.4.1. Several studies have documented discharge of nutrients 

from construction sites believed to derive from natural soil constituents (Burton et al. 1976; Tan and 

Thirumurthi 1978; Daniel et al. 1979; Shields and Sanders 1986). 

Nutrients can also derive from construction materials and historic contamination. While some level of 

nitrogen and phosphorus is naturally present in most soils, the presence of nutrients from other sources 

varies widely. Roofing materials treated with phosphate washes and binders during preparation for use 

may release phosphorus (NRC 2008). Phosphorus from blasting explosive residue on a construction site 

has been documented (USEPA 2009b). 

Fertilizers are also frequently used during the surface stabilization phase of construction to encourage 

vegetation growth. They can be applied directly or as a component of a hydroseeding mixture or other 

product. Organic materials such as mulch, compost, and straw are also used to encourage vegetation and 

contain elevated nutrient levels relative to site soils. When insufficiently managed, they can contribute 

nutrients to construction site discharges (Glanville et al. 2004). Faucette et al. (2007) documented 

elevated nitrogen and soluble phosphorus levels in construction site stormwater runoff from areas treated 

with straw blankets containing mineral fertilizer for erosion control. Hydroseeding operations, in which 

seed, fertilizers, lime and, in some cases, tackifiers, are applied to soil in a one-step operation are more 

likely to discharge nutrients than conventional seed-bed preparation operations during which fertilizers 

and lime are tilled into the soil. Nutrient discharges from fertilizer use on construction sites have been 

documented in several studies (Taylor and Roff 1986; Horner et al. 1990; Faucette et al. 2007; Clausen 

2007; Kalainesan 2007; Faucette et al. 2008; Chen et al. 2009). 

Decades of application of phosphorus-containing fertilizers and human, animal, and industrial waste to 

the land surface has significantly elevated phosphorus levels in many surface soil horizons in developed 

areas of the United States (Brady and Weil 1999). Land recently used as cropland may have elevated soil 

phosphorus levels from many years of fertilizer application. This phosphorus can be mobilized through 

erosion (Paul and Meyer 2001). 

Other studies documenting the presence of nutrients in construction site stormwaters and/or their 

elevation in receiving waters due to construction activity include White (1976), Barton (1977), Barrett et 

al. (1995a), Harbor et al. (1995), Fossati et al. (2001), Kayhanian et al. (2001), Wong (2005), and Selbig 

and Bannerman (2008). Some studies monitored for and found no elevation in nutrient levels downstream 

of construction sites (Peterson and Nyquist 1972; Extence 1978; Young and Mackie 1991; Cleveland and 

Fashokun 2006). 
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Phosphorus and nitrogen can be found in both particulate and soluble forms on construction sites. Soluble 

nitrogen and phosphorus compounds are the typical form found in fertilizers so as to be immediately 

available to vegetation. Relatively low levels of soluble nitrogen compounds and very low levels of 

soluble phosphorus compounds also occur in natural terrestrial soils. Soluble nitrogen compounds leach 

more readily through soil than phosphorus compounds. Soluble forms of nitrogen and phosphorus can 

dissolve in and be transported with stormwater flows. 

Most nitrogen in terrestrial soils is found in particulate organic form (Brady and Weil 1999). Phosphorus 

is typically found in particulate organic matter or bound to sediment. Nutrients form adsorption 

complexes with clay minerals and organic matter. Phosphorus readily binds to sediment, even when 

initially introduced to soil in soluble form such as fertilizer. Once bound to soil, it does not leach readily. 

Because phosphorus has such a strong association with sediment, discharges of total phosphorus tend to 

increase and decrease with total suspended solid discharges from sites (Selbig and Bannerman 2008). 

Discharges of organic particulate nitrogen also vary closely with suspended sediment discharges (Daniel 

et al. 1979). 

Some studies have documented strong associations between turbidity or sediment discharges and nutrient 

discharges from construction sites, suggesting that discharged nutrients were associated with particulate 

matter eroded from the construction sites (Daniel et al. 1979; Shields and Sanders 1986). Daniel et al. 

(1979) found 99 percent of total phosphorus discharges and 90 percent of total nitrogen discharges from 

three residential construction sites to be associated with sediment. Because of stormwater’s tendency to 

transport finer particles from eroded soils and nutrients’ tendency to be associated with fine sediments 

and organic matter, eroded sediments are frequently enriched in nutrient content relative to the parent 

soils (Novotny and Chesters 1989). Once in a waterbody, nutrients can, under certain conditions, detach 

from sediments and enter the water column (Bilotta and Brazier 2008). 

3.3.1.1 Nitrogen, Phosphorus, and Aquatic Resource Impacts 

While nutrients are necessary for the primary production (organic matter derived from photosynthetic 

activity) that forms the base of many aquatic food chains, they can become pollutants at elevated levels. 

Eutrophication is a process by which excessive nutrient levels increase the growth rates of primary 

producers in aquatic systems, particularly algae, which can drastically alter a waterbody’s ecological 

balance and impair human use of the waterbody (Wetzel 2000). Gradual eutrophication of some types of 

surface waters is a natural process, but nutrient levels elevated by human activity can drastically change 

the rate at which the process takes place (Goldman and Horne 1994). 

Both nitrogen and phosphorus must be available for eutrophication to proceed. Freshwater system 

eutrophication is typically limited by phosphorus availability, and marine systems are typically limited by 

nitrogen availability. Because many undisturbed stream phosphorus levels are naturally low, even small 

increases can impact water quality. Nitrogen can also be the primary nutrient limiting freshwater primary 

production in some regions of the United States, particularly portions of the Northeast and the Pacific 

Northwest with granitic or basaltic geology (USEPA 2006d) and in other waters on a seasonal basis. 

Elevated nutrient levels can change the quantity and quality of algal communities in both marine and 

freshwater systems. High concentrations of algal biomass, commonly referred to as algal “blooms,” 

accumulate in the water column faster than natural processes (e.g., settling, zooplankton grazing, benthic 

organism filtering, senescence) can remove them. 

Algal blooms can significantly reduce surface water transparency and therefore reduce the light available 

for submerged aquatic vegetation (SAV) photosynthesis. Elevated nutrient levels in the water column also 
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November 2009 3-9 

encourage the growth of algae on the surface of submerged vegetation, which further decreases light 

availability. Light attenuation reduces photosynthesis levels and the maximum depth at which SAVs can 

survive. Deterioration in water quality due to eutrophication has been a major factor in the decline of 

SAV in many ecosystems (e.g., Staver et al. 1996). A number of estuaries have experienced loss of high 

quality SAV beds (e.g., eelgrass, turtlegrass) because of reduced light availability due to eutrophication 

from elevated nitrogen loadings. Impacts to aquatic vegetation subsequently decrease food and habitat 

availability for other aquatic organisms. 

Excessive nutrients in shallow, wadeable streams can lead to dense mats of algal growth, particularly 

filamentous algae (e.g., Cladophora spp.) on bottom substrates. Such growths alter the habitat quality of 

the substrate and lead to predictable changes in aquatic macroinvertebrate populations. The proportion of 

pollutant-tolerant species in the community increases. Proportions and abundances of various feeding 

guilds shift, changing food quality and availability for fish and other organisms. Elevated nutrient levels, 

in combination with elevated levels of fine-grained sedimentation, can foster the growth of aquatic 

macrophyte beds, particularly in stream pools (King and Ball 1964; Taylor and Roff 1986). 

Decomposition of excessive algal biomass reduces dissolved oxygen levels in surface waters, lessening 

the amount available to other aquatic organisms. This can impair organisms’ functioning and health and 

possibly kill them. In surface waters stratified by temperature and/or salinity gradients, the settling of 

large amounts of organic matter can create low oxygen or anoxic conditions in bottom waters and allow 

only a few pollution-tolerant species to persist in the benthic community. Large diurnal swings in surface 

water oxygen content have also been observed in some waters. Supersaturation of dissolved oxygen takes 

place during daylight hours when photosynthesis takes place. At night, oxygen levels fall dramatically 

due to plant respiration. Such shifts have been shown to cause many summer fish kill events. 

Coldwater fish species (e.g., salmonids) that depend on cool, well-oxygenated bottom waters as a refuge 

during summer are particularly vulnerable to eutrophication impacts and may be extirpated from affected 

systems. Low levels of dissolved oxygen can affect amphibians by reducing respiratory efficiency, 

metabolic energy, reproductive rate, and ultimately survival (USFWS 2005). 

Eutrophication can also lead to shifts in fish community composition due to modifications of trophic 

feeding relationships. For example, the proportion of large, piscivorous fish species that rely on sight to 

catch their prey may decline in a surface water as water clarity falls. Filter-feeding planktivores (e.g., 

gizzard shad) may greatly increase in abundance. If eutrophication conditions are extreme, a fish 

community can be reduced to pollution-tolerant bottom feeders (e.g., carp). 

Eutrophic conditions can also foster the growth of certain types of algal blooms with toxic effects on 

aquatic organisms and human beings. Brown or red tides are intense algal growths that can release 

neurotoxins into the water column, harming some forms of aquatic life. Toxic effects on aquatic 

organisms can cause population declines and localized extinctions (Burkholder 1998). 

These types of blooms usually require beach closings and temporary bans on seafood harvesting because 

consuming shellfish from affected waters can cause shellfish poisoning in humans (Burkholder 1998). 

Human exposure to marine algal neurotoxins has been associated with a range of neurobehavioral 

abnormalities and is an emerging area of study (Friedman and Levin 2005). Illnesses most frequently 

linked to neurophysiological disturbance are Amnesic Shellfish Poisoning, Ciguatera Fish Poisoning, and 

Possible Estuarine Associated Syndrome, which is associated with exposure to substances from the 

dinoflagellate Pfiesteria piscicida through the food chain. 
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In freshwater systems, toxins from blue-green algal (Cyanophyta) species (e.g., Microcystis, 

Cylindrospermum) can be of concern. Blue-green algae toxins are classified according to mode of action 

and include hepatotoxins (e.g., microcystins), neurotoxins (e.g., anatoxins), skin irritants, and others 

(WHO 2003). Increased levels of organic material from algal blooms can also increase concentrations of 

chlorination disinfection by-products, including haloacetic acids and trihalomethanes, in treated drinking 

water. Severe morbidity and mortality in domestic animals due to toxin-contaminated drinking water has 

been documented (WHO 2003). Microcystins, in particular, have been associated with acute liver damage 

and possibly liver cancer in laboratory animals. 

Some algal blooms may not be directly harmful to humans but may still impair human use of aquatic 

resources. Increased levels of organic material associated with algal blooms can clog drinking water 

intakes and cause unpleasant tastes and odors in drinking water (Boyd 1990) (see Chapter 9). Excessive 

algae growths can reduce the attractiveness and viability of surface waters for a variety of recreational 

activities including swimming, boating, fishing, wildlife viewing, and other outings. Excessive growths 

can also clog screens on water intakes for irrigation, industrial use, and drinking water. 

Sedimentation levels also increase over the long term in eutrophic waterbodies, particularly lakes, 

reservoirs, and other impoundments with quieter flow conditions. Sediment cores obtained from studies 

of former lake environments have documented large increases in annual sediment deposits after the onset 

of eutrophication (Goldman and Horne 1994). These elevated sedimentation levels, acting in concert with 

sediment deposition from construction activities and streambed erosion due to land use change, can 

decrease depth and volume of surface waters over time. Chapter 8 discusses EPA’s analysis of economic 

impacts associated with loss of reservoir capacity. 

Algal growth also increases surface water turbidity, the various aquatic life and human resource impacts 

of which are discussed in Sections 2.3 and 2.4.

The nitrogen compound ammonia can function as a nutrient, the impacts of which are discussed above. 

Ammonia is also toxic to many aquatic organisms at relatively low levels under certain water conditions. 

Ammonia toxicity is primarily attributable to the unionized form (NH3) as opposed to the ammonium ion 

(NH4+) form. Ammonia toxicity typically increases with pH such that above pH levels of 9, the most 

toxic form (unionized ammonia) is the predominant fraction. Temperature also influences ammonia 

toxicity. Low dissolved oxygen levels found in the bottom waters of eutrophic waterbodies can increase 

the potential for ammonia toxicity, particularly when a surface water is stratified. The low redox 

conditions found in these waters favor the anaerobic microbial conversion of nitrite or nitrate to ammonia. 

3.3.1.2 Nitrogen and Phosphorus Criteria and Surface Water Impairment 

As noted in Section 2.6, Section 305(b) of the Clean Water Act (CWA) requires states, territories, and 

other jurisdictions of the United States to submit reports to EPA on the quality of their surface waters 

every two years If a waterbody fails to meet any one of its designated uses, CWA Section 303(d) requires 

a state or other entity to list the waterbody as “impaired" and not meeting its designated uses. Excessive 

nutrients (phosphorus, nitrogen) and its associated biological response (i.e., chlorophyll a, DO levels, 

transparency) are a common reason for a waterbody failing to meet its designated uses. 

Appropriate levels of nitrogen and phosphorus vary among surface waters. Nutrient levels associated with 

eutrophication vary among regions with the country due to differences in geology, climate, and soil types. 

EPA has published a series of technical guidance documents that provide methods for setting nutrient 

water quality criteria for (1) lakes and reservoirs (USEPA 2000c); (2) rivers and streams (USEPA 2000d); 

(3) estuaries and coastal marine waters (USEPA 2001); and (4) wetlands (USEPA 2008c). EPA has used 
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this guidance to develop tables of recommended nutrient criteria for lakes/reservoirs and streams/rivers 

for several ecoregions across the country (USEPA 2009d). States, tribes, and other entities have started to 

develop their own numeric nutrient criteria, using EPA recommendations as a starting point. 

The Assessment TMDL Tracking and Implementation System (ATTAINS) provides information on water 

quality conditions reported by the states to EPA under Sections 305(b) and 303(d) of the Clean Water 

Act. The information available in ATTAINS is updated as data are processed and are used to generate the 

biennial National Water Quality Inventory Report to Congress. This information reflects only the status of 

those waters that have been assessed. 

Table 3-1 presents information on surface waters with nutrient-related impairments (algal growth, 

ammonia, noxious aquatic plants, nutrients, and organic enrichment/oxygen depletion) by EPA Region as 

reported by the states in ATTAINS. Appendix A provides information on the state water report year for 

which data were available for populating the table below as of September 17, 2009. 

It should be noted that individual waters may be impaired by more than one pollutant. Although states 

tend to target their monitoring efforts to those surface waters they believe to be impaired, the total area of 

impaired surface waters due to nutrients is probably underestimated due to the low percentage of surface 

waters that were assessed. As of September 17, 2009, states had assessed only 26 percent of the nation’s 

reach miles, 42 percent of its lake acres, and 20 percent of its bay and estuary square miles. 

Table 3-1: Nutrient-Related Impairment in 305(b)-Assessed Waters, by EPA Region 

EPA 

Region Stream/River (miles) Lake/Pond/Reservoir (acres) Bay/Estuary (sq. miles) 

1 2,267 229,728 759 

2 10,485 219,203 591 

3 8,009 61,179 4,039 

4 11,522 262,978 22 

5 72,350 1,244,029 

6 13,162 742,606 724 

7 22,369 487,346 

8 11,298 995,262 

9 20,816 804,315 111 

10 31,722 506,696 

Nation 204,000 5,553,342 6,246 

Source: EPA ATTAINS database (USEPA 2009a) as of 9/17/09. 

Water quality in the United States has also been assessed through a series of national, probability-based 

surveys known as the National Aquatic Resource Surveys. These surveys use randomized sampling 

designs, core indicators, and consistent monitoring methods and laboratory protocols to provide 

statistically defensible assessments of water quality at the national scale. The Wadeable Streams 

Assessment (USEPA 2006d) is a statistical survey of the smaller perennial streams and rivers that, 

according to the report, comprise 90 percent of all perennial reach (i.e., stream and river) miles in the 

United States. Elevated nutrient and sedimentation levels are the top stressors of streams. According to 

the survey, 32 percent of streams have “poor” nitrogen conditions, and 21 percent have “fair” conditions 

relative to reference streams. Also, 31 percent have “poor” phosphorus conditions, and 16 percent have 

“fair” conditions relative to reference streams. The survey also examined the association between 

stressors and biological condition and found that high levels of nitrogen or phosphorus more than double 

the risk for poor biological condition (see Figure 3-1). 
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Figure 3-1: Extent of Stressors and Their Relative Risk to the Biological Condition of the 

Nation’s Streams 

Source: USEPA (2006d). 

Several threatened and endangered (T&E) species are vulnerable to eutrophication from nutrient 

pollution. One study postulated that 25 percent of all current freshwater T&E species are adversely 

impacted by eutrophication (Richter et al. 1997). Mollusks are frequently vulnerable to eutrophication 

impacts. Examples include several species of freshwater snails and mussels (USFWS 2000; Biber 2002; 

and Kozlowski and Vallelian 2009). Fish species and aquatic plants are also vulnerable. Threatened fish 

species include the Waccamaw silverside (Menida extensa), paleback darter (Etheostoma pallididorsum), 

and an arctic grayling subspecies (Thymallus arcticus montanus) found in Montana and Wyoming 

(NatureServe Explorer 2009). 

To quantitatively evaluate the water quality benefits from reducing nitrogen and phosphorus loadings 

from construction sites, EPA developed an approach that relies on the empirical relationship between instream sediment and nitrogen and phosphorus levels. A full discussion of this approach is provided in 

Chapter 6. 

3.3.2 Organic Compounds and Materials 

A wide variety of organic compounds and materials can be discharged from construction sites. Organic 

compounds are present on construction sites as natural soil constituents, components of a variety of 

building and equipment materials, and historic soil contamination. The nature and frequency of these 

materials’ occurrences on and discharge from construction sites has not been well characterized in the 

literature. Given the proprietary nature of the composition of many commercially available products and 

the enormous variety of possible compounds in these materials, research in this area can be challenging. 

Pavement materials such as asphalt and petroleum-based asphalt pavement rejuvenators contain a variety 

of organic compounds. There are many concrete additives whose environmental effects have not been 

thoroughly characterized (e.g., air entraining agents, water reducers, strength accelerating agents, and 

pumping acids) (NRC 2001c). Benzothiazole has been detected in leachate from shredded scrap tires 

commonly used as pavement additives and is a likely toxin (Eldin 2002). N, 4-dimethylbenzamine has 

been detected in methacrylate bridge deck sealer and is also highly toxic (NRC 2001c). 
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PCB compounds have been banned for use in manufacturing in the United States due to their 

carcinogenicity, persistence in the environment, and bioaccumulation potential. Because they were widely 

used historically and are very stable in the environment, their residues are frequently found in soils of 

areas where they have been manufactured or used, including many industrial and urban sites. PCBs bind 

well to sediments and are often transported with them. 

Organic compounds can have toxic effects on aquatic organisms and/or influence surface water oxygen 

levels when present at sufficient levels. Discharges of organic compounds and materials of all types show 

a strong correlation to suspended sediment discharges (Barrett et al. 1995b). Oil and grease, other 

petroleum hydrocarbons, PAHs, and many organic pesticides adsorb to sediment and travel with it as it 

erodes. As sediments accumulate in receiving waters, concentrations of associated organic pollutants can 

increase to levels above those found in the adjacent water column. Over time these sediment levels can 

become high enough to cause adverse impacts to organisms living in, interacting with, or connected 

through the food chain to those sediments. 

Information on some of the better characterized pollutants known to be of concern, including high organic 

content material, petroleum hydrocarbons, PAHs, and organic pesticides, is provided below. 

3.3.2.1 Organic Matter and Dissolved Oxygen 

Organic matter consists of biodegradable carbon-based material. Organic material is a natural constituent 

of many construction site soils. A common construction site practice is to stockpile topsoil, roots, and 

other vegetative debris from site clearing on-site for use in final site landscaping. If not protected from 

erosion, these stockpiles can contribute organic matter, sediment, turbidity, and adsorbed pollutants to 

surface waters. Developers may also import topsoil for use during landscaping. Wood mulch, compost, 

straw, organic geotextiles (i.e., biodegradable structural sheets used to reduce slope erosion), and other 

organic materials used for soil erosion control and landscaping can increase organic material discharges if 

improperly maintained and allowed to erode (Glanville et al. 2004). Taylor and Roff (1986) documented 

extensive erosion of straw mulch from a construction site, sufficient to clog culverts downstream of the 

site. 

Elevated loadings of organic material can increase levels of oxygen-demanding substances (e.g., as 

measured by biological oxygen demand (BOD) and chemical oxygen demand (COD)) in receiving 

waters. Microbes aerobically break down the organic compounds. Elevated BOD and COD levels can 

lower dissolved oxygen levels in surface water, leading to several of the impacts associated with nutrientderived oxygen depletion discussed in Section 3.3.1. 

Excessive algal growth has been documented in sedimentation basin waters at some construction sites 

(Kalainesan 2007; USEPA 2009b). These blooms can deplete oxygen levels diurnally and elevate organic 

matter content in water discharged from these basins. 

Some studies have documented a decline in the organic matter content of surface water sediment due to 

construction activity (Barton 1977; Young and Mackie 1991). This effect is derived from the erosion of 

largely inorganic soil particles from construction sites and the high levels of those sediments entering the 

surface water. Some aquatic organisms depend on organic matter in surface water sediments as a food 

source. Elevated levels of sediment containing low levels of organic matter can reduce the food value of 

this material for these organisms. 
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3.3.2.2 Petroleum Hydrocarbons 

Petroleum hydrocarbons include a variety of crude and refined oil products, including gasoline, diesel 

fuel, and lubricating oils. These products contain a diverse array of organic compounds, many poorly 

characterized in terms of environmental behavior and impact. Petroleum hydrocarbons are often 

monitored in water as a group, either as total petroleum hydrocarbons or as oil and grease. 

Petroleum-derived aliphatic hydrocarbons on construction sites typically derive from human activity, 

either current or historic. Many historic industrial sites have some level of petroleum hydrocarbon 

contamination in site soils and, at some sites, groundwater as well. Improper operation, fueling, and 

maintenance of construction equipment, and poor housekeeping practices (e.g., improper storage of oil, 

diesel fuel, and gasoline products) can lead to leakage or spillage of materials containing petroleum 

hydrocarbons (Garton 1977). Accidental rupturing of petroleum transport lines can also occur during 

construction (USFWS 2005, citing two incidents during utility line trench construction in Texas). 

Asphalt-based products (pavement material, asphalt shingles) are commonly used on construction sites 

and contain a variety of organic compounds including n-alkanes, carboxylic acids such as n-alkanoic 

acids, benzoic acids, thiaarenes, and PAHs (see below). Kayhanian et al. (2001) documented oil and 

grease in construction site stormwaters. 

Petroleum hydrocarbons tend to adsorb to soil and sediment particles and travel with them as they erode 

and settle in surface waters. Petroleum hydrocarbons discharged directly to water spread quickly to cover 

water surfaces. Microbial activity in surface waters eventually degrades the more structurally simple 

components of petroleum materials (e.g., 40–80 percent of a crude oil) (Hoffman et al. 1995). 

Petroleum hydrocarbons can affect aquatic organisms through direct toxicity, smothering, and changes in 

water quality. Water quality impacts can include light reduction, pH alteration, dissolved oxygen 

reduction, and dissolution of some product components in the water column. Oil, grease, fuel, and other 

petroleum hydrocarbons typically contain toxic and carcinogenic constituents. Organisms may be 

impacted directly or through impacts to food sources (USFWS 2005, Hoffman et al. 1995). 

Fish can be exposed to petroleum hydrocarbons through contact with dissolved fractions in the water 

column, particulate fractions and contaminated sediments and ingestion of contaminated food and water. 

Floating egg masses can come into contact with surface hydrocarbon layers (Malins and Hodgins 1981). 

Garton (1977) documented a diesel spill at a construction site that resulted in a fish kill. These dynamics 

affect other aquatic organisms, as well. Waterfowl can be affected by external oiling, ingestion, egg 

oiling, and habitat changes. 

3.3.2.3 Polycyclic Aromatic Hydrocarbons 

PAHs are a large and diverse class of organic compounds deriving from both natural and anthropogenic 

sources. Natural sources include forest fires, volcanic emissions, and oil seeps. Anthropogenic sources 

include thermal combustion by industrial, municipal, power plant, vehicular, and household entities. 

PAHs from these sources can be deposited from the atmosphere onto soil over wide areas. They can 

accumulate in soils, particularly in urbanized areas, and subsequently erode after ground disturbance. 

PAHs are also found in construction materials. Coal tar-based sealcoat for driveway and parking lot 

surfaces contains very high levels of PAHs (Mahler et al. 2005; Van Metre et al. 2006) and has been 

found to contaminate stormwater and sediment in downstream surface waters (Science Daily 2009). It has 

been inferred that these sealcoat products are contributing to PAH contamination of many U.S. lakes. 
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PAHs are also found in oil, asphalt, and tar products used for pavement and roofing projects, some 

foundry sands, creosote wood preservatives, and shredded scrap tires used as a pavement additive. The 

PAHs in asphalt include sulfur-containing PAHs, such as dibenzothiophene, which are more water 

soluble and bioaccumulative than some other PAHs (NRC 2008). Several studies have noted road 

construction sites as possible contributors of PAHs to surface waters (Ohio EPA 1998d, 1998e, 1999b). 

A number of commonly used solvents (e.g., toluene, trichloroethane, and dichloroethane) can transport 

PAHs and have frequently contaminated soil and groundwater at industrial and urban sites. Historic 

industrial sites may contain PAHs from other sources as well, such as disposal of coal tar and combustion 

by-products. Historic fill materials on industrial and urban sites may also contain PAHs. 

In general, PAHs have low solubility in water, high melting and boiling points, and low vapor pressure 

(Hoffman et al. 1995). PAHs are persistent in the environment and do not break down easily in water. 

They also have bioaccumulation potential (USEPA 2008d). In the aquatic environment, most PAHs 

associate with soil and sediment particles rather than partitioning to the water column. Their adsorption to 

sediment increases with increasing organic content of the sediment. PAHs may accumulate in sediments 

to concentrations much higher than those measured in the water column. 

PAHs can adversely affect mammals (including human beings), birds, fish, amphibians, invertebrates, and 

plants. PAHs at low concentrations can stimulate or inhibit growth of aquatic bacteria and algae. At 

higher concentrations, however, they interfere with cell division and photosynthesis (Eisler 1987). Effects 

on aquatic invertebrates include inhibited reproduction, delayed emergence, sediment avoidance, and 

mortality. Egg and larval stages are more susceptible. Effects on amphibians and reptiles include impaired 

reproduction, reduced growth and development, tumors, and cancer. Effects on fish include fin erosion, 

liver abnormalities, cataracts, and immune system impairments (Eisler 1987; Van Veld et al. 1990; 

Mahler et al. 2005; USFWS 2005). PAHs from application of coal tar-based pavement sealcoat have been 

documented as the cause of a fish kill (Maryland DEP 2000). 

PAHs can transfer to humans through consumption of fish from surface waters contaminated with 

sufficient levels of PAHs (USEPA 2004b). PAHs have been found to cause birth defects and liver and 

blood problems in some animals (USEPA 2008d). PAHs are a known carcinogen in many animals and are 

suspected to be carcinogenic in human beings as well (USEPA 2008d). 

3.3.2.4 Organic Pesticides 

A large number of pesticides are organic compounds. Organic pesticides have been and continue to be 

used extensively on both agricultural and urban lands (e.g., building foundation and lawn pest control). 

Some pesticides, including many organochlorine pesticides used historically but since banned in the 

United States, create residues that are able to persist in soils for long periods of time. These residues can 

erode when soils are disturbed. Pesticide residues on construction sites can derive both from activities 

associated with construction (e.g., landscaping and vegetation removal activity) as well as from historical 

pesticide applications on urban and agricultural lands that are now being redeveloped. Chlorpyrifos and 

diazinon have been detected in construction site stormwaters in California (Kayhanian et al. 2001). 

Organochlorine pesticides that strongly adsorb to clay and organic matter and can be transported by 

erosion and overland runoff include 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethylene DDT, aldrin, mirex, 

kepone, dieldrin, endosulfan, toxaphene, lindane, heptachlor, chlordane, and difocol (Alberta ARD 2009). 

Except for endosulfan, use of these pesticides was generally banned in the United States because of their 

toxicity, persistence in the environment, and/or potential to bioaccumulate. For example, even though 

DDT usage peaked and began to decline in the 1960s and was banned in 1972 in the United States, DDT 
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November 2009 3-16 

and its breakdown products - DDD (1,1-dichloro-2,2-bis(p-chlorophenyl)ethane) and DDE (1,1-dichloro2,2-bis(p-chlorophenyl)ethylene) persists widely in many agricultural soils and surface water sediments in 

current and former agricultural watersheds (New Jersey DEP 1999). 

Persistence, bioaccumulation, and toxicity properties vary among organic pesticides. Some organic 

pesticides in current use are generally believed to degrade more quickly once applied in the environment 

and have fewer toxic effects. 

Due to their hydrophobic nature, pesticides tend to adsorb to sediments and organic materials and, 

therefore, tend to erode, travel, and settle with sediment particles in surface waters. Similar to other 

organic compounds, pesticides are often found at low or undetectable levels in the water column, but can 

accumulate to problematic levels in surface water sediments, particularly fine-grained organic materials. 

Biomagnification of pesticides occurs as higher trophic level organisms, such as fish-eating birds and 

mammals, ingest pesticides in prey. Ingestion can also occur during feeding activities that involve 

sediment contact (e.g., probing of sediment with bill, grooming). 

Once in a surface water, pesticides can remain associated with sediment or can detach and enter the water 

column given appropriate conditions (Bilotta and Brazier 2008). Aquatic organisms come into contact 

with pesticide residues during contact with or ingestion of contaminated water, sediment, and prey. 

Herbicides can remain active even at low concentration levels and damage aquatic vegetation (USFWS 

1998). Several studies have documented morphological, developmental, and biochemical alterations in a 

variety of amphibians exposed to atrazine (USFWS 2005). 

Most human sediment-related exposure to contaminants is through indirect routes in which pollutants 

transfer from sediments to the water column or to aquatic organisms. A number of fish consumption 

advisories or fishing bans are due to chlordane, DDT, and its metabolites (DDD and DDE), all of which 

are commonly found in sediments. 

3.3.3 Metals 

Metals derive from a variety of sources on construction sites. Iron, aluminum, and manganese are 

common constituents of natural soils. Other metals can also occur naturally, but their distribution is 

highly variable. Elevated concentrations of these other metals are more commonly associated with human 

activity. A variety of metals can be a concern in the aquatic environment. These include aluminum, 

antimony, arsenic, cadmium, chromium, copper, iron, lead, manganese, mercury, nickel, selenium, silver, 

and zinc. 

Construction equipment and materials can be sources of metals. A wide variety of metal materials are 

used for roofing, pipes, and structural frameworks and supports. Galvanized metal (e.g., for pipes, fence 

supports, and roofing) can elevate zinc levels in construction site stormwater runoff (Horner et al. 1990; 

NRC 2008). Cement and concrete products, wastes, and equipment washwaters can have elevated metal 

levels (e.g., of chromium) due to industrial wastes incorporated during the manufacturing process 

(USEPA 2006a). High levels of aluminum and iron have also been documented in cement wastes (NRC 

2008). Preserved and waterproofed wood products can leach high levels of copper (NRC 2008). One 

study attributed elevated levels of construction site metal discharge (chromium) to pressure-treated wood 

products (Ohio EPA 1997d). 

A variety of materials used for road and highway construction contain metals, including pressure-treated 

wood, coal fly ash, phosphogypsum, and asphalt mixes containing municipal incinerator ash, shredded 

scrap tires, foundry sand, and recycled shingles. In laboratory studies, these materials have been found to 
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November 2009 3-17 

leach heavy metals at levels toxic to water fleas (Daphnia magna) and green algae (Selenastrum 

capricornutum). Leached metals included arsenic, copper, zinc, lead, aluminum, mercury, and vanadium 

(USEPA 1999; NRC 2001c; Eldin 2002). Although adsorption to soil particles ameliorates the metal 

leachates’ toxic effects (NRC 2001), these soil particles may subsequently erode from construction sites 

and enter surface waters. Loose particles of road and highway construction materials can also erode and 

enter surface waters. 

Landscaping materials can contain metals, as well. Some fertilizers contain elevated levels of metals due 

to industrial wastes incorporated during their manufacture. Glanville et al. (2004) documented elevated 

levels of several metals in composted biosolids, yard waste, and bioindustrial waste relative to topsoil. 

Compost can be used to reduce total erosion (and therefore total loadings of metals) from construction 

sites. However, if compost products contain elevated metal levels and are allowed to erode, they can 

contribute metals to construction site discharges. Metals content varies widely among composted products 

and is heavily dependent on the source materials used. 

Historic contamination can also be a source of metals. Lead is a common soil contaminant near roads and 

in urbanized areas due to its use in lead-based paint and as a gasoline additive for many years. Soil in fruit 

orchards active prior to 1950 are frequently contaminated with high levels of lead and arsenic due to 

widespread use of lead arsenate pesticides during the late 19th and early 20th centuries (New Jersey DEP 

1999). These pesticides were also widely used for golf course and lawn care (Folkes et al. 2001). Soils on 

lands used to grow cotton prior to 1950 often contain high levels of arsenic due to use of arsenic acids to 

defoliate cotton prior to harvest. Lead and arsenic pesticide residues do not decay over time and remain in 

surface soil layers until a disturbance, such as construction activity, mobilizes them through erosion. 

Metals have been documented in construction site stormwaters and at elevated levels in downstream 

receiving waters due to construction activity. Documented metals include aluminum (Huckabee et al. 

1975), cadmium (Kayhanian et al. 2001), chromium (Kayhanian et al. 2001), copper (Horner et al. 1990; 

Barrett et al. 1995a; Kayhanian et al. 2001; Wong 2005; Clausen 2007), iron (Extence 1978; Shields and 

Sanders 1986; Barrett et al. 1995; Chen et al. 2009), lanthanum (Huckabee et al. 1975), lead (Shields and 

Sanders 1986; Horner et al. 1990; Kayhanian et al. 2001; Clausen 2007), manganese (Huckabee et al. 

1975; Shields and Sanders 1986), nickel (Kayhanian et al. 2001), samarium (Huckabee et al. 1975), silver 

(Kayhanian et al. 2001), and zinc (Huckabee et al. 1975; Shields and Sanders 1986; Horner et al. 1990; 

Barrett et al. 1995a; Kayhanian et al. 2001; Clausen 2007). Some studies attributed metals to natural 

constituents in site soils or groundwater (Huckabee et al. 1975; Extence 1978; Shields and Sanders 1986; 

Yew and Makowski 1989; Barrett et al. 1995a). In many studies, the source of the metals was not 

identified. The presence and level of metals is highly variable among site discharges. Some studies 

examined but detected no elevation in metals levels in receiving waters downstream of construction sites 

(Peterson and Nyquist 1972). 

Heavy metals can form complexes with clay minerals and organic matter in soil or can be a component of 

particulate matter in soil. Organic matter, in particular, has a high capacity to bind with metals. Sediments 

with higher levels of organic matter typically have higher concentrations of zinc, lead, chromium, copper, 

mercury, and cadmium (Paul and Meyer 2001). 

Metals also tend to associate with finer sized sediment particles. Because stormwater tends to transport 

finer particles from sites, the sediment in stormwater tends to be enriched in metals relative to the parent 

soil. Novotny and Chesters (1989) discussed metals that can be present at elevated levels in sediment 

relative to the original soil, including lead, copper, zinc, aluminum, iron, chromium, and nickel. 
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November 2009 3-18 

Metals also show a strong correlation with suspended solids discharges. One study of construction site 

discharges noted strong correlations between total suspended solids (TSS) and particulate copper, zinc, 

and chromium discharge levels, suggesting that the metals travel with sediment as it erodes (Kayhanian et 

al. 2001). Barrett et al. (1995a) found strong correlations between iron and TSS discharge concentrations 

downstream of a construction site. Barrett et al. (1995b) stated that lead and iron are the metals most 

strongly associated with particulate matter, followed by copper and cadmium. 

Metals tend to adsorb to sediment in the aquatic environment. Because metals do not biodegrade, they can 

accumulate on waterbody beds as they settle from the water column with sediments. Due to this 

accumulation in bottom sediments over time, metals levels may often be below levels of concern or 

detection in the water column but reach levels high enough to have adverse impacts on organisms living 

in or near bottom sediments. For example, lead concentrations tend to be highest in benthic organisms, 

perhaps due to feeding among bottom sediments (USFWS 1998). Metals can also desorb from sediments 

and enter the water column under appropriate conditions (e.g., anaerobic conditions) (Bilotta and Brazier 

2008). 

During dredging or flooding, sediment containing metals may be reintroduced into the water column 

because of the disturbance. Metals attached to suspended sediment can be transported downstream, 

potentially contaminating areas far from their point of origin. Disturbance of contaminated sediments can 

also release metal contaminants into water drawn from surface waters for drinking water supply. When 

present at sufficiently high levels, metals can cause dredged sediment to be classified as toxic, making its 

disposal more costly. 

A variety of organisms in streams in urbanized areas have exhibited elevated metal concentrations, 

including algae, mollusks, arthropods, and annelids. Organisms are directly exposed to both dissolved 

metals in the water column and metals associated with ingested sediments and organic matter. The 

potential for toxicity depends on the bioavailability of the metal, the means and length of organism 

exposure, and the life stage exposed. The bioavailability of the metal depends on the nature of the metal 

discharge and the aquatic environment. 

Acute and chronic ambient water quality criteria have been established for most heavy metals and contain 

adjustment factors to reflect dissolved and particulate metal forms and water column hardness (USEPA 

2007d). A recent examination of the water quality criteria for copper by EPA uses the Biotic Ligand 

Model (BLM) and incorporates data on receiving water levels of dissolved organic carbon, pH, major 

cations and anions, alkalinity, and temperature. The presence of other types of pollutants or materials can 

be important as some act additively, synergistically, or antagonistically with certain metals. For example, 

the bioavailability of cationic metals in sediments decreases with increasing organic content and the 

presence of binding sulfide compounds (USEPA 2005b). 

While trace amounts of certain metals are necessary for normal biological functions in many aquatic 

species, higher concentrations can be acutely or chronically toxic and adversely affect behavior, growth, 

development, metabolism, reproductive success, and survival in aquatic species, waterfowl, and 

mammals. Exposure to metals has been shown to reduce photosynthetic efficiency and algal colonization, 

lowering primary production rates and diminishing food available to other organisms. Elevated metal 

levels can inhibit plant growth and can have adverse effects on the health of benthic organisms 

(Masterson and Bannerman 1994). Metals can be absorbed through skin, gills, intestines, and other 

organs. Early organism life stages and organisms with longer exposure durations are more vulnerable 

(USFWS 2005). Some metals are able to progress and bioaccumulate through the food chain, imposing 
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November 2009 3-19 

heavier metal exposures on organisms higher in the tropic structure. Impacts from elevated metals levels 

can reduce organism abundance and alter community structure. 

Iron and manganese are more common and less toxic to aquatic organisms than heavy metals. They are 

highly sensitive to redox conditions and are more soluble under low dissolved oxygen conditions. High 

levels of dissolved iron and manganese can be found seasonally in anaerobic bottom waters of stratified 

lakes. High levels can also be found in low-oxygen groundwaters. In solution, these metals can create 

highly colored water. Low-oxygen groundwaters containing high levels of iron and/or manganese may be 

discharged from construction sites due to grading, excavation, and dewatering activities. If these 

groundwaters are discharged to surface waters, aeration and rapid oxidation can deposit large amounts of 

red iron oxides or black manganese oxides on surface water substrates. These deposits can coat stream 

substrate and aquatic vegetation, fill interstitial spaces, and create turbid, highly colored water. Chisholm 

and Downs (1978) discuss the discharge of such groundwater from a construction site in West Virginia. 

3.3.4 Dissolved Inorganic Ions 

A number of dissolved inorganic ions can discharge from construction sites, including calcium, chloride, 

sodium, potassium, magnesium, sulfate, and bicarbonate. These ions are naturally occurring components 

of many soils and, in some cases, of precipitation. Levels vary geographically. Elevated levels relative to 

freshwater surface waters can be found in some groundwater. Construction site activity may increase 

discharge of ions to surface waters through soil disturbance and erosion, groundwater disturbance, use of 

dust suppressants (e.g., calcium chloride), fertilizers (which may contain chloride, potassium, or other 

inorganic ions), lime (calcium carbonate), cement, concrete, and other building materials. High levels of 

calcium, magnesium, and sodium have been documented in cement wastes (NRC 2008). 

Several studies have documented the presence of dissolved inorganic ions in construction site 

stormwaters and at elevated levels in downstream receiving waters due to construction activity. Ions that 

have been documented include calcium (Huckabee et al. 1975; Tan and Thirumurthi 1978; Nodvin et al. 

1986; Shields and Sanders 1986), bicarbonate (Tan and Thirumurthi 1978; Foassati et al. 2001), chloride 

(Tan and Thirumurthi 1978; Shields and Sanders 1986; Chen et al. 2009), magnesium (Huckabee et al. 

1975; Nodvin et al. 1986; Shields and Sanders 1986), potassium (Nodvin et al. 1986), sodium (Tan and 

Thirumurthi 1978; Nodvin et al. 1986), and sulfate (Huckabee et al. 1975; Tan and Thirumurthi 1978; 

Shields and Sanders 1986; Chen et al. 2009). Some studies monitored but did not detect changes in 

dissolved inorganic ion levels in receiving waters downstream of construction sites (Burton et al. 1976; 

Cramer and Hopkins 1982; Hedrick et al. 2006). 

Shields and Sanders (1986) and Chen et al. (2009) identified soil erosion as the source of ions. Chisholm 

and Downs (1978) identified construction site use of a calcium chloride dust suppressant as the source. 

Huckabee et al. (1975) identified acid rock drainage as the source. Nodvin et al. (1986) documented 

elevated levels of magnesium, sodium, and potassium in a stream due to leaching of material from 

concrete freshly poured for bridge footers. 

Dissolved inorganic ions generally exhibit low toxicity to aquatic organisms. However, ions can influence 

several surface water attributes including salinity and pH (discussed below). They can also influence the 

toxicity of co-discharged metals. For example, large concentrations of calcium in surface waters can 

decrease toxicity of several metals, including aluminum (Fredman 1995). In wetlands and anaerobic 

sediments, however, microbial processes can exchange calcium ions with mercury and facilitate the 

release of mercury into these systems. 
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Elevated or widely fluctuating chloride levels can impair aquatic organism growth, reproduction, and 

survival (New Hampshire DES 2007). Some aquatic plants are sensitive to salinity changes (e.g., some 

studies of the genus Potamogeton have shown it to be very sensitive to water column salinity and mineral 

or ionic profile changes (USFWS 1998)). Irrigation water that contains dissolved salts or pollutants can 

harm crops and damage soil quality (Clark et al. 1985). 

Depending on whether fish are fresh or salt water species, they have been reported to tolerate chloride 

levels of 400 to 30,000 mg/L. EPA has set an acute freshwater criterion for chloride at 680 mg/L and a 

chronic criterion at 230 mg/L (USEPA 1988). Chloride toxicity increases when it co-occurs with other 

ions such as potassium or magnesium. Dissolved inorganic ions can also affect organism osmoregulation. 

Some sensitive species (e.g., mayflies) have been lost from surface waters due to elevated ion levels 

(NRC 2008). 

3.3.5 pH Level 

Construction sites use materials that can alter surface water pH levels, including large quantities of 

cement and concrete. Wastes from concrete truck and other equipment washouts have a high pH (12 pH 

units) and can alter surface water pH if discharged at sufficient levels (Canadian DFO 2009). Stormwater 

can also become elevated in pH if it comes into contact with freshly placed concrete. Sand-cement bags 

may be used as temporary headwalls. Nodvin et al. (1986) documented elevated pH levels in a stream in 

Nevada downstream of a bridge construction site. pH levels increased from 6.83 to a maximum of 11.22. 

The authors postulated that leaching of calcium oxide hydration products from the concrete poured for the 

bridge footers was the source. 

Some materials used on construction sites for soil stabilization and fill are also high in pH. These include 

lime and cement materials, cement kiln dust, lime kiln dust, and crushed limestone. Lime can also be 

added to soils as a fertilizer. 

Natural site soil and rock constituents, such as limestone or sulfide-containing rock and soil, can also alter 

surface water pH if discharged at sufficient levels. Huckabee et al. (1975) documented acidification of a 

small stream in Tennessee due to iron sulfides in rock used for roadbed fill. Upstream of the fill, stream 

pH was 6.5 to 7.0. Downstream of the fill for several miles, stream pH was 4.5 to 5.9. Acidification took 

place shortly after completion of the project and continued to persist for more than 30 years. Yew and 

Makowski (1989) also documented stream acidification due to use of pyritic shales in road construction. 

EPA (USEPA 2009b) described a highway construction site in Washington where disturbance of 

sedimentary shale rock raised pH levels as high as 9.6 in stormwater and dewatered groundwater. Chen et 

al. (2009) documented significant increases in sulfate levels downstream of a construction site. The 

authors believed the sulfate derived from construction site erosion and contributed to increased stream 

acidity after project completion. Shields and Sanders (1986) also attributed pH changes in downstream 

receiving waters to soil constituents. 

Change in pH levels in receiving waters was highly variable among sites. In addition to the studies 

described above, several studies monitored but detected no change in receiving water pH levels due to 

construction activity (Peterson and Nyquist 1972; Barton 1977; Extence 1978; Lenat et al. 1981; Cline et 

al. 1982; Cramer and Hopkins 1982; Taylor and Roff 1986; Foasatti et al. 2001; Hedrick et al. 2006). 

Aquatic communities have low species richness at pH levels above or below 6 to 8.5 units (Kalff 2002). 

Atmospheric acid deposition has been documented to eliminate species as pH decreases and to extirpate 

fish from lakes once long-term levels fall below 5 pH units. At high (alkaline) pH levels, LC50 (lethal 

concentration) values for salmonids fall in the 9–10 pH unit range. Few fish survive even short-term 
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November 2009 3-21 

exposures to pH levels greater than 11 (Alabaster and Lloyd 1980). Some aquatic plants are also sensitive 

to pH changes (e.g., some studies of the genus Potamogeton have shown it to be very sensitive to water 

column pH changes) (USFWS 1998). Low species richness at high or low pH levels can be attributable, 

in some cases, to other factors working in concert with altered pH levels. These factors can include 

increased metal toxicity (e.g., aluminum), high salt levels, and high water temperature. 

3.3.6 Pathogens 

Pathogens may be present on construction sites due to the use of manure products for fertilization and 

landscaping and improper disposal of on-site sanitary wastes. Pathogens are transported by attachment to 

sediment particles. The transport and fate of bacterial indicator species Escherichia coli and Salmonella

spp. have been shown to be highly influenced by their relationship with flocculated suspended sediment 

and bed sediments in a river (Droppo et al. 2009). Bacteria counts were consistently higher within 

sediments than within the water column. Bed sediments were found to represent a possible reservoir of 

pathogens for subsequent remobilization and transport 

Fungi can also function as pathogens. Aspergillus sydowii, a common terrestrial fungus, can enter marine 

environments through upstream erosion. This fungus has emerged as a widespread sea fan pathogen in the 

Caribbean. Another fungus dispersed in eroded soil is known to cause a disease called “valley fever” in 

California sea otters (Lafferty et al. 2004).
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4 Summary of Literature on Construction Site Discharges to 

Surface Waters 

A large number of publicly available studies issued in peer-reviewed, government, and other publications 

document pollutant discharges from construction sites to surface waters. EPA reviewed many of these 

studies and refers to the information they contain throughout this document. This chapter provides an 

overview of the studies EPA reviewed (Section 4.1) as well as a brief summary of each individual study 

(Section 4.2). Section 4.3 presents state reports of construction site discharges. 

4.1 Overview of Impacts in Literature 

Studies documenting the results of research on construction site discharges have been published for many 

years. The earliest paper summarized in this chapter was published in 1959, and the most recent papers 

were published in 2009. Research characterizing the nature and magnitude of construction site discharges 

is ongoing. 

Available studies cover a range of construction types, geographic locations, and surface water types. 

Studies of the impact of highway construction are more numerous than studies of impacts from other 

types of construction. Studies of impacts to streams and small rivers are more numerous than studies of 

impacts to other types of surface waters. Impacts to marine systems are poorly characterized. EPA was 

unable to locate an explanation for these aspects of the literature. One possible explanation for the 

frequency of stream ecosystems in the literature is the high frequency with which they are affected by 

construction site discharges and the ease with which they can be divided into “altered” and “unaltered” 

zones (i.e., controls) for study of construction site discharge effects. 

Most of the studies EPA chose for review examined surface water impacts downstream of construction 

sites. EPA also reviewed a smaller number of studies characterizing the nature of construction site 

stormwaters prior to their discharge to receiving waters. 

The literature indicates that construction sites can affect a number of stormwater and receiving water 

parameters, including physical, chemical, and biological parameters. Most studies, however, characterized 

only a subset of all potentially affected parameters, perhaps due to resource limitations. Suspended 

sediment and turbidity were the most commonly monitored parameters. Basic water quality parameters 

(pH, dissolved oxygen, some major dissolved ions, biological oxygen demand (BOD)), nutrients, metals, 

and toxic organic compounds were also monitored. Nutrients were monitored in a wide variety of forms, 

though only a subset of possible forms was studied in most cases. Likewise, when metals were monitored, 

only a subset of possible metals was typically chosen for observation. Specific organic compounds were 

rarely monitored. Summaries of study findings for specific pollutants are provided in Chapters 2 and 3. 

Studies varied in whether they measured in-stream pollutant concentrations, total pollutant loads, or both. 

Measurement of pollutant concentrations was more common than measurement of pollutant loads. Each 

measurement provides a different type of information. Measurement of pollutant concentration is useful 

for determining certain acute and chronic effects associated with individual discharge events (e.g., surface 

water appearance, organism toxicity). Total load measurements provide information on the cumulative 

effects of a discharge source and can be particularly important for pollutants such as sediment, metals, 

and certain toxic and persistent organic compounds that can settle and persist in surface waters for several 

or more years. 
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A complication with the measurement of pollutant concentrations in construction site stormwater is that 

they can be diluted by the greater volume of stormwater runoff generated by many construction sites. An 

observer relying solely on pollutant concentration data to determine the effects of construction activity on 

stormwater discharges may incorrectly conclude that a construction site has no impact because 

concentration measurements do not reflect the cumulative impact of the total volume of runoff from the 

site. For example, Clausen (2007) observed that copper, lead, and zinc concentrations in stormwater 

runoff were unchanged by construction activity at one site, even though the total load of these pollutants 

discharged from the site increased. 

Physical stream condition was discussed in a number of studies, particularly when sedimentation impacts 

were noted. Observed impacts included increased substrate embeddedness, complete burial of substrate 

beneath a sediment layer, elimination of pools and other stream microhabitats, changes in channel 

morphology, and loss of stream baseflow. 

Biological parameters were monitored less frequently than water quality parameters. Macroinvertebrates, 

particularly benthic macroinvertebrates, were monitored most frequently, followed by monitoring of fish. 

Only a few studies examined impacts to primary producers or animals other than macroinvertebrates or 

fish (e.g., amphibians, mollusks, reptiles). Impacts to organisms were noted in most studies in which they 

were monitored. Impacts included reduction of abundance, diversity, and biomass. Increased drift and 

avoidance behavior, change in community composition, and mortality were also documented. 

Other study limitations included limits on the spatial and temporal extent of monitoring. Few studies were 

designed to characterize the full spatial extent of impacts from construction sites’ discharges. Most 

studies examined conditions within surface waters less than a mile downstream of construction sites. 

Several studies documented surface water impacts several miles downstream of construction sites (King 

and Ball 1964; Wolman and Shick 1967; Vice et al. 1969; Huckabee et al. 1975; Hainly 1980; Nodvin et 

al. 1986; Ohio EPA 1997f). One study documented impacts as far as 56 miles downstream (Fossati et al. 

2001). 

Many studies were also limited in the length of time they were able to monitor, and most were not 

designed to characterize the full temporal extent of impacts. Most monitoring took place during active 

construction. A number of studies included monitoring during pre-construction and post-construction 

periods, as well. Elevated suspended sediment and turbidity concentrations in downstream surface waters 

were generally noted to decline within a year or less of construction activity cessation and site surface 

stabilization with sufficient vegetation. 

A number of studies documented longer-term impacts from construction site discharges. These impacts 

included elevated sedimentation of surface water substrates (Guy 1963; Chisholm and Downs 1978; Tsui 

and McCart 1981; Cline et al. 1982; Taylor and Roff; Stout and Coburn 1989; Barrett et al. 1995a; Reid 

and Anderson 1999). Elevated sedimentation levels in areas immediately downstream of construction 

sites were typically documented as requiring one to several years to clear. Monitoring of sedimentation of 

downstream surface water substrates as construction sediments migrated downstream was typically not 

included in the studies. Lee et al. (2009) noted that many years may be required after completion of 

construction in a watershed for construction-associated sediments to fully migrate downstream. 

Longer-term effects to macroinvertebrate and fish populations were also documented (Peterson and 

Nyquist 1972; Huckabee et al. 1975; Reed 1977; Chisholm and Downs 1978; Tsui and McCart 1981; 

Cline et al. 1982; Taylor and Roff 1986; Reid and Anderson 1999). Macroinvertebrate population 

improvements were usually noted to occur as excess sedimentation was flushed from surface water 

substrates, and fish population improvements were usually noted as substrate and macroinvertebrate 
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November 2009 4-3 

conditions improved. Recovery was often documented to require more than a year following construction 

completion, with some studies noting recovery as still incomplete several years after construction 

cessation (Taylor and Roff 1986; Montgomery County DEP 2009). In one case, damage was still noted 30 

years after construction cessation because of stream acidification from iron sulfide rocks used as roadbed 

fill (Kucken et al. 1994). 

Implementation of sediment and erosion controls varied widely among sites described in the studies, 

though a large number of sites, including many in older studies, utilized them to some degree. 

Requirements for use of sediment and erosion controls on construction sites have gradually become more 

stringent over time. Older studies tend to document discharges from sites with lower levels of sediment 

erosion and control. However, although older studies often do not reflect current requirements, they do 

provide valuable information on the nature of pollutant discharges from construction sites and their 

effects on surface waters. In addition, current practices vary among states and localities and are not 

always successfully implemented (particularly during large precipitation events) or maintained over time. 

Because current requirements have reduced, rather than eliminated, construction site discharges, 

information from older studies continues to be relevant for full evaluation of potential impacts. 

Construction site discharges are dependent on site conditions (e.g., slope, vegetative cover, soil type), 

rainfall, nature of construction activity, presence and effectiveness of sediment and erosion abatement 

practices, and other factors. Given the large number of construction sites active every year (greater than 

80,000) and limitations in resources available for construction site discharge and impact study and 

documentation, the collection of studies summarized here only partially describes the nature of 

construction site discharges taking place each year in the United States. 

To quantify current national water quality impacts from construction site sediment discharges and how 

they would decrease under various regulatory options, EPA used several methodologies, including 

Spatially Referenced Regressions on Watershed Attributes (SPARROW) models, to estimate surface 

water sediment, nitrogen, and phosphorus levels deriving from construction activity. These 

methodologies and the results of the analyses are described in more detail in Chapter 6 of this document. 

Table 4-1 summarizes information from studies EPA reviewed that studied the effect of construction site 

activity on physical, chemical, and/or biological parameters of site stormwaters or receiving waters. 

Specific information on suspended sediment concentration, suspended sediment yield, and turbidity levels 

are provided in Table 4-2, Table 4-3, and Table 4-4. 

 “Altered Water Characteristics” as listed in Table 4-1 are water condition alterations identified by a study 

as being due to construction activity. “Unaltered Water Characteristics” are water conditions identified by 

a study as unchanged by construction activity. In some studies, conditions listed under “Unaltered Water 

Characteristics” changed, but the cause was identified by the study as something other than construction 

activity.
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November 2009 

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts 

Study Location Construction 

Type 

Monitoring 

Points 

Sediment & 

Erosion Controls 

Altered Water 

Characteristics 

U

C

Barrett et al. (1995a) Texas Highway Stream Rock Berms Silt Fence 

BOD51

Chemical Oxygen Demand (COD) Copper Fecal Coliform1Fecal Strep Iron Nitrate Sedimentation Total Coliform Total Organic Carbon1

TSS Turbidity Volatile Suspended Solids Zinc 

Cadmium ChromiuLead 

Barton et al. (1972) Utah Highway Stream Unknown Macroinvertebrates Barton (1977) Ontario, Canada Highway Stream Ponds Sedimentation Basin Straw Mulch Turfing 

Benthic Macroinvertebrates Fish Nitrate Sedimentation Sediment Organic Content Suspended Sediment 

AmmonDissolveHardnesNitrite pH Phospha

Burton et al. (1976) Florida Highway Stream Lake 

Earthen Berms Hay Bales Mulching Plastic Sheeting Sedimentation Basins Seeding Sod Placement Visqueen Slope Drains 

Orthophosphate [load] Phosphorus, Total Dissolved [load] Silicon, Dissolved [load] Suspended Sediment [conc. + load] Turbidity 

DissolveStormwa

Highway – Park Avenue Stream 

Rock Dam Sedimentation Basin Silt Fence Vegetative Buffer 

TSS Benthic 

Substrat

Carline et al. Trout Re(2003) Pennsylvania 

Highway – Rock Road Stream Sedimentation Basin Silt Fence TSS Benthic 

SubstratTrout Re
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts 

Study Location Construction 

Type 

Monitoring 

Points 

Sediment & 

Erosion Controls 

Altered Water 

Characteristics 

U

C

Chen et al. (2009) 

West Virginia Highway Stream 

Grass seeding Mulch Sedimentation Basin Silt Fence 

Acidity Alkalinity (possible) Calcium (possible) Chloride Conductivity (possible) Iron Macroinvertebrates Nitrate Sulfate TSS Turbidity 

AmmonPhosphaTemperaWater D

Chisholm & Downs (1978) 

West Virginia Highway Stream Seeding 

Dissolved Solids Macroinvertebrates Sedimentation Stream Substrate 

Residential - Traditional Storm Sewer Unspecified Practices 

Ammonia [yield] Copper [yield] Lead [yield] Nitrate + Nitrite [yield] Runoff Volume TKN [conc.]1

TKN [yield] Total Phosphorus [conc.] Total Phosphorus [yield] TSS [yield] Zinc [yield] 

AmmonCopper Lead [coNitrate + TSS [coZinc [co

Clausen (2007) Connecticut 

Residential – Enhanced Best Management Practices (BMPs) 

Ditch 

Earthen Berm Hay Bales Silt Fence Topsoil stockpile cover 

Ammonia [conc.] Copper [conc.] Lead [conc.] Nitrate + Nitrite [conc.] Runoff Volume1

TKN [conc.] Total Phosphorus [conc.] Total Phosphorus [yield] TSS –[conc.] TSS –[yield] Zinc [yield]1

AmmonBOD [coCopper Fecal CoLead [yiNitrogen TKN [yiZinc [co

Cleveland & Fashokun (2006) 

Texas Highway Ditch Rock Filter Dam TSS Phosphorus 

AmmonNitrate Nitrite Turbidit
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts 

Study Location Construction 

Type 

Monitoring 

Points 

Sediment & 

Erosion Controls 

Altered Water 

Characteristics 

U

C

Cline et al. (1982) Colorado Highway Stream Unknown 

Epilithon Macroinvertebrates Substrate composition TSS 

DissolvepH Total Di

Cramer & Hopkins (1982) Louisiana Highway Wetland Unknown 

Color Turbidity 

DissolvepH Salinity 

Downs & Appel 1986 

West Virginia Highway Stream Unknown Suspended Sediment 

Duck (1985) Scotland, Great Britain Road Stream Lake Unknown 

Sedimentation Sediment Load Suspended Sediment Turbidity 

Eckhardt et al. (1976) Pennsylvania Highway Stream Mulching Seeding Suspended Sediment Embler & Fletcher (1983) 

South Carolina Unknown Stream Unknown Suspended Sediment Turbidity 

Extence (1978) Great Britain Highway Stream River Unknown 

Iron Macroinvertebrates Sedimentation Suspended Sediment 

BOD Nitrate OrthophpH 

Fossati et al. (2001) Bolivia Highway River Unknown 

Bicarbonates Macroinvertebrates Phosphates Sedimentation Suspended Sediment Turbidity 

Calcium ChlorideConductMagnesiNitrates pH PotassiuSodium Sulfates Tempera

Garton (1977) West Virginia Highway Spring Unknown Fish Petroleum Hydrocarbons Suspended Sediment Guy (1963) Maryland Residential Stream Unknown Sedimentation Suspended Sediment Hainly (1980) Pennsylvania Highway Stream Unknown Suspended Sediment 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts 

Study Location Construction 

Type 

Monitoring 

Points 

Sediment & 

Erosion Controls 

Altered Water 

Characteristics 

U

C

Hedrick et al. (2006) Tennessee Highway Stream 

Buffered Disposal of 

Acidic Rock 

Rock Check Dams Silt Fence 

None 

Calcium ConductMagnesipH Suspend

Hedrick et al. (2007) 

West Virginia Highway Streams Silt Fence 

Benthic Macroinvertebrates (short 

term) 

Sediment Yield 

Benthic 

(long 

Sedimen

Helm (1978) Pennsylvania Highway Stream Unknown Suspended Sediment Helsel (1985) Ohio Highway Stream River 

Benches Dams Dikes Excelsior Matting Hay Bales Jute Matting Mulching Rock-Lined Channels Sedimentation Basins Seeding 

Suspended Sediment 

Huckabee et al. (1975) 

North Carolina Highway Stream Unknown 

Aluminum Bicarbonate Calcium Fish Lanthanum Magnesium Manganese Periphyton pH Salamander Samarium Stream Substrate Sulfate Zinc 

Arsenic Cadmium Chloride Cobalt Copper Iron Lead Mercury PhosphaPotassiuSelenium Sodium 

Hunt & Grow (2001) Ohio Unknown Stream None 

Fish Habitat Quality Indices Sedimentation Substrate Embeddedness 

Keller (1962) Maryland Multiple River Unknown Suspended Sediment 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts 

Study Location Construction 

Type 

Monitoring 

Points 

Sediment & 

Erosion Controls 

Altered Water 

Characteristics 

U

C

King & Ball (1964) Michigan Highway River Unknown 

Fish Macroinvertebrates Primary Production Sedimentation Suspended Sediment Turbidity 

Lee et al. (2009) Kansas 

Commercial Highway Residential 

Stream Unknown Sediment Yield 

Lenat et al. (1981) 

North Carolina Highway Stream Sedimentation Basin Silt Fence Macroinvertebrates Substrate Composition Suspended Sediment Dissolve

pH Tempera

Line (2009) North Carolina Highway Streams Lake 

Coir Baffles Flocculent Mulching Rock Dams Sediment Traps Sedimentation Basins Seeding Silt Fence Skimmer Outlet Slope Drains Turbidity Curtain 

Suspended Sediment Yield Turbidity 

Line & White (2007) 

North Carolina Residential Stream Sediment Trap 

Ammonia Nitrate Peak Runoff Runoff – Rainfall Ratio Stream Baseflow TKN Total Nitrogen Total Phosphorus TSS 

Lubliner & Golding (2005) Washington 

Commercial Residential Transportation Utility 

Stream 

Blanket Erosion Control Mulch Storm Drain Protection Sedimentation Basin Vegetation 

Transparency TSS Turbidity 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts 

Study Location Construction 

Type 

Monitoring 

Points 

Sediment & 

Erosion Controls 

Altered Water 

Characteristics 

U

C

Montgomery County DEP (2009) 

Maryland Multiple Streams 

Baffles Basin Forebays Limit Disturbance Oversized + Dual Perforated Risers Sedimentation Basin –Skimmers Stabilization 

Channel Aggradation Channel Sinuosity1

Fish Macroinvertebrates Sedimentation Stream “Flashiness” Substrate Embeddedness TKN TSS 

Nodvin et al. (1986) California Bridge Stream Lake Unknown 

Alkalinity Calcium Conductivity Magnesium pH Potassium Sodium Turbidity 

Ohio EPA (1997f) Ohio Golf Course Residential Stream Sedimentation Basin 

Fish Macroinvertebrates Mollusks Sedimentation Substrate Embeddedness Turbidity 

Owens et al. (2000) Wisconsin Residential Commercial Stormwater Discharge Not included in study Suspended Sediment Peterson & Nyquist (1972) Alaska Highway Bridge Stream Unknown Conductivity Macroinvertebrates Turbidity 

AlkaliniAmmonCalcium Carbon DDissolveIron MagnesiNitrate Nitrite OrthophpH Silica Sulfate Tempera
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts 

Study Location Construction 

Type 

Monitoring 

Points 

Sediment & 

Erosion Controls 

Altered Water 

Characteristics 

U

C

Reed (1977) Virginia Highway Streams 

Hay Bales Mulching Rock Dam Sedimentation Basin Seeding 

Fish Macroinvertebrates 

Reed (1980) Pennsylvania Highway Streams 

Mulching Rock Dams Sedimentation Basins Seeding 

Suspended Sediment 

Reid & Anderson (1999) 

Multiple Sites in North America 

Pipeline Streams Rivers Unknown 

Benthic Macroinvertebrates Channel Morphology Fish Sedimentation Substrate Composition Substrate Embeddedness Suspended Sediment Turbidity 

Selbig et al. (2004) Wisconsin Residential Stream 

Construction Phasing Deep Tilling Earthen Berms Inlet Protection Silt Fence Stockpile Seeding Stone Tracking Pads Straw Bales Vegetative Buffers 

Runoff Volume Suspended Sediment Total Sediment 

Fine SedFish Habitat MacroinStream MTempera

Selbig & Bannerman (2008) 

Wisconsin Residential Stormwater Basin 

Earthen Berms Erosion Fabric Rock Trenches Sediment Basins Seeding Silt Fence 

Phosphorus - total Runoff Volume Total Solids TSS 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts 

Study Location Construction 

Type 

Monitoring 

Points 

Sediment & 

Erosion Controls 

Altered Water 

Characteristics 

U

C

Shields & Sanders (1986) Mississippi Waterway Stream 

Polymer Flocculant Sedimentation Basin Disposal Area Design 

Multiple2

Arsenic Cadmium ChromiuCopper – Mercury Nitrate – Nitrate + Organic PhosphoTKN [loZinc – to

Stephens et al. (1996) Utah Highway Stream Reservoir Tan & Thirumurthi (1978) 

Nova Scotia, Canada Highway Lake Diversion Channel Sedimentation Basin 

Conductivity Nitrate + Nitrite Total Dissolve Solids Turbidity 

Multiple

Taylor & Roff (1986) Ontario Highway Stream 

Sedimentation Basin Straw Mulch Turfing 

Fish Macroinvertebrates Macrophytes Nitrate Sedimentation Sediment Organic Content Suspended Sediment 

AmmonDissolveHardnespH Phospho

Tsui & McCart (1981) 

British Columbia, Canada 

Pipeline Stream Unknown 

Macroinvertebrates Sedimentation TSS Turbidity 

Vice et al. (1969) Virginia Highway Stream Unknown Suspended Sediment Turbidity Walling & Gregory (1970) 

Devon, England Residential Stream Unknown Suspended Sediment 

Ward & Appel (1988) 

West Virginia Highway Stream Unknown Suspended Sediment 

Wark & Keller (1963) 

Maryland, Virginia, and West Virginia 

Multiple Streams Rivers Unknown Suspended Sediment 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 4-1: Studies Documenting Construction Site Discharges to Surface Waters and Their Impacts 

Study Location Construction 

Type 

Monitoring 

Points 

Sediment & 

Erosion Controls 

Altered Water 

Characteristics 

U

C

Welsh & Ollivier (1998) California Highway Streams Unknown 

Amphibians Fine sediment depth Substrate Embeddedness 

Werner (1983) West Virginia Highway Spring Unknown Fish Suspended Sediment Whitney & Bailey (1959) Montana Highway Stream Unknown Fish Wolman & Schick (1967) Maryland Commercial Stream Unknown Sedimentation Stream Substrate Wong (2005) Hawaii Highway Stream Reservoir Unknown Multiple4 MultipleYew & Makowski (1989) 

TennesseeNorth Carolina Border 

Highway Stream Unknown 

Alkalinity Fish Metals pH 

Yorke & Herb (1978) Maryland All types Streams Unknown Suspended Sediment [yield] Young & Mackie (1991) 

Northwest Territories, Canada 

Pipeline Stream Stream Stabilization Vegetative Buffer Macroinvertebrates Sedimentation TSS 

DissolveHardnesNitrogen pH Phospho

1 Parameter declined between baseline and construction estimates. 2 Alkalinity, Ammonia- total [conc.], Biological Oxygen Demand [conc.], Calcium – dissolved [conc.], Carbonate [conc.], Chemical Oxygen Demand [conc.], Chlo

Hardness, Iron – total [conc. AND daily loading], Lead-total [conc. AND daily loading], Magnesium – dissolved [conc.], Manganese- total [conc. AND daily loadinloading], Nitrite – total [conc.], pH, Phosphorus-total [conc.], Specific Conductance, Sulfate [conc.], Total Dissolved Solids [conc. AND daily loading], Total Kjeld

Total Nitrogen [conc. AND daily loading], TSS [conc.], Turbidity, and Zinc-total [daily loading]. 3 Alkalinity, ammonia, color, coliforms, dissolved oxygen, hardness, iron, manganese, pH, potassium, silica, temperature, total organic carbon 4 Copper (possible), Nitrite + Nitrate, Sediment Yield, Specific Conductance, Total Phosphorus, Total Suspended Solids, Turbidity. 5 2,4-D, 2,4,5-T, Aldrin, Aluminum, Arsenic, Barium, Beryllium, Cadmium, Chlordane, Chlorpyrifos, Chromium, Cobalt, DDD, DDE, DDT, DEF, Diazinon, Dichl

Dissolved Oxygen, Disulfoton, Endosulfan-alpha, Endrin, Ethion, Fonofos, Heptachlor, Heptachlor epoxide, Iron, Lead, Lindane, Lithium, Malathion, Manganese, 

Methoxychlor, Mirex, Molybdenum, Nickel, Parathion, Perthane, pH, Phorate, PCB, PCN, Selenium, Silver, Silvex (2,4,5-TP) Strontium, Temperature, Total NitroVanadium, Zinc. 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-13 

Table 4-2, Table 4-3, and Table 4-4 summarize findings on suspended sediment concentrations, sediment 

yield, and turbidity levels, respectively, from the studies reviewed by EPA. Sediment in construction site 

stormwaters or receiving waters was measured either as a concentration (Table 4-2) or a total yield from 

the site or the surface water’s watershed basin (Table 4-3). Suspended sediment concentrations are 

measured in milligrams per liter (mg/L). Sediment yield is total sediment load expressed as a mass of 

sediment normalized by the area of the contributing watershed. It represents the mass of sediment eroded 

from the land, channels, and mass wasting, minus the sediment that is redeposited prior to the point of 

measurement. Sediment yield is expressed in terms of total yield or in terms of an increase due to 

construction site discharge and is sometimes specified per unit area or period of time. Turbidity is 

expressed in terms of NTUs (Table 4-4). 

For most studies, the data presented in Table 4-2, Table 4-3, and Table 4-4 reflect the averaging of 

multiple samples rather than the comparison of single grab samples. The number of samples and the 

length of time over which they are averaged vary widely among studies. 

Table 4-2: Summary of Suspended Sediment Concentrations from Selected Studies 

Suspended Sediment Concentration (mg/L) 

Study Location Unaffected Site Affected Site 

Barrett et al. (1995a) Texas 13.9 79 

Barton (1977) Ontario, Canada 2.8 5-50.01

Chisholm & Downs (1978) West Virginia — 3,051.2 

Connecticut – Enhanced site 42 67 

Clausen (2007) Connecticut – Traditional site No statistical difference. 

Cleveland & Fashokun (2006) Texas 212.5 1410.3 

Colorado 16.4 10.8 

Colorado 2.2 18.9 

Colorado 3.8 91.0 Cline et al. (1982) 

Colorado 2.6 121.6 

Duck (1985) Scotland 0.3-3.4 7.4-712.8 

Embler & Fletcher (1983) South Carolina <303 60-1303

Fossati et al. (2001) Bolivia 4 5-2,5744

Hedrick et al. (2006) Tennessee No statistical difference. 

Helsel (1985) Ohio 125.0 261.0 

Washington — 33.7 

Lubliner & Golding (2005) Washington — 131.2 

Washington — 628.7 

Wisconsin – summer 

construction 

100 15,000 

Owens et al. (2000) Wisconsin – winter 

construction 

550 2,400 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-14 

Table 4-2: Summary of Suspended Sediment Concentrations from Selected Studies 

Suspended Sediment Concentration (mg/L) 

Study Location Unaffected Site Affected Site 

7-85 12-185 Pennsylvania – Site 2 

42-1006

 146-2436

3-55 9-145 Pennsylvania – Site 2A 

38-636

 100-2096

11-175 58-705 Pennsylvania – Site 2B 

65-776

 309-4086

8-95 16-305

Reed (1980) 

Pennsylvania – Site 3 

36-576

 227-2856

Shields & Sanders (1986) Mississippi 119 347 

Taylor & Roff (1986) Ontario, Canada 5-10 30-206 

Tsui & McCart (1981) British Columbia, Canada 0-7 0-10,660 

Wolman & Schick (1967) Maryland - Cockeysville 1,500 80,000 

Wolman & Schick (1967) 

Multiple sites in Baltimore, 

Maryland and District of 

Columbia region 

3,000 to 150,000 + 

Hawaii – Site 16265700 5 10 

Hawaii – Site 16266500 3 2 

Hawaii – Site 16267500 3 12 

Hawaii – Site 16269500 2 5 

Hawaii – Site 16270900 5 2 

Wong (2005)7

Hawaii – Site 16275000 3 5 

Young & Mackie (1991) Northwest Territories, Canada <2 >300 

1

 Average concentration varied with stage of construction activity. 2

 Modeled estimate. 

3

 Peak values. 

4

 Study sites extended up to 56 miles downstream of construction activity. 

5

 Average daily-mean base-flow values. 6

 Average daily-mean storm event values. 7

 Wong (2005) measured concentration as a long-term geometric mean. 

Studies vary in the degree to which construction activity increased suspended sediment yield for multiple 

reasons, including variability in the percentage of the watershed under study containing construction 

activity, nature of construction activity and associated land disturbance, precipitation intensity, site 

characteristics (e.g., soil type and slope), and sediment and erosion control practices utilized. A number of 

the yields in Table 4-3 were calculated for watersheds containing other land uses besides construction 

(e.g., forest, mature development, pasture, cropland). This method of calculation lowers estimated 

sediment yields because it incorporates lower sediment yields associated with these other land uses. 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-15 

Table 4-3: Summary of Sediment Yield Data from Selected Studies 

Sediment Yield 

Study Location Unaffected Site Affected Site Units 

Burton et al. (1976) Florida Up to 67 tons/acre/year 

Carline et al. (2003) Pennsylvania – Rock 

Road 

 5 tons/acre/year 

Connecticut – 

Enhanced BMP site 

0.0021 0.024 tons/acre/year 

Clausen (2007) Connecticut – 

Traditional BMP site 

0.00081

 0.10 tons/acre/year 

Wisconsin – Site G2 – 12.27 tons/acre/year 

Daniel et al. (1979) Wisconsin – Site G3 7.58 tons/acre/year 

Wisconsin – Site G5 5.84 tons/acre/year 

Downs and Appel (1986) West Virginia 0.2 9.7 tons/acre/year 

Duck (1985) Scotland, Great 

Britain 

0.03 3.8 tons/acre/year 

Eckhardt (1976) Pennsylvania 62-103 tons/acre/year 

Guy (1963) Maryland – 39 tons/acre/year 

Guy and Ferguson (1962) Virginia – 2 tons/acre/year 

Helsel (1985) Ohio 0.7-1.2 15-25 tons/acre/year 

North Carolina – 

Tilly Up 

0.01 7.3 tons/acre/year 

North Carolina – 

Tilly Down 

0.07 3.50 tons/acre/year 

North Carolina – 

Ellery Up 

0.04 2.02 tons/acre/year 

North Carolina – 

Ellery Down 

0.20 1.35 tons/acre/year 

Line (2009) 

North Carolina – 

King’s Mill 

0.09 1.63 tons/acre/year 

Line and White (2007) North Carolina 0.16 2.80 tons/acre/year 

Nelson and Booth (2002) Washington 0.11 0.43 tons/acre/year 

Wisconsin – summer 

construction 

0.18 3.38 tons/acre/year 

Owens et al. (2000) Wisconsin – winter 

construction 

0.18 0.82 tons/acre/year 

Pennsylvania –Site 2 0.17 8 tons/acre/year 

Pennsylvania – Site 

2A 

0.12 6 tons/acre/year 

Pennsylvania – Site 

2B 

0.21 7 tons/acre/year Reed (1980) 

Pennsylvania – Site 3 0.20 9.5 tons/acre/year 

Wisconsin – land 

disturbance phase 

57.6 76.2 tons/storm event 

Selbig et al. (2004) Wisconsin – house 

construction phase 

9.7 10.4 tons/storm event 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-16 

Table 4-3: Summary of Sediment Yield Data from Selected Studies 

Sediment Yield 

Study Location Unaffected Site Affected Site Units 

Wisconsin – major 

land disturbance 

phase 

0.004 0.038-0.06 tons/acre/year 

Selbig & Bannerman (2008) 

Wisconsin – house 

construction phase 

0.004 0.009 tons/acre/year 

Vice et al. (1969) Virginia 0.038 63-76 tons/acre/year 

Ward and Appel (1988) West Virginia 0.3 4.4 tons/acre/year 

Maryland - Baltimore – 219 tons/acre/year 

Maryland - Towson – 125 tons/acre/year 

Maryland - 

Cockeysville 

Wolman and Schick (1967) – 112 tons/acre/year 

Maryland - Gwynns 

Falls 

– 18 tons/acre/year 

Yorke and Herb (1978) 

Maryland – multiple 

sites 

 7.2-100.8 tons/acre/year 

1

 Modeled estimate. 

Table 4-4: Summary of Turbidity Findings from Selected Studies 

Turbidity (NTU) 

Study Author Location Unaffected site Affected site 

Barrett, et al. (1995) Texas 6 72 

Chisholm & Downs (1978) West Virginia 2.3 7.1 

Embler & Fletcher (1983) South Carolina <251 50- 801

North Carolina – Tilly Up 25 1,530 

North Carolina – Tilly Down 54 1,197 

North Carolina – Ellery Down 140 504 Line (2009) 

North Carolina – King’s Mill 41 593 

Washington 2.2 2.2 

Washington 5.2 6.4 

Washington 5.5 5.2 

Washington 6.4 25.7 

Washington 9.8 45.0 

Lubliner & Golding (2005) 

Washington 17.6 12.0 

Shields & Sanders (1986) Mississippi 18 40 

Tsui & McCart (1981) British Columbia 0.5-2.3 0.7-5,000 

Hawaii – Site 16226200 5.6 9.0 

Hawaii – Site 16265700 1.4 11 

Hawaii – Site 16266500 0.8 1.8 

Hawaii – Site 16267500 3.5 14 

Hawaii – Site 16269500 3.8 2.2 

Hawaii – Site 16270900 3.1 2.2 

Hawaii – Site 16273950 13 3.8 

Wong (2005)2

Hawaii – Site 16275000 0.7 1.0 1 Peak values. 

2

 Wong (2005) measures concentrations as a long-term geometric mean. 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-17 

4.2 Individual Study Summaries 

This section provides brief summaries of studies of construction site discharges reviewed by EPA. 

Additional information is available in the original studies. Individual study summaries are presented in 

chronological order with the exception of state reports, which are summarized at the end of the section. 

Whitney and Bailey (1959) documented large declines in fish abundance in a stream section that was 

channelized as part of a highway construction project. Small fish declined 85 percent in number and large 

fish declined 94 percent in number. 

Guy and Ferguson (1962) described a lake in Virginia that accumulated approximately 235,000 tons of 

sediment between 1938 and 1957 due to construction and development taking place in 68 percent of the 

lake’s watershed during this period. 

Keller (1962) documented elevated suspended sediment discharge in a river in Maryland. Sediment 

concentrations and total flux were significantly higher at a monitoring station downstream of a rapidly 

developing area relative to those measured at a monitoring station in an upstream rural watershed. 

Guy (1963) described a 58-acre residential construction site in Kensington, Maryland with an annual 

sediment yield of 39 tons/acre from 1957 to 1962. Suspended sediment concentrations from 1,490 to 

65,000 mg/L were measured in the receiving stream. Early in the project, sediment from the construction 

site fully buried the receiving stream bed, which, under natural conditions, had a rock and gravel bottom. 

Much of the sediment was flushed from the channel by 1962, approximately 2 years after the peak of 

construction activity on the site. 

Wark and Keller (1963), in a study of sediment sources in multiple Potomac River watershed subbasins, 

found the highest sediment yields in subbasins with significant levels of construction activity. Rates were 

10 to 50 times higher than those in rural areas and varied with construction activity intensity. Annual 

sediment yields in watersheds of 4.1 to 72.8 square miles with large amounts of construction activity 

ranged from 1.7 to 3.6 tons per acre. 

King and Ball (1964) documented impacts to a river in Michigan from highway construction. The 

highway project crossed eight major tributaries to the river at distances of 2 to 5 miles from the river. The 

authors observed elevated turbidity, suspended sediment, and sedimentation levels in the river. The 

sediments were primarily inorganic. Primary production declined 70 percent once construction activity 

began. Macroinvertebrate biomass declined. Smallmouth bass populations also declined, which the 

authors attributed to the sedimentation of the river’s pools, important habitat for the bass. The authors also 

noted capture of sediments by patches of aquatic vegetation able to grow in the river because of nutrient 

discharges from a sewage treatment facility. The authors expressed concern that the combination of 

aquatic vegetation and sedimentation would create a mud flat ecosystem in place of the former coarse 

substrate of the river. 

Wolman and Schick (1967) examined multiple studies conducted in the Mid-Atlantic region and found 

that sediment flux in streams receiving construction site discharges was two to several hundred times 

greater than that in streams draining rural or wooded areas. The authors found annual sediment yields of 

18 to 219 tons per acre from construction sites 1.2 to 151 acres in size. The authors stated that, under nonconstruction conditions, annual sediment yields average 0.3-0.8 tons per acre, with lower levels (e.g., 0.02 

tons per acre) in predominantly wooded watersheds. The authors noted that higher sediment yields were 

found in those watersheds with the greatest proportion of land disturbed by construction and the least 

“dilution” by lower sediment yields from other land uses. For example, the highest sediment yield noted 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-18 

in the study, 219 tons/acre/year, was associated with a drainage area of 1.2 acres that was completely 

disturbed by construction activity. The study noted sediment concentrations in construction-affected 

surface waters of 3,000 to greater than 150,000 mg/L, whereas the highest concentration found in waters 

draining predominantly natural or agricultural areas was 2,000 mg/L. 

The study also described in detail the impacts of sedimentation from an individual construction site in 

Maryland. Virtually all stream sections contained sand or silt deposits, in contrast to natural conditions 

consisting of a predominantly cobble streambed and riffle and pool sequences. Portions of the stream bed 

had sand deposits up to 2 feet thick that traveled downstream in dunes. At all sites the authors examined, 

construction sediment impacts were visible for the entire length of the stream between the construction 

site and the stream’s confluence with a reservoir (up to 2 miles). The authors noted that sediment from 

construction activity was nearly absent from a different stream in the same area seven years after the 

cessation of construction upstream. The authors did not describe the manner in which this sediment was 

redistributed downstream of the site of original impact. The authors noted the difficulty of predicting the 

duration and extent of sediment storage in different waters. 

Vice et al. (1969) documented that highway construction constituted 1 to 10 percent of a 4.5 square mile 

Virginia watershed’s area at any given time but contributed 85 percent of total sediment yield. 

Measurements were taken nearly 2 miles downstream from the construction area, indicating that the 

construction impacts were not solely localized to the active construction site. The authors also noted 

significant turbidity, which imparted a reddish color to the affected stream. Turbidity and suspended 

sediment concentrations increased as construction activity increased and abated as construction activity 

decreased. The stream carried a 90-day mean concentration of 20,000 to 25,000 mg/L of suspended 

sediment during precipitation events for several months. 

Walling and Gregory (1970) documented a 2- to 10-fold and occasionally up to 100-fold increase in 

suspended sediment concentrations in waters draining areas undergoing construction. 

Barton et al. (1972, as cited in Barton 1977 and Barrett et al. 1995) found that highway construction 

extirpated invertebrates in a Utah stream bottom. The stream bottom recolonized within 6 months of 

construction completion. 

Peterson and Nyquist (1972) studied highway and bridge construction impacts on an Alaskan stream. The 

stream was still recovering from damage from past placer mining activity. The authors documented 

elevated turbidity and conductivity levels downstream of the site. No other water quality parameters 

changed (see Table 4-1). They also documented severe declines in the number and diversity of benthic 

macroinvertebrates downstream of the site. Macroinvertebrate abundance and density approached normal 

levels one year after construction. 

