Lead: Human Exposure and Health Risk 

Assessments for Selected Case Studies 

Volume II. Appendices 

 

EPA-452/R-07-014b 

October 2007 

Lead: Human Exposure and Health Risk Assessments for 

Selected Case Studies 

Volume II. Appendices 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 
 

DISCLAIMER
 

This document has been reviewed by the Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency (EPA), and approved for publication. This draft 

document has been prepared by staff from the Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, in conjunction with ICF International (through Contract 

No. EP-D-06-115). Any opinions, findings, conclusions, or recommendations are those of the 

authors and do not necessarily reflect the views of the EPA or ICF International. Mention of 

trade names or commercial products is not intended to constitute endorsement or 

recommendation for use. This document is being provided to the Clean Air Scientific Advisory 

Committee for their review, and made available to the public for comment. Any questions or 

comments concerning this document should be addressed to Zachary Pekar, U.S. Environmental 

Protection Agency, Office of Air Quality Planning and Standards, C504-06, Research Triangle 

Park, North Carolina 27711 (email: pekar.zachary@epa.gov). 
 

 

 

List of Appendices 

Appendix A. Sources, Emissions and Air Quality in the U.S. with Particular Focus 

on Urban Areas 

Appendix B. Background on Case Studies 

Appendix C. Media Concentrations for the General Urban Case Study 

Appendix D. Media Concentrations for the Primary Pb Smelter Case Study 

Appendix E. Media Concentrations for the Secondary Pb Smelter Case Study 

Appendix F. Pb in Outdoor Soil and Dust near Roadways 

Appendix G. Approaches for Estimating Indoor Dust Pb Concentrations 

Appendix H. Blood Lead (PbB) Prediction Methods, Models, and Inputs 

Appendix I. Blood Lead (PbB) Modeling Estimates 

Appendix J. Performance Evaluation of Blood Pb (PbB) Models 

Appendix K. Risk (IQ Decrement) Estimates 

Appendix L. Sensitivity Analysis Approach and Results 

Appendix M. Qualitative Discussion of Sources of Uncertainty and Quantitative Analysis of 

Two Design Features 

Appendix N. Additional General Urban and Primary Pb Smelter Case Study Analyses 

Appendix O. Location-specific Urban Case Study Analyses 

Appendix P. Primary and Secondary Pb Smelter Case Studies’ Subarea Analyses 

i 

 

October 31, 2007 

Appendix A. Sources, Emissions and Air Quality in the U.S. 

with Particular Focus on Urban Areas 

Prepared by: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 
 

TABLE OF CONTENTS 

A. SOURCES, EMISSIONS AND AIR QUALITY IN THE U.S. WITH 

PARTICULAR FOCUS ON URBAN AREAS....................................................................... A-1
 

A.1 SOURCES AND EMISSIONS....................................................................... A-1 

A.1.1 Types of Pb Sources............................................................................... A-2 

A.1.1.1 Stationary Sources......................................................................... A-2 

A.1.1.2 Mobile Sources ............................................................................. A-4 

A.1.1.3 Resuspension of Previously Deposited Pb and other Sources ...... A-6 

A.1.2 Number and Geographic Distribution of Sources.................................. A-7 

A.1.3 Largest Pb Point Sources in the 2002 NEI............................................. A-9 

A.1.4 Data Sources, Limitations and Confidence.......................................... A-11 

A.2 AIR QUALITY MONITORING DATA...................................................... A-14 

A.2.1 Ambient Pb Measurement Methods..................................................... A-14 

A.2.1.1 Inlet Design................................................................................. A-15 

A.2.1.2 Volume of Air Sampled .............................................................. A-15 

A.2.1.3 Sampling Frequency ................................................................... A-15 

A.2.1.4 Sample Analysis.......................................................................... A-16 

A.2.2 Pb-TSP ................................................................................................. A-16 

A.2.2.1 Monitor Locations....................................................................... A-16 

A.2.2.2 Data Analysis Details.................................................................. A-18 

A.2.2.2.1 Screening Criteria ................................................................... A-19 

A.2.2.2.2 Urban Sites.............................................................................. A-19 

A.2.2.2.3 Source-oriented Sites .............................................................. A-20 

A.2.2.2.4 Population Associations.......................................................... A-21 

A.2.2.2.5 Statistical Metrics ................................................................... A-22 

A.2.2.3 Current Concentrations ............................................................... A-22 

A.2.2.3.1 Source-oriented Sites .............................................................. A-29 

A.2.2.3.2 Urban Sites.............................................................................. A-39 

A.2.3 Pb-PM10................................................................................................ A-44 

A.2.3.1 Data Analysis Details.................................................................. A-44 

A.2.3.2 Current Concentrations ............................................................... A-45 

A.2.4 Pb-PM2.5 ............................................................................................... A-50 

A.2.4.1 Data Analysis Details.................................................................. A-52 

A.2.4.2 Current Concentrations ............................................................... A-53 

A-i 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

REFERENCES ....................................................................................................... A-56 

LIST OF FIGURES 

Figure A-1. Emissions density from all Pb sources in the 2002 NEI..................................... A-7 

Figure A-3. Geographic distribution of point sources with >1 tpy Pb emissions in 

Figure A-6. Distribution of Pb-TSP concentrations (represented by 6 different statistics) 

Figure A-7. Percentages of Pb-TSP monitored populations residing in areas exceeding 

Figure A-12. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

Figure A-13. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

Figure A-14. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

Figure A-15. Distribution of Pb-TSP annual mean concentrations at different categories 

Figure A-16. Distribution of Pb-TSP maximum quarterly mean concentrations at different 

Figure A-17. Distribution of Pb-TSP maximum monthly mean concentrations at different 

Figure A-18. Distribution of Pb-TSP second maximum monthly mean concentrations at 

Figure A-20. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

Figure A-21. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

 at urban monitoring sites located in metropolitan areas (CBSAs) with 

Figure A-22. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

 at urban monitoring sites located in CBSA’s with less than 1 million 

Figure A-23. Percentages of Pb-TSP urban monitored populations residing in areas 

Figure A-25. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

Figure A-2. Emissions density from all stationary sources of Pb in 2002 NEI...................... A-8 

2002 NEI........................................................................................................... A-10 

Figure A-4. Pb-TSP monitoring sites: 2003-2005................................................................ A-17 

Figure A-5. Change in the number of Pb-TSP monitoring sites from 1980 to 2005............ A-18 

 at the 189 Pb-TSP monitoring sites, 2003-2005. ............................................. A-24 

various concentrations (for 4 different statistics), 2003-2005 .......................... A-25 

Figure A-8. Pb-TSP annual means (for all sites), 2003-2005. ............................................. A-26 

Figure A-9. Pb-TSP maximum quarterly means (for all sites), 2003-2005. ........................ A-27 

Figure A-10. Maximum monthly Pb-TSP means (all sites), 2003-2005............................ A-28 

Figure A-11. Second maximum monthly Pb-TSP means (all sites), 2003-200 ................. A-29 

 at source-oriented monitoring sites, 2003-2005........................................... A-31 

 at non-source-oriented monitoring sites, 2003-2005. .................................. A-32 

 at monitoring sites near previous large emission sources, 2003-2005......... A-33 

of sites, 2003-2005....................................................................................... A-34 

categories of sites, 2003-2005...................................................................... A-35 

categories of sites, 2003-2005....................................................................... A-36 

different categories of sites, 2003-2005....................................................... A-37 

Figure A-19. Medians, means, and population-weighted means for 4 site-level statistics. 

 (All y-axis are in concentrataion units of μg/m3

). ....................................... A-38 

 at urban monitoring sites, 2003-2005. ......................................................... A-40 

1 million or more population, 2003-2005..................................................... A-41 

population, 2003-2005. ................................................................................. A-42 

(represented by 4 different statistics) exceeding various levels.................... A-43 

Figure A-24. Pb-PM10 (NATTS) monitoring sites network............................................... A-44 

 at all Pb monitoring sites, 2003-2005. ......................................................... A-46 

A-ii 
 

 

 

 

Figure A-26. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

 at urban monitoring sites, 2003-2005. ......................................................... A-47 

Figure A-27. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

 at urban monitoring sites in CBSAs of > 1 million population, 2003-2005.A-48 

Figure A-32. Distribution of Pb-PM2.5 concentrations (represented by 3 different statistics) 

Figure A-28. Pb-PM10 annual means (for all sites), 2003-2005......................................... A-49 

Figure A-29. Pb-PM10 maximum quarterly means (for all sites), 2003-2005.................... A-50 

Figure A-30. Pb-PM2.5 (CSN) monitoring sites. ................................................................ A-51 

Figure A-31. Pb-PM2.5 (IMPROVE) monitoring sites. ...................................................... A-52 

 at all Pb-PM2.5 monitoring sites, 2003-2005................................................ A-54 

Figure A-33. Pb-PM2.5 annual means (for all sites), 2003-2005. ....................................... A-55 

LIST OF TABLES 

Table A-1. Source categories emitting greater than 5 tpy of Pb in the 2002 NEI. ..................... A-3 

Table A-2. Lead emissions from leaded aviation gas use in the 2002 NEI version 3. ............... A-5 

Table A-3. Size distribution of point sources within the 2002 NEI and associated estimated 

emissions. ................................................................................................................. A-9 

Table A-4. Point Sources with Pb emissions in 2002 NEI greater than or equal to 5 tpy........ A-11 

LIST OF ATTACHMENTS 

Attachment A-1. Largest Stationary Source Categories for Pb in the 2002 NEI .................... A-58 

Attachment A-2. Additional Details of Air Quality Analyses................................................. A-61 

A-iii 

 

 

A. SOURCES, EMISSIONS AND AIR QUALITY IN THE U.S. WITH 

PARTICULAR FOCUS ON URBAN AREAS 

Due to its physical and chemical properties, lead (Pb) exists in the environment 

predominantly in solid form. Consequently upon emission into the air, Pb deposits onto surfaces 

or exists in the atmosphere as a component of atmospheric aerosol, and usually in the form of 

various Pb compounds (CD1

, Section 2.1). The National Ambient Air Quality Standard 

(NAAQS) for Pb pertains to the Pb content of all Pb compounds that may be emitted to air. 

The major environmental transport pathway for anthropogenic Pb is the atmosphere, in 

which it can also undergo secondary dispersal via the deposition and resuspension of particles 

containing Pb (CD, p 2-52 and Section 2.3.1). Airborne Pb particles generally have a bimodal 

distribution with the greater mass of Pb found in the fine fraction (CD, p. 2-52), for which 

deposition is slower and less efficient than for larger particles (CD, p. 2-59). Accordingly Pb 

may be widely dispersed (CD, pp. 2-52, 3-3). Wet and dry deposition are the ultimate paths by 

which Pb particles are removed from the atmosphere. 

This appendix describes information on sources and emissions of Pb to the atmosphere 

(Section A.1), and Pb air monitoring data (Section A.2). 

A.1 SOURCES AND EMISSIONS 

The purpose of this section is to summarize available information on sources and 

emissions of Pb into the ambient air. The section does not provide a comprehensive list of all 

sources of Pb, nor does it provide estimates of emission rates or emission factors for all source 

categories. Rather, the discussion here is intended to identify the larger sources, either on a 

national or local scale, and provide some characterization of their emissions and distribution 

within the U.S. The primary data source for this discussion is the National Emissions Inventory 

(NEI) for 2002 (USEPA, 2007a). As a result of Clean Air Act requirements, emissions standards 

implemented for a number of source categories since then are projected to result in considerably 

lower emissions at the current time or in the near future. 

It is noted that the Pb emissions estimates in the NEI, and presented in this Appendix, are 

a mixture of estimates specific to Pb (regardless of the compound in which it may have been 

emitted) and estimates specific to the Pb compounds emitted. That is, emissions estimates for 

some of the point sources are in terms of mass of Pb compounds, whereas the nonpoint source 

and mobile source emissions estimates are in terms of mass of the Pb only. For the point 

1

 As in Volume I, the Air Quality Criteria Document for Lead (USEPA, 2006a) is abbreviated here as 

“CD”. 

A-1
 
 

sources, approximately 80% are reported as mass of Pb and most of the other 20% are reported 

as mass of Pb compounds. The high molecular weight of Pb (as compared to elements with 

which it is associated in Pb compounds), however, reduces the impact of this reporting 

inconsistency. 

The larger categories of Pb sources are presented in Section A.1.1, while Section A.1.2 

describes the number of and geographic distribution of Pb sources and associated emissions. 

Section A.1.3 describes the largest Pb stationary sources in the NEI. Lastly, the data sources, 

limitations of and confidence in the Pb emissions and source information presented here is 

discussed in Section A.1.4. 

A.1.1 Types of Pb Sources 

Lead is emitted from a wide variety of source types, some of which are small individually 

but the cumulative emissions of which are large, and some for which the opposite is true. The 

categories of Pb sources estimated in the 2002 NEI to emit –as a category- more than 5 tons per 

year (tpy) of Pb are listed in Table A-1. The main sources of emissions in the 2002 NEI are 

comprised primarily of combustion-related emissions and industrial process-related emissions. 

Point source emissions account for about 66% of the national Pb emissions in the 2002 NEI. The 

point source emissions are roughly split between combustion and industrial processes, while 

mobile, nonroad sources (emissions associated with general aviation aircraft leaded fuel) account 

for 29%. 

A.1.1.1 Stationary Sources 

Table A-1 presents emissions estimates for stationary sources grouped into descriptive 

categories. Presence and relative position of a source category on this list does not necessarily 

provide an indication of the significance of the emissions from individual sources within the 

source category. A source category, for example, may be composed of many small (i.e., lowemitting) sources, or of just a few very large (high-emitting) sources. Such aspects of a source 

category, which may influence its potential for human and ecological impacts, are included in the 

short descriptions of the largest stationary source categories presented in Attachment A-1. The 

relative sizes of stationary sources represented in the NEI, and the geographic distribution of the 

larger sources are presented in Sections A.1.2 and A.1.3. 

A-2
 
 

Table A-1. Source categories emitting greater than 5 tpy of Pb in the 2002 NEI. 

Source Category Description Total Emissions (tpy)a 

 ALL CATEGORIES 1,697b 

Mobile sources 491c 

Industrial/Commercial/ Institutional Boilers & Process Heaters 190 

Utility Boilers 168d 

Iron and Steel Foundries 110 

Primary Lead Smelting 59 

Hazardous Waste Incineration 47 

Secondary Lead Smelting 43 

Military Installations 33 

Municipal Waste Combustors 33 

Integrated Iron & Steel Manufacturing 32 

Pressed and Blown Glass and Glassware Manufacturing 32 

Stainless and Non Stainless Steel Manufacturing: EAF 32 

Mining 31 

Lead Acid Battery Manufacturing 27 

Secondary Nonferrous Metals 24 

Portland Cement Manufacturing 22 

Primary Copper Smelting 22 

Primary Metal Products Manufacturing 21 

Industrial and Commercial Machinery Manufacturing 18 

Fabricated Metal Products Manufacturing 14 

Electrical and Electronics Equipment Manufacturing 12 

Waste Disposal - Solid Waste Disposal 11 

Industrial Inorganic Chemical Manufacturing 10 

Pulp & Paper Production 10 

Sewage Sludge Incineration 10 

Mineral Products Manufacturing 9 

Secondary Aluminum Production 9 

Synthetic Rubber Manufacturing 9 

Secondary Copper Smelting 8 

Transportation Equipment Manufacturing 8 

Ferroalloys Production 7 

Nonferrous Foundries 7 

Stationary Reciprocating Internal Combustion Engines 7 

Commercial and Industrial Solid Waste Incineration 6 

Primary Nonferrous Metals--Zinc, Cadmium and Beryllium 6 

Residential Heating 6 

Asphalt Processing and Asphalt Roofing Manufacturing 5 

Autobody Refinishing Paint Shops 5 a

Some values here differ from those in the CD (Table 2-8) due to changes in the 2002 NEI subsequent to CD publication. 

Additionally, values just above 5 tpy have been rounded to 5. 

b

Includes 91 tpy Pb emissions from 109 smaller categories (57 tpy in MACT categories and 34 tpy in non MACT). c

 This value is not yet reflected in 2002 NEI (vers 3); it will be reflected in version 4, estimated for 2008 release. d

 This estimate of 168 tons, which is based on the 2002 NEI, has uncertainties and differs from estimates in some other studies 

and inventories. For example, the estimated lead emissions reported to the U.S. EPA’s Toxic Release Inventory for year 2004 is 

about 90 tons for this sector, and the projected estimate for year 2010 presented in the 1998 EPA Utility Air Toxics Study Report to 

Congress (U.S. EPA, 1998) is 92 tons. 

A-3
 
 

 

 

 

A.1.1.2 Mobile Sources 

Thirty-five years ago, combustion of leaded gasoline was the main contributor of Pb to 

the air. In the early 1970s, EPA set national regulations to gradually reduce the Pb content in 

gasoline. In 1975, unleaded gasoline was introduced for motor vehicles equipped with catalytic 

converters. EPA banned the use of leaded gasoline in highway vehicles after December 1995. 

While Pb is not added to jet fuel that is used in commercial aircraft, military aircraft, or other 

turbine engine aircraft, currently lead is still added to aviation gasoline (commonly referred to as 

“avgas”) used in most piston-engine aircraft and some types of race cars. Lead emissions from 

the combustion of avgas are discussed below. Vehicles used in racing are not regulated by the 

EPA under the Clean Air Act and can therefore use alkyl-Pb additives to boost octane. EPA has 

formed a voluntary partnership with the National Association for Stock Car Auto Racing 

(NASCAR) with the goal of permanently removing alkyl-Pb from racing fuels used in the Nextel 

Cup, Busch and Craftsman Truck Series (CD, p. 2-50). In January of 2006, NASCAR agreed to 

switch to unleaded fuel in its race cars and trucks beginning in 2008. NASCAR initiated this 

switch in 2007. 

Lead is also present as a trace contaminant in gasoline and diesel fuel and is a component 

of lubricating oil (CD, pp. 2-45 to 2-48). Inventory estimates from these sources are not 

currently available. Additional mobile sources of Pb include brake wear, tire wear, and loss of 

Pb wheel weights (CD, pp. 2-48 to 2-50). Emission rates for Pb from brake wear have been 

published but inventory estimates have not yet been developed from these data (Schauer et al., 

2006). Robust estimates of Pb from tire wear and wheel weights are not available. Currently, Pb 

from combustion of leaded avgas is the only mobile source of Pb included in the 2002 NEI. 

Currently, there are two main types of leaded avgas used, 100 Octane and 100 Octane 

Low Lead (100 LL), which can contain up to 1.12 grams Pb per liter (g/L) (0.009347 pounds per 

gallon, lb/gal) and 0.56 g Pb/L (0.004673 lb/gal), respectively (ASTM D 910). The vast majority 

of leaded avgas used is 100LL. In 2002 approximately 280 million gallons of avgas were 

supplied to the U.S. (DOE, 2006), contributing an estimated 491 tons of lead to the air – 

comprising 29% of the national Pb inventory. 2 

Lead emission estimates from piston-engine aircraft in the 2002 NEI are allocated to 

3,410 airports located throughout the United States (USEPA, 2007b). These Pb emissions are 

2

 Lead emissions from general aviation are calculated as the product of the fuel consumed, the 

concentration of Pb in the fuel and the factor 0.75 to account for an estimated 25% of Pb being retained in the engine 

and/or exhaust system of the aircraft. The estimate of 25% Pb retention was derived from estimates from light-duty 

gas vehicles operating on leaded fuel and is an upper-bound estimate of the amount of Pb retained in a piston-engine 

aircraft. Smaller retention values would proportionally increase the overall mobile source Pb inventory. 

A-4
 
 

 

allocated to each airport based on its percentage of piston-engine operations nationwide. These 

operations for 2002 can be found in the Terminal Area Forecast (TAF) system, which is the 

official forecast of aviation activity at FAA facilities. Airport-specific Pb emissions estimates in 

the NEI include Pb emitted during the entire flight (i.e., not limited to the landing and take-off 

cycle and local operations). EPA is using this allocation approach for Pb because it is important 

to account for all of the Pb emitted by avgas use. There is currently not an alternative approach 

for incorporating all the Pb emissions from aircraft into the NEI. EPA understands that 

allocating lead emissions to airports from operations outside the landing-takeoff cycle and local 

flying operations has a tendency to overstate the local emissions near airports because longer 

duration (e.g., itinerant) flights emit lead at altitude as well as in the local area near the airport. 

Airport-specific Pb emissions estimates in the 2002 NEI do not include the following 

airport-related sources of Pb: evaporative losses of Pb from fuel storage and distribution, 

military aircraft combustion emissions, and the small amounts of tetraethyl-lead (TEL) discarded 

on the tarmac by pilots after their fuel check. Lead emissions from fuel storage and distribution 

are estimated to total 0.3 tons nationally and are included in the NEI, but not assigned to specific 

airports. Data regarding military piston engine aircraft emissions are supplied to EPA by states. 

The 2002 version 3 inventory estimates for this category did not include state-submitted data, but 

future updates to the NEI will include these estimates. 

These current NEI estimates provide a valuable comparison with other ambient sources 

of Pb. Future upgrades to these estimates and assessments specific to individual airports could 

include more refined local data including characteristics of local operations (e.g., landings and 

take-offs), Pb retention in piston engines, and fuel consumption rates. 

Among the airports in the 2002 NEI where piston-engine aircraft operate, approximately 

one percent of US airports listed have estimated Pb emissions of greater than one ton per year, a 

greater percentage has estimated Pb emissions between one ton and 0.1 ton per year, while the 

majority of airports are estimated to have Pb emissions less than 0.1 ton per year. Table A-2 

below demonstrates these estimated emission ranges. 

Table A-2. Lead emissions from leaded aviation gas use in the 2002 NEI version 3. 

Emissions Total 

Range Number of Emissions 

(tpy) Airports (tpy) 

< 0.1 2,104 76.7 

0.1 to 1.0 1,270 367.5 

> 1 36 47.1 

Summary 3,410 491.3 

A-5 
 

A.1.1.3 Resuspension of Previously Deposited Pb and other Sources 

Resuspension of soil-bound Pb particles and contaminated road dust has been reported to 

be a significant source of airborne Pb (CD, Section 2.3.3, and p. 2-62). Quantitative estimates of 

resuspension-related emissions, however, are not included in the 2002 NEI. Studies of emissions 

in southern California indicate that Pb in resuspended road dust may represent between 40% and 

90% of Pb emissions in that area (CD, p. 2-65). Lead concentrations in suspended soil and dust, 

however, vary significantly (CD, p. 2-65). In general, the main drivers of particle resuspension 

are typically mechanical stressors such as vehicular traffic, construction and agricultural 

operations, and to a lesser extent, the wind. Lead resuspended in soil near roadways that was in 

place during the use of leaded gasoline may be a notable emissions source if or when such soil is 

disturbed (e.g., road widening or building construction). 

Understanding the physics of resuspension from natural winds requires analyzing the 

wind stresses on individual particles and although this analysis can be accurate on a small scale, 

predicting resuspension on a large scale generally focuses on empirical data for soil movement 

due to three processes: saltation, surface creep, and suspension (CD, pp. 2-62 to 2-63). Rather 

than a continuous process, resuspension may occur as a series of events. Short episodes of high 

wind speed, dry conditions, and other factors conducive to resuspension may dominate annual 

averages of upward flux (CD, p. 2-65). All of these factors complicate emissions estimates (CD, 

Section 2.2.1) such that quantitative estimates for these processes remain an area of significant 

uncertainty. 

Other sources not currently included in the NEI are emissions of Pb from natural sources, 

such as wind-driven resuspension of soil with naturally occurring Pb, sea salt spray, volcanoes, 

wild forest fires, and biogenic sources (CD, Section 2.2.1). Estimates for these emissions, some 

of which have significant variability (CD, p. 2-13) have not been developed for the NEI, as 

quantitative estimates for these processes remain an area of significant uncertainty. 

A-6
 
 

A.1.2 Number and Geographic Distribution of Sources 

The geographic distribution and magnitude of Pb emissions in the U.S. from all sources 

identified in the 2002 NEI is presented in Figure A-1, in terms of emissions density (defined here 

as tons per area, square mile, per county). This presentation indicates a broad distribution of Pb 

emissions across the U.S., with the highest emitting counties scattered predominantly within a 

broad swath from Minnesota to southern New England and southward. 

Figure A-1. Emissions density from all Pb sources in the 2002 NEI. 

Within the NEI, emissions from stationary sources may be associated with specific 

“points” (i.e., point sources) or with activities estimated to occur with some frequency within an 

“area” such as a county (area sources) or with mobile sources (see Section 1.1.1.2). Emissions 

from all stationary sources represented in the NEI are presented in Figure A-2, in terms of 

emissions density (tons per area, square mile, per county). 

A-7
 
 

Figure A-2. Emissions density from all stationary sources of Pb in 2002 NEI. 

There are some 13,067 point sources (industrial, commercial or institutional) in the 2002 

NEI, each with one or more processes that emit Pb to the atmosphere (Table A-3). Most of these 

sources emit less than 0.1 tpy Pb. There are approximately 1,300 point sources of Pb in the NEI 

with estimates of emissions greater than or equal to 0.1 tpy and these point sources, combined, 

emit 1058 tpy, or 94% of the Pb point source emissions. In other words, 94% of Pb point source 

emissions are emitted by the largest 10% of these sources. 

A-8
 
 

 

 

 

Table A-3. Size distribution of point sources within the 2002 NEI and associated estimated 

emissions. 

Average 

Emissions Total Emissions 

Range Number Emissions per Source 

(tpy) of Sources (tpy) (tpy) 

< 0.1 11,800 73 <0.01 

0.1 to 1.0 1,028 326 0.3 

1.0 to 5 210 421 2 

> 5 29 301 10 

Summary 13,067 1121 

A.1.3 Largest Pb Point Sources in the 2002 NEI 

While Section A.1.1 focuses on source categories that rank highest due to cumulative 

national Pb emissions, this section is intended to consider Pb emissions on the individual source 

level. The geographic distribution of point sources estimated to emit greater than 1 tpy is 

presented in Figure A-3. 

A-9
 
 

Figure A-3.	 Geographic distribution of point sources with >1 tpy Pb emissions in 2002 

NEI. 

As mentioned in Section A.1.2, the 2002 NEI includes 30 facilities with emissions 

estimated to be greater than or equal to 5 tons per year (see Table A-3). Most of these sources 

(Table A-4) are metallurgical industries, followed by waste disposal facilities and manufacturing 

processes. 

A-10
 
 

Table A-4. Point Sources with Pb emissions in 2002 NEI greater than or equal to 5 tpy. 

Source Category Name State County Name 

2002 Point 

Emissions (TPY)a 

Primary Lead Smelting MO Jefferson County 58.8 

Military Installation OK Pittsburg County 17.2 

Mining MO Reynolds County 15.4 

Copper Refiningb

 TX Potter County 13.9 

Primary Copper Smelting AZ Gila County 12.8 

Electric Arc Furnaces IL Peoria County 12.5 

Secondary Lead Smelting MO Iron County 12.4 

Integrated Iron & Steel Manufacturing IN Lake County 11.3 

Pressed and Blown Glass and Glassware Manufacturing TN Madison County 10.9 

Military Installation PA Franklin County 10.4 

Hazardous Waste Incineration AR Union County 10.2c 

Lead Acid Battery Manufacturing KY Madison County 9.9 

Industrial and Commercial Machinery Manufacturing KS Marshall County 8.2 

Synthetic Rubber Products Manufacturing - Fabric 

Coating IN Cass County 7.4 

Commercial and Industrial Solid Waste Incineration AR Clark County 7.3 

Iron and Steel Foundries OH Cuyahoga County 7.3 

Integrated Iron & Steel Manufacturing IN Porter County 7.2 

Integrated Iron & Steel Manufacturing IN Lake County 6.1 

Mineral Products Manufacturing NM Socorro County 6.1 

Commercial and Industrial Solid Waste Incineration CT Windham County 5.8 

Ferroalloys Production OH 

Washington 

County 5.7 

Nonferrous Foundries NE Nemaha County 5.5 

Portland Cement Manufacturing MD Frederick County 5.4 

Hazardous Waste Incineration OH Lorain County 5.4 

Coke Oven VA Buchanan County 5.1 

Iron and Steel Foundries IA Jefferson County 5.1 

Mining MO Reynolds County 5 

a

 (USEPA, 2007a)

b

 This entry is included in the total provided for “secondary nonferrous metals” in Table A-1. c

Following compliance with the MACT standards in 2008, Pb emissions are estimated to be 0.7 tpy. 

A.1.4 Data Sources, Limitations and Confidence 

The Pb emissions information presented in the previous sections is drawn largely from 

EPA’s NEI for 2002 (USEPA, 2007a). The NEI is based on information submitted from State, 

Tribal and local air pollution agencies and data obtained during the preparation of technical 

support information for EPA’s hazardous air pollutant regulatory programs. EPA has recently 

developed version 3 of the NEI for 2002 and that version is anticipated to be posted on the 

EPA’s CHIEF website soon at (http://www.epa.gov/ttn/chief/net/2002inventory.html). The 

information presented in this document is based on version 3. 

A-11
 
 

The process of identifying sources that emit Pb into the air has been ongoing since before 

the Clean Air Act of 1970. The comprehensiveness of emission inventories generally, and the 

NEI, specifically, depends upon knowledge of source types emit Pb, their locations and their 

operating characteristics, as well as the reporting of this information to the inventory. As noted 

above, the NEI relies on information that is available from a variety of sources for this 

information. There are numerous steps, each with its own uncertainties, associated with the 

development of this information for use in the emissions inventory. First, the categories emitting 

Pb must be identified. Second, the sources’ processes and control devices must be known. 

Third, the activity throughputs and operating schedules of these sources must be known. Finally, 

we must have emission factors to relate emissions to the operating throughputs, process 

conditions and control devices. The process, control device, throughputs and operating 

schedules are generally available for each source. However, the emission factors represent 

average emissions for a source type and average emissions may differ significantly from source 

to source. In some cases, emissions testing provides source-specific information. In others, 

emissions factors must be estimated from similar sources or source categories or other 

information. More information on emission factors and the estimation of emissions is found in 

the introduction to EPA’s Compilation of Air Pollutant Emissions Factors (USEPA, 2006). 

Further information on emission factors is available at: http://www.epa.gov/ttn/chief/ap42/. 

The NEI is limited with regard to Pb emissions estimates for some sources such as 

resuspended road dust (Section 2.2.2.3), biomass burning and trace levels of Pb in motor fuel and 

lubricating oil (Section 2.2.2.2), and others. We have not yet developed estimates for the NEI of 

Pb emissions associated with resuspension of Pb residing in roadway dust and nearby surface 

soil. Emissions estimates are also not yet in the NEI for the miscellaneous categories of on-road 

emissions (e.g., combustion of fuel with Pb traces, lubricating oil, mechanical wear of vehicle 

components, etc.) and Pb that may be emitted from wildfires. 

The 2002 NEI underwent extensive 3-month external review, including a review of the 

process for developing the inventory which includes extensive quality assurance and quality 

control steps (QA/QC). For example, we created a QA/QC process and tracking database to 

provide feedback reports to point source data providers at regular intervals during the QA of the 

data. The feedback reports included the following 4 QC reports: data integrity, 

latitude/longitudes QC, stack parameters QC, and emissions QC. Further, there was additional 

QA/QC conducted for emission inventory information for facilities that are included in the Risk 

and Technology Review (RTR) source categories (60FR14734). As a result we have strong 

confidence in the quality of the data for these facilities. Version 3 of the 2002 NEI used in RTR 

has undergone additional peer review and QA/QC based on comments received to Docket # 

EPA-HQ-OAR-2006-0859. 

A-12
 
 

In summary, generic limitations to the 2002 NEI include the following: 

•	 Consistency: The 2002 NEI for Pb is a composite of emissions estimates generated by 

state and local regulatory agencies, industry, and EPA. Because the estimates 

originated from a variety of sources, as well as for differing purposes, they will in turn 

vary in quality, whether Pb is reported for particular source types, method of reporting 

compound classes, level of detail, and geographic coverage. 

•	 Variability in Quality and Accuracy of Emission Estimation Methods: The accuracy of 

emission estimation techniques varies with pollutants and source categories. In some 

cases, an estimate may be based on a few or only one emission measurement at a 

similar source. The techniques used and quality of the estimates will vary between 

source categories and between area, major, and mobile source sectors. Generally, the 

more review and scrutiny given to emissions data by states and other agencies, the 

more certainty and accuracy there is in that data. 

A-13
 
 

A.2 AIR QUALITY MONITORING DATA 

The EPA has been measuring Pb in the atmosphere since the 1970s. For the most part, 

Pb concentrations have decreased dramatically over that period. This decrease is primarily 

attributed to the removal of Pb from gasoline; however, some individual locations still have Pb 

concentrations above the level of the NAAQS. The following sections describe the ambient Pb 

measurement methods, the sites and networks where these measurements are made, as well as 

how the ambient Pb concentrations vary geographically and temporally. 

Ambient air Pb concentrations are measured by four monitoring networks in the United 

States, all funded in whole or in part by EPA. These networks provide Pb measurements for 

three different size classes of airborne particulate matter (PM): total suspended PM (TSP), PM 

less than or equal to 2.5 μm in diameter (PM2.5), and PM less than or equal to 10 μm in diameter 

(PM10). The networks include the Pb TSP network, the PM2.5 Chemical Speciation Network 

(CSN), the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, and 

the National Air Toxics Trends Stations (NATTS) network. The subsections below describe 

each network and the Pb measurements made at these sites. 

In addition to these four networks, various organizations have operated other sampling 

sites yielding data on ambient air concentrations of Pb, often for limited periods and/or for 

primary purposes other than quantification of Pb itself. Most of these data are accessible via 

EPA’s Air Quality System (AQS): http://www.epa.gov/ttn/airs/airsaqs/. In an effort to gather as 

much air toxics data, including Pb, into one database, the EPA and State and Territorial Air 

Pollution Program Administrators and the Association of Local Air Pollution Control Officials 

(STAPPA/ALAPCO) created the Air Toxics Data Archive. The Air Toxics Data Archive can be 

accessed at: http://vista.cira.colostate.edu/atda/. 

A.2.1 Ambient Pb Measurement Methods 

A number of methods are used to collect Pb and measure Pb concentrations in the 

atmosphere. Most methods use similar sample collection approaches. Ambient air is drawn 

through an inlet for a predetermined amount of time (typically 24 hours) and the PM is collected 

on a suitable filter media. After the sample has been collected, the filter may be used to 

determine the mass of PM collected prior to then being used for determination of Pb. The filter 

is chemically extracted and analyzed to determine the Pb concentration in the particulate 

material. The concentration of Pb found in the atmosphere, in μg/m3

, is calculated based on the 

concentration of Pb in the volume extracted, the size of the collection filter, and the volume of 

air drawn through the filter. 

A-14
 
 

 

The primary factors affecting the measurements made are the sampling frequency, 

duration of sampling, type of inlet used, volume of air sampled, and the method of analyzing the 

filter for Pb content. The following paragraphs describe how these factors affect the Pb 

measurements. 

A.2.1.1 Inlet Design 

In ambient air monitors, a number of inlet designs have been developed that allow certain 

particle size ranges to be sampled. The inlets use either impaction or cyclone techniques to 

remove particles larger than a certain size (the size cutpoint) from the sample stream. Three 

particle size cutpoints are used in ambient Pb measurements including TSP, PM2.5, PM10. The 

TSP inlet is designed to allow as much suspended particulate into the sampling device as 

possible while protecting against precipitation and direct deposition on to the filter (nominally 25 

to 45 micrometers) (USEPA, 2004c). 

Sampling systems employing inlets other than the TSP inlet will not collect Pb contained 

in the PM larger than the size cutpoint. Therefore, they do not provide an estimate of the total Pb 

in the ambient air. This is particularly important near sources which may emit Pb in the larger 

PM size fractions (e.g., fugitive dust from materials handling and storage). 

A.2.1.2 Volume of Air Sampled 

The amount of Pb collected is directly proportional to the volume of air sampled. Two 

different sampler types have evolved for PM and Pb sampling – a high-volume and a lowvolume sampler. High-volume samplers draw between 70 and 100 m3

/hr of air through an 8 inch 

by 10 inch filter (0.05 m2

 filter area). Low-volume samplers typically draw 1 m3/hr through a 47 

mm diameter filter (0.002 m2 filter area). Currently all Federal Reference Method (FRM) and 

Federal Equivalence Method (FEM) for Pb-TSP are based on high-volume samplers. 

A.2.1.3 Sampling Frequency 

The frequency of Pb sampling used in the U.S. varies between one sample every day (1 in 

1 sampling) to the more common frequency of one sample every 6 days (1 in 6 sampling). Semicontinuous methods for the measurement of ambient metals (including Pb) are currently being 

explored which would allow for more frequent sampling (as frequent as 1 sample per hour), but 

much more work is needed on these methods before they can be deployed in a network setting. 

More frequent sampling reduces the uncertainty in estimates of quarterly or annual 

averages associated with temporal variations in ambient concentrations. However, the costs of 

sampling and analysis are directly tied to sample frequency. As such, it is necessary to evaluate 

the reduction in measurement error versus the increase in sampling and analysis costs when 

A-15
 
 

 

selecting the required sampling frequency. A discussion of the observed temporal variation of 

Pb measurements is given later in this section. 

A.2.1.4 Sample Analysis 

After the samples have been collected on filters and the filters have been weighed, the 

filters are analyzed for Pb content. A number of analytical methods can be used to analyze the 

filters for Pb content including x-ray fluorescence analysis (XRF), proton-induced x-ray 

emission (PIXE), neutron activation analysis (NAA), atomic absorption (AA), or inductivelycoupled plasma mass spectrometry (ICP/MS) (CD, pp. 2-80 to 2-81). A detailed discussion of 

these methods was given in the 1986 CD (USEPA, 1986), and the reader is referred to that 

document for more information on these analytical methods. A search conducted on the AQS 

database3 shows that the method detection limits for all of these analytical methods (coupled 

with the sampling methods) are very low, ranging from 0.01 μg/m3 to as low as 0.00001 μg/m3, 

and are more than adequate for determining compliance with the current NAAQS. 

A.2.2 Pb-TSP 

This network is comprised of state and locally managed Pb monitoring stations which 

measure Pb in TSP, i.e., particles up to 25 to 45 microns. These stations use samplers and 

laboratory analysis methods which have either FRM or FEM status. The FRM and FEM method 

descriptions can be found in the U.S. Code of Federal Regulations, Section 40 part 50, Appendix 

G. Sampling is conducted for 24-hour periods, with a typical sampling schedule of 1 in 6 days. 

Some monitoring agencies “composite” samples by analyzing several consecutive samples 

together to save costs and/or increase detection limits. 

A.2.2.1 Monitor Locations 

The locations of Pb-TSP sites in operation between 2003 and 2005 are shown in Figure 

A-4. State and local agencies are required to operate two Pb-TSP monitors in any area which has 

exceeded the NAAQS in the last two years (40 CFR 58 Appendix D). State and local agencies 

have the latitude to operate more monitors beyond the minimum requirement. Agencies which 

operate these sites report the data to EPA’s AQS where they are accessible via several web-based 

tools. EPA’s series of annual air quality trends reports have used data from this network to 

quantify trends in ambient air Pb concentrations. The most recent Trends report for Pb-TSP can 

be found at http://www.epa.gov/airtrends/lead.html. 

A review of the Pb-TSP network's coverage of the highest Pb emitting sources (as 

identified in the current version of the 2002 NEI) was conducted as part of preparing this 

3

 EPA’s AQS can be accessed at http://www.epa.gov/ttn/airs/airsaqs/ 

A-16
 
 

 

 

 

 

document. This review indicates that many of the highest Pb emitting sources in the 2002 NEI 

do not have nearby Pb-TSP monitors. This review indicates that only 2 of 26 facilities (both Pb 

smelters4) identified as emitting greater than 5 tpy have a Pb-TSP monitor within 1 mile. The 

lack of monitors near large sources should be addressed in the network design for the revised 

rule in order to get monitors at these locations in the future. Additionally, none of the 189 PbTSP sites included in the 2003-2005 analysis described in Sections A.2.2.2 and A.2.2.3 are 

located within a mile of airports identified in the NEI as an airport where piston-engine aircraft 

operate (i.e., aircraft that still use leaded aviation fuel).5 

Figure A-4. Pb-TSP monitoring sites: 2003-2005. 

The number of sites in the Pb-TSP network has decreased significantly since the 1980s 

(see Figure A-5). The number of sites in the network reached its highest point in 1981 (946 

sites). About 250 sampling sites operated during 2005. This decline in the number of Pb-TSP 

4

 Primary and secondary smelters were the source types given particular priority at the time of the last Pb 

NAAQS review (USEPA, 1990; USEPA, 1991). 

5 While there are limited historical data (going back to 1993) in AQS for 12 Pb-TSP monitoring sites 

operating within one mile of 11 of these airports, time constraints have limited the extent of our analysis here of 

these data or of other such data that may be available elsewhere. 

A-17
 
 

sites is attributable to the dramatic decrease in Pb concentrations observed since the 1980s and 

the need to fund new monitoring objectives (e.g., PM2.5 and ozone monitoring). Lead-TSP sites 

in lower concentration areas were shut down to free up resources needed for monitoring of other 

pollutants such as PM2.5 and ozone. 

Figure A-5. Change in the number of Pb-TSP monitoring sites from 1980 to 2005. 

A.2.2.2 Data Analysis Details 

Lead-TSP data collected in 2003-2005 (parameter code 12128, durations ‘7’ and ‘C’ ) 

were extracted from EPA’s AQS on May 22, 2007. Most of the monitors reporting data for that 

timeframe utilized FRM or FEM, and therefore, are candidates for comparisons to the NAAQS. 

Some of the Pb-TSP monitors, however, were placed for nonregulatory purposes (e.g., for toxics 

monitoring initiatives) and utilize methods other than a FRM or FEM. Although measurements 

from these monitors cannot be compared to the NAAQS for purposes of nonattainment 

decisions, they were considered worthy for inclusion in this national Pb-TSP characterization. 

The non-FRM/FEM Pb-TSP methods typically have lower uncertainties and detection limits than 

the FRM/FEM. Detection limits vary significantly even for the data generated using FRM or 

FEM. In summary aggregations, the AQS generally substitutes one half the method detection 

level (MDL) for reported concentration readings less than or equal MDL. That protocol was not 

A-18
 
 

 

 

utilized in this national aggregation; data were used ‘as reported’ to AQS. Only a small number 

of Pb-TSP measurements for 2003-2005 were exceptional events (e.g., structural fires, chemical 

spills, sandblasting); none of the exceptional event flag-flagged data, however, were concurred 

(i.e., approved) by the associated EPA Regional Office. Data flags were ignored in this analysis. 

A.2.2.2.1 Screening Criteria 

Measurements of Pb-TSP with 24-hour sample collection duration were reported to AQS 

for more than 350 monitors for the years 2003 to 2005. 189 of those monitors met the following 

screening criteria and were used in this national characterization. The completeness criteria 

employed for this national characterization were: 1) a minimum of 10 observations per quarter, 

2) for at least one full year (all 4 quarters), and 3) at least 9 months with 4 observations each6

; all 

three criteria had to be met for inclusion. 209 monitors met the 3-pronged criteria; of these 209 

monitors, 20 were collocated with another complete monitor. Only one monitor from each 

collocated pair (i.e., from each site location) was kept in the analysis, specifically the one with 

highest 3-year maximum quarterly mean. Thus, data from 189 monitors at 189 distinct locations 

were actually used; 109 of these monitors/sites had 3 complete years, 36 monitors/sites had 2 

complete years, and 44 monitors/sites had only one compete year. Complete quarters that were 

not part of a complete year were used. Likewise, all complete months were used, even if they 

did not correspond to the complete years. The 189 sites have an average of about 10 complete 

quarters and 28 complete months. The 189 utilized monitors are listed along with various 

summary and demographic data in Attachment A-2, Table 1. 

A.2.2.2.2 Urban Sites 

The 189 monitors are located in 86 counties, in 23 States. 140 of the 189 sites were 

deemed ‘urban’ and aggregated as such. Sites were labeled ‘urban’ if they located within a 

defined urbanized area or urban cluster (per 2000 Census geographic definitions). All of the 

‘urban’ designated sites were located in a Core Based Statistical Area (CBSA) per 2003 CBSA 

geographic definitions. CBSA is a collective term for both metropolitan and micropolitan 

statistical areas. A metro area contains a core urban area of 50,000 or more population, and a 

micro area contains an urban core of at least 10,000 (but less than 50,000) population. Each 

metro or micro area consists of one or more whole counties and includes the counties containing 

the core urban area, as well as any adjacent counties that have a high degree of social and 

economic integration with the urban core. The monitors in the analysis map to 65 unique 

6

 Quarterly means calculated with less than ten observations, annual means calculated with only three 

quarters, and monthly means derived with less than four observations were also considered valid if that mean value 

exceeded the level of the current standard (i.e., 1.5 μg/m3 for quarterly mean). 

A-19
 
 

CBSA’s. Only 10 of the 189 monitors are not located within a CBSA. CBSA’s do not always 

exclusively encompass wholes or parts of urbanized areas and/or urbanized clusters. 39 of the 

189 Pb monitoring sites are located in a CBSA but are not classified as ‘urban’. Although 

‘urban’ locations (i.e., parts of urbanized areas or urban clusters) are found in counties not 

defined as (or part of) a CBSA, all of the 140 urban sites in this characterization are located in a 

CBSA. 91 of the 140 urban sites are located in CBSA’s with 1 million or greater population. 

Note that the 65 CBSA’s containing the Pb-TSP monitoring sites are generally among the largest 

in the nation (with respect to total population). Almost 75 percent of the Pb-TSP CBSA’s are 

larger (in population) than the 75 percent of all U.S. CBSA’s. With respect to total CBSA 

population, the 5 overall largest CBSA’s and 18 of the largest 25 contain at least one Pb-TSP 

monitor. 

A.2.2.2.3 Source-oriented Sites 

Monitoring sites were classified as being “source oriented” with regard to sources of Pb 

emissions if: 1) they met a graduated (or sliding scale of ) cumulative emission ton per year by 

distance criterion, or 2) they were classified as source oriented in previous EPA analysis. Sixty 

of the 189 Pb-TSP sites met at least one of these criteria. Of the 60 total source-oriented sites, 40 

met the first criterion and 51 met the second. 

The graduated cumulative emission ton per year to distance criterion (criterion #1) 

utilized the 2002 (version 3) national emission inventory (NEI) for Pb point sources and Pb area 

nonpoint sources. The Pb point source emissions were assigned to the specific facility point 

locations (longitude/latitude coordinates), and the area nonpoint inventory was allocated to 

Census tracts and assumed uniform across those extents. To meet the graduated “sourceoriented” criterion, a Pb monitoring site had to be within at least one multiplier of 0.1 miles 

(checking up to 1 mile away) for a corresponding multiplier of 0.1 tpy of total point and nonpoint 

emissions (e.g., Within 0.1 mile of a cumulative 0.1 tpy, within 0.2 miles of a cumulative 0.2 tpy, 

within 0.3 miles of a cumulative 0.3 tpy, ..., or within 1.0 miles of a cumulative 1.0 tpy) The 

area nonpoint contribution to the comparison cumulative inventory was based on the composite 

emission densities of the Census tract in which a site was located and all other tracts with 

population centroids within a mile of the monitoring site. 

The sites ‘classified as source oriented in previous EPA analysis’ (criterion #2) were 

identified via a reference list that was last updated in 2003 (but currently under review); this list 

has been utilized in recent EPA Trends Report analysis. The list encompasses 114 sites. Many 

of the monitoring sites on this list did not have data that met the data completeness criteria for 

2003–2005 because they have permanently discontinued Pb monitoring, most ostensibly because 

the associated nearby Pb emission source(s) has implemented controls, closed operations, and/or 

A-20
 
 

reduced production. Some ambient monitoring sites continue monitoring even after significant 

assumed reductions in nearby new Pb emissions. Sites were not screened out of the sourceoriented classification in those instances. In addition to including such sites in the sourceoriented category, these sites were separately reviewed to see if they still had higher 

concentrations than nonsource sites because of previously emitted Pb becoming resuspended into 

the air and/or possible emission estimate errors. These sites are termed, “’previous’ sourceoriented sites” in relevant figures and tables. 

There are only nine sites that were categorized as “previous” source-oriented in this 

national analysis. The particular circumstances related to the emission sources associated with 

these nine monitoring sites vary considerably. In some instances the emission sources have been 

closed for more than a decade and the facility locations have undergone remediation. For other 

sources, production and clean-up status was not fully ascertained. In the case of one emission 

source (that has numerous nearby monitoring sites), production was presumably halted at the end 

of 2003 and no significant clean-up activity has yet been undertaken. For the monitoring sites 

associated with this source, two sets of statistics were generated (or attempted). Statistics 

representing the entire 3-year period were calculated and used everywhere applicable except for 

the “previous” category, and statistics representing the post-production period (2004-2005) were 

generated and used for the “previous” classification. Note that some of these monitoring sites 

met the data completeness criteria for the 3-year period (2003-2005) but not for the 2-year period 

(2004-2005). Because of the small number of sites included in the “previous” source-oriented 

classification and the uncertainty in the emission source status, results for this category should be 

viewed with caution. 

A.2.2.2.4 Population Associations 

Two population statistics were summarized with the Pb concentration data, the ‘total 

population’ within 1 mile of the site (a.k.a., a “radial mile”) and the ‘under age 5 population’ 

within 1 mile of the site. Populations assigned sites were based on Census block group 

population densities, specifically the density of the block group in which the site was located and 

(if relevant) the density of other block groups with population centroids within 1 mile of the site. 

The average population density (expressed in square miles) was multiplied by pi (3.143) to 

obtain a radial mile population (i.e., the number of people living within a one mile radius of the 

monitoring site). Population data and block group definitions utilized are from the 2000 Census. 

The median size of populations associated with the Pb-TSP monitors in this analysis is 

about 6,200 and the corresponding under age 5 median population is around 420. These median 

populations are slightly smaller than the overall U.S. block group median radial mile populations 

(19 percent smaller for total and 7 percent smaller for under age 5). Attachment A-2, Table 1 

A-21
 
 

 

shows the assigned site-level populations; CBSA information for each site is also shown. Based 

on the radial mile population association (described above) approximately 1.73 million people 

(0.125 million under the age of 5) are in proximity of a 2003-2005 Pb-TSP monitor included in 

this analysis. 

A.2.2.2.5 Statistical Metrics 

Four basic statistics were computed for the 2003-2005 Pb-TSP concentration data: annual 

means, maximum quarterly means, maximum monthly means, and second maximum monthly 

means. These metrics were calculated at the site level. They were calculated both for the overall 

3-year period (2003-2005) and for each of the three individual years (2003, 2004, and 2005). 

The former set of statistics (representing the overall 3-year period) were the general focus of the 

analysis, and unless otherwise stated, figures, maps, and text should be assumed that type. Note 

that the 3-year annual mean statistic is actually the average of the annual means for the complete 

years; thus it is the average of three annual means, the average of two annual means, or the only 

available single complete annual mean. Annual means were computed from quarterly means. 

The 3-year maximum quarterly mean statistic represents the highest quarterly mean of the 

complete ones (sites have from four to 12 complete quarters), and the 3-year maximum monthly 

mean represents the highest monthly mean of the complete ones (each site has from nine to 36 

complete months). Two additional 3-year metrics were also calculated but, like the individual 

year statistics for the four basic metrics, utilized sparingly. These two metrics are 1) the average 

of the three overall highest monthly means for the 3-year period (year nonspecific), and 2) the 

average of the annual maximum monthly means. 

Population weighted means were also calculated for the four basic metrics for various 

aggregation levels. The site-level means were weighted by total population. To compute the 

population weighted measures, 1) the mean for each site in a specific category was multiplied by 

its associated population (i.e., within a mile radius), 2) these products (of #1) and the associated 

populations were summed, and 3) the sum of the products of #1 were divided by the population 

sums. Theoretically, these population weighted means show the average concentration exposure 

for each individual within a mile of a monitoring site. That supposition, of course, assumes that 

concentrations reported at the monitor are uniform over the entire radial mile. 

A.2.2.3 Current Concentrations 

In the following subsections, analyses are presented for the different categorizations of 

Pb-TSP monitoring sites described above. These categories include “all Pb-TSP sites meeting 

screening criteria”, and the following subsets: sites in urban areas, sites in urban areas of 

population greater than 1 million, sites that are source-oriented, sites that are not known to be 

source-oriented, and sites that were previously source-oriented. 

A-22
 
 

The site-level Pb-TSP concentrations for all computed statistics are shown in Attachment 

A-2, Table 1. The distributions of sites for the four basic (3-year) statistics (annual mean, 

maximum quarterly mean, maximum monthly mean, and second maximum monthly mean) and 

the two additional 3-year statistics (average of three overall highest monthly means and average 

of 3 annual maximum monthly means) are shown in Figure A-6; the boxes depict inter-quartile 

ranges and medians, whiskers depict the 5th and 95th percentiles, and asterisks identify composite 

averages. Additional points on the distributions for these statistics are given in Attachment A-2, 

Table 2. For example, the national composite average annual mean was 0.09 μg/m3, and the 

corresponding median annual mean was 0.02 μg/m3. The national composite average maximum 

quarterly mean was 0.17 μg/m3 and the corresponding median maximum quarterly mean was 

0.03 μg/m3

. The national composite average maximum monthly mean was 0.31 μg/m3 and the 

median maximum monthly mean was 0.04 μg/m3

. The national composite average second 

maximum monthly mean was 0.21 μg/m3 and the median value was 0.03 μg/m3. The national 

composite average of the mean of the three overall highest monthly averages was 0.31 μg/m3

 and 

the median value was 0.04 μg/m3

. The national composite average of the mean of the annual 

highest monthly means was 0.21 μg/m3 and the median value was 0.03 μg/m3. 

A-23
 
 

Annual mean Max quarterly Max monthly 

Concentration (μg/m3 ) 

2nd max monthly Average of 3 Average of annual 

mean mean mean overall max max monthly 

monthly means means 

Figure A-6.	 Distribution of Pb-TSP concentrations (represented by 6 different statistics) 

at the 189 Pb-TSP monitoring sites, 2003-2005. 

Figure A-7 shows cumulative percentages of total monitored populations associated with 

each of the four Pb metrics for various levels [≥0.02 μg/m3

 , ≥ 0.05 μg/m3, ≥ 0.20 μg/m3, ≥ 0.50 

μg/m3, and ≥ 1.54 μg/m3]. Note that site statistics were rounded to two decimal places before 

comparing to stated levels. The phrase “monitored populations” refers to populations residing in 

proximity to monitors as described in Section A.2.2.2.4. The site-level values for the four 

statistical metrics (annual average, maximum quarterly mean, maximum quarterly mean, and 

second maximum monthly mean) are mapped in Figures A-8 through A-11. As seen when 

comparing these figures, the geographic locations of the high (and low) concentration values for 

all three metrics are generally the same. In fact, there are significant correlations among all four 

3-year (2003-2005) summary metrics; see Attachment A-2, Table 3. 

A-24
 
 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

percent of monitored population 

a nnua l m e a n m a x q u a r t e r l y m e a n m a x m ont hl y m e a n 2 nd m a x m ont hl y m e a n 

26.9% 

7.7% 

2.2% 

0.7% 

0.1% 

54.4% 

26.0% 

5.1% 

1.6% 

0.2% 

65.8% 

31.3% 

8.7% 

3.3% 

1.1% 

55.6% 

22.4% 

7.3% 

2.1% 

0.3% 

> 0. 02 > 0. 05 > 0. 20 > 0. 50 > 1. 54 

μg/m3 

Figure A-7. Percentages of Pb-TSP monitored populations residing in areas exceeding 

various concentrations (for 4 different statistics), 2003-2005. 

A-25
 
 

≥ 1.55

0.50 – 1.55

0.20 – 0.50

0.05 – 0.20

< 0.05

Concentration range

(μg/m3)

≥ 1.55 

0.50 – 1.55 

0.20 – 0.50 

0.05 – 0.20 

< 0.05 

Concentration range 

(μg/m3) 

Figure A-8. Pb-TSP annual means (for all sites), 2003-2005. 

A-26
 
 

 

≥ 1.55

0.50 – 1.55

0.20 – 0.50

0.05 – 0.20

Concentration range

(μg/m3)

≥ 1.55 

0.50 – 1.55 

0.20 – 0.50 

0.05 – 0.20 

Concentration range 

(μg/m3) 

< 0.05 

Figure A-9. Pb-TSP maximum quarterly means (for all sites), 2003-2005. 

A-27
 
 

≥ 1.55

0.50 – 1.55

0.20 – 0.50

0.05 – 0.20

< 0.05

Concentration range

(μg/m3)

≥ 1.55 

0.50 – 1.55 

0.20 – 0.50 

0.05 – 0.20 

< 0.05 

Concentration range 

(μg/m3) 

Figure A-10. Maximum monthly Pb-TSP means (all sites), 2003-2005. 

A-28
 
 

≥ 1.55

0.50 – 1.55

0.20 – 0.50

0.05 – 0.20

< 0.05

Concentration range

(μg/m3)

≥ 1.55 

0.50 – 1.55 

0.20 – 0.50 

0.05 – 0.20 

< 0.05 

Concentration range 

(μg/m3) 

Figure A-11. Second maximum monthly Pb-TSP means (all sites), 2003-200 

The site-level ratios of 1) maximum quarterly mean to annual mean, 2) maximum 

monthly mean to annual mean, and second maximum monthly mean to annual mean are 

presented in Attachment A-2, Table 4. For all TSP-Pb sites included in the analysis, the national 

median for the ratio of site-level maximum quarterly average to site-level annual mean was about 

1.8; the national median for the ratio of site-level maximum monthly mean to site-level annual 

mean was about 2.8; and the national median for the ratio of site-level second maximum monthly 

mean to site-level annual mean was about 2.1. 

A.2.2.3.1 Source-oriented Sites 

As seen in the previously discussed Figure A-6, the national (“all sites”) means are 

substantially higher than the national medians for all four statistical metrics (annual mean, 

maximum quarterly mean, maximum monthly mean, and second maximum monthly mean). 

This is due to a small number of monitors with significantly higher levels. These monitors with 

higher concentrations are almost exclusively associated with industrial point sources. 

A-29
 
 

Eliminating the source-oriented monitors from the national aggregations lowers most of the 

corresponding distribution statistics and makes the means more comparable to the medians. 

The distributions of the site-level metrics for the source-oriented sites, the non-sourceoriented sites, and the “previous” source-oriented sites, are presented in Figures A-12, A-13, and 

A-14, respectively. For comparison purposes, Figures A-15 through A-18 present the categorical 

data distributions for each of the four statistical metrics on the same scales. In all of these 

figures, the boxes depict inter-quartile ranges and medians, whiskers depict the 5th and 95th 

percentiles, and asterisks identify composite averages. Additional points on the distributions of 

these statistical metrics for these three categories of monitoring sites are given in Attachment A2, Table 2. The medians, means, and population-weighted means of the site-level values of the 

three statistical metrics are presented in Figure A-19 for the source-oriented and other groupings 

of monitoring sites. 

Per Figure A-16, the median maximum quarterly mean for source-oriented sites (0.25 

μg/m3

) is about 14 times greater than the same statistic for non-source-oriented sites (0.02 

μg/m3

); in fact, that median (50th percentile) maximum quarterly mean for non-source-oriented 

sites is approximately the same value as the 5th percentile for source-oriented sites. Almost 95 

percent of all monitors identified as being source-oriented had a maximum quarterly average of 

0.02 μg/m3 or more, and over 25 percent had maximum quarterly average of 0.50 μg/m3 or more. 

A-30
 
 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean mean mean 

Figure A-12. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

at the source-oriented monitoring sites, 2003-2005. 

A-31
 
 

 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean mean mean 

Figure A-13. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

at the non-source-oriented monitoring sites, 2003-2005. 

A-32
 
 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean mean mean 

Figure A-14. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

at the monitoring sites near previous large emission sources, 2003-2005. 

A-33
 
Concentration (μg/m3 ) 

All sites Source- Not source Previous Urban Urban CBSA Urban CBSA 

oriented oriented source­ (189 sites) (140) pop. ≥ 1M pop. < 1M (60) (129) oriented (9) (91) (49) 

Figure A-15. Distribution of Pb-TSP annual mean concentrations at different categories of sites, 2003-2005

A-34
 
Concentration (μg/m3 ) 

All sites Source- Not source Previous Urban Urban CBSA Urban CBSA 

(189 sites) oriented oriented source- (140) pop. ≥ 1M pop. < 1M (60) (129) oriented (9) (91) (49) 

Figure A-16. Distribution of Pb-TSP maximum quarterly mean concentrations at different categories of si

A-35
 
Concentration (μg/m3 ) 

All sites Source- Not source Previous Urban Urban CBSA Urban CBSA 

oriented oriented source­ (189 sites) (140) pop. ≥ 1M pop. < 1M (60) (129) oriented (9) (91) (49) 

Figure A-17. Distribution of Pb-TSP maximum monthly mean concentrations at different categories of sit

A-36
 
Concentration (μg/m3 ) 

All sites Source- Not source Previous Urban Urban CBSA Urban CBSA 

oriented oriented source­ (189 sites) (140) pop. ≥ 1M pop. < 1M (60) (129) oriented (9) (91) (49) 

Figure A-18. Distribution of Pb-TSP second maximum monthly mean concentrations at different categorie

2005. 

A-37
 
 

mean

median

population weighted mean

mean

median

population weighted mean

mean

median

population weighted mean

mean

median

population weighted mean

0.000 

0.050 

0.100 

0.150 

0.200 

0.250 

0.300 

all sites sourceoriented 

sites 

non sourceoriented 

sites 

previous 

sourceoriented 

urban sites urban sites 

in CBSAs ≥

1M pop. 

urban sites 

in CBSAs 

<1M pop. 

0.000 

0.100 

0.200 

0.300 

0.400 

0.500 

0.600 

all sites sourceoriented 

sites 

non sourceoriented 

sites 

previous 

sourceoriented 

urban sites urban sites 

in CBSAs ≥

1M pop. 

urban sites 

in CBSAs 

<1M pop. 

0.000 

0.100 

0.200 

0.300 

0.400 

0.500 

0.600 

0.700 

0.800 

0.900 

1.000 

all sites sourceoriented 

sites 

non sourceoriented 

sites 

previous 

sourceoriented 

urban sites urban sites 

in CBSAs ≥

1M pop. 

urban sites 

in CBSAs 

<1M pop. 

0.000 

0.100 

0.200 

0.300 

0.400 

0.500 

0.600 

0.700 

all sites sourceoriented 

sites 

non sourceoriented 

sites 

previous 

sourceoriented 

urban sites urban sites 

in CBSAs ≥

1M pop. 

urban sites 

in CBSAs 

<1M pop. 

Annual Mean Max Quarterly Mean 

Max monthly mean 2nd Max Monthly Mean 

mean 

median 

population weighted mean 

mean 

median 

population weighted mean 

mean 

median 

population weighted mean 

mean 

median 

population weighted mean 

Figure A-19. Medians, means, and population-weighted means for 4 site-level statistics. (All 

y-axes are in concentration units of μg/m3). 

Although 60 Pb-TSP monitoring sites met the source oriented classification criteria, that 

number does not correspond to the number of represented or ‘covered’ sources of significant 

emissions. Recall that the emission sliding scale was based on the aggregate emissions within 

one mile of the site (See Section A.2.2.2.3). Thus, instead of having only one significant source 

within a specified range, a site tagged as source-oriented could actually have several nearby 

moderate sized emission sources and/or many nearby small sources. However, the majority of 

the source-oriented sites in this national analysis do have just one nearby significant emission 

source. Furthermore, many of these significant emission sources have multiple Pb-TSP monitors 

A-38
 
 

in the vicinity. For example, the Herculaneum primary Pb smelter has 7 nearby Pb-TSP 

monitoring sites that are included in this national characterization (as well as others that operated 

during 2003-2005 but that did not meet the screening criteria). Thus, the 60 source-oriented sites 

really represent fewer than 60 significant emission sources. For the 60 source-oriented sites, 

there are only 37 unique closest emission sources (i.e., NEI site ID’s). The 60 source-oriented 

sites are located in 29 different counties. 

Although the “previous” source-oriented category contains only a limited number of sites 

(nine) with varied and undetermined circumstances, the distribution statistics for that category 

(for all three metrics) are generally much higher than the non-source-oriented levels; for 

example, the “previous” median maximum quarterly mean of 0.10 μg/m3

 is more than five times 

higher than the comparable non-source-oriented level of 0.02 μg/m3

. 

A.2.2.3.2 Urban Sites 

 The distributions of the site-level values for the four statistical metrics for the set of 140 

sites classified as “urban” are presented in Figure A-20. The distributions for the subset of sites 

(n = 91) located in a CBSA with one million or more population are presented in Figure A-21, 

and for the subset of sites (n=49) located in a CBSA with less than a million population, in 

Figure A-22. In these figures, the boxes depict inter-quartile ranges and medians, whiskers 

depict the 5th and 95th percentiles, and asterisks identify composite averages. Additional points 

on the distributions for these statistics for these three groupings of monitoring sites are given in 

Attachment A-2, Table 2. 

Previously mentioned Figures A-15 through A-18 plot on uniform scales the four 

statistical metrics for these three categories of urban sites. The median and mean values for all 

three concentration metrics are lower for sites in less populated CBSA’s than they are for sites in 

high population CBSA’s. Figure A-23 shows cumulative percentages of urban monitored 

populations (“total” and “under age 5”) associated with each of the three Pb metrics for various 

concentration ranges [≥0.01 μg/m3 (for annual mean only), ≥ 0.05 μg/m3, ≥ 0.20 μg/m3, ≥ 0.50 

μg/m3, and ≥ 1.55 μg/m3]. The phrase “monitored populations” refers to the number of people 

residing in proximity to monitors as described in Section A.2.2.2.4. Figure A-23, for urban 

monitored populations, resembles Figure A-7 (for all monitored populations) because the large 

majority of the monitored population resides in urban areas. 

A-39
 
 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean mean mean 

Figure A-20. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

at the 140 urban monitoring sites, 2003-2005. 

A-40
 
 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean mean mean 

Figure A-21. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

at the 91 urban monitoring sites located in metropolitan areas (CBSAs) with 1 

million or more population, 2003-2005. 

A-41
 
 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean mean mean 

Figure A-22. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

at the 49 urban monitoring sites located in CBSA’s with less than 1 million 

population, 2003-2005. 

A-42
 
26.2% 

6.7% 1.7% 0.6% 

54.8% 

25.5% 

4.4% 

1.1% 

66.6% 

30.8% 

7.8% 2.6% 

56.0% 

21.8% 

6.6% 1.8% 

0% 

10% 20% 30% 40% 50% 60% 70% 

> 0.02 > 0.05 > 0.20 > 0.50 

μg/m3 

percent of monitored population 

annual mean max quarterly mean max monthly mean 2nd max mont

Figure A-23. Percentages of Pb-TSP urban monitored populations residing in areas (represented by 4 diff

exceeding various levels. (Note: Site statistics were rounded to 2 decimal places before comparin

A-43
 
 

A.2.3 Pb-PM10 

The NATTS network operated in 2003-2005 included 23 sites in mostly urban, but some 

rural, areas (Figure A-24). These sites are also operated by 21 state or local host agencies. All 

collect particulate matter as PM10 for toxic metals analysis, typically on a 1 in 6 day sampling 

schedule. Lead in the collected sample is generally quantified via the ICP/MS method. The 

standard operating procedure for metals by ICP/MS is available at: 

http://www.epa.gov/ttn/amtic/airtox.html. These NATTS sites are relatively new, with 2004 

being the first year in which all were operating. The AQS can be accessed at 

http://www.epa.gov/ttn/airs/airsaqs/. 

Figure A-24. Pb-PM10 (NATTS) monitoring sites network. 

A.2.3.1 Data Analysis Details 

Lead-PM10 data collected in 2003-2005 (parameter code 82128, duration ‘7’) were 

extracted from EPA’s AQS on May 22, 2007. Most of the monitors reporting such data are in 

the NATTS network. The same screening criteria utilized for Pb-TSP were implemented for PbPM10 with one variation; because of the limited amount of available data, only three valid 

quarters were required (instead of all four) to make a valid year. Thus the criteria used were: 1) 

a minimum of 10 observations per quarter, 2) for at least three quarters of one calendar year, and 

3) at least 9 months with 4 observations each; all three criteria had to be met for inclusion. Forty 

A-44
 
 

monitors met the three-part criteria. Of these 40 monitors, two were collocated with another 

complete monitor. Only one monitor from each collocated pair (i.e., from each site location) was 

kept in the analysis, specifically the one with highest maximum quarterly mean. Thus, data from 

38 monitors at 38 distinct site locations were actually used. Seven of the 38 sites had complete 

data (i.e., 3 or 4 valid quarters) for each of the three years (2003-2005), 10 sites had only two 

years of complete data; and 21 sites had only one complete year of data. Complete quarters that 

were not part of a complete year were used. Likewise, all complete months were used, even if 

they did not correspond to the complete years. The 38 sites have an average of about 7 complete 

quarters and 19 complete months. 

As with the Pb-TSP data processing, the PM10 data were used “as reported”; that is, 1⁄2 

MDL substitutions were not made for reported concentrations less than or equal to MDL. PbPM10 sites were categorized similarly to the Pb-TSP sites. However, no Pb-PM10 sites fell into 

the source-oriented classification. 25 of the 38 Pb-PM10 sites were classified as urban; 20 of 

those 25 sites are located in CBSA’s of 1 million or more population and the other 5 are located 

in smaller CBSA’s. The 38 Pb-PM10 monitors are listed with various summary and demographic 

data in Attachment A-2, Table 5. 

Three statistical metrics were computed for the Pb-PM10 data: annual means, maximum 

quarterly means, and maximum monthly means. These metrics were calculated at the site level. 

They were calculated only for the overall 3-year period (2003-2005), . Note that the 3-year 

annual mean statistic is actually the average of the annual means for the complete years; thus it is 

the average of three annual means, the average of two annual means, or the only available single 

complete annual mean. The 3-year maximum quarterly mean statistic represents the highest 

quarterly mean of the complete quarters (sites have from three to 12 complete quarters), and the 

3-year maximum monthly mean represents the highest monthly mean of the complete months 

(each site has from nine to 36 complete months). 

A.2.3.2 Current Concentrations 

Monitoring site-level concentrations for each of the 3 statistical metrics (annual mean, 

maximum quarterly mean, and maximum monthly mean) are provided in Attachment A-2, Table 

5. Figure A-26 shows the distributions of the annual means, maximum quarterly averages, and 

maximum monthly means for the 38 Pb-PM10 sites. The national composite average annual 

mean for Pb-PM10 was 0.006 μg/m3 for the 3-year period, 2003-2005; the corresponding median 

annual mean was also 0.006 μg/m3

. The national composite average maximum quarterly mean 

was 0.012 μg/m3

 for 2003-2005 and the corresponding median maximum quarterly mean was 

0.009 μg/m3. The national composite average maximum monthly mean was 0.021 μg/m3 and the 

median maximum monthly mean was 0.014 μg/m3. Figure A-27 shows distribution boxplots for 

A-45
 
 

the 25 urban sites and Figure A-28 shows distribution boxplots for the 20 urban sites located in 

CBSA’s with one million or more population. In these three figures (A-26 through A-28), the 

boxes depict inter-quartile ranges and medians, whiskers depict the 5th and 95th percentiles, and 

asterisks identify composite averages. Additional points on the distribution for these statistics 

are given in Attachment A-2, Table 6. Concentration (μg/m3 ) 

Max quarterly Max monthly Annual mean 

mean mean 

Figure A-25. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

at all 28 monitoring sites, 2003-2005. 

A-46
 
 

Annual mean Max quarterly Max monthly 

Concentration (μg/m3 ) 

mean mean 

Figure A-26. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

at the 25 urban monitoring sites, 2003-2005. 

A-47
 
 

Concentration (μg/m3 ) 

Max quarterly Max monthly Annual mean 

mean mean 

Figure A-27. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

at the urban monitoring sites located in CBSAs of > 1 million population, 

2003-2005. 

Site-level annual means are mapped in Figure A-27 and the corresponding maximum 

quarterly means are mapped in Figure A-28. 

A-48
 
 

Concentration range

(μg/m3)

≥ 0.015

0.010 - 0.015

0.005 - 0.010

< 0.005

Concentration range Concentration range 

(μg/m3) 

≥ 0.015 

0.010 - 0.015 

0.005 - 0.010 

< 0.005 

Figure A-28. Pb-PM10 annual means (for all sites), 2003-2005. 