Huckabee et al. (1975) documented impacts to a small stream from a highway construction project in 

Great Smoky Mountains National Park in Tennessee. Stream acidification took place shortly after 

completion of the project and resulted in a fish kill. Ten years later, the authors documented elevated 

sulfate, calcium, and metal (aluminum, magnesium, zinc, manganese, lanthanum, and samarium) levels 

and acidification in the stream due to iron sulfides in rock used for roadbed fill in 1963. Upstream of the 

site, stream pH was 6.5 to 7.0. Downstream of the site, stream pH was 4.5 to 5.9. In addition, a dense 

white-yellow precipitate coated stream substrate rocks and periphyton more than 1 mile downstream. The 

impacted stream was nearly devoid of fish (e.g., brook trout), salamander larvae, and macroinvertebrates 

for up to 4 miles downstream of the fill. Conditions improved gradually with increasing distance 

downstream. Fish killed by the stream water showed clear evidence of gill hyperplasia. The elevated 

acidity, sulfate, and heavy metal levels were documented as responsible for fish and salamander 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-19 

mortality. Kucken et al. (1994) documented that these conditions continued to persist 30 years after 

construction completion. Two species of stream-breeding salamander were nearly eliminated, and 

abundances of two other species were halved (Kucken et al. 1994). 

Burton et al. (1976) documented impacts to a Florida stream and lake from highway construction. The 

authors documented increases in turbidity and suspended sediment concentrations, as well as in mean 

loads per storm for suspended solids, total dissolved phosphorus, dissolved silicon (silica), and 

orthophosphate. Loads of nitrate, nitrite, and ammonia also increased, but the authors postulated that these 

may have been associated with an upstream sewage discharges (even though these discharges affected 

both the upstream and downstream monitoring stations). The authors attributed the elevated levels of 

suspended solids, phosphorus, and dissolved silicon to soil erosion from the construction site. Total 

dissolved solids loads and total stormwater discharge did not change due to construction activity. The lake 

downstream experienced elevated turbidity and sedimentation levels. 

Eckhardt (1976, as cited in Barrett et al 1995b) documented impacts from highway construction on a 

Pennsylvania stream. Suspended sediment discharge from the construction site comprised approximately 

50 percent of the total suspended sediment load in the stream over a 3.5-year period. High sediment yields 

from the site continued after completion of construction because of delay in vegetation growth. 

White (1976, as cited in Taylor and Roff 1986) documented increases in nitrogen and other inorganic ions 

in a stream downstream of a road construction site. 

Barton (1977) studied highway construction site impacts to an Ontario stream and associated ponds up to 

approximately 1 mile downstream over the life of the construction project. The study documented 

elevated suspended sediment and sedimentation levels (see Table 4-2). Sedimentation levels increased 10-

fold. The decline in stream sediment organic matter content reflected the erosion of low organic matter 

soil from the construction site. An increase in nitrate concentrations was observed during the final 

construction stages. Suspended sediment and sedimentation levels declined with increasing distance 

downstream due to sediment accumulation in several ponds along the stream channel, though elevated 

sedimentation levels were also seen downstream of the ponds after heavy spring flows. The number and 

standing crop of fish declined from 24 kg/ha to 10 kg/ha immediately downstream of the construction site 

during construction but returned to normal within 8 months of construction completion. The authors 

postulated that fish may have migrated from the area during construction to avoid high suspended 

sediment levels. No impacts on fish were documented further downstream, and no fish mortality was 

observed at any point. Benthic macroinvertebrate abundance and species number remained steady, but 

species composition changed. Areas completely denuded by construction activity were quickly 

recolonized by species known to be strong recolonizers. The authors postulated that some invertebrates 

may have sheltered in protected areas during periods of high sediment levels and others drifted in from 

unaffected areas. No changes were recorded in ammonia, dissolved oxygen, hardness, nitrite, pH, or 

phosphate levels. 

Garton (1977, as cited in Barrett et al. 1995) documented an incident in which a highway construction site 

in West Virginia discharged large quantities of sediment to a karst cavern system. Springs discharging 

from the caverns were being used as the water supply for a fish hatchery. Sediment accumulation on the 

gills of trout in the hatchery killed 150,000 fish during one event. Other fish kills were the result of a 

diesel fuel spill on the site that subsequently washed into the caverns. 

Reed (1977) documented impacts to several Virginia streams from highway construction. The authors 

documented declines in both macroinvertebrate and fish diversity and abundance due to elevated 

sediment discharges and consequent sedimentation from construction sites. Populations recovered 
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November 2009 4-20 

somewhat after sediment discharges declined and excess sediments were flushed from the streams, though 

many had still not fully recovered several to 22 months following construction completion. 

Chisholm and Downs (1978) studied highway construction impacts to Turtle Creek in West Virginia, also 

discussed in Downs and Appel (1986) (see below). Portions of the stream under study were in a relocated 

channel. The authors documented elevated turbidity, dissolved solids, sedimentation, and TSS levels (up 

to 11,100 mg/L). The authors recorded up to 8 to 10 inches of silt and clay on the stream bed. 

Sedimentation levels varied during the study period as excess sediments were flushed downstream by 

heavy winter and spring flows, followed by additional sedimentation during lower flow periods. The 

authors postulated that the elevated dissolved solids levels derived from use of calcium chloride as a 

wetting agent to reduce dust on the construction site during the early and middle phases of construction. 

Calcium chloride is highly soluble and leaches readily. Dissolved solids concentrations decreased toward 

the end of the construction project. 

The authors also observed iron precipitates deriving from groundwater discharges. The precipitates 

elevated stream turbidity and imparted a reddish-orange color to it. The authors stated that such 

discharges are common in recently excavated areas in the watershed. 

Heavy sedimentation levels and relocation of portions of the stream channel initially extirpated or heavily 

degraded macroinvertebrate populations over several miles of stream. Organism diversity and abundance 

recovered within a year after construction completion as stream substrate was flushed of excess 

sedimentation, then stabilized. The authors stated that organism drift from unimpacted tributaries 

upstream and good quality construction and revegetation of the new stream channels aided community 

recovery. 

Extence (1978) documented elevated sedimentation levels and suspended solids and iron concentrations 

in a stream and river in Great Britain downstream of a highway construction site. Sedimentation 

proceeded to the point where fine sediment fully covered the former rocky substrate of the river. River 

channel width decreased as a consequence of sediment accumulation. High flow events periodically 

transported some of the sediments downstream. Sedimentation of the substrate reduced benthic 

invertebrate density and diversity. The authors attributed the elevated iron levels to erosion of ironcontaining sediment from the construction site. The authors did not observe impacts to water column pH, 

BOD, orthophosphate, or nitrate levels. 

Helm (1978, as cited in Barrett et al. 1995b) documented impacts from highway construction on a stream. 

Construction discharges contributed approximately 50 percent, or 8,000 tons, of the approximately 16,000 

tons of suspended sediment discharged in the stream during the construction period. The stream already 

contained elevated sediment levels due to steep slopes, fine coal wastes, coal-washing operations, and 

other land uses in the upstream portion of the watershed. 

Tan and Thirumurthi (1978) studied water quality changes in four lakes during highway construction 

activity taking place 30 to 400 feet from the lake shorelines. The lakes served as a drinking water supply 

for the city of Halifax, Nova Scotia. In one lake, elevated turbidity, total dissolved solids, and nitrate + 

nitrite-nitrogen levels were documented. Total dissolved solids were primarily composed of chlorides, 

sulfate, and sodium and calcium carbonates. Elevated conductivity was documented in two lakes. No 

water quality changes were documented in the other two lakes. Table 4-1 lists the parameters for which 

no water quality changes were detected. The authors postulated that construction activity was the source 

of the water quality changes and that elevated nitrogen levels derived from the site’s disturbed soils and 

vegetation. The authors stated that the highway projects had caused serious erosion, producing suspended 

sediment concentrations as high as 12,836 mg/L and very high turbidity concentrations in site runoff. The 
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November 2009 4-21 

authors noted that sediment control devices treating the runoff to the primary affected lake performed 

poorly. 

Yorke and Herb (1978) documented impacts to streams draining several Maryland watersheds over a 

multi-year period of development. The authors documented elevated suspended sediment yields from 

areas undergoing construction. The authors stated that an earlier study (Yorke and Davis 1972) had 

documented a 30 percent increase in stormwater runoff to streams due to construction taking place in 15 

percent of the watershed. 

Daniel et al. (1979) documented elevated yields of suspended sediment, total phosphorus, organic 

nitrogen, nitrate + nitrite, ammonia, and dissolved phosphorus from three residential construction sites in 

Wisconsin. 

Hainly (1980, as cited in USFWS 2003 and Barrett et al. 1995b) found that highway construction site 

discharges elevated turbidity levels in a stream up to 6 miles downstream. The construction site 

contributed 9,100 tons of suspended sediment to the stream or more than 25 percent of the total suspended 

sediment load of 35,500 tons in the stream during the study period. The author observed little 

sedimentation immediately downstream of the construction site. 

Reed (1980) monitored a highway construction site in Pennsylvania before, during, and after construction 

activity. The author documented elevated suspended sediment yields in four streams during construction. 

These yields fell approximately to pre-construction levels during the 2- to 3-year period following project 

completion. Suspended sediment concentrations also increased during construction and fell during the 2-

to 3-year period following project completion. 

Lenat et al. (1981) documented elevated suspended solids levels in the water column and increased 

fractions of sand and gravel in the substrates of two streams downstream of a construction project. These 

discharges impacted invertebrates in the stream. A suite of benthic macroinvertebrate species adapted to 

the new sandy substrate and associated periphyton downstream of the construction site was able to 

establish itself in high numbers during low flow periods. During high flow periods, however, this 

community was unable to persist. On average, organism density and habitat availability was lower 

downstream of the construction sites, but no species were entirely lost from the ecosystem. The authors 

noted that all stream sites in the study were impacted by discharges from upstream agricultural activities 

and gravel roads and so already contained a somewhat stress-tolerant benthic invertebrate community. 

Tsui and McCart (1981) documented impacts to a stream in British Columbia due to construction of a gas 

pipeline crossing. Suspended sediment levels were elevated up to 10,660 mg/L and turbidity levels up to 

5,000 NTU within 300 feet downstream of the construction site. Sedimentation levels increased. The 

authors documented a large reduction (up to 74 percent) in benthic macroinvertebrate abundance. Excess 

sediment was flushed downstream within 2 years of pipeline installation. The invertebrate community had 

not yet recovered after 2 years but was progressing in that direction. 

Cline et al. (1982) examined stream sites within approximately 1,600 feet downstream of a highway 

construction project in a multi-year study. Construction activity elevated TSS levels 10- to 100-fold at 

downstream sites. The exception was one site where a reservoir release between the upstream and 

downstream sampling points influenced observations. TSS levels returned to background conditions 

within 2 weeks following cessation of construction. Construction also increased the percentage of fine 

sediments in the stream’s substrate. This effect dissipated within 1 year of construction cessation, in large 

part due to high stream flow during the snowmelt period, which transported excess sediment downstream. 

Periphyton was generally less abundant and contained more detritus at downstream sites. The authors 
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November 2009 4-22 

noted that fine sediment tended to collect in the periphyton. Downstream sites also had lower algal 

diversity. These effects were still apparent 1 year after construction completion. Macroinvertebrate 

density and biomass declined at some, but not all, downstream sites, and remained depressed for a year 

following construction completion. Taxa composition changed to favor more pollution-tolerant species. 

The authors postulated that construction activity timing had been such that high snowmelt flows and 

reservoir releases had transported sediments downstream and improved conditions at unaffected stream 

sites. 

Cramer and Hopkins (1982, as cited in Barrett et al. 1995b) studied impacts to a Louisiana wetland from 

highway construction. The authors documented elevated turbidity and a change in water color deriving 

from construction discharges. Changes in salinity, dissolved oxygen, and pH were attributed to other 

factors. Turbidity decreased and water color began to return to normal levels once construction ceased. 

Embler and Fletcher (1983, as cited in Barrett et al. 1995b) studied construction site impacts to a South 

Carolina stream. Turbidity levels increased from below 25 NTU to peak levels of 50 to 80 NTU. 

Suspended sediment levels increased from below 30 mg/L to peak levels of 60 to 130 mg/L. 

Werner (1983, as cited in Donaldson 2004) documented a case in which improper highway construction 

practices created multiple instances of turbid water in karst springs in West Virginia. The turbid water 

clogged the gills of trout, killing a large number of them. 

Helsel (1985) documented impacts to an Ohio stream and river from highway construction. Construction 

significantly elevated sediment yield and suspended sediment loads downstream. 

Duck (1985) documented impacts to a stream and lake from gravel road construction in Scotland. A 

stream flowing into the lake carried a sediment load 20 times greater than normal. A turbid plume was 

observed to extend over 1,600 feet into the lake. The author documented deposition of at least 2,010 tons 

of sediment on more than 11 acres of lake bed during a 2-month period. The author estimated that the 

lake, under natural conditions, would have otherwise taken 20 to 25 years to accumulate this quantity of 

sediment. Suspended sediment concentrations in the affected streams returned to normal levels after 

completion of construction. 

Downs and Appel (1986) documented construction site impacts to streams and rivers from highway 

construction in West Virginia. Suspended sediment loads in Trace Fork, a small stream in the study, 

increased from 830 to 2,385 tons downstream of the construction site for a 14-month period. Suspended 

sediment concentrations of up to 7,520 mg/L were measured in the stream. The largest daily mean 

concentration was 2,500 mg/L. Turtle Creek, another small stream, had elevated suspended sediment 

loads of 34,000 tons over 2 years. The authors were unable to gather sufficient data to determine impacts 

to suspended sediment in the Coal River, a river downstream of the smaller streams affected by 

construction activity. The authors postulated that the difficulty in measuring the impact to the larger river 

was due to masking of the construction discharge signal by dilution from the river’s large flow volume 

and by large sediment contributions from other sediment sources in the river’s watershed. 

Nodvin et al. (1986) observed large sediment plumes extending across most of the length of a 2,200-foot 

wide lake whose inlet lay approximately 5.1 miles downstream of a bridge construction project. The 

sediment derived from preparation of an upstream area on a tributary stream for bridge footer 

emplacement. The authors documented elevated levels of alkalinity, conductivity, pH, and calcium in the 

stream. Maximum changes observed were 6.83 to 11.22 for pH, 9 to 341 micro-siemens per centimeter 

for conductivity, 70 to 2001 micro-equivalents per liter for alkalinity, and 54 to 2227 micro-equivalents 

per liter for calcium. Smaller increases in magnesium, sodium, and potassium levels were also recorded. 
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November 2009 4-23 

The authors postulated that leaching of calcium oxide hydration products from the cement in the concrete 

poured for the bridge footers was the pollutants’ source. Conductivity and alkalinity were found to be 

elevated as far as the lake outlet approximately 5.6 miles downstream. Elevated pollutant levels ceased 

and dispersed within the 24 hours following the concrete pouring. The authors postulated that the shortterm duration of the effects explains why the phenomena they documented have not been more widely 

reported in the literature. 

Shields and Saunders (1986) documented stream impacts from waterway (canal) construction, a portion 

of which required stream rechannelization. The authors recorded elevations in specific conductance, 

turbidity, color, COD, total alkalinity, hardness, ammonia, phosphorus, sulfate, iron, lead and manganese 

during construction. Mean values were 50 to 100 percent higher than levels measured before the start of 

construction. Concentrations of dissolved calcium, dissolved magnesium, chloride, total nitrogen, total 

nitrite, and total Kjeldahl nitrogen also increased. Stream temperature and average daily loadings of total 

metals (including iron, manganese, lead, and zinc), nutrients, and dissolved solids increased. Coliform 

bacteria densities decreased. The authors ascribed these changes to increased sediment input from the 

construction site and reduced shading of the surface water due to removal of riparian vegetation during 

construction. Total organic nitrogen, phosphorus, zinc, iron, and manganese were all significantly 

correlated with turbidity discharges. Average daily suspended solids loadings remained approximately the 

same, though the authors ascribed this result to water quality sampling issues. The authors attributed the 

water quality changes to erosion from the construction site, including excavations that exposed formerly 

deeply buried materials to precipitation and erosion. 

Taylor and Roff (1986) continued study of a small stream in southern Ontario impacted by both highway 

and sewer trunk line construction, extending the work documented in Barton (1977) (summarized above). 

Monitoring took place for six years following completion of highway building activities and included the 

land stabilization and revegetation phase of construction activity. During this period, part of the 

downstream study area was additionally impacted by sewage line construction. 

The authors documented elevated suspended sediment and sedimentation levels downstream of the 

construction site that declined over several years as vegetation stabilized the former construction site and 

excess sediment migrated downstream. Site revegetation, in particular, reduced sediment discharges. The 

proportion of organic material in the suspended and bedded sediments increased over time as 

predominantly inorganic sediment from the construction site was flushed downstream. Stream sediment 

conditions closest to the construction site returned to reference levels approximately 5 years after 

construction site stabilization. Sediment conditions further downstream, however, did not fully recover 

during the study period due to additional disturbance from sewage line construction and probable input of 

sediments flushed downstream from sites closer to the original construction site. The stream bed remained 

covered by up to 6 inches of inorganic sediment. The authors documented extensive growths of 

previously absent aquatic plants downstream of the construction site, which they attributed to elevated 

sedimentation levels. 

Elevated nitrate levels were also detected downstream of the construction site. Levels were higher in areas 

closer to the construction site and slightly lower at sites further downstream. The authors noted that 

phosphorus and nitrogen fertilizers were applied with straw mulch as part of the highway site stabilization 

process and that heavy rains washed much of the initial mulch application and, most likely, much of the 

fertilizer from the site (site culverts were clogged with straw). Elevated phosphorus levels were not 

detected. 
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November 2009 4-24 

The authors documented that the changes in stream conditions favored some species of invertebrates and 

fish and decreased or failed to favor others. For the first 2.5 years of the study, macroinvertebrate species 

tolerant of sedimentation and low-level organic enrichment increased in abundance and biomass. The 

authors postulated that the macroinvertebrates were responding to nutrient-enriched conditions in the 

stream deriving from construction site nutrient discharges. Species less tolerant of sedimentation 

increased in abundance more slowly or declined. Their loss led to an overall decline in community 

diversity. The magnitude of modification to the macroinvertebrate community declined with distance 

downstream from the construction site. In the final 3 years of the sturdy, diversity increased as excess 

sediments were flushed from the stream and formerly declining species populations strengthened. 

Fish abundance increased downstream of the construction site during the study period. The authors 

postulated that this effect was due to the elevated abundance of macroinvertebrates and the growth of 

aquatic plants. The authors noted that fish populations declined downstream of the sewage line 

construction site, probably due to migration of fish from the area to avoid high suspended sediment 

levels. Population levels rebounded once construction was complete. Community composition changed to 

favor mid-water feeder species over bottom feeder species. 

The authors postulated that several of the changes to the stream community were “relatively permanent” 

and that return to original conditions would take many years (assuming no further disturbance from new 

construction sites). 

Ward and Appel (1988) continued the study initially described in Downs and Appel (1986) (see above). 

Over a period of nearly 5 years, the Trace Fork watershed saw completion and stabilization of 

construction activity for one highway segment, a 2-year period without construction, followed by a 

second construction phase. Sediment loads in Trace Fork were elevated during the first construction 

phase, declined during the inactive phase, and increased again during the second phase of construction. 

Stout and Coburn (1989) studied macroinvertebrates that consume leaf detritus in a Tennessee stream 

2 years after completion of highway construction upstream. Observation sites were located within 

0.5 miles of the former construction site. The authors found stream substrate downstream of the 

construction site to have returned to conditions similar to those of upstream sites. The total abundance and 

number of macroinvertebrate taxa was lower downstream of the former construction site than expected. 

The authors attributed this observation not to construction sedimentation impacts but to the removal of 

riparian vegetation during construction, which reduced detrital inputs to the stream and elevated stream 

temperature. 

Yew and Makowski (1989, as cited in Barrett et al. 1995b) documented impacts to several streams near 

the Tennessee-North Carolina border from highway construction. Exposure of pyritic shale during 

construction resulted in discharge of sulfuric acid to the streams. Conditions toxic to fish existed in the 

streams due to low pH levels of 4.0 to 4.4, low alkalinity, and elevated toxic metal concentrations. 

Young and Mackie (1991) studied oil pipeline construction impacts to a stream in the Northwest 

Territories, Canada. Part of the construction process involved burying a portion of the pipeline beneath 

the stream. Downstream sampling stations were located within 1,500 feet of the construction zone. The 

authors documented elevated TSS concentrations downstream of the construction site that peaked at 3,000 

mg/L. These sediments contained significantly less organic matter than the usual stream sediments. 

Macroinvertebrate drift increased as suspended sediment concentrations increased. The authors attributed 

this effect to the increased levels of sediment on the stream bed and in the water column. Total biomass 

and species richness recovered by the warm season following completion of construction. Sedimentation 

also increased downstream of the construction site, but excess sediments were flushed downstream by the 
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November 2009 4-25 

end of the warm season following construction completion. Water chemistry parameters did not change 

during the study. 

Barrett et al. (1995) studied highway construction impacts to a stream in Texas. The authors calculated 

percentage changes in concentrations of a number of parameters (see Table 4-1) but did not assess the 

statistical significance of the observed differences. Large increases in concentrations of TSS, turbidity, 

and iron were observed downstream of the site. Moderate increases were observed in concentrations of 

zinc, volatile suspended solids, and nitrate. Smaller increases were observed in copper, COD, total 

coliform, and fecal strep. Small declines were observed in concentrations of BOD5, fecal coliform, and 

total organic carbon. No change was observed in cadmium, chromium, or lead concentrations. The 

authors found iron discharges to be highly correlated with TSS discharges from the construction site but 

were not able to identify a source for the elevated zinc discharges. The authors observed elevated 

sedimentation levels in the stream downstream of the construction site. This excess sediment was flushed 

further downstream by storm flows within approximately 1.5 years following the cessation of 

construction. 

Stephens et al. (1996) stated that road construction increased loadings of sediment, nutrients, and trace 

elements to a receiving stream and downstream reservoir used as a drinking water supply. 

Welsh and Ollivier (1998) documented deposition of sediment from a California highway construction 

site into a stream. A single storm event deposited sediment layers 0.1 to 2 inches thick on the substrate of 

several streams within 0.5 to 1 mile of the construction site. Mean fine sediment depth and percentage of 

substrate embeddedness was significantly greater in impacted stream pools. The sediment filled 

interstitial spaces in the stream’s substrate and significantly reduced the density of two salamander 

species and one frog species. 

Reid and Anderson (1999) reviewed 27 studies of water quality impacts from pipeline stream and river 

crossing construction at various sites in North America. Pipeline crossing construction frequently requires 

construction activity directly in a surface water. Most earthmoving activity takes place over the course of 

a few days to a few weeks, though site stabilization can require additional time. Impacts varied among 

sites. Multiple studies documented increases in downstream levels of turbidity, suspended sediment, 

sedimentation, and stream embeddedness downstream during and after construction. Changes in channel 

morphology and substrate composition were also observed. Turbidity levels up to 4,700 NTU and 

suspended sediment levels up to 11,600 mg/L have been documented. Multiple studies also documented 

reductions in benthic invertebrate abundance and diversity and reductions in fish abundance. Invertebrate 

community compositions generally shifted to include a larger proportion of pollution-tolerant species. 

Decreases in invertebrate abundance and changes in community composition were attributed to increased 

drift downstream and habitat degradation. The authors also found some studies documenting little or no 

impact to fish communities. 

Of those studies that looked at post-construction recovery, multiple studies documented recovery of 

downstream areas to pre-construction conditions within 2 to 4 years following installation of the crossing. 

Turbidity and suspended sediment levels typically decreased once major earthmoving activity was 

complete. Excess sediments were usually flushed downstream within 6 weeks to 2 years. Recovery of instream cover (pools, rocks, woody debris, and submersed vegetation) took 4 years in one study. 

Invertebrate communities moved toward recovery as excess sediments were removed and recolonization 

from undisturbed areas upstream took place. Fish populations also moved toward recovery with 

improvements in sediment conditions and invertebrate communities. 
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November 2009 4-26 

Owens et al. (2000) studied sediment yields in stormwater prior to its on-site treatment from one small 

commercial and one small residential construction site in Wisconsin. The authors found that construction 

activity significantly increased sediment yields from the sites relative to sediment yields from undisturbed 

rural areas (see Table 4-3). Average event mean concentrations of suspended sediment also increased 

significantly. Increases in sediment yield and event mean suspended sediment concentration were less at 

the site that was active during winter, when rainfall events were less intense, than at the site that was 

active during the summer, when rainfall events were more intense. 

Fossati et al. (2001) studied a river in Bolivia impacted by highway construction. The small river 

downstream of the construction site was transformed by excess sediments into a braided stream system. 

The authors documented a 500-fold increase in suspended sediment concentrations, which ranged from 

2,574 mg/L near the construction site to 5 to 267 mg/L further downstream. Turbidity impacts were 

observable 56 miles downstream. Smaller increases in phosphate and bicarbonate concentrations were 

observed. Sedimentation levels increased significantly. Aquatic invertebrate density declined 200-fold, 

and the total number of taxa declined 6-fold. Authors attributed the invertebrate decline to various 

impacts associated with increased suspended sediment concentrations. No changes in pH, temperature, or 

concentrations of chlorides, sulfates, nitrates, calcium, magnesium, sodium, or potassium were observed. 

Hunt and Grow (2001) documented impacts to a high quality stream downstream of a construction site 

that failed to comply with discharge requirements. Sedimentation created a silt and clay layer 12 to 16 

inches thick on the stream bed. The stream’s substrate, gravel and cobble under natural conditions, was 

highly embedded 5,000 feet downstream. Number of fish species decreased from 26 at the upstream site 

to 19 at the downstream site. Fish abundance decreased from 525 to 230 individuals. Pollutant-tolerant 

species increased 237 percent and pollutant-intolerant species decreased 33 percent. Fish biomass 

declined 62 percent. Measures of overall habitat quality significantly declined. The authors attributed the 

decline in aquatic community health to the severe embeddedness of the stream’s substrate. 

Kayhanian et al. (2001) studied 15 California highway construction sites with widely varying 

characteristics over a 2-year period. In stormwater discharges from the sites, the authors documented 

varying levels of particulate cadmium, nitrate, nitrite, ammonia, total Kjeldahl nitrogen, suspended and 

dissolved solids, turbidity, chemical oxygen demand, oil and grease, total and fecal coliform, 

chlorpyrifos, diazinon, and dissolved and particulate chromium, copper, lead, nickel, silver, zinc, and 

phosphorus. The authors attributed TSS and turbidity levels runoff to disturbed soils on the sites. They 

stated that the sources of the other pollutants were unknown. Because levels of these pollutants varied 

among sites, the authors speculated that site-specific soil and vegetation conditions influenced their 

discharge. The authors also noted correlations between TSS concentrations and particulate copper, 

chromium, and zinc concentrations, indicating that these pollutants are traveling with sediment during 

erosion. 

Buckner et al. (2002) stated that sediment is the top source of impairment in the Tennessee, Cumberland, 

and Mobile River basins, a region containing high levels of aquatic organism diversity. The study noted 

that a large number of aquatic species in this region are threatened, endangered, or being considered for 

listing. Construction activity is one source of sediment in the region. The authors described an incident 

where improper control of runoff from a road construction site in Tennessee resulted in stream vegetation 

loss and stream bank collapse. Four feet of sediment accumulated on the stream bottom, extirpating the 

existing biological community. The water supply for a downstream town was contaminated. The authors 

also noted a report of significant levels of sedimentation in the Cahaba River due to sewer trunk line and 

residential home construction. 
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November 2009 4-27 

Nelson and Booth (2002) noted that construction sites had one of the highest sediment yields per unit of 

land area of all land uses in western Washington, approximately four times greater than natural sediment 

yield for the region. Construction comprised 0.3 percent of the area of the watershed under study and 

contributed 0.7 percent of total watershed sediment yield. 

Carline et al. (2003) documented statistically significant increases in TSS concentrations and suspended 

sediment loads at sampling sites within 1,200 feet downstream of two different road construction sites. 

The authors monitored the site during the entire construction process. Total TSS export at one site 

increased at least 13.5 percent relative to upstream levels after installation of concrete-lined drainage 

channels on site. The authors noted that interpretation of data from this site was complicated by probable 

elevated sediment export from an additional construction site upstream of the observed construction site. 

Total TSS export at the second study site was 13.6 percent higher. The authors did not find impacts to 

stream substrate composition, benthic macroinvertebrate communities, or trout redd occurrence 

downstream of either site. 

Selbig et al. (2004) studied discharges from a residential construction site over the life of the project. The 

authors found that discharge volumes and total and suspended sediment loads were significantly greater in 

the stream downstream of a residential construction site compared to upstream of the site. The authors 

noted that careful implementation of a wide range of sediment and erosion control practices significantly 

reduced discharge volumes and sediment loads during the life of the project. The authors detected no 

significant impacts on stream geomorphology, macroinvertebrate communities, and fish communities 

downstream of the site. However, the stream drained a heavily agricultural area and was ranked very good 

to good for macroinvertebrate community quality, poor for fish community quality, and fair for overall 

habitat quality prior to the start of construction activity. Changes in temperature and fine sediment levels 

were attributed to processes unrelated to construction activity. 

Lubliner and Golding (2005), in a study designed to examine a wide range of construction sites, visited 

183 sites in Washington. They found 44 to be discharging runoff at the time of their visit. Other sites were 

not discharging due to the episodic nature of precipitation events, lower than normal rainfall, on-site 

infiltration, and use of stormwater management practices. Of the 44 discharging sites, 6 discharged 

directly to surface waters and 38 infiltrated discharges or directed them to a municipal stormwater sewer. 

Two of the six sites’ discharges elevated turbidity levels 100 feet downstream. The authors stated that the 

low incidence of receiving water turbidity impacts might have reflected the lower level of precipitation 

during the study and efforts at construction sites to avoid discharging to surface waters. 

Wong (2005), in a multi-year study, documented elevated total sediment yield, TSS concentrations, and 

turbidity levels at multiple stream sites within approximately 0.5 miles of a highway construction project 

that were most likely attributable to construction activity. Elevations were seen in both long-term 

geometric means as well as 90th and 98th percentile values. Some sites were also disturbed by golf course 

construction. TSS and turbidity levels declined after completion of construction except for TSS levels at 

two sites and turbidity levels at one site. Elevated nitrite + nitrate, total phosphorus, copper, and specific 

conductance levels were also thought to derive from construction activity, but were detected at 

substantially fewer stream sites. No effects from construction activity were seen on a reservoir 

approximately 0.5 miles downstream of the project. 

Cleveland and Fashokun (2006) documented a two- to six-fold increase in TSS concentrations due to 

highway construction activity. 

Hedrick et al. (2006) found no statistically significant difference in water quality between sites 0.2 km 

downstream of construction activity and unaffected sites. The authors speculated that the lack of elevated 
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November 2009 4-28 

suspended sediment levels was due to frozen soil conditions during the peak construction period, 

decreased rainfall intensities, and/or effective sediment erosion and control practices. The authors also 

noted that it was likely that the construction site elevated sediment discharges to the stream, but that they 

were difficult to detect statistically because the construction site constituted a small portion of the 

stream’s total watershed, site discharges were diluted approximately 3,300-fold in the stream, and the 

stream’s suspended sediment concentration was naturally highly variable. 

Hedrick et al. (2007) examined impacts from a West Virginia highway construction project during a 

3-year period at two stream sites 300 to 800 feet downstream. The construction site significantly 

increased total sediment export downstream, particularly prior to the installation of silt fences. During this 

time, benthic macroinvertebrate community health declined in one stream. Community health recovered 

once silt fences reduced sediment export from construction activity. Over the total duration of the study, 

construction activity did not have a significant impact on the benthic macroinvertebrate community’s 

health. The authors postulated that stream areas downstream of the construction site were successfully 

recolonized by organisms from unaffected stream areas. The authors also found little difference in the 

percentage of fine sediment present at upstream versus downstream sampling sites. 

Clausen (2007) documented changes in stormwater runoff quality in storm sewer and ditch discharges 

from two watersheds under construction, one using traditional sediment and erosion control practices and 

the other using more rigorous (“enhanced”) control practices. Though runoff volume typically increases 

during construction activity, it declined 97 percent from the enhanced BMP watershed. The authors 

attributed this change to sediment and erosion control practices that retained water on-site and the 

placement on-site of soil with higher infiltration values than the site’s original soil. Runoff volume 

doubled in the traditional watershed. In the enhanced BMP watershed, both TSS and phosphorus total 

export and concentrations increased. Total export of nitrogen stayed the same, though nitrogen discharge 

concentrations increased. The author attributed increased nitrogen concentrations to fertilizer use. In the 

traditional watershed, total export of both sediment and nutrients increased. TSS concentrations did not 

change, which the author attributed to adequate sediment control practices. Nitrogen and phosphorus 

concentrations stayed the same or declined. Zinc export from the enhanced BMP watershed declined 

during construction, and copper and lead export stayed the same. Metals export from the traditional 

watershed increased. Fecal coliform and BOD levels did not change. 

Line and White (2007) compared stormwater runoff immediately downstream of an area undergoing 

residential development to a nearby undeveloped area. Construction activity elevated the percentage of 

rainfall leaving the area as stream runoff to 55 percent versus 21 percent at the unaffected site. Runoff 

volume was 68 percent greater. The site elevated TSS, total phosphorus, nitrate, TKN, ammonia, and total 

nitrogen yields (see Table 4-1). Stream baseflow declined to 0 percent of flow versus 25 percent at the 

unaffected site and peak discharge rates increased more than four times. 

Selbig and Bannerman (2008) found that runoff volume from an area undergoing residential construction 

in Wisconsin increased relative to pre-construction conditions. The authors documented that the Low 

Impact Development (LID) techniques used to develop the area, however, had significantly reduced 

runoff volume relative to conventional development techniques. TSS, total solids, and total phosphorus 

yields also increased during construction. 

Chen et al. (2009) found statistically significant increases in TSS and turbidity levels at stream sites 

approximately 100 to 3,400 feet downstream of construction activity. The authors noted that the 

magnitude of the increase in TSS and turbidity levels was less than that recorded in many other studies 

and thought this was possibly due to a combination of sediment and erosion controls at the construction 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-29 

site and the predominance of woodland and pasture in the contributing watersheds. Total iron, sulfate, 

chloride, and potentially other water quality parameters were elevated during construction activity, and 

chloride, sulfate, nitrate, and acidity were elevated after project completion. The authors thought the 

nitrate may derive from the use of mulches, fertilizer, and hydroseeding for erosion and sediment control 

at the construction site. The authors documented a decline in overall stream ecosystem health as indicated 

by the percentage of chironomidae, the Hilsenhoff Biotic Index, and Ephemeroptera, Plectoptera, and 

Trichoptera (EPT) presence. Stream health remained good to excellent overall during and after 

construction, however, which authors partially attributed to the overall wooded and rural nature of the 

affected watershed and to the possibility that the stream may not have exceeded certain thresholds for fine 

sediment accumulation critical to macroinvertebrate health. 

Lee et al. (2009) documented substantially higher sediment yields per unit area from watersheds with 

higher levels of road and building construction activity compared to watersheds with lower levels of 

activity. Sediment yields were 1.6 tons/acre/year for the watershed with the greatest level of construction 

activity and fell as low as 0.2 tons/acre/year for already urbanized or less-developed watersheds. The 

author also observed elevated levels of fine sediment deposition in areas with high levels of construction 

activity. The watershed under study was listed as biologically impaired due, in part, to excess suspended 

sediments. 

Line (2009) described impacts to streams and a lake from highway construction in North Carolina. The 

lakes were located in a residential neighborhood that valued them for their aesthetic and recreational 

values. Construction discharges elevated suspended sediment and turbidity levels in tributary streams and 

the lake. One watershed (“Ellery-down”) was also impacted by discharges from widening of a municipal 

road and residential construction. Larger increases in suspended sediment yield were found in watersheds 

that contained a larger proportion of construction activity and/or contained construction activities 

particularly prone to erosion. 

Montgomery County Department of Environmental Protection (2009) is the most recent annual report for 

a multi-year study of impacts to surface waters from construction and land use change on previously 

undeveloped lands in the county. The report described changes seen in three large watersheds undergoing 

development. Enhanced sediment and erosion controls were in use and generally performed as expected. 

Some monitoring was conducted immediately downstream of construction sites, while other monitoring 

was conducted further downstream in order to examine impacts deriving from larger areas containing 

multiple construction projects whose locations changed over time. The report noted that impacts were in 

part deriving from disturbed lands where construction had started but was subsequently halted due to an 

economic downturn. Disturbed lands were left in an unstabilized condition. Stream chemistry was 

monitored downstream of one construction project. TSS concentrations increased during construction, 

declined during site stabilization, and returned to pre-construction levels during the post-construction 

period. TKN levels may have increased when sediment and erosion control structures from the 

construction phase were converted to permanent stormwater control structures. Substrate embeddedness 

impacts were documented downstream of two of six construction projects during 2007. Embeddedness 

levels improved to pre-construction levels once construction was completed. Another stream underwent 

channel aggradation and straightening during three years of active construction in the watershed. Total 

water discharge to the stream increased during this time, and flow was “flashier.” As construction activity 

declined in the watershed during two to three years, the channel started to erode accumulated sediments 

and to widen. Flow became less flashy. Calculation of Index of Biological Integrity scores for benthic 

macroinvertebrates and fish for multiple stations throughout the three large study watersheds indicated 

degradation of aquatic ecosystem health from construction activity. Scores for most areas dropped from 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-30 

“good” or “excellent” to “fair” or “poor.” Areas with more intense construction activity scored more 

poorly. The authors noted a shift in macroinvertebrate community structure to include more generalist and 

pollution-tolerant organisms. One area of one watershed showed indications of moving toward recovery 

of pre-construction conditions. 

4.3 State Reports of Construction Discharge Impacts 

A number of states have identified construction as a source of damage or impairment to surface waters in 

written reports and in required reporting of impaired waters under Sections 303(d) and 305(b) of the 

Clean Water Act. 

Ohio’s state water quality reports from the early 1990s through 2006 include 29 reports identifying water 

quality degradation due to construction activity (Ohio EPA 1994, 1995, 1996a, 1996b, 1996c, 1997a, 

1997b, 1997c, 1997e, 1997f, 1998a, 1998b, 1998c, 1999a, 1999b, 1999c, 1999d, 2000, 2001b, 2003a, 

2003b, 2004, 2005, 2006a, 2006b, 2006c, 2006d, 2007b, 2007c). Water quality reports from Ohio EPA 

evaluate biological indicators of water quality and, where possible, identify the waterbody’s sources of 

impairment. Multiple reports identify fish and macroinvertebrate communities impaired by elevated 

suspended sediment and/or sedimentation levels from construction activity upstream. Several types of 

construction are identified as sediment and/or turbidity sources in the studies, including road (Ohio EPA 

1997e, 1998c, 1999b, 2001a), bridge (Ohio EPA 2001a, 2001b), highway (Ohio EPA 1994, 1998b, 

1998d, 1999d, 2005, 2005, 2007a, 2007b), residential (Ohio EPA 1997d, 2006d), commercial (Ohio EPA 

2005), golf course (Ohio EPA 1997f, 1998e, 1999d, 2004, 2006b, 2007c), and airport (Ohio EPA 1996a, 

1998c, 2001b). 

Ohio EPA (1997f) documented impacts to a stream from a combination golf course and residential 

housing construction site. Elevated turbidity and sedimentation were observed up to 10 miles 

downstream. Within a short distance downstream of the site, dissolved oxygen concentrations were 

depressed, and TSS, fecal coliform, ammonia, and nitrate+nitrite concentrations were elevated. The 

stream bed was covered with 4 to 8 inches of silt. The authors stated that the construction site was the 

primary source of the pollutants but upstream agricultural sources also contributed. Discharges from the 

site degraded stream habitat. Approximately 100 dead fingernail clam (Musculium transversum) 

individuals were found in a downstream pool, and the macroinvertebrate community consisted of 

pollution-tolerant species. Healthy populations of fingernail clams and pollution-sensitive 

macroinvertebrates were found upstream and several miles downstream of the site. The fish community 

was also somewhat impacted. 

Ohio EPA documented road construction sites as possible contributors to elevated polycyclic aromatic 

hydrocarbon (PAH) levels in at least three rivers: Little Cuyahoga River, Little Miami River, and Scioto 

River (Ohio EPA 1998d, 1998e, 1999b). PAHs are found in asphalt and tar used in road construction and 

repair, and it is possible for them to contaminate sediment and construction site discharges during rain 

events (Ohio EPA 1998d). Ohio EPA also attributed increased chromium concentrations in receiving 

waters to pressure-treated wood products used at a nearby residential construction site (Ohio EPA 1997d). 

The state of California has expressed concern about discharges from construction sites and has 

specifically discussed the impacts of these discharges on threatened and endangered California coho 

salmon and steelhead trout and their habitat (McEwan and Jackson 1996; California Department of Fish 

and Game 2004). The state noted, for example, that discharges from the construction of Interstate 5 in 

California depleted spawning gravels for coho salmon (California Department of Fish and Game 2004). 
Environmental Impact and Benefits Assessment for the C&D Regulation 

November 2009 4-31 

The states of Michigan and Missouri have also issued reports that identify construction as a source of 

water quality degradation (Missouri DNR 2004, 2005; Michigan DEQ 2006). 

Table 4-5 summarizes data from the ATTAINS database, a national database of water quality information 

submitted to EPA by all states, territories, and the District of Columbia (see Section 2.6, for more 

information). This table presents information on surface waters identified as impaired due to construction 

activity as of September 17, 2009. Though substantial, the numbers in Table 4-5 probably underestimate 

the level of impairment due to construction activity because states are not required to identify surface 

water impairment sources. As of September 17, 2009, 29 states include “Construction” on their lists of 

possible impairment sources. Because significant effort can be necessary to identify a surface water’s 

source of impairment, it is likely that a number of construction sites contributing to surface water 

impairment have not been identified as impairment sources. In addition, as discussed in Section 2.6, a 

large percentage of surface waters have not yet been assessed for impairment. 

Table 4-5: Construction Impairment in 305(b)-Assessed Waters 

Stream/ 

River 

(miles) 

Lake/ 

Pond/ 

Reservoir 

(acres) 

Bay/ 

Estuary 

(sq. miles) 

Coastal 

Shoreline 

(miles) 

Ocean/ 

Near 

Coastal 

(sq. miles) 

Wetland 

(acres) 

Great 

Lake 

Shoreline 

(miles) 

Great 

Lake Open 

Water (sq. 

miles) 

12,273 300,855 16 3 3 13,971 – – 

Source: EPA ATTAINS database (EPA 2009a) as of 9/17/09. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-1 

5 Overview of Benefits from Regulation 

This chapter provides an overview of the potential benefits to society related to the reduced sediment 

turbidity and reduced nutrient discharges from construction sites that will result from the final regulation. 

Sediments and other pollutants from construction sites (e.g., nutrients) may have a wide range of effects 

on water resources located in the vicinity of construction sites. These environmental changes affect 

economic productivity (e.g., drinking water supply and storage and navigation) as well as environmental 

services valued by humans (e.g., recreation, public and private property ownership, existence services 

such as aquatic life, wildlife, and habitat designated uses). Related market benefits (e.g., avoided costs of 

producing various market goods and services) and nonmarket benefits are additive (Freeman 2003). In all 

cases, benefits are conceptualized and estimated based on established welfare theoretic models (Freeman 

2003; Just et al. 2004). 

This chapter provides a conceptual framework for understanding the benefits likely to be achieved by the 

regulation, a qualitative discussion of those benefits, and a review of the benefit estimation methods used 

in EPA’s analysis of this regulation. The following chapters quantify and estimate the economic value of 

these benefit categories in greater detail. 

5.1 Conceptual Framework for Valuation of Environmental Services 

The economic benefits of the final regulation reflect the money-metric values associated with the 

resulting improvements in water and other resources. The conceptual approach for estimating the 

monetary value of benefits of a policy involves an evaluation of changes in social welfare realized by 

consumers and producers, and quantification of these changes in money-metric terms. Such measures are 

based on standardized and widely accepted concepts within applied welfare economics (Just et al. 2004). 

They reflect the degree of well-being derived by economic agents (e.g., people and firms) given different 

levels of goods and services, including those associated with environmental quality. For market goods, 

analysts typically use money-denominated measures of consumer and producer surplus, which provide an 

approximation of exact welfare effects (Freeman 2003). For nonmarket goods, such as aquatic habitat, 

values must be assessed using nonmarket valuation methods (Freeman 2003). In such cases, valuation 

estimates are typically restricted to effects on individual households (or consumers), and either represent 

consumer surplus or analogous exact Hicksian welfare measures (e.g., compensating variation). The 

choice of welfare measure (i.e., value) is often determined by the valuation context. 

The economic value of the changes in environmental services provided by surface waters from the 

regulation is a product of three sets of functional relationships (Freeman 2003): 

3⁄4 The effect of the regulation on environmental quality (Q)

3⁄4 Human uses of the affected resources and their dependence on environmental quality (X)

3⁄4 The economic value of the uses of the environment (V). 

The first functional relationship relates the final regulation to changes in environmental quality. In this 

case, water quality is a function of sediment and other pollutant loadings (S) from construction sites, instream pollutant concentrations (vector P), and other relevant attributes (vector A) such as stream flow 

and velocity. Because the final regulation affects private activities that influence sediment and other 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-2 

pollutant discharges from construction sites, pollutant loadings are a function of private responses to the 

final regulation (R). This relationship may be represented as: 

= APRSfQ ),),(( (Eq. 5-1) 

For this analysis, water quality changes are estimated in terms of sediment and nutrient concentrations in 

receiving waters using SPARROW, an empirical relationship between sediment and nutrient 

concentrations in surface waters, and DCP. (see Chapter 4 and Appendix C through Appendix F for 

details). 

The second set of functional relationships describes the human values of the affected water resources and 

their dependence on water quality. In the following analyses, pollutant discharges are treated as an 

“input” to the water quality, and, as a result, as an “input” to ecological services provided by the affected 

water bodies. Like any other input, the value of changes in pollutant discharges from construction sites is 

determined by its impact on water quality. Water quality, in turn, influences human uses of the affected 

resources, leading to changes in use values. It may also lead to changes in ecosystem services that 

generate nonuse values. 

For example, water quality is often characterized based on its suitability for recreational activities (e.g., 

whether the water is boatable, fishable, or swimmable) or its ability to support specified uses (e.g., 

primary/secondary contact recreation, public drinking water supply, or commercial fishing). To link water 

quality changes from reduced construction site discharges to effects on human uses and support for 

aquatic and terrestrial species habitat, EPA used a Water Quality Index (WQI) for this analysis 

(McClelland 1974; Dunnette 1979; Harrison et al. 2000; CCME 2001; Cude 2001; Carruthers and 

Wazniak 2003; Gupta et al. 2003; USEPA 2004b). Section 10.1 provides detail on the WQI and its 

application to the benefits analysis for the regulation. The WQI presents water quality in a way linked to 

suitability for varying human uses, but does not in itself identify associated changes in human behavior. 

Behavioral changes and associated welfare effects are implied in the proposed benefit transfer approach 

for measuring economic values. Additional details of this approach are provided below and in the 

following chapters. 

In addition to water quality (Q), the level of human uses or other ecological services (vector X) of the 

environmental resource depends on a variety of other factors (e.g., type and physical characteristics of the 

waterbody, proximity to populated areas, presence of recreational amenities, scenery and etc). If vector I

represents these other inputs into the production of environmental services or uses based on the affected 

water resource, the second functional relationship can be expressed as: 

= IQfX ),( (Eq. 5-2) 

The vector of environmental services or human uses (X) affected by changes in water quality and 

sediment discharges could include services received through purchases of market goods produced with 

surface water resources (e.g., commercial fish), goods, services, and activities produced within the 

household, such as water-based recreation, and existence values associated with water quality 

improvements. 

The third set of relationships defines the economic value (V) of the ecological services or human uses of 

the environment. This relationship “embodies the value judgment society has adopted for economic 

purposes” (Freeman 2003). For simplicity, and following standard approaches in benefit cost analysis 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-3 

(Boardman et al. 2001), this analysis assumes that the value function is a simple aggregation of 

individuals’ values.2

 

The relationship between individual or household utility (U) and water quality (Q) can be more formally 

described though the following utility function: 

= BYQXUU ),),(( (Eq. 5-3) 

Individuals are assumed to derive utility from X, as well as from other goods and services (Y), which are 

not related to water resources. For example, a subset of household goods and services, such as recreation, 

are “produced” with water quality (Q) and other inputs such as travel. To account for variation in this 

utility structure across individuals, B denotes a vector of individual or household characteristics, such as 

demographic characteristics, that influence the form or parameters of the utility function. 

Maximization of Equation 4-3 with respect to an income (M) constraint produces the following indirect 

utility function: 

= BMPQVV );,,( (Eq. 5-4) 

where P represents the vector of prices associated with goods and services (X and Y ). 

The total dollar value associated with a change in water quality from Q0 to Q1 (holding other variables 

constant) can therefore be expressed using the compensating variation(CV) measure from the following 

equation: 

);,,();,,( 000 = 001 − BCVYPQVBYPQV (Eq. 5-5) 

Other things being equal, an individual is better off with a higher level of water quality (Q1) than a lower 

level (Q0). CV in this equation represents the individual’s maximum willingness to pay for the specified 

water quality improvement, leaving the individual just as well off after payment and higher water quality 

as with lower water quality. 

From the above, CV (or willingness to pay, WTP) may be derived as a function of pre- and post-change 

environmental quality, prices, income, and individual attributes: 

);,,,( = 0010 BYPQQCVCV (Eq. 5-6) 

This function also provides the basic conceptual foundation for constructing a benefit transfer function, 

which can be used to predict WTP values for defined changes in water quality (van Houtven et al. 2007). 

5.1.1 Applicable Benefit Categories 

There are likely to be several categories of benefits of the regulation; some benefits are linked to direct 

use of market goods and services, and others pertain to nonmarket goods and services. The following 

sections outline the most important categories of the expected benefits, and approaches used to estimate 

each category. In cases where estimates were not possible due to data limitations or other constraints, this 

is also noted below. 

 

2

 This function could also incorporate weights or environmental justice concepts. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-4 

5.1.2 Market Benefits 

Some of the social benefits expected from the regulation will manifest themselves in economic markets 

through changes in price, cost, or quantity of market-valued activities. For benefit endpoints traded in 

markets, such as increased yields from commercial fisheries, benefits can be measured using standard 

market approaches based on data including market prices and quantities (Boardman et al. 2001). 

Competitive prices can also be used to measure benefits related to avoided costs associated with market 

goods and services. In analyzing benefits of the regulation, EPA used the avoided cost method to 

monetize three benefit categories: (1) maintenance of navigational waterways, (2) reservoir dredging, and 

(3) drinking water treatment. Other applicable market benefits (e.g., commercial fishing) are discussed 

qualitatively. The avoided cost approach represents an appropriate measure of social benefits for cases in 

which a policy change reduces costs to producers of a market-supplied good, but in which price effects 

are minimal (Boardman et al. 2001, p. 70-74). 

5.1.2.1 Maintenance of Navigational Waterways 

The primary effect of pollutant discharges from construction sites on navigation and transportation is 

accumulation of sediment in navigational channels and harbors (USEPA 2004b). Section 2.4.1 of this 

report provides detail on sediment impacts on navigational waterways. Keeping these areas passable 

requires extensive dredging, which can be costly. Implementation of the regulation is expected to result in 

less frequent dredging of navigable waterways due to reduced sediment discharges from construction sites 

and, as a result, cost savings to the government and private entities responsible for maintenance of 

navigational shipping channels, harbors, and other waterways. EPA used the avoided cost of navigational 

waterways dredging as a measure of benefits resulting from the final regulation. Chapter 7 of this report 

details methods and presents results of EPA’s analysis of benefits to navigation expected from the 

regulation. 

5.1.2.2 Water Supply and Use 

3⁄4 Reservoir Dredging. Water storage facilities (e.g., reservoirs) serve many functions, including 

providing drinking water, flood control, hydropower supply, and recreational opportunities. Sediment 

in streams can be carried into reservoirs, where it can settle and build up layers of silt over time. An 

increase in sedimentation rates will reduce reservoir capacity. Section 2.4.2 of this report provides 

detail on sediment impacts on the reservoir water storage capacity. To replace this capacity, sediment 

must be dredged from reservoirs, or new reservoirs must be constructed (Clark et al. 1985). The 

regulation is expected to reduce the amount of sediment entering reservoirs and, as a result, the need 

for sediment mitigation measures in these reservoirs and the cost of reservoir maintenance. Because 

sediment mitigation is often accomplished through dredging, which itself is environmentally 

destructive, an additional benefit to reduced reservoir sediment buildup is the reduced environmental 

impact of dredging sediment and disposing of it. EPA used the avoided cost of reservoir dredging as a 

measure of benefits resulting from the final regulation. Chapter 8 of this report details methods and 

presents results of EPA’s analysis of benefits from reduced sedimentation of reservoirs due to the 

regulation. 

3⁄4 Drinking Water Treatment and Household Water Use. Discharges from construction sites can affect 

the quality and cost of providing drinking water. Sediment and other discharged pollutants negatively 

affect water quality, and require increased spending on treatment measures such as settlement ponds, 

filtration, and chemical treatment. There is an additional cost of removing sludge that is created 

during the treatment process (USEPA 2007b). EPA used the avoided cost of treating drinking water 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-5 

for sediment and disposing of sludge resulting from reduced turbidity in source water as a measure of 

benefits resulting from the final regulation. Chapter 9 of this report details methods and presents 

results of EPA’s analysis of benefits to drinking water treatment plants. Reducing nutrient loadings to 

surface waters is expected to reduce eutrophication which is one of the main causes of taste and odor 

impairment in drinking water. Taste and odor in drinking water has a major negative impact on the 

public perception of drinking water safety and the drinking water industry due to a significant 

increase in drinking water treatment costs from foul taste and odor in the source waters. The final 

regulation is expected to reduce the cost of drinking water treatment by improving taste and odor in 

the source waters. EPA, however, was unable to estimate a value of improved taste and odor of 

drinking water, due to data limitations (see Chapter 9 for more detail). 

3⁄4 Industrial Water Use. Sediment may have negative effects on industrial water users. Suspended 

sediment increases the rate at which hydraulic equipment, pumps, and other equipment wear out, 

causing accelerated depreciation of capital equipment. Sediment can also clog cooling water systems 

at power plants and other large industrial facilities (Clark et al. 1985). Section 2.4.4 of this report 

provides detail on sediment impacts on industrial water uses. The final regulation is expected to 

benefit industrial water users by reducing sediment concentrations in source waters and thus 

increasing the useful life of industrial equipment. The Agency, however, was not able to quantify this 

benefit category due to data limitations. 

3⁄4 Agricultural Water Use. Irrigation water that contains sediment or other pollutants from construction 

sites can harm crops and reduce agricultural productivity (Clark et al. 1985). Section 2.4.5 of this 

report provides more detail on sediment impacts on agricultural water uses. The final regulation is 

expected to benefit agricultural producers by reducing sediment discharges and, as a result, sediment 

deposition on farm land; this would lead to improvements in land productivity and enhanced 

marketability of agricultural products. The Agency, however, was not able to quantify this benefit 

category due to data limitations. 

5.1.2.3 Commercial Fishing 

Sediment and other discharges from construction sites can greatly reduce fish populations by inhibiting 

reproduction and survival of an aquatic species. These population reductions would reduce the size of 

commercial harvest. These changes negatively affect subsistence anglers, commercial anglers and fish 

sellers, and consumers of fish and fish products. Chapters 2 and 3 of this report provide some detail on 

aquatic life impacts from increased sediment and other pollutant concentrations in fresh and marine 

waters. Reducing sediment and other pollutant discharges from construction sites is expected to enhance 

aquatic life habitat and thus contribute to reproduction and survival of commercially harvested species, 

which in turn will lead to an increase in producer and consumer surplus. EPA did not quantify market 

benefits from improved commercial harvest resulting from this regulation due to data limitations. The 

nonmarket value of improved habitat for commercial fish species may be implicitly accounted for in the 

WTP for changes in environmental services provided by surface waters affected by construction site 

discharges stemming from the regulation (see Chapter 10 of this report for details). 

5.1.2.4 Public and Private Property Ownership 

3⁄4 Property Values. Aesthetic degradation of land and water resources resulting from sediment and other 

pollutant discharges (e.g., increased water turbidity) can reduce the market value of property and thus 

affect the financial status of property owners. For example, a hedonic price study by Bejranonda et al. 

(1999) found that “the rate of sediment inflow entering the lakes has a negative influence on lakeside 

property rent” (p. 216). Sediment discharges also have a significant impact on stream morphology. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-6 

For example, higher coarse sediment load leads to an increase in width of the river bed and, as a 

result, bank erosion (Wheeler et al. 2003). A 1993 study of Lake Erie’s housing market found that 

“erosion-prone lakeshore property will be discounted” (Kriesel et al. 1993). Stabilization of stream 

banks leads to an increase in the value of surrounding property (Streiner and Loomis 1996). 

Therefore, the final regulation is expected to enhance nearby property value in two ways: (1) by 

improving the aesthetic quality of land and water resources (e.g., reduced turbidity) and (2) by 

improving stream morphology and stabilizing river banks. 

Due to data limitations, EPA did not estimate changes in market values of properties located near 

water bodies expected to benefit from reduced sediment discharges from construction sites as a result 

of this regulation. The nonmarket component (i.e., increased satisfaction with the property) may be 

implicitly accounted for in WTP for improvements in environmental services provided by surface 

waters affected by construction site discharges (see Chapter 10 of this report for details). 

3⁄4 Flood Damages. Sedimentation can also increase the severity of property damages from flooding. 

Sedimentation of river beds can reduce river capacity and result in higher flood levels and more 

frequent flooding. Additionally, sediments carried by flood waters can damage property and can be 

expensive to remove, particularly in developed areas. Clark et al. (1985) estimated flooding damages 

attributable to sediment discharges to be $1.5 billion (2008$), annually. Section 2.4.6 of this report 

provides detail on sediment impacts on flood control and stormwater management. This regulation is 

expected to reduce flooding damages by reducing sediment discharges from construction sites. The 

Agency, however, was unable to estimate benefits from reduced flood damages due to data 

limitations. 

3⁄4 Ditch Maintenance. Sediment in discharges from construction sites can clog ditches and culverts. 

Such sedimentation can increase flooding, which may result in damages to bridges, roads, farmland, 

and other structures in the nearby areas. Preventing these damages may require increased 

maintenance efforts such as dredging (Clark et al. 1985). Section 2.4.6 of this report provides more 

detail on sediment impacts on stormwater management. The final regulation is expected to reduce the 

costs of ditch maintenance by reducing the amount of sediment deposited in ditches. This benefit 

category is not quantified due to data limitations. 

5.1.3 Nonmarket Benefits 

EPA expects that this regulation will reduce the sediment and other pollutant discharges to surface waters 

and will enhance or protect aquatic ecosystems currently under stress, effects for which humans may hold 

value but cannot express this value in the form of a market transaction. The decrease in sediment and 

other pollutant loadings is expected to improve protection of resident species, 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 or use values. Improvements in water quality 

such as decrease in turbidity will also favor increased recreational uses such as swimming, boating, and 

outings. Finally, the Agency expects that the regulation will augment nonuse values (e.g., option, 

existence, and bequest values) of the affected water resources. 

The expected nonmarket benefits of water quality improvements fall into two broad categories: 

nonmarket use benefits and nonmarket nonuse benefits. The following sections describe these benefit 

categories and the nonmarket approaches to valuing these benefits in more detail. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-7 

5.1.3.1 Nonmarket Use Benefits 

Direct use benefits include the value of improved environmental goods and services used and valued by 

people (even if these services and goods are not traded in markets). Aesthetic degradation of water 

resources resulting from sediment and turbidity discharges from construction sites can reduce owner 

satisfaction with the property and the residential area in general. It can also adversely affect recreational 

opportunities. Adverse impacts of sediment and other pollutant discharges and, as a result, increased 

turbidity of surface waters on recreational activities are summarized below. 

3⁄4 Outings. Activities that take place near water such as hiking, jogging, picnicking, and wildlife may be 

adversely affected by sediment and other pollutant discharges into the water. While these activities do 

not involve contact with the water, murky and visually unpleasant water and odors may greatly 

detract from the enjoyment gained through these activities. Decreases in fish populations may cause a 

reduction in wildlife near the resource, affecting wildlife viewing. Pollutants may negatively affect 

local flora and fauna, reducing the aesthetic appeal of the area near the resource and negatively 

impacting wildlife viewing. 

3⁄4 Recreational Fishing. As noted in Chapter 2 of this report, sediment and other discharges from 

construction sites can reduce fish populations by inhibiting reproduction and survival of an aquatic 

species. This may lead to fewer and smaller fish, and a reduction of the game fish population. In 

addition, sediments and other pollutants reduce the aesthetics of the waterbody, which may reduce 

anglers’ utility of their fishing experience. Additionally, turbidity caused by sediment and other 

pollutant discharges may affect recreational anglers by reducing the distance over which fish can see 

lures, resulting in lower catch rates (Clark et al. 1985). 

3⁄4 Boating. Polluted water greatly reduces the aesthetic appeal of recreational boating activities. 

Turbidity caused by sediment and other pollutant discharges may affect the safety of boating. 

Turbidity may obscure underwater obstacles, making collisions more likely. Increased sediment 

concentrations may create sandbars, increasing the chances of running aground. Clark et al. (1985) 

estimated that turbidity (from all sources) may be responsible for as many as 200 boating fatalities 

and many more injuries each year. Using the value for a statistical life’s worth of risks ($7 million) 

and multiplying it by the number of boating fatalities yields the value of preventing these fatalities of 

$1.4 billion per year. Even if reducing discharges from construction sites prevents a few fatalities and 

injuries each year, the expected monetary value of preventing boating accidents can be significant. 

3⁄4 Swimming. Turbidity and other problems (e.g., eutrophication) associated with discharges from 

construction sites may greatly reduce a swimmer’s aesthetic enjoyment of a resource. Additionally, 

turbidity may create safety hazards for the swimmer by reducing the ability to see underwater hazards 

or increasing diving accidents by impairing the ability to gauge water depth. 

3⁄4 Hunting. Similar to the effect on outings, discharged pollutants may greatly detract from the hunters’ 

aesthetic enjoyment of a water resource. Damage to flora and fauna may also cause a reduction in the 

game population, reducing the number and quality of the game available. 

Improved water quality from reducing sediment and other pollutant discharges from construction sites is 

expected to enhance the quality of living in the areas affected by construction activities, thereby resulting 

in welfare gain to the resident populations. The final regulation is also expected to enhance recreational 

uses of water resources affected by sediment discharges from construction sites, thereby resulting in 

welfare gain to recreational users of these resources. Improved water quality from reducing sediment and 

other pollutant discharges from construction sites may translate into two components of recreational 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-8 

benefits: (1) an increase in the value of a recreational trip resulting from a more enjoyable experience, and 

(2) an increase in recreational participation. 

5.1.3.2 Nonmarket Nonuse Benefits 

Even if no human activities (or uses) are affected by environmental changes caused by sediment and other 

pollutants from construction sites, such environmental changes may still affect social welfare. For a 

variety of reasons, including bequest, altruism, and existence motivations, individuals may value the 

knowledge that water quality is being maintained, that ecosystems are being protected, and that 

populations of individual species are healthy completely independent of their use value. It is often 

difficult to quantify the relationship between changes in pollutant discharges and the improvements in 

societal well-being that are not associated with current use of the affected ecosystem or habitat. That these 

values 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 (although 

some portion of these donations may be motivated by use values). Notwithstanding challenges involved 

in estimation of nonuse values, there is a substantial literature devoted to such issues (Bateman et al. 

2002). This literature provides insight into analysts’ ability to estimate nonuse values within various types 

of policy contexts, and for various types of resources. 

5.1.3.3 Nonmarket Valuation Methods 

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 sediment, nutrient, and other 

pollutant discharges from construction sites and the specific ecological changes, ranging from a lack of 

water quality monitoring data for many locations, to time lags between water quality changes and changes 

in biological community condition. In addition, it may be 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. 

A variety of nonmarket valuation methods exist for estimating nonmarket use value, including both 

revealed and stated preference methods (Freeman 2003). Where appropriate data are available or may be 

collected, revealed preference methods can represent a preferred set of methods for estimating use values. 

These methods use observed behavior to infer users’ value for environmental goods and services. 

Examples of revealed preference methods include travel cost, hedonic pricing, and random utility (or site 

choice) models. Compared to nonuse values, use values are often considered relatively easy to estimate, 

due to their relationship to observable behavior, the variety of revealed preference methods available, and 

public familiarity with the recreational services provided by surface water bodies. 

In contrast to direct use values, nonuse values are often considered more difficult to estimate. Stated 

preference methods, or benefit transfer based on stated preference studies, are the generally accepted 

techniques for estimating these values (USEPA 2000b; OMB 2003). Stated preference methods rely on 

carefully designed surveys, which either (1) ask people about their WTP for particular ecological 

improvements, such as increased protection of aquatic species or habitats with particular attributes, or (2) 

ask people to choose between competing hypothetical “packages” of ecological improvements and 

household cost (Bateman et al. 2003). In either case, values are estimated by statistical analysis of survey 

responses. 

Nonuse values may be more difficult to assess than use values for several reasons. First, nonuse values 

are not associated with easily observable behavior. Second, nonuse values may be held by both users and 

nonusers of a resource. Because nonusers may be less familiar with a resource, their values may be 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-9 

different from the nonuse values for users of the same resource. Third, the development of a defensible 

stated preference survey is often a time- and resource-intensive process. Fourth, even carefully designed 

surveys may be subject to certain biases associated with the hypothetical nature of survey responses 

(Mitchell and Carson 1989; Bateman et al. 2003). Finally, efforts to disaggregate total WTP into its use 

and nonuse components have proved troublesome (Carson et al. 2000). 

To evaluate nonmarket benefits of this regulation, EPA developed a benefit transfer approach based on a 

meta-analysis of surface water valuation studies. Benefit transfer may be described as the “practice of 

taking and adapting value estimates from past research ... and using them ... to assess the value of a 

similar, but separate, change in a different resource” (Smith et al. 2002, p. 134). It involves adapting 

research conducted for another purpose to estimate values within a particular policy context (Bergstrom 

and De Civita 1999). Although the use of primary research to estimate values is generally preferred, the 

realities of the policy process often dictate that benefit transfer is the only option for assessing certain 

types of nonmarket values (Rosenberger and Johnston 2007). In the benefit transfer used for analyzing 

benefits of the regulation, the meta-analysis is of WTP that incorporates both use and nonuse values. 

Although the potential limitations and challenges of benefit transfer are well established (Desvousges et 

al. 1998), the Agency also emphasizes that benefit transfers are a nearly universal component of benefit 

cost analyses conducted by and for government agencies. As noted by Smith et al. (2002, p. 134), “nearly 

all benefit cost analyses rely on benefit transfers, whether they acknowledge it or not.” Benefits transfer 

methods may be placed into three general categories: (1) transfer of an unadjusted fixed-value estimate 

generated from a single study; (2) the use of expert judgment to aggregate or otherwise alter benefits to be 

transferred from a site or set of sites, and (3) estimation of a value estimator model or benefits transfer 

function, often based on data gathered from multiple sites (Bergstrom and De Civita 1999). 

Given the generally unreliable performance of unadjusted single-site transfers, EPA used meta-analysis of 

45 surface water valuation studies that use stated preference techniques to estimate a benefit function that 

allows forecasting of total WTP estimates (including use and nonuse values) in a variety of policy 

contexts, thereby providing more valid benefit estimates for transfer applications (USEPA 2000b; 

Johnston et al. 2005). Because stated preference surveys describe a range of environmental services 

corresponding to different levels of water quality (e.g., suitable for boating or swimmable), the total WTP 

derived from meta-analysis implicitly accounts for a wide range of nonmarket use and nonuse values, 

including recreation, aesthetic value of near-water properties, enhanced quality of source water for 

drinking and household use, as well as existence and bequest values. 

The technical details involved in the estimation of original meta-analyses are presented in Appendix G as 

well as in sources such as Bateman and Jones (2003), USEPA (2004d), Johnston et al. (2005, 2006), 

Rosenberger and Phipps (2007), and Shrestha et al. (2007). Chapter 10 of this report describes application 

of the meta-analysis results for estimating benefits of water quality improvements resulting from the 

regulation. 

5.2 Summary of Effects 

Ultimately, changes in sediment and other pollutant loadings from construction sites related to the final 

regulation affect humans through influences on the production of goods and services that enter into 

individuals’ utility functions. Table 5-1 summarizes some of the important ways in which sediment and 

other pollutant discharges from construction sites affect the quantity and quality of services provided by 

the natural environment and how these services in turn provide economic value to society through various 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-10 

use and nonuse related societal welfare mechanisms.3 EPA was not able to bring the same depth of 

analysis to all of the environmental services affected by the regulation 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 (including 

maintenance to navigable waterways, reservoir dredging, drinking water treatment, and WTP for 

improvements in large river and streams and some coastal waters), quantify but not monetize other 

benefits (changes in pollutant concentrations in lakes), and assess still other benefits only qualitatively 

(e.g., commercial fishing, flood damages, and property values).

 

3

 Underlying this table is a conceptual framework based on the valuation of ecosystem services (Daily 1997; Simpson and 

Christensen 1997; van Houtven et al. 2005). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 5-1: Summary of Benefits from Reducing Sediment and Other Pollutant Discharges from Construct

Entities Affected by Environmental Changes 

Activity 

Environmental Service Flows 

Potentially Affected by 

Discharges from Construction 

Sites Firms Governments Individuals 

Methods Used in E

from th

Recreation 3⁄4 Outings 3⁄4 Boating 3⁄4 Swimming 3⁄4 Fishing

Aesthetics, water clarity, water safety, degree of sedimentation, eutrophication, weed growth, fish and shellfish populations 

9 Use & Nonuse Values Benefits Transfer 

Commercial Fishing and Shellfishing 

Fish and shellfish populations 9 9 Use & Nonuse Values Qualitative Discussion 

Property Ownership Aesthetics, safety of property from 

flooding, property value 

9 9 Use & Nonuse Values Benefits Transfer 

Water Conveyance and Supply 3⁄4 Water conveyance 3⁄4 Water storage 3⁄4 Water treatment

Turbidity, degree of sedimentation 9 9 Use Values Avoided cost to governm

3⁄4 Reservoir dredging 3⁄4 Drinking water treatmen

Water Transportation Degree of sedimentation of 

navigational channels, harbors, and other waterways 

9 9 Use Values Avoided cost to firms an

Water Use 3⁄4 Industrial 3⁄4 Municipal 3⁄4 Agricultural 

Turbidity 9 9 Use Values Qualitative Discussion 

Knowledge (No Direct Uses) 

Environmental health Nonuse Benefits Transfer 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 5-12 

Figure 5-1 depicts data sources, analytic steps, and models in EPA’s analysis of benefits from reducing 

discharges of sediments and nutrients from construction sites. Chapters 7 through 11 detail methods used 

in these analyses and present results. 

Figure 5-1: Calculation of Monetized Benefits from the Regulation 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-1 

6 Water Quality Modeling 

Changes in water quality resulting from the various regulatory options were assessed using the U.S. 

Geological Survey’s SPARROW models (SPAtially Referenced Regressions On Watershed attributes). 

SPARROW models for the coterminous United States for sediment, nitrogen, and phosphorus were used 

to characterize baseline water quality conditions. For each regulatory option, reductions in sediment 

discharge from constructions sites were predicted as described in Development Document for Final 

Effluent Guidelines and Standards for the Construction and Development Category (USEPA 2009b). 

These reductions were modeled in SPARROW and resulting changes in in-stream sediments and nutrient 

concentrations were evaluated to assess the environment impact of each option. 

EPA did not calculate reductions in discharge levels of other pollutants (e.g., turbidity, metals, pesticides) 

under each of the alternative regulatory options due to insufficient data on the frequency and magnitude 

of their presence in construction site discharges in the United States. It was therefore not possible to 

quantify water quality improvements associated with reduction of their discharge. These pollutants are 

instead discussed qualitatively in Chapters 2 and 3. Some of these pollutants erode and travel with 

sediment discharges and are therefore addressed to some degree by the same practices that reduce 

sediment discharges. 

This chapter describes the methodology used to estimate the water quality impacts of the alternative 

regulatory options and is organized as follows: Section 6.1 describes the general SPARROW modeling 

approach. Section 6.2 provides details on the SPARROW sediment model. Section 6.3 describes the 

approach used to estimate water quality impacts in estuaries where no model flow predictions were 

available from SPARROW. Section 6.4 describes the approach for modeling nutrient reductions 

associated with reduced discharges. Section 6.5 provides a brief summary of predicted sediment loading 

reductions that are used in the model. Section 6.6 discusses model results. 

6.1 SPARROW Model Documentation4 

6.1.1 General Overview 

SPARROW is a watershed modeling technique for estimating contaminant source contributions and 

transport in surface waters. SPARROW employs a statistically estimated nonlinear regression model with 

contaminant supply and process components, including surface-water flow paths, non-conservative 

transport processes, and mass-balance constraints. Regression equation parameters are estimated by 

correlating stream water-quality records with GIS (Geographic Information System) data on pollutant 

sources (point and nonpoint) and climatic and hydrogeologic properties (e.g., precipitation, topography, 

vegetation, soils, and water routing) that affect contaminant transport. The SPARROW parameterestimating procedure also provides measures of uncertainty in water-quality predictions. 

SPARROW model infrastructure consists of a detailed watershed – stream reach network to which all 

monitoring stations and watershed properties are spatially referenced. Digital elevation models (DEMs) 

are used to delineate watershed boundaries and to identify overland flowpaths. The spatially distributed 

model structure allows separate statistical estimation of land- and water-related rates of pollutant delivery 

 

4

 Discussion adapted from Schwarz et al. (2006). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-2 

from sources to streams, and transport of pollutants to downstream locations within the stream network 

(reaches, reservoirs and estuaries). Mechanistic separation of terrestrial and aquatic features of large 

watersheds and improved parameter estimation techniques represent significant advances in water-quality 

modeling of important contaminant sources and watershed properties that control transport over large 

spatial scales. SPARROW has been applied in the analysis of suspended sediment, surface-water nitrogen 

and phosphorus nutrients, pesticides, organic carbon, and fecal bacteria, and is potentially applicable to 

other measures of water quality. The SPARROW software is written in Statistical Analysis System 

Interactive Matrix Language (SAS IML). 

The remainder of this chapter provides a brief description of SPARROW modeling concepts, model 

specification, data sources, and estimation results. Appendix C of this report provides more detail on the 

nature of the general SPARROW methodology. Appendix D provides more detail on the suspended 

sediment SPARROW model developed by USGS (Schwarz 2008a). Appendix C also provides more detail 

on the total nitrogen (TN) and total phosphorus (TP) SPARROW models developed by USGS (Smith et 

al. 1997). 

6.1.2 Modeling Concept 

The broad objective of SPARROW modeling is to establish a mathematical relationship between waterquality measurements made at a network of monitoring stations and attributes of the contributing 

watersheds. The model may be used to satisfy a variety of water-quality information objectives. One 

objective is to describe past or present water-quality conditions for a state or region on the basis of 

monitoring data. A second objective is to identify and quantify the sources of pollution that give rise to 

in-stream water-quality conditions. A third objective is water quality simulation: the ability to portray 

counterfactual conditions for specified inputs. Water-quality simulations can depict the in-stream effects 

of changes in contaminant sources associated with alternative potential pollution-control strategies, 

providing an important step in the analysis of benefits associated with specific Best Management 

Practices (BMP) or other regulatory approaches. For the analysis described in this document, SPARROW 

is used to evaluate the effectiveness of a range of regulatory options for controlling construction related 

runoff and improving water quality as measured by in-stream Total Suspended Solids (TSS), TN and TP 

concentrations. A fourth objective of SPARROW modeling is to test one or more hypotheses about the 

nature and importance of factors and processes that may have influenced water quality at the locations 

where samples were collected. 

The dependent variable in SPARROW models is the mass flux, the mass of contaminant that passes a 

specific stream location per unit time, and the laws of conservation of mass apply. Mass accounting in 

SPARROW models is supported by the explicit spatial structure defined by the stream network. The 

imposition of mass balance greatly improves the interpretability of model coefficients. For example, 

assuming mass balance, the coefficient associated with the reach time-of-travel variable is interpreted as a 

first-order decay rate. Mass balance provides a basis for contaminant mass flux accounting, whereby mass 

flux can be allocated to its various sources, both spatially and topically. Spatial variability is modeled as a 

function of natural and human-related properties of watersheds that influence the supply and transport of 

contaminants. Estimates of long-term mean mass flux are developed from water-quality and streamflow 

monitoring data that are regularly collected at fixed locations on streams and rivers. 
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November 2009 6-3 

6.1.3 Model Infrastructure 

A flow diagram is provided in Figure 6-1 to illustrate the functional linkages between the major spatial 

components of SPARROW models. Mass flux estimates at monitoring stations are derived from stationspecific models that relate contaminant concentrations from individual water-quality samples to 

continuous records of streamflow and time. The stream reach, inclusive of its incremental contributing 

drainage basin, is the most elemental spatial unit of the SPARROW model infrastructure. Explanatory 

data (e.g., climate, topography, land use) are frequently compiled according to geographic units that are 

not coincident with the drainage basin boundaries of river reaches. These data may be collected at 

different spatial scales and according to spatial units that reflect political boundaries (e.g., counties) or 

other non-hydrologic features of the landscape. 

Figure 6-1: Schematic of the Major SPARROW Model 

Components 

Source: Modified from Alexander et al. (2002a). 

The estimation of a SPARROW model requires estimates of long-term mean mass flux from a spatially 

distributed set of monitoring stations, having sufficiently long periods of record, inclusive of a wide range 

of spatial scales and expressing considerable variation in predictor variable conditions. Long-term mean 

mass flux is obtained by relating infrequently collected water-quality samples to continuous 

measurements of streamflow and functions of time. In order to have consistency across stations having 

different periods of record, it is necessary to detrend the mass flux estimates to a common base year prior 

to computing the long-term mean. 

A vector- or raster-based digital representation of the stream and river network topology is the most 

fundamental component of the spatial infrastructure that supports the SPARROW model (Figure 6-2).

The node topology is defined according to an upstream (from-) and downstream (to-) node attribute table. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-4 

Figure 6-2: Schematic Illustrating a Vector Stream Reach Network with Node Topology 

and Water/Contaminant Reach-Node Routing Table 

The reach-type indicator has possible values of “0” (Stream Reach), “1”(Impoundment Reach), and “2” Outlet Reach for 

Impoundment.

SPARROW separately estimates the sediment and contaminant attenuation in reservoirs and lakes. The 

assessment of pollutant mass flux to coastal estuaries requires an expanded reach network that includes 

shoreline features and the identification of reaches that terminate at estuaries. Shoreline reaches are used 

to define coastal drainage areas – areas that discharge runoff directly to the estuary without transport 

through a stream reach. The SPARROW node and routing architecture also can fully support the 

modeling of contaminant transport along “off-reach” (landscape) flow paths according to flow directions 

defined by landscape topography as reflected, for example, in DEMs. 

The explanatory variables evaluated in SPARROW reflect current knowledge of natural and humanrelated sources and the physical, chemical, or biological properties of the terrestrial and aquatic 

ecosystems that affect supply and transport of contaminants in watersheds. Point- and nonpoint-source 

variables may include direct measures of the supply of contaminant mass to the landscape and streams 

and reservoirs (e.g., municipal and industrial wastewater discharge, fertilizer application), or they may 

serve as surrogate indicators of the contaminant mass supplied by point and diffuse sources in watersheds, 

such as land-use/land-cover data or census data on human and livestock populations. Climatic and 

landscape properties that affect contaminant transport may include measures of water-balance terms (e.g., 

solar radiation, precipitation, evaporation, evapotranspiration), soil characteristics (e.g., permeability, 

moisture content), water-flow path properties (e.g., slope, hydraulic roughness, topographic index), or 

management practices and activities (e.g., tile drains, conservation tillage, BMPs). 

6.1.4 Model Specification 

The specification of a SPARROW model consists of identifying the explanatory variables and functional 

forms. Conceptually, the contaminant mass flux leaving a reach is the sum of two components. The first 

component is the mass flux generated within upstream reaches that is delivered to the reach via the stream 

network. Losses of contaminant mass from the stream network may occur at points where flow is 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-5 

diverted, and contaminant mass will generally be attenuated by stream or reservoir processes. The second 

component consists of source loading that is generated within the reach’s incremental watershed and 

delivered to the stream network within the reach. A number of source-dependent processes affect the 

amount of source loading reaching the stream network and transported to the reach’s downstream outlet 

node. The processes affecting delivery to the stream network are called land-to-water delivery processes, 

and may include both surface and sub-surface elements. Land-to-water delivery processes determine the 

amount of contaminant generated within an incremental drainage area delivered to the area’s 

corresponding reach. 

Large digital spatial data sets are now readily available, and advances in digital topographic and stream 

network data [e.g., National Hydrography Dataset (NHD), National Land Cover Database (NLCD)] have 

made spatially distributed modeling more feasible. Inclusion in the SPARROW model of detailed 

information on the geographic locations where contaminants are released in watersheds has particular 

relevance to the policy and management applications of the model. The source terms used in the model 

can be generally classified as intensive and extensive measures of contaminant mass. Intensive measures 

are direct measures of pollutant mass, such as fertilizer application, livestock waste, atmospheric 

deposition, or sewage-effluent mass flux. The associated source parameter is a dimensionless coefficient 

that, together with standardized expressions of the land-to-water delivery factor, describes the proportion 

or fraction of the source input that is delivered to streams. Extensive measures of contaminant mass are 

surrogate indicators and include measures of watershed properties such as specific land-use area and 

sewered population, considered to be proportional to the actual contaminant mass generated by a 

particular type of source. The associated model coefficients are expressed as the contaminant mass 

generated per unit of the source type (e.g., kilograms kilometer-2 year-1; kilograms person-1 year-1). 

Combined with the land-to-water delivery factor, the standardized source coefficient indicates the mean 

quantity of contaminant mass per unit of the surrogate source measure that is delivered to streams. For 

land-use terms, the standardized coefficient gives what is frequently cited as an export coefficient. 

Landscape variables in SPARROW describe properties of the landscape that relate to climatic, natural- or 

human-related terrestrial processes affecting contaminant transport. The model structure allows tests of 

hypotheses about the influence of specific features of the landscape on contaminant transport. Landscape 

variables may include water-balance terms (e.g., precipitation, evapotranspiration) related to climate and 

vegetation, soil properties (e.g., organic content, permeability, moisture content), topographic water flowpaths variables (e.g., overland flow, topographic index, and slope), or management practices and 

activities, including tile drainage, conservation tillage practices, and BMPs related to stream riparian 

properties. Particular types of land-use classes, such as wetlands or impervious cover, may also be 

potentially used to describe transport properties of the landscape. 

Stream attenuation processes that act on contaminant mass as it travels along stream reaches are 

frequently modeled as first-order reaction rate processes. A first-order decay process implies that the rate 

of removal of the contaminant from the water column per unit of time is proportional to the concentration 

or mass that is present in a given volume of water (a zero-order process corresponds to a constant rate of 

removal per unit of time). Accordingly, in basic forms of the SPARROW model, the fraction of the 

contaminant mass originating from the upstream node and transported along a reach to its downstream 

node is estimated as a function of the mean water time of travel. Attenuation processes that act on 

contaminant mass as it travels through a lake or reservoir are often modeled as a net removal process, 

with the loss coefficient expressed as either a first-order reaction rate or a mass-transfer coefficient (also 

referred to as an apparent settling velocity). These mass-balance models typically assume steady-state and 

uniformly mixed conditions in the waterbody. 
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6.1.5 Model Estimation 

The SPARROW model equation is a nonlinear function of its parameters, and the model must be 

estimated using nonlinear techniques. SPARROW utilizes a nonlinear weighted least squares (NWLS) 

estimation method, implemented in SAS. Model parameters are evaluated for statistical significance and 

the quantification of uncertainty. The key parameter statistics include the estimated mean coefficient 

values, estimated variance of these coefficient estimators, and measures of statistical significance based 

on the t statistics. Parameters are also evaluated for physical interpretability, to test hypotheses about the 

importance of different contaminant sources and the hydrologic and biogeochemical processes that are 

represented in the model. The interpretability of the parameters and their relation to specific processes are 

enhanced in SPARROW by the use of a mass balance, mechanistic structure that explicitly separates the 

terrestrial and aquatic properties of watersheds and accounts for nonlinear interactions among watershed 

properties. The sign of SPARROW model coefficients can be evaluated to determine the direction of 

influence of any explanatory variable on in-stream estimates of mean-annual mass flux. Coefficients 

associated with source inputs expressed in areal units describe the mass per unit area delivered to streams 

from these land areas. Other source coefficients that are expressed in dimensionless units provide a 

measure of the fraction of the contaminant that is delivered from each source to streams, rivers, and 

reservoirs. 

6.2 SPARROW Sediment Model5

The following describes results from a SPARROW suspended sediment model developed for the 

coterminous United States and described in Schwarz (2008a), which is reprinted in full in Appendix D of 

this document. The analysis is based on mass flux estimates compiled from more than 1,800 long-term 

monitoring stations operated by the U.S. Geological Survey (USGS) during the period 1975-2007. The 

SPARROW model is structured on the Reach File 1 (RF1) stream network, consisting of approximately 

62,000 reach segments. The reach network has been modified to include more than 4,000 reservoirs, an 

important landscape feature affecting the delivery of suspended sediment. The model identifies six 

sources of sediment, including the stream channel and five classes of land use (urban, forested, federal 

non-forested, agricultural and other). The delivery of sediment from landform sources to RF1 streams is 

mediated by soil permeability, erodibility, slope, and rainfall; streamflow is found to affect the amount of 

sediment mobilized from the stream channel. The results show agricultural land and the stream channel to 

be major sources of sediment. Per unit area, federal non-forested and urban lands are the largest landform 

sediment sources. Reservoirs are identified as major sites for sediment attenuation. 

6.2.1 Sediment Model Data Sources 

The spatial framework of the SPARROW sediment model is the vector-based 1:500,000 scale River RF1 

hydrography, originally developed by EPA (USEPA 1996b) and enhanced to include areal hydraulic load 

information for selected reservoirs, shoreline reaches, and reach catchment areas derived from the USGS 

Hydro 1K DEM (USGS 2006a). The enhanced network, consisting of 62,776 reach segments, including 

shoreline reaches, 61,214 delineated reach catchments, and 2,171 individual reservoirs, has been used to 

support numerous national SPARROW modeling efforts for the coterminous United States. The RF1 

reach network for the current SPARROW sediment model was further enhanced by inclusion of areal 

hydraulic load information for approximately 2,000 additional large reservoirs from the National 

 

5

 Adapted from Schwarz (2008a). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-7 

Inventory of Dams (NID, USACE 1996). The dependent variable in the SPARROW sediment model is 

given by long-term mean sediment mass flux, detrended to the base year 1992. In-stream sediment 

concentrations and stream discharge measurements over the water year (WY) period 1975-2007, have 

been obtained from the National Stream Quality Accounting Network (USGS 2006b), the USGS 

NAWQA Program (USGS 2001), and the USGS National Water Information System (NWIS; USGS 

2008), a database encompassing USGS water quality monitoring stations and water quality monitoring 

activities done in cooperation with State governments. The sediment model is estimated using 1,828 

monitoring stations on the RF1 stream network (Figure 6-3). 

While the RF1 network provides reasonably comprehensive national coverage of major rivers, streams 

and other surface water bodies, coverage is limited in certain important respects. First, RF1 network 

coverage is limited to the coterminous United States, thus excluding Alaska and Hawaii. In addition, 

while RF1 1:500,000-scale network reaches have associated data or estimates of stream discharge and 

velocity that are required to specify the SPARROW model, the network excludes the majority of the 

nation’s total stream mileage, and smaller streams in particular. The linear coverage of the RF1 network is 

approximately 700,000 miles (USEPA 2007e). By contrast, coverage of the USGS National Hydrographic 

Dataset (NHD) at 1:24,000 - 1:100,000 scale is currently over 7 million miles (USGS 2007b). Nonhomogeneity of coverage may lead to an under-estimation of the impacts of construction-related sediment 

on smaller stream reaches. As construction activities may be concentrated along lower-order streams not 

included in the RF1 network, the relative share of total sediments contributed by construction activities in 

these reaches may be high during active construction. By contrast, the specific impacts of construction 

activities may diminish in magnitude relative to contributions from spatially extensive and diffuse land 

uses, including agriculture, at the spatial level of RF1 reaches. 

Data on land cover and land use have been developed from the 2001 USGS National Land Cover Data Set 

Retrofit Change Product (Multi-Resolution Land Characteristics Consortium (MRLC) 2001), derived 

from Landsat TM/ETM remotely sensed imagery at 30-meter resolution and classified according to the 

eight Anderson Level I categories. For use within SPARROW, data were transformed to 1 km2

 cells 

within a Lambert map projection as consistent with Hydro 1K. The 1 km2

 cells were then resolved to 

catchments associated with specific RF1 reaches. For model estimation, land use was given by the 1992 

values of the 2001 NLCD Retrofit Change Product. The 2001 values of the Retrofit Change Product were 

used to simulate water quality conditions for 2001. Federal range and barren land was included in the 

model separately from private land. Federal land extent, taken from the Federal Land coverage of the 

National Atlas (USGS 2003) and transformed to 1 km2

 cells, was apportioned using the 1 km2

transformation of the 1992 NLCD Change Product for Anderson Level 1 range and barren land classes. 

For model simulation of 2001 conditions, federal range and barren land were similarly estimated using the 

land use estimates from the 2001 NLCD Retrofit Change Product. 
Environmental Impact and Benefits Assessment for the C&D Category 

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Figure 6-3: Location of 1,828 Water Quality Monitoring Stations Used in the SPARROW 

Sediment Model, in Relation to the Reach File 1 (RF1) Reach Network 

Variables determining the delivery of contaminants from the land to RF1 streams include soil erodibility 

(RUSLE k-factor), soil permeability, mean slope, and precipitation (RUSLE r-factor). Slope, soil 

erodibility, and permeability were obtained from the State Soil Geographic (STATSGO) database (USDA 

1994), converted to a 1-km2

 grid and averaged over RF1 catchments. The RUSLE rainfall factor was 

derived by interpolating a digitized national map of rainfall factor isoline contours, creating a continuous 

1 km2

 grid surface, which was subsequently averaged over individual RF1 catchments. 

6.2.2 Model Estimation Results 

The nonlinear least squares estimation results of a preliminary version of the SPARROW suspended 

sediment model are given in Table 6-1. The model includes six source terms: five measured by area of 

specific land use (expressed in km2

), and the sixth by the length of stream channel. The five land use 

sources are urban land, forested land, federal nonforested land, agricultural land, and other land. The 

federal land class excludes federal forested land, which is incorporated in the forested land class. 

Agricultural land includes cropland, pasture land and orchards. Other land consists of nonfederal range 

and barren land. Among all the land classes, only wetlands and land covered by water, ice or snow are 

excluded as a potential source. The source described as “streambed” relates to stream channels as a direct 

source of sediment, and is measured in terms of stream length (expressed in meters). The model specifies 

two in-stream sediment attenuation processes: attenuation in streams, distinguished by three streamflow 

classes (below 500 cubic feet per second (cfs), 500–1,000 cfs, and greater than 1,000 cfs); and reservoir 

attenuation, specified as a function of areal hydraulic load. 
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Table 6-1: Estimation Results for the SPARROW Suspended Sediment Model 

Parameter Units Estimate Standard Error p-value 

Source Coefficients 

Urban land kg/km2/yr 47,130 9,925 0.000 

Forested land kg/km2

/yr 634 898 0.480 

Federal nonforested land kg/km2

/yr 64,344 12,411 0.000 

Agricultural land kg/km2

/yr 18,047 3,623 0.000 

Other land kg/km2

/yr 11,343 3,186 0.000 

Streambed (reach length) kg/m/yr 28.80 6.40 0.000 

Land to Water Delivery Factors 

Slope – 0.804 0.087 0.000 

Soil permeability – -0.778 0.094 0.000 

R-factor – 0.821 0.081 0.000 

K-factor – 1.292 0.279 0.000 

Flow [< 500 cfs] (Reach) – 0.154 0.100 0.125 

Flow [> 500 cfs] (Reach) – 0.721 0.354 0.042 

Stream Attenuation Factors 

Travel time (Q < 500 cfs) day-1 -0.007 0.016 0.673 

Travel time (500 < Q < 1,000 cfs) day-1 -0.233 0.057 0.000 

Travel time (Q > 1,000 cfs) day-1 0.009 0.047 0.854 

Reservoir settling velocity m/yr 36.49 5.552 0.000 

Number of Observations 1,828 

RMSE 1.414 

R-square 0.711 

Source: Schwarz (2008a). 

With the exception of forested land, all of the source variables are highly statistically significant. The 

largest intrinsic sediment yield is associated with federal range and barren land; urban land has the second 

highest intrinsic yield. Stream channels are also a statistically significant source of sediment. Land-towater delivery for land sources is strongly mediated by the four delivery variables: soil permeability, soil 

erodibility, slope, and rainfall. With the exception of soil permeability, an increase in these factors results 

in an increase in the share of sediment delivered to streams. Permeable soils reduce the delivery of 

sediment, presumably because more runoff infiltrates, leaving less overland flow to carry sediment. 

Greater streamflow causes an increase in the amount of sediment generated from stream channels, with 

the largest effect associated with streams with flow greater than 500 cfs. Reservoir retention is statistically 

significant and indicates sediment settles at a mean velocity of 36 meters per year. The preliminary model 

does not find evidence of sediment attenuation in streams, as in-stream attenuation in small and large 

streams is not statistically different from zero. Additional investigation would be necessary to determine 

if this result is real, or if there are additional reach attributes, currently absent from the model, that 

identify a subset of reaches where sediment attenuation takes place. 

6.2.3 Model Simulation 

The estimated SPARROW suspended sediment model for base water year 1992 is used to simulate waterquality conditions for 2001, with and without proposed changes in the regulation of construction activity. 

The simulation of suspended sediment mass flux for water year 2001 without changes in regulation is 

obtained with all land use-related source variables set to 2001 values based on the NLCD Retrofit Change 

Product. Flow weighted concentration is estimated by dividing simulated mass flux estimates by mean 

streamflow over the period WY 1975-2006. Although the SPARROW model does not explicitly include a 

term for construction loading, such loading is implicitly accounted for in the urban land component of the 

model. Therefore, the 2001 pre-compliance loading from construction (the “base-case” scenario loading) 
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November 2009 6-10 

is incorporated in the 2001 mass flux attributed to urban land that is obtained by evaluating the urban land 

variable in the SPARROW model using the 2001 NLCD Change Product value. 

The absence in the SPARROW model of an explicit term for construction loading requires the 

development of an indirect method for assessing changes in sediment mass flux arising from different 

construction industry regulation scenarios. Suspended sediment loading under alternative regulation 

scenarios has been estimated by EPA using a variation of the USLE. The USLE method determines the 

amount of soil that is mobilized and delivered, under a regulatory scenario, to the edge of a construction 

site. To evaluate the impact that changes in these loadings have on RF1 stream sediment mass flux and 

concentration, it is first necessary to assess the rate at which “edge of site” loads are subsequently 

delivered to RF1 streams. We use the estimated rate of delivery from agricultural land (explicitly included 

in the model) that can be factored into a mobilization (“edge of site”) delivery component and a streamdelivery component. The method uses edge-of-site measurements of agricultural sediment erosion from 

the 1992 National Resources Inventory (NRI) to isolate the stream-delivery component from the overall 

rate of delivery from agricultural land and applies this component to the change in construction loading to 

determine the change in loading to RF1 streams. Thus, the approach assumes that the delivery of sediment 

from the edge of a site to an RF1 stream is the same for both construction and agriculture activities; 

although the mobilization and delivery of sediment to the edge of the site between these activities is 

allowed to differ. 

6.3 Extension of SPARROW to Estuaries and Coastal Waters 

EPA used SPARROW output to evaluate sediment deliveries to estuaries. While SPARROW predicts 

mass flux in all reaches, it does not estimate concentrations in reaches where flow estimates are not 

available. Of the 2,067 estuarine/coastal reaches in the RF1 dataset, SPARROW provided TSS 

concentrations in more than 92 percent of the reaches. Of those reaches, 772 are retained for water quality 

analysis based on the availability of data for other parameters needed in calculating Water Quality Index 

values (see Chapter 10). Of the 772 reaches used in the analysis, SPARROW provides TSS 

concentrations for 729 reaches in this set directly. In the remaining 43 estuarine reaches, sediment 

concentrations were calculated using the SPARROW predicted mass flux and dissolved concentration 

potentials (DCPs) for estuaries (USEPA 1997). The National Oceanic and Atmospheric Administration 

(NOAA) developed DCPs to provide an approach for predicting the ambient concentration of 

conservative contaminants that are introduced into an estuary and are then subject to mixing and dilution. 

Using this approach, EPA was able to estimate TSS concentrations associated with annual quantities of 

sediment discharged into estuaries as predicted by SPARROW without detailed hydrodynamic modeling 

of estuaries. 

Concentration calculations using DCPs presume that water quality constituents are dissolved, 

conservative pollutants dispersed under well-mixed, steady-state conditions. These criteria are not strictly 

met for TSS, although the finest particle fractions approximately meet these criteria due to their extremely 

low settling velocities and relatively stable composition. Because current and proposed sediment 

abatement practices remove coarser particle fractions first, construction site sediment discharges are 

expected to contain primarily finer particles. Therefore, the DCP methodology criteria are assumed to be 

applicable for estimating changes in estuary water quality associated with changes in construction site 

sediment discharge. 

NOAA has calculated DCP values for most major estuaries in the Southeast (NOAA and EPA 1989a); 

Northeast (NOAA and EPA 1989b); Gulf of Mexico (NOAA and EPA 1989c); and West Coast (NOAA 
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November 2009 6-11 

and EPA 1991) of the coterminous United States. For these estuaries, DCP values and baseline TSS 

concentrations calculated using DCPs are shown in Appendix E in Table E-1 through Table E-4 for 

Southeastern, Gulf, Northeastern, and West Coast estuaries, respectively. Data on these and additional 

estuaries are found via the NOAA Coastal Geospatial Data Project (http://coastalgeospatial.noaa.gov). 

Total sediment loadings estimated in SPARROW for reaches draining to estuaries were aggregated on the 

basis of 5-digit NOAA Estuarine Drainage Area (EDA) codes. Estimated loadings were calculated in 

SPARROW as Suspended Sediment Concentrations (SSC). Suspended Sediment Concentrations needed 

to be adjusted before the application of DCP methods, for two reasons. First, reported SSC values include 

all particle size fractions and coarse sediment fractions are not assumed to behave as conserved within the 

water column due to higher settling velocities. Approximately 127,000 sediment records for which 

particle size distributions were recorded, collected at 1805 water quality gauging stations obtained from 

USGS (Schwarz 2008b) were analyzed. It was found that on average over these samples, 78.4 percent of 

suspended sediment mass was classified as “fine” (diameter < 0.0625 mm). This percentage varied to 

some extent by 2-digit USGS Water Resources Regions, from 71 percent (Region 14) to 89.5 percent 

(Region 9); and was subject to even wider at-site inter-seasonal and inter-annual variations. It is assumed 

that the implied sediment size distribution of SPARROW output is equivalent to observed distributions. 

However, only 15 of the 1,805 water quality gauging stations for which sediment size fraction 

information was available were terminal or coastal reaches (draining directly into bays or estuaries), so 

that no regional or estuary-specific sediment size distributions could be identified. 

A second consideration is that USGS (2000) has determined that SSC and TSS, while carrying the same 

physical interpretation, are subject to systematic differences in measurement due to differing laboratory 

protocols. SSC values in the SPARROW output are divided by 1.3 to adjust for this difference, as 

described in Section 6.6. The use of this adjustment factor yields TSS concentrations that are roughly 77 

percent of corresponding SSC values. Since this adjustment is reasonably close to the nation-wide 

(average of samples) percentage of fine particles in SSC (78 percent), SPARROW SSC values were 

converted to TSS equivalent, and the TSS values assumed to conform approximately to the assumptions 

underlying DCP calculations. In order to determine how well these assumptions are reflected in observed 

estuarine water quality conditions, data on suspended sediment size distribution for 623 samples taken 

within San Francisco Bay were obtained from the USGS (http://sfbay.wr.usgs.gov/access/wqdata/). The 

percentage of fine particles (< 0.0625 mm) was 71 percent for these samples. It is evident that suspended 

sediment accounting within estuaries is complex, and ambient TSS levels, in addition to the percentage of 

fine particles, are observed to reflect many influences beyond the annual riverine input of sediment. As an 

additional check on SPARROW outputs to estuaries, the aggregated annual loadings per unit of 

contributing watershed area were calculated for each channel contributing sediment to estuaries, and 

found to be reasonable in most cases. 

Annual sediment loads as estimated by SPARROW were aggregated by estuary, unit-converted and 

divided by 10,000 tons per year (the DCP unit loading), and multiplied by DCP coefficients to obtain 

ambient estuarine TSS concentrations. Baseline estimates of estuarine TSS concentrations are presented 

in Appendix E in Table E-1 through Table E-4. Concentrations varied from below 1 mg/L (one 

Southeastern and multiple Northeastern estuaries) to a maximum of 833 mg/L (San Antonio Bay, TX), 

and averaged 71.5 mg/L over all estuaries. 

SPARROW-simulated TSS concentrations were compared to measured values from monitoring data. 

Measured observations were obtained from the EPA National Coastal Assessment 

(http://www.epa.gov/emap/nca/html/data/index.html), and augmented for San Francisco Bay with data 
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November 2009 6-12 

from USGS (http://sfbay.wr.usgs.gov/access/wqdata/). Estimated TSS concentrations obtained from 

SPARROW predictions and application of the DCP approach in general exceed measured values, 

although agreement is remarkably good in many instances. In particular, DCP estimates of TSS for 

Delaware Bay (40.0 mg/L) are very close to observed values (38.45 mg/L), and virtually identical for 

Long Island Sound (9.7 mg/L predicted vs. 9.8 mg/L observed). Results for San Francisco Bay illustrate 

the difficulty in comparing model predictions with observations. DCP TSS estimates of 89.3 mg/L are 

slightly below the NOAA EMAP average of 71 observations (132 mg/L); but above the mean of the 

larger USGS sample (34 mg/L). Results suggest that SPARROW sediment loading predictions, 

transformed by the DCP approach, provide a reasonable basis for evaluating changes in TSS due to 

specific regulatory options, although predicted TSS values may differ from physical observation in 

absolute value in certain settings. 

In the final analysis, the DCP approach was only used for the 43 reaches without flow predictions in 

SPARROW. The number of reaches in each estuary where the DCP approach was used to calculate TSS 

concentrations is shown in Appendix E in Table E-5 through Table E-8 . 

6.4 Modeling Changes in Nutrient Concentrations 

Reducing soil erosion and sediment runoff from construction sites not only reduces sediment loadings 

into streams, but can also reduce concentration of other pollutants that are present in the soil. To evaluate 

the water quality benefits of these ancillary reductions, EPA developed an approach that relies on the 

empirical relationship observed between baseline in-stream suspended sediment concentrations and TN 

and TP concentrations to derive estimates of the post-compliance nutrient reductions. 

In evaluating potential approaches to estimating in-stream nutrient concentration reductions EPA faced 

several key challenges: (1) locating nationally available data on the nutrient content of soil or sediment at 

sufficient resolution; (2) accounting for the impact of the sediment delivery processes from edge of site to 

the stream on nutrient concentrations; (3) and estimating total nutrients as compared with particulate, 

dissolved, and other forms of nutrients. These challenges are in addition to the overall challenge of 

modeling the complexity of the nitrogen or phosphorus cycles and delivery processes, on a national scale. 

Based on literature, EPA determined that most of the total nitrogen and phosphorus in sediment 

associated with runoff are in the solid phase and therefore there is little difference between total and solid 

phase nutrient concentrations (Schuman et al. 1973a, b, as cited in Sharpley 1985; Haith 2009). It is 

estimated that up to approximately 90 percent of nutrients delivered to streams are bound to sediments 

(Schuman et al. 1973a, b, as cited in Sharpley 1985; Daniel et al. 1979); this is particularly true in the case 

of phosphorus and organic nitrogen. Several studies of construction site discharges have shown high 

correlations between sediment discharges and nutrients discharges (Daniel et al. 1979; Novotny and 

Chesters 1989; Harbor et al. 1995). Studies also indicate the ability of several construction site sediment 

erosion and control technologies to reduce nutrient (and other pollutants, e.g., metal, hydrocarbons, 

bacteria) discharges from construction sites (Horner et al. 1990; Stahre and Urbanos 1990; Bhaduri et al. 

1995; Harbor et al. 1995; Lentz et al. 2002; USEPA 2006b; Bachand et al. 2007; Faucette et al. 2008; 

USEPA 2009b). Therefore, EPA assumed that the change in sediment-bound TN and TP would be 

representative of the overall change in TN and TP delivered to streams. While this simplification does not 

account for the complex processes that control nutrient fluxes through streams, EPA believes that it still 

provides reasonable estimates of the scale of changes that may be directly related to changes in sediment 

discharge from construction sites. 
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6.4.1 Modeling Approach Based on SPARROW Nutrient Models 

USGS developed SPARROW models for TN and TP transport for the nontidal coterminous United States 

as described in Smith et al. (1997). As with the sediment model, the SPARROW nutrient models 

regression equations relate measured nutrient transport rates in streams to spatially referenced descriptors 

of pollution sources and land-surface and stream-channel characteristics. Observed TN and TP transport 

rates were derived from water-quality records for 414 stations in the National Stream Quality Accounting 

Network. Nutrient sources identified in the regression equations include point sources, applied fertilizer, 

livestock waste, nonagricultural land, and atmospheric deposition (TN model only). Surface 

characteristics found to be significant predictors of land-water delivery include soil permeability, stream 

density, and air temperature (TN model only). 

The delivery processes, explanatory variables, and their coefficients are slightly different in each of the 

three SPARROW models (sediment, TN, and TP). As a result, it was not possible to utilize the nutrient 

models to predict changes in nutrient concentrations associated with construction sediment discharge by 

specifying the same input parameters used in the SPARROW sediment model. However, since the three 

models are based on the same hydrological network and baseline conditions, it is possible to relate 

predicted concentrations at the level of individual stream reaches. In this application, EPA used the 

baseline estimates for the three models to determine ratios between TN and sediment and between TP and 

sediment that were then used in conjunction with the predicted change in sediment concentrations to 

determine the change in nutrient concentrations, as represented by the following equation 

baseline

baseline

scenario scenario TSS

nutrient

nutrient TSS

][

][ Δ Δ= ][][ × (Eq. 6-1)

where Δ[nutrient] represents the change in nutrient concentration associated with the change in TSS 

concentration (Δ[TSS]). The variable [nutrient] represents the in-stream concentration of either TN or TP. 

The nutrient-to-TSS ratio ([nutrient]/[TSS]) is calculated using SPARROW model outputs of TSS, TN 

and TP mass flux related to land-based runoff for the baseline conditions, as indicated in the equations 

below. The ratios were calculated for each individual reach as follows (where IL = incremental sediment 

mass flux per reach): 

( )

totalTSS

totalTN TN population related TN deposition catmospheri

baseline

baseline

IL

IL IL IL

TSS

TN

,

, , ,

][

][ − − = − (Eq. 6-2) 

( )

totalTSS

totalTP TP population related

baseline

baseline

IL

ILIL

TSS

TP

,

, ,

][

][ − − = (Eq. 6-3) 

Note that, since the regulation specifically targets reducing stormwater runoff and the associated sediment 

and nutrients and therefore does not address other potential sources of nutrients, EPA excluded from the 

calculations of the nutrient-to-sediment ratios nutrient loadings associated with population and 

atmospheric deposition sources.6

 To mitigate the effects of outlier TSS concentration on the estimated 

nutrient-to-sediment ratio, the top 5 percent of nutrient-to-TSS ratios were replaced by the 95th percentile 

 

6

 An example of population-related sources is effluent from publicly owned treatment works (POTWs). Atmospheric 

deposition is only a source in the nitrogen model. 
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November 2009 6-14 

value of the distribution. This is similar to the approach used to summarize SPARROW-predicted TSS 

concentrations at the national level which replaces values above the 95th percentile with the 95th percentile 

value. National statistics of the reach-specific nutrient-to-sediment ratios are summarized in Table 6-2. 

Table 6-2: Nutrient-to-Sediment Ratios 

Average Median Min Max 

TN/TSS 0.006 0.005 0.000 0.028 

TP/TSS 0.001 0.001 0.000 0.003 

EPA averaged the reach-level ratios to the HUC8 watershed level to obtain a nutrient-to-sediment ratio 

that represents the average relationship between in-stream nutrients and suspended sediments within each 

basin. EPA later uses these watershed-average ratios to estimate reach-specific changes in suspended 

sediment concentrations for each scenario to yield estimated reach-level changes in nutrient 

concentration. 

EPA compared the ratios described above to values found in the literature, databases, and fate and 

transport models (Table 6-3). As shown in the table, the nutrient-to-sediment ratios calculated as 

described above based on SPARROW model outputs fall within the range of values published in the 

literature for both TN and TP. While differences are expected between soil and in-stream sediment 

concentrations, the values for both range widely in the United States and are shown in Table 6-3 for 

reference. 

Table 6-3: Literature Values of Nitrogen and Phosphorus in Soil and Sediment 

Data Source Data Type Soil/Sediment Nitrogen (%) Phosphorus (%) 

Ratios used in this analysis 

based on SPARROW1

Modeled values 

based on field data Sediment 0 to 2.77 0 to 0.32 

Haith et al. (1996) Field data Sediment 0.30 0.13 

Sednet 

(Wilkinson et al. 2004) Field data Subsoils 0.1 0.025 

Tisdale et al. (1985) Field data Natural, top 1 ft 0.03 to 0.4 - 

Haith et al. (1996) Field data Surface 30 cm <0.05 to ≥0.2 ≤0.09 to 0.68 

Havlin et al. (1999) 

Modeled values based 

on field data Surface soil - 0.005 to 0.15 

NRCS (2008)2 Field data Soil (varied) 0 to 3.57 0.0001 to 0.58 

SWAT (2005) 

Modeled values based 

on field data Surface soil min 0.0073 0.00254

Notes: Shaded rows indicate ranges that reflect geographically-specific data. For example, the Natural Resources Conservation Service 

(NRCS 2008) dataset is based on over 4,000 and 3,500 data points for TN and TP concentrations in soil, respectively, with a distribution of 

samples across the continental United States. The data provide useful references when evaluating the range of ratios obtained from 

SPARROW outputs but are insufficient for extrapolation to a reach-specific analysis. 1 Presented ranges are for reach-level values excluding top 5 percent. 2 Range of soil values seen in the continental United States (TN: 4,176 data points; TP: 3564 data points). 3

 Nitrate only. 

4 Mineral pool concentrations only. 
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6.5 Pollutant Load Modeling 

The general procedures that EPA used to estimate sediment load reductions from construction sites are 

described in EPA’s Development Document for Final Effluent Guidelines and Standards for the 

Construction and Development Category (USEPA 2009b). The results of this analysis are also detailed in 

that document. This analysis only considers stormwater sediment discharges to receiving waters and does 

not analyze dry-weather increases in surface water sediment and turbidity levels from dewatering 

discharges, wind deposition, construction activity taking place in surface waters, groundwater seepage, 

and vehicle and equipment washwaters. 

Table 6-4 presents a national summary of sediment loadings calculated by EPA for the baseline and four 

regulatory options, as well as reductions under each regulatory option. Option 1 is expected to result in a 

reduction of 34 percent of construction sediment discharges nationally, while Option 2, Option 3, and 

Option 4 are expected to result in reductions of 70 percent, 87 percent, and 77 percent, respectively. Total 

reductions under Option 2 are 1.80 million tons of sediment, 2.25 million tons under Option 3, and 

1.98 million tons under Option 4 compared to 870,000 tons under Option 1. 

Table 6-4: Baseline Construction Sediment Loading Summary and Post-Compliance 

Reductions 

Scenario Sediment Loading 

(million tons) 

Sediment Discharge 

Reduction (million tons) 

Percent Reduction from 

Baseline 

Baseline 2.58 – – 

Option 1 1.71 0.87 34% 

Option 2 0.78 1.80 70% 

Option 3 0.34 2.25 87% 

Option 4 0.60 1.98 77% 

Table 6-5 presents estimates of the amount of sediment entering reaches from construction related activity 

under each of these four options, by EPA region. The table shows the baseline amount of sediment 

entering reaches as well as post-compliance conditions and reductions for a regulatory option.
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November 2009 

Table 6-5: Construction Sediment Loading Summary and Reductions by Option and EPA Region 

Baseline Option 1 Option 2 Option 3 

EPA 

Region 

Loading 

(mil. tons) 

Loading 

(mil. tons) 

Reduction 

(mil. tons)

Percent 

Reduction

Loading 

(mil. tons)

Reduction 

(mil. tons)

Percent 

Reduction

Loading 

(mil. tons) 

Reduction 

(mil. tons)

Percent 

Reduction

Loadin

(mil. ton

1 0.02 0.01 0.01 34% 0.01 0.01 69% 0.00 0.02 85% 0.01 2 0.04 0.02 0.01 34% 0.01 0.02 69% 0.00 0.03 86% 0.01 3 0.15 0.10 0.05 34% 0.05 0.11 70% 0.02 0.13 87% 0.04 4 0.90 0.60 0.30 34% 0.27 0.63 70% 0.12 0.78 87% 0.22 5 0.28 0.19 0.10 34% 0.09 0.20 70% 0.04 0.25 87% 0.07 6 0.85 0.56 0.29 34% 0.25 0.60 70% 0.11 0.74 87% 0.16 7 0.23 0.15 0.08 34% 0.07 0.16 70% 0.03 0.20 87% 0.05 8 0.04 0.03 0.01 34% 0.01 0.03 70% 0.01 0.04 87% 0.01 9 0.04 0.02 0.01 34% 0.01 0.03 70% 0.00 0.03 87% 0.01 

10 0.03 0.02 0.01 34% 0.01 0.02 68% 0.00 0.02 85% 0.01 

Total 2.58 1.71 0.87 34% 0.78 1.80 70% 0.33 2.25 87% 0.60 

Note: Due to rounding, percentage reduction may not match the shown (rounded) reduction divided by the shown (rounded) baseline. 
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6.6 Water Quality Modeling Results 

This section presents the results of water quality modeling using the SPARROW model for sediment and 

the above described estimation approach for nutrients. The SPARROW sediment model simulations 

generate estimates of water sediment content as Suspended Sediment Concentration (SSC), as consistent 

with water quality gauging records used to estimate SPARROW sediment models. By contrast, ambient 

sediment concentrations measured as Total Suspended Solids (TSS) are required in the calculation of the 

water quality index (details of the water quality index can be found in Chapter 10). Although SSC and 

TSS are interpreted in a similar manner as measures of the concentration of suspended solid-phase 

material in surface water bodies, Gray et al. (2000) have determined that differences in laboratory 

protocols result in systematic differences between SSC and TSS when measuring a given ambient 

concentration of suspended sediment. SSC measurements are systematically higher than TSS, particularly 

when the sand component of total sediments exceeds roughly 25 percent of total (dry) sediment mass. 

Therefore, for the analysis described in this document, SPARROW estimates of SSC have been divided 

by a factor of 1.3 to obtain the corresponding TSS values. 

Construction activity discharges impact the majority of RF1 reaches in the coterminous United States. 

During the nine-year period from 1992 through 2001, approximately 71 percent of RF1 reach watersheds 

contained some level of construction. Construction discharges differ from other types of industrial 

discharges in this respect. Other types of industrial dischargers tend to be fewer in number and less widely 

distributed across the United States. 

The intensity of construction activity, however, varies widely among individual RF1 watersheds. 

Approximately 43,900 RF1 watersheds had a net increase in urban acreage during the 1992-2001 time 

period. Approximately 17,200 RF1 watersheds had no change (~16,400 watersheds) or a minor decrease 

(~800 watersheds) in urban acreage. Figure 6-4 illustrates the highly uneven distribution of construction 

acres among RF1 watersheds during the 1992-2001 time period. Watersheds represented in the figure 

account for 93 percent of all construction acres during the period, as shown in Figure 6-5, which shows 

the cumulative distribution of acres of construction by RF1 watersheds in decreasing order of construction 

acres by watershed and includes the national total number of acres of construction during the period. As 

the two figures show, relatively few watersheds account for a large fraction of acres of construction 

nationally. In Figure 6-5, for example, the 5 percent of RF1 watersheds containing the largest number of 

construction acres per watershed encompassed, as a group, over 2.4 million construction acres, or nearly 

50 percent of all construction acreage for the 1992-2001 time period. 
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Figure 6-4: Plot of Construction Acres by RF1 Watershed Percentile Group (1992-200

-

200,000400,000600,000800,000

1,000,0001,200,0001,400,000

1% 6% 11% 16% 21% 26% 31% 36% 41% 46% 51% 56% 61% 66% 71% 76% 81% 

RF1 Watershed Percentile Rank Based on Total Construction Acres

Total Acres of Construction per Pertentile Group (1992-2001)

Most construction acres per watershed Least construction a
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Figure 6-5: Cumulative Plot of Construction Acres by RF1 Watershed (1992-2001) 

-

500,000

1,000,0001,500,0002,000,0002,500,0003,000,0003,500,0004,000,0004,500,0005,000,0005,500,000

1% 6% 11% 16% 21% 26% 31% 36% 41% 46% 51% 56% 61% 66% 71% 76% 81% 8

RF1 Watershed Percentile Rank Based on Total Construction Acres

Total Acres of Construction (1992-2001)

Total Construction Acres (1992-2001) = 5,297,286 

Most construction acres per watershed Least construction acre
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Figure 6-6 through Figure 6-15 illustrate the distribution of construction acreage by EPA region. 

Figure 6-6: EPA Region 1: Percent Urban Change 1992–2001 by RF1 Watershed 

Figure 6-7: EPA Region 2: Percent Urban Change 1992–2001 by RF1 Watershed 
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Figure 6-8: EPA Region 3: Percent Urban Change 1992–2001 by RF1 Watershed 

Figure 6-9: EPA Region 4: Percent Urban Change 1992–2001 by RF1 Watershed 
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Figure 6-10: EPA Region 5: Percent Urban Change 1992–2001 by RF1 Watershed 

Figure 6-11: EPA Region 6: Percent Urban Change 1992–2001 by RF1 Watershed 
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Figure 6-12: EPA Region 7: Percent Urban Change 1992–2001 by RF1 Watershed 

Figure 6-13: EPA Region 8: Percent Urban Change 1992–2001 by RF1 Watershed 
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November 2009 6-24 

Figure 6-14: EPA Region 9: Percent Urban Change 1992–2001 by RF1 Watershed 

Figure 6-15: EPA Region 10: Percent Urban Change 1992–2001 by RF1 Watershed 

Because construction sites are distributed so unevenly among watersheds, this chapter summarizes water 

quality information in two ways. One set of tables included in this chapter presents information for the 

relatively small number of RF1 watersheds that contain most construction acreage for the 1992-2001 time 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-25 

period. A second set of tables presents information for all RF1 reaches directly receiving discharges from 

construction sites, regardless of the level of construction activity in their watersheds. 

An important factor to consider when examining the data summarized in the tables below is that episodic 

precipitation events are the primary cause of construction site discharges to surface waters. Most TSS and 

associated nutrient discharges from construction sites, therefore, take place during or shortly after 

precipitation events. Once a precipitation event ceases, discharges from construction sites generally cease 

within a short time period. The estimates presented in the tables below, however, are not intended to 

reflect surface water TSS and nutrient concentrations associated with individual, episodic storm events. 

They instead represent average concentrations estimated to exist in RF1 reaches over multi-year time 

periods. EPA uses these long-term estimates because the data and modeling resources currently available 

to EPA do not permit a finer level of time resolution. 

EPA expects that, in general, surface water TSS and nutrient concentrations in the short time periods 

following storm events would be much higher than the concentrations presented in the tables below. TSS 

concentrations as high as 7,000 mg/L have been documented due to construction site discharges in 

downstream surface waters following precipitation events (Wolman and Schick 1967; Chisholm and 

Downs 1978; Downs and Appel 1986). Sediment and nutrient concentration reductions for the evaluated 

regulatory options would be higher, as well. During periods when there is no precipitation event to cause 

a discharge from a construction site, surface water sediment and nutrient concentrations would generally 

be lower. 

A second important factor to consider when examining the data in the tables below is that construction 

site sediment and nutrient discharges also have strong geospatial variability. Geospatial differences occur 

as a result of variation in soil type, topography, distance from construction site to surface water, receiving 

surface water characteristics, and weather patterns, among other factors. These differences are reflected to 

a certain extent by the differences in surface water TSS and nutrient concentrations among different 

groups of reaches, as reflected by reaches having progressively greater construction acreage within their 

watershed (presented in Table 6-16 through Table 6-20) and among the various EPA regions (presented in 

Table 6-22 through Table 6-31). In general, however, the data presented below are averaged over large 

geographic regions because of limitations in the data and modeling resources currently available to EPA. 

When summarizing TSS and nutrient concentrations, EPA calculated statistics based on reach-length 

weighted concentrations to account for the wide variation among reaches in length. EPA believes that this 

adjustment provides a more representative characterization of the distribution of concentrations by giving 

more weight to the longer reaches. Additionally, EPA has replaced concentrations that have 

concentrations above the 95th percentile (e.g., TSS greater than 6,157.8 mg/L under baseline conditions) 

with the 95th percentile value since these reaches could potentially be considered outliers. Finally, the data 

presented in the tables are for those reaches receiving direct discharges from construction sites. The 

decrease in loadings and concentrations in these reaches also translate into decreased concentrations in 

downstream reaches. While improvements in these downstream reaches are included in the benefits 

analysis, changes in concentration occurring downstream reaches that do not receive direct discharges 

from construction sites are not included in the tables in this chapter. 

While summaries cannot adequately describe the full extent of temporal and geospatial variations in TSS 

and nutrient concentrations, they are nevertheless useful for interpreting broad-scale changes in water 

quality as a result of the evaluated regulatory options. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-26 

6.6.1 Current Water Quality Impacts to Surface Water from Construction Site 

Discharges 

To estimate water quality impacts associated with construction site discharges under current conditions, 

EPA used SPARROW modeling of a hypothetical scenario in which all sediment discharges from 

construction activity are prevented from entering surface waters. Under this hypothetical scenario, loading 

from in-scope construction sites (one acre and greater in size) decrease by 2.58 million tons per year, 

relative to baseline conditions. It is important to note that a certain level of sediment is present in surface 

waters under natural, undisturbed conditions and that some sediment is necessary for the natural 

biological function of surface waters. Current levels of sediment discharge to the U.S. surface water 

network are much higher, however, than would be observed under natural, undisturbed conditions due to 

sediment discharges from construction, agriculture, and eroding streambeds associated with land use 

change and other human activity (see Section 2.1). Therefore, while construction sediment discharges 

represent approximately 0.15 percent of total sediment in surface waters, they represent a greater 

percentage of total sediment pollution in the nation’s waters. Given the relatively small percentage of U.S. 

land area dedicated to construction activity on an annual basis (~0.04 percent of coterminous United 

States), construction sites have a disproportionately high rate of sediment contribution to surface waters 

relative to other land uses. 

Predicted changes, relative to baseline conditions, in TSS concentration for a hypothetical scenario with 

zero discharge from construction sites are presented in Table 6-6 through Table 6-9. The tables show the 

distribution of TSS concentrations and reach miles improved. Average TSS concentrations decrease from 

956.4 mg/L under baseline conditions to 954.1 mg/L under the zero discharge scenario. This represents a 

reduction of 2.4 mg/L or 0.3 percent. Median concentrations decrease from 289.3 to 287.5 mg/L, 

representing a change of 1.8 mg/L or 0.6 percent. In examining the spatial distribution of concentrations 

in the baseline and zero discharge scenarios, shown in Table 6-7 and Table 6-8, respectively, the highest 

concentrations of TSS are seen in Regions 6, 7, 8, and 9, and this is also where the largest reductions are 

predicted. Table 6-9 shows the distribution and magnitude of reductions in improved reach miles. Over 77 

percent of improved reach miles are expected to have a decrease in TSS concentration that is greater than 

zero but less than 1 mg/L. Approximately 18 percent of improved reach miles will decrease in TSS 

concentration by 1 to 5 mg/L. Greater than 5 mg/L reductions are estimated in 4.2 percent of improved 

reach miles. These improvements are largely driven by Regions 4, 6, 7 and 10. Eliminating sediment 

discharge from construction sites also reduces sediment accumulation in reservoirs by 3.7 million pounds 

per year. 

Table 6-6: Distribution of TSS Concentrations based on SPARROW Output for 31,927 RF1 

Reaches Receiving Construction Sediment Discharges 

Distribution of TSS Concentrations by 

Percentile1,2 (mg/L) 

Scenario 

Reach 

Count 

Reach 

Miles 

Average 

TSS 

(mg/L)1,2

Reduction in 

Average TSS

(mg/L) 5th 25th 50th 75th 95th 

Baseline 31,927 412,062 956.4 – 29.7 109.9 289.3 880.0 6,157.8 

Hypothetical 

No Discharge 

Scenario 31,927 412,062 954.1 2.4 28.6 108.7 287.5 877.9 6,156.8 1

Reach-length weighted. 

2

Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-27 

Table 6-7: Summary of Baseline TSS Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TSS Concentrations by Percentile1,2 

EPA (mg/L) 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TSS 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 77.5 9.8 25.2 41.2 57.2 118.7 

2 1,111 12,560 110.2 10.5 38.8 79.0 135.7 283.1 

3 2,012 25,471 180.6 44.5 83.6 141.7 228.3 447.4 

4 7,564 83,058 236.8 33.2 91.4 180.6 311.1 596.8 

5 3,918 56,909 296.0 20.6 60.9 196.1 430.4 880.4 

6 4,634 70,687 1,567.7 82.9 259.5 641.6 2,137.4 6,157.8 

7 3,063 46,903 1,800.8 177.6 484.7 1,050.3 2,008.6 6,157.8 

8 4,300 57,513 2,024.6 79.2 359.7 1,131.2 3,104.9 6,157.8 

9 1,439 22,280 1,486.7 31.6 157.6 434.1 1,932.3 6,157.8 

10 2,673 25,940 239.6 20.6 52.6 108.2 233.1 882.4 

Nation 31,927 412,062 956.4 29.7 109.9 289.3 880.0 6,157.8 1

Reach-length weighted. 

2

Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced with the 95th percentile value. 

Table 6-8: Summary of TSS Concentrations Under the Hypothetical No Discharge 

Scenario, by EPA Region 

Distribution of TSS Concentrations by Percentile1,2 (mg/L) EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TSS 

(mg/L)1,2

5th 25th 50th 75th 95th 

1 1,213 10,740 77.3 9.7 25.2 41.1 57.1 118.6 

2 1,111 12,560 110.2 10.5 38.8 79.0 135.5 281.7 

3 2,012 25,471 180.4 44.4 83.6 141.5 228.2 447.3 

4 7,564 83,058 235.3 32.2 90.4 178.8 310.1 590.9 

5 3,918 56,909 295.8 20.5 60.9 195.8 430.4 880.3 

6 4,634 70,687 1,562.0 75.1 254.0 634.6 2,123.9 6,156.8 

7 3,063 46,903 1,797.9 173.9 481.7 1,048.3 2,007.3 6,156.8 

8 4,300 57,513 2,017.5 75.4 354.5 1,126.6 3,104.2 6,156.8 

9 1,439 22,280 1,480.9 29.8 152.6 430.2 1,900.9 6,156.8 

10 2,673 25,940 238.4 19.4 51.4 107.5 233.1 882.4 

Nation 31,927 412,062 954.1 28.6 108.7 287.5 877.9 6,156.8 1

Reach-length weighted. 

2

Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-28 

Table 6-9: Water Quality Impacts: Improvements in TSS Concentrations Under the 

Hypothetical No Discharge Scenario, by EPA Region 

Range of Reductions in TSS Concentration (mg/L) 

0 < ∆ TSS < 1 1 ≤ ∆ TSS < 5 5 ≤ ∆ TSS < 10 10 ≤ ∆ TSS < 50 50 ≤ ∆ TSS 

EPA 

Region 

Total 

Improved 

Reach 

Miles1 Reach 

Miles 

% of 

Total 

Reach 

Miles 

% of 

Total 

Reach 

Miles 

% of 

Total 

Reach 

Miles 

% of 

Total 

Reach 

Miles 

% of 

Total 

1 10,740 10,421 97.0% 219 2.0% 64 0.6% 36 0.3% 0 0.0% 

2 12,560 12,488 99.4% 72 0.6% 0 0.0% 0 0.0% 0 0.0% 

3 25,471 24,256 95.2% 1,073 4.2% 112 0.4% 30 0.1% 0 0.0% 

4 83,028 64,885 78.1% 14,320 17.2% 2,236 2.7% 1,401 1.7% 186 0.2% 

5 56,906 54,801 96.3% 1,974 3.5% 97 0.2% 34 0.1% 0 0.0% 

6 70,630 33,122 46.9% 27,840 39.4% 4,506 6.4% 4,501 6.4% 661 0.9% 

7 46,859 31,518 67.3% 13,896 29.7% 816 1.7% 584 1.2% 44 0.1% 

8 57,322 47,070 82.1% 9,967 17.4% 227 0.4% 49 0.1% 10 0.0% 

9 22,204 17,373 78.2% 4,319 19.4% 283 1.3% 200 0.9% 29 0.1% 

10 25,920 23,283 89.8% 1,527 5.9% 559 2.2% 478 1.8% 72 0.3% 

Nation 411,640 319,218 77.5% 75,206 18.3% 8,900 2.2% 7,314 1.8% 1,002 0.2% 1

 Does not include 40 freshwater reaches (422 reach miles) for which SPARROW does not predict a concentration. 

Table 6-10 through Table 6-15 present the reductions in TN and TP concentrations associated with 

reduced sediment discharge under the hypothetical zero discharge scenario. As described in Section 6.4, 

reductions in TN and TP concentrations are calculated based on the predicted reduction of in-stream 

sediment concentrations. The distribution of improved reach miles for TN and TP is therefore the same as 

for TSS, which is presented above in Table 6-9. 

Reductions in average TN are predicted to be 0.02 mg/L or 0.1 percent. Reductions in the median 

concentrations are 0.01 mg/L or 0.1 percent and 95th percentile concentrations with 0.04 mg/L or 0.3 

percent reductions. 

Reductions in average TP concentrations are predicted to be 6.0 μg/L or 0.2 percent. Reductions in the 

median concentrations are 2.2 μg/L or 0.1 percent and 95th percentile concentrations with 22.3 μg/L or 

0.6 percent reductions. 

The geographic distribution of TN and TP concentrations is similar to TSS concentrations, with the 

highest concentrations seen in Regions 6, 7, 8, and 9. In general, these regions are also predicted to see 

the largest reductions in concentrations. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-29 

Table 6-10: Distribution of TN Concentrations based on SPARROW Output for 31,927 RF1 

Reaches Receiving Construction Sediment Discharges 

Distribution of TN Concentrations by 

Percentile (mg/L) 1,2

Scenario 

Reach 

Count 

Reach 

Miles 

Average TN 

(mg/L)1,2

Reduction 

in Average 

TN (mg/L) 5th 25th 50th 75th 95th 

Baseline 31,927 412,062 18.22 – 0.37 0.91 1.65 3.88 14.95 

Hypothetical 

No Discharge 

Scenario 31,927 412,062 18.21 0.02 0.36 0.90 1.64 3.86 14.91 1

Reach-length weighted. 

2

Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced 

with the 95th percentile value. 

Table 6-11: Summary of Baseline TN Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TN Concentrations by Percentile (mg/L) 1

EPA Region Reach 

Count 

Reach 

Miles 

Average TN

(mg/L)1 5th 25th 50th 75th 95th 

1 1,213 10,740 1.32 0.24 0.59 0.85 1.27 2.57 

2 1,111 12,560 19.43 0.33 0.95 1.37 1.98 3.85 

3 2,012 25,471 1.88 0.61 1.02 1.35 1.95 4.35 

4 7,564 83,058 4.39 0.29 0.70 1.06 1.59 3.06 

5 3,918 56,909 7.76 0.51 1.49 3.68 7.50 15.86 

6 4,634 70,687 26.15 0.45 1.07 1.83 3.26 12.38 

7 3,063 46,903 22.87 1.10 2.64 5.11 9.56 21.50 

8 4,300 57,513 40.01 0.50 1.08 2.44 5.99 28.66 

9 1,439 22,280 46.56 0.24 0.55 1.28 3.35 54.43 

10 2,673 25,940 5.30 0.23 0.49 0.83 1.58 5.29 

Nation 31,927 412,062 18.22 0.37 0.91 1.65 3.88 14.95 1

Reach-length weighted. 

Table 6-12: Summary of TN Concentrations Under the Hypothetical No Discharge 

Scenario, by EPA Region 

Distribution of TN Concentrations by Percentile (mg/L) 1,2

EPA Region Reach 

Count 

Reach 

Miles 

Average TN 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 1.32 0.23 0.59 0.85 1.27 2.57 

2 1,111 12,560 19.43 0.33 0.95 1.37 1.98 3.85 

3 2,012 25,471 1.88 0.61 1.02 1.35 1.95 4.34 

4 7,564 83,058 4.38 0.28 0.70 1.06 1.58 3.06 

5 3,918 56,909 7.75 0.51 1.48 3.68 7.49 15.86 

6 4,634 70,687 26.11 0.42 1.06 1.82 3.23 12.33 

7 3,063 46,903 22.85 1.08 2.61 5.10 9.49 21.50 

8 4,300 57,513 39.99 0.48 1.07 2.42 5.96 28.64 

9 1,439 22,280 46.51 0.23 0.54 1.28 3.26 54.43 

10 2,673 25,940 5.29 0.23 0.49 0.83 1.58 5.29 

Nation 31,927 412,062 18.21 0.36 0.90 1.64 3.86 14.91 1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced 

with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-30 

Table 6-13: Distribution of TP Concentrations based on SPARROW Output for 31,927 RF1 

Reaches Receiving Construction Sediment Discharges 

Distribution of TP Concentrations by 

Percentile (μg/L) 1,2

Scenario 

Reach 

Count 

Reach 

Miles 

Average 

TP 

(μg/L)1,2

Reduction 

in Average 

TP (μg/L) 5th 25th 50th 75th 95th 

Baseline 31,927 412,062 3,921.7 – 30.5 104.4 253.2 685.3 2,907.3 

Hypothetical 

No Discharge 

Scenario 31,927 412,062 3,915.6 6.0 29.5 103.3 251.0 683.2 2,885.0 1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced 

with the 95th percentile value. 

Table 6-14: Summary of Baseline TP Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TP Concentrations by Percentile (μg/L) 1

EPA Region Reach 

Count 

Reach 

Miles 

Average 

TP (μg/L)1 5th 25th 50th 75th 95th 

1 1,213 10,740 109.5 9.9 29.6 49.2 86.5 233.8 

2 1,111 12,560 1,697.5 16.6 47.2 91.2 152.1 403.0 

3 2,012 25,471 209.3 37.7 80.2 135.4 233.5 633.6 

4 7,564 83,058 885.6 30.0 81.8 139.5 241.9 547.4 

5 3,918 56,909 651.3 18.6 75.6 225.3 475.9 1,246.4 

6 4,634 70,687 8,110.6 63.4 208.4 519.5 1,216.9 5,557.8 

7 3,063 46,903 3,380.2 140.6 377.8 603.9 1,012.7 2,853.1 

8 4,300 57,513 7,282.3 69.0 249.5 659.0 1,449.5 5,803.0 

9 1,439 22,280 13,544.9 37.0 114.9 489.0 1,652.3 30,733.9 

10 2,673 25,940 965.7 22.1 56.3 113.4 303.0 1,318.7 

Nation 31,927 412,062 3,921.7 30.5 104.4 253.2 685.3 2,907.3 

1

Reach-length weighted. 

Table 6-15: Summary of TP Concentrations Under the Hypothetical No Discharge 

Scenario, by EPA Region 

Distribution of TP Concentrations by Percentile (μg/L) 1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TP (μg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 109.3 9.9 29.4 49.1 86.5 233.7 

2 1,111 12,560 1,697.5 16.6 47.2 91.1 151.9 402.9 

3 2,012 25,471 209.1 37.7 80.0 135.1 233.2 633.4 

4 7,564 83,058 884.4 29.2 81.1 138.5 239.9 547.3 

5 3,918 56,909 651.1 18.5 75.5 225.3 475.8 1,246.4 

6 4,634 70,687 8,094.3 57.6 202.5 516.2 1,207.3 5,557.7 

7 3,063 46,903 3,376.8 138.4 374.8 601.9 1,008.8 2,850.7 

8 4,300 57,513 7,277.0 66.6 246.4 656.6 1,445.2 5,780.1 

9 1,439 22,280 13,512.6 35.3 111.2 474.1 1,644.4 30,733.9 

10 2,673 25,940 964.6 21.0 55.4 112.2 297.9 1,267.0 

National 31,927 412,062 3,915.6 29.5 103.3 251.0 683.2 2,885.0 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced 

with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-31 

6.6.2 Estimated Changes in TSS Concentrations in RF1 Watersheds with Most 

Developed Acres (1992-2001) Receiving Sediment Loading from Construction 

Sites 

Table 6-16 summarizes the characteristics of different groups of modeled watersheds in terms of the 

relative amount of development they contain. The table provides information for three particular 

subgroups that represent the “top” watersheds in terms of the amount of developed land during the period 

of 1992-2001: top 1 percent, top 10 percent, and top 25 percent. As discussed above, development is not 

evenly distributed during the nine-year period under examination and each of the watershed subsets 

therefore account for a relatively disproportionate share of construction acres. For example, the top 10 

percent of watersheds account for over 58 percent of the increase in developed land in the coterminous 

United States, while the top 25 percent account for 76 percent of all developed land. These watersheds are 

also distributed unevenly across EPA regions both in terms of total reach miles and the number of 

waterbody reaches, as shown in Table 6-17. For example, approximately 22 percent of reach miles within 

the top 1 percent are found in Region 5 even though the region accounts for approximately 14 percent of 

overall RF1 reach miles. 

These differences are reflected in TSS concentration estimates shown in Table 6-16 through Table 6-20 

for each of the three top groups, respectively, and across the four regulatory options (see Chapter 1 for 

option descriptions). As shown in the tables, the top 1 percent of watersheds in terms of construction 

acres tend to have the highest sediment concentrations, both under current conditions and under the four 

options. While the three top groups result in a similar fraction of miles showing improvements in TSS 

concentrations across the three groups, reaches within the top 1 percent of watersheds exhibit a higher 

magnitude of TSS concentration reductions than reaches in watersheds that contain comparatively fewer 

construction acres. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 Table 6-16: Reaches with Largest Increase in Developed Land Area (1992-2001)Percent of RF1 Watersheds1

Number of RF1 

Watersheds2

% of Total 

Increase in 

Coterminous 

U.S. Developed 

Land 

Total Increase in 

Developed Land 

Area (1992-2001) 

(million acres) 

Average Increase 

in Developed 

Land Area per 

RF1 Watersheds 

(1992-2001) 

(acres) 

Total Land A

(million acr

Top 1% of RF1 Watersheds 319 21.7% 1.1 3,596.4 52.6 Top 10% of RF1 Watersheds 3,192 58.3% 3.1 968.0 305.2 Top 25% of RF1 Watersheds 7,981 76.0% 4.0 504.4 572.9 All RF1 Watersheds in Coterminous U.S. (100% of Reaches) 

31,927 93.5% 5.0 155.1 1,291.9 

1 Top 1% of RF1 watersheds had 1,841 acres or greater of developed land area (1992-2001); Top 10% of watersheds had 321 acres or greater of developed land area (19

watersheds had 124 acres or greater of developed land area (1992-2001). 

2 RF1 watersheds included in count are those that receive construction loadings and for which both NLCD and SPARROW data are available. Table 6-17: Distribution of RF1 Watersheds that Receive Direct Loadings by Increase in Developed Land by EPA Region 

Top 1% of Watersheds Top 10% of Watersheds Top 25% of Watersheds 

C

EPA (

Region Reach Miles % of Miles Reach Miles % of Miles Reach Miles % of Miles Reach 

1 21 0% 911 1% 2,088 1% 10,742 50 1% 1,502 2% 3,646 3% 12,563 410 6% 4,582 7% 9,774 7% 25,474 1,472 20% 14,276 22% 33,331 24% 83,055 1,590 22% 9,733 15% 19,670 14% 56,906 1,540 21% 15,428 24% 31,300 22% 70,687 736 10% 7,042 11% 16,350 12% 46,908 450 6% 4,758 7% 13,362 9% 57,59 947 13% 3,915 6% 6,827 5% 22,2

10 172 2% 2,139 3% 5,239 4% 25,94

Nation 7,387 100% 64,284 100% 141,587 100% 412,06
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 Table 6-18: Estimated Changes in RF1 Reach TSS Concentration by Policy Option: Top 1% of RF1 WateDirect Construction Loadings by Increase in Developed Land Area (1992-2001) 

Range of Reductions in TSS Concentrations (mg/L) 

Total Improved 0 < ∆ TSS < 1 1 ≤ ∆ TSS < 5 5 ≤ ∆ TSS < 10 10 ≤ ∆ TSS < 

Scenario 

Median 

TSS 

(mg/L)1

Average 

TSS 

(mg/L)1

RF1 

Miles 

% of 

Total 

Miles2 RF1 Miles

% of Total 

Improved 

Miles 

RF1 

Miles 

% of Total 

Improved 

Miles 

RF1 

Miles 

% of Total 

Improved 

Miles 

RF1 

Miles 

% of T

Impr

Mil

Baseline 425.6 1,073.3 – – – – – – – – – – Option 1 425.2 1,069.2 6,932 93.8% 4,891 70.6% 1,288 18.6% 354 5.1% 381 5Option 2 424.9 1,067.0 6,932 93.8% 4,242 61.2% 1,509 21.8% 416 6.0% 683 9Option 3 424.7 1,065.9 7,372 99.8% 4,457 60.5% 1,418 19.2% 618 8.4% 748 10Option 4 424.8 1,066.5 6,932 93.8% 4,184 60.4% 1,359 19.6% 511 7.4% 776 111 River-mile weighted. 2 Total miles (7,387) are based on top 1% of reaches by area of urban development from 1992 to 2001. Table 6-19: Estimated Changes in RF1 Reach TSS Concentration by Policy Option: Top 10% of RF1 WateDirect Construction Loadings by Increase in Developed Land Area (1992-2001) 

Range of Reductions in TSS Concentrations (mg/L) 

Total Improved 0 < ∆ TSS < 1 1 ≤ ∆ TSS < 5 5 ≤ ∆ TSS < 10 10 ≤ ∆ TSS < 

Scenario 

Median 

TSS 

(mg/L)1

Average 

TSS 

(mg/L)1

RF1 

Miles 

% of 

Total 

Miles2

RF1 

Miles 

% of Total 

Improved 

Miles 

RF1 

Miles 

% of Total 

Improved 

Miles 

RF1 

Miles 

% of Total 

Improved 

Miles 

RF1 

Miles 

% of T

Impr

Mil

Baseline 289.1 947.7 – – – – – – – – – – Option 1 287.5 945.0 60,992 94.9% 48,452 79.4% 9,547 15.7% 1,708 2.8% 1,067 1.8Option 2 287.0 943.6 60,992 94.9% 41,568 68.2% 13,413 22.0% 2,970 4.9% 2,674 4.4Option 3 286.0 943.9 64,202 99.9% 42,342 66.0% 14,308 22.3% 3,643 5.7% 3,419 5.3Option 4 286.8 943.3 60,992 94.9% 40,599 66. 6% 13,551 22.2% 3,355 5.5% 3,044 5.01 River-mile weighted. 2 Total miles (64,284) are based on top 10% of reaches by area of urban development from 1992 to 2001. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 Table 6-20: Estimated Changes in RF1 Reach TSS Concentration by Policy Option: Top 25% of RF1 Watein Developed Land Area (1992-2001) 

Range of Reductions in TSS Concentrations (mg/L) 

Total Improved 0 < ∆ TSS < 1 1 ≤ ∆ TSS < 5 5 ≤ ∆ TSS < 10 10 ≤ ∆ TSS < 

Scenario 

Median 

TSS 

(mg/L)1

Average 

TSS 

(mg/L)1

RF1 

Miles 

% of 

Total 

Miles2

RF1 

Miles 

% of Total 

Improved 

Miles 

RF1 

Miles 

% of Total 

Improved 

Miles 

RF1 

Miles 

% of Total 

Improved 

Miles 

RF1 

Miles 

% of 

Impr

Mi

Baseline 283.5 929.9 – – – – – – – – – – 

Option 1 282.6 928.2 134,556 95.0% 114,258 84.9% 16,259 12.1% 2,336 1.7% 1,406 1.1Option 2 281.9 927.3 134,556 95.0% 100,071 74.4% 25,718 19.1% 4,638 3.5% 3,635 2.7Option 3 281.5 926.8 141,498 99.9% 101,252 71.6% 29,060 20.5% 5,898 4.2% 4,630 3.3Option 4 281.5 927.1 134,556 95.0% 97,345 72.3% 27,184 20.2% 5,288 3.9% 4,132 3.1

1 River-mile weighted. 2 Total miles (141,489) are based on top 25% of reaches by area of urban development from 1992 to 2001. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-35 

6.6.3 Estimated Changes in TSS, TN, and TP Concentrations for all RF1 Watersheds 

Receiving Loading from Construction Sites 

Table 6-21 summarizes national average reach-length weighted TSS concentrations in RF1 reaches as 

estimated by SPARROW under both the baseline scenario representing current conditions and the four 

regulatory options (see Chapter 1 for option descriptions). Information is presented for all reaches in the 

RF1 network that receive sediment loading from construction sites. 

Table 6-21 also presents information on the distribution of TSS concentrations under the baseline 

scenario representing current conditions and the four regulatory options. TSS concentrations in the RF1 

network vary over a wide range. Values for the 5th and 95th percentile bounds under baseline conditions 

are 29.7 and 6,157.8 mg/L respectively. Median (50th percentile) TSS concentrations are approximately 

289.3 mg/L under the baseline scenario. Under Option 1, the median concentration decreases 0.8 mg/L to 

288.6 mg/L. Median concentration under Option 2 decreases 1.4 mg/L to 287.9 mg/L; under Option 3, it 

decreases 1.6 mg/L to 287.7 mg/L; under Option 4 it decreases 1.5 mg/L to 287.8 mg/L. These numbers 

represent the national average concentration reduction and do not provide information on the geospatial 

variability in TSS concentration reduction, which is high. 

Table 6-21: Distribution of TSS Concentration based on SPARROW Output for 31,927 RF1 

Reaches Receiving Construction Sediment Discharges

Distribution of TSS Concentrations, by 

percentile (mg/L)1,2

Policy 

Option 

Reach 

Count 

Reach 

Miles 

Percent of 

total 

construction 

acres, ‘92-‘01 

Average 

TSS 

(mg/L)1,2

Reduction 

in 

Average 

TSS 5th 25th 50th 75th 95th 

Baseline 31,927 412,062 93.05% 956.4 – 29.7 109.9 289.3 880.0 6,157.8

Option 1 31,927 412,062 93.05% 955.0 1.5 29.1 109.2 288.6 878.5 6,157.8

Option 2 31,927 412,062 93.05% 954.5 2.0 28.8 108.9 287.9 878.3 6,157.8

Option 3 31,927 412,062 93.05% 954.2 2.2 28.7 108.8 287.7 878.0 6,156.8

Option 4 31,927 412,062 93.05% 954.4 2.1 28.7 108.8 287.8 878.3 6,157.8

1

Reach-length weighted. 

2

Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced with the 95th percentile value. 

Table 6-22 through Table 6-26 detail TSS concentrations by EPA region under baseline conditions and 

for each of the four regulatory options considered. The highest average TSS concentrations are found in 

Regions 6, 7, 8, and 9 which have average reach-length weighted TSS concentrations greater than 

1,400 mg/L under all scenarios. Median TSS concentrations in these regions—ranging from 434.1 to 

1,131.2 mg/L under baseline conditions—are also higher than median concentrations in Regions 1, 2, 3, 

4, 5, and 10 where the highest median TSS concentration is 196.1 mg/L. Under Option 1, TSS 

concentrations decline a small amount (by less than 1 percent) from baseline concentrations. TSS 

concentrations are further reduced under Option 2 and again under Option 3. Under Option 4, 

concentrations are reduced relative to baseline and concentrations generally fall between those under 

Option 2 and Option 3. The distribution of concentrations among regions is consistent with the baseline 

under each of the options.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-36 

Table 6-22: Summary of Baseline TSS Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TSS Concentrations, by percentile (mg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TSS 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 77.5 9.8 25.2 41.2 57.2 118.7 

2 1,111 12,560 110.2 10.5 38.8 79.0 135.7 283.1 

3 2,012 25,471 180.6 44.5 83.6 141.7 228.3 447.4 

4 7,564 83,058 236.8 33.2 91.4 180.6 311.1 596.8 

5 3,918 56,909 296.0 20.6 60.9 196.1 430.4 880.4 

6 4,634 70,687 1,567.7 82.9 259.5 641.6 2,137.4 6,157.8 

7 3,063 46,903 1,800.8 177.6 484.7 1,050.3 2,008.6 6,157.8 

8 4,300 57,513 2,024.6 79.2 359.7 1,131.2 3,104.9 6,157.8 

9 1,439 22,280 1,486.7 31.6 157.6 434.1 1,932.3 6,157.8 

10 2,673 25,940 239.6 20.6 52.6 108.2 233.2 882.4 

Nation 31,927 412,062 956.4 29.7 109.9 289.3 880.0 6,157.8 

1

Reach-length weighted. 

2

Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced with the 95th percentile value. 

Table 6-23: Summary of Option 1 TSS Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TSS Concentrations, by percentile (mg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TSS 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 77.5 9.8 25.2 41.2 57.1 118.7 

2 1,111 12,560 110.2 10.5 38.8 79.0 135.6 282.7 

3 2,012 25,471 180.5 44.5 83.6 141.5 228.3 447.4 

4 7,564 83,058 236.2 32.9 90.9 179.9 311.0 593.7 

5 3,918 56,909 295.9 20.6 60.9 196.0 430.4 880.4 

6 4,634 70,687 1,564.7 78.8 256.8 639.6 2,130.3 6,157.8 

7 3,063 46,903 1,798.8 175.9 482.6 1,048.4 2,007.4 6,157.8 

8 4,300 57,513 2,017.8 75.4 354.5 1,126.6 3,104.2 6,157.8 

9 1,439 22,280 1,481.5 29.8 153.0 430.7 1,902.6 6,157.8 

10 2,673 25,940 239.1 20.3 52.1 107.7 233.2 882.4 

Nation 31,927 412,062 955.0 29.1 109.2 288.6 878.5 6,157.8 

1

Reach-length weighted. 

2

Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-37 

Table 6-24: Summary of Option 2 TSS Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TSS Concentrations, by percentile (mg/L) 1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TSS 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 77.4 9.7 25.2 41.1 57.1 118.7 

2 1,111 12,560 110.2 10.5 38.8 79.0 135.5 282.2 

3 2,012 25,471 180.4 44.5 83.6 141.5 228.3 447.3 

4 7,564 83,058 235.7 32.3 90.7 179.0 310.7 592.9 

5 3,918 56,909 295.9 20.6 60.9 195.9 430.4 880.3 

6 4,634 70,687 1,563.2 76.2 254.4 637.3 2,128.6 6,157.8 

7 3,063 46,903 1,798.4 175.8 482.6 1,048.4 2,007.3 6,157.8 

8 4,300 57,513 2,017.7 75.4 354.5 1,126.6 3,104.2 6,157.8 

9 1,439 22,280 1,481.2 29.8 152.6 430.3 1,901.1 6,157.8 

10 2,673 25,940 238.8 20.0 51.6 107.5 233.2 882.4 

Nation 31,927 412,062 954.5 28.8 108.9 287.9 878.3 6,157.8 

1

Reach-length weighted. 

2

Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced with the 95th percentile value. 

Table 6-25: Summary of Option 3 TSS Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region

Distribution of TSS Concentrations, by percentile (mg/L) 1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TSS 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 77.3 9.7 25.2 41.1 57.1 118.6 

2 1,111 12,560 110.2 10.5 38.8 79.0 135.5 281.9 

3 2,012 25,471 180.4 44.4 83.6 141.5 228.2 447.3 

4 7,564 83,058 235.5 32.3 90.5 178.9 310.2 592.2 

5 3,918 56,909 295.8 20.6 60.9 195.9 430.4 880.2 

6 4,634 70,687 1,562.5 75.4 254.2 637.0 2,124.3 6,156.8 

7 3,063 46,903 1,798.1 175.8 481.7 1,048.4 2,007.3 6,156.8 

8 4,300 57,513 2,017.6 75.4 354.5 1,126.6 3,104.2 6,156.8 

9 1,439 22,280 1,481.0 29.8 152.6 430.3 1,901.0 6,156.8 

10 2,673 25,940 238.6 20.0 51.4 107.5 233.2 882.4 

Nation 31,927 412,062 954.2 28.7 108.8 287.7 878.0 6,156.8 

1

Reach-length weighted. 

2

Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-38 

Table 6-26: Summary of Option 4 TSS Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TSS Concentrations, by percentile (mg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TSS 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 77.4 9.7 25.2 41.1 57.1 118.7 

2 1,111 12,560 110.2 10.5 38.8 79.0 135.5 282.2 

3 2,012 25,471 180.4 44.5 83.6 141.5 228.3 447.3 

4 7,564 83,058 235.6 32.3 90.7 179.0 310.3 592.5 

5 3,918 56,909 295.8 20.6 60.9 195.9 430.4 880.3 

6 4,634 70,687 1,562.8 75.6 254.2 637.3 2,127.0 6,157.8 

7 3,063 46,903 1,798.3 175.8 481.8 1,048.4 2,007.3 6,157.8 

8 4,300 57,513 2,017.7 75.4 354.5 1,126.6 3,104.2 6,157.8 

9 1,439 22,280 1,481.2 29.8 152.6 430.3 1,901.1 6,157.8 

10 2,673 25,940 238.9 20.0 51.8 107.5 233.2 882.4 

Nation 31,927 412,062 954.4 28.7 108.8 287.8 878.3 6,157.8 

1

Reach-length weighted. 

2

Any TSS concentration above the 95th percentile is assumed to be an outlier and was replaced with the 95th percentile value. 

The following tables describe the number of RF1 reach miles improved and the distribution of TSS 

concentration reductions for each of the four options analyzed for this regulation. As shown in Table 

6-27, which summarizes the effects of sediment reduction at the national level, Options 1, 2 and 3 

improve 94.7 percent of modeled RF1 reach miles receiving construction sediment loading. Options 4 is 

predicted to lead to improvements in 99.9 percent of RF1 reach miles that receive direct construction 

loadings. Under Option 1, approximately 93 percent of the improved reach miles show reductions in TSS 

concentrations of greater than zero but less than 1 mg/L. Under Options 2 through 4, approximately 84 to 

86 percent of the improved reach miles are predicted to see a decrease of less than 1 mg/L. While under 

Option 1, only about 6 percent of improved reach miles are expected see a reduction of 1 to 5 mg/L, under 

Options 2 through 4, 11 to 12 percent of improved reach miles are expected to improve by 1 to 5 mg/L. 

Reductions greater than 50 mg/L are expected in less than or equal 0.2 percent of improved miles under 

all options. 

As discussed at the start of Section 6.6, the reduction in loadings and concentrations in reaches that 

receive construction sediment discharge directly also translate into decreased concentrations in 

downstream reaches. The total number of reach miles that are improved under the four options analyzed 

is therefore greater than presented in the tables within this chapter. When considering all RF1 reaches, 

including those that do not receive construction sediment discharge directly, the results show a total of 

431,074 reach miles improving under Options 1, 2, and 4. These miles account for 68.7 percent of the 

total RF1 reach network. Option 3 has an even broader effect, reducing TSS concentrations in a total of 

472,402 reach miles (75.3 percent of the RF1 reach network). 

Table 6-28 summarizes Option 1’s sediment reductions at the regional level. Nationwide, 93 percent of 

improved RF1 reach miles under Option 1 experience TSS concentration reductions less than 1 mg/L. 

While all but Region 2 will experience reduction of 1 to 5 mg/L, the number of regions experiencing 

reductions decreases at higher reduction ranges. All but Regions 2, 3, and 5 will experience reductions 

between 5 and 10 mg/L, encompassing approximately 0.8 percent of all improved reaches nationally. All 

but Regions 1, 2, 3, and 5 are predicted to see some reductions between 10 and 50 mg/L encompassing 

0.5 percent of all improved reaches nationally. Only four regions (4, 6, 7, and 10) will experience 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-39 

reductions in TSS concentrations greater than 50 mg/L, with a national total of 0.1 percent of improved 

reaches. 

As shown in Table 6-29, Option 2 is predicted to result in the same number of total improved reach miles 

(390,374 miles), but the magnitude of reductions in TSS concentrations in these miles is expected to be 

greater. All regions are predicted to see a reduction in TSS concentrations of up to 5 mg/L in some reach 

miles. Approximately 86 and 11 percent of all reach miles improved will experience up to 1 and 5 mg/L 

reductions, respectively. Some reach miles in all but Region 2 are predicted to improve by 5 to 10 mg/L 

with approximately 1.6 percent of reach miles improved across the nation in this range. Approximately 

1.2 percent of all reach miles improved will see a decrease of 10 to 50 mg/L. Regions 2, 3, and 5 are the 

only regions without reductions in this range. Nationally, 0.2 percent of all improved reach miles will 

experience a reduction of greater than 50 mg/L under Option 2. Among regions, Region 6 is predicted to 

have the greatest number of reach miles with reductions greater than 50 mg/L, followed by Regions 4, 10, 

7, and 8. 

Option 3 (Table 6-30) is estimated to reduce TSS concentrations by more than Option 2 with an 

additional 21,266 miles of improved rivers and generally a greater magnitude of reductions across the 

reduction ranges and across regions. Reductions of up to 1 mg/L and 5 mg/L are expected for all regions 

with a national total of 84 and 12 percent of improved reach miles, respectively. Some reach miles are 

expected to improve by 5 to 10 mg/L in all regions except Region 2, encompassing 2.0 percent of all 

improved reach miles nationally. All regions but Regions 2, 3, and 5 are expected to experience 

improvement in some reach miles by 10 to 50 mg/L including 1.5 percent of all improved reach miles 

nationally. Greater than 50 mg/L reductions in TSS concentrations are expected in 0.2 percent of all 

improved reach miles nationally with improvements in Regions 4 and 7 through 10. 

Option 4 (Table 6-31) is estimated to reduce TSS concentrations in the same number of reach miles as 

Options 1 and 2, but the reductions are of slightly greater magnitude. Therefore, under Option 4, there is a 

slightly smaller percentage of total miles improved by up to 1 mg/L and a larger percentage for all other 

ranges. The spatial distribution of reductions, however, is the same as Option 3, with respect to the 

regions where reductions are predicted in each of the reduction ranges. Approximately 11.9 and 

1.8 percent of improved reach miles are expected to see 1 to 5 mg/L and 5 to 10 mg/L reductions, 

respectively. Reductions of 10 mg/L to 50 mg/L are expected in 5,414 miles or 1.4 percent of all 

improved reach miles, and 0.2 percent of improved reach miles are expected to improve by greater than 

50 mg/L under Option 4. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 6-27: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS Conce

Range of Reductions in TSS Concentrations (mg/L)

0 < ∆ TSS < 1 1 ≤ ∆ TSS < 5 5 ≤ ∆ TSS < 10 10 ≤ ∆ TSS < 

Policy 

Option 

Total RF1 

Miles 

Total Miles 

with TSS 

Reductions1

Percent of 

Total 

Reach 

Miles with 

TSS 

Reductions1

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% 

To

Imp

M

Option 1 412,062 390,374 94.7% 361,569 92.6% 23,624 6.1% 2,994 0.8% 1,832 0.Option 2 412,062 390,374 94.7% 336,168 86.1% 42,481 10.9% 6,382 1.6% 4,738 1.Option 3 412,062 411,640 99.9% 346,379 84.2% 50,221 12.2% 8,084 2.0% 6,147 1.Option 4 412,062 390,374 94.7% 330,850 84.8% 46,339 11.9% 7,029 1.8% 5,414 1.1 Not included in the total are 40 reaches (422 reach miles) for which SPARROW does not predict concentrations. The 99.9% fraction represents all reach miles over whic

concentrations.

Table 6-28: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS ConceOption 1, by EPA Region 

Range of Reductions in TSS Concentration (mg/L) 

0 < Δ TSS < 1 1 ≤ Δ TSS < 5 5 ≤ Δ TSS < 10 10 ≤ Δ TSS < 50 

EPA 

Region 

Total 

Improved 

Reach 

Miles1

Reach 

Miles 

% of 

Total 

Reach 

Miles 

% of 

Total 

Reach 

Miles 

% of 

Total 

Reach 

Miles 

% of 

Total 

1 10,740 10,610 98.8% 110 1.0% 20 0.2% 0 0.0% 2 12,554 12,554 100.0% 0 0.0% 0 0.0% 0 0.0% 3 25,471 25,164 98.8% 307 1.2% 0 0.0% 0 0.0% 4 82,955 76,258 91.9% 5,657 6.8% 633 0.8% 297 0.4% 5 56,906 56,474 99.2% 432 0.8% 0 0.0% 0 0.0% 6 64,954 50,162 77.2% 11,569 17.8% 1,751 2.7% 1,249 1.9% 7 41,102 37,682 91.7% 3,040 7.4% 257 0.6% 109 0.3% 8 49,967 49,350 98.8% 570 1.1% 25 0.1% 21 <0.1% 9 19,853 18,943 95.4% 778 3.9% 78 0.4% 54 0.3% 

10 25,873 24,374 94.2% 1,161 4.5% 230 0.9% 102 0.4% 

Nation 390,374 361,569 92.6% 23,624 6.1% 2,994 0.8% 1,832 0.5% 

1 Total does not include 40 freshwater reaches (422 reach miles) for which SPARROW does not predict concentrations.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 6-29: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS ConcOption 2, by EPA Region 

Range of Reductions in TSS Concentration (mg/L) 

0 < Δ TSS < 1 1 ≤ Δ TSS < 5 5 ≤ Δ TSS < 10 10 ≤ Δ TSS < 50 

EPA 

Region 

Total 

Improved 

Reach 

Miles1

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

1 10,740 10,564 98.4% 128 1.2% 29 0.3% 20 0.2% 2 12,554 12,542 99.9% 12 0.1% 0 0.0% 0 0.0% 3 25,471 24,751 97.2% 691 2.7% 30 0.1% 0 0.0% 4 82,955 69,708 84.0% 10,787 13.0% 1,396 1.7% 903 1.1% 5 56,906 55,616 97.7% 1,236 2.2% 54 0.1% 0 0.0% 6 64,954 39,267 60.5% 18,668 28.7% 3,685 5.7% 2,970 4.6% 7 41,102 33,876 82.4% 6,323 15.4% 495 1.2% 394 1.0% 8 49,967 48,047 96.2% 1,770 3.5% 104 0.2% 37 0.1% 9 19,853 18,041 90.9% 1,515 7.6% 165 0.8% 132 0.7% 

10 25,873 23,758 91.8% 1,352 5.2% 425 1.6% 282 1.1% 

Nation 390,374 336,168 86.1% 42,481 10.9% 6,382 1.6% 4,738 1.2% 

1 Total does not include 40 freshwater reaches (422 reach miles) for which SPARROW does not predict concentrations.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 6-30: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS ConcOption 3, by EPA Region 

Range of Reductions in TSS Concentration (mg/L) 

0 < Δ TSS < 1 1 ≤ Δ TSS < 5 5 ≤ Δ TSS < 10 10 ≤ Δ TSS < 50 

EPA 

Region 

Total 

Improved 

Reach 

Miles1

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

1 10,740 10,538 98.1% 120 1.1% 63 0.6% 20 0.2% 2 12,560 12,501 99.5% 59 0.5% 0 0.0% 0 0.0% 3 25,471 24,493 96.2% 908 3.6% 70 0.3% 0 0.0% 4 83,028 66,798 80.5% 12,850 15.5% 2,021 2.4% 1,186 1.4% 5 56,906 55,180 97.0% 1,635 2.9% 91 0.2% 0 0.0% 6 70,630 40,665 57.6% 21,400 30.3% 4,178 5.9% 3,845 5.4% 7 46,859 37,950 81.0% 7,724 16.5% 667 1.4% 504 1.1% 8 57,322 54,799 95.6% 2,273 4.0% 203 0.4% 37 0.1% 9 22,204 19,952 89.9% 1,844 8.3% 227 1.0% 169 0.8% 

10 25,920 23,503 90.7% 1,408 5.4% 565 2.2% 388 1.5% 

Nation 411,640 346,379 84.2% 50,221 12.2% 8,084 2.0% 6,147 1.5% 

1 Total does not include 40 freshwater reaches (422 reach miles) for which SPARROW does not predict concentrations.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 6-31: Total RF1 Miles that Receive Direct Construction Loadings with Improvements in TSS ConcOption 4, by EPA Region 

Range of Reduction in TSS Concentration (mg/L) 

0 < Δ TSS < 1 1 ≤ Δ TSS < 5 5 ≤ Δ TSS < 10 10 ≤ Δ TSS < 50 

EPA 

Region 

Total 

Improved 

Reach 

Miles1

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

Reach 

Miles 

% of 

Total 

Improved 

Miles 

1 10,740 10,581 98.5% 136 1.3% 4 <0.1% 20 0.2% 2 12,554 12,546 99.9% 8 0.1% 0 0.0% 0 0.0% 3 25,471 24,662 96.8% 779 3.1% 30 0.1% 0 0.0% 4 82,955 68,280 82.3% 11,758 14.2% 1,752 2.1% 991 1.2% 5 56,906 55,617 97.7% 1,235 2.2% 54 0.1% 0 0.0% 6 64,954 36,432 56.1% 20,548 31.6% 3,927 6.1% 3,552 5.5% 7 41,102 33,170 80.7% 6,887 16.8% 568 1.4% 462 1.1% 8 49,967 47,677 95.4% 2,048 4.1% 196 0.4% 37 0.1% 9 19,853 17,940 90.4% 1,610 8.1% 149 0.8% 142 0.7% 

10 25,873 23,944 92.6% 1,329 5.1% 351 1.4% 211 0.8% 

Nation 390,374 330,850 84.8% 46,339 11.9% 7,029 1.8% 5,414 1.4% 

1 Total does not include 40 freshwater reaches (422 reach miles) for which SPARROW does not predict concentrations. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-44 

6.6.4 Impacts on Reservoir Sedimentation 

Construction discharges affect not only sediment concentrations in receiving water bodies, but they also 

affect the amount of sedimentation that takes place in reservoirs. As described in Section 6.2, reservoirs 

provide major sites for sediment attenuation within the hydrographic network. This attenuation is 

estimated in SPARROW for more than 4,000 reservoirs distributed throughout the RF1 network. The 

model estimates that more than 444 million cubic yards accumulate under baseline conditions. The 

accumulating sediment may hinder reservoir functions and need to be periodically dredged, as discussed 

in greater detail in Chapter 2. The policy options reduce this amount to various degrees. As shown in 

Table 6-32, Option 1 reduces the amount of sediment accumulating in reservoirs slightly as compared to 

current conditions (by 558,800 cubic yards or approximately 0.1 percent). Most of this reduction is 

concentrated in Regions 4, 6, and 7. Options 2, 3, and 4 show greater reductions in reservoir 

sedimentation, with estimated reductions of 1.16, 1.45, and 1.32 million cubic yards, respectively (or 

0.3 percent). They also show more geographically distributed effects, with significant reductions observed 

in Regions 4, 5, 6, 7, 9, and 10. 

Table 6-32: Sediment Accumulation in Reservoirs by Policy Option and EPA Region 

Sediment Accumulation 

(thousand cubic yards) 

Reduction in Sediment Accumulation 

(thousand cubic yards) EPA 

Region Baseline Option 1 Option 2 Option 3 Option 4 Option 1 Option 2 Option 3 Option 4

1 1,278 1,277 1,275 1,275 1,276 1.4 2.9 3.6 2.1

2 2,725 2,725 2,724 2,724 2,724 0.5 1.1 1.4 1.0

3 5,338 5,335 5,332 5,331 5,332 2.6 5.3 6.6 5.5

4 45,667 45,568 45,462 45,411 45,440 98.8 205.1 255.7 226.5

5 35,940 35,933 35,924 35,920 35,923 7.9 16.3 20.3 17.2

6 128,381 127,973 127,530 127,320 127,397 408.5 851.2 1,061.7 984.4

7 42,238 42,213 42,186 42,173 42,179 24.8 51.6 64.3 59.1

8 126,435 126,433 126,430 126,429 126,430 2.2 4.5 5.6 4.7

9 41,860 41,855 41,850 41,847 41,849 4.8 10.0 12.4 10.5

10 14,744 14,737 14,729 14,726 14,732 7.3 14.8 18.4 11.8

Nation 444,606 444,047 443,443 443,156 443,283 558.8 1,162.7 1,449.9 1,322.9

6.6.5 Estimated Changes in Nutrient Concentrations for all RF1 Watersheds Receiving 

Loading from Construction Sites 

Table 6-33 and Table 6-34 summarize national average reach-length weighted TN and TP concentrations 

and their distribution under the baseline conditions and the four regulatory options (see Chapter 1 for 

option descriptions) for reaches receiving construction sediment discharges. TN and TP concentrations in 

the RF1 network vary over a wide range. Nutrient criteria established by EPA for rivers and streams vary 

across ecoregions from 0.12 mg/L to 2.18 mg/L for TN and from 10 μg/L to 128 μg/L for TP (USEPA 

2009d). 