A-49
 
 

 

Concentration range

(μg/m3)

≥ 0.015

0.010 - 0.015

0.005 - 0.010

< 0.005

Concentration range Concentration range 

(μg/m3) 

≥ 0.015 

0.010 - 0.015 

0.005 - 0.010 

< 0.005 

Figure A-29. Pb-PM10 maximum quarterly means (for all sites), 2003-2005 

A.2.4 Pb-PM2.5 

Two networks measure Pb in PM2.5, the EPA CSN and the IMPROVE network. The 

CSN consists of 54 long-term trends sites (commonly referred to as the Speciation Trends 

Network or STN sites) and about 150 supplemental sites, all operated by state and local 

monitoring agencies. Most STN sites operate on a 1 in 3 day sampling schedule, while most 

supplemental sites operate on a 1 in 6 day sampling schedule. All sites in the CSN network 

determine the Pb concentrations in PM2.5 samples and, as such, do not measure Pb in the size 

fraction >2.5 μm in diameter. Lead is quantified via the XRF method. The standard operating 

procedure for metals by XRF is available at: 

http://www.epa.gov/ttnamti1/files/ambient/pm25/spec/xrfsop.pdf. Data are managed through the 

AQS. 

A-50
 
 

The IMPROVE network is administered by the National Park Service, largely with 

funding by EPA, on behalf of federal land management agencies and state air agencies that use 

the data to track trends in rural visibility. Lead in the PM2.5 is quantified via the XRF method, as 

in the CSN. Data are managed and made accessible mainly through the VIEWS website 

(http://vista.cira.colostate.edu/views/), but also are available via the AQS. Samplers are operated 

by several different federal, state, and tribal host agencies on the same 1 in 3 day schedule as the 

STN. 

The locations of the CSN are shown in Figure A-30. Nearly all of the CSN sites are in 

urban areas, often at the location of highest known PM2.5 concentrations. The first CSN sites 

generally began operation around 2000. 

Figure A-30. Pb-PM2.5 (CSN) monitoring sites. 

In the IMPROVE network, PM2.5 monitors are placed in “Class I” areas (including 

National Parks and wilderness areas) and are mostly in rural locations (Figure A-31). The oldest 

of these sites began operation in 1988, while many others began in the mid 1990s. There are 110 

formally designated IMPROVE sites, which are located in or near national parks and other Class 

I visibility areas, virtually all of these being rural. Approximately 80 additional sites at various 

urban and rural locations, requested and funded by various parties, are also informally treated as 

part of the network. 

A-51
 
 

Figure A-31. Pb-PM2.5 (IMPROVE) monitoring sites. 

A.2.4.1 Data Analysis Details 

2003-2005 Pb-PM2.5 data (parameter code 88128, duration ‘7’) were extracted from 

EPA’s AQS on May 22, 2007. Data generated with IMPROVE collection/analysis methods 

were excluded from the central focus of this national characterization on the basis that most of 

the monitors utilizing those methods are located in rural or remote areas distant from both Pb 

sources and large populations. Most remaining data are associated with EPA’s CSN program. 

The same screening criteria utilized for Pb-PM10 were also implemented for Pb-PM2.5: 1) 

a minimum of 10 observations per quarter, 2) for at least 3 quarters of one calendar year, and 3) 

at least 9 months with 4 observations each; all three criteria had to be met for inclusion. 278 

monitors met the data completeness criteria. Of these 278 monitors, 7 were collocated with 

another complete monitor. Only one monitor from each collocated pair (i.e., from each site 

location) was kept in the analysis, specifically the one with highest maximum quarterly mean. 

Thus, data from 271 monitors at 271 distinct locations were actually used. 192 of the 271 sites 

had complete data (i.e., 3 or 4 valid quarters) for each of the three years (2003-2005), 40 sites 

had only two years of complete data; and 39 sites had only one complete year of data. Complete 

A-52
 
 

 

quarters that were not part of a complete year were used. Likewise, all complete months were 

used, even if they did not correspond to the complete years. The 38 sites have an average of 

about 10 complete quarters and 29 complete months Pb-PM2.5 data were used “as reported”; 1⁄2 

MDL substitutions were not made for reported concentrations less than or equal MDL. 

PM2.5 sites were categorized similarly to the sites in the other size cuts. Only 8 Pb-PM2.5 

sites were classified as source-oriented. 216 of the 271 Pb-PM2.5 sites were classified as urban; 

99 of those 216 sites are located in CBSAs of 1 million or more population and the other 117 are 

located in smaller CBSAs. The 271 Pb-PM2.5 monitors are listed with various summary and 

demographic data in Attachment A-2, Table 7. 

A.2.4.2 Current Concentrations 

The site-level Pb-PM2.5 concentrations for each of the three statistics (annual mean, 

maximum quarterly mean, and maximum monthly mean) during the three-year period, 2003­

2005, are shown in Attachment A-2, Table 7. Figure A-33 shows the distributions of the three 

statistical metrics for the 271 Pb-PM2.5 sites; the boxes depict inter-quartile ranges and medians, 

whiskers depict the 5th and 95th percentiles, and asterisks identify composite averages. 

Additional points on the distribution for these statistics are given in Attachment A-2, Table 8. 

The national composite average annual mean was 0.004 μg/m3 for the 3-year period, 2003-2005; 

the corresponding median annual mean was 0.003 μg/m3. The national composite average 

maximum quarterly mean was 0.008 μg/m3 for 2003-2005 and the corresponding median 

maximum quarterly mean was 0.005 μg/m3

. The national composite average maximum monthly 

mean was 0.013 μg/m3

 and the median maximum monthly mean was 0.007 μg/m3. As also 

shown in Attachment A-2, Table 8, the median and mean site-level annual mean and maximum 

quarterly mean levels for source-oriented sites were approximately double those for the nonsource-oriented sites. Figure A-34 maps the annual means for Pb-PM2.5 sites. 

A-53
 
 

Concentration (μg/m3 ) 

Max quarterly Max monthly Annual mean 

mean mean 

Figure A-32. Distribution of Pb-PM2.5 concentrations (represented by 3 different statistics) 

at all 271 monitoring sites, 2003-2005. 

A-54
 
 

Concentration range

(μg/m3)

≥ 0.015

0.010 - 0.015

0.005 - 0.010

< 0.005

Concentration range Concentration range 

(μg/m3) 

≥ 0.015 

0.010 - 0.015 

0.005 - 0.010 

< 0.005 

Figure A-33. Pb-PM2.5 annual means (for all sites), 2003-2005. 

A-55
 
 

 

 

 

 

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A-56
 
 

 

 

 

 

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A-57
 
 

 

 

 

Attachment A-1. Largest Stationary Source Categories for Pb in the 2002 NEI. 

Boilers and Process Heaters 

Materials including coal, oil, natural gas (or, at times, other substances such as wood and petroleum coke) are 

burned in boilers and process heaters to produce steam. With regard to boilers, the steam is used to produce 

electricity or provide heat, while process heaters are used in industrial processes. Lead is present naturally in the 

fuel and is emitted to air following combustion. The extent of emissions depends on the concentration of Pb in the 

fuel, the quantity of fuel burned, and PM control devices applied. 

Industrial, commercial and institutional boilers and process heaters are used at a wide variety of facilities (e.g., 

refineries, chemical and manufacturing plants, etc), as well as in a “stand alone” mode to provide heat for large 

building complexes. Consequently, there are thousands of these sources throughout the country, generally located in 

urban areas, and they range widely in size. Most coal-fired industrial boilers emit about 0.06 tpy, with the larger ones 

emitting about 0.07 tpy due to the use of high efficiency particulate matter (PM) control devices (ERG, 2002a). [ 

Among utility boilers, coal-fired boilers have the highest Pb emissions, oil-fired utility plants emit somewhat lower 

amounts, and gas-fired plants emit very low levels of Pb (USEPA, 1998). There are approximately 1,300 coal-fired 

electric utility boilers in the U.S. ranging in size from 25 to approximately 1,400 MWe. Based on emission factor 

calculations, a 325 MWe coal-fired boiler would be expected to emit approximately 0.021 tpy Pb, based on the use of 

an electrostatic precipitator for PM control (USEPA, 1998). Although there are exceptions, coal-fired utility boilers 

tend to be located in non-urban areas. 

Iron and Steel Foundries 

Iron and steel foundries melt scrap, ingot, and other forms of iron and steel and pour the molten metal into molds for 

particular products. While located in 44 of the lower 48 states (in both cities and rural areas), the 650 existing 

foundries in the U.S., are most heavily concentrated in the Midwest (IN, IL, OH, MI, WI, and MN) - roughly 40% of 

foundries with almost 60% of U.S. production (USEPA, 2002a). Most are iron foundries operated by manufacturers 

of automobiles and large industrial equipment and their suppliers. The largest Pb emission sources at iron foundries 

are large furnaces, emissions from which generally range from about 0.3 to 3 tpy (generally released at heights of 25-

30 feet), depending on the throughput of the furnace, the type and operating characteristics of the emission control 

system, and the Pb content in the metal charged to the furnace. Regulations promulgated in 2004 are projected to 

yield emissions reductions of approximately 25 tpy for this category (USEPA, 2004b). 

Hazardous Waste Incineration/ Combustion Facilities 

Hazardous waste combustors include hazardous waste incinerators, as well as boilers and industrial furnaces that 

burn hazardous waste for energy or material recovery (e.g., production of halogen acid from the combustion of 

chlorine-bearing materials). Industrial furnaces burning hazardous waste include cement kilns, lightweight aggregate 

kilns, and hydrochloric acid production furnaces. Lead is a trace contaminant in the hazardous waste, fossil fuels, 

and raw materials used in the combustors. In 2005, there were nearly 270 hazardous waste combustor sources in 

operation in the United States (70 FR at 59530), with approximately 40 percent of them in the states of Texas and 

Louisiana. As a result of emissions standards promulgated in 2005, EPA estimates that cumulative Pb emissions 

from hazardous waste combustors will be reduced to approximately 4.0 tons per year by the compliance date in 2008 

(USEPA, 2005), a 95% reduction from 1990 levels. 

Primary Lead Smelting 

At primary Pb smelters, Pb-bearing ore concentrates are smelted to produce Pb metal. Lead is emitted from primary 

Pb smelters as process emissions, process fugitive emissions, and fugitive dust emissions (CD, p. 2-21). U.S. EPA 

promulgated a national emissions standard in 1999 for this category which includes an emissions limit for Pb (U.S. 

EPA 1999a). In the 1990s, there were three operating primary Pb smelters in the U.S: one in Montana and two in 

Missouri, emitting an estimated total of about 260 tpy Pb. In 2002, there were two in operation (estimated emissions 

shown in Table A-1); one of the two had less than 1 tpy Pb emissions. As of 2004, there was only one operating 

primary Pb smelter in the U.S., located in Missouri with estimated total emissions of about 28 tpy in year 2005 (CD, p. 

2-20). Thus, total Pb emissions from this category have decreased about 90% since 1990. 

Secondary Lead Smelting 

Secondary Pb smelters are recycling facilities that use blast, rotary, reverberatory, and/or electric furnaces to recover 

Pb metal from Pb-bearing scrap materials, primarily Pb-acid batteries. This category does not include remelters and 

refiners or primary Pb smelters. At secondary Pb smelters, Pb may be emitted from process emissions, process 

fugitive emissions and fugitive dust emissions from wind or mechanically induced entrainment of dust from stockpile 

and plant yards and roadways. In 1995, U.S. EPA promulgated a national emissions standard for this category which 

includes an emissions limit for Pb (USEPA, 1995). In 2002, there were 15 secondary smelters operating in 11 states, 

most of which are in the eastern half of the U.S. Estimates of total emissions (process and fugitive) for individual 

A-58
 
 

 

 

 

 

 

 

Attachment A-1. Largest Stationary Source Categories for Pb in the 2002 NEI. 

facilities as of 2002 range between 1 and 4 tpy, with one facility having total lead emissions of about 12 tpy (USEPA, 

2007a; EC/R, 2006). Total Pb emissions (tpy) for this category decreased about 60% from 1990 to 2002. 

Military Installations 

This source category includes sources that are military facilities. The types of sources contributing to Pb emissions 

from this category include, among others, rocket and engine test facilities, ammunition manufacturing, weapons 

testing, waste combustion and boilers. While there are over 300 military facilities in the NEI, only 10% emit over 0.1 

tpy of Pb and only 3% emit over 1 tpy. The two largest facilities (listed in Table A-4) are a missile ammunition 

production plant and a weapons testing facility and these two facilities account for over 75% of the category 

emissions. 

Mining 

This category includes various mining facilities that extract ore from the earth containing Pb, zinc, copper and/or other 

non-ferrous metals (such as gold and silver), and/or non-metallic minerals such as talc and coal. This category does 

not include the smelting or refining of the metals and minerals. These facilities produce ore concentrates (such as 

Pb, zinc, and copper concentrates) that are transported to other facilities where further processes, such as smelting 

and refining take place. The 2002 NEI indicates that there are 3 mining facilities in the U.S. emitting greater than 0.5 

tpy Pb, one of which emits more than 5 tpy. This facility is in Missouri and produces Pb, zinc, and copper 

concentrates that are shipped to customers for further processing. 

Integrated Iron & Steel Manufacturing 

Integrated iron and steel manufacturing includes facilities engaged in the production of steel from iron ore. The 

processes include sinter plants, blast furnaces that produce iron, and basic oxygen process furnaces that produce 

steel, as well as several ancillary processes including hot metal transfer, desulfurization, slag skimming, and ladle 

metallurgy. There are currently 17 facilities in this source category each of whom emit from 2 to 8 tpy of Pb. Stack 

heights range from 30 - 50 feet. The facilities are located in 9 states, mostly in the Midwest (USEPA, 2003a). EPA 

promulgated a national emissions standard in 2003 for this category which includes an emissions limit for PM (as a 

surrogate for metal HAP, including Pb) (USEPA, 2003b). 

Municipal Waste Combustors: Small & Large 

Municipal waste combustors (MWCs) incinerate municipal or municipal-type solid waste. The amount of municipal 

waste incinerated (about 14% of U.S. municipal waste) has remained stable over the past decade. The amount of Pb 

emitted from municipal waste combustors depends on the amount of Pb in the refuse, with typical sources including 

paper, inks, cans and other metal scrap and plastics (CD, pp. 2-35 to 2-36). As of 2005, MACT standards were 

completed for all existing and new municipal waste incineration units, resulting in nationwide Pb emissions of less 

than 10 tons per year, roughly a 97% reduction since 1990. There are currently 66 large MWC plants and 26 small 

MWC plants operating nationally, with individual large MWC plants projected to emit less than 0.1 tpy Pb, and small 

MWC plants less than 0.02 tpy Pb (ERG, 2002b,c; Stevenson, 2002). However, there are a few MWC facilities that 

emit about 2 tons per year. 

Pressed and Blown Glass and Glassware Manufacturing 

This category includes manufacturers of flat glass, glass containers, and other pressed and blown glass and 

glassware, with Pb emitted primarily from the pressed and blown glass industry sector. Some container plants also 

make a leaded-glass product, but this is not typical of container glass plants. Lead may also be added to flat glass 

for use in microwaves and flat-screen TVs. Emissions from individual facilities may range from a few pounds per 

year up to several tons per year depending on Pb content of their glass and the level of control. Furnace stacks for 

these facilities are typically of the order of 35-60 feet high. As of 2005, about 22 tons of Pb is emitted from glass 

manufacturing annually in the U.S. Glass plants are located in 35 States (RTI, 2006). U.S. EPA is currently 

developing an emissions regulation for this category, scheduled for promulgation in December 2007. 

Electric Arc Furnace Steelmaking 

In the steelmaking process that uses an electric arc furnace (EAF), the primary raw material is scrap metal, which is 

melted and refined using electric energy. Since scrap metal is used instead of molten iron, there are no cokemaking 

or ironmaking operations associated with steel production that use an EAF. There are currently 141 EAFs at 93 

facilities, with estimated total nationwide Pb and Pb compound emissions of approximately 80 tons, and the average 

per facility is approximately 0.75 tpy. Stack heights range from heights of 30 - 50 feet. The facilities are located in 32 

states; mostly in the northeast and Midwest, with ninety percent of the facilities located in urban areas. This 

information is drawn from multiple sources (Lehigh, 1982; Calspan, 1977; RTI, 2005). U.S. EPA is developing a 

hazardous air pollutant (HAP) emissions regulation for this category, scheduled for promulgation in December 2007. 

Lead Acid Battery Manufacturing 

The Pb acid battery manufacturing category includes establishments primarily engaged in manufacturing storage 

batteries from Pb alloy ingots and Pb oxide. The Pb oxide may be prepared by the battery manufacturer or may be 

purchased from a supplier. There has been a general decline in number of facilities, with 58 facilities currently in 

operation (data obtained from the Battery Council International (BCI)). The estimated range of facility-specific Pb and 

A-59
 
 

 

 

Attachment A-1. Largest Stationary Source Categories for Pb in the 2002 NEI. 

Pb compound emissions is from 1 x 10-5 to just below 10 tpy, with an average of about 0.5 tpy. The facilities are 

located in urban and rural areas of 23 states and Puerto Rico (2002 NEI). 

Primary Copper Smelting 

This source category includes all industries which refine copper concentrate from mined ore to anode grade copper, 

using pyrometallic processes. Seven primary copper smelters are currently operating in the U.S. Six of these seven 

smelters use conventional smelter technology which includes batch converter furnaces for the conversion of matte 

grade copper to blister copper, while the seventh uses a continuous flash furnace. Two of the three largest smelters 

are located in AZ, and the third is in Utah. The largest facility emitted an estimated 12.8 tons Pb in 2002, while 

emissions for the other two large facilities are estimated to be between 0.1 to 5 tpy. No other source in this category 

emits more than 0.1 tpy. In 2002, U.S. EPA promulgated a national emissions standard, including limits for PM (as a 

surrogate for metal HAP, including Pb), for this category (USEPA, 2002c). 

Portland Cement Manufacturing 

Portland cement manufacturing is an energy intensive process in which cement is made by grinding and heating a 

mixture of raw materials such as limestone, clay, sand, and iron ore in a rotary kiln (a large furnace fueled by coal, oil, 

gas, coke and/or various waste materials such as tires). Lead, a trace contaminant both of the raw materials and 

some fuel materials (e.g., coal, tires), is emitted with particulate material from the kiln stacks, which range in height 

from approximately 10 meters to more than 100 meters. Relatively smaller Pb emissions occur from grinding, 

cooling, and materials handling steps in the manufacturing process. These facilities are generally located in areas 

with limestone deposits and in rural areas or near small towns. The largest numbers of facilities are in Pennsylvania 

and California, although a significant percentage of facilities are in the Midwest. As of 2004, there were 107 Portland 

cement plants in the U.S. (O’Hare, 2006), with all but three reporting less than 1 tpy of Pb emissions. The highest 

estimated Pb emissions for a facility in the 2002 NEI is 5.4 tpy. In 1999, U.S. EPA promulgated a national emissions 

standard, including a limit for PM (as a surrogate for metal HAP, including Pb), for this category (USEPA, 1999b). 

A-60
 
 

Attachment A-2. 

Additional Details of Air Quality Analyses 

A-61
 
Attachment A-2

Table 1a. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

3-year data capture 

comp. 

years 

comp. 

qtrs 

comp. 

months 

annual 

mean 

qu

011090003 2 31.79056 -85.97917 AL Pike Troy, AL 29,605 461 31 4.5 1 2 10 31 0.6875 1

011090006 1 31.79278 -85.98056 AL Pike Troy, AL 29,605 461 31 4.5 1 2 10 31 0.3808 0

060250005 1 32.67611 -115.48333 CA Imperial El Centro, CA 142,361 16,385 1,290 1 0.0 2 11 34 0.0175 0

060371103 2 34.06659 -118.22688 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 29,329 1,633 1 0.3 3 12 36 0.0225 0

060371301 1 33.92899 -118.21071 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 47,423 5,066 1 0.0 3 12 34 0.0188 0060371601 1 34.01407 -118.06056 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 13,333 1,066 1 0.0 2 9 27 0.0186 0060374002 2 33.82376 -118.18921 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 20,131 1,232 1 0.0 3 12 36 0.0149 0060374004 2 33.79236 -118.17533 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 61,497 6,697 1 0.0 2 10 28 0.0112 0060375001 1 33.92288 -118.37026 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 19,148 1,680 1 0.0 1 5 14 0.0222 0060375005 1 33.95080 -118.43043 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 33,968 1,358 1 0.0 1 7 17 0.0057 0060651003 2 33.94603 -117.40063 CA Riverside Riverside-San Bernardino-Ontario, C 3,254,821 16,320 1,278 1 0.0 3 12 36 0.0097 0

060658001 3 33.99958 -117.41601 CA Riverside Riverside-San Bernardino-Ontario, C 3,254,821 16,247 1,678 1 0.0 3 12 35 0.0121 0060711004 1 34.10374 -117.62914 CA San Bernardino Riverside-San Bernardino-Ontario, C 3,254,821 18,777 1,578 1 0.0 3 12 35 0.0142 0060719004 1 34.10688 -117.27411 CA San Bernardino Riverside-San Bernardino-Ontario, C 3,254,821 14,861 1,755 1 0.0 3 12 36 0.0186 0080010005 1 39.79601 -104.97754 CO Adams Denver-Aurora, CO 2,157,756 2,025 183 1.9 1 3 12 36 0.1697 0

080010006 1 39.82574 -104.93699 CO Adams Denver-Aurora, CO 2,157,756 3,313 256 1 0.0 3 12 31 0.0304 0

080310002 4 39.75119 -104.98762 CO Denver Denver-Aurora, CO 2,157,756 22,019 974 1 0.0 3 12 34 0.0315 0

080310015 1 39.70012 -104.98714 CO Denver Denver-Aurora, CO 2,157,756 14,438 809 1 0.0 1 7 20 0.0153 0

080410011 1 38.83139 -104.82778 CO El Paso Colorado Springs, CO 537,484 10,581 552 1 0.0 3 12 35 0.0156 0

080650001 1 39.24778 -106.29139 CO Lake Edwards, CO 49,471 5,903 361 1 0.0 2 11 28 0.0165 0

100010002 1 38.98472 -75.55556 DE Kent Dover, DE 126,697 352 22 0.0 1 4 12 0.0033 0

100031007 1 39.55111 -75.73083 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 2,041 209 0.0 1 4 10 0.0039 0

100031008 1 39.57778 -75.61111 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 3,170 160 0.0 1 4 9 0.0052 0

100032004 1 39.73944 -75.55806 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 34,053 2,649 1 0.0 1 4 11 0.0097 0

100051002 1 38.64444 -75.61306 DE Sussex Seaford, DE 156,638 5,450 390 1 0.0 1 4 12 0.0033 0120571065 5 27.89222 -82.53861 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 14,463 612 1 0.0 1 4 12 0.0049 0120571066 1 27.96028 -82.38250 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 5,793 465 1 1.3 1 3 12 35 0.5835 1120571073 1 27.96583 -82.37944 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 4,541 340 1 1.3 1 3 12 35 0.1934 0120571075 5 28.05000 -82.37806 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 10,691 490 1 0.0 1 4 12 0.0041 0121030004 5 27.94639 -82.73194 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 13,048 557 1 0.0 1 4 12 0.0028 0

121030018 5 27.78556 -82.74000 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 11,289 571 1 0.0 2 8 24 0.0042 0121033005 1 27.87583 -82.69639 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 2,151 58 1 0.0 3 12 36 0.0006 0

130890003 2 33.69833 -84.27333 GA DeKalb Atlanta-Sandy Springs-Marietta, GA 4,247,981 7,888 663 1 0.0 3 12 36 0.1000 0

132150011 1 32.43083 -84.93167 GA Muscogee Columbus, GA-AL 281,768 10,871 1,037 1 0.3 1 1 1 10 34 0.1000 0

150032004 1 21.39667 -157.97167 HI Honolulu Honolulu, HI 876,156 23,622 1,207 1 0.1 3 12 35 0.0014 0

170310001 1 41.67275 -87.73246 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 13,648 971 1 0.0 3 12 35 0.0143 0

170310022 2 41.68920 -87.53932 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 22,040 1,708 1 0.2 3 12 36 0.0270 0

170310026 1 41.87333 -87.64507 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 28,739 1,203 1 0.0 3 12 34 0.0405 0170310052 1 41.96743 -87.74982 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 42,187 2,877 1 0.0 3 12 32 0.0214 0170313103 1 41.96528 -87.87639 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 10,302 670 1 0.0 3 12 34 0.0149 0

170313301 1 41.78278 -87.80528 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 23,749 1,678 1 0.0 3 12 35 0.0308 0

170314201 1 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 6,070 303 1 0.0 2 8 24 0.0113 0170316003 1 41.87194 -87.82611 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 14,862 1,071 1 0.0 3 12 32 0.0303 0171170002 2 39.39804 -89.80975 IL Macoupin St. Louis, MO-IL 2,721,491 40 2 0.0 3 12 36 0.0103 0

171190010 1 38.69417 -90.15361 IL Madison St. Louis, MO-IL 2,721,491 8,014 529 1 1.3 1 3 12 34 0.0768 0

171193007 2 38.86056 -90.10583 IL Madison St. Louis, MO-IL 2,721,491 5,397 360 1 0.1 3 12 36 0.0150 0

171430037 1 40.69889 -89.58474 IL Peoria Peoria, IL 366,899 12,643 1,109 1 0.0 3 12 35 0.0137 0

171630010 2 38.61222 -90.16028 IL St. Clair St. Louis, MO-IL 2,721,491 3,512 430 1 0.3 3 12 36 0.0433 0

180350008 1 40.15806 -85.42111 IN Delaware Muncie, IN 118,769 2,108 104 1 0.0 1 3 12 34 0.2944 0

180350009 2 40.15944 -85.41556 IN Delaware Muncie, IN 118,769 980 82 0.0 1 1 6 13 2.6732 4

180890023 1 41.65278 -87.43944 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 5,959 603 1 6.5 1 3 12 34 0.0389 0

180892008 1 41.63944 -87.49361 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 7,144 612 1 0.0 3 12 33 0.0219 0180892011 2 41.59250 -87.47194 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 9,815 729 1 0.0 3 12 34 0.0368 0

A-6

2
 
Attachment A-2

Table 1a. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

3-year data capture 

comp. 

years 

comp. 

qtrs 

comp. 

months 

annual 

mean 

qu

180930004 1 38.88944 -86.55194 IN Lawrence Bedford, IN 45,922 393 32 0.0 2 10 26 0.0270 0

180970063 1 39.76083 -86.29722 IN Marion Indianapolis-Carmel, IN 1,525,104 12,176 875 1 1.7 1 3 12 36 0.0320 0

180970076 1 39.75889 -86.28972 IN Marion Indianapolis-Carmel, IN 1,525,104 9,171 602 1 1.7 1 3 12 35 0.0142 0

180970078 1 39.81110 -86.11447 IN Marion Indianapolis-Carmel, IN 1,525,104 14,196 1,175 1 0.0 2 11 33 0.0108 0

181010001 1 38.89028 -86.76083 IN Martin 84 5 0.0 3 12 34 0.0272 0181630006 2 37.97167 -87.56722 IN Vanderburgh Evansville, IN-KY 342,815 13,666 817 1 0.0 3 12 33 0.0065 0

260490021 4 43.04722 -83.67028 MI Genesee Flint, MI 436,141 9,889 994 1 0.0 3 12 36 0.0100 0261130001 1 44.31056 -84.89194 MI Missaukee Cadillac, MI 44,962 58 3 0.0 3 12 33 0.0032 0261630001 2 42.22861 -83.20833 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 14,329 798 1 0.0 3 12 35 0.0087 0261630005 1 42.26722 -83.13222 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 11,314 923 1 0.2 2 11 34 0.0166 0261630015 4 42.30278 -83.10667 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 17,729 1,771 1 0.0 3 12 36 0.0178 0261630019 1 42.43083 -83.00028 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 28,362 2,628 1 0.0 3 12 34 0.0103 0

261630027 1 42.29222 -83.10694 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 6,024 516 1 1.1 1 1 5 14 0.0256 0

261630033 2 42.30667 -83.14889 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 17,402 1,843 1 0.5 3 12 34 0.0236 0

270370001 1 44.83333 -93.11500 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 5,074 404 1 3.2 1 2 8 24 0.0781 0270370020 1 44.76535 -93.03248 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 162 7 0.0 3 12 32 0.0051 0270370421 1 44.77720 -93.04097 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 478 24 0.0 1 9 27 0.0037 0270370423 1 44.77500 -93.06278 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 886 83 0.0 3 12 34 0.0018 0270370442 1 44.74036 -93.00556 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 168 11 0.3 2 10 28 0.0027 0

270530050 1 45.00123 -93.26712 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 16,318 923 1 0.0 3 12 35 0.0051 0270530963 1 44.95540 -93.25827 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 46,218 3,929 1 0.2 3 12 36 0.0039 0270530964 1 44.88855 -93.19538 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 209 0 1 0.0 1 4 14 0.0045 0

270530965 1 45.00448 -93.24005 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 19,106 1,095 1 0.4 3 12 35 0.0039 0270530966 1 44.98133 -93.26615 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 17,156 439 1 0.0 3 12 35 0.0047 0270530967 1 44.99646 -93.23488 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 14,621 580 1 0.4 1 1 7 20 0.0075 0

270530968 1 44.89301 -93.23323 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 11,243 789 1 0.0 1 6 18 0.0019 0270531007 1 45.04182 -93.29873 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 14,889 1,118 1 0.0 3 12 35 0.0026 0271231003 1 44.96322 -93.19023 MN Ramsey Minneapolis-St. Paul-Bloomington, 2,968,806 9,247 474 1 0.1 3 12 33 0.0065 0271377001 1 47.52336 -92.53631 MN St. Louis Duluth, MN-WI 275,486 8,942 428 1 0.1 3 12 33 0.0047 0271377555 1 46.73264 -92.16337 MN St. Louis Duluth, MN-WI 275,486 4,527 287 1 0.0 3 12 34 0.0014 0290930016 1 37.62528 -91.12917 MO Iron 58 4 0.0 1 3 12 34 0.6918 1

290930021 1 37.65417 -91.13056 MO Iron 58 4 0.0 1 3 12 36 0.5460 0

290930023 1 37.50333 -90.69556 MO Iron 138 7 0.0 1 1 *# 1 6 18 0.2291 0290930024 1 37.47972 -90.69028 MO Iron 32 2 0.0 1 1 *# 1 6 18 0.5898 0

290930025 1 37.51056 -90.69750 MO Iron 138 7 0.0 1 1 *# 1 5 14 0.2477 0290930026 1 37.45917 -90.68639 MO Iron 32 2 0.0 1 1 *# 1 5 15 0.2266 0

290930027 1 37.48611 -90.69000 MO Iron 32 2 0.0 1 1 * 3 12 33 0.2678 0

290930029 1 37.47167 -90.68944 MO Iron 32 2 0.0 1 1 * 3 12 32 0.2824 0

290930030 1 37.46639 -90.69000 MO Iron 32 2 0.0 1 1 *# 1 6 18 0.1665 0

290990004 1 38.26330 -90.37850 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 2 8 24 1.1300 1290990005 3 38.26722 -90.37944 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 3 12 36 0.3711 0290990008 1 38.26194 -90.39417 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 58.8 1 1 10 31 0.0910 0290990009 1 38.28444 -90.38194 MO Jefferson St. Louis, MO-IL 2,721,491 9,804 820 1 0.0 1 2 11 31 0.0957 0290990010 1 38.24110 -90.37680 MO Jefferson St. Louis, MO-IL 2,721,491 2,799 215 1 0.0 1 2 11 34 0.0388 0

290990011 1 38.26820 -90.37380 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 3 12 36 0.4778 1290990013 1 38.27361 -90.38000 MO Jefferson St. Louis, MO-IL 2,721,491 3,570 318 1 58.8 1 3 12 35 0.2633 0290990015 1 38.26167 -90.37972 MO Jefferson St. Louis, MO-IL 2,721,491 1,988 178 1 58.8 1 3 12 36 1.4501 1291892003 1 38.64972 -90.35056 MO St. Louis St. Louis, MO-IL 2,721,491 12,303 512 1 0.0 2 11 34 0.0063 0

295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL 2,721,491 9,140 783 1 0.0 1 4 11 0.0134 0340231003 1 40.47222 -74.47139 NJ Middlesex New York-Northern New Jersey-Lon 18,323,002 13,850 1,124 1 1.7 1 2 10 27 0.0403 0

360470122 1 40.71980 -73.94788 NY Kings New York-Northern New Jersey-Lon 18,323,002 92,660 5,785 1 0.1 2 9 22 0.0276 0360632008 1 43.08216 -79.00099 NY Niagara Buffalo-Niagra Falls, NY Metropoli 1,170,111 6,795 386 1 0.0 1 4 12 0.0054 0360713001 1 41.46107 -74.36343 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 1,481 99 1.8 1 2 9 26 0.0606 0

A63
 
Attachment A-2

Table 1a. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

3-year data capture 

comp. 

years 

comp. 

qtrs 

comp. 

months 

annual 

mean 

qu

360713002 1 41.45887 -74.35392 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 1,257 86 1.8 1 2 9 26 0.1257 0

360713004 1 41.47633 -74.36827 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 6,816 434 1 0.0 2 9 26 0.0305 0360850067 1 40.59733 -74.12619 NY Richmond New York-Northern New Jersey-Lon 18,323,002 21,834 1,373 0.0 1 4 11 0.0059 0

390170015 2 39.48990 -84.36407 OH Butler Cincinnati-Middletown, OH-KY-IN 2,009,632 4,668 373 1 0.0 2 8 24 0.0107 0390290019 1 40.63111 -80.54694 OH Columbiana East Liverpool-Salem, OH 112,075 5,385 322 1 0.0 3 12 36 0.0144 0

390290020 1 40.63972 -80.52389 OH Columbiana East Liverpool-Salem, OH 112,075 6,414 354 1 0.0 3 12 36 0.0158 0

390290022 1 40.63500 -80.54667 OH Columbiana East Liverpool-Salem, OH 112,075 3,318 202 1 0.0 3 12 36 0.0139 0

390350038 1 41.47694 -81.68194 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 7,329 585 1 0.1 3 12 36 0.0205 0

390350042 1 41.48222 -81.70889 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 18,776 1,575 1 0.0 2 11 35 0.0169 0

390350049 1 41.44667 -81.65111 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 9,720 758 1 0.0 1 1 3 12 36 0.1214 0390350050 1 41.44250 -81.64917 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 8,771 695 1 0.0 1 1 3 12 36 0.0362 0390350061 2 41.47506 -81.67596 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 6,141 444 1 0.3 1 1 3 12 36 0.0477 0390350069 1 41.51918 -81.63794 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 23,566 1,961 1 0.1 1 6 35 0.0170 0390490025 1 39.92806 -82.98111 OH Franklin Columbus, OH 1,612,694 15,220 1,226 1 0.6 1 3 12 36 0.0114 0390510001 1 41.57528 -83.99639 OH Fulton Toledo, OH 659,188 1,503 110 1 0.3 1 2 11 36 0.1332 0

390910003 1 40.34306 -83.75500 OH Logan Bellefontaine, OH 46,005 1,536 108 1 0.1 3 12 36 0.0922 0

390910005 1 40.34278 -83.76028 OH Logan Bellefontaine, OH 46,005 1,546 126 1 0.1 1 3 12 36 0.1058 0

390910006 1 40.34111 -83.75778 OH Logan Bellefontaine, OH 46,005 1,217 87 1 0.1 1 3 12 36 0.1578 0

390910007 1 40.34472 -83.75444 OH Logan Bellefontaine, OH 46,005 2,156 185 1 0.1 3 12 36 0.1497 0

391670008 1 39.43361 -81.50250 OH Washington Parkersburg-Marietta, WV-OH 164,624 1,947 114 0.0 3 12 36 0.0054 0

391670009 1 39.37696 -81.53730 OH Washington Parkersburg-Marietta, WV-OH 164,624 314 21 0.0 1 5 14 0.0073 0401159005 2 36.98580 -94.84920 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0412 0

401159006 1 36.98460 -94.82490 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0316 0401159007 1 36.97190 -94.85180 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0505 0401159008 1 36.97160 -94.82500 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0312 0410510246 7 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR- 1,927,881 24,303 1,771 1 0.0 1 4 11 0.0081 0

420030002 1 40.50056 -80.07194 PA Allegheny Pittsburgh, PA 2,431,087 19,559 1,045 1 0.0 3 12 30 0.0096 0

420032001 1 40.39667 -79.86361 PA Allegheny Pittsburgh, PA 2,431,087 10,120 769 1 0.2 3 12 35 0.0396 0420070505 1 40.68500 -80.32500 PA Beaver Pittsburgh, PA 2,431,087 6,497 218 1 0.0 2 11 31 0.0563 0420110005 1 40.46630 -75.75890 PA Berks Reading, PA 373,638 692 44 4.8 1 2 11 33 0.0618 0420110717 1 40.47667 -75.75917 PA Berks Reading, PA 373,638 575 39 1 4.8 1 2 11 30 0.1301 0420111717 1 40.37722 -75.91444 PA Berks Reading, PA 373,638 7,376 390 1 2.1 1 3 12 33 0.2570 0

420210808 1 40.34806 -78.88278 PA Cambria Johnstown, PA 152,598 2,606 115 1 0.0 3 12 36 0.0383 0

420250105 1 40.80306 -75.60833 PA Carbon Allentown-Bethlehem-Easton, PA-N 740,395 8,477 513 1 0.0 2 11 33 0.0779 0

420450002 1 39.83556 -75.37250 PA Delaware Philadelphia-Camden-Wilmington, P 5,687,147 10,156 859 1 0.0 3 12 35 0.0372 0

421010449 1 39.98250 -75.08306 PA Philadelphia Philadelphia-Camden-Wilmington, P 5,687,147 8,653 413 1 0.0 1 1 3 12 31 0.0203 0

421290007 1 40.16667 -79.87500 PA Westmoreland Pittsburgh, PA 2,431,087 7,739 445 1 0.0 3 12 36 0.0352 0450031001 1 33.43253 -81.89233 SC Aiken Augusta-Richmond County, GA-SC 499,684 437 24 0.0 1 4 12 0.0000 0

450130007 1 32.43654 -80.67785 SC Beaufort Hilton Head Island-Beaufort, SC 141,615 4,928 330 1 0.0 3 12 34 0.0006 0

450190003 2 32.88394 -79.97754 SC Charleston Charleston-North Charleston, SC 549,033 4,401 275 1 0.0 3 12 34 0.0014 0

450190046 1 32.94275 -79.65718 SC Charleston Charleston-North Charleston, SC 549,033 63 4 0.0 3 12 33 0.0005 0450190047 1 32.84461 -79.94804 SC Charleston Charleston-North Charleston, SC 549,033 7,000 294 1 0.0 1 4 12 0.0022 0

450410001 1 34.19794 -79.79885 SC Florence Florence, SC 193,155 3,426 224 1 0.0 1 4 13 0.0010 0

450410002 1 34.16764 -79.85040 SC Florence Florence, SC 193,155 1,795 106 1 0.0 2 8 24 0.0011 0

450430006 1 33.36378 -79.29426 SC Georgetown Georgetown, SC 55,797 5,247 427 1 0.3 1 2 11 32 0.0072 0

450430007 1 33.34973 -79.29821 SC Georgetown Georgetown, SC 55,797 1,579 119 0.3 3 12 35 0.0002 0

450430009 1 33.37399 -79.28570 SC Georgetown Georgetown, SC 55,797 2,447 185 1 0.3 3 12 35 0.0038 0

450430010 1 33.36960 -79.29840 SC Georgetown Georgetown, SC 55,797 6,173 511 1 0.3 3 12 33 0.0049 0

450450008 2 34.84045 -82.40291 SC Greenville Greenville, SC 559,940 7,967 381 1 0.0 3 12 34 0.0023 0

450452002 1 34.94165 -82.22961 SC Greenville Greenville, SC 559,940 7,266 494 1 0.0 3 12 32 0.0001 0

450470001 1 34.18111 -82.15224 SC Greenwood Greenwood, SC 66,271 7,853 667 1 0.0 3 12 32 0.0028 0

450470002 1 34.16520 -82.16048 SC Greenwood Greenwood, SC 66,271 1,490 116 0.0 3 12 31 0.0071 0

450510002 2 33.70460 -78.87745 SC Horry Myrtle Beach-Conway-North Myrtle 196,629 4,510 227 1 0.0 3 12 35 0.0009 0

A-6

4
 
Attachment A-2

Table 1a. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

3-year data capture 

comp. 

years 

comp. 

qtrs 

comp. 

months 

annual 

mean 

qu

450630005 2 33.78560 -81.11978 SC Lexington Columbia, SC 647,158 736 66 0.0 1 4 12 0.0018 0

450631002 2 33.96900 -81.06533 SC Lexington Columbia, SC 647,158 8,086 551 1 0.0 3 12 32 0.0046 0

450790006 4 34.00740 -81.02329 SC Richland Columbia, SC 647,158 17,143 574 1 0.0 1 4 12 0.0030 0450790007 2 34.09584 -80.96230 SC Richland Columbia, SC 647,158 4,405 233 1 0.0 3 12 36 0.0004 0

450790019 1 33.99330 -81.02414 SC Richland Columbia, SC 647,158 15,569 287 1 0.0 3 12 35 0.0048 0

450790021 1 33.81655 -80.78114 SC Richland Columbia, SC 647,158 123 10 0.0 3 12 35 0.0001 0

450830001 2 34.94774 -81.93255 SC Spartanburg Spartanburg, SC 253,791 7,505 552 1 0.0 3 12 34 0.0018 0450850001 1 33.92423 -80.33774 SC Sumter Sumter, SC 104,646 4,990 407 1 0.0 3 12 35 0.0025 0450910005 1 34.96303 -81.00085 SC York Charlotte-Gastonia-Concord, NC-SC 1,330,448 3,453 221 1 0.0 2 11 27 0.0021 0

470930027 1 35.98306 -83.95222 TN Knox Knoxville, TN 616,079 8,586 826 1 5.8 1 1 9 26 0.0182 0470931017 1 35.97500 -83.95444 TN Knox Knoxville, TN 616,079 7,817 763 1 5.8 1 1 9 26 0.0143 0471570044 1 35.08750 -90.07250 TN Shelby Memphis, TN-MS-AR 1,205,204 6,730 548 1 0.0 1 1 1 6 17 0.0100 0471633001 1 36.52556 -82.27333 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 3 12 35 0.1249 0

471633002 3 36.52472 -82.26806 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 3 12 36 0.0614 0

471633003 1 36.52806 -82.26833 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 3 12 35 0.0651 0

471870100 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 2 8 23 0.2527 0

471870102 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 2 8 23 0.2575 0471871101 1 35.79944 -86.66500 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 2 8 24 0.0811 0480610006 1 25.89251 -97.49382 TX Cameron Brownsville-Harlingen, TX 335,227 14,803 1,422 1 0.0 3 12 35 0.0053 0

480850003 1 33.14250 -96.82472 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 3.2 1 3 12 35 0.2271 0

480850007 2 33.14722 -96.82556 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 1 3.2 1 3 12 34 0.1186 0

480850009 1 33.14472 -96.82889 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 3.2 1 3 12 33 0.4961 0

481130018 1 32.74556 -96.78250 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 6,451 491 1 0.0 3 12 34 0.0274 0

481130057 2 32.77890 -96.87306 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 4,591 578 1 0.0 3 12 35 0.0362 0

481130066 2 32.73972 -96.78278 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 8,270 622 1 0.0 1 1 1 7 20 0.0090 0481410033 1 31.77694 -106.50167 TX El Paso El Paso, TX 679,622 13,680 1,005 1 0.0 1 6 17 0.0120 0

482011034 4 29.76799 -95.22058 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 14,785 1,770 1 0.0 3 12 36 0.0081 0

484790016 1 27.51083 -99.51972 TX Webb Laredo, TX 193,117 14,880 1,441 1 0.0 3 12 36 0.0121 0490351001 1 40.70861 -112.09472 UT Salt Lake Salt Lake City, UT 968,858 215 23 1 0.0 2 11 32 0.0421 0

721270003 1 18.44917 -66.05306 PR San Juan San Juan-Caguas-Guaynabo, PR 2,509,007 319 5 1 3 12 36 0.0014 0

 * These sites were classified as "previous" source-oriented but because production (and related lead emissions) at the associated source was not terminated until December, 2003, only # Data for 2004-2005 did not meet completeness criteria..

A-6

5
 
Attachment A-2

Table 1b. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

max 

quarterly 

mean, 

2003 

max 

monthly 

mean, 

2003 

2nd max 

monthly 

mean, 

2003 

ma

quarte

mea

200

011090003 2 31.79056 -85.97917 AL Pike Troy, AL 29,605 461 31 4.5 1 1.9233 2.6600 2.4200 1.22

011090006 1 31.79278 -85.98056 AL Pike Troy, AL 29,605 461 31 4.5 1 0.9100 1.6900 0.8900 0.84

060250005 1 32.67611 -115.48333 CA Imperial El Centro, CA 142,361 16,385 1,290 1 0.0 0.0248 0.0404 0.0357 0.01

060371103 2 34.06659 -118.22688 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 29,329 1,633 1 0.3 0.0627 0.1460 0.0260 0.02

060371301 1 33.92899 -118.21071 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 47,423 5,066 1 0.0 0.0300 0.0440 0.0360 0.03060371601 1 34.01407 -118.06056 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 13,333 1,066 1 0.0 0.0300 0.0480 0.0340 0.02060374002 2 33.82376 -118.18921 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 20,131 1,232 1 0.0 0.0400 0.0960 0.0440 0.014060374004 2 33.79236 -118.17533 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 61,497 6,697 1 0.0 0.0938 0.1020 0.0840 0.014060375001 1 33.92288 -118.37026 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 19,148 1,680 1 0.0 0.0667 0.1700 0.0220 0.01

060375005 1 33.95080 -118.43043 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 33,968 1,358 1 0.0 0.00

060651003 2 33.94603 -117.40063 CA Riverside Riverside-San Bernardino-Ontario, C 3,254,821 16,320 1,278 1 0.0 0.0113 0.0160 0.0120 0.01

060658001 3 33.99958 -117.41601 CA Riverside Riverside-San Bernardino-Ontario, C 3,254,821 16,247 1,678 1 0.0 0.0179 0.0200 0.0200 0.014060711004 1 34.10374 -117.62914 CA San Bernardino Riverside-San Bernardino-Ontario, C 3,254,821 18,777 1,578 1 0.0 0.0343 0.0800 0.0200 0.01060719004 1 34.10688 -117.27411 CA San Bernardino Riverside-San Bernardino-Ontario, C 3,254,821 14,861 1,755 1 0.0 0.0773 0.1420 0.0680 0.014080010005 1 39.79601 -104.97754 CO Adams Denver-Aurora, CO 2,157,756 2,025 183 1.9 1 0.1739 0.2509 0.2016 0.13080010006 1 39.82574 -104.93699 CO Adams Denver-Aurora, CO 2,157,756 3,313 256 1 0.0 0.0388 0.0443 0.0406 0.04080310002 4 39.75119 -104.98762 CO Denver Denver-Aurora, CO 2,157,756 22,019 974 1 0.0 0.0290 0.0467 0.0284 0.02080310015 1 39.70012 -104.98714 CO Denver Denver-Aurora, CO 2,157,756 14,438 809 1 0.0 0.0212 0.0305 0.0196 0.01

080410011 1 38.83139 -104.82778 CO El Paso Colorado Springs, CO 537,484 10,581 552 1 0.0 0.0117 0.0165 0.0120 0.01080650001 1 39.24778 -106.29139 CO Lake Edwards, CO 49,471 5,903 361 1 0.0 0.0192 0.0277 0.0209 0.02

100010002 1 38.98472 -75.55556 DE Kent Dover, DE 126,697 352 22 0.0 0.0040 0.0051 0.0041 

100031007 1 39.55111 -75.73083 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 2,041 209 0.0 0.0046 0.0058 0.0051 

100031008 1 39.57778 -75.61111 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 3,170 160 0.0 0.0063 0.0081 0.0058 100032004 1 39.73944 -75.55806 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 34,053 2,649 1 0.0 0.0115 0.0163 0.0161 100051002 1 38.64444 -75.61306 DE Sussex Seaford, DE 156,638 5,450 390 1 0.0 0.0042 0.0048 0.0042 120571065 5 27.89222 -82.53861 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 14,463 612 1 0.0 0.0062 0.0094 0.0080 120571066 1 27.96028 -82.38250 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 5,793 465 1 1.3 1 0.7400 1.3800 0.7800 1.26120571073 1 27.96583 -82.37944 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 4,541 340 1 1.3 1 0.2533 0.4800 0.4400 0.23120571075 5 28.05000 -82.37806 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 10,691 490 1 0.0 0.0054 0.0105 0.0072 121030004 5 27.94639 -82.73194 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 13,048 557 1 0.0 0.0041 0.0067 0.0039 121030018 5 27.78556 -82.74000 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 11,289 571 1 0.0 0.0056 0.0103 0.0093 0.00

121033005 1 27.87583 -82.69639 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 2,151 58 1 0.0 0.0067 0.0200 0.0000 0.00

130890003 2 33.69833 -84.27333 GA DeKalb Atlanta-Sandy Springs-Marietta, GA 4,247,981 7,888 663 1 0.0 0.1000 0.1000 0.1000 0.10132150011 1 32.43083 -84.93167 GA Muscogee Columbus, GA-AL 281,768 10,871 1,037 1 0.3 1 1 0.1000 0.1000 0.1000 0.10

150032004 1 21.39667 -157.97167 HI Honolulu Honolulu, HI 876,156 23,622 1,207 1 0.1 0.0029 0.0072 0.0021 0.00

170310001 1 41.67275 -87.73246 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 13,648 971 1 0.0 0.0157 0.0250 0.0180 0.02

170310022 2 41.68920 -87.53932 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 22,040 1,708 1 0.2 0.0286 0.0360 0.0350 0.03170310026 1 41.87333 -87.64507 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 28,739 1,203 1 0.0 0.0613 0.0860 0.0620 0.05170310052 1 41.96743 -87.74982 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 42,187 2,877 1 0.0 0.0250 0.0280 0.0260 0.02170313103 1 41.96528 -87.87639 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 10,302 670 1 0.0 0.0180 0.0240 0.0220 0.014170313301 1 41.78278 -87.80528 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 23,749 1,678 1 0.0 0.0750 0.1950 0.0360 0.05170314201 1 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 6,070 303 1 0.0 0.0133 0.0175 0.0160 0.01

170316003 1 41.87194 -87.82611 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 14,862 1,071 1 0.0 0.0373 0.0480 0.0400 0.03

171170002 2 39.39804 -89.80975 IL Macoupin St. Louis, MO-IL 2,721,491 40 2 0.0 0.0113 0.0140 0.0100 0.01171190010 1 38.69417 -90.15361 IL Madison St. Louis, MO-IL 2,721,491 8,014 529 1 1.3 1 0.3280 0.9100 0.0620 0.15171193007 2 38.86056 -90.10583 IL Madison St. Louis, MO-IL 2,721,491 5,397 360 1 0.1 0.0173 0.0320 0.0240 0.01171430037 1 40.69889 -89.58474 IL Peoria Peoria, IL 366,899 12,643 1,109 1 0.0 0.0167 0.0220 0.0180 0.01171630010 2 38.61222 -90.16028 IL St. Clair St. Louis, MO-IL 2,721,491 3,512 430 1 0.3 0.0563 0.0940 0.0720 0.05180350008 1 40.15806 -85.42111 IN Delaware Muncie, IN 118,769 2,108 104 1 0.0 1 0.2341 0.3394 0.3138 0.46

180350009 2 40.15944 -85.41556 IN Delaware Muncie, IN 118,769 980 82 0.0 1 0.8073 1.2183 0.967273 4.09

180890023 1 41.65278 -87.43944 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 5,959 603 1 6.5 1 0.0435 0.0620 0.0510 0.06

180892008 1 41.63944 -87.49361 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 7,144 612 1 0.0 0.0277 0.0413 0.0335 0.02180892011 2 41.59250 -87.47194 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 9,815 729 1 0.0 0.0453 0.0610 0.0420 0.03180930004 1 38.88944 -86.55194 IN Lawrence Bedford, IN 45,922 393 32 0.0 0.0270 0.0270 0.0270 0.02180970063 1 39.76083 -86.29722 IN Marion Indianapolis-Carmel, IN 1,525,104 12,176 875 1 1.7 1 0.0508 0.0812 0.0584 0.07

A -66 
Attachment A-2

Table 1b. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

max 

quarterly 

mean, 

2003 

max 

monthly 

mean, 

2003 

2nd max 

monthly 

mean, 

2003 

ma

quarte

mea

200

180970076 1 39.75889 -86.28972 IN Marion Indianapolis-Carmel, IN 1,525,104 9,171 602 1 1.7 1 0.0143 0.0190 0.0178 0.01

180970078 1 39.81110 -86.11447 IN Marion Indianapolis-Carmel, IN 1,525,104 14,196 1,175 1 0.0 0.0057 0.0110 0.0075 0.01

181010001 1 38.89028 -86.76083 IN Martin 84 5 0.0 0.0299 0.0358 0.0270 0.02181630006 2 37.97167 -87.56722 IN Vanderburgh Evansville, IN-KY 342,815 13,666 817 1 0.0 0.0051 0.0075 0.0060 0.01

260490021 4 43.04722 -83.67028 MI Genesee Flint, MI 436,141 9,889 994 1 0.0 0.0153 0.0189 0.0166 0.01

261130001 1 44.31056 -84.89194 MI Missaukee Cadillac, MI 44,962 58 3 0.0 0.0040 0.0042 0.0039 0.00

261630001 2 42.22861 -83.20833 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 14,329 798 1 0.0 0.0096 0.0101 0.0100 0.01261630005 1 42.26722 -83.13222 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 11,314 923 1 0.2 0.0247 0.0340 0.0208 0.02261630015 4 42.30278 -83.10667 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 17,729 1,771 1 0.0 0.0207 0.0247 0.0214 0.02261630019 1 42.43083 -83.00028 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 28,362 2,628 1 0.0 0.0136 0.0141 0.0141 0.01261630027 1 42.29222 -83.10694 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 6,024 516 1 1.1 1 0.01

261630033 2 42.30667 -83.14889 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 17,402 1,843 1 0.5 0.0410 0.0601 0.0406 0.02270370001 1 44.83333 -93.11500 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 5,074 404 1 3.2 1 0.2100 0.0380 0.11

270370020 1 44.76535 -93.03248 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 162 7 0.0 0.0086 0.0200 0.0100 0.00

270370421 1 44.77720 -93.04097 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 478 24 0.0 0.0057 0.0100 0.0060 0.00

270370423 1 44.77500 -93.06278 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 886 83 0.0 0.0050 0.0100 0.0060 0.00

270370442 1 44.74036 -93.00556 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 168 11 0.3 0.0062 0.0080 0.0020 0.00

270530050 1 45.00123 -93.26712 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 16,318 923 1 0.0 0.0079 0.0100 0.0080 0.00

270530963 1 44.95540 -93.25827 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 46,218 3,929 1 0.2 0.0071 0.0080 0.0075 0.00

270530964 1 44.88855 -93.19538 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 209 0 1 0.0 0.0114 0.0180 0.0080 270530965 1 45.00448 -93.24005 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 19,106 1,095 1 0.4 0.0080 0.0100 0.0080 0.00

270530966 1 44.98133 -93.26615 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 17,156 439 1 0.0 0.0080 0.0120 0.0060 0.00

270530967 1 44.99646 -93.23488 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 14,621 580 1 0.4 1 0.00

270530968 1 44.89301 -93.23323 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 11,243 789 1 0.0 0.00270531007 1 45.04182 -93.29873 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 14,889 1,118 1 0.0 0.0067 0.0080 0.0060 0.00

271231003 1 44.96322 -93.19023 MN Ramsey Minneapolis-St. Paul-Bloomington, 2,968,806 9,247 474 1 0.1 0.0100 0.0200 0.0100 0.01

271377001 1 47.52336 -92.53631 MN St. Louis Duluth, MN-WI 275,486 8,942 428 1 0.1 0.0362 0.0900 0.0080 0.00

271377555 1 46.73264 -92.16337 MN St. Louis Duluth, MN-WI 275,486 4,527 287 1 0.0 0.0020 0.0040 0.0020 0.00

290930016 1 37.62528 -91.12917 MO Iron 58 4 0.0 1 0.6593 1.2160 1.1720 0.78

290930021 1 37.65417 -91.13056 MO Iron 58 4 0.0 1 0.5850 0.9840 0.7820 0.71

290930023 1 37.50333 -90.69556 MO Iron 138 7 0.0 1 1 *# 0.3433 0.6320 0.4275 0.01290930024 1 37.47972 -90.69028 MO Iron 32 2 0.0 1 1 *# 0.6677 1.6026 0.9927 0.03290930025 1 37.51056 -90.69750 MO Iron 138 7 0.0 1 1 *# 0.3263 0.6320 0.4189 0.06

290930026 1 37.45917 -90.68639 MO Iron 32 2 0.0 1 1 *# 0.2523 0.3555 0.3370 0.06290930027 1 37.48611 -90.69000 MO Iron 32 2 0.0 1 1 * 0.8761 1.4414 0.9300 0.16

290930029 1 37.47167 -90.68944 MO Iron 32 2 0.0 1 1 * 0.7148 1.4740 1.1410 0.08

290930030 1 37.46639 -90.69000 MO Iron 32 2 0.0 1 1 *# 0.2017 0.3330 0.2797 0.00290990004 1 38.26330 -90.37850 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 1.47

290990005 3 38.26722 -90.37944 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 0.5438 0.7900 0.6157 0.67

290990008 1 38.26194 -90.39417 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 58.8 1 0.1500 0.2583 0.1350 0.13

290990009 1 38.28444 -90.38194 MO Jefferson St. Louis, MO-IL 2,721,491 9,804 820 1 0.0 1 0.1664 0.1440 0.1100 0.14

290990010 1 38.24110 -90.37680 MO Jefferson St. Louis, MO-IL 2,721,491 2,799 215 1 0.0 1 0.0680 0.1040 0.0900 0.07

290990011 1 38.26820 -90.37380 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 0.5321 1.0490 0.7327 1.304

290990013 1 38.27361 -90.38000 MO Jefferson St. Louis, MO-IL 2,721,491 3,570 318 1 58.8 1 0.2717 0.4850 0.3355 0.86

290990015 1 38.26167 -90.37972 MO Jefferson St. Louis, MO-IL 2,721,491 1,988 178 1 58.8 1 1.4906 2.2993 1.9442 1.47

291892003 1 38.64972 -90.35056 MO St. Louis St. Louis, MO-IL 2,721,491 12,303 512 1 0.0 0.0000 0.0000 0.0000 0.05

295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL 2,721,491 9,140 783 1 0.0 0.02340231003 1 40.47222 -74.47139 NJ Middlesex New York-Northern New Jersey-Lo 18,323,002 13,850 1,124 1 1.7 1 0.0419 0.0875 0.0360 0.114360470122 1 40.71980 -73.94788 NY Kings New York-Northern New Jersey-Lo 18,323,002 92,660 5,785 1 0.1 0.0293 0.0350 0.0300 0.03

360632008 1 43.08216 -79.00099 NY Niagara Buffalo-Niagra Falls, NY Metropoli 1,170,111 6,795 386 1 0.0 0.0060 0.0080 0.0080 

360713001 1 41.46107 -74.36343 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 1,481 99 1.8 1 0.0820 0.1580 0.0940 0.074

360713002 1 41.45887 -74.35392 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 1,257 86 1.8 1 0.2417 0.2080 0.1700 0.23360713004 1 41.47633 -74.36827 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 6,816 434 1 0.0 0.0313 0.0333 0.0320 0.03360850067 1 40.59733 -74.12619 NY Richmond New York-Northern New Jersey-Lo 18,323,002 21,834 1,373 0.0 0.0082 0.0140 0.0125 390170015 2 39.48990 -84.36407 OH Butler Cincinnati-Middletown, OH-KY-IN 2,009,632 4,668 373 1 0.0 0.01

 A-67 
Attachment A-2

Table 1b. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

max 

quarterly 

mean, 

2003 

max 

monthly 

mean, 

2003 

2nd max 

monthly 

mean, 

2003 

ma

quarte

mea

200

390290019 1 40.63111 -80.54694 OH Columbiana East Liverpool-Salem, OH 112,075 5,385 322 1 0.0 0.0253 0.0300 0.0300 0.014

390290020 1 40.63972 -80.52389 OH Columbiana East Liverpool-Salem, OH 112,075 6,414 354 1 0.0 0.0247 0.0300 0.0240 0.01

390290022 1 40.63500 -80.54667 OH Columbiana East Liverpool-Salem, OH 112,075 3,318 202 1 0.0 0.0367 0.0800 0.0200 0.01

390350038 1 41.47694 -81.68194 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 7,329 585 1 0.1 0.0300 0.0600 0.0300 0.02390350042 1 41.48222 -81.70889 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 18,776 1,575 1 0.0 0.0233 0.0300 0.0200 0.02

390350049 1 41.44667 -81.65111 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 9,720 758 1 0.0 1 1 0.2367 0.4500 0.2500 0.13

390350050 1 41.44250 -81.64917 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 8,771 695 1 0.0 1 1 0.0400 0.0700 0.0500 0.054

390350061 2 41.47506 -81.67596 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 6,141 444 1 0.3 1 1 0.3600 0.5600 0.4700 0.02

390350069 1 41.51918 -81.63794 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 23,566 1,961 1 0.1 0.0233 0.0290 0.0280 

390490025 1 39.92806 -82.98111 OH Franklin Columbus, OH 1,612,694 15,220 1,226 1 0.6 1 0.0167 0.0200 0.0200 0.01

390510001 1 41.57528 -83.99639 OH Fulton Toledo, OH 659,188 1,503 110 1 0.3 1 0.2667 0.5300 0.2500 0.24

390910003 1 40.34306 -83.75500 OH Logan Bellefontaine, OH 46,005 1,536 108 1 0.1 0.1467 0.2700 0.2000 0.13

390910005 1 40.34278 -83.76028 OH Logan Bellefontaine, OH 46,005 1,546 126 1 0.1 1 0.1300 0.1900 0.1100 0.14

390910006 1 40.34111 -83.75778 OH Logan Bellefontaine, OH 46,005 1,217 87 1 0.1 1 0.1967 0.3200 0.2100 0.26

390910007 1 40.34472 -83.75444 OH Logan Bellefontaine, OH 46,005 2,156 185 1 0.1 0.1500 0.2100 0.1500 0.22

391670008 1 39.43361 -81.50250 OH Washington Parkersburg-Marietta, WV-OH 164,624 1,947 114 0.0 0.0100 0.0100 0.0100 0.00391670009 1 39.37696 -81.53730 OH Washington Parkersburg-Marietta, WV-OH 164,624 314 21 0.0 0.04

401159005 2 36.98580 -94.84920 OK Ottawa Miami, OK 33,194 1,573 117 0.0 

401159006 1 36.98460 -94.82490 OK Ottawa Miami, OK 33,194 1,573 117 0.0 

401159007 1 36.97190 -94.85180 OK Ottawa Miami, OK 33,194 1,573 117 0.0 

401159008 1 36.97160 -94.82500 OK Ottawa Miami, OK 33,194 1,573 117 0.0 

410510246 7 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 24,303 1,771 1 0.0 0.0101 0.0110 0.0105 

420030002 1 40.50056 -80.07194 PA Allegheny Pittsburgh, PA 2,431,087 19,559 1,045 1 0.0 0.0255 0.0280 0.0260 0.01

420032001 1 40.39667 -79.86361 PA Allegheny Pittsburgh, PA 2,431,087 10,120 769 1 0.2 0.0567 0.1140 0.0660 0.03

420070505 1 40.68500 -80.32500 PA Beaver Pittsburgh, PA 2,431,087 6,497 218 1 0.0 0.0913 0.1920 0.0483 0.09

420110005 1 40.46630 -75.75890 PA Berks Reading, PA 373,638 692 44 4.8 1 0.0757 0.1000 0.0760 0.094

420110717 1 40.47667 -75.75917 PA Berks Reading, PA 373,638 575 39 1 4.8 1 0.1238 0.1980 0.1580 0.18420111717 1 40.37722 -75.91444 PA Berks Reading, PA 373,638 7,376 390 1 2.1 1 0.3860 0.4840 0.4560 0.39

420210808 1 40.34806 -78.88278 PA Cambria Johnstown, PA 152,598 2,606 115 1 0.0 0.0364 0.0460 0.0320 0.04

420250105 1 40.80306 -75.60833 PA Carbon Allentown-Bethlehem-Easton, PA-N 740,395 8,477 513 1 0.0 0.0992 0.1300 0.1240 0.11

420450002 1 39.83556 -75.37250 PA Delaware Philadelphia-Camden-Wilmington, P 5,687,147 10,156 859 1 0.0 0.0364 0.0380 0.0380 0.04

421010449 1 39.98250 -75.08306 PA Philadelphia Philadelphia-Camden-Wilmington, P 5,687,147 8,653 413 1 0.0 1 1 0.0350 0.0380 0.0360 0.02421290007 1 40.16667 -79.87500 PA Westmoreland Pittsburgh, PA 2,431,087 7,739 445 1 0.0 0.0369 0.0400 0.0400 0.03

450031001 1 33.43253 -81.89233 SC Aiken Augusta-Richmond County, GA-SC 499,684 437 24 0.0 0.0000 0.0000 0.0000 

450130007 1 32.43654 -80.67785 SC Beaufort Hilton Head Island-Beaufort, SC 141,615 4,928 330 1 0.0 0.0022 0.0070 0.0016 0.00

450190003 2 32.88394 -79.97754 SC Charleston Charleston-North Charleston, SC 549,033 4,401 275 1 0.0 0.0041 0.0104 0.0048 0.00450190046 1 32.94275 -79.65718 SC Charleston Charleston-North Charleston, SC 549,033 63 4 0.0 0.0032 0.0068 0.0035 0.00450190047 1 32.84461 -79.94804 SC Charleston Charleston-North Charleston, SC 549,033 7,000 294 1 0.0 0.0037 0.0058 0.0052 

450410001 1 34.19794 -79.79885 SC Florence Florence, SC 193,155 3,426 224 1 0.0 0.0026 0.0063 0.0023 

450410002 1 34.16764 -79.85040 SC Florence Florence, SC 193,155 1,795 106 1 0.0 0.0000 0.00450430006 1 33.36378 -79.29426 SC Georgetown Georgetown, SC 55,797 5,247 427 1 0.3 1 0.0166 0.0420 0.0077 0.01

450430007 1 33.34973 -79.29821 SC Georgetown Georgetown, SC 55,797 1,579 119 0.3 0.0017 0.0054 0.0000 0.00

450430009 1 33.37399 -79.28570 SC Georgetown Georgetown, SC 55,797 2,447 185 1 0.3 0.0081 0.0158 0.0094 0.004

450430010 1 33.36960 -79.29840 SC Georgetown Georgetown, SC 55,797 6,173 511 1 0.3 0.0102 0.0132 0.0102 0.01

450450008 2 34.84045 -82.40291 SC Greenville Greenville, SC 559,940 7,967 381 1 0.0 0.0071 0.0125 0.0066 0.00

450452002 1 34.94165 -82.22961 SC Greenville Greenville, SC 559,940 7,266 494 1 0.0 0.0000 0.0000 0.0000 0.00

450470001 1 34.18111 -82.15224 SC Greenwood Greenwood, SC 66,271 7,853 667 1 0.0 0.0053 0.0086 0.0082 0.00450470002 1 34.16520 -82.16048 SC Greenwood Greenwood, SC 66,271 1,490 116 0.0 0.0100 0.0202 0.0168 0.01

450510002 2 33.70460 -78.87745 SC Horry Myrtle Beach-Conway-North Myrtle 196,629 4,510 227 1 0.0 0.0018 0.0032 0.0020 0.00

450630005 2 33.78560 -81.11978 SC Lexington Columbia, SC 647,158 736 66 0.0 0.0033 0.0052 0.0050 450631002 2 33.96900 -81.06533 SC Lexington Columbia, SC 647,158 8,086 551 1 0.0 0.0179 0.0356 0.0090 0.00

450790006 4 34.00740 -81.02329 SC Richland Columbia, SC 647,158 17,143 574 1 0.0 0.0069 0.0090 0.0072 

450790007 2 34.09584 -80.96230 SC Richland Columbia, SC 647,158 4,405 233 1 0.0 0.0006 0.0018 0.0000 0.00

450790019 1 33.99330 -81.02414 SC Richland Columbia, SC 647,158 15,569 287 1 0.0 0.0097 0.0138 0.0104 0.00450790021 1 33.81655 -80.78114 SC Richland Columbia, SC 647,158 123 10 0.0 0.0012 0.0038 0.0000 0.00

 A-68 
Attachment A-2

Table 1b. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

max 

quarterly 

mean, 

2003 

max 

monthly 

mean, 

2003 

2nd max 

monthly 

mean, 

2003 

ma

quarte

mea

200

450830001 2 34.94774 -81.93255 SC Spartanburg Spartanburg, SC 253,791 7,505 552 1 0.0 0.0035 0.0060 0.0058 0.00

450850001 1 33.92423 -80.33774 SC Sumter Sumter, SC 104,646 4,990 407 1 0.0 0.0064 0.0108 0.0066 0.004

450910005 1 34.96303 -81.00085 SC York Charlotte-Gastonia-Concord, NC-SC 1,330,448 3,453 221 1 0.0 0.0042 0.0082 0.0050 0.00

470930027 1 35.98306 -83.95222 TN Knox Knoxville, TN 616,079 8,586 826 1 5.8 1 0.0100 0.0100 0.0100 0.02470931017 1 35.97500 -83.95444 TN Knox Knoxville, TN 616,079 7,817 763 1 5.8 1 0.0100 0.0100 0.0100 0.01471570044 1 35.08750 -90.07250 TN Shelby Memphis, TN-MS-AR 1,205,204 6,730 548 1 0.0 1 1 0.0100 0.0100 0.0100 0.01

471633001 1 36.52556 -82.27333 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 0.1515 0.1940 0.1718 0.15471633002 3 36.52472 -82.26806 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 0.0719 0.0846 0.0730 0.10471633003 1 36.52806 -82.26833 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 0.0679 0.0844 0.0810 0.12

471870100 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 0.9867 1.9120 0.8200 0.12471870102 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 0.6953 0.9460 0.6000 0.08471871101 1 35.79944 -86.66500 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 0.0853 0.1160 0.1120 0.30480610006 1 25.89251 -97.49382 TX Cameron Brownsville-Harlingen, TX 335,227 14,803 1,422 1 0.0 0.0085 0.0090 0.0090 0.00

480850003 1 33.14250 -96.82472 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 3.2 1 0.3006 0.4436 0.3518 0.24

480850007 2 33.14722 -96.82556 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 1 3.2 1 0.1337 0.2458 0.2223 0.124480850009 1 33.14472 -96.82889 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 3.2 1 0.6600 0.8914 0.6658 0.59

481130018 1 32.74556 -96.78250 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 6,451 491 1 0.0 0.0318 0.0640 0.0250 0.08

481130057 2 32.77890 -96.87306 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 4,591 578 1 0.0 0.0611 0.1016 0.0708 0.044

481130066 2 32.73972 -96.78278 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 8,270 622 1 0.0 1 1 0.0178 0.0260 0.0217 0.02481410033 1 31.77694 -106.50167 TX El Paso El Paso, TX 679,622 13,680 1,005 1 0.0 0.0585 0.0600 0.0540 

482011034 4 29.76799 -95.22058 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 14,785 1,770 1 0.0 0.0136 0.0230 0.0140 0.02

484790016 1 27.51083 -99.51972 TX Webb Laredo, TX 193,117 14,880 1,441 1 0.0 0.0142 0.0230 0.0147 0.01

490351001 1 40.70861 -112.09472 UT Salt Lake Salt Lake City, UT 968,858 215 23 1 0.0 0.0628 0.1188 0.0752 0.07

721270003 1 18.44917 -66.05306 PR San Juan San Juan-Caguas-Guaynabo, PR 2,509,007 319 5 1 0.0042 0.0125 0.0000 0.00

 * These sites were classified as "previous" source-oriented but because production (and related lead emissions) at the associated source was not terminated until December, 2003, only data for 2004-2005 were considered for the "previous" source oriented characterization. # Data for 2004-2005 did not meet completeness criteria..