Estimated TN concentrations under baseline conditions range between 0.37 and 14.95 mg/L for the 5th

and 95th percentiles, respectively. Median (50th percentile) TN concentrations are approximately 

1.65 mg/L under the baseline scenario. Under Options 1 and 2, the median concentration remains 

relatively unchanged at 1.65 mg/L; under Options 3 and 4, it decreases 0.01 mg/L to 1.64 mg/L. Overall, 

the largest reduction in TN concentrations is predicted under Option 3 with a 0.5 percent reduction in 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-45 

median TN concentrations relative to baseline levels. These numbers represent the national average 

concentration reduction, however, and do not reflect the wide geospatial variability in TN concentration 

reductions. 

Table 6-33: Distribution of TN Concentration Based on SPARROW Output for 31,927 

RF1 Reaches Receiving Construction Sediment Discharges 

Distribution of TN Concentrations, by Percentile 

(mg/L)1,2 Policy 

Option 

Reach 

Count 

Reach 

Miles 

Average 

TN 

(mg/L)1,2

Reduction 

in Average 

TN 5th 25th 50th 75th 95th 

Baseline 31,927 412,062 18.22 – 0.37 0.91 1.65 3.88 14.95 

Option 1 31,927 412,062 18.21 0.01 0.37 0.91 1.65 3.87 14.91 

Option 2 31,927 412,062 18.21 0.02 0.36 0.91 1.65 3.87 14.91 

Option 3 31,927 412,062 18.21 0.02 0.36 0.90 1.64 3.87 14.91 

Option 4 31,927 412,062 18.21 0.02 0.36 0.91 1.65 3.87 14.91 

1

Reach-length weighted. 2

Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 

Values for TP concentrations for the 5th and 95th percentile bounds under baseline conditions are 30.5 and 

2,907.3 μg/L, respectively. Median (50th percentile) TP concentrations are approximately 253.2 μg/L 

under the baseline scenario. Under Option 1, the median concentration decreases 1.4 μg/L to 251.8 μg/L. 

The median concentration under Option 2 decreases 1.9 μg/L to 251.3 μg/L and under Option 3 and 4, it 

decreases 2.1 μg/L to 251.1 μg/L. These numbers represent the national average concentration reduction, 

however, and do not reflect the wide geospatial variability in TP concentration reductions. 

Table 6-34: Distribution of TP Concentration Based on SPARROW Output for 31,927 

RF1 Reaches Receiving Construction Sediment Discharges 

Distribution of TP Concentrations, by Percentile 

(μg/L)1,2 Policy 

Option 

Reach 

Count 

Reach 

Miles 

Average 

TP 

(μg/L)1,2

Reduction 

in Average 

TP 5th 25th 50th 75th 95th 

Baseline 31,927 412,062 3,921.6 30.5 104.4 253.2 685.3 2,907.3 

Option 1 31,927 412,062 3,916.3 5.3 29.9 103.8 251.8 683.7 2,886.4 

Option 2 31,927 412,062 3,915.9 5.7 29.6 103.4 251.3 683.6 2,886.4 

Option 3 31,927 412,062 3,915.7 5.9 29.6 103.3 251.1 683.5 2,885.1 

Option 4 31,927 412,062 3,915.9 5.8 29.6 103.4 251.1 683.5 2,886.4 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 

Changes in nutrient concentrations are based on in-stream sediment reductions, as described in Section 

6.4. Therefore, the distribution and magnitude of percent change in reach miles will be the same as for 

TSS, as presented in Section 6.6.3. These tables are not recreated below for TN and TP. 

Table 6-35 through Table 6-39 detail TN concentrations by EPA region under baseline conditions and for 

each of the four regulatory options considered. The highest average TN concentrations are found in 

Regions 8 and 9, which have average reach-length weighted TN concentrations between 40.01 and 

46.56 mg/L under all scenarios. However, the highest median TN concentrations are found in Regions 5 

and 7, with concentrations of 3.68 and 5.11 mg/L, respectively. Regions 8 and 9 appear to have a skewed 

distribution with a smaller portion of reach miles at high concentrations skewing the average higher than 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-46 

other regions. Under all four options, TN concentrations decline a small amount, approximately 

0.1 percent, from baseline concentrations. 

Table 6-35: Summary of Baseline TN Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TN Concentrations, by Percentile (mg/L)1 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TN (mg/L)1

5th 25th 50th 75th 95th 

1 1,213 10,740 1.32 0.24 0.59 0.85 1.27 2.57 

2 1,111 12,560 19.43 0.33 0.95 1.37 1.98 3.85 

3 2,012 25,471 1.88 0.61 1.02 1.35 1.95 4.35 

4 7,564 83,058 4.39 0.29 0.70 1.06 1.59 3.06 

5 3,918 56,909 7.76 0.51 1.49 3.68 7.50 15.86 

6 4,634 70,687 26.15 0.45 1.07 1.83 3.26 12.38 

7 3,063 46,903 22.87 1.10 2.64 5.11 9.56 21.50 

8 4,300 57,513 40.01 0.50 1.08 2.44 5.99 28.66 

9 1,439 22,280 46.56 0.24 0.55 1.28 3.35 54.43 

10 2,673 25,940 5.30 0.23 0.49 0.83 1.58 5.29 

Nation 31,927 412,062 18.22 0.37 0.91 1.65 3.88 14.95 

1

Reach-length weighted. 

Table 6-36: Summary Option 1 TN Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TN Concentrations, by Percentile (mg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TN 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 1.32 0.24 0.59 0.85 1.27 2.57 

2 1,111 12,560 19.43 0.33 0.95 1.37 1.98 3.85 

3 2,012 25,471 1.88 0.61 1.02 1.35 1.95 4.35 

4 7,564 83,058 4.39 0.28 0.70 1.06 1.58 3.06 

5 3,918 56,909 7.76 0.51 1.49 3.68 7.49 15.86 

6 4,634 70,687 26.12 0.43 1.06 1.83 3.24 12.37 

7 3,063 46,903 22.86 1.08 2.62 5.10 9.53 21.50 

8 4,300 57,513 39.99 0.48 1.07 2.42 5.96 28.64 

9 1,439 22,280 46.51 0.23 0.54 1.28 3.26 54.43 

10 2,673 25,940 5.29 0.23 0.49 0.83 1.58 5.29 

Nation 31,927 412,062 18.21 0.36 0.91 1.65 3.87 14.91 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-47 

Table 6-37: Summary of Option 2 TN Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TN Concentrations, by Percentile (mg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TN 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 1.32 0.24 0.59 0.85 1.27 2.57 

2 1,111 12,560 19.43 0.33 0.95 1.37 1.98 3.85 

3 2,012 25,471 1.88 0.61 1.02 1.35 1.95 4.34 

4 7,564 83,058 4.38 0.28 0.70 1.06 1.58 3.06 

5 3,918 56,909 7.76 0.51 1.48 3.68 7.49 15.86 

6 4,634 70,687 26.12 0.43 1.06 1.83 3.23 12.35 

7 3,063 46,903 22.85 1.08 2.62 5.10 9.49 21.50 

8 4,300 57,513 39.99 0.48 1.07 2.42 5.96 28.64 

9 1,439 22,280 46.51 0.23 0.54 1.28 3.26 54.43 

10 2,673 25,940 5.29 0.23 0.49 0.83 1.58 5.29 

Nation 31,927 412,062 18.21 0.36 0.91 1.65 3.87 14.91 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 

Table 6-38: Summary of Option 3 TN Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TN Concentrations, by Percentile (mg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TN 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 1.32 0.24 0.59 0.85 1.27 2.57 

2 1,111 12,560 19.43 0.33 0.95 1.37 1.98 3.85 

3 2,012 25,471 1.88 0.61 1.02 1.35 1.95 4.34 

4 7,564 83,058 4.38 0.28 0.70 1.06 1.58 3.06 

5 3,918 56,909 7.75 0.51 1.48 3.68 7.49 15.86 

6 4,634 70,687 26.11 0.42 1.06 1.83 3.23 12.34 

7 3,063 46,903 22.85 1.08 2.61 5.10 9.49 21.50 

8 4,300 57,513 39.99 0.48 1.07 2.42 5.96 28.64 

9 1,439 22,280 46.51 0.23 0.54 1.28 3.26 54.43 

10 2,673 25,940 5.29 0.23 0.49 0.83 1.58 5.29 

Nation 31,927 412,062 18.21 0.36 0.90 1.64 3.87 14.91 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-48 

Table 6-39: Summary of Option 4 TN Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TN Concentrations, by Percentile (mg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TN 

(mg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 1.32 0.24 0.59 0.85 1.27 2.57 

2 1,111 12,560 19.43 0.33 0.95 1.37 1.98 3.85 

3 2,012 25,471 1.88 0.61 1.02 1.35 1.95 4.34 

4 7,564 83,058 4.38 0.28 0.70 1.06 1.58 3.06 

5 3,918 56,909 7.76 0.51 1.48 3.68 7.49 15.86 

6 4,634 70,687 26.11 0.42 1.06 1.83 3.23 12.34 

7 3,063 46,903 22.85 1.08 2.62 5.10 9.49 21.50 

8 4,300 57,513 39.99 0.48 1.07 2.42 5.96 28.64 

9 1,439 22,280 46.51 0.23 0.54 1.28 3.26 54.43 

10 2,673 25,940 5.29 0.23 0.49 0.83 1.58 5.29 

Nation 31,927 412,062 18.21 0.36 0.90 1.64 3.87 14.91 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 

Table 6-40 through Table 6-44 detail TP concentrations by EPA region under baseline conditions and for 

each of the four regulatory options considered. The highest average TP concentrations are found in 

Regions 2, 6, 7, 8, and 9, which have average reach-length weighted TP concentrations greater than 

1,600 μg/L under all scenarios. These regions, with the exception of Region 2, also show the highest 

median TP concentrations (ranging from 489.0 to 659.0 μg/L under baseline conditions). Option 1 shows 

the smallest TP concentration reductions, with Options 2 and 3 showing slightly higher reductions. 

Option 4 shows TP concentration reductions that are between those under Option 2 and Option 3, similar 

to trends observed for TN. 

Table 6-40: Summary of Baseline TP Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TP Concentrations, by Percentile (μg/L)1 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TP 

(μg/L)1 5th 25th 50th 75th 95th 

1 1,213 10,740 109.5 9.9 29.6 49.2 86.5 233.8 

2 1,111 12,560 1,697.5 16.6 47.2 91.2 152.1 403.0 

3 2,012 25,471 209.3 37.7 80.2 135.4 233.5 633.6 

4 7,564 83,058 885.6 30.0 81.8 139.5 241.8 547.4 

5 3,918 56,909 651.2 18.6 75.6 225.3 475.9 1,246.4 

6 4,634 70,687 8,110.6 63.4 208.4 519.5 1,216.9 5,557.8 

7 3,063 46,903 3,380.2 140.6 377.8 603.9 1,012.7 2,853.1 

8 4,300 57,513 7,282.3 69.0 249.5 659.0 1,449.5 5,803.0 

9 1,439 22,280 13,544.9 37.0 114.9 489.0 1,652.3 30,733.9 

10 2,673 25,940 965.7 22.1 56.3 113.4 303.0 1,318.7 

Nation 31,927 412,062 3,921.6 30.5 104.4 253.2 685.3 2,907.3 

1

Reach-length weighted. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-49 

Table 6-41: Summary of Option 1 TP Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TP Concentrations, by Percentile (μg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TP 

(μg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 109.4 9.9 29.5 49.2 86.5 233.8 

2 1,111 12,560 1,697.5 16.6 47.2 91.2 152.0 403.0 

3 2,012 25,471 209.2 37.7 80.1 135.3 233.4 633.6 

4 7,564 83,058 885.1 29.8 81.4 139.1 241.1 547.4 

5 3,918 56,909 651.2 18.6 75.5 225.3 475.8 1,246.4 

6 4,634 70,687 8,096.5 61.3 205.2 517.0 1,213.2 5,557.7 

7 3,063 46,903 3,377.4 138.4 375.7 603.3 1,010.4 2,853.1 

8 4,300 57,513 7,277.2 66.6 246.4 656.7 1,445.2 5,780.2 

9 1,439 22,280 13,513.0 36.2 111.7 476.2 1,644.4 30,733.9 

10 2,673 25,940 965.2 21.5 56.1 112.3 303.0 1,314.0 

Nation 31,927 412,062 3,916.3 29.9 103.8 251.8 683.7 2,886.4 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 

Table 6-42: Summary of Option 2 TP Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TP Concentrations, by Percentile (μg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TP 

(μg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 109.4 9.9 29.5 49.2 86.5 233.8 

2 1,111 12,560 1,697.5 16.6 47.2 91.2 152.0 402.9 

3 2,012 25,471 209.1 37.7 80.0 135.2 233.3 633.5 

4 7,564 83,058 884.7 29.3 81.2 138.9 240.2 547.3 

5 3,918 56,909 651.1 18.6 75.5 225.3 475.8 1,246.4 

6 4,634 70,687 8,095.2 59.0 203.8 516.6 1,211.4 5,557.7 

7 3,063 46,903 3,377.2 138.4 374.9 602.8 1,008.8 2,853.1 

8 4,300 57,513 7,277.2 66.6 246.4 656.6 1,445.2 5,780.1 

9 1,439 22,280 13,512.8 35.3 111.4 475.1 1,644.4 30,733.9 

10 2,673 25,940 964.9 21.5 55.7 112.3 300.4 1,289.3 

Nation 31,927 412,062 3,915.9 29.6 103.4 251.3 683.6 2,886.4 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 6-50 

Table 6-43: Summary of Option 3 TP Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TP Concentrations, by Percentile (μg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TP 

(μg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 109.4 9.9 29.4 49.2 86.5 233.7 

2 1,111 12,560 1,697.5 16.6 47.2 91.1 152.0 402.9 

3 2,012 25,471 209.1 37.7 80.0 135.1 233.3 633.5 

4 7,564 83,058 884.5 29.2 81.2 138.6 240.0 547.3 

5 3,918 56,909 651.1 18.5 75.5 225.3 475.8 1,246.4 

6 4,634 70,687 8,094.6 58.6 203.6 516.2 1,209.8 5,557.7 

7 3,063 46,903 3,376.9 138.4 374.9 602.2 1,008.8 2,850.8 

8 4,300 57,513 7,277.0 66.6 246.4 656.6 1,445.2 5,780.1 

9 1,439 22,280 13,512.7 35.2 111.4 474.5 1,644.4 30,733.9 

10 2,673 25,940 964.7 21.5 55.6 112.3 297.9 1,277.5 

Nation 31,927 412,062 3,915.7 29.6 103.3 251.1 683.5 2,885.1 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 

Table 6-44: Summary of Option 4 TP Concentration in RF1 Reaches Receiving 

Construction Sediment Discharges, by EPA Region 

Distribution of TP Concentrations, by Percentile (μg/L)1,2 EPA 

Region 

Reach 

Count 

Reach 

Miles 

Average 

TP 

(μg/L)1,2 5th 25th 50th 75th 95th 

1 1,213 10,740 109.4 9.9 29.5 49.2 86.5 233.8 

2 1,111 12,560 1,697.5 16.6 47.2 91.2 152.0 402.9 

3 2,012 25,471 209.1 37.7 80.0 135.2 233.3 633.5 

4 7,564 83,058 884.6 29.3 81.2 138.9 240.2 547.3 

5 3,918 56,909 651.1 18.6 75.5 225.3 475.8 1,246.4 

6 4,634 70,687 8,094.9 58.8 203.7 516.2 1,211.2 5,557.7 

7 3,063 46,903 3,377.1 138.4 374.9 602.4 1,008.8 2,853.1 

8 4,300 57,513 7,277.1 66.6 246.4 656.6 1,445.2 5,780.1 

9 1,439 22,280 13,512.8 35.3 111.4 475.0 1,644.4 30,733.9 

10 2,673 25,940 965.0 21.5 55.8 112.3 301.2 1,297.2 

Nation 31,927 412,062 3,915.9 29.6 103.4 251.1 683.5 2,886.4 

1

Reach-length weighted. 

2

 Nutrient reductions are based on TSS reductions. Any TSS concentration above the 95th percentile is assumed to be an outlier and was 

replaced with the 95th percentile value. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-1 

7 Benefits to Navigation 

Navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are an integral part of 

the United States’ industrial transportation network. Navigable channels are prone to reduced 

functionality due to sediment build-up, which can reduce the navigable depth and width of the waterway 

(Clark et al. 1985). Removing sediment to keep navigable waterways passable requires dredging. The 

U.S. Army Corps of Engineers (USACE) spends an average of more than $572 million (2008$) every 

year to dredge waterways and keep them passable (USACE 2009). 

Implementation of the regulation is expected to result in less frequent dredging of navigable waterways 

due to reduced sediment runoff from construction sites. This will result in avoided costs to the 

government and private entities responsible for maintenance of navigational shipping channels, harbors, 

rivers, and other waterways. For example, prior studies of benefits of the Conservation Reserve program 

found that a reduction of 205 million tons of sediment erosion from farmland would result in 

approximately $526 million (2008$) in benefits to navigation from decreased dredging (Ribaudo 1989). 

Finally, unless there is overdredging to compensate or sedimentation is monitored so that dredging 

activity may be timed optimally, water bodies will be on average less navigable even with a dredging 

program. 

This chapter presents EPA’s analysis of the navigable waterway maintenance costs that would be avoided 

by implementation of the regulation. This approach represents an appropriate measure of social benefits 

for cases in which a policy change reduces costs to producers (in this case “producers” of navigable 

waterways), but in which price effects are minimal (cf. Boardman et al. 2001, p. 70-74). Additional 

environmental benefits of reduced dredging are not estimated due to the highly place-specific nature of 

these benefits and difficulty in estimating these costs over the national scale. Hence, in this regard the 

Agency’s benefit estimate in this chapter is understated. This analysis includes the following steps: 

3⁄4 Identifying navigable waterways that are regularly dredged and estimating the frequency of 

regular dredging in each waterway 

3⁄4 Estimating the navigable waterway maintenance cost per cubic yard of sediment dredged 

3⁄4 Estimating the total cost of navigable waterway maintenance under the baseline and postcompliance scenarios 

3⁄4 Estimating cost saving from decreased dredging of navigable waterways due to the reduction 

in sediment runoff from construction sites. 

7.1 Data Sources 

EPA relied on the USACE Dredging Information System in analyzing benefits to navigation from the 

regulation. The dredging database catalogs all USACE dredging contracts from 1990 to 2008 and all 

USACE-conducted dredging jobs from 1995 to 2008, and provides information on the location, dates, 

cost, and amount of sediment dredged for each dredging job or contract. For the purpose of this analysis, 

a “dredging occurrence” refers to a single record in these data, containing a location and time of dredging, 

while a “dredging job” refers to a single location of dredging that may have more than one dredging 

occurrence, and thus be repeated in the data. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-2 

Where the USACE Dredging Information System did not contain cost or quantity of sediment dredged for 

a listed dredging occurrence, EPA estimated the missing information from other jobs. The Agency 

calculated 10th, 50th, and 90th percentile costs and quantity boundaries and used these to fill in all 

incomplete records in that region. EPA included 50th percentile (median) estimates in the summary data 

presented in Table 7-1 through Table 7-4 and in the midpoint estimate of total dredging costs. The 10th

and 90th percentile cost and amount dredged estimates were used in the low and high total dredging cost 

estimates, respectively. For more details, see Section 7.4.3: Sensitivity Analysis. 

Table 7-1 summarizes dredging information for dredging jobs for the period 1995–2008, including 

dredging performed by USACE and dredging contracted to other firms. The cost data were adjusted to 

2008$ using the construction cost index (CCI) (Construction Cost Index 2008). During this period, 

USACE funded nearly 3,400 dredging occurrences, worth more than $9 billion (2008$). This dredging 

removed more than 2.6 billion cubic yards of sediment from more than 1,100 navigable reaches. The 

region with the most dredging was Region 4, reporting more than 900 dredging occurrences between 

1995 and 2008, about 65 per year. Region 6 reported the largest volume of sediment removed, with nearly 

1.5 billion cubic yards removed from navigable waterways over this 14-year period. Though benefits are 

also dependent on the amount of sediment reduction in a region, regions with more frequent dredging are 

more likely to experience greater benefits from reduced sedimentation in navigable waterways. 

Table 7-1: Dredging in U.S. Navigable Waterways, 

1995–2008 

EPA 

Region 

Number of 

Dredging 

Occurrences 

Total Sediment 

Removed 

(millions of cubic 

yards) 

Total Cost 

(millions of 2008$) 

1 79 3.7 $45.7 

2 278 111.4 $801.1 

3 291 124.5 $902.8 

4 921 540.9 $3,121.3 

5 739 112.8 $517.8 

6 647 1,483.3 $2,706.4 

7 22 17.3 $39.5 

8 1 0.5 $2.3 

9 129 67.7 $449.3 

10 290 153.5 $499.9

Total 3,397 2,615.7 $9,086.3 

Source: Dredging Information System (USACE 2009). 

Table 7-2 summarizes dredging activity from 1995 through 2008 by type of entity performing dredging 

work (private or government). As noted above, USACE spent more than $9 billion (2008$) over the past 

14 years to fund nearly 3,400 dredging occurrences. The majority of dredging work (58 percent) was 

contracted out, with USACE performing about 42 percent of the work over this period. The individual 

costs of these dredging occurrences range considerably, from around $10,000 to more than $100 million, 

but averaging about $3 million (2008$). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-3 

Table 7-2: Dredging of Navigable Waterways Performed or Contracted by USACE, 

1995–2008 

Reported 

Occurrences 

of Dredging 

Percent of 

Total 

Reported 

Dredging 

Occurrences 

Reported 

Sediment 

Dredged 

(millions of 

cubic yards) 

Percent of 

Total 

Reported 

Sediment 

Dredged 

Total 

Reported 

Cost (millions 

of 2008$) 

Percent of 

Total 

Reported 

Cost 

USACE 1,414 42% 1,067.0 41% $3,963 44% 

Contract 1,983 58% 1,548.7 59% $5,124 56% 

Total 3,397 100% 2,615.7 100% $9,086 100% 

Source: Dredging Information System (USACE 2009). 

7.2 Identifying Waterways That Are Dredged, the Frequency of Dredging, and 

the Quantity of Sediment Dredged 

7.2.1 Determining Dredging Job Locations 

Location information is given by latitude–longitude coordinates for some jobs, but for others, location 

information was limited to the name of the job (usually the waterway dredged) and the USACE district 

that performed the job. To identify the nearest reach segment, EPA used an unprojected version of the 

ERF 1.2 (Enhanced Reach File) from USGS. For this analysis, EPA researched latitude–longitude 

coordinates for jobs where they were not provided and linked them to Reach File Version 1.0 (RF1) 

reaches. Each latitude/longitude of interest was matched to the nearest point in the ERF 1.2 universe of 

points using a spherical model of the earth and a standard haversine distance formula. To associate the 

point with an RF1 reach number, EPA used the “RR” (River Reach) attribute from the ERF 1.2 attribute 

table. No reach types were excluded from consideration in the nearest reach calculation. Because these 

coordinates may be inexact, dredging activity was aggregated to the state level for the calculations in this 

analysis and is presented at a regional level. 

7.2.2 Identifying the Baseline Frequency of Dredging 

Continuous sediment deposition in a navigable waterway is likely to require repeated dredging to 

maintain navigability. Between 1995 and 2008, 1,272 sites required dredging on at least one occasion, 

and 551 required dredging more than once. Table 7-3 shows these statistics for each EPA region. The 

number of dredging jobs varies by region, and is likely to be influenced by the size of the region, its 

number of navigable waterways, and their economic importance. Because the data available from USACE 

reflect a relatively short period of time (14 years) and dredging data show a large degree of variability in 

the frequency of dredging occurrences, EPA calculated an average frequency of recurrence for each 

dredging job by dividing 14 years by the number of occurrences of each job over these 14 years.7

 The 

EPA-estimated regional average recurrence intervals range from 9.6 to 14 years, including jobs that 

occurred only once in 14 years. For all dredging jobs in the United States, the average frequency of 

recurrence is about 10 years. Excluding jobs that occurred only once over this period, the regional average 

recurrence interval ranges from 4.4 to 7 years, averaging slightly less than 5 years at the national level. 

 

7

 As described later in this section, EPA used the number of days between dredging occurrences and imposed a minimum 

number of intervening days for a dredging occurrence to be considered unique. Occurrences separated by fewer than this 

minimum number of days were considered as a single occurrence. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-4 

Prior studies (Clark et al. 1985; Ribaudo 1989) of the avoided costs from reduction in sedimentation of 

navigable waterways, shipping channels, and harbors assumed a linear relationship between sediment 

runoff and dredging activity, implying that every ton of sediment entering navigable waterways would 

eventually be dredged up. Because dredging is an expensive and environmentally disruptive activity, it is 

unlikely that every navigable waterway that receives sediment will eventually be dredged. 

For this analysis, EPA assumed that navigable waterways that are regularly dredged would benefit from 

reducing the amount of sediment that needs to be dredged up. Although other waterways may benefit 

from reduced sedimentation, EPA based its estimation of reduced dredging activity based on historic 

dredging data from USACE. For dredging jobs that occurred only once between 1995 and 2008, EPA 

assumed that this job or one similar to it will occur over the same time frame in the future. However, 

these single occurrence jobs are excluded from the low range estimate of the analysis (see Section 7.4.3: 

Sensitivity Analysis). 

Table 7-3: Dredging Jobs and Recurrence Intervals, 1995- 2008 

EPA 

Region Number of Jobs 

Number of 

Recurring 

Jobs 

Average 

Interval 

(years) 

Average Interval 

for Recurring 

Jobs (years) 

1 46 11 11.8 4.6 

2 105 48 9.7 4.7 

3 133 50 10.7 5.3 

4 294 126 10.0 4.6 

5 293 139 9.6 4.7 

6 226 104 9.6 4.4 

7 18 2 13.2 7.0 

8 1 0 14.0 N/A 

9 45 21 9.6 4.5 

10 111 50 9.9 4.9 

Total 1,272 551 10.0 4.7 

Source: Dredging Information System (USACE 2009). 

EPA used the USACE dredging data to identify navigable waterways that are dredged (dredging jobs), as 

well as the frequency at which dredging activity occurs. For each dredging job included in the analysis, 

the Agency identified: 

3⁄4 The number of occurrences of this dredging job 

3⁄4 The time elapsed between occurrences of dredging. 

EPA excluded from this analysis any occurrences where the time elapsed since the last dredging 

occurrence was less than 30 days due to uncertainty as to whether these occurrences were continuations of 

an earlier occurrence of dredging. From this information, EPA established a dredging schedule for each 

recurring dredging job for the next 14 years. For the 721 jobs that only occurred once from 1995 to 2008, 

the number of future dredging jobs is set to one, except in the low estimate, where it is set to zero (see 

Section 7.4.3: Sensitivity Analysis). For other dredging jobs, EPA divided 14 years by the number of 

occurrences of that job between 1995 and 2008 to obtain an average interval of dredging for that job in 

years. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-5 

7.2.3 Identifying the Amount of Sediment Dredged 

For most jobs, the USACE data reports sediment dredged (in cubic yards), but in cases where this data 

was missing, EPA filled in the missing data with percentile values, as described in Section 7.1. In

addition, for occurrences of the same job that were within 30, 90, or 1808

 days of one another, the 

occurrences were consolidated into one occurrence and total amount of sediment dredged was summed. 

The total quantity of sediment dredged for each job (single location) over the past 14 years was divided 

by the number of occurrences of that job to calculate an average quantity of sediment dredged for an 

occurrence of that job. EPA assumed that this quantity of sediment would be dredged each time the job 

occurs in the future under the baseline scenario, and that it would be reduced due to the regulation (see 

Section 7.4.1).

7.3 Estimating the Navigational Maintenance Cost per Cubic Yard of Sediment 

Removed 

Table 7-4 shows the average cost of dredging for each EPA region. Average unit dredging costs vary 

considerably over these regions, from around $1.80 per cubic yard in Region 6 to nearly $11 per cubic 

yard in Region 1. The average unit cost of dredging for the entire United States is $3.40 per cubic yard. 

Table 7-4: Costs of Recurring Dredging, 1995-2006

EPA Region 

Total Cost 

(millions of 2008$)

Total Sediment Removed

(millions of cubic yards)

Average Cost per 

cubic yard 

1 $50.1 4.8 $10.49 

2 $1,057.3 147.0 $7.19 

3 $1,091.7 157.2 $6.95 

4 $3,862.2 705.9 $5.47 

5 $625.7 133.4 $4.69 

6 $3,443.4 1,915.7 $1.80 

7 $48.6 18.6 $2.60 

8 $2.3 0.5 $4.49 

9 $544.2 84.1 $6.47 

10 $717.7 196.7 $3.65 

Total $11,443.1 3,363.9 $3.40 

Source: USACE Dredging Information System (2009). 

Though USACE does not disaggregate its reported dredging costs, the cost of sediment dredging typically 

includes the following components (Sohngen and Rausch 1998): 

3⁄4 The cost of dredging sediment from the bed and loading onto the boat 

3⁄4 The cost of transporting the material to a disposal facility 

3⁄4 The cost of confining or disposing of the dredged sediment. 

 

8

 The threshold for determining whether an occurrence was an extension of the previous occurrence varies between the low 

(180 days), midpoint (90 days), and high (30 days) estimates. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-6 

7.4 Estimating the Total Cost of Navigable Waterway Maintenance Under 

Different Policy Scenarios 

Avoided costs of navigational waterway dredging were estimated by comparing estimated future dredging 

costs under the no policy scenario with estimated future dredging costs under the post-compliance 

scenario. 

7.4.1 Estimating the Reduction in Sediment Dredging in Navigable Waterways Due to 

the Reduction in Discharge from Construction Sites 

EPA estimated changes in sediment deposition in navigable waterways under different regulatory 

scenarios using output from SPARROW. Chapter 6 provides a description of SPARROW as well as input 

data used in this analysis. For the post-compliance scenarios, EPA calculated the percentage change in 

sediment yield from the baseline for each RF1 reach in the SPARROW analysis. The quantity of sediment 

for each dredging job (calculated as described in Section 7.2.3) was multiplied by this percentage change 

in sediment yield to obtain a post-compliance quantity of sediment to be dredged each time a job occurs 

in the next 14 years9

. If an RF1 reach was not assigned to a dredging job due to data limitations or the 

reach assigned to that dredging job was not included in the SPARROW model (such as coastal reaches), 

the state-wide average reduction in sediment yield was applied to that job. 

7.4.2 Estimating the Total Cost of Navigable Waterway Maintenance Under the Baseline 

and Post-Compliance Scenarios 

EPA estimated future dredging costs over a period of 14 years from 2010 to 2023 under both the baseline 

and post-compliance scenarios. For this cost calculation, average cost per cubic yard of sediment dredged, 

total amount of sediment dredged, and the average interval between dredging jobs were calculated for 

each relevant job in the USACE Dredging Information System. For the post-compliance scenarios, total 

amount of sediment dredged was determined as described in Section 7.4.1. 

Because costs will occur whenever the waterbody is dredged rather than on an annual basis, each reach 

will have a unique stream of costs derived from its individual dredging frequency, cost, dredging volume, 

and reduction in sediment. Thus, EPA calculated the present value of dredging costs over the 14-year 

period using a dredging schedule for each dredging job and then discounted the estimated values using 

both 3 and 7 percent annual interest rates. EPA estimated the present value of each dredging job as 

follows: 

3⁄4 Estimating the average recurrence interval for the job over the 14-year period for each job by 

dividing 14 years by its number of occurrences in the USACE dredging data. This produces I

in the equation below. 

3⁄4 Entering this information along with the cost per cubic yard dredged, number of cubic yards 

dredged, and percentage of sediment requiring dredging in the post-compliance scenario into 

the formula below: 

( ) ∑ ( ) =

∗ ⎥

⎦

⎤ ⎢

⎣

⎡

+

∗∗ =

I

n

nI

pcb

d

CRQ PV

/14

1 1 (Eq. 7-1) 

 

9

 EPA estimated future dredging for 14 years to reflect the 14 years of historic data provided by USACE. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-7 

Where: 

PV = Present Value 

Σ is the sum of the expression in brackets for the number of dredging occurrences of this 

dredging job 

I = recurrence interval in years (including fractions of a year) for this dredging job 

14/I = number of periods between recurrence intervals for which costs will be discounted 

Qb = cubic yards dredged under the baseline scenario 

Rpc = percentage of cubic yards remaining in the post-compliance scenario 

C = cost per cubic yards dredged 

d = annual discount rate10

d*I = the discount rate for the period, being the annual rate d prorated for the length of the 

period I in years 

n = the number of the specific dredging occurrence. 

For example, if the Mississippi River is scheduled to be dredged every 2 years between 2010 and 2023, 

there will be no dredging costs for year 1, but the dredging costs in year 2 will be equal to the cost per 

cubic yard multiplied by the amount of sediment dredged, then discounted by 2 years. There will again be 

no dredging costs for year 3. Dredging costs will then be incurred in year 4 as in year 2, but discounted by 

4 years, and so on. By the end of 2023, the Mississippi River will have been dredged seven times. The 

sum of the discounted costs of these six dredging occurrences is the total present value of the dredging 

cost of the Mississippi River for the next 14 years. Repeating this process for every dredging job in this 

analysis yields an estimate of national dredging costs over the next 14 years. 

Alternately, the frequency can be interpreted as occurring at the beginning of each interval rather than at 

the end, so that in the example above, the Mississippi River would be dredged in year 1, then in year 3, 

etc., and discounted accordingly. This approach was taken for EPA’s high estimate (see below). 

7.4.3 Sensitivity Analysis 

To account for uncertainty in projecting future dredging costs, EPA adjusted the assumptions described in 

Section 7.2 to provide a range of benefits estimates. EPA estimated low, midpoint, and high levels of 

dredging activity for this sensitivity analysis as follows: 

Low estimate: 

3⁄4 Assuming a minimum recurrence interval (below which occurrences are assumed to be 

continuations of the previous occurrence of the same job) of 180 days 

 

10 EPA estimated costs using both 3 and 7 percent discount rates, in accordance with OMB (2003, 33-34). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-8 

3⁄4 Assigning 10th percentile cost and cubic yards dredged estimates for jobs lacking these data 

3⁄4 Excluding jobs with only one occurrence over the 14 years from 1995 to 2008. 

Midpoint estimate: 

3⁄4 Assuming a minimum recurrence interval of 90 days to include jobs with more closely spaced 

recurrences 

3⁄4 Using 50th percentile (median) cost and cubic yards dredged estimates for jobs lacking these 

data 

3⁄4 Including jobs with only one occurrence over the 14 years from 1995 to 2008 under the 

assumption that these jobs or similar jobs will occur once over the next 14 years. 

High estimate: 

3⁄4 Assuming a minimum recurrence interval of 30 days 

3⁄4 Using 90th percentile cost and cubic yards dredged estimates for jobs lacking these data 

3⁄4 Including jobs with only one occurrence between 1995 and 2008 under the assumption that 

these jobs or similar jobs will occur once over the next 14 years. 

This analysis also presents costs for all estimates using both 3 and 7 percent discount rates. 

7.4.4 Annualizing Future Dredging Costs 

Though dredging costs will not be incurred at a regular annual rate, EPA annualized its 14-year cost 

projection in order to facilitate the comparison of these costs to other costs calculated for the analysis of 

the regulation that are measured on an annual timeframe. This annualized cost was calculated using the 

same interest rates used to discount future dredging costs. Table 7-5 presents estimated total annualized 

dredging (cubic yards removed) and the estimated costs for navigable waterway dredging for the baseline 

scenario, including low, midpoint, and high estimates. 

Under the baseline scenario, midpoint dredging costs projected over the period of 2010 to 2023 are 

expected to range between $468 and 620 million per year (annualized using a 7 and a 3 percent discount 

rate, respectively). The high and low estimates produce an overall range between $360 million and $655 

million. Regions 4 and 6 are expected to incur the highest costs, as they have the largest percentage of 

dredging activity and amounts of sediment dredged. In the low estimate, Region 8 incurs no dredging 

costs because the sole dredging job recorded in Region 8 in the USACE Dredging Information System 

occurs only once and was thus excluded. Regions 1 and 7 also have considerably lower dredging costs 

than other regions, due to the small number of recorded dredging jobs in these areas. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 7-5: Annualized Dredging Costs Under the Baseline Scenario (millions of 2008$) 

Sediment Dredged 

(millions of cubic yards) 

Cost using 3% discount rate 

(millions of 2008$) 

Cost using 7

EPA (million

Region Low Mid High Low Mid High Low 

1 1.4 3.7 6.6 $1.4 $3.0 $3.6 $1.0 2 84.5 111.5 130.5 $39.4 $54.1 $56.9 $29.8 $3 95.5 124.6 134.8 $47.5 $61.6 $63.2 $36.2 $4 405.5 540.8 575.9 $160.9 $213.2 $221.3 $122.4 $5 85.4 112.7 149.2 $23.6 $34.9 $44.6 $17.9 $6 1,240.5 1,483.3 1,503.8 $148.4 $185.6 $191.5 $112.3 $7 7.4 17.3 18.8 $1.0 $2.5 $2.8 $0.7 8 0.0 0.5 0.5 $0.0 $0.1 $0.1 $0.0 9 60.5 67.7 82.3 $27.6 $31.1 $33.1 $21.1 $

10 112.2 153.5 178.3 $24.7 $34.2 $37.6 $18.9 $

Total 2,093.0 2,615.7 2,780.8 $474.5 $620.4 $654.7 $360.3 $4
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-10 

7.5 Estimating the Avoided Costs from Decreased Dredging of Navigable 

Waterways 

The avoided costs for each post-compliance scenario are calculated as the difference in total annualized 

dredging costs between the baseline and each post-compliance scenario, and are considered the benefits to 

navigation resulting from the regulation. Table 7-6, Table 7-7, Table 7-8, and Table 7-9 present 

annualized avoided costs from reduced dredging of navigable waterway for each of the Agency’s policy 

options, including low, midpoint, and high estimates for cost reductions under each policy option. Each of 

these estimates was calculated using both 3 and 7 percent rates to discount and annualize costs. Because 

the discount rate does not make a significant difference in the overall avoided costs, all values discussed 

are those calculated assuming a 3 percent discount rate. 

Annualized savings from reduced dredging activity range from $1.0 to $3.4 million. EPA estimates that 

Regions 4 and 6 will benefit from the most substantial reductions in dredging costs under all policy 

options. This is due to a large amount of dredging activity in these regions, and a large percentage 

reduction in sediment runoff expected as a result of the regulation. Due to the lack of significant dredging 

activity in Region 8, no noticeable benefits are expected in this region. 

Option 1, which requires non-numeric effluent limitations for all sites, is EPA’s least stringent policy 

option. It is predicted to produce a range of avoided costs between $1.0 and $1.3 million with a midpoint 

estimate of slightly less than $1.3. EPA predicts that this option would prevent 8.5 million cubic yards of 

sediment from entering navigable waters each year. 

Option 2 requires ATS on sites with more than 30 acres disturbed at one time and imposes a 13 NTU 

turbidity standard while requiring non-numeric effluent limitations on all sites and is similar to the option 

EPA proposed previously in 2008. This option will prevent an estimated 17.6 million cubic yards of 

sediment from entering navigable water bodies and requiring dredging. The midpoint estimate for avoided 

costs under this option is $2.6 million per year, ranging from $2.1 to $2.8 million between the low and 

high estimates. 

Option 3, EPA’s most stringent policy option, requires ATS on sites with more than 10 acres disturbed at 

one time, imposes a 13 NTU turbidity standard on these sites, and requires non-numeric effluent 

limitations on all sites. This option would prevent approximately 22.0 million cubic yards of sediment 

from building up in navigable waterways each year. Avoided costs from this action range from $2.7 to 

$3.4 million, with a midpoint of $3.3 million. 

Option 4 requires passive treatment systems on all sites with 10 or more acres disturbed at one time, and 

establishes a numeric turbidity standard of 250 NTU (expressed as a daily maximum value) for sites 

required to implement passive treatment. In addition, all sites will be required to meet non-numeric 

effluent limitations. Avoided costs from Option 4 range between $2.4 and $3.0 million, with a midpoint 

estimate of $2.9 million. The requirements of Option 4 produce larger reductions in dredged sediment 

than those of Option 2, as turbidity treatment is required on more sites. Option 4 is not as effective as 

Option 3 in reducing sediment in navigable waters, since the two options have the same criteria for 

disturbed acres, but Option 4 has a less stringent turbidity standard. The total reduction in sediment 

dredged from navigable waters is expected to be 20.0 million cubic yards under Option 4. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 7-6: Reductions in Dredging and Annualized Avoided Costs Under Option 1 

Reduction in Sediment Dredged 

(thousands of cubic yards) 

Avoided Costs Using 3% Discount Rate 

(thousands of 2008$) 

Avoided Costs Usi

EPA (thousand

Region Low Mid High Low Mid High Low M

11 0.3 1.0 1.3 $0.3 $0.8 $0.9 $0.3 

2 16.4 21.3 24.2 $7.6 $10.4 $10.9 $7.4 3 40.7 51.5 55.0 $20.0 $25.0 $25.6 $19.7 4 871.5 1,001.7 1,058.3 $262.5 $320.0 $329.8 $258.1 5 21.0 31.5 50.9 $8.3 $12.7 $18.3 $8.2 6 5,901.7 6,660.6 6,739.7 $628.8 $739.1 $769.7 $611.5 7 5.2 9.3 10.6 $0.8 $1.6 $1.7 $0.7 8 0.0 0.0 0.0 $0.0 $0.0 $0.0 $0.0 9 56.4 81.6 95.5 $28.7 $41.3 $43.3 $28.2 

10 486.8 614.8 795.1 $69.7 $106.2 $131.8 $68.3 

Total 7,400.0 8,473.3 8,830.6 $1,026.7 $1,257.2 $1,331.9 $1,002.4 $

Table 7-7: Reductions in Dredging and Annualized Avoided Costs Under Option 2 

Reduction in Sediment Dredged 

(thousands of cubic yards) 

Avoided Costs Using 3% Discount Rate 

(thousands of 2008$) 

Avoided Costs Usi

EPA (thousan

Region Low Mid High Low Mid High Low M

1 0.7 2.1 2.7 $0.7 $1.7 $1.9 $0.7 2 33.8 43.8 49.9 $15.6 $21.4 $22.4 $15.2 3 84.5 106.7 114.0 $41.4 $51.9 $53.0 $40.8 4 1,806.7 2,076.3 2,193.3 $543.1 $661.9 $682.2 $534.0 5 43.2 65.0 105.1 $17.1 $26.2 $37.6 $16.8 6 12,309.2 13,891.9 14,056.6 $1,311.4 $1,541.4 $1,605.1 $1,275.4 $17 10.8 19.4 22.0 $1.6 $3.2 $3.6 $1.5 8 0.0 0.1 0.1 $0.0 $0.0 $0.0 $0.0 9 116.9 168.5 197.4 $59.5 $85.4 $89.4 $58.5 

10 990.2 1,251.4 1,618.7 $141.8 $216.1 $268.1 $138.8 

Total 15,395.9 17,625.0 18,359.8 $2,132.1 $2,609.2 $2,763.4 $2,081.7 $2
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 7-8: Reductions in Dredging and Annualized Avoided Costs Under Option 3 

Reduction in Sediment Dredged 

(thousands of cubic yards) 

Avoided Costs Using 3% Discount Rate 

(thousands of 2008$) 

Avoided Costs Usi

EPA (thousan

Region Low Mid High Low Mid High Low M

1 0.9 2.6 3.4 $0.8 $2.1 $2.3 $0.8 2 42.1 54.5 62.1 $19.4 $26.7 $27.9 $18.9 3 105.3 132.9 142.0 $51.6 $64.7 $66.0 $50.8 4 2,251.4 2,587.4 2,733.0 $676.6 $824.5 $849.8 $665.2 5 53.8 81.0 130.8 $21.2 $32.6 $46.9 $20.9 6 15,356.2 17,330.6 17,536.2 $1,636.0 $1,922.9 $2,002.4 $1,591.0 $17 13.5 24.1 27.5 $2.0 $4.0 $4.5 $1.9 8 0.0 0.1 0.1 $0.0 $0.0 $0.0 $0.0 9 145.7 209.8 245.9 $74.2 $106.3 $111.3 $72.9 

10 1,229.5 1,554.0 2,010.3 $176.0 $268.4 $333.0 $172.4 

Total 19,198.2 21,977.0 22,891.2 $2,657.7 $3,252.1 $3,444.1 $2,594.9 $3

Table 7-9: Reductions in Dredging and Annualized Avoided Costs Under Option 4 

Reduction in Sediment Dredged 

(thousands of cubic yards) 

Avoided Costs Using 3% Discount Rate 

(thousands of 2008$) 

Avoided Costs Usi

EPA (thousan

Region Low Mid High Low Mid High Low M

1 0.6 1.7 2.3 $0.6 $1.4 $1.6 $0.6 2 33.7 43.5 49.6 $15.5 $21.3 $22.3 $15.1 3 92.1 115.9 123.8 $45.2 $56.5 $57.7 $44.5 4 1,964.1 2,252.6 2,374.9 $574.7 $699.0 $720.3 $565.1 5 43.4 67.2 107.2 $16.9 $26.1 $37.2 $16.7 6 14,395.8 16,242.8 16,434.8 $1,531.7 $1,800.0 $1,874.4 $1,489.6 $17 12.4 22.3 25.3 $1.8 $3.7 $4.2 $1.8 8 0.0 0.1 0.1 $0.0 $0.0 $0.0 $0.0 9 127.8 174.1 205.7 $65.1 $88.3 $92.6 $64.0 

10 815.4 1,043.3 1,351.7 $116.0 $178.8 $222.5 $113.6 

Total 17,485.3 19,963.5 20,675.5 $2,367.5 $2,875.1 $3,032.8 $2,310.9 $2
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 7-13 

7.6 Sources of Uncertainty and Limitations 

Key limitations of EPA’s analysis of cost savings to drinking water treatment facilities are outlined 

below. The SPARROW model for suspended sediments that was used for estimating TSS concentrations 

in the source waters also has a number of limitations. These limitations are discussed in detail in 

Chapter 6.

3⁄4 The USACE dredging database identifies dredging jobs by name, which is usually the name 

of the waterbody dredged. However, the data lack standardized naming conventions, so it is 

possible that the same waterbody is dredged under different job names. This may result in the 

exclusion of dredging job names that only appear once in the database, but in fact were 

carried out in the same water bodies as a differently named job, which would result in a 

downward bias in EPA’s dredging frequency calculations and the project costs. 

3⁄4 The navigable waterway data provide latitude/longitude information for some dredging jobs, 

which are used to link dredging jobs to RF1 reaches, but these data are incomplete. In cases 

where latitude/longitude information was not available for a particular job, EPA matched it to 

an RF1 reach using the job name. This is a potential source of inaccuracy, as the job name is 

often the waterway name, and may not be very specific (in cases such as the Mississippi or 

Colorado rivers). Additionally, if no RF1 reach could be identified for the dredging job, the 

average reduction for the state in which the job occurred was used. It is unclear whether this 

would lead to an over- or underestimate of benefits. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 8-1 

8 Benefits to Water Storage 

Reservoirs are water impoundments, often manmade, that serve many functions, including providing 

drinking water, flood control, hydropower supply, and recreational opportunities. Sediment in streams can 

be carried into reservoirs, where it may settle and build up layers of silt over time. An increase in 

sedimentation rates will reduce the useful life of a reservoir, unless measures are taken to reclaim some of 

its capacity. Historically, the United States Geological Survey (USGS) has recorded an average of 1.2 

billion kilograms of sediment deposition per reservoir each year (USGS 2007c). 

The regulation is expected to reduce the amount of sediment entering reservoirs and, as a result, the cost 

of reservoir maintenance. Though there are multiple options for sediment mitigation, based on a literature 

review EPA used the avoided cost of reservoir dredging as a measure of the benefits of the regulation to 

water storage. This approach represents an appropriate measure of social benefits for cases in which a 

policy change reduces costs to producers (in this case producers of water supplies for human use), but in 

which price effects are minimal (cf. Boardman et al. 2001, p. 70-74). A description of other amelioration 

measures can be found in Section 8.1. 

The analysis of these benefits includes the following steps: 

3⁄4 Estimating the unit cost of sediment removal from reservoirs 

3⁄4 Estimating the sediment accumulation in reservoirs under the baseline scenario and the postcompliance EPA policy options 

3⁄4 Estimating the cost that would be incurred to dredge this accumulated sediment under each 

scenario and the avoided cost of reservoir dredging expected from this regulation. 

The remainder of this chapter provides details of this analysis. 

8.1 Review of Literature on Reservoir Sedimentation 

Research into reservoir sedimentation was conducted in the 1980s as part of a larger research effort into 

the economic effects of sediment runoff from agricultural land use. As sediment has similar effects 

regardless of its source, many of the insights from these studies will be applicable to sediment discharge 

from construction activities. 

Crowder (1987) estimated that the United States was losing about 0.22 percent of its reservoir capacity 

each year due to sedimentation. Clark et al. (1985) notes that total U.S. reservoir capacity is filling up 

slowly and has enough excess capacity dedicated to hold sediment build-up over hundreds of years. 

However, the study goes on to conclude that while total reservoir sedimentation is manageable, 

sedimentation is far from uniform and that in about 15 percent of U.S. reservoirs, sedimentation rates 

exceeded 3 percent of capacity annually, and in the more extreme cases, 10 percent per year (Clark et al. 

1985). The Reservoir Sedimentation Handbook (Morris and Fan 1997), an engineering guide, details 

several methods of counteracting the effects of sedimentation to maintain reservoir functionality: 

3⁄4 Sediment routing encompasses a group of techniques that seek to allow sediment-laden water 

to pass around or through the reservoir, not allowing the sediment to settle. All of these 

techniques involve timing sediment-laden flows from events such as storms and floods and 

operating the pass-through or bypass mechanism optimally. Pass-through techniques may be 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 8-2 

implemented at existing reservoirs; however, the bypass mechanisms are most cost-effective 

if incorporated into the initial design of the reservoir. SPARROW does not take into account 

any of these techniques in its assumptions about sediment removed from reaches by 

reservoirs, EPA was not able to find any information on either the prevalence or effectiveness 

of these techniques. 

3⁄4 Flushing involves scouring accumulated sediment from the reservoir by partially or fully 

draining the reservoir and allowing the erosive force of the water to carry the sediment 

through the dam and downstream of the reservoir. This does not appear to be a very common 

practice in the United States, as this creates extraordinarily high sediment concentrations 

downstream of the flushed reservoir, sometimes in excess of 1,000,000 mg/L and thus may 

require special environmental permission according to Morris and Fan (1997). Flushing is not 

one-hundred percent effective at removing settled sediment, and the reservoir may still 

require dredging. The process also requires interrupting the service of the reservoir. 

3⁄4 Dredging is a practical and common approach to sediment removal once a reservoir has been 

built. Morris and Fan (1997) note that with the exception of draining a reservoir and 

excavating settled sediment (which is often more expensive and less common than dredging), 

dredging may be the only feasible option for sediment removal in many reservoirs. Dredging 

may be limited by available funds, as well as by the amount of space available to dispose of 

the dredged material. Due to the cost of transporting dredged sediment, a lack of nearby 

disposal sites may limit the amount of sediment that can be removed using dredging. 

In a study focused on the economic costs of reservoir sedimentation due to agricultural runoff, Crowder 

(1987) describes several options for mitigating reservoir sedimentation. These options are: 

3⁄4 Including sediment pools in the initial construction of the reservoir to collect sediment entering 

the reservoir and maintaining these pools to increase the useful life of the reservoir 

3⁄4 Dredging the reservoir 

3⁄4 Replacing the lost capacity by expanding or replacing the reservoir. 

According to Crowder (1987), constructing sediment pools at the outset is more than five times less costly 

than dredging the reservoir. This study estimates that reservoir capacity costs between $564 and $1,316 

(2008$) per acre-foot to construct (regardless of whether it is built as excess capacity for sediment or to 

replace capacity lost from sedimentation), and that dredging costs $4,700 (2008$) per acre-foot of 

material dredged. He estimates that only 20,000 (32.3 million cubic yards) acre-feet of sediment are 

removed from reservoirs each year by dredging, while sediment pools in reservoirs accumulate 1 million 

acre-feet (1.6 billion cubic yards) of sediment per year, and that new capacity would have to account for 

the remaining 620,000 acre-feet (1 billion cubic yards) lost to sediment each year (Crowder 1987). His 

estimate of total economic damages from reservoir sedimentation is $1.3 billion (2008$) per year, based 

on an assumed $94 million (2008$) in annual dredging expenditures and the discounted cost of replacing 

all remaining lost capacity in 10 to 20 years. 

The analysis used by Crowder (1987) assumes that all reservoir sediment that is not dredged or 

accumulated by sediment pools is accounted for with the construction of new capacity. This assumption 

implies that additional reservoir capacity can be built unlimitedly to maintain the useful capacity of 

reservoirs, an assumption that the author acknowledges is unrealistic. Although Crowder (1987) finds that 

building excess storage capacity may be more cost effective than dredging reservoirs, it may not be a 

feasible option in many cases. Moreover, in comparing costs of reservoir dredging and building new 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 8-3 

reservoir capacity, Crowder (1987) does not take into account ecological effects of new reservoir capacity 

construction. The value of ecological services lost due to new reservoir construction may outweigh 

avoided costs compared to reservoir dredging. The study does also not take into account any opportunity 

costs of increasing the amount of land used for water storage. The objective of Crowder (1987) is to 

quantify the impact of sedimentation on water storage reservoirs by assuming that all capacity lost to 

sedimentation that is not currently recovered through dredging will need to be replaced by new capacity. 

While this approach is appropriate for a theoretical estimation of the cost impact of reservoir 

sedimentation, EPA’s objective is to quantify the avoided costs of reduced reservoir sedimentation due to 

reduced construction site stormwater discharges, and thus only takes into account actual measures being 

taken to reduce reservoir sedimentation (i.e., dredging). Therefore, this analysis assumes that excess 

sediment is removed from reservoirs by dredging rather than building new reservoir capacity. 

8.2 Data Sources 

Information on reservoir locations, uses, and historic sedimentation rates is available from the Reservoir 

Sedimentation Survey Information System (RESIS) database (USGS 2007b). Formally known as RESISII, this database was initially developed by the U.S. Department of Agriculture Natural Resources 

Conservation Service, and was provided to EPA by USGS. 

The database includes 4,227 sedimentation survey results from 1,819 reservoirs located across the 

coterminous United States. Results from 1775 to 1992 are represented, with the majority of measurements 

covering the mid-20th century and on. RESIS does not include all U.S. reservoirs, nor does it contain 

information about dredging activity in reservoirs or about any other methods for recapturing lost water 

storage capacity. 

Table 8-1 details sediment deposition per year recorded by RESIS in each EPA region. More than 1.2 

billion kilograms of sediment are deposited in the average U.S. reservoir each year. The most sediment 

deposition is recorded in Region 6 with 6.6 billion kilograms per reservoir per year, almost four times 

more than the national average. Region 9 also reports large masses of sediment deposition each year of 

1.8 billion kilograms per reservoir, which is still greater than the national average. Other regions have 

average annual deposition per reservoir ranging from 800,000 kilograms in Region 1 to 440 million in 

Region 4. 

Table 8-1: Average Annual Sedimentation Deposition 

Recorded by RESIS

EPA 

Region 

Number of 

Reservoirs 

Average Annual Sediment Deposition 

per Reservoir (million kg/year) 

1 9 0.8 

2 18 25.1 

3 81 71.5 

4 168 440.4 

5 272 25.9 

6 338 6,568.9 

7 329 57.2 

8 208 436.0 

9 285 1,817.6 

10 110 67.2 

Total 1,818 1,234.0 

Source: RESIS (USGS 2007b). 
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November 2009 8-4 

Changes in sediment attenuation in reservoirs will be predicted by the SPARROW model, the details of 

which can be found in Chapter 6. 

8.3 Estimating the Unit Cost of Sediment Removal from Reservoirs 

Because RESIS does not provide information on reservoir dredging, EPA assumed for the purpose of this 

analysis that the average unit cost of dredging sediment from a navigable waterway is approximately 

equal to the average unit cost of dredging sediment from a reservoir. Crowder (1987) does provide an 

estimate of reservoir dredging cost per unit of sediment dredged, but gives no empirical basis for this 

estimate. Because the USACE dredging database provides costs and quantity dredged for more than 3,300 

dredging jobs in the past 12 years, this unit cost of dredging has more robust empirical foundations. EPA 

does not expect there to be any significant difference between the costs of reservoir dredging and the 

costs of navigable waterway dredging, as all of the variable costs for dredging are related to the quantity 

of sediment dredged. Morris and Fan (1997) cite a unit cost of $4.45 (2008$) per cubic yard for sediment 

dredging at Lake Springfield, Illinois, which is quite close to the $4.41 average cost per cubic yard for 

EPA Region 5 in the navigational dredging data. Section 7.3 provides details on estimating the unit cost 

of navigable waterway dredging that will be used as a surrogate for the unit cost of reservoir dredging. 

This analysis will use average costs at the regional level. 

8.4 Estimating the Total Cost of Reservoir Dredging Under Different Policy 

Options 

8.4.1 Estimating Sediment Accumulation in Reservoirs and the Amount of Sediment 

Expected To Be Dredged 

EPA estimated the annual sediment attenuation rate in each reservoir under the baseline and the three 

policy options using output data from the SPARROW model. Chapter 6 provides a description of the 

model. This analysis uses data output from SPARROW that predicts the amount of sediment (in 

kilograms) settling in reservoirs in a reach. To estimate the amount of sediment flowing into and settling 

in reservoirs in each region, EPA summed the values in this field under the baseline scenario and each 

policy option.11 The amount of sediment settling in reservoirs under each scenario forms the basis for the 

estimation of the quantity of sediment dredged from reservoirs and the costs of this dredging. 

For the purposes of this analysis, EPA assumed, consistent with Crowder (1987), that all sediment 

entering reservoirs must be removed in order to maintain the current water storage capacity in the United 

States. The frequency of dredging is highly site-specific, depending on many factors including the 

average sediment concentration of the river or stream, the size of the reservoir and excess storage 

capacity, and any sediment routing practices. For this analysis, EPA chose a general frequency of 

reservoir dredging based on information presented by the USACE in a Final Dredged Material 

Management Plan and Environmental Impact Statement for reservoirs in Washington (USACE 2002). 

This report states that “dredging cycles may vary from 2 to 10 years” (USACE 2002, p. 66). EPA used 

these frequencies as high and low estimates, and employed their arithmetic mean of 6 years for a midpoint 

 

11 EPA conducted a preliminary analysis of baseline costs including and excluding outliers, and found that the exclusion of 

outliers does not have a significant effect on the total cost of reservoir dredging. The values used for this analysis include 

reservoir sedimentation for all reservoirs on reaches modeled by SPARROW. 
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November 2009 8-5 

estimate. This approach provides a range of benefits estimates to account for uncertainty in the frequency 

of reservoir dredging. 

8.4.2 Estimating the Total Cost of Reservoir Dredging Under the Baseline Scenario and 

Policy Options 

The cost of dredging the sediment settling out in reservoirs is estimated by using the average dredging 

cost per region as described in Section 8.3. Because this cost is given per cubic yard, the sediment 

attenuation in reservoirs given by SPARROW will be converted from kilograms to cubic yards using a 

sediment density of 1.5 g/cm3 (Hargrove 2007). This translates to a conversion of 1,147 kilograms per 

cubic yard. The equation below summarizes the calculation of costs for one cycle of dredging before 

discounting and annualization. 

[ ] [ ] ∑ [ ] =

⎥

⎦

⎤ ⎢

⎣

⎡ ⎟ ∗

⎠

⎞ ⎜

⎝

⎛ ∗=

10

R 1 147,1 R

R

reservoirs C S TC I (Eq. 8-1) 

Where: 

TCreservoirs = total cost of dredging all sediment settling in reservoirs in all regions 

R = region number 

I = the assumed interval in years of reservoir dredging; varied between 2, 6, and 10 

SR = sum of all reservoir sediment settlement predicted by SPARROW in a region in 

kilograms 

1,147 = kilograms in a cubic yard 

CR = regional average of historic dredging job cost per cubic yard, 1995-2006 

The resulting costs were discounted and annualized over the assumed interval using both 3 and 7 percent 

discount rates, in accordance with OMB Circular A-4 (OMB 2003). 

Table 8-2 presents the total amount of sediment that is estimated to be dredged per year and the estimated 

cost of this dredging under the baseline scenario, including low, midpoint, and high estimates. The 445 

million cubic yards predicted by SPARROW to settle annually in reservoirs is expected to cost between 

$1.5 and $1.7 billion to dredge under the baseline scenario. Region 8 has the highest dredging costs, and 

though less sediment is predicted to accumulate in Region 8 reservoirs than in those in Region 6, the 

former has a much higher unit cost of dredging. Regions 1, 2, and 3 are all predicted to accumulate less 

than 6 million cubic yards of sediment per year each, and accordingly are estimated to have lower overall 

dredging costs. For other regions, midpoint estimates of dredging costs range between $50 and $526 

million. 
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November 2009 8-6 

Table 8-2: Estimated Cost of Reservoir Dredging Under the Baseline Scenario 

EPA Estimated Cost (millions of 2008$) 

Region Sediment Dredged (yd3) Low Mid High 

1 1,278,280 $11.7 $12.4 $13.2 

2 2,725,285 $17.1 $18.2 $19.3 

3 5,337,766 $32.3 $34.4 $36.5 

4 45,666,958 $218.0 $231.8 $246.2 

5 35,940,396 $147.1 $156.4 $166.2 

6 128,381,390 $201.3 $214.0 $227.3 

7 42,237,651 $96.0 $102.1 $108.4 

8 126,434,846 $495.0 $526.4 $559.1 

9 41,859,743 $236.2 $251.2 $266.8 

10 14,743,876 $46.9 $49.9 $53.0 

Total 444,606,190 $1,501.6 $1,596.8 $1,696.0 

8.5 Estimating the Avoided costs from Reduced Reservoir Dredging 

The difference between the anticipated dredging costs under the baseline and a particular policy option 

represents the avoided costs of that policy option. Table 8-3, Table 8-4, Table 8-5, and Table 8-6 present 

reductions in sedimentation and subsequent avoided costs from reduced reservoir dredging for each 

policy option, including low, midpoint, and high estimates under these options. Because the range of 

estimates is relatively small between the low and high estimates, the values presented below are midpoint 

estimates unless otherwise stated. Benefits were estimated assuming both 3 and 7 percent discount rates, 

consistent with OMB Circular A-4 (OMB 2003). 

Avoided costs from a reduction in reservoir sedimentation range from $1,072 (Option 1, low estimate, 7 

percent discount rate) to $3.6 million (Option 3, high estimate, 3 percent discount rate), depending on the 

policy option, the assumed frequency of reservoir dredging, and the discount rate. The largest savings are 

predicted in Region 6 under all options, as SPARROW predicts the largest overall reductions in sediment 

accumulation in this region. Region 4 is also expected to benefit substantially relative to other regions due 

to both large reductions in construction discharges in this region and a relatively high unit cost of 

dredging (estimated from USACE data). 

Option 1, which requires non-numeric effluent limitations for all sites, is EPA’s least stringent policy 

option. This option is expected to reduce reservoir sedimentation by about 560 thousand cubic yards 

nationally every year, which is estimated to save between $1.2 (7 percent discount rate) and $1.4 million 

(3 percent discount rate) in dredging costs per year. 

Option 2 requires ATS on sites with more than 30 acres disturbed at one time and imposes a 13 NTU 

turbidity standard while requiring non-numeric effluent limitations on all sites and is similar to the option 

EPA proposed previously. This action is estimated to prevent about 1.2 million cubic yards of sediment 

from building up in reservoirs each year saving between $2.6 (at a 7 percent discount rate) and $2.9 (at a 

3 percent discount rate) million in dredging costs per year. 

Option 3, EPA’s most stringent policy option, requires ATS on sites with more than 10 acres disturbed at 

one time, imposes a 13 NTU turbidity standard on these sites, and requires non-numeric effluent 

limitations on all sites. Its reduction in sediment deposition in reservoirs is estimated to be 1.5 million 

cubic yards annually. The estimated avoided costs of reduced dredging are between $3.2 and $3.6 million. 
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November 2009 8-7 

Option 4 requires passive treatment systems on all sites with 10 or more acres disturbed at one time, and 

establishes a numeric turbidity standard of 250 NTU (expressed as a daily maximum value) for sites 

required to implement passive treatment. In addition, all sites will be required to meet non-numeric 

effluent limitations. Because Option 4 requires passive rather than active treatment of sediment and has a 

higher turbidity standard, the estimated reductions in reservoir sediment buildup are lower than those 

from Option 3. However, the estimated reductions from Option 4 are still greater than those expected 

from Option 2, as that option requires treatment on fewer sites. Expected avoided costs from Option 4 are 

estimated to be $2.9 million assuming a 7 percent discount rate, and $3.2 million assuming a 3 percent 

rate. Regions 4 and 6 together account for more than half of the monetized benefits of reduced reservoir 

sedimentation, due to the expected reductions in construction discharges in these regions. Option 4 is 

estimated to reduce reservoir sedimentation by 1.3 million cubic yards annually. 

Table 8-3: Reduction in Reservoir Dredging and Avoided Costs Under Option 1 

Avoided Costs (thousands of 2008$) 

EPA 3% Discount Rate 7% Discount Rate 

Region 

Annual 

Reduction in 

Sediment yd3) Low Mid High Low Mid High 

1 1,423 $13.0 $13.8 $14.7 $10.8 $12.5 $14.4 

2 535 $3.4 $3.6 $3.8 $2.8 $3.2 $3.7 

3 2,554 $15.5 $16.5 $17.5 $12.8 $14.9 $17.1 

4 98,832 $471.7 $501.6 $532.8 $391.4 $453.6 $522.5 

5 7,878 $32.2 $34.3 $36.4 $26.8 $31.0 $35.7 

6 408,507 $640.5 $681.1 $723.4 $531.4 $615.9 $709.4 

7 24,768 $56.3 $59.8 $63.6 $46.7 $54.1 $62.3 

8 2,165 $8.5 $9.0 $9.6 $7.0 $8.2 $9.4 

9 4,833 $27.3 $29.0 $30.8 $22.6 $26.2 $30.2 

10 7,293 $23.2 $24.7 $26.2 $19.3 $22.3 $25.7 

Total 558,788 $1,291.5 $1,373.4 $1,458.7 $1,071.6 $1,241.9 $1,430.5 

Table 8-4: Reduction in Reservoir Dredging and Avoided Costs Under Option 2 

 Avoided Costs (thousand 2008$) 

EPA 3% Discount Rate 7% Discount Rate 

Region 

Annual 

Reduction in 

Sediment (yd3) Low Mid High Low Mid High 

1 2,876 $26.3 $28.0 $29.7 $21.8 $25.3 $29.1 

2 1,096 $6.9 $7.3 $7.8 $5.7 $6.6 $7.6 

3 5,278 $32.0 $34.0 $36.1 $26.5 $30.7 $35.4 

4 205,148 $979.1 $1,041.2 $1,105.8 $812.4 $941.5 $1,084.5 

5 16,292 $66.7 $70.9 $75.3 $55.3 $64.1 $73.9 

6 851,211 $1,334.6 $1,419.2 $1,507.4 $1,107.4 $1,283.3 $1,478.2 

7 51,568 $117.2 $124.6 $132.3 $97.2 $112.7 $129.8 

8 4,476 $17.5 $18.6 $19.8 $14.5 $16.8 $19.4 

9 9,992 $56.4 $60.0 $63.7 $46.8 $54.2 $62.5 

10 14,804 $47.1 $50.1 $53.2 $39.1 $45.3 $52.2 

Total 1,162,741 $2,683.8 $2,853.8 $3,031.2 $2,226.8 $2,580.6 $2,972.6 
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November 2009 8-8 

Table 8-5: Reduction in Reservoir Dredging and Avoided Costs Under Option 3 

 Avoided Costs (thousand 2008$) 

EPA 3% Discount Rate 7% Discount Rate 

Region 

Annual 

Reduction in 

Sediment (yd3) Low Mid High Low Mid High 

1 3,567 $32.6 $34.7 $36.9 $27.1 $31.4 $36.1 

2 1,363 $8.5 $9.1 $9.7 $7.1 $8.2 $9.5 

3 6,573 $39.8 $42.3 $45.0 $33.0 $38.3 $44.1 

4 255,706 $1,220.4 $1,297.7 $1,378.4 $1,012.6 $1,173.5 $1,351.7 

5 20,293 $83.1 $88.3 $93.8 $68.9 $79.9 $92.0 

6 1,061,735 $1,664.7 $1,770.2 $1,880.2 $1,381.2 $1,600.7 $1,843.8 

7 64,312 $146.1 $155.4 $165.0 $121.2 $140.5 $161.9 

8 5,575 $21.8 $23.2 $24.7 $18.1 $21.0 $24.2 

9 12,446 $70.2 $74.7 $79.3 $58.3 $67.5 $77.8 

10 18,375 $58.5 $62.2 $66.1 $48.5 $56.2 $64.8 

Total 1,449,945 $3,345.8 $3,557.9 $3,778.9 $2,776.1 $3,217.2 $3,705.9 

Table 8-6: Reduction in Reservoir Dredging and Avoided Costs Under Option 4 

 Avoided Costs (thousand 2008$) 

EPA 3% Discount Rate 7% Discount Rate 

Region 

Annual 

Reduction in 

Sediment (yd3) Low Mid High Low Mid High 

1 2,126 $19.4 $20.7 $22.0 $16.1 $18.7 $21.5 

2 1,022 $6.4 $6.8 $7.2 $5.3 $6.2 $7.1 

3 5,497 $33.3 $35.4 $37.6 $27.6 $32.0 $36.9 

4 226,512 $1,081.1 $1,149.6 $1,221.0 $897.0 $1,039.5 $1,197.4 

5 17,172 $70.3 $74.7 $79.4 $58.3 $67.6 $77.9 

6 984,437 $1,543.5 $1,641.3 $1,743.3 $1,280.7 $1,484.2 $1,709.6 

7 59,072 $134.2 $142.7 $151.6 $111.4 $129.1 $148.7 

8 4,697 $18.4 $19.6 $20.8 $15.3 $17.7 $20.4 

9 10,505 $59.3 $63.0 $67.0 $49.2 $57.0 $65.7 

10 11,828 $37.6 $40.0 $42.5 $31.2 $36.2 $41.7 

Total 1,322,867 $3,003.6 $3,193.9 $3,392.4 $2,492.1 $2,888.1 $3,326.8 

8.6 Sources of Uncertainty and Limitations 

Key limitations of EPA’s analysis of avoided costs to drinking water treatment facilities are outlined 

below. The SPARROW model for suspended sediments that was used for estimating TSS concentrations 

in the source waters also has a number of limitations. These limitations are discussed in detail in 

Chapter 6.

3⁄4 The reliance of the SPARROW model on the RF1 network is a significant limitation because the 

reservoirs located off the RF1 network are omitted from this analysis. This omission is likely to 

bias the estimated savings from reduced reservoir dredging downward. 

3⁄4 There is uncertainty as to the uniformity of sediment density because it is related to the type of 

soil in the area. Using a single density to convert volume to weight for all sediment may reduce 

the accuracy of the resulting cost estimates. However, the direction of this potential bias is 

uncertain. 
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November 2009 8-9 

3⁄4 The lack of data on reservoir dredging results in significant uncertainty as to the types of 

reservoirs that are dredged and the cost of this dredging. The appropriateness of the avoided cost 

approach is conditional upon the estimates representing actual dredging costs that are no longer 

imposed on reservoirs. If typical reservoirs do not reduce dredging, and hence costs, but rather 

undertake other actions in response to reduced sediment loads, then actual benefits may diverge 

from estimates provided above. 

3⁄4 Neither RESIS nor the SPARROW model take into account sediment build-up in natural water 

storage facilities such as glacial lakes and ponds, so any activity to mitigate sedimentation of 

these areas is not included in this analysis, resulting in a potential downward bias of this estimate. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 9-1 

9 Benefits to Drinking Water Treatment 

Drinking water treatment plants provide the essential services of removing contaminants from water taken 

from surface and ground sources, making the water potable. Sediment is one of the contaminants that 

must be removed from drinking water before it can be consumed, so a reduction in sediment levels in 

water taken up by drinking water treatment plants is expected to result in lower treatment costs for these 

plants. This section describes the methodology EPA used to estimate the reduction in drinking water 

treatment costs that should result from a reduction of sediment discharge from construction sites. This 

approach represents an appropriate measure of social benefits for cases in which a policy change reduces 

costs to producers (in this case producers of drinking water), but in which price effects are minimal (cf. 

Boardman et al. 2001, p. 70-74). 

9.1 Construction Discharge Effects on Drinking Water Treatment Costs 

9.1.1 Sediment 

The primary concern of sediment in terms of drinking water treatment is the turbidity that results from 

suspended sediment in the water column. As turbidity increases in drinking water influent, the variable 

costs of water treatment to mitigate this turbidity increase. 

The variable costs associated with treating drinking water for turbidity are twofold: 

3⁄4 Adding chemicals coagulants that bond to the sediment particles and help them sink out of 

the water column, and 

3⁄4 Disposing of the sludge which results from this treatment. 

Several attempts have been made in the past two decades to isolate these elements of drinking water 

treatment costs: 

3⁄4 Forster et al. (1987) estimated $172 (2008$) per million gallons for drinking water sediment 

treatment, while Holmes (1988) performed several estimates, with ranges between $7.45 and 

$212 (2008$) per million gallons. It is unclear in these studies what elements were included 

in these estimates. 

3⁄4 Moore and McCarl (1987) estimated a total of $41 (2008$) per million gallons to treat water 

and dispose of the sludge for drinking water treatment plants in Oregon. 

3⁄4 Dearmont et al. (1998) put the chemical costs of turbidity treatment alone at $96 (2008$) per 

million gallons for Texas drinking water. 

This broad range of cost estimates for sediment and turbidity treatment may be due to the fact that the 

studies measure costs per million gallons of water, without respect to the specific sediment levels in 

drinking water influent. 

9.1.2 Nutrients 

In addition to removing sediment from drinking water, treatment plants must also meet standards for 

certain nutrients. Nitrogen and phosphorus are the two primary nutrients of concern in construction 
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November 2009 9-2 

discharges. While there is no EPA standard for phosphorus levels in drinking water, nitrogen is regulated 

by EPA with maximum contaminant levels (MCLs) of 10 mg/L for nitrate and 1 mg/L for nitrite; total 

nitrogen (nitrate + nitrite) is limited to 10 mg/L (USEPA undated). DeZuane (1997) cites EPA surface 

water surveys that found that only 1.2 percent of surveyed supplies had total nitrogen concentrations 

exceeding 10 mg/L. 

EPA recommends ion exchange, reverse osmosis, and electrodialysis to remove nitrogen from source 

water (USEPA undated). Reverse osmosis and electrodialysis are systems with operating costs related to 

the volume of water treated rather than the amount of contaminants, while ion exchange does have the 

variable cost of the ion exchange resin to which the contaminant bonds. Therefore, drinking water 

treatment facilities that use reverse osmosis and electrodialysis incur no additional cost associated with 

nitrogen removal. Although facilities that use ion exchange may incur operating costs associated with 

nitrogen removal, the process is also used to remove many other contaminants, and estimating the fraction 

of variable cost attributable to nitrogen removal is not feasible within the national-level analysis. 

Benefits from reduced nitrogen loadings to drinking water source reaches would only be realized if their 

nitrogen loadings are above the MCL for nitrate and nitrite. Table 9-1 presents total nitrogen (TN) 

concentrations as predicted by SPARROW for the reaches in the drinking water treatment benefits 

analysis. As shown, the top 4 percent of these reaches are predicted to have concentrations above the 10 

mg/L threshold. However, because EPA’s methodology treats values above the 95th percentile as outliers, 

benefits for these reaches would not be able to be monetized. 

Table 9-1: TN Concentrations Predicted by SPARROW for Reaches 

Serving as Drinking Water Sources1

Distribution of TN Concentrations TN Concentration (mg/L) 

Minimum 0.0 

25th percentile 0.9 

50th percentile 1.3 

75th percentile 2.4 

95th percentile 8.0 

96th percentile 10.0 

Maximum 2,503.7 

Average 4.8 

1

 Only includes reaches that are both modeled by SPARROW and included in the federal drinking water 

database SDWIS, see Section 9.5 for details. 

9.1.3 Increase in Primary Productivity and Associated Water Quality Impacts from 

Nutrient Discharges (Eutrophication) 

In addition to the meeting the nitrogen standards discussed above, indirect construction-related nutrient 

impacts can result in the development of harmful algal blooms (HABs) in surface drinking water 

reservoirs. These algal blooms, particularly those comprised of blue-green algae (Cyanobacteria), can 

pose potential health threats and aesthetic problems for the reservoir water’s potability. These include the 

production of taste and odor compounds, production of cyanotoxins, and the potential for increase 

disinfection by-product formation. 

Cyanobacteria and actinomycetes produce the organic compound geosmin, which is typically released on 

the death or lysis of the cell. This compound is responsible for the muddy smell and unpleasant taste in 

drinking water that originates from surface water bodies experiencing algal blooms. The human sensory 
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November 2009 9-3 

system is highly sensitive to geosmin. Studies show that the nose can detect this compound at 

concentrations as low as 5 parts per trillion and that taste can detect geosmin at 0.7 parts per billion. The 

second taste and odor compound found in drinking water is 2-methylisoborneol (or MIB). It is also 

produced by blue-green algae and detected by humans at very low levels of concentration. Together, these 

two compounds are responsible for the taste and odor associated with drinking water treatment, which can 

be quite strong in the presence of algal bloom. Taste and odor problems associated with drinking water 

are a pervasive and significant problem for many municipalities. In 1996, a survey of 377 American and 

Canadian water utilities conducted by the American Water Works Association reports that 43 percent of 

the utilities had a taste and odor problem lasting more than one week and 16 percent consider their 

drinking water taste and odor problem to be serious (Suffet et al. 1996).

Large concentrations of cyanobacteria can also be problematic because of the harmful toxins they can 

produce. Cyanobacterial toxins can be either found inside of the cell, “intracellular,” or outside the cell, 

“extracellular”. The cyanotoxins can cause adverse impacts including death not only to aquatic organisms 

that come in direct contact with them but also to livestock, domestic animals, waterfowl, and in some 

cases to humans (WHO 1999). These toxins belong to a very diverse group of chemical substances that 

can be classified according to their biological effects on the systems and organs that they affect most 

strongly including: hepatotoxins, neurotoxins, cytotoxins, irritants and gastrointestinal toxins, and other 

cyanotoxins whose toxicological or ecotoxicological profile is still only partially known (Funari and 

Testai 2008). 

Additionally, eutrophication can lead to an increase in organic carbon (both particulate and dissolved) in 

the waterbody. This additional carbon can result in the increased formation of trihalomethanes, a 

dangerous by-product arising from disinfection of the waterbody with a halogen-containing compound. 

The chlorine or bromine in the disinfectant reacts with organic compounds in the water to form these trisubstituted methanes that are considered environmental pollutants and are often carcinogenic to humans. 

Currently, the EPA has set the limit of this total trihalomethanes at 80 ppb in treated water (USEPA 

1998). 

Common water treatment methods include filtration, softening, reverse osmosis, chlorination, and 

distillation (Parsons and Jefferson 2006). Other, more specialized treatments include using powdered 

activated carbon (PAC) to absorb pollutants, and ozonation. Using PAC treatment requires activated 

carbon to be added to an aerobic or anaerobic treatment system where it serves as the “buffer” against 

toxics in the wastewater. To treat geosmin and MIB with this method typically requires large doses of 

activated carbon, and thus can be impractical, particularly for large facilities. Ozonation uses radical 

formation to eliminate organic particles via coagulation or chemical oxidation. However, like PAC 

treatment, this method is expensive especially for large treatment facilities; it also can add harmful biproducts to the water. Ozonation has stronger germicidal properties than chlorination however because it 

rapidly reacts with bacteria, viruses, and protozoans. 

The city of Waco, Texas is constructing a water clarification facility as part of the second phase of the 

Water Quality and Quantity project as a result of a comprehensive five-year study of Lake Waco which 

identified a significant cyanobacteria population that was dominating the lake as a result of nutrient 

loading from the watershed. This abundance of blue-green algae further exacerbated the pre-existing taste 

and odor issues that the reservoir already faced. This new facility, with a construction cost of $40 million 

dollars, proposes to improve the taste of the finished water by implementing Dissolved Air Flotation 

(DAF) technology (Waco Water Utilities Services 2009). DAF works by attaching air bubbles to particles 

suspended in water and floating them to the surface for tank collection. This technology has proven 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 9-4 

successful in other systems and is particularly effective in waters with a significant amount of lightweight 

particles (such as algae). 

The effectiveness of a specific drinking water treatment for the removal of cyanobacterial toxins depends 

on the total concentration of the algal toxins, the form of the toxin, and whether it is intracellular or 

extracellular (Westrick 2008). Intracellular cyanotoxins can be removed by conventional treatment and 

membrane filtration. Extracellular cyanobacterial toxins are more costly to remove because conventional 

treatment (e.g., flocculation, coagulation, sedimentation and filtration) is usually not effective and 

advanced treatment processes must be implemented unless the contaminant is oxidized through 

disinfection (Westrick 2008). 

Due to data limitations, EPA was unable to estimate changes in drinking water treatment costs associated 

with improvements in taste and odor of source waters resulting from reduced sediment and nutrient 

discharges from construction sites. 

9.2 Avoided Costs of Drinking Water Treatment from Reducing Sediment 

Discharge from Construction Sites 

This section describes the estimation of the total expenditures to remove sediments from drinking water 

and the avoided costs expected with the reduction of sediment discharge anticipated from the regulation. 

It involves the following steps: 

3⁄4 Identifying RF1 reaches modeled by SPARROW that are sources for drinking water 

treatment plants 

3⁄4 Determining TSS reductions in these reaches 

3⁄4 Estimating the chemical cost of treating the turbidity caused by TSS in these reaches 

3⁄4 Estimating the cost of disposing of the sludge generated from this turbidity treatment 

3⁄4 Estimating the total costs of drinking water treatment 

3⁄4 Estimating the total avoided costs between the baseline and post-compliance scenarios. 

Error! Reference source not found. outlines these steps in more detail. The ovals at the top represent 

primary data sources. The gray boxes contain calculations or conversions, and the white boxes contain the 

results of these calculations or conversions that serve as input for the next step in the calculation. The lefthand side of the diagram shows the calculation of the chemical cost of treating turbidity, while the righthand side shows the calculation of the cost of sludge disposal. Finally, the pale yellow boxes at the 

bottom of the figure represent the costs under baseline and post-compliance scenarios and the difference 

between them. 

Sediment concentrations and drinking water influent volumes were estimated for each surface drinking 

water intake in the United States. EPA calculated the costs of treatment chemicals and sludge disposal by 

surface drinking water intake; the specific steps are described in more detail in the following sections. 

Costs under the baseline and post-compliance scenarios were compared, and the difference represents the 

national avoided costs. To address uncertainty in its assumptions, EPA conducted a sensitivity analysis 

that varies the assumptions described in the following sections, and the results of the following analyses 

present low, midpoint, and high benefits estimates (see Section 9.8 for details). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 9-5 

Figure 9-1: Steps in Calculating the Cost of Treating Turbidity in Drinking Water 

Alum dose—Turbidity relationship 

TSS—Turbidity conversion 

Alum unit cost 

Chemical usage under baseline and postcompliance scenarios 

Reduction in total drinking water treatment costs by RF1 

reach from baseline to post-compliance scenarios 

Distance to disposal facility 

Sludge generation equation 

Probability of off-site disposal 

Influent turbidity under baseline and post 

compliance scenarios 

Sludge generation under baseline and postcompliance scenarios 

Sludge disposal under baseline and postcompliance scenarios 

Chemical costs under baseline and postcompliance scenarios 

Sludge disposal costs under baseline and 

post-compliance scenarios 

Total treatment costs by RF1 reach under 

baseline scenario 

Total treatment costs by RF1 reach under 

post-compliance scenario 

SPARROW results: TSS 

concentrations by RF1 

reach under baseline and 

post-compliance scenarios 

SDWIS: Drinking water 

influent volumes by RF1 

reach
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November 2009 9-6 

9.3 Data Sources 

Information on drinking water intake points and volumes were provided by EPA’s Safe Drinking Water 

Information System (SDWIS), Federal Version. The federal version of SDWIS stores the information on 

approximately 160,000 public water systems used to monitor the quality and safety of drinking water 

(USEPA 2006b). For this analysis, EPA was only interested in surface water intakes, as these are the only 

type that would be affected by discharge from construction sites. Location information and average daily 

flow were estimated from the latitude-longitude and population data provided by SDWIS, respectively. 

EPA obtained data on the chemicals used to treat turbidity based on data from the American Society of 

Civil Engineers (ASCE) and the American Water Works Association (AWWA). These data also contain a 

formula for the calculation of sludge disposal costs (ASCE/AWWA 2005). 

Estimations of coagulant dosing for various turbidity levels were taken from research reports by Cornell 

University’s AguaClara Project. This project focuses on improving drinking water quality through 

engineering research and offers calculations and other engineering information for both researchers and 

professionals in the field (Menéndez et al. 2007). 

The costs of alum and alternative coagulant inputs were obtained by EPA for the Long Term 2 Enhanced 

Surface Water Treatment Rule. The Technologies & Costs Document for this rule included research on 

the expenses of public water systems and potential changes resulting from the rule (USEPA 2005c). For 

this purpose, many details of the costs of drinking water treatment were obtained. This document reports 

cost data in 1998 dollars, so its data are be adjusted to 2008 dollars. 

The equation used to estimate the volume of sludge generated from turbidity and TSS treatment comes 

from the EPA document Identification and Characterization of Typical Drinking Water Treatment 

Residuals (USEPA 2007a), which examines the byproducts of drinking water purification from different 

methods of treatment. 

Calculations of the percentage of facilities that dispose of any of their residual sludge off-site and of the 

percentage of total residual sludge generated that is disposed of off-site are obtained from the Drinking 

Water Treatment Plant Master Survey Response Database (henceforth the Drinking Water Survey 

Database) prepared by EPA for the Drinking Water Effluent Limitation Guidelines rule (USEPA 2007b). 

9.4 Modeling Sediment Concentrations and Reductions 

EPA modeled sediment concentrations in RF1 stream reaches under both the current and final scenarios 

using the SPARROW model. A detailed description of this model can be found in Chapter 6. 

Drinking water treatment facilities located on waterbodies with typically higher levels of TSS and 

turbidity may have systems in place that reduce total suspended solids and turbidity levels in influent 

water before it is chemically treated. EPA assumed, as per Ngo and Nichols (1998), that facilities with 

baseline TSS concentrations exceeding 1,000 mg/L would employ a process such as plain sedimentation, 

during which influent water is allowed to settle for extended periods of days or weeks. The effectiveness 

of this process depends on the size of the sedimentation basin and the amount of time the water is left to 

settle. Studies by Yao (1973) and DeZuane (1997) suggest that the effectiveness of plain sedimentation 

ranges from 30 and 90 percent with a midpoint estimate of 60 percent. Thus, EPA varied the effectiveness 
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November 2009 9-7 

of plain sedimentation basins between 90, 60, and 30 percent TSS removal for the low, mid, and high 

benefit estimates, respectively.12 

This reduction is applied to influent water TSS concentrations at facilities that are estimated to have 

influent water with TSS concentrations exceeding 1,000 mg/L in the baseline. EPA deems this approach 

reasonable, as it brings treatment costs within the range of values published in existing literature 

presented in Section 9.1. 

9.5 Identifying Reaches with Drinking Water Intakes and Their Intake Volumes 

To estimate the avoided costs of drinking water treatment resulting from the regulation, EPA used the 

drinking water intake database compiled by USGS. The database provides information on public water 

systems, including intakes, treatment plants, sampling sites, distribution systems, wells, and consecutive 

connections from EPA’s SDWIS database. Each drinking water intake is matched to the RF1 reach and 

has the corresponding “USGS River Reach Number.” The database, however, does not provide 

information on the intake volume. For this analysis, EPA approximated each intake volume based on the 

population served by each drinking water system and the number of intakes corresponding to that system. 

The population served by a particular drinking water system was divided by the number of intakes in that 

system to obtain the average population served per intake. From these data the daily flow of each intake 

was estimated using the following equation determined by EPA for the Revised Arsenic Rule (USEPA 

2000a): 

062.1 F 067.0 ∗= P (Eq. 9-1) 

Where: 

F = average daily flow in thousands of gallons per day 

P = population served 

Table 9-2 summarizes public surface drinking water intakes by EPA region and the total estimated daily 

flow for all intakes in that region. Overall, public water systems in the United States withdraw nearly 18 

billion gallons of water per day from surface water sources, an average of 2.8 million gallons per day per 

intake. 

 

12 The more effective the plain sedimentation process, the lower the treatment costs to the affected 

facilities, and consequently, the lower their potential savings from reduced influent turbidity. 
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Table 9-2: Public Water Intakes and Estimated 

Average Daily Flow by EPA Region 

EPA 

Region 

Number of Public 

Water Intakes 

Total Estimated Daily 

Flow (million 

gallons/day) 

1 639 1,105 

2 478 2,286 

3 881 2,301 

4 868 2,913 

5 525 2,373 

6 821 2,309 

7 353 715 

8 549 701 

9 748 2,543 

10 487 560 

Total 6,349 17,809 

Source: SDWIS, Federal Version (USEPA 2006c). 

9.6 Estimating the Cost of Chemical Turbidity Treatment 

Reducing sediment concentrations in drinking water influent is expected to reduce the turbidity level of 

the water, requiring smaller doses of chemical coagulants to treat. EPA estimated the amount of 

coagulants needed to treat a given level of turbidity according to the following steps: 

3⁄4 Converting sediment concentrations (given in mg/L) into nephelometric turbidity units 

(NTU) using an equation determined by ASCE/AWWA (2005): 

b

TSS T = (Eq. 9-2) 

 Where: 

T = turbidity (in NTU) 

TSS = TSS (in mg/L) 

b = 0.8, 1.5, and 2.2 for the low , midpoint and high estimates,(as the value of b decreases, 

a given level of TSS generates more turbidity, leading to higher treatment costs). 

3⁄4 Determining the doses of alum (the primary coagulant used to treat turbidity) required to treat 

this turbidity level using relationships determined by researchers of the AguaClara Project 

(Menéndez et al. 2007). 

3⁄4 Multiplying this coagulant dose (given per volume of water treated) by the total volume of a 

drinking water intake to obtain the total coagulant usage for water from that intake. 

3⁄4 Multiplying this amount of coagulant by the unit cost of the alum. 

For this analysis, EPA assumed that alum is the coagulant used to treat turbidity, since ferrous sulfate is 

likely to be used only on water with low turbidity (high doses of iron may have negative effects on water 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 9-9 

quality). No cost information was available for ferrous sulfate, but ferric chloride was quoted at $497 per 

ton (2008$); additionally, dosing information for iron compounds could not be determined. For the Long 

Term 2 Enhanced Surface Water Treatment Rule, EPA determined that alum costs between $286 and 

$373 per ton (2008$) (USEPA 2005c). EPA’s assumption that all turbidity will be treated with alum may 

understate treatment costs and savings. EPA estimated avoided costs using the cost of both dry stock and 

liquid stock alum to present a range of savings estimates in the sensitivity analysis. The lower cost of 

liquid alum was used in the low estimate, but it should be noted that the sludge generation equation in 

Section 9.7.1, calculates sludge volume based on dry alum as a coagulant. Therefore, the cost of dry alum 

is used for the midpoint and high estimates. The approximate dose of alum used to treat water of a given 

turbidity can be estimated using the following equation: 

Al = T − 28)log(33 (Eq. 9-3) 

Where: 

Al = alum dose in milligrams per liter 

T = influent water turbidity in NTU (after any reduction from plain sedimentation). 

After calculating the dose of alum necessary to treat the turbidity in the influent water at a given intake, 

this dose was multiplied by the total daily influent volume of the intake (converted from gallons to liters 

using a conversion factor of 3.8 liters to one gallon) to obtain the total amount of alum needed to treat 

turbidity at the intake per day. This total daily amount of alum was multiplied by 365 to obtain an annual 

estimate of the amount of alum needed to treat turbidity from the intake. EPA then used a conversion 

factor of 907x106

 milligrams per ton to estimate the amount of alum required for turbidity treatment in 

tons per year. Finally, the estimated annual amount of alum was multiplied by the unit cost of alum to 

arrive at a final annual cost of alum for each drinking water intake. The following equation presents this 

calculation of the annual cost of alum. 

Al CAl

Al QTC ⎟ ∗∗

⎠

⎞ ⎜

⎝

⎛

∗

∗

∗= 365

10907

8.3

6 (Eq. 9-4) 

Where: 

TCAl = total annual alum cost in 2008$

Q = daily plant flow in gallons 

Al = alum dose in milligrams per liter 

3.8 = liters in one gallon 

907*106 = milligrams in one ton 

365 = days in one year 

CAl = cost of one ton of alum in 2008$ 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 9-10 

The estimated annual cost of turbidity treatment was calculated under the baseline and post-compliance 

scenarios for each surface drinking water intake in the SDWIS database where TSS concentration 

information was available, a total of 6,132 intakes. 

9.7 Estimating the Cost of Sludge Disposal 

The alum added to the water to treat turbidity binds to the particles of sediment, making heavier lumps of 

coagulated sediment that settle out of the water column. This residual material, known as sludge, is 

proportional to the amount of sediment in the water and the amount of coagulant added. Drinking water 

treatment facilities must regularly dispose of this sludge, either by discharging it to a receiving waterbody 

or wastewater treatment plant, or by having it hauled away for off-site disposal. The cost of sludge 

disposal can be significant. 

The estimation of the cost of sludge disposal has several steps: 

3⁄4 Estimating the amount of sludge generated 

3⁄4 Calculating the probability that a generated quantity of sludge will be disposed of off-site (as 

opposed to discharged to a receiving waterbody) 

3⁄4 Estimating the distance to the disposal location 

3⁄4 Calculating the cost of sludge disposal 

9.7.1 Estimating the Amount of Sludge Generated 

A reduction in the turbidity of the water will result in a reduction in the amount of sludge generated from 

the chemicals added to treat turbidity, and thus a reduction in the cost of sludge disposal. Sludge 

quantities are a function of the amount of chemical coagulants added to treat the water and the TSS levels 

of the water. EPA (USEPA 2007c) derived the following relationship between sludge generation and 

chemical coagulants: 

QS ∗∗= [ CaCH + 6.20.234.8 MgCH + CaNCH + 6.1 MgNCH + CO + + 9.244.0 + + ASSFeAl ] 2 (Eq. 9-5) 

Where: 

S = sludge production (lb/day) 

Q = plant flow (mgd) 

CaCH = calcium hardness removed as CaCO3 (mg/L) 

MgCH = magnesium hardness removed as CaCO3 (mg/L) 

 CaNCH = noncarbonated calcium hardness removal as CaCO3 (mg/L) 

MgNCH = noncarbonated magnesium hardness removed as CaCO2 (mg/L) 

CO2 = carbon dioxide removed by lime addition, as CaCO3

Al = dry alum dose (mg/L) (as 17.1 percent Al2O3) 
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November 2009 9-11 

Fe = iron dose as Fe (mg/L) 

SS = raw water suspended solids (mg/L) 

A = other additives (mg/L) 

EPA used this equation in conjunction with the estimated sediment concentrations in the source water, the 

quantity of water taken up by the drinking water intake, and the alum doses required to treat turbidity to 

estimate the amount of sludge generated from turbidity treatment. 

EPA recognizes that the estimated volume of sludge takes into account the residuals generated from alum 

treatment of TSS, and thus does not represent the actual volumes of sludge generated from the entire 

water treatment process. Leaving out the other chemical additives does not affect the difference in sludge 

generation from turbidity treatment under the baseline and post-compliance scenarios, as they would be 

held constant under both scenarios. 

9.7.2 Calculating the Probability That the Sludge Will Require Off-Site Disposal 

Public water systems may use different sludge disposal methods. Some facilities dispose of their sludge 

off-site; some discharge to water bodies under an NPDES permit or discharge to a wastewater treatment 

plant. Facilities may also discharge a certain amount of their residual sludge and dispose of the remainder 

off-site. Facilities discharging sludge into receiving waters or to a wastewater treatment plant do not incur 

sludge disposal costs. To calculate the amount of sludge disposed of off-site and resulting in disposal 

costs to a drinking water treatment facility, EPA calculated the probability of off-site disposal. 

The Agency used facility data from the drinking water survey database to calculate the number of 

facilities reporting any off-site disposal and the percent of sludge being disposed of off-site at these 

facilities. The survey found 207 out of 511 facilities (41 percent) reporting some off-site disposal. At 

these facilities, on average 85 percent of all residual solid sludge was disposed of off-site. To determine 

the likelihood that a generated quantity of sludge will result in off-site disposal costs for a facility, EPA 

multiplied the probability that the sludge was generated at a facility performing any off-site disposal (41 

percent) by the probability that a unit of sludge from this facility will be disposed of off-site (85 percent). 

The estimated probability (Pr) that a given volume of sludge generated will be disposed of off-site, thus 

incurring sludge disposal costs, is 35 percent. 

9.7.3 Estimating the Distance to the Disposal Location 

The cost of sludge disposal is also a function of the distance to the disposal location. A sample of 10 

facilities and their disposal locations was taken randomly from the Drinking Water Survey Database. 

Because location information was available for both the facilities and their disposal locations, EPA 

calculated the driving distance between the facility and its discharge locations for each of the 10 facilities. 

If a facility had more than one listed discharge location, an average distance was used. Based on this 

sample of 10 facilities, the distance to a disposal location can vary between 1 and 200 miles, and the 

estimated average distance (Z) to disposal locations is 21.6 miles. 

9.7.4 Calculating the Cost of Sludge Disposal 

EPA estimated the annual cost of sludge disposal as a function of sludge volumes and distance to the 

disposal facility using the following equation from EPA (USEPA 1996a): 
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November 2009 9-12 

Y [ ] ( ) S ∗∗= ∗ + [ + (SZ ∗ ∗ ∗ Pr0005.365)25.083.3(Pr0005.36512.54 )] (Eq. 9-6) 

Where: 

Y = annual cost of sludge disposal ($/year) 

54.12 = disposal cost per ton of sludge ($) 

S = total sludge production from treating turbidity in drinking water (lbs/day) 

365 = days per year 

0.0005 = tons per lb 

Pr = the estimated probability of off-site sludge disposal (35 percent) 

3.83 = fixed transportation cost per pound of sludge ($) 

0.25 = variable transportation cost per mile per pound of sludge ($) 

Z = average transportation distance of 21.6 miles. 

The cost of sludge disposal was estimated under both the baseline and post-compliance scenarios for all 

surface drinking water intakes in SDWIS for which TSS concentrations were available. 

9.8 Sensitivity Analysis 

To account for uncertainty in drinking water treatment costs EPA varied several of the parameters of this 

analysis, including the effectiveness of pre-sedimentation, the amount of turbidity generated by a unit of 

TSS, and the cost of alum, to provide a range of benefits estimates. The values of these inputs in each 

estimate range are as follows: 

Low Estimate: 

3⁄4 TSS levels exceeding 1,000 mg/L in the baseline are reduced by 90 percent for all options 

3⁄4 TSS = 2.2 * Turbidity 

3⁄4 CAl = $250 

Midpoint Estimate: 

3⁄4 TSS levels exceeding 1,000 mg/L in the baseline are reduced by 60 percent for all options 

3⁄4 TSS = 1.5 * Turbidity 

3⁄4 CAl = $327 

High Estimate: 

3⁄4 TSS levels at exceeding 1,000 mg/L in the baseline are reduced by 30 percent for all options 

3⁄4 TSS = 0.8 * Turbidity 

3⁄4 CAl = $327 
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November 2009 9-13 

9.9 Estimating the Total Costs of Drinking Water Treatment Under Different 

Policy Scenarios 

EPA estimated the total national costs of treating drinking water for turbidity by summing the estimated 

annual costs of chemical coagulants added to reduce turbidity and the estimated annual cost of disposing 

of the sludge resulting from this chemical treatment. Table 9-3 presents annual estimated total drinking 

water treatment costs for the baseline scenario, including low, midpoint, and high estimates for avoided 

costs. Estimated drinking water treatment costs for TSS and turbidity for facilities included in EPA’s 

analysis range from $343 to $651 million nationwide under the baseline scenario, with a midpoint value 

of $521 million. 

Table 9-3: Drinking Water Turbidity Treatment Costs Under the Baseline Scenario 

Average Treated Turbidity (NTU) 

Treatment Cost 

EPA (millions of 2008$) 

Region Low Mid High Low Mid High 

1 26.7 40.1 76.9 $6.6 $10.5 $14.9 

2 50.3 84.4 177.9 $21.3 $33.9 $44.7 

3 71.7 105.6 198.9 $45.5 $61.6 $72.8 

4 110.8 163.3 307.7 $60.0 $82.5 $98.2 

5 136.8 263.8 613.3 $52.6 $83.6 $105.8 

6 151.5 367.2 960.3 $60.3 $90.1 $110.8 

7 166.0 422.9 1,129.5 $18.4 $33.9 $44.5 

8 121.8 254.5 619.4 $18.3 $25.5 $30.3 

9 123.8 256.3 620.5 $54.4 $91.5 $119.1 

10 43.9 64.4 120.8 $5.1 $7.8 $10.2 

Total 104.0 205.9 486.0 $342.7 $520.9 $651.3 

Cost per million gallons1 $58.4 $88.7 $110.9 

1

 Calculated for the estimated 17,373 million gallons per day of intake for public water systems in SDWIS. 

It is important to note that these avoided cost estimates do not take into account any treatment or disposal 

avoided costs to public wastewater treatment facilities to which drinking water treatment facilities may 

route their sludge, nor any environmental benefits from reduced volumes of sludge discharged directly 

into surface waters by direct drinking water treatment facilities. Therefore, the total benefits of reducing 

turbidity in drinking water treatment are understated. 

9.10 Estimating Avoided costs from Lower Sediment and Turbidity in Drinking 

Water Influent 

The total avoided costs from lowered turbidity resulting from lower TSS concentrations in drinking water 

influent were estimated as the difference between drinking water turbidity treatment under the baseline 

and post-compliance scenarios. Table 9-4, Table 9-5, Table 9-6, and Table 9-7 present estimated savings 

in drinking water treatment costs from lowered turbidity for the three policy options EPA considered, as 

well as EPA’s final policy option. The tables include low, midpoint, and high estimates for savings under 

each of these options.

The estimated savings from reduced TSS and turbidity treatment at drinking water facilities are between 

$978,400 and $2.1 million, varying between the least and most stringent policy options and between the 

low and high estimates. Region 5 benefits the most from the TSS reductions expected from this regulatory 
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November 2009 9-14 

action. The avoided costs in Region 5 under Option 1 account for more than half of the total national 

savings. Other regions receive a larger portion of benefits under Options 2 through 4, though Region 5 

still accounts for the greatest proportion of the total cost savings, though Regions 4 and 6 also show 

significant savings under Options 2 through 4. 

Option 1, which requires non-numeric effluent limitations for all sites, is EPA’s least stringent policy 

option. Average turbidity reductions are less than 1 NTU for this policy option, and the estimated savings 

drinking water treatment costs range between $978,400 and $1.3 million, with a midpoint estimate of $1.2 

million. 

Option 2 requires ATS on sites with more than 30 acres disturbed at one time and imposes a 13 NTU 

turbidity standard while requiring non-numeric effluent limitations on all sites and is similar to the option 

EPA proposed previously. This option reduces turbidity between 0.4 and 1.3 NTU, translating to $1.4 

million to $1.9 million in avoided costs, with a midpoint estimate of $1.8 million. 

Option 3, EPA’s most stringent policy option, requires ATS on sites with more than 10 acres disturbed at 

one time, imposes a 13 NTU turbidity standard on these sites, and requires non-numeric effluent 

limitations on all sites. This option reduces treated turbidity by an average of 0.7 NTU in the midpoint, 

ranging from 0.4 to 1.4 between the low and high estimates. Total estimated avoided costs for this option 

are between $1.7 and $2.1 million, with a midpoint estimate just below $2.1 million. 

Option 4, requires passive treatment systems on all sites with 10 or more acres disturbed at one time, and 

establishes a numeric turbidity standard of 250 NTU (expressed as a daily maximum value) for sites 

required to implement passive treatment. In addition, all sites will be required to meet non-numeric 

effluent limitations. National average turbidity reductions from Option 4 range from 0.4 NTU to 1.3 

NTU, with a midpoint estimate of 0.6 NTU. Total avoided costs for Option 4 range between $1.5 and 

$1.9 million, with a midpoint estimate of $1.8 million. While Option 4 is less stringent than Option 3, it is 

estimated to reduce turbidity in drinking water sources by nearly as much and to produce similar 

monetized benefits. 

As construction site discharge is more likely to contain smaller particles that contribute less to TSS and 

more to turbidity, the high estimates for Options 2, 3, and 4 may be more relevant because EPA uses a 

conversion factor between TSS and turbidity that takes this into account. The high estimate for turbidity 

reductions under Option 2 is 1.3 NTU nationwide. Under Option 3, the high estimate is on average 

around 1.4 NTU nationwide. For Option 4, the selected option, nationwide reductions are 1.4 NTU. In 

Region 5, high estimates of reductions under all options are 4.1 NTU. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 9-15 

Table 9-4: Reduction in Drinking Water Treatment Costs Under Option 1 

Average Reduction in Treated Turbidity 

(NTU) 1 EPA Avoided costs (thousands of 2008$) 

Region Low Mid High Low Mid High 

1 0.0 0.0 0.0 $4.0 $5.0 $5.8 

2 0.1 0.5 1.8 $44.1 $91.2 $129.8 

3 0.0 0.0 0.1 $8.0 $9.2 $9.3 

4 0.4 0.5 1.0 $136.2 $161.1 $164.5 

5 1.2 1.9 3.9 $515.0 $603.4 $616.6 

6 0.3 0.5 1.1 $162.4 $194.1 $201.0 

7 0.1 0.2 0.4 $11.3 $14.1 $15.8 

8 0.0 0.0 0.0 $1.1 $1.3 $1.4 

9 0.1 0.1 0.2 $7.6 $9.9 $11.3 

10 0.1 0.2 0.3 $88.7 $110.4 $110.4 

Total 0.2 0.4 0.9 $978.4 $1,199.8 $1,265.9 

1

 Average turbidity reductions shown as 0.0 are not actually zero, but not sufficiently large to show at this level of significant digits.

Table 9-5: Reduction in Drinking Water Treatment Costs Under Option 2 

Average Reduction in Treated Turbidity 

(NTU) 1 EPA Avoided costs (thousands of 2008$) 

Region Low Mid High Low Mid High 

1 0.0 0.0 0.1 $8.2 $10.2 $11.7

2 0.1 0.6 1.8 $50.8 $99.4 $138.4

3 0.1 0.1 0.2 $24.0 $27.7 $27.8

4 0.7 1.0 1.9 $258.9 $307.6 $318.3

5 1.2 2.0 4.1 $526.6 $618.4 $633.7

6 0.7 1.1 2.3 $341.5 $408.3 $422.5

7 0.2 0.4 0.8 $23.5 $29.3 $33.0

8 0.0 0.0 0.1 $2.2 $2.7 $2.9

9 0.1 0.2 0.4 $15.8 $20.5 $23.3

10 0.2 0.3 0.6 $195.9 $244.9 $244.9

Total 0.4 0.6 1.3 $1,447.4 $1,769.1 $1,856.6 1

 Average turbidity reductions shown as 0.0 are not actually zero, but not sufficiently large to show at this level of significant digits.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 9-16 

Table 9-6: Reduction in Drinking Water Treatment Costs Under Option 3 

Average Reduction in Treated Turbidity 

EPA (NTU) Avoided costs (thousands of 2008$) 

Region Low Mid High Low Mid High 

1 0.0 0.1 0.1 $10.2 $12.7 $14.6

2 0.1 0.6 1.8 $54.1 $103.7 $143.2

3 0.1 0.1 0.2 $31.8 $36.6 $36.7

4 0.8 1.2 2.2 $312.7 $370.7 $383.2

5 1.2 2.0 4.1 $532.4 $626.4 $643.0

6 0.9 1.4 2.8 $428.6 $513.1 $531.2

7 0.3 0.5 1.0 $29.3 $36.6 $41.2

8 0.0 0.0 0.1 $2.7 $3.4 $3.7

9 0.1 0.2 0.5 $20.1 $26.9 $31.4

10 0.3 0.4 0.7 $237.0 $320.3 $320.3

Total 0.4 0.7 1.4 $1,658.9 $2,050.5 $2,148.5

Table 9-7: Reduction in Drinking Water Treatment Costs Under Option 4 

Average Reduction in Treated Turbidity 

EPA (NTU) Avoided costs (thousands of 2008$) 

Region Low Mid High Low Mid High 

1 0.0 0.0 0.1 $6.8 $8.5 $9.7

2 0.1 0.6 1.8 $50.3 $98.8 $137.8

3 0.1 0.1 0.2 $25.6 $29.6 $29.6

4 0.7 1.1 2.0 $281.1 $333.5 $344.8

5 1.2 2.0 4.1 $526.8 $618.8 $634.2

6 0.8 1.3 2.6 $396.9 $474.5 $490.6

7 0.2 0.4 0.9 $27.0 $33.7 $37.8

8 0.0 0.0 0.1 $2.2 $2.8 $3.1

9 0.1 0.2 0.4 $16.4 $21.5 $24.7

10 0.2 0.2 0.4 $145.5 $181.9 $181.9

Total 0.4 0.6 1.3 $1,478.7 $1,803.5 $1,894.3

9.11 Sources of Uncertainty/Limitations 

Key limitations of EPA’s analysis of avoided costs to drinking water treatment facilities are outlined 

below. The SPARROW model for suspended sediments that was used for estimating TSS concentrations 

in the source waters also has a number of limitations. These limitations are discussed in detail in 

Chapter 6. 

3⁄4 EPA’s analysis of reductions in TSS and turbidity in surface waters is limited to the RF1 

reaches modeled by SPARROW. Therefore, reductions at any facilities that are not located on 

RF1 reaches would not be included in this assessment of benefits to drinking water treatment, 

leading to an underestimation of benefits. 

3⁄4 Sediment filtration systems and pre-sedimentation (allowing water to sit and sediment to 

filter out before treatment) at drinking water treatment facilities reduce the sediment 

concentration of the water before it enters chemical treatment, so that the turbidity level of 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 9-17 

the water entering the facility is not the turbidity level that is eventually treated with 

coagulants. Assuming that the differential between pre- and post- compliance sediment 

concentration is proportional to the differential between pre- and post-compliance turbidity 

treatment introduces uncertainty, as the lower sediment levels may be more or less affected 

by the pre-sedimentation and filtration processes. EPA’s analysis attempts to account for this 

uncertainty by varying the maximum amount of TSS and turbidity treated by a given drinking 

water treatment facility. 

3⁄4 If a drinking water treatment facility produces sludge that is toxic (due to other pollutants in 

the water besides sediment), its disposal costs may be significantly higher because toxic 

sludge disposal is more restricted and costly. If the facility cannot separate the sludge 

generated by sediment treatment from the sludge generated by treatment of toxics (which is 

likely the case), then all of its sludge will be characterized as toxic. This analysis may 

understate the cost of disposal (and thus the avoided costs of smaller quantities of sludge to 

be disposed of) for facilities that generate toxic sludge. 

3⁄4 Reducing nutrient loadings to surface waters is expected to reduce eutrophication which is 

one of the main causes of taste and odor impairment in drinking water. Taste and odor in 

drinking water has a major negative impact on the public perception of drinking water safety 

and the drinking water industry due to a significant increase in drinking water treatment costs 

from foul taste and odor in the source waters. Although the final regulation is expected to 

reduce the cost of drinking water treatment to improve taste and odor, the Agency was unable 

to monetize this benefit category due to data limitations. 

3⁄4 The analysis presumes that reduced costs of drinking water treatment will not result in 

appreciable changes in prices (costs) of drinking water paid by households. Given the 

marginal costs of reduced turbidity treatment relative to other costs of drinking water 

production, this assumption seems valid. However, if substantial price effects do occur, 

additional benefits will accrue at the consumer level.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-1 

10 Nonmarket Benefits from Water Quality Improvements 

As discussed in the preceding chapters of this document, sediments, nutrients, and other pollutants from 

construction sites may have a wide range of effects on water resources located in the vicinity of the 

construction sites. These environmental changes affect environmental services valued by humans, 

including recreation; commercial fishing; public and private property ownership; navigation; water supply 

and use; and existence services such as aquatic life, wildlife, and habitat designated uses. These 

nonmarket benefits (Freeman 2003) are in addition to market benefits (e.g., avoided costs of producing 

various market goods and services) estimated in prior chapters. 

This chapter describes the use of meta-analysis of surface water valuation studies for estimating benefits 

of water quality improvements resulting from the regulation. The technical details involved in the 

estimation of meta-analysis are presented in Appendix G as well as in sources such as Bateman and Jones 

(2003), Johnston et al. (2005, 2006), Shrestha et al. (2007), and Rosenberger and Phipps (2007). 

10.1 Water Quality Index 

To link water quality changes from reduced sediment discharge to effects on human uses and support for 

aquatic and terrestrial species habitat, this analysis utilizes a water quality index (WQI). The index 

translates water quality measurements, gathered for multiple parameters that are indicative of various 

aspects of water quality, into a single numerical indicator. The parameters used in formulating the WQI 

are determined based on waterbody type, scientific understanding of ecosystem response to varying 

conditions, and available data. 

Most importantly for the present analysis, the WQI provides the link between specific pollutant levels, as 

reflected in individual index parameters (e.g., dissolved oxygen concentrations), and the presence of 

aquatic species and suitability for particular recreational uses. The WQI value, which is measured on a 

scale from 0 to 100, reflects varying water quality, with 0 for poor quality and 100 for excellent. 

Numerous water quality indices have been developed and documented in the literature since the 1960s 

(Horton 1965); however, no standardized approach has emerged. EPA Region 10 was one of the first 

designers of the WQI framework in use today (Brown et al. 1970). The National Sanitation Foundation 

WQI (McClelland 1974) and Oregon WQI (Dunnette 1979) proposed subsequent iterations that built on 

this original framework. McClelland (1974) relied on a survey of water quality experts to identify water 

quality parameters to be included in the index, the structure and the relationship between the parameters, 

and the weights given to the parameters in the WQI equation. This is a procedure known as the Delphi 

Method (Dalkey 1968). 

The WQI used in this analysis builds on McClelland’s work, and the methodology developed by Dunnette 

(1979), which was subsequently updated by Cude (2001) to better account for spatial and morphologic 

variability in the natural characteristics of streams. To support the analysis of changes in sediment 

loadings from construction sites relative to baseline conditions, EPA adapted Cude’s methodology to the 

national scale by developing subindex curves for TSS at the level of ecoregions. EPA also further 

modified the WQI methodology to apply to estuarine reaches, building on prior applications of WQI 

concepts to saltwater environments (Harrison et al. 2000; Canadian Council of Ministers of the 

Environment 2001; Carruthers and Wazniak 2003; Gupta et al. 2003; and USEPA 2007a). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-2 

Implementing the WQI methodology involves three key steps: (1) obtaining water quality measurements 

for each parameter included in the WQI; (2) transforming measurements to subindex values expressed on 

a common scale; and (3) weighting and aggregating the individual parameter subindices to obtain an 

overall WQI value that reflects waterbody conditions across the parameters. 

In compiling water quality data for each parameter, EPA used a combination of average water quality 

field measurements and SPARROW modeling results as input values for each of the parameters included 

in the WQI. Data sources for measurements are discussed in Section 10.1.4, and SPARROW modeling 

results were discussed in Chapter 6. 

The second step in the implementation of the WQI involves the transformation of parameter 

measurements into subindex values that express water quality conditions on a common scale of 0 to 100. 

With the exception of the TSS parameter, EPA used the subindex transformation curves developed by 

Dunnette (1979) and Cude (2001) for the Oregon Water Quality Index. In the case of TSS concentrations, 

EPA adapted the approach developed by Cude (2001) to account for the wide range of natural or 

background sediment concentrations that result from varying geologic and other region-specific 

conditions, and to reflect the national context of the analysis. TSS subindex curves were developed for 

each Level III ecoregion (USEPA 2009d) using baseline TSS concentrations calculated in SPARROW at 

the RF1 reach level.13 For each of the 85 Level III ecoregions intersected by the RF1 reach network, EPA 

derived the TSS transformation curve by assigning a score of 100 to the 25th percentile of the reach-level 

TSS concentrations in the ecoregion (i.e., using the 25th percentile as a proxy for “reference” 

concentrations), and a score of 70 to the median concentration. An exponential equation was then fitted to 

the two concentration points. 

The final step in implementing the WQI involves combining the individual parameter subindices into a 

single WQI value that reflects the overall water quality across the parameters. Following McClelland’s 

approach, EPA calculated the overall WQI for a given reach using a weighted geometric mean function as 

follows: 

∏=

=

n

i

Wi WQIr Qi

1

 (Eq. 10-1) 

Where: 

WQIr = the multiplicative water quality index (from 0 to 100) for reach r

Qi = the water quality subindex measure for parameter i

Wi = the weight of the i-th parameter 

n = the number of parameters 

The WQI methodology used for rivers and streams and for estuaries are described in Section 10.1.1 and 

Section 10.1.2, respectively. 

 13 The selected data exclude outlier TSS concentration values, defined as values that exceed the 95th percentile based on the 

national population of all RF1 reaches. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-3 

10.1.1 Freshwater WQI for Rivers and Streams 

The freshwater WQI used by EPA to evaluate water quality in rivers and streams includes six parameters 

selected to represent major stream impairment categories: dissolved oxygen (DO), biochemical oxygen 

demand (BOD), fecal coliform (FC), total nitrogen (TN), total phosphorus (TP), and total suspended 

solids (TSS). These freshwater WQI parameters are based on the set of parameters used in the WQI 

developed by Cude (2001) but exclude temperature and pH. Temperature and pH are not listed as 

common causes of impairment nationally or with respect to the uses assessed in this economic 

valuation.14 Additionally, temperature may be considered to be indirectly reflected in the DO parameter 

because DO is highly temperature dependent. In addition, the WQI developed by Cude (2001) does not 

explicitly account for turbidity associated with water quality impacts from TSS and eutrophication. 

Table 10-1 presents parameter-specific functions used for transforming water quality data into water 

quality subindices for freshwater waterbodies. The equation parameters for each of the 85 ecoregionspecific TSS subindex curves are provided in Appendix F.

 14 EPA (2009a) reports 38,228 miles and 26,569 miles of rivers or streams impaired or threatened due to temperature and pH, 

respectively, as compared to much higher incidence of impairment due to pathogens (112,084 miles), sediment (85,248 

miles), and nutrients (81,279 miles). Temperature and pH are similarly responsible for a relatively small share of 

impairments noted in estuaries and bays. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-4 

Table 10-1: Freshwater Water Quality Subindices

Parameter Concentrations Concentration Unit Subindex 

Dissolved Oxygen (DO) 

DO saturation ≤100% 

DO ≤ 3.3 Mg/L 10 

DO 3.3 < DO < 10.5 Mg/L -80.29+31.88*DO-1.401*DO2

DO 10.5 ≤DO Mg/L 100 

100% < DO saturation ≤ 275% 

DO N/A Mg/L 100 * exp((DOsat - 100) *-1.197 E-2) 

275% < DO saturation 

DO N/A Mg/L 10 

Fecal Coliform (FC) 

FC ≤ 50 Lbs/100 mL 98 

FC 50 < FC ≤ 1,600 Lbs/100 mL 98 * exp(FC - 50) * -9.9178 E-4 

FC > 1,600 Lbs/100 mL 10 

Total Nitrogen (TN) 

TN ≤ 3 Mg/L 100 * exp(TN * -0.4605) 

TN > 3 Mg/L 10 

Total Phosphorus (TP) 

TP ≤ 0.25 Mg/L 100 - 299.5 * TP - 0.1384 * TP2 

TP > 0.25 Mg/L 10 

Total Suspended Solids (TSS)1

TSS ≤ TSS10 Mg/L 10 

TSS TSS10 < TSS ≤ TSS100 Mg/L a * exp(TSS*b); where a and b are 

ecoregion-specific values 

TSS > TSS100 Mg/L 100 

Biochemical Oxygen Demand, 5-day (BOD) 

BOD ≤ 8 Mg/L 100 * exp(BOD * -0.1993) 

BOD > 8 Mg/L 10 

1

 TSS10 and TSS100 are ecoregion-specific TSS concentration values that correspond to subindex scores of 10 and 100, respectively. 

Source: Cude (2001) (ecoregion-specific curves were developed for TSS based on Cude's methodology). 

Table 10-2 lists the freshwater WQI parameters and the weights used in aggregating the subindex values 

into the overall WQI for inland rivers and streams. EPA (USEPA 2002) revised the weights originally 

developed by McClelland (1974) by redistributing the weights to the six parameters retained in the EPA 

WQI so that the ratio among the parameters is maintained and the weights sum of 1. 

Table 10-2: Original and Revised Weights for Freshwater WQI Parameters 

Parameters 

Original Weights 

(McClelland 1974) 

Revised Weights (USEPA 

2002) 

Dissolved oxygen (% saturation and mg/L) 0.17 0.24 

Fecal coliform (colonies/100 mL) 0.16 0.22 

Biochemical oxygen demand, 5-day (mg/L) 0.11 0.15 

Total nitrogen (mg/L) 0.10 0.14 

Total phosphorus (mg/L) 0.10 0.14 

Total suspended solids (mg/L) 0.08 0.11 

pH (standard) 0.11 -- 

Temperature (C) 0.10 -- 

Total Solids (mg/L) 0.07 -- 

Total 1.00 1.00 
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November 2009 10-5 

10.1.2 WQI Methodology for Estuaries 

The methodology used in calculating a WQI score for estuaries and coastal waterbodies adapts the 

approach outlined above for rivers and streams to account for the different characteristics and response of 

saltwater systems. EPA reviewed several applications of WQI concepts to saltwater systems (Harrison et 

al. 2000; Carruthers and Wazniak 2003; Gupta et al. 2003; and USEPA 2007a) to identify water quality 

parameters pertinent to estuaries and their relative importance in characterizing overall water quality. 

The estuarine WQI includes parameters that are common to both freshwater and saltwater systems—

dissolved oxygen, nitrogen, phosphorus, fecal coliform, and suspended solids—and replaces biochemical 

oxygen demand with chlorophyll-a (ChA). Chlorophyll-a assesses the amount of phytoplankton present in 

the waterbody. While phytoplankton is a natural component of the waterbody’s food web, changes in its 

abundance (e.g., high ChA), species composition, and productivity are commonly the first biological 

response to nutrient enrichment (Chesapeake Bay Program 2008). Subsequent decomposition of the 

phytoplankton may reduce available dissolved oxygen and induce hypoxia and anoxia. 

Similarly to the WQI for rivers and streams, the data for each parameter included in the WQI for estuaries 

are transformed to a standard (0–100) scale using subindex curves. Table 10-3 presents parameter-specific 

functions used in transforming water quality data to subindices for estuaries. The curve used for ChA is 

derived from the curves used for TN, since both parameters indicate trophic status. 

Table 10-3: Estuarine Water Quality Subindices 

Parameter Concentrations Concentration Unit Subindex 

Dissolved Oxygen (DO) 

DO saturation ≤100% 

DO ≤3.3 mg/L 10 

DO 3.3<DO<10.5 mg/L -80.29+31.88*DO-1.401*DO2

DO 10.5≤ DO mg/L 100 

100% < DO saturation ≤ 275% 

DO N/A mg/L 100 * exp((DOsat - 100) *-1.197 E-2) 

275% < DO saturation 

DO N/A mg/L 10 

Fecal Coliform (FC) 

FC ≤50 lbs/100 mL 98 

FC 50 < FC ≤1,600 lbs/100 mL 98 * exp(FC - 50) * -9.9178 E-4 

FC > 1,600 lbs/100 mL 10 

Total Nitrogen (TN) 

TN ≤3 mg/L 100 * exp(TN * -0.4605) 

TN > 3 mg/L 10 

Total Phosphorus (TP) 

TP ≤ 0.25 mg/L 100 - 299.5 * TP - 0.1384 * TP2

TP > 0.25 mg/L 10 

Total Suspended Solids (TSS)1

TSS ≤TSS10 mg/L 10 

TSS TSS10 < TSS ≤TSS100 mg/L a * exp(TSS*b); where a and b are 

ecoregion-specific values 

TSS > TSS100 mg/L 100 
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November 2009 10-6 

Table 10-3: Estuarine Water Quality Subindices 

Parameter Concentrations Concentration Unit Subindex 

Chlorophyll-a (ChA) 

ChA ≤ 40 μg/L 100 *exp(ChA * -0.05605) 

ChA > 40 μg/L 10 

1

 TSS10 and TSS100 are ecoregion-specific TSS concentration values that correspond to subindex scores of 10 and 100, respectively. 

Source: Cude (2001) (ecoregion-specific curves were developed for TSS based on Cude’s methodology). 

Table 10-4 shows the weighting scheme used to combine the parameters into an overall WQI. These 

weights are a modification of the ones used for the freshwater WQI. 

Table 10-4: Weights for Estuarine WQI Parameters 

Parameters Estuary WQI Weights1

Dissolved oxygen (% saturation and mg/L) 0.26 

Fecal coliform (colonies/100mL) 0.25 

Total nitrogen (mg/L) 0.15 

Total phosphorus (mg/L) 0.15 

Total suspended solids (mg/L) 0.11 

Chlorophyll-a (mg/L) 0.08 

Total 1.00 1

 Weights used in the freshwater WQI were redistributed among estuarine WQI parameters to account for the removal of BOD and the

addition of ChA. 

10.1.3 Relation between WQI and Suitability for Human Uses 

Once an overall WQI value is calculated, it can be related to suitability for potential uses. Vaughan (1986) 

developed a water quality ladder (WQL) that can be used to indicate whether water quality is suitable for 

various human uses (i.e., boating, rough fishing, game fishing, swimming, and drinking without 

treatment). Vaughan identified “minimally acceptable parameter concentration levels” for each of the five 

potential uses. Water quality is deemed acceptable for each use if none of the six parameters exceeds the 

threshold concentration levels. Vaughan used a scale of zero to 10 instead of the WQI scale of zero to 100 

to classify water quality based on its suitability for potential uses. Table 10-5 presents water use 

classifications and the corresponding WQL and WQI values. 

Table 10-5: Water Quality Classifications 

Water Quality Classification WQL Value WQI Value1

... drinking without treatment 9.5 95 

... swimming 7.0 70 

... game fishing 5.0 50 

... rough fishing 4.5 45 

... boating 2.5 25 1

 The WQI value corresponding to a given classification of water quality equals the WQL value multiplied by 10. 

Source: Vaughan (1986). 
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November 2009 10-7 

10.1.4 Sources of Data on Ambient Water Quality 

For river and streams, EPA used the following data sources to obtain ambient concentrations for the six 

parameters included in the WQI: 

3⁄4 The SPARROW model outputs provided data for baseline and post-compliance 

concentrations of total nitrogen, total phosphorus, and total suspended solids as discussed in 

Chapter 6. 

3⁄4 The USGS National Water Information System (NWIS) provided concentration data for three 

parameters: (1) fecal coliform, (2) dissolved oxygen, and (3) biochemical oxygen demand. 

3⁄4 EPA’s Storage and Retrieval (STORET) data warehouse provided additional data on fecal 

coliform counts and biochemical oxygen demand (USEPA 2008e). The NWIS database did 

not include data for these two parameters for 88.6 percent and 91.3 percent of the 9,444 RF1 

reaches, respectively. To address these data gaps, EPA augmented fecal coliform and 

biochemical oxygen demand data by adding observations from the STORET data warehouse. 

The addition of these observations increased the fecal coliform and biochemical oxygen 

demand data, so that 71.9 percent and 33.8 percent, respectively, of the 9,444 reaches were 

covered. 

Baseline freshwater WQI values are based on baseline data for 60,017 reaches in the coterminous United 

States.15 Baseline concentrations for all WQI parameters were available for a total of 9,444 reaches. EPA 

used a successive average approach to address the data gaps in the remaining freshwater reaches. The 

approach involves assigning the average of ambient concentrations for a WQI parameter within a 

hydrologic unit to reaches within the same hydrologic unit with missing data and progressively expanding 

the geographical scope of the hydrologic unit (HUC8, HUC6, HUC4, and HUC2) to fill in all missing 

data.16 This approach assumes that waterbody reaches located in the same watershed generally share 

similar characteristics. Using this estimation approach, EPA was able to compile water quality data for a 

total of 60,017 freshwater reaches (including 50,573 reaches estimated from HUC-based averages). 

The following sources provided data for estuaries and coastal waterbodies: 

3⁄4 The SPARROW model outputs provided data for baseline and post-compliance 

concentrations of suspended sediment. Some sediment concentrations were calculated from 

sediment flux using Dissolved Concentration Potentials as discussed in Chapter 6. 

3⁄4 EPA’s Environmental Monitoring & Assessment Program, National Coastal Assessment 

Monitoring Data (EMAP-NCA) database provided data for approximately 3,000 sampling 

stations in the United States, for all pertinent parameters except fecal coliform (USEPA 

2008a). 

 

15 No ambient concentration data were available for Alaska and Hawaii. 

16 Hydrologic Unit Codes (HUCs) are cataloguing numbers that uniquely identify hydrologic features such as surface drainage 

basins. The HUCs consist of 8 to 14 digits, with each set of 2 digits giving more specific information about the hydrologic 

feature. The first pair of values designate the region (of which there are 21), the next pair the subregion (total of 222), the

third pair the basin or cataloguing unit (total of 352), and the fourth pair the subbasin, or accounting unit (total of 2,262) 

(USGS 2007a). Digits after the first eight offer more detailed information, but are not always available for all waters. In this 

discussion, a HUC level refers to a set of waters that have that number of HUC digits in common. For example, the HUC6 

level includes all reaches for which the first six digits of their HUC are the same. 
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November 2009 10-8 

3⁄4 The National Estuarine Research Reserve System database (NERRS) provided data for 25 

estuarine reserves, each containing multiple water monitoring stations, for all relevant 

parameters except fecal coliform (NOAA 2008). 

3⁄4 EPA’s STORET data warehouse provided additional data on all relevant parameters, 

including fecal coliform counts, but excluding chlorophyll-a (USEPA 2008e). 

3⁄4 EPA also obtained data from the following state and local agencies through special requests: 

 The Houston Area Research Center (HARC) is a nonprofit organization that supports the 

water monitoring data collection for the Texas Commission on Environmental Quality. 

HARC provided data spanning the period of 1999 to 2005 (Gonzalez 2009). 

 The New Hampshire Department of Environmental Quality collects coastal water quality 

data in support of its Coastal Program. Data include measurements made from 1999 to 

2008 (Hunt 2009). 

 The Southern Carolina Estuarine and Coastal Assessment Program is a collaboration 

between the South Carolina Department of Natural Resources (SCDNR) and the South 

Carolina Department of Health and Environmental Control (SCDHEC) providing 

monitoring and periodic reports on the conditions of the state’s estuarine habitats. Data 

are available for the period of 1999 to 2004 (VanDolah 2009). 

 The Connecticut Department of Environmental Protection, on behalf of the collaboration 

with EPA on the Long Island Sound Study, conducts a Long Island Sound Water Quality 

Monitoring Program. In total, 28 stations are monitored each year. Data include 

measurements made from 1999 to 2008 (Lyman 2009). 

 The Washington Department of Ecology conducts marine water quality monitoring at 85 

stations in Puget Sound, Grays Harbor, and Willapa Bay. About 40 stations are monitored 

each year on a monthly basis, with some stations monitored on a rotating schedule. Data 

cover the period of 1975 to 2008 (Thom 2009).

 The Oregon Department of Environmental Quality collects coastal water quality data in 

support of its Water Quality program. Data include measurements made from 1991 to 

2008 (Marxer 2009). 

In the case of estuaries and coastal reaches, EPA used a similar approach to estimate missing 

concentration values based on the average concentrations available for reaches within consecutively 

coarser HUC levels. Thus, for RF1 reaches where no empirical measurement could be obtained from any 

of the sources listed above, the parameter value was estimated by averaging the corresponding values for 

adjacent estuarine/coastal reaches within the same HUC8 watershed. Due to the smaller scale of estuarine 

waterbodies, EPA used the average of concentration values for HUC8, HUC6, and HUC 4 to estimate 

average parameter concentrations for reaches where data gaps existed and did not estimate values based 

on HUC2 averages. Using this approach, EPA compiled ambient water quality data for all WQI 

parameters for 772 reaches out of the total 2,067 estuarine/coastal reaches in the coterminous United 

States. Due to data limitations 1,295 coastal reaches were excluded from the analysis. 

EPA calculated baseline WQI for all reaches based on modeled, measured, or extrapolated parameter 

values. The WQI was calculated using the methodology outlined above according to the type of reach 

(either an inland stream or an estuarine reach). SPARROW identifies estuarine and coastal reaches using 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-9 

a specific flag.17 As done throughout the analysis (see Chapter 6), in calculating WQI values, EPA 

replaced all outlier TSS concentrations equal or above the 95th percentile with the 95th percentile value 

(6,157.8 mg/L in the baseline). Based on this threshold, a total of 3,106 reaches are considered outliers. 

Table 10-6 shows the distribution of reach miles by WQI value and EPA region for the baseline scenario 

(existing conditions). 

Table 10-6: Percentage of Reach Miles in Coterminous 48 States by WQI Classification 

for EPA Regions: Baseline Scenario 

EPA Region WQI<25 25≤WQI<50 50<WQI<70 70<WQI 

1 0.2% 8.3% 82.7% 8.8% 

2 0.5% 13.0% 36.4% 50.1% 

3 5.0% 43.4% 45.0% 6.6% 

4 0.8% 35.8% 42.2% 21.2% 

5 6.1% 63.4% 27.1% 3.3% 

6 1.0% 64.6% 28.1% 6.3% 

7 17.9% 70.9% 10.2% 1.1% 

8 5.0% 51.2% 24.8% 18.9% 

9 19.0% 73.2% 7.3% 0.6% 

10 0.1% 19.2% 35.5% 45.2% 

National Average 5.6% 50.3% 29.0% 15.1%

Note: Data include both freshwater and saltwater reaches. 

10.1.5 Estimated Changes in Water Quality (ΔWQI) from the Regulation 

To estimate benefits of water quality improvements expected from the regulation, EPA calculated the 

WQI for each policy option. In calculating the post-compliance WQI, the Agency used option-specific 

TSS, TN, and TP concentrations based on SPARROW output as described in Chapter 6. The sediment 

and nutrient concentration estimates for each policy option reflect the expected reduction in sediment 

discharge and associated nutrients under the policy options. Although reductions in sediment and nutrient 

discharges from construction sites may also reduce loadings of other pollutants that are included in the 

WQI, the other parameters were held constant in this analysis for all policy options. 

Each RF1 reach that has an improved WQI value relative to its baseline WQI value contributes to the 

estimated economic benefits of a policy option. Based on the estimated WQI value under the baseline 

scenario, EPA categorized each RF1 reach using four WQI ranges (WQI < 25, 25≤WQI<50, 

50≤WQI<70, and 70≤WQI). WQI values of less than 25 indicate that water is not suitable for boating (the 

recreational use with the lowest required WQI), whereas WQI values greater than 70 indicate that waters 

are swimmable (the recreational use with the highest required WQI). For each WQI category under the 

baseline scenario and policy options in a given state, EPA estimated the weighted average WQI using 

reach miles as weights. 

The difference in WQI between baseline conditions and a given policy option (hereafter denoted as 

ΔWQI) is a measure of the change in water quality attributable to the policy option. To monetize benefits 

of the regulation, EPA used three ranges of water quality improvements 0.01< ΔWQI ≤ 0.1, 

0.1 < ΔWQI ≤ 0.5, and 0.5 < ΔWQI. Water quality improvements below 0.01 WQI units are not 

 

17 “Terminal” reaches that terminate into the Great Lakes or along the Canadian border were treated as inland rivers and 

streams. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-10 

calculated because they are so small EPA does not expect any benefits from these improvements. For 

each combination of the baseline water quality category and the improvement range, the Agency 

estimated average ΔWQI and the corresponding percentage of total reach miles in the state. In this 

analysis, the numbers of reach miles improved under each option – i.e., miles that experience a change in 

the WQI value – differ from the numbers presented in Chapter 6 which instead looked at absolute 

changes in TSS, TN, or TP concentrations. Additionally, data presented in this chapter cover all reaches 

within the RF1 network, including those that do not receive construction discharge directly but may 

nevertheless see improvement due to changes in water quality in upstream reaches. As noted in Chapter 

6, when considering all RF1 reaches, including those that do not receive construction sediment discharge 

directly, the analysis shows a total of 431,074 reach miles improving under Options 1, 2, and 4. These 

miles account for 68.7 percent of the total RF1 reach network. Option 3 has an even broader effect, 

reducing TSS concentrations in a total of 472,402 reach miles (75.3 percent of the RF1 reach network). 

Table 10-7, Table 10-8, Table 10-9, and Table 10-10 summarize changes in ambient water quality 

resulting from Options 1, 2, 3, and 4 respectively. Appendix H provides more detail on water quality 

improvements by the baseline water quality category. 

EPA estimated that Option 1 will not result in significant water quality improvements; this option 

improves water quality in only 73,054 reach miles18 or 11.6 percent of the 627,679 miles modeled in the 

analysis. EPA Region 4 shows the most water quality improvements, with 29,911 reach miles (33.1 

percent of reach miles modeled in this region) being affected. Region 6 is expected to have the next 

largest water quality improvement with 23,355 reach miles being affected under the post-compliance 

scenario. 

EPA’s Option 2 is estimated to improve ambient water quality in 112,429 reach miles (17.9 percent) that 

receive construction discharges nationwide. EPA’s Regions 1, 4, and 6 are each estimated to experience 

improved water quality on 20 percent or greater of RF1 reach miles included in the analysis. EPA’s water 

quality analysis predicts that Regions 4 and 6 will experience the most improvements in terms of both 

reach miles (45,817 and 31,540) and percentage of total regional reach length (50.7 percent and 

33.2 percent). EPA’s analysis also indicates that Region 6 would benefit the most in terms of the 

estimated magnitude of water quality improvements with 3.1 percent of reach miles modeled estimated to 

improve by greater than 0.5 WQI units. Conversely, EPA Region 2 is estimated to improve the least, with 

1,532 reach miles benefiting from higher water quality. 

Option 3 yields the most significant results overall with 129,747 reach miles expected to improve under 

the post-compliance scenario. The estimated scale of improvements ranges from 3.2 percent to 

55.2 percent of reach miles in EPA’s Region 8 and 4, respectively. EPA estimated that Region 4 will 

experience the largest water quality improvements, with 49,945 reach miles improved (55.2 percent of the 

reach miles modeled). EPA’s analysis also shows that Region 6 will have the most reach miles (3,487) 

improving by greater than 0.5 WQI units, while Region 2 will have the lowest water quality 

improvements with 2,147 reach miles showing any improvement under the post-compliance scenario. 

Option 4 generates the second most improvements in water quality when compared to the other three 

regulatory options. EPA calculates that a total of 113,963 reach miles (18.2 percent of reaches in the 

analysis) will be improved under this option. As with the other three options, Region 4 is expected to 

have the most improved reaches with 47,130 reach miles (52.1 percent) experiencing some improvements 

in water quality. EPA Region 2 is expected to improve the least in terms of reach miles, with 1,438 miles 

 

18 For the purpose of the discussion, “reach miles” refers to both freshwater and estuarine RF1 reaches. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-11 

(9.5 percent) improving. EPA Region 8 is expected to have the least improvements in terms of percentage 

of reach miles improved. Only 1.3 percent (1,739 miles) are expected to improve in the region under 

Option 4. Region 6 has the most miles with water quality improvements greater than 0.5 WQI units, 

3,313 miles. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 10-7: Estimated Water Quality Improvements Under Option 11

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI T

EPA 

Region 

Reach Miles 

Modeled 

Total Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Re

M

1 16,182 18,324 1,696 10.5% 9.3% 31 0.2% 0.2% 29 0.2% 0.2% 12 15,140 16,110 415 2.7% 2.6% 0 0.0% 0.0% 0 0.0% 0.0% 3 28,904 33,617 1,539 5.3% 4.6% 114 0.4% 0.3% 0 0.0% 0.0% 14 90,435 94,525 26,210 29.0% 27.7% 3,172 3.5% 3.4% 529 0.6% 0.6% 295 68,285 71,550 2,931 4.3% 4.1% 132 0.2% 0.2% 4 <0.1% <0.1% 36 95,098 98,681 17,902 18.8% 18.1% 4,227 4.4% 4.3% 1,227 1.3% 1.2% 237 60,909 60,909 4,196 6.9% 6.9% 562 0.9% 0.9% 168 0.3% 0.3% 48 130,311 130,311 495 0.4% 0.4% 64 0.1% 0.1% 362 0.3% 0.3% 9 54,228 56,492 1,360 2.5% 2.4% 134 0.3% 0.2% 151 0.3% 0.3% 1

10 68,189 69,524 3,541 5.2% 5.1% 1,322 1.9% 1.9% 542 0.8% 0.8% 5

Nation 627,679 650,043 60,285 9.6% 9.3% 9,757 1.6% 1.5% 3,012 0.5% 0.5% 73

1 Reach miles include both freshwater and estuarine RF1 reaches. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 10-8: Estimated Water Quality Improvements Under Option 21

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI T

EPA 

Region 

Reach Miles 

Modeled 

Total Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Re

M

1 16,182 18,324 3,035 18.8% 16.6% 235 1.5% 1.3% 41 0.3% 0.2% 32 15,140 16,110 1,527 10.1% 9.5% 5 <0.1% <0.1% 0 0.0% 0.0% 13 28,904 33,617 4,483 15.5% 13.3% 108 0.4% 0.3% 30 0.1% 0.1% 44 90,435 94,525 37,936 42.0% 40.1% 6,594 7.3% 7.0% 1,288 1.4% 1.4% 455 68,285 71,550 5,889 8.6% 8.2% 409 0.6% 0.6% 85 0.1% 0.1% 66 95,098 98,681 21,442 22.6% 21.7% 7,185 7.6% 7.3% 2,912 3.1% 3.0% 317 60,909 60,909 6,719 11.0% 11.0% 1,000 1.6% 1.6% 261 0.4% 0.4% 78 130,311 130,311 1,365 1.1% 1.1% 90 0.1% 0.1% 367 0.3% 0.3% 19 54,228 56,492 2,088 3.9% 3.7% 219 0.4% 0.4% 186 0.3% 0.3% 2

10 68,189 69,524 4,295 6.3% 6.2% 1,592 2.3% 2.3% 1,044 1.5% 1.5% 6

Nation 627,679 650,043 88,779 14.1% 13.7% 17,436 2.8% 2.7% 6,214 1.0% 1.0% 112

1 Reach miles include both freshwater and estuarine RF1 reaches.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 10-9: Estimated Water Quality Improvements Under Option 31

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI T

EPA 

Region 

Reach Miles 

Modeled 

Total Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

R

M

1 16,182 18,324 3,717 23.0% 20.3% 301 1.9% 1.6% 41 0.3% 0.2% 42 15,140 16,110 2,142 14.2% 13.3% 5 <0.1% <0.1% 0 0.0% 0.0% 23 28,904 33,617 6,084 21.1% 18.1% 163 0.6% 0.5% 30 0.1% 0.1% 64 90,435 94,525 40,092 44.3% 42.4% 8,244 9.1% 8.7% 1,610 1.8% 1.7% 495 68,285 71,550 7,228 10.6% 10.1% 630 0.9% 0.9% 85 0.1% 0.1% 76 95,098 98,681 23,679 24.9% 24.0% 8,228 8.7% 8.3% 3,487 3.7% 3.5% 357 60,909 60,909 7,473 12.3% 12.3% 1,300 2.1% 2.1% 319 0.5% 0.5% 98 130,311 130,311 3,677 2.8% 2.8% 111 0.1% 0.1% 376 0.3% 0.3% 49 54,228 56,492 2,834 5.2% 5.0% 282 0.5% 0.5% 222 0.4% 0.4% 3

10 68,189 69,524 4,405 6.5% 6.3% 1,812 2.7% 2.6% 1,170 1.7% 1.7% 7

Nation 627,679 650,043 101,332 16.1% 15.6% 21,075 3.4% 3.2% 7,340 1.2% 1.1% 129

1 Reach miles include both freshwater and estuarine RF1 reaches.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table 10-10: Estimated Water Quality Improvements Under Option 41

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI T

EPA 

Region 

Reach Miles 

Modeled 

Total Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

R

M

1 16,182 18,324 2,338 14.5% 12.8% 156 1.0% 0.9% 29 0.2% 0.2% 22 15,140 16,110 1,433 9.5% 8.9% 5 <0.1% <0.1% 0 0.0% 0.0% 13 28,904 33,617 4,620 16.0% 13.7% 108 0.4% 0.3% 30 0.1% 0.1% 44 90,435 94,525 38,507 42.6% 40.7% 7,159 7.9% 7.6% 1,465 1.6% 1.6% 475 68,285 71,550 5,979 8.8% 8.4% 377 0.6% 0.5% 85 0.1% 0.1% 66 95,098 98,681 21,693 22.8% 22.0% 7,819 8.2% 7.9% 3,313 3.5% 3.4% 327 60,909 60,909 6,860 11.3% 11.3% 1,205 2.0% 2.0% 280 0.5% 0.5% 88 130,311 130,311 1,282 1.0% 1.0% 90 0.1% 0.1% 367 0.3% 0.3% 19 54,228 56,492 2,166 4.0% 3.8% 197 0.4% 0.4% 186 0.3% 0.3% 2

10 68,189 69,524 3,894 5.7% 5.6% 1,380 2.0% 2.0% 943 1.4% 1.4% 6

Nation 627,679 650,043 88,772 14.1% 13.7% 18,495 3.0% 2.9% 6,696 1.1% 1.0% 113

1 Reach miles include both freshwater and estuarine RF1 reaches.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-16 

10.2 Willingness to Pay for Water Quality Improvements 

To estimate nonmarket benefits of water quality improvements resulting from the regulation, EPA used a 

benefits transfer function based on meta-analysis results presented in Appendix G of this report. The 

general approach follows standard methods illustrated by Johnston et al. (2005) and Shrestha et al. (2007), 

among many others (see Rosenberger and Phipps 2007). This function allows the Agency to forecast 

willingness to pay (WTP) based on assigned values for model variables, chosen to represent a resource 

change in the regulation’s policy context. EPA’s meta-analysis results imply a simple benefit function of 

the following general form: 

ln(WTP) = intercept + ∑ nt(coefficie )(Independ Variableent )Values i i (Eq. 10-2) 

Here, ln(WTP) is the dependent variable in the meta-analysis—the natural log of WTP for water quality 

improvements. The metadata include independent variables characterizing specific details of the 

resource(s) valued, such as the baseline resource conditions; the extent of resource improvements and 

whether they occur in estuarine or freshwater; the geographic region and scale of resource improvements 

(e.g., the number of waterbodies); resource characteristics (e.g., baseline conditions, the extent of water 

quality change, and ecological services affected by resource improvements); characteristics of surveyed 

populations (e.g., users, nonusers); and other specific details of each study. Table 10-11 provides the 

estimated regression equation intercept (5.71), variable coefficients (coefficienti), and the corresponding 

independent variable names. Appendix G provides detail on the metadata, model specification, and 

justification for the functional form. 

EPA assigned a value to each model variable corresponding with theory, characteristics of the water 

resources, and sites affected by the regulation and the policy context. Table 10-11 presents a complete list 

of assigned variable values. 

EPA followed Johnston et al. (2006) in assigning values for methodological attributes (i.e., variables 

characterizing the study methodology used in the original source studies), which are set at mean values 

from the metadata except in cases where theoretical considerations dictate alternative specifications. This 

follows general guidance from Bergstrom and Taylor (2006) that meta-analysis benefit transfer should 

incorporate theoretical expectations and structures, at least in a weak form. In this instance, three of the 

methodology variables, discrete, WQI_study, and outlier_bids are all included with an assigned value of 

one. Year_index is given the value of 9.68, which corresponds to the mean year that the studies were 

conducted, 2002. Nonparam is set to zero because most studies included in metadata used parametric 

methods to estimate WTP values. Other study and methodology variables (volunt, mail, lump_sum, 

non_reviewed, median_WTP) are assigned a zero value. 

EPA used state-specific median household income, as provided by the U.S. Census Bureau’s 2006 

American Communities Survey (U.S. Census Bureau 2006a), to assign a value for the income variable 

(income). To be consistent with the WTP estimates, which are all estimated in 2008 dollars, EPA used the 

Consumer Price Index to adjust the value from the 2006 survey to 2008 dollars. The variable nonusers 

was set to zero because water quality improvements resulting from reduced sediment discharge from 

construction sites would benefit both users and nonusers of the affected resources. 

The regulation is expected to affect water quality at a regional level because construction sites are located 

throughout the entire United States. The Agency set a dummy variable denoting multiple regions (mr) to 

zero because the expected magnitude of water quality improvement is relatively modest and, as a result, 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-17 

EPA’s analysis focuses primarily on in-state or local resource improvements. The Mountain Plain 

regional dummy variable (mp) is set to zero because the magnitude of the regional effect suggests that 

spurious or otherwise unexplained effects (e.g., the effect of specific researchers who appear more than 

once in the data) may drive their overall magnitude. Appendix G provides more detail on regression 

results. 

To account for the regional scale of the water quality effect in freshwater bodies resulting from the 

regulation, the variable regional_fresh is assigned a value of one. Other variables relating to waterbody 

type (i.e., single_lake, single_river, salt_pond, multiple_river, num_riv_pond) are set to zero. In 

estimating WTP for water quality improvements in estuaries and coastal reaches, EPA set all waterbody 

type variables (including regional_fresh) to zero because the default value is a resource change taking 

place in estuaries.19

Water quality improvements resulting from the regulation are likely to enhance a variety of water 

resource uses, including fishing, swimming, and boating. Therefore, variables denoting multiple uses 

(allmult) and recreational fishing (fish_use) are assigned a value of one, while the variable denoting 

nonspecified uses (nonspec) is set to zero. However, the variable fishplus is given a value of zero because 

it is unlikely that the regulation will cause more than a 50 percent increase in the fish population. Baseline 

water quality (baseline) and change in water quality (quality_ch) are assigned state-specific values as 

described in Section 10.1. For a broader discussion of issues involved in the specification of variable 

levels for meta-analysis benefit transfer, see Johnston et al. (2005, 2006), among others. 

 

19 The water quality model used in this analysis (SPARROW) does not go beyond the terminal reach to estimated water quality 

in large waterbodies, and these waterbodies are not fully characterized. However, EPA did calculate change in WQI for 

several more complex systems such as the Great Lakes and estuaries using ambient water quality data from other sources 

discussed in Section 11.1.4. EPA used mixing zone dilution factors to estimate the expected change in WQI in complex 

waterbodies under the post-compliance scenario (see Chapter 6 for details). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-18 

Table 10-11: Independent Variable Assignments 

Variable 

Type Variable Coefficient 

Assigned 

Value Explanation 

intercept 5.7109 . 

year_index -0.08043 9.68 Set to 9.68 to reflect the mean year that the 

studies in the dataset were conducted. 

discrete -0.1248 1 Set to one to reflect survey efforts that 

employed discrete choice elicitation methods, 

which are preferred over other approaches, 

such as open-ended and payment card 

methods. 

volunt -1.3233 0 Set to zero because hypothetical voluntary 

payment mechanisms are not even potentially 

incentive compatible (Mitchell and Carson 

1989). 

mail -0.2013 0 Set to zero because mail surveys are believed 

to be of less quality than in-person interviews. 

lump_sum 0.5569 0 Set to zero because the policy option will be 

paid for over a period of years. 

WQI -0.3275 1 Set to one because of the methodological use 

of the WQI in the meta-analysis. 

nonparam -0.6698 0 Set to zero because most studies used in the 

meta analysis used regression analysis to 

calculate willingness to pay values. 

non_reviewed -0.2718 0 Set to zero because studies published in peerreviewed journals are preferred. 

median_WTP -0.5358 0 Set to zero because only average or mean 

WTP values in combination with the number

of affected households will mathematically 

yield total benefits if the distribution of WTP 

is not perfectly symmetrical. 

Study and 

Methodology 

Variables 

outlier_bids -0.8837 1 Set to one because survey data that exclude 

such responses are preferable; outlier bids are 

often excluded from the analysis of stated 

preference data because these bids (often 

identified as greater than a certain percentage 

of a respondent’s income) may indicate that 

a respondent did not consider his or her 

budget constraints and or supplementary 

goods. 

Surveyed 

Population 

income 0.0000027 Varies Median annual household income data from 

the 2006 American Communities Survey; 

median household income values assigned 

separately for each state (U.S. Census Bureau 

2006a). 

nonusers -0.4036 0 Set to zero in order to estimate the total value 

for aquatic habitat improvements, including 

both use and nonuse values; for nonuser 

population, the total value of water resource 

improvements includes nonuse values only 

(Freeman 2003). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-19 

Table 10-11: Independent Variable Assignments 

Variable 

Type Variable Coefficient 

Assigned 

Value Explanation 

single_river -0.4279 0 

single_lake -0.06316 0 

multiple_river -1.4752 0 

regional_fresh 0.1588 Varies 

Waterbody 

Type 

Variables 

salt_pond 0.9849 0 

For valuing improvement in freshwater 

bodies, regional_fresh is set to one because 

the expectation is that multiple freshwater 

bodies within a state would be affected by the 

regulation. For saltwater reaches, all 

waterbody type variables are set to zero 

because the default value in the model is a 

resource change taking place in estuaries. 

num_riv_ 

pond 

0.1173 0 Indicates the number of rivers or salt ponds 

affected by a policy, and is set to zero because 

this analysis assumes that the effluent 

guidelines will affect the entire 

watershed/region; this variable assignment is 

constant across study regions. 

mr -0.8846 0 

Geographic 

Region and 

Scale 

Variables 

mp 1.6337 0 

Regional variables are omitted from the 

predictive portion of the analysis (i.e., set to 

zero) because the regression results suggest 

that these variables may be picking spurious 

or other unexplained effects (e.g., author’s 

effect). 

allmult -0.3728 1 Set to one because it is assumed that multiple 

species would benefit from water quality 

improvements 

nonspec -0.4042 0 Set to zero because it is assumed that multiple 

species would benefit from water quality 

improvements. 

lnquality_ch 0.4065 Varies Set to the natural log of average change in the 

WQI for a given analytic scenario. 

fish_use -0.3317 1 Set to one because the regulation is expected 

to benefit a variety of aquatic species and 

therefore enhance recreational fishing 

opportunities. 

fishplus 0.4432 0 Set to zero because the regulation is not 

expected to result in a fish population change 

of 50 percent or greater. 

Resource 

Improvement 

Variables 

lnbase 0.02610 Varies Set to the natural log of the average base 

water quality for a given state. 

Economic values of water quality improvements are calculated at the state level and organized by analytic 

scenario. EPA calculated the state-level average of baseline and post-compliance WQI changes 

corresponding to 12 analytic scenarios reflecting combinations of four levels of baseline water quality 

conditions (WQI≤ 25, 25<WQI≤50, 50<WQI≤70, and 70<WQI) and three different ranges of the 

expected water quality improvements (0.01< ΔWQI≤0.1, 0.1<ΔWQI≤0.5, 0.5<ΔWQI). EPA then 

calculated state-level WTP values for each scenario. 

For each analytic scenario, coefficient estimates for each variable, taken from meta-analysis results (Table 

10-11, column 3) are multiplied by the variable levels chosen above (Table 10-11, column 4). The sum of 

these products represents the predicted natural log of WTP (ln_WTP) for a given analytic scenario, as 

indicated by Equation 10-2. The final step uses a standard formula to transform this predicted natural log 

into the desired WTP estimate. This formula is given by: 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-20 

WTP = exp(ln_WTP + σe

2

/2) (Eq. 10-3) 

Where: 

exp(·) = the exponential operator 

ln_WTP = the predicted natural log of WTP for a given analytic scenario 

σe

2

 = the model residual variance (0.1876) taken from Table G-3 in Appendix G. 

The total WTP regression model presented above can be used to predict WTP for each of the studies in 

the database; however, estimates derived from regression models are subject to some degree of error and 

uncertainty. To better characterize the uncertainty or error bounds around predicted WTP, EPA used a 

procedure described by Krinsky and Robb (1986). The procedure involves sampling the variance–

covariance matrix of the estimated coefficients, which is a standard output of the statistical package used 

to estimate the meta-analysis model. WTP values are then calculated for each drawing from the variance–

covariance matrix and an empirical distribution of WTP values is constructed. By varying the number of 

drawings, it is possible to generate an empirical distribution with a desired degree of accuracy (Krinsky 

and Robb 1986). The low and high estimate of WTP values is then identified based on the 10th and 90th 

percentile of WTP values from the empirical distribution. These bounds may help decision-makers 

understand the uncertainty associated with the benefit results. 

EPA used the Krinsky and Robb (1986) procedure to estimate the 10th, 50th, and 90th percentiles of total 

WTP for each EPA region, based on the results of the total WTP regression model. Table 10-12 presents 

the results of these calculations. The Agency notes that this analysis provides confidence limits for WTP 

estimates related to the covariance matrix of meta-analysis parameter estimates. It does not, however, 

assess the sensitivity of results to changes in meta-regression model assumptions or specifications (cf. 

Johnston et al. 2005, 2006) or assumptions implied in benefit aggregation (cf. Loomis 1996; Loomis et al. 

2000; Bateman et al. 2006). As noted above, however, the Agency has to the extent practicable made 

assumptions and specifications that lead to conservative benefit estimates.

Table 10-12 presents 10th, 50th, and 90th percentiles of household WTP for water quality improvements 

resulting form reduced sediment discharge from construction sites by EPA region and policy option.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 Table 10-12: Estimates of Annual Household Willingness to Pay for Water Quality Improvement by RegioOption (2008$) 

Option 1 Option 2 Option 3 

EPA 

Region 

Low 

10% 

Mid 

50% 

High 

90% 

Low 

10% 

Mid 

50% 

High 

90% 

Low 

10% 

Mid 

50% 

High 

90% 

Low 

10% 

1 $0.14 $0.87 $1.96 $0.36 $2.13 $4.74 $0.50 $2.94 $6.54 $0.30 2 $0.04 $0.30 $0.66 $0.17 $1.10 $2.45 $0.25 $1.63 $3.66 $0.16 3 $0.09 $0.56 $1.24 $0.28 $1.71 $3.79 $0.40 $2.40 $5.31 $0.29 4 $1.14 $4.39 $9.07 $1.90 $7.05 $14.37 $2.17 $7.87 $15.96 $1.96 5 $0.09 $0.49 $1.07 $0.21 $1.13 $2.45 $0.27 $1.43 $3.09 $0.22 6 $1.19 $3.98 $7.87 $1.88 $5.83 $11.28 $2.16 $6.59 $12.68 $2.05 7 $0.42 $1.62 $3.32 $0.70 $2.63 $5.34 $0.82 $2.97 $5.98 $0.76 8 $0.45 $0.80 $1.25 $0.47 $0.93 $1.52 $0.54 $1.29 $2.29 $0.47 9 $0.17 $0.64 $1.30 $0.26 $0.98 $2.01 $0.30 $1.16 $2.37 $0.26 

10 $0.64 $2.20 $4.41 $0.98 $3.04 $5.93 $1.10 $3.33 $6.42 $0.84 

National $0.49 $1.85 $3.78 $0.83 $3.10 $6.33 $0.97 $3.63 $7.41 $0.86 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-22 

As shown in Table 10-12 the mid-point estimates of household WTP for water quality improvements 

resulting from the regulation range from $1.85 to $3.63 per household per year under different policy 

options. The estimated WTP values vary greatly across EPA regions depending on the expected water 

quality improvements and water resource and population characteristics in a given region. 

Option 1 is estimated to improve ambient water quality in 11.6 percent of RF1 reach miles modeled in the 

analysis. This option results in the least water quality improvements across all EPA regions. EPA Region 

4 is expected to the have the greatest improvements in water quality under this option. The estimated 

annual WTP per household in Region 4 is $4.39 (mid-point estimate). Region 2 has the smallest 

household WTP, with a mid-point estimate of $0.30 per household. Nationwide, the estimated annual 

household WTP is $1.85 (mid-point estimate). 

Option 2 is estimated to improve ambient water quality in 17.9 percent of RF1 reach miles modeled. The 

estimated national average WTP for water quality improvements resulting from the regulation is $3.10 

per household per year. Regions 4 and 6 are estimated to see improvements in water quality in 50.7 and 

33.2 percent of their total RF1 reach miles (Table 10-8). EPA’s analysis suggests that Region 4 

households would be willing to pay the most, with a mid-point estimate of $7.05 per household per year, 

for water quality improvements resulting from the regulation. Region 6 has the second largest household 

WTP of $5.83. Conversely, households located in Region 2 are estimated to have the lowest WTP for 

water quality improvements from the regulation, $1.10. 

Policy Option 3 yields the most significant results overall in terms of reach miles expected to improve 

under the post-compliance scenario (20.7 percent). The estimated scale of improvements ranges from 

3.2 percent to 55.6 percent of total reach miles in Region 8 and 4, respectively (Table 10-9). Nationwide, 

the estimated annual per-household WTP for water quality improvements under Option 3 is $3.63 (midpoint estimate). 

Option 4 is estimated to generate improvements in 18.2 percent of reach miles nationally. These 

improvements result in a national household WTP of $3.17 per year (mid-point estimate).. As with the 

other options, Region 4 has the largest annual household WTP value with a mid-point estimate of $7.23. 

Conversely, EPA Region 8 has the lowest WTP value with a mid-point estimate of $0.91. 

10.3 Estimating Total WTP for Water Quality Improvements 

For each policy option, EPA calculated reach-level WTP as follows. First EPA estimated mean state-level 

per-household WTP for each combination of the baseline water quality category (WQI baseline) and the 

expected change in WQI (ΔWQI). Then, the Agency assigned each reach in the analysis a mean 

household WTP value based on reach location, baseline water quality, and change in water quality. The 

WTP was then multiplied by the number of households in a given state in 2006 and the percentage of 

reach miles in that state that comprise a given reach. The number of households per state was calculated 

by taking U.S. Census Bureau population estimates for 2006 for each state and dividing by the average 

number of people per household for a given state as reported in U.S. Census Bureau (2006a, 2006b). The 

total WTP equation for each reach is provided below (Equation 10-4): 

TWTP WQIWTP WQI),( StateHH PercentRiverMiles) reach = baseline Δ × × (Eq. 10-4) 

Where: 

TWTPreach = the reach-level welfare change from improved water quality 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-23 

WTP = the estimated state-level per-household WTP for water quality 

improvement for a given combination of the baseline water quality 

category (WQI baseline) and the expected change in water quality under the 

post-compliance scenario (ΔWQI) 

StateHH = the number of households in a given state 

PercentRiverMiles = the percentage of total reach miles in a given state that are comprised of a 

given reach. 

Finally, EPA aggregated reach-level benefits to the regional level. The regional benefits for the ten EPA 

regions were then combined to calculate the national benefit of the regulation. Table 10-13 presents 

estimated benefits of the regulation by EPA region and policy option.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 Table 10-13 :Regional Willingness to Pay for Water Quality Improvement (Millions 2008$) 

Option 1 Option 2 Option 3 

EPA 

Region 

Lower 

10% 

Bound 

Mean 

Upper 

90% 

Bound 

Lower 

10% 

Bound 

Mean 

Upper 

90% 

Bound 

Lower 

10% 

Bound 

Mean 

Upper 

90% 

Bound 

Lower 

10% 

Bound

1 $0.81 $4.93 $11.10 $2.06 $12.07 $26.84 $2.84 $16.67 $37.03 $1.71 2 $0.45 $3.10 $6.94 $1.76 $11.50 $25.67 $2.63 $17.14 $38.36 $1.71 3 $1.07 $6.45 $14.23 $3.27 $19.70 $43.59 $4.58 $27.61 $61.08 $3.36 4 $26.41 $101.44 $209.34 $43.94 $162.70 $331.84 $50.10 $181.69 $368.63 $45.36 5 $1.78 $9.92 $21.64 $4.27 $22.97 $49.85 $5.43 $29.01 $62.88 $4.37 6 $15.62 $52.44 $103.61 $24.72 $76.74 $148.49 $28.45 $86.74 $166.91 $27.01 7 $2.28 $8.86 $18.12 $3.84 $14.37 $29.15 $4.49 $16.24 $32.67 $4.17 8 $1.76 $3.17 $4.91 $1.85 $3.66 $5.97 $2.12 $5.08 $9.03 $1.84 9 $2.67 $9.95 $20.26 $4.04 $15.32 $31.28 $4.72 $18.09 $36.97 $4.08 

10 $2.90 $10.01 $20.09 $4.45 $13.86 $27.03 $5.03 $15.16 $29.25 $3.82 

National $55.75 $210.27 $430.24 $94.19 $352.90 $719.72 $110.39 $413.41 $842.81 $97.44 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-25 

EPA estimates that total annual benefits of water quality improvements resulting from reduced sediment 

discharge from construction sites range from $210.3 million under Option 1 to $413.4 million under 

Option 3. The estimated mean regional benefits vary from $3.1 to $181.7 million per year, depending on 

the level of construction activity, average rainfall, water resource characteristics, and resident population 

in a given region and stringency of the policy option. 

As shown in Table 10-13, Option 1 generates the least water quality improvements of the four regulatory 

options. Thus, this option yields the smallest benefits at the regional and national levels. The national 

benefits of water quality improvements under this option range from $55.8 million to $430.2 million per 

year, with a mid-point estimate of $210.3 million. Region 4 gains the most benefit from water quality 

improvement, with a total value of $101.4 million per year (48.2 percent of the total national benefits). 

Region 6 has the second largest benefits ($52.4 million per year), which account for 24.9 percent of the 

total national benefits. 

Under Option 2, the mid-point estimate of national benefits is $352.9 million per year, with low and high 

estimates of $94.2 million and $719.7 million per year. Region 4 gains the most from water quality 

improvements resulting from the regulation ($162.7 million per year). EPA Region 8 receives the smallest 

annual benefits, $3.7 million 

Under Option 3, the estimated national benefits of water quality improvement from the regulation are 

$413.4 million per year, with a low estimate of $110.4 and a high estimate of $842.8 million. As with the 

other policy options Region 4 receives the largest benefits from water quality improvements, accounting 

for 43.9 percent ($181.7 million per year) of the total national benefits. Region 8 is estimated to gain least 

under Option 3, with the total regional benefits estimated at $5.1 million per year. 

Option 4 is expected to generate national benefits of $361.0 million per year with a low estimate of $97.4 

million and a high estimate of $734.3 million. EPA Region 4 gains the largest benefits, with a total of 

$159.9 million per year. Region 8 has the smallest improvements in water quality under this option, with 

improvements in only 1.3 percent of reach miles, and thus has the smallest annual benefits ($3.6 million). 

10.4 Uncertainty and Limitations 

A number of issues are common to all benefit transfers. Benefit transfer involves adapting research 

conducted for another purpose in the available literature to address the policy questions at hand. Because 

benefits analysis of environmental regulations rarely affords enough time to develop original stated 

preference surveys that are specific to the policy effects, benefit transfer is often the only option to inform 

a policy decision. As a result, they are nearly universal in benefit-cost analyses (Smith et al. 2002). 

Benefit transfers are by definition characterized by a difference between the context in which resource 

values are estimated and that in which benefit estimates are desired (Rosenberger and Phipps 2007). The 

ability of meta-analysis to adjust for the influence of study, economic, and resource characteristics on 

WTP can minimize, but not eliminate, potential biases (Smith et al. 2002; Rosenberger and Stanley 2006; 

Rosenberger and Phipps 2007). As is typical in applied benefit transfers, the meta-analysis model used in 

this analysis provides a close, but not perfect, match to the context in which values are desired. Therefore, 

some beneficial effects associated with reducing sediment discharges from construction sites are not 

accounted for in the estimated WTP values. For example, surface water valuation studies included in 

meta-data focused primarily on changes in ambient water quality. Other effects associated with sediment 

discharges (e.g., sedimentation of river beds, stability and erosion of river banks, and changes in the 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-26 

stream carrying capacity) were not included in the original study scenarios and, as a result, from the 

estimated WTP for reducing sediment discharges from construction sites. 

Some related and additional limitations inherent to the meta-analysis model and the subsequent benefit 

transfer include: 

3⁄4 The Agency notes, as detailed by Loomis (1996), Loomis et al. (2000) and Bateman et al. 