 A-69 
Attachment A-2

Table 2. Pb-TSP monitoring site distribution statistics 

All sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 189 0.0000 0.0010 0.0019 0.0032 0.0042 0.0052 0.0071 0.0097 0.0114 0.0143 0.0166 0.0934 0.0203 0.0272 0.0316 0.0396 0.0606 0.09

max quarter mean 189 0.0000 0.0031 0.0041 0.0063 0.0071 0.0100 0.0126 0.0179 0.0224 0.0254 0.0299 0.1738 0.0367 0.0495 0.0627 0.0820 0.1259 0.18

max monthly mean 189 0.0000 0.0054 0.0080 0.0100 0.0112 0.0140 0.0200 0.0288 0.0320 0.0380 0.0430 0.3079 0.0503 0.0880 0.1000 0.1460 0.2200 0.29

2nd max monthly mean 189 0.0000 0.0035 0.0051 0.0072 0.0100 0.0117 0.0140 0.0196 0.0240 0.0280 0.0340 0.2066 0.0380 0.0440 0.0726 0.1000 0.1428 0.23

average of 3 overall highest monthly means 189 0.0000 0.0043 0.0060 0.0075 0.0101 0.0122 0.0147 0.0228 0.0279 0.0320 0.0373 0.2253 0.0400 0.0500 0.0811 0.1033 0.1496 0.25average of 3 annual max monthly means 189 0.0000 0.0042 0.0058 0.0080 0.0100 0.0112 0.0150 0.0203 0.0252 0.0299 0.0344 0.1942 0.0400 0.0496 0.0753 0.1073 0.1380 0.20

Source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 60 0.0072 0.0095 0.0142 0.0229 0.0375 0.0440 0.0616 0.0775 0.0933 0.1122 0.1253 0.2596 0.1455 0.1815 0.2281 0.2549 0.2655 0.33

max quarter mean 60 0.0100 0.0180 0.0221 0.0309 0.0731 0.0880 0.1206 0.1502 0.1829 0.2064 0.2470 0.4781 0.2800 0.3272 0.3526 0.5107 0.6866 0.71

max monthly mean 60 0.0100 0.0311 0.0378 0.0420 0.1000 0.1580 0.1814 0.2311 0.2881 0.3577 0.4263 0.8572 0.5200 0.6320 0.7663 0.9280 1.0307 1.45

2nd max monthly mean 60 0.0100 0.0205 0.0310 0.0380 0.0871 0.1070 0.1484 0.1690 0.2230 0.2670 0.2943 0.5738 0.3485 0.4336 0.4568 0.5645 0.7310 0.92

Not source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 129 0.0000 0.0006 0.0014 0.0021 0.0028 0.0038 0.0045 0.0051 0.0057 0.0081 0.0100 0.0162 0.0113 0.0142 0.0153 0.0175 0.0214 0.02

max quarter mean 129 0.0000 0.0022 0.0033 0.0042 0.0060 0.0067 0.0080 0.0100 0.0114 0.0138 0.0179 0.0322 0.0229 0.0253 0.0280 0.0343 0.0386 0.04

max monthly mean 129 0.0000 0.0051 0.0062 0.0080 0.0090 0.0105 0.0120 0.0140 0.0160 0.0220 0.0290 0.0525 0.0320 0.0360 0.0404 0.0480 0.0600 0.08

2nd max monthly mean 129 0.0000 0.0025 0.0040 0.0052 0.0060 0.0080 0.0100 0.0115 0.0125 0.0148 0.0196 0.0358 0.0230 0.0270 0.0300 0.0360 0.0390 0.04

Previous source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 9 0.0090 0.0090 0.0090 0.0100 0.0100 0.0203 0.0203 0.0362 0.0362 0.0477 0.0477 0.0595 0.0477 0.0799 0.0799 0.1000 0.1000 0.11

max quarter mean 9 0.0100 0.0100 0.0100 0.0209 0.0209 0.0350 0.0350 0.0550 0.0550 0.1000 0.1000 0.1206 0.1000 0.1027 0.1027 0.1654 0.1654 0.23

max monthly mean 9 0.0100 0.0100 0.0100 0.0380 0.0380 0.0420 0.0420 0.1000 0.1000 0.1000 0.1000 0.1942 0.1000 0.1400 0.1400 0.3080 0.3080 0.45

2nd max monthly mean 9 0.0100 0.0100 0.0100 0.0280 0.0280 0.0360 0.0360 0.0940 0.0940 0.1000 0.1000 0.1467 0.1000 0.1025 0.1025 0.2200 0.2200 0.26

Urban sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 140 0.0001 0.0012 0.0021 0.0032 0.0045 0.0052 0.0074 0.0097 0.0112 0.0138 0.0149 0.0594 0.0168 0.0187 0.0230 0.0304 0.0365 0.04

max quarter mean 140 0.0006 0.0032 0.0042 0.0067 0.0080 0.0104 0.0131 0.0174 0.0214 0.0247 0.0260 0.1100 0.0300 0.0364 0.0405 0.0612 0.0766 0.09max monthly mean 140 0.0018 0.0062 0.0081 0.0103 0.0120 0.0149 0.0204 0.0287 0.0315 0.0360 0.0400 0.1958 0.0440 0.0502 0.0800 0.1000 0.1164 0.18

2nd max monthly mean 140 0.0000 0.0040 0.0059 0.0080 0.0100 0.0118 0.0145 0.0192 0.0220 0.0253 0.0305 0.1295 0.0355 0.0385 0.0430 0.0670 0.0870 0.10Urban sites, located in MSA's ≥ 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 91 0.0006 0.0026 0.0042 0.0051 0.0075 0.0090 0.0103 0.0113 0.0142 0.0150 0.0178 0.0711 0.0205 0.0225 0.0276 0.0315 0.0368 0.03

max quarter mean 91 0.0033 0.0060 0.0071 0.0100 0.0114 0.0133 0.0197 0.0220 0.0252 0.0267 0.0300 0.1343 0.0353 0.0400 0.0567 0.0667 0.0773 0.09

max monthly mean 91 0.0067 0.0082 0.0110 0.0124 0.0160 0.0200 0.0290 0.0340 0.0360 0.0400 0.0440 0.2442 0.0500 0.0601 0.0960 0.1029 0.1460 0.18

2nd max monthly mean 91 0.0000 0.0067 0.0080 0.0103 0.0120 0.0160 0.0200 0.0230 0.0255 0.0315 0.0360 0.1530 0.0380 0.0406 0.0500 0.0778 0.0880 0.10

Urban sites, located in MSA's < 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 49 0.0001 0.0006 0.0010 0.0014 0.0018 0.0025 0.0030 0.0046 0.0048 0.0065 0.0100 0.0378 0.0121 0.0143 0.0156 0.0175 0.0305 0.07

max quarter mean 49 0.0006 0.0020 0.0026 0.0034 0.0037 0.0063 0.0069 0.0085 0.0126 0.0166 0.0179 0.0649 0.0224 0.0248 0.0279 0.0386 0.0585 0.10

max monthly mean 49 0.0018 0.0048 0.0053 0.0063 0.0072 0.0102 0.0108 0.0144 0.0209 0.0286 0.0310 0.1060 0.0320 0.0400 0.0404 0.0800 0.0920 0.13

2nd max monthly mean 49 0.0018 0.0025 0.0034 0.0042 0.0054 0.0072 0.0090 0.0106 0.0132 0.1700 0.0200 0.0861 0.0240 0.0300 0.0310 0.0400 0.0560 0.13

 A-70 
Attachment A-2

Table 3. Pb-TSP metric correlations 

Statistic (Q = quarterly, M = monthly) 

All sites 

annual 

mean, 2003-

2005 

max Q 

mean, 2003-

2005 

max M 

mean, 2003-

2005 

2nd max M 

mean, 2003-

2005 

avg. of 3 

overall 

highest M 

means, 

2003-2005 

avg. of 3 

annual max 

M means, 

2003-2005 

number of sites 189 189 189 189 189 189 

mean (μg/m3) 0.0934 0.1738 0.3079 0.2066 0.2253 0.1942 

Correlation coeeficients annual mean, 2003-2005 1.00 0.96 0.88 0.97 0.94 0.90 max Q mean, 2003-2005 1.00 0.94 0.99 0.98 0.94 max M mean, 2003-2005 1.00 0.92 0.98 0.97 2nd max M mean, 2003-2005 1.00 0.97 0.93 average of 3 overall highest M means, 2003-2005 1.00 0.98 average of 3 annual max M means, 2003-2005 1.00 

Statistic (Q = quarterly, M = monthly) 

annual 

mean, 2003-

2005 

max Q 

mean, 2003-

2005 

ma

mean

20

number of sites 140 140 14

mean (μg/m3) 0.0594 0.1100 0.1

Correlation coeeficients annual mean, 2003-2005 1.00 0.95 0.

max Q mean, 2003-2005 1.00 0.

max M mean, 2003-2005 1.

2nd max M mean, 2003-2005 

average of 3 overall highest M means, 2003-2005 

average of 3 annual max M means, 2003-2005 

Statistic (Q = quarterly, M = monthly)

Source-oriented sites 

annual 

mean, 2003-

2005 

max Q 

mean, 2003-

2005 

max M 

mean, 2003-

2005 

2nd max M 

mean, 2003-

2005 

avg. of 3 

overall 

highest M 

means, 

2003-2005 

avg. of 3 

annual max 

M means, 

2003-2005 

number of sites 60 60 60 60 60 60 

mean (μg/m3) 0.2596 0.4781 0.8572 0.5738 0.6259 0.5333 

Correlation coeeficients annual mean, 2003-2005 1.00 0.95 0.85 0.96 0.93 0.88 max Q mean, 2003-2005 1.00 0.93 0.99 0.98 0.92 max M mean, 2003-2005 1.00 0.89 0.97 0.96 2nd max M mean, 2003-2005 1.00 0.97 0.90 average of 3 overall highest M means, 2003-2005 1.00 0.97 average of 3 annual max M means, 2003-2005 1.00 

Statistic (Q = quarterly, M = monthly) 

Urban sites in 

annual 

mean, 2003-

2005 

max Q 

mean, 2003-

2005 

ma

mean

20

number of sites 91 91 9

mean (μg/m3) 0.0711 0.1343 0.2

Correlation coeeficients annual mean, 2003-2005 1.00 0.95 0.

max Q mean, 2003-2005 1.00 0.

max M mean, 2003-2005 1.

2nd max M mean, 2003-2005 

average of 3 overall highest M means, 2003-2005 

average of 3 annual max M means, 2003-2005 

A -71 
Table 4. Pb-TSP metric ratio distribution statistics 

Attachment A-2 

TSP Category Ratio Sites min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 

ratio of max quarterly mean to annual mean 1.0000 1.1135 1.2080 1.2852 1.3433 1.3848 1.4837 1.5299 1.6127 1.7079 1.7846 2.3541 1.8893 2.0025 2.1421 2.3164 2.547

All sites ratio of max monthly mean to annual mean 189 1.0000 1.3553 1.5556 1.8176 1.9537 2.1475 2.2817 2.4036 2.5471 2.6265 2.8310 4.4159 2.9634 3.5018 4.0128 4.4273 4.987

ratio of 2nd max monthly mean to annual mean 0.0000 1.1211 1.3057 1.4296 1.5822 1.6784 1.7597 1.8311 1.9092 2.0346 2.1246 2.5728 2.2033 2.3015 2.4453 2.5908 2.765

ratio of max quarterly mean to annual mean 1.0000 1.0787 1.2484 1.3167 1.3529 1.3966 1.5077 1.5205 1.5559 1.7079 1.7571 2.0471 1.8085 1.9070 1.9759 2.1285 2.348

Source-oriented sites ratio of max monthly mean to annual mean 60 1.0000 1.4735 1.8318 1.9768 2.1641 2.2716 2.3528 2.5202 2.5824 2.7379 2.9086 3.7485 3.0625 3.5073 3.6900 4.0401 4.509

ratio of 2nd max monthly mean to annual mean 1.0000 1.4088 1.6543 1.6815 1.7864 1.8292 1.8838 1.9438 2.0124 2.1000 2.1723 2.4356 2.2784 2.4017 2.5034 2.5293 2.639

ratio of max quarterly mean to annual mean 1.0000 1.1368 1.2034 1.2648 1.3095 1.3826 1.4753 1.5591 1.6332 1.7079 1.8151 2.4980 1.9665 2.0293 2.2498 2.4153 2.689

Non-source-oriented sites ratio of max monthly mean to annual mean 129 1.0000 1.3140 1.4769 1.6578 1.8680 2.0164 2.2496 2.3671 2.5200 2.5851 2.7967 4.7287 2.9508 3.4823 4.0858 4.5723 5.592

ratio of 2nd max monthly mean to annual mean 0.0000 1.0746 1.2445 1.3294 1.4296 1.5582 1.6657 1.7556 1.8372 1.9200 2.1185 2.6371 2.1818 2.2633 2.3912 2.6316 2.824

ratio of max quarterly mean to annual mean 1.0000 1.0000 1.0000 1.0000 1.0000 1.2844 1.2844 1.4961 1.4961 1.5195 1.5195 2.2049 1.5195 1.7266 1.7266 1.9492 1.949

Previous source-oriented sites ratio of max monthly mean to annual mean 13 1.0000 1.0000 1.0000 1.0000 1.0000 1.7515 1.7515 1.8746 1.8746 2.7626 2.7626 3.4751 2.7626 2.7863 2.7863 3.7062 3.706

ratio of 2nd max monthly mean to annual mean 1.0000 1.0000 1.0000 1.0000 1.0000 1.7759 1.7759 2.1414 2.1414 2.5969 2.5969 4.9066 2.5969 3.0986 3.0986 8.4131 8.413

ratio of max quarterly mean to annual mean 1.0000 1.1605 1.2504 1.3070 1.3536 1.4227 1.5010 1.5712 1.6427 1.7167 1.7773 2.3313 1.8683 2.0072 2.1508 2.3167 2.589

Urban sites ratio of max monthly mean to annual mean 140 1.0000 1.3391 1.5925 1.7771 1.9220 2.1133 2.2936 2.4105 2.5423 2.6341 2.8310 4.2395 2.9518 3.4976 4.0307 4.4998 5.112

ratio of 2nd max monthly mean to annual mean 0.0000 1.1889 1.3271 1.4700 1.6053 1.6784 1.7660 1.8606 1.9967 2.1038 2.1650 2.6532 2.2452 2.3637 2.5051 2.6213 2.785

ratio of max quarterly mean to annual mean 1.0000 1.1077 1.2080 1.2745 1.3095 1.3759 1.4753 1.5167 1.6157 1.6829 1.7366 2.3159 1.7900 1.8451 2.0496 2.3164 2.431

Urban sites in CBSAs > 1M population ratio of max monthly mean to annual mean 91 1.0000 1.3046 1.4769 1.6578 1.8346 1.9910 2.2676 2.3690 2.5370 2.5619 2.6726 4.0747 2.8714 2.9508 3.5868 4.0253 4.647

ratio of 2nd max monthly mean to annual mean 0.0000 1.0746 1.3057 1.4089 1.4965 1.5855 1.6553 1.7491 1.7890 1.8960 2.0982 2.4155 2.1414 2.2633 2.3912 2.5004 2.596

ratio of max quarterly mean to annual mean 1.0000 1.2648 1.3522 1.3838 1.4152 1.5299 1.5608 1.6127 1.6901 1.9505 2.0025 2.3597 2.0091 2.1715 2.2498 2.6223 2.724

Urban sites in CBSAs < 1M population ratio of max monthly mean to annual mean 49 1.0000 1.4392 1.7365 1.9615 2.0898 2.2033 2.3056 2.5035 2.6417 2.9528 3.1207 4.5456 3.5286 4.4055 4.5723 5.0000 5.549

ratio of 2nd max monthly mean to annual mean 1.0000 1.3116 1.6698 1.7451 1.8787 1.9843 2.0346 2.1235 2.1673 2.2033 2.3435 3.0946 2.3684 2.6953 2.7642 2.9302 3.819

A- 72 
Attachment A-2

Table 5. Pb-PM10 monitoring site information 

site poc lat long state county_name cbsa_name cbsa_pop00 urban 

data completeness 

(complete periods) 

qtrs years months annual mean 

080770017 1 39.06363 -108.56102 CO Mesa Grand Junction, CO 116,255 1 1 4 13 0.0049 

110010043 1 38.91889 -77.01250 DC District of Columbia Washington-Arlington-Alexandria, 4,796,183 1 2 7 20 0.0048 

120571065 5 27.89222 -82.53861 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 1 2 8 23 0.0062 

120573002 5 27.96565 -82.23040 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 2 8 24 0.0035 121030018 5 27.78556 -82.74000 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 1 1 4 12 0.0022 121030026 5 27.85004 -82.71459 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 1 1 6 17 0.0023 130890002 1 33.68801 -84.29033 GA DeKalb Atlanta-Sandy Springs-Marietta, GA 4,247,981 1 3 12 34 0.0026 170314201 6 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 1 4 12 0.0060 211930003 1 37.28306 -83.22028 KY Perry 3 12 34 0.0040 250250042 6 42.32944 -71.08278 MA Suffolk Boston-Cambridge-Quincy, MA-NH 4,391,344 1 1 6 22 0.0049 261630033 1 42.30667 -83.14889 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 1 3 12 35 0.0212 

295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL 2,721,491 1 2 10 30 0.0127 

360850106 1 40.57811 -74.18430 NY Richmond New York-Northern New Jersey-Lo 18,323,002 1 4 11 0.0071 

360850111 1 40.57997 -74.19872 NY Richmond New York-Northern New Jersey-Lo 18,323,002 1 4 11 0.0074 360850131 1 40.58806 -74.16882 NY Richmond New York-Northern New Jersey-Lo 18,323,002 1 4 10 0.0069 360850132 1 40.58061 -74.15158 NY Richmond New York-Northern New Jersey-Lo 18,323,002 1 4 11 0.0095 410390060 7 44.02631 -123.08374 OR Lane Eugene-Springfield, OR 322,959 1 1 3 9 0.0023 410510030 7 45.49742 -122.67467 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 1 4 11 0.0056 

410510080 7 45.49667 -122.60222 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 2 7 22 0.0055 410510244 8 45.53500 -122.69889 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 2 7 21 0.0065 410510246 7 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 2 8 23 0.0097 410610119 7 45.33897 -117.90480 OR Union La Grande, OR 24,530 2 7 20 0.0016 410670111 7 45.47020 -122.81585 OR Washington Portland-Vancouver-Beaverton, OR 1,927,881 1 1 3 11 0.0025 440070022 1 41.80795 -71.41500 RI Providence Providence-New Bedford-Fall Rive 1,582,997 1 3 12 36 0.0098 440070029 1 41.81644 -71.43790 RI Providence Providence-New Bedford-Fall Rive 1,582,997 1 1 4 13 0.0061 450250001 2 34.61537 -80.19879 SC Chesterfield 2 8 20 0.0029 481390017 1 32.47361 -97.04250 TX Ellis Dallas-Fort Worth-Arlington, TX 5,161,544 1 6 17 0.0151 481410041 1 31.76054 -106.50045 TX El Paso El Paso, TX 679,622 1 1 4 12 0.0118 482011035 1 29.73371 -95.25759 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 12 36 0.0077 482011039 1 29.67005 -95.12849 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 12 31 0.0056 490110004 1 40.90297 -111.88447 UT Davis Ogden-Clearfield, UT 442,656 1 2 10 29 0.0059 530110030 7 45.64168 -122.68123 WA Clark Portland-Vancouver-Beaverton, OR 1,927,881 1 1 3 11 0.0032 530330080 1 47.56833 -122.30806 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 11 32 0.0046 530630016 1 47.66083 -117.35722 WA Spokane Spokane, WA 417,939 1 1 4 12 0.0059 530630050 1 47.69545 -117.37030 WA Spokane Spokane, WA 417,939 1 4 12 0.0049 530630052 1 47.66512 -117.42909 WA Spokane Spokane, WA 417,939 1 4 12 0.0037 530630053 1 47.68220 -117.30480 WA Spokane Spokane, WA 417,939 1 4 12 0.0051 550270007 1 43.43500 -88.52778 WI Dodge Beaver Dam, WI 85,897 1 4 10 0.0054 

A -73 
Attachment A-2

Table 6. Pb-PM10 monitoring site distribution statistics 

All sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 38 0.0016 0.0022 0.0023 0.0026 0.0032 0.0037 0.0046 0.0049 0.0049 0.0054 0.0056 0.0063 0.0056 0.0059 0.0061 0.0065 0.0071 0

max quarter mean 38 0.0027 0.0030 0.0032 0.0046 0.0049 0.0055 0.0066 0.0078 0.0082 0.0085 0.0087 0.0117 0.0090 0.0098 0.0106 0.0113 0.0117 0

max monthly mean 38 0.0030 0.0040 0.0047 0.0061 0.0075 0.0085 0.0094 0.0106 0.0116 0.0123 0.0135 0.0205 0.0142 0.0146 0.0151 0.0160 0.0190 0

Urban sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 25 0.0022 0.0023 0.0023 0.0025 0.0029 0.0046 0.0048 0.0049 0.0052 0.0056 0.0056 0.0064 0.0059 0.0060 0.0061 0.0062 0.0065 0

max quarter mean 25 0.0030 0.0032 0.0032 0.0034 0.0049 0.0056 0.0076 0.0081 0.0085 0.0085 0.0088 0.0126 0.0092 0.0101 0.0106 0.0108 0.0113 0

max monthly mean 25 0.0040 0.0045 0.0047 0.0051 0.0073 0.0094 0.0097 0.0106 0.0114 0.0123 0.0136 0.0235 0.0142 0.0145 0.0151 0.0190 0.0211 0

Urban sites, located in MSA's ≥ 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 20 0.0022 0.0022 0.0024 0.0025 0.0029 0.0039 0.0047 0.0048 0.0052 0.0056 0.0056 0.0065 0.0058 0.0061 0.0061 0.0063 0.0071 0

max quarter mean 20 0.0030 0.0031 0.0033 0.0040 0.0049 0.0064 0.0080 0.0085 0.0085 0.0087 0.0090 0.0136 0.0095 0.0101 0.0105 0.0109 0.0142 0

max monthly mean 20 0.0045 0.0046 0.0049 0.0056 0.0077 0.0095 0.0101 0.0111 0.0120 0.0130 0.0139 0.0259 0.0143 0.0145 0.0148 0.0170 0.0223 0

Urban sites, located in MSA's < 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 5 0.0023 0.0023 0.0023 0.0023 0.0036 0.0049 0.0049 0.0049 0.0054 0.0059 0.0059 0.0061 0.0059 0.0059 0.0059 0.0059 0.0059 0max quarter mean 5 0.0032 0.0032 0.0032 0.0032 0.0044 0.0056 0.0056 0.0056 0.0069 0.0081 0.0081 0.0089 0.0081 0.0095 0.0108 0.0108 0.0108 0max monthly mean 5 0.0040 0.0040 0.0040 0.0040 0.0062 0.0085 0.0085 0.0085 0.0098 0.0111 0.0111 0.0140 0.0111 0.0161 0.0211 0.0211 0.0211 0

 A-74 
Table 7. Pb-PM2.5 monitoring site information 

Attachment A-2 

site poc lat long state county_name cbsa_name cbsa_pop00 urban data completeness (complete periods) 3-year metrics qtrs years months annual mean max quarterly mean max monthly mean 010050002 5 31.66414 -85.60623 AL Barbour Eufaula, AL-GA 31,636 2 8 25 0.0026 0.0033 0.0053 010730023 5 33.55306 -86.81500 AL Jefferson Birmingham-Hoover, AL 1,052,238 1 3 12 36 0.0180 0.0296 0.0475 010731009 5 33.45972 -87.30556 AL Jefferson Birmingham-Hoover, AL 1,052,238 3 12 34 0.0021 0.0032 0.0044 010732003 5 33.49972 -86.92417 AL Jefferson Birmingham-Hoover, AL 1,052,238 1 3 12 36 0.0450 0.0967 0.2091 010890014 5 34.69083 -86.58306 AL Madison Huntsville, AL 342,376 1 3 12 34 0.0024 0.0040 0.0057 010970003 5 30.76972 -88.08750 AL Mobile Mobile, AL 399,843 1 3 12 36 0.0038 0.0060 0.0096 011011002 5 32.40694 -86.25639 AL Montgomery Montgomery, AL 346,528 1 3 12 34 0.0045 0.0083 0.0115 011030011 5 34.51861 -86.97694 AL Morgan Decatur, AL 145,867 1 3 11 30 0.0029 0.0042 0.0060 011130001 5 32.47639 -84.99917 AL Russell Columbus, GA-AL 281,768 1 1 3 9 0.0030 0.0037 0.0063 020200018 5 61.20667 -149.82083 AK Anchorage Municipa Anchorage, AK 319,605 1 1 6 17 0.0043 0.0067 0.0101 020900010 6 64.84111 -147.72000 AK Fairbanks North Star Fairbanks, AK 82,840 1 1 3 8 0.0034 0.0053 0.0070 040130019 5 33.48385 -112.14257 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 2 7 20 0.0030 0.0057 0.0100 040134009 5 33.40642 -112.14434 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 4 9 0.0062 0.0123 0.0228 040137003 5 33.28936 -112.15732 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 4 10 0.0027 0.0049 0.0067 040137020 5 33.47333 -111.85418 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 4 11 0.0026 0.0038 0.0058 040138006 5 33.43671 -112.09141 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 1 5 13 0.0042 0.0067 0.0084 040139997 7 33.50364 -112.09500 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 3 12 36 0.0027 0.0047 0.0069 040139998 5 33.45513 -111.99610 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 1 6 18 0.0033 0.0047 0.0075 040191028 5 32.29515 -110.98230 AZ Pima Tucson, AZ 843,746 1 3 12 35 0.0017 0.0022 0.0035 050030005 5 33.13944 -91.95000 AR Ashley 1 2 9 21 0.0027 0.0055 0.0082 051190007 5 34.75611 -92.27583 AR Pulaski Little Rock-North Little Rock, AR 610,518 1 3 12 33 0.0029 0.0042 0.0061 051450001 5 35.24861 -91.71528 AR White Searcy, AR 67,165 1 2 9 22 0.0026 0.0046 0.0063 060070002 5 39.75750 -121.84222 CA Butte Chico, CA 203,171 1 3 12 36 0.0026 0.0039 0.0054 060190008 5 36.78139 -119.77222 CA Fresno Fresno, CA 799,407 1 3 12 36 0.0030 0.0050 0.0066 060250005 5 32.67611 -115.48333 CA Imperial El Centro, CA 142,361 1 3 12 36 0.0119 0.0172 0.0342 060290014 5 35.35611 -119.04028 CA Kern Bakersfield, CA 661,645 1 3 11 32 0.0026 0.0046 0.0061 060371103 5 34.06659 -118.22688 CA Los Angeles Los Angeles-Long Beach-Santa An 12,365,627 1 3 12 36 0.0053 0.0098 0.0228 060631009 5 39.80833 -120.47167 CA Plumas 1 3 12 36 0.0025 0.0041 0.0054 060658001 5 33.99958 -117.41601 CA Riverside Riverside-San Bernardino-Ontario, 3,254,821 1 3 12 36 0.0058 0.0088 0.0151 060670006 5 38.61417 -121.36694 CA Sacramento Sacramento--Arden-Arcade--Rosev 1,796,857 1 3 12 36 0.0022 0.0031 0.0047 060670010 5 38.55833 -121.49194 CA Sacramento Sacramento--Arden-Arcade--Rosev 1,796,857 1 3 12 36 0.0029 0.0037 0.0052 060730003 5 32.79139 -116.94167 CA San Diego San Diego-Carlsbad-San Marcos, C 2,813,833 1 3 12 36 0.0039 0.0059 0.0078 060731002 5 33.12778 -117.07417 CA San Diego San Diego-Carlsbad-San Marcos, C 2,813,833 1 3 12 36 0.0035 0.0050 0.0064 060850005 5 37.34850 -121.89500 CA Santa Clara San Jose-Sunnyvale-Santa Clara, C 1,735,819 1 3 12 36 0.0026 0.0063 0.0138 060990005 5 37.64167 -120.99361 CA Stanislaus Modesto, CA 446,997 1 3 12 36 0.0033 0.0065 0.0090 061072002 5 36.33222 -119.29028 CA Tulare Visalia-Porterville, CA 368,021 1 3 12 36 0.0034 0.0046 0.0060 061112002 5 34.27750 -118.68472 CA Ventura Oxnard-Thousand Oaks-Ventura, C 753,197 1 3 12 33 0.0020 0.0032 0.0042 080010006 5 39.82574 -104.93699 CO Adams Denver-Aurora, CO 2,157,756 1 3 12 36 0.0077 0.0163 0.0185 080410011 5 38.83139 -104.82778 CO El Paso Colorado Springs, CO 537,484 1 3 12 34 0.0019 0.0028 0.0048 080670008 5 37.26861 -107.87500 CO La Plata Durango, CO 43,941 1 1 3 8 0.0014 0.0016 0.0024 080770003 5 39.09083 -108.56389 CO Mesa Grand Junction, CO 116,255 1 1 3 8 0.0015 0.0021 0.0031 080770017 5 39.06363 -108.56102 CO Mesa Grand Junction, CO 116,255 1 2 9 25 0.0023 0.0035 0.0056 081230008 5 40.20917 -104.82306 CO Weld Greeley, CO 180,936 3 12 36 0.0020 0.0034 0.0054 090090027 5 41.30111 -72.90278 CT New Haven New Haven-Milford, CT 824,008 1 2 8 20 0.0029 0.0043 0.0066 100010003 5 39.15500 -75.51806 DE Kent Dover, DE 126,697 1 3 12 34 0.0024 0.0038 0.0051 100032004 5 39.73944 -75.55806 DE New Castle Philadelphia-Camden-Wilmington, 5,687,147 1 3 12 32 0.0042 0.0084 0.0114 110010042 6 38.88083 -77.03250 DC District of Columbia Washington-Arlington-Alexandria, 4,796,183 1 1 6 18 0.0037 0.0058 0.0075 110010043 5 38.91889 -77.01250 DC District of Columbia Washington-Arlington-Alexandria, 4,796,183 1 3 12 36 0.0035 0.0063 0.0093 120330004 6 30.52500 -87.20417 FL Escambia Pensacola-Ferry Pass-Brent, FL 412,153 1 3 12 36 0.0019 0.0026 0.0042 120571075 5 28.05000 -82.37806 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 1 1 4 12 0.0023 0.0034 0.0052 120573002 5 27.96565 -82.23040 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 2 8 24 0.0027 0.0042 0.0069 120730012 5 30.43972 -84.34833 FL Leon Tallahassee, FL 320,304 1 3 12 36 0.0020 0.0034 0.0049 120861016 5 25.79417 -80.20611 FL Miami-Dade Miami-Fort Lauderdale-Miami Bea 5,007,564 1 3 12 36 0.0020 0.0068 0.0163 121030026 5 27.85004 -82.71459 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 1 1 5 16 0.0025 0.0039 0.0088 130210007 5 32.77944 -83.64694 GA Bibb Macon, GA 222,368 3 12 34 0.0029 0.0069 0.0147 A -75 
Table 7. Pb-PM2.5 monitoring site information 

Attachment A-2 

site poc lat long state county_name cbsa_name cbsa_pop00 urban data completeness (complete periods) 3-year metrics qtrs years months annual mean max quarterly mean max monthly mean 130510017 5 32.09278 -81.14417 GA Chatham Savannah, GA 293,000 1 2 8 22 0.0017 0.0029 0.0041 130590001 5 33.94583 -83.37222 GA Clarke Athens-Clarke County, GA 166,079 1 3 12 29 0.0021 0.0029 0.0041 130690002 5 31.52430 -82.76510 GA Coffee Douglas, GA 45,022 3 12 30 0.0013 0.0022 0.0032 130890002 5 33.68750 -84.29028 GA DeKalb Atlanta-Sandy Springs-Marietta, G 4,247,981 1 3 12 36 0.0027 0.0042 0.0077 131150005 5 34.26333 -85.27250 GA Floyd Rome, GA 90,565 3 12 33 0.0023 0.0030 0.0040 132150011 5 32.43083 -84.93167 GA Muscogee Columbus, GA-AL 281,768 1 3 12 32 0.0036 0.0101 0.0086 132450091 5 33.43333 -82.02194 GA Richmond Augusta-Richmond County, GA-SC 499,684 1 3 12 32 0.0025 0.0038 0.0067 132950002 5 34.96611 -85.29750 GA Walker Chattanooga, TN-GA 476,531 1 1 3 9 0.0033 0.0040 0.0051 150032004 5 21.39667 -157.97167 HI Honolulu Honolulu, HI 876,156 1 3 12 34 0.0010 0.0021 0.0031 160270004 5 43.56240 -116.56323 ID Canyon Boise City-Nampa, ID 464,840 1 3 12 36 0.0022 0.0046 0.0096 170310057 5 41.91473 -87.72273 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 3 12 35 0.0071 0.0115 0.0172 170310076 5 41.75137 -87.71375 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 3 12 36 0.0054 0.0063 0.0087 170314201 5 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 3 12 36 0.0040 0.0054 0.0085 170434002 5 41.77120 -88.15250 IL DuPage Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 2 8 23 0.0047 0.0063 0.0072 171150013 5 39.86694 -88.92556 IL Macon Decatur, IL 114,706 3 12 35 0.0067 0.0142 0.0228 171192009 5 38.90278 -90.14306 IL Madison St. Louis, MO-IL 2,721,491 1 3 12 32 0.0090 0.0208 0.0413 180030004 5 41.09472 -85.10194 IN Allen Fort Wayne, IN 390,156 1 2 7 20 0.0257 0.1674 0.3091 180372001 5 38.39139 -86.92917 IN Dubois Jasper, IN 52,511 1 1 4 12 0.0042 0.0051 0.0063 180390003 5 41.66778 -85.96944 IN Elkhart Elkhart-Goshen, IN 182,791 1 1 4 12 0.0044 0.0048 0.0056 180650003 5 40.01167 -85.52361 IN Henry New Castle, IN 48,508 3 12 36 0.0037 0.0055 0.0074 180890022 5 41.60667 -87.30472 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 3 11 32 0.0097 0.0128 0.0204 180892004 5 41.58528 -87.47444 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 2 8 24 0.0090 0.0120 0.0244 180970078 5 39.81110 -86.11447 IN Marion Indianapolis-Carmel, IN 1,525,104 1 3 12 36 0.0048 0.0071 0.0087 181411008 5 41.69361 -86.23667 IN St. Joseph South Bend-Mishawaka, IN-MI 316,663 1 1 4 12 0.0042 0.0054 0.0072 181630012 5 38.02167 -87.56944 IN Vanderburgh Evansville, IN-KY 342,815 1 3 12 34 0.0031 0.0057 0.0080 191130037 5 42.00833 -91.67861 IA Linn Cedar Rapids, IA 237,230 1 3 12 35 0.0033 0.0044 0.0071 191530030 5 41.60306 -93.64306 IA Polk Des Moines-West Des Moines, IA 481,394 1 3 12 33 0.0027 0.0037 0.0058 191630015 5 41.53000 -90.58750 IA Scott Davenport-Moline-Rock Island, IA 376,019 1 3 12 33 0.0065 0.0084 0.0118 201730010 5 37.70111 -97.31389 KS Sedgwick Wichita, KS 571,166 1 2 9 18 0.0021 0.0032 0.0053 202090021 5 39.11750 -94.63556 KS Wyandotte Kansas City, MO-KS 1,836,038 1 3 12 36 0.0048 0.0066 0.0100 210190017 5 38.45917 -82.64056 KY Boyd Huntington-Ashland, WV-KY-OH 288,649 1 3 12 35 0.0043 0.0060 0.0096 210590005 5 37.78083 -87.07556 KY Daviess Owensboro, KY 109,875 1 1 4 12 0.0037 0.0044 0.0061 210590014 5 37.74111 -87.11806 KY Daviess Owensboro, KY 109,875 1 2 8 19 0.0023 0.0038 0.0036 210670012 5 38.06500 -84.50000 KY Fayette Lexington-Fayette, KY 408,326 1 3 12 36 0.0038 0.0066 0.0101 211110043 5 38.23222 -85.82528 KY Jefferson Louisville-Jefferson County, KY-IN 1,161,975 1 3 12 36 0.0042 0.0070 0.0100 211110048 5 38.24056 -85.73167 KY Jefferson Louisville-Jefferson County, KY-IN 1,161,975 1 3 12 36 0.0048 0.0071 0.0133 211170007 5 39.07250 -84.52500 KY Kenton Cincinnati-Middletown, OH-KY-IN 2,009,632 1 3 12 36 0.0048 0.0106 0.0170 211250004 5 37.08722 -84.06333 KY Laurel London, KY 52,715 1 3 12 36 0.0037 0.0048 0.0095 211451004 5 37.06556 -88.63778 KY McCracken Paducah, KY-IL 98,765 1 3 12 36 0.0029 0.0043 0.0059 211930003 5 37.28306 -83.22028 KY Perry 3 12 34 0.0041 0.0059 0.0079 212270007 5 36.99333 -86.41833 KY Warren Bowling Green, KY 104,166 1 3 12 35 0.0033 0.0056 0.0098 220150008 5 32.53417 -93.74972 LA Bossier Shreveport-Bossier City, LA 375,965 3 12 32 0.0046 0.0089 0.0147 220330009 5 30.46111 -91.17694 LA East Baton Rouge Baton Rouge, LA 705,973 1 3 11 31 0.0051 0.0101 0.0198 240030019 5 39.10111 -76.72944 MD Anne Arundel Baltimore-Towson, MD 2,552,994 1 2 7 17 0.0033 0.0061 0.0087 240053001 5 39.31083 -76.47444 MD Baltimore Baltimore-Towson, MD 2,552,994 1 2 9 26 0.0054 0.0080 0.0101 240330030 5 39.05528 -76.87833 MD Prince George's Washington-Arlington-Alexandria, 4,796,183 1 1 4 12 0.0039 0.0069 0.0099 250130008 5 42.19446 -72.55571 MA Hampden Springfield, MA 680,014 2 10 25 0.0025 0.0035 0.0045 250250042 6 42.32944 -71.08278 MA Suffolk Boston-Cambridge-Quincy, MA-N 4,391,344 1 3 12 35 0.0027 0.0039 0.0056 260050003 5 42.76778 -86.14861 MI Allegan Allegan, MI 105,665 1 3 11 35 0.0035 0.0055 0.0079 260330901 5 46.49361 -84.36417 MI Chippewa Sault Ste. Marie, MI 38,543 1 3 12 36 0.0023 0.0038 0.0046 260770008 5 42.27806 -85.54194 MI Kalamazoo Kalamazoo-Portage, MI 314,866 1 3 11 33 0.0050 0.0068 0.0097 260810020 5 42.98417 -85.67139 MI Kent Grand Rapids-Wyoming, MI 740,482 1 3 12 35 0.0048 0.0083 0.0104 261130001 5 44.31056 -84.89194 MI Missaukee Cadillac, MI 44,962 3 12 33 0.0022 0.0057 0.0102 261150005 5 41.76389 -83.47194 MI Monroe Monroe, MI 145,945 3 12 32 0.0042 0.0050 0.0074 261610008 5 42.24056 -83.59972 MI Washtenaw Ann Arbor, MI 322,895 1 2 10 30 0.0038 0.0060 0.0087 A-76 
Table 7. Pb-PM2.5 monitoring site information 

Attachment A-2 

site poc lat long state county_name cbsa_name cbsa_pop00 urban data completeness (complete periods) 3-year metrics qtrs years months annual mean max quarterly mean max monthly mean 261630001 5 42.22861 -83.20833 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 1 3 12 36 0.0042 0.0051 0.0063 261630033 5 42.30667 -83.14889 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 1 3 12 33 0.0118 0.0182 0.0329 270530963 5 44.95540 -93.25827 MN Hennepin Minneapolis-St. Paul-Bloomington 2,968,806 1 3 12 36 0.0031 0.0041 0.0072 270953051 5 46.20703 -93.75941 MN Mille Lacs 3 11 31 0.0017 0.0023 0.0036 271095008 5 43.99691 -92.45037 MN Olmsted Rochester, MN 163,618 1 3 12 35 0.0027 0.0043 0.0067 271230871 5 44.96145 -93.03589 MN Ramsey Minneapolis-St. Paul-Bloomington 2,968,806 1 2 9 27 0.0042 0.0073 0.0084 280350004 5 31.32364 -89.28717 MS Forrest Hattiesburg, MS 123,812 1 3 12 35 0.0048 0.0128 0.0302 280430001 5 33.83611 -89.79722 MS Grenada Grenada, MS 23,263 3 11 31 0.0017 0.0032 0.0056 280470008 5 30.39014 -89.04972 MS Harrison Gulfport-Biloxi, MS 246,190 1 3 11 32 0.0023 0.0034 0.0062 280490018 5 32.29681 -90.18831 MS Hinds Jackson, MS 497,197 1 3 12 31 0.0046 0.0071 0.0112 280670002 5 31.68844 -89.13506 MS Jones Laurel, MS 83,107 1 3 12 35 0.0030 0.0073 0.0180 290470005 5 39.30306 -94.37639 MO Clay Kansas City, MO-KS 1,836,038 3 12 36 0.0026 0.0040 0.0050 290530001 5 38.79500 -92.91806 MO Cooper 3 12 33 0.0020 0.0028 0.0041 290990012 5 38.43778 -90.36139 MO Jefferson St. Louis, MO-IL 2,721,491 1 3 12 36 0.0089 0.0126 0.0191 291860005 5 37.89694 -90.42222 MO Ste Genevieve 3 12 34 0.0045 0.0084 0.0095 292070001 5 36.97000 -90.14000 MO Stoddard 1 4 11 0.0034 0.0044 0.0068 295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL 2,721,491 1 3 12 36 0.0095 0.0140 0.0192 300530018 5 48.38417 -115.54806 MT Lincoln 1 3 12 35 0.0017 0.0029 0.0039 300630031 5 46.87491 -113.99525 MT Missoula Missoula, MT 95,802 1 3 12 36 0.0020 0.0035 0.0051 310550019 5 41.24722 -95.97556 NE Douglas Omaha-Council Bluffs, NE-IA 767,041 1 3 12 35 0.0030 0.0042 0.0055 320030560 5 36.15861 -115.11083 NV Clark Las Vegas-Paradise, NV 1,375,765 1 1 5 15 0.0025 0.0039 0.0061 320030561 5 36.16399 -115.11393 NV Clark Las Vegas-Paradise, NV 1,375,765 1 2 7 20 0.0025 0.0044 0.0086 320310016 5 39.52508 -119.80772 NV Washoe Reno-Sparks, NV 342,885 1 3 12 36 0.0024 0.0040 0.0060 330110020 5 43.00056 -71.46806 NH Hillsborough Manchester-Nashua, NH 380,841 1 3 12 34 0.0034 0.0053 0.0062 330150014 5 43.07528 -70.74806 NH Rockingham Boston-Cambridge-Quincy, MA-N 4,391,344 1 3 12 36 0.0024 0.0028 0.0036 340070003 5 39.92304 -75.09762 NJ Camden Philadelphia-Camden-Wilmington, 5,687,147 1 3 11 33 0.0042 0.0052 0.0069 340230006 6 40.47279 -74.42251 NJ Middlesex New York-Northern New Jersey-Lo 18,323,002 1 3 12 24 0.0045 0.0063 0.0114 340273001 5 40.78763 -74.67630 NJ Morris New York-Northern New Jersey-Lo 18,323,002 1 3 12 35 0.0027 0.0038 0.0059 340390004 5 40.64144 -74.20836 NJ Union New York-Northern New Jersey-Lo 18,323,002 1 3 12 36 0.0044 0.0059 0.0067 350010023 5 35.13426 -106.58551 NM Bernalillo Albuquerque, NM 729,649 1 2 8 22 0.0013 0.0020 0.0027 360050083 6 40.86586 -73.88075 NY Bronx New York-Northern New Jersey-Lo 18,323,002 1 3 12 36 0.0040 0.0059 0.0067 360050110 5 40.81616 -73.90207 NY Bronx New York-Northern New Jersey-Lo 18,323,002 1 3 12 36 0.0047 0.0064 0.0079 360290005 6 42.87684 -78.80988 NY Erie Buffalo-Niagra Falls, NY Metropol 1,170,111 1 3 12 36 0.0106 0.0157 0.0192 360310003 5 44.39309 -73.85892 NY Essex 3 12 34 0.0015 0.0021 0.0028 360551007 5 43.14620 -77.54813 NY Monroe Rochester, NY 1,037,831 1 2 7 20 0.0031 0.0040 0.0048 360556001 5 43.16100 -77.60357 NY Monroe Rochester, NY 1,037,831 1 1 5 15 0.0031 0.0037 0.0045 360610062 1 40.72052 -74.00409 NY New York New York-Northern New Jersey-Lo 18,323,002 1 1 4 12 0.0070 0.0092 0.0190 360632008 1 43.08216 -79.00099 NY Niagara Buffalo-Niagra Falls, NY Metropol 1,170,111 1 1 4 11 0.0052 0.0063 0.0065 360710002 1 41.49947 -74.00973 NY Orange Poughkeepsie-Newburgh-Middleto 621,517 1 1 4 11 0.0034 0.0040 0.0053 360810124 6 40.73620 -73.82317 NY Queens New York-Northern New Jersey-Lo 18,323,002 1 3 12 36 0.0038 0.0055 0.0068 361010003 5 42.09071 -77.21025 NY Steuben Corning, NY 98,726 3 12 36 0.0028 0.0034 0.0042 361030001 1 40.74583 -73.42028 NY Suffolk New York-Northern New Jersey-Lo 18,323,002 1 1 4 11 0.0032 0.0039 0.0051 370210034 5 35.60972 -82.35083 NC Buncombe Asheville, NC 369,171 1 3 12 36 0.0019 0.0031 0.0052 370350004 5 35.72889 -81.36556 NC Catawba Hickory-Lenoir-Morganton, NC 341,851 1 3 12 35 0.0025 0.0036 0.0060 370510009 5 35.04142 -78.95311 NC Cumberland Fayetteville, NC 336,609 1 2 8 22 0.0021 0.0037 0.0057 370570002 5 35.81444 -80.26250 NC Davidson Thomasville-Lexington, NC 147,246 1 2 8 23 0.0032 0.0047 0.0087 370670022 5 36.11056 -80.22667 NC Forsyth Winston-Salem, NC 421,961 1 3 12 36 0.0026 0.0036 0.0063 370810013 5 36.10917 -79.80111 NC Guilford Greensboro-High Point, NC 643,430 1 2 9 23 0.0028 0.0043 0.0064 371070004 5 35.23146 -77.56879 NC Lenoir Kinston, NC 59,648 3 12 34 0.0026 0.0046 0.0062 371190041 5 35.24028 -80.78556 NC Mecklenburg Charlotte-Gastonia-Concord, NC-S 1,330,448 1 3 12 36 0.0029 0.0042 0.0052 371590021 5 35.55187 -80.39504 NC Rowan Salisbury, NC 130,340 1 1 4 11 0.0032 0.0040 0.0057 371830014 5 35.85611 -78.57417 NC Wake Raleigh-Cary, NC 797,071 1 3 12 34 0.0021 0.0038 0.0041 380150003 5 46.82543 -100.76821 ND Burleigh Bismarck, ND 94,719 1 3 12 36 0.0012 0.0023 0.0036 380171004 5 46.93375 -96.85535 ND Cass Fargo, ND-MN 174,367 3 12 36 0.0019 0.0027 0.0038 380530002 5 47.58120 -103.29950 ND Mc Kenzie 3 12 35 0.0012 0.0026 0.0040 A-77 
Table 7. Pb-PM2.5 monitoring site information 

Attachment A-2 

site poc lat long state county_name cbsa_name cbsa_pop00 urban data completeness (complete periods) 3-year metrics qtrs years months annual mean max quarterly mean max monthly mean 390171004 5 39.53000 -84.39250 OH Butler Cincinnati-Middletown, OH-KY-IN 2,009,632 1 3 12 36 0.0092 0.0147 0.0273 390350038 6 41.47694 -81.68194 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 1 3 12 35 0.0120 0.0163 0.0282 390350060 5 41.49396 -81.67854 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 1 3 12 36 0.0123 0.0207 0.0270 390490081 6 40.08778 -82.95972 OH Franklin Columbus, OH 1,612,694 1 3 12 34 0.0038 0.0052 0.0073 390530003 5 38.94996 -82.10910 OH Gallia Point Pleasant, WV-OH 57,026 1 6 16 0.0043 0.0072 0.0085 390610040 5 39.12861 -84.50417 OH Hamilton Cincinnati-Middletown, OH-KY-IN 2,009,632 1 2 8 25 0.0056 0.0069 0.0113 390610042 5 39.10500 -84.55111 OH Hamilton Cincinnati-Middletown, OH-KY-IN 2,009,632 1 1 4 11 0.0079 0.0114 0.0286 390810017 5 40.36610 -80.61500 OH Jefferson Weirton-Steubenville, WV-OH 132,008 1 1 5 11 0.0127 0.0150 0.0193 390870010 5 38.51972 -82.66556 OH Lawrence Huntington-Ashland, WV-KY-OH 288,649 1 3 12 33 0.0059 0.0095 0.0137 390930016 5 41.43944 -82.16167 OH Lorain Cleveland-Elyria-Mentor, OH 2,148,143 1 1 4 10 0.0157 0.0244 0.0450 390933002 5 41.46306 -82.11444 OH Lorain Cleveland-Elyria-Mentor, OH 2,148,143 2 8 20 0.0238 0.0337 0.0465 390950026 5 41.62056 -83.64139 OH Lucas Toledo, OH 659,188 1 3 12 36 0.0035 0.0053 0.0069 390990014 5 41.09587 -80.65843 OH Mahoning Youngstown-Warren-Boardman, O 602,964 1 3 12 35 0.0131 0.0253 0.0382 391130031 5 39.75944 -84.14444 OH Montgomery Dayton, OH 848,153 1 3 11 25 0.0042 0.0079 0.0085 391510017 5 40.78667 -81.39444 OH Stark Canton-Massillon, OH 406,934 1 1 4 10 0.0114 0.0148 0.0186 391510020 5 40.80056 -81.37333 OH Stark Canton-Massillon, OH 406,934 1 2 8 24 0.0060 0.0082 0.0157 391530023 5 41.08806 -81.54167 OH Summit Akron, OH 694,960 1 3 11 29 0.0050 0.0069 0.0098 400450890 5 36.08518 -99.93494 OK Ellis 3 12 34 0.0012 0.0019 0.0027 401091037 5 35.61278 -97.47222 OK Oklahoma Oklahoma City, OK 1,095,421 1 3 12 36 0.0022 0.0033 0.0046 401431127 5 36.20490 -95.97654 OK Tulsa Tulsa, OK 859,532 1 3 12 36 0.0031 0.0045 0.0056 410170120 5 44.06390 -121.31258 OR Deschutes Bend, OR 115,367 1 1 4 11 0.0014 0.0018 0.0021 410290133 5 42.31408 -122.87924 OR Jackson Medford, OR 181,269 1 3 12 35 0.0019 0.0029 0.0035 410390060 5 44.02631 -123.08374 OR Lane Eugene-Springfield, OR 322,959 1 3 12 35 0.0015 0.0025 0.0041 410510246 6 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 3 12 36 0.0075 0.0182 0.0398 410610119 5 45.33897 -117.90480 OR Union La Grande, OR 24,530 2 8 20 0.0012 0.0020 0.0026 420010001 5 39.92000 -77.31000 PA Adams Gettysburg, PA 91,292 3 12 35 0.0037 0.0070 0.0082 420030008 6 40.46556 -79.96111 PA Allegheny Pittsburgh, PA 2,431,087 1 3 12 36 0.0112 0.0141 0.0252 420030021 5 40.41361 -79.94139 PA Allegheny Pittsburgh, PA 2,431,087 1 1 3 9 0.0073 0.0083 0.0129 420030064 6 40.32361 -79.86833 PA Allegheny Pittsburgh, PA 2,431,087 1 2 9 23 0.0143 0.0239 0.0356 420270100 5 40.81139 -77.87703 PA Centre State College, PA 135,758 1 3 12 35 0.0032 0.0043 0.0061 420290100 5 39.83444 -75.76861 PA Chester Philadelphia-Camden-Wilmington, 5,687,147 3 12 32 0.0046 0.0086 0.0105 420430401 5 40.24500 -76.84472 PA Dauphin Harrisburg-Carlisle, PA 509,074 1 3 12 34 0.0063 0.0122 0.0190 420450002 5 39.83556 -75.37250 PA Delaware Philadelphia-Camden-Wilmington, 5,687,147 1 3 12 34 0.0042 0.0057 0.0073 420490003 5 42.14175 -80.03861 PA Erie Erie, PA 280,843 1 3 12 34 0.0057 0.0153 0.0323 420692006 5 41.44278 -75.62306 PA Lackawanna Scranton--Wilkes-Barre, PA 560,625 1 3 12 33 0.0054 0.0087 0.0115 420710007 5 40.04667 -76.28333 PA Lancaster Lancaster, PA 470,658 1 3 12 35 0.0073 0.0175 0.0231 420950025 5 40.62806 -75.34111 PA Northampton Allentown-Bethlehem-Easton, PA- 740,395 1 3 12 36 0.0065 0.0095 0.0152 420990301 5 40.45694 -77.16556 PA Perry Harrisburg-Carlisle, PA 509,074 3 12 36 0.0035 0.0056 0.0084 421010004 7 40.00889 -75.09778 PA Philadelphia Philadelphia-Camden-Wilmington, 5,687,147 1 3 12 36 0.0052 0.0071 0.0090 421010136 5 39.92750 -75.22278 PA Philadelphia Philadelphia-Camden-Wilmington, 5,687,147 1 3 12 34 0.0038 0.0061 0.0104 421255001 5 40.44528 -80.42083 PA Washington Pittsburgh, PA 2,431,087 3 12 36 0.0050 0.0067 0.0084 421290008 5 40.30469 -79.50567 PA Westmoreland Pittsburgh, PA 2,431,087 1 3 12 35 0.0051 0.0070 0.0097 421330008 5 39.96528 -76.69944 PA York York-Hanover, PA 381,751 1 3 12 34 0.0058 0.0112 0.0169 440070022 5 41.80795 -71.41500 RI Providence Providence-New Bedford-Fall Rive 1,582,997 1 3 12 36 0.0065 0.0432 0.1103 440071010 5 41.84092 -71.36094 RI Providence Providence-New Bedford-Fall Rive 1,582,997 1 1 5 14 0.0030 0.0037 0.0051 450190046 5 32.94275 -79.65718 SC Charleston Charleston-North Charleston, SC 549,033 2 6 17 0.0019 0.0026 0.0039 450190049 5 32.79098 -79.95869 SC Charleston Charleston-North Charleston, SC 549,033 1 3 12 36 0.0022 0.0035 0.0048 450250001 5 34.61712 -80.19879 SC Chesterfield 3 12 36 0.0021 0.0035 0.0044 450450009 5 34.90105 -82.31307 SC Greenville Greenville, SC 559,940 1 3 12 36 0.0026 0.0050 0.0060 450790019 5 33.99330 -81.02414 SC Richland Columbia, SC 647,158 1 3 12 34 0.0048 0.0092 0.0122 460990006 5 43.54429 -96.72644 SD Minnehaha Sioux Falls, SD 187,093 1 3 12 36 0.0022 0.0031 0.0052 470370023 5 36.17633 -86.73890 TN Davidson Nashville-Davidson--Murfreesboro 1,311,789 1 3 12 34 0.0038 0.0065 0.0107 470654002 5 35.05093 -85.12631 TN Hamilton Chattanooga, TN-GA 476,531 1 3 12 34 0.0038 0.0050 0.0071 470931020 5 36.01944 -83.87361 TN Knox Knoxville, TN 616,079 1 3 11 29 0.0040 0.0052 0.0059 470990002 5 35.11611 -87.47000 TN Lawrence Lawrenceburg, TN 39,926 3 12 35 0.0021 0.0030 0.0040 A-78 
Table 7. Pb-PM2.5 monitoring site information 

Attachment A-2 

site poc lat long state county_name cbsa_name cbsa_pop00 urban data completeness (complete periods) 3-year metrics qtrs years months annual mean max quarterly mean max monthly mean 471570047 5 35.16895 -90.02157 TN Shelby Memphis, TN-MS-AR 1,205,204 1 3 12 36 0.0033 0.0045 0.0076 471631007 5 36.54065 -82.52167 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 1 3 12 33 0.0031 0.0049 0.0086 471650007 5 36.29778 -86.65278 TN Sumner Nashville-Davidson--Murfreesboro 1,311,789 1 3 12 32 0.0027 0.0051 0.0068 480430002 5 30.36580 -103.64910 TX Brewster 3 11 20 0.0014 0.0025 0.0043 480430101 5 29.30250 -103.16782 TX Brewster 3 12 25 0.0009 0.0018 0.0028 481130050 5 32.77417 -96.79778 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 1 3 12 34 0.0027 0.0041 0.0055 481130069 5 32.81995 -96.86008 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 1 3 12 36 0.0036 0.0077 0.0169 481390015 5 32.43694 -97.02500 TX Ellis Dallas-Fort Worth-Arlington, TX 5,161,544 3 11 31 0.0029 0.0057 0.0085 481410044 5 31.76567 -106.45523 TX El Paso El Paso, TX 679,622 1 3 12 34 0.0036 0.0060 0.0090 481410053 5 31.75852 -106.50105 TX El Paso El Paso, TX 679,622 1 3 12 34 0.0078 0.0148 0.0236 481670014 5 29.26332 -94.85657 TX Galveston Houston-Sugar Land-Baytown, TX 4,715,407 3 11 32 0.0021 0.0028 0.0041 482010024 5 29.90111 -95.32694 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 12 35 0.0041 0.0066 0.0087 482010026 5 29.80250 -95.12555 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 11 31 0.0028 0.0038 0.0056 482010055 5 29.69574 -95.49924 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 11 32 0.0020 0.0026 0.0037 482011034 5 29.76799 -95.22058 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 11 31 0.0026 0.0073 0.0160 482011039 7 29.67005 -95.12849 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 2 10 30 0.0023 0.0042 0.0072 482030002 5 32.66900 -94.16745 TX Harrison Marshall, TX 62,110 3 11 32 0.0019 0.0027 0.0035 482430004 5 30.66938 -104.02463 TX Jeff Davis 3 11 25 0.0008 0.0014 0.0028 482450022 5 29.86395 -94.31776 TX Jefferson Beaumont-Port Arthur, TX 385,090 3 11 32 0.0019 0.0030 0.0049 482570005 5 32.56917 -96.31583 TX Kaufman Dallas-Fort Worth-Arlington, TX 5,161,544 1 3 11 30 0.0024 0.0063 0.0128 482730314 5 27.42694 -97.29861 TX Kleberg Kingsville, TX 31,963 3 11 29 0.0010 0.0017 0.0024 483030001 5 33.59085 -101.84759 TX Lubbock Lubbock, TX 249,700 1 2 10 28 0.0010 0.0024 0.0062 483390078 5 30.35030 -95.42514 TX Montgomery Houston-Sugar Land-Baytown, TX 4,715,407 3 11 32 0.0031 0.0042 0.0058 483550034 5 27.81180 -97.46563 TX Nueces Corpus Christi, TX 403,280 1 3 12 36 0.0013 0.0021 0.0033 483611100 5 30.19417 -93.86694 TX Orange Beaumont-Port Arthur, TX 385,090 3 11 32 0.0020 0.0028 0.0041 490110004 5 40.90297 -111.88447 UT Davis Ogden-Clearfield, UT 442,656 1 2 10 29 0.0035 0.0059 0.0071 490353006 5 40.73639 -111.87222 UT Salt Lake Salt Lake City, UT 968,858 1 3 12 36 0.0042 0.0077 0.0131 490494001 5 40.34139 -111.71361 UT Utah Provo-Orem, UT 376,774 1 3 12 36 0.0034 0.0072 0.0095 500070012 5 44.48028 -73.21444 VT Chittenden Burlington-South Burlington, VT 198,889 1 3 12 35 0.0023 0.0029 0.0037 510870014 5 37.55833 -77.40028 VA Henrico Richmond, VA 1,096,957 1 2 8 24 0.0030 0.0042 0.0064 511390004 5 38.66333 -78.50472 VA Page 2 8 24 0.0027 0.0045 0.0081 515200006 5 36.60778 -82.16444 VA Bristol (City) Kingsport-Bristol-Bristol, TN-VA 298,484 1 3 12 35 0.0036 0.0057 0.0083 517600020 5 37.51056 -77.49833 VA Richmond (City) Richmond, VA 1,096,957 1 1 4 12 0.0027 0.0033 0.0064 517700014 5 37.25611 -79.98500 VA Roanoke (City) Roanoke, VA 288,309 1 2 8 24 0.0074 0.0140 0.0283 530330024 6 47.75333 -122.27722 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 35 0.0030 0.0046 0.0073 530330032 6 47.54556 -122.32222 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 2 7 22 0.0078 0.0134 0.0201 530330048 6 47.61846 -122.32972 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 33 0.0032 0.0052 0.0089 530330057 6 47.56333 -122.33833 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 33 0.0074 0.0150 0.0260 530330080 6 47.57027 -122.30860 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 36 0.0034 0.0055 0.0075 530630016 5 47.66083 -117.35722 WA Spokane Spokane, WA 417,939 1 1 4 11 0.0038 0.0062 0.0087 540390011 5 38.44861 -81.68389 WV Kanawha Charleston, WV 309,635 2 9 25 0.0026 0.0043 0.0048 540391005 5 38.36806 -81.69361 WV Kanawha Charleston, WV 309,635 1 2 8 25 0.0043 0.0067 0.0077 540511002 5 39.91597 -80.73406 WV Marshall Wheeling, WV-OH 153,172 1 1 6 19 0.0065 0.0081 0.0124 550270007 5 43.43500 -88.52778 WI Dodge Beaver Dam, WI 85,897 3 12 36 0.0036 0.0059 0.0083 550590019 5 42.50472 -87.80930 WI Kenosha Chicago-Naperville-Joliet, IL-IN-W 9,098,316 3 12 36 0.0038 0.0057 0.0073 550710007 5 44.13861 -87.61611 WI Manitowoc Manitowoc, WI 82,887 2 8 25 0.0039 0.0060 0.0113 550790026 5 43.06111 -87.91250 WI Milwaukee Milwaukee-Waukesha-West Allis, 1,500,741 1 3 12 36 0.0058 0.0115 0.0245 551198001 5 45.20389 -90.60000 WI Taylor 3 12 36 0.0020 0.0030 0.0050 551330027 5 43.02028 -88.21500 WI Waukesha Milwaukee-Waukesha-West Allis, 1,500,741 1 3 12 36 0.0097 0.0185 0.0217 720610001 5 18.42472 -66.11639 PR Guaynabo San Juan-Caguas-Guaynabo, PR 2,509,007 1 3 12 36 0.0018 0.0026 0.0058 780100012 5 17.71444 -64.78528 VI St Croix 1 1 5 12 0.0003 0.0007 0.0009 A-79 
Attachment A-2

Table 8. Pb_PM2.5 monitoring site distribution statistics 

All sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 271 0.0003 0.0014 0.0019 0.0020 0.0022 0.0024 0.0026 0.0027 0.0029 0.0031 0.0033 0.0043 0.0035 0.0038 0.0040 0.0042 0.0046 0

max quarter mean 271 0.0007 0.0022 0.0028 0.0030 0.0034 0.0037 0.0039 0.0042 0.0044 0.0047 0.0052 0.0076 0.0056 0.0059 0.0063 0.0067 0.0072 0

max monthly mean 271 0.0009 0.0033 0.0040 0.0044 0.0050 0.0053 0.0057 0.0061 0.0064 0.0068 0.0073 0.0123 0.0079 0.0085 0.0089 0.0098 0.0112 0

Source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 8 0.0036 0.0036 0.0036 0.0043 0.0043 0.0053 0.0063 0.0063 0.0067 0.0067 0.0073 0.0086 0.0079 0.0079 0.0106 0.0106 0.0110 0

max quarter mean 8 0.0067 0.0067 0.0067 0.0101 0.0101 0.0107 0.0114 0.0114 0.0122 0.0122 0.0132 0.0143 0.0142 0.0142 0.0148 0.0148 0.0153 0

max monthly mean 8 0.0077 0.0077 0.0077 0.0086 0.0086 0.0136 0.0186 0.0186 0.0190 0.0190 0.0191 0.0215 0.0192 0.0192 0.0228 0.0228 0.0257 0

Not source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 263 0.0003 0.0014 0.0019 0.0020 0.0022 0.0024 0.0026 0.0027 0.0029 0.0030 0.0032 0.0042 0.0034 0.0037 0.0038 0.0042 0.0045 0

max quarter mean 263 0.0007 0.0022 0.0027 0.0030 0.0034 0.0037 0.0039 0.0042 0.0043 0.0046 0.0050 0.0073 0.0055 0.0057 0.0061 0.0066 0.0070 0

max monthly mean 263 0.0009 0.0033 0.0040 0.0043 0.0049 0.0053 0.0056 0.0060 0.0063 0.0067 0.0072 0.0120 0.0076 0.0084 0.0087 0.0096 0.0104 0

Urban sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 216 0.0003 0.0017 0.0020 0.0023 0.0024 0.0026 0.0027 0.0030 0.0031 0.0033 0.0035 0.0046 0.0037 0.0039 0.0042 0.0045 0.0049 0max quarter mean 216 0.0007 0.0025 0.0031 0.0035 0.0038 0.0040 0.0042 0.0044 0.0047 0.0051 0.0055 0.0082 0.0059 0.0063 0.0066 0.0071 0.0079 0max monthly mean 216 0.0009 0.0036 0.0045 0.0051 0.0055 0.0058 0.0061 0.0063 0.0067 0.0072 0.0077 0.0135 0.0085 0.0088 0.0097 0.0104 0.0123 0Urban sites, located in MSA's > 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 99 0.0018 0.0022 0.0025 0.0027 0.0027 0.0030 0.0031 0.0033 0.0036 0.0038 0.0041 0.0055 0.0042 0.0047 0.0048 0.0054 0.0065 0max quarter mean 99 0.0026 0.0033 0.0037 0.0039 0.0042 0.0045 0.0051 0.0054 0.0058 0.0061 0.0063 0.0093 0.0066 0.0069 0.0071 0.0083 0.0106 0max monthly mean 99 0.0036 0.0047 0.0052 0.0058 0.0064 0.0068 0.0072 0.0075 0.0078 0.0086 0.0088 0.0160 0.0099 0.0104 0.0128 0.0160 0.0185 0Urban sites, located in MSA's < 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 117 0.0003 0.0013 0.0017 0.0020 0.0021 0.0023 0.0025 0.0026 0.0028 0.0030 0.0032 0.0038 0.0033 0.0035 0.0037 0.0038 0.0042 0max quarter mean 117 0.0007 0.0021 0.0026 0.0029 0.0034 0.0037 0.0038 0.0040 0.0042 0.0044 0.0046 0.0072 0.0050 0.0053 0.0057 0.0060 0.0068 0

max monthly mean 117 0.0009 0.0031 0.0037 0.0042 0.0051 0.0053 0.0056 0.0059 0.0060 0.0062 0.0064 0.0114 0.0070 0.0079 0.0086 0.0090 0.0097 0

 A-80 
Appendix B: Background on Case Studies 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

Table of Contents 

Table of Contents ................................................................................................................. B-i 

List of Exhibits .................................................................................................................... B-ii 

List of Attachments............................................................................................................B-iii 

B. BACKGROUND ON CASE STUDIES ............................................................................ B-1
 

B.1. PRIMARY PB SMELTER CASE STUDY................................................................. B-1 

B.1.1. Description of Case Study Location ................................................................ B-1 

B.1.2. Description of Primary Pb Smelter.................................................................. B-2 

B.1.3. Human Exposure Measurements ..................................................................... B-4 

B.1.4. Emissions ......................................................................................................... B-6 

B.1.5. Summary of Environmental Data .................................................................... B-7 

B.1.5.1. Air Monitoring.................................................................................. B-8 

B.1.5.2. Soil .................................................................................................. B-10 

B.1.5.3. Indoor Dust ..................................................................................... B-10 

B.1.5.4. Deposition....................................................................................... B-11 

B.2. SECONDARY PB SMELTER CASE STUDY ........................................................ B-11 

B.2.1. Description of Case Study Location .............................................................. B-12 

B.2.2. Description of Secondary Pb Smelter............................................................ B-13 

B.2.3. Human Exposure Measurements ................................................................... B-15 

B.2.4. Emissions ....................................................................................................... B-15 

B.2.5. Summary of Environmental Data .................................................................. B-16 

B.2.5.1. Air Monitoring................................................................................ B-16 

B.2.5.2. Soil .................................................................................................. B-18 

B.2.5.3. Indoor Dust ..................................................................................... B-18 

B.2.5.4. Deposition....................................................................................... B-18 

REFERENCES ................................................................................................................... B-19 

B-i 
List of Exhibits 

Exhibit B-1. Facility Location Map – Primary Pb Smelter ........................................................B-3 

Exhibit B-2. Summary of 2001 PbB Measurements for Herculaneum Residents......................B-4 

Exhibit B-3. Summary of 2002 PbB Measurements for Herculaneum Residents......................B-5 

Exhibit B-4. Percentage of Tested Children with PbB Levels above 10 μg/dL in Jefferson 

County (1997 through 2003; 2005) .......................................................................B-6 

Exhibit B-5. Summary of Environmental Data Sources for Primary Pb Smelter Case Study....B-8 

Exhibit B-6. Distribution of 2005 Annual Average Values for PB-TSP Measurements at 

Monitor Sites across the United States Relative to Monitors near the Primary 

Pb Smelter..............................................................................................................B-9 

Exhibit B-9. Summary of Environmental Data Sources for Secondary Pb Smelter Case 

Exhibit B-10. Distribution of 2005 Annual Average Values for PB-TSP at Monitor Sites across 

Exhibit B-7. Population Data around Secondary Pb Smelters in the United States .................B-12 

Exhibit B-8. Facility Location Map – Secondary Pb Smelter ..................................................B-14 

Study....................................................................................................................B-16 

the United States Relative to Monitors near the Secondary Pb Smelter............B-18 

B-ii 
List of Attachments 

Attachment B-1. Air Monitoring Locations around the Primary Pb Smelter ...........................B-21 

Attachment B-2. Average Annual Pb Concentrations from AQS Monitors Located around 

Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS Located around 

the Primary Pb Smelter .................................................................................B-22 

the Primary Pb Smelter ................................................................................ B-23 

Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter ...........B-32 

Attachment B-5. Post-Excavation Soil Sampling Results for Pb – Primary Pb Smelter..........B-50 

Attachment B-6. Recontamination Soil Sampling Results for Pb – Primary Pb Smelter.........B-56 

Attachment B-7. Average Soil Pre-Excavation, Post-Excavation, and Recontamination 

Pb Results for 31 Residential Locations within One mile of the 

Primary Pb Smelter .......................................................................................B-64 

Attachment B-9. Soil and Air Deposition Monitoring Locations around the Primary Pb 

Attachment B-13. Average Annual Pb Concentrations from AQS Monitors Located around 

Attachment B-8. Indoor Dust/Wipe Sample Results for Pb – Primary Pb Smelter..................B-65 

Smelter ..........................................................................................................B-68 

Attachment B-10. Soil Deposition Monitoring Results for Pb – Primary Pb Smelter..............B-69 

Attachment B-11. Air Deposition Monitoring Results for Pb – Primary Pb Smelter...............B-73 

Attachment B-12. Air Monitoring Locations around the Secondary Pb Smelter .....................B-76 

the Secondary Pb Smelter ...........................................................................B-77 

B-iii 

 

 

B. BACKGROUND ON CASE STUDIES 

This Appendix provides descriptions of the primary lead (Pb) smelter and secondary Pb 

smelter case study locations, accompanied by an overview of the available human exposure 

measurements (i.e., human blood Pb [PbB] levels), emissions, and environmental data for each 

site. The primary Pb smelter is discussed in Section B.1; the secondary Pb smelter is discussed 

in Section B.2. 

B.1. PRIMARY PB SMELTER CASE STUDY 

The Herculaneum Lead Smelter (HLS) is currently the largest source of Pb metal and the 

only currently operating Pb smelter in the United States (Missouri Department of Natural 

Resources [MDNR], 2005). The HLS facility (hereafter referred to as the “primary Pb smelter”) 

represents a relatively large point source that has been active for more than a century (MDNR, 

2005) and for which a large amount of site-specific data characterizing both media 

concentrations (soil, indoor dust, and ambient air) and human PbB levels is available. Pb 

contaminant conditions for the area surrounding this facility are dominated by emissions from 

this facility, with older historical automobile and other point source emissions being of relatively 

lesser importance. Environmental sampling conducted around the primary Pb smelter has shown 

Pb contamination throughout the community surrounding the smelter. Available environmental 

data are discussed in Section B.1.5 and presented in Attachments B-1 through B-13. 

B.1.1. Description of Case Study Location 

The primary Pb smelter facility is located in Herculaneum, Missouri. The City of 

Herculaneum is in Jefferson County, about 42 kilometers (km) (26 miles [mi]) southwest of St. 

Louis, and its approximate area is 9 square kilometers (km2

). As of 2000, an estimated 37,562 

people were living within a 10-km radius of the primary Pb smelter (2,064 within 2 km; 14,237 

between 2 and 5 km; and 21,261 between 5 and 10 km). Of this population in 2000, 3,880 were 

children less than 7 years of age (171 within 2 km; 1,545 between 2 and 5 km; and 2,164 

between 5 and 10 km) (U.S. Census Bureau, 2005).1 

1

 In 2002, the company that owns the primary Pb smelter facility offered a voluntary property acquisition of 

homes within a specified geographic area, approximately 3/8 mile around the smelter. The 2000 U.S. Census 

population counts in the U.S. Census blocks that comprise the buy-out area were excluded from these population 

estimates (since it is known that individuals no longer reside in these areas). 