(2006), among others, that there are numerous uncertainties and associated assumptions 

required to aggregate WTP across spatial jurisdictions. While these uncertainties are well 

known, the literature does not agree on appropriate, standardized guidance for benefit 

aggregations, and applied benefit-cost analysis almost universally requires simplifying 

assumptions in order to generate defensible welfare aggregations. In an ideal context, analysts 

would have information necessary to estimate spatially referenced distance decay 

relationships for all changes resulting from policies under consideration (cf. Bateman et al. 

2006). However, the Agency notes that even the most advanced literature provides only 

simple illustrations of such issues, and none methodologically sufficient to support regulatory 

analysis. In analyzing benefits of the final regulation, EPA assumed that households would 

gain no benefits from water quality improvements in aquatic resources located outside of 

their state of residence. As a result, the population considered in the benefits analysis of the 

regulation does not represent all the households that are likely to hold values for water 

resources in a given state. Residents of other states may hold values for water resources 

outside of their home state, in particular if such resources have personal, regional, or national 

significance. Even if per household WTP for out-of-state residents are small they can be very 

large in the aggregate if these values are held by a substantial fraction of the population 

3⁄4 Some resource valuation studies have found that respondents in the typical contingent market 

situation may overstate their WTP compared to their likely behavior in a real-world situation. 

However, the magnitude of hypothetical bias on the estimated WTP is uncertain. Following 

standard benefit transfer approaches, including meta-analytic transfers, this analysis proceeds 

under the assumption that each source study provides a valid, unbiased estimate of the 

welfare measure under consideration (cf. Moeltner et al. 2007; Rosenberger and Phipps 

2007). To minimize potential hypothetical bias, EPA set independent variable values to 

reflect best benefit transfer practices.

3⁄4 The estimation of WTP may be sensitive to differences in the environmental water quality 

measures. Studies that did not use the WQI were mapped to the WQI so a comparison could 

be made across studies. The dummy variable (WQI) captures the effect of a study using 

(WQI=1) or not using the WQI (WQI=0). It was found that studies that did not use the WQI 

had lower WTP values. This may indicate that there may have been some systematic biases in 

the mapping of studies that did not use the WQI. In analyzing the benefits of this regulation, 

EPA set the WQI to one to reduce uncertainty in WTP estimates associated with studies that 

did not include WQI as a native survey instrument. See Appendix G for a detailed discussion 

of water quality measures used in the original studies included in the meta-analysis. 

3⁄4 Transfer error may occur when benefit estimates from a study site are adopted to forecast the 

benefits of a policy site. Rosenberger and Stanley (2006) define transfer error as the 

difference between the transferred and actual, generally unknown, value. While meta-analysis 

is fairly accurate when estimating benefit function, transfer error may be a problem in cases 

where the sample size is small. While meta-analyses have been shown to outperform other 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 10-27 

function-based transfer methods in many cases, this result is not universal (Shrestha et al. 

2007). This notwithstanding, results reviewed by Rosenberger and Phipps (2007) are “very 

promising” for the performance of meta-analytic benefit transfers relative to alternative 

transfer methods. 

Additional limitations and uncertainties are associated with the use of WQI to link water quality changes 

from reduced sediment discharges to effects on human uses and support for aquatic and terrestrial species 

habitat: 

3⁄4 The estimated changes in WQI reflect only water quality improvements resulting directly 

from reductions in total suspended solid and nutrient loadings. They do not include 

improvements in water quality indicators indirectly associated with pollutant loadings, (e.g., 

dissolved oxygen). This is likely to result in underestimation of the expected water quality 

changes resulting from the proposed regulation because the combined impact of several 

pollutants on ambient water quality is likely to be greater than the sum of the individual 

impacts of reducing concentrations of sediments and nutrients. 

3⁄4 The WQI index used in EPA’s analysis uses TSS concentrations as a proxy for water 

turbidity. It does not include turbidity as a separate parameter in calculation of the WQI. This 

omission may understate the expected change in WQI resulting from the final regulation. 

3⁄4 The methodology used to translate in-stream sediment and nutrient concentrations into subindex scores employs nonlinear transformation curves. Water quality changes that fall outside 

of the sensitive part of the transformation curve (i.e., above/below the upper/lower bounds) 

yield no benefit in the analysis. 

Limitations and uncertainty associated with the use of SPARROW are discussed in Chapter 6 of this 

report.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 11-1 

11 Total Estimated Benefits 

This chapter summarizes findings from EPA’s analysis of the expected benefits from the regulation. EPA 

considered a wide range of policy options in developing this regulation and presents here the three policy 

options for which a full cost-benefit analysis was performed. These options are: 

3⁄4 Option 1: nonnumeric effluent limitations on all sites. 

3⁄4 Option 2: in addition to the requirements of Option 1, active sediment treatment is required 

on sites with 30 or more acres disturbed at one time and treated discharge is subject to a 

turbidity standard of 13 NTU. 

3⁄4 Option 3: in addition to the requirements of Option 1, active sediment treatment is required 

on sites with 10 or more acres disturbed at one time and treated discharge is subject to a 

turbidity standard of 13 NTU. 

3⁄4 Option 4: in addition to the requirements of Option 1, passive sediment treatment is required 

on sites with 10 or more acres disturbed at one time, and treated discharge is subject to a 

turbidity standard of 280 NTU. 

Chapter 1 describes these policy options in more detail. 

11.1 Summary of the Estimated Benefits 

The Agency analyzed benefits of the regulation in four benefits categories: navigation, water storage, 

drinking water treatment, and nonmarket benefits of water quality improvement. The previous chapters of 

this report provide the details of the methods and data used in the analysis of the four monetized benefit 

categories (i.e., list benefit categories). See Chapters 7, 8, and 9 for a discussion of the avoided cost 

methods used to estimate benefits to navigation, water storage, and treatment. Chapter 10 and Appendix G 

provide a discussion of the methods used to estimate the willingness to pay (WTP) for water quality 

improvements resulting from the policy options considered for the regulation. 

EPA was unable to quantify benefits stemming from: 

3⁄4 Reduced damages to industrial and agricultural water users 

3⁄4 Improved market value of properties near surface waters 

3⁄4 Reduced cost of stormwater system maintenance and flood damages 

3⁄4 Benefits to commercial fishing and shellfishing 

3⁄4 Reduced cost of drinking water treatment to improve taste and odor resulting from 

eutrophication of drinking water sources. 

Chapter 5 of this report describes these benefits qualitatively. In addition, limitations inherent in EPA’s 

analysis of water quality improvements and monetization of benefits are likely to result in understatement 

of benefits resulting from the final regulation. Section 11.2 below summarizes key limitations of EPA’s 

analysis of benefits stemming from reducing construction site discharges.. 

Table 11-1 presents low, midpoint, and high estimates of benefits under each policy option, consisting of 

benefits to navigation, water storage, drinking water treatment, and WTP. Table 11-2, Table 11-3, Table 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 11-2 

11-4,and Table 11-5 detail total benefits by EPA region, policy option, and estimate range. It should be 

noted that these tables incorporate the confidence intervals of 10, 50, and 90 percent from the WTP 

analysis into the low, mid, and high sensitivity analyses performed for the avoided cost estimates. Though 

these are conceptually different, they are both intended to present a range of values to account for some of 

the uncertainty inherent in these estimates. The sensitivity analyses create a range by varying EPA’s 

assumptions underlying the analysis, while the confidence interval presents high and low bounds from the 

meta-analysis regression. 

All tables present benefits for navigable waterway and reservoir dredging calculated using both 3 and 7 

percent discount rates. Because the discount rate only applies to two of the four monetized benefits 

categories, which represent at most 5 percent of total benefits, varying it has little effect on the total 

benefits estimate. EPA calculated benefits for drinking water treatment and WTP using a single-year 

timeframe, which did not require discounting or additional calculations to present annual values. All 

benefits presented reflect annual values. The remaining discussion presents the benefits estimates 

assuming a 3 percent discount rate; the associated tables present results for both discount rates. 

Total national benefits vary significantly among the three regulatory options. Under Option 1, the 

estimated benefits range from approximately $59.0 million to approximately $434.2 million, with a 

midpoint estimate of $214.0 million. Estimated avoided costs range from $3.3 million to $4.1 million, 

with a midpoint of $3.8 million, and WTP ranges from $55.8 to $430.2 million , with a midpoint estimate 

of $210.3 million. 

For Option 2, the estimated benefits range from $100.5 million to $727.4 million, with a midpoint 

estimate of $360.1 million. The estimated WTP for water quality improvements from reduced sediment 

discharges from construction sites under Option 2 ranges from $94.2 to $719.7 million, with a midpoint 

value of $352.9 million. Estimated cost savings range from $6.3 million to $7.7 million per year, with a 

midpoint estimate of $7.2 million. 

Under Option 3, total benefits are estimated to be between $118.0 and $852.2 million, with a midpoint 

estimate of $422.3 million. The avoided costs are estimated to be between $7.7 and $9.4 million per year, 

with a midpoint estimate of $8.9 million. WTP under Option 3 ranges from $110.4 million to $842.8 

million , with a midpoint estimate of $413.4 million. 

Under Option 4, the final regulation, the estimated benefits range from $104.3 million to $742.7 million, 

with a midpoint estimate of $368.9 million. Nonmarket benefits estimated based on household WTP for 

surface water quality improvements account for 93, 98, and 99 percent of total benefits from the 

regulation in the low, mid, and high estimates, respectively. The estimated WTP for water quality 

improvements from reduced sediment discharges from construction sites under Option 4 ranges from 

$97.4 to $734.3 million, with a midpoint estimate of $361.0 million. The estimated cost savings to 

industry and government through reduced costs of navigable waterway maintenance, reservoir dredging, 

and drinking water treatment range from $6.8 million to $8.3 million per year, with a midpoint estimate of 

$7.9 million. Under Option 4, avoided cost benefits account for 7, 2, and 1 percent of total benefits in the 

low, mid, and high estimates, respectively. Because this option requires passive treatment at sites with 

more than 10 acres of land disturbed and establishes a numeric effluent limit, its benefits are more than 

double those of Option 1, which does not establish numeric criteria for sediment discharge. It also 

produces more benefits than Option 2, which requires active treatment of sediment but on fewer sites. 

Benefits under Option 4 are lower than those under Option 3, which would require active sediment 

treatment on the same sites where Option 4 requires passive treatment, which is less burdensome. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 11-3 

Table 11-1: Annual Total National Benefits by Benefits Category (millions of 

2008$) 

3% Discount Rate 7% Discount Rate 

Benefit Category Low Mid High Low Mid High 

Option 1 

Navigation $1.0 $1.3 $1.3 $1.0 $1.2 $1.3 

Water Storage1 $1.3 $1.4 $1.5 $1.1 $1.2 $1.4 

Drinking Water1 $1.0 $1.2 $1.3 $1.0 $1.2 $1.3 

Avoided Costs $3.3 $3.8 $4.1 $3.1 $3.7 $4.0 

WTP1 $55.8 $210.3 $430.2 $55.8 $210.3 $430.2 

Total2 $59.0 $214.1 $434.3 $58.8 $213.9 $434.2 

Option 2 

Navigation $2.1 $2.6 $2.8 $2.1 $2.5 $2.7 

Water Storage1 $2.7 $2.9 $3.0 $2.2 $2.6 $3.0 

Drinking Water1 $1.4 $1.8 $1.9 $1.4 $1.8 $1.9 

Avoided Costs $6.3 $7.2 $7.7 $5.8 $6.9 $7.5 

WTP1 $94.2 $352.9 $719.7 $94.2 $352.9 $719.7 

Total2 $100.5 $360.1 $727.4 $99.9 $359.8 $727.2 

Option 3 

Navigation $2.7 $3.3 $3.4 $2.6 $3.2 $3.4 

Water Storage1 $3.3 $3.6 $3.8 $2.8 $3.2 $3.7 

Drinking Water1 $1.7 $2.1 $2.1 $1.7 $2.1 $2.1 

Avoided Costs $7.7 $8.9 $9.4 $7.0 $8.4 $9.2 

WTP1 $110.4 $413.4 $842.8 $110.4 $413.4 $842.8 

Total2 $118.0 $422.3 $852.2 $117.4 $421.8 $852.0 

Option 4 

Navigation $2.4 $2.9 $3.0 $2.3 $2.8 $3.0 

Water Storage1 $3.0 $3.2 $3.4 $2.5 $2.9 $3.3 

Drinking Water1 $1.5 $1.8 $1.9 $1.5 $1.8 $1.9 

Avoided Costs $6.8 $7.9 $8.3 $6.3 $7.5 $8.2 

WTP1 $97.4 $361.0 $734.3 $97.4 $361.0 $734.3 

Total2 $104.3 $368.9 $742.7 $103.7 $368.5 $742.5 1

 These savings were calculated for a one-year timeframe, did not require discounting, and are equal under both discount rates. 2

 Totals may not equal the sum of categories due to rounding. 

Table 11-2, Table 11-3, Table 11-4, and Table 11-5 detail total monetized benefits (including benefits to 

navigation, water storage, drinking water, and water quality) by region. Region 4 benefits the most from 

this regulation under all policy options, as it experiences the most widespread changes in terms of 

improved reach miles and the most significant reductions in sediment concentrations in these reach miles. 

This leads to higher WTP estimates in Region 4, which account for the largest proportion of benefits. 

Region 6 benefits second most, though monetized benefits in this region are about half of those in Region 

4. Regions 4 and 6 together account for more than half of the benefits under all of the options. For Region 

4, midpoint benefits estimates are $102.4, $164.7, $184.2, and $169.2 million, respectively under the four 

policy options. For Region 6, midpoint benefits estimates for the four options are $54.1, $80.1 $90.9, and 

$86.0 million, respectively. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 11-4 

Table 11-2: Annual Total National Benefits Under Option 1 (millions of 2008$) 

Avoided Dredging Costs Discounted at 

3%1

Avoided Dredging Costs Discounted at 

7%1 EPA 

Region Low Mid High Low Mid High 

1 $0.8 $5.0 $11.1 $0.8 $5.0 $11.1 

2 $0.5 $3.2 $7.1 $0.5 $3.2 $7.1 

3 $1.1 $6.5 $14.3 $1.1 $6.5 $14.3 

4 $27.3 $102.4 $210.4 $27.2 $102.4 $210.4 

5 $2.3 $10.6 $22.3 $2.3 $10.6 $22.3 

6 $17.1 $54.1 $105.3 $16.9 $54.0 $105.3 

7 $2.3 $8.9 $18.2 $2.3 $8.9 $18.2 

8 $1.8 $3.2 $4.9 $1.8 $3.2 $4.9 

9 $2.7 $10.0 $20.3 $2.7 $10.0 $20.3 

102 $3.1 $10.3 $20.4 $3.1 $10.2 $20.4 

Total3 $59.0 $214.1 $434.3 $58.8 $213.9 $434.2 1

 Only avoided costs of dredging navigable waterways and reservoirs required discounting and annualization. Avoided costs of 

drinking water treatment and willingness-to-pay were estimated on a single-year basis. 

2

 Benefits estimates in this region are not zero, but less than $500 annually. 

3

 Totals may not be equal to the sum of regional data because the WTP model estimates the national total rather than summing 

regional totals. 

Table 11-3: Annual Total National Benefits Under Option 2 (millions of 2008$) 

Avoided Dredging Costs Discounted at 

3%1

Avoided Dredging Costs Discounted at 

7%1 EPA 

Region Low Mid High Low Mid High 

1 $2.1 $12.1 $26.9 $2.1 $12.1 $26.9 

2 $1.8 $11.6 $25.8 $1.8 $11.6 $25.8 

3 $3.4 $19.8 $43.7 $3.4 $19.8 $43.7 

4 $45.7 $164.7 $333.9 $45.5 $164.6 $333.9 

5 $4.9 $23.7 $50.6 $4.9 $23.7 $50.6 

6 $27.7 $80.1 $152.0 $27.4 $79.9 $152.0 

7 $4.0 $14.5 $29.3 $4.0 $14.5 $29.3 

8 $1.9 $3.7 $6.0 $1.9 $3.7 $6.0 

9 $4.2 $15.5 $31.5 $4.2 $15.5 $31.5 

10 $4.8 $14.4 $27.6 $4.8 $14.4 $27.6 

Total2 $100.5 $360.1 $727.4 $99.9 $359.8 $727.2 1

 Only avoided costs of dredging navigable waterways and reservoirs required discounting and annualization. Avoided costs 

of drinking water treatment and willingness-to-pay were estimated on a single-year basis. 2

 Totals may not be equal to the sum of regional data because the WTP model estimates the national total rather than summing 

regional totals. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 11-5 

Table 11-4: Annual Total National Benefits Under Option 3 (millions of 2008$) 

Avoided Dredging Costs Discounted at 

3%1

Avoided Dredging Costs Discounted at 

7%1 EPA 

Region Low Mid High Low Mid High 

1 $2.9 $16.7 $37.1 $2.9 $16.7 $37.1 

2 $2.7 $17.3 $38.5 $2.7 $17.3 $38.5 

3 $4.7 $27.8 $61.2 $4.7 $27.8 $61.2 

4 $52.3 $184.2 $371.2 $52.1 $184.0 $371.2 

5 $6.1 $29.8 $63.7 $6.0 $29.7 $63.7 

6 $32.2 $90.9 $171.3 $31.9 $90.7 $171.2 

7 $4.7 $16.4 $32.9 $4.6 $16.4 $32.9 

8 $2.1 $5.1 $9.1 $2.1 $5.1 $9.1 

9 $4.9 $18.3 $37.2 $4.9 $18.3 $37.2 

10 $5.5 $15.8 $30.0 $5.5 $15.8 $30.0 

Total2 $118.0 $422.3 $852.2 $117.4 $421.8 $852.0 1

 Only avoided costs of dredging navigable waterways and reservoirs required discounting and annualization. Avoided costs 

of drinking water treatment and willingness-to-pay were estimated on a single-year basis. 2

 Totals may not be equal to the sum of regional data because the WTP model estimates the national total rather than summing 

regional totals. 

Table 11-5: Annual Total National Benefits Under Option 4 (millions of 2008$) 

Avoided Dredging Costs Discounted at 

3%1

Avoided Dredging Costs Discounted at 

7%1 EPA 

Region Low Mid High Low Mid High 

1 $1.7 $10.4 $23.1 $1.7 $10.4 $23.1 

2 $1.8 $11.4 $25.5 $1.8 $11.4 $25.5 

3 $3.5 $20.4 $45.1 $3.5 $20.4 $45.1 

4 $47.3 $169.2 $342.4 $47.1 $169.1 $342.4 

5 $5.0 $24.2 $51.8 $5.0 $24.2 $51.8 

6 $30.5 $86.0 $161.8 $30.2 $85.8 $161.8 

7 $4.3 $15.4 $31.0 $4.3 $15.4 $31.0 

8 $1.9 $3.6 $5.8 $1.9 $3.6 $5.8 

9 $4.2 $15.6 $31.7 $4.2 $15.6 $31.7 

10 $4.1 $12.6 $24.4 $4.1 $12.6 $24.4 

Total2 $104.3 $368.9 $742.7 $103.7 $368.5 $742.5 1

 Only avoided costs of dredging navigable waterways and reservoirs required discounting and annualization. Avoided costs 

of drinking water treatment and willingness-to-pay were estimated on a single-year basis. 2

 Totals may not be equal to the sum of regional data because the WTP model estimates the national total rather than summing 

regional totals. 

11.2 Sources of Uncertainty and Limitations 

EPA notes that quantifying and monetizing impacts of reducing sediment discharges from construction 

sites is challenging. As a result, total national benefits estimates of the regulation are subject to the 

limitations and uncertainties inherent in the valuation approaches used for assessing benefits to 

navigation, water storage, drinking water treatment, and nonmarket benefits of water quality 

improvement. Because the combined effect of these limitations and uncertainties is likely to 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 11-6 

underestimate the national level of benefits of this regulation, the estimated benefits should be interpreted 

in the context of these limitations. 

The preceding sections of this report discuss specific limitations and uncertainties associated with 

estimating benefits to navigation, water storage, drinking water treatment, and nonmarket benefits of 

water quality improvement. This section summarizes the limitations inherent in EPA’s analysis of 

reducing discharges from the construction sites. 

11.2.1 Water Quality Model Limitations 

To estimate benefits of reduced sediment loadings to surface water, EPA relied on SPARROW. The 

SPARROW model for suspended sediments has a number of limitations, some of which are inherent to 

the methodology and some the result of the particular model application. The key model limitations are: 

3⁄4 Reliance on the Reach File 1 network. While the RF1 network provides reasonably 

comprehensive national coverage of major rivers, streams, and other surface waterbodies, 

coverage is limited in certain important respects. First, RF1 network coverage is limited to 

the coterminous United States, thus excluding Alaska and Hawaii. In addition, while the RF1 

1:500,000-scale network reaches have associated data or estimates of stream discharge and 

velocity that are required to specify the SPARROW model, the network excludes the majority 

of the nation’s total stream mileage, and smaller streams in particular. The linear coverage of 

the RF1 network is approximately 700,000 miles (USEPA 2007e). By contrast, coverage of 

the USGS National Hydrographic Dataset, at 1:24,000 - 1:100,000 scale, is currently over 7 

million miles (USGS 2007b). Given that RF1 accounts only for 10 percent of the total reach 

miles, the impacts of construction-related sediment on smaller stream reaches are likely to be 

significantly understated. As construction activities may be concentrated along lower-order 

streams not included in the RF1 network, the relative share of total sediments contributed by 

construction activities may be high on these reaches during active construction phases. By 

contrast, the specific impacts of construction activities may diminish in importance relative to 

contributions from spatially extensive and diffuse land uses, including agriculture, at the level 

of RF1 reaches. Moreover, approximately 20–30 percent of sediment discharged from 

construction sites does not reach the RF1 network and is instead retained on the land surface 

and in smaller streams and reservoirs. These loads are omitted from the current analysis. 

3⁄4 Estimation of changes in nutrient concentrations associated with changes in sediment loading. 

The approach used to estimate changes in nutrient concentration resulting from reduction in 

sediment loadings is based on modeled long-term relationships between sediment and 

nutrient loadings within each of the modeled watersheds. This assumption follows 

observations from case studies discussed in Chapter 4 of this report, which suggest a 

correlation between sediment loadings from construction sites and elevated nitrogen and 

phosphorus loadings. By using a fixed relationship, the approach assumes that nutrients are 

bound to the sediments and that methods used to reduce sediment runoff from construction 

sites are equally effective in reducing nitrogen and phosphorus runoff from these sites. There 

is currently insufficient information to assess the extent to which actual reductions may differ 

from this assumption. While phosphorus is often attached to the sediment and therefore may 

be more readily addressed by control measures that retain sediments on site, nitrogen is 

typically found in soluble forms, and sediment control measures may be less effective in 

reducing nitrogen loading. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 11-7 

3⁄4 Omission of all ponds and lakes and reservoirs located off the RF1 network from the water 

quality analysis. All lakes, ponds, and reservoirs located off the RF1 network are not included 

in the SPARROW model and thus are excluded from estimation of monetized benefits. The 

National Water Quality Inventory Report to Congress (USEPA 2009e) reports 40.6 million 

acres of lakes and reservoirs in the coterminous United States. The RF1 network includes 

approximately 3.9 million acres or 9.5 percent of the total lakes and reservoir acres in the 

United States (USEPA 2007e).20 Omission of these waterbody types from the analysis of 

monetized benefits is likely to lead to understatement of benefits in two benefit categories: 

(1) nonmarket benefits of water quality improvements resulting from the regulation and 

(2) reservoir dredging. 

3⁄4 Restriction of the water quality analysis to the description of long-term mean water quality 

conditions. Construction activities are, by contrast, transient in nature, extending over weeks 

or months. Construction activities (unlike agricultural activities) are spatially compact, so 

they are a sub-grid phenomenon with respect to the specification of the national scale of the 

SPARROW model. The restriction to mean water quality conditions precludes an analysis of 

the frequency with which conditions of extreme sediment transport conditions (e.g., during an 

active construction period) occur. Although the predicted changes in average water quality 

conditions may be small, the expected changes in sediment concentrations under extreme 

sediment transport conditions may be significant. 

11.2.2 Focus on Selected Pollutants of Concern (Sediment and Nutrients) 

Existing case studies of environmental impacts associated with construction activities demonstrated that a 

number of pollutants are found in construction site discharges, including turbidity, BOD, metals, toxic 

organics, trash and debris, and other miscellaneous pollutants. However, EPA’s analysis of benefits from 

reduced construction site discharges focuses only on water quality improvements resulting directly from 

reductions in total suspended solid and nutrient loadings. It does not include improvements resulting from 

reductions in other pollutant loadings, nor does it include improvements in water quality indicators 

indirectly associated with pollutant loadings (e.g., increase in primary productivity such as algae growth, 

changes in dissolved oxygen, and turbidity). This is likely to result in underestimation of the expected 

water quality changes resulting from the regulation because the combined impact of several pollutants on 

ambient water quality conditions is likely to be greater than the sum of the individual impacts of reducing 

concentrations of sediments and nutrients. 

11.2.3 Omission of Several Benefit Categories from the Analysis of Monetized Benefits 

Due to data limitations, EPA did not estimate benefits in several benefit categories. Although the 

magnitude of benefits in the omitted categories is unknown, they may not be trivial. Chapter 5 of this 

report provides a qualitative discussion of the omitted benefit categories. A brief summary of the omitted 

benefit categories is provided below: 

3⁄4 Market values of properties located near waterbodies. Reducing sediment discharges from 

construction sites is likely to increase market values of properties located in the vicinity of 

 

20 The estimated total lake/reservoir acres do not include the Great Lakes. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 11-8 

construction sites by enhancing the aesthetic quality of the affected land and water resources 

(e.g., reducing erosion of river banks and improving water clarity).21

3⁄4 Flood damages. Reducing sediment discharges from construction sites is expected to reduce 

flooding damages by decreasing sedimentation of river beds and improving river capacity. 

Clark et al. (1985) estimated flooding damages attributable to sediment discharges to be $1.5 

billion (2008$), annually. Therefore, even a small reduction in the frequency and severity of 

flooding is likely to generate significant benefits. 

3⁄4 Ditch maintenance. The regulation is expected to reduce the costs of ditch maintenance by 

reducing the amount of sediment deposited in ditches. 

3⁄4 Industrial water use. The regulation is expected to benefit industrial water users by reducing 

sediment concentrations in source waters and thus increasing the useful life of industrial 

equipment. 

3⁄4 Agricultural water use. The regulation is expected to benefit agricultural producers by 

reducing sediment discharges and, as a result, sediment deposition on farm land; this would 

lead to improvements in land productivity and enhanced marketability of agricultural 

products. 

3⁄4 Drinking water treatment.22 The regulation is expected to reduce the cost of drinking water 

treatment to improve taste and odor. Reducing nutrient loadings to surface waters is expected 

to reduce eutrophication which is one of the main causes of taste and odor impairment in 

drinking water. Taste and odor in drinking water has a major negative impact on the public 

perception of drinking water safety and the drinking water industry due to a significant 

increase in drinking water treatment costs from foul taste and odor in the source waters. 

Chapter 9 of this report provides detail on eutrophication impacts on portable water. 

11.2.4 Limitations Inherent in the Estimate of Nonmarket Benefits 

To estimate nonmarket benefits of water quality improvements resulting from the regulation, EPA used a 

benefits transfer function based on meta-analysis of surface water valuation studies. Key limitations of 

this approach that are likely to lead to underestimation on nonmarket benefits are outlined below: 

3⁄4 Using benefit transfer from existing surface water valuation studies to estimate nonmarket 

benefits of the regulation. A number of issues are common to all benefit transfers. Benefit 

transfer involves adapting research conducted for another purpose in the available literature 

to address the policy questions at hand. As is typical in applied benefit transfers, the metaanalysis model used in the analysis of C&D benefits provides a close, but not perfect, match 

to the context in which values are desired. Therefore, some beneficial effects associated with 

reducing sediment discharges from construction sites are not accounted for in the estimated 

WTP values. For example, surface water valuation studies included in meta-data focused 

primarily on changes in ambient water quality. Other effects associated with sediment 

 

21 The nonmarket component (i.e., increased satisfaction with the property) may be implicitly accounted for in WTP for 

improvements in environmental services provided by surface waters affected by construction site discharges (see Chapter 10

for detail), however this does not encompass increased market values of real estate. 

22 Although EPA estimated a change in drinking water cost associated with reducing turbidity in the source water this estimate 

does not account for any changes in the cost of treating T/O. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 11-9 

discharges (e.g., sedimentation of river beds, stability and erosion of river banks, and changes 

in the stream carrying capacity) were not included in the original study scenarios and, as a 

result, from the estimated WTP for reducing sediment discharges from construction sites. 

3⁄4 Restriction of WTP to the state-level improvements in water quality. In analyzing benefits of 

the C&D rule, EPA assumed that households would gain no benefits from water quality 

improvements in aquatic resources located outside of their state of residence. Although 

empirical literature shows that WTP for environmental quality improvements is inversely 

related to the distance from the water resource in question, WTP values are likely to be 

positive for out-of-state residents if improvements occur in aquatic resources of regional or 

national significance (e.g., Chesapeake Bay, Great Lakes). It is also possible that some of the 

out-of-state households have non zero WTP for water quality improvements in water 

resources that have only local significance. Even if per household WTP for out-of-state 

residents are small they can be very large in the aggregate if these values are held by a 

substantial fraction of the population. 

3⁄4 Percent of the total reach miles expected to improve maybe understated. Excluding smaller 

streams from water quality analysis may understate the percentage of both (1) the total reach 

miles estimated to experience water quality improvements and/or (2) reach miles associated 

with higher reductions in pollutant concentrations. Because WTP for regional level water 

quality improvements is a function of the magnitude of the expected water quality change and 

the geographic scale of improvement (i.e., percent of reach miles expected to improve), the 

estimated WTP value may be understated. In addition, approximately 67 percent of 

estuarine/coastal reaches are excluded from the analysis due to data limitations. 

3⁄4 Proximity of water quality improvements to residential areas is not accounted for. Most 

construction impacts are to surface waters near major population centers. Because people 

tend to place a higher value on improvements in waterbodies closer to where they live 

omitting the distance effect from the analysis may lead to understatement of benefits.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 12-1 

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Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 A-1 

Appendix A – Status of Available Data on Waterbody Assessments 

This appendix presents the year of the data available from each state on waterbody assessments. These are 

the data used in the National Water Quality Inventory Report to Congress (USEPA 2009e) and presented 

in Sections 2.6 and 3.3.1.2. 

Table A-1: Year of Available Waterbody Assessment Data, as of September 17, 2009

State Name Assessed Waters Report Year Impaired Waters Report Year

Alabama 2008 2008 

Alaska 2008 2008 

American Samoa 2008 2008 

Arizona 2008 2004 

Arkansas 2004 2004 

California 2004 2006 

Colorado 2008 2008 

Connecticut 2008 2008 

Delaware 2006 2006 

District Of Columbia 2008 2008 

Florida 2002 2002 

Georgia 2008 2008 

Guam 2008 2008 

Hawaii 2006 2006 

Idaho 2008 2008 

Illinois 2006 2006 

Indiana 2008 2008 

Iowa 2008 2008 

Kansas 2008 2008 

Kentucky 2008 2008 

Louisiana 2008 2006 

Maine 2008 2008 

Maryland 2002 2006 

Massachusetts 2006 2006 

Michigan 2008 2008 

Minnesota 2008 2008 

Mississippi 2008 2008 

Missouri 2008 2008 

Montana 2006 2006 

N. Mariana Islands 2008 2008 

Nebraska 2008 2008 

Nevada 2006 2004 

New Hampshire 2006 2006 

New Jersey 2006 2006 

New Mexico 2008 2008 

New York 2008 2008 

North Carolina 2006 2006 

North Dakota 2008 2008 

Ohio 2008 2008 

Oklahoma 2008 2008 

Oregon 2006 2006 

Pennsylvania 2006 2004 

Puerto Rico 2008 2008 

Rhode Island 2008 2008 

South Carolina 2008 2008 

South Dakota 2008 2008 

Tennessee 2008 2008 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 A-2 

Texas 2008 2008 

Utah 2006 2006 

Vermont 2008 2008 

Virgin Islands 2008 2008 

Virginia 2008 2008 

Washington 2008 2008 

West Virginia 2006 2006 

Wisconsin 2006 2006 

Wyoming 2008 2008 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 B-1 

Appendix B – List of Federally Threatened and Endangered Aquatic 

Species Potentially Impacted by Sediment and Turbidity 

Table B-1: List of Federal Threatened and Endangered Aquatic Species Potentially 

Impacted by Sediment 

Common name Scientific Name Threatened/Endangered 

Status 

Fish 

Ala balik (trout) Salmo platycephalus Endangered 

Alabama cavefish Speoplatyrhinus poulsoni Endangered 

Alabama sturgeon Scaphirhynchus suttkusi Endangered 

Amber darter Percina antesella Endangered 

Apache trout Oncorhynchus apache Threatened 

Arkansas River shiner Notropis girardi Threatened 

Ash Meadows Amargosa pupfish Cyprinodon nevadensis mionectes Endangered 

Ash Meadows speckled dace Rhinichthys osculus nevadensis Endangered 

Asian bonytongue Scleropages formosus Endangered 

Atlantic salmon Salmo salar Endangered 

Ayumodoki (loach) Hymenophysa curta Endangered 

Bayou darter Etheostoma rubrum Threatened 

Beautiful shiner Cyprinella formosa Threatened 

Beluga sturgeon Huso huso Threatened 

Big Bend gambusia Gambusia gaigei Endangered 

Big Spring spinedace Lepidomeda mollispinis pratensis Threatened 

Blackside dace Phoxinus cumberlandensis Threatened 

Blue shiner Cyprinella caerulea Threatened 

Bluemask (=jewel) Darter Etheostoma sp. Endangered 

Bonytail chub Gila elegans Endangered 

Borax Lake chub Gila boraxobius Endangered 

Boulder darter Etheostoma wapiti Endangered 

Bull Trout Salvelinus confluentus Threatened 

Cahaba shiner Notropis cahabae Endangered 

Cape Fear shiner Notropis mekistocholas Endangered 

Catfish Pangasius sanitwongsei Endangered 

Cherokee darter Etheostoma scotti Threatened 

Chihuahua chub Gila nigrescens Threatened 

Chinook salmon Oncorhynchus (=Salmo) tshawytscha Endangered 

Chum salmon Oncorhynchus (=Salmo) keta Threatened 

Cicek (minnow) Acanthorutilus handlirschi Endangered 

Clear Creek gambusia Gambusia heterochir Endangered 

Clover Valley speckled dace Rhinichthys osculus oligoporus Endangered 

Coho salmon Oncorhynchus (=Salmo) kisutch Endangered 

Colorado pikeminnow (=squawfish) Ptychocheilus lucius Endangered 

Comanche Springs pupfish Cyprinodon elegans Endangered 

Conasauga logperch Percina jenkinsi Endangered 

Cui-ui Chasmistes cujus Endangered 

Delta smelt Hypomesus transpacificus Threatened 

Desert dace Eremichthys acros Threatened 

Desert pupfish Cyprinodon macularius Endangered 

Devils Hole pupfish Cyprinodon diabolis Endangered 

Devils River minnow Dionda diaboli Threatened 

Duskytail darter Etheostoma percnurum Endangered 

Etowah darter Etheostoma etowahae Endangered 

Foskett speckled dace Rhinichthys osculus ssp. Threatened 

Fountain darter Etheostoma fonticola Endangered 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 B-2 

Table B-1: List of Federal Threatened and Endangered Aquatic Species Potentially 

Impacted by Sediment 

Common name Scientific Name Threatened/Endangered 

Status 

Gila chub Gila intermedia Endangered 

Gila topminnow (incl. Yaqui) Poeciliopsis occidentalis Endangered 

Gila trout Oncorhynchus gilae Threatened 

Goldline darter Percina aurolineata Threatened 

Greenback cutthroat trout Oncorhynchus clarki stomias Threatened 

Gulf sturgeon Acipenser oxyrinchus desotoi Threatened 

Hiko White River springfish Crenichthys baileyi grandis Endangered 

Humpback chub Gila cypha Endangered 

Hutton tui chub Gila bicolor ssp. Threatened 

Ikan temoleh (minnow) Probarbus jullieni Endangered 

Independence Valley speckled dace Rhinichthys osculus lethoporus Endangered 

June sucker Chasmistes liorus Endangered 

Kendall Warm Springs dace Rhinichthys osculus thermalis Endangered 

Lahontan cutthroat trout Oncorhynchus clarki henshawi Threatened 

Leon Springs pupfish Cyprinodon bovinus Endangered 

Leopard darter Percina pantherina Threatened 

Little Colorado spinedace Lepidomeda vittata Threatened 

Little Kern golden trout Oncorhynchus aguabonita whitei Threatened 

Loach minnow Tiaroga cobitis Threatened 

Lost River sucker Deltistes luxatus Endangered 

Maryland darter Etheostoma sellare Endangered 

Mexican blindcat (catfish) Prietella phreatophila Endangered 

Miyako tango (=Toyko bitterling) Tanakia tanago Endangered 

Moapa dace Moapa coriacea Endangered 

Modoc Sucker Catostomus microps Endangered 

Mohave tui chub Gila bicolor mohavensis Endangered 

Nekogigi (catfish) Coreobagrus ichikawai Endangered 

Neosho madtom Noturus placidus Threatened 

Niangua darter Etheostoma nianguae Threatened 

North American green sturgeon Acipenser medirostris Threatened 

Okaloosa darter Etheostoma okaloosae Endangered 

Oregon chub Oregonichthys crameri Endangered 

Owens pupfish Cyprinodon radiosus Endangered 

Owens tui chub Gila bicolor snyderi Endangered 

Ozark cavefish Amblyopsis rosae Threatened 

Pahranagat roundtail chub Gila robusta jordani Endangered 

Pahrump poolfish Empetrichthys latos Endangered 

Paiute cutthroat trout Oncorhynchus clarki seleniris Threatened 

Palezone shiner Notropis albizonatus Endangered 

Pallid sturgeon Scaphirhynchus albus Endangered 

Pecos bluntnose shiner Notropis simus pecosensis Threatened 

Pecos gambusia Gambusia nobilis Endangered 

Pygmy madtom Noturus stanauli Endangered 

Pygmy Sculpin Cottus paulus (=pygmaeus) Threatened 

Railroad Valley springfish Crenichthys nevadae Threatened 

Razorback sucker Xyrauchen texanus Endangered 

Relict darter Etheostoma chienense Endangered 

Rio Grande silvery minnow Hybognathus amarus Endangered 

Roanoke logperch Percina rex Endangered 

San Marcos gambusia Gambusia georgei Endangered 

Santa Ana sucker Catostomus santaanae Threatened 

Scioto madtom Noturus trautmani Endangered 

Shortnose sturgeon Acipenser brevirostrum Endangered 

Shortnose Sucker Chasmistes brevirostris Endangered 

Slackwater darter Etheostoma boschungi Threatened 

Slender chub Erimystax cahni Threatened 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 B-3 

Table B-1: List of Federal Threatened and Endangered Aquatic Species Potentially 

Impacted by Sediment 

Common name Scientific Name Threatened/Endangered 

Status 

Smalltooth sawfish Pristis pectinata Endangered 

Smoky madtom Noturus baileyi Endangered 

Snail darter Percina tanasi Threatened 

Sockeye salmon Oncorhynchus (=Salmo) nerka Endangered 

Sonora chub Gila ditaenia Threatened 

Spikedace Meda fulgida Threatened 

Spotfin Chub Erimonax monachus Threatened 

Steelhead Oncorhynchus (=Salmo) mykiss Endangered 

Thailand giant catfish Pangasianodon gigas Endangered 

Tidewater goby Eucyclogobius newberryi Endangered 

Topeka shiner Notropis topeka (=tristis) Endangered 

Totoaba (seatrout or weakfish) Cynoscion macdonaldi Endangered 

Unarmored threespine stickleback Gasterosteus aculeatus williamsoni Endangered 

Vermilion darter Etheostoma chermocki Endangered 

Virgin River Chub Gila seminuda (=robusta) Endangered 

Waccamaw silverside Menidia extensa Threatened 

Warm Springs pupfish Cyprinodon nevadensis pectoralis Endangered 

Warner sucker Catostomus warnerensis Threatened 

Watercress darter Etheostoma nuchale Endangered 

White River spinedace Lepidomeda albivallis Endangered 

White River springfish Crenichthys baileyi baileyi Endangered 

White sturgeon Acipenser transmontanus Endangered 

Woundfin Plagopterus argentissimus Endangered 

Yaqui catfish Ictalurus pricei Threatened 

Yaqui chub Gila purpurea Endangered 

Yellowfin madtom Noturus flavipinnis Threatened 

Amphibians 

African viviparous toads Nectophrynoides spp. Endangered 

Arroyo (=arroyo southwestern) toad Bufo californicus (=microscaphus) Endangered 

Barton Springs salamander Eurycea sosorum Endangered 

California red-legged frog Rana aurora draytonii Threatened 

California tiger Salamander Ambystoma californiense Endangered 

California tiger Salamander (Sonoma) Ambystoma californiense Endangered 

Cameroon toad Bufo superciliaris Endangered 

Cheat Mountain salamander Plethodon nettingi Threatened 

Chinese giant salamander Andrias davidianus (=davidianus d.) Endangered 

Chiricahua leopard frog Rana chiricahuensis Threatened 

Desert slender salamander Batrachoseps aridus Endangered 

Frosted Flatwoods salamander Ambystoma cingulatum Threatened 

Golden coqui Eleutherodactylus jasperi Threatened 

Goliath Frog Conraua goliath Threatened 

Guajon Eleutherodactylus cooki Threatened 

Houston toad Bufo houstonensis Endangered 

Israel painted frog Discoglossus nigriventer Endangered 

Japanese giant salamander Andrias japonicus (=davidianus j.) Endangered 

Mississippi gopher Frog Rana capito sevosa Endangered 

Monte Verde golden toad Bufo periglenes Endangered 

Mountain yellow-legged frog Rana muscosa Endangered 

Panamanian golden frog Atelopus varius zeteki Endangered 

Puerto Rican crested toad Peltophryne lemur Threatened 

Red Hills salamander Phaeognathus hubrichti Threatened 

Reticulated flatwoods salamander Ambystoma bishopi Endangered 

San Marcos salamander Eurycea nana Threatened 

Santa Cruz long-toed salamander Ambystoma macrodactylum croceum Endangered 

Shenandoah salamander Plethodon shenandoah Endangered 

Sonora tiger Salamander Ambystoma tigrinum stebbinsi Endangered 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 B-4 

Table B-1: List of Federal Threatened and Endangered Aquatic Species Potentially 

Impacted by Sediment 

Common name Scientific Name Threatened/Endangered 

Status 

Stephen Island frog Leiopelma hamiltoni Endangered 

Texas blind salamander Typhlomolge rathbuni Endangered 

Wyoming Toad Bufo baxteri (=hemiophrys) Endangered 

Panamanian golden frog Atelopus varius zeteki Endangered 

Mollusks 

Cumberland elktoe Alasmidonta atropurpurea Endangered 

Dwarf wedgemussel Alasmidonta heterodon Endangered 

Appalachian elktoe Alasmidonta raveneliana Endangered 

Fat three-ridge (mussel) Amblema neislerii Endangered 

Ouachita rock pocketbook Arkansia wheeleri Endangered 

Birdwing pearlymussel Conradilla caelata Endangered 

Fanshell Cyprogenia stegaria Endangered 

Tampico pearlymussel Cyrtonaias tampicoensis tecomatensis Endangered 

Dromedary pearlymussel Dromus dromas Endangered 

Chipola slabshell Elliptio chipolaensis Threatened 

Tar River spinymussel Elliptio steinstansana Endangered 

Purple bankclimber (mussel) Elliptoideus sloatianus Threatened 

Cumberlandian combshell Epioblasma brevidens Endangered 

Oyster mussel Epioblasma capsaeformis Endangered 

Curtis pearlymussel Epioblasma florentina curtisii Endangered 

Yellow blossom (pearlymussel) Epioblasma florentina florentina Endangered 

Tan riffleshell Epioblasma florentina walkeri (=E. walkeri) Endangered 

Upland combshell Epioblasma metastriata Endangered 

Catspaw (=purple cat's paw pearlymussel) Epioblasma obliquata obliquata Endangered 

White catspaw (pearlymussel) Epioblasma obliquata perobliqua Endangered 

Southern acornshell Epioblasma othcaloogensis Endangered 

Southern combshell Epioblasma penita Endangered 

Green blossom (pearlymussel) Epioblasma torulosa gubernaculum Endangered 

Northern riffleshell Epioblasma torulosa rangiana Endangered 

Tubercled blossom (pearlymussel) Epioblasma torulosa torulosa Endangered 

Turgid blossom (pearlymussel) Epioblasma turgidula Endangered 

Shiny pigtoe Fusconaia cor Endangered 

Finerayed pigtoe Fusconaia cuneolus Endangered 

Cracking pearlymussel Hemistena lata Endangered 

Pink mucket (pearlymussel) Lampsilis abrupta Endangered 

Finelined pocketbook Lampsilis altilis Threatened 

Higgins eye (pearlymussel) Lampsilis higginsii Endangered 

Orangenacre mucket Lampsilis perovalis Threatened 

Arkansas fatmucket Lampsilis powellii Threatened 

Speckled pocketbook Lampsilis streckeri Endangered 

Shinyrayed pocketbook Lampsilis subangulata Endangered 

Alabama lampmussel Lampsilis virescens Endangered 

Carolina heelsplitter Lasmigona decorata Endangered 

Scaleshell mussel Leptodea leptodon Endangered 

Louisiana pearlshell Margaritifera hembeli Threatened 

Alabama moccasinshell Medionidus acutissimus Threatened 

Coosa moccasinshell Medionidus parvulus Endangered 

Gulf moccasinshell Medionidus penicillatus Endangered 

Ochlockonee moccasinshell Medionidus simpsonianus Endangered 

Nicklin's pearlymussel Megalonaias nicklineana Endangered 

Ring pink (mussel) Obovaria retusa Endangered 

Littlewing pearlymussel Pegias fabula Endangered 

White wartyback (pearlymussel) Plethobasus cicatricosus Endangered 

Orangefoot pimpleback (pearlymussel) Plethobasus cooperianus Endangered 

Clubshell Pleurobema clava Endangered 

James spinymussel Pleurobema collina Endangered 
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November 2009 B-5 

Table B-1: List of Federal Threatened and Endangered Aquatic Species Potentially 

Impacted by Sediment 

Common name Scientific Name Threatened/Endangered 

Status 

Black clubshell Pleurobema curtum Endangered 

Southern clubshell Pleurobema decisum Endangered 

Dark pigtoe Pleurobema furvum Endangered 

Southern pigtoe Pleurobema georgianum Endangered 

Cumberland pigtoe Pleurobema gibberum Endangered 

Flat pigtoe Pleurobema marshalli Endangered 

Ovate clubshell Pleurobema perovatum Endangered 

Rough pigtoe Pleurobema plenum Endangered 

Oval pigtoe Pleurobema pyriforme Endangered 

Heavy pigtoe Pleurobema taitianum Endangered 

Fat pocketbook Potamilus capax Endangered 

Alabama (=inflated) heelsplitter Potamilus inflatus Threatened 

Triangular Kidneyshell Ptychobranchus greenii Endangered 

Rough rabbitsfoot Quadrula cylindrica strigillata Endangered 

Winged Mapleleaf Quadrula fragosa Endangered 

Cumberland monkeyface (pearlymussel) Quadrula intermedia Endangered 

Appalachian monkeyface (pearlymussel) Quadrula sparsa Endangered 

Stirrupshell Quadrula stapes Endangered 

Pale lilliput (pearlymussel) Toxolasma cylindrellus Endangered 

Purple bean Villosa perpurpurea Endangered 

Cumberland bean (pearlymussel) Villosa trabalis Endangered 

Crustaceans 

Madison Cave isopod Antrolana lira Threatened 

Conservancy fairy shrimp Branchinecta conservatio Endangered 

Longhorn fairy shrimp Branchinecta longiantenna Endangered 

Vernal pool fairy shrimp Branchinecta lynchi Threatened 

San Diego fairy shrimp Branchinecta sandiegonensis Endangered 

Cave crayfish Cambarus aculabrum Endangered 

Cave crayfish Cambarus zophonastes Endangered 

Illinois cave amphipod Gammarus acherondytes Endangered 

Noel's Amphipod Gammarus desperatus Endangered 

Vernal pool tadpole shrimp Lepidurus packardi Endangered 

Lee County cave isopod Lirceus usdagalun Endangered 

Nashville crayfish Orconectes shoupi Endangered 

Shasta crayfish Pacifastacus fortis Endangered 

Squirrel Chimney Cave shrimp Palaemonetes cummingi Threatened 

Alabama cave shrimp Palaemonias alabamae Endangered 

Kentucky cave shrimp Palaemonias ganteri Endangered 

Kauai cave amphipod Spelaeorchestia koloana Endangered 

Riverside fairy shrimp Streptocephalus woottoni Endangered 

Peck's cave amphipod Stygobromus (=Stygonectes) pecki Endangered 

Hay's Spring amphipod Stygobromus hayi Endangered 

California freshwater shrimp Syncaris pacifica Endangered 

Socorro isopod Thermosphaeroma thermophilus Endangered 

Corals 

Coral, elkhorn Acropora palmata Threatened 

Coral, staghorn Acropora cervicornis Threatened 

Source: USFWS 2009 
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November 2009 C-1 

Appendix C – SPARROW Model Documentation 

C.1 General Overview 

SPARROW (SPAtially Referenced Regressions On Watershed attributes) is a watershed modeling 

technique for estimating contaminant source contributions and transport in watersheds and surface waters. 

SPARROW employs a statistically estimated nonlinear regression model with contaminant supply and 

process components, including surface-water flow paths, non-conservative transport processes, and massbalance constraints. Regression equation parameters are estimated by correlating stream water-quality 

records with GIS (Geographic Information System) data on pollutant sources (atmospheric, point, 

nonpoint) and climatic and hydrogeologic properties (e.g., precipitation, topography, vegetation, soils, 

water routing) that affect contaminant transport. The procedure for estimating SPARROW parameters 

also provides measures of uncertainty in model coefficients and in water-quality predictions. The 

SPARROW software is written in SAS (Statistical Analysis System) IML (Interactive Matrix Language). 

SPARROW model infrastructure consists of a detailed watershed – stream reach network to which all 

monitoring stations and GIS data on watershed properties are spatially referenced. Digital elevation 

models (DEMs) are used to delineate watershed boundaries and to identify overland flowpaths. The 

spatially distributed model structure allows separate statistical estimation of land- and water-related 

parameters that quantify rates of pollutant delivery from sources to streams, and transport of pollutants to 

downstream locations within the stream network (i.e., reaches, reservoirs and estuaries). Mechanistic 

separation of terrestrial and aquatic features of large watersheds, and improved parameter estimation 

techniques represent significant advances in water-quality modeling to objectively evaluate alternative 

hypotheses about important contaminant sources and watershed properties that control transport over 

large spatial scales. 

SPARROW has been applied in the analysis of suspended sediment, surface-water nutrients, pesticides, 

organic carbon, and fecal bacteria, and is potentially applicable to other measures of water quality. Recent 

applications of SPARROW have provided estimates of nutrient sources and long-term rates of nutrient 

removal in surface waters (e.g., Smith et al. 1997; Alexander et al. 2000, 2001, 2002a, 2002b). The model 

has demonstrated particular utility for quantifying long-distance transport and delivery of nutrients to 

sensitive downstream locations (e.g., estuaries, reservoirs, drinking water intakes). The earliest version of 

the SPARROW model was developed to describe contaminant transport in surface waters of the State of 

New Jersey (Smith et al. 1994). Federal and State environmental managers are currently using 

SPARROW to assess the sources of nutrient loadings in streams, including targeting of nutrient reduction 

strategies in the Chesapeake Bay watershed (Preston and Brakebill 1999) and in waters of the State of 

Kansas (Kansas Dept. Health and Environment 2004) as well as for developing TMDLs (Total Maximum 

Daily Loads) in the Connecticut River Basin (NEIWPCC 2004). Other applications encompass New 

England watersheds (Moore et al. 2004), New Zealand river basins (Alexander et al. 2002a; Elliott et al. 

2005), North Carolina coastal watersheds (McMahon et al. 2003), and watersheds in Tennessee and 

Kentucky (Hoos 2005). SPARROW models are currently under development for the Delaware River 

Basin and are being planned for selected regions of the U.S. under the U.S. Geological Survey (USGS) 

National Water-Quality Assessment (NAWQA) Program. 
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C.2 Modeling Concept 

C.2.1 Objectives of SPARROW Modeling 

The broad objective of SPARROW modeling is to establish a mathematical relation between waterquality measurements made at a network of monitoring stations and attributes of the watersheds 

containing the stations. Once constructed, the model may be used to satisfy a variety of water-quality 

information objectives. One common modeling objective is to describe past or present water-quality 

conditions for a state or region on the basis of monitoring data. The underlying challenge is to extrapolate 

from a sample of water-quality measurements made at a finite number of stream and river locations to an 

area containing un-sampled locations. The usual limitations in attempting this are: (1) sparse sampling, 

reflecting the high cost of monitoring; and/or (2) unrepresentative (nonrandom or targeted) sampling 

undertaken to characterize water quality at specific locations, especially those suspected of having waterquality problems. In the absence of an interpretive model such as SPARROW, a single monitoring design 

cannot be optimal for both of these distinct objectives. Because the Federal Clean Water Act requires 

state governments to collect and report both types of information, the distinction between the two types of 

monitoring is of great practical importance. “Probabilistic” monitoring has been promoted by the 

U.S. Environmental Protection Agency (EPA) (Yoder 1997) to obtain a spatially unbiased, broad 

overview of water-quality conditions in State waters. An important limitation of this approach is that the 

monitoring data alone do not provide detailed information on the geography of water-quality conditions 

and give little understanding of the factors (sources and processes) that explain those conditions. Targeted 

monitoring has been used extensively by the States to identify specific streams with water-quality 

problems and to gage compliance with State water-quality regulatory standards and criteria. These data, 

however, provide a spatially biased description of water-quality conditions in watersheds. SPARROW is 

effective in integrating samples from these different monitoring approaches to provide both a 

geographically representative description of water-quality conditions as well as insight into the sources 

and watershed processes that control water quality. 

A second objective of SPARROW modeling is to identify and quantify the sources of pollution that give 

rise to in-stream water-quality conditions. SPARROW distinguishes between source categories, such as 

point sources, atmospheric sources, and agriculture; and individual sources, defined as the rate of supply 

of contaminant of a particular category originating in the watershed and draining to a specific stream 

reach. The ability to develop quantitative information on pollution sources in SPARROW models stems 

from the ability to trace, for each contaminant category, the predicted in-stream flux through a given 

stream reach to the individual sources in each of the upstream reach watersheds contributing 

contamination to that reach. Sources may be quantified either in mass units or in terms of their percent 

contribution to the total contaminant flux to the reach. An example application of SPARROW in 

quantifying pollution sources is in TMDL analyses (e.g., McMahon and Roessler 2002). In general, the 

Clean Water Act requires TMDL analyses for any stream reach in which the concentration of a 

contaminant exceeds the applicable water-quality standard when all pollution discharge limits are met. 

The ultimate objective of TMDL-related modeling is to establish a hypothetical waste-load allocation for 

all individual sources affecting the reach in question that would meet the standard. In theory, an infinite 

number of hypothetical load allocations will satisfy the standard, and choosing the official allocation 

requires comparing many possible solutions, utilizing simulation (see below) in search of a least-cost or 

other optimum solution. Prior to conducting the hypothetical analyses, however, a great deal of 

preliminary quantitative information on the actual or baseline relations between watershed sources and instream conditions is useful. The percentage contributions (shares) of individual point sources are of 
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November 2009 C-3 

particular interest because point sources are usually the only sources subject to direct regulation. The 

share contributions of individual nonpoint sources are also of interest as a means of identifying the 

important stakeholders to include in discussions of voluntary pollution reductions. This describes the 

current application of SPARROW: to evaluate the effectiveness of a range of regulatory options for 

controlling and mitigating sediments associated with construction activities, with explicit focus on the 

impacts of such options on in-stream water quality as measured by Total Suspended Solids (TSS). 

A third objective is water quality simulation. Simulation refers to the use of a calibrated model to predict 

conditions on the basis of a set of altered, typically hypothetical model inputs. The ability to portray 

counterfactual conditions for specified inputs is one of the most powerful justifications for models; there 

are often no alternative methods for conducting controlled experiments on complex systems. Waterquality model simulations can depict the in-stream effects of changes in contaminant sources associated 

with alternative future pollution-control strategies, providing a critical step in the analysis of the costs and 

benefits associated with specific Best Management Practices (BMPs) or regulatory approaches. 

Simulating alternative waste-load allocations in a TMDL analysis (see above) is a prime example of a 

water-quality simulation application. In the present application, SPARROW simulation is used to conduct 

experiments focused on specific regulatory options (BMPs) for managing construction site sediment that 

would not be feasible to conduct physically outside a limited number of test sites. 

A fourth objective of SPARROW modeling is hypothesis testing. One common feature of the preceding 

modeling objectives is that they each make use of predictions of the dependent variable by a calibrated 

model. Another class of modeling objectives focuses on the calibration process and its results directly. 

The SPARROW estimation process explores the predictive value of a set of potential explanatory 

variables and may also compare alternative mathematical forms. The selection of a final set of predictors 

and mathematical form usually has the primary objective of maximizing the accuracy of model 

predictions of the dependent variable, but an alternative modeling objective may be to test one or more 

hypotheses about the nature and importance of factors and processes that may have influenced water 

quality at the locations where samples were collected. Hypothesis tests are performed for each of the 

model parameters estimated in the calibration, and these serve as indicators of an empirical relation 

between the independent variables associated with each parameter and the dependent variable of the 

model. Because the coefficients in a SPARROW model are specified to conform to physical processes, 

and the potential explanatory variables are selected on the basis of theoretical or logical connections to the 

dependent variable, a statistically strong parameter provides evidence of the physical relationship. For 

example, if multiple categories of potential sources of the contaminant are being evaluated, the model 

estimation process provides useful hypothesis tests on the importance of each category. “Importance” is 

measured by the correlation between contaminant inputs from the source category and downstream 

monitored loads of the contaminant. That correlation will tend to be stronger when the mass contribution 

of the category to the total mass of contaminant flowing past many of the monitoring stations is large, but 

model estimation may also indicate a source category is important even when the mass contribution is 

small, provided the amount contributed to stream loads by each unit of source is consistent from place to 

place. 

The importance of factors and processes potentially related to the transport of contaminants from sources 

to stream channels and within stream channels may be tested through calibration of the “land to water” 

and “in-stream decay” terms in the model. These terms dictate the fraction of the contaminant mass that 

completes the terrestrial and aquatic phases of transport within the watershed draining to each stream 

reach. The land-to-water terms describe the land-surface characteristics that influence both overland and 

subsurface transport from sources to stream channels. Similarly, the in-stream decay terms describe the 
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November 2009 C-4 

effects of channel characteristics on downstream transport. In sum, an important objective in building and 

calibrating SPARROW models is to gain insight and to test hypotheses concerning the role of specific 

contaminant sources and hydrologic processes in supplying and transporting contaminants in watersheds. 

C.2.2 SPARROW Mass Balance Approach 

SPARROW models constructed to date, like most water-quality models, are expressed in the form of a 

mass balance. Such models describe the movement of mass in space and/or the change of mass in time. 

The law of conservation of mass implies that certain basic accounting rules must apply to a mass balance, 

water-quality model, such as: (1) the sum of fluxes entering the confluence of two streams equals the flux 

leaving the confluence; (2) the sum of the fluxes attributable to each contaminant source must equal total 

flux; and (3) a doubling of all sources in the model results in an exact doubling of the predicted flux at 

each location. Because the dependent variable in SPARROW models (the mass of contaminant that 

passes a specific stream location per unit time) is, in mathematical terms, linearly related to all sources of 

contaminant mass in the model, all accounting rules relating to the conservation of mass will apply. Mass 

accounting in SPARROW models is also supported by the explicit spatial structure defined by the stream 

network. 

There are a number of advantages to the mass balance approach. Because of the linear relation between 

flux and sources, there is an expectation that the estimation of flux over spatial scales smaller and larger 

than that covered by the model’s sample data will yield reasonably accurate results. The imposition of 

mass balance greatly improves the interpretability of model coefficients. For example, assuming mass 

balance, the coefficient associated with the reach time-of-travel variable is interpreted as a first-order 

decay rate and, because point-source loadings are delivered directly to the stream network, a reasonable 

null hypothesis for coefficients associated with point sources is that they equal 1.0 (Smith et al. 1997). 

The enhanced interpretability of the model coefficients in turn facilitates the comparison of coefficient 

estimates from the model with other estimates described in the literature. These comparisons have been 

generally favorable, especially for the model components that quantify in-stream nitrogen decay rates 

(Alexander et al. 2000, 2002a; McMahon et al. 2003), nutrient and suspended sediment removal rates in 

reservoirs (Schwarz et al. 2001; Alexander et al. 2002a), and the nutrient export associated with various 

land uses and pollutant sources (e.g., Alexander et al. 2001, 2002a, 2004; McMahon et al. 2003). Mass 

balance provides a basis for flux accounting, whereby flux can be allocated to its various sources, both 

spatially and topically. For example, mass balance makes it possible to attribute nutrients discharged to 

the Gulf of Mexico to specific sources within the Mississippi basin (Alexander et al. 2000), thereby 

providing guidance in managing the reduction of this discharge. This approach is also sensitive to the 

effects of natural and human-related processes that supply and remove contaminants from watersheds 

over long periods. Thus, this approach de-emphasizes the quantification of short-term cycling and 

transformation processes, which are often central to the functioning of many dynamic mechanistic 

models, in favor of processes that have a long-term impact on elemental budgets in aquatic ecosystems. 

C.2.3 Time and Space Scales of the Model 

SPARROW models are structurally designed to explain spatial variability in the long-term mean-annual 

or mean-seasonal flux of contaminants in streams. Spatial variability is modeled as a function of natural 

and human-related properties of watersheds that influence the supply and transport of contaminants. 

Estimates of the long-term mean flux are developed from water-quality and streamflow monitoring data 

that are regularly collected at fixed locations on streams and rivers. The basic form of SPARROW models 

is structured to describe the long-term, steady-state water-quality and flow conditions in streams. 
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Contaminant source inputs are assumed to be in balance with the estimated sinks and measured in-stream 

water-quality load such that there is conservation of mass among the model components. The principal 

objective supported by this model structure is the quantification of the location and rates (and statistical 

uncertainties) of the supply, transport, and fate of contaminants within the terrestrial and aquatic 

ecosystems of watersheds. In the current specifications of SPARROW models, temporal variability in 

contaminant loads, including intra- and inter-annual variations in water quality and streamflow reflected 

in the monitoring data, are explicitly modeled and accounted for in a step prior to modeling spatial 

variability in loads with SPARROW. 

The computation of mean-annual or mean-seasonal flux (the SPARROW response variable) requires the 

prior application of a water-quality flux-estimation model constructed on the basis of streamflow and 

water-quality records from regularly monitored stream locations. The flux-estimation model explicitly 

accounts for temporal variability in contaminant loads related to streamflow, season of the year, and 

trends (either continuous or abrupt) with time. A base-year load estimate ensures that the stream waterquality loads and the contaminant source data (which are commonly reported only periodically, e.g., the 

U.S. Agricultural Census reports every five years) are contemporaneous. Therefore, the mean-annual 

loads used to calibrate SPARROW models describe the mean load that would be expected to occur during 

a particular base year under long-term mean streamflow conditions. 

The steady-state mass-balance structure of the basic SPARROW model quantifies the long-term net 

effects of biogeochemical and hydrologic processes on contaminant transport in terrestrial and aquatic 

ecosystems. Modeling the effects of these processes is typically of greatest interest for non-conservative 

chemical, physical, and biological properties of water, such as nutrients, pesticides, fecal coliform 

bacteria, organic carbon, and suspended sediment. Many of these constituents are subject to chemical 

transformations or degradation during transport and may be stored over short or long periods. In 

SPARROW models that are estimated using long-term water-quality records, biogeochemical cycling and 

storage processes that temporarily immobilize or remove contaminants from flow paths are generally in 

steady state with those processes that mobilize or release contaminants from storage. Hence, the effects of 

transformation and removal processes that operate on relatively short intra-annual time scales (e.g., daily, 

weekly, seasonal), or over multi-year time scales that are less frequent than the period of model 

estimation, are not likely to be detected as contaminant losses in the steady-state form of SPARROW 

models. Limitations in our knowledge of temporal lags in chemical transport and their causes also create 

uncertainties in the periods over which steady-state conditions apply. For example, the time scales for the 

transport of sediment in streams reflect erosion, storage, and transport processes that operate from 

seasonal to decadal or even longer (e.g., century) periods (Trimble and Crosson 2000). 

Temporal changes in mean conditions also can be explicitly modeled in SPARROW using the current 

model structure and software. One approach is to estimate multiple steady-state models that explain 

mean-annual or mean-seasonal stream contaminant loads for separate multi-year periods. Alternatively, 

the current software permits the estimation of a single SPARROW model having time-dimensioned 

dependent and independent variables – each assumed to pertain to different steady-state conditions. In this 

approach, temporal changes in mean-annual stream contaminant flux may be modeled as a function of 

temporal changes in contaminant sources, land use, and climatic factors (e.g., precipitation, streamflow, 

and temperature) over similar multi-year periods as those used to estimate stream loads at each 

monitoring station. The coefficients in this model can be time dependent or restricted to take common 

values over the full period of the analysis. This type of model structure has considerable data demands 

that require the development of historical data on contaminant sources and climatic/hydrologic variability 

in the watersheds and reaches (including stream velocity, which is sensitive to average streamflow and 
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November 2009 C-6 

thus varies in response to changes in mean streamflow across different periods). Such a model allows 

users to explicitly test for temporal changes in model parameters to determine whether changes are 

evident in process rates or contaminant export coefficients with time. As an alternative to the explicit 

estimation of a SPARROW model with time-dimensioned coefficients, existing SPARROW models can 

be used to simulate changes in mean-annual stream water-quality loads as a function of changes in source 

inputs. This approach assumes that the process rates and contaminant export coefficients of the model do 

not change with time. 

C.2.4 Accuracy and Complexity of SPARROW Models 

It is generally the case for SPARROW, as for any model with statistically estimated parameters, that 

model accuracy (bias and precision) and complexity (number of statistically significant or sensitive 

parameters) are dependent on the information content of the water-resources data used in model 

calibration. Investigations of hydrologic models have demonstrated that both the quantity and quality of 

calibration data define the information content and have important effects on parameter estimation and 

precision (e.g., Gupta and Sorooshian 1985; Yapo et al. 1996). Increasing the quantity of data can 

improve the precision provided the data give new, independent information about the values of the model 

parameters. Data quality, as defined by Gupta and Sorooshian (1985), generally increases as the data 

become more “representative” of the range of watershed properties that affect transport and the range of 

conditions present in the sampled watersheds. An independent set of measurements are preferred for 

estimating parameter values, such that the data reflect the most extreme combinations of watershed 

conditions for the various properties. These general statistical guidelines have implications for the time 

and space scales required to develop SPARROW data sets and accurate models. First, a sufficiently large 

number of water-quality monitoring stations are required. In SPARROW models, the monitoring-station 

loads serve as the response variable observations in the nonlinear spatial regressions. The number of 

stations has a demonstrable effect on the statistical power of the regression – i.e., the capacity of the 

model to detect the effect of an explanatory factor on stream loads. Models with more stations generally 

have greater power, which typically supports more complex models – i.e., models with a larger number of 

statistically significant parameters and functional components. Second, the amount of spatial variability in 

the stream monitoring data and explanatory factors should reflect as broad and representative a range of 

watershed conditions as possible. The most complex SPARROW models typically have been developed 

for regions that have relatively large spatial variability (greater than one order of magnitude) in the 

watershed properties that affect contaminant transport. Watershed properties that vary over a wide range 

within a modeled region generally provide more information about the response of stream loads to 

different levels of a given watershed property and are more likely to be statistically significant in 

SPARROW models. 

The spatial variability of a given variable is most readily increased in SPARROW models by expanding 

the spatial domain of the model to include larger drainage areas. This, of course, has the effect of 

increasing the number of monitoring stations, which also contributes to the potential for greater model 

complexity. In selecting water-quality monitoring sites for modeling, it is especially important to obtain 

sites that are located on a wide range of stream sizes (especially small streams) and are inclusive of 

impoundments of varying size. Sediment and nutrient removal rates are highly responsive to the hydraulic 

characteristics of streams and reservoirs, such as flow volume and velocity (Alexander et al. 2000, 

2002a). Accounting for the wide variation in these properties can provide more accurate estimates of 

nutrient removal and improve the separation of land and water effects on transport in the model. 

Estimation errors are much larger for constituents that are most affected by high flows, such as suspended 
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November 2009 C-7 

sediment, total phosphorus, and fecal coliform bacteria. These constituents are generally more difficult to 

measure and exhibit larger variability in concentrations in streams, which can produce less precise 

estimates of the mean-annual flux. By contrast, dissolved substances, such as nitrate, sulfate, and total 

dissolved solids exhibit less variability with flow, and their fluxes can generally be more accurately 

estimated. 

C.2.5 Comparison of SPARROW with Other Watershed Models 

A wide variety of hydrologic and water-quality models have been used to describe contaminant sources 

and transport in watersheds and surface waters. These models can be characterized on the basis of their 

process complexity and temporal and spatial scales (e.g., Singh 1995). The level of complexity or process 

detail represented by model descriptions of hydrologic and biogeochemical processes commonly varies 

with the extent to which deterministic (mechanistic) and statistical/empirical methods are used to 

describe and estimate these processes (Figure 12-1; Alexander et al. 2002b). All models reflect some 

blend of these methods, but most place greater emphasis on one or the other type of model structure and 

process specification. 

Figure 12-1: A Simple Continuum of Model Types Based on the Level of Statistical and 

Mechanistic Descriptions of Contaminant Sources and Biogeochemical Processes 

In general, purely statistical models tend to reflect more simplistic model constructs. These models have a 

simple correlative mathematical structure and typically assume limited a priori knowledge of various 

processes. Conventional versions of these models are expressed as simple linear (or log linear) 

correlations of stream measurements with watershed sources and landscape properties (e.g., Peierls et al. 

1991; Howarth et al. 1996; Caraco et al. 2003). The methods have the advantage of being readily applied 

in large watersheds (often relying on generally available stream monitoring records) and can readily 

quantify the errors in model parameters and predictions. Simple correlative approaches, however, offer 

little mechanistic explanation of contaminant sources and transport. They generally lack spatial detail on 

the distribution of sources and sinks within watersheds, rarely account for nonlinear interactions between 

sources and loss processes, and do not impose mass-balance constraints on contaminant transport. The 

most purely statistical approaches are found in artificial neural network and kriging techniques. These 

models commonly provide an excellent fit to the observations, but provide little understanding of the 

processes that affect contaminant transport. 
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By contrast, mechanistic water-quality models have complex mass-balance structures that simulate 

hydrologic and contaminant transport processes, often according to relatively fine temporal scales 

[e.g., Soil Water Assessment Tool (SWAT), Srinivasan et al. 1993; Agricultural Nonpoint Source Model 

(AGNPS), Young et al. 1995; Hydrologic Simulation Program-Fortran (HSPF), Bicknell et al. 2001]. The 

components of these models frequently provide a highly detailed temporal description (e.g., daily, hourly) 

of the response of stream contaminants to climatic variability, and the effects of coarser temporal 

variations in land use and management activities are often superimposed on the more detailed climatic 

variations. The mathematical descriptions of these responses are frequently based on a priori assumptions 

about the dominant processes and their reaction rates. The complexity of mechanistic simulation models 

creates intensive data and calibration requirements, which generally limits their application to relatively 

small watersheds. Because mechanistic water-quality models are frequently calibrated manually, robust 

measures of uncertainty in model parameters and predictions cannot be quantified. Despite the common 

use of mechanistic models, there are growing concerns about whether sufficient water-resources data and 

knowledge of biogeochemical processes exist to reliably support the general use of such highly complex 

descriptions of processes (Jakeman and Hornberger 1993; Beven 2002). Without sufficient data, there is 

limited ability to apply formal parameter-estimation techniques required to quantify model uncertainties 

and to identify unique models having sensitive and uncorrelated parameters. 

By comparison to other types of water-quality models, SPARROW may be best characterized as a hybrid 

process-based and statistical modeling approach for estimating pollutant sources and contaminant 

transport in surface waters. The mechanistic mass transport components of SPARROW include surfacewater flow paths (channel time of travel, reservoirs), non-conservative transport processes (i.e., first-order 

in-stream and reservoir decay), and mass-balance constraints on model inputs (sources), losses (terrestrial 

and aquatic losses/storage), and outputs (riverine nutrient export). The statistical features of the model 

involve the use of nonlinear parameter-estimation for spatially correlating stream water-quality records 

with geographic data on pollutant sources (e.g., atmospheric, point-, nonpoint) and climatic and 

hydrogeologic properties (e.g., precipitation, topography, vegetation, soils, water routing). Parameter 

estimation ensures that the calibrated model will not be more complex than can be supported by the data. 

This provides an objective statistical approach for evaluating alternative hypotheses about important 

contaminant sources and controlling transport processes over large spatial scales in watersheds. 

SPARROW has been shown to improve the accuracy and interpretability of model parameters and the 

predictions of nutrient loadings and sources in streams as compared with conventional statistical 

modeling approaches (Smith et al. 1997; Alexander et al. 2000, 2002a, 2002b). 

A number of inter-model comparisons have been previously conducted that compare the performance and 

properties of SPARROW to those of other water-quality models. These include national comparisons 

with SWAT and comparisons in the Chesapeake Bay watershed with HSPF (see Alexander et al. 2001). 

Inter-model comparisons also have been conducted with statistical and quasi-deterministic models in 

watersheds of the northeastern U.S. (see Alexander et al. 2002b; Seitzinger et al. 2002). Finally, 

evaluations of the SPARROW technique also are included as part of a number of recent National 

Research Council (NRC) reports (2000, 2001a, 2001b, 2002a, 2002b). At least two of the NRC reports 

(2000, 2001a) have noted the advantages of statistical modeling approaches, such as SPARROW, for 

general water-quality assessment and use in TMDL assessments. 

C.2.6 Model Infrastructure 

The parameters of SPARROW models are statistically estimated with nonlinear regression techniques by 

spatially correlating water-quality flux estimates at monitoring stations with watershed data on sources, 
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and landscape and surface-water properties that affect transport. The calibrated models are then used to 

predict flux, total and disaggregated by contributing source, for stream reaches throughout a river 

network. A flow diagram is provided in Figure 12-2 to illustrate the functional linkages between the 

major spatial components of SPARROW models. Pre-processing steps are required to develop reach-level 

information for the major components of the SPARROW model infrastructure as shown in Figure 12-3.

Monitoring station flux estimation refers to the estimates of long-term flux used as the response variable 

in the model. Flux estimates at monitoring stations are derived from station-specific models that relate 

contaminant concentrations from individual water-quality samples to continuous records of streamflow 

and time. The stream reach, inclusive of its incremental contributing drainage basin, is the most elemental 

spatial unit of the SPARROW model infrastructure. Stream reaches typically define the length of stream 

channel that extends from one stream tributary junction to another. Explanatory data (e.g., climate, 

topography, land use) are frequently compiled according to geographic units that are not coincident with 

the drainage basin boundaries of river reaches. These data may be collected at different spatial scales and 

according to spatial units that reflect political (e.g., counties) or other non-hydrologic features of the 

landscape. 

Figure 12-2: Schematic of the Major SPARROW Model Components 

Source: Modified from Alexander et al. (2002). 
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Figure 12-3: Location of 1,828 Water Quality Monitoring Stations Used in the SPARROW 

Sediment Model, in Relation to the Reach File 1 (RF1) Reach Network 

C.2.7 Use of Monitoring Data 

The estimation of a SPARROW model requires estimates of long-term mean flux from a spatially 

distributed set of monitoring stations within the study area, having sufficiently long periods of record. In 

addition, it is necessary to include data from a diverse set of monitoring stations, inclusive of a wide range 

of spatial scales and expressing considerable variation in predictor variable conditions. Often, monitoring 

stations have different periods of record. Long-term variations in hydrologic conditions, combined with 

long-term trends in water quality, imply mean fluxes computed over different periods may not be directly 

comparable. If there are trends in water quality, the estimate of long-term mean water-quality flux will 

depend on the period or window through which the water-quality data are acquired. Additionally, water 

quality is rarely measured with the frequency necessary to directly estimate long-term mean flux, so that 

indirect methods are needed to estimate flux from available measurements to account for hydrologic 

conditions during periods when water-quality measurements are not made. The estimation procedure must 

account for the fact that water-quality measurements are not always collected in a random manner, 

implying the arithmetic average of flux is not a reliable estimate of mean flux. Rather, the measurements 

of flux must be related in some way to other information, collected on a continuous basis, which exhibits 

a close relation to flux, typically streamflow. The limitation, therefore, in constructing a mass-balance 

model using long-term mean flux is that only water-quality measurements that can be associated with a 

continuous record of streamflow will be suitable for model estimation. 

Trends in water quality also create problems for the mass balance approach in relating water quality to its 

predictor variables, principally the source variables. Ideally, the source variables will consist of a time 

series of estimates, and these source variables would be included in SPARROW as long-term averages in 

the same way as flux estimates. However, source information is rarely available for multiple periods. 

Even if source information is available over time, the period over which it is compiled will rarely match 

the period of the flux information; for example, point-source and land-use data are infrequently available 

due to the high cost of compiling this information. To address the problem of incompatible periods of 

record, a SPARROW model is typically specified for a single base year; with all water-quality and source 
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information assumed to pertain to a given point in time. The severest constraint on the period of the 

analysis is imposed by source information that is available only for a single year. Consequently, the base 

year is generally selected to be in the middle of the range of available years for this ‘one-time’ 

information. 

C.2.8 Model Specification for Monitoring Station Flux Estimation 

The extrapolation of infrequently sampled water-quality data and removal of trend require the 

specification of a model of flux. The model must relate infrequently measured data to variables that are 

measured continuously over time; and to accommodate de-trending, the model must include a function of 

time. These requirements suggest a model that relates infrequently measured concentration to the 

variables streamflow and time. The inclusion of streamflow as an explanatory factor serves another 

purpose in the analysis. Because flux is typically positively related to streamflow, water-quality sampling 

is commonly biased towards high flow events. The inclusion of flow in the water-quality model 

effectively conditions the estimation of flux so as to remove the effects of high-flow sampling bias. The 

estimation of mean flux by a station-specific model need not account for all processes affecting flux 

within a basin; as this task is assigned to the SPARROW model. All that is required is that the estimated 

mean flux at a station be reflective of long-term average processes within the basin. Station-specific flux 

models need not be structurally accurate; they need only be predictively accurate. A causal explanation of 

flux is ultimately obtained through application of a SPARROW model, whereby variations in mean flux 

conditions across stations, estimated either explicitly or implicitly, are correlated with variations in basin 

attributes across space. 

Cohn et al. (1992a) have suggested a simple seven-parameter model in which the logarithm of 

contaminant concentration (c) is related via a linear model to an intercept, the logarithm of flow, the 

square of the logarithm of flow, decimal time T, decimal time squared, and a seasonal harmonic 

consisting of two trigonometric terms – the sine and cosine of 2 times decimal time. Vecchia (2000) has 

argued that there are important long-term lags affecting the relation between water quality and flow. He 

suggests a specification that relates the log of contaminant concentration to a set of compound flow terms 

consisting of moving averages of flows, of various lengths, in addition to time trend terms. A 

specification that generalizes both the seven-parameter and the lag flow models takes the form 

( ) ( ) i Qt tXtTt = β + β + β + eXThQMc~ (Eq. C-1) 

Where Qt is a p-element row vector consisting of current and lagged logarithms of flow, M(Q) is a vector 

function that transforms the p-element logged flows into a Kq element row vector; βQ is a Kq element 

vector consisting of coefficients associated with the transformed flow terms; h(Tt) is a KT element row 

vector function of decimal time; βT is a KT element vector of coefficients associated with the transformed 

decimal time terms; Xt is a KX element row vector of other exogenous variables affecting water quality; βX

is a KX element vector of coefficients associated with the other exogenous variables; and et is the normally 

distributed error term, independent over time and uncorrelated with each of the predictor variables. 

C.2.9 Tools for Flux Estimation 

The water-quality models described above can be estimated using ordinary least squares if the waterquality data do not include any censored observations. For cases in which observations are censored, 

Cohn et al. (1992b) suggest estimation via adjusted maximum likelihood. The standard maximum 

likelihood method for type I censored data (data for which the censoring threshold is known) is the Tobit 
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model. The method of adjusted maximum likelihood combines the Tobit model with an adjustment to 

correct for first-order bias in the coefficient estimates caused by estimation using a small sample. The 

method of adjusted maximum likelihood is implemented within the USGS program Load Estimator 

(LOADEST) 2000 (Runkle et al. 2004). In addition to estimates of the parameters and their covariance 

matrix, the program uses retransformation methods to produce unbiased estimates of daily and annual 

flux. A simple averaging of the daily or annual estimates over all days or years yields an estimate of longterm mean flux. The estimation of the model used to detrend flow requires a maximum likelihood method 

capable of correcting for serial correlation in the errors. This capability is included in the PARMA model 

developed by Vecchia (2000), and in standard statistical packages such as SAS (SAS 1993). The more 

recently developed program Fluxmaster (Schwarz et al. 2006) includes methods to estimate the timeseries flow model using maximum likelihood, detrend flow, and estimate the water-quality model via 

adjusted maximum likelihood. The program also computes unbiased, detrended estimates of long-term 

mean flux, and provides an estimate of the associated standard error. The exact methods used in 

Fluxmaster differ from those used in LOADEST 2000, but they are a close approximation, and identical if 

there are no censored observations. Fluxmaster (Schwarz et al. 2006) is used in the present study. 

C.2.10 Guidance for Specifying Monitoring Station Flux Models 

The ideal water-quality record has sufficient observations to reliably estimate the coefficients in the 

model. Models that include both flow and time require a fairly long record, one that includes the base date 

for detrending, and encompassing a wide range of hydrologic conditions. One consideration that places 

limits on the length of the record is the need for flux estimates to be representative of base-year 

conditions. This does not imply that the record must be representative of the hydrologic conditions in the 

base year: the SPARROW model is estimated using mean estimates of water quality with the intention of 

removing variations due to hydrologic conditions. Rather, long-term patterns in water quality should be 

adequately captured by the model specification. This implies the period of record should not be so long as 

to violate the assumption that water-quality trend is reasonably approximated by a simple linear function 

of time. It is generally true that longer water-quality records display more complex patterns of trend. A 

record that is too long runs the risk of misrepresenting the trend in water quality for any given year, 

adding noise to the detrended flux estimate. For national analyses, Smith et al. (1997) and Alexander et al. 

(2000) have based mean flux on water-quality data that span a 15 to 20 year period. Regional analyses by 

Preston and Brakebill (1999) and Moore et al. (2004) have used water-quality data that span 20 to 

25 years. Shorter periods of about six years were used by Alexander et al. (2002a). Shorter records may 

certainly be used to obtain an estimate of mean flux, although a shorter record will generally result in a 

larger standard error in the mean flux estimate. For very short records, however, the specification of trend 

becomes unreliable. It is recommended that trend terms be excluded from the model if record spans less 

than three years. 

One of the principal determinants of accuracy in flux estimation is the number of observations input to the 

water-quality model. Generally, it is recommended that an estimate of flux depend on no fewer than 

15 uncensored water-quality observations. However, it may be necessary to deviate from this threshold in 

situations in which a site is subject to high variability in streamflow. The final consideration for inclusion 

of data for a station in the SPARROW analysis is that the standard error of the mean flux estimate not be 

too large. SPARROW has the capability of weighting observations according to their standard errors; 

therefore wholesale exclusion of high-error flux estimates is unnecessary. If, however, a high-error flux 

estimate enters the model as an upstream source for some downstream flux observation (the nested basin 

arrangement) the potential for biasing estimates increases. High-error flux estimates also present 
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problems in assessing the error of SPARROW predictions if the predictions are conditioned on observed, 

upstream flux estimates. In national analyses, we have frequently excluded mean flux estimates that have 

a standard error greater than about 20 to 30 percent. 

C.2.11 Stream Network Topology 

A vector- or raster-based digital representation of the stream and river network topology is the most 

fundamental component of the spatial infrastructure that supports the SPARROW model (Figure 12-4).

Vector representations are based on point and line (arc) GIS features, whereas raster representations are 

based on a cellular (areal) structure. In either instance, a stream reach network explicitly defines surfacewater flow paths that spatially connect contaminant sources and landscape features with water quality 

observations at downstream monitoring stations. In a vector-reach topology, a stream reach represents the 

length of stream channel that extends from one tributary junction to another. Reach nodes are point 

features that are associated with the location of tributary junctions, hence reach boundaries. Reach nodes 

will also occur at the locations where reaches overlay with the shorelines of impoundments (reservoirs, 

lakes), and with stream water-quality monitoring sites. In a raster representation of streams, nodes may 

also define various intermediate locations along the stream reach between tributary junctions. Whether 

digitally represented in vector or raster form, the reach topology must define either a set of reach nodes or 

raster cells that are linked hydrologically to indicate the direction of water flow. The node topology must 

be defined according to an upstream (from-) and downstream (to-) node attribute table (Figure 12-4). This 

tabulation of surface-water flow paths is required for the routing of water and contaminants through the 

river network by SPARROW navigation software during estimation and application of the model. Thus, 

the reach-node table defines the fundamental data infrastructure of the model. 

Figure 12-4: Schematic Illustrating a Vector Stream Reach Network with Node Topology 

and Water/Contaminant Reach-Node Routing Table 

The reach-type indicator has possible values of “0” (Stream Reach), “1”(Impoundment Reach), and “2” 

Outlet Reach for Impoundment 

The SPARROW model structure supports distributary and diversion reaches in the stream network; 

including braided channels or reaches where water is diverted to canals or other water bodies. The 
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SPARROW model assumes that contaminants are diverted in proportion to volume of flow. Therefore, an 

estimate is required for each reach of the diversion fraction – a measure of the fraction of streamflow that 

is diverted in distributary reaches. A diversion fraction of 1.0 is assigned to reaches without water 

diversions. In the example distributary reaches shown in Figure 12-4, the fraction of the flow in reach 7 

that is diverted to the two downstream reaches is 0.7 for reach 5 and 0.3 for reach 6. The SPARROW 

reach topology also supports the separate designation of impoundments (e.g., lakes, reservoirs) associated 

with stream reaches. This designation is used in SPARROW to separately estimate the sediment and 

contaminant attenuation in reservoirs and lakes. In Figure 12-4, reaches 9-11 are associated with a 

reservoir. A “reach-type” indicator is used to identify reaches associated with impoundments separately 

from conventional stream reaches. The outlet reach of an impoundment is coded separately from other 

interior impoundment reaches to facilitate the pollutant attenuation calculations. 

In addition to the reach properties listed in Figure 12-4, the reach length and an estimate of the meanannual streamflow of the reach is also required. Estimates of mean water velocity are required to estimate 

in-stream contaminant attenuation as a function of the water time of travel (alternatively, in-stream 

attenuation can be estimated as a function the reach length). Measures of the areal water load are needed 

for impoundments for use in estimating the contaminant attenuation in these water bodies; the areal water 

load is computed as the quotient of the outflow to surface area of the waterbody (see Alexander et al. 

2002a). Digital representations of the drainage basin boundaries associated with river reaches are also 

needed to estimate the incremental and total drainage area of the reaches and to support the digital overlay 

of drainage boundaries with polygonal boundaries (vector or raster) that define the locations of 

contaminant sources (point and diffuse) and various landscape properties. 

The assessment of pollutant loadings to coastal estuaries requires an expanded reach network that 

includes shoreline features and the identification of reaches that terminate at estuaries. The node points of 

shoreline reaches include the downstream nodes of the terminating reaches. Shoreline reaches are used to 

define coastal drainage areas – areas that discharge runoff directly to the estuary without transport 

through a stream reach. Because the discharge for a shoreline reach does not accumulate from any 

upstream location, the diversion fraction for these features is set to zero. Shoreline reaches also have no 

stream attenuation so travel time is set to zero. 

Spatial referencing is a critical step in SPARROW modeling that is necessary to construct the watershed 

attribute data used as explanatory variables and to verify the accuracy of the hydrologic connectivity of 

the stream reaches, which governs routing and accumulation of mass in the model. Spatial referencing 

entails the use of GIS techniques to digitally establish the geographic relation between stream reaches in 

the river network and the various watershed attributes that are used to specify the model. Watershed 

attribute data sets (e.g., topography, land use, climate) are frequently compiled and reported according to 

geographic units that are not coincident with the drainage basin boundaries of river reaches; for example 

at finer spatial scales or according to spatial units that reflect cultural (e.g., counties, states) or other nonhydrologic features of the landscape. Watershed attributes may also be geographically located according 

to precise locations coded as latitude and longitude, which must be digitally linked to a watershed or 

nearby river reach. These may include the locations of stream monitoring gages or municipal wastewater 

treatment outfalls. Verification of automated GIS operations is often required to ensure accurate spatial 

referencing; including, for example, comparisons of the river name of a gage with that of a river reach or 

the comparison of the reported drainage area of a gage with that estimated for a river reach. Finally, the 

SPARROW node and routing architecture also can fully support the modeling of contaminant transport 

along “off-reach” (landscape) flow paths according to flow directions defined by landscape topography as 

reflected, for example, in DEMs, although this capability is not currently implemented in the sediment 
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model. Such architecture facilitates the incorporation of high-resolution spatial data sets that delineate 

sources and other explanatory variables at scales finer than an incremental drainage area for the reach. 

C.2.12 Watershed Sources and Explanatory Variables 

The explanatory variables evaluated in SPARROW models reflect current knowledge of natural and 

human-related sources and the important physical, chemical, or biological properties of the terrestrial and 

aquatic ecosystems that affect supply and transport of contaminants in watersheds, along with practical 

considerations of availability. Point- and diffuse-source variables may include direct measures of the 

introduction or supply of contaminant mass to the landscape and streams and reservoirs (e.g., municipal 

and industrial wastewater discharge, fertilizer application). Alternatively, source variables may serve as 

surrogate indicators of the contaminant mass supplied by point and diffuse sources in watersheds, such as 

land-use/land-cover data or census data on human and livestock populations. Watershed data on sources 

and other properties (e.g., climate, topography, land-use) must be spatially referenced to the drainage 

basins and stream reaches of the SPARROW river network. Figure 12-5 shows the relation between areas 

of source polygons and the incremental drainage basin of a hypothetical stream reach. Estimates of the 

diffuse sources associated with the drainage basins of stream reaches can be obtained using GIS 

operations to digitally overlay the drainage basin boundaries of stream reaches with the polygonal areas 

associated with the diffuse source data. Once quantitative measures of the overlap in watershed areas are 

obtained (Figure 12-5), estimates of the area-weighted sum of source characteristics (Sn,j) for reach j and 

source type n can be calculated as: 

∑ ( ) ∈

= )(

*

, ,, /

jPk

k jn kjkn AASS (Eq. C-2) 

where P(j) is the set of all source-related polygons that intersect the incremental drainage polygon for 

stream reach j, Sn,k is the quantity of source-type n associated with polygon k, Aj,k is the sub-area of reach 

j’s incremental drainage that intersects the source-related polygon k, and A*

k is the total area of the sourcerelated polygon k. 

Figure 12-5: Schematic Illustrating Digital Overlay of Stream Reach Drainage Area and 

Polygonal Areas Associated with Diffuse Sources 

If it is known that a particular contaminant source is associated with a specific land use (or some grouping 

of land uses) – for example, fertilizer is associated with cultivated land – and if the spatial scale of landuse information is similar to the scale at which watersheds are delineated, then the method described 

above can be modified to obtain a more refined estimate of sources within a reach incremental drainage 
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area. The enhanced method requires only that the area term Aj,k in Equation C-2 be redefined to represent 

the area of the associated land use that intersects the reach j incremental drainage and source polygon k, 

and that the area term A*

k be redefined to equal the total area of the associated land use within the source 

polygon k. 

Climatic and landscape properties that affect contaminant transport may include measures of waterbalance terms (e.g., solar radiation, precipitation, evaporation, evapotranspiration), soil characteristics 

(e.g., permeability, moisture content), water-flow path properties (e.g., slope, hydraulic roughness, 

topographic index), or management practices and activities (e.g., tile drains, conservation tillage, BMPs). 

Estimates of various climatic and landscape characteristics or properties are often calculated as an areaweighted mean estimate (Zi,j) for stream reach j and landscape property i according to: 

∑ ( ) ∈

= )(

*

, ,, / ~

jPk

j ji kjki AAZZ (Eq. C-3) 

where P(j) is the set of all land-characteristics polygons that intersect the incremental drainage polygon 

for stream reach j, Zi,k is the landscape property i associated with polygon k, Aj,k is the sub-area of reach 

j’s incremental drainage that intersects the landscape property’s polygon k, and A*

j is the total drainage 

area of the reach watershed j. Figure 12-6 shows the relation between areas of landscape polygons and the

incremental drainage basin of a hypothetical stream reach that is described in Equation C-3. Note that the 

area-weighted mean estimate defined in Equation C-3 differs from the area-weighted sum given by 

Equation C-2 in that the area ratio terms sum to 1.0 in Equation C-3 but not in Equation C-2. 

Figure 12-6: Schematic Illustrating Digital Overlay of Stream Reach Drainage Area and 

Polygonal Areas Associated with Landscape Properties 

The basic SPARROW models described are designed to model long-term mean contaminant loadings in 

streams, implying explanatory variables in the model should be computed to reflect long-term conditions. 

Variables that describe contaminant sources and landscape characteristics may be averaged over multiple 

years, corresponding to the available period of record for water-quality monitoring data, or may reflect 

the conditions during a specified base year used to estimate stream water-quality loadings, as in the 

sediment study. 
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C.3 Model Specification 

The specification of a SPARROW model consists of identifying the explanatory variables and functional 

forms for the associated processes the model is to include. The process is described here in generic terms, 

supplemented by specific descriptions of the specification of the SPARROW sediment model. 

C.3.1 Model Equation and Specification of Terms 

Conceptually, the contaminant load or flux leaving a reach is the sum of two components. The first 

component is the load generated within upstream reaches that is delivered to the reach via the stream 

network. Losses of flux from the stream network may occur at points where flow is diverted. 

Additionally, in moving through the reach, flux will generally be attenuated by stream or reservoir 

processes. The second component consists of source flux that is generated within the reach’s incremental 

watershed and delivered to the stream network somewhere along the reach segment. A number of sourcedependent processes, in addition to stream attenuation processes, affect the amount of source flux 

reaching the stream network and transported to the reach’s downstream outlet node. For flux originating 

on the landscape, the processes affecting delivery to the stream network are called land-to-water delivery 

processes, and may include both surface and sub-surface elements. A conceptual illustration of the 

pertinent spatial relations is given in Figure 12-7. A connecting reach is generally defined as a stream 

segment that connects the confluence of two stream segments; a headwater reach is a reach that is defined 

without an upstream confluence. Figure 12-7 shows five complete reaches, two of which are headwater 

reaches, with one of the headwater reaches classified as a reservoir. Each reach is embedded within a 

Figure 12-7: Conceptual Illustration of a Reach Network for Five Incremental Watersheds. 

Model Equation C-4 Describes the Supply and Transport of Load within an Individual 

Reach and its Incremental Watershed. 

Source: McMahon et al. (2003). 

color-coded area representing the reach’s incremental drainage – the area that drains directly to the reach 

without passing through another reach. Because there are five reaches, there are five incremental drainage 

areas. Land-to-water delivery processes determine the amount of contaminant generated within an 

incremental drainage area, excluding contaminant generated directly on a reach, which is then delivered 

to the area’s corresponding reach. In the figure, two monitoring stations, X and Y, form the boundaries of 

a nested basin, defined as all reaches above a monitored reach i, containing monitoring station X, but 

exclusive of reaches above and including all upstream monitoring stations – in this case the single 
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monitoring station Y. In-stream attenuation processes associated with headwater reaches affect only the 

transport of contaminants from their incremental drainage; in-stream attenuation processes for all nonheadwater reaches affect the transport of contaminants from their own incremental drainage and also from 

the incremental drainage of all upstream reaches (including reaches in upstream nested basins). 

This conceptual model can be formalized through a mathematical equation. Let F*

i be the modelestimated flux for contaminant leaving reach i. This flux is related to the flux leaving adjacent reaches 

upstream of reach i, denoted by F’

j where j indexes the set J(i) of adjacent reaches upstream of reach i, 

plus additional flux that is generated within the incremental reach segment i. In most cases, the set of 

adjacent upstream reaches J(i) will consist of either two reaches, if reach i is the result of a confluence, or 

no reaches if reach i is a headwater reach (see Figure 12-7). The functional relations determining reach i 

flux are given by: 

( ) () ( ) RS

R

i

S

i

N

n

i D

D

RS nnin i

R

i

S

i

iJj

F i j ZZAF ZZAZDSSδ ,;, θθ θα ,;,'; θθ

1

,

)(

* '

⎥

⎦

⎤ ⎢

⎣

⎡ ⎥ +

⎦

⎤ ⎢

⎣

⎡ = ∑ ∑ ∈ =

 (Eq. C-4) 

The first summation term represents the amount of flux that leaves upstream reaches and is delivered 

downstream to reach i, where F’j equals measured flux, Fj

M, if upstream reach j is monitored or, if it is 

not, is given by the model-estimated flux F*j. δi is the fraction of upstream flux delivered to reach i. If 

there are no diversions, then δi is set to 1. Otherwise, this fraction is defined by the fraction of streamflow 

leaving upstream reaches that is delivered to reach i. A(.) is the stream delivery function representing 

attenuation processes acting on flux as it travels along the reach pathway. This function defines the 

fraction of flux entering reach i at the upstream node that is delivered to the reach’s downstream node. 

The factor is a function of measured stream and reservoir characteristics, denoted by the vectors ZS

 and 

ZR

, with corresponding coefficient vectors θS and θR. If reach i is a stream, then only the ZS and θS terms 

determine the value of A(.); conversely, if reach i is a reservoir then the terms that determine A(.) consist 

of ZR

 and θR.

The second summation term represents the amount of flux introduced to the stream network at reach i. 

This term is composed of the flux originating in specific sources, indexed by n = 1,...,NS. Associated with 

each source is a source variable, denoted Sn. Depending on the nature of the source, this variable could 

represent the mass of the source available for transport to streams, or the area of a particular land use. The 

variable αn is a source-specific coefficient. This coefficient retains the units that convert source variable 

units to flux units. The function Dn(.) represents the land-to-water delivery factor. For sources associated 

with the landscape, this function along with the source-specific coefficient determines the amount of 

contaminant delivered to streams. The land-to-water delivery factor is a source-specific function of a 

vector of delivery variables, denoted by Zi

D, and an associated vector of coefficients θD. For point sources 

that are described by a measured (same units as flux) discharge of mass directly to the stream channel 

(e.g., municipal wastewater effluent measured in kilograms year

-1

), the delivery factor should be 1.0, with 

no underlying factors acting as determinants, and the source-specific coefficient should be close to 1.0. 

The last term in the equation, the function A’(.), represents the fraction of flux originating in and delivered 

to reach i that is transported to the reach’s downstream node. This function is similar in form to the stream 

delivery factor defined previously; however, the default assumption in SPARROW models is that if reach 

i is classified as a stream (as opposed to a reservoir reach), the contaminants introduced to the reach from 

its incremental drainage area receive the square root of the reach’s full in-stream delivery. This 

assumption is consistent with the notion that contaminants are introduced to the reach network at the 

midpoint of reach i and thus experience only half of the reach’s time of travel. For reaches classified as 
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reservoirs, the default assumption is that the contaminant receives the full attenuation defined for the 

reach. 

The nonlinear model structure in Equation C-4 contains several key features. The additive contaminant 

source components and multiplicative land and water transport terms are conceptually consistent with the 

physical mechanisms that explain the supply and movement of contaminants in watersheds. Total 

modeled flux for a reach is shown to be decomposed into its individual sources. Because this same 

decomposition is done for all upstream reaches, it is possible in this framework to perform flux 

accounting, whereby total flux is attributed to its source components. All processes are spatially 

referenced with respect to the stream network according to the reach in which they operate. This means, 

for example, that a reservoir at reach i affects the transport of all contaminants entering the reach network 

upstream (but not downstream) of reach i. The additive source components also provide a mathematical 

structure in the model that preserves mass. This can be seen by noting that a doubling of each of the 

source variables Sn,i, along with a doubling of all upstream sources, as represented by a doubling of F’j

results in an exact doubling of modeled flux F*i. Finally, the modeled flux at any reach i is conditioned on 

monitored fluxes entering the stream network anywhere upstream of reach i. This approach to nested 

basins serves to isolate errors introduced in any upstream basin from incremental errors that arise in a 

downstream basin, making it defensible to treat nested basins as independent observations. 

C.3.2 Contaminant Sources 

The selection of potential contaminant source variables in SPARROW models initially depends upon a 

user’s particular knowledge of a watershed as well as inferences that can be derived from the research 

literature about the major sources that contribute pollutants to watersheds Knowledge of the geography of 

these sources, based on direct measures or surrogate indicators of the contaminant mass supplied to the 

land surface and surface waters, is also critical to provide an effective use of the spatially distributed 

structure in SPARROW. The compilation and availability of large digital spatial data sets has become 

much more common (e.g., Brakebill and Preston 1999) and the advances in many types of digital 

topographic and stream network data [e.g., National Hydrography Dataset (NHD), National Land Cover 

Database (NLCD)] have made spatially distributed modeling more feasible. The inclusion of detailed 

information in the SPARROW model on the geographic locations where contaminants are released in 

watersheds has particular relevance to the potential policy and management applications of the model. 

The source terms used in the model can be generally classified as intensive and extensive measures of 

contaminant mass. The former measures are typically descriptive of direct measures of pollutant mass, 

such as fertilizer application, livestock waste, atmospheric deposition, or sewage-effluent loadings. In 

these cases, the source-specific parameter (α) is expressed as a dimensionless coefficient that, together 

with standardized expressions of the land-to-water delivery factor, describes the proportion or fraction of 

the source input that is delivered to streams. This fraction would be expected to be less than 1.0 but 

greater than zero, reflecting the removal of contaminants in soils and ground water. 

Extensive measures of contaminant mass also may be used in SPARROW models. These are surrogate 

indicators of contaminant mass and include measures of watershed properties such as specific land-use 

area and sewered population that are considered to be proportional to the actual mass loadings generated 

by a general type of a contaminant source. The empirical estimates of the source coefficients in the model 

provide a quantitative measure of the proportion of the mass loading that is associated with a specified 

source. If extensive measures are used, the associated model coefficients are expressed as the contaminant 

mass generated per unit of the source type (e.g., kilograms kilometer-2 year-1; kilograms person-1 year-1). If 

combined with the land-to-water delivery factor and expressed as a standardized source coefficient, the 
Environmental Assessment and Benefits Document for the C&D Category 

November 2009 C-20 

coefficient indicates the mean quantity of contaminant mass per unit of the surrogate source measure that 

is delivered to streams. For land-use terms, the standardized coefficient gives what is frequently cited as 

an export coefficient (Beaulac and Reckhow 1982; Johnes et al. 1996). Observed values of export 

coefficients have been reported in the literature for various land-use/land-cover types, such as crop, urban, 

and forested lands (Beaulac and Reckhow 1982); and often compare favorably with corresponding 

estimates by SPARROW (e.g., Alexander et al. 2004). Other sources of guidance include sediment 

erosion estimates generated from the Universal Soil Loss Equation (USLE) as part of the 

U.S. Department of Agriculture (USDA) National Resources Inventory (NRI) (Natural Resources 

Conservation Service 2005). 

C.3.3 Landscape Variables 

Landscape variables in SPARROW describe properties of the landscape that relate to climatic, natural- or 

human-related terrestrial processes affecting contaminant transport. These include properties that are 

assumed on some conceptual or empirical basis to control the rates of contaminant processing and 

transport, and which are widely available. The model structure allows tests of hypotheses about the 

influence of specific features of the landscape on contaminant transport. Landscape variables may include 

water-balance terms (e.g., precipitation, evapotranspiration) related to climate and vegetation, soil 

properties (e.g., organic content, permeability, moisture content), topographic water flow-paths variables 

(e.g., TOPMODEL overland flow, topographic index, and slope) (Beven and Kirkby 1979), or 

management practices and activities, including tile drainage, conservation tillage practices, and BMPs 

related to stream riparian properties. Particular types of land-use classes, such as wetlands or impervious 

cover, may also be potentially used to describe transport properties of the landscape. 

The land-to-water delivery factor in Equation C-4, Dn(Zi

D;θD), is a source-specific function of a vector of 

delivery variables, denoted by Zi

D, and an associated vector of coefficients θD. In basic SPARROW 

models, the land-to-water delivery factor has been expressed in exponential functional form. For source n, 

the fraction of contaminant mass generated in the incremental reach drainage area and delivered to the 

reach (excluding source coefficient term) is estimated as 

( ) ⎭

⎬

⎫

⎩

⎨

⎧ = ∑=

M D

m

Dm

D

D imnm

D ZD in Z

1

, θ exp; θω (Eq. C-5) 

where Zm,i

D represents delivery variable m for the incremental drainage of reach i, θDm is its corresponding 

coefficient, ωnm is an indicator variable that is 1.0 if delivery variable m affects source n and zero 

otherwise, and MD is the number of delivery variables. Log-transformed delivery variables, such as the 

logarithm of soil permeability, may provide an improved fit to the data. Under the log-transformation, the 

land-to-water coefficient is interpreted as the percent change in flux delivered to streams, derived from all 

sources to which the land-to-water variable is applied, from a one-percent increase in the land-to-water 

delivery variable. The full effect of landscape variables on the delivery of source n to streams is 

determined by the product of the delivery factor in Equation C-5 and the source coefficient αn. Therefore, 

modification of delivery factor specification can be expected to change the mean of the delivery factor, 

resulting in a corresponding counter-adjustment of the source coefficient αn. In order to improve the 

interpretability of the source coefficient, and to provide some stability in its value across alternative 

specifications of the land-to-water delivery factor, it is recommended that the delivery variables Zm,i

D in 

Equation C-5 be expressed as differences from their mean value over all reaches. 
Environmental Assessment and Benefits Document for the C&D Category 

November 2009 C-21 

C.3.4 Stream Transport 

Stream attenuation processes that act on contaminant flux as it travels along stream reaches are frequently 

modeled as first-order reaction rate processes (Chapra 1997). A first-order decay process implies that the 

rate of removal of the contaminant from the water column per unit of time is proportional to the 

concentration or mass that is present in a given volume of water (a zero-order process corresponds to a 

constant rate of removal per unit of time). In a first-order decay process, the fraction of contaminant 

removed over a given stream distance is estimated as an exponential function of a first-order reaction rate 

coefficient (in reciprocal time units) and the cumulative water time of travel over this distance. A reaction 

rate is estimated on a volumetric basis, and therefore is expected to depend on properties of the water 

column that are proportional to water volume, such as streamflow and water-column depth (Stream Solute 

Workshop 1990). Accordingly, in basic forms of the SPARROW model, the fraction of the contaminant 

mass originating from the upstream node and transported along reach i to its downstream node is 

estimated as a function of the mean water time of travel (Tc

S

i; units of time) in reach i and stream class c 

defined according to discrete intervals of mean streamflow or depth (in this case, Zi

S

 = {Tc

S

i}, c = 1,...,CS, 

where Tc

S

i is nonzero only for the streamflow class corresponding to reach i), and a stream-size dependent 

loss rate coefficient (θSc; units of time) such that: 

( ) ⎭

⎬

⎫

⎩

⎨

⎧

−= ∑=

CS

c

i

S

RS cSc

R

i

S

i ZZA T

1

θθ exp,;, θ (Eq. C-6) 

Mean water time of travel is estimated as the quotient of the reach channel length and mean water 

velocity. The most accurate estimates of water velocity are obtained from time-of-travel dye studies 

(Jobson 1996), but in some cases may be obtained from instantaneous measurements of water velocity 

taken during flow gage site visits. Empirical geomorphic relations, which use regression methods to relate 

time-of-travel measurements to channel and basin properties (e.g., streamflow, slope), also are available 

for regions of the U.S. and other countries (e.g., Jobson 1996; USEPA 1996b; Jowett 1998; Alexander et 

al. 1999) and can be used to estimate the time-of-travel of stream reaches for a given river network. 

Alternatively, channel length may be used in Equation C-6 where water velocity estimates are not 

available. This assumes that the water time of travel is proportional to the channel length, and estimates of 

the removal rate are expressed as reciprocal length (e.g., see Alexander et al. 2002a; McMahon et al. 

2003). 

C.3.5 Reservoir and Lake Transport 

Attenuation processes that act on contaminant mass as it travels through a lake or reservoir are often 

modeled as a net removal process, with the loss coefficient expressed as either a first-order reaction rate 

or a mass-transfer coefficient (also referred to as an apparent settling velocity) (Chapra 1997). Both of 

these loss expressions have been used in empirical mass-balance lake models for phosphorus (e.g., 

Vollenweider 1976; Reckhow and Chapra 1983) and nitrogen (e.g., Kelly et al. 1987; Molot and Dillon 

1993), although the use of the mass-transfer rate is more common. These mass-balance models typically 

assume steady-state and uniformly mixed conditions in the waterbody. The reaction rate is expressed in 

reciprocal time units and its estimation and use are dependent on knowledge of the water depth and 

surface area of the waterbody (volume). The apparent settling velocity is expressed in units of length per 

time and is estimated as a function of the ratio of the reservoir outflow and the surface area of the 

reservoir sediments (assumed equal to the surface area of the waterbody); this ratio, denoted qi

R for reach 

i, is termed the areal hydraulic load and is a measure of the water displacement or velocity in the 
Environmental Assessment and Benefits Document for the C&D Category 

November 2009 C-22 

reservoir. The term “apparent” indicates that the settling velocity measures the net effect of various 

processes that remove the contaminant from the water column and deliver it to the sediments, and 

processes that may add contaminant to the water column. The function that relates the areal hydraulic 

load to the fraction of flux attenuated in a reservoir is 

( ) ( ) 1

0 1

1

,;, −

+

= R

iR

RS

R

i

S

i

q

A

θ

ZZ θθ (Eq. C-7) 

where θR0 is a parameter, to be estimated in the model, representing the apparent settling velocity. 

C.4 Model Estimation 

The SPARROW model equation, given in Equation C-4, is a nonlinear function of its parameters, and the 

model must be estimated using nonlinear techniques. The model errors are assumed to be independent 

across observations with zero mean, while the variance of each observation may be observation specific. 

SPARROW utilizes a nonlinear weighted least squares (NWLS) estimation method, which does not 

assume the precise distribution of the residuals. Several algorithms exist for obtaining NWLS estimates, 

and the Levenberg-Marquardt Least-Squares method, implemented in SAS, is used to estimate the 

SPARROW model (SAS 1999). Unlike linear models, the statistical properties of the estimated 

parameters for nonlinear models are not precisely known in finite samples. For this reason, much of the 

theory of nonlinear estimation has focused on characterizing asymptotic properties – the statistical 

properties of the coefficient estimates as the sample size goes to infinity. Emphasis in the following 

sections is on interpretation of estimated model parameters. 

C.4.1 Evaluation of Model Parameters 

The objective of parameter evaluation in SPARROW modeling is to determine whether a converged 

model provides statistically sound and physically interpretable coefficient values. The first objective 

entails the appraisal of model parameters for statistical significance and the quantification of uncertainty. 

This provides important information for identifying unique model specifications (parameters and values 

for which the model predictions are sensitive) and determining the level of model complexity (number 

and types of explanatory variables and model functions) that can be empirically supported by the stream 

monitoring data. The emphasis on parameter estimation in SPARROW models reflects the objective of 

identifying the important contaminant sources and factors affecting mean-annual contaminant transport 

over large spatial scales in soils and in ground and surface waters. The key parameter statistics include the 

estimated mean coefficient values, estimated variance of these coefficient estimators, and measures of 

statistical significance based on the t statistics (ratio of the coefficient value to its standard error). These 

statistics are biased in finite samples but consistent as sample size goes to infinity; the t statistics are 

asymptotically distributed as standard normal. The p-values are based on a two-tailed probability from a 

Student’s t distribution. The p-values can be used to identify statistically significant model coefficients – 

those that are statistically distinguishable from zero – and can be used to refine the parameter set to 

identify parsimonious SPARROW models. 

The second complementary objective is the evaluation of the parameters for physical interpretability. This 

objective entails the evaluation of the sign and magnitude of model coefficients to test hypotheses about 

the importance of different contaminant sources and the hydrologic and biogeochemical processes that are 

represented by the explanatory variables of the model. SPARROW model parameters reflect the net 
Environmental Assessment and Benefits Document for the C&D Category 

November 2009 C-23 

effects over large spatial scales of an aggregate set of hydrologic and biogeochemical processes and 

human-related activities. The interpretability of the parameters and their relation to specific processes is 

enhanced in SPARROW by the use of a mass balance, mechanistic structure that explicitly separates the 

terrestrial and aquatic properties of watersheds and accounts for nonlinear interactions among watershed 

properties, together with an emphasis on the statistical estimation of parameter values. The sign of 

SPARROW model coefficients can be evaluated to determine the direction of influence of any 

explanatory variable on in-stream estimates of mean-annual flux. The direction of influence is assessed 

for consistency with the anticipated response based on available theoretical or empirical information 

about processes related to individual explanatory factors. For example, a negative sign on the soil 

permeability coefficient indicates that total loads in streams are inversely related to permeability – i.e., instream loads of contaminants are generally lower in watersheds with highly permeable soils. The sign of 

the coefficient is also important in estimating physically meaningful contaminant source terms in 

SPARROW, which are generally expected to contribute positive contaminant mass to the watershed 

system. 

Coefficients associated with source inputs expressed in areal units describe the mass per unit area 

delivered to streams from these land areas. These areal expressions of contaminant transport (export) can 

be directly compared with ranges of export coefficients reported in the literature (e.g., Beaulac and 

Reckhow 1982). Estimated SPARROW coefficients associated with specific land uses generally compare 

favorably with published export coefficients. Other source coefficients that are expressed in 

dimensionless units provide a measure of the fraction of the contaminant that is delivered from each 

source to streams, rivers, and reservoirs. These coefficients can be evaluated to determine how reasonably 

they reflect the net mean rates of contaminant removal by a source as part of the delivery to aquatic 

systems. 

C.5 Model Prediction 

SPARROW output contains prediction results paired with measures of accuracy. A number of technical 

issues can arise in the derivation of these statistics; most of these caused by the nonlinear nature of the 

SPARROW model. The prediction equation is similar to the calibration Equation C-4. Because 

predictions are generated for specific sources, however, it is necessary to decompose flux into sourcespecific components. Let F*

i,n denote the reach i model-estimated flux associated with source n, and let 

F’

j,n be the source-n flux from upstream reach j. If reach j is monitored (that is, j אI, where I is the set of 

monitored reaches), and predictions are conditioned on measured flux, then F’

j,n is apportioned from the 

measured flux Fj

M according to 

M

N j

m

mj

nj

nj F

F

F F y

⋅=

∑=1

, *

, *

, ' (Eq. C-8) 
Environmental Assessment and Benefits Document for the C&D Category 

November 2009 C-24 

Otherwise, if reach j is not a monitored reach (that is, j בI) or predictions are not conditioned on 

measurements, then F’

j,n is set equal to F*j,n. The equation defining the model-estimated flux for source n, 

F*

i,n, is given by 

( ) ( )( ) RS

R

i

S

iD

D

inninRS i

R

i

S

i

iJj

F ni nj δ ZZAF ,;, , θαθθ ZZAZDS ,;,'; θθ

)(

, ' , *

⎥ +

⎦

⎤ ⎢

⎣

⎡ = ∑∈

 (Eq. C-9) 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 D-1 

Appendix D – A Preliminary SPARROW Model of Suspended Sediment 

for the Coterminous United States (Schwarz 2008a) 
A Preliminary SPARROW Model of Suspended Sediment for 

the Conterminous United States 

Open-File Report 2008–1205 

U.S. Department of the Interior 

U.S. Geological Survey 
A Preliminary SPARROW Model of Suspended Sediment 

for the Conterminous United States 

By Gregory E. Schwarz 

National Water-Quality Assessment Program 

Open-File Report 2008–1205 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia: 2008 

For product and ordering information:
 

World Wide Web: http://www.usgs.gov/pubprod
 

Telephone: 1–888–ASK–USGS
 

For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, 


natural hazards, and the environment:
 

World Wide Web: http://www.usgs.gov
 

Telephone: 1–888–ASK–USGS
 

Suggested citation:
 

Schwarz, G.E., 2008, A Preliminary SPARROW model of suspended sediment for the conterminous United States: U.S. 


Geological Survey Open-File Report 2008–1205, 8 p., available only online at http://pubs.usgs.gov/of/2008/1205.
 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the 


U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report. 
iii 

Contents 

Abstract...........................................................................................................................................................1
 

Introduction.....................................................................................................................................................1
 

Sediment Model Data Sources ...................................................................................................................1
 

Preliminary Model Estimation Results .......................................................................................................3
 

Model Simulation...........................................................................................................................................5
 

Model Limitations ..........................................................................................................................................6
 

References Cited............................................................................................................................................6
 

Figures 

1. Location of 1,828 water-quality monitoring stations used in the SPARROW sediment model, in 


relation to the Reach File 1 (RF1) stream network .................................................................2
 

Tables 

1. Preliminary estimation results for the SPARROW suspended sediment model..............................4
 
A Preliminary SPARROW Model of Suspended Sediment 

for the Conterminous United States

By Gregory E. Schwarz

Abstract

This report describes the results of a preliminary Spatially 

Referenced Regression on Watershed attributes (SPARROW) 

model of suspended sediment for the conterminous United 

States. The analysis is based on flux estimates compiled from 

more than 1,800 long-term monitoring stations operated by the 

U.S. Geological Survey (USGS) during the period 1975-2007. 

The SPARROW model is structured on the Reach File 1 (RF1) 

stream network, consisting of approximately 62,000 reach segments. The reach network has been modified to include more 

than 4,000 reservoirs, an important landscape feature affecting the delivery of suspended sediment. The model identifies 

six sources of sediment, including the stream channel and 

five classes of land use: urban, forested, Federal nonforested, 

agricultural and other, noninundated land. The delivery of 

sediment from landform sources to RF1 streams is mediated 

by soil permeability, erodibility, slope, and rainfall; streamflow is found to affect the amount of sediment mobilized from 

the stream channel. The results show agricultural land and 

the stream channel to be major sources of sediment flux. Per 

unit area, Federal nonforested and urban lands are the largest 

landform sediment sources. Reservoirs are identified as major 

sites for sediment attenuation. This report includes a description for how the model results can be used to assess changes 

in instream sediment flux and concentration resulting from 

proposed changes in the regulation of sediment discharge from 

construction sites.

Introduction

This report describes the results of a preliminary Spatially 

Referenced Regression on Watershed attributes (SPARROW) 

model of suspended sediment for the conterminous United 

States.

Sediment Model Data Sources

The spatial framework of the SPARROW sediment model 

is the vector-based 1:500,000-scale River Reach File (RF) 

1 hydrography, originally developed by the U.S. EPA (U.S. 

Environmental Protection Agency, 1996) and subsequently 

enhanced to include areal hydraulic load information for 

selected reservoirs (Ruddy and Hitt, 1990), shoreline reaches, 

and reach catchment areas derived from the U.S. Geological 

Survey (USGS) HYDRO1k Digital Elevation Model (DEM) 

(U.S. Geological Survey, 2006a). The enhanced network 

(Nolan and others, 2002), consisting of 62,776 reach segments, including shoreline reaches, 61,214 delineated reach 

catchments, and 2,171 individual reservoirs, has been used to 

support numerous national SPARROW modeling efforts for 

the conterminous United States (for example, Alexander and 

others, 2000). The RF1 reach network for the current SPARROW sediment model was further enhanced by the inclusion 

of areal hydraulic load information for approximately 2,000 

additional large reservoirs (reservoir storage greater than 500 

acre-feet), identified from the National Inventory of Dams 

(NID) (U.S. Army Corps of Engineers, 2006) and linked to the 

RF1 network according to dam geographic coordinates, river 

name, and drainage area.

Included in the original RF1 network are reach estimates 

of mean streamflow and mean velocity, the latter being converted to reach time of travel using the RF1 measure of reach 

length. The attributes of mean streamflow and mean velocity 

are used to assess various sediment mobilization and attenuation processes associated with the stream channel. Because 

catchment areas used to derive the original RF1 estimates of 

mean streamflow are not compatible with catchments included 

in the enhanced RF1 network, an alternative measure of mean 

streamflow was used to compute flow-weighted sediment concentration. The alternative measure is based on an interpolation of USGS streamgage estimates from the 1975-2006 water 

years (WYs), with extrapolation of streamflow upstream of 

gages based on runoff estimated at downstream or neighboring stations and apportioned to the land surface according to 

the enhanced RF1 catchments (David Wolock, U.S. Geological Survey, 2008, written commun.). The available period for 

these data is 1 year less than the period for this study, and a 

water year consists of the period October 1st of the previous 

calendar year through September 30th of the enumerated water 

year.

The dependent variable in the SPARROW sediment 

model is given by long-term mean sediment flux. Long-term 
2 A Preliminary SPARROW Model of Suspended Sediment for the Conterminous United States

mean sediment flux is estimated using the maximum likelihood approach developed by Cohn (2005), as implemented in 

the Fluxmaster program (Schwarz and others, 2006). Instream 

sediment concentrations and stream discharge measurements 

over the WY period 1975-2007 have been obtained from the 

National Stream Quality Accounting Network (NASQAN) 

(Alexander and others, 1996; U.S. Geological Survey, 2006b), 

the USGS National Water-Quality Assessment (NAWQA) 

Program (Mueller and Spahr, 2005), and the USGS National 

Water Information System (NWIS) (U.S. Geological Survey, 

2008), a database encompassing USGS water-quality monitoring stations as well as water-quality monitoring activities 

done in cooperation with State governments. Sampling for 

suspended sediment (USGS water-quality parameter 80154) 

is typically done periodically, not daily. Sample data are 

weighted by channel cross-sectional flow geometry (depth, 

width) and correlated with stream discharge at the time of 

sampling. A linear regression model is estimated that relates 

log-transformed instantaneous suspended-sediment concentration to log-transformed mean daily streamflow, which is measured continuously via water-surface height (stage) coupled 

with a previously estimated relation between surface height 

and instantaneous flow. Included in the regression are the sine 

and cosine of decimal time to capture a seasonal signal, and 

a linear time trend to be used for detrending flux. To support 

the detrending of flux, a companion model of daily streamflow 

is estimated for each water-quality station. The streamflow 

model relates the logarithm of daily streamflow to a secondorder harmonic of the sine and cosine of decimal time, and 

a linear time trend term. To account for serial correlation in 

the daily values, the model is estimated using time-series 

methods that assume a 30-day autoregression in the residuals. 

The water-quality and streamflow models are used to simulate daily flux, with both water-quality and streamflow trends 

removed, for all days within the 33-year period WY 1975-

2007 for which a daily streamflow value is available for every 

day in the same WY. Thus, if streamflow is not available for 

any day within a given WY, no simulated water-quality flux is 

computed for any day in that WY. The simulated estimates of 

flux, detrended to the base year 1992, for all complete WYs 

within the 33-year period, are averaged to obtain a detrended 

flux reflecting long-term mean hydrologic conditions. 

The sediment model is estimated using 1,828 monitoring 

stations located on the RF1 stream network (fig. 1). Stations were selected for inclusion in the model if they had at 

least 15 concentration measurements during the period WY 

1975-2007, and the standard error of the flux estimate did not 

exceed 80 percent of the flux estimate. Of the stations included 

in the analysis, 90 percent had streamflow records in excess 

of 5 years, 70 percent had records exceeding 19 years, and 50 

percent had records that exceeded 32 years. Approximately 

700 monitoring stations have been indexed to the RF1 stream 

Figure 1. Location of 1,828 water-quality monitoring stations used in the SPARROW sediment model, in relation to the 

Reach File 1 (RF1) stream network.
Preliminary Model Estimation Results 3

network as part of previous studies (Alexander and others, 

2000). The remaining monitoring stations were linked to RF1 

reaches via their association with USGS streamgages, which 

are linked to the National Hydrography Dataset (NHD) reach 

network (Stewart and others, 2006). The location of these 

streamgages was transferred from NHD to RF1 using information on streamgage latitude and longitude (based on the NHD 

location), stream name, and reported drainage area. If multiple 

stations were present for the same RF1 reach, the alternative 

stations were first ranked in terms of drainage area, number 

of days predictions were made, the number of water-quality 

observations used in estimation, the coefficient of variation of 

the flux estimate, and whether the water-quality and streamflow records were sufficient to support detrending of flux to 

the base year. (To be detrended, the water-quality and streamflow records must span at least 3 years and, if extended no 

more than 15 percent in duration, must include the detrending 

date June 30, 1992.) The station with the lowest sum of these 

ranks was selected for inclusion in the model. 

Most of the source variables for the SPARROW sediment 

model are expressed as extensive measures of land use. Data 

on land cover and land use have been developed from the 2001 

USGS National Land Cover Data (NLCD) Set Retrofit Change 

Product [Multi-resolution Land Characteristics (MRLC), 

2001], derived from Landsat Thematic Mapper/Embedded Trace Macrocells (TM/ETM) remotely sensed imagery 

at 30-meter resolution and classified according to the eight 

Anderson Level I categories. These data were transformed to 

1-square kilometer (km2

) cells within a Lambert map projection as consistent with HYDRO1k for use within SPARROW. 

The 1-km2 cells are then resolved to catchments associated 

with specific RF1 reaches. For model estimation, land use was 

assigned the 1992 values of the 2001 NLCD Retrofit Change 

Product; the 2001 values of the Retrofit Change Product were 

used to simulate water-quality conditions for 2001. 

Federal nonforested (range and barren) land was included 

in the model separately from private land. Federal land extent, 

taken from the Federal Land coverage of the National Atlas 

(U.S. Geological Survey, 2003) and transformed to 1-km2 cells 

in Lambert projection, was apportioned into Federal range 

and Federal barren land using the 1-km2

 transformation of 

the 1992 NLCD Change Product for Anderson Level I range 

and barren land classes (see above). For model simulation of 

2001 conditions, Federal range and barren land were similarly 

estimated using the land-use estimates from the 2001 NLCD 

Retrofit Change Product.

Variables governing the estimated delivery of contaminants from the land to RF1 streams include soil erodibility 

[the revised universal soil loss equation (RUSLE) K factor], 

soil permeability (inches/hour; depth integrated), mean slope 

(percent), and precipitation (RUSLE R factor). Slope, soil 

erodibility, and permeability were obtained from the State Soil 

Survey Geographic (STATSGO) database (U.S. Department of 

Agriculture, 1994), converted to a 1-km2

 grid in the Lambert 

projection, and averaged over RF1 catchments. The RUSLE 

rainfall factor was derived by interpolating a digitized national 

map of rainfall factor isoline contours (Wischmeier and Smith, 

1978), creating a continuous 1-km2 grid surface in the Lambert 

projection. The grid coverage was subsequently averaged over 

individual RF1 catchments. 

Preliminary Model Estimation Results

The nonlinear, least-squares estimation results of a preliminary version of the SPARROW suspended sediment model 

are given in table 1. The preliminary model includes six source 

terms, five of which are measured by area of specific land 

use (urban, forested, Federal nonforested, agricultural, and 

other land, expressed in km2), and an additional source given 

by the length of the stream channel. The Federal land class 

consists only of Federal range and barren land; it excludes 

Federal forested land, which is incorporated in the forested 

land class. Agricultural land includes cropland, pasture land, 

and orchards. Other land consists of non-Federal range and 

barren land. Among all the land classes, only wetlands and 

land covered by water, ice, or snow are excluded as a potential 

source. The source described as “streambed” relates to stream 

channels as a direct source of sediment, and is measured in 

terms of stream length (expressed in meters). 

The transport of sediment from the land surface to RF1 

rivers is mediated by a land-to-water delivery factor that is 

expressed as a function (see Equation 1.28 in Schwarz and 

others, 2006) of logarithm-transformed values of soil permeability, soil erodibility (USLE K factor), land-surface slope, and 

the USLE rainfall factor. The streambed source is mediated 

by the logarithm of streamflow, distinguished by streamflows 

above and below 500 cubic feet per second (ft3

/s). Although 

the mediation of the streambed source by streamflow is not 

a land process, the manner in which the process is specified in SPARROW is mathematically equivalent to treating 

streamflow as a land-to-water variable affecting the streambed 

source, and for this reason streamflow is listed as a land-towater variable in table 1. To facilitate interpretation of the 

source coefficients, the delivery variables are all expressed as 

deviations from their mean value.

Streamflow was used as the mediating factor affecting 

the mobilization of sediment from the stream channel. There 

are two principal physical factors affecting the mobilization 

of sediment from the streambed: the energy of the stream, 

represented by stream velocity; and the availability of channel 

material, which is proportional to the area of the streambed per 

unit of channel length. Direct measurements of channel velocity, width, and depth (the determinants of streambed area per 

unit channel length) are not available for most reaches in the 

RF1 network. Streamflow was deemed to be a viable surrogate 

for these variables because it is highly correlated with them 

(Schwarz and others, 2006) (in fact, the estimate of velocity 

included with the RF1 network is derived from streamflow), 

and because estimates of streamflow exist for all reaches. 
4 A Preliminary SPARROW Model of Suspended Sediment for the Conterminous United States

The model specifies two instream sediment-attenuation 

processes: attenuation in streams (see Equation 1.30 in 

Schwarz and others, 2006), distinguished by three streamflow 

classes (less than 500 ft3/s, 500-1,000 ft3/s, and greater than 

1,000 ft3/s); and reservoir attenuation, specified as a function 

of areal hydraulic load (see Equation 1.34 in Schwarz and 

others, 2006). The three streamflow classes used to distinguish instream decay are characteristic of the streamflow at 

the 1,828 monitoring stations: 1,040 stations have streamflow 

less than 500 ft3/s, and 539 stations have streamflow exceeding 

1,000 ft3/s. Attenuation in reservoirs is specified to be a function of the ratio of the reservoir settling velocity, the estimated 

mean rate at which sediment moves vertically in water, to areal 

hydraulic load (the ratio of streamflow to reservoir surface 

area), which represents the velocity a particle at the surface of 

the stream would need to travel in order to reach the bottom of 

the reservoir within the average period that the streamflow is 

impounded in the reservoir.

The estimation results given in table 1 characteristically 

reflect the large uncertainty associated with sediment modeling. The model root mean squared error (RMSE) is 1.4, implying that predicted sediment flux or concentration in any given 

reach has an error of approximately 140 percent (Schwarz 

and others, 2006). This compares with the much smaller 0.3 

RMSE obtained with total nitrogen models (Alexander and 

others, 2008). Despite this uncertainty, many of the model 

coefficients are statistically significant. With the exception of 

forested land, all of the source variables are highly statistically 

significant. The largest intrinsic sediment yield is associated 

with Federal range and barren land; urban land has the second 

highest intrinsic yield. Stream channels are also a statistically 

significant source of sediment.

Land-to-water delivery for land sources is strongly 

mediated by the four delivery variables—soil permeability, 

soil erodibility, slope, and rainfall. As would be expected and 

with the exception of soil permeability, the presence of higher 

levels of these factors results in greater sediment delivery to 

streams. Conversely, permeable soils reduce the delivery of 

sediment, presumably because more of the water runoff infiltrates into the ground leaving less overland flow to transport 

Table 1. Preliminary estimation results for the SPARROW suspended sediment model.

[Kg/km2/yr = kilograms per square kilometer per year; kg/m/yr = kilograms per meter per year; ft3/s = cubic feet per second; and m/yr = meters per 

year]

Parameter Units Estimate Standard Error p-value

Source Coefficients

Urban land kg/km2

/yr 47,130 9,925 0.000

Forested land kg/km2

/yr 634 898 0.480

Federal non-forested land kg/km2

/yr 64,344 12,411 0.000

Agricultural land kg/km2

/yr 18,047 3,623 0.000

Other land kg/km2

/yr 11,343 3,186 0.000

Streambed (reach length) kg/m/yr 28.80 6.40 0.000

Land-to-Water Delivery Factors

Slope – 0.804 0.087 0.000

Soil permeability – -0.778 0.094 0.000

R-factor – 0.821 0.081 0.000

K-factor – 1.292 0.279 0.000

Flow [< 500 ft3

/s] (Reach) – 0.154 0.100 0.125

Flow [> 500 ft3

/s] (Reach) – 0.721 0.354 0.042

Stream Attenuation Factors

Travel time (Q < 500 ft3

/s) day-1 -0.007 0.016 0.673

Travel time (500 < Q < 1,000 ft3

/s) day-1 -0.233 0.057 0.000

Travel time (Q > 1,000 ft3

/s) day-1 0.009 0.047 0.854

Reservoir settling velocity m/yr 36.49 5.552 0.000

Number of Observations 1,828

Root mean squared error (RMSE) 1.414

R-square 0.711
Model Simulation 5

sediment. Greater streamflow causes an increase in the amount 

of sediment generated from stream channels, with the largest 

effect associated with streams having flows greater than 500 

ft3/s. 

Reservoir retention is statistically significant and indicates sediment settles at a mean velocity of 36 meters per 

year (m/yr), comparable to estimates of reservoir attenuation 

obtained for phosphorus (Alexander and others, 2008). The 

preliminary model indicates medium-sized streams (flow 

500-1,000 ft3/s) have a statistically significant negative rate 

of instream attenuation, indicating that medium streams are a 

source of sediment, in addition to the stream-channel source 

identified above. Unlike the streambed source of sediment (see 

above), which is dictated by channel length and, thus, explicitly tied to a physical entity, the implied “source” of sediment 

arising from a negative rate of instream attenuation represents 

a proportional “enhancement” of sediment already suspended 

in the stream (see Equation 1.30 in Schwarz and others, 2006). 

Because this proportional enhancement of sediment is not 

associated with a physical source, it is inconsistent with mass 

balance and represents an anomalous finding of the model that 

is not yet explainable. Instream attenuation in small and large 

streams is not significantly statistically different from zero. 

Thus, the preliminary model does not find evidence for sediment loss in streams.

Model Simulation

The estimated SPARROW suspended sediment model for 

base WY 1992 can be used to simulate water-quality conditions for 2001, with and without U.S. EPA proposed changes 

in the regulation of construction activity. The simulation of 

suspended sediment flux for WY 2001, without changes in 

regulation, is obtained using the model prediction equation, 

described as Equation 1.120 in Schwarz and others (2006), 

with all land use-related source variables set to 2001 values 

according to the 2001 NLCD Retrofit Change Product. Flowweighted average sediment concentration is estimated by 

dividing simulated flux estimates by mean streamflow over the 

period WY 1975-2006, obtained from USGS streamgages and 

interpolated to RF1 reaches (Wolock, U.S. Geological Survey, 

2008, written commun.). Although the SPARROW model 

does not explicitly include a source term for construction, such 

loading is implicitly accounted for in the urban land component of the model. Therefore, the 2001 precompliance loading 

from construction (that is, the “base-case” scenario loading) is 

incorporated in the 2001 loading attributed to urban land that 

is obtained by evaluating the urban land variable in the SPARROW model using the 2001 NLCD Change Product value.

The absence of an explicit term for construction loading 

in the SPARROW model necessitates the development of an 

indirect method for assessing changes in sediment loading 

arising from different construction industry regulation scenarios. Suspended sediment loading under alternative regulation 

scenarios has been estimated by U.S. EPA using a variation 

of the Universal Soil Loss Equation (USLE). The USLE 

method determines the amount of soil that is mobilized and 

delivered, under a proposed regulation scenario, to the edge 

of a construction site. To evaluate the impact that changes in 

these loadings have on RF1 stream-sediment flux and flowweighted concentration, it is first necessary to assess the rate 

at which “edge of site” loads are subsequently delivered to 

RF1 streams. To do this, we use the estimated rate of delivery 

from agricultural land, a source that is explicitly included in 

the model, and that can be factored into a mobilization, “edge 

of site” delivery component and a stream-delivery component. 

The method described below isolates the stream-delivery component from the overall rate of delivery from agricultural land 

and applies this component to the change in construction loading to determine the change in loading to RF1 streams. Thus, 

the approach assumes that the delivery of sediment from the 

edge of a site to an RF1 stream is the same for both construction and agriculture activities; the mobilization and delivery 

of sediment to the edge of the site between these activities 

is allowed to differ. Given that urban areas, as compared to 

agricultural areas, generally exhibit higher rates of runoff, with 

compressed runoff duration periods for a given precipitation 

event, the assumption probably leads to an underestimate in 

the change in stream-sediment flux from proposed regulation 

of the construction industry. It would not be necessary to make 

this assumption if the analysis was based instead on a factorization of urban land delivery; however, as is indicated below, 

the information necessary to do this is not available.

The amount of sediment mobilized from agricultural 

land, delivered to the edge of field, and subsequently transported to an RF1 stream, is estimated in SPARROW as the 

product of the agricultural land-source coefficient and the 

associated land-to-water delivery factor. This quantity, denoted 

KAG and expressed in units of yield as kilograms per square 

kilometer per year (kg/km2/yr), is conceptually divided into 

two components: a component representing the amount of 

sediment mobilized from agricultural land and delivered to the 

edge of site, denoted KEOS-AG, and a component representing 

the fraction of this material that is subsequently delivered to an 

RF1 stream, denoted KRF1. If ∆LEOS(S) represents the change in 

construction sediment loading to the edge of site, as estimated 

by U.S. EPA using the USLE method, and ∆LRF1(S) represents 

the change in sediment loading to RF1 streams associated with 

construction regulation scenario S, then the two loadings are 

related according to

( ) ( ) (1) EOS ( )

RF1 RF1 EOS AG

EOS-AG

S

S S

L

L KL K

K

∆

∆ = ⋅∆ =

Given estimates of the regulation-induced change in 

sediment delivered to each RF1 reach, instream processes 

associated with attenuation in channels and reservoirs, as 

described by the SPARROW model estimates, can be applied 

to estimate changes in sediment flux and concentration for 

all RF1 reaches. Additionally, because SPARROW imposes 
6 A Preliminary SPARROW Model of Suspended Sediment for the Conterminous United States

mass balance, the reservoir attenuation process can be used to 

assess changes in the amount of sediment retained in each of 

the approximately 4,000 reservoirs linked directly to the RF1 

network. 

To implement the method described by Equation 1, it 

is necessary to have estimates of KAG, the delivery factor 

for agricultural land, and KEOS-AG, the amount of soil erosion 

mobilized from agricultural land and delivered to the edge of 

site. KAG is reach specific and is estimated in SPARROW as 

a function of the land-to-water delivery factors for agriculture (see Equation 1.28 in Schwarz and others, 2006) and the 

empirically estimated values of the agricultural land-source 

and land-to-water delivery coefficients. The value for KEOS-AG

is obtained using information on soil erosion included in the 

1992 National Resources Inventory (NRI) (U.S. Department 

of Agriculture, 1994), which reports county estimates of soil 

erosion rates for cropland, pasture, and orchards—the land 

classes encompassed by the class labeled “agricultural land” in 

the NLCD Retrofit Change Product. (KRF1 is based on agricultural land because erosion rates for urban land are not reported 

by the NRI.) The USLE-based county erosion rates for each 

of the three land classes were weighted according to the share 

of county land in the respective class (as reported in the 1992 

NRI) and then averaged. If there was no county estimate for 

cropland erosion, an average erosion rate for that county was 

not computed. The county average erosion rate was apportioned to RF1 catchments according to a 1-km2

 grid of agricultural land area derived from the 1992 NLCD Retrofit Change 

Product. For 6,329 catchments where an NRI erosion rate was 

not available, the erosion rate was estimated by determining 

an agricultural land weighted average of all available erosion 

rates for catchments in the same 8-digit hydrologic cataloging 

unit. Lack of an 8-digit cataloging unit average necessitated 

using a 6-digit cataloging unit average for 713 catchments, and 

the remaining 54 catchments were estimated using a 4-digit 

cataloging unit average.

Model Limitations 

The preliminary SPARROW model for suspended sediment described above has certain limitations, some of which 

are inherent to the methodology and some the result of the 

particular model application. An example of the former is the 

restriction of the analysis to the description of long-term mean 

water-quality conditions. As explained in Schwarz and others 

(2006), this restriction is a consequence of imposing mass 

balance on the predictions. One of the benefits of the mass 

balance methodology is that it facilitates the interpretation of 

model coefficients, and enables the comparison of coefficient 

estimates to estimates obtained by other studies in the literature; however, the restriction to mean water-quality conditions 

precludes an analysis of the frequency with which conditions 

of extreme sediment transport occur. 

A second example of a methodology-imposed limitation concerns the use of the statistical method to estimate 

model coefficients. The statistical method provides considerable insight into the evaluation of model fit and the empirical 

relevance of individual model processes. It also enables the 

estimation of prediction uncertainty. However, reasonable 

precision in the statistical estimation of model coefficients 

is generally possible only if the number of specified model 

parameters is limited to those associated with sources and 

delivery processes that have the greatest influence on water 

quality. The resulting model is parsimonious, but may be 

overly simplistic in terms of the range of processes affecting 

sediment transport. 

The RF1 reach network is fairly coarse, and its use in the 

present application limits the ability to predict water-quality 

conditions in smaller streams. The median headwater catchment area in RF1 is 88 km2

, implying water-quality conditions 

in streams with smaller catchments are unresolved. Additionally, the smallest monitored catchment in the SPARROW sediment model is 13 km2

, with only five percent of the monitored 

catchments less than 100 km2. A SPARROW analysis structured on a denser reach network, such as the National Hydrography Dataset, would relax the reach network limitation, but 

the large number of reaches associated with this network 

would make it difficult to conduct a national analysis. 

The large error obtained in the present analysis implies 

the prediction of sediment flux or concentration in any given 

reach segment is imprecise. Although this error compromises 

the ability to describe water-quality conditions in any given 

reach, it does not preclude using the model to characterize 

water quality in a large grouping of reaches. As long as the 

error across reaches is sufficiently independent, the assessment 

of mean water quality in a group of reaches becomes more 

precise as the size of the group increases. 

With the exception of reservoirs, the preliminary model 

does not find evidence of sediment attenuation in streams. The 

result implies that sediment transport in streams is not in a 

steady state. Additional investigation is necessary to determine 

if this result is real, or if there are additional reach attributes, 

currently absent from the model, that identify a subset of 

reaches where sediment attenuation takes place. 

References Cited 

Alexander, R.B., Slack, J.R., Ludtke, A.S., Fitzgerald, K.K., 

Schertz, T.L., Briel, L.I., and Buttleman, K.P., 1996, Data 

from selected U.S. Geological Survey National Stream 

Water-Quality Networks (WQN): U.S. Geological Survey Digital Data Series DDS–37, available online at 

http://pubs.usgs.gov/dds/wqn96cd/. (Accessed January 30, 

2008.)
References Cited 7

Alexander R.B., Smith, R.A., and Schwarz, G.E., 2000, Effect 

of stream channel size on the delivery of nitrogen to the 

Gulf of Mexico: Nature, v. 403, no. 6771, p. 758–761.

Alexander, R.B., Smith, R.A., Schwarz, G.E., Boyer, E.W., 

Nolan, J.V., and Brakebill, J.W., 2008, Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the 

Mississippi River Basin: Environmental Science & Technology, v. 42, issue 3, p. 822–830.

Cohn, T.A., 2005, Estimating contaminant loads in rivers—An 

application of adjusted maximum likelihood to type 1 

censored data: Water Resources Research 41:W07003, 

doi:10.1029/2004WR003833.

Mueller, D.K., and Spahr, N.E., 2005, Water-quality, streamflow, and ancillary sata for nutrients in streams and 

rivers across the Nation, 1992–2001: U.S. Geological 

Survey Digital Data Series DDS–152, available online at 

http://pubs.usgs.gov/ds/2005/152/. (Accessed 1/30/2008.)

Nolan, J.V., Brakebill, J.W., Alexander, R.B., and 

Schwarz, G.E., 2002, ERF1_2—Enhanced River 

Reach File 2.0: U.S. Geological Survey OpenFile Report 02–40. (Also available online at 

http://water.usgs.gov/GIS/metadata/usgswrd/XML/erf1_2.

xml.)

Ruddy, B.C., and Hitt, K.J., 1990, Summary of selected characteristics of large reservoirs in the United States and Puerto 

Rico, 1988: U.S. Geological Survey Open-File Report 

90–163.

Schwarz, G.E., Hoos, A.B., Alexander, R.B., and Smith, 

R.A., 2006, The SPARROW surface water-quality 

model: Theory, application and user documentation: 

U.S. Geological Survey Techniques and Methods, section B, chapter 6-B3, 248 p., available only online at 

http://pubs.usgs.gov/tm/2006/tm6b3/. (Accessed June 10, 

2008.)

Steffen, L.J., 1996, A reservoir sedimentation survey information system—RESIS, in Proceedings of the Sixth Federal 

Interagency Sedimentation Conference, Las Vegas, NV, 

March 10–14, 1996: Sponsored by the Subcommittee on 

Sedimentation, Interagency Advisory Committee on Water 

Data, p. 29–37.

Stewart, D.W., Rea, A.H., and Wolock, D.M., 2006, USGS 

streamgages linked to the medium resolution NHD: U.S. 

Geological Survey Data Series DS–195.

U.S. Army Corps of Engineers (USACE), 2006, 

USACE National Inventory of Dams Web site at 

http://crunch.tec.army.mil/nidpublic/webpages/nid.cfm. 

(Accessed June 17, 2005.)

U.S. Department of Agriculture, 1994, State Soil Survey Geographic Data Base: U.S. Department of Agriculture, Soil 

Conservation Service, National Cooperative Soil Survey, 

Miscellaneous Publication Number 1492.

U.S. Environmental Protection Agency, 1996, The U.S. 

Environmental Protection Agency Reach File Version 1.0 

(RF1) for the Conterminous United States in BASINS: 

U.S. Environmental Protection Agency Web page at 

http://www.epa.gov/waterscience/BASINS/metadata/rf1.htm. 

(Accessed June 5, 2008.)

U.S. Geological Survey, 2003, Federal lands 

of the United States, available online at 

http://nationalatlas.gov/mld/fedlanp.html. (Accessed June 

5, 2008.)

U.S. Geological Survey, 2006a, HYDRO1k 

Documentation, available online at 

http://edc.usgs.gov/products/elevation/gtopo30/hydro/

readme.html. (Accessed June 5, 2008.)

U.S. Geological Survey, 2006b, National Stream Water Quality Network (NASQAN) Published Data, available online at 

http://water.usgs.gov/nasqan/data/finaldata.html. (Accessed 

January 30, 2008.)

U.S. Geological Survey, 2008, USGS Water Data for the 

Nation, available online at

http://waterdata.usgs.gov/nwis. (Accessed February 4, 

2008.)

Wischmeier, W.H., and Smith, D.D., 1978, Predicting rainfall 

erosion losses—A guide to conservation planning: USDA 

Agriculture Handbook 537, 58 p.
Schwarz—A Preliminary SPARROW Model of Suspended Sediment for the Conterminous United States—Open-File Report 2008–1205 

Printed on recycled paper 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 E-1 

Appendix E – Estuary TSS Concentration Calculations Using 

Dissolved Concentration Potentials 

As described in Section 6.3, EPA used SPARROW predicted sediment loadings in combination with 

dissolved concentration potentials (DCPs) for calculating sediment concentrations where flow estimates 

were not available from SPARROW. This appendix includes all the tables referenced in Section 6.3. 

DCP values and baseline TSS concentrations calculated using DCPs are shown in Table E-1 through 

Table E-4 for Southeastern, Gulf, Northeastern, and West Coast estuaries, respectively. Data on these and 

additional estuaries are found via the NOAA Coastal Geospatial Data Project 

(http://coastalgeospatial.noaa.gov). 

Table E-1: TSS Concentrations (mg/L) Estimated by 

DCP, Southeastern Estuaries 

Estuary DCP 

Base TSS 

(mg/l) 

Albemarle/Pamlico Sound, NC, VA 0.14 33.7 

Altamaha River, GA 0.37 82.8 

Biscayne Bay, FLA 0.4 0.2 

Bogue Sound, NC 1.47 3.0 

Broad River, SC 4.92 16.3 

Cape Fear River, NC 0.61 69.7 

Charleston Harbor, SC 0.44 2.5 

Indian River, FL 1.02 3.4 

N and S Santee Rivers, SC 2.91 456.8 

New River, NC 7.68 15.3 

Ossabaw Sound, GA 1.99 81.0 

Savannah River, SC, GA 0.43 61.2 

St Catherines/Sapelo Sound, GA 7.56 17.2 

St Helena Sound, SC 0.95 10.8 

St Johns River, FL 0.83 13.3 

St. Andrews/St. Simons Sound, GA 2.43 47.4 

Winyah Bay, SC, NC 0.39 109.0 

Table E-2: TSS Concentrations (mg/L) Estimated by 

DCP, Gulf of Mexico Estuaries 

Estuary DCP 

Base TSS 

(mg/l) 

Apalachee Bay, FL, GA 0.36 8.2 

Apalachicola Bay, FL 0.17 27.7 

Arkansas Bay, TX 6.02 152.7 

Atchafalaya and Vermillion Bays, LA 0.04 254.8 

Brazos River, TX 1.11 598.3 

Calcasieu Lake, LA 1.18 48.3 

Charlotte Harbor, FL 0.76 15.1 

Choctawhatchee Bay, FL, AL 0.7 51.7 

Corpus Cristi Inner Harbor, TX 4.67 555.0 

Galveston Bay, TX 0.4 93.4 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 E-2 

Table E-2: TSS Concentrations (mg/L) Estimated by 

DCP, Gulf of Mexico Estuaries 

Estuary DCP 

Base TSS 

(mg/l) 

Laguna Madre, TX 0.34 21.7 

Matagorda Bay, TX 0.81 212.0 

Mississippi River, LA, MS 0.01 198.4 

Mississippi Sound, MS, LA, AL 0.17 127.5 

Mobile Bay, AL 0.08 87.7 

Pensacola Bay, FL 0.46 44.1 

Perdido Bay, FL, AL 2.89 34.5 

Sabine Lake, LA, TX 0.38 76.3 

San Antonio Bay, TX 1.30 833.3 

St Andrew Bay, FL 0.76 2.2 

Suwanee River, FL 0.38 24.5 

Tampa Bay, FL 1.03 10.4 

Ten Thousand Islands, FL 1.94 1.6 

Table E-3: TSS Concentrations (mg/L) Estimated by 

DCP, Northeastern Estuaries 

Estuary DCP 

Base TSS 

(mg/l) 

Barnegat Bay, NJ 1.36 3.7 

Blue Hill Bay, ME 1.03 1.7 

Buzzards Bay, MA 1.04 3.8 

Cape Cod Bay, MA 0.69 0.5 

Casco Bay, ME 0.61 3.1 

Chesapeake Bay, VA, MD, DE, PA, DC 0.072 91.6 

Chincoteague Bay, MD, VA 3.08 2.0 

Delaware Bay, DE, NJ, PA, MD 0.14 40.0 

Englishman Bay, ME 0.92 4.7 

Gardiners Bay, NY 1.77 2.0 

Great Bay, NE, NH 1.54 7.6 

Great South Bay, NY 5.07 12.0 

Hudson River/Raritan Bay, NY, NJ, MA, CT 0.2 52.4 

Long Island Sound, NY, CT, MA 0.054 9.7 

Massachusetts Bay, MA 0.27 2.3 

Merrimack River, NH., MA 1.01 32.0 

Muscongus Bay, ME 2.25 6.5 

Narragansett Bay, MA, RI 0.52 8.2 

Narragauges Bay, ME 1.54 4.2 

Passaquoddy Bay, ME 0.27 1.5 

Penobscot Bay, ME 0.13 7.1 

Saco Bay, ME, NH 0.45 4.2 

Sheepscop Bay, ME, NH 0.088 0.2 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 E-3 

Table E-4: TSS Concentrations (mg/L) Estimated by 

DCP, West Coast Estuaries 

Estuary DCP Base TSS (mg/l) 

Alsea River, OR 2.086 37.4 

Columbia River, WA, OR 0.032 77.0 

Coos Bay, OR 2.27 50.9 

Eel River, CA 0.39 65.3 

Grays Harbor, WA 0.278 21.2 

Humboldt Bay, CA 8 38.7 

Klamath River, OR, CA 0.31 79.8 

Monterey Bay, CA 1.05 148.2 

Nehalam River, OR 1.556 38.5 

Netarts Bay, OR 12.564 13.0 

Puget Sound, WA 0.039 9.7 

Rogue River, OR 0.556 125.4 

San Diego Bay, CA 12.31 88.9 

San Francisco Bay, CA 0.104 89.3 

San Pedro Bay, CA 4.41 104.0 

Santa Monica Bay, CA 1.371 30.0 

Siletz Bay, OR 2.424 42.8 

Siuslaw River, OR 1.853 46.5 

Tillamook Bay, OR 1.049 21.3 

Umpqua River, OR 0.793 110.6 

Willapa Bay, WA 0.466 12.1 

Yaquina Bay, OR 3.356 25.7 

In the final analysis, the DCP approach was only used in 43 reaches without flow predictions. The 

number of reaches in each estuary where the DCP approach was used to calculate TSS concentrations is 

shown in Table E-5 through Table E-8 . 

Table E-5: Total Number Reaches in the Analysis located in Southeastern Estuaries that 

Use DCP to Calculate TSS by Estuary 

Estuary DCP Number of Reaches 

Albemarle/Pamlico Sound, NC, VA 0.14 2 

Altamaha River, GA 0.37 0 

Bogue Sound, NC 1.47 0 

Broad River, SC 4.92 0 

Cape Fear River, NC 0.61 0 

Charleston Harbor, SC 0.44 1 

Indian River, FL 1.02 0 

N and S Santee Rivers, SC 2.91 1 

New River, NC 7.68 1 

Ossabaw Sound, GA 1.99 1 

Savannah River, SC, GA 0.43 0 

St Catherines/Sapelo Sound, GA 7.56 0 

St Helena Sound, SC 0.95 0 

St Johns River, FL 0.83 2 

Winyah Bay, SC, NC 0.39 1 

Note: Only estuaries with reaches included in the analysis are listed in the table. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 E-4 

Table E-6: Total Number Reaches in the Analysis located in Gulf of Mexico Estuaries 

that Use DCP to Calculate TSS by Estuary 

Estuary DCP Number of Reaches 

Apalachicola Bay, FL 0.17 0 

Charlotte Harbor, FL 0.76 0 

Choctawhatchee Bay, FL, AL 0.7 2 

Galveston Bay, TX 0.4 0 

Pensacola Bay, FL 0.46 0 

Perdido Bay, FL, AL 2.89 0 

Sabine Lake, LA, TX 0.38 0 

St Andrew Bay, FL 0.76 1 

Suwanee River, FL 0.38 0 

Tampa Bay, FL 1.03 2 

Note: only estuaries with reaches included in the analysis are listed in the table. 

Table E-7: Total Number Reaches in the Analysis located in Northeastern Estuaries 

that Use DCP to Calculate TSS by Estuary 

Estuary DCP Number of Reaches 

Barnegat Bay, NJ 1.36 1 

Buzzards Bay, MA 1.04 0 

Cape Cod Bay, MA 0.69 0 

Casco Bay, ME 0.61 1 

Delaware Bay, DE, NJ, PA, MD 0.14 0 

Gardiners Bay, NY 1.77 0 

Great Bay, NE, NH 1.54 0 

Great South Bay, NY 5.07 0 

Hudson River/Raritan Bay, NY, NJ, MA, CT 0.2 2 

Long Island Sound, NY, CT, MA 0.054 1 

Massachusetts Bay, MA 0.27 0 

Narragansett Bay, MA, RI 0.52 2 

Saco Bay, ME, NH 0.45 1 

Sheepscop Bay, ME, NH 0.088 0 

Note: Only estuaries with reaches included in the analysis are listed in the table.

Table E-8: Total Number Reaches in the Analysis located in West Coast Estuaries that 

Use DCP to Calculate TSS by Estuary 

Estuary DCP Number of Reaches 

Alsea River, OR 2.086 0 

Coos Bay, OR 2.27 5 

Grays Harbor, WA 0.278 3 

Nehalam River, OR 1.556 0 

Netarts Bay, OR 12.564 0 

Puget Sound, WA 0.039 4 

Rogue River, OR 0.556 1 

San Diego Bay, CA 12.31 1 

San Francisco Bay, CA 0.104 2 

San Pedro Bay, CA 4.41 1 

Santa Monica Bay, CA 1.371 0 

Siletz Bay, OR 2.424 0 

Siuslaw River, OR 1.853 0 

Tillamook Bay, OR 1.049 2 

Umpqua River, OR 0.793 0 

Willapa Bay, WA 0.466 1 

Yaquina Bay, OR 3.356 0 

Note: Only estuaries with reaches included in the analysis are listed in the table.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 F-1 

Appendix F – TSS Subindex Curve Parameters 

This appendix provides the ecoregion-specific parameters used in estimating the TSS water quality 

subindex, as follows: 

 If [TSS] ≤ TSS100 Subindex = 100 

 If TSS100 < [TSS] ≤ TSS10 Subindex = a exp(b [TSS]) 

 If [TSS] > TSS10 Subindex = 10 

 where [TSS] is the measured concentration and TSS10, TSS100, a, and b are specified in Table F-1.

Table F-1: TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS100 TSS10

10.1.2 Columbia Plateau 126.56 -0.0038 63 668 

10.1.3 Northern Basin and Range 112.42 -0.0007 160 3,457 

10.1.4 Wyoming Basin 123.36 -0.001 220 2,513 

10.1.5 Central Basin and Range 121.22 -0.0018 109 1,386 

10.1.6 Colorado Plateaus 144.44 -0.001 363 2,670 

10.1.7 Arizona/New Mexico Plateau 126.76 -0.0004 668 6,349 

10.1.8 Snake River Plain 146.39 -0.0027 142 994 

10.2.1 Mojave Basin and Range 119.34 -0.0015 121 1,653 

10.2.2 Sonoran Desert 112.39 -0.0002 567 12,097 

10.2.4 Chihuahuan Desert 214.39 -0.0005 1,419 6,130 

11.1.1 California Coastal Sage, Chaparral, and Oak Woodlands 127.97 -0.0012 205 2,124 

11.1.2 Central California Valley 171.86 -0.0044 122 646 

11.1.3 Southern and Baja California Pine-Oak Mountains 115.12 -0.0007 197 3,491 

12.1.1 Madrean Archipelago 261.35 -0.0005 2,053 6,527 

13.1.1 Arizona/New Mexico Mountains 120.98 -0.0004 477 6,233 

15.4.1 Southern Florida Coastal Plain 116.95 -0.0405 4 61 

5.2.1 Northern Lakes and Forests 157.76 -0.0233 20 118 

5.2.2 Northern Minnesota Wetlands 154.99 -0.0186 24 147 

5.3.1 Northern Appalachian and Atlantic Maritime Highlands 174.99 -0.0261 21 110 

5.3.3 North Central Appalachians 245.15 -0.0176 51 182 

6.2.10 Middle Rockies 144.64 -0.0038 98 703 

6.2.11 Klamath Mountains 238.9 -0.0068 129 467 

6.2.12 Sierra Nevada 185.36 -0.0116 53 252 

6.2.13 Wasatch and Uinta Mountains 124.28 -0.0014 160 1,800 

6.2.14 Southern Rockies 153.42 -0.0031 140 881 

6.2.15 Idaho Batholith 184.23 -0.0142 43 205 

6.2.3 Columbia Mountains/Northern Rockies 180.7 -0.0168 35 172 

6.2.4 Canadian Rockies 396.62 -0.0308 45 119 

6.2.5 North Cascades 240.95 -0.0193 46 165 

6.2.7 Cascades 192.94 -0.0181 36 164 

6.2.8 Eastern Cascades Slopes and Foothills 178.82 -0.0145 40 199 

6.2.9 Blue Mountains 148.35 -0.0037 107 729 

7.1.7 Strait of Georgia/Puget Lowland 181.06 -0.0224 27 129 

7.1.8 Coast Range 174.78 -0.0114 49 251 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 F-2 

Table F-1: TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS100 TSS10

7.1.9 Willamette Valley 210.3 -0.0114 65 267 

8.1.1 Eastern Great Lakes and Hudson Lowlands 144.62 -0.0104 36 257 

8.1.2 Lake Erie Lowland 112.79 -0.0049 25 494 

8.1.3 Northern Appalachian Plateau and Uplands 322.68 -0.0113 103 307 

8.1.4 North Central Hardwood Forests 148.68 -0.0108 37 250 

8.1.5 Driftless Area 117.97 -0.0012 141 2,057 

8.1.6 S. Michigan/N. Indiana Drift Plains 191.44 -0.0143 46 206 

8.1.7 Northeastern Coastal Zone 158.48 -0.0164 28 168 

8.1.8 Maine/New Brunswick Plains and Hills 156.02 -0.025 18 110 

8.1.10 Erie Drift Plain 133.08 -0.0037 78 700 

8.2.1 Southeastern Wisconsin Till Plains 121.34 -0.0042 46 594 

8.2.2 Huron/Erie Lake Plains 145.17 -0.0058 65 461 

8.2.3 Central Corn Belt Plains 187.95 -0.0033 191 889 

8.2.4 Eastern Corn Belt Plains 235.18 -0.003 282 1,053 

8.3.1 Northern Piedmont 175.82 -0.0042 135 683 

8.3.2 Interior River Valleys and Hills 149.68 -0.0013 303 2,081 

8.3.3 Interior Plateau 220.47 -0.0037 217 836 

8.3.4 Piedmont 224.11 -0.0048 169 648 

8.3.5 Southeastern Plains 205.3 -0.0085 85 356 

8.3.6 Mississippi Valley Loess Plains 492.49 -0.0048 333 812 

8.3.7 South Central Plains 184.36 -0.0045 136 648 

8.3.8 East Central Texas Plains 162.32 -0.0013 362 2,144 

8.4.1 Ridge and Valley 186.83 -0.0063 99 465 

8.4.2 Central Appalachians 166.76 -0.0062 82 454 

8.4.3 Western Allegheny Plateau 183.67 -0.0032 190 910 

8.4.4 Blue Ridge 216.16 -0.0087 89 353 

8.4.5 Ozark Highlands 175.16 -0.0018 317 1,591 

8.4.6 Boston Mountains 329.77 -0.0062 193 564 

8.4.7 Arkansas Valley 283.25 -0.004 261 836 

8.4.8 Ouachita Mountains 212.77 -0.0048 157 637 

8.4.9 Southwestern Appalachians 207.09 -0.0071 103 427 

8.5.1 Middle Atlantic Coastal Plain 182.17 -0.0178 34 163 

8.5.2 Mississippi Alluvial Plain 131.35 -0.0029 93 888 

8.5.3 Southern Coastal Plain 138.62 -0.0144 23 183 

8.5.4 Atlantic Coastal Pine Barrens 283.76 -0.0463 23 72 

9.2.1 Aspen Parkland/Northern Glaciated Plains 136.43 -0.0005 640 5,226 

9.2.2 Lake Manitoba and Lake Agassiz Plain 174.13 -0.0042 131 680 

9.2.3 Western Corn Belt Plains 135.01 -0.0009 347 2,892 

9.2.4 Central Irregular Plains 201.19 -0.001 673 3,002 

9.3.1 Northwestern Glaciated Plains 133.98 -0.0006 483 4,325 

9.3.3 Northwestern Great Plains 130.6 -0.0004 636 6,424 

9.3.4 Nebraska Sand Hills 289.85 -0.0066 162 510 

9.4.1 High Plains 125.61 -0.0005 507 5,061 

9.4.2 Central Great Plains 156.84 -0.0005 925 5,505 

9.4.3 Southwestern Tablelands 137.77 -0.0003 1,280 8,743 

9.4.4 Flint Hills 270.93 -0.0009 1,084 3,666 

9.4.5 Cross Timbers 134.97 -0.0006 523 4,337 

9.4.6 Edwards Plateau 173.77 -0.001 544 2,855 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 F-3 

Table F-1: TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS100 TSS10

9.4.7 Texas Blackland Prairies 134.23 -0.0005 624 5,194 

9.5.1 Western Gulf Coastal Plain 124.47 -0.0025 88 1,009 

9.6.1 Southern Texas Plains/Interior Plains and Hills with 

Xerophytic Shrub and Oak Forest 

166.67 -0.0003 1,602 9,378 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-1 

Appendix G – Meta-Analysis Results 

EPA used function-based benefit transfer to estimate benefits of surface water quality improvements due 

to reductions in sediment runoff from construction sites under the regulatory analysis options considered 

for the analysis of the regulation. The benefit function was derived using meta-analysis, following the 

general approach of Johnston et al. (2005), Shrestha et al. (2007), and others, following conceptual 

methods outlined by Bergstrom and Taylor (2006). The recent literature has given increasing emphasis to 

the potential use of meta-analysis to conduct and inform function-based benefit transfer (Johnston et al. 

2005; Bergstrom and Taylor 2006; Rosenberger and Stanley 2006; Shrestha et al. 2007). For the present 

analysis, the meta-regression model was based on a model specification and data developed originally for 

the 316(b) Phase II Cooling Water Intake rule, and extended to generate a benefit function more suitable 

for assessing changes in ambient water quality from reducing sediment discharges. Chapter A12, 

“Methods for Estimating Non-use Benefits,” in the Regional Analysis document for the final Phase II rule 

provides details on the original meta-analysis (USEPA 2004d); revisions are detailed below. These 

revisions included adding new studies to the metadata, re-estimating the willingness-to-pay (WTP) 

function to better account for ecological services potentially affected by sediments and nutrients, and 

testing additional functional forms and statistical approaches. 

As stated by Rosenberger and Johnston (2007, p. 1-2): 

One of the primary advantages of meta-analysis as a benefit transfer tool relates to its capacity to allow more 

appropriate adjustments of welfare measures based on patterns observed in the literature. Within a benefit transfer 

context, transfer error is often inversely related to the correspondence between a study site and a policy site among 

various dimensions (Rosenberger and Phipps 2007). The probability of finding a good fit between a single (or multiple) 

study site and a policy site, however, is usually low (Boyle and Bergstrom 1992; Spash and Vatn 2006). If, on the other 

hand, empirical studies contribute to a body of WTP estimates (i.e., metadata), and if empirical value estimates are 

systematically related to variations in resource, study, and site characteristics, then meta-regression analysis may 

provide a viable tool for estimating a more universal transfer function with distinct advantages over unit value or other 

function-based transfer methods (Johnston et al. 2003; Rosenberger and Loomis 2000a; Rosenberger and Stanley 

2006). More specifically, Rosenberger and Phipps (2007) posit a meta-valuation function as the envelope of a set of 

empirically-defined valuation functions reported in the literature. 

In the present case, EPA identified 45 valuation studies that use stated preference techniques to elicit 

benefit values for water quality improvements. To examine the relative influence of study, economic, and 

resource characteristics on WTP for improving surface water quality, the Agency conducted a regressionbased meta-analysis of 115 estimates of WTP for water quality improvements, provided by the 45 original 

studies. Analytic methods and model specifications follow established methods in the published literature 

(e.g., Johnston et al. 2005; Bergstrom and Taylor 2006). The estimated econometric model is used as the 

basis of a function-based benefit transfer, to calculate WTP for improving water quality in waterbodies 

affected by sediment runoff from construction sites. 

The following discussion summarizes the results of EPA’s meta-analysis of surface water valuation 

studies and the use of the resulting benefit function for transfer. 

G.1 Literature Review of Water Resource Valuation Studies 

As outlined in the introduction, EPA conducted a meta-analysis of water resource valuation studies to 

examine the relative influence of study, economic, and resource characteristics on total WTP for water 

quality improvements. The Agency analyzed 45 studies, published between 1981 and 2008, that applied 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-2 

generally accepted, standard valuation methods to determine total (including use and nonuse) values 

associated with aquatic habitat improvements. These 45 studies all used stated preference techniques to 

assess WTP, but varied in other respects, including the survey administration methods used, the specific 

environmental change valued, and the geographic region affected by the environmental changes. Studies 

using stated preference approaches are preferred to studies using revealed preference approaches, because 

they elicit total household WTP (including use and nonuse values). Revealed preference studies allow to 

estimate use values only. Data from the 45 studies result in a total of 115 observations for the metaanalysis because 30 studies provide more than one usable estimate of total WTP for aquatic habitat 

improvements. 

When constructing metadata for subsequent meta-analysis, analysts must determine the optimal scope of 

the metadata (Rosenberger and Johnston 2007), interpreted as the exact definition of the dependent 

variable in the meta-regression model, which, in turn, defines the set of source studies that can be 

considered for inclusion in the metadata. The primary tradeoff is often between maintaining close 

similarity among dependent variables versus including additional information (i.e., observations) in the 

metadata. Similarity in dependent variable definition and study attributes within the metadata can be 

important for two reasons. First, theory may dictate that certain types of estimated values are not strictly 

comparable (e.g., Hicksian compensating variation from a stated preference model versus Marshallian 

consumer surplus from a travel cost model). Second, model fit may be improved by narrowing the 

metadata, for example to include only valuation studies that use a particular valuation approach (e.g., 

stated preference methods, as in Johnston et al. (2005); or travel cost model estimates, as in Smith and 

Kaoru (1990)). Such study selection issues may be framed in terms of a requirement that at a minimum, 

studies included in metadata satisfy both commodity consistency and welfare consistency (Bergstrom and 

Taylor 2006). The former implies that “the commodity (Q) being valued should be approximately the 

same within and across studies” (Bergstrom and Taylor 2006, p. 353). The latter implies that “measures 

of WTP within and across studies ... should represent the same ... welfare change measure, or ex-post 

calibrations [are] made to account for theoretical differences between welfare change measures” 

(Bergstrom and Taylor 2006, p. 355). 