B-1
 
B.1.2. Description of Primary Pb Smelter 

The primary Pb smelter facility is located at 881 Main Street in Herculaneum, Missouri 

(see Exhibit B-1). The property associated with this facility covers 52 acres and consists of 3 

main areas: (1) the smelter plant, which is located on the east side of Main Street; (2) office 

buildings located on the west side of Main Street; and (3) a 40- to 50-foot (ft) high furnace waste 

(i.e., slag) storage pile that covers 24 acres. The facility is bordered on the east by the 

Mississippi River, on the southeast by Joachim Creek, on the west and north-northwest by 

residential areas, and on the south-southwest by the slag pile. A large part of the slag pile is 

located in the floodplain wetlands of Joachim Creek and the Mississippi River. 

The principal processing occurring at the facility includes: (1) sintering, smelting, and 

refining of Pb ore; (2) sulfuric acid production from waste sulfur-containing gases generated by 

the sintering operation; and (3) wastewater treatment. Sources at the facility include various 

stacks and vents from plant processes, fugitive emissions from ore handling operations, wind 

erosion from the slag pile, and fugitive emissions from transport of Pb concentrate over local 

roads. A Pb ore concentrate, consisting of approximately 80 percent Pb sulfide, is processed at 

the smelter. The ore is transported by truck from eight Pb mines near Viburnum, Missouri, 

approximately 121 km (75 mi) south-southwest of Herculaneum. The smelting operation 

generates a molten slag, 20 percent of which is sent to the slag storage pile as waste. Stack and 

fugitive emissions from the facility and deposition of these emissions to soil and surface water 

have resulted in elevated Pb concentrations in the surrounding areas (MDNR, 1999), as cited in 

Agency for Toxic Substances and Disease Registry (ATSDR) (2003). 

B-2
 
Exhibit B-1. Facility Location Map – Primary Pb Smelter 

B-3
 
 

B.1.3. Human Exposure Measurements 

PbB levels at or above 10 micrograms (μg) per deciliter (dL) have been recorded for 

Herculaneum residents, including children less than 72 months of age (ATSDR, 2002; 2003). 

The U.S. Department of Health and Human Services (DHSS) and the Jefferson County Health 

Department (JCHD), in cooperation with ATSDR, have offered PbB testing to the residents of 

Herculaneum and surrounding communities. Results of two such testing events conducted in 

2001 and 2002 have been documented in DHSS/ATSDR health consultation reports (ATSDR, 

2002; 2003) and are summarized here. 

A total of 935 Herculaneum residents were tested in 2001. A summary of PbB results by 

age group is provided in Exhibit B-2. Of the children less than 72 months old that were tested in 

2001, 33 (28 percent) had PbBs of 10 μg/dL or greater. In the area closest to the primary Pb 

smelter, 30 out of 67 (45 percent) of the children less than 72 months of age who were tested in 

2001 had PbBs equal to or above 10 μg/dL (ATSDR, 2002). 

Exhibit B-2. Summary of 2001 PbB Measurements for Herculaneum Residents 

PbB (μg/dL) Number of Individuals 

Tested a 

Percent of Individuals 

Tested in PbB Range b 

Children Less than 72 Months of Age 

0 to 9 85 72% 

10 to 19 27 23% 

20 to 29 5 4% 

30 or Higher 1 1% 

Children Between 6 and 17 Years of Age 

0 to 9 149 92% 

10 to 19 13 8% 

20 to 29 0 -

30 or Higher 0 -

Adults 18 Years of Age or Older 

0 to 24 653 >99% 

25 to 39 1 <1% 

40 to 49 0 -

50 or Higher 1 <1% 

a Data derived from ATSDR (2002).
 b

 percentile estimates (based on reported values and the total sample size of the study) have been added to the 

tables to facilitate interpretation of the results. 

B-4 
In September 2002, DHSS and JCHD conducted a voluntary community-wide PbB 

testing event, during which 340 Herculaneum residents were tested. Exhibit B-3 summarizes 

results sorted by age group for Herculaneum residents. As shown in Exhibit B-3, of the children 

less than 72 months old that were tested in 2002, 8 (14 percent) had PbBs of 10 μg/dL or higher. 

Exhibit B-3. Summary of 2002 PbB Measurements for Herculaneum Residents 

PbB (μg/dL) Number of Individuals 

Tested a 

Percent of Individuals 

Tested in PbB Range b 

Children Less than 72 Months of Age 

0 to 9 50 86% 

10 to 19 6 10% 

20 to 29 2 4% 

30 or Higher 0 -

Children Between 6 and 17 Years of Age 

0 to 9 127 98% 

10 to 19 2 2% 

20 to 29 0 -

30 or Higher 0 -

Adults 18 Years of Age or Older 

0 to 24 147 96% 

25 to 39 5 3% 

40 to 49 1 1% 

50 or Higher 0 -

a Data derived from ATSDR (2003, Tables 1 to 3). 

b

 percentile estimates (based on reported values and the total sample size of the study) have been added to the 

tables to facilitate interpretation of the results. 

While summarized data for Herculaneum are not available for more recent years than 

2002, county-level information on the numbers of children with PbB levels above 10 μg/dL is 

available from the State of Missouri web site through 2005 (although 2004 data are not 

available). While not necessarily specific to the town of Herculaneum, it is noted that 

the percentage of tested children with PbB levels above 10 μg/dL in Jefferson County declined 

slightly in 2005 as compared to 2002 and 2003 (see Exhibit B-4). 

B-5
 
 

 

 

 

 

Exhibit B-4. Percentage of Tested Children with PbB Levels above 10 μg/dL in Jefferson 

County (1997 through 2003; 2005) 

Parameter 

Number of Children Tested 

Year 

1997 

367 

1998 

412 

1999 

293 

2000 

656 

2001 

1207 

2002 

1355 

2003 

2070 

2005 

1607 

Percent Tested Above 

10 μg/dL 

8% 3% 4% 4% 4% 2% 2% 1% 

Note: Data derived from State of Missouri Department of Health and Senior Services (DHSS) (2007). 

B.1.4. Emissions 

The Pb emissions estimates used for the National Ambient Air Quality Standard 

(NAAQS) scenario for the primary Pb smelter case study were obtained from U.S. EPA Region 

7 and reflect the proposed 2007 Revision of the State Implementation Plan (SIP) developed for 

the facility (MDNR, 2007a; 2007b). Rather than representing current conditions at the facility, 

these emissions represent the maximum allowable Pb emissions (per the proposed 2007 SIP) 

estimated to result in meeting the current NAAQS.2 

2

 Several different Herculaneum emission situations are alternately discussed within this report and other 

appendices. While they are related, each is distinct and provides a different type of information. The 2002 NEI 

emissions (discussed in Appendix A) are emissions reported by the Doe Run Company to the state. While these 

emissions may be derived from stack tests and should reflect 2002 production levels, emissions such as building or 

storage pile fugitives and emissions from materials handling or activity on facility roads may be less completely 

accounted for in the 2002 reported values. These 2002 National Emissions Inventories NEI emissions should not be 

confused with current conditions or maximum allowable Pb emissions. “Current conditions at the facility” may be 

described as the actual emissions being released from all facility-related sources at present, given current controls, 

work practices, and process throughputs. The “maximum allowable Pb emissions” refers to the emissions allowed 

under the proposed 2007 SIP revision. The 2007 SIP revision proposes a portfolio of controls focused on reducing 

Pb emissions from sources identified as significant contributors to recent NAAQS exceedances (e.g., Pb emissions 

associated with materials handling, activity on facility roads, building fugitives, among others). A lesser contributor 

to air Pb concentrations in Herculaneum, but a large source of measured emissions, is the facility’s main stack. Due 

to the main stack height and the high process temperature, considerable dispersion occurs resulting in a low impact 

from the main stack on the air concentrations in the City of Herculaneum. As a result of this condition, in 

combination with lower actual production and other process controls at the Herculaneum plant, the reported main 

stack emissions (either in the 2002 NEI or the current actual emissions) are considerably lower than their SIP 

allowable level. Altogether, the maximum allowable emissions from facility-related sources have been modeled 

by Missouri in their 2007 SIP revision for the purpose of meeting the 1.5 μg/m3

 per quarter NAAQS. Thus, it is the 

maximum allowable emissions under the proposed 2007 SIP revision that are used for the current NAAQS scenario 

for this case study. 

B-6
 
The proposed 2007 SIP describes maximum allowable Pb emissions from processes at 

the facility, fugitive emissions from transferring of materials, fugitive emissions from storage at 

the slag pile and other process storage piles, building fugitives, and emissions associated with 

dust from roadways in the vicinity of the smelter. Particle sizes for emissions from road segment 

emission points around the primary Pb smelter ranged from 1.6 to 25.3 micrometers (μm). 

Particle sizes for emissions from all other emission points at the primary Pb smelter ranged from 

1.6 to 45 μm. Note that EPA has not completed its review of the proposed 2007 SIP revision 

associated with these emissions. Consequently, the dispersion model runs completed for this 

assessment using these emissions should be considered illustrative only. Emissions and release 

parameters, particle size inputs, and other inputs used for fate and transport modeling of the 

primary Pb smelter are provided in Appendix D, Attachments D-1 to D-6. 

B.1.5. Summary of Environmental Data 

The environmental data sets available for the primary Pb smelter case study are 

summarized in Exhibit B-5. These data are discussed in the sections following this exhibit. 

B-7
 
 

 

Exhibit B-5. Summary of Environmental Data Sources for Primary Pb Smelter Case Study 

Medium Data Set a Timeframe Locations Comments 

Ambient air 

EPA Air Quality System 

(AQS) Database 2001 to 2005 9 locations 

Total suspended particulate 

matter (TSP) monitors 

measuring Pb located within 

10 km of facility; see 

Attachments B-1 and B-2 

Monitors not in AQS b 2001 to 2003 4 locations 

TSP monitors measuring Pb 

located along roads; see 

Attachments B-1 and B-3 

Residential Soil 

Pre-excavation 2000 to 2004 

Over 900 locations 

around the primary 

Pb smelter 

Locations within 

approximately 2.4 km (1.5 mi) 

of facility; see Attachment B-4 

Post-excavation 2000 to 2004 

Approximately 300 

locations around the 

primary Pb smelter 

Locations within 

approximately 2.4 km (1.5 mi) 

of facility; see Attachment B-5 

Recontamination 

assessment 2002 to 2006 31 residences 

Locations within 

approximately 1.6 km (1 mi) of 

facility; see Attachments B-6 

and B-7 

Indoor dust Recontamination 

assessment 2002 to 2006 17 residences 

Locations within 

approximately 1.6 km (1 mi) of 

facility; see Attachment B-8 

Deposition to soil Soil boxes 2003 to 2004 c 10 locations See Attachments B-9 and 

B-10 

Deposition to air Filters 2003 to 2004 c 10 locations See Attachments B-9 and 

B-11 

a

 Several data sources existed, including analyses conducted by the U.S. EPA, the primary Pb smelter facility, 

ATSDR, MDNR, and various consultants. Aside from the U.S. EPA’s AQS air monitoring data, the data
 

represented in this table were obtained electronically from the U.S. EPA Region 7 (2006). The data presented in this
 

table are the only environmental data discussed and summarized for the primary Pb smelter in this Appendix and in
 

the associated attachments. Attempts were made to obtain environmental data from sources outside the U.S. EPA,
 

but no additional data were received within the time available for this assessment. b

 The four monitors not in AQS were placed by the Superfund program for their objectives, and are additional to the
 

nine AQS monitors in place for U.S. EPA’s air monitoring program objectives. The data for the four Superfund
 

monitors are not stored in AQS, but were received directly from the U.S. EPA Region 7. c

 These are the most recent data available from the U.S. EPA Region 7. 

B.1.5.1. Air Monitoring 

As shown in Exhibit B-5, two air monitoring data sets are available from the U.S. EPA 

for the primary Pb smelter. Attachment B-1 shows the locations of the 13 air monitoring 

locations relative to the facility. 

 Air monitoring data for the nine AQS monitors are provided by year in Attachment B-2. 

These data indicate a reduction in average annual Pb concentration between 2001 and the 

subsequent years. The largest difference was observed for Monitor ID 290990005 (located near 

a public school, approximately 0.8 km (0.5 mi) from the smelter’s main stack [see Attachment 

B-1]), where average annual Pb concentrations decreased from 2.10 μg/m3

 in 2001 to 0.28 to 

B-8
 
0.44 μg/m3

 for the subsequent years. It is additionally noted that for 2005, however, the most 

recent year for which annual average values are reported in Attachment B-2, exceedances of the 

NAAQS (1.5 μg/dL as a maximum quarterly average) occurred at a different monitor during 

three of the four quarters (USEPA, 2007). Air monitoring data for the four additional monitoring 

sites not in AQS are provided by year in Attachment B-3. In general, data were collected from 

the four monitors for portions of years over the period of 2001 through 2003. A complete year’s 

set of data (for 2002) was available for only two monitors (Full-Scale Analysis IDs 100 and 

102). 

For comparison purposes, the average annual Pb concentrations for 2005 from AQS 

monitors located around the primary Pb smelter were compared to AQS monitor results across 

the United States. Exhibit B-6 shows the distribution of average annual Pb concentrations in 

TSP for 208 monitoring sites across the United States (with average annual monitored Pb 

concentrations sorted in ascending order). The 2005 monitor results for the nine AQS monitors 

located in the vicinity of the primary Pb smelter are indicated using a solid square (■), while all 

other monitors are indicated using a solid diamond (♦). The 2005 annual average Pb 

concentrations for the 208 monitoring sites ranged from 0.046 to 1.56 μg/m3

. The 1.56 μg/m3 

maximum annual average is associated with monitoring site 290990015, one of the monitoring 

sites identified within 10 km of the primary Pb smelter. Of the 208 monitoring site locations, the 

nine within 10 km of the primary Pb smelter all fall within the top 30 percent of annual average 

values for all 208 monitoring sites, with four of the nine monitoring sites in the top 10 percent. 

Exhibit B-6. Distribution of 2005 Annual Average Values for PB-TSP Measurements at 

Monitor Sites across the United States Relative to Monitors near the Primary Pb Smelter Pb TSP Concentration (μg/m 3) 1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

Monitors Across the United States (N=208) 

■- Monitor Around Primary Pb Smelter 

B-9
 
B.1.5.2. Soil 

As shown in Exhibit B-5, three soil data sets are available from the U.S. EPA for the 

primary Pb smelter: pre-excavation, post-excavation, and recontamination assessment data. 

Pre-excavation soil samples were collected from residential locations around the smelter prior to 

soil removal activities. Pre-excavation soil sample results for over 900 residential locations 

around the primary Pb smelter are presented in Attachment B-4. Average soil concentrations at 

these sampling locations ranged from 53 to 23,350 milligrams per kilogram (mg/kg). 

Based on pre-excavation sampling results, PB-contaminated soil in a subset of the 900 

sampled residential yards near the smelter was removed, replaced with clean backfill, and 

re-seeded with grass. Post-excavation soil data were available for over 300 residential locations. 

Post-excavation soil samples were collected immediately following excavation, prior to the yards 

being backfilled with clean soil. Post-excavation results are presented in Attachment B-5. 

Average soil concentrations at these properties ranged from 70 to 2,757 mg/kg. 

The U.S. EPA has recently conducted post-remediation residential yard soil sampling at 

31 locations within a radius of approximately 1.6 km (1 mi) of the primary Pb smelter to 

determine whether residential yards in which PB-contaminated soil was removed and replaced 

with clean soil are becoming recontaminated. Results from the recontamination assessment 

samples are provided in Attachment B-6. For most of the 31 recontamination assessment 

locations within 1.3 km (0.8 mi) of the facility, average Pb concentrations in the replacement 

“clean” soil increased between 2002 and 2006. Refer to Attachment B-7 for a summary of the 

pre-excavation, post-excavation, and recontamination assessment data for these 31 residential 

locations. 

B.1.5.3. Indoor Dust 

The interiors of 17 of the 31 residential properties identified for the soil recontamination 

assessment were also assessed for Pb levels in indoor dust. Indoor dust removal (in which areas 

inside homes were wiped and/or vacuumed) was performed at these residences prior to 

recontamination sampling. Attachment B-8 provides a summary of recontamination indoor dust 

sample results for these 17 properties. Carpet dust samples collected during recontamination 

sampling events at these residences contained Pb concentrations that ranged from 122 to 

4,350 mg/kg. Pb loadings in window sill wipe samples ranged from 5.6 to 1,385 μg per square 

foot (ft2

). No general patterns were identified at homes during successive sampling events. Pb 

concentrations and/or loadings may have increased, decreased, or remained generally the same 

(see Attachment B-8). This lack of pattern may be attributed in part to inconsistent house 

cleaning protocols within the homes. 

B-10
 
 

 

 

B.1.5.4. Deposition 

As shown in Exhibit B-5, soil boxes3 were set up at 10 locations (primarily along roads) 

within approximately 1.8 km (1.1 mi) of the main stack at the primary Pb smelter. Deposition 

monitoring locations are shown in Attachment B-9. From 2003 to 2004, samples were collected 

monthly to measure Pb deposition on soil; results for these locations are presented in Attachment 

B-10. Maximum concentrations at the nine locations (excluding the control site) ranged from 25 

to 406 mg/kg in 2003 and from 25.3 to 527 mg/kg in 2004. The overall average Pb 

concentration in these soil boxes across all nine locations increased from 49 mg/kg in 2003 to 

96.5 mg/kg in 2004, an increase of almost 100 percent. 

Air deposition monitoring data were available for the same 10 locations around the 

primary Pb smelter for which soil box monitoring data were available (see Attachment B-9). 

Dry deposition samples were collected monthly at two levels (1 ft and 10 ft) above the ground 

surface from April 2003 through April 2004. Data collected at each level for these locations are 

presented in Attachment B-11. The annual Pb deposition rates at a height of 1 ft for the nine 

monitoring locations (excluding the control site) ranged from 0.34 to 22 mg/ft2

, and the overall 

average Pb deposition rate across all nine locations at the height of 1 ft was 4.8 mg/ft2. The 

annual Pb deposition rates at a height of 10 ft for the nine monitoring locations ranged from 0.26 

to 33 mg/ft2, and the overall average Pb deposition rate across all nine locations at the height of 

10 ft was 5.0 mg/ft2

. The average annual Pb air deposition rates at each level by location are 

provided in Attachment B-11. 

B.2. SECONDARY PB SMELTER CASE STUDY 

The secondary Pb smelter case study focused on the impacts of emissions from a smaller 

point source (compared to the primary Pb smelter) located in Alabama. Fewer site-specific data 

characterizing media concentrations and human exposure levels were available for this study 

area than for the primary Pb smelter case study. However, recent air concentration data from the 

area surrounding the facility and facility characterization data (including emission estimates) 

were readily available. 

3

 Clean soil is placed in containers that measure approximately 2 ft by 3 ft, 8 to 12 inches deep and are set 

on the ground. Soil box measurements were taken by placing an X-ray fluorescence (XRF) meter directly on the 

soil surface in the soil box. Soil boxes were intended to provide a repeatable means of measuring Pb deposition on 

soil that would be less likely to be disturbed than soil in residential yards (Staley et al., 2002). 

B-11
 
 

 

B.2.1. Description of Case Study Location 

The secondary Pb smelter case study location is in Troy, Alabama. Troy is a city located 

in Pike County, positioned in the south-central portion of the state, and its approximate area is 

68 km2

. As of 2000, an estimated 17,910 people were living within a 10-km radius of the facility 

(2,186 within 2 km; 10,634 between 2 and 5 km; and 5,090 between 5 and 10 km). Of this 

population, 1,672 are children less than 7 years of age (187 [11 percent] within 2 km; 896 

[54 percent] between 2 and 5 km; and 589 [35 percent] between 5 and 10 km) (U.S. Census 

Bureau, 2005). 

As of 2002, 15 secondary Pb smelters in the United States were operating in 11 states 

(EC/R Incorporated, 2006). Population data (total population and population of children less 

than 7 years of age) around these 15 facilities are provided in Exhibit B-7. Of these 15 facilities, 

the secondary Pb smelter in Troy, Alabama, had the highest percentage (at 11 percent) of 

children less than 7 years of age living within 2 km of the facility. The percentage of children 

less than 7 years of age living within 2 km of secondary Pb smelters in other parts of the United 

States ranged from 0 to 6 percent (see Exhibit B-7). 

Exhibit B-7. Population Data around Secondary Pb Smelters in the United States 

No. Location 

Pop

0 to 2 km 

ulation Numbers at Select Distances a 

2 to 5 km 5 to 10 km 

Total Children 0 to 7 Total Children 0 to 7 Total Children 0 to 7 

1 Troy, AL 2,186 187 11% 10,634 896 54% 5,090 589 35% 

2 Vernon, CA 29,609 5,334 2% 323,643 55,079 24% 1,122,949 172,709 74% 

3 City of Industry, CA 15,311 1,858 2% 141,005 19,517 20% 565,507 77,962 78% 

4 Tampa, FL 6,302 650 2% 34,361 4,232 14% 201,068 24,718 84% 

5 Muncie, IN 1,352 152 3% 5,535 600 13% 51,174 4,074 87% 

6 Indianapolis, IN 5,649 716 3% 41,129 4,872 20% 155,030 19,261 78% 

7 Baton Rouge, LA 2,931 251 3% 13,427 1,715 19% 52,086 7,247 82% 

8 Eagan, MN 6,034 929 5% 33,383 4,756 24% 132,923 14,486 72% 

9 Boss, MO 2,064 171 4% 14,237 1,545 40% 21,261 2,164 56% 

10 Forest City, MO 22 4 2% 79 6 3% 1,676 159 95% 

11 Middletown, NY 983 0 0% 33,589 4,016 54% 33,791 3,719 50% 

12 Lyon Station, PA 1,059 111 3% 12,569 995 30% 26,684 2,356 71% 

13 Reading, PA 9,416 746 4% 58,609 7,444 37% 112,425 11,834 59% 

14 College Grove, TN 335 36 6% 1,233 108 19% 4,476 434 75% 

15 Frisco, TX 5,097 863 4% 27,691 4,938 24% 92,476 14,620 72% 

a

 Data derived from U.S. Census Bureau (2005). 

B-12 
B.2.2. Description of Secondary Pb Smelter 

The location of this facility is bordered by US-231 to the north-northeast and by a 

railroad line and Henderson Highway along the north-northwestern and western boundaries of 

the facility. The area located directly west of Henderson Highway is forested. To the south and 

south-southwest are other industries and businesses. Big Creek appears to be the closest major 

water body, located approximately 0.8 km (0.5 mi) south-southeast from the center of the 

facility. The City of Troy is located north and east of the facility and north of US-231 (see 

Exhibit B-8). 

Secondary Pb smelters produce Pb from scrap and provide the primary means for 

recycling PB-acid automotive batteries. Approximately 95 percent of all PB-acid batteries are 

recycled at secondary Pb smelters. Secondary Pb smelters perform three basic unit operations: 

battery breaking, smelting, and refining and alloying. Battery breaking is accomplished by either 

crushing or cutting battery cases into pieces. The plastic, spent acid, and PB-bearing materials 

are then separated. PB-bearing materials are processed in one of three types of smelting 

furnaces: blast, reverberatory, or rotary. Molten Pb from these furnaces is further processed in 

refining kettles and subsequently cast into molds. The waste stream from the furnaces (i.e., slag) 

is either returned to the primary smelting furnace or treated in a separate furnace dedicated to 

slag cleaning to recover additional Pb. Three types of emission sources occur at secondary Pb 

facilities: process sources, process fugitive sources, and fugitive dust sources. The types of 

sources at the secondary Pb smelter analyzed in these assessments include: blast furnace, 

agglomeration furnace, alloying kettles and heating system, flue dust storage bins, and slag 

treatment furnace. Stack emissions from the facility and fugitive emissions associated with 

materials storage and handling and roadway dust have resulted in releases of Pb to the air and 

soil (EC/R Incorporated, 2006). 

B-13
 
Exhibit B-8. Facility Location Map – Secondary Pb Smelter 

B-14
 
 

B.2.3. Human Exposure Measurements 

No information on children’s PbB levels specific to the area around the secondary Pb 

smelter was identified. However, the Lead Poisoning Prevention Branch of the Centers for 

Disease Control and Prevention (CDC) collected PbB surveillance data for children less than 72 

months of age in Pike County, Alabama, in 2005. Of the 154 children tested by the CDC, there 

were 19 (approximately 12 percent) confirmed cases of elevated PbB (i.e., PbB above 10 μg/dL). 

For children less than 72 months of age in the state of Alabama and in the United States as a 

whole, the confirmed elevated PbBs as a percent of children tested in 2005 was 1.4 percent and 

1.6 percent, respectively (CDC, 2005). Note, however, that the statistics for children in Pike 

County do not necessarily represent PbBs for children living in Troy, Alabama, or children living 

in the areas immediately impacted by emissions from the secondary Pb smelter. In addition, it is 

not known to what extent older housing (with elevated concentrations of Pb in drinking water 

and paint) may be contributing to elevated Pb levels in the surveyed population. 

B.2.4. Emissions 

As of June 9, 1994, when the U.S. EPA proposed the secondary Pb smelter MACT 

standard (59 FR 63941), 23 secondary Pb smelters were operating in the United States. As of 

2002, 15 facilities were operating. Of these 15 facilities, the secondary Pb smelter analyzed in 

this study is the third highest emitter of Pb (EC/R Incorporated, 2006). 

The estimates for process emissions for the secondary Pb smelter analyzed in this 

assessment were calculated from Pb emissions measured during stack tests performed in 2005 

and 2006 (URS Corporation, 2005a; 2005b; 2006b). Fugitive emissions for four fugitive sources 

(associated with the smelter building, materials handling, loader traffic, and truck traffic) were 

estimated based on 1987 Prevention of Significant Deterioration (PSD) data (URS Corporation, 

2006a), which were the most recent available data on fugitive emissions from the facility. The 

cumulative Pb emissions from this facility, including facility process and fugitive emissions were 

estimated to be 3.11 tons per year (tons/year). 

Particle sizes for emissions from point sources at the facility ranged from 0.5 to 10 μm, 

and particle sizes for emissions from area sources at the facility ranged from 1.25 to 22.5 μm. 

Emissions and release parameters, particle size inputs, and other inputs for fate and transport 

modeling for the facility are provided in Appendix E, Attachments E-1 and E-2. 

The emissions used in this assessment differ slightly from those used in the pilot-scale 

assessment, which matched estimates for the facility contained in the 2002 National Emissions 

Inventory (NEI). The 2002 NEI process emissions were estimated based on stack tests 

B-15
 
 

 

 

 

performed in December 1997, November 1999, and February 2000 (EC/R Incorporated, 2006), 

and fugitive emissions were estimated by comparing the modeled concentrations from the 

process emissions to background Pb concentrations and monitored concentrations (EC/R 

Incorporated, 2006). The cumulative emissions estimate in the 2002 NEI, and modeled in the 

pilot-scale assessment, including facility process and fugitive emissions, was approximately 

4.6 tons/year. For this assessment, the use of more recent stack test data has produced a process 

emissions estimate that is approximately 30 percent lower. 

B.2.5. Summary of Environmental Data 

The environmental data sets available for the secondary Pb smelter case study are 

summarized in Exhibit B-9. 

Exhibit B-9. Summary of Environmental Data Sources for 

Secondary Pb Smelter Case Study 

Medium Data Set a Timeframe Locations Comments 

Ambient air EPA’s AQS 1998 to 2002 b

 2 locations 

TSP monitors measuring Pb 

located 400 and 680 meters 

(m) from the facility; see 

Attachments B-12, B-13. 

Residential soil No data identified. 

Indoor dust No data identified. 

Deposition No data identified. 

a

 In general, site characterization information was lacking for this secondary Pb smelter. Data, with the exception of 

limited air monitoring data, were not available based on information from the U.S. EPA Region 4. Information from 

the Alabama Department of Environmental Management (ADEM) indicates relevant soil data may be available from 

the facility (ADEM, 2006); however, no data have been obtained to date. 

b

 Monitor values from 1998 to 2002 were obtained from U.S. EPA’s Air Quality System (AQS) database for the 

purpose of comparing monitored values to modeled air concentrations (see Appendix E). Note that the comparison 

of these monitoring data to modeling results (presented in Appendix E) is limited by the fact that the modeled 

emissions are based on a combination of emission estimates from 1987, 2005, and 2006 and thus may not be 

completely representative of the emissions captured in these monitoring data. 

B.2.5.1. Air Monitoring 

As shown in Exhibit B-9, average annual Pb concentrations in the vicinity of the 

secondary Pb smelter were available from U.S. EPA’s AQS database (USEPA, 2007) for two air 

monitors located near the facility (see Attachment B-12). Data from these two air monitoring 

sites for 1998 through 2002 (see Attachment B-13) were compared to the modeled air 

concentrations. These years of monitoring data were selected to correspond to the years of 

B-16
 
 

 

 

 

meteorological data used in the air modeling.4 Over this period, average annual Pb 

concentrations at the monitor closer to the facility ranged from 0.28 to 0.47 μg/m3, with the 

lowest average annual concentration in the year 2002. Average annual Pb concentrations at the 

second monitor ranged from 0.14 to 0.20 μg/m3

. While no exceedances of the NAAQS 

(1.5 μg/dL as a maximum quarterly average) occurred during the 1998 to 2002 time period, it is 

noted that since that time, an exceedance has occurred (during the 4th quarter of 2003) (MDNR, 

2007b). 

For comparison purposes, the average annual Pb concentrations for 2005 from AQS 

monitors located around the secondary Pb smelter case study location were compared to AQS 

monitor results across the United States. Exhibit B-10 shows the distribution of average annual 

Pb concentrations in TSP for 208 monitoring sites across the United States (with average annual 

Pb concentrations per location sorted in ascending order). The 2005 results for the two AQS 

monitoring sites located in the vicinity of the secondary Pb smelter are indicated using a solid 

square (■), while all other monitors are indicated using a solid diamond (♦). The annual average 

Pb concentrations for the 208 monitoring sites ranged from 0.046 to 1.56 μg/m3

. 

4

 Note that the emissions data used in this modeling represent stack testing performed in 2005 and 2006 and 

fugitives emission estimates from 1987. Given that these emissions data, when used together, are not clearly 

representative of any specific time period, the decision was made to use monitoring data corresponding to the years 

of meteorological data used in the modeling (i.e., 1998 to 2002). 

B-17
 
Exhibit B-10. Distribution of 2005 Annual Average Values for PB-TSP at Monitor Sites 

across the United States Relative to Monitors near the Secondary Pb Smelter Pb TSP Concentration (μg/m 3) 1.8 

1.6 

1.4 

1.2
 

1
 

0.8 

0.6 

0.4 

0.2
 

0
 

■- Monitor Around Secondary Pb Smelter 

Monitors Across the United States (N=208) 

B.2.5.2. Soil 

No soil measurement data for Pb were identified in the vicinity of the secondary Pb 

smelter case study location. For the human exposure and health risk assessments, soil 

concentrations were estimated by defining the spatial pattern of soil concentrations around the 

facility using air and soil model results and then adjusting the magnitude of the concentrations 

based on measured concentrations from a similar facility. See Appendix E for details. 

B.2.5.3. Indoor Dust 

No indoor dust data for Pb were available from homes located in the vicinity of the 

secondary Pb smelter. Indoor dust concentrations were estimated using an empirical model that 

relates ambient air concentrations to indoor dust concentrations, as discussed in Appendix E. 

B.2.5.4. Deposition 

No Pb deposition monitoring data were identified in the vicinity of the secondary Pb 

smelter case study location. Pb deposition resulting from emissions from the secondary Pb 

smelter was modeled using U.S. EPA’s AERMOD air dispersion model, as discussed in 

Appendix E. 

B-18
 
 

 

 

 

 

 

 

 

 

 

REFERENCES 

Agency for Toxic Substances and Disease Registry (ATSDR). (2002) Health Consultation; Public Health 

Implications From Attending or Working at Herculaneum Schools; Herculaneum Lead Smelter Site; 

Herculaneum, Jefferson County, Missouri. June. 

Agency for Toxic Substances and Disease Registry (ATSDR). (2003) Health Consultation; Blood Lead Results for 

2002 Calendar Year; Herculaneum Lead Smelter Site; Herculaneum, Jefferson County, Missouri, EPA 

Facility ID MOD006266373. August. 

Alabama Department of Environmental Management (ADEM). (2006) Conversations With Representative of 

ADEM Regarding Available Measured Data From Media in and Around the Secondary Pb Smelter in Troy, 

AL. October 31 and November 1, 2006. 

Centers for Disease Control and Prevention (CDC). (2005) CDC Surveillance Data, 1997 to 2005. Available online 

at: http://0-www.cdc.gov.mill1.sjlibrary.org/nceh/lead/surv/stats.htm. 

EC/R Incorporated. (2006) Secondary Lead Smelter Industry: Residual Risk Assessment (Draft). Research Triangle 

Park, NC: Prepared for U.S. EPA Office of Air and Radiation, Office of Air Quality Planning and 

Standards; May. 

Missouri Department of Natural Resources (MDNR). (1999) Preliminary Assessment: Herculaneum Lead Smelter 

Site, Jefferson County, Missouri. Division of Environmental Quality; March 30, 1999. 

Missouri Department of Natural Resources (MDNR). (2005) Analysis of Lead Recontamination and Deposition in 

Soils Adjacent to the Doe Run Company's Herculaneum Smelter, Herculaneum, Missouri; February 2002 

Through July 2005. Division of Environmental Quality; November 8, 2005. 

Missouri Department of Natural Resources (MDNR). (2007a) 2007 Revision of the State Implementation Plan for 

the Herculaneum Lead Nonattainment Area, As Adopted by the Missouri Air Conservation Commission. 

April 26, 2007. 

Missouri Department of Natural Resources (MDNR). (2007b) Doe Run - Herculaneum State Implementation Plan 

(SIP) Dispersion Modeling Review. Memorandum From Jeffry D. Bennett to John Rustige. February 12, 

2007. Available online at: http://www.dnr.mo.gov/env/apcp/herculaneumsip.htm. 

Staley, C. S.; Ritter, P. D.; Rood, A. S. (2002) Quality Assurance Project Plan for Lead Deposition at 

Herculaneum, Missouri. Prepared for U.S. EPA National Exposure Research Laboratory, Technology 

Support Center, and U.S. Department of Energy; August. 

State of Missouri Department of Health and Senior Services (DHSS). (2007) Data & Statistical Reports; Childhood 

Lead Poisoning Prevention; Blood Lead Screening for 1997-2003 and 2005. Available online at: 

http://www.dhss.mo.gov/ChildhoodLead/Reports.html. 

URS Corporation. (2005a) Periodic NESHAP-Required Inorganic Lead Source Emissions Testing Program 

Conducted February 15, 2005 on Stack No. 10. 

URS Corporation. (2005b) Periodic NESHAP-Required Inorganic Lead Source Emissions Testing Program 

Conducted October 18, 2005 on Stack No. 4. 

URS Corporation. (2006a) Memorandum From Billy R. Nichols at URS Corporation to Ronald W. Gore at Alabama 

Department of Environmental Management (ADEM) Regarding 2005 Annual Emission Estimates for the 

Secondary Pb Smelter. April 26, 2006. 

B-19
 
 

URS Corporation. (2006b) Periodic NESHAP-Required Inorganic Lead Source Emissions Testing Program 

Conducted February 7 and 8, 2006 on Stack No. 1 and Stack No. 5. 

U.S. Census Bureau. (2005) United States Census 2000: Summary File 1. Public Information Office. Available 

online at: http://www.census.gov/Press-Release/www/2001/sumfile1.html. 

U.S. Environmental Protection Agency (USEPA). (2007) Air Quality System (AQS) Database. Available online at: 

http://www.epa.gov/ttn/airs/airsaqs/aqsweb/aqswebwarning.htm. 

B-20
 
Attachment B-1. Air Monitoring Locations around the Primary Pb Smelter 

B-21
 
Attachment B-2. Average Annual Pb Concentrations from AQS Monitors
 

Located around the Primary Pb Smelter
 

Monitor ID 

Average Monitored Pb Concentrations (μg/m3

)

a,b 

2001 2002 2003 2004 2005 

290990004 -- -- -- 1.27 0.94 

290990005 2.10 0.39 0.31 0.44 0.28 

290990008 0.27 0.068 0.10 0.097 0.10 

290990009 0.33 0.054 0.086 0.11 0.063 

290990010 0.13 0.074 0.033 0.046 0.046 

290990011 1.52 0.51 0.41 0.56 0.31 

290990013 0.98 0.24 0.20 0.44 0.16 

290990015 3.79 1.29 1.31 1.37 1.56 

290990016 -- -- -- 0.30 0.20 

a

 Data are for average annual Pb concentrations in total suspended particulate matter (TSP) and were calculated
 

from the daily U.S. EPA Air Quality System (AQS) data, including data from State and Local Air
 

Monitoring Stations (SLAMS) and other air monitoring networks (designated as 'others' in the AQS database). 

The daily data were extracted from AQS using an AMP350 report, with the mean daily statistic selected 

and the units selected as reported. Events and nulls were not included in the AMP350 report.
 b

 "--" indicates that data were not available.
 

B-22
 
Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS 

Located around the Primary Pb Smelter
 

Full-Scale 

Sampling Dates and Results (μg/m3

)

 a,b,c 

2001 2002 2003 

Analysis ID Date Date Date 

100 -- -- 3-Jan-02 0.5 1-Jan-03 0.316 

100 -- -- 7-Jan-02 0.52 4-Jan-03 1.26 

100 -- -- 10-Jan-02 0.51 7-Jan-03 0.547 

100 -- -- 13-Jan-02 4.5 10-Jan-03 0.291 

100 -- -- 16-Jan-02 0.97 13-Jan-03 1.03 

100 -- -- 19-Jan-02 2.2 16-Jan-03 1.09 

100 -- -- 22-Jan-02 2.4 19-Jan-03 0.531 

100 -- -- 25-Jan-02 0.75 22-Jan-03 0.095 

100 -- -- 28-Jan-02 2 25-Jan-03 0.811 

100 -- -- 5-Feb-02 1.5 28-Jan-03 2.28 

100 -- -- 8-Feb-02 0.97 31-Jan-03 0.118 

100 -- -- 11-Feb-02 0.59 3-Feb-03 0.15 

100 -- -- 14-Feb-02 0.33 9-Feb-03 1.29 

100 -- -- 18-Feb-02 2.3 12-Feb-03 0.901 

100 -- -- 21-Feb-02 0.24 15-Feb-03 0.514 

100 -- -- 26-Feb-02 0.23 ND 18-Feb-03 0.406 

100 -- -- 1-Mar-02 3.8 21-Feb-03 0.527 

100 -- -- 4-Mar-02 0.57 24-Feb-03 0.119 

100 -- -- 7-Mar-02 1.7 27-Feb-03 0.05 ND 

100 -- -- 11-Mar-02 2.3 2-Mar-03 0.095 

100 -- -- 14-Mar-02 1.3 5-Mar-03 0.138 

100 -- -- 17-Mar-02 0.78 8-Mar-03 1.63 

100 -- -- 20-Mar-02 0.24 ND 11-Mar-03 1.99 

100 -- -- 23-Mar-02 0.25 ND 14-Mar-03 1.53 

100 -- -- 26-Mar-02 0.41 17-Mar-03 2.86 

100 -- -- 29-Mar-02 0.76 20-Mar-03 2.07 

100 -- -- 1-Apr-02 0.93 23-Mar-03 0.352 

100 -- -- 4-Apr-02 0.24 ND 26-Mar-03 0.58 

100 -- -- 7-Apr-02 0.61 29-Mar-03 0.05 ND 

100 -- -- 10-Apr-02 4.9 1-Apr-03 0.399 

100 -- -- 16-Apr-02 2 4-Apr-03 0.397 

100 -- -- 18-Apr-02 3 7-Apr-03 0.238 

100 -- -- 22-Apr-02 0.41 10-Apr-03 0.19 

100 -- -- 25-Apr-02 0.23 ND 13-Apr-03 1.95 

100 -- -- 28-Apr-02 0.25 ND 16-Apr-03 0.376 

100 -- -- 1-May-02 2.2 19-Apr-03 5.48 

100 -- -- 4-May-02 0.55 22-Apr-03 0.357 

100 -- -- 7-May-02 2 25-Apr-03 0.092 

100 -- -- 10-May-02 3.58 28-Apr-03 3.37 

100 -- -- 13-May-02 0.144 1-May-03 0.309 

100 -- -- 16-May-02 0.932 4-May-03 0.715 

100 -- -- 19-May-02 0.0913 7-May-03 0.59 

100 -- -- 22-May-02 2.33 10-May-03 0.437 

100 -- -- 25-May-02 0.193 13-May-03 1.4 

100 -- -- 29-May-02 1.59 16-May-03 2.08 

100 -- -- 31-May-02 0.397 19-May-03 0.493 

100 -- -- 3-Jun-02 0.32 22-May-03 0.108 

100 -- -- 6-Jun-02 0.359 25-May-03 0.505 

100 -- -- 9-Jun-02 0.326 28-May-03 0.242 

100 -- -- 12-Jun-02 0.716 31-May-03 0.165 

100 -- -- 15-Jun-02 0.141 3-Jun-03 0.21 

100 -- -- 18-Jun-02 1.1 6-Jun-03 0.603 

B-23
 
Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS 

Located around the Primary Pb Smelter
 

Full-Scale 

Sampling Dates and Results (μg/m3

)

 a,b,c 

2001 2002 2003 

Analysis ID Date Date Date 

100 -- -- 21-Jun-02 1.49 9-Jun-03 0.121 

100 -- -- 24-Jun-02 2.17 12-Jun-03 0.627 

100 -- -- 27-Jun-02 0.24 15-Jun-03 0.063 

100 -- -- 30-Jun-02 0.091 18-Jun-03 1.51 

100 -- -- 3-Jul-02 0.861 21-Jun-03 0.216 

100 -- -- 6-Jul-02 1.68 24-Jun-03 0.433 

100 -- -- 9-Jul-02 0.439 27-Jun-03 0.184 

100 -- -- 12-Jul-02 2.92 30-Jun-03 0.803 

100 -- -- 15-Jul-02 1.04 6-Jul-03 0.06 

100 -- -- 18-Jul-02 1.09 -- --

100 -- -- 22-Jul-02 0.771 -- --

100 -- -- 29-Jul-02 0.553 -- --

100 -- -- 4-Aug-02 0.225 -- --

100 -- -- 7-Aug-02 0.511 -- --

100 -- -- 10-Aug-02 1.28 -- --

100 -- -- 13-Aug-02 0.181 -- --

100 -- -- 16-Aug-02 0.994 -- --

100 -- -- 19-Aug-02 1.27 -- --

100 -- -- 22-Aug-02 0.547 -- --

100 -- -- 25-Aug-02 0.064 -- --

100 -- -- 28-Aug-02 0.204 -- --

100 -- -- 31-Aug-02 0.465 -- --

100 -- -- 3-Sep-02 0.439 -- --

100 -- -- 6-Sep-02 4.11 -- --

100 -- -- 9-Sep-02 1.19 -- --

100 -- -- 12-Sep-02 0.473 -- --

100 -- -- 15-Sep-02 0.0875 -- --

100 -- -- 18-Sep-02 0.739 -- --

100 -- -- 21-Sep-02 0.107 -- --

100 -- -- 24-Sep-02 0.223 -- --

100 -- -- 27-Sep-02 0.183 -- --

100 -- -- 30-Sep-02 0.395 -- --

100 -- -- 3-Oct-02 1.57 -- --

100 -- -- 6-Oct-02 0.21 -- --

100 -- -- 9-Oct-02 0.983 -- --

100 13-Oct-01 0.41 12-Oct-02 0.498 -- --

100 16-Oct-01 0.24 15-Oct-02 0.256 -- --

100 18-Oct-01 1.7 18-Oct-02 0.457 -- --

100 23-Oct-01 0.32 21-Oct-02 4.63 -- --

100 26-Oct-01 0.24 ND 24-Oct-02 1.89 -- --

100 29-Oct-01 5 27-Oct-02 1.26 -- --

100 1-Nov-01 1.4 30-Oct-02 0.359 -- --

100 4-Nov-01 0.69 2-Nov-02 0.053 -- --

100 8-Nov-01 0.71 5-Nov-02 0.506 -- --

100 11-Nov-01 3.9 8-Nov-02 0.319 -- --

100 14-Nov-01 2.8 11-Nov-02 0.129 -- --

100 16-Nov-01 1 14-Nov-02 0.627 -- --

100 19-Nov-01 0.45 17-Nov-02 0.485 -- --

100 22-Nov-01 1.1 20-Nov-02 0.765 -- --

100 26-Nov-01 2 23-Nov-02 0.498 -- --

100 28-Nov-01 0.24 ND 26-Nov-02 0.818 -- --

100 1-Dec-01 0.66 29-Nov-02 0.518 -- --

B-24
 
Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS 

Located around the Primary Pb Smelter
 

Full-Scale 

Sampling Dates and Results (μg/m3

)

 a,b,c 

2001 2002 2003 

Analysis ID Date Date Date 

100 4-Dec-01 4.6 2-Dec-02 0.954 -- --

100 7-Dec-01 2.5 5-Dec-02 0.057 -- --

100 10-Dec-01 2.5 8-Dec-02 0.112 -- --

100 13-Dec-01 0.25 ND 11-Dec-02 2.57 -- --

100 17-Dec-01 0.31 14-Dec-02 0.264 -- --

100 19-Dec-01 0.23 ND 17-Dec-02 1.89 -- --

100 22-Dec-01 0.24 ND 20-Dec-02 0.382 -- --

100 26-Dec-01 0.27 23-Dec-02 0.895 -- --

100 28-Dec-01 1.3 26-Dec-02 0.086 -- --

100 31-Dec-01 0.27 29-Dec-02 1.72 -- --

100 Summary: 2001 

Max = 5 

Avg = 1.3 2002 

Max = 4.9 

Avg = 1 2003 

Max = 5.5 

Avg = 0.79 

101 -- -- 3-Jan-02 0.25 ND -- --

101 -- -- 7-Jan-02 0.25 ND -- --

101 -- -- 10-Jan-02 0.3 -- --

101 -- -- 13-Jan-02 17 -- --

101 -- -- 16-Jan-02 0.35 -- --

101 -- -- 19-Jan-02 0.6 -- --

101 -- -- 22-Jan-02 0.55 -- --

101 -- -- 25-Jan-02 0.24 ND -- --

101 -- -- 28-Jan-02 0.34 -- --

101 -- -- 31-Jan-02 0.24 ND -- --

101 -- -- 5-Feb-02 0.52 -- --

101 -- -- 8-Feb-02 0.3 -- --

101 -- -- 11-Feb-02 0.23 ND -- --

101 -- -- 14-Feb-02 0.27 -- --

101 -- -- 18-Feb-02 0.6 -- --

101 -- -- 21-Feb-02 0.24 ND -- --

101 -- -- 26-Feb-02 0.24 ND -- --

101 -- -- 1-Mar-02 0.65 -- --

101 -- -- 7-Mar-02 1.6 -- --

101 -- -- 11-Mar-02 0.24 ND -- --

101 -- -- 14-Mar-02 1.2 -- --

101 -- -- 17-Mar-02 0.65 -- --

101 -- -- 20-Mar-02 0.46 -- --

101 -- -- 23-Mar-02 0.25 ND -- --

101 -- -- 26-Mar-02 0.24 ND -- --

101 -- -- 29-Mar-02 0.48 -- --

101 -- -- 1-Apr-02 0.26 ND -- --

101 -- -- 4-Apr-02 1.8 -- --

101 -- -- 7-Apr-02 0.26 ND -- --

101 -- -- 10-Apr-02 0.69 -- --

101 -- -- 16-Apr-02 1.8 -- --

101 -- -- 18-Apr-02 0.55 -- --

101 -- -- 25-Apr-02 0.25 ND -- --

101 -- -- 28-Apr-02 0.27 ND -- --

101 -- -- 1-May-02 0.34 -- --

101 -- -- 4-May-02 0.51 -- --

101 -- -- 7-May-02 0.54 -- --

101 -- -- 10-May-02 2.14 -- --

101 -- -- 13-May-02 0.054 -- --

101 -- -- 16-May-02 0.28 -- --

B-25
 
Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS 

Located around the Primary Pb Smelter
 

Full-Scale 

Sampling Dates and Results (μg/m3

)

 a,b,c 

2001 2002 2003 

Analysis ID Date Date Date 

101 -- -- 19-May-02 0.0617 -- --

101 -- -- 22-May-02 0.921 -- --

101 -- -- 25-May-02 0.123 -- --

101 -- -- 29-May-02 0.562 -- --

101 -- -- 31-May-02 0.0993 -- --

101 -- -- 3-Jun-02 0.677 -- --

101 -- -- 6-Jun-02 0.962 -- --

101 -- -- 9-Jun-02 0.245 -- --

101 -- -- 12-Jun-02 0.085 -- --

101 -- -- 15-Jun-02 0.0693 -- --

101 -- -- 18-Jun-02 0.261 -- --

101 -- -- 21-Jun-02 0.375 -- --

101 -- -- 24-Jun-02 0.935 -- --

101 -- -- 27-Jun-02 0.0751 -- --

101 -- -- 30-Jun-02 0.05 ND -- --

101 -- -- 3-Jul-02 0.225 -- --

101 -- -- 6-Jul-02 1.11 -- --

101 -- -- 9-Jul-02 1.66 -- --

101 -- -- 12-Jul-02 3.58 -- --

101 -- -- 15-Jul-02 0.655 -- --

101 -- -- 18-Jul-02 0.131 -- --

101 -- -- 22-Jul-02 0.092 -- --

101 -- -- 26-Jul-02 1.36 -- --

101 -- -- 29-Jul-02 0.213 -- --

101 -- -- 1-Aug-02 1.29 -- --

101 -- -- 4-Aug-02 0.22 -- --

101 -- -- 7-Aug-02 9.13 -- --

101 -- -- 10-Aug-02 0.656 -- --

101 -- -- 13-Aug-02 0.05 ND -- --

101 -- -- 16-Aug-02 6.68 -- --

101 -- -- 19-Aug-02 1.69 -- --

101 -- -- 22-Aug-02 0.059 -- --

101 -- -- 25-Aug-02 0.701 -- --

101 -- -- 28-Aug-02 10 -- --

101 -- -- 31-Aug-02 0.378 -- --

101 -- -- 3-Sep-02 1.22 -- --

101 -- -- 6-Sep-02 1.09 -- --

101 13-Oct-01 0.096 -- -- -- --

101 16-Oct-01 0.075 -- -- -- --

101 18-Oct-01 0.18 -- -- -- --

101 23-Oct-01 0.3 ND -- -- -- --

101 26-Oct-01 0.23 ND -- -- -- --

101 29-Oct-01 1.4 -- -- -- --

101 1-Nov-01 0.41 -- -- -- --

101 4-Nov-01 0.23 ND -- -- -- --

101 8-Nov-01 0.26 -- -- -- --

101 11-Nov-01 2.4 -- -- -- --

101 14-Nov-01 1.5 -- -- -- --

101 16-Nov-01 0.24 ND -- -- -- --

101 19-Nov-01 0.24 ND -- -- -- --

101 22-Nov-01 0.38 -- -- -- --

101 26-Nov-01 0.24 ND -- -- -- --

B-26
 
Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS 

Located around the Primary Pb Smelter
 

Full-Scale 

Sampling Dates and Results (μg/m3

)

 a,b,c 

2001 2002 2003 

Analysis ID Date Date Date 

101 28-Nov-01 1.7 -- -- -- --

101 1-Dec-01 0.62 -- -- -- --

101 4-Dec-01 0.25 ND -- -- -- --

101 7-Dec-01 1.7 -- -- -- --

101 10-Dec-01 1.4 -- -- -- --

101 13-Dec-01 0.3 -- -- -- --

101 17-Dec-01 0.24 ND -- -- -- --

101 19-Dec-01 0.23 ND -- -- -- --

101 22-Dec-01 0.23 ND -- -- -- --

101 26-Dec-01 0.22 ND -- -- -- --

101 28-Dec-01 0.23 ND -- -- -- --

101 31-Dec-01 0.24 ND -- -- -- --

101 Summary: 2001 

Max = 2.4 

Avg = 0.52 2002 

Max = 17 

Avg = 1.1 2003 --

102 -- -- 03-Jan-02 0.59 01-Jan-03 0.147 

102 -- -- 07-Jan-02 0.65 04-Jan-03 0.326 

102 -- -- 10-Jan-02 1.4 07-Jan-03 0.63 

102 -- -- 13-Jan-02 15 10-Jan-03 0.257 

102 -- -- 16-Jan-02 4.4 13-Jan-03 0.388 

102 -- -- 19-Jan-02 0.24 ND 16-Jan-03 0.322 

102 -- -- 22-Jan-02 25 19-Jan-03 0.986 

102 -- -- 28-Jan-02 8.1 22-Jan-03 0.172 

102 -- -- 31-Jan-02 0.39 25-Jan-03 0.684 

102 -- -- 05-Feb-02 2.7 28-Jan-03 1.52 

102 -- -- 08-Feb-02 5 31-Jan-03 2.33 

102 -- -- 11-Feb-02 4.4 03-Feb-03 2.69 

102 -- -- 14-Feb-02 14 06-Feb-03 0.342 

102 -- -- 18-Feb-02 13 09-Feb-03 0.265 

102 -- -- 21-Feb-02 0.38 12-Feb-03 0.46 

102 -- -- 26-Feb-02 0.25 15-Feb-03 0.05 ND 

102 -- -- 01-Mar-02 6.1 18-Feb-03 0.173 

102 -- -- 04-Mar-02 4.4 21-Feb-03 0.281 

102 -- -- 07-Mar-02 11 24-Feb-03 0.279 

102 -- -- 14-Mar-02 17 27-Feb-03 0.056 

102 -- -- 17-Mar-02 0.26 ND 02-Mar-03 0.181 

102 -- -- 20-Mar-02 0.23 ND 05-Mar-03 0.363 

102 -- -- 23-Mar-02 2.4 08-Mar-03 1.85 

102 -- -- 26-Mar-02 0.45 11-Mar-03 3.25 

102 -- -- 29-Mar-02 0.81 14-Mar-03 0.224 

102 -- -- 01-Apr-02 13 17-Mar-03 1.25 

102 -- -- 04-Apr-02 0.24 ND 20-Mar-03 0.349 

102 -- -- 07-Apr-02 6.4 23-Mar-03 0.504 

102 -- -- 10-Apr-02 0.86 26-Mar-03 0.476 

102 -- -- 16-Apr-02 11 29-Mar-03 0.107 

102 -- -- 18-Apr-02 3.1 01-Apr-03 1.56 

102 -- -- 25-Apr-02 1.1 04-Apr-03 4.11 

102 -- -- 28-Apr-02 0.25 ND 07-Apr-03 0.184 

102 -- -- 01-May-02 0.87 10-Apr-03 0.16 

102 -- -- 04-May-02 0.6 13-Apr-03 0.441 

102 -- -- 07-May-02 0.98 16-Apr-03 10 

102 -- -- 10-May-02 0.551 19-Apr-03 4.33 

102 -- -- 13-May-02 0.679 22-Apr-03 0.215 

B-27
 
Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS 

Located around the Primary Pb Smelter
 

Full-Scale 

Sampling Dates and Results (μg/m3

)

 a,b,c 

2001 2002 2003 

Analysis ID Date Date Date 

102 -- -- 16-May-02 2.19 28-Apr-03 0.435 

102 -- -- 19-May-02 0.148 01-May-03 0.926 

102 -- -- 22-May-02 3.84 04-May-03 0.671 

102 -- -- 25-May-02 1.72 13-May-03 1.41 

102 -- -- 29-May-02 0.645 16-May-03 0.319 

102 -- -- 31-May-02 1.26 19-May-03 0.512 

102 -- -- 03-Jun-02 2.27 22-May-03 0.11 

102 -- -- 06-Jun-02 0.441 25-May-03 0.05 ND 

102 -- -- 09-Jun-02 1.96 28-May-03 0.245 

102 -- -- 12-Jun-02 0.962 31-May-03 0.274 

102 -- -- 15-Jun-02 0.365 03-Jun-03 0.188 

102 -- -- 18-Jun-02 2.89 06-Jun-03 0.381 

102 -- -- 21-Jun-02 1.12 09-Jun-03 1.35 

102 -- -- 24-Jun-02 1.72 12-Jun-03 0.418 

102 -- -- 27-Jun-02 1.06 15-Jun-03 0.096 

102 -- -- 30-Jun-02 0.273 18-Jun-03 0.406 

102 -- -- 03-Jul-02 1.23 21-Jun-03 0.475 

102 -- -- 06-Jul-02 0.747 24-Jun-03 2.33 

102 -- -- 09-Jul-02 0.739 27-Jun-03 0.469 

102 -- -- 12-Jul-02 0.616 30-Jun-03 2.29 

102 -- -- 15-Jul-02 0.522 03-Jul-03 0.964 

102 -- -- 18-Jul-02 0.967 06-Jul-03 1.15 

102 -- -- 22-Jul-02 0.667 -- --

102 -- -- 26-Jul-02 6.48 -- --

102 -- -- 29-Jul-02 0.913 -- --

102 -- -- 01-Aug-02 1.18 -- --

102 -- -- 04-Aug-02 0.663 -- --

102 -- -- 07-Aug-02 0.434 -- --

102 -- -- 10-Aug-02 0.932 -- --

102 -- -- 13-Aug-02 2.86 -- --

102 -- -- 16-Aug-02 4.93 -- --

102 -- -- 19-Aug-02 1.04 -- --

102 -- -- 22-Aug-02 3.8 -- --

102 -- -- 25-Aug-02 0.135 -- --

102 -- -- 28-Aug-02 0.262 -- --

102 -- -- 31-Aug-02 0.205 -- --

102 -- -- 03-Sep-02 0.411 -- --

102 -- -- 06-Sep-02 0.586 -- --

102 -- -- 09-Sep-02 0.614 -- --

102 -- -- 12-Sep-02 0.318 -- --

102 -- -- 21-Sep-02 0.29 -- --

102 -- -- 24-Sep-02 0.261 -- --

102 -- -- 27-Sep-02 0.314 -- --

102 -- -- 30-Sep-02 4.56 -- --

102 -- -- 03-Oct-02 1.53 -- --

102 -- -- 06-Oct-02 0.611 -- --

102 -- -- 09-Oct-02 1.77 -- --

102 -- -- 12-Oct-02 0.412 -- --

102 -- -- 15-Oct-02 0.17 -- --

102 16-Oct-01 0.31 18-Oct-02 2.44 -- --

102 18-Oct-01 16 21-Oct-02 0.759 -- --

102 23-Oct-01 2.5 24-Oct-02 0.215 -- --

B-28
 
Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS 

Located around the Primary Pb Smelter
 

Full-Scale 

Sampling Dates and Results (μg/m3

)

 a,b,c 

2001 2002 2003 

Analysis ID Date Date Date 

102 26-Oct-01 0.25 27-Oct-02 0.152 -- --

102 29-Oct-01 14 30-Oct-02 0.125 -- --

102 01-Nov-01 18 02-Nov-02 0.069 -- --

102 04-Nov-01 0.48 05-Nov-02 0.099 -- --

102 08-Nov-01 0.83 08-Nov-02 10.7 -- --

102 11-Nov-01 0.58 11-Nov-02 0.15 -- --

102 14-Nov-01 4.2 14-Nov-02 1.07 -- --

102 16-Nov-01 0.99 17-Nov-02 0.108 -- --

102 19-Nov-01 0.4 20-Nov-02 0.708 -- --

102 22-Nov-01 13 23-Nov-02 0.287 -- --

102 26-Nov-01 65 26-Nov-02 0.145 -- --

102 28-Nov-01 0.24 ND 29-Nov-02 0.15 -- --

102 04-Dec-01 7.5 02-Dec-02 0.776 -- --

102 07-Dec-01 0.85 05-Dec-02 0.896 -- --

102 10-Dec-01 1.4 08-Dec-02 0.376 -- --

102 13-Dec-01 0.22 ND 11-Dec-02 0.919 -- --

102 17-Dec-01 0.23 ND 14-Dec-02 0.568 -- --

102 19-Dec-01 0.85 17-Dec-02 2.32 -- --

102 22-Dec-01 5.1 20-Dec-02 0.224 -- --

102 26-Dec-01 0.49 23-Dec-02 0.233 -- --

102 28-Dec-01 0.53 26-Dec-02 0.083 -- --

102 31-Dec-01 0.25 29-Dec-02 5.24 -- --

102 Summary: 2001 

Max = 65 

Avg = 6.2 2002 

Max = 25 

Avg = 2.4 2003 

Max = 10 

Avg = 1 

103 -- -- 3-Jan-02 0.92 10-Jan-03 0.402 

103 -- -- 7-Jan-02 0.43 13-Jan-03 0.621 

103 -- -- 10-Jan-02 0.73 16-Jan-03 0.23 

103 -- -- 13-Jan-02 1.3 19-Jan-03 0.155 

103 -- -- 16-Jan-02 1.5 22-Jan-03 0.058 

103 -- -- 19-Jan-02 0.25 ND 25-Jan-03 0.326 

103 -- -- 22-Jan-02 2.1 28-Jan-03 0.864 

103 -- -- 25-Jan-02 0.59 31-Jan-03 0.075 

103 -- -- 28-Jan-02 1.9 3-Feb-03 0.069 

103 -- -- 31-Jan-02 0.46 6-Feb-03 0.283 

103 -- -- 5-Feb-02 1 9-Feb-03 0.566 

103 -- -- 8-Feb-02 0.61 12-Feb-03 0.65 

103 -- -- 11-Feb-02 0.49 15-Feb-03 0.05 ND 

103 -- -- 14-Feb-02 0.38 18-Feb-03 1.22 

103 -- -- 18-Feb-02 1.4 21-Feb-03 0.104 

103 -- -- 21-Feb-02 0.32 24-Feb-03 0.135 

103 -- -- 26-Feb-02 0.24 ND 27-Feb-03 0.05 ND 

103 -- -- 1-Mar-02 6.1 2-Mar-03 0.085 

103 -- -- 4-Mar-02 0.49 5-Mar-03 0.105 

103 -- -- 7-Mar-02 0.94 8-Mar-03 0.377 

103 -- -- 11-Mar-02 1.2 11-Mar-03 0.993 

103 -- -- 14-Mar-02 1.5 14-Mar-03 0.395 

103 -- -- 17-Mar-02 0.25 ND 17-Mar-03 2.2 

103 -- -- 20-Mar-02 0.26 ND 20-Mar-03 0.655 

103 -- -- 23-Mar-02 0.25 ND 23-Mar-03 0.422 

103 -- -- 26-Mar-02 0.3 26-Mar-03 0.421 

103 -- -- 29-Mar-02 2.1 29-Mar-03 0.056 

103 -- -- 1-Apr-02 0.62 1-Apr-03 0.236 

B-29
 
Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS 

Located around the Primary Pb Smelter
 

Full-Scale 

Sampling Dates and Results (μg/m3

)

 a,b,c 

2001 2002 2003 

Analysis ID Date Date Date 

103 -- -- 4-Apr-02 0.24 ND 4-Apr-03 0.169 

103 -- -- 7-Apr-02 1.4 7-Apr-03 0.205 

103 -- -- 10-Apr-02 3.8 10-Apr-03 0.113 

103 -- -- 16-Apr-02 1.2 13-Apr-03 0.908 

103 -- -- 18-Apr-02 1.7 16-Apr-03 0.218 

103 -- -- 22-Apr-02 1.1 19-Apr-03 2.15 

103 -- -- 25-Apr-02 0.23 ND 22-Apr-03 0.145 

103 -- -- 28-Apr-02 0.25 ND 25-Apr-03 0.093 

103 -- -- 1-May-02 1.8 1-May-03 0.242 

103 -- -- 4-May-02 0.4 7-May-03 0.455 

103 -- -- 7-May-02 0.42 10-May-03 0.369 

103 -- -- 10-May-02 1.43 13-May-03 0.679 

103 -- -- 13-May-02 0.0822 16-May-03 0.14 

103 -- -- 22-May-02 1.53 19-May-03 0.383 

103 -- -- 25-May-02 0.232 22-May-03 0.078 

103 -- -- 29-May-02 0.906 25-May-03 0.06 

103 -- -- 31-May-02 0.449 28-May-03 0.164 

103 -- -- 3-Jun-02 0.342 31-May-03 0.166 

103 -- -- 6-Jun-02 0.338 3-Jun-03 0.105 

103 -- -- 9-Jun-02 0.35 6-Jun-03 1.15 

103 -- -- 15-Jun-02 0.204 9-Jun-03 0.126 

103 -- -- 18-Jun-02 0.86 12-Jun-03 0.511 

103 -- -- 21-Jun-02 1.11 15-Jun-03 0.05 ND 

103 -- -- 24-Jun-02 1.06 18-Jun-03 0.907 

103 -- -- 27-Jun-02 0.46 21-Jun-03 0.133 

103 -- -- 30-Jun-02 0.097 24-Jun-03 0.32 

103 -- -- 3-Jul-02 0.68 27-Jun-03 0.098 

103 -- -- 6-Jul-02 0.286 30-Jun-03 0.453 

103 -- -- 9-Jul-02 0.342 3-Jul-03 0.159 

103 -- -- 12-Jul-02 0.276 6-Jul-03 0.051 

103 -- -- 15-Jul-02 0.244 -- --

103 -- -- 18-Jul-02 0.878 -- --

103 -- -- 22-Jul-02 0.728 -- --

103 -- -- 26-Jul-02 0.537 -- --

103 -- -- 29-Jul-02 0.422 -- --

103 -- -- 1-Aug-02 2.59 -- --

103 -- -- 4-Aug-02 0.258 -- --

103 -- -- 7-Aug-02 0.159 -- --

103 -- -- 10-Aug-02 0.379 -- --

103 -- -- 13-Aug-02 0.077 -- --

103 -- -- 16-Aug-02 0.46 -- --

103 -- -- 19-Aug-02 0.756 -- --

103 -- -- 22-Aug-02 0.296 -- --

103 -- -- 25-Aug-02 0.057 -- --

103 -- -- 28-Aug-02 0.107 -- --

103 -- -- 31-Aug-02 0.33 -- --

103 -- -- 3-Sep-02 0.291 -- --

103 -- -- 6-Sep-02 1.11 -- --

103 13-Oct-01 0.994 -- -- -- --

103 16-Oct-01 0.56 -- -- -- --

103 18-Oct-01 0.96 -- -- -- --

103 23-Oct-01 0.32 ND -- -- -- --

B-30
 
Attachment B-3. Air Monitoring Results for Pb from Monitors Not In AQS 

Located around the Primary Pb Smelter
 

Full-Scale 

Sampling Dates and Results (μg/m3

)

 a,b,c 

2001 2002 2003 

Analysis ID Date Date Date 

103 26-Oct-01 0.33 -- -- -- --

103 29-Oct-01 2.5 -- -- -- --

103 01-Nov-01 0.86 -- -- -- --

103 04-Nov-01 0.25 -- -- -- --

103 08-Nov-01 0.87 -- -- -- --

103 11-Nov-01 0.59 -- -- -- --

103 14-Nov-01 3.6 -- -- -- --

103 16-Nov-01 1 -- -- -- --

103 19-Nov-01 0.35 -- -- -- --

103 22-Nov-01 1.1 -- -- -- --

103 26-Nov-01 2.9 -- -- -- --

103 28-Nov-01 0.23 ND -- -- -- --

103 01-Dec-01 0.85 -- -- -- --

103 04-Dec-01 2.1 -- -- -- --

103 07-Dec-01 1.3 -- -- -- --

103 10-Dec-01 2.3 -- -- -- --

103 13-Dec-01 0.26 -- -- -- --

103 17-Dec-01 0.24 ND -- -- -- --

103 19-Dec-01 0.39 -- -- -- --

103 22-Dec-01 0.25 ND -- -- -- --

103 26-Dec-01 0.4 -- -- -- --

103 Summary: 2001 

Max = 3.6 

Avg = 1 2002 

Max = 6.1 

Avg = 0.8 2003 

Max = 2.2 

Avg = 0.39 

a

 Daily data obtained from U.S. EPA Region 7 (2006). 

b

 "--" indicates that no sample was collected during that time. 

c

 A value qualified with an "ND" represents a non-detect. The value presented is the detection limit. For the 

purpose of calculating averages, one-half the detection limit was used as the value for non-detects. 

B-31
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

140 03-Oct-01 920 853 460 1060 823 

141 03-Oct-01 1500 724 1470 818 1128 

142 03-Oct-01 377 602 762 731 618 

143 03-Oct-01 757 1390 1200 563 978 

144 03-Oct-01 1680 1030 685 719 1029 

145 03-Oct-01 2770 2210 1070 783 1708 

146 03-Oct-01 1280 809 433 731 813 

147 03-Oct-01 2640 1530 596 674 1360 

148 03-Oct-01 670 1360 13100 465 3899 

149 03-Oct-01 2820 2080 1540 1400 1960 

150 03-Oct-01 403 1330 350 748 708 

151 03-Oct-01 783 913 736 1240 918 

152 04-Oct-01 803 1140 660 696 825 

153 04-Oct-01 270 5530 1140 486 1857 

154 04-Oct-01 4220 2160 1440 1360 2295 

155 04-Oct-01 1260 873 1360 612 1026 

156 04-Oct-01 1260 1450 636 2190 1384 

157 05-Oct-01 1330 1550 1460 1630 1493 

158 04-Oct-01 3100 9390 756 781 3507 

159 04-Oct-01 1660 5780 428 440 2077 

160 04-Oct-01 1150 853 927 269 800 

161 04-Oct-01 1720 1790 1420 846 1444 

162 04-Oct-01 1670 1800 526 2320 1579 

163 04-Oct-01 13600 4870 2190 8450 7278 

164 04-Oct-01 6900 10700 8360 5270 7808 

165 04-Oct-01 6640 6500 7760 6200 6775 

166 05-Oct-01 16600 11800 5970 8860 10808 

167 05-Oct-01 28000 32100 8490 14200 20698 

168 05-Oct-01 16700 18600 10400 2130 11958 

169 05-Oct-01 12800 5640 4610 15800 9713 

170 05-Oct-01 8670 4140 3950 4060 5205 

171 10-Oct-01 1400 2120 461 1470 1363 

172 10-Oct-01 851 1530 1270 728 1095 

173 10-Oct-01 1160 1090 751 1570 1143 

174 10-Oct-01 1270 1260 2530 1320 1595 

175 10-Oct-01 2750 2580 5200 1260 2948 

176 10-Oct-01 1720 2030 1620 515 1471 

177 10-Oct-01 2760 3370 2190 7510 3958 

178 08-Oct-01 4950 3690 1040 649 2582 

179 08-Oct-01 1010 1800 1270 1250 1333 

180 08-Oct-01 1330 2010 1220 899 1365 

181 08-Oct-01 1070 2260 1160 976 1367 

182 08-Oct-01 22500 5110 886 302 7200 

183 08-Oct-01 1980 3020 1210 1050 1815 

184 08-Oct-01 5830 4370 1510 1520 3308 

185 09-Oct-01 2230 1670 796 936 1408 

186 09-Oct-01 1020 1220 652 366 815 

187 09-Oct-01 833 898 795 1050 894 

188 09-Oct-01 2350 1820 1100 886 1539 

189 09-Oct-01 1110 1070 1680 849 1177 

190 09-Oct-01 930 818 922 910 895 

191 09-Oct-01 1730 2180 24000 3600 7878 

B-32
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

192 09-Oct-01 3150 1230 710 1180 1568 

193 10-Oct-01 5740 1590 14600 11200 8283 

194 10-Oct-01 3670 998 1360 3520 2387 

195 10-Oct-01 7240 1820 906 1880 2962 

196 10-Oct-01 1180 2310 1550 979 1505 

197 11-Oct-01 2210 5630 2430 1870 3035 

198 11-Oct-01 857 850 423 112 561 

199 11-Oct-01 648 330 310 117 351 

200 11-Oct-01 559 156 710 296 430 

201 11-Oct-01 373 86 95 212 192 

202 12-Oct-01 211 160 389 203 241 

203 12-Oct-01 870 579 1090 -- 846 

204 12-Oct-01 183 308 174 184 212 

205 11-Oct-01 326 157 251 66 200 

206 11-Oct-01 234 236 201 220 223 

207 09-Oct-01 1040 1140 1150 826 1039 

208 10-Oct-01 3050 2150 1890 1800 2223 

209 10-Oct-01 1510 2030 1390 1100 1508 

210 10-Oct-01 7490 546 1870 3830 3434 

211 10-Oct-01 2400 2200 952 642 1549 

212 10-Oct-01 163 273 341 642 355 

213 10-Oct-01 8500 1640 3340 1020 3625 

214 11-Oct-01 2100 2010 1150 1010 1568 

215 11-Oct-01 1320 1020 1160 1420 1230 

216 11-Oct-01 948 1070 1010 962 998 

217 10-Oct-01 541 754 826 668 697 

218 11-Oct-01 1320 671 588 562 785 

219 11-Oct-01 685 858 1150 773 867 

220 11-Oct-01 1050 1770 714 1020 1139 

221 02-Aug-04 395 470 202.7 -- 356 

222 11-Oct-01 1340 676 469 1610 1024 

223 11-Oct-01 424 555 474 199 413 

224 11-Oct-01 772 504 459 581 579 

225 11-Oct-01 1170 592 511 651 731 

226 11-Oct-01 323 381 357 606 417 

227 11-Oct-01 475 526 124 612 434 

228 11-Oct-01 324 680 343 479 457 

229 11-Oct-01 374 511 307 5430 1656 

230 11-Oct-01 333 423 492 148 349 

231 09-Oct-01 501 706 889 873 742 

232 09-Oct-01 1580 1870 1060 1220 1433 

233 09-Oct-01 1640 3810 900 686 1759 

234 09-Oct-01 1100 2350 721 600 1193 

235 09-Oct-01 1200 1480 636 599 979 

236 12-Oct-01 1420 614 731 1280 1011 

237 09-Oct-01 1250 792 1810 981 1208 

238 11-Oct-01 -- 492 1300 3420 1737 

239 09-Oct-01 9820 2440 1630 2730 4155 

240 09-Oct-01 2320 3070 4230 1460 2770 

241 12-Oct-01 691 4130 392 634 1462 

242 12-Oct-01 495 860 525 460 585 

243 12-Oct-01 313 354 539 638 461 

B-33
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

244 12-Oct-01 671 547 530 731 620 

245 12-Oct-01 586 785 595 700 667 

246 12-Oct-01 703 801 468 760 683 

247 12-Oct-01 498 813 537 484 583 

248 12-Oct-01 431 368 670 524 498 

249 12-Oct-01 279 568 1020 1690 889 

250 12-Oct-02 914 864 830 1200 952 

251 12-Oct-01 4130 2980 2540 857 2627 

252 12-Oct-01 2330 1160 1360 1430 1570 

253 11-Oct-01 413 1180 2140 964 1174 

254 11-Oct-01 1010 1700 1100 1090 1225 

255 11-Oct-01 756 890 1360 1290 1074 

256 11-Oct-01 2090 2480 1130 1800 1875 

257 09-Oct-01 967 1400 993 933 1073 

258 11-Oct-01 1680 1420 1430 1660 1548 

259 11-Oct-01 1290 3420 1670 4400 2695 

260 11-Oct-01 1200 1460 1470 807 1234 

261 11-Oct-01 934 1550 1730 1830 1511 

262 11-Oct-01 1990 1980 1040 1280 1573 

263 09-Oct-01 1890 1160 1220 1430 1425 

264 11-Oct-01 1650 2220 1360 1300 1633 

265 09-Oct-01 1090 1010 1060 885 1011 

266 11-Oct-01 2390 2460 1210 1850 1978 

267 11-Oct-01 1440 1770 1230 1930 1593 

268 11-Oct-01 1040 1080 1220 1040 1095 

269 11-Oct-01 1230 981 1050 1160 1105 

270 11-Oct-01 4270 909 917 1030 1782 

271 11-Oct-01 1360 1060 897 709 1007 

272 11-Oct-01 612 2060 658 687 1004 

273 09-Oct-01 315 340 630 232 379 

274 09-Oct-01 703 719 520 664 652 

275 09-Oct-01 694 731 660 393 620 

276 09-Oct-01 254 443 136 216 262 

277 09-Oct-01 868 797 349 522 634 

278 09-Oct-01 245 204 59 48 139 

279 10-Oct-01 1230 1330 982 822 1091 

280 10-Oct-01 21100 893 475 441 5727 

281 08-Oct-01 1120 1910 1090 957 1269 

282 08-Oct-01 7650 6940 3380 4920 5723 

283 08-Oct-01 4400 3060 2250 2010 2930 

284 08-Oct-01 4690 6760 3270 4850 4893 

285 08-Oct-01 4690 6760 3270 4850 4893 

286 08-Oct-01 8380 8590 6850 6870 7673 

287 08-Oct-01 6020 5650 2420 3580 4418 

288 08-Oct-01 19900 20500 9766 9020 14797 

289 08-Oct-01 1880 602 950 596 1007 

290 08-Oct-01 887 636 2220 1750 1373 

291 08-Oct-01 662 398 538 1240 710 

292 08-Oct-01 2510 1510 3510 2530 2515 

293 08-Oct-01 436 698 682 528 586 

294 08-Oct-01 189 330 534 409 366 

295 10-Oct-01 1130 3180 1580 1070 1740 

B-34
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

296 08-Oct-01 3100 3180 2240 1680 2550 

297 08-Oct-01 1630 1650 1940 1810 1758 

298 08-Oct-01 993 1080 3700 2010 1946 

299 10-Oct-01 129 7280 2880 2160 3112 

300 10-Oct-01 688 1190 1670 1800 1337 

301 09-Oct-01 4130 6070 1220 989 3102 

302 10-Oct-01 223 13000 5320 2230 5193 

303 09-Oct-01 1220 1120 180 640 790 

304 09-Oct-01 500 667 381 203 438 

305 09-Oct-01 569 506 650 630 589 

306 09-Oct-01 818 664 917 1170 892 

307 10-Oct-01 498 465 492 744 550 

308 10-Oct-01 954 1360 1050 695 1015 

309 10-Oct-01 824 581 529 580 629 

310 09-Oct-01 648 714 809 838 752 

311 09-Oct-01 977 875 808 926 897 

312 09-Oct-01 657 728 593 619 649 

313 10-Oct-01 890 720 612 607 707 

314 10-Oct-01 11200 1110 177 159 3162 

315 10-Oct-01 590 858 393 375 554 

316 10-Oct-01 825 957 794 854 858 

317 10-Oct-01 658 436 533 503 533 

318 10-Oct-01 509 578 484 1470 760 

319 10-Oct-01 1100 1540 1320 397 1089 

320 10-Oct-01 827 962 -- -- 895 

321 10-Oct-01 1200 1040 1160 2790 1548 

322 10-Oct-01 2570 3400 1590 2190 2438 

323 10-Oct-01 814 720 1320 1220 1019 

324 10-Oct-01 2130 2490 2650 1810 2270 

325 02-Oct-01 2970 2470 1300 916 1914 

326 08-Oct-01 20700 10600 8880 2590 10693 

327 08-Oct-01 6490 8670 2650 3930 5435 

328 08-Oct-01 8080 6010 3470 2990 5138 

329 08-Oct-01 5160 2510 996 1040 2427 

330 09-Oct-01 1040 1900 1330 2040 1578 

331 12-Oct-01 1800 1480 1470 1400 1538 

332 12-Oct-01 1530 1720 594 1810 1414 

333 12-Oct-01 1150 1620 1730 1540 1510 

334 12-Oct-01 831 619 1360 1210 1005 

335 12-Oct-01 1630 4470 944 1600 2161 

336 19-Oct-01 11400 11600 8180 7050 9558 

337 18-Oct-01 1080 1770 563 854 1067 

338 18-Oct-01 999 1050 753 772 894 

339 18-Oct-01 660 3900 1600 1060 1805 

340 15-Oct-01 945 814 953 954 917 

341 15-Oct-01 742 2060 1010 778 1148 

342 15-Oct-01 1290 807 562 244 726 

343 15-Oct-01 959 1080 1566 1220 1206 

344 15-Oct-01 801 364 637 472 569 

345 15-Oct-01 1230 59 419 1080 697 

346 15-Oct-01 730 348 396 281 439 

347 19-Oct-01 371 726 964 394 614 

B-35
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

348 15-Oct-01 860 527 892 430 677 

349 15-Oct-01 388 334 266 210 300 

350 15-Oct-01 128 490 488 161 317 

351 17-Oct-01 624 869 316 379 547 

352 17-Oct-01 1250 857 425 1480 1003 

353 19-Oct-01 2320 2740 1160 2860 2270 

354 17-Oct-01 1370 3900 1350 1050 1918 

355 17-Oct-01 180 392 413 413 350 

356 17-Oct-01 300 263 144 100 202 

357 17-Oct-01 826 798 496 960 770 

358 17-Oct-01 919 560 288 771 635 

359 17-Oct-01 886 617 128 143 444 

360 17-Oct-01 1110 549 806 -- 822 

361 17-Oct-01 624 886 257 544 578 

362 15-Oct-01 907 9421 699 1110 3034 

363 15-Oct-01 890 2160 947 -- 1332 

364 15-Oct-01 372 1110 1240 1060 946 

365 15-Oct-01 564 913 1220 521 805 

366 15-Oct-01 231 838 926 244 560 

367 15-Oct-01 173 330 250 915 417 

368 18-Oct-01 302 480 688 319 447 

369 16-Oct-01 12100 5170 9140 4290 7675 

370 18-Oct-01 1380 855 480 519 809 

371 16-Oct-01 2740 977 1300 1850 1717 

372 18-Oct-01 65 210 169 135 145 

373 16-Oct-01 237 209 197 200 211 

374 16-Oct-01 691 228 354 197 368 

375 16-Oct-01 510 341 159 434 361 

376 16-Oct-01 179 666 1080 41 492 

377 16-Oct-01 257 229 113 151 188 

378 16-Oct-01 435 382 498 391 427 

379 16-Oct-01 237 413 330 309 322 

380 16-Oct-01 342 448 614 281 421 

381 17-Oct-01 466 618 532 529 536 

382 17-Oct-01 454 559 726 629 592 

383 19-Oct-01 270 383 311 433 349 

384 19-Oct-01 294 288 815 768 541 

385 16-Oct-01 367 1690 391 1080 882 

386 16-Oct-01 4970 4250 3700 2680 3900 

387 16-Oct-01 3130 2750 3180 2010 2768 

388 16-Oct-01 1280 1570 5100 1170 2280 

389 18-Oct-01 1120 8100 159 756 2534 

390 18-Oct-01 1800 1750 1400 1400 1588 

391 16-Oct-01 1380 1010 1150 936 1119 

392 18-Oct-01 977 1330 758 1500 1141 

393 16-Oct-01 1130 1923 425 741 1055 

394 16-Oct-01 319 904 584 396 551 

395 18-Oct-01 523 782 758 766 707 

396 18-Oct-01 634 800 903 452 697 

397 18-Oct-01 377 60 658 529 406 

398 18-Oct-01 289 155 263 868 394 

399 18-Oct-01 691 464 408 416 495 

B-36
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

400 18-Oct-01 451 1010 391 440 573 

401 15-Oct-01 814 1040 567 969 848 

402 15-Oct-01 2970 3080 396 513 1740 

403 18-Oct-01 1670 2290 1440 1230 1658 

404 15-Oct-01 655 636 401 545 559 

405 18-Oct-01 679 516 688 519 601 

406 15-Oct-01 748 1110 311 896 766 

407 15-Oct-01 440 514 324 346 406 

408 15-Oct-01 470 682 -- 573 575 

409 15-Oct-01 1010 1060 489 1620 1045 

410 15-Oct-01 928 1090 682 1500 1050 

411 15-Oct-01 982 541 791 444 690 

412 15-Oct-01 768 867 -- 649 761 

413 16-Oct-01 874 1110 1340 767 1023 

414 16-Oct-01 1160 1150 621 814 936 

415 16-Oct-01 1160 1130 609 245 786 

416 16-Oct-01 1240 866 1070 1260 1109 

417 16-Oct-01 9530 3450 537 2060 3894 

418 16-Oct-01 1640 1290 331 329 898 

419 19-Oct-01 332 560 165 440 374 

420 18-Oct-01 733 455 524 529 560 

421 18-Oct-01 774 559 341 307 495 

422 18-Oct-01 492 800 281 639 553 

423 18-Oct-01 530 804 793 440 642 

424 18-Oct-01 562 1320 578 619 770 

425 16-Oct-01 1040 1360 1030 1139 1142 

426 16-Oct-01 949 1240 850 1110 1037 

427 18-Oct-01 1230 4410 2010 2230 2470 

428 17-Oct-01 836 1540 778 934 1022 

429 17-Oct-01 1710 1490 1160 1940 1575 

430 17-Oct-01 1530 1170 597 471 942 

431 17-Oct-01 1990 1820 426 321 1139 

432 17-Oct-01 945 1250 560 323 770 

433 17-Oct-01 2050 2990 1970 9410 4105 

434 17-Oct-01 1270 2660 3930 1140 2250 

435 19-Oct-01 2670 594 1520 1170 1489 

436 17-Oct-01 556 1880 1090 1460 1247 

437 05-Oct-01 3850 5830 5610 3240 4633 

438 17-Oct-01 515 2150 285 228 795 

439 18-Oct-01 1880 1220 1960 3230 2073 

440 16-Oct-01 1380 1070 1480 1880 1453 

441 16-Oct-01 3780 3230 2240 2430 2920 

442 19-Oct-01 13500 5180 5590 6500 7693 

443 16-Oct-01 3500 5010 1630 754 2724 

444 18-Oct-01 1890 1540 1830 1920 1795 

445 18-Oct-01 710 719 998 1650 1019 

446 18-Oct-01 3670 645 1050 1290 1664 

447 18-Oct-01 564 775 352 631 581 

448 18-Oct-01 436 854 516 2010 954 

449 18-Oct-01 858 446 544 719 642 

450 18-Oct-01 322 635 527 491 494 

451 18-Oct-01 781 821 661 800 766 

B-37
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

452 18-Oct-01 435 249 726 657 517 

453 18-Oct-01 403 740 556 552 563 

454 18-Oct-01 682 618 578 788 667 

455 18-Oct-01 422 402 690 577 523 

456 19-Oct-01 697 4780 858 408 1686 

457 18-Oct-01 674 430 390 509 501 

458 18-Oct-01 124 333 1610 638 676 

459 18-Oct-01 566 732 406 240 486 

460 18-Oct-01 865 562 453 670 638 

461 18-Oct-01 489 386 599 487 490 

462 18-Oct-01 518 950 548 552 642 

463 17-Oct-01 829 416 100 194 385 

464 17-Oct-01 342 718 424 580 516 

465 17-Oct-01 357 530 343 487 429 

466 17-Oct-01 553 596 401 581 533 

467 19-Oct-01 778 33 370 495 419 

468 17-Oct-01 1330 1310 707 381 932 

469 17-Oct-01 89 286 464 230 267 

470 19-Oct-01 1770 903 398 1350 1105 

471 19-Oct-01 1230 1390 624 379 906 

472 19-Oct-01 815 835 494 720 716 

473 15-Oct-01 1670 534 933 1520 1164 

474 15-Oct-01 569 158 1030 884 660 

475 15-Oct-01 98 168 299 280 211 

476 19-Oct-01 603 744 592 607 637 

477 16-Oct-01 264 1670 2730 1900 1641 

478 16-Oct-01 1390 999 560 878 957 

479 16-Oct-01 412 439 570 613 509 

480 16-Oct-01 669 110 854 602 559 

481 16-Oct-01 156 862 335 189 386 

482 16-Oct-01 2280 1340 1860 2820 2075 

483 16-Oct-01 795 661 1660 1020 1034 

484 17-Oct-01 2440 2340 1330 1210 1830 

485 17-Oct-01 1620 1830 826 1390 1417 

486 17-Oct-01 2450 1240 809 702 1300 

487 18-Oct-01 1060 3930 1810 974 1944 

488 17-Oct-01 887 847 1370 625 932 

489 18-Oct-01 489 618 2760 904 1193 

490 16-Oct-01 529 721 399 550 550 

491 18-Oct-01 1400 353 956 784 873 

492 17-Oct-01 434 903 608 634 645 

493 17-Oct-01 429 399 492 542 466 

494 17-Oct-01 592 986 955 1270 951 

495 17-Oct-01 1640 440 641 749 868 

496 23-Oct-01 1560 1170 2020 1170 1480 

497 23-Oct-01 2440 3120 1460 1700 2180 

498 23-Oct-01 1190 775 1590 1810 1341 

499 23-Oct-01 313 372 396 365 362 

500 23-Oct-01 453 301 2820 518 1023 

501 23-Oct-01 6830 1260 3470 4900 4115 

502 23-Oct-01 2250 3100 2000 2000 2338 

503 23-Oct-01 3120 2370 3350 2030 2718 

B-38
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

504 23-Oct-01 2530 1550 5480 3190 3188 

505 23-Oct-01 1110 1570 2250 1380 1578 

506 23-Oct-01 1020 1100 1010 1250 1095 

507 23-Oct-01 2640 7230 1120 2030 3255 

508 23-Oct-01 534 464 988 1040 757 

509 23-Oct-01 837 755 1560 1170 1081 

510 23-Oct-01 716 617 768 888 747 

511 23-Oct-01 2830 2550 1060 -- 2147 

512 23-Oct-01 2130 3110 1390 1420 2013 

513 23-Oct-01 5350 3330 1090 1300 2768 

514 23-Oct-01 1020 1690 1290 1500 1375 

515 23-Oct-01 970 1420 2260 2070 1680 

516 23-Oct-01 1400 1570 1630 1090 1423 

517 23-Oct-01 1120 1370 1350 1270 1278 

518 23-Oct-01 972 1510 1480 1460 1356 

519 23-Oct-01 1110 797 1110 1590 1152 

520 23-Oct-01 5490 1770 -- -- 3630 

521 23-Oct-01 3590 2150 12700 7510 6488 

522 23-Oct-01 505 1040 852 420 704 

523 23-Oct-01 32800 13300 24100 23200 23350 

524 23-Oct-01 2530 1860 3070 3400 2715 

525 23-Oct-01 863 2150 2110 2440 1891 

526 24-Oct-01 2950 2470 1600 1610 2158 

527 24-Oct-01 1480 1400 1040 684 1151 

528 24-Oct-01 642 601 533 619 599 

529 24-Oct-01 720 1300 903 1070 998 

530 24-Oct-01 1050 749 801 1700 1075 

531 24-Oct-01 511 438 641 882 618 

532 24-Oct-01 1640 1490 8220 8520 4968 

533 24-Oct-01 215 659 677 624 544 

534 24-Oct-01 12100 8330 5310 11700 9360 

535 24-Oct-01 1130 2540 2240 2270 2045 

536 24-Oct-01 213 211 530 373 332 

537 24-Oct-01 197 171 -- -- 184 

538 24-Oct-01 1780 2070 1290 1750 1723 

539 24-Oct-01 408 203 171 529 328 

540 24-Oct-01 1180 1370 870 644 1016 

541 24-Oct-01 518 386 831 381 529 

542 24-Oct-01 806 594 1150 747 824 

543 24-Oct-01 1180 1280 868 942 1068 

544 24-Oct-01 2020 814 304 353 873 

545 25-Oct-01 8630 7640 7030 4840 7035 

546 25-Oct-01 615 1150 430 930 781 

547 25-Oct-01 1020 1650 1920 686 1319 

548 25-Oct-01 1890 2250 1770 3750 2415 

549 25-Oct-01 2110 2650 3260 3690 2928 

550 25-Oct-01 1860 2820 2930 1530 2285 

551 25-Oct-01 7670 14600 308 1120 5925 

552 25-Oct-01 11500 7460 2620 5670 6813 

553 25-Oct-01 11300 5310 4030 3570 6053 

554 25-Oct-01 772 1870 1700 1440 1446 

555 26-Oct-01 1570 340 4260 2730 2225 

B-39
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

556 26-Oct-01 705 645 578 416 586 

557 26-Oct-01 916 1100 394 662 768 

558 26-Oct-01 671 389 352 438 463 

559 26-Oct-01 539 616 576 824 639 

560 26-Oct-01 4690 7370 7580 6990 6658 

561 26-Oct-01 942 247 432 817 610 

562 26-Oct-01 1570 1320 632 501 1006 

563 26-Oct-01 993 772 1090 859 929 

564 26-Oct-01 462 313 539 558 468 

565 26-Oct-01 332 1000 2290 1750 1343 

566 26-Oct-01 690 366 928 1210 799 

567 26-Oct-01 478 298 1080 1310 792 

568 26-Oct-01 917 475 466 1490 837 

569 26-Oct-01 427 420 468 546 465 

570 26-Oct-01 2170 2120 3600 4110 3000 

571 26-Oct-01 1010 599 2870 2170 1662 

572 26-Oct-01 2520 1380 2850 4000 2688 

573 27-Sep-01 904 632 684 553 693 

574 07-Nov-00 1800 5000 2000 1700 2625 

576 08-Nov-00 1400 1600 2000 1000 1500 

577 19-Jul-02 1977 1657 1620 1717 1743 

578 02-Nov-01 241 292 195 111 210 

579 24-Sep-01 1920 1170 1490 1530 1528 

580 11-Oct-01 -- 492 1300 3420 1737 

581 31-Oct-01 1170 795 618 764 837 

582 30-Oct-01 1120 1770 1020 1100 1253 

583 31-Oct-01 450 281 354 207 323 

584 31-Oct-01 2550 1920 1170 774 1604 

585 01-Nov-01 1880 2130 1550 1960 1880 

586 02-Nov-01 1490 2260 1630 1320 1675 

587 02-Nov-01 3710 1520 1440 2050 2180 

588 02-Nov-01 460 489 102 294 336 

589 02-Nov-01 5540 2410 -- -- 3975 

590 02-Nov-01 267 396 143 165 243 

591 02-Nov-01 1740 835 538 441 889 

592 02-Nov-01 538 540 365 381 456 

593 02-Nov-01 204 407 360 203 294 

594 02-Nov-01 298 466 375 214 338 

595 02-Nov-01 894 399 625 1090 752 

596 02-Nov-01 4480 3670 2000 2440 3148 

597 02-Nov-01 3020 1450 2350 1160 1995 

598 02-Nov-01 1850 1620 1450 1640 1640 

599 02-Nov-01 519 428 858 343 537 

600 02-Nov-01 994 1360 1730 542 1157 

601 02-Nov-01 2050 1990 2910 2540 2373 

602 02-Nov-01 421 458 705 1100 671 

603 02-Nov-01 622 844 3170 1400 1509 

604 02-Nov-01 1230 1230 1250 1210 1230 

605 02-Nov-01 515 321 520 293 412 

606 02-Nov-01 539 703 849 729 705 

607 02-Nov-01 761 937 839 1120 914 

608 02-Nov-01 1470 1130 1110 956 1167 

B-40
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

609 02-Nov-01 1070 958 946 299 818 

610 02-Nov-01 870 1160 1140 938 1027 

611 02-Nov-01 1860 2090 2200 2100 2063 

612 02-Nov-01 1160 4770 2280 1160 2343 

613 02-Nov-01 1840 2680 1190 1170 1720 

614 02-Nov-01 1380 1830 2170 794 1544 

615 02-Nov-01 1030 1340 1420 1660 1363 

616 31-Oct-01 941 446 531 256 544 

617 31-Oct-01 1320 1300 1060 1500 1295 

618 31-Oct-01 1020 635 1060 1210 981 

619 05-Nov-01 179 181 283 571 304 

620 05-Nov-01 1370 410 221 311 578 

621 05-Nov-01 2200 2820 4800 1880 2925 

622 05-Nov-01 815 1460 186 238 675 

623 05-Nov-01 977 110 199 185 368 

624 05-Nov-01 393 126 195 672 347 

625 06-Nov-01 1680 1350 1020 868 1230 

626 06-Nov-01 488 657 554 717 604 

627 06-Nov-01 2650 2580 1300 1240 1943 

628 06-Nov-01 822 745 633 901 775 

629 06-Nov-01 1240 906 476 555 794 

630 06-Nov-01 803 562 502 769 659 

631 06-Nov-01 685 498 -- -- 592 

632 06-Nov-01 441 355 1710 719 806 

633 06-Nov-01 910 587 653 428 645 

634 06-Nov-01 965 760 584 421 683 

635 06-Nov-01 788 682 274 351 524 

636 06-Nov-01 721 330 449 444 486 

637 08-Nov-01 1360 1140 1220 1050 1193 

638 08-Nov-01 492 682 605 367 537 

639 08-Nov-01 725 706 647 696 694 

640 08-Nov-01 346 368 122 170 252 

641 08-Nov-01 -- 496 462 662 540 

642 08-Nov-01 1370 2020 2270 1180 1710 

643 08-Nov-01 644 944 -- -- 794 

644 08-Nov-01 747 515 -- -- 631 

645 06-Nov-01 596 702 1190 854 836 

646 06-Nov-01 766 621 626 518 633 

647 06-Nov-01 1040 846 413 882 795 

648 06-Nov-01 480 760 795 1010 761 

649 06-Nov-01 1060 631 532 862 771 

650 06-Nov-01 384 600 491 566 510 

651 06-Nov-01 522 690 565 490 567 

652 06-Nov-01 619 704 587 623 633 

653 06-Nov-01 256 180 -- 160 199 

654 06-Nov-01 1450 1190 808 844 1073 

655 06-Nov-01 1040 816 541 647 761 

656 06-Nov-01 328 409 316 263 329 

657 06-Nov-01 765 356 952 892 741 

658 06-Nov-01 556 580 517 261 479 

659 06-Nov-01 530 890 318 368 527 

660 08-Nov-01 695 815 771 450 683 

B-41
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

661 08-Nov-01 1030 244 2320 3030 1656 

662 08-Nov-01 920 1410 588 715 908 

663 08-Nov-01 380 470 690 753 573 

664 08-Nov-01 1030 776 677 534 754 

665 08-Nov-01 2590 1880 2350 2780 2400 

666 08-Nov-01 408 283 -- -- 346 

667 08-Nov-01 822 874 831 895 856 

668 08-Nov-01 1760 1050 1080 1500 1348 

669 08-Nov-01 588 255 607 502 488 

670 31-Oct-01 505 651 545 256 489 

671 31-Oct-01 448 555 422 580 501 

672 31-Oct-01 1210 3070 1380 2090 1938 

673 31-Oct-01 1660 1580 1980 2340 1890 

674 07-Nov-00 2400 1000 1400 2600 1850 

675 17-Oct-01 525 657 584 533 575 

676 06-Feb-02 1633 1440 1173 1210 1364 

677 26-Nov-02 1197 1220 2857 3177 2113 

678 26-Nov-02 1747 1210 3680 -- 2212 

679 22-Feb-02 655 287 241 594 444 

680 05-Mar-02 552 315 641 580 522 

681 05-Mar-02 541 524 525 801 598 

682 06-Mar-02 2247 1350 551 615 1191 

683 06-Mar-02 552 634 650 740 644 

684 04-Mar-02 4037 4443 4647 14300 6857 

685 08-Mar-02 1487 916 538 568 877 

686 07-Mar-02 585 1129 2103 3797 1904 

687 20-Mar-02 466 1477 547 587 769 

688 20-Mar-02 1009 2147 805 563 1131 

689 20-Mar-02 827 1075 322 378 651 

690 22-Mar-02 464 298 164 203 282 

691 22-Mar-02 148 205 358 184 224 

692 22-Mar-02 1627 1753 1370 1357 1527 

693 22-Mar-02 1147 2900 2562 2217 2207 

699 04-Oct-01 13600 4870 2190 8450 7278 

703 15-Apr-02 474 295 599 286 414 

706 22-Mar-02 6780 1070 -- -- 3925 

707 15-Apr-02 961 906 -- -- 934 

708 08-Aug-02 653 1040 693 443 707 

709 19-Dec-02 754 469 347 332 476 

710 15-Aug-03 730 672 773 1036 803 

711 15-Apr-02 1360 1343 1183 2577 1616 

714 15-Aug-03 853 1347 901 779 970 

718 22-Jul-04 1363 -- -- -- 1363 

723 08-Aug-02 967 536 590 999 773 

725 15-Apr-02 1177 1920 1893 1327 1579 

726 18-Jul-02 3200 2583 2253 2630 2667 

728 08-May-02 482 328 422 538 443 

729 30-Jan-02 329 411 311 282 333 

730 31-Jan-02 209 433 236 295 293 

731 08-Mar-02 183 196 211 132 181 

732 08-Mar-02 462 340 212 243 314 

733 31-Jan-02 231 191 190 165 194 

B-42
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

734 22-Mar-02 148 133 73 64 105 

735 31-Jan-02 128 96 235 56 129 

736 31-Jan-02 62 55 47 49 53 

737 05-Feb-02 72 53 57 61 61 

738 06-Jun-02 110 ND 130 ND 154 130 ND 85 

739 06-Jun-02 140 ND 140 ND 150 ND 181 99 

740 06-Jun-02 152 130 ND 159 120 ND 109 

741 06-Jun-02 150 ND 140 ND 130 ND 192 101 

742 06-Jun-02 185 159 170 ND 140 ND 125 

743 06-Jun-02 140 ND 130 ND 202 140 ND 102 

744 06-Jun-02 165 415 220 152 238 

745 06-Jun-02 227 130 ND 140 ND 172 134 

746 06-Jun-02 200 267 140 ND 130 ND 151 

747 06-Jun-02 130 ND 154 120 ND 120 ND 85 

748 10-Jun-02 120 ND 155 140 ND 352 159 

749 10-Jun-02 397 142 287 140 ND 224 

750 10-Jun-02 523 296 194 342 339 

751 10-Jun-02 130 ND 149 157 141 128 

752 10-Jun-02 130 ND 175 186 150 ND 125 

753 10-Jun-02 150 ND 212 140 ND 140 ND 107 

754 11-Jun-02 231 207 257 193 222 

755 11-Jun-02 207 378 131 140 ND 197 

756 11-Jun-02 283 201 224 140 ND 195 

757 11-Jun-02 181 150 ND 140 ND 140 ND 99 

758 11-Jun-02 140 ND 219 140 ND 140 ND 107 

759 11-Jun-02 150 ND 140 ND 150 ND 140 ND 73 

760 11-Jun-02 130 ND 140 ND 140 ND 120 ND 66 

761 11-Jun-02 140 ND 250 150 ND 288 171 

762 11-Jun-02 146 170 ND 130 ND 175 118 

763 11-Jun-02 355 624 130 ND 140 ND 279 

764 26-Jul-04 332 124 145 193 199 

765 12-Jun-02 167 163 133 130 ND 132 

766 31-May-02 159 169 120 ND 197 146 

767 31-May-02 156 163 156 110 ND 133 

768 31-May-02 469 118 163 110 ND 201 

769 31-May-02 370 339 153 216 270 

770 31-May-02 305 232 150 ND 128 185 

771 31-May-02 264 173 168 178 196 

772 31-May-02 465 279 140 132 254 

773 31-May-02 686 576 288 171 430 

774 31-May-02 120 ND 220 160 120 ND 125 

775 31-May-02 256 299 131 107 198 

776 31-May-02 120 ND 221 182 127 148 

777 31-May-02 192 328 133 144 199 

778 31-May-02 1120 398 436 393 587 

779 31-May-02 224 232 110 ND 177 172 

780 31-May-02 291 213 100 ND 257 203 

781 31-May-02 238 215 187 214 214 

782 31-May-02 178 142 110 ND 120 ND 109 

783 31-May-02 253 268 110 ND 195 193 

784 03-Jun-02 458 306 149 144 264 

785 03-Jun-02 201 201 121 110 ND 145 

B-43
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

786 03-Jun-02 250 130 ND 120 ND 170 136 

787 04-Jun-02 170 140 ND 130 ND 150 ND 95 

788 04-Jun-02 147 166 130 ND 140 ND 112 

789 04-Jun-02 140 ND 150 ND 150 ND 139 90 

790 04-Jun-02 358 165 289 316 282 

791 04-Jun-02 140 ND 150 ND 149 130 ND 90 

792 05-Jun-02 130 ND 140 ND 140 ND 152 89 

793 06-Jun-02 142 100 ND 226 338 189 

794 11-Jun-02 183 144 150 ND 150 ND 119 

796 19-Jun-02 205 182 282 135 201 

797 20-Jun-02 -- 265 161 150 ND 167 

798 12-Jun-02 182 158 -- 180 ND 143 

799 22-Jun-02 213 150 ND 148 140 ND 127 

800 19-Jun-02 140 ND 169 170 ND 203 132 

801 20-Jun-02 288 300 170 ND 157 ND 188 

802 20-Jun-02 150 ND 170 150 ND 150 ND 99 

803 18-Jun-02 218 150 ND 165 150 ND 133 

804 18-Jun-02 150 ND 150 ND 180 ND 180 ND 83 

805 19-Jun-02 170 ND 140 ND 250 170 ND 123 

806 18-Jun-02 170 ND 170 ND 150 ND 130 ND 78 

807 18-Jun-02 187 150 ND 212 150 ND 137 

808 19-Jun-02 204 147 ND 173 170 ND 134 

809 19-Jun-02 184 189 228 148 187 

810 18-Jun-02 245 217 346 371 295 

811 19-Jun-02 140 ND 150 ND 151 160 114 

812 20-Jun-02 231 189 150 ND 170 ND 145 

813 18-Jun-02 173 183 140 ND 140 ND 124 

814 19-Jun-02 257 163 ND 140 130 ND 136 

815 19-Jun-02 184 150 ND 170 ND 193 134 

816 20-Jun-02 588 270 272 365 374 

817 20-Jun-02 197 263 150 ND 150 ND 153 

818 27-Jun-02 203 274 207 199 221 

819 27-Jun-02 140 ND 170 ND 170 ND 222 116 

820 27-Jun-02 202 298 376 244 280 

821 27-Jun-02 520 335 277 156 322 

822 27-Jun-02 205 333 132 194 216 

823 27-Jun-02 252 212 212 205 220 

824 27-Jun-02 367 286 180 ND 194 234 

825 27-Jun-02 221 249 192 192 214 

826 27-Jun-02 221 191 153 163 182 

827 26-Jun-02 269 180 199 150 ND 181 

828 26-Jun-02 384 451 308 150 ND 305 

829 26-Jun-02 144 188 161 130 ND 140 

830 26-Jun-02 140 149 179 140 ND 135 

831 26-Jun-02 130 ND 130 ND 130 ND 120 ND 64 

832 26-Jun-02 304 110 ND 467 727 388 

833 26-Jun-02 150 ND 150 ND 197 150 ND 106 

834 03-Jul-02 2080 5770 1270 1490 2653 

835 09-Jul-02 185 247 155 198 196 

836 09-Jul-02 264 181 113 117 169 

837 09-Jul-02 176 247 218 170 203 

838 09-Jul-02 148 223 161 185 179 

B-44
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

839 03-Jul-02 52.7 129 93 218 123 

840 03-Jul-02 207 118 169 272 192 

841 03-Jul-02 -- 164 151 134 150 

842 09-Jul-02 172 80 91 41 96 

843 09-Jul-02 150 110 86 214 140 

844 03-Jul-02 99 111 169 211 148 

845 09-Jul-02 198 135 122 115 143 

846 03-Jul-02 149 35 35 56 69 

847 09-Jul-02 109 92 304 583 272 

848 12-Jul-02 340 743 119 81 321 

849 12-Jul-02 347 62 195 273 219 

850 11-Jul-02 73 121 51 36 70 

851 11-Jul-02 78 101 61 32 68 

852 11-Jul-02 184 140 121 116 140 

853 11-Jul-02 518 1210 156 252 534 

854 11-Jul-02 343 653 199 107 326 

855 11-Jul-02 418 483 305 361 392 

856 11-Jul-02 236 164 82 161 161 

857 11-Jul-02 330 371 164 208 268 

858 11-Jul-02 191 83 207 150 158 

859 12-Jul-02 104 107 140 96 112 

860 12-Jul-02 223 230 284 226 241 

861 12-Jul-02 193 233 167 236 207 

862 11-Jul-02 228 261 50 81 155 

863 11-Jul-02 154 173 111 173 153 

864 11-Jul-02 25 56 85 71 59 

865 17-Jul-02 248 277 197 251 243 

866 12-Jul-02 96 341 141 128 177 

867 12-Jul-02 129 417 120 85 188 

868 15-Jul-02 159 277 223 165 206 

869 15-Jul-02 274 299 206 188 242 

870 15-Jul-02 298 -- 143 186 209 

871 15-Jul-02 199 341 212 130 221 

872 15-Jul-02 287 298 220 285 273 

873 17-Jul-02 127 183 219 152 170 

874 17-Jul-02 143 150 116 118 132 

875 18-Jul-02 254 232 91 246 206 

876 11-Jul-02 177 280 311 526 324 

877 11-Jul-02 148 89 11 111 90 

878 18-Jul-02 326 330 297 329 321 

879 11-Jul-02 168 242 181 116 177 

880 17-Jul-02 271 441 569 443 431 

881 17-Jul-02 265 218 303 265 263 

882 15-Jul-02 441 328 120 207 274 

883 12-Jul-02 352 355 289 243 310 

884 18-Jul-02 200 238 109 249 199 

885 09-Jul-02 228 500 230 235 298 

886 09-Jul-02 395 293 179 188 264 

887 09-Jul-02 257 214 181 191 211 

888 09-Jul-02 215 274 295 252 259 

889 09-Jul-02 175 385 206 308 269 

890 09-Jul-02 268 293 311 193 266 

B-45
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

891 12-Jul-02 342 370 387 374 368 

892 12-Jul-02 436 454 359 244 373 

893 12-Jul-02 303 230 287 310 283 

894 12-Jul-02 498 342 314 548 426 

895 16-Jul-02 307 244 147 122 205 

896 16-Jul-02 156 192 70 98 129 

897 16-Jul-02 86 133 16 90 81 

898 15-Jul-02 174 187 186 155 176 

899 15-Jul-02 194 211 190 163 190 

900 15-Jul-02 133 108 15 186 111 

901 15-Jul-02 129 69 235 164 149 

902 16-Jul-02 185 201 104 135 156 

903 11-Jul-02 170 83 82 137 118 

904 18-Jul-02 100 179 248 289 204 

905 11-Jul-02 177 207 243 130 189 

906 15-Jul-02 239 217 196 183 209 

907 15-Jul-02 134 265 234 190 206 

908 16-Jul-02 133 206 171 130 160 

909 16-Jul-02 229 162 140 84 154 

910 15-Jul-02 33 127 128 194 121 

911 16-Jul-02 116 184 192 155 162 

912 16-Jul-02 55 163 121 186 131 

913 16-Jul-02 243 225 141 227 209 

914 18-Jul-02 296 276 295 235 276 

915 18-Jul-02 348 361 213 173 274 

916 18-Jul-02 515 635 175 281 402 

917 18-Jul-02 513 369 287 295 366 

918 18-Jul-02 337 208 207 293 261 

919 18-Jul-02 181 165 391 214 238 

920 18-Jul-02 363 361 287 367 345 

921 18-Jul-02 446 360 221 343 343 

922 03-Jul-02 1220 879 1480 621 1050 

923 11-Jul-02 4810 3970 -- -- 4390 

924 26-Jun-02 150 ND 120 ND 140 ND 140 ND 69 

925 07-Aug-02 199 121 112 108 135 

926 07-Aug-02 538 291 173 235 309 

927 07-Aug-02 262 156 37 97 138 

928 07-Aug-02 318 296 247 152 253 

929 06-Aug-02 317 195 184 264 240 

930 06-Aug-02 344 266 223 157 248 

931 06-Aug-02 292 317 239 153 250 

932 06-Aug-02 279 258 154 205 224 

933 07-Aug-02 504 314 205 381 351 

934 07-Aug-02 279 304 141 306 258 

935 07-Aug-02 269 181 183 145 195 

936 07-Aug-02 299 210 209 217 234 

937 06-Aug-02 357 371 262 196 297 

938 06-Aug-02 148 141 137 179 151 

939 06-Aug-02 193 268 171 117 187 

940 06-Aug-02 119 197 210 118 161 

941 07-Aug-02 314 417 167 236 284 

942 06-Aug-02 283 362 148 119 228 

B-46
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

943 06-Aug-02 368 196 148 225 234 

944 26-Jun-02 150 ND 140 ND 137 140 ND 88 

945 11-Jun-02 210 257 150 ND 133 169 

946 12-Jun-02 140 ND 158 720 150 ND 256 

947 05-Jun-02 150 ND 133 ND 154 140 ND 91 

948 29-Aug-02 280 325 332 183 280 

949 29-Aug-02 597 351 299 259 377 

950 29-Aug-02 79.6 148 107 166 125 

951 29-Aug-02 348 223 120 185 219 

952 29-Aug-02 264 276 167 83.5 198 

953 27-Aug-02 295 536 482 616 482 

954 29-Aug-02 247 391 374 295 327 

955 27-Aug-02 313 343 244 376 319 

956 27-Aug-02 278 302 316 283 295 

957 27-Aug-02 216 225 134 331 227 

958 27-Aug-02 374 202 282 160 255 

959 27-Aug-02 333 113 182 289 229 

960 27-Aug-02 385 310 234 115 261 

961 27-Aug-02 230 245 106 219 200 

962 27-Aug-02 186 349 238 127 225 

963 27-Aug-02 288 315 368 222 298 

964 27-Aug-02 319 433 206 313 318 

965 27-Aug-02 225 198 333 388 286 

966 27-Aug-02 225 210 225 305 241 

967 27-Aug-02 166 235 240 240 220 

968 27-Aug-02 197 425 229 177 257 

969 27-Aug-02 478 416 284 164 336 

970 27-Aug-02 241 235 104 210 198 

971 27-Aug-02 409 244 188 182 256 

972 27-Aug-02 263 184 188 303 235 

973 27-Aug-02 157 268 262 243 233 

974 27-Aug-02 337 183 367 189 269 

975 04-Sep-02 284 330 373 374 340 

976 04-Sep-02 160 246 203 138 187 

977 04-Sep-02 433 279 124 222 265 

978 04-Sep-02 210 285 366 237 275 

979 04-Sep-02 289 264 136 212 225 

980 04-Sep-02 319 710 252 312 398 

981 03-Sep-02 14 109 130 89 86 

982 03-Sep-02 243 160 266 187 214 

983 03-Sep-02 142 74 197 130 136 

984 03-Sep-02 215 138 116 163 158 

985 03-Sep-02 68 64 155 118 101 

986 03-Sep-02 234 255 226 169 221 

987 03-Sep-02 188 271 142 211 203 

988 03-Sep-02 148 66 42 25 70 

989 03-Sep-02 110 185 195 26 129 

990 03-Sep-02 201 182 260 195 210 

991 03-Sep-02 25 -- 135 223 128 

992 30-Aug-02 207 233 229 125 199 

993 30-Aug-02 95 149 130 106 120 

994 30-Aug-02 239 171 234 202 212 

B-47
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

995 30-Aug-02 276 215 152 298 235 

996 30-Aug-02 464 230 213 312 305 

997 30-Aug-02 99 13 50 184 87 

998 30-Aug-02 62 88 192 218 140 

999 11-Sep-02 229 350 145 184 227 

1000 12-Sep-02 311 513 370 231 356 

1001 11-Sep-02 117 187 146 123 143 

1002 12-Sep-02 251 200 121 59 158 

1003 12-Sep-02 167 201 164 108 160 

1004 12-Sep-02 342 168 128 114 188 

1005 12-Sep-02 237 157 74 124 148 

1006 12-Sep-02 203 160 177 55 149 

1007 12-Sep-02 602 309 329 185 356 

1008 12-Sep-02 192 224 262 188 217 

1009 12-Sep-02 104 141 172 272 172 

1010 10-Sep-02 236 193 139 108 169 

1011 11-Sep-02 253 179 287 318 259 

1012 11-Sep-02 84 260 146 119 152 

1013 11-Sep-02 64 123 19 81 72 

1014 11-Sep-02 156 170 111 139 144 

1015 10-Sep-02 256 222 207 95 195 

1016 06-Sep-02 149 133 36 120 110 

1017 11-Sep-02 198 215 98 157 167 

1018 10-Sep-02 137 58 40 122 89 

1019 10-Sep-02 197 203 221 245 217 

1020 11-Sep-02 -- 92 219 121 144 

1021 15-Jul-02 170 114 208 175 167 

1022 06-Sep-02 206 160 230 138 184 

1023 30-Aug-02 158 169 165 174 167 

1024 06-Sep-02 355 381 170 186 273 

1025 06-Sep-02 37 41 72 96 62 

1026 30-Aug-02 108 60 155 115 110 

1027 30-Aug-02 24 70 82 137 78 

1028 06-Sep-02 48 115 113 48 81 

1029 30-Aug-02 131 177 126 174 152 

1030 30-Aug-02 212 199 128 163 176 

1031 30-Aug-02 215 7 51 129 101 

1032 06-Sep-02 123 123 114 180 135 

1033 06-Sep-02 10 89 131 137 92 

1034 06-Sep-02 27 122 159 156 116 

1035 06-Sep-02 125 119 136 26 102 

1036 30-Aug-02 504 389 173 282 337 

1037 06-Sep-02 175 285 139 175 194 

1038 06-Sep-02 92 151 175 241 165 

1039 06-Sep-02 170 300 241 228 235 

1040 10-Sep-02 113 106 268 174 165 

1041 10-Sep-02 118 199 192 56 141 

1042 10-Sep-02 314 166 185 335 250 

1043 11-Sep-02 175 166 299 206 212 

1044 10-Sep-02 221 170 65 152 152 

1045 11-Sep-02 783 59 125 45 253 

1046 11-Sep-02 107 264 133 191 174 

B-48
 
Attachment B-4. Pre-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Property Average 

(mg/kg) d Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

1047 11-Sep-02 145 33 -- 67 82 

1048 10-Sep-02 194 281 215 343 258 

1049 11-Sep-02 64 27 43 107 60 

1050 10-Sep-02 204 239 153 224 205 

1051 11-Sep-02 135 155 119 137 137 

1052 10-Sep-02 169 216 210 -- 198 

1053 11-Sep-02 326 267 172 304 267 

1054 10-Sep-02 122 152 170 106 138 

1055 10-Sep-02 221 200 80 150 163 

1056 10-Sep-02 215 217 136 224 198 

1057 10-Sep-02 151 237 91 206 171 

1058 10-Sep-02 225 129 183 207 186 

1059 10-Sep-02 202 170 200 247 205 

1060 10-Sep-02 133 58 143 101 109 

1061 10-Sep-02 114 173 178 96 140 

1062 10-Sep-02 74 37 101 140 88 

1063 16-Oct-02 122 92 98 142 114 

1064 30-Oct-02 86 42 49 74 63 

1065 30-Oct-02 85 92 117 77 93 

1066 12-Mar-03 91.4 101.6 77 75.6 86 

1067 11-Mar-03 1033 1070 506 -- 870 

1068 19-Jun-03 717 542 162 -- 474 

1069 15-Jul-03 2137 1230 1203 1217 1447 

1074 07-Aug-03 859 787 530 810 747 

1075 08-Nov-01 1760 1050 1080 1500 1348 

1076 15-Oct-01 982 541 791 444 690 

1079 02-Nov-01 16300 2800 11300 6290 9173 

1080 08-Nov-01 2110 6090 606 680 2372 

1081 02-Nov-01 5260 10000 8750 6390 7600 

1082 11-Apr-03 100 133 -- -- 117 

1084 02-Nov-01 5680 1580 4460 1160 3220 

1086 08-Nov-01 606 -- -- -- 606 

1088 01-Apr-04 935 814 781 715 811 

1090 22-Jul-04 -- -- -- 632 632 

a

 Data were obtained from U.S. EPA Region 7 (2006).
 

b

 A value qualified with an "ND" represents a non-detect. The value presented is the detection limit. 

For the purpose of calculating the property average, one-half the detection limit was used as the value for nondetects.
 

c

 "--" indicates that no sample was collected for that quadrant.
 

d

 Not provided by U.S. EPA Region 7. Averages were calculated by ICF. 

B-49
 
Attachment B-5. Post-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b Property 

Average 

(mg/kg) c Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

141 17-Jun-02 944 523 1170 587 806 

142 16-Jun-03 157 215 212 237 205 

143 16-Jun-03 197 265 207 264 233 

145 03-Apr-02 880 598 445 493 604 

146 19-May-03 206 146 270 309 233 

147 03-Apr-02 368 349 201 247 291 

148 21-May-02 193 296 208 290 247 

149 16-Apr-02 1370 612 462 308 688 

150 16-Dec-02 198 225 -- 260 228 

151 14-Jan-02 281 463 483 279 377 

153 15-Jun-04 280 295 218 127 230 

154 17-Jan-02 1550 1457 764 786 1139 

155 03-Jun-03 109 288 283 378 265 

156 10-Dec-02 540 332 195 505 393 

157 08-Jul-02 778 895 876 353 726 

158 19-Jan-02 675 288 455 539 489 

159 27-Jun-02 280 193 217 196 222 

160 26-Jun-02 398 216 188 232 259 

161 28-Jun-02 490 1297 502 534 706 

162 12-Jul-02 2630 2137 1400 766 1733 

163 13-May-02 1898 2946 2078 1688 2153 

172 21-Oct-02 466 189 769 1010 609 

175 28-Mar-02 549 104 391 601 411 

176 22-Jan-02 1217 687 1018 633 889 

177 13-Nov-02 690 1001 1021 860 893 

178 27-Nov-02 307 153 71 48 145 

179 05-Nov-01 334 397 447 254 358 

180 03-Jun-02 572 240 285 288 346 

181 20-Nov-01 907 401 771 603 671 

182 14-Dec-01 1347 1273 911 697 1057 

183 09-Nov-01 560 834 659 562 654 

184 07-Feb-02 716 167 475 321 420 

185 25-Apr-02 125 208 127 123 146 

191 10-Mar-04 -- -- 500 129 315 

196 07-Dec-01 643 981 760 243 657 

197 09-Jan-02 872 825 680 847 806 

200 27-Feb-02 155 -- 311 -- 233 

207 11-Jan-02 978 890 485 648 750 

208 07-Oct-02 499 714 490 1057 690 

211 07-Oct-03 794 857 693 672 754 

212 23-Jan-02 -- -- -- 568 568 

213 29-Aug-02 567 684 457 645 588 

214 05-Mar-02 564 517 1076 496 663 

215 22-Aug-03 647 386 487 762 571 

216 23-Mar-04 358 661 400 249 417 

217 11-Apr-03 627 473 553 271 481 

218 16-Sep-03 473 300 294 445 378 

220 19-Aug-03 451 475 592 394 478 

222 15-Mar-02 139 85 152 166 136 

225 02-Jun-04 104 110 286 186 172 

226 20-May-02 -- -- -- 155 155 

230 15-Feb-02 -- 119 190 -- 155 

231 21-Mar-02 203 415 429 281 332 

232 25-Jun-02 520 840 946 275 645 

B-50
 
Attachment B-5. Post-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b Property 

Average 

(mg/kg) c Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

233 13-Feb-02 578 238 357 285 365 

234 13-Nov-02 74 669 369 254 342 

235 06-Aug-03 440 532 701 485 540 

237 21-Aug-03 454 551 589 641 559 

238 17-Oct-02 2693 -- 1095 1100 1629 

239 04-Mar-02 1400 690 976 1487 1138 

240 15-Apr-02 488 451 798 220 489 

241 07-May-04 342 270 -- -- 306 

242 23-Aug-04 49 58 168 271 137 

246 12-Aug-04 261 258 -- 336 285 

251 26-Aug-02 678 813 793 246 633 

253 17-Dec-03 599 652 411 563 556 

254 16-Sep-03 178 668 557 400 451 

255 16-Sep-03 922 463 446 543 594 

256 19-Sep-03 536 1040 679 1663 980 

257 17-Oct-01 523 660 294 333 453 

258 26-Sep-03 907 972 976 689 886 

259 29-Aug-02 615 376 527 656 544 

260 16-Oct-03 292 1143 705 213 588 

261 11-Mar-02 246 244 721 849 515 

262 16-Jan-02 395 1110 913 822 810 

263 15-Oct-01 1197 497 603 1243 885 

264 18-Sep-03 790 345 1097 860 773 

265 28-Jan-02 1083 939 694 571 822 

266 02-Oct-03 1563 653 871 747 959 

267 22-Oct-03 1620 1830 1123 1280 1463 

268 26-Sep-03 1087 463 922 842 829 

269 07-Oct-03 1087 1026 940 702 939 

272 16-Jan-04 248 444 432 450 394 

273 14-Mar-02 -- -- 165 -- 165 

277 01-Apr-04 205 251 -- 181 212 

279 19-May-03 116 203 252 321 223 

280 14-Jul-04 221 165 264 269 230 

282 19-Jun-02 1640 3900 1270 1227 2009 

283 21-Jun-02 1487 356 597 605 761 

284 15-Mar-04 355 474 209 296 334 

285 15-Mar-04 355 474 209 296 334 

287 15-May-02 1990 1815 1550 1432 1697 

295 18-Jul-02 1900 2953 1093 895 1710 

299 24-Jul-02 -- 2930 850 195 1325 

300 09-Aug-02 1260 1150 310 1033 938 

301 18-Jul-02 239 162 235 210 212 

302 07-Nov-01 232 270 64 136 176 

303 15-Mar-04 660 277 -- 214 384 

304 08-Apr-04 326 538 -- -- 432 

306 14-Oct-02 1290 492 192 223 549 

308 15-Aug-03 1011 532 784 444 693 

311 10-Aug-04 568 272 417 291 387 

314 28-Mar-02 575 430 -- -- 503 

316 06-May-04 471 444 129 536 395 

319 20-Apr-04 927 551 -- -- 739 

320 04-May-04 727 811 -- -- 769 

321 01-Apr-02 402 604 645 1107 690 

323 26-Sep-02 1536 1540 556 334 992 

B-51
 
Attachment B-5. Post-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b Property 

Average 

(mg/kg) c Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

324 19-Aug-02 111 539 394 317 340 

325 09-Apr-04 2757 -- -- -- 2757 

326 07-Jun-02 1340 1313 884 1253 1198 

327 07-Jun-02 921 1011 2223 373 1132 

328 06-Jun-02 2047 648 1663 756 1279 

330 24-Jan-02 220 195 146 234 199 

331 24-Jan-02 416 731 724 360 558 

337 01-Jul-03 623 833 509 467 608 

339 08-Feb-02 147 259 124 80 153 

340 17-Jan-02 823 1277 944 -- 1015 

341 21-Nov-02 371 1530 310 565 694 

344 01-Apr-02 958 -- 1058 6177 2731 

347 20-Dec-01 -- 113 88 -- 101 

353 18-Oct-02 100 210 152 317 195 

354 23-Feb-04 -- 625 147 411 394 

355 08-Feb-02 -- 130 193 179 167 

357 30-Apr-04 291 393 369 262 329 

358 30-Apr-04 369 490 -- 576 478 

360 04-May-04 240 142 335 -- 239 

361 22-Apr-04 629 233 568 -- 477 

363 30-Jun-04 331 596 482 -- 470 

364 02-Jul-04 393 517 563 -- 491 

365 08-Jun-04 331 684 173 426 404 

371 09-Oct-02 96 241 84 60 120 

384 25-Mar-04 -- -- 544 153 349 

385 20-Dec-01 -- 60 -- 79 70 

386 20-Feb-04 1274 837 1267 1072 1113 

387 27-Feb-04 1253 1001 838 -- 1031 

388 22-Aug-02 292 123 267 425 277 

389 07-Mar-02 304 324 239 254 280 

390 04-Aug-03 684 1167 519 530 725 

391 29-Jan-02 706 709 1220 752 847 

392 08-Nov-02 401 187 191 376 289 

395 07-Mar-02 344 435 550 449 445 

396 11-Sep-03 401 687 792 317 549 

398 18-Feb-02 -- -- -- 160 160 

400 21-May-04 155 210 -- 174 180 

403 28-Feb-02 445 209 149 376 295 

404 19-Jul-02 1113 408 -- -- 761 

405 29-Jul-03 356 885 589 341 543 

406 19-Jul-02 229 -- -- -- 229 

407 22-Feb-02 318 312 -- -- 315 

408 03-Jul-02 339 164 320 308 283 

410 10-Apr-02 653 862 1490 532 884 

411 15-Aug-03 632 564 564 353 528 

412 13-Aug-03 417 442 719 456 509 

413 29-Jan-02 382 546 267 343 385 

416 12-Apr-04 398 2000 -- -- 1199 

418 17-Jun-02 186 154 -- -- 170 

419 17-Jun-02 -- 263 -- 169 216 

422 04-May-04 97 163 -- 108 123 

425 21-Feb-02 287 285 112 146 208 

426 18-Jan-02 1180 1913 987 710 1198 

428 29-Jul-03 525 148 159 237 267 

B-52
 
Attachment B-5. Post-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b Property 

Average 

(mg/kg) c Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

430 30-Apr-03 704 469 282 286 435 

431 10-Dec-02 478 1527 114 -- 706 

432 02-Jun-03 118 234 220 -- 191 

433 20-Jun-02 64 532 1423 4100 1530 

435 10-Jul-02 2253 592 1807 835 1372 

436 10-Jul-02 872 3163 -- -- 2018 

440 09-Aug-02 880 328 1380 1650 1060 

442 16-Feb-04 859 111 768 608 587 

444 18-Feb-02 224 321 488 294 332 

445 27-Apr-04 568 365 820 942 674 

446 29-Aug-02 1372 1073 596 884 981 

447 13-Mar-02 178 222 -- 89 163 

448 20-Dec-02 315 616 366 165 366 

449 27-Apr-04 162 304 227 258 238 

450 13-May-02 -- 130 176 163 156 

451 14-Apr-04 266 209 235 222 233 

454 13-Feb-02 274 191 206 63 184 

456 18-Aug-04 184 123 212 175 174 

458 29-Apr-04 -- -- 228 170 199 

468 22-Apr-04 264 238 323 -- 275 

470 29-Jul-04 1550 439 -- 305 765 

477 23-Oct-02 1070 733 1210 2233 1312 

484 16-Jan-02 395 1110 913 822 810 

485 22-Jul-04 628 713 961 688 748 

486 24-Sep-03 734 963 779 791 817 

491 02-Jun-04 358 -- 508 395 420 

492 30-Apr-04 164 257 446 231 275 

493 05-Feb-02 72 159 129 144 126 

495 25-Jun-04 303 304 418 328 338 

496 04-Jun-03 287 279 -- 204 257 

497 22-Aug-02 148 60 429 -- 212 

498 09-Aug-02 1042 686 608 482 705 

500 18-Feb-04 -- -- 310 -- 310 

501 23-Jan-04 1930 675 1180 811 1149 

503 25-Feb-04 1323 797 -- -- 1060 

504 13-Jun-02 353 177 83 174 197 

511 03-Apr-02 863 1773 204 209 762 

512 15-Nov-01 688 752 777 567 696 

513 15-Nov-01 736 824 743 245 637 

514 24-Jan-02 209 605 233 840 472 

517 06-May-04 604 584 471 439 525 

518 10-Jun-04 380 493 313 602 447 

520 23-Feb-04 224 -- -- -- 224 

526 16-Jul-02 1007 891 1117 944 990 

528 03-Aug-04 -- -- 130 -- 130 

531 11-Jan-02 567 -- -- -- 567 

532 10-Mar-04 481 1840 114 244 670 

535 05-May-03 274 148 283 118 206 

540 10-Sep-03 307 502 1018 568 599 

542 13-Jan-04 762 357 426 318 466 

544 08-May-03 479 223 681 -- 461 

557 27-Aug-03 878 789 467 523 664 

558 15-Feb-02 53 -- -- 90 72 

564 11-Feb-02 57 335 114 55 140 

B-53
 
Attachment B-5. Post-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b Property 

Average 

(mg/kg) c Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

573 30-Oct-01 273 236 214 202 231 

574 09-Oct-01 614 1057 1363 507 885 

575 21-Sep-01 1670 1947 186 905 1177 

576 26-Sep-01 1160 654 512 619 736 

577 30-Sep-02 1523 1187 469 594 943 

579 02-Oct-01 1180 937 343 382 711 

580 17-Oct-02 2693 -- 1095 1100 1629 

581 12-Mar-02 731 403 90 192 354 

584 23-Feb-04 436 1027 153 -- 539 

585 03-Dec-03 371 669 632 574 562 

588 11-Feb-02 202 159 -- -- 181 

593 11-Feb-02 -- 123 -- -- 123 

596 20-Feb-04 796 235 -- -- 516 

597 15-Apr-03 1257 854 618 893 906 

600 09-Aug-02 264 866 -- -- 565 

601 22-Jul-02 1333 445 848 2010 1159 

604 13-Oct-03 809 380 719 680 647 

605 03-Aug-04 378 -- -- -- 378 

606 03-Aug-04 -- 333 -- -- 333 

608 06-Mar-02 847 372 764 882 716 

609 24-Jun-04 435 402 827 -- 555 

612 07-Mar-02 304 324 239 254 280 

613 15-Aug-02 432 476 130 661 425 

614 04-Aug-03 1753 432 904 427 879 

615 08-Sep-03 641 802 268 548 565 

617 17-Oct-02 654 1247 535 781 804 

622 11-Feb-02 553 878 -- -- 716 

625 21-Mar-02 182 434 425 651 423 

626 29-Jan-02 220 217 172 221 208 

627 28-Jan-02 989 511 192 2177 967 

628 07-Aug-03 536 288 238 440 376 

629 28-Mar-02 506 351 248 219 331 

632 06-Apr-04 100 -- 91 155 115 

635 21-May-03 182 341 -- -- 262 

636 01-Jul-02 168 -- 92 154 138 

637 19-Dec-02 245 277 1497 320 585 

642 20-Jun-03 395 881 739 425 610 

644 29-Jul-03 338 399 152 272 290 

655 08-Jun-04 623 473 975 769 710 

657 06-May-04 151 -- 279 228 219 

658 16-Aug-04 70 220 243 -- 178 

660 05-Sep-03 126 165 297 149 184 

663 11-Mar-02 -- 102 244 218 188 

664 15-Apr-04 431 420 325 305 370 

668 15-Aug-03 716 753 606 409 621 

670 12-Apr-02 388 492 519 372 443 

672 12-Dec-02 623 779 289 375 517 

674 01-Oct-01 627 854 1413 -- 965 

676 19-Dec-02 589 277 273 771 478 

677 28-Jun-02 922 605 1253 2840 1405 

678 28-Jun-02 1247 897 1025 -- 1056 

681 26-Apr-04 343 214 381 377 329 

684 17-Jul-03 63 388 209 193 213 

688 14-May-03 458 703 560 618 585 

B-54
 
Attachment B-5. Post-Excavation Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b Property 

Average 

(mg/kg) c Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

692 13-Oct-03 1147 930 -- -- 1039 

693 08-Oct-02 1004 802 1683 1513 1251 

699 13-May-02 1898 2800 2055 1688 2110 

711 02-Oct-02 566 533 427 301 457 

714 08-Sep-03 1010 307 740 363 605 

718 03-Aug-04 628 -- -- -- 628 

725 09-Aug-04 669 810 779 782 760 

726 15-Oct-02 402 300 429 496 407 

729 14-Mar-02 192 237 -- 131 187 

795 11-Jul-02 1273 626 1207 1293 1100 

820 06-May-04 135 186 -- -- 161 

821 23-Dec-02 180 -- -- -- 180 

832 16-Jan-03 -- -- 77 101 89 

847 09-Jan-03 -- -- -- 76 76 

853 13-May-04 84 146 -- -- 115 

889 09-Jan-03 -- 419 -- -- 419 

996 10-Jan-03 -- 91 -- -- 91 

1074 22-Aug-03 317 307 635 650 477 

1075 15-Aug-03 575 607 489 476 537 

1076 15-Aug-03 433 576 723 -- 577 

1079 30-May-02 81 95 -- -- 88 

1080 26-Sep-02 -- 514 -- -- 514 

1081 22-May-02 361 109 741 768 495 

1083 11-Jul-03 102 685 309 194 323 

1084 26-Jul-02 856 2150 462 -- 1156 

1087 05-Apr-04 1723 843 667 863 1024 

1088 19-Apr-04 380 197 263 295 284 

1090 03-Aug-04 -- -- -- 463 463 

a

 Data were obtained from U.S. EPA Region 7 (2006).
 

b

 "--" indicates that no sample was collected for that quadrant.
 

c

 Not provided by U.S. EPA Region 7. Averages were calculated by ICF. 