The requirement of welfare consistency implies that—outside of preference calibration approaches that 

explicitly account for theoretical differences between welfare constructs (e.g., Smith et al. 2002)—metaanalyses should not combine data representing theoretically distinct welfare measures. For example, the 

ad hoc combination of stated and revealed data for meta-analysis would generally violate the condition of 

welfare consistency. Although one may introduce independent variables in regression models (typically 

dummy variable intercept shifters) to account for such theoretical differences, Smith and Pattanayak 

(2002) argue that such methods are unlikely to represent appropriate adjustments for fundamental 

differences in theoretical welfare measures. 

G.1.1 Identifying Water Resource Valuation Studies 

EPA identified surface water valuation studies used in the total WTP meta-analysis by conducting an indepth search of the economic literature. EPA used a variety of sources and search methods to identify 

relevant studies: 

3⁄4 Review of EPA’s research and bibliographies dealing with non-market benefits associated 

with water quality changes 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-3 

3⁄4 Systematic review of recent issues of key resource economics journals (e.g., Land 

Economics, Marine Resource Economics, Journal of Environmental Economics and 

Management) 

3⁄4 Searches of online reference and abstract databases (e.g., Environmental Valuation Resource 

Inventory (EVRI), Benefits Use Valuation Database (BUVD), AgEcon Search) 

3⁄4 Visits to home pages of authors known to have published stated preference studies and/or 

water quality valuation research 

3⁄4 Searches of Web sites of agricultural and resource economics departments at several colleges 

and universities 

3⁄4 Searches of Web sites of organizations and agencies known to publish environmental and 

resource economics valuation research (e.g., Resources for the Future (RFF), National Center 

for Environmental Economics (NCEE)). 

From this review, EPA identified approximately 300 surface water valuation studies that were potentially 

relevant for this analysis and compiled a bibliographic database to organize the literature review process. 

Sixty-seven of these studies met the criteria identified for inclusion in the meta-analysis.23 These criteria 

were designed to ensure both commodity and welfare consistency as noted above, and include: 

3⁄4 Specific amenity valued: Selected studies were limited to those in which the environmental 

quality change being valued affects ecological services provided by surface waterbodies, 

including aquatic life support, recreational activities (such as fishing, boating, and 

swimming), and nonuse value

3⁄4 Values estimated: Selected studies were limited to those that used stated preference 

techniques to elicit household WTP. 

3⁄4 Study location: Selected studies were limited to those that surveyed U.S. and Canadian 

populations to value resources 

3⁄4 Research methods: Selected studies were limited to those that applied research methods 

supported by journal literature. 

The Agency compiled extensive information from the 67 selected studies. Of these studies, 45 were 

utilized in the model estimation. Reasons for the difference between the total number of studies in the 

final metadata (67) and studies represented in the final model (45) include unavailability of information 

for certain key regressors for all studies. 

The tradeoff between the number of regressors or independent variables that may be included in a metaregression analysis (K) and the number of studies that are included in the metadata (N) is a fundamental 

tradeoff in most meta-analyses in the valuation literature (Moeltner et al. 2007). That is, if a study 

considered for inclusion in the metadata does not provide information for a certain regressor that analysts 

might wish to include in the meta-regression, analysts must generally choose between omitting the 

regressor from the meta-regression or omitting the study from the metadata. As a result, researchers 

wishing to increase the number of explanatory variables in meta-regression models (increasing K) often 

do so at the cost of reductions in the number of studies or observations in the metadata (reducing N).

 

23 The remaining studies were either earlier unpublished versions or slightly modified versions of the included studies, focused

on water resources outside of the United States and Canada, used secondary research methods, or valued environmental 

quality changes that were not directly linked to changes in water quality (e.g., change in recreational catch rates). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-4 

Conversely, increases in the number of observations in meta-regression models are sometimes only 

possible if one reduces the number of independent variables in the model. Hence, there is a tradeoff 

between the quantity of information in the metadata (i.e., the number of observations or studies) and the 

possible risk of omitted variables bias due to the omission of influential regressors. See Moeltner et al. 

(2007) for additional discussion of this “N versus K” tradeoff in meta-analysis. 

The complete data set used in the meta-analysis is provided in the docket for the regulation (DCN 6-

7900), and includes the following information: 

3⁄4 Full study citation 

3⁄4 Study location 

3⁄4 Sample data and description (e.g., size, response rate, income) 

3⁄4 Resource characteristics (e.g., affected waterbody type, recreational uses, baseline quality) 

3⁄4 Description of environmental quality change, including geographic scale, affected species, 

and affected recreational uses (e.g., water quality change from fishable to boatable) 

3⁄4 Quantitative measure of environmental quality change measured in terms of improvements in 

Water Quality Index (WQI)24 and/or percentage reduction in pollutant concentration 

3⁄4 Study WTP values updated to 2007 dollars 

3⁄4 WTP estimation characteristics (i.e., parametric versus non-parametric, inclusion of protest 

bids and outlier bids, WTP description). 

G.1.2 Description of Studies Selected for Total WTP Meta-Analysis 

The 45 studies that EPA used in the total WTP meta-analysis were conducted between 1981 and 2008, 

and applied standard, generally accepted stated preference valuation methods to assess WTP. Studies 

were excluded if they did not conform to general tenets of economic theory, or if they applied methods 

not generally accepted in the literature. 

All selected studies focus on environmental quality changes that affect surface water resources in the 

United States. Beyond this general similarity, the studies vary in several respects. Differences include the 

specific environmental change valued, scale of environmental improvement, geographic region affected 

by environmental changes, types of values estimated, survey administration methods, demographics of 

the survey sample, and statistical methods employed. The 45 studies include 25 journal articles, 6 reports, 

5 Ph.D. dissertations, 7 academic or staff papers, 1 book, and 1 master’s thesis. 

The 45 studies selected for the meta-analysis provided 115 observations in the final data set because 

multiple estimates of WTP were available from 30 studies. The availability of multiple observations from 

single studies is common in meta-analyses of this type (e.g., Bateman and Jones 2003; Johnston et al. 

2005). Some of the characteristics that allowed multiple observations to be derived from a single study 

include the extent of the amenity change, the respondent population type, elicitation method(s), 

 

24 Additional details on the WQI and the use of the WQI in survey instruments are provided by McClelland (1974), Vaughan 

(1986), and Mitchell and Carson (1989, p. 342). This index is linked to specific pollutant levels, which in turn are linked to 

presence of aquatic species and suitability for particular recreational uses. The WQI allows the use of objective water quality

parameters (e.g., dissolved oxygen concentrations) to characterize ecosystem services or uses provided by a given 

waterbody. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-5 

waterbody type, number of waterbodies affected, recreational activities affected by the quality change, 

and species affected by the quality change. These variations are often due to experimental design driven 

by the key research questions or hypotheses. Table G-1 lists key study and resource characteristics and 

indicates the number of observations derived from each study. 

Surveys in 26 studies were administered by mail; 9 studies collected information through personal 

interviews in the home, onsite, or in a centralized location, 9 surveys were conducted by telephone, and 1 

conducted by computer administration. Study sample sizes range from 96 to 4,033 responses. 

Table G-1: Selected Summary Information for Studies 

Author(s) and Year Observations State 

Waterbody 

Type 

Type of Water Quality 

Improvement 

Affected 

Recreational Uses 

Aiken (1985) 1 CO all freshwater general water quality fishing 

Anderson and 

Edwards (1986) 

1 RI salt 

pond/marshes 

general water quality fishing and swimming 

Azevedo et al. (2001) 5 IA lake nutrients fishing and swimming 

Bockstael et al. 

(1988) 

1 DC, MD, 

VA 

estuary general water quality; 

phosphorus and nitrogen 

swimming, beach, 

boating, fishing, 

outings 

Bockstael et al. 

(1989) 

2 MD estuary general water quality swimming 

Breffle et al. (1999) 2 WI estuary general water quality fishing 

Cameron (1988) 1 TX Estuary general water quality fishing 

Cameron and 

Huppert (1989) 

1 CA river/stream wildlife habitat game fishing 

Carson and Mitchell 

(1993) 

4 National multiple general water quality boating; fishing; 

swimming 

Carson et al. (1994) 2 CA estuary DDT and PCBs fishing 

Clonts and Malone 

(1990) 

3 AL river/stream general water quality multiple uses 

Croke et al. (1986-

87) 

9 IL river/stream general water quality multiple uses 

De Zoysa (1995) 2 OH river and lake sediment and nutrients; 

wildlife habitat 

multiple uses 

Desvousges et al. 

(1987) 

12 PA river/stream general water quality multiple uses 

Hayes et al. (1992) 2 RI estuary general water quality swimming; fishing 

Herriges and Shogren 

(1996) 

2 IA lake general water quality boating and fishing 

Hite (2002) 2 MS river/stream general water quality multiple uses 

Huang et al. (1997) 2 NC estuary general water quality fishing 

Hushak and Bielen 

(1999) 

2 OH, MI river/stream general water quality multiple uses 

Kaoru (1993) 1 MA salt 

pond/marshes 

fecal coliform fishing 

Lant and Roberts 

(1990) 

3 IA, IL river/stream sediment boating, fishing, and 

swimming 

Lant and Tobin 

(1989) 

9 IA, IL river/stream general water quality boating, fishing 

Lichtkoppler and 

Blaine (1999) 

1 OH multiple PCBs and general water 

quality 

all recreational uses 

Lindsey (1994) 8 MD estuary nutrients multiple uses 

Lipton (2004) 1 MD estuary general water quality boating 

Loomis (1996) 1 WA river/stream general water quality fishing 

Loomis et al. (2000) 2 CO river/stream general water quality multiple uses 

Lyke (1993) 2 WI lake general water quality fishing 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-6 

Table G-1: Selected Summary Information for Studies 

Author(s) and Year Observations State 

Waterbody 

Type 

Type of Water Quality 

Improvement 

Affected 

Recreational Uses 

Matthews et al. 

(1999) 

2 MN river/stream phosphorus boating and fishing 

Olsen et al. (1991) 3 ID, MT, 

OR, WA 

river/stream wildlife habitat fishing 

Opaluch et al. (1998) 1 NY estuary general water quality shellfishing 

Roberts and Leitch 

(1997) 

1 MN, SD lake general water quality multiple uses 

Rowe et al. (1985) 1 CO river/stream general water quality boating, fishing, and 

swimming 

Sanders et al. (1990) 4 CO river/stream general water quality swimming 

Schulze et al. (1995) 2 MT river and lake hazardous pollutants boating, fishing, and 

swimming 

Shrestha and 

Alavalapati (2004) 

2 FL multiple phosphorus and wildlife 

habitat 

multiple uses 

Stumborg et al. 

(2001) 

2 WI lake phosphorus multiple uses 

Sutherland and 

Walsh (1985) 

1 MT river and lake general water quality swimming 

Viscusi et al. (2008) 2 National river and lake general water quality multiple uses 

Welle (1986) 6 MN all freshwater acid rain game fishing and 

wildlife viewing 

Wey (1990) 2 RI salt pond/ 

marshes 

general water quality Other 

Whitehead et al. 

(2002) 

1 NC river/stream general water quality fishing, boating, 

swimming 

Whitehead and 

Groothuis (1992) 

3 NC river/stream sediment and nutrients multiple uses 

Whitehead et al. 

(1995) 

2 NC estuary general water quality boating, fishing, and 

swimming 

Whittington et al. 

(1994) 

1 TX estuary heavy metals and pesticides multiple uses 

The Agency’s review of the relevant economic literature showed that available surface water valuation 

studies focus primarily on general water quality rather than specific pollutants or changes. Even in cases 

in which specific pollutants are the primary policy issue, the stated preference surveys from which 

welfare estimates are derived often characterize water quality changes only in general (i.e., non-pollutant 

specific) terms. Hence, the associated welfare measures are conditioned on this general description. Of 

the 45 studies, 26 presented only WTP values for changes in general water quality (approximately 60 

percent). Of the studies that did address specific changes, eight specified nutrients and/or sediment, four 

addressed hazardous pollutants including heavy metals and pesticides, three addressed wildlife habitat, 

one addressed acid deposition, one addressed fecal coliform bacteria, one presented values for both 

changes in general water quality and nutrient reductions, and one presented values for both changes in 

general water quality and wildlife habitat. Preliminary model estimates showed no evidence that the type 

of pollutant considered had a statistically significant influence on WTP across and within studies. For this 

reason, EPA used a standardized scale to define both the baseline water quality and the water quality 

change valued in the original study. Additional details are provided below. 

From these 45 studies, the Agency compiled a data set for the meta-analysis of total WTP values. EPA 

specified a regression model based on these data to estimate a range of total household benefits for 

surface water and aquatic habitat improvements. General empirical methods follow those outlined by 

Johnston et al. (2005), following standard approaches in the meta-analysis literature. The model and 

results are described in the next section. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-7 

G.2 Total WTP Meta-Analysis Regression Model and Results 

EPA estimated both trans-log and semi-log meta-regression models based on 115 WTP estimates for 

improvements in water resources, derived from 45 original studies.25 These metadata, the model 

specification, model results, and interpretation of the results are described in the following sections. EPA, 

however, notes that only the trans-log model is used in the analysis of benefits from reduced sediment 

discharges. The alternative specification (semi-log) is presented for comparative purposes only. 

In a frequently cited work, Glass (1976) characterizes meta-analysis as “the statistical analysis of a large 

collection of results for individual studies for the purposes of integrating the findings. It provides a 

rigorous alternative to the casual, narrative discussion of research studies which is commonly used to 

make some sense of the rapidly expanding research literature” (p. 3; cited in Poe et al. 2001, p. 138). 

Meta-analysis is being increasingly explored as a potential means to estimate resource values in cases 

where original targeted research is impractical, or as a means to reveal systematic components of WTP 

(e.g., Smith and Osborne 1996; Santos 1998; Rosenberger and Loomis 2000a; Poe et al. 2001; Woodward 

and Wui 2001; Bateman and Jones 2003). While the literature often urges caution in the use and 

interpretation of benefit transfers for direct policy application (e.g., Desvousges et al. 1998; Poe et al. 

2001; Navrud and Ready 2007), such methods are “widely used in the United States by government 

agencies to facilitate benefit-cost analysis of public policies and projects affecting natural resources” 

(Bergstrom and De Civita 1999). Transfers based on meta-analysis are likewise common in both the 

United States and Canada (Bergstrom and De Civita 1999; Bergstrom and Taylor 2006). 

Depending on the suitability of available data, a meta-analysis can provide a superior alternative to the 

calculation and use of a simple arithmetic mean WTP over the available observations, as it allows 

estimation of the systematic influence of study, economic, and natural resource attributes on WTP 

(USEPA 2000b; Rosenberger and Phipps 2007; Shrestha et al. 2007). The primary advantage of a 

regression-based (statistical) approach is that it accounts for differences among study characteristics that 

may contribute to changes in WTP, to the extent permitted by available data (Johnston et al. 2005; 

Rosenberger and Phipps 2007). An additional advantage is that meta-analysis can reveal systematic 

factors influencing WTP, allowing analysts to assess whether, for example, WTP estimates are (on 

average) sensitive to scope (Smith and Osborne 1996). 

There is, however, some controversy regarding whether regression-based meta-analyses should be used 

for direct benefit transfer. Many contemporary sources in the literature note the potential ability of 

regression-based meta-analyses to generate benefit functions better able to adjust and forecast benefits at 

policy sites in question, and either explicitly or implicitly favor the use of meta-analysis over alternative 

benefit transfer approaches (e.g., Johnston et al. 2005; Bergstrom and Taylor 2006; Moeltner et al. 2007; 

Rosenberger and Johnston 2007; Rosenberger and Phipps 2007; Shrestha et al. 2007). EPA (USEPA 

2000b) characterizes meta-analysis as “the most rigorous” benefit transfer method. In contrast, the EPA 

Science Advisory (2007) Board’s “Advisory on EPA’s Issues in Valuing Mortality Risk Reduction” 

recommends against the use of regression-based meta-analysis for VSL (value of statistical life) transfers, 

and other authors advise caution in such uses (Navrud and Ready 2007). The primary disagreement is 

whether it is appropriate to use meta-analysis results as a reduced form model to estimate benefits, and 

 

25 In its analysis of nonuse benefits for the final 316(b) Phase II rule, EPA also specified trans- and semi-log regression models

similar to the models estimated for Phase II discussed in this section. See Chapter A12, “Methods for Estimating Non-use 

Benefits,” in the Regional Analysis document for further details regarding both the log-log and semi-log regression models 

estimated in EPA’s analysis of nonuse benefits for the final Phase II rule (USEPA 2004d); 

http://www.epa.gov/ost/316b/casestudy/final.htm). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-8 

whether the empirical ability of meta-analysis in many cases to generate benefit transfers with reduced 

transfer errors offsets the lack of an underlying theoretical model to “calibrate” benefit estimates across 

studies (cf. Smith and Pattanayak 2002; Smith et al. 2002). While the Agency recognizes this ongoing 

controversy, it notes that there are a large number of practitioners and publications supporting the use of 

regression-based meta-analysis for benefit transfer. It also removes the element of subjective judgment 

associated with selecting a single study or value for benefit transfer. Hence, the following model is 

presented as a means to provide a benefit function that capitalizes on the substantial information available 

for existing water quality valuation studies, notwithstanding potential concerns voiced by some regarding 

the use of meta-analyses for such purposes. 

G.2.1 Metadata Total WTP Regression Model 

Meta-analysis is largely an empirical, data-driven process, but one in which variable and model selection 

is guided by theory (Bergstrom and Taylor 2006). Given a reliance on information available from the 

underlying studies that comprise the metadata, meta-analysis models most often represent a middle 

ground between model specifications that would be most theoretically appropriate and those 

specifications that are possible given available data. Smith and Osborne (1996), Rosenberger and Loomis 

(2000a), Poe et al. (2001), Bateman and Jones (2003), Dalhuisen et al. (2003), Johnston et al. (2005, 

2006), Bergstrom and Taylor (2006), Moeltner et al. (2007), and others provide insight into the mechanics 

of specifying and estimating meta-equations in resource economics applications. 

Past meta-analyses have incorporated a range of different statistical methods, with none universally 

accepted as superior (Johnston et al. 2005). EPA followed recent work of Bateman and Jones (2003) and 

Johnston et al. (2005) in applying a multilevel model specification to the metadata, to address potential 

correlation among observations gathered from single studies. Also following prior work (e.g., Smith and 

Osborne 1996; Poe et al. 2001), EPA applied the Huber-White robust variance estimation. As described 

by Smith and Osborne (1996, p. 293), “this approach treats each study as the equivalent of a sample 

cluster with the potential for heteroskedasticity...across clusters.” Weighted models are avoided 

following the arguments of Bateman and Jones (2003).26

To guide development of the model and variable specifications, EPA relied upon a set of general 

principles. These principles are designed to help prevent excessive data manipulations and other factors 

that may lead to misleading model results. The general principles include, all else being equal: 

3⁄4 Fewer and simpler data transformations are preferred to more extensive ones. 

3⁄4 In the absence of overriding theoretical considerations, continuous variables are generally 

preferred to discrete variables derived from underlying continuous distributions. 

3⁄4 Models should attempt to capture elements of the scope and scale of resource changes. 

3⁄4 Models should distinguish WTP associated with different types of resources and resource 

uses, particularly where relevant to the policy question at hand. 

3⁄4 Following the “weak structural utility theoretic” (WSUT) approach of Bergstrom and Taylor 

(2006, p. 352), exogenous structural constraints are avoided to afford the flexibility necessary 

 

26 For comparison, models were also estimated using ordinary least squares (OLS) with robust variance estimation, weighted 

least squares with robust variance estimation, and multilevel models with standard (non-robust) variance estimation. None 

of these models outperformed the illustrated model in terms of overall model significance and fit, and statistical significance

of individual coefficients (see Section G.2.2 for further details concerning the specification of the model). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-9 

to appropriately model empirical patterns that may not necessarily flow from an underlying 

theoretical modeling structure. The dependent variable in the meta-analysis is the natural 

logarithm of estimated household WTP for water quality improvements in aquatic habitat, as 

reported in each original study. For this analysis, original study values were adjusted to 

2007$ based on the relative change in Consumer Price Index (CPI) from the study year to 

2007. Total WTP over the sample ranged from $5.33 to $502.70, with a mean value of 

$83.09. 

As noted above, two model specifications are estimated (cf. Johnston et al. 2005). For the first 

specification, all right-hand-side variables are linear, resulting in a standard semi-log functional form. The 

second specification is identical to the semi-log model, except for the specification of the explanatory 

variable measuring water quality change and baseline as natural logs. This results in a trans-log functional 

form, also common in empirical applications (Johnston et al. 2001, 2005). Both the semi-log and trans-log 

models have advantages related to (1) their fit to the data, (2) the intuitive results that are provided, and 

(3) their common use in the empirical valuation and meta-analysis literature (e.g., Smith and Osborne 

1996; Santos 1998; Johnston et al. 2001, 2005, 2006). The trans-log model, however, has the additional 

structural advantage that estimated WTP is necessarily zero when water quality change is also zero (cf. 

Johnston et al. 2001)—a property suggested by theory that analysts may wish to weight against model fit 

considerations when choosing a model for benefit estimation. While linear forms are also common in this 

literature (Rosenberger and Loomis 2000a, 2000b; Poe et al. 2001; Bateman and Jones 2003), 

specifications requiring more intensive data transformations (e.g., Box-Cox, log-log) are less common. 

As noted in the preceding section, the metadata include independent variables characterizing specific 

details of the resource(s) valued such as the baseline resource conditions; the extent of resource 

improvements and whether they occur in estuarine or freshwater; the geographic region and scale of 

resource improvements (e.g., the number of waterbodies); resource characteristics (e.g., baseline 

conditions, the extent of water quality change, and ecological services affected by resource 

improvements); characteristics of surveyed populations (e.g., users, nonusers); and other specific details 

of each study. For ease of exposition, these variables are categorized into those characterizing (1) study 

and methodology, (2) surveyed populations, (3) geographic region and scale, and (4) resource 

improvements. Attributes included within each category are summarized below. 

Study and methodology variables characterize such features as: 

3⁄4 The year in which a study was conducted 

3⁄4 The payment vehicle and elicitation format (e.g., discrete choice versus open-ended, 

voluntary versus non-voluntary, interview versus mail versus phone) 

3⁄4 WTP estimation methods and conventions (e.g., approaches to protest and outlier bids, use of 

parametric versus non-parametric statistical methods, estimation of mean or median WTP, the 

use of annual or lump-sum payments) 

3⁄4 Whether the original survey represented water quality changes using the WQI. 

Surveyed populations variables characterize such features as: 

3⁄4 The average income of respondents 

3⁄4 Whether the survey specifically targeted nonusers. 

Geographic region and scale variables characterize such features as: 
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November 2009 G-10 

3⁄4 The number of waterbodies affected by the policy 

3⁄4 Whether the study considered water quality improvements in all waterbodies in a region 

3⁄4 The geographic area of the country in which the study was conducted. 

Resource improvement variables characterize such features as: 

3⁄4 The extent of water quality change estimated as a difference between the baseline and postchange water quality index 

3⁄4 Baseline water quality index 

3⁄4 Those studies for which recreational uses such as fishing are specifically noted in the survey 

3⁄4 Aquatic species affected by resource improvements (e.g., game fish and shellfish) 

3⁄4 Those studies identifying large increases in fish populations (i.e., greater than 50 percent). 

Although the interpretation and calculation of most independent variables requires little explanation, a 

few variables require additional detail. These include the variables characterizing surface water quality 

and its measurement. Many (23) observations in the metadata characterize quality changes using variants 

of the WQI (e.g., Mitchell and Carson 1989). This scale is linked to specific pollutant levels, which, in 

turn, are linked to the presence of aquatic species and recreational uses. However, some observations 

provide water quality measures using other, primarily descriptive, means that differ from the WQI. 

To allow consistent comparisons of water quality change using a single scale, EPA mapped all water 

quality measures to the original WQI developed by McClelland (1974). WQI values were therefore 

developed for those studies that did not originally use this index. This scale was chosen for two reasons: 

3⁄4 WQI values are linked to specific pollutant levels including sediment concentrations, which, 

in turn, are linked to the presence of aquatic species and suitability for particular recreational 

uses. Therefore, the WQI can be used to link water quality changes from reduced sediment 

runoff to effects on human uses and support for aquatic species habitat. Chapter 10 of this 

report provides detail on application of the WQI to estimating resource improvements from 

reduced sediment runoff from construction sites. 

3⁄4 A large number of the original studies in the metadata included WQI measures as “native” 

components of the original surveys. Hence, for these studies, no additional transformations 

were required. 

While not all studies in the metadata included the WQI as a native survey component, in most cases the 

descriptions of water quality (present in the studies that did not apply the WQI) rendered mapping of 

water quality measures to the WQI straightforward. In cases where baseline and improved (or declined) 

water quality was not defined by suitability for recreational activities (e.g., boating, fishing, and 

swimming) or corresponding qualitative measures (e.g., poor, fair, good), EPA used descriptive 

information available from studies (e.g., amount/indication of the presence of specific pollutants, 

historical decline of the quality of the resource) to approximate the baseline level of water quality and the 

magnitude of the change.27 For studies that valued discrete changes in the size of species populations, 

EPA characterized the baseline quality based on the current presence and prevalence of the species at 

 

27 For example, a study by Huang et al. (1997) described current water quality as degraded from 1981 levels in terms of 

reduced fish catches (60 percent) and reduced number of open shellfish beds (25 percent). However, because the water 

resource was still supporting recreational fishery, the baseline water quality was set to “fishable” on the WQI. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-11 

hand, and assumed population increases to correspond to modest increases in water quality in order to be 

conservative.28 To account for the uncertainty involved in mapping those studies that are not based on the 

WQI, EPA introduced the binary variable WQI, which indicates those studies in which WQI 

measurements were an original component of the survey instrument. This approach is based largely on the 

published methods of Johnston et al. (2005), drawn from prior Agency work for the 316(b) Phase II 

Cooling Water Intake rule. 

Variables incorporated in the final model are listed and described in Table G-2.

Table G-2: Variables and Descriptive Statistics for the Total WTP Regression Model 

Variable Description 

Units and 

Measurement 

Mean 

(Std. Dev.) 

ln_WTP Natural log of WTP for specified resource improvements. Natural log of dollars 

(Range: 2.12 to6.22) 

4.42 

(0.75) 

year_indx Year in which the study was conducted, converted to an index by 

subtracting 1980. 

Year index (Range: 1 

to 28) 

9.68 

(6.42) 

discrete Binary (dummy) variable indicating that WTP was estimated 

using a discrete choice survey instrument. 

Binary 

(Range: 0 or 1) 

0.28 

(0.45) 

volunt Binary (dummy) variable indicating that WTP was estimated 

using a payment vehicle described as voluntary as opposed to, for 

example, property taxes. 

Binary 

(Range: 0 or 1) 0.05 

(0.22) 

mail Binary (dummy) variable indicating that the survey was 

conducted through mail (default value for this dummy is a phone 

survey). 

Binary 

(Range: 0 or 1) 0.48 

(0.52) 

lump_sum Binary (dummy) variable indicating that payments were to occur 

on something other than an annual basis over a long period of 

time, such as property taxes. For example, some studies specified 

that payments would occur over a five-year period. 

Binary 

(Range: 0 or 1) 0.13 

(0.34) 

nonparam Binary (dummy) variable indicating that WTP was estimated 

using non-parametric methods. 

Binary 

(Range: 0 or 1) 

0.50 

(0.50) 

quality_ch The change in mean water quality, specified on the WQI 

(McClelland 1974; Mitchell and Carson 1989). Defined as the 

difference between baseline and post-compliance quality. Where 

the original study (survey) did not use the WQI, EPA mapped 

water quality descriptions to analogous levels on the WQI to 

derive water quality change (see text). 

WQI units 

(Range: 2.5 to 65) 21.3 

(10.4) 

lnquality_ch1 Natural log of the change in mean water quality (quality_ch), 

specified on the WQI (McClelland 1974; Mitchell and Carson 

1989). 

WQI units 

(Range: 0.92 to 4.17) 2.91 

(0.59) 

WQI Binary (dummy) variable indicating that the original survey 

reported resource changes using a standard WQI. 

Binary 

(Range: 0 or 1) 

0.34 

(0.48) 

non-reviewed Binary (dummy) variable indicating that the study was not 

published in a peer-reviewed journal. 

Binary 

(Range: 0 or 1) 

0.32 

(0.47) 

outlier_bids Binary (dummy) variable indicating that outlier bids were 

excluded when estimating WTP. 

Binary 

(Range: 0 or 1) 

0.94 

(0.24) 

median_WTP Binary (dummy) variable indicating that the study reported 

median, not mean, WTP. 

Binary 

(Range: 0 or 1) 

0.04 

(0.20) 

income Mean income of survey respondents, either as reported by the 

original survey or calculated by EPA based on U.S. Census 

Bureau averages for the original surveyed region. 

Dollars 

(Range: 34,955 to 

158,347) 

5,7049.59 

(13,946.64) 

 

28 For example, a study by Lyke (1993) describes the baseline conditions as follows: (1) “there are no naturally reproducing 

lake trout in Lake Michigan; all lake trout found there are from hatcheries.” (2) “Lake Superior stocks of self-reproducing 

lake trout were much reduced, but not wiped out, and both natural and hatchery-raised lake trout are found there.” These 

baseline conditions correspond to the “game-fishable” level on the WQI. The study estimates WTP for restoring natural 

populations of lake trout to the Wisconsin Great Lakes. Therefore, the expected change that will occur within the “gamefishable” category is likely to be small. 
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Table G-2: Variables and Descriptive Statistics for the Total WTP Regression Model 

Variable Description 

Units and 

Measurement 

Mean 

(Std. Dev.) 

nonusers Binary (dummy) variable indicating that the survey was 

implemented over a population of nonusers (default category for 

this dummy is a survey of any population that includes users). 

Binary 

(Range: 0 or 1) 0.09 

(0.29) 

single_river2 Binary (dummy) variable indicating that resource change 

explicitly took place over a single river (default is a change in an 

estuary or that takes place on a national scale). 

Binary 

(Range: 0 or 1) 0.23 

(0.42) 

single_lake3 Binary (dummy) variable indicating that resource change 

explicitly took place over a single lake (default is a change in an 

estuary or that takes place on a national scale). 

Binary 

(Range: 0 or 1) 

0.08 

(0.28) 

regional_fresh Binary (dummy) variable indicating that resource change 

explicitly took place over an entire region such as a state (default 

is a change in an estuary or a change that takes place on a national 

scale). 

Binary 

(Range: 0 or 1) 0.36 

(0.48) 

multiple_river Binary (dummy) variable indicating that resource change 

explicitly took place over multiple rivers (default is a change in an 

estuary or that takes place on a national scale). 

Binary 

(Range: 0 or 1) 0.06 

(0.24) 

salt_pond Binary (dummy) variable indicating that resource change 

explicitly took place over multiple salt ponds (default is a change 

in an estuary or that takes place on a national scale). 

Binary 

(Range: 0 or 1) 0.03 

(0.18) 

num_riv_pond Number of rivers or salt ponds affected by policy; if unspecified 

num_riv_pond = 0. (In the present data, only studies addressing 

rivers and lakes specified >1 number of waterbodies. All others 

specified either 1 waterbody, or the number was unspecified.) 

Number of specified 

rivers or ponds 

(Range: 0 to 15) 

1.01 

(2.98) 

mr Binary (dummy) variable indicating that the survey included 

respondents from more than one of the EPA regions. 

Binary 

(Range: 0 or 1) 

0.08 

(0.26) 

mp Binary (dummy) variable indicating that the survey included 

respondents from the Mountain Plain region.4

Binary 

(Range: 0 or 1) 

0.02 

(0.13) 

allmult Binary (dummy) variable indicating that either all or multiple 

aquatic species are affected by the resource change. 

Binary 

(Range: 0 or 1) 

0.18 

(0.39) 

nonspec Binary (dummy) variable indicating that the study did not specify 

what species would be affected by water quality improvements. 

Binary 

(Range: 0 or 1) 

0.42 

(0.50) 

fish_use Binary (dummy) variable identifying studies in which changes in 

fishing use are specifically noted in the survey. 

Binary 

(Range: 0 or 1) 

0.56 

(0.50) 

fishplus Binary (dummy) variable identifying studies in which a fish 

population or harvest change of 50 percent or greater is reported 

in the survey. 

Binary 

(Range: 0 or 1) 0.08 

(0.28) 

baseline Baseline water quality, specified on the WQI. WQI units (Range: 

1.61 to 5.2) 

39.79 

(20.37) 

lnbase Natural log of baseline water quality, specified on the WQI. WQI units (Range: 5 

to 70) 

3.46 

(0.80) 1

 The variable quality_ch is defined earlier in this table as the difference between baseline and post-compliance quality, specified on the WQI 

(Mitchell and Carson 1989). 

2

 Examples of rivers and streams considered in the studies include the Columbia, Potomac, Elwha, Eagle, and Tar-Pamlico rivers. 3

 Includes one study that focused on a segment of the Lake Erie shoreline. 4

 The Mountain Plain region includes the following states: Colorado, Iowa, Kansas, Missouri, Montana, Nebraska, North Dakota, South 

Dakota, Utah, Wyoming.

G.2.2 Total WTP Regression Model and Results 

As noted above, EPA estimated the meta-analysis regression using a multilevel, random-effects 

specification. This model follows the general approaches of Bateman and Jones (2003) and Johnston et al. 

(2005), among others. Multilevel (or hierarchical) models may be estimated as either random-effects or 

random-coefficients models, and are described in detail elsewhere (Singer 1998). The fundamental 

distinction between these and classical linear models is the two-part modeling of the equation error to 

account for hierarchical data. Here, the metadata are comprised of multiple observations per study, and 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-13 

there is a corresponding possibility of correlated errors among observations that share a common study or 

author. 

The common approach to modeling such potential correlation is to divide the residual variance of 

estimates into two parts, a random error that is independently and identically distributed across all studies 

and for each observation, and a random effect that represents systematic variation related to each study. 

The model is estimated as a two-level hierarchy, with level one corresponding to WTP estimates 

(individual observations), and level two corresponding to individual studies. The random effect may be 

interpreted as a deviation from the mean equation intercept associated with individual studies (Bateman 

and Jones 2003). The model is estimated using a maximum likelihood estimator, assuming that random 

effects show a multivariate normal distribution. Following Bateman and Jones (2003), observations are 

unweighted. Covariances are obtained using the Huber-White covariance estimator (Smith and Osborne 

1996). Random-effects models such as the multilevel model applied here are becoming increasingly 

standard in resource economics applications, and are estimable using a variety of readily available 

software packages. 

A Note on Model Specification 

As noted above, EPA considered two functional forms in this analysis: semi-log and trans-log. In both 

cases, the dependent variable is the natural log of estimated household WTP for surface water quality 

improvement, as shown in Table G-2. For Model One, all right-hand-side variables are linear, resulting in 

a semi-log functional form common in meta-analysis (e.g., Smith and Osborne 1996; Johnston et al. 

2003). While linear forms are also common (Rosenberger and Loomis 2000a, 2000b; Poe et al. 2001; 

Bateman and Jones 2003), the semi-log and trans-log forms were chosen based on statistical performance 

and ability to capture curvature in the valuation function, and because they allow independent variables to 

influence WTP in a multiplicative rather than additive manner. 

Model Two is a trans-log model, identical to the semi-log specification save for the inclusion of water 

quality measures (baseline and quality_ch) as natural logarithms. This form—common in the hedonic 

modeling literature (Johnston et al. 2001) and illustrated by Johnston et al. (2005) within the metaanalysis literature—shares many advantages of the semi-log functional form, but also incorporates the 

desirable quality that WTP is constrained to zero when quality change is also equal to zero. 

Following standard econometric practice and the “weak structural utility theory” approach to metaanalysis summarized above (Bergstrom and Taylor 2006), the final models are specified based on 

guidance from theory and prior literature. For example, Arrow et al. (1993) made a fundamental 

distinction between discrete choice and open-ended payment mechanisms (such as iterative bidding and 

payment cards). Hence, this distinction is made in the final model (i.e., including the variable 

discrete_ch). Similarly, other “survey methodology” variables in the model were chosen based on 

theoretical considerations and prior findings in the literature (e.g., voluntary versus mandatory payment 

vehicles, parametric versus non-parametric, treatment of protest and outlier bids, use of mean versus 

median WTP). Also included are variables characterizing the scope and scale of the resource change, 

based on theoretical expectations that such factors should be relevant to welfare estimates. 

Few variables were excluded solely because of lack of statistical significance. Individual variables were 

only excluded if they could not be shown to be statistically significant in any version of the model 

(restricted or unrestricted), and there was no overriding rationale for retaining the variable in the model. 

For example, variables distinguishing different types of discrete choice instruments (e.g., conjoint versus 

dichotomous choice) added no significant explanatory power to the model (p = 0.58). 
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It is important to note that although empirical considerations play a role in model development, certain 

variables were retained in the model for theoretical reasons, even if significance levels were low. Such 

specification of meta-analysis models using a combination of theoretical guidance and empirical 

considerations is standard in modeling efforts (Bergstrom and Taylor 2006). 

Table G-3 presents results of the total WTP regression model. 

Table G-3: Estimated Multilevel Model Results for the Trans-log and Semi-log Total 

WTP Regression Models: WTP for Aquatic Habitat Improvements 

Trans-log Model Semi-log Model 

Variable Parameter Estimate Standard Error Parameter Estimate Standard Error 

intercept 5.7109 3 0.9352 6.0946 3 0.5127 

year_indx -0.08043 3 0.01482 -0.06707 3 0.01430 

discrete -0.1248 0.2230 -0.1696 0.1735 

volunt -1.3233 3 0.1653 -1.30493 0.1661 

nonparam -0.66983 0.1434 -0.68923 0.1164 

income 2.698 x 10-6 3.9 x 10-6 -1.16 x 10-7 3.238 x 10-6

WQI -0.3275 0.2692 -0.36311 0.2096 

outlier_bids -0.88373 0.2855 -0.78293 0.2164 

single_river -0.4279 3 0.1412 -0.27242 0.1222 

single_lake -0.06316 0.2386 -0.1268 0.2223 

multiple_river -1.47523 0.3540 -1.50543 0.3184 

regional_fresh 0.1588 0.1505 0.22191 0.1168 

salt_pond 0.98493 0.3580 1.13573 0.3142 

num_riv_pond 0.11733 0. 02806 0.11453 0.02510 

mr -0.88463 0.1832 -0.79323 0.1343 

mp 1.63373 0.2980 1.51683 0.2574 

nonusers -0.40363 0.1314 -0.44513 0.1301 

allmult -0.37282 0.1644 -0.40443 0.1266 

nonspec -0.40422 0.1731 -0.2988 0.1351 

quality ch (lnquality_ch 

in trans-log model) 0.40651 0.1488 0.032083 0.006337 

fish_use -0.33172 0.1291 -0.44803 0.1034 

fishplus 0.44322 0.1820 0.40173 0.1217 

baseline (lnbase in 

trans-log model) 0.02610 0.1183 0.005205 0.003739 

mail -0.2013 0.1466 -0.30733 0.1155 

lump_sum 0.55692 0.2387 0.71883 0.1873 

non_reviewed -0.27181 0.1625 -0.37443 0.1234 

median_wtp -0.53583 0.1875 -0.46752 0.1898 

-2 Log Likelihood 133.93 115.52 

Covariance Factors: 

Study Level (σu) 0 0 

Residual (σ e) 0.18763 0.15993 

1

 Significant at the 0.10 level. 

2

 Significant at the 0.05 level.

3

 Significant at the 0.01 level. 

G.2.3 Interpretation of Total WTP Regression Analysis Results 

Regression results reveal strong systematic elements influencing WTP. The analysis finds both 

statistically significant and intuitive patterns that influence WTP for surface water quality improvements. 

In general, the statistical fit of the three estimated equations is good; model results suggest a considerable 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-15 

systematic component of WTP variation that allows forecasting of WTP based on site and study 

characteristics. Likelihood ratio tests (Table G-3) show that model variables are jointly significant at the 

p<0.01 level for the trans-log model and the p < 0.05 level for the semi-log model. In both models, the 

majority of independent variables are statistically significant at p<0.05, with most statistically significant 

at p<0.01 (Table G-3). Signs of significant parameter estimates generally correspond with intuition, 

where prior expectations exist. As shown in Table G-3, the random effect is statistically insignificant (i.e., 

study level covariance factors are zero). Considering these factors, the statistical performance of both 

models compare favorably to prior meta-analyses in the valuation literature. 

Despite differences in the functional form of the two models, statistical results are robust across models. 

In most cases, coefficient magnitudes and standard errors vary to only a small degree. Measures of 

equation fit are similar, and both models are significant at the p<0.05 level or better. Such results mirror 

those of Johnston et al. (2005), whose earlier meta-analysis of WTP for water quality improvements finds 

a high degree of robustness to changes in model specification. 

The initial discussion emphasizes results of the trans-log model. Although policy implications of both 

model specifications are nearly identical for moderate to relatively large water quality improvements 

(e.g., more than 5 percent increase in WQI), the trans-log model provides more accurate WTP estimates 

when the expected water quality change is very small (e.g., less than 1 percent increase in WQI). 

One of the primary means to assess the validity of benefit transfers—and the only one that may be applied 

in cases wherein the true value for the study site is unknown—is value surface tests (Bergstrom and De 

Civita 1999). These tests involve assessments of ways in which “different factors may cause values to 

vary across sites, providing guidance for adjustments needed to make a valid transfer of value estimates 

from the study site(s) to the policy site” (Bergstrom and De Civita 1999). Following general approaches 

for such value surface assessments—which generally involve comparisons of empirical patterns found via 

meta-analysis to theoretical expectations or norms—the Agency concluded that most results of the 

estimated value surface suggest an appropriate benefit function. Results of these value surface 

assessments are detailed in the following sections. 

Resource Improvement Effects 

Seven variables characterize resource improvements and uses; most are of the expected sign. The 

coefficient on the quality_ch variable is positive and statistically significant (p<0.01), indicating that 

larger water quality improvements generate larger WTP. This is an important result, and indicates that 

WTP is sensitive to the scope of water quality improvements. The estimated model showed that WTP 

values are not sensitive to the baseline water quality from which water quality change would occur. The 

estimated parameter on the variable baseline representing the baseline water quality from which water 

quality change would occur is not statistically significant (p>0.1). This finding differs from that of 

Johnston et al. (2005), which shows that WTP for marginal water quality improvements declines as 

baseline water quality improves. Here, the value is positive but is only significant at p<0.17 in the semilog model. In the trans-log model, this parameter is not statistically significant. The reason for this result 

is unknown, but may be related to highly valued uses that are associated with larger values on the WQI. 

For example, increases beginning at higher levels on the WQI may cross thresholds allowing such highly 

valued uses as swimming and drinking, such that increases at these high-quality levels may be valued 

more highly than otherwise similar changes at lower baselines, which may not allow such uses. Such 

thresholds, or other non-convex preference patterns, may lead to unexpected results for the baseline water 

quality variable (baseline). 
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Both models reveal that water quality changes associated with recreational fishing uses lead to a 

significant decrease in total WTP values, compared to improvements that do not affect fishing. The 

variable fish_use identifies those studies that specified effects of water quality improvements on 

recreational fishing (e.g., increase in catch rates). The associated parameter estimate is significant 

(p<0.05) and has the negative sign. In contrast, the variable fishplus identifies those studies for which the 

associated survey identified particularly large gains in fish populations or harvest rates (>50 percent). The 

positive and statistically significant result (p<0.05) indicates that large gains in fish populations or 

harvests are associated with statistically significant increases in total WTP. Results suggest that while 

water quality improvements targeting fishing uses may not be valued particularly highly on average, very 

large resultant improvements in fish populations or harvests are associated with increases in WTP, ceteris 

paribus. 

The variables all_mult and nonspec indicate that water quality improvements affect multiple species 

(all_mult) or unspecified species (nonspec), respectively. The default category from which these variables 

allow systematic variations in WTP is a focus on particular aquatic species affected by water quality (e.g., 

shellfish or game fish). The associated coefficients are negative, indicating that WTP is lower when a 

survey instrument does not specify what aquatic species would be affected (nonspec) or when all or 

multiple species are affected (all_mult). The latter finding seems to be counterintuitive at first. However, 

when a survey instrument focuses on the effect of water quality on particular species, it is a likely 

indication that these effects are of significant concern to the affected communities, which typically leads 

to a higher WTP. That is, this result suggests that WTP is higher when water improvements can be shown 

to offer targeted benefits to specific, and often high-profile, species groups—as opposed to cases in which 

improvements benefit an often poorly characterized group of species. 

Geographic Region and Scale Effects 

Ten binary variables characterize geographic region and scale; seven are statistically significant at 

p<0.10. The default category from which these variables allow systematic variations in WTP is an 

estuarine waterbody. Also included in this default are a small number of observations addressing national 

level improvements. Compared to this baseline, WTP associated with rivers is lower (single_river and

multiple_river both have negative and significant values). Single_lake has a negative value, but it is not 

significant. WTP for water quality gains in salt ponds (salt_pond) is higher than for estuaries (p<0.05). 

This is not surprising since water quality gains in salt ponds correspond to an increase in the number of 

acres of shellfish beds. 

Of particular importance for the general validity of empirical findings, the model results further suggest 

that WTP is sensitive to the number of waterbodies under consideration and geographic scale of 

improvement (regional_fresh). Of the waterbody categories distinguished above, both rivers and salt 

ponds allowed variation in numbers of affected waterbodies explicitly described by the survey. This 

variation is captured by the variable num_riv_pond (see Table G-3).29 The associated parameter estimate 

is statistically significant (p<0.01) and indicates that WTP increases with the number of waterbodies 

considered. The parameter estimate on the regional_fresh variable is positive and significant (p<0.10) in 

the semi-log model, indicating that large-scale regional water quality improvements lead to an increase in 

WTP. These results, combined with the statistical significance of the water quality change variables noted 

above, suggest that WTP values (in this case for water quality improvements) are strongly sensitive to 

 

29 Technically, this variable is the sum of two interaction variables: (1) an interaction between multiple_river and the number 

of waterbodies noted in the survey (0 if unspecified) and (2) an interaction between salt_pond and the number of 

waterbodies noted in the survey (0 if unspecified). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-17 

scope, both in terms of the number of waterbodies considered, geographic scale of improvement, and the 

magnitude of water quality change. In the trans-log model, the regional_fresh variable is positive but not 

statistically significant (p>0.30). 

Finally, the regional indicator variables mp and mult_reg are statistically significant at p<0.01, suggesting 

that there are significant differences among WTP estimates from surveys in different geographical regions 

of the United States. The parameter estimate on the mult_reg variable is negative, indicating that WTP for 

non-local water quality improvements (e.g., out of state) are lower compared to in-state or local resource 

improvements. This is consistent with prior findings that WTP for water quality improvements declines 

with the distance from the resource (Bateman et al. 2006). The magnitude of the Pacific Mountain (mp) 

regional effect suggests that spurious or otherwise unexplained effects (e.g., the effect of specific 

researchers who appear more than once in the data) may drive their overall magnitude. For example, the 

size of the positive parameter estimate associated with WTP in the Pacific Mountain dummy (mp) leads in 

many cases to relatively large increases in WTP for Pacific Northwest policies. Hence, EPA believes that 

particular, spurious, or unexplained aspects of studies from this region may have caused the associated 

parameter estimate to have a larger-than-expected influence on WTP. Although effects of regional 

dummy variables often escape simple, intuitive characterization, EPA notes that they are often 

statistically significant in meta-analysis found in the valuation literature. Similar issues are found by 

Johnston et al. (2005), for example. 

Surveyed Populations Effects 

Only two variables, nonusers and income, are used to characterize surveyed populations. In particular, the 

nonusers variable is of substantial policy relevance. The negative and strongly significant (p<.01) 

parameter estimate indicates that surveys of nonusers only, who by definition have only nonuse values for 

the resource improvements in question (cf. Freeman 2003, p. 142), generate lower WTP values than 

surveys that include users, who may have both use and nonuse values. Based on this statistically 

significant result, it is possible to use this model to estimate nonuse values, interpreted as the mean WTP 

values estimated by surveys of nonusers only. Such methods, however, may underestimate nonuse values 

of the general population, if the nonuse values of users exceed those of nonusers (Whitehead and 

Blomquist 1991a,b). 

The income parameter estimate is positive in the trans-log model, as expected, but is not statistically 

significant. Such lack of statistical significance for income parameters is not uncommon in meta-analyses 

found in the literature (e.g., Johnston et al. 2005). 

Study and Methodology Effects 

As often found in meta-analyses within the valuation and benefit transfer literature (Navrud and Ready 

2007), a variety of study and methodology effects can be shown to influence WTP for water quality 

improvements. While expected, this does indicate that the methodological approach influences WTP, as 

argued by Arrow et al. (1993). Of nine variables characterizing study and methodological effects, eight 

are statistically significant at p<0.10. Among these is the year in which a study was conducted (year_indx, 

a continuous variable), with later studies associated with lower WTP. This is the expected result, as the 

focus of survey design over time has often been on the reduction of survey biases that would otherwise 

result in an overstatement of WTP (Arrow et al. 1993). 

Model results reveal that voluntary (voluntary=1) payment vehicles (i.e., surveys that describe 

hypothetical payments as voluntary) are associated with reduced WTP estimates. This result counters 

common intuition and empirical findings that voluntary payment vehicles are associated with 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-18 

overstatements of true WTP (Carson et al. 2000). The reason for this counter-intuitive finding is 

unknown, but may reflect an unwillingness among respondents to offer large voluntary payments, given 

the fear that others will free-ride (Johnston et al. 2005). Reduced WTP estimates are also associated with 

studies applying non-parametric methods to WTP estimation (nonparam). Survey elicitation method does 

not have a strong effect in this model; studies using discrete choice formats have lower WTP values, but 

this difference is not statistically significant. 

Smaller WTP estimates are associated with studies that eliminate or trim outlier bids when estimating 

WTP (outlier_bids=1; p<0.01). Studies that report median WTP (median_WTP; p<0.01) have lower WTP 

values. 

Lower WTP is associated with the use of the WQI in the original survey (WQI=1). This parameter is, 

however, significant in the semi-log model only (p<0.1). As is the case with a variety of study design 

variables, there is no necessary expectation with respect to the direction of this effect. Nonetheless, this 

finding might suggest the capacity of such scales to clarify the specific magnitude and implications of 

water quality change, and hence (perhaps) reduce methodological misspecification or symbolic biases that 

might act to systematically inflate estimated WTP. 

Survey format variables also have an effect on WTP, as might be expected. Mail has a negative and 

statistically significant coefficient (p<0.01) in the semi-log model, compared to the default of telephone 

surveys or interviews. This parameter is negative, but not statistically significant (p>0.17) in the trans-log 

model. It may be possible that the interview and telephone survey format results in larger WTP values 

either because the respondents are better able to understand the valuation scenario, or because 

respondents may feel pressure from interviewers to bias their WTP estimates upward. Finally, studies that 

ask respondents to report an annual payment (as opposed to a lump_sum payment) have higher WTP 

estimates (p<0.05). This likely to reflect the fact that annual payments are regarded as an infinite 

contribution and may reflect the respondent’s uncertainly regarding his future income and budget 

constraints. 

G.2.4 Model Selection 

To select the model for estimating benefits of water quality improvements from the regulation, EPA 

calculated WTP values for a range of WQI changes using both semi-log and trans-log models. In all cases 

the baseline WQI is set to 50, which approximates the average WQI value across all RF1 reaches in the 

United States. EPA assigned values to other independent variables corresponding with theory, 

characteristics of the water resource, and the policy context. Table 10-11 in Chapter 10 provides a 

complete list of values assigned to the remaining independent regressors. 

As shown in Table G-4, both the semi-log and trans-log models yield similar WTP for water quality 

changes greater than five points as measured by WQI. However, the semi-log model is not sensitive to 

very small water quality changes (i.e., changes less than one point on the WQI index). Because the 

expected water quality changes from the regulation are relatively small, the Agency selected the trans-log 

specification for estimating benefits from reducing sediment runoff from construction sites, as a more 

conservative option. 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-19 

Table G-4: Comparison of WTP for Different Changes in WQI Based on Semi-log and 

Trans-log Models 

Model 

20 Point 

Change in 

WQI 

10 Point 

Change in 

WQI 

5 Point 

Change in 

WQI 

1 Point 

Change in 

WQI 

0.50 Point 

Change 

in WQI 

0.10 Point 

Change 

in WQI 

0.01 Point 

Change 

in WQI 

Trans-log 105.0186 79.2313 59.7760 31.0738 23.4436 12.1869 4.7796 

Semi-log 89.0567 64.6167 55.0407 48.4122 47.6419 47.0345 46.8989 

Semi-log Error Term = 0.1599. 

Trans-log Error Term =0.1876. 

G.3 Model Limitations 

The validity and reliability of benefit transfer—including that based on meta-analysis—depends on a 

variety of factors. While benefit transfer can provide valid measures of use and nonuse benefits, tests of 

its performance have provided mixed results (e.g., Desvousges et al. 1998; Vandenberg et al. 2001; Smith 

et al. 2002; Shrestha et al. 2007). Nonetheless, benefit transfers are increasingly applied as a core 

component of benefit cost analyses conducted by EPA and other government agencies (Bergstrom and De 

Civita 1999; Rosenberger and Phipps 2007). Moreover, Smith et al. (2002, p. 134) argue that “nearly all 

benefit cost analyses rely on benefit transfers, whether they acknowledge it or not.” Given the increasing 

[or as Smith et al. (2002) might argue, universal] use of benefit transfers, an increasing focus is on the 

empirical properties of applied transfer methods and models. 

Although the statistical performance of the model is good, EPA notes several limitations of the model. 

These limitations stem largely from information available from the original studies, as well as degrees of 

freedom and statistical significance. An important factor in any benefit transfer is the ability of the study 

site or estimated valuation equation to approximate the resource and context under which benefit 

estimates are desired. As is common, the meta-analysis model presented here provides a close but not 

perfect match to the context in which values are desired. Although all of the studies used in the metaanalysis valued changes in water quality improvements, many studies did not specify the cause of water 

quality impairment in the baseline or focused on causes that are different from the pollutant of concern in 

the regulation (i.e., sediment). Preliminary models, however, suggest no systematic patterns in WTP 

associated with such factors, at least in the present metadata. 

Additional limitations relate to the paucity of demographic variables available for inclusion in the model. 

The only demographic variable incorporated in the analysis (income) was not statistically significant. 

Moreover, other demographic variables are unavailable. 

The estimated model is statistically significant and allows estimation of WTP based on study and site 

characteristics. However, strictly speaking, model findings are relative to the specific case studies 

considered, and must be viewed within the context of the 115-observation data set, with all the 

appropriate caveats. Although this represents a fairly standard-to-large sample size for a meta-analysis in 

this context (the 45 studies in the analysis gather data from a total of 23,589 respondents), it is relatively 

small relative to other statistical applications in resource and environmental economics. Model results are 

also subject to choices regarding functional form and statistical approach, although many of the primary 

model effects are robust to reasonable changes in functional form and/or statistical methods. The rationale 

for the specific functional form chosen here (the semi-log form) is detailed above. 

As in all cases, results of the meta-analysis are dependent on the sample of studies available for the given 

resource change (Navrud and Ready 2007), and may be subject to various selection biases if the available 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 G-20 

literature does not provide a representative, unbiased perspective on welfare estimates associated with 

resource changes (Rosenberger and Johnston 2007). In this case, however, the Agency took various steps 

to ameliorate such potential biases, including the incorporation of both peer-reviewed and gray literature 

to avoid possible publication biases (Rosenberger and Johnston 2007), and the use of a comprehensive 

literature review in the attempt to avoid—as much as possible—other types of selection biases. 

The relatively large (positive) magnitude of the parameter estimate for the Pacific Mountain U.S. regional 

dummy variable (mp) leads EPA to question the appropriate interpretation of this effect. While it is 

theoretically possible that WTP for water quality changes is substantially higher in the Pacific Northwest 

(e.g., people who live in this region are outdoor enthusiasts), the magnitude of the effect suggested by the 

model seems unlikely from an intuitive perspective. As suggested above, it is possible that spurious, 

unexplained factors influence the magnitude of this parameter in the present model. However, 

assessments of preliminary model runs suggest that this effect is relatively robust given the present data 

and selection of variables available. Nonetheless, EPA recommends that the magnitude of the predicted 

shift in WTP associated with the Pacific Mountain region should be viewed with caution. 

Finally, as noted above, there is some controversy over the appropriateness of meta-analysis for benefit 

transfer. While recognizing this controversy, the Agency emphasizes that the broader literature provides 

support for the use of various types of meta-analysis for benefit transfer (cf. USEPA 2000b; Bergstrom 

and Taylor 2006; Shrestha et al. 2007). 
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 H-1 

Appendix H – Water Quality Index Tables 

This appendix presents detailed versions of Table 10-7 through Table 10-10, showing WQI reach mile 

and percentage improvements by baseline water quality index range, improvement range, and EPA 

Region for each policy option.
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-1: Estimated Water Quality Improvements Under Option 1 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI To

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Rea

Mi

<26 33 37 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 1,343 1,520 77 5.76% 5.09% 0 0.00% 0.00% 9 0.69% 0.61% 50-70 13,387 15,159 1,481 11.06% 9.77% 31 0.23% 0.21% 20 0.15% 0.13% 1

>70 1,420 1,608 138 9.69% 8.56% 0 0.00% 0.00% 0 0.00% 0.00% 

1 

Total 16,182 18,324 1,696 10.48% 9.25% 31 0.19% 0.17% 29 0.18% 0.16% 1

<26 82 87 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 1,958 2,084 36 1.82% 1.71% 0 0.00% 0.00% 0 0.00% 0.00% 50-70 5,507 5,860 177 3.21% 3.01% 0 0.00% 0.00% 0 0.00% 0.00% 

>70 7,592 8,078 202 2.67% 2.51% 0 0.00% 0.00% 0 0.00% 0.00% 

2 

Total 15,140 16,110 415 2.74% 2.57% 0 0.00% 0.00% 0 0.00% 0.00% 

<26 1,448 1,684 51 3.49% 3.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 12,552 14,599 645 5.14% 4.42% 21 0.17% 0.14% 0 0.00% 0.00% 50-70 13,006 15,127 780 5.99% 5.15% 93 0.71% 0.61% 0 0.00% 0.00% 

>70 1,898 2,207 64 3.39% 2.91% 0 0.00% 0.00% 0 0.00% 0.00% 

3 

Total 28,904 33,617 1,539 5.33% 4.58% 114 0.39% 0.34% 0 0.00% 0.00% 1

<26 714 746 135 18.93% 18.11% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 32,338 33,801 7,903 24.44% 23.38% 1,003 3.10% 2.97% 154 0.48% 0.45% 950-70 38,191 39,918 12,232 32.03% 30.64% 1,696 4.44% 4.25% 282 0.74% 0.71% 14

>70 19,192 20,060 5,939 30.95% 29.61% 473 2.46% 2.36% 94 0.49% 0.47% 6

4 

Total 90,435 94,525 26,210 28.98% 27.73% 3,172 3.51% 3.36% 529 0.59% 0.56% 29

<26 4,175 4,375 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 43,315 45,386 899 2.08% 1.98% 33 0.08% 0.07% 4 0.01% 0.01% 50-70 18,508 19,393 1,859 10.04% 9.58% 99 0.53% 0.51% 0 0.00% 0.00% 1

>70 2,287 2,396 173 7.56% 7.22% 0 0.00% 0.00% 0 0.00% 0.00% 

5 

Total 68,285 71,550 2,931 4.29% 4.10% 132 0.19% 0.18% 4 0.01% 0.01% 3
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-1: Estimated Water Quality Improvements Under Option 1 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI To

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Rea

Mi

<26 931 966 101 10.82% 10.43% 7 0.71% 0.68% 13 1.43% 1.38% 

26-50 61,473 63,789 7,303 11.88% 11.45% 995 1.62% 1.56% 440 0.72% 0.69% 850-70 26,731 27,738 8,464 31.66% 30.52% 2,419 9.05% 8.72% 587 2.19% 2.12% 11

>70 5,963 6,188 2,034 34.10% 32.86% 805 13.51% 13.02% 187 3.14% 3.02% 3

6 

Total 95,098 98,681 17,902 18.82% 18.14% 4,227 4.44% 4.28% 1,227 1.29% 1.24% 23

<26 10,877 10,877 13 0.12% 0.12% 0 0.00% 0.00% 11 0.10% 0.10% 

26-50 43,158 43,158 2,096 4.86% 4.86% 212 0.49% 0.49% 132 0.31% 0.31% 250-70 6,199 6,199 1,933 31.18% 31.18% 328 5.29% 5.29% 26 0.41% 0.41% 2

>70 675 675 155 22.95% 22.95% 22 3.28% 3.28% 0 0.00% 0.00% 

7 

Total 60,909 60,909 4,196 6.89% 6.89% 562 0.92% 0.92% 168 0.28% 0.28% 4

<26 6,557 6,557 0 0.00% 0.00% 0 0.00% 0.00% 68 1.04% 1.04% 

26-50 66,701 66,701 29 0.04% 0.04% 28 0.04% 0.04% 190 0.28% 0.28% 50-70 32,368 32,368 363 1.12% 1.12% 36 0.11% 0.11% 100 0.31% 0.31% 

>70 24,685 24,685 103 0.42% 0.42% 0 0.00% 0.00% 4 0.02% 0.02% 

8 

Total 130,311 130,311 495 0.38% 0.38% 64 0.05% 0.05% 362 0.28% 0.28% 

<26 10,282 10,712 128 1.24% 1.19% 0 0.00% 0.00% 7 0.07% 0.07% 

26-50 39,696 41,353 1,170 2.95% 2.83% 128 0.32% 0.31% 144 0.36% 0.35% 150-70 3,946 4,111 62 1.57% 1.51% 0 0.00% 0.00% 0 0.00% 0.00% 

>70 304 316 0 0.00% 0.00% 6 2.06% 1.97% 0 0.00% 0.00% 

9 

Total 54,228 56,492 1,360 2.51% 2.41% 134 0.25% 0.24% 151 0.28% 0.27% 1
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-1: Estimated Water Quality Improvements Under Option 1 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI To

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Rea

Mi

<26 39 40 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 13,116 13,373 263 2.01% 1.97% 189 1.44% 1.41% 28 0.21% 0.21% 50-70 24,190 24,664 1,043 4.31% 4.23% 410 1.69% 1.66% 213 0.88% 0.86% 1

>70 30,844 31,448 2,235 7.24% 7.11% 723 2.34% 2.30% 301 0.98% 0.96% 3

10 

Total 68,189 69,524 3,541 5.19% 5.09% 1,322 1.94% 1.90% 542 0.79% 0.78% 5

<26 35,137 36,080 427 1.22% 1.18% 7 0.02% 0.02% 99 0.28% 0.27% 

26-50 315,650 325,764 20,421 6.47% 6.27% 2,609 0.83% 0.80% 1,100 0.35% 0.34% 2450-70 182,033 190,537 28,393 15.60% 14.90% 5,112 2.81% 2.68% 1,227 0.67% 0.64% 34

>70 94,859 97,662 11,042 11.64% 11.31% 2,030 2.14% 2.08% 586 0.62% 0.60% 13

Nation

Total 627,679 650,043 60,285 9.60% 9.27% 9,757 1.55% 1.50% 3,012 0.48% 0.46% 73
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-2: Estimated Water Quality Improvements Under Option 2 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI To

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Re

Mi

<26 33 37 3 8.79% 7.76% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 1,343 1,520 176 13.10% 11.57% 5 0.40% 0.35% 9 0.69% 0.61% 50-70 13,387 15,159 2,606 19.46% 17.19% 229 1.71% 1.51% 32 0.24% 0.21% 2

>70 1,420 1,608 250 17.63% 15.57% 0 0.00% 0.00% 0 0.00% 0.00% 

1 

Total 16,182 18,324 3,035 18.75% 16.56% 235 1.45% 1.28% 41 0.25% 0.22% 3

<26 82 87 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 1,958 2,084 90 4.59% 4.31% 0 0.00% 0.00% 0 0.00% 0.00% 50-70 5,507 5,860 766 13.91% 13.07% 0 0.00% 0.00% 0 0.00% 0.00% 

>70 7,592 8,078 671 8.84% 8.31% 5 0.07% 0.06% 0 0.00% 0.00% 

2 

Total 15,140 16,110 1,527 10.09% 9.48% 5 0.03% 0.03% 0 0.00% 0.00% 1

<26 1,448 1,684 144 9.93% 8.54% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 12,552 14,599 1,907 15.19% 13.06% 28 0.22% 0.19% 0 0.00% 0.00% 150-70 13,006 15,127 2,174 16.72% 14.37% 80 0.62% 0.53% 30 0.23% 0.20% 2

>70 1,898 2,207 258 13.59% 11.69% 0 0.00% 0.00% 0 0.00% 0.00% 

3 

Total 28,904 33,617 4,483 15.51% 13.34% 108 0.37% 0.32% 30 0.10% 0.09% 4

<26 714 746 221 30.97% 29.63% 13 1.89% 1.80% 0 0.00% 0.00% 

26-50 32,338 33,801 11,822 36.56% 34.97% 1,959 6.06% 5.80% 458 1.42% 1.36% 1450-70 38,191 39,918 17,431 45.64% 43.67% 3,450 9.03% 8.64% 639 1.67% 1.60% 21

>70 19,192 20,060 8,462 44.09% 42.18% 1,171 6.10% 5.84% 191 0.99% 0.95% 9

4 

Total 90,435 94,525 37,936 41.95% 40.13% 6,594 7.29% 6.98% 1,288 1.42% 1.36% 45

<26 4,175 4,375 31 0.75% 0.71% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 43,315 45,386 2,136 4.93% 4.71% 89 0.21% 0.20% 25 0.06% 0.05% 250-70 18,508 19,393 3,268 17.66% 16.85% 320 1.73% 1.65% 60 0.33% 0.31% 3

>70 2,287 2,396 454 19.84% 18.94% 0 0.00% 0.00% 0 0.00% 0.00% 

5 

Total 68,285 71,550 5,889 8.62% 8.23% 409 0.60% 0.57% 85 0.12% 0.12% 6
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-2: Estimated Water Quality Improvements Under Option 2 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI To

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Re

Mi

<26 931 966 138 14.86% 14.32% 0 0.00% 0.00% 46 4.93% 4.76% 

26-50 61,473 63,789 11,393 18.53% 17.86% 1,896 3.08% 2.97% 825 1.34% 1.29% 1450-70 26,731 27,738 8,353 31.25% 30.11% 4,035 15.09% 14.55% 1,488 5.57% 5.36% 13

>70 5,963 6,188 1,558 26.13% 25.18% 1,255 21.04% 20.27% 554 9.29% 8.95% 3

6 

Total 95,098 98,681 21,442 22.55% 21.73% 7,185 7.56% 7.28% 2,912 3.06% 2.95% 31

<26 10,877 10,877 47 0.43% 0.43% 0 0.00% 0.00% 11 0.10% 0.10% 

26-50 43,158 43,158 3,854 8.93% 8.93% 420 0.97% 0.97% 179 0.42% 0.42% 450-70 6,199 6,199 2,546 41.07% 41.07% 557 8.99% 8.99% 71 1.15% 1.15% 3

>70 675 675 273 40.40% 40.40% 22 3.28% 3.28% 0 0.00% 0.00% 

7 

Total 60,909 60,909 6,719 11.03% 11.03% 1,000 1.64% 1.64% 261 0.43% 0.43% 7

<26 6,557 6,557 13 0.20% 0.20% 0 0.00% 0.00% 68 1.04% 1.04% 

26-50 66,701 66,701 253 0.38% 0.38% 39 0.06% 0.06% 190 0.28% 0.28% 50-70 32,368 32,368 773 2.39% 2.39% 51 0.16% 0.16% 105 0.32% 0.32% 

>70 24,685 24,685 326 1.32% 1.32% 0 0.00% 0.00% 4 0.02% 0.02% 

8 

Total 130,311 130,311 1,365 1.05% 1.05% 90 0.07% 0.07% 367 0.28% 0.28% 1

<26 10,282 10,712 239 2.33% 2.23% 0 0.00% 0.00% 7 0.07% 0.07% 

26-50 39,696 41,353 1,741 4.39% 4.21% 213 0.54% 0.51% 179 0.45% 0.43% 250-70 3,946 4,111 102 2.58% 2.48% 0 0.00% 0.00% 0 0.00% 0.00% 

>70 304 316 6 2.00% 1.92% 6 2.06% 1.97% 0 0.00% 0.00% 

9 

Total 54,228 56,492 2,088 3.85% 3.70% 219 0.40% 0.39% 186 0.34% 0.33% 2
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-2: Estimated Water Quality Improvements Under Option 2 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI To

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Re

Mi

<26 39 40 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 13,116 13,373 220 1.68% 1.64% 257 1.96% 1.92% 117 0.89% 0.87% 50-70 24,190 24,664 1,538 6.36% 6.24% 250 1.03% 1.01% 420 1.74% 1.70% 2

>70 30,844 31,448 2,537 8.23% 8.07% 1,084 3.52% 3.45% 508 1.65% 1.62% 4

10 

Total 68,189 69,524 4,295 6.30% 6.18% 1,592 2.33% 2.29% 1,044 1.53% 1.50% 6

<26 35,137 36,080 836 2.38% 2.32% 13 0.04% 0.04% 132 0.38% 0.37% 

26-50 315,650 325,764 33,591 10.64% 10.31% 4,907 1.55% 1.51% 1,981 0.63% 0.61% 4050-70 182,033 190,537 39,557 21.73% 20.76% 8,972 4.93% 4.71% 2,844 1.56% 1.49% 51

>70 94,859 97,662 14,795 15.60% 15.15% 3,543 3.74% 3.63% 1,257 1.32% 1.29% 19

Nation 

Total 627,679 650,043 88,779 14.14% 13.66% 17,436 2.78% 2.68% 6,214 0.99% 0.96% 112
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-3: Estimated Water Quality Improvements Under Option 3 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI T

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of Reach

Miles 

Modeled 

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of Reach

Miles 

Modeled 

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of Reach

Miles 

Modeled 

% of 

Total 

Reach 

Miles 

Re

M

<26 33 37 3 8.79% 7.76% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 1,343 1,520 223 16.60% 14.66% 5 0.40% 0.35% 9 0.69% 0.61% 250-70 13,387 15,159 3,142 23.47% 20.73% 295 2.21% 1.95% 32 0.24% 0.21% 3,4

>70 1,420 1,608 349 24.60% 21.73% 0 0.00% 0.00% 0 0.00% 0.00% 3

1 

Total 16,182 18,324 3,717 22.97% 20.29% 301 1.86% 1.64% 41 0.25% 0.22% 4,0

<26 82 87 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 1,958 2,084 155 7.90% 7.42% 0 0.00% 0.00% 0 0.00% 0.00% 150-70 5,507 5,860 1,026 18.63% 17.51% 0 0.00% 0.00% 0 0.00% 0.00% 1,0

>70 7,592 8,078 961 12.66% 11.90% 5 0.07% 0.06% 0 0.00% 0.00% 9

2 

Total 15,140 16,110 2,142 14.15% 13.29% 5 0.03% 0.03% 0 0.00% 0.00% 2,1

<26 1,448 1,684 155 10.74% 9.23% 0 0.00% 0.00% 0 0.00% 0.00% 1

26-50 12,552 14,599 2,449 19.51% 16.77% 35 0.28% 0.24% 0 0.00% 0.00% 2,450-70 13,006 15,127 3,135 24.10% 20.72% 121 0.93% 0.80% 30 0.23% 0.20% 3,2

>70 1,898 2,207 346 18.21% 15.66% 7 0.38% 0.33% 0 0.00% 0.00% 3

3 

Total 28,904 33,617 6,084 21.05% 18.10% 163 0.56% 0.48% 30 0.10% 0.09% 6,2

<26 714 746 193 27.00% 25.83% 44 6.18% 5.92% 0 0.00% 0.00% 2

26-50 32,338 33,801 12,663 39.16% 37.46% 2,401 7.43% 7.10% 618 1.91% 1.83% 15,50-70 38,191 39,918 18,276 47.85% 45.78% 4,249 11.12% 10.64% 771 2.02% 1.93% 23,

>70 19,192 20,060 8,960 46.68% 44.66% 1,549 8.07% 7.72% 220 1.15% 1.10% 10,

4 

Total 90,435 94,525 40,092 44.33% 42.41% 8,244 9.12% 8.72% 1,610 1.78% 1.70% 49,
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-3: Estimated Water Quality Improvements Under Option 3 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI T

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of Reach

Miles 

Modeled 

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of Reach

Miles 

Modeled 

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of Reach

Miles 

Modeled 

% of 

Total 

Reach 

Miles 

Re

M

<26 4,175 4,375 58 1.39% 1.32% 0 0.00% 0.00% 0 0.00% 0.00% 5

26-50 43,315 45,386 2,595 5.99% 5.72% 137 0.32% 0.30% 25 0.06% 0.05% 2,750-70 18,508 19,393 3,983 21.52% 20.54% 493 2.67% 2.54% 60 0.33% 0.31% 4,5

>70 2,287 2,396 592 25.88% 24.70% 0 0.00% 0.00% 0 0.00% 0.00% 5

5 

Total 68,285 71,550 7,228 10.59% 10.10% 630 0.92% 0.88% 85 0.12% 0.12% 7,9

<26 931 966 185 19.90% 19.18% 0 0.00% 0.00% 66 7.11% 6.86% 2

26-50 61,473 63,789 13,385 21.77% 20.98% 2,174 3.54% 3.41% 1,010 1.64% 1.58% 16,50-70 26,731 27,738 8,577 32.09% 30.92% 4,786 17.90% 17.25% 1,741 6.51% 6.28% 15,

>70 5,963 6,188 1,532 25.69% 24.76% 1,268 21.27% 20.50% 671 11.25% 10.84% 3,4

6 

Total 95,098 98,681 23,679 24.90% 24.00% 8,228 8.65% 8.34% 3,487 3.67% 3.53% 35,

<26 10,877 10,877 34 0.31% 0.31% 13 0.12% 0.12% 11 0.10% 0.10% 5

26-50 43,158 43,158 4,404 10.21% 10.21% 527 1.22% 1.22% 233 0.54% 0.54% 5,150-70 6,199 6,199 2,718 43.85% 43.85% 732 11.81% 11.81% 76 1.22% 1.22% 3,5

>70 675 675 317 46.97% 46.97% 28 4.13% 4.13% 0 0.00% 0.00% 3

7 

Total 60,909 60,909 7,473 12.27% 12.27% 1,300 2.13% 2.13% 319 0.52% 0.52% 9,0

<26 6,557 6,557 26 0.39% 0.39% 0 0.00% 0.00% 68 1.04% 1.04% 9

26-50 66,701 66,701 1,348 2.02% 2.02% 39 0.06% 0.06% 190 0.28% 0.28% 1,550-70 32,368 32,368 1,826 5.64% 5.64% 72 0.22% 0.22% 114 0.35% 0.35% 2,0

>70 24,685 24,685 477 1.93% 1.93% 0 0.00% 0.00% 4 0.02% 0.02% 4

8 

Total 130,311 130,311 3,677 2.82% 2.82% 111 0.09% 0.09% 376 0.29% 0.29% 4,1

<26 10,282 10,712 295 2.87% 2.75% 0 0.00% 0.00% 7 0.07% 0.07% 3

26-50 39,696 41,353 2,334 5.88% 5.64% 275 0.69% 0.67% 215 0.54% 0.52% 2,850-70 3,946 4,111 199 5.03% 4.83% 0 0.00% 0.00% 0 0.00% 0.00% 1

>70 304 316 6 2.00% 1.92% 6 2.06% 1.97% 0 0.00% 0.00% 1

9 

Total 54,228 56,492 2,834 5.23% 5.02% 282 0.52% 0.50% 222 0.41% 0.39% 3,3
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-3: Estimated Water Quality Improvements Under Option 3 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI T

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of Reach

Miles 

Modeled 

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of Reach

Miles 

Modeled 

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of Reach

Miles 

Modeled 

% of 

Total 

Reach 

Miles 

Re

M

<26 39 40 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 13,116 13,373 224 1.70% 1.67% 314 2.39% 2.35% 117 0.89% 0.87% 650-70 24,190 24,664 1,616 6.68% 6.55% 358 1.48% 1.45% 446 1.85% 1.81% 2,4

>70 30,844 31,448 2,566 8.32% 8.16% 1,140 3.69% 3.62% 607 1.97% 1.93% 4,3

10 

Total 68,189 69,524 4,405 6.46% 6.34% 1,812 2.66% 2.61% 1,170 1.72% 1.68% 7,3

<26 35,137 36,080 949 2.70% 2.63% 57 0.16% 0.16% 152 0.43% 0.42% 1,1

26-50 315,650 325,764 39,780 12.60% 12.21% 5,908 1.87% 1.81% 2,416 0.77% 0.74% 48,50-70 182,033 190,537 44,498 24.44% 23.35% 11,106 6.10% 5.83% 3,270 1.80% 1.72% 58,

>70 94,859 97,662 16,106 16.98% 16.49% 4,004 4.22% 4.10% 1,502 1.58% 1.54% 21,

Nation 

Total 627,679 650,043 101,332 16.14% 15.59% 21,075 3.36% 3.24% 7,340 1.17% 1.13% 129
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-4: Estimated Water Quality Improvements Under Option 4 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI To

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Re

M

<26 33 37 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 1,343 1,520 131 9.76% 8.62% 0 0.00% 0.00% 9 0.69% 0.61% 50-70 13,387 15,159 1,982 14.81% 13.08% 156 1.16% 1.03% 20 0.15% 0.13% 2

>70 1,420 1,608 224 15.80% 13.95% 0 0.00% 0.00% 0 0.00% 0.00% 

1 

Total 16,182 18,324 2,338 14.45% 12.76% 156 0.96% 0.85% 29 0.18% 0.16% 2

<26 82 87 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 1,958 2,084 90 4.59% 4.31% 0 0.00% 0.00% 0 0.00% 0.00% 50-70 5,507 5,860 713 12.95% 12.17% 0 0.00% 0.00% 0 0.00% 0.00% 

>70 7,592 8,078 630 8.30% 7.80% 5 0.07% 0.06% 0 0.00% 0.00% 

2 

Total 15,140 16,110 1,433 9.47% 8.90% 5 0.03% 0.03% 0 0.00% 0.00% 1

<26 1,448 1,684 144 9.93% 8.54% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 12,552 14,599 2,031 16.18% 13.91% 28 0.22% 0.19% 0 0.00% 0.00% 250-70 13,006 15,127 2,229 17.14% 14.74% 80 0.62% 0.53% 30 0.23% 0.20% 2

>70 1,898 2,207 217 11.41% 9.81% 0 0.00% 0.00% 0 0.00% 0.00% 

3 

Total 28,904 33,617 4,620 15.98% 13.74% 108 0.37% 0.32% 30 0.10% 0.09% 4

<26 714 746 234 32.85% 31.43% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 32,338 33,801 12,208 37.75% 36.12% 2,084 6.44% 6.16% 531 1.64% 1.57% 1450-70 38,191 39,918 17,484 45.78% 43.80% 3,785 9.91% 9.48% 714 1.87% 1.79% 2

>70 19,192 20,060 8,581 44.71% 42.77% 1,290 6.72% 6.43% 220 1.15% 1.10% 10

4 

Total 90,435 94,525 38,507 42.58% 40.74% 7,159 7.92% 7.57% 1,465 1.62% 1.55% 47

<26 4,175 4,375 58 1.39% 1.32% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 43,315 45,386 2,184 5.04% 4.81% 68 0.16% 0.15% 25 0.06% 0.05% 250-70 18,508 19,393 3,242 17.52% 16.72% 308 1.67% 1.59% 60 0.33% 0.31% 3

>70 2,287 2,396 495 21.65% 20.66% 0 0.00% 0.00% 0 0.00% 0.00% 

5 

Total 68,285 71,550 5,979 8.76% 8.36% 377 0.55% 0.53% 85 0.12% 0.12% 6
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-4: Estimated Water Quality Improvements Under Option 4 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI To

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Re

M

<26 931 966 185 19.90% 19.18% 0 0.00% 0.00% 46 4.93% 4.76% 

26-50 61,473 63,789 11,913 19.38% 18.68% 2,085 3.39% 3.27% 945 1.54% 1.48% 1450-70 26,731 27,738 8,090 30.26% 29.17% 4,456 16.67% 16.07% 1,698 6.35% 6.12% 14

>70 5,963 6,188 1,504 25.22% 24.31% 1,278 21.44% 20.66% 624 10.46% 10.08% 3

6 

Total 95,098 98,681 21,693 22.81% 21.98% 7,819 8.22% 7.92% 3,313 3.48% 3.36% 32

<26 10,877 10,877 34 0.31% 0.31% 13 0.12% 0.12% 11 0.10% 0.10% 

26-50 43,158 43,158 3,922 9.09% 9.09% 524 1.22% 1.22% 193 0.45% 0.45% 450-70 6,199 6,199 2,604 42.01% 42.01% 639 10.31% 10.31% 76 1.22% 1.22% 3

>70 675 675 300 44.40% 44.40% 28 4.13% 4.13% 0 0.00% 0.00% 

7 

Total 60,909 60,909 6,860 11.26% 11.26% 1,205 1.98% 1.98% 280 0.46% 0.46% 8

<26 6,557 6,557 26 0.39% 0.39% 0 0.00% 0.00% 68 1.04% 1.04% 

26-50 66,701 66,701 250 0.37% 0.37% 39 0.06% 0.06% 190 0.28% 0.28% 50-70 32,368 32,368 711 2.20% 2.20% 51 0.16% 0.16% 105 0.32% 0.32% 

>70 24,685 24,685 296 1.20% 1.20% 0 0.00% 0.00% 4 0.02% 0.02% 

8 

Total 130,311 130,311 1,282 0.98% 0.98% 90 0.07% 0.07% 367 0.28% 0.28% 1

<26 10,282 10,712 251 2.44% 2.34% 0 0.00% 0.00% 7 0.07% 0.07% 

26-50 39,696 41,353 1,807 4.55% 4.37% 191 0.48% 0.46% 179 0.45% 0.43% 250-70 3,946 4,111 101 2.56% 2.46% 0 0.00% 0.00% 0 0.00% 0.00% 

>70 304 316 6 2.00% 1.92% 6 2.06% 1.97% 0 0.00% 0.00% 

9 

Total 54,228 56,492 2,166 3.99% 3.83% 197 0.36% 0.35% 186 0.34% 0.33% 2
Environmental Impact and Benefits Assessment for the C&D Category 

November 2009 

Table H-4: Estimated Water Quality Improvements Under Option 4 

Baseline Scenario Water Quality Improvements by WQI Change 

0.01 < ΔWQI < 0.1 0.1 < ΔWQI < 0.5 0.5 < ΔWQI To

EPA 

Region 

Baseline 

Water 

Quality Reach Miles 

Modeled 

Total 

Reach 

Miles in 

RF1 

Network 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles 

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Reach 

Miles

% of 

Reach 

Miles 

Modeled

% of 

Total 

Reach 

Miles 

Re

M

<26 39 40 0 0.00% 0.00% 0 0.00% 0.00% 0 0.00% 0.00% 

26-50 13,116 13,373 236 1.80% 1.77% 180 1.37% 1.35% 96 0.74% 0.72% 50-70 24,190 24,664 1,342 5.55% 5.44% 259 1.07% 1.05% 385 1.59% 1.56% 

>70 30,844 31,448 2,317 7.51% 7.37% 941 3.05% 2.99% 461 1.50% 1.47% 3

10 

Total 68,189 69,524 3,894 5.71% 5.60% 1,380 2.02% 1.99% 943 1.38% 1.36% 6

<26 35,137 36,080 932 2.65% 2.58% 13 0.04% 0.04% 132 0.38% 0.37% 

26-50 315,650 325,764 34,773 11.02% 10.67% 5,199 1.65% 1.60% 2,168 0.69% 0.67% 4250-70 182,033 190,537 38,497 21.15% 20.20% 9,735 5.35% 5.11% 3,087 1.70% 1.62% 5

>70 94,859 97,662 14,569 15.36% 14.92% 3,548 3.74% 3.63% 1,309 1.38% 1.34% 19

Nation 

Total 627,679 650,043 88,772 14.14% 13.66% 18,495 2.95% 2.85% 6,696 1.07% 1.03% 113