B-55
 
Attachment B-6. Recontamination Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

184 16-Apr-02 62 69 58 ND 57 ND 

184 21-May-02 54 67 61 49 ND 

184 24-Jun-02 92 69 67 48 ND 

184 23-Jul-02 48 48 ND 58 49 ND 

184 23-Aug-02 86 60 47 ND 50 

579 16-Apr-02 109 125 105 79 

579 21-May-02 95 101 75 55 

579 21-Jun-02 92 92 137 109 

579 23-Jul-02 93 87 67 61 

579 22-Aug-02 80 157 83 100 

579 23-Sep-02 69 92 67 66 

151 11-Feb-02 67 ND 84 65 ND 63 ND 

151 14-Mar-02 56 60 ND 75 56 ND 

151 16-Apr-02 58 ND 62 ND 60 ND 64 

151 22-May-02 51 ND 50 ND 54 50 ND 

151 24-Jun-02 54 ND 64 58 49 ND 

151 22-Jul-02 56 54 66 57 

151 23-Aug-02 62 58 50 47 ND 

151 25-Sep-02 64 52 ND 64 59 

151 07-Nov-02 60 63 41 55 

151 10-Dec-02 50 49 53 53 

151 15-Jan-03 53 ND 53 ND 61 55 

151 12-Mar-03 53 48 57 57 

151 20-Jun-03 142 59 49 ND 70 

151 22-Sep-03 74 127 70 61 

151 22-Dec-03 49.7 52.8 37.5 43.5 

151 22-Mar-04 53 ND 92 85.9 83.6 

151 21-Jun-04 67 75.2 50.8 67.6 

151 23-Sep-04 96.8 100.3 38.2 60 

151 16-Dec-04 43 ND 69.8 51.4 58.8 

151 28-Mar-05 127 146 85 86 

151 07-Jul-05 83.6 106.1 79 85 

151 03-Oct-05 81 83 67 139 

151 02-May-06 59 83 67 101 

493 17-Apr-02 47 ND 53 ND 49 ND 51 ND 

493 21-May-02 48 ND 60 44 ND 45 ND 

493 24-Jun-02 53 ND 63 60 51 ND 

493 24-Jul-02 45 ND 46 ND 41 ND 41 ND 

493 22-Aug-02 45 ND 38 ND 38 55 

493 25-Sep-02 45 ND 58 45 ND 42 ND 

493 07-Nov-02 49 54 ND 57 ND 60 

493 09-Dec-02 51 ND 50 ND 53 ND 51 

493 21-Jan-03 72 46 ND 45 50 ND 

493 14-Mar-03 37 ND 43 47 53 

340 06-Feb-02 59 ND 58 ND 61 55 ND 

340 14-Mar-02 74 56 ND 82 69 

340 16-Apr-02 53 ND 66 59 397 

340 22-May-02 45 ND 47 ND 54 ND 48 

340 24-Jun-02 54 54 ND 55 62 ND 

340 24-Jul-02 54 47 ND 68 51 

340 26-Aug-02 49 ND 47 80 66 

340 24-Sep-02 48 ND 53 65 65 

340 07-Nov-02 44 ND 50 ND 56 99 

340 10-Dec-02 63 69 67 111 

340 17-Mar-03 74 58 80 126 

B-56
 
Attachment B-6. Recontamination Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

340 23-Jun-03 63 62 101 106 

340 23-Sep-03 117 96 105 91 

340 22-Dec-03 66 55 119 124 

340 22-Mar-04 67.4 91.9 199 91.7 

340 22-Jun-04 77.1 78.8 153 163 

340 23-Sep-04 134.7 116 141.6 324 

340 16-Dec-04 107.1 128.9 163.3 223 

340 29-Mar-05 97 161 107 155 

340 08-Jul-05 214 97 146 156 

340 03-Oct-05 187 172 258 302 

340 02-May-06 161 261 201 300 

197 11-Feb-02 73 62 ND 63 ND 69 ND 

197 14-Mar-02 97 74 66 ND 65 

197 17-Apr-02 96 51 64 ND 48 ND 

197 21-May-02 100 60 54 48 ND 

197 24-Jun-02 74 95 65 172 

197 22-Jul-02 183 61 75 51 ND 

197 23-Aug-02 89 62 60 55 

197 24-Sep-02 164 61 155 53 

197 07-Nov-02 130 81 208 123 

197 10-Dec-02 281 127 302 172 

197 17-Mar-03 78 103 179 82 

197 23-Jun-03 76 133 69 67 

197 23-Sep-03 104 122 130 66 

197 22-Dec-03 81 131 184 105 

197 22-Mar-04 120 188 363 108 

197 21-Jun-04 132 152.7 124 76.7 

197 23-Sep-04 145.4 261.7 332.8 124 

197 16-Dec-04 201.3 63.7 130.1 69.2 

197 30-Mar-05 283 235 145 112 

197 07-Jul-05 143 252 209 91 

197 04-Oct-05 186 182 145 130 

197 02-May-06 148 205 156 181 

531 17-Apr-02 63 ND 65 ND 67 ND 57 ND 

531 22-May-02 54 ND 58 56 ND 54 ND 

531 24-Jun-02 50 ND 50 ND 51 53 

531 22-Jul-02 164 80 52 ND 41 ND 

531 23-Aug-02 73 53 58 49 ND 

531 24-Sep-02 51 65 43 46 

531 07-Nov-02 85 50 53 ND 46 ND 

531 10-Dec-02 53 44 ND 44 ND 41 ND 

531 15-Jan-03 63 56 ND 59 58 ND 

531 12-Mar-03 62 94 38 ND 47 

531 20-Jun-03 48 67 83 60 

531 23-Sep-03 64 60 68 77 

531 22-Dec-03 57.6 61.5 41.9 35 

531 22-Mar-04 63.8 64.6 56 67.6 

531 21-Jun-04 56.1 92.5 55.9 50.6 

531 23-Sep-04 192.3 123.3 90.9 67.9 

531 16-Dec-04 179.7 131 92.1 72.1 

531 28-Mar-05 127 103 67 99 

531 07-Jul-05 73 130 128 75 

531 04-Oct-05 101 111 57 ND 65 

531 02-May-06 47 87 65 46 ND 

626 11-Feb-02 65 ND 64 ND 56 ND 71 

B-57
 
Attachment B-6. Recontamination Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

626 14-Mar-02 55 ND 58 ND 98 69 

626 16-Apr-02 60 58 ND 69 ND 56 ND 

626 20-May-02 52 ND 65 51 47 ND 

626 24-Jun-02 74 48 ND 49 ND 51 ND 

626 23-Jul-02 47 41 ND 43 40 ND 

626 23-Aug-02 45 ND 45 ND 48 41 ND 

626 24-Sep-02 45 ND 45 ND 59 49 

626 30-Oct-02 43 ND 50 ND 40 ND 48 ND 

626 10-Dec-02 43 ND 50 50 49 ND 

626 15-Jan-03 52 48 ND 50 53 

626 17-Mar-03 60 53 ND 58 ND 45 ND 

212 20-May-02 61 49 ND 90 116 

212 21-Jun-02 77 323 103 66 

212 23-Jul-02 56 141 127 117 

212 22-Aug-02 54 75 116 116 

212 23-Sep-02 53 57 113 88 

212 01-Nov-02 65 63 101 88 

212 12-Dec-02 78 77 84 76 

212 14-Mar-03 66 122 88 121 

212 23-Jun-03 112 61 156 115 

212 22-Sep-03 131 95 242 145 

212 22-Dec-03 87 122 100 147 

212 22-Mar-04 56.6 69.7 187 77 ND 

212 21-Jun-04 131 93.6 175 150 

212 23-Sep-04 88.5 201.7 696.3 235.7 

212 16-Dec-04 87.2 117 406.3 153 

212 29-Mar-05 99 94 210 119 

212 07-Jul-05 147 178 461 215 

212 04-Oct-05 98 157 412 214 

212 01-May-06 109 185 271 229 

454 17-Apr-02 52 ND 53 ND 51 ND 50 ND 

454 20-May-02 48 ND 44 ND 50 ND 46 ND 

454 24-Jun-02 95 42 ND 49 ND 49 ND 

454 24-Jul-02 50 ND 40 ND 48 ND 57 

454 22-Aug-02 46 49 ND 45 ND 46 ND 

454 25-Sep-02 45 ND 46 ND 46 ND 48 ND 

454 07-Nov-02 56 52 ND 43 ND 52 ND 

454 09-Dec-02 53 ND 42 ND 52 ND 49 ND 

454 13-Jan-03 47 53 59 54 

454 14-Mar-03 43 34 ND 39 38 ND 

239 20-May-02 89 ND 63 54 ND 71 

239 25-Jun-02 284 51 44 ND 48 

239 23-Jul-02 52 50 42 ND 43 ND 

239 26-Aug-02 208 87 45 ND 89 

239 23-Sep-02 254 64 48 50 

239 07-Nov-02 159 55 56 63 ND 

239 10-Dec-02 160 104 70 63 

239 17-Mar-03 104 93 59 52 

444 16-Apr-02 58 ND 65 55 ND 60 ND 

444 21-May-02 44 ND 50 ND 49 50 ND 

444 25-Jun-02 118 56 47 ND 45 ND 

444 24-Jul-02 61 51 62 69 

444 23-Aug-02 56 49 ND 47 98 

444 25-Sep-02 133 ND 130 ND 137 ND 119 ND 

444 07-Nov-02 50 ND 54 ND 52 ND 95 

B-58
 
Attachment B-6. Recontamination Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

444 12-Dec-02 47 ND 52 58 54 

444 20-Jan-03 57 48 79 63 

444 14-Mar-03 76 47 57 62 

444 23-Jun-03 3187 43 ND 84 131 

444 22-Sep-03 60 46 51 83 

444 22-Dec-03 513 57.2 54 107 

444 22-Mar-04 256 62 74.5 55.4 

444 21-Jun-04 128 51.4 ND 57.4 81 

444 23-Sep-04 160.3 237.7 196.7 209 

444 16-Dec-04 203.7 280.5 96 259.3 

444 28-Mar-05 123 123 109 186 

674 31-May-02 99 92 -- 88 

674 25-Jun-02 109 63 83 85 

674 23-Jul-02 62 136 99 77 

674 23-Aug-02 95 98 -- 90 

674 25-Sep-02 140 138 -- 127 ND 

674 07-Nov-02 137 191 -- 178 

674 12-Dec-02 183 231 -- 177 

674 15-Jan-03 201 166 -- 133 

674 14-Mar-03 205 104 -- 175 

674 23-Jun-03 175 118 -- 134 

263 16-Sep-02 74 44 ND 50 93 

263 01-Nov-02 63 49 ND 58 79 

263 09-Dec-02 73 46 45 44 ND 

263 17-Mar-03 65 50 ND 81 63 

263 23-Jun-03 58 57 68 60 

581 16-Sep-02 67 69 134 63 

581 01-Nov-02 55 ND 69 55 ND 44 ND 

581 09-Dec-02 54 55 65 67 

581 25-Jul-05 78 113 134 107 

581 04-Oct-05 65 132 109 103 

581 02-May-06 80 122 171 119 

240 16-Sep-02 90 61 ND 91 ND 114 ND 

240 30-Oct-02 99 78 ND 80 ND 81 ND 

240 10-Dec-02 78 ND 76 ND 81 ND 84 ND 

240 14-Mar-03 79 ND 80 ND 77 145 

240 23-Jun-03 128 100 ND 98 ND 98 ND 

240 23-Sep-03 84 76 ND 122 129 

240 22-Dec-03 79.4 121.5 62.5 165.5 

240 22-Mar-04 110 139.5 85.4 117.5 

240 21-Jun-04 107.3 ND 147 91.5 ND 131.5 

240 23-Sep-04 93.7 177 91.3 196.5 

240 16-Dec-04 103.4 ND 179 97 193.5 

240 28-Mar-05 106 163 80 ND 184 

240 07-Jul-05 242 232 138 266 

240 04-Oct-05 125 224 115 275 

240 01-May-06 124 177 120 157 

257 11-Feb-02 52 54 62 87 

257 14-Mar-02 71 70 79 75 

257 15-Apr-02 63 60 71 74 

257 21-May-02 122 76 69 57 

257 21-Jun-02 79 76 73 99 

257 23-Jul-02 54 50 57 54 

257 22-Aug-02 60 54 65 46 ND 

257 23-Sep-02 81 103 66 59 

B-59
 
Attachment B-6. Recontamination Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

257 01-Nov-02 81 77 88 64 

257 12-Dec-02 61 58 179 120 

257 14-Mar-03 61 60 63 57 

257 23-Jun-03 98 56 120 74 

257 23-Sep-03 133 151 72 85 

257 22-Dec-03 75 68 76 68 

257 22-Mar-04 89 73 92 68 ND 

257 21-Jun-04 101.6 123 107.4 70.7 

257 23-Sep-04 107.5 222 126 136 

257 16-Dec-04 162.3 128.7 78.9 79.3 

257 29-Mar-05 90 143 127 97 

576 06-Feb-02 71 67 55 120 

576 14-Mar-02 68 62 74 69 ND 

576 17-Apr-02 64 63 69 69 

576 21-May-02 74 77 76 68 

576 25-Jun-02 140 76 55 63 

576 23-Jul-02 69 44 ND 53 65 

576 23-Aug-02 55 63 74 65 

576 25-Sep-02 78 79 75 59 

576 07-Nov-02 104 54 75 62 

576 12-Dec-02 111 62 76 60 

576 15-Jan-03 63 71 60 79 

576 14-Mar-03 100 68 85 94 

576 23-Jun-03 68 53 81 57 

576 22-Sep-03 91 45 94 101 

576 22-Dec-03 64.8 56.6 85.7 78 

576 22-Mar-04 83.7 53 ND 71.9 78.9 

576 21-Jun-04 85.7 69.3 78.5 76.5 

576 23-Sep-04 127.8 112.4 101 91.3 

576 16-Dec-04 85.9 99.8 80.9 85.8 

576 28-Mar-05 121 120 76.7 89 

576 07-Jul-05 192 169 145 163 

576 03-Oct-05 147 141 105 137 

576 02-May-06 97 71 92 127 

207 06-Feb-02 53 ND 58 ND 67 ND 82 

207 14-Mar-02 177 ND 160 ND 230 ND 150 ND 

207 16-Apr-02 59 67 59 93 

207 22-May-02 54 ND 52 ND 53 ND 95 

207 21-Jun-02 69 54 52 ND 50 ND 

207 23-Jul-02 65 52 52 45 ND 

207 22-Aug-02 46 ND 75 53 68 

207 23-Sep-02 70 59 52 ND 51 

207 23-Oct-02 51 54 ND 55 56 ND 

207 09-Dec-02 50 51 ND 46 ND 56 

207 14-Mar-03 65 49 48 47 

207 23-Jun-03 50 46 44 74 

207 22-Sep-03 110 106 40 107 

207 22-Dec-03 87 51.6 45.1 68 

207 22-Mar-04 63.6 69.2 63.3 157.7 

207 21-Jun-04 61.7 70.2 ND 80.9 70.2 

207 23-Sep-04 111.3 104 179 169 

207 16-Dec-04 126 83.3 75.1 100.4 

207 29-Mar-05 120 123 65 ND 133 

207 14-Jul-05 100 100 75 115 

207 04-Oct-05 69 90 84 107 

B-60
 
Attachment B-6. Recontamination Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

207 01-May-06 98 166 83 137 

347 15-Feb-02 61 ND 62 ND 68 59 

347 14-Mar-02 160 58 ND 59 64 ND 

347 16-Apr-02 53 ND 59 ND 57 ND 56 ND 

347 20-May-02 107 58 57 56 

347 25-Jun-02 98 56 63 55 

347 24-Jul-02 55 62 66 54 

347 26-Aug-02 60 57 56 121 

347 24-Sep-02 67 71 69 138 

347 07-Nov-02 86 90 126 61 

347 10-Dec-02 74 84 125 113 

347 17-Mar-03 121 164 152 140 

347 23-Jun-03 150 88 179 137 

347 23-Sep-03 245 210 212 132 

347 22-Dec-03 224 128.5 181.5 295 

347 22-Mar-04 175 100 311 216 

347 21-Jun-04 138 76.1 170.3 233 

347 23-Sep-04 268 404.3 338 423.3 

347 16-Dec-04 163 358.3 331.7 290.3 

347 30-Mar-05 239 426 207 177 

347 07-Jul-05 298 376 235 341 

347 04-Oct-05 154 271 357 447 

347 01-May-06 250 382 264 515 

176 13-Feb-02 116 72 ND 67 ND 73 ND 

176 14-Mar-02 78 67 69 79 

176 17-Apr-02 59 81 62 ND 59 ND 

176 22-May-02 45 ND 57 50 ND 54 

176 25-Jun-02 53 ND 98 55 53 

176 24-Jul-02 60 140 56 54 

176 23-Aug-02 70 102 50 42 

176 25-Sep-02 73 114 75 66 

176 07-Nov-02 60 ND 50 69 54 

176 12-Dec-02 56 88 59 70 

176 15-Jan-03 50 ND 97 61 67 

176 23-Mar-04 152 244 121 134 

176 21-Jun-04 206.7 103.7 94.7 110.7 

176 23-Sep-04 674 244.7 169.7 170.7 

176 16-Dec-04 139.7 205.3 137.7 151.6 

176 28-Mar-05 241 189 136 150 

176 08-Jul-05 233 360 136 193 

176 03-Oct-05 201 306 301 231 

512 06-Feb-02 51 86 46 ND 64 ND 

512 14-Mar-02 135 80 78 61 ND 

512 17-Apr-02 60 ND 81 61 ND 65 

512 22-May-02 58 158 61 73 

512 25-Jun-02 60 88 52 57 

512 23-Jul-02 67 127 51 53 

512 26-Aug-02 79 154 59 61 

512 24-Sep-02 71 106 70 57 

512 07-Nov-02 99 131 59 69 

512 10-Dec-02 148 234 82 92 

512 23-Jun-03 114 260 95 85 

512 23-Sep-03 130 281 110 182 

512 22-Dec-03 128 290 150 88 

512 22-Mar-04 116 315 191 94.6 

B-61
 
Attachment B-6. Recontamination Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

512 22-Jun-04 112 211.7 84.8 79.4 

512 23-Sep-04 249.3 328.7 202 235 

512 16-Dec-04 102.4 284 75 202.8 

512 30-Mar-05 196 295 188 167 

512 08-Jul-05 184 247 111 180 

512 04-Oct-05 147 259 170 111 

512 02-May-06 275 351 189 187 

398 08-Oct-02 -- -- -- 51 ND 

398 31-Oct-02 -- -- -- 38 ND 

398 09-Dec-02 -- -- -- 58 

398 13-Jan-03 -- -- -- 58 

181 07-Nov-02 193 82 58 73 

181 10-Dec-02 117 64 53 60 

181 17-Mar-03 120 60 99 73 

181 23-Jun-03 141 78 77 57 ND 

181 23-Sep-03 163 65 87 131 

181 22-Dec-03 96 72.9 74.5 76.2 

181 22-Mar-04 164 80 92 89.9 

181 22-Jun-04 237.3 112 101.5 106 

181 23-Sep-04 219 141.7 68.2 114.3 

181 16-Dec-04 195 141 68.69 162 

181 30-Mar-05 177 89 90 136 

181 07-Jul-05 140 167 98 113 

181 04-Oct-05 196 218 127 205 

181 02-May-06 220 281 113 95 

328 30-Oct-03 51.7 68.8 -- --

328 22-Dec-03 173 123 -- --

328 22-Mar-04 144 169 -- --

328 22-Jun-04 95.7 137.3 -- --

328 23-Sep-04 212.3 131.5 -- --

328 16-Dec-04 173.3 399 -- --

328 29-Mar-05 196 136 -- --

328 07-Jul-05 255 144 -- --

328 03-Oct-05 236 181 -- --

328 18-May-06 213 248 -- --

684 22-Dec-03 90.3 53 41 38.8 

684 22-Mar-04 73.6 60.7 77.5 59.9 

684 22-Jun-04 126.4 59.4 ND 75.6 72.4 

684 23-Sep-04 88.9 121.3 126 104.8 

684 16-Dec-04 144.2 227 147 171.3 

684 28-Mar-05 182 171 151 142 

684 08-Jul-05 101 118 116 132 

684 04-Oct-05 91 126 107 109 

684 02-May-06 129 140 169 168 

575 22-Dec-03 257 285 181 250 

575 22-Mar-04 451 530 280 217 

575 21-Jun-04 462 518 208 264 

575 23-Sep-04 495 458.7 325 485 

575 16-Dec-04 837.8 854.5 367.7 299.3 

575 30-Mar-05 551 638 395 296 

575 07-Jul-05 1507 528 557 437 

575 04-Oct-05 390 266 304 512 

575 02-May-06 488 258 258 240 

224 28-Mar-05 44 ND 43 ND 49 ND 55 ND 

224 07-Jul-05 52 ND 68 54 ND 54 ND 

B-62
 
Attachment B-6. Recontamination Soil Sampling Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4 

224 03-Oct-05 42 ND 48 ND 50 422 

224 02-May-06 39 37 ND 44 ND 94 

402 28-Mar-05 76.5 48 -- 50 

402 07-Jul-05 57 ND 61 ND -- 60 ND 

402 03-Oct-05 62 52 ND -- 47 ND 

402 01-May-06 50 ND 48 -- 41 ND 

1078 31-Jan-02 405 -- -- --

1078 14-Mar-02 173 ND -- -- --

1078 17-Apr-02 138 -- -- --

1078 21-May-02 107 -- -- --

1078 25-Jun-02 106 -- -- --

1078 24-Jul-02 250 -- -- --

1078 26-Aug-02 102 -- -- --

1078 24-Sep-02 94 -- -- --

1078 07-Nov-02 80 -- -- --

1078 10-Dec-02 100 -- -- --

1078 14-Mar-03 154 -- -- --

1078 23-Jun-03 206 -- -- --

1078 23-Sep-03 164 -- -- --

1078 22-Dec-03 106 -- -- --

1078 22-Mar-04 184 -- -- --

1078 21-Jun-04 263.8 -- -- --

1078 23-Sep-04 845.6 -- -- --

1078 16-Dec-04 130.5 -- -- --

1078 28-Mar-05 151 -- -- --

1078 07-Jul-05 209 -- -- --

1078 03-Oct-05 287 -- -- --

1078 01-May-06 277 -- -- --

1079 22-Dec-03 67 121 -- --

1079 22-Mar-04 111.7 105.6 -- --

1079 22-Jun-04 231.3 227.7 -- --

1079 23-Sep-04 362 329.7 -- --

1079 16-Dec-04 275 338.3 -- --

1079 28-Mar-05 338 230 -- --

1079 07-Jul-05 345 164 -- --

1079 03-Oct-05 622 590 -- --

1079 02-May-06 370 1276 -- -- a

 Data were obtained from U.S. EPA Region 7 (2006).

b

 A value qualified with a "ND" represents a non-detect. The value presented is the detection limit. For the 

purpose of calculating the property average by year, one-half the detection limit was used as the value for nondetects.
 c

 "--" indicates that no sample was collected for that quadrant.
 

B-63
 
Attachment B-7. Average Soil Pre-Excavation, Post-Excavation, and Recontamination Pb 

Results for 31 Residential Locations within One Mile of the Primary Pb Smelter
 

Full-Scale Pre-Excavation Post-Excavation Averages (mg/kg) b,c 

Analysis ID (mg/kg) a (mg/kg) a 2002 2003 2004 2005 2006 

151 918 377 47.3 59.8 65.3 97.3 77.5 

176 1471 889 62.1 62.5 191.3 223.1 

181 1367 671 87.5 90.1 130.8 146.3 177.3 

184 3308 420 51.1 -- -- -- --

197 3035 806 93.8 106.9 162 176.1 172.5 

207 1039 750 52.2 64.9 96.9 95.7 121 

212 355 568 93.1 119.4 180.4 200.3 198.5 

224 579 -- -- -- -- 63.4 43.4 

239 4155 1138 81.3 77 -- -- --

240 2770 489 50.1 86.2 119.4 175.8 144.5 

257 1073 453 73.1 82.3 108.2 114.3 --

263 1425 885 54.1 59.6 -- -- --

328 5138 1279 -- 104.1 182.8 191.3 230.5 

340 917 1015 59.2 90.2 141.3 171 230.8 

347 614 101 69.2 172.4 249.8 294 352.8 

398 394 160 34.2 58 -- -- --

402 1740 -- -- -- -- 41.7 31.2 

444 1795 332 49.4 244.2 149 135.3 --

454 667 184 28.9 41.4 -- -- --

493 466 126 32.5 40.8 -- -- --

512 2013 696 80 159.4 180.2 187.9 250.5 

531 618 567 44.6 56.2 91 92.3 55.5 

575 -- 1177 -- 243.3 440.8 531.8 311 

576 1500 736 70.2 74.8 84.7 133.8 96.8 

579 1528 711 91.5 -- -- -- --

581 837 354 60 -- -- 105.1 123 

626 604 208 37.1 39.6 -- -- --

674 1850 965 120.8 156.8 -- -- --

684 6857 213 -- 55.8 106.6 128.8 151.5 

1078 -- -- 146.9 157.5 356 215.7 277 

1079 9173 88 -- 94 247.7 381.5 823 

a

 All available pre-excavation and post-excavation results by quadrant are provided in Attachments B-4 and B-5, 

respectively.
 b

 Soil samples from up to four quadrants were collected on each date. The results for the quadrants were first averaged 

(using one-half the detection limit as the value for non-detects) before determining the final overall average by year for 

each location.
 c During the process of summarizing post-excavation and recontamination Pb results for the 31 locations, it was noted 

that, in general, post-excavation sampling results (collected during 2001 and 2002) were higher than the Pb results for 

recontamination samples collected subsequently in 2002 or 2003. This observation is due to the fact that post-excavation 

samples were collected prior to backfilling the excavated areas with clean soil.
 

B-64
 
Attachment B-8. Indoor Dust/Wipe Sample Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Round No. Date 

Carpet Dust a, b Wipe 

Pb Loading 

Pb 

Concentration 

Window Sill 

Pb Loading 

Other Wipe 

Pb Loading 

(mg/ft2

) (mg/kg) (μg/ft2) (μg/ft2) 

184 Recon #01 16-Apr-02 43 3300 1385 120 

184 Recon #02 29-May-02 28.4 4350 881 47 

184 Recon #03 26-Jun-02 25.7 3364 630 54 

184 Recon #04 24-Jul-02 46.6 3874 1257 69 

579 Recon #01 16-Apr-02 0.54 370 413 17 

579 Recon #02 31-May-02 0.402 383 293 12 

579 Recon #03 28-Jun-02 0.986 539 173 16 

579 Recon #04 2-Aug-02 0.548 728 201 9 

579 Recon #05 26-Aug-02 0.216 826 225 6.2 

151 Recon #01 16-Apr-02 2.1 1000 165 66 

151 Recon #02 28-May-02 2.09 918 75 22 

151 Recon #03 28-Jun-02 0.448 786 38 14 

151 Recon #04 22-Jul-02 0.468 895 27 12 

151 Recon #05 28-Aug-02 0.322 559 49 13 

151 Recon #06 30-Sep-02 0.696 655 36 11 

151 Recon #07 23-Oct-02 2.17 710 36 4.4 

151 Recon #08 4-Dec-02 0.619 642 14 7.7 

151 Recon #09 10-Jan-03 0.471 675 13 6.9 

151 Recon #10 26-Feb-03 0.437 612 23 5.3 

151 Recon #11 1-Apr-03 0.623 644 26 8.5 

151 Recon #12 16-Jul-03 0.487 435 40 4.6 

151 Recon #13 15-Oct-03 0.567 394 17 3.3 

151 Recon #14 7-Jan-04 0.605 477 6.7 3.8 

151 Recon #15 14-Apr-04 -- -- 60 5.1 

151 Recon #16 8-Jul-04 -- -- 9.4 3.4 

151 Recon #17 8-Oct-04 -- -- 55 3.1 

151 Recon #18 10-Jan-05 -- -- 7.5 2.9 

151 Recon #19 19-Apr-05 -- -- 28 11 

151 Recon #20 5-Jul-05 -- -- 17 5.2 

151 Recon #21 7-Oct-05 -- -- 23 6.6 

151 Recon #22 24-Apr-06 -- -- 21 7.6 

493 Recon #01 17-Apr-02 1.4 600 353 19 

493 Recon #02 24-May-02 0.258 695 75 15 

493 Recon #03 16-Jul-02 2.38 664 67 18 

493 Recon #06 20-Sep-02 0.616 426 32 12 

493 Recon #07 24-Oct-02 1.01 629 45 12 

493 Recon #08 3-Dec-02 0.523 681 17 11 

493 Recon #09 27-Jan-03 2.25 845 31 10 

493 Recon #10 25-Feb-03 0.631 313 16 6.1 

493 Recon #11 24-Mar-03 1.07 613 28 11 

340 Recon #01 17-Apr-02 0.66 2200 352 14 

340 Recon #02 30-May-02 2.15 3711 508 24 

340 Recon #03 26-Jun-02 0.826 2191 638 19 

340 Recon #04 30-Jul-02 0.497 2551 185 22 

340 Recon #05 6-Sep-02 0.512 1510 60 6.8 

340 Recon #07 14-Nov-02 0.334 900 141 7.3 

340 Recon #11 3-Apr-03 0.806 1032 576 16 

340 Recon #12 30-Jun-03 0.998 1665 912 8.5 

340 Recon #13 17-Oct-03 0.824 1377 156 10 

197 Recon #01 22-Apr-02 4.7 1900 264 35 

197 Recon #02 4-Jun-02 11.3 2603 109 48 

197 Recon #03 18-Jul-02 6.26 1783 105 25 

531 Recon #01 22-Apr-02 1.6 950 101 18 

B-65
 
Attachment B-8. Indoor Dust/Wipe Sample Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Round No. Date 

Carpet Dust a, b Wipe 

Pb Loading 

Pb 

Concentration 

Window Sill 

Pb Loading 

Other Wipe 

Pb Loading 

(mg/ft2

) (mg/kg) (μg/ft2) (μg/ft2) 

531 Recon #02 30-May-02 1.6 1778 35 15 

531 Recon #03 27-Jun-02 1.3 1461 10 7.6 

531 Recon #04 25-Jul-02 3.19 2477 10 6.1 

531 Recon #05 28-Aug-02 1.67 2409 11 7.8 

531 Recon #07 24-Oct-02 1.44 860 16 7.2 

531 Recon #10 26-Feb-03 1.46 336 23 21 

531 Recon #11 9-Apr-03 1.7 579 52 14 

531 Recon #12 23-Jul-03 2 428 15 11 

531 Recon #14 7-Jan-04 1.85 639 46 11 

531 Recon #15 9-Apr-04 2.24 1208 35 14 

531 Recon #16 3-Aug-04 0.811 761 21 8.2 

531 Recon #17 8-Nov-04 1.02 400 51 9.2 

531 Recon #19 22-Mar-05 2.25 647 15 10 

531 Recon #20 5-Jul-05 0.56 137 6.9 8.4 

531 Recon #21 5-Oct-05 0.106 122 53 11 

531 Recon #22 25-Apr-06 0.168 233 41 6.7 

626 Recon #01 23-Apr-02 0.53 290 110 44 

626 Recon #02 30-May-02 0.393 457 82 6.1 

626 Recon #04 26-Jul-02 0.616 410 129 5.5 

626 Recon #07 25-Oct-03 0.349 317 71 4.4 

212 Recon #01 30-Apr-02 0.46 610 62 10 

212 Recon #02 28-May-02 0.327 557 22 14 

212 Recon #04 26-Jul-02 0.332 659 62 3.3 

212 Recon #05 4-Sep-02 0.578 734 21 3.2 

212 Recon #06 2-Oct-02 0.324 531 9 2.6 

212 Recon #07 8-Nov-02 0.316 650 6.3 2.2 

212 Recon #08 18-Dec-02 0.332 490 6.2 3.2 

212 Recon #09 31-Jan-03 0.451 586 12 3.6 

212 Recon #10 25-Feb-03 0.524 671 10 4.4 

212 Recon #11 8-Apr-03 0.439 512 24 4.4 

212 Recon #12 9-Jul-03 0.395 477 6.9 4 

212 Recon #14 7-Jan-04 0.283 455 14 2.3 

212 Recon #15 15-Apr-04 0.334 457 16 4.6 

212 Recon #17 10-Nov-04 0.229 589 11 6 

212 Recon #19 29-Mar-05 0.137 321 9 

212 Recon #20 6-Jul-05 0.338 422 6 11 

212 Recon #22 25-Apr-06 0.0305 660 22 9.1 

454 Recon #01 30-Apr-02 0.22 450 35 8.5 

454 Recon #02 3-Jun-02 1.75 1502 33 9.3 

454 Recon #03 18-Jul-02 0.22 517 17 8.5 

454 Recon #07 28-Oct-02 0.235 526 31 9.2 

454 Recon #08 4-Dec-02 0.299 550 28 6.8 

454 Recon #09 3-Feb-03 0.0142 247 9 5.9 

454 Recon #10 26-Feb-03 0.319 224 16 5.7 

239 Recon #01 30-Apr-02 26 3000 405 12 

B-66
 
Attachment B-8. Indoor Dust/Wipe Sample Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Round No. Date 

Carpet Dust a, b Wipe 

Pb Loading 

Pb 

Concentration 

Window Sill 

Pb Loading 

Other Wipe 

Pb Loading 

(mg/ft2

) (mg/kg) (μg/ft2) (μg/ft2) 

239 Recon #02 28-May-02 22.6 2124 251 18 

239 Recon #03 1-Jul-02 25 1944 292 10 

239 Recon #04 2-Aug-02 31 2862 85 11 

239 Recon #05 27-Aug-02 11.8 1682 56 8.2 

444 Recon #01 6-May-02 9.3 2300 905 72 

444 Recon #02 7-Jun-02 6.6 2588 1134 41 

674 Recon #02 31-May-02 4.62 1669 40 102 

674 Recon #03 25-Jun-02 2.15 1394 33 29 

674 Recon #04 25-Jul-02 3 1482 25 24 

674 Recon #05 27-Aug-02 2.06 1459 15 12 

674 Recon #07 22-Oct-02 2.88 1273 11 31 

674 Recon #08 3-Dec-02 1.54 1056 20 31 

674 Recon #09 3-Jan-03 2.28 1088 16 10 

674 Recon #10 21-Feb-03 2.28 742 11 5.4 

674 Recon #11 15-Apr-03 2.09 927 18 11 

263 Recon #06 17-Sep-02 0.378 1336 176 4 

263 Recon #07 24-Oct-02 0.673 1786 31 6.4 

263 Recon #08 2-Dec-02 0.649 1619 34 5.1 

263 Recon #09 7-Jan-03 0.514 1196 104 4.4 

263 Recon #10 24-Feb-03 0.182 745 23 4 

263 Recon #11 24-Mar-03 0.635 1119 57 9.5 

263 Recon #12 30-Jun-03 1.39 980 95 6.9 

581 Recon #06 27-Sep-02 0.489 369 124 43 

581 Recon #07 31-Oct-02 1.19 566 99 6.3 

581 Recon #08 11-Dec-02 1.05 426 34 6.5 

581 Recon #09 8-Jan-03 1.51 376 24 7.5 

581 Recon #20 25-Jul-05 0.0201 131 32 5.3 

581 Recon #21 3-Oct-05 0.0483 143 41 8.4 

581 Recon #22 25-Apr-06 0.108 271 155 7 

240 Recon #06 26-Sep-02 4.48 1795 505 26 

240 Recon #07 23-Oct-02 5.04 1633 199 19 

240 Recon #08 4-Dec-02 3.99 1700 159 21 

240 Recon #09 3-Jan-03 3.58 1591 96 18 

240 Recon #10 20-Feb-03 13.8 2877 68 15 

240 Recon #11 20-Mar-03 8.22 1813 62 20 

240 Recon #12 3-Jul-03 2.93 1075 409 15 

240 Recon #13 1-Oct-03 1.7 873 188 19 

240 Recon #14 7-Jan-04 1.12 929 133 11 

240 Recon #15 7-Apr-04 1.45 1064 108 14 

240 Recon #16 16-Jul-04 0.95 805 171 13 

240 Recon #17 18-Oct-04 3.03 1170 455 8.3 

240 Recon #18 10-Jan-05 1.06 735 72 11 

240 Recon #19 19-Apr-05 1.08 834 84 20 

240 Recon #20 7-Jul-05 0.68 816 599 28 

240 Recon #21 5-Oct-05 0.585 766 89 18 

240 Recon #22 5-May-06 0.843 1040 502 24 

398 Recon #06 7-Oct-02 0.49 354 19 5.2 

398 Recon #07 28-Oct-02 0.342 244 17 5.5 

398 Recon #08 3-Dec-02 0.95 322 10 3.7 

398 Recon #09 7-Jan-03 0.499 470 5.6 2.8 

a

 Data were obtained from U.S. EPA Region 7 (2006). 

b

 "--" indicates that no measurement was taken on that date. 

B-67
 

Attachment B-9. Soil and Air Deposition Monitoring Locations 

around the Primary Pb Smelter 

B-68
 
Attachment B-10. Soil Deposition Monitoring Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Round 1 Round 2 Round 3 Round 4 

181

6-Mar-03 40 ND 39 ND 38 ND --

11-Apr-03 58 ND 119 54 ND 63 ND

7-May-03 36 ND 42 ND 47 ND --

6-Jun-03 116 58 ND 52 ND 51 ND

11-Jul-03 79.1 55 ND 67.1 --

11-Aug-03 80.3 82.3 54 ND 97.8

15-Sep-03 57 ND 89.1 78.1 --

15-Oct-03 70.3 51.3 98.9 --

18-Nov-03 90.5 90.2 140 111

17-Dec-03 165 81.4 107 132

19-Jan-04 34.9 80.9 105 52.9

19-Feb-04 186 159 87.8 --

19-Mar-04 81.9 115 136 --

21-Apr-04 95.8 213 177 --

24-May-04 142 37 ND 36 ND --

24-Jun-04 130 51 ND 139 105

27-Aug-04 50.8 70.4 119 --

207

6-Mar-03 48 ND 31 ND 44 ND 36 ND

11-Apr-03 50 ND 50 ND 51 ND --

7-May-03 35 ND 48 ND 35 ND --

6-Jun-03 56 ND 35 ND 31 ND --

11-Jul-03 53 ND 39 ND 46 ND --

11-Aug-03 59 ND 48 ND 56 ND --

15-Sep-03 35 ND 51 ND 39 ND --

15-Oct-03 33.4 39.9 30 --

17-Nov-03 34 ND 59.4 46.2 --

17-Dec-03 54.4 26 ND 37.3 --

19-Jan-04 38 ND 31 ND 32 ND --

19-Feb-04 64.3 30 35 --

19-Mar-04 43.4 55 42.1 --

21-Apr-04 43.8 48.6 46.1 --

24-May-04 59.3 135 27 ND --

24-Jun-04 52 ND 64.5 37 ND --

27-Aug-04 36.2 137 34.1 --

240

6-Mar-03 30 ND 38 ND 37 ND --

16-Apr-03 49 ND 46 ND 43 ND --

7-May-03 42 ND 48 ND 53 ND --

6-Jun-03 35 ND 65 ND 56 ND --

11-Jul-03 62 ND 74 ND 59 ND --

11-Aug-03 54 ND 64 ND 63 ND --

15-Sep-03 50 ND 45 ND 47 ND --

15-Oct-03 33 ND 44.3 47.4 --

18-Nov-03 46.5 45.8 37.9 --

17-Dec-03 48.7 58.9 30 ND --

19-Jan-04 63.2 57.7 45 ND --

19-Feb-04 51.1 91 69.9 --

19-Mar-04 47 ND 75.5 53.2 --

21-Apr-04 52.7 49 ND 64.4 --

24-May-04 43 ND 62 94.9 --

24-Jun-04 67 ND 46 ND 84.1 --

27-Aug-04 46.7 36 ND 37.4 --

B-69
 
Attachment B-10. Soil Deposition Monitoring Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Round 1 Round 2 Round 3 Round 4 

286

14-Feb-03 25 ND 26 ND 21 ND --

11-Apr-03 60.2 65.5 39 ND --

7-May-03 32 ND 27 ND 29 ND --

6-Jun-03 51 ND 27 ND 48 ND --

11-Jul-03 32 ND 40 ND 30 ND --

11-Aug-03 80.4 47 ND 65.7 43 ND

15-Sep-03 28 ND 30 ND 36 ND --

15-Oct-03 38.4 28.4 92.4 --

17-Nov-03 64.8 105 52.9 --

17-Dec-03 198 119 129 --

19-Jan-04 83.9 90.1 103 --

19-Feb-04 161 106 117 --

19-Mar-04 58.9 30 ND 39.1 --

21-Apr-04 275 190 216 --

24-May-04 155 152 217 --

24-Jun-04 330 402 302 --

27-Aug-04 66.5 278 59.3 289

444

6-Mar-03 31 ND 32 ND 34 ND --

11-Apr-03 90 ND 47 ND 56 ND --

7-May-03 32 ND 24 ND 53.5 --

6-Jun-03 69 ND 50 ND 48 ND --

11-Jul-03 81 ND 71 ND 39 ND --

11-Aug-03 70 65 ND 49 ND --

15-Sep-03 53 50 ND 56.1 --

15-Oct-03 47.4 29 ND 29 ND --

17-Nov-03 73.6 65.2 59.2 --

17-Dec-03 79.4 62.4 41.9 --

19-Jan-04 58.8 38 ND 36 ND --

19-Feb-04 69.3 83.8 63.9 --

19-Mar-04 84.3 46.1 96.2 --

21-Apr-04 68.4 131 147 --

24-May-04 107 89.4 60.4 --

24-Jun-04 160 71.5 55 ND --

27-Aug-04 119 50 ND 102 --

531

6-Mar-03 22 ND 23 ND 22 ND --

11-Apr-03 34 ND 46 ND 35 ND --

7-May-03 20 ND 22 ND 28 ND --

6-Jun-03 33 ND 34 ND 29 ND --

11-Jul-03 28 ND 31 ND 26 ND --

11-Aug-03 49 ND 57 ND 44 ND --

15-Sep-03 22 ND 22 ND 34 ND --

15-Oct-03 19 ND 19 ND 21 ND --

17-Nov-03 19 ND 19 ND 20 ND --

17-Dec-03 24 ND 27 ND 26 ND --

19-Jan-04 28 ND 28 ND 31 ND --

19-Feb-04 19 ND 20 ND 20 ND --

19-Mar-04 23 ND 23 ND 52.2 --

21-Apr-04 28 ND 31.8 29.7 --

24-May-04 41.4 24 ND 24 ND --

24-Jun-04 24 ND 29 ND 23 ND --

27-Aug-04 25 ND 26 ND 23 ND --

B-70
 
Attachment B-10. Soil Deposition Monitoring Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Round 1 Round 2 Round 3 Round 4 

576

6-Mar-03 36 ND 40 ND 42 ND --

11-Apr-03 71 ND 42 ND 46 ND --

7-May-03 43 ND 35 ND 40 ND --

6-Jun-03 43 ND 55 ND 47 ND --

11-Jul-03 38 ND 46 ND 50 ND --

11-Aug-03 53 ND 51 ND 44 ND --

15-Sep-03 38 ND 50 ND 40 ND --

15-Oct-03 24 ND 41 ND 35 ND --

17-Nov-03 38.8 32.9 29 ND --

17-Dec-03 60.4 34 ND 35.1 --

19-Jan-04 42 ND 50 ND 45 ND --

19-Feb-04 41 30 ND 49.5 --

19-Mar-04 36 ND 74.6 42 ND --

21-Apr-04 68.4 63.3 36 ND --

24-May-04 62.6 53.6 35.7 --

24-Jun-04 49 ND 42 ND 64.9 --

27-Aug-04 54.4 35.9 28 ND --

1071

7-Jan-03 23 ND 22 ND 26 ND --

14-Feb-03 32 ND 35 ND 28 ND --

11-Apr-03 135 119 102 --

7-May-03 47 37.4 37.8 --

6-Jun-03 115 73.9 133 --

11-Jul-03 205 153 144 --

11-Aug-03 336 622 259 --

15-Sep-03 288 301 294 --

15-Oct-03 330 143 219 228

17-Nov-03 309 218 281 --

17-Dec-03 265 206 176 189

19-Jan-04 188 317 188 --

19-Feb-04 404 271 311 291

19-Mar-04 278 306 434 --

21-Apr-04 602 515 464 --

24-May-04 210 229 360 --

24-Jun-04 279 285 499 279

27-Aug-04 166 143 143 --

1072

(Control)

7-Mar-03 24 ND 21 ND 21 ND --

11-Apr-03 30 ND 36 ND 39 ND --

7-May-03 22 ND 22 ND 20 ND --

6-Jun-03 22 ND 26 ND 30 ND --

11-Jul-03 33 ND 33 ND 33 ND --

11-Aug-03 32 ND 25 ND 26 ND --

15-Sep-03 26 ND 28 ND 28 ND --

15-Oct-03 17 ND 16 ND 17 ND --

18-Nov-03 17 ND 15 ND 19 ND --

17-Dec-03 20 ND 13 ND 18 ND --

19-Jan-04 24 ND 17 ND 24 ND --

19-Feb-04 20 17 20 --

19-Mar-04 13 ND 19 ND 22 ND --

21-Apr-04 28 ND 36 ND 23 ND --

24-May-04 21 ND 20 ND 19 ND --

24-Jun-04 30 ND 30 ND 31 ND --

27-Aug-04 20 ND 21 ND 21 ND --

B-71
 
Attachment B-10. Soil Deposition Monitoring Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/kg) a, b, c 

Round 1 Round 2 Round 3 Round 4 

1073

7-Jan-03 25 ND 20 ND 28 ND --

14-Feb-03 30 ND 24 ND 25 ND --

11-Apr-03 64.1 63 ND 103 77.2

7-May-03 29 ND 26 ND 28 ND --

6-Jun-03 46.1 41.1 57.8 --

11-Jul-03 93.9 46.4 95.2 --

11-Aug-03 165 108 190 --

15-Sep-03 97.6 85.7 125 --

15-Oct-03 54.5 68.1 75.8 --

18-Nov-03 74.8 82.3 109 50.8

17-Dec-03 87 55.2 146 80.3

19-Jan-04 131 144 107 --

19-Feb-04 172 125 104 --

19-Mar-04 36.9 30 ND 187.7 --

21-Apr-04 207 103 301 --

24-May-04 95.8 93 189 --

24-Jun-04 162 114 257 --

27-Aug-04 205 34.5 136 -- a

 Data were obtained from U.S. EPA Region 7 (2006). 

b

 "--" indicates that no sample was during that time. 

c

 A value qualified with an "ND" represents a non-detect. The value presented is the detection limit. For the purpose 

of calculating averages, one-half the detection limit was used as the value for non-detects. 

B-72
 
Attachment B-11. Air Deposition Monitoring Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/ft2

) a, b 

Height = 1 ft Height = 10 ft 

181

7-Apr-03 0.774 10.318

7-May-03 10.928 6.041

6-Jun-03 3.657 5.266

11-Jul-03 3.826 3.861

12-Aug-03 2.669 3.543

15-Sep-03 13.584 15.058

15-Oct-03 7.877 6.202

17-Nov-03 5.903 5.32

17-Dec-03 15.137 11.899

19-Jan-04 7.203 5.162

19-Feb-04 8.152 4.927

19-Mar-04 6.943 10.346

21-Apr-04 7.852 6.829

Annual Averages: 7.3 7.3 

207

7-Apr-03 3.343 4.432

7-May-03 3.684 2.699

6-Jun-03 0.516 0.459

11-Jul-03 2.118 1.986

12-Aug-03 1.006 1.054

15-Sep-03 2.306 2.591

15-Oct-03 1.203 1.494

17-Nov-03 1.497 2.698

17-Dec-03 2.552 3.163

19-Jan-04 2.739 3.025

19-Feb-04 1.093 2.699

19-Mar-04 5.124 6.831

21-Apr-04 4.194 4.202

Annual Averages: 2.4 2.9 

240

7-Apr-03 3.924 4.128

7-May-03 3.727 4.01

6-Jun-03 1.131 1.068

11-Jul-03 1.666 2.045

12-Aug-03 1.333 1.337

15-Sep-03 2.418 2.164

15-Oct-03 1.62 1.676

17-Nov-03 1.64 2.322

17-Dec-03 3.769 4.657

19-Jan-04 3.627 3.698

19-Feb-04 1.975 1.603

19-Mar-04 4.521 5.57

21-Apr-04 3.363 4.105

Annual Averages: 2.7 3.0 

286

7-Apr-03 11.904 12.295

7-May-03 10.046 11.758

6-Jun-03 2.579 2.57

11-Jul-03 4.09 4.249

12-Aug-03 1.047 2.624

15-Sep-03 3.86 2.916

15-Oct-03 2.488 2.808

17-Nov-03 5.848 5.581

17-Dec-03 11.737 14.01

19-Jan-04 8.328 3.179

19-Feb-04 4.011 5.487

19-Mar-04 9.145 20.996

21-Apr-04 20.312 33.171

Annual Averages: 7.3 9.4 

B-73
 
Attachment B-11. Air Deposition Monitoring Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/ft2

) a, b 

Height = 1 ft Height = 10 ft 

444

7-Apr-03 3.937 4.234

7-May-03 5.204 3.422

6-Jun-03 1.122 0.798

11-Jul-03 2.712 2.333

12-Aug-03 0.803 0.887

15-Sep-03 1.765 3.073

15-Oct-03 2.547 1.371

17-Nov-03 2.376 3.008

17-Dec-03 3.757 4.646

19-Jan-04 2.878 5.938

19-Feb-04 0.452 1.842

19-Mar-04 4.835 7.211

21-Apr-04 7 8.862

Annual Averages: 3.0 3.7 

531

7-Apr-03 2.645 1.523

7-May-03 1.035 1.193

6-Jun-03 0.452 0.263

11-Jul-03 0.917 0.835

12-Aug-03 0.341 0.484

15-Sep-03 0.887 0.606

15-Oct-03 0.514 0.527

17-Nov-03 0.877 0.542

17-Dec-03 1.713 1.644

19-Jan-04 1.735 2.191

19-Feb-04 0.822 1.073

19-Mar-04 3.525 1.922

21-Apr-04 3.323 2.063

Annual Averages: 1.4 1.1 

576

7-Apr-03 1.991 1.994

7-May-03 1.827 1.519

6-Jun-03 0.716 0.514

11-Jul-03 1.396 1.417

12-Aug-03 0.596 0.742

15-Sep-03 0.972 1.406

15-Oct-03 0.671 0.966

17-Nov-03 1.183 1.275

17-Dec-03 2.02 1.99

19-Jan-04 2.209 1.786

19-Feb-04 0.596 1.556

19-Mar-04 3.777 3.707

21-Apr-04 3.923 4.399

Annual Averages: 1.7 1.8 

1071

7-Apr-03 14.764 17.635

7-May-03 19.453 7.265

6-Jun-03 4.673 4.611

11-Jul-03 5.802 4.397

12-Aug-03 6.804 6.784

15-Sep-03 16.903 31.997

15-Oct-03 5.247 8.909

17-Nov-03 5.925 4.734

17-Dec-03 16.435 13.384

19-Jan-04 12.265 10.1

19-Jan-04 7.927 8.057

19-Mar-04 22.039 13.635

21-Apr-04 10.718 12.532

Annual Averages: 11 11 

B-74
 
Attachment B-11. Air Deposition Monitoring Results for Pb – Primary Pb Smelter 

Full-Scale 

Analysis ID Sampling Date 

RESULTS (mg/ft2

) a, b 

Height = 1 ft Height = 10 ft 

1072

(Control)

7-Apr-03 0.588 12.125

7-May-03 0.774 0.601

6-Jun-03 0.268 0.292

11-Jul-03 0.363 0.317

12-Aug-03 0.3 0.456

15-Sep-03 0.236 0.241

15-Oct-03 0.203 0.238

17-Nov-03 0.28 0.426

17-Dec-03 0.805 0.7

19-Jan-04 0.676 0.313

19-Feb-04 0.33 0.282

19-Mar-04 0.718 0.642

21-Apr-04 2.382 1.771

Annual Averages: 0.61 1.4 

1073

7-Apr-03 7.798 8.346

7-May-03 6.195 6.507

6-Jun-03 2.296 1.677

11-Jul-03 3.844 6.033

12-Aug-03 1.722 1.983

15-Sep-03 7.751 4.782

15-Oct-03 4.969 4.071

17-Nov-03 5.051 3.52

17-Dec-03 7.816 8.113

19-Jan-04 4.733 5.148

19-Feb-04 3.601 4.754

19-Mar-04 6.899 7.082

21-Apr-04 8.554 5.393

Annual Averages: 5.5 5.2 

a

 Data were obtained U.S. EPA Region 7 (2006). 

b

 "--" indicates that no sample was taken during that time. 

B-75
 
Attachment B-12. Air Monitoring Locations around the Secondary Pb Smelter 

B-76
 
Attachment B-13. Average Annual Pb Concentrations from AQS Monitors 

Located around the Secondary Pb Smelter 

Monitor ID 

Facility 

(meters) 

Average Annual Pb Concentrations (μg/m3

) a 

1998 1999 2000 2001 2002 

11090003 290 to 480 0.47 0.47 0.38 0.44 0.28 

11090006 570 to 750 0.16 0.18 0.19 0.20 0.14 

a

 Data are for average annual Pb concentrations in total suspended particulate matter (TSP) 

and were calculated from the U.S. EPA's AQS monthly composite data and weighted by the 

number of days in a month. The data were extracted from AQS using an AMP350 report, with 

the units selected as reported. Events and nulls were not included in the AMP350 report. 

B-77
 

Appendix C: Media Concentrations for the General Urban Case Study 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

Table of Contents 

Table of Contents.................................................................................................................C-i 

List of Exhibits ....................................................................................................................C-ii 

C. MEDIA CONCENTRATIONS FOR THE GENERAL URBAN CASE STUDY ........ C-1
 

C.1. AIR............................................................................................................................... C-1 

C.1.1. Ambient Air Concentrations............................................................................ C-1 

C.1.2. Inhalation Exposure Concentrations................................................................ C-5 

C.2. SOIL............................................................................................................................. C-6 

C.3. INDOOR DUST......................................................................................................... C-20 

REFERENCES ................................................................................................................... C-22 

C-i 
List of Exhibits 

Exhibit C-1. Air Quality Scenarios included in the General Urban Case Study ........................C-2 

Exhibit C-2. Ambient Air Ratios of Monthly or Quarterly Average Concentrations to 

Annual Average Concentration..............................................................................C-3 

Exhibit C-3. Estimated Annual Average Ambient Air Concentrations by Air Quality 

Scenario..................................................................................................................C-3 

Exhibit C-4. Estimated Annual Average Inhalation Exposure Air Concentrations 

for the Air Quality Scenarios .................................................................................C-6 

Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measurements ......C-9 

Exhibit C-6. Pb Concentrations Measured in Urban Soils in the United States.......................C-19 

Exhibit C-7. Estimated Annual Indoor Dust Pb Concentrations from the Hybrid 

Mechanistic-Empirical Model for the Air Quality Scenarios ..............................C-20 

Exhibit C-8. Estimated Annual Indoor Dust Pb Concentrations from the Air-Only 

Regression-Based Model for the Air Quality Scenarios......................................C-21 

C-ii 
 

 

 

 

C. MEDIA CONCENTRATIONS FOR THE GENERAL URBAN CASE 

STUDY 

This appendix presents the methodology used to calculate the concentration of lead (Pb) 

in various media for the general urban case study, along with the resulting media concentrations. 

Section C.1 describes the estimation of ambient air and inhalation exposure concentrations; 

Section C.2 examines soil concentrations; and Section C.3 covers indoor dust concentrations. 

C.1. AIR 

C.1.1. Ambient Air Concentrations 

The air quality scenarios included in the general urban case study are summarized in 

Exhibit C-1. Two current conditions scenarios are included. The first is based on the 95th 

percentile monitoring site in urban areas of larger than one million residents, with regard 

to maximum quarterly average Pb-total suspended particulate matter (TSP) concentration for the 

time period 2003 to 2005 (using data from the U.S. EPA Air Quality System [AQS] database 

(USEPA, 2007).1

 It was derived by first calculating the maximum quarterly average 

concentration of Pb in TSP for the time period 2003 to 2005 for each monitoring site that met 

completeness criteria and that is located in an urban area with more than one million residents. 

The value shown in Exhibit C-1 for this first scenario is the 95th percentile of the distribution of 

those maximum quarterly average values. The value for the second current conditions scenario 

is the arithmetic mean of those maximum quarterly average values. The third value is for the 

current National Ambient Air Quality Standard (NAAQS) scenario for Pb, and the last four 

values are for the alternative NAAQS scenarios included in this assessment. 

1

 These statistics and their derivation are described in Appendix A. 

C-1
 
 

 

 

 

Exhibit C-1. Air Quality Scenarios included in the General Urban Case Study 

Air Quality Scenario Level 

(μg/m3

) 

Averaging 

Time Notes a 

Current conditions (95th 

percentile) 0.87 

Calendar 

Quarter 

(maximum) 

This value is the 95th percentile of the maximum quarterly 

average concentration of Pb in TSP (for period 2003 to 

2005) among monitor locations in urban areas having more 

than one million residents. 

Current conditions 

(mean) 0.14 

Calendar 

Quarter 

(maximum) 

This value is the mean of the maximum quarterly average 

concentrations of Pb in TSP (for period 2003 to 2005) 

among monitor locations in urban areas having more than 

one million residents. 

Current NAAQS 1.5 

Calendar 

Quarter 

(maximum) 

--

Alternative NAAQS 1 0.2 

Calendar 

Quarter 

(maximum) 

--

Alternative NAAQS 2 0.5 Monthly 

(maximum) --

Alternative NAAQS 3 0.2 Monthly 

(maximum) --

Alternative NAAQS 4 0.05 Monthly 

(maximum) --

a

 The data used to derive the current conditions concentrations are Pb-TSP monitoring data in the U.S. 

EPA AQS database for 2003 to 2005, which met certain adequacy criteria. This is further described in
 

Appendix A. 

Ratios relating these maximum quarterly or monthly average concentrations to annual 

average concentrations were used to estimate the annual average ambient air concentrations used 

in this assessment. The ratios were developed using the same data set as that described above for 

developing the current conditions scenarios. The ratios and their basis and application for this 

assessment are provided in Exhibit C-2 below. 

C-2
 
 

 

 

Exhibit C-2. Ambient Air Ratios of Monthly or Quarterly Average Concentrations to 

Annual Average Concentration 

Ratio Description (unitless) 

Value Notes a 

95th percentile ratio of maximum 

quarterly to annual average PbTSP concentrations 

7.6 

1) For each monitoring site in urban areas of more than 

one million residents, the maximum quarterly average and the 

annual average Pb-TSP concentrations, and the ratios of the 

former to the latter, were derived. This value is the 95th 

percentile of the distribution of the ratios. 

2) This ratio was used to derive the annual average concentration 

for the current conditions (95th percentile) scenario. 

Mean ratio of maximum 

quarterly to annual average PbTSP concentrations 

2.5 

1) For each monitoring site in urban areas of more than 

one million residents, the maximum quarterly average and the 

annual average Pb-TSP concentrations, and the ratios of the 

former to the latter, were derived. This value is the 

arithmetic mean of these ratios. 

2) This ratio was used to derive the annual average concentration 

for the current and alternative NAAQS scenarios for which the 

averaging time is calendar quarter. 

Mean ratio of maximum monthly 

to annual average Pb-TSP 

concentrations 

4.0 

1) For each monitoring site in urban areas of more than 

one million residents, the maximum monthly average and the 

annual average Pb-TSP concentrations, and the ratios of the 

former to the latter, were derived. This value is the 

arithmetic mean of these ratios. 

2) This ratio was used to derive the annual average concentration 

for the alternative NAAQS scenarios for which the averaging 

time is monthly. a Data derived from U.S. EPA (2007). 

The ratios were applied to the concentrations in Exhibit C-1 to estimate the seven annual 

average ambient air concentrations (i.e., one for each air quality scenario) (see Exhibit C-3). 

Exhibit C-3. Estimated Annual Average Ambient Air Concentrations 

by Air Quality Scenario 

Air Quality Scenario 

Current conditions (95th percentile) 

Annual Average 

Pb Concentration 

(μg/m3

) 

0.11 

Current conditions (mean) 0.056 

Current NAAQS (1.5 μg/m3

, maximum quarterly average) 0.60 

Alternative NAAQS 1 (0.2 μg/m3

, maximum quarterly average) 0.080 

Alternative NAAQS 2 (0.5 μg/m3

, maximum monthly average) 0.13 

Alternative NAAQS 3 (0.2 μg/m3

, maximum monthly average) 0.050 

Alternative NAAQS 4 (0.05 μg/m3

, maximum monthly average) 0.013 

C-3
 
 

 

 

 

 

 

 

 

 

 

 

The following provides a more detailed description (than that provided in Exhibit C-2) of 

the derivation of the annual average Pb-TSP concentrations used for the seven air quality 

scenarios included in the general urban case study. 

The annual average concentration for the current conditions (95th percentile) scenario was 

estimated using the calculation shown below. 

CC 95th −A = CC 95th −Q ÷ R95th −Q:A 

where: 

CC95th-A = Annual average concentration for the current conditions 

(95th percentile) scenario (micrograms [μg] 

per cubic meter [m3]) 

CC95th -Q = Maximum quarterly average concentration for the current 

conditions (95th percentile) scenario (μg/m3) (from Exhibit C-1) 

R95th -Q:A = 95th percentile ratio of maximum quarterly to annual average 

concentrations (unitless) (from Exhibit C-2) 

A similar calculation was used to estimate the annual average concentration for the 

current conditions (mean) scenario, which is shown below. 

CC Mean−A Mean = CC −Q Mean ÷ R −Q:A 

where: 

CCMean-A = Annual average concentration for the current conditions (mean) 

scenario (μg/m3) 

CCMean-Q = Maximum quarterly average concentration for the current 

conditions (mean) scenario (μg/m3

) (from Exhibit C-1) 

RMean-Q:A = Mean ratio of maximum quarterly to annual average 

concentrations (unitless) (from Exhibit C-2) 

The annual average concentrations for the current NAAQS scenario and the alternative 

NAAQS scenario for which the averaging time is calendar quarter were estimated by replacing 

CCMean-Q in the above equation with the maximum quarterly average levels for each scenario 

(i.e., 1.5 and 0.2 μg/m3, respectively). 

C-4
 
 

 

 

 

 

 

 

Lastly, the annual average concentrations for the alternative NAAQS scenarios for which 

the averaging time is monthly were estimated using the calculation below. 

ALT A M M = ALT ÷ R :A 

where: 

ALTA = Annual average concentration for alternative NAAQS 

scenarios (for which averaging time is monthly) 

(μg/m3) 

ALTM = Maximum monthly average concentration for alternative 

NAAQS scenarios (for which averaging time is monthly), 

(μg/m3) (from Exhibit C-1) 

RM:A = Mean ratio of maximum monthly to annual average (unitless) 

(from Exhibit C-2) 

C.1.2. Inhalation Exposure Concentrations 

Inhalation exposure concentrations of Pb were estimated for the population of interest 

(young children) from the annual ambient air concentrations using age group- and locationspecific relationships for Pb developed from modeling performed for U.S. EPA’s 1999 Nationalscale Air Toxics Assessment (USEPA, 2006), one of the U.S. EPA’s National Air Toxics 

Assessment (NATA) activities. These relationships account for air concentration differences 

indoors and outdoors, as well as for mobility or time spent in various locations (e.g., outdoors at 

home, inside at home) for the population of interest. 

The NATA national-scale assessment produced air concentrations of Pb (and other 

hazardous air pollutants) for each U.S. Census tract using the Assessment System for Population 

Exposure Nationwide (ASPEN) model, and corresponding exposure concentrations of Pb for 

each of five age groups at each U.S. Census tract using the Hazardous Air Pollutant Exposure 

Model (HAPEM). The median ratio of ambient Pb concentration to Pb exposure concentration 

from the NATA national-scale assessment for the 0- to 4-year-old age group across all the U.S. 

Census tracts was identified as the best estimate of the relationship between ambient and 

inhalation exposure concentrations for use in this risk assessment. Data for 0- to 4-year-olds 

were used because this group is the closest age group for which outputs are available when 

compared to the age group of interest for this assessment. The result of applying this ratio, 

which was 0.43, to the annual ambient air concentration is shown in Exhibit C-4. 

C-5
 
 

 

Exhibit C-4. Estimated Annual Average Inhalation Exposure Air Concentrations 

for the Air Quality Scenarios 

Air Quality Scenario 

Current conditions (95th percentile) 

Annual Average 

Inhalation Exposure 

Concentration 

(μg/m3

) 

0.049 

Current conditions (mean) 0.024 

Current NAAQS (1.5 μg/m3

, max quarterly average) 0.26 

Alternative NAAQS 1 (0.2 μg/m3

, max quarterly average) 0.034 

Alternative NAAQS 2 (0.5 μg/m3

, max monthly average) 0.054 

Alternative NAAQS 3 (0.2 μg/m3

, max monthly average) 0.021 

Alternative NAAQS 4 (0.05 μg/m3

, max monthly average) 5.4E-03 

Use of ratios for the 0 to 4 age group across the United States, rather than ratios for 0 to 7 

year-olds in only urban areas, contributes some uncertainty to the estimate of children’s 

inhalation exposure concentrations. The use of the arithmetic mean of the ambient-to-inhalation 

exposure concentration ratios also creates some uncertainty in that it does not capture the interindividual and inter-location variability in this relationship. In addition, there is some 

uncertainty in the magnitude of the air concentrations generated using the ASPEN model for the 

NATA assessment. In a comparison to monitoring data across the country, the ASPEN-modeled 

air concentrations generally underestimated monitored concentrations (USEPA, 2006; Section on 

Comparison to Monitored Values). However, the relationship between ambient air 

concentrations and exposure concentrations (i.e., the comparison used here) is not expected to be 

affected by underestimated ambient air concentrations from the NATA assessment. Also, some 

of the exposure modeling inputs used in the NATA simulations were not specific to Pb and 

thus may introduce additional uncertainties. For example, the penetration factor, which is used 

to estimate the fraction of the pollutant in outdoor air that reaches indoor air, that was used for 

Pb in the NATA assessment is based on a study that examined the penetration of hexavalent 

chromium particles, which are generally more reactive than Pb particles (Long et al., 2004). 

C.2. SOIL 

In order to determine the soil Pb concentration used for the general urban case study, a 

survey of the literature regarding Pb concentrations in urban surface soils was undertaken. 

Information regarding the studies identified during that survey is presented in Exhibit C-5, and 

the range of soil Pb concentrations presented in these papers is shown in Exhibit C-6. Out of 

these studies, it was determined that an interim version of the National Study of Lead and 

Allergens in Housing (NSLAH) as cited in (USEPA, 2000) provided the most recent, nationally 

representative data for a generalized urban area. When compared to the regional- and stateC-6
 
 

 

 

focused studies presented in Exhibit C-5, the NSLAH goal of producing nationally representative 

information provided an advantage in the effort to develop a concentration for a generalized area. 

Relative to Succop et al. (2001), which is one of the two other national studies identified 

in the literature, NSLAH presents data that are more accurately representative across public and 

private housing compared to the Succop et al. (2001) data that focus solely on public housing. 

NSLAH also has several advantages over the other national survey, the National Survey of LeadBased Paint in Housing (NSLBPH), which is presented in USEPA (2000). As a larger and more 

recent survey, NSLAH is better able to capture current conditions across the country, and it 

utilizes the American Society for Testing and Materials (ASTM) standard E1727-95 core 

sampling protocol, a standard procedure for residential Pb sampling (USEPA, 2000). The 

NSLAH summary statistics also do not censor non-detect values as is done in NSLBPH, which 

can positively skew soil Pb concentrations. Time and resource limitations dictated the use of 

readily accessible data from the interim NSLAH rather than data from the final version of the 

report. 

The interim NSLAH surveyed 706 homes located in all 50 states and the District of 

Columbia with construction dates ranging from pre-1940 to 1998. While the surveyed homes are 

distributed throughout the United States, they are located across both urban and non-urban areas. 

Soil samples taken to a depth of one-half inch (in) were collected from five sites on each 

dwelling property between 1998 and 1999. A single soil sample was taken near the house main 

entrance, while one drip-line sample was taken from the wall containing the main entry and 

another was taken from a randomly chosen second wall. Similarly, one mid-yard sample was 

taken from the wall containing the main entry and another was taken from a randomly chosen 

second wall. The dripline samples were a composite of three core samples, while the mid-yard 

samples were a composite of up to four core samples. The interim2

 NSLAH yard-wide 

arithmetic mean soil Pb concentration, which is 198 μg of Pb per gram (g) of soil, was chosen as 

the soil Pb concentration for the general urban case study. Although NSLAH does provide data 

that are specific to child play areas in a yard, which may better represent exposures for children 

because they may spend significantly more time in these particular portions of the yard, the yardwide average soil concentrations were used because the play area samples were collected from 

only half the total sites in the study. The arithmetic average of the yard-wide average soil 

concentrations was used because it represents the expected value of the exposure concentration 

2

 The term “interim” is used here to indicate that the data comes from a version of NSLAH that predates the 

final version of the report. 

C-7
 
 

 

of a child who randomly "samples" from the underlying distribution of exposures. The average 

accounts for weights that were assigned to the samples from the various houses based on 

selection probabilities with the purpose of producing data that are nationally representative. 

There is some uncertainty associated with the use of a single average soil Pb concentration in 

that it does not capture inter-city and inter-house variability, which can be significant due to 

different historical and current land uses, housing vintages, renovation activities, and 

other more minor factors. 

C-8
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Study Citation 

Adgate et al., 1998 

Location and Sampling Scheme 

• Jersey City, New Jersey • Ten homes • Samples collected October 1994 to January 

1995 

• Soil collected from yards of 10 homes 

screened for participation in the Childhood Lead Exposure Assessment and Reduction Study (CLEARS) 

• Samples collected in bare, unvegetated 

areas of the subject child's primary outdoor activity area 

• All samples were surface soil (top 5 

centimeters [cm]) 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) 

• Geometric mean (GM): 540 parts per million 

(ppm) 

• Range: 70 to 2,080 ppm • n = 10 

Other R

• Study exam

indoor dust 

• Used ratios 

sources 

• Outdoor so

act as esseindoor dust 

• Outdoor so

about as mindoor dust 

Bornschein et al., 

1987 

• Inner-city neighborhood in Cincinnati, Ohio • Five square mile area for sampling • Exterior surface dust scrapings were taken 

from asphalt, concrete, or brick near the dwelling, or hard-packed soil devoid of vegetation 

• Eighty houses total (20th century public, 19th 

century rehabilitated, 19th century satisfactory, and 19th century deteriorated) 

• All (n=80): mean 1,360.32 ppm; 

range 76 to 54,519 ppm 

• Public (n=20): GM 247.88 ppm; 

range 7 to 812 ppm 

• Rehabilitated (n=29): GM 1,654.49 ppm; 

range 253 to 11,889 ppm 

• Satisfactory (n=9): GM 7,361.54 ppm; 

range 1,500 to 54,519 ppm 

• Deteriorated (n=22): GM 2,791.19 ppm; 

range 108 to 25,180 ppm 

• Concentrat

influenced bthe lowest cpublic hous

• Seventy-fiv

occupied by had externa>1,000 ppm 

Chirenje et al., 2004 

• Gainesville, Florida, relatively undeveloped, 

low population/traffic density, and Miami, Florida, developed, high population/traffic density 

• Locations were sampled according to land 

use characterization as residential, commercial, public parks, or public buildings. 

• Sampling depths: 0 to 20 cm from surface in 

Gainesville; 0 to 10 cm in Miami 

Miami: 

• Combined: median 98 ppm; GM 92.9 ppm; 

arithmetic mean 152 ppm; range 2.13 to 1091 ppm; 55 percent of samples were 51 to 200 ppm 

• Residential median 121 ppm (n=60) • Commercial median 146 ppm (n=60) • Public parks median 82 ppm (n=60) • Public buildings median 84 ppm (n=60) 

Gainesville: 

• Combined median 15 ppm; GM 16.4 ppm; 87 

percent of samples <50 ppm 

• Residential median 20.4 ppm (n=39) • Commercial median 19.2 ppm (n=41) • Public parks median 7.23 ppm (n=38) • Public buildings median 17.4 ppm (n=44) 

• In Miami, a

concentrati10 cm were from those 

• Concluded 

was due to activity, lesPb mobility/organic carcontent ver

• Pb patterns 

slightly diffeand Miami. 

• Residential 

generally h

C-9
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Elhelu et al., 1995 

Study Citation 

randomly from 239 unpaved front yards of homes (typically row houses) 

• Sites sampled in each of 8 political wards (30 

each, except for one) 

• Samples were taken at a depth of 15 cm 

from sites that are 1 meter (m) from each of the surveyed dwellings 

Location and Sampling Scheme 

• Washington, District of Columbia • Duplicate soil samples were collected 

Surveyed homes were an average of 

• Medians for eight wards ranged from 

53.7 ppm to 471.4 ppm 

• Seven wards had medians > 129 ppm • Four wards had medians > 221 ppm • Two wards had medians > 440 ppm • Range: 10.2 to 6015 ppm 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) 

• Authors sug

concentratiareas adjacsuggested source of P

Other R

Gasana and 

Charmorro, 2002 

•• One hundred and twenty homes in Miami, 

Florida (Little Haiti and Liberty City) 

• Samples were taken from soil as well as 

floors, windows, wells, tap water, and air 

• The presence of Pb paint was also 

investigated 

• Investigations were tailored to areas most 

utilized by children less than 6 years old 4.5 meters (m) from the road 

• n = 121 • Mean: 275 ppm • Median: 153 ppm • Range: 25 to 1612 ppm 

• The playgro

had the hig

Johnson and 

Bretsch, 2002 

• Syracuse, New York • Samples of soil were collected at 194 

locations within a 600 m by 600 m grid laid out over the City of Syracuse (residential areas, and a city-wide mix of house lots, parks and playgrounds, and street side locations emphasized) 

• At most sites, two kinds of samples were 

• Average: 80 ppm • 95 percent of the soil samples collected had 

values in the range of 20 to 800 ppm 

• Found no s

concentrati0 to 10 cm 

• No other Pb 

acquired: (1) a bulk sample of 0.5 to 1 kilogram (kg) from a single location, integrated over a 0 to 10 cm depth; and (2) a composite 0 to 1 cm surface core sample obtained from within a 1 square meter area 

summary st

Kassa et al., 2000 

• Toledo, Ohio • Sampled from January 1995 to August 1998 • One-half inch (in) coring device was used to 

collect soil samples around homes and in play areas adjacent to the home 

• All pre-1950 housing (n=145 houses) • Sampling depth not specified 

• Range: 400 to more than 5,000 ppm • 77 houses had exterior soil levels greater 

than 5,000 ppm 

• 41 houses had soil levels surrounding the 

house between 2,000 to 5,000 ppm 

• 63 surrounding play areas had 

concentrations from 400 to 2,000 ppm 

• No other Pb 

were report

C-10
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Khandler and 

Friedman, 2000 

Study Citation 

• New York City, New York • Thirty-five soil samples were collected from 

10 different parks; collected from relatively undisturbed sites 30 to 1,000 feet (ft) from highways to park roads 

Location and Sampling Scheme 

range 147 to 583 ppm 

• Forest Park: mean 502 ppm; 

range 125 to 1,040 ppm 

• Kissena Park: mean 166.54 ppm; 

range 161.82 to 175 ppm 

• Owl’s Head Park: mean 240.55 ppm; 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) 

• All parks: range 26 to 1,040 ppm • Central Park: mean 150.96 ppm; 

range 26 to 225 ppm 

• Clove Lake Park: mean 149 ppm; 

range 120.42 to 177 ppm 

• Conference House Park: mean 311.68 ppm; 

range 177.41 to 303.70 ppm 

• Prospect Park: mean 190.97 ppm; 

maximum 321.01 ppm. 

• There was 

Pb in all parenovated l

• Soils with h

of metals whighway 

Other R

Lejano and Ericson, 

2005 

• Pacoima, California (large amount of 

highways present) 

• Study occurred over a 5-month period in 

2002 

• Two hundred and ten soil samples were 

collected, from the side of the highways, schools and parks (and >100 m away as a control). 

Mean Pb levels: 

• Random: 111.0 ppm • Schools: 66.7 ppm • Parks: 51.6 ppm • San Fernando Road: 171.3 ppm • Whiteman Airport: 111.6 ppm (without 

outlier); 232.5 ppm (with outlier) 

• Interstate 5: 118.6 ppm • Interstate 118: 102.1 ppm • Interstate 210: 43.3 ppm • Riverside Park and Fort Washington 

Park: mean 272.45 ppm; range 49 to 444 ppm 

• The total an

found to be close to ma

• The study c

unexpected deposited ba long perio

Liberti and Pichtel, 

1997 

• City of Muncie in Center Township, Delaware 

County, Indiana 

• One hundred and fifty samples; 3 samples 

from each of 25 quadrants at 2 soil depths 

• Sampling depth: 0 to 5 cm and 10 to 25 cm 

from surface 

Depth of 0 to 5 cm: 

• Mean ± S.D. 203.8 ± 35.9 ppm; • range 81.1 to 466.3 ppm 

 Depth of 10 to 25 cm: 

• Mean ± S.D 172.2 ± 28.9 ppm; • range 53.9 to 344.8 ppm 

• Pb concent

higher in thto the subs

• Highest con

city center a

• The majorit

residual forrelatively im

C-11
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Study Citation 

Mielke, 1994 

Location and Sampling Scheme 

• New Orleans, Louisiana • Soil samples were collected from the surface 

2.5 cm within inner-city, mid-city, and suburban residential communities 

• Samples collected within 1 m from street, 

within 1 m of house-sides (foundations), and from open spaces (e.g., vacant land or parks far from streets) 

• n = 3,704 (sampled from 283 U.S. Census 

tracts in the city) 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) 

Inner-city 

• Foundation: median 840 ppm; 

range 8 to 69,000 ppm (n=201) 

• Streetside: median 342 ppm; 

range 4 to 9,450 ppm (n=723) 

• Open space: median 212 ppm; 

range 10 to 10,600 (n=74) 

Mid-city 

• Foundation: median 110 ppm; 

range 1 to 24,400 ppm (n=220) 

• Streetside: median 110 ppm; 

range 1 to 6,340 ppm (n=765) 

• Open space: median 40 ppm; 

range 2 to 3,960 (n=80) 

Other R

• Pb peaked 

inner-city asuburban a

• Bare soils i

residential shad the higsoils along 

• The lowest 

open areas 

Sheets et al., 2001 

• Springfield, Missouri • Nine sampling locations, including three near 

heavy-traffic streets and two more than 30 m from residential street 

• At each site, samples were collected in 1999 

at depths of 1, 8, and 15 cm and at three distances (1, 2, and 3 m) from air sample stations; same-depth samples were averaged at each site 

• Excess vegetation was removed before 

samples were collected 

Site average 107 ± 8 ppm;  range 18 ppm to 302 ppm Average concentrations for the 9 sites: 

• Depth 1 cm: 99.5 ± 73 ppm • Depth 8 cm: 104 ± 79 ppm • Depth 15 cm: 116 ± 89 ppm 

Lowest site concentrations: 

• Depth 1 cm: 18.0 ± 0.8 ppm • Depth 8 cm: 19.3 ± 13 ppm • Depth 15 cm: 20.8 ± 4.4 ppm 

Highest site concentrations: 

• Depth 1 cm: 228 ± 17 ppm • Depth 8 cm: 255 ± 5.8 ppm • Depth 15 cm: 302 ± 6.9 ppm 

• Soil Pb was 

increasing s

• Sampling lo

vegetated 

• Authors not

are relativesites 

Shinn et al., 2000 

• Chicago, Illinois • Sampled bar soil in four-block urban 

residential area and measured Pb (n=62) 

• Properties were located on either side of two 

North/South residential streets within the study area 

• Developed surface plots of Pb levels via 

kriging; analyzed patterns by reviewing historical data for potential sources 

• Sampling depth not specified • Pre-1930 housing in area 

• Overall mean 2,180 ppm; median 1,775 ppm; 

range 175 to 7,935 ppm 

• Eastern street median 2289 ppm; 

range 253 to 7,935 ppm 

• Western street median 1,263 ppm; 

range 175 to 4,158 ppm 

• Pb distribut

random dis

• Pb surface 

existing and sources witnearby stre

• Five sampli

>5,000 ppm 

C-12
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Study Citation 

Succop et al., 2001 

Location and Sampling Scheme 

• Sampling was conducted in 67 public 

housing developments nationwide (a total of 482 dwelling units and associated areas were individually sampled) 

• Data includes 1,222 soil samples • Soil samples collected from locations near 

building foundation, elsewhere in the yard, or near walkways 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) 

• Near the building foundations: median 

194 ppm 

• Near walkways: median 177 ppm • In yards: median 145 ppm • The maximum concentration, 3,900 ppm, was 

found in a foundation sample 

• For 28 housing development assessments, at 

least 1 sample greater than or equal to 400 ppm 

Other R

• No other da

Sutherland and 

Tolosa, 2001 

• Manoa basin, Oahu, Hawaii • Sampled two transects at low speed 

roadways (near park and school) out to 50 m from road 

• First sample (0 m) from road deposited 

sediment which was curbside area at edge of road 

• For each site, Pb was analyzed in topsoil (0 

to 2.5 cm) and subsoil (7.5 to 10 cm) 

• Five supplemental soil samples collected 

from grass-covered recreational field >100 m from roadway; 10 “control” locations sampled from relatively undisturbed areas 

• Park transect: max of 375 ppm (5 m from 

road); road deposited sediment 285 ppm 

• School transect: max of 200 ppm in road 

deposited sediment; all soil samples 25 to 50 ppm, out to 50 m 

• Measurements for both transects drop to 

<50 ppm within 5 to 10 m 

• Median local background soil concentrations: 

surface samples 13 ± 1; subsurface 14 ± 3 ppm 

• Authors sug

study data of metals in prolong consystems 

Sutherland et al., 

2000 

• Samples collected 78 roadside (within 2 m) 

and 10 background locations within the Manoa watershed, Oahu, Hawaii 

• For each site, Pb was analyzed in topsoil (0 

to 2.5 cm) and subsoil (7.5 to 10 cm) 

• Total Pb in roadside samples: median 56 

± 30 ppm; range 10 ppm to 4,870 ppm 

• 10th percentile: 19 ppm • 25th percentile: 34 ppm • 75th percentile: 120 ppm • 90th percentile: 170 ppm • Total Pb in background samples: median 14 

± 2 ppm 

• Same samp

as in Teich

• Appears tha

are based oSutherland concentratidepths. 

• Enrichment 

based on thanthropogePb was the enhanced m

• Enrichment 

four to five background 

C-13
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Study Citation 

Sutherland, 2000 

Location and Sampling Scheme 

• Samples collected 78 roadside and 10 

background locations within the Manoa watershed, Oahu, Hawaii 

• For each site, Pb was analyzed in topsoil (0 

to 2.5 cm) and subsoil (7.5 to 10 cm) 

• All sites had some grass cover. • Reported total Pb and HCl extractable (i.e., 

labile) Pb 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) 

• Total Pb in roadside topsoil samples: median 

58 ± 27 ppm; range 14 to 4,870 ppm 

• Total Pb in background topsoil 

samples: median: 13 ± 1 ppm; range: 10 to 22 ppm 

Other R

• Roadside la

times highe

• Subsoil con

topsoil concroadside an

Tiechman et al., 

1993 

• Alameda County, California • Soils were collected from the yards of homes 

adjacent the freeway, within a 1-mile radius 

• Sampling occurred at least 20 m away from 

the homes to control for Pb from paint 

• Nineteen subsurface samples were taken 

• Surface samples: average 567.7 ppm; 

range 195.3 ppm to 2,026.6 ppm 

• Subsurface samples: average 618.3 ppm; 

range 369.8 to 1,045.7 ppm 

• Ninety perc

from subsuexceeding t

• Soil downw

contained Pfound on thpercent 

Tong, 1990 

• Cincinnati, Ohio, roadside dusts and soils • Sixty sites (n=60) were sampled from either 

0 to 5 cm in depth or 15 to 20 cm from the surface 

• Housing in the study area were grouped into 

those built before 1950 and those built after 1960 

• Samples were taken from the edge of the 

curb closest to the roadway and 30 m from the roadway 

Street dusts and soils: 

• 0 to 5 cm: arithmetic mean 1,004.1 

± 1,007.8 ppm 

• 15 to 20 cm: arithmetic mean 1,301.0 

± 1,313.6 ppm 

Housing age before 1950: 

• 0 to 5 cm: arithmetic mean 1,256.2 

± 1,254.3 ppm 

• 15 to 20 cm: arithmetic mean 1,602.4 

± 1,563.8 ppm 

Housing age after 1960 

• 0 to 5 cm: arithmetic mean 752.0 

± 557.4 ppm 

• 15 to 20 cm: arithmetic mean 999.7 

± 744.7 ppm 

• Ranges not 

C-14
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Study Citation 

Turer and Maynard, 

2003 

Location and Sampling Scheme 

• Corpus Christi, Texas • Two sample sites in Texas were chosen 

along the highway: one in the city center with mostly automotive traffic, and the second near oil refineries with truck traffic 

• Twenty-two samples were taken along a 

transect perpendicular to the highway in Corpus Christi 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) 

• City center: range 20 (3 miles from the road 

at 32.5 cm deep) to 820 ppm (3 meters from the road at the 0 to 10 cm depth) 

• Industrial area: range 15 to 650 ppm (at 5 to 

15 cm depth) 

Other R

• Concluded 

low mobility insoluble or

• About 40 pe

vehicle exhat site 1 anremained in 

Turer et al., 2001 

• Cincinnati, Ohio; Interstate 75 (I-75) through 

city; 58 samples 

• Sampling conducted adjacent to highways 

on median between lanes (within ~50 m of road) 

• Sampling depth: 0 to 1 cm; also sampled 1 

to 5 cm 

• Range for 0 to 1 cm samples: 166 to 

942 ppm; range for 1 to 5 cm samples: 59 to 1,073 ppm 

• Some samples taken at depth of 10 to 15 cm 

contained total Pb between 1,000 to 2,000 ppm 

• Performed 

determine fhistorically currently in percent of Pstudy area 

• Removal vi

proposed aremobilizatrunoff may removal me

USEPA, 1993; 1996 

• Cincinnati, Ohio • Sampled three neighborhoods: (A) 

Pendleton; (B) Findlay, Back, Dandridge; and (C) Glencoe, Mohawk 

• Compared soil Pb concentrations before and 

after a total neighborhood Pb abatement project (Area C was abated after this study) 

• Sampled 1989 to 1992 • Sampling depth: Surface, 0 to 2 cm, 13 to 

15 cm 

• n = 8,127 soil samples 

Pre-abatement surface scrapings 

• GM (95 percentile) • Area A: 189 (1,996) ppm (n=242) • Area B: 101 (776) ppm (n=273) • Area C: 154 (1,653) ppm (n=311) 

0 to 2 cm soil samples: 

• Area A: 200 (2,659) ppm (n=195) • Area B: 103 (780) ppm (n=230) • Area C: 140 (1,200) ppm (n=224) 

13 to 15 cm soil samples: 

• Area A: 215 (1,612) ppm (n=185) • Area B: 162.4 (383) ppm (n=230) • Area C: 114 (848) ppm (n=217) 

Data analysis by U.S. EPA (2000): 

• Building: GM 233.9 ppm; 

range 7.1 to 630 ppm 

• No measura

found excesources we

• Study indica

a significanother sourc

• Bare areas: GM 220.9 ppm; 

range 5.4 to 4552 ppm 

• Play area: GM 94.6 ppm; 

range 20.0 ppm to 192 ppm 

C-15
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Study Citation 

• Baltimore, Maryland • Pre-1950 housing • Lower Park Heights and Walbrook Junction 

(control area) neighborhoods were the 

Location and Sampling Scheme 

Pre-abatement soil levels (n=57): 

• TriMean: 503.6 ± 268.2 ppm 

(TriMean= (Lower Quartile + 2*median +Upper Quartile)/4)) 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) Other R

USEPA, 1993; 1996 

sampling sites 

• Sixty-three properties were studied • Using a 15-cm soil coring device, nine 

composite samples were taken from the top 2 cm and 9 from the bottom 2 cm of the soil 

• Sampled 1988 to 1989 • Samples were taken pre and post soil 

• Range: 100 to 1,450 ppm 

Control (n=147) 

• Mean 501.3 ± 312.1 ppm 

Reported in U.S. EPA (2000): Dripline top 2 cm: GM 635.9 ppm; range 96 to 4,400 ppm Mid-yard top 2 cm: GM 287.0 ppm; 

• No measura

found excesources we

• Study indica

significant sother sourc

abatement from the foundation, mid-yard, 

and boundary line 

• Sampling depth: 0 to 2 cm and 13 to 15 cm 

from surface 

range 31 to 3,500 ppm Remote top 2 cm: GM 337.0 ppm; range 77.2 to 1850 ppm 

USEPA, 1993; 1996 

• Boston, Massachusetts • Sampled 1989 to 1991 • Preliminary sampling to determine eligibility 

consisted of measurements from 150 contaminated properties throughout the city 

• Eligible properties had at least two samples 

> 1,500 ppm at the time of preliminary testing 

• 37 houses were found eligible • Three to four composite soil samples taken 

within 2 m of the houses 

• Sampling depth: 0 to 2 cm from surface 

Study Group Results (SPI): 

• Pre-abatement (n=35): • Median: 2,413 ppm • Arithmetic mean: 2,625 ppm 

• Children's P

areas wherwere high (abatement was control

C-16
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Study Citation 

USEPA, 2000; 

Westat Inc., 1995; 

1996 

Location and Sampling Scheme 

• National Survey of Lead-Based Paint in 

Housing surveyed randomly selected 381 housing units (284 private and 97 public houses) in 30 counties across the United States 

• Three core soil samples were taken from 

each dwelling unit: one near the main entrance, one along the drip line (soil next to the housing until), and one at a remote location away from the building, but still on property 

• Sampling 1989 to 1990 • Housing construction years included pre1940 to 1979 • Sampling depth: 10 cm 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) 

Data from Westat Inc. (1996): Private housing 

• All locations (n=762): mean 

324 ppm; median 54 ppm; 1 to 22,974 ppm 

• Entrance (n=260): arithmetic mean 327 ppm; 

GM 85 ppm; median 64.8 ppm; range 2.84 to 6,829 

• Dripline (n=249): arithmetic mean 448 ppm; 

GM 74 ppm; median 56.2 ppm; range 1.16 to 22,974 ppm 

• Remote (n=253): arithmetic mean 204 ppm; 

GM 46; median 46.7 ppm;  range 1.45 to 6,951 ppm 

Analysis by U.S. EPA (2000) 

• Yard-wide average: arithmetic mean 

235 ppm; GM 61.9 ppm; median 49.2 ppm; range 4.63 to 7,030 ppm 

Other R

• Study found 

statistical pprivate and housing un

• Additional s

U.S. Censubetween buCensus regpaint, and a

• Degree of u

of Pb paint predictors f

• In the U.S. 

households used to calc

• Yard-wide a

of (1) the avsample resresults for tsamples 

C-17
 
Exhibit C-5. Selected Data – Pb in Urban Surface Soil and Related Urban Measuremen

Study Citation 

USEPA, 2000; 

Westat Inc., 2002 

Location and Sampling Scheme 

• National Survey of Lead and Allergens in 

Housing surveyed 831 homes in all 50 states (preliminary data evaluated by U.S. EPA (2000) included 706 houses in all 50 states) 

• 375 of the homes also had children's play 

area bare soil tested 

• Sampled 1998 to 1999 • A single soil sample was taken near the 

house main entrance, one drip-line sample was taken from the wall containing the main entry and another was taken from a randomly chosen second wall, and one midyard sample was taken from the wall containing the main entry and another from a random second wall. The dripline samples were a composite of three core samples, while the mid-yard samples were a composite of up to four samples. 

• Housing construction years were pre-1940 to 

1998 

• Sampling depth: top 0.5 in 

Reported Pb Concentration(s) 

(total Pb unless otherwise specified) 

Results for five sampling sites at all 831 homes: 

• Main Entry (n=707): arithmetic mean 

234.8 ppm; GM 43.3 ppm; median 40.2 ppm 

• Wall 1 Dripline (n=704): arithmetic mean 

242.9 ppm; GM 44.5 ppm; median 38.8 ppm 

• Wall 2 Dripline (n=704): arithmetic mean 

404.1 ppm; GM 49.0 ppm; median 40.3 ppm 

• Wall 1 Mid-yard (n=723): arithmetic mean 

87.3 ppm; GM 28.1 ppm; median 27.0 ppm 

• Wall 2 Mid-yard (n=728): arithmetic mean 

123.4 ppm; GM 29.9 ppm; median 29.1 ppm 

Results for housing where children's play area bare soil was sampled: 

• 51 percent > 20 ppm • 30 percent > 59 ppm • 5 percent > 400 ppm • 2 percent > 2,000 ppm 

Analysis of interim data by U.S. EPA (2000): 

• Yard-wide average with no adjustment to 

non-detects: arithmetic mean 200 ppm; GM 53.0 ppm; median 41.4 ppm; range 0 to 9,270 ppm 

Other R

• Only house

used to calc

• Yard-wide a

of (1) the avsample resresults for tsamples 

• Yard-wide a

prior to 194(arithmetic 297 ppm bano adjustm

• The highes

generally fothe lowest i

C-18
 
 

Chirenje et al., 2004 

USEPA, 2000 (Yard-wide Interim NSLAH) Elhelu et al., 1995 Sutherland, 2000 USEPA, 2000 (Yard-wide NSLP) Johnson and Bretsch, 2002 Chirenje et al., 2004 Sheets et al., 2001 Kassa et al., 2000 Liberti and Pichtel, 1 997 USEPA, 2000 (Cincinnati, OH) Gasana and Chamo rrow, 2002 USEPA, 2 000 (Baltimore, MD) Adgate et al., 1 998 Tong, 1990 Bornschein et al., 1987 Sh

Exhibit C-6. Pb Concentrations Measured in Urban Soils in the United States 

ma x: 9,270 6,015 4,870 7,030 5,000 4,552 3,500 5

0 

500 

1000 1500 2000 2500 3000 3500 

Total Measured Pb in Outdoor Surface Soil (ppm)

Study (Author, Year) 

a This chart is intended to convey general levels of total Pb measured in urban soils for which means or medians were reported. Fo

vertical line represents the approximate range of total Pb reported in upper surface soil samples. The square mark or box represent

Pb for all samples in that study; the geometric (preferred) or arithmetic mean was reported in the study. In some cases, only the meconcentrations for selected study locations or sample categories were reported; these cases are represented by a box. R efer to cited 

details on individual studies. 

C-19 
 

 

C.3. INDOOR DUST 

For the general urban case study, both the hybrid model and the air-only regressionbased model (described in Appendix G) are used to generate separate indoor dust Pb 

concentration estimates. In addition, the fraction of Pb originating from recent air and other 

sources (i.e., contributions from indoor paint, outdoor soil/dust, and additional sources including 

historical air) is estimated in both cases. 

For the hybrid model, the fraction of Pb mass from recent air-derived sources is 

calculated by dividing the hybrid model air-dust Pb loading by the total Pb loading; this fraction 

is then applied to the total Pb concentration to derive the indoor dust (recent air) portion of the 

indoor dust Pb concentration. The indoor dust (other) portion is the remainder of the indoor dust 

Pb concentration. The indoor dust (recent air), indoor dust (other), and indoor dust (total) 

estimates for the hybrid model are provided in Exhibit C-7 below. 

Exhibit C-7. Estimated Annual Indoor Dust Pb Concentrations from the Hybrid 

Mechanistic-Empirical Model for the Air Quality Scenarios 

Air Quality Scenario 

Current conditions (95th percentile) 

Indoor Dust Pb 

Sources 

Recent air 

Dust Pb 

Concentration 

(μg/g) 

180 

Other 17 

Total 198 

Current conditions (mean) 

Recent air 122 

Other 24 

Total 146 

Current NAAQS (1.5 μg/m3

, max quarterly average) 

Recent air 418 

Other 8 

Total 426 

Alternative NAAQS 1 (0.2 μg/m3

, max quarterly average) 

Recent air 149 

Other 21 

Total 169 

Alternative NAAQS 2 (0.5 μg/m3

, max monthly average) 

Recent air 189 

Other 17 

Total 206 

Alternative NAAQS 3 (0.2 μg/m3

, max monthly average) 

Recent air 114 

Other 25 

Total 140 

Alternative NAAQS 4 (0.05 μg/m3

, max monthly average) 

Recent air 47 

Other 41 

Total 88 

C-20
 
 

 

For the air-only regression-based model, the indoor dust (other) portion of the indoor dust 

Pb concentration estimate is the intercept (60 μg/g) and the indoor dust (recent air) portion is the 

slope of the function multiplied by the ambient air concentration. The indoor dust (recent air), 

indoor dust (other), and indoor dust (total) estimates for the air-only regression-based model are 

provided in Exhibit C-8 below. 

Exhibit C-8. Estimated Annual Indoor Dust Pb Concentrations from the Air-Only 

Regression-Based Model for the Air Quality Scenarios 

Air Quality Scenario 

Current conditions (95th percentile) 

Indoor Dust Pb 

Sources 

Recent air 

Dust Pb 

Concentration 

(μg/g) 

97 

Other 60 

Total 157 

Recent air 47 

Current conditions (mean) Other 60 

Total 107 

Recent air 506 

Current NAAQS (1.5 μg/m3

, max quarterly average) Other 60 

Total 566 

Alternative NAAQS 1 (0.2 μg/m3

, max quarterly average) 

Recent air 68 

Other 60 

Total 128 

Alternative NAAQS 2 (0.5 μg/m3

, max monthly average) 

Recent air 106 

Other 60 

Total 166 

Alternative NAAQS 3 (0.2 μg/m3

, max monthly average) 

Recent air 42 

Other 60 

Total 102 

Alternative NAAQS 4 (0.05 μg/m3

, max monthly average) 

Recent air 11 

Other 60 

Total 71 

C-21
 
 

 

 

 

 

 

 

 

REFERENCES 

Adgate, J. L.; Rhoads, G. G.; Lioy, P. J. (1998) The Use of Isotope Ratios to Apportion Sources of Lead in Jersey 

City, NJ, House Dust Wipe Samples. Sci. Total Environ. 221(2-3): 171-180. 

Bornschein, R. L.; Succop, P. A.; Krafft, K. M.; Clark, C. S.; Peace, B.; Hammond, P. B. (1987) Exterior Surface 

Dust Lead, Interior House Dust Lead and Childhood Lead Exposure in an Urban Environment. Vol. 20: 

322-332. Columbia, MO: Trace Substances in Environmental Health. Proceedings of University of 

Missouri's 20th Annual Conference. Conference in Trace Metals in Environmental Health. 

Chirenje, T.; Ma, L. Q.; Reeves, M.; Szulczewski, M. (2004) Lead Distribution in Near-Surface Soils of Two 

Florida Cities: Gainesville and Miami. Geoderma. 119(2): 113-120. 

Elhelu, M. A.; Caldwell, D. T.; Hirpassa, W. D. (1995) Lead in Inner-City Soil and Its Possible Contribution to 

Children's Blood Lead. Arch. Environ. Health. 50(2): 165-169. 

Gasana, J.and Chamorro, A. (2002) Environmental Lead Contamination in Miami Inner-City Area. J. Expo. Anal. 

Environ. Epidemiol. 12(4): 265-272. 

Johnson, D. L. and Bretsch, . J. K. (2002) Soil Lead and Children's Blood Lead Levels in Syracuse, NY, USA. 

Environmental Geochemistry and Health. 24: 375-385. 

Kassa, H.; Bisesi, M. S.; Khuder, S. A.; Park, P. C. (2000) Assessment of a Lead Management Program for InnerCity Children. Environmental Health. 15-19. 

Khandker, E. H.and Friedman, G. M. (2000) Geochemical Study of Trace Metals in Soils of New York City Parks. 

Nothern Geology and Environmental Sciences. 22: 50-88. 

Lejano, R. P.and Ericson, J. E. (2005) Tragedy of the Temporal Commons: Soil-Bound Lead and the Anachronicity 

of Risk. Journal of Environmental Planning and Management. 48(2): 301-320. 

Liberti, M.and Pichtel, J. (1997) Spatial Distribution of Trace Metals in Delaware County, Indiana, Surface Soils. 

Proceedings of the Indiana Academy of Science. 106: 233-245. 

Long, T.; Johnson, T.; Laurenson, J.; Rosenbaum, A. (2004) Development of Penetration and Proximity 

Microenvironment Factor Distributions for the HAPEM5 in Support of the 1999 National-Scale Air Toxics 

Assessment (NATA). Memorandum prepared for Ted Palma, U.S. EPA, Office of Air Quality Planning and 

Standards (OAQPS); April 5. 

Mielke, H. W. (1994) Lead in New Orleans Soils: New Images of an Urban Environment. Environmental 

Geochemistry and Health. 16: 123-128. 

Sheets, R. W.; Kryger, J. R.; Biagioni, R. N.; Probst, S.; Boyer, R.; Barke, K. (2001) Relationship Between Soil 

Lead and Airborne Lead Concentrations at Springfield, Missouri, USA. Science of Total Environment. 271: 

79-85. 

Shinn, N. J.; Bing-Canar, J.; Cailas, M.; Peneff, N.; Binns, H. J. (2000) Determination of Spatial Continuity of Soil 

Lead Levels in an Urban Residential Neighborhood. Environmental Research. 82(Section A): 46-52. 

Succop, P.; Clark, S.; Tseng, C.-Y.; Bornschein, R.; Chen, M. (2001) Evaluation of Public Housing Lead Risk 

Assessment Data. Environmental Geochemistry and Health. 23: 1-15. 

Sutherland, R. A. (2000) Depth Variation in Copper, Lead, and Zinc Concentrations and Mass Enrichment Ratios in 

Soils of an Urban Watershed. J. Environ. Qual. 29: 1414-1422. 

C-22
 
 

 

 

 

 

 

 

 

 

 

Sutherland, R. A.and Tolosa, C. A. (2001) Variation in Total and Extractable Elements With Distance From Roads 

in an Urban Watershed, Honolulu, Hawaii. Water, Air, and Soil Pollution. 127(4): 315-338. 

Sutherland, R. A.; Tolosa, C. A.; Tack, F. M. G.; Verloo, M. G. (2000) Characterization of Selected Element 

Concentrations and Enrichment Ratios in Background and Anthropogenically Impacted Roadside Areas. 

Archives of Environmental Contamination and Toxicology. 38: 428-438. 

Teichman, J.; Coltrin, D.; Prouty, K.; Bir, W. A. (1993) A Survey of Lead Contamination in Soil Along Interstate 

880, Alameda County, CA. American Industrial Hygiene Association Journal. 54(9): 557-559. 

Tong, S. T. (1990) Roadside Dusts and Soils Contamination in Cincinnati, Ohio. Environmental Management. 

14(1): 107-114. 

Turer, D.; Maynard, J. B.; Sansalone, J. J. (2001) Heavy Metal Contamination in Soils of Urban Highways: 

Comparison Between Runoff and Soil Concentrations at Cincinnati, Ohio. Water, Air, and Soil Pollution. 

132: 293-314. 

Turer, D. G.and Maynard, J. B. (2003) Heavy Metal Contamination in Highway Soils. Comparison of Corpus 

Christi, Texas and Cincinnati, Ohio Shows Organic Matter Is Key to Mobility. Clean Technologies and 

Environmental Policy. 4(4): 235-245. 

U.S. Environmental Protection Agency (USEPA). (1993a) Urban Soil Lead Abatement Demonstration Project. Vol. 

IV. Cincinnati Report. EPA/600/AP-93/001d. Washington, DC: Office of Research and Development; July. 

U.S. Environmental Protection Agency (USEPA). (1993b) Urban Soil Lead Abatement Demonstration Project. 

Volume II. Part 2. Boston Report. EPA/600/AP-93/001b. Research Triangle Park, NC: Office of Research 

and Development; July. 

U.S. Environmental Protection Agency (USEPA). (1993c) Urban Soil Lead Abatement Demonstration Project. 

Volume III. Part 1. Baltimore Report. EPA/600/AP-93/001c. Research Triangle Park, NC: Office of 

Research and Development; July. Available online at: http://www.epa.gov/oppt/lead/pubs/es_con.htm. 

U.S. Environmental Protection Agency (USEPA). (1996) Urban Soil Lead Abatement Demonstration Project. 

Volume 1: EPA Integrated Report. EPA/600/P-93/001aF. Washington, DC: Office of Research and 

Development; April. 

U.S. Environmental Protection Agency (USEPA). (2000) Hazard Standard Risk Analysis Supplement - TSCA 

Section 403: Risk Analysis to Support Standards to Lead in Paint, Dust, and Soil: Supplemental Report. 

EPA 747-R-00-004. Available online at: http://www.epa.gov/lead/pubs/403risksupp.htm. 

U.S. Environmental Protection Agency (USEPA). (2006a) 1999 National-Scale Air Toxics Assessment. Available 

online at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html. 

U.S. Environmental Protection Agency (USEPA). (2007) Air Quality System (AQS) Database. Available online at: 

http://www.epa.gov/ttn/airs/airsaqs/aqsweb/aqswebwarning.htm. 

Westat Inc. (1995) Report on the National Survey of Lead-Based Paint in Housing, Base Report. US Environmental 

Protection Agency (USEPA) and HUD; June. 

Westat Inc. (1996) Distributions of Soil Lead in the Nation's Housing Stock. EPA 747-R-96-003. Washington, D.C.: 

Office of Pollution Prevention and Toxics; May. 

Westat Inc. (2002) National Survey of Lead and Allergens in Housing. Volume I: Analysis of Lead Hazards. Final 

Report. Revision 7.1. Washington, D.C.: Office of Health Homes and Lead Hazard Control, U.S. 

Department of Housing and Urban Development. 

C-23
 

Appendix D: Media Concentrations for the Primary Pb Smelter Case Study 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

 

Table of Contents 

Table of Contents .................................................................................................................D-i 

List of Exhibits ....................................................................................................................D-ii 

List of Attachments............................................................................................................D-iii 

D. MEDIA CONCENTRATIONS FOR THE PRIMARY PB SMELTER CASE 

STUDY................................................................................................................................. D-1
 

D.1. SPATIAL TEMPLATE ............................................................................................... D-2 

D.2. AIR............................................................................................................................... D-6 

D.2.1. Air Dispersion Modeling ................................................................................. D-6 

D.2.2. Air Concentrations...........................................................................................D-7 

D.2.3. Inhalation Exposure Concentrations..............................................................D-11 

D.2.4. Air Modeling Performance Evaluation..........................................................D-13 

D.3. OUTDOOR SURFACE SOIL ................................................................................... D-14 

D.4. INDOOR DUST......................................................................................................... D-16 

REFERENCES ................................................................................................................... D-20 

D-i 
List of Exhibits 

Exhibit D-1. Ratios of the Maximum-to-Mean Block-level Annual Average Air Concentrations 

in each Block Group ............................................................................................. D-4 

Exhibit D-2. Spatial Template for the Primary Pb Smelter Case Study (Including U.S. Census 

Exhibit D-3. Annual Average Population-weighted Air Concentrations for the Primary Pb 

Exhibit D-4. Wind Rose of Meteorological Data used for Primary Pb Smelter Case Study 

Exhibit D-5. Annual Average Air Concentration Isopleths for the Current NAAQS Scenario 

Exhibit D-6. Ratios of Inhalation Exposure Concentrations to Ambient Air Concentrations 

Exhibit D-7. Annual Average Population-weighted Inhalation Exposure Concentrations 

Exhibit D-9. Modeled Indoor Dust Pb Concentrations for the Primary Pb Smelter Case 

Exhibit D-10. Annual Average Population-weighted Indoor Pb Dust Exposure 

Blocks and Block Groups with Children Less than 7 Years of Age) ................... D-5 

Smelter Case Study............................................................................................... D-8 

(Direction from which Wind is Blowing)............................................................. D-9 

for the Primary Pb Smelter Case Study .............................................................. D-10 

from the NATA National-scale Air Toxics Assessment .................................... D-12 

for the Primary Pb Smelter Case Study .............................................................. D-12 

Exhibit D-8. Average Pre-excavation Soil Measurements and Best-fit Trend Line................ D-15 

Study................................................................................................................... D-17 

Concentrations for the Primary Pb Smelter Case Study ................................... D-19 

D-ii 
List of Attachments 

Attachment D-1. Emission Parameters for Point Sources for the Primary Pb Smelter 

Case Study.................................................................................................... D-22 

Attachment D-2. Emission Parameters for Volume Sources for the Primary Pb Smelter 

Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter 

Attachment D-4. Hourly Emissions Factors by Emission Point for the Primary Pb Smelter 

Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter 

Attachment D-6. Building Downwash Parameters for the Primary Pb Smelter 

Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the 

Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 maxAttachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3

 maxAttachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3

 max
 

Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max­

 Case Study.................................................................................................... D-23 

Case Study ................................................................................................... D-24 

 Case Study ................................................................................................... D-33 

Case Study .................................................................................................. D-37 

 Case Study.................................................................................................... D-45 

Primary Pb Smelter Case Study .................................................................. D-48 

monthly) Scenario for the Primary Pb Smelter Case Study......................... D-59 

monthly) Scenario for the Primary Pb Smelter Case Study......................... D-70 

monthly) Scenario for the Primary Pb Smelter Case Study....................... D-81 

quarterly) Scenario for the Primary Pb Smelter Case Study...................... D-92 

D-iii 

D. MEDIA CONCENTRATIONS FOR THE PRIMARY PB SMELTER 

CASE STUDY 

This appendix discusses methods, results, limitations, and uncertainties associated with 

the estimation of environmental media concentrations for the primary lead (Pb) smelter case 

study included in the human exposure and health risk assessments. These media concentrations 

were estimated using a combination of modeling approaches and monitoring data. Estimates 

presented in this appendix are specified with regard to number of decimal places, which results 

in various numbers of implied significant figures. This is not intended to convey greater 

precision for some estimates than others; it is simply an expedient and initial result of the 

software used for the calculation. Greater attention is given to significant figures in the 

presentation of estimates in the main body of the report. 

For this analysis, five air quality scenarios were evaluated, including meeting the current 

National Ambient Air Quality Standard (NAAQS) and four possible alternative standards, as 

described below: 

•	 Meeting an air concentration of 1.5 microgram per cubic meter (μg/m3), based on a 

maximum calendar quarter average (i.e., current NAAQS scenario); 

•	 Meeting an air concentration of 0.2 μg/m3, based on a maximum calendar quarter 

averaging period; 

•	 Meeting an air concentration of 0.5 μg/m3, based on a maximum monthly averaging 

period; 

•	 Meeting an air concentration of 0.2 μg/m3

, based on a maximum monthly averaging 

period; and 

•	 Meeting an air concentration of 0.05 μg/m3

, based on a maximum monthly averaging 

period. 

This analysis focused on three primary environmental media and their exposure 

concentrations: ambient air, indoor dust, and outdoor soil/dust. Estimated inhalation and indoor 

dust exposure concentrations differed for the five air quality scenarios because they both were 

based, at least in part, on the estimated ambient air concentrations, which varied across scenarios. 

The outdoor soil/dust exposure concentrations estimated for the current NAAQS scenario were 

also used for the alternative NAAQS scenarios (i.e., it was assumed that reductions in ambient 

air concentrations associated with the alternative NAAQS scenarios did not have a significant 

D-1 
 

 

 

 

 

 

 

impact on soil concentrations).1 The approaches used and estimated exposure concentrations for 

air, outdoor soil, and indoor dust are described in the remainder of this appendix. 

D.1. SPATIAL TEMPLATE 

The outer boundary of the study area for the primary Pb smelter case study was set to 

approximately 10 kilometers (km), which was expected to capture the population experiencing 

the most significant impacts of the facility’s emissions, while recognizing limitations of the 

modeling tools, demands of associated ("downstream") analyses, and available time and 

2 resources.

The 29 U.S. Census block groups that are predominantly within 10 km of the facility 

were selected to define the spatial extent of the study area (U.S. Census Bureau, 2005). Because 

of the irregular shape of block groups, not all of the block groups that overlap with the 10-km 

radius around the facility were included, and some that were included have portions falling 

outside this 10-km radius. Block groups falling along the 10-km radius were generally included 

if most of their area fell within the radius. All U.S. Census block centroids within these 29 block 

groups were included as receptors in the air dispersion model runs (i.e., air model results were 

output for each U.S. Census block centroid). There are 1,321 U.S. Census blocks within these 

block groups. Of these U.S. Census blocks, 14 were located either within facility boundaries or 

adjacent to the facility in the Mississippi River.3

 These 14 U.S. Census blocks were removed 

from the assessment. A total of 1,307 U.S. Census block centroids were included as receptors in 

the air dispersion model simulations, including blocks within the study area with zero 

population. The U.S. Census blocks with no children less than 7 years of age were included in 

1

 Derivation of outdoor soil/dust estimates for the current NAAQS scenario is further discussed in 

Section D.3. 

2 Previous analyses of modeled air concentrations of Pb from the primary Pb smelter performed using the 

pilot assessment scenario indicated a potential contribution from the smelter to air concentrations at distances of 

more than 50 km (ICF, 2006). Within 10 km, however, air Pb concentrations estimated in the pilot assessment were 

reduced by 0.43 percent for U.S. Census blocks and block groups with at least one child under 7 years of age from 

the highest concentrations predicted outside the primary Pb smelter property. Although this assessment utilized a 

different set of emissions data than the pilot assessment, the overall trends in air Pb concentrations are expected to 

be similar. See Appendix M for a discussion of sources of uncertainty associated with this assessment. 

3

 All territory in the United States is delineated into U.S. Census blocks (U.S. Census Bureau, 2005). 

Therefore, large water bodies like the Mississippi River often contain U.S. Census blocks, although there is no 

population associated with these blocks. 

D-2 
the modeling simulations to aid in understanding the patterns of air concentrations in the study 

area. These locations, however, were not included in the exposure assessment and are not 

included in exhibits summarizing modeling results (with the exception of isopleths diagrams), 

because the exposure assessment focuses on the effects of Pb in children less than 7 years of age. 

The elevation of each block centroid was generated using U.S. Geological Survey (USGS) 

digital elevation model files (U.S. Department of the Interior U.S. Geological Survey, 1993) and 

the AERMAP preprocessor model (USEPA, 2004). 

For purposes of efficiency (i.e., to provide sufficient spatial resolution to capture 

significant concentration gradients, while minimizing the number of computations required for 

estimating other media concentrations, blood Pb (PbB) levels, and associated risks), the spatial 

template for primary Pb smelter case study is a combination of block-level results in areas of 

larger air Pb concentration gradients and block group-level results in areas of more gradual 

changes in air Pb concentrations. The spatial template used here was developed in the pilot 

assessment. In the pilot assessment, the annual average concentration in each block group was 

calculated by spatially weighting estimates derived at the block level from the pilot analysis 

modeling scenario. The area of each block was obtained from the U.S. Census Bureau (2005). 

The decision of whether to include the block or block group in the spatial template was made by 

considering the range of block-level concentrations within a block group (see Exhibit D-1). If 

the ratio of the maximum block-level air concentration in the block group to the mean annual 

average air concentration in the block group was greater than 2.0, the individual U.S. Census 

blocks in the block group were included. Otherwise, the full block group was included. This 

method generally resulted in assessment at the block level near the facility. Some U.S. Census 

blocks located far from the facility that fall within very large block groups were also evaluated 

individually. A total of 22 U.S. Census block groups and 115 U.S. Census blocks (all with at 

least one child less than 7 years of age) comprise the spatial template for the primary Pb smelter 

case study (see Exhibit D-2). 

D-3 
Exhibit D-1. Ratios of the Maximum-to-Mean Block-level Annual Average 

Air Concentrations in each Block Group 

D-4 
Exhibit D-2. Spatial Template for the Primary Pb Smelter Case Study (Including U.S. 

Census Blocks and Block Groups with Children Less than 7 Years of Age) 

D-5 
 

 

 

D.2. AIR 

The air concentrations of Pb resulting from emissions at the primary Pb smelter facility 

were estimated using the ISC-PRIME air dispersion model (USEPA, 1995; Schulman et al., 

1997), as described in Section D.2.1. The outputs from this modeling were processed to estimate 

air concentrations for each air quality scenario as described in Section D.2.2. These air 

concentrations were used to estimate inhalation exposure concentrations (as described in 

Section D.2.3) and as inputs to the calculation of indoor dust concentrations (as described in 

Section D.4). Model performance analysis is described in Section D.2.4. 

D.2.1. Air Dispersion Modeling 

Air dispersion modeling for this case study (for the current NAAQS scenario) relied on 

the model and the emissions and source parameters used in developing the 2007 proposed 

revision to the State Implementation Plan for the primary Pb smelter (Missouri Department of 

Natural Resources (MDNR), 2007; 2007). The air dispersion model ISC-PRIME was used for 

the air quality modeling. The meteorological data used for the model simulations included 24 

consecutive months (April 1, 1997, to March 31, 1999) of on-site data.4

 These meteorological 

data were also used for the analysis of model performance submitted with the proposed revision 

to the SIP (MDNR, 2007). Emissions, release parameters, particle size parameters, and building 

downwash inputs were all provided by U.S. EPA Region 7 in the form of an input runstream file 

(USEPA, 2007). All of the inputs used in this modeling are presented in Attachments D-1 

through D-6. Monthly average air concentrations were output from the dispersion model at each 

receptor (i.e., block or block group, as described in Section D.1) and total suspended particulate 

matter (TSP) monitor location (see Appendix B). Use of these air concentrations in the current 

NAAQS scenario, and derivation of air concentrations for the alternative NAAQS scenarios is 

described in Section D.2.2. 

4 Although air quality modeling guidance generally suggests that five consecutive years of meteorological 

data be used for modeling annual average air concentrations, in the primary Pb smelter case study, 24 consecutive 

months of on-site meteorological data were used for modeling Pb concentrations at receptor locations. The use of 

on-site meteorological data, even with coverage of less than five years, was considered preferable to the use of 

meteorological data from the nearest National Weather Service station, which is located in St Louis, Missouri 

approximately 31 miles (50 km) from the facility, because they are much more likely to capture local meteorological 

conditions. Note, however, that the use of two years of meteorological data limits the ability of this assessment to 

fully capture year-to-year variability in meteorological conditions. 

D-6 
D.2.2. Air Concentrations 

The monthly air concentration model results calculated at the centroid of each U.S. 

Census block group, block, and monitor receptor point for the 137 U.S. Census blocks or block 

groups with at least one child less than 7 years of age, generated as described in Section D.2.1, 

were averaged over both years of the modeling period to generate one set of representative 

annual average air concentrations for the current NAAQS scenario. 

To confirm that the estimated air concentrations for this scenario were at or below the 

current NAAQS standard, the concentrations were also averaged quarterly and compared to the 

current NAAQS (1.5 μg/m3

, max quarterly average). None of the modeled quarterly averaged 

Pb air concentrations exceeded the current NAAQS; therefore, annual averages for the current 

NAAQS scenario were calculated directly from the model results (see Exhibit D-3). 

Monthly and quarterly averages were also compared to four alternative NAAQS 

scenarios including: maximum monthly average alternative scenarios of 0.5 μg/m3

, 0.2 μg/m3, 

and 0.05 μg/m3

; and one maximum quarterly alternative scenario of 0.2 μg/m3. For these 

alternative scenarios there were several modeled U.S. Census blocks which did not meet the 

alternative NAAQS, in which case a ratio was developed from the maximum monthly or 

quarterly averaged value and the alternative NAAQS. This roll-back factor was then applied to 

scale down the concentrations at each of the 1,307 receptors and a new combined annual average 

was calculated from the scaled data set (i.e., a proportional rollback of all modeled locations was 

implemented). These 1,307 receptors were narrowed down to the 137 U.S. Census blocks and 

block groups included in the exposure assessment by (1) spatially weighting and averaging 

results for all blocks within each block group selected (see Section D.1) and (2) removing all 

blocks with no children less than 7 years of age. 

The air concentration estimates modeled for the 137 U.S. Census blocks and block 

groups with at least one child less than 7 years of age are presented in Attachments D-7 through 

D-11 for all scenarios. Exhibit D-3 presents the distribution of annual average populationweighted Pb air concentrations associated with the five NAAQS scenarios. Population-weighted 

ambient air concentrations were calculated by first sorting the block/block groups in increasing 

ambient air concentration order. Then the percentage of children living in block/block groups 

less than or equal to the maximum ambient air concentration of those block/block groups was 

calculated. The ambient air concentration of the block/block group associated with the 

minimum, 5th, median, 95th, and maximum percentile was selected. 

A wind rose created from 24 consecutive months (April 1, 1997 to March 31, 1999) of 

on-site meteorological data at the primary Pb smelter shows that the predominant direction in 

D-7 
which the wind is blowing from is the west and south (see Exhibit D-4). Exhibit D-5 shows the 

isopleths of the block-level modeled air concentration results for all 1,307 U.S. Census blocks 

modeled using the air dispersion model. 

Exhibit D-3. Annual Average Population-weighted Air Concentrations for the 

Primary Pb Smelter Case Study 

Average Annual Population-weighted Pb Air Concentration (μg/m3) a 

Alternative NAAQS Scenario 

Statistic b 

Current 

NAAQS 

Scenario 1 

0.2 μg/m3 , 

Max Quarterly 

2 

0.5 μg/m3 , 

Max Monthly 

3 

0.2 μg/m3 , 

Max Monthly 

4 

0.05 μg/m3 , 

Max Monthly 

Maximum 0.740 0.161 0.326 0.130 0.033 

95th Percentile 0.153 0.033 0.067 0.027 0.007 

Median 0.042 0.009 0.019 0.007 0.002 

5th Percentile 0.015 0.003 0.007 0.003 0.001 

Minimum 0.006 0.001 0.003 0.001 < 0.001 

a The 137 U.S. Census blocks and block groups with at least one child less than 7 years of age were used to
 

create this summary. 

b

 The statistic (e.g., 95th percentile, median) may not be at the same location for each of the data results 

presented here.
 

D-8 
 

Exhibit D-4. Wind Rose of Meteorological Data used for Primary Pb Smelter Case Study 

(Direction from which Wind is Blowing) 

Note: Wind rose from 24 consecutive months (April 1, 1997 to March 31, 1999) of on-site meteorological 

data at the primary Pb smelter (17,520 hours of data). 

D-9 
 

Exhibit D-5. Annual Average Air Concentration Isopleths for the Current NAAQS 

Scenario for the Primary Pb Smelter Case Study 

D-10 
 

D.2.3. Inhalation Exposure Concentrations 

Inhalation exposure concentrations of Pb were estimated for the population of interest 

(young children) from the estimated ambient air concentrations using age group- and locationspecific relationships for Pb developed from modeling the U.S. EPA 1999 National-scale Air 

Toxics Assessment (USEPA, 2006), one of the U.S. EPA’s National Air Toxics Assessment 

(NATA) activities. These relationships account for air concentration differences indoors and 

outdoors, as well as for mobility or time spent in different locations (e.g., outdoors at home, 

inside at home, etc.) for the population of interest. 

The U.S. EPA 1999 National-scale Air Toxics Assessment produced air concentrations of 

Pb (and other hazardous air pollutants [HAPs]) for each U.S. Census tract (using the Assessment 

System for Population Exposure Nationwide model [ASPEN]), and corresponding exposure 

concentrations of Pb for each of five age-groups at each U.S. Census tract (using the Hazardous 

Air Pollutant Exposure Model [HAPEM]). The relationships (or ratios) between ambient air Pb 

concentration and Pb inhalation exposure concentration from the U.S. EPA’s 1999 Nationalscale Air Toxics Assessment for the 0 to 4 age group (the closest age group for which outputs are 

available to the age group of interest for this assessment) ranged from 0.37 to 0.42 for the U.S. 

Census tracts within the study area for the primary Pb smelter case study. The ratios are 

presented in Exhibit D-6. It was assumed that these U.S. Census tract specific ratios provided a 

reasonable approximation of the ratios for the U.S. Census blocks and block groups contained 

within each tract. 

The resulting distribution of annual average inhalation exposure concentrations 

associated with the five air quality scenarios is presented in Exhibit D-7. Population-weighted 

annual average inhalation exposure concentrations were calculated by first sorting the 

block/block groups in increasing inhalation exposure concentration order. Then the percentage 

of children living in block/block groups less than or equal to the maximum annual average 

inhalation exposure concentration of those block/block groups was calculated. The annual 

average inhalation exposure concentration of the block/block group associated with the 

minimum, 5th, median, 95th, and maximum percentile was selected. 

Use of ratios for the 0 to 4 age group (rather than for 0 to 7) contributes some uncertainty 

in the estimate of children’s inhalation exposure concentrations. In addition, there is some 

uncertainty in the magnitude of the air concentrations generated using the ASPEN model for the 

U.S. EPA’s 1999 National-scale Air Toxics Assessment (USEPA, 2006). In a comparison to 

monitoring data across the country, the ASPEN-modeled air concentrations generally 

underestimated monitored concentrations (USEPA, 2006; Section on Comparison to Monitored 

D-11 
 

 

Values). However, the relationship between ambient air concentrations and inhalation exposure 

concentrations (i.e., the comparison used here) is not expected to be affected by underestimated 

ambient air concentrations from the U.S. EPA’s 1999 National-scale Air Toxics Assessment (see 

Exhibit D-6. In addition, some of the exposure modeling inputs used in the NATA simulations 

were not specific to Pb and thus may introduce additional uncertainties. For example, the 

penetration factor, which is used to estimate the fraction of the pollutant in outdoor air that 

reaches indoor air, used for Pb in the NATA assessment is based on a study that examined the 

penetration of hexavalent chromium particles, which are generally more reactive than Pb 

particles (Long et al., 2004). 

Exhibit D-6. Ratios of Inhalation Exposure Concentrations to Ambient Air 

Concentrations from the NATA National-scale Air Toxics Assessment 

U.S. Census Tract ID Inhalation Exposure Concentration: 

Ambient Air Concentration 

17133600200 0.40

17133600300 0.39 

29099700104 0.40 

29099700601 0.42 

29099700603 0.40 

29099700605 0.38 

29099700700 0.41 

29099700800 0.40 

29099700900 0.37 

29099701000 0.39 

Exhibit D-7. Annual Average Population-weighted Inhalation Exposure 

Concentrations for the Primary Pb Smelter Case Study 

Annual Average Population-weighted Pb Inhalation 

Exposure Concentration (μg/m3) a 

Statistic b Alternative NAAQS Scenario Current 

NAAQS 

Scenario 

1 

0.2 μg/m3 , 

Max Quarterly 

2 

0.5 μg/m3 , 

Max Monthly 

3 

0.2 μg/m3 , 

Max Monthly 

4 

0.05 μg/m3 , 

Max Monthly 

Maximum 0.310 0.067 0.136 0.055 0.014 

95th Percentile 0.064 0.014 0.028 0.011 0.003 

Median 0.017 0.004 0.007 0.003 0.001 

5th Percentile 0.006 0.001 0.003 0.001 < 0.001 

Minimum 0.002 < 0.001 0.001 < 0.001 < 0.001 

D-12 
 

a

 The 137 U.S. Census blocks and block groups with at least one child less than the age of 7 were used to
 

create this summary. 

b

 The statistic (e.g., 95th percentile, median) may not be at the same location for each of the data results 

presented here.
 

D.2.4. Air Modeling Performance Evaluation 

The results from the air Pb modeling performed for the primary Pb smelter case study in 

this assessment were not compared directly to available monitoring data because they represent 

facility conditions (e.g., emissions) that do not currently exist (as discussed in Appendix B). 

Instead, this performance evaluation relied on an “actual value” analysis conducted by the 

primary Pb smelter case study facility and reviewed by the State of Missouri, which used the 

2007 proposed SIP modeling configuration, but replaced the hypothetical facility conditions with 

“actual values.” This actual value modeling conducted by the primary Pb smelter case study 

facility included three separate evaluations comparing model predictions to measured Pb 

concentrations at five monitor sites in the primary Pb smelter case study area. These 

comparisons included: 

•	 Day-to-day evaluation of modeling output compared to monitor values. The review of 

the model performance evaluation conducted by the State of Missouri concluded that all 

sites demonstrated a pattern of overall accuracy for directional prediction (i.e., high 

modeled days were high monitored days and low modeled days were low monitored 

days), suggesting that the model was performing well in relating wind direction to Pb 

transport (MDNR, 2007). 

•	 Source contribution analysis. Significant sources of Pb for each monitor (e.g., in-plant 

roads and yard dust, blast furnace) were identified using chemical mass balance (CMB) 

of monitor filter residue. The results of this analysis were compared with relative 

contributions predicted by the dispersion model for individual modeled sources. The 

review of the model performance evaluation concluded that there was generally good 

agreement between the CMB results and the air dispersion results in terms of major 

sources contributing Pb at each monitor (MDNR, 2007). 

•	 Comparison of overall average modeled results with monitored Pb levels. This 

performance evaluation involved comparing modeled results (for 247 days simulated for 

2005) at six monitor locations with actual measured Pb values for that same period at 

those locations. Results of this evaluation suggested a slight over-prediction bias 

(< 10 percent) for those sites likely to have the greatest impacts from the primary Pb 

smelter facility (MDNR, 2007). 

This evaluation of model performance for the actual value modeling scenario increases 

confidence in estimates developed for the current NAAQS scenario using the 2007 proposed SIP 

revision modeling configuration. 

D-13 
 

 

 

D.3. OUTDOOR SURFACE SOIL 

Outdoor surface soil concentrations were estimated from the soil sample measurements in 

the area for each spatial unit (i.e., U.S. Census blocks and block groups) with at least one child 

less than 7 years of age in the study area. The extent and types of soil data sets available for the 

calculations are described in Appendix B. The two data sets used here are the “pre-excavation” 

and “recontamination” data sets. 

Many of the yards within 1.5 km of the primary Pb smelter facility have been excavated 

and filled with clean soil in the last 10 years. The U.S. EPA has taken soil samples from 31 of 

these sites on multiple occasions since 2002. These measurements are called “recontamination” 

samples. The U.S. EPA database also contains soil samples for more than 900 locations labeled 

as “pre-excavation.” These samples were taken from November 2000 to August 2004 and were 

the basis for decisions on soil replacement in those locations.5 The sample depth for both data 

sets is less than an inch (in) (USEPA, 2001). Depending on the location of the modeled block or 

block group in the study area (within or outside of the soil cleanup area), the soil concentrations 

for this assessment were calculated using either the recontamination or pre-excavation data set. 

All U.S. Census blocks within the soil cleanup area (approximately 1.5 km) were 

identified from the Gradient Corporation report (Gradient Corporation, 2004). For these U.S. 

Census blocks with at least one child less than 7 years of age, soil concentrations were estimated 

from the recontamination soil samples taken in 2005. For U.S. Census blocks for which there 

were one or more soil measurements available, the block soil concentration was set to the 

average (arithmetic mean) of those measurements. For U.S. Census blocks for which there were 

no measurements, but for which there were nearby measurements (i.e., across the street), the soil 

concentration was set to the average of the nearby measurements. For other U.S. Census blocks, 

the average of all of the recontamination soil measurements within 500 meters (m) was 

calculated and set as the value for the block. 

Outside of the soil cleanup area, soil concentrations were estimated using a regression 

equation of the pre-excavation soil concentrations. The distance of each pre-excavation soil 

sample to the main stack was measured using a geographical information system (GIS). The 

measurements were grouped according to distance from the main stack (used as a reference point 

for distance from the facility and its associated sources), with separate groups for each 500-m 

5 Based on these sample results a number of yards in locations within 1.5 km of the facility have been filled 

with clean soil. 

D-14 
 

increment. The arithmetic mean for each group was calculated, resulting in five arithmetic mean 

average values for soil concentration, and these values were plotted versus distance from the 

facility. A regression power equation (R2 of 0.92) was calculated from the samples (see 

Exhibit D-8). Note that pre-excavation soil samples taken within 1.5 km of the facility were 

included to develop the regression equation; however, the equation was not used to estimate 

soil concentrations at U.S. Census blocks within the 1.5-km soil clean-up area (as indicated in 

Exhibit D-8). The distance of each U.S. Census block and block group centroid from the main 

stack was measured in GIS. Soil concentrations for the U.S. Census blocks and block groups 

outside the soil cleanup area were then calculated using the regression equations based on 

distance from the stack. 

Exhibit D-8. Average Pre-excavation Soil Measurements and Best-fit Trend Line 

y = 2E+07x-1.4417 

R2 = 0.9233 

0 

500 

1000 

1500 

2000 

2500 

3000 

3500 

4000 

Pb soil concentration (mg/kg) 

Average of 

measurements 

Best-fit line 

Line indicating distance beyond 

which regression equation was used 

to estimate soil concentrations 

0 500 1000 1500 2000 2500 

Distance from main stack (m) 

All calculated soil concentrations used in the five scenarios for the 137 U.S. Census 

blocks and block groups with at least one child less than 7 years of age are summarized in 

Attachments D-7 through D-11 with an indication of which method was used to calculate the 

values. Note that due to the soil cleanup within 1.5 km of the stack, the soil Pb concentration 

estimates (consistent with soil measurements) near the facility are in some cases lower than those 

in the more distant locations. It is recognized that the estimated Pb concentrations within the 

D-15 
 

remediation zone (i.e., within 1.5 km of the facility) likely underestimate the current 

contributions of the primary Pb smelter to outdoor soil/dust Pb concentrations as a result of 

continued recontamination of outdoor soil/dust near the facility. While this is source of 

uncertainty in the risk results (e.g., underestimating contribution from the outdoor soil/dust 

pathway close to the primary Pb smelter case study facility), the impact of this limitation on 

results is reduced by the selection of different indoor dust Pb prediction models for the two 

different parts of the study area. That is, in the locations within the soil cleanup area, the indoor 

dust Pb prediction model does not rely on soil Pb concentrations, while in locations outside of 

the soil cleanup area the indoor dust Pb prediction model does take soil Pb concentrations into 

account (see Section D.4 and Appendix G for more details). 

D.4. INDOOR DUST 

For estimating indoor dust concentrations for residences in the primary Pb smelter case 

study, two dust prediction models were used. 

•	 For locations within 1.5 km of the facility: a site-specific regression model that predicts 

indoor dust Pb concentration as a function of air concentration (referred to as H5 model 

in Attachments D-7 through D-11) is used. 

•	 For locations more than 1.5 km away from the facility, a regression model (based on data 

from communities near various Pb point sources) that predicts Pb dust concentrations 

given soil and air concentrations (referred to as the air+soil regression-based model) is 

used (USEPA, 1989). 

For a more detailed explanation of these indoor Pb dust concentration prediction models see 

Appendix G. 

Exhibit D-9 presents a summary of the Pb indoor dust concentrations generated in the 

primary Pb smelter case study for the five different air quality scenarios. Exhibit D-9 also shows 

the number of children residing in areas associated with different estimates of Pb indoor dust 

concentration. All estimated indoor dust Pb concentrations for residences with at least one child 

less than 7 years of age in the primary Pb smelter case study are presented in Attachments D-7 

through D-11. 

D-16 
Exhibit D-9. Modeled Indoor Dust Pb Concentrations for the Primary Pb Smelter Case S

Number of U.S. Census Blocks/ Block Groups with Indoor Dust 

Pb Concentrations 

Greater than Value in First Column a 

Number of Children Living in Area w

Concentrations Greater than Value 

Alternative NAAQS Scenario Alternative NAAQ

Indoor Dust Pb 

Concentration 

(μg/g) Current 

NAAQS 

Scenario 

1 

0.2 μg/m3 , 

Max 

Quarterly 

2 

0.5 μg/m3 , 

Max 

Monthly 

3 

0.2 μg/m3 , 

Max 

Monthly 

4 

0.05 μg/m3 , 

Max 

Monthly 

Current NAAQS 

Scenario 

1 

0.2 μg/m3 , 

Max 

Quarterly 

2 

0.5 μg/m3 , 

Max 

Monthly 

0.2 

M

30 137 137 137 137 137 3,880 3,880 3,880 350 129 111 122 108 102 3,845 3,481 3,661 2

100 81 56 63 56 41 1,646 884 965 500 24 4 13 4 0 98 8 41 

1,000 11 0 4 0 0 39 0 8 3,000 0 0 0 0 0 0 0 0 5,000 0 0 0 0 0 0 0 0 

a The 137 U.S. Census blocks and block groups with children ages 0 to 7 in the 2000 U.S. Census (U.S. Census Bureau, 2005) were us

summary. Note that U.S. Census blocks without children were excluded.
 

b Number of children ages 0 to 7 from the 2000 U.S. Census were used in this analysis (U.S. Census Bureau, 2005).
 

October 2007 D-17 Draft- 
 

 

The Pb indoor dust concentrations estimated for the five scenarios for this primary Pb 

smelter case study fall within the range presented by the U.S. EPA (1989) but they are not in the 

high-end of the range. Studies summarized in U.S. EPA (1989) contained measurements of 

house dust ranging from 10 to 35,000 parts per million (ppm). A high value of 100,000 ppm was 

measured in one home within 2 km of a Pb smelting facility (USEPA, 1989). In this case study, 

the maximum dust concentration of Pb predicted at a receptor location is 5,300 ppm at 300 m 

from the main stack of the primary Pb smelter. Exhibit D-10 presents a summary of the annual 

average population-weighted indoor Pb dust exposure concentrations generated in the primary 

Pb smelter case study for the five different NAAQS scenarios. Population-weighted indoor dust 

Pb concentrations were calculated by first sorting the block/block groups in increasing 

population-weighted concentration order. Then the percentage of children living in block/block 

groups less than or equal to the maximum indoor dust Pb concentration of those block/block 

groups was calculated. The indoor dust Pb concentration of the block/block group associated 

with the minimum, 5th, median, 95th, and maximum percentile was selected. 

In a study of Pb concentrations in household dust near a facility that has operated as a 

secondary Pb smelter since 1972 and as a primary smelter for the previous 200 years in the 

Czech Republic, Rieuwerts et al. (1999) measured indoor dust Pb concentrations in houses in a 

neighborhood adjacent to the facility (the neighborhood ranges from approximately 0 to 500 m 

away from the facility according to a figure). Measured Pb concentrations in household dust 

from 14 homes ranged from 861 to 5,890 ppm, with a geometric mean (GM) of 1,668 ppm. 

Indoor Pb dust concentrations predicted for this case study are similar, ranging from 1,500 to 

5,300 ppm out to 500 m from the facility, with a GM of 3,100 ppm. (MDNR, 2007) 

D-18 
 

 

Exhibit D-10. Annual Average Population-weighted Indoor Pb Dust Exposure 

Concentrations for the Primary Pb Smelter Case Study 

Annual Average Population-weighted Indoor Dust Pb 

Exposure Concentrations (μg/g) a 

Alternative NAAQS Scenario Statistic b Current 

NAAQS 

Scenario 

1 

0.2 μg/m3 , 

Max Quarterly 

2 

0.5 μg/m3 , 

Max Monthly 

3 

0.2 μg/m3 , 

Max Monthly 

4 

0.05 μg/m3 , 

Max Monthly 

Maximum 1944 648 1077 557 383 

95th Percentile 219 152 172 149 138 

Median 84 68 73 67 63 

5th Percentile 53 45 47 44 43 

Minimum 41 38 39 38 38 

a

 The 137 U.S. Census blocks and block groups with at least one child less than 7 years of age were used to 

create this summary. 

b

 The statistic (e.g., 95th percentile, median) may not be at the same location for each of the data results 

presented here. 

D-19 
 

 

 

 

 

 

 

 

 

REFERENCES 

Gradient Corporation. (2004) RAGS Part D Interim Deliverables Report for Community Risk Assessment; 

Herculaneum, Missouri (Draft). Prepared for the Don Run Company; October. 

ICF International. (2006) Lead Human Exposure and Health Risk Assessments and Ecological Risk Assessment for 

Selected Areas, Pilot Phase. External Review Draft Technical Report. Prepared for the U.S. EPA Office of 

Air Quality Planning and Standards (OAQPS). December. 

Long, T.; Johnson, T.; Laurenson, J.; Rosenbaum, A. (2004) Development of Penetration and Proximity 

Microenvironment Factor Distributions for the HAPEM5 in Support of the 1999 National-Scale Air Toxics 

Assessment (NATA). Memorandum prepared for Ted Palma, U.S. EPA, Office of Air Quality Planning and 

Standards (OAQPS); April 5. 

Missouri Department of Natural Resources (MDNR). (2007a) 2007 Revision of the State Implementation Plan for 

the Herculaneum Lead Nonattainment Area, As Adopted by the Missouri Air Conservation Commission. 

April 26. 

Missouri Department of Natural Resources (MDNR). (2007b) 2007 Revision of the State Implementation Plan for 

the Herculaneum Lead Nonattainment Area, Public Hearing; March 20, 2007. Emission Source Description 

on Table 2: 27 of 43. Division of Environmental Quality. Available online at: 

http://www.dnr.mo.gov/env/apcp/docs/2007revision.pdf. 

Missouri Department of Natural Resources (MDNR). (2007c) Doe Run - Herculaneum State Implementation Plan 

(SIP) Dispersion Modeling Review. Memorandum From Jeffry D. Bennett to John Rustige. February 12. 

Available online at: http://www.dnr.mo.gov/env/apcp/herculaneumsip.htm. 

Rieuwerts, J. S.; Farago, M.; Cikrt, M.; and Bencko, V. (1999) Heavy Metal Concentrations in and Around 

Households Near a Secondary Lead Smelter. Environmental Monitoring and Assessment. 58: 317-335. 

Schulman, L. L.; Stimaitis, D. G.; Scire, J. S. (1997) Addendum to ISC3 User's Guide: The Prime Plume Rise and 

Building Downwash Model. Earth Tech Document A287. A-99-05, II-A-12. Palo Alto, CA: Electric Power 

Research Institute; November. Available online at: 

http://www.epa.gov/scram001/7thconf/iscprime/useguide.pdf. 

U.S. Census Bureau. (2005) United States Census 2000: Summary File 1. Public Information Office. Available 

online at: http://www.census.gov/Press-Release/www/2001/sumfile1.html. 

U.S. Department of the Interior U.S. Geological Survey. (1993) USGIS DIGITAL ELEVATION MODELS 

(DEMs): User's Guide 5. Reston, Virginia. 

U.S. Environmental Protection Agency (USEPA). (1989) Review of National Ambient Air Quality Standard for 

Lead: Exposure Analysis Methodology and Validation. EPA-450/2-89-011. Research Triangle Park, NC: 

Office of Air Quality Planning and Standards; June. 

U.S. Environmental Protection Agency (USEPA). (1995) User's Guide for the Industrial Source Complex (ISC3) 

Dispersion Models, Volume 1- User Instructions. Washington, D.C.: Office of Air Quality Planning and 

Standards (OAQPS). Available online at: 

http://www.epa.gov/scram001/userg/regmod/isc3v1.pdf#search=%22%22user's%20guide%20for%20the% 

20industrial%20source%20complex%22%22. 

U.S. Environmental Protection Agency (USEPA). (2001) Quality Assurance Project Plan for a Site Characterization 

at the Herculaneum Lead Smelter. Herculaneum, Missouri, CERCLIS ID No.: MOD 006266373. Prepared 

for U.S. EPA, Region 7, Superfund Division by U.S. EPA Region 7 Superfund Technical Assessment and 

Response Team 2; September. 

D-20 
 

 

 

 

 

U.S. Environmental Protection Agency (USEPA). (2004) User's Guide for the AERMOD Terrain Preprocessor 

(AERMAP). EPA-454/B-03-003. RTP, NC: Office of Air Quality Planning and Standards Emissions, 

Monitoring, and Analysis Division. 

U.S. Environmental Protection Agency (USEPA). (2006) 1999 National-Scale Air Toxics Assessment. Available 

online at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html. 

U.S. Environmental Protection Agency (USEPA). (2007) Email From Richard Daye, U.S. EPA Region 7, to 

Zachary Pekar, Office of Air Quality Planning and Standards. Re: MDNR - Re: Fw: Modeling input/output 

files? April 26. 

D-21 
Attachment D-1. Emission Parameters for Point Sources for the Primary Pb Smelter Case 

Emission 

Point ID Emission Point Description 

Hourly Emissions 

or Emissions 

Factor? 

UTM x 

(m) 

UTM y

 (m) 

Elevation 

(m) 

Source Type 

(Point, Area or 

Volume) 

Point Emission 

Annual 

Average 

Emission 

Rate (g/s) 

Stack 

Height (m) 

Stack Ga

Temper

(K) 

Pb 

30001 Main stack - GEP stack height (167.67 is actual stack ht) No 729534 4237767 131.98 Point 4.17 100.75 346.640004 Dross kettle heat stack No 729588 4237885 130.76 Point 8.58E-04 21.3 391.

40005 Dross kettle heat stack No 729587 4237895 130.76 Point 8.58E-04 21.3 391.

50007 New baghouse No. 8 stack (part of 2000 SIP) No 729596 4237797 131.06 Point 4.31E-02 30.48 285.5

50008 New baghouse No. 9 stack (part of 2000 SIP) No 729596 4237792 131.06 Point 0.297 30.48 276.1

50011 Kettle setting heat stack No 729579 4237787 131.06 Point 1.65E-03 18.8 989.

50012 Kettle setting heat stack No 729579 4237796 131.06 Point 1.65E-03 18.8 989.

50013 Kettle setting heat stack No 729579 4237805 131.06 Point 1.65E-03 18.8 989.

50014 Kettle setting heat stack No 729579 4237813 131.06 Point 1.65E-03 18.8 989.

50015 Kettle setting heat stack No 729579 4237822 130.76 Point 1.65E-03 18.8 989.

50016 Kettle setting heat stack No 729579 4237831 130.76 Point 1.65E-03 18.8 989.

50017 Kettle setting heat stack No 729579 4237840 130.76 Point 1.65E-03 18.8 989.

50018 Kettle setting heat stack No 729579 4237849 130.76 Point 1.65E-03 18.8 989.

60001 Strip mill heat stack No 729434 4237560 129.24 Point 1.13E-04 21.3 699.

60002 Strip mill heat stack No 729475 4237560 130.76 Point 1.13E-04 21.3 699.

60003 Strip mill baghouse No 729456 4237562 130.76 Point 5.93E-06 7.6 297 

60004 Low alpha baghouse No 729477 4237483 128.02 Point 1.80E-03 6.1 327.

60005 Strip mill vent No 729440 4237549 129.24 Point 1.17E-03 16.8 297 

60006 Strip mill vent No 729450 4237549 129.24 Point 1.17E-03 16.8 297 

60007 Strip mill vent No 729460 4237549 130.76 Point 1.17E-03 16.8 297 

60008 Strip mill vent No 729470 4237549 130.76 Point 1.17E-03 16.8 297 

D-22
 
Attachment D-2. Emission Parameters for Volume Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) 

Volume E

Annual 

Average 

Emission 

Rate (g/s) 

Releas

Height 

above 

ground

level (m

Pb 

10001A1 New dump concentrate hopper (Part of 2000 SIP) yes - Hourly Factors 729460 4237585 131.06 Volume 2.31E-03 0.61 10001A2 New dump concentrate storage (Part of 2000 SIP) yes - Hourly Factors 729520 4237550 129.54 Volume 4.62E-03 4.27 10001B1 Load concentrate rail car yes - Hourly Factors 729520 4237585 129.84 Volume 7.62E-03 4.27 10001B2 Dump concentrate and secondary unloader (new location) yes - Hourly Factors 729547 4238029 132.59 Volume 2.31E-03 6.40 

20001A Load sinter railcar/dump sinter No 729520 4237585 129.84 Volume 3.02E-05 4.27 

20001B Load sinter railcar/dump sinter No 729560 4237920 131.98 Volume 3.02E-05 6.40 

20002 Sinter unloading (NE corner of sinter building) No 729520 4237935 132.89 Volume 3.02E-05 3.66 

20003 Sinter loading/unloading (truck/rail) (at sinter building) No 729550 4237550 128.63 Volume 3.02E-05 4.27 20004 Fume Loading No 729540 4237980 133.2 Volume 2.41E-04 4.27 

20004B New Railcar fume unloading (Part of 2002 SIP-wet vs dry loading) yes - Hourly Factors 729544 4237424 125 Volume 1.93E-03 0.91 

20004C New Railcar fume unloading (Part of 2002 SIP-wet vs dry loading) yes - Hourly Factors 729538 4237429 125 Volume 7.23E-04 3.66 

20005A Sinter mix room No 729519 4237854 132.28 Volume 3.37E-04 18.30 

20005B Sinter mix room No 729519 4237843 132.28 Volume 3.37E-04 18.30 

20005C Sinter mix room No 729519 4237832 132.28 Volume 3.37E-04 18.30 20005D Sinter mix room No 729519 4237821 132.28 Volume 3.37E-04 18.30 

20005E Sinter mix room No 729519 4237810 131.98 Volume 3.37E-04 18.30 

20005F Sinter mix room No 729519 4237799 131.98 Volume 3.37E-04 18.30 

20006 Sinter building fugitives No 729546 4237904 131.98 Volume 2.31E-03 20.00 

20007 #3 Baghouse roof vents No 729540 4237699 131.37 Volume 3.72E-04 21.30 

30002 Blast furnace No 729583 4237960 131.37 Volume 1.40E-03 9.30 

30011 #5 Baghouse roof vent No 729524 4238016 133.2 Volume 1.93E-04 21.30 

30012 #5 Baghouse roof vent No 729524 4237999 133.2 Volume 1.93E-04 21.30 

30013 #5 Baghouse roof vent No 729524 4237982 133.2 Volume 1.93E-04 21.30 

40006 New dross plant fugitives (part of 2000 SIP) No 729578 4237885 130.76 Volume 4.33E-03 7.62 

50006 New refinery plant fugitives (part of 2000 SIP w/install BH# 8&9) No 729578 4237810 131.06 Volume 3.17E-03 5.49 

70001 Fugitive dross handling Yes - Hourly Emissions have been averaged 729636 4238220 128.32 Volume 3.67E-04 2.00 

70007 Fugitive slag handling Yes - Hourly Emissions have been averaged 729239 4237241 118.57 Volume 4.63E-06 2.00 70009 Fugitive secondaries handling Yes - Hourly Emissions have been averaged 729492 4237630 130.45 Volume 4.76E-05 2.00 

D-23
 
Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) Release Height (m) Length of x Side of Area (m) Length of y Side of Area (m) 

70002 Fugitive dross wind erosion Yes - hourly emissions have been averaged 729620 4238201 130.45 Area 2.00 30.00 40.00 

70004 Fugitive concentrate wind erosion Yes - hourly emissions have been averaged 729515 4237391 124.97 Area 2.00 15.00 150.00 

70006 Fugitive sinter wind erosion Yes - hourly emissions have been averaged 729537 4237395 124.97 Area 2.00 15.00 150.00 

70008A Fugitive slag storage wind erosion Yes - hourly emissions have been averaged 728878 4237050 128 Area 2.00 166.00 275.00 

70008B Fugitive slag storage wind erosion Yes - hourly emissions have been averaged 729150 4237150 128 Area 2.00 75.00 175.00 

70010 Fugitive secondaries wind erosion Yes - hourly emissions have been averaged 729482 4237609 130.45 Area 2.00 20.00 40.00 

70100 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727276 4237113 132.59 Area 0 10.00 64.48 

70101 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727340 4237103 131.06 Area 0 74.17 10.00 

70102 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727415 4237101 128.02 Area 0 74.17 10.00 

70103 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727489 4237110 128.93 Area 0 10.00 58.12 

70104 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727547 4237113 131.67 Area 0 10.00 58.12 

70105 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727605 4237116 132.28 Area 0 10.00 64.48 

70106 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727669 4237116 132.89 Area 0 10.00 64.48 

70107 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727734 4237106 134.42 Area 0 54.90 10.00 

70108 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727788 4237103 138.99 Area 0 54.90 10.00 

70109 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727844 4237110 144.17 Area 0 10.00 62.86 

70110 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727906 4237110 137.77 Area 0 10.00 62.86 

70111 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 727969 4237110 124.97 Area 0 10.00 49.97 

D-24
 
Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) Release Height (m) Length of x Side of Area (m) Length of y Side of Area (m) 

70112 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728019 4237110 124.66 Area 0 10.00 49.97 

70113 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728069 4237110 124.36 Area 0 10.00 38.69 

70114 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728103 4237105 125.58 Area 0 10.00 77.39 

70115 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728106 4237182 128.63 Area 0 10.00 51.57 

70116 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728108 4237234 130.45 Area 0 10.00 51.57 

70117 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728109 4237285 134.72 Area 0 10.00 61.21 

70118 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728109 4237348 135.94 Area 0 10.00 86.75 

70119 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728132 4237432 132.89 Area 0 10.00 76.58 

70120 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728161 4237502 130.15 Area 0 10.00 84.57 

70121 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728187 4237583 131.67 Area 0 10.00 72.68 

70122 New area source input (Hwy 55 to Joachim bridge) segment AB Yes - hourly factors 728203 4237653 128.63 Area 0 10.00 32.85 

70150 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728209 4237686 128.63 Area 0 10.00 50.46 

70151 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728221 4237735 134.42 Area 0 10.00 50.46 

70152 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728233 4237784 130.45 Area 0 10.00 55.89 

70153 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728246 4237838 130.45 Area 0 10.00 55.89 

70154 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728258 4237893 128.63 Area 0 10.00 49.99 

70155 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728268 4237942 125.88 Area 0 10.00 49.99 

70156 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728278 4237992 124.97 Area 0 10.00 74.83 

70157 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728307 4238061 124.05 Area 0 10.00 65.31 

70158 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728340 4238118 122.22 Area 0 10.00 65.31 

D-25
 
Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) Release Height (m) Length of x Side of Area (m) Length of y Side of Area (m) 

70159 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728372 4238175 113.69 Area 0 10.00 63.25 

70160 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728402 4238230 112.17 Area 0 10.00 63.25 

70161 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728432 4238286 118.57 Area 0 10.00 94.58 

70162 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728474 4238370 119.48 Area 0 10.00 50.33 

70163 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728499 4238414 119.48 Area 0 10.00 50.33 

70164 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728523 4238458 120.09 Area 0 10.00 52.79 

70165 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728545 4238506 120.7 Area 0 10.00 52.79 70166 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728568 4238554 121.62 Area 0 10.00 50.82 

70167 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728592 4238599 119.18 Area 0 10.00 50.82 

70168 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728617 4238643 121.01 Area 0 10.00 65.74 

70169 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728648 4238700 124.36 Area 0 10.00 52.91 

70170 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728668 4238749 137.16 Area 0 10.00 43.73 

70171 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728679 4238790 138.99 Area 0 10.00 75.98 

70172 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728681 4238963 147.22 Area 0 98.04 10.00 

70173 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728676 4239030 153.62 Area 0 66.93 10.00 

70174 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728668 4239120 151.18 Area 0 90.59 10.00 

70175 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728646 4239176 162.76 Area 0 62.01 10.00 

70176 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728617 4239239 165.81 Area 0 68.72 10.00 

70177 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728621 4239236 165.81 Area 0 53.87 10.00 

70178 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728674 4239229 172.52 Area 0 53.87 10.00 

D-26
 
Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) Release Height (m) Length of x Side of Area (m) Length of y Side of Area (m) 

70179 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728727 4239222 174.96 Area 0 97.42 10.00 

70180 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728823 4239204 173.13 Area 0 54.02 10.00 

70181 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728877 4239197 171.6 Area 0 54.02 10.00 

70182 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728929 4239189 165.2 Area 0 65.51 10.00 70183 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 728992 4239169 166.42 Area 0 51.82 10.00 

70184 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729044 4239161 160.32 Area 0 51.82 10.00 

70185 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729095 4239153 163.07 Area 0 91.32 10.00 

70186 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729183 4239134 168.25 Area 0 53.37 10.00 

70187 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729232 4239112 166.73 Area 0 53.37 10.00 

70188 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729280 4239092 162.15 Area 0 52.18 10.00 

70189 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729320 4239059 165.51 Area 0 52.18 10.00 

70190 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729359 4239026 161.54 Area 0 90.62 10.00 

70191 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729420 4238959 164.9 Area 0 52.17 10.00 70192 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729454 4238919 161.85 Area 0 52.17 10.00 

70193 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729487 4238879 162.46 Area 0 83.81 10.00 

70194 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729541 4238814 159.11 Area 0 66.20 10.00 

70195 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729585 4238766 154.23 Area 0 57.75 10.00 

70196 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729611 4238717 162.46 Area 0 76.20 10.00 

70197 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729620 4238642 155.45 Area 0 73.49 10.00 

70198 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729622 4238568 155.14 Area 0 62.33 10.00 

D-27
 
Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) Release Height (m) Length of x Side of Area (m) Length of y Side of Area (m) 

70199 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729609 4238447 156.67 Area 0 10.00 61.78 

70200 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729585 4238400 149.66 Area 0 10.00 53.76 

70201 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729560 4238352 147.52 Area 0 10.00 53.76 

70202 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729538 4238289 145.08 Area 0 10.00 66.19 

70203 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729522 4238227 145.39 Area 0 10.00 64.25 

70204 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729495 4238145 142.34 Area 0 10.00 86.59 

70205 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729482 4238084 140.51 Area 0 10.00 61.57 

70206 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729464 4238029 141.43 Area 0 10.00 58.43 

70207 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729448 4237982 141.43 Area 0 10.00 49.28 

70208 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729437 4237926 135.33 Area 0 10.00 56.75 

70209 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729428 4237881 133.81 Area 0 10.00 45.40 

70210 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729413 4237815 133.81 Area 0 10.00 68.57 

70211 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729393 4237764 133.2 Area 0 10.00 54.98 70212 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729377 4237717 132.59 Area 0 10.00 49.28 

70213 New area source input (Joachim bridge exit to plant entrance) segment BC Yes - hourly factors 729375 4237713 132.59 Area 0 10.00 5.45 

70250 New area source input (plant entrance to NW corner of Stip Mill Blding/SMB) segment CD Yes - hourly factors 729367 4237692 132.28 Area 0 10.00 21.62 

70251 New area source input (plant entrance to NW corner of Stip Mill Blding/SMB) segment CD Yes - hourly factors 729367 4237689 132.28 Area 0 68.70 10.00 

70252 New area source input (plant entrance to NW corner of Stip Mill Blding/SMB) segment CD Yes - hourly factors 729393 4237625 130.76 Area 0 51.46 10.00 

70300 New area source input (NW corner of SMB to conc. hopper) segment DE Yes - hourly factors 729416 4237574 129.24 Area 0 46.05 10.00 

70350 New area source input (conc. hopper to SW corner SMB) segment EF Yes - hourly factors 729461 4237564 130.76 Area 0 23.47 10.00 

D-28
 
Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) Release Height (m) Length of x Side of Area (m) Length of y Side of Area (m) 

70351 New area source input (conc. hopper to SW corner SMB) segment EF Yes - hourly factors 729482 4237561 130.15 Area 0 17.74 10.00 

70352 New area source input (conc. hopper to SW corner SMB) segment EF Yes - hourly factors 729495 4237555 130.15 Area 0 21.78 10.00 

70353 New area source input (conc. hopper to SW corner SMB) segment EF Yes - hourly factors 729497 4237493 128.32 Area 0 10.00 41.34 

70354 New area source input (conc. hopper to SW corner SMB) segment EF Yes - hourly factors 729497 4237493 128.32 Area 0 29.47 10.00 

70355 New area source input (conc. hopper to SW corner SMB) segment EF Yes - hourly factors 729493 4237439 128.02 Area 0 10.00 25.79 

70356 New area source input (conc. hopper to SW corner SMB) segment EF Yes - hourly factors 729479 4237432 125.58 Area 0 10.00 18.62 

70357 New area source input (conc. hopper to SW corner SMB) segment EF Yes - hourly factors 729459 4237425 125.58 Area 0 10.00 22.52 

70358 New area source input (conc. hopper to SW corner SMB) segment EF Yes - hourly factors 729434 4237423 129.24 Area 0 10.00 26.67 

70400 New area source input (NW corner SMB to SW corner of SMB ) segment DF Yes - hourly factors 729411 4237555 127.71 Area 0 10.00 22.81 

70401 New area source input (NW corner SMB to SW corner of SMB ) segment DF Yes - hourly factors 729410 4237532 128.02 Area 0 10.00 23.48 

70402 New area source input (NW corner SMB to SW corner of SMB ) segment DF Yes - hourly factors 729405 4237505 128.32 Area 0 10.00 28.02 

70403 New area source input (NW corner SMB to SW corner of SMB ) segment DF Yes - hourly factors 729403 4237485 128.32 Area 0 10.00 19.58 

70404 New area source input (NW corner SMB to SW corner of SMB ) segment DF Yes - hourly factors 729404 4237482 129.84 Area 0 24.35 10.00 

70405 New area source input (NW corner SMB to SW corner of SMB ) segment DF Yes - hourly factors 729413 4237461 129.84 Area 0 30.68 10.00 

70406 New area source input (NW corner SMB to SW corner of SMB ) segment DF Yes - hourly factors 729423 4237431 127.71 Area 0 16.78 10.00 

70450 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG Yes - hourly factors 729429 4237416 129.24 Area 0 23.40 10.00 

70451 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG Yes - hourly factors 729438 4237394 129.24 Area 0 31.73 10.00 

70452 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG Yes - hourly factors 729454 4237366 124.97 Area 0 28.05 10.00 

70453 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG Yes - hourly factors 729471 4237343 126.19 Area 0 31.66 10.00 

70454 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG Yes - hourly factors 729490 4237318 124.97 Area 0 10.98 10.00 

D-29
 
Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) Release Height (m) Length of x Side of Area (m) Length of y Side of Area (m) 

70500 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729587 4237602 127.71 Area 0 29.96 10.00 

70501 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729592 4237573 127.71 Area 0 19.13 10.00 

70502 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729593 4237528 127.1 Area 0 10.00 27.21 

70503 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729592 4237505 125.88 Area 0 10.00 23.16 

70504 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729589 4237478 124.36 Area 0 10.00 27.29 

70505 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729586 4237453 123.75 Area 0 10.00 25.13 

70506 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729583 4237425 123.75 Area 0 10.00 27.67 

70507 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729577 4237400 123.14 Area 0 10.00 27.05 

70508 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729569 4237384 124.66 Area 0 10.00 18.86 

70509 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729552 4237366 124.66 Area 0 10.00 25.99 

70510 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729540 4237351 124.97 Area 0 10.00 18.70 

70511 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729527 4237337 124.97 Area 0 10.00 19.92 

70512 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729514 4237323 121.31 Area 0 10.00 19.24 

70513 New area source input (North end of Slag Haul Road to refinery dock) segment GH Yes - hourly factors 729499 4237316 124.97 Area 0 10.00 17.85 

70550 New area source input (South Slag Haul Road paved) segment GK Yes - hourly factors 729479 4237311 127.71 Area 0 10.00 20.25 

70551 New area source input (South Slag Haul Road paved) segment GK Yes - hourly factors 729460 4237298 125.58 Area 0 10.00 21.51 

70552 New area source input (South Slag Haul Road paved) segment GK Yes - hourly factors 729451 4237280 128.02 Area 0 10.00 17.86 

70553 New area source input (South Slag Haul Road paved) segment GK Yes - hourly factors 729450 4237278 128.02 Area 0 24.48 10.00 

70600 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729611 4237950 130.15 Area 0 10.00 23.58 

70601 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729611 4237950 130.15 Area 0 35.37 10.00 

D-30
 
Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) Release Height (m) Length of x Side of Area (m) Length of y Side of Area (m) 

70602 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729612 4237883 128.93 Area 0 10.00 31.74 

70603 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729611 4237846 128.93 Area 0 10.00 37.18 

70604 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729610 4237821 128.63 Area 0 10.00 24.97 

70605 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729606 4237784 128.93 Area 0 10.00 37.80 

70606 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729606 4237753 129.24 Area 0 10.00 29.92 

70607 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729606 4237753 129.24 Area 0 24.48 10.00 

70608 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729605 4237693 127.71 Area 0 10.00 35.82 

70609 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729603 4237661 125.27 Area 0 10.00 32.69 

70610 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729601 4237635 127.71 Area 0 10.00 25.94 

70611 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729598 4237614 127.71 Area 0 10.00 21.09 

70612 New area source input (north end of main building to refinery dock unpaved) segment HL Yes - hourly factors 729591 4237604 127.71 Area 0 10.00 14.00 

70650 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729512 4237946 132.89 Area 0 10.00 17.32 

70651 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729496 4237936 133.81 Area 0 10.00 16.15 

70652 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729493 4237904 133.5 Area 0 10.00 28.36 

70653 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729493 4237902 132.89 Area 0 21.01 10.00 

70654 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729493 4237859 132.59 Area 0 10.00 26.79 

70655 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729483 4237846 132.59 Area 0 10.00 18.66 

70656 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729473 4237826 133.2 Area 0 10.00 21.02 70657 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729465 4237795 132.89 Area 0 10.00 31.49 

70658 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729465 4237792 132.89 Area 0 17.22 10.00 

D-31
 
Attachment D-3. Emission Parameters for Area Sources for the Primary Pb Smelter Cas

Emission 

Point ID Emission Point Description Hourly Emissions or Emissions Factor? UTM x (m) UTM y (m) Elevation (m) Source Type (Point, Area or Volume) Release Height (m) Length of x Side of Area (m) Length of y Side of Area (m) 

70659 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729470 4237776 132.59 Area 0 23.56 10.00 

70660 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729474 4237753 132.28 Area 0 28.24 10.00 

70661 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729480 4237726 132.28 Area 0 24.63 10.00 

70662 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729481 4237701 131.67 Area 0 20.90 10.00 

70663 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729480 4237660 131.37 Area 0 10.00 20.94 

70664 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729480 4237659 131.37 Area 0 19.45 10.00 

70665 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729482 4237640 130.45 Area 0 13.43 10.00 

70666 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729482 4237626 130.45 Area 0 19.37 10.00 

70667 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729488 4237606 130.45 Area 0 19.36 10.00 

70668 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729497 4237588 130.45 Area 0 20.08 10.00 

70669 New area source input (sinter plant to sinter storage) segment IJ Yes - hourly factors 729511 4237572 130.15 Area 0 11.66 10.00 

70700 New area source input (south Slag Haul Road unpaved) segment KM Yes - hourly factors 729427 4237243 122.53 Area 0 10.00 29.75 

70701 New area source input (south Slag Haul Road unpaved) segment KM Yes - hourly factors 729386 4237233 127.71 Area 0 10.00 43.12 

70702 New area source input (south Slag Haul Road unpaved) segment KM Yes - hourly factors 729346 4237218 128.02 Area 0 10.00 42.69 

70703 New area source input (south Slag Haul Road unpaved) segment KM Yes - hourly factors 729322 4237208 128.02 Area 0 10.00 25.49 

D-32
 
Attachment D-4. Hourly Emissions Factors by Emission Point for the Primary Pb Smelter Ca

Emission Emissions Factor for Hour of Day 

Point ID Hr1 Hr2 Hr3 Hr4 Hr5 Hr6 Hr7 Hr8 Hr9 Hr10 Hr11 Hr12 Hr13 Hr14 Hr15 Hr16 Hr17 Hr18 Hr19 Hr20 

10001A1 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

10001A2 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

10001B1 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

10001B2 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

20004B 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00 0.00 

20004C 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

70100 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70101 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70102 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70103 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70104 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70105 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70106 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70107 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70108 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70109 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70110 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70111 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70112 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70113 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70114 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70115 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70116 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70117 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70118 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70119 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70120 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70121 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70122 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70150 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70151 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70152 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70153 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70154 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70155 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70156 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70157 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70158 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70159 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70160 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70161 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70162 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70163 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70164 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70165 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70166 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70167 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70168 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70169 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70170 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70171 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70172 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70173 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70174 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

D-33
 
Attachment D-4. Hourly Emissions Factors by Emission Point for the Primary Pb Smelter Ca

Emission Emissions Factor for Hour of Day 

Point ID Hr1 Hr2 Hr3 Hr4 Hr5 Hr6 Hr7 Hr8 Hr9 Hr10 Hr11 Hr12 Hr13 Hr14 Hr15 Hr16 Hr17 Hr18 Hr19 Hr20 

70175 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70176 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70177 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70178 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70179 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70180 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70181 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70182 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70183 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70184 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70185 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70186 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70187 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70188 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70189 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70190 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70191 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70192 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70193 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70194 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70195 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70196 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70197 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70198 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70199 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70200 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70201 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70202 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70203 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70204 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70205 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70206 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70207 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70208 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70209 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70210 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70211 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70212 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70213 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70250 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70251 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70252 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70300 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70350 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70351 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70352 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70353 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70354 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70355 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70356 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70357 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70358 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70400 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70401 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

D-34
 
Attachment D-4. Hourly Emissions Factors by Emission Point for the Primary Pb Smelter Ca

Emission Emissions Factor for Hour of Day 

Point ID Hr1 Hr2 Hr3 Hr4 Hr5 Hr6 Hr7 Hr8 Hr9 Hr10 Hr11 Hr12 Hr13 Hr14 Hr15 Hr16 Hr17 Hr18 Hr19 Hr20 

70402 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70403 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70404 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70405 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70406 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70450 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70451 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70452 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70453 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70454 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70500 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70501 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70502 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70503 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70504 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70505 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70506 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70507 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70508 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70509 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70510 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70511 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70512 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70513 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70550 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70551 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70552 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70553 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70600 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70601 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70602 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70603 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70604 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70605 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70606 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70607 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70608 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70609 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70610 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70611 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70612 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70650 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70651 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70652 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70653 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70654 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70655 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70656 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70657 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70658 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70659 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70660 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70661 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70662 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

D-35
 
Attachment D-4. Hourly Emissions Factors by Emission Point for the Primary Pb Smelter Ca

Emission Emissions Factor for Hour of Day 

Point ID Hr1 Hr2 Hr3 Hr4 Hr5 Hr6 Hr7 Hr8 Hr9 Hr10 Hr11 Hr12 Hr13 Hr14 Hr15 Hr16 Hr17 Hr18 Hr19 Hr20 

70663 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70664 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70665 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70666 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70667 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70668 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70669 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70700 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70701 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70702 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

70703 0.05 0.075 0.075 0.075 0.125 0.50 1.25 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 0.50 

D-36
 
Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter Case 

Emission 

Point ID 

Emission Point Description 

Mass Fraction Particle Diameter (μm) P

Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 B

30001 Main stack - GEP stack height (167.67 is actual stack ht) 0.00 0.00 0.11 0.10 0.12 0.21 0.28 0.19 1.57 4.77 7.24 11.94 17.65 24.08 35.09 40.99 4.99 4.99 4

40004 Dross kettle heat stack 0.00 0.00 0.17 0.19 0.16 0.20 0.27 0.00 1.57 4.76 6.98 12.30 16.98 23.58 34.06 45.01 5.72 5.72 5

40005 Dross kettle heat stack 0.00 0.00 0.17 0.19 0.16 0.20 0.27 0.00 1.57 4.76 6.98 12.30 16.98 23.58 34.06 45.01 5.72 5.72 5

50007 New baghouse No. 8 stack (part of 2000 SIP) 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

50008 New baghouse No. 9 stack (part of 2000 SIP) 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 550011 Kettle setting heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

50012 Kettle setting heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 550013 Kettle setting heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 550014 Kettle setting heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 550015 Kettle setting heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 550016 Kettle setting heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 550017 Kettle setting heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 550018 Kettle setting heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 560001 Strip mill heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

60002 Strip mill heat stack 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

60003 Strip mill baghouse 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

60004 Low alpha baghouse 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

60005 Strip mill vent 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

60006 Strip mill vent 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

60007 Strip mill vent 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

60008 Strip mill vent 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 5

10001A1 New dump concentrate hopper (Part of 2000 SIP) a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

10001A2 New dump concentrate storage (Part of 2000 SIP) a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

10001B1 Load concentrate rail car a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 110001B2 Dump concentrate and secondary unloader (new location) a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

20001A Load sinter railcar/dump sinter a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 120001B Load sinter railcar/dump sinter a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

20002 Sinter unloading (NE corner of sinter building) a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

20003 Sinter loading/unloading (truck/rail) (at sinter building) a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 120004 Fume Loading a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

20004B New Railcar fume unloading (Part of 2002 SIP-wet vs dry loading) a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

20004C New Railcar fume unloading (Part of 2002 SIP-wet vs dry loading) a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

20005A Sinter mix room a 0.00 0.01 0.12 0.13 0.12 0.28 0.18 0.16 1.57 4.72 7.12 12.08 17.04 23.97 33.86 44.21 5.35 5.35 5

20005B Sinter mix room a 0.00 0.01 0.12 0.13 0.12 0.28 0.18 0.16 1.57 4.72 7.12 12.08 17.04 23.97 33.86 44.21 5.35 5.35 5

20005C Sinter mix room a 0.00 0.01 0.12 0.13 0.12 0.28 0.18 0.16 1.57 4.72 7.12 12.08 17.04 23.97 33.86 44.21 5.35 5.35 520005D Sinter mix room a 0.00 0.01 0.12 0.13 0.12 0.28 0.18 0.16 1.57 4.72 7.12 12.08 17.04 23.97 33.86 44.21 5.35 5.35 5

20005E Sinter mix room a 0.00 0.01 0.12 0.13 0.12 0.28 0.18 0.16 1.57 4.72 7.12 12.08 17.04 23.97 33.86 44.21 5.35 5.35 5

20005F Sinter mix room a 0.00 0.01 0.12 0.13 0.12 0.28 0.18 0.16 1.57 4.72 7.12 12.08 17.04 23.97 33.86 44.21 5.35 5.35 5

20006 Sinter building fugitives a 0.00 0.01 0.12 0.13 0.12 0.28 0.18 0.16 1.57 4.72 7.12 12.08 17.04 23.97 33.86 44.21 5.35 5.35 520007 #3 Baghouse roof vents a 0.00 0.01 0.12 0.13 0.12 0.28 0.18 0.16 1.57 4.72 7.12 12.08 17.04 23.97 33.86 44.21 5.35 5.35 530002 Blast furnace a 0.00 0.00 0.11 0.10 0.12 0.21 0.28 0.19 1.57 4.77 7.24 11.94 17.65 24.08 35.09 40.99 4.99 4.99 430011 #5 Baghouse roof vent a 0.00 0.00 0.11 0.10 0.12 0.21 0.28 0.19 1.57 4.77 7.24 11.94 17.65 24.08 35.09 40.99 4.99 4.99 430012 #5 Baghouse roof vent a 0.00 0.00 0.11 0.10 0.12 0.21 0.28 0.19 1.57 4.77 7.24 11.94 17.65 24.08 35.09 40.99 4.99 4.99 430013 #5 Baghouse roof vent a 0.00 0.00 0.11 0.10 0.12 0.21 0.28 0.19 1.57 4.77 7.24 11.94 17.65 24.08 35.09 40.99 4.99 4.99 4

D-37
 
Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter Case 

Emission 

Point ID 

Emission Point Description 

Mass Fraction Particle Diameter (μm) P

Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 B

40006 New dross plant fugitives (part of 2000 SIP) a 0.00 0.00 0.17 0.19 0.16 0.20 0.27 0.00 1.57 4.76 6.98 12.30 16.98 23.58 34.06 45.01 5.72 5.72 5

50006 New refinery plant fugitives (part of 2000 SIP w/install BH# 8&9) a 0.00 0.00 0.05 0.08 0.13 0.33 0.19 0.22 1.57 4.80 7.04 12.03 17.62 23.93 33.64 42.76 5.86 5.86 570001 Fugitive dross handling a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70007 Fugitive slag handling a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70009 Fugitive secondaries handling a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70002 Fugitive dross wind erosion a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170004 Fugitive concentrate wind erosion a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170006 Fugitive sinter wind erosion a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70008A Fugitive slag storage wind erosion a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170008B Fugitive slag storage wind erosion a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70010 Fugitive secondaries wind erosion a 0.07 0.20 0.20 0.18 0.16 0.19 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70100 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70101 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70102 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70103 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70104 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70105 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70106 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70107 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70108 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70109 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70110 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70111 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70112 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70113 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70114 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70115 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70116 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70117 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

D-38
 
Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter Case 

Emission 

Point ID 

Emission Point Description 

Mass Fraction Particle Diameter (μm) P

Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 B

70118 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70119 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70120 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70121 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70122 New area source input (Hwy 55 to Joachim bridge) 

segment AB a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70150 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170151 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170152 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170153 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170154 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70155 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70156 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170157 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170158 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170159 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70160 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70161 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170162 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170163 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170164 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70165 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70166 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170167 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170168 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

D-39
 
Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter Case 

Emission 

Point ID 

Emission Point Description 

Mass Fraction Particle Diameter (μm) P

Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 B

70169 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70170 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170171 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170172 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70173 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70174 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170175 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170176 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170177 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170178 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70179 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70180 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170181 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170182 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170183 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70184 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70185 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170186 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170187 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170188 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70189 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70190 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170191 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170192 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

D-40
 
Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter Case 

Emission 

Point ID 

Emission Point Description 

Mass Fraction Particle Diameter (μm) P

Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 B

70193 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70194 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170195 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170196 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70197 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70198 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170199 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170200 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170201 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170202 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70203 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70204 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170205 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170206 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170207 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70208 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70209 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170210 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170211 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170212 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70213 New area source input (Joachim bridge exit to plant entrance) segment BC a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70250 New area source input (plant entrance to NW corner of Stip Mill Blding/SMB) segment CD a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170251 New area source input (plant entrance to NW corner of Stip Mill Blding/SMB) segment CD a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170252 New area source input (plant entrance to NW corner of Stip Mill Blding/SMB) segment CD a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

D-41
 
Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter Case 

Emission 

Point ID 

Emission Point Description 

Mass Fraction Particle Diameter (μm) P

Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 B

70300 New area source input (NW corner of SMB to conc. hopper) segment DE a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70350 New area source input (conc. hopper to SW corner SMB) segment EF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170351 New area source input (conc. hopper to SW corner SMB) segment EF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170352 New area source input (conc. hopper to SW corner SMB) segment EF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70353 New area source input (conc. hopper to SW corner SMB) segment EF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70354 New area source input (conc. hopper to SW corner SMB) segment EF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170355 New area source input (conc. hopper to SW corner SMB) segment EF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170356 New area source input (conc. hopper to SW corner SMB) segment EF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170357 New area source input (conc. hopper to SW corner SMB) segment EF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170358 New area source input (conc. hopper to SW corner SMB) segment EF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70400 New area source input (NW corner SMB to SW corner of SMB ) segment DF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70401 New area source input (NW corner SMB to SW corner of SMB ) segment DF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170402 New area source input (NW corner SMB to SW corner of SMB ) segment DF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170403 New area source input (NW corner SMB to SW corner of SMB ) segment DF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170404 New area source input (NW corner SMB to SW corner of SMB ) segment DF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70405 New area source input (NW corner SMB to SW corner of SMB ) segment DF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70406 New area source input (NW corner SMB to SW corner of SMB ) segment DF a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170450 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170451 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170452 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70453 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70454 New area source input (SW corner SMB to North end of Slag Haul Road) segment FG a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170500 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170501 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

D-42
 
Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter Case 

Emission 

Point ID 

Emission Point Description 

Mass Fraction Particle Diameter (μm) P

Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 B

70502 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70503 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170504 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170505 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70506 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

D-43
 
Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter Case 

Emission 

Point ID 

Emission Point Description 

Mass Fraction Particle Diameter (μm) P

Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 B

70507 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70508 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170509 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170510 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70511 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70512 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170513 New area source input (North end of Slag Haul Road to refinery dock) segment GH a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170550 New area source input (South Slag Haul Road paved) segment GK a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170551 New area source input (South Slag Haul Road paved) segment GK a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170552 New area source input (South Slag Haul Road paved) segment GK a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70553 New area source input (South Slag Haul Road paved) segment GK a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70600 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170601 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170602 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170603 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70604 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70605 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170606 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170607 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170608 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70609 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70610 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170611 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170612 New area source input (north end of main building to refinery dock unpaved) segment HL a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

D-44
 
Attachment D-5. Particle Size Inputs by Emission Point for the Primary Pb Smelter Case 

Emission 

Point ID 

Emission Point Description 

Mass Fraction Particle Diameter (μm) P

Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin1 Bin2 B

70650 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70651 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70652 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70653 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70654 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70655 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70656 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70657 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70658 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70659 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70660 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70661 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70662 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70663 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70664 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70665 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70666 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70667 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70668 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70669 New area source input (sinter plant to sinter storage) 

segment IJ a 0.05 0.05 0.10 0.05 0.35 0.41 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70700 New area source input (south Slag Haul Road unpaved) segment KM a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

70701 New area source input (south Slag Haul Road unpaved) segment KM a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170702 New area source input (south Slag Haul Road unpaved) segment KM a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 170703 New area source input (south Slag Haul Road 

unpaved) segment KM a 0.05 0.10 0.16 0.29 0.20 0.20 - - 1.57 3.88 7.75 12.63 17.57 25.25 - - 1.00 1.00 1

a Emission point description derived from MDNR (2007b). 

D-45 
Attachment D-6. Building Downwash Parameters for the Primary Pb Smelter Case St

Emission Building Building Downwash Parameters (categorized in 10's of degrees) 

Point ID Parameter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 

BUILDHGT 27.43 41.10 41.10 22.86 21.30 21.30 21.30 21.30 21.30 21.30 21.30 21.30 21.30 21.30 27.43 41.10 41.10 41.10 41.10 41.10 41.10 27.30 21.30 21.30 21.30 21.30 21.30 21.30 21.30 21.3

BUILDWID 20.27 45.21 72.28 77.54 39.79 40.68 40.33 38.75 36.00 38.75 40.33 40.68 39.79 37.70 20.95 52.47 43.90 87.00 52.24 45.21 71.64 77.54 39.79 40.68 40.33 38.75 36.00 38.75 40.33 40.6

30001 BUILDLEN 12.16 74.07 162.98 137.22 37.70 34.45 30.17 24.96 19.00 24.96 30.17 34.45 37.70 39.79 17.29 72.71 71.95 142.00 83.88 74.07 85.92 80.25 37.70 34.45 30.17 24.96 19.00 24.96 30.17 34.4

XBADJ -54.05 79.53 71.08 22.93 -32.76 -26.83 -20.09 -12.74 -5.00 -3.36 -1.62 0.17 1.95 3.68 36.47 -152.58 -157.68 -231.00 -239.30 -153.60 -234.05 -160.15 -4.94 -7.62 -10.08 -12.22 -14.00 -21.60 -28.55 -34.6

YBADJ -13.03 42.71 53.25 41.32 -23.58 -25.63 -26.91 -27.37 -27.00 -25.81 -23.83 -21.13 -17.79 -13.91 -20.85 41.90 20.26 24.50 14.45 -42.71 -52.93 -41.32 23.58 25.63 26.91 27.37 27.00 25.81 23.83 21.1

BUILDHGT 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.1

BUILDWID 52.24 62.90 71.64 61.97 67.64 71.26 72.71 71.95 69.00 72.64 74.07 73.26 70.21 65.04 59.44 59.91 48.19 87.00 52.24 62.90 71.64 61.97 67.64 71.26 72.71 71.95 69.00 72.64 74.07 73.2

40004 BUILDLEN 83.88 86.21 85.92 70.21 65.04 59.44 52.47 43.90 34.00 35.62 45.21 54.42 61.97 67.64 71.26 83.22 81.62 142.00 83.88 86.21 85.92 70.21 65.04 59.44 52.47 43.90 34.00 35.62 45.21 54.4

XBADJ 29.84 24.77 18.94 -64.64 -69.20 -73.22 -75.44 -75.36 -73.00 -72.93 -75.70 -77.16 -76.27 -73.07 -67.64 -110.38 -112.67 -113.00 -113.71 -110.97 -104.86 -5.57 4.16 13.78 22.97 31.46 39.00 37.31 30.49 22.7

YBADJ 18.24 29.92 40.70 45.28 39.25 32.01 23.81 14.88 5.50 -3.70 -12.79 -21.49 -29.53 -36.68 -43.50 21.85 9.32 -29.50 -18.24 -29.92 -40.70 -45.28 -39.25 -32.01 -23.81 -14.88 -5.50 3.70 12.79 21.4

BUILDHGT 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.1

BUILDWID 52.24 62.90 54.42 61.97 67.64 71.26 72.71 71.95 69.00 72.64 74.07 73.26 70.21 65.04 59.44 59.91 48.19 87.00 52.24 62.90 54.42 61.97 67.64 71.26 72.71 71.95 69.00 72.64 74.07 73.2

40005 BUILDLEN 83.88 86.21 73.26 70.21 65.04 59.44 52.47 43.90 34.00 35.62 45.21 54.42 61.97 67.64 71.26 83.22 81.62 142.00 83.88 86.21 73.26 70.21 65.04 59.44 52.47 43.90 34.00 35.62 45.21 54.4

XBADJ 20.16 15.71 -66.27 -71.66 -74.86 -77.35 -77.92 -76.12 -72.00 -70.21 -71.34 -71.29 -69.08 -64.76 -58.48 -100.64 -102.65 -103.00 -104.04 -101.92 -6.98 1.44 9.83 17.91 25.45 32.21 38.00 34.59 26.13 16.8

YBADJ 15.52 25.56 44.08 38.09 30.94 22.85 14.07 4.86 -4.50 -13.37 -21.84 -29.65 -36.55 -42.34 -47.63 19.37 8.56 -28.50 -15.52 -25.56 -44.08 -38.09 -30.94 -22.85 -14.07 -4.86 4.50 13.37 21.84 29.6

BUILDHGT 41.10 27.30 27.43 27.43 21.30 21.30 18.30 18.30 22.86 22.86 22.86 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 27.30 18.30 18.30 18.30 18.30 18.30 18.30 22.86 22.86 22.86 41.1

BUILDWID 80.82 65.20 20.95 20.34 39.79 40.68 203.33 79.02 49.00 51.55 52.54 73.26 70.21 65.04 59.44 52.47 48.19 87.00 52.24 65.20 131.44 157.17 178.13 193.67 203.33 206.80 112.00 124.19 132.61 73.2

50007 BUILDLEN 153.73 78.35 17.29 19.11 37.70 34.45 98.90 29.25 19.00 27.22 34.61 54.42 61.97 67.64 71.26 72.71 81.62 142.00 83.88 78.35 192.17 176.20 154.88 128.85 98.90 65.95 80.00 79.48 100.14 54.4

XBADJ 44.98 38.16 -106.33 -107.40 -99.42 -95.31 -82.61 -81.30 -73.55 -82.62 -89.17 -127.52 -138.39 -145.05 -147.31 -145.10 -200.29 -200.64 -198.71 -116.51 -46.04 -40.22 -33.17 -25.11 -16.29 -6.98 -6.45 3.14 12.63 73.1

YBADJ 55.18 45.23 9.48 -7.63 -7.27 -21.15 -37.13 41.09 34.14 22.50 10.17 50.64 32.75 13.87 -6.22 -26.32 17.10 -37.05 -40.89 -45.23 5.02 13.93 22.43 30.24 37.13 42.90 -65.64 -58.82 -50.21 -50.6

BUILDHGT 41.10 21.30 27.43 27.43 21.30 21.30 21.30 18.30 18.30 22.86 22.86 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 18.30 18.30 18.30 18.30 18.30 18.30 18.30 18.30 22.86 22.86 41.1

BUILDWID 80.82 29.14 20.95 20.34 39.79 40.68 40.33 79.02 79.00 51.55 52.54 73.26 70.21 65.04 59.44 52.47 48.19 87.00 52.24 101.72 131.44 157.17 178.13 193.67 203.33 206.80 204.00 124.19 132.61 73.2

50008 BUILDLEN 153.73 37.51 17.29 19.11 37.70 34.45 30.17 29.25 26.00 27.22 34.61 54.42 61.97 67.64 71.26 72.71 81.62 142.00 83.88 202.30 192.17 176.20 154.88 128.85 98.90 65.95 36.00 79.48 100.14 54.4

XBADJ 50.69 -127.64 -101.40 -103.09 -95.86 -92.61 -86.55 -80.54 -80.81 -83.89 -91.42 -130.67 -142.35 -149.70 -152.51 -150.68 -206.10 -206.49 -204.43 -55.88 -50.98 -44.53 -36.73 -27.81 -18.05 -7.74 2.81 4.41 14.87 76.2

YBADJ 56.45 21.36 12.63 -3.67 -2.62 -15.95 -28.80 46.90 33.99 28.22 15.58 55.57 37.06 17.43 -3.52 -24.57 17.86 -37.31 -42.16 -6.30 1.87 9.97 17.78 25.04 31.55 37.09 41.51 -64.53 -55.61 -55.5

BUILDHGT 41.10 27.30 27.30 27.43 21.30 21.30 21.30 18.30 18.30 22.86 22.86 22.86 41.10 41.10 41.10 41.10 41.10 41.10 41.10 27.30 27.30 21.30 21.30 21.30 21.30 18.30 18.30 22.86 22.86 22.8

BUILDWID 80.82 65.20 72.47 20.34 39.79 40.68 40.33 79.02 79.00 51.55 52.54 51.94 70.21 65.04 59.44 52.47 43.90 87.00 52.24 65.20 72.47 37.70 39.79 40.68 40.33 79.02 204.00 51.55 52.54 51.9

50011 BUILDLEN 153.73 78.35 80.52 19.11 37.70 34.45 30.17 29.25 26.00 27.22 34.61 40.95 61.97 67.64 71.26 72.71 71.95 142.00 83.88 78.35 80.52 39.79 37.70 34.45 30.17 29.25 36.00 27.22 34.61 40.9

XBADJ 58.05 53.56 45.29 -88.83 -80.08 -75.80 -69.22 -63.20 -64.00 -68.12 -77.16 -83.86 -132.37 -142.35 -148.01 -149.17 -145.80 -211.00 -211.79 -131.91 -125.82 42.14 42.39 41.35 39.05 33.95 -14.00 40.90 42.55 42.9

YBADJ 40.68 33.22 48.82 -13.65 -9.97 -20.45 -30.31 48.42 38.50 35.58 25.57 14.79 51.32 33.21 13.30 -7.23 -27.53 -20.50 -26.39 -33.22 -48.82 -0.81 9.97 20.45 30.31 -48.42 37.00 -35.58 -25.57 -14.7

BUILDHGT 41.10 41.10 27.30 27.43 21.30 21.30 18.30 18.30 22.86 22.86 22.86 22.86 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 27.30 18.30 18.30 18.30 18.30 18.30 22.86 22.86 22.86 22.8

BUILDWID 80.82 77.23 72.47 20.34 39.79 40.68 203.33 79.02 49.00 51.55 52.54 51.94 70.21 65.04 59.44 52.47 43.90 87.00 52.24 62.90 72.47 157.17 178.13 193.67 203.33 79.02 49.00 51.55 52.54 51.9

50012 BUILDLEN 153.73 160.80 80.52 19.11 37.70 34.45 98.90 29.25 19.00 27.22 34.61 40.95 61.97 67.64 71.26 72.71 71.95 142.00 83.88 86.21 80.52 176.20 154.88 128.85 98.90 29.25 19.00 27.22 34.61 40.9

XBADJ 49.19 36.89 37.50 -95.72 -85.87 -80.30 -66.59 -64.76 -57.00 -66.55 -74.08 -79.36 -126.58 -135.46 -140.22 -140.72 -136.94 -202.00 -202.93 -197.68 -118.02 -51.90 -46.72 -40.12 -32.31 35.51 38.00 39.33 39.47 38.4

YBADJ 39.12 59.07 44.32 -19.43 -16.87 -28.25 -41.52 39.56 35.50 26.71 17.11 6.99 44.43 27.42 8.80 -10.31 -29.09 -20.50 -24.83 -51.90 -44.32 25.74 32.02 37.34 41.52 -39.56 -35.50 -26.71 -17.11 -6.99 

BUILDHGT 41.10 41.10 27.30 27.30 21.30 18.30 18.30 22.86 22.86 22.86 22.86 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 27.30 27.30 18.30 18.30 18.30 22.86 22.86 22.86 22.86 41.1

BUILDWID 80.82 77.23 72.47 77.54 39.79 193.67 76.63 51.55 49.00 51.55 52.54 73.26 70.21 65.04 59.44 52.47 43.90 87.00 52.24 62.90 72.47 77.54 178.13 193.67 76.63 51.55 49.00 51.55 52.54 73.2

50013 BUILDLEN 153.73 160.80 80.52 80.25 37.70 128.85 33.60 27.22 19.00 27.22 34.61 54.42 61.97 67.64 71.26 72.71 71.95 142.00 83.88 86.21 80.52 80.25 154.88 128.85 33.60 27.22 19.00 27.22 34.61 54.4

XBADJ 40.33 28.43 29.70 21.86 -91.65 -93.22 -66.64 -55.79 -57.00 -64.99 -71.01 -109.36 -120.80 -128.56 -132.42 -132.26 -128.07 -193.00 -194.06 -189.23 -110.23 -102.11 -40.94 -35.62 33.04 28.57 38.00 37.77 36.39 54.9

YBADJ 37.55 55.99 39.82 51.36 -23.76 -45.13 39.96 34.35 26.50 17.85 8.66 52.30 37.54 21.64 4.30 -13.38 -30.66 -20.50 -23.27 -48.83 -39.82 -51.36 38.92 45.13 -39.96 -34.35 -26.50 -17.85 -8.66 -52.3

BUILDHGT 41.10 41.10 27.30 27.30 21.30 18.30 22.86 22.86 22.86 22.86 22.86 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 27.30 27.30 18.30 18.30 22.86 22.86 22.86 22.86 22.86 41.1

BUILDWID 80.82 77.23 72.47 77.54 39.79 71.92 52.54 51.55 49.00 51.55 52.54 73.26 70.21 65.04 59.44 52.47 43.90 87.00 52.24 62.90 72.47 77.54 178.13 71.92 52.54 51.55 49.00 51.55 52.54 73.2

50014 BUILDLEN 153.73 160.80 80.52 80.25 37.70 45.56 34.61 27.22 19.00 27.22 34.61 54.42 61.97 67.64 71.26 72.71 71.95 142.00 83.88 86.21 80.52 80.25 154.88 45.56 34.61 27.22 19.00 27.22 34.61 54.4

XBADJ 32.45 20.91 22.77 15.73 -96.80 -68.93 -55.61 -57.18 -57.00 -63.60 -68.27 -105.36 -115.66 -122.44 -125.49 -124.74 -120.20 -185.00 -186.18 -181.71 -103.30 -95.98 -35.79 23.36 21.00 29.96 38.00 36.38 33.66 50.9

YBADJ 36.16 53.25 35.82 46.22 -29.89 41.08 33.63 26.47 18.50 9.97 1.14 45.37 31.41 16.49 0.30 -16.12 -32.05 -20.50 -21.88 -46.09 -35.82 -46.22 45.05 -41.08 -33.63 -26.47 -18.50 -9.97 -1.14 -45.3

BUILDHGT 41.10 41.10 27.30 27.30 27.30 22.86 22.86 22.86 22.86 22.86 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 27.30 27.30 27.30 22.86 22.86 22.86 22.86 22.86 41.10 41.1

BUILDWID 80.82 77.23 72.47 77.54 80.25 51.94 52.54 51.55 49.00 51.55 74.07 73.26 70.21 65.04 59.44 52.47 43.90 87.00 52.24 62.90 72.47 77.54 80.25 51.94 52.54 51.55 49.00 51.55 74.07 73.2

50015 BUILDLEN 153.73 160.80 80.52 80.25 77.54 40.95 34.61 27.22 19.00 27.22 45.21 54.42 61.97 67.64 71.26 72.71 71.95 142.00 83.88 86.21 80.52 80.25 77.54 40.95 34.61 27.22 19.00 27.22 45.21 54.4

XBADJ 23.59 12.45 14.98 8.84 2.43 -56.86 -58.69 -58.74 -57.00 -62.04 -88.79 -100.86 -109.87 -115.54 -117.70 -116.28 -111.33 -176.00 -177.32 -173.25 -95.50 -89.09 -79.97 15.91 24.08 31.52 38.00 34.82 43.58 46.4

YBADJ 34.60 50.18 31.32 40.43 48.32 31.98 25.17 17.60 9.50 1.11 49.49 37.57 24.51 10.71 -4.20 -19.20 -33.61 -20.50 -20.31 -43.01 -31.32 -40.43 -48.32 -31.98 -25.17 -17.60 -9.50 -1.11 -49.49 -37.5

BUILDHGT 41.10 41.10 27.30 27.30 27.30 27.30 27.30 22.86 22.86 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 27.30 27.30 27.30 27.30 27.30 22.86 22.86 41.10 41.10 41.1

BUILDWID 80.82 62.90 72.47 77.54 80.25 80.52 78.35 51.55 49.00 72.64 74.07 73.26 70.21 65.04 59.44 52.47 43.90 87.00 52.24 62.90 72.47 77.54 80.25 80.52 78.35 51.55 49.00 72.64 74.07 73.2

50016 BUILDLEN 153.73 86.21 80.52 80.25 77.54 72.47 65.20 27.22 19.00 35.62 45.21 54.42 61.97 67.64 71.26 72.71 71.95 142.00 83.88 86.21 80.52 80.25 77.54 72.47 65.20 27.22 19.00 35.62 45.21 54.4

XBADJ 14.72 78.59 7.19 1.95 -3.35 -8.55 -13.49 -60.30 -57.00 -73.45 -85.71 -96.36 -104.09 -108.65 -109.91 -107.83 -102.47 -167.00 -168.46 -164.80 -87.71 -82.20 -74.18 -63.92 -51.71 33.08 38.00 37.82 40.50 41.9

YBADJ 33.04 39.93 26.82 34.65 41.43 46.95 51.04 8.74 0.50 51.04 41.03 29.78 17.62 4.92 -8.70 -22.28 -35.17 -20.50 -18.75 -39.93 -26.82 -34.65 -41.43 -46.95 -51.04 -8.74 -0.50 -51.04 -41.03 -29.7

BUILDHGT 41.10 41.10 41.10 27.30 27.30 27.30 27.30 27.30 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 27.30 27.30 27.30 27.30 27.30 41.10 41.10 41.10 41.1

BUILDWID 52.24 62.90 71.64 77.54 80.25 80.52 78.35 73.80 69.00 72.64 74.07 73.26 70.21 65.04 59.44 52.47 43.90 87.00 52.24 62.90 71.64 77.54 80.25 80.52 78.35 73.80 69.00 72.64 74.07 73.2

50017 BUILDLEN 83.88 86.21 85.92 80.25 77.54 72.47 65.20 55.95 34.00 35.62 45.21 54.42 61.97 67.64 71.26 72.71 71.95 142.00 83.88 86.21 85.92 80.25 77.54 72.47 65.20 55.95 34.00 35.62 45.21 54.4

XBADJ 75.72 70.13 62.42 -4.95 -9.14 -13.05 -16.57 -19.58 -64.00 -71.88 -82.63 -91.86 -98.30 -101.75 -102.11 -99.37 -93.61 -158.00 -159.59 -156.34 -148.33 -75.30 -68.40 -59.42 -48.63 -36.37 30.00 36.26 37.42 37.4YBADJ 17.19 36.86 55.40 28.86 34.53 39.15 42.59 44.72 50.50 42.18 32.58 21.98 10.72 -0.86 -13.20 -25.35 -36.74 -20.50 -17.19 -36.86 -55.40 -28.86 -34.53 -39.15 -42.59 -44.72 -50.50 -42.18 -32.58 -21.9

BUILDHGT 41.10 41.10 41.10 27.30 27.30 27.30 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 41.10 27.30 27.30 27.30 41.10 41.10 41.10 41.10 41.10 41.1

BUILDWID 52.24 62.90 71.64 77.54 80.25 80.52 72.71 71.95 69.00 72.64 74.07 73.26 70.21 65.04 59.44 52.47 43.90 87.00 52.24 62.90 71.64 77.54 80.25 80.52 72.71 71.95 69.00 72.64 74.07 73.2

50018 BUILDLEN 83.88 86.21 85.92 80.25 77.54 72.47 52.47 43.90 34.00 35.62 45.21 54.42 61.97 67.64 71.26 72.71 71.95 142.00 83.88 86.21 85.92 80.25 77.54 72.47 52.47 43.90 34.00 35.62 45.21 54.4

XBADJ 66.85 61.67 54.62 -11.84 -14.92 -17.55 -54.67 -60.25 -64.00 -70.32 -79.56 -87.36 -92.52 -94.86 -94.32 -90.91 -84.74 -149.00 -150.73 -147.88 -140.54 -68.41 -62.61 -54.92 2.20 16.35 30.00 34.70 34.34 32.9

YBADJ 15.63 33.78 50.90 23.08 27.64 31.36 54.56 48.77 41.50 33.32 24.12 14.19 3.83 -6.65 -17.70 -28.43 -38.30 -20.50 -15.63 -33.78 -50.90 -23.08 -27.64 -31.36 -54.56 -48.77 -41.50 -33.32 -24.12 -14.1

BUILDHGT 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.2

BUILDWID 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.4

60001 BUILDLEN 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.8

XBADJ -20.39 -20.16 -19.32 -17.89 -15.92 -13.46 -10.60 -7.41 -4.00 -3.77 -3.42 -2.96 -2.42 -1.81 -1.13 -0.43 0.29 1.00 -7.00 -14.79 -22.13 -28.80 -34.60 -39.34 -42.88 -45.13 -46.00 -48.77 -50.07 -49.8

YBADJ -22.50 -23.32 -23.44 -22.84 -21.54 -19.59 -17.05 -13.99 -10.50 -6.69 -2.68 1.41 5.46 9.34 12.94 16.14 18.86 21.00 22.50 23.32 23.44 22.84 21.54 19.59 17.05 13.99 10.50 6.69 2.68 -1.4

BUILDHGT 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.2

BUILDWID 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.4

60002 BUILDLEN 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.8

XBADJ -27.51 -34.18 -39.82 -44.25 -47.33 -48.97 -49.13 -47.79 -45.00 -44.14 -41.94 -38.47 -33.83 -28.16 -21.63 -14.45 -6.83 1.00 0.12 -0.77 -1.63 -2.45 -3.19 -3.83 -4.36 -4.75 -5.00 -8.40 -11.54 -14.3

YBADJ 17.87 15.20 12.07 8.57 4.81 0.91 -3.03 -6.87 -10.50 -13.81 -16.71 -19.09 -20.90 -22.07 -22.57 -22.39 -21.52 -20.00 -17.87 -15.20 -12.07 -8.57 -4.81 -0.91 3.03 6.87 10.50 13.81 16.71 19.0

BUILDHGT 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.2BUILDWID 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.4

60003 BUILDLEN 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.8

XBADJ -26.18 -29.57 -32.05 -33.57 -34.06 -33.52 -31.96 -29.43 -26.00 -25.08 -23.41 -21.02 -17.99 -14.41 -10.40 -6.07 -1.56 3.00 -1.21 -5.39 -9.40 -13.13 -16.46 -19.28 -21.53 -23.11 -24.00 -27.46 -30.08 -31.7

YBADJ -1.19 -3.34 -5.38 -7.27 -8.93 -10.33 -11.40 -12.14 -12.50 -12.48 -12.09 -11.33 -10.22 -8.80 -7.12 -5.21 -3.16 -1.00 1.19 3.34 5.38 7.27 8.93 10.33 11.40 12.14 12.50 12.48 12.09 11.3

BUILDHGT 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 15.24 15.24 15.24 15.24 15.24 15.24 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 6.71 

BUILDWID 58.62 65.45 70.30 73.01 73.51 71.77 67.84 61.86 54.00 61.86 67.84 71.77 73.51 50.52 52.80 53.48 52.54 50.00 52.54 65.45 70.30 73.01 73.51 71.77 67.84 61.86 54.00 61.86 67.84 71.7

60004 BUILDLEN 61.86 67.84 71.77 73.51 73.01 70.30 65.45 58.62 50.00 58.62 65.45 70.30 73.01 46.69 41.45 34.96 27.39 19.00 27.39 67.84 71.77 73.51 73.01 70.30 65.45 58.62 50.00 58.62 65.45 70.3

XBADJ -5.21 -13.26 -20.90 -27.91 -34.08 -39.20 -43.14 -45.77 -47.00 -56.18 -63.66 -69.20 -72.64 -88.43 -89.32 -87.49 -83.01 -76.00 -75.37 -54.59 -50.86 -45.59 -38.94 -31.10 -22.31 -12.85 -3.00 -2.43 -1.79 -1.10 

YBADJ 26.88 30.93 34.05 36.14 37.12 36.98 35.72 33.36 30.00 25.72 20.67 14.98 8.84 25.89 14.20 2.07 -10.12 -22.00 -33.21 -30.93 -34.05 -36.14 -37.12 -36.98 -35.72 -33.36 -30.00 -25.72 -20.67 -14.9

D-46
 
Attachment D-6. Building Downwash Parameters for the Primary Pb Smelter Case St

Emission Building Building Downwash Parameters (categorized in 10's of degrees) 

Point ID Parameter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 

BUILDHGT 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.2BUILDWID 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.4

60005 BUILDLEN 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.8

XBADJ -10.60 -11.88 -12.79 -13.32 -13.45 -13.16 -12.48 -11.41 -10.00 -11.58 -12.82 -13.66 -14.09 -14.09 -13.66 -12.82 -11.58 -10.00 -16.79 -23.08 -28.66 -33.37 -37.07 -39.64 -41.01 -41.13 -40.00 -40.96 -40.67 -39.1YBADJ -14.69 -13.92 -12.74 -11.17 -9.26 -7.07 -4.66 -2.11 0.50 3.10 5.60 7.93 10.02 11.81 13.24 14.27 14.86 15.00 14.69 13.92 12.74 11.17 9.26 7.07 4.66 2.11 -0.50 -3.10 -5.60 -7.93 

BUILDHGT 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.2

BUILDWID 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.4

60006 BUILDLEN 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.8

XBADJ -12.34 -15.30 -17.79 -19.75 -21.11 -21.82 -21.87 -21.26 -20.00 -21.43 -22.21 -22.32 -21.75 -20.52 -18.66 -16.24 -13.32 -10.00 -15.06 -19.66 -23.66 -26.94 -29.41 -30.98 -31.61 -31.28 -30.00 -31.11 -31.27 -30.4

YBADJ -4.84 -4.53 -4.08 -3.51 -2.83 -2.07 -1.24 -0.38 0.50 1.36 2.18 2.93 3.60 4.15 4.58 4.87 5.01 5.00 4.84 4.53 4.08 3.51 2.83 2.07 1.24 0.38 -0.50 -1.36 -2.18 -2.93 

BUILDHGT 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.2

BUILDWID 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.4

60007 BUILDLEN 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.8

XBADJ -14.07 -18.72 -22.79 -26.18 -28.77 -30.48 -31.27 -31.11 -30.00 -31.28 -31.61 -30.98 -29.41 -26.94 -23.66 -19.66 -15.06 -10.00 -13.32 -16.24 -18.66 -20.52 -21.75 -22.32 -22.21 -21.43 -20.00 -21.26 -21.87 -21.8

YBADJ 5.01 4.87 4.58 4.15 3.60 2.93 2.18 1.36 0.50 -0.38 -1.24 -2.07 -2.83 -3.51 -4.08 -4.53 -4.84 -5.00 -5.01 -4.87 -4.58 -4.15 -3.60 -2.93 -2.18 -1.36 -0.50 0.38 1.24 2.07 

BUILDHGT 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.24 15.2

BUILDWID 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.4

60008 BUILDLEN 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.80 50.52 46.69 41.45 34.96 27.39 19.00 27.39 34.96 41.45 46.69 50.52 52.80 53.48 52.54 50.00 52.54 53.48 52.8

XBADJ -15.81 -22.14 -27.79 -32.61 -36.43 -39.14 -40.67 -40.96 -40.00 -41.13 -41.01 -39.64 -37.07 -33.37 -28.66 -23.08 -16.79 -10.00 -11.58 -12.82 -13.66 -14.09 -14.09 -13.66 -12.82 -11.58 -10.00 -11.41 -12.48 -13.1

YBADJ 14.86 14.27 13.24 11.81 10.02 7.93 5.60 3.10 0.50 -2.11 -4.66 -7.07 -9.26 -11.17 -12.74 -13.92 -14.69 -15.00 -14.86 -14.27 -13.24 -11.81 -10.02 -7.93 -5.60 -3.10 -0.50 2.11 4.66 7.07 

D-47
 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

46 

te

trations 

d Indoor D

7006031 737 0.032 0.013 40 Regression equation from EPA soil measurements vs. distance 20 

7009003 254 0.027 0.010 51 Regression equation from EPA soil measurements vs. distance 17 50 

7008004 197 0.089 0.036 186 Regression equation from EPA soil measurements vs. distance 57 99 

7006052 187 0.015 6.0E-03 51 Regression equation from EPA soil measurements vs. distance 10 50 

7006013 176 0.153 0.064 231 Regression equation from EPA soil measurements vs. distance 97 115 

7001044 164 0.017 7.0E-03 30 Regression equation from EPA soil measurements vs. distance 11 42 

7010001 145 0.019 8.0E-03 37 Regression equation from EPA soil measurements vs. distance 12 45 

7008007 141 0.057 0.023 105 Regression equation from EPA soil measurements vs. distance 36 70 

7006053 139 0.031 0.012 91 Regression equation from EPA soil measurements vs. distance 20 64 

7009001 120 0.046 0.017 85 Regression equation from EPA soil measurements vs. distance 29 62 

7008005 104 0.066 0.027 132 Regression equation from EPA soil measurements vs. distance 42 79 

6015002 95 0.134 0.056 282 Regression equation from EPA soil measurements vs. distance 85 134 

7008002 92 0.062 0.025 100 Regression equation from EPA soil measurements vs. distance 39 68 

D-48 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

65 

te

trations 

d Indoor D

7009002 86 0.045 0.017 91 Regression equation from EPA soil measurements vs. distance 29 

6012052 79 0.093 0.039 107 Regression equation from EPA soil measurements vs. distance 60 70 

7007003 77 0.083 0.034 195 Regression equation from EPA soil measurements vs. distance 53 102 

7007005 74 0.034 0.014 73 Regression equation from EPA soil measurements vs. distance 22 58 

7008003 72 0.047 0.019 83 Regression equation from EPA soil measurements vs. distance 30 62 

7007001 70 0.054 0.022 111 Regression equation from EPA soil measurements vs. distance 35 72 

7006054 63 0.047 0.018 139 Regression equation from EPA soil measurements vs. distance 30 82 

7006051 62 0.031 0.012 55 Regression equation from EPA soil measurements vs. distance 20 51 

7008006 58 0.057 0.023 112 Regression equation from EPA soil measurements vs. distance 36 72 

7007004 49 0.065 0.026 146 Regression equation from EPA soil measurements vs. distance 41 84 

7002029 46 0.133 0.054 222 Regression equation from EPA soil measurements vs. distance 85 112 

7006011 45 0.100 0.042 185 Regression equation from EPA soil measurements vs. distance 64 99 

2001044 34 0.026 0.010 44 Regression equation from EPA soil measurements vs. distance 17 47 

D-49 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

160 

te

trations 

d Indoor D

7002016 29 0.095 0.039 354 Regression equation from EPA soil measurements vs. distance 61 7002033 23 0.122 0.050 245 Regression equation from EPA soil measurements vs. distance 78 120 

8001017 22 0.059 0.024 120 Regression equation from EPA soil measurements vs. distance 38 75 

6014015 15 0.189 0.079 277 Regression equation from EPA soil measurements vs. distance 121 132 

6014027 14 0.449 0.188 223 Re-contamination sample in block NA NA 

8001030 14 0.078 0.031 145 Regression equation from EPA soil measurements vs. distance 50 84 

6014025 13 0.223 0.093 116 Re-contamination samples nearby NA NA 

7002032 13 0.107 0.044 242 Regression equation from EPA soil measurements vs. distance 68 119 

7002021 12 0.101 0.041 211 Regression equation from EPA soil measurements vs. distance 65 108 

6012003 12 0.022 9.0E-03 38 Regression equation from EPA soil measurements vs. distance 14 45 

6015001 11 0.171 0.072 42 Re-contamination sample in block NA NA 

3001003 11 0.034 0.013 43 Regression equation from EPA soil measurements vs. distance 21 47 

3001000 11 0.012 5.0E-03 27 Regression equation from EPA soil measurements vs. distance 8 41 

D-50 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

74 

te

trations 

d Indoor D

8001036 10 0.076 0.031 117 Regression equation from EPA soil measurements vs. distance 48 6012053 9 0.090 0.038 97 Regression equation from EPA soil measurements vs. distance 57 67 

2001050 9 0.019 7.0E-03 32 Regression equation from EPA soil measurements vs. distance 12 43 

6015016 8 0.238 0.100 105 Re-contamination sample in block NA NA 

8001035 8 0.071 0.029 119 Regression equation from EPA soil measurements vs. distance 45 75 

8001031 8 0.068 0.027 144 Regression equation from EPA soil measurements vs. distance 44 84 

8001037 8 0.063 0.025 113 Regression equation from EPA soil measurements vs. distance 40 72 

2001041 8 0.013 5.0E-03 28 Regression equation from EPA soil measurements vs. distance 8 42 

6012016 8 0.026 0.011 42 Regression equation from EPA soil measurements vs. distance 17 46 

7002030 7 0.116 0.047 205 Regression equation from EPA soil measurements vs. distance 74 106 

6012001 7 0.031 0.013 43 Regression equation from EPA soil measurements vs. distance 20 47 

6014051 6 0.444 0.186 184 Re-contamination sample in block NA NA 

6014044 6 0.240 0.101 159 Re-contamination samples nearby NA NA 

D-51 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

NA 

te

trations 

d Indoor D

6015017 6 0.222 0.093 153 

Re-contamination sample in block NA 

7002028 6 0.110 0.045 189 Regression equation from EPA soil measurements vs. distance 70 100 

6012021 6 0.042 0.018 53 Regression equation from EPA soil measurements vs. distance 27 50 

6014039 5 0.675 0.282 294 Re-contamination sample in block NA NA 

6014046 5 0.458 0.192 129 Re-contamination sample in block NA NA 

6015012 5 0.163 0.068 63 Re-contamination sample in block NA NA 

6015019 5 0.098 0.041 176 Re-contamination sample in block NA NA 

6012051 5 0.094 0.039 89 Regression equation from EPA soil measurements vs. distance 60 64 

2001058 5 0.023 9.0E-03 34 Regression equation from EPA soil measurements vs. distance 15 44 

6012013 5 0.028 0.012 42 Regression equation from EPA soil measurements vs. distance 18 46 

8001006 5 0.038 0.015 87 Regression equation from EPA soil measurements vs. distance 25 63 

8001049 4 0.088 0.035 585 Regression equation from EPA soil measurements vs. distance 56 244 

8001045 4 0.084 0.034 376 Regression equation from EPA soil measurements vs. distance 54 168 

D-52 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

117 

te

trations 

d Indoor D

7002031 4 0.110 0.045 237 Regression equation from EPA soil measurements vs. distance 70 6012014 4 0.024 0.010 39 Regression equation from EPA soil measurements vs. distance 16 45 

2001029 4 0.016 6.0E-03 38 Regression equation from EPA soil measurements vs. distance 10 45 

2001015 4 0.010 4.0E-03 26 Regression equation from EPA soil measurements vs. distance 7 41 

3001065 4 0.012 5.0E-03 28 Regression equation from EPA soil measurements vs. distance 8 42 

2001023 4 6.0E-03 2.0E-03 20 Regression equation from EPA soil measurements vs. distance 4 39 

7002011 3 0.097 0.040 556 Regression equation from EPA soil measurements vs. distance 62 234 

7002012 3 0.130 0.053 519 Regression equation from EPA soil measurements vs. distance 83 220 

6014018 3 0.158 0.066 400 Regression equation from EPA soil measurements vs. distance 101 177 

8001000 3 0.067 0.027 461 Regression equation from EPA soil measurements vs. distance 43 199 

8001044 3 0.081 0.033 373 Regression equation from EPA soil measurements vs. distance 52 167 

6012057 3 0.089 0.037 124 Regression equation from EPA soil measurements vs. distance 57 76 

2001059 3 0.037 0.015 36 Regression equation from EPA soil measurements vs. distance 24 44 

D-53 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

62 

te

trations 

d Indoor D

6012049 3 0.076 0.032 84 Regression equation from EPA soil measurements vs. distance 49 2001056 3 0.021 8.0E-03 35 Regression equation from EPA soil measurements vs. distance 13 44 

8001034 3 0.076 0.030 112 Regression equation from EPA soil measurements vs. distance 48 72 

8001032 3 0.067 0.027 141 Regression equation from EPA soil measurements vs. distance 42 83 

8001029 3 0.069 0.028 129 Regression equation from EPA soil measurements vs. distance 44 78 

2001057 3 0.022 9.0E-03 35 Regression equation from EPA soil measurements vs. distance 14 44 

6012044 3 0.054 0.022 68 Regression equation from EPA soil measurements vs. distance 34 56 

6012030 3 0.046 0.019 62 Regression equation from EPA soil measurements vs. distance 29 54 

6012019 3 0.032 0.014 46 Regression equation from EPA soil measurements vs. distance 21 48 

8001042 3 0.045 0.018 106 Regression equation from EPA soil measurements vs. distance 29 70 

6012022 3 0.031 0.013 51 Regression equation from EPA soil measurements vs. distance 20 50 

2001030 3 0.024 9.0E-03 48 Regression equation from EPA soil measurements vs. distance 15 49 

6014043 2 0.386 0.162 150 Re-contamination samples nearby NA NA 

D-54 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

NA 

te

trations 

d Indoor D

6014028 2 0.413 0.173 179 

Re-contamination samples nearby NA 

6015015 2 0.231 0.097 98 Re-contamination samples nearby NA NA 6014021 2 0.224 0.094 95 Re-contamination sample in block NA NA 

6015018 2 0.152 0.064 160 Re-contamination samples nearby NA NA 

8001047 2 0.092 0.037 447 Regression equation from EPA soil measurements vs. distance 59 194 

6012065 2 0.126 0.053 136 Regression equation from EPA soil measurements vs. distance 80 81 

7002014 2 0.110 0.045 276 Regression equation from EPA soil measurements vs. distance 70 132 

8001019 2 0.088 0.036 230 Regression equation from EPA soil measurements vs. distance 56 115 

6012062 2 0.075 0.031 108 Regression equation from EPA soil measurements vs. distance 48 70 

8001023 2 0.077 0.031 158 Regression equation from EPA soil measurements vs. distance 49 89 

2001051 2 0.017 7.0E-03 32 Regression equation from EPA soil measurements vs. distance 11 43 

6012041 2 0.046 0.019 60 Regression equation from EPA soil measurements vs. distance 30 53 

2001060 2 0.019 8.0E-03 37 Regression equation from EPA soil measurements vs. distance 12 45 

D-55 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

45 

te

trations 

d Indoor D

6012005 2 0.024 0.010 38 Regression equation from EPA soil measurements vs. distance 15 6012006 2 0.024 0.010 38 Regression equation from EPA soil measurements vs. distance 15 45 

3001017 2 0.042 0.016 87 Regression equation from EPA soil measurements vs. distance 27 63 

3001055 2 0.010 4.0E-03 24 Regression equation from EPA soil measurements vs. distance 6 40 

6014042 1 0.740 0.310 129 Re-contamination samples nearby NA NA 

6014052 1 0.283 0.118 216 Re-contamination sample in block NA NA 

6014032 1 0.669 0.280 162 Re-contamination samples nearby NA NA 

6014033 1 0.708 0.296 162 Re-contamination samples nearby NA NA 

6014049 1 0.333 0.139 167 Re-contamination sample in block NA NA 

6014029 1 0.380 0.159 135 Re-contamination sample in block NA NA 

6014050 1 0.274 0.115 171 Re-contamination sample in block NA NA 

6015013 1 0.173 0.072 53 Re-contamination samples nearby NA NA 6015011 1 0.137 0.057 123 Re-contamination samples nearby NA NA 

D-56 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

287 

te

trations 

d Indoor D

7002006 1 0.128 0.052 703 Regression equation from EPA soil measurements vs. distance 82 7002009 1 0.099 0.040 958 Regression equation from EPA soil measurements vs. distance 63 380 

6014006 1 0.154 0.065 153 Regression equation from EPA soil measurements vs. distance 98 87 

7002017 1 0.126 0.051 323 Regression equation from EPA soil measurements vs. distance 80 149 

6014007 1 0.160 0.067 200 Regression equation from EPA soil measurements vs. distance 102 104 

3001019 1 0.102 0.040 169 Regression equation from EPA soil measurements vs. distance 65 93 

2001066 1 0.028 0.011 41 Regression equation from EPA soil measurements vs. distance 18 46 

7002025 1 0.070 0.028 179 Regression equation from EPA soil measurements vs. distance 44 97 

6012031 1 0.044 0.018 60 Regression equation from EPA soil measurements vs. distance 28 53 

8001003 1 0.046 0.019 109 Regression equation from EPA soil measurements vs. distance 30 71 

6012018 1 0.028 0.012 43 Regression equation from EPA soil measurements vs. distance 18 47 

6012004 1 0.023 0.010 38 Regression equation from EPA soil measurements vs. distance 15 45 

3001015 1 0.027 0.010 70 Regression equation from EPA soil measurements vs. distance 17 57 

D-57 
Attachment D-7. Estimated Media Concentrations in Current NAAQS Scenario for the Primary Pb 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Predic

From 

Recent 

Air a 

Concen (μ

From 

Other a 

37 

te

trations 

d Indoor D

2001003 1 6.0E-03 2.0E-03 17 Regression equation from EPA soil measurements vs. distance 4 3001063 1 0.014 6.0E-03 30 Regression equation from EPA soil measurements vs. distance 9 42 

3001009 1 0.027 0.011 60 Regression equation from EPA soil measurements vs. distance 17 53 

3001066 1 0.013 5.0E-03 30 Regression equation from EPA soil measurements vs. distance 8 42 

2001104 1 0.023 9.0E-03 56 Regression equation from EPA soil measurements vs. distance 15 52 

2001101 1 0.023 9.0E-03 58 Regression equation from EPA soil measurements vs. distance 15 52 

2001022 1 7.0E-03 3.0E-03 22 Regression equation from EPA soil measurements vs. distance 5 39 

a “Other” refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air) and “recent air” refe

associated with outdoor ambient air. The H5 model does not separate out recent air from other air. Therefore, “NA” is indicated in thesmodel. 

D-58 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

7006031 737 0.014 6.0E-03 40 Regression equation from EPA soil measurements vs. distance 9 46 7009003 254 0.012 4.0E-03 51 Regression equation from EPA soil measurements vs. distance 8 50 7008004 197 0.039 0.016 186 Regression equation from EPA soil measurements vs. distance 25 99 7006052 187 7.0E-03 3.0E-03 51 Regression equation from EPA soil measurements vs. distance 4 50 7006013 176 0.067 0.028 231 Regression equation from EPA soil measurements vs. distance 43 115 7001044 164 8.0E-03 3.0E-03 30 Regression equation from EPA soil measurements vs. distance 5 42 7010001 145 9.0E-03 3.0E-03 37 Regression equation from EPA soil measurements vs. distance 5 45 7008007 141 0.025 0.010 105 Regression equation from EPA soil measurements vs. distance 16 70 7006053 139 0.014 5.0E-03 91 Regression equation from EPA soil measurements vs. distance 9 64 7009001 120 0.020 8.0E-03 85 Regression equation from EPA soil measurements vs. distance 13 62 7008005 104 0.029 0.012 132 Regression equation from EPA soil measurements vs. distance 19 79 6015002 95 0.059 0.025 282 Regression equation from EPA soil measurements vs. distance 38 134 7008002 92 0.027 0.011 100 Regression equation from EPA soil measurements vs. distance 17 68 

D-59 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

7009002 86 0.020 7.0E-03 91 Regression equation from EPA soil measurements vs. distance 13 65 6012052 79 0.041 0.017 107 Regression equation from EPA soil measurements vs. distance 26 70 7007003 77 0.037 0.015 195 Regression equation from EPA soil measurements vs. distance 23 102 7007005 74 0.015 6.0E-03 73 Regression equation from EPA soil measurements vs. distance 10 58 7008003 72 0.021 8.0E-03 83 Regression equation from EPA soil measurements vs. distance 13 62 7007001 70 0.024 0.010 111 Regression equation from EPA soil measurements vs. distance 15 72 7006054 63 0.021 8.0E-03 139 Regression equation from EPA soil measurements vs. distance 13 82 7006051 62 0.014 5.0E-03 55 Regression equation from EPA soil measurements vs. distance 9 51 7008006 58 0.025 0.010 112 Regression equation from EPA soil measurements vs. distance 16 72 7007004 49 0.029 0.012 146 Regression equation from EPA soil measurements vs. distance 18 84 7002029 46 0.059 0.024 222 Regression equation from EPA soil measurements vs. distance 37 112 7006011 45 0.044 0.018 185 Regression equation from EPA soil measurements vs. distance 28 99 2001044 34 0.012 5.0E-03 44 Regression equation from EPA soil measurements vs. distance 7 47 

D-60 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

7002016 29 0.042 0.017 354 Regression equation from EPA soil measurements vs. distance 27 160 7002033 23 0.054 0.022 245 Regression equation from EPA soil measurements vs. distance 34 120 8001017 22 0.026 0.011 120 Regression equation from EPA soil measurements vs. distance 17 75 6014015 15 0.083 0.035 277 Regression equation from EPA soil measurements vs. distance 53 132 6014027 14 0.198 0.083 223 Re-contamination sample in block NA NA 8001030 14 0.034 0.014 145 Regression equation from EPA soil measurements vs. distance 22 84 6014025 13 0.098 0.041 116 Re-contamination samples nearby NA NA 7002032 13 0.047 0.019 242 Regression equation from EPA soil measurements vs. distance 30 119 7002021 12 0.045 0.018 211 Regression equation from EPA soil measurements vs. distance 29 108 6012003 12 0.010 4.0E-03 38 Regression equation from EPA soil measurements vs. distance 6 45 6015001 11 0.075 0.032 42 Re-contamination sample in block NA NA 3001003 11 0.015 6.0E-03 43 Regression equation from EPA soil measurements vs. distance 9 47 3001000 11 5.0E-03 2.0E-03 27 Regression equation from EPA soil measurements vs. distance 3 41 

D-61 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

8001036 10 0.033 0.013 117 Regression equation from EPA soil measurements vs. distance 21 74 6012053 9 0.040 0.017 97 Regression equation from EPA soil measurements vs. distance 25 67 2001050 9 8.0E-03 3.0E-03 32 Regression equation from EPA soil measurements vs. distance 5 43 6015016 8 0.105 0.044 105 Re-contamination sample in block NA NA 8001035 8 0.031 0.013 119 Regression equation from EPA soil measurements vs. distance 20 75 8001031 8 0.030 0.012 144 Regression equation from EPA soil measurements vs. distance 19 84 8001037 8 0.028 0.011 113 Regression equation from EPA soil measurements vs. distance 18 72 2001041 8 6.0E-03 2.0E-03 28 Regression equation from EPA soil measurements vs. distance 4 42 6012016 8 0.012 5.0E-03 42 Regression equation from EPA soil measurements vs. distance 7 46 7002030 7 0.051 0.021 205 Regression equation from EPA soil measurements vs. distance 33 106 6012001 7 0.014 6.0E-03 43 Regression equation from EPA soil measurements vs. distance 9 47 6014051 6 0.195 0.082 184 Re-contamination sample in block NA NA 6014044 6 0.106 0.044 159 Re-contamination samples nearby NA NA 

D-62 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

6015017 6 0.098 0.041 153 Re-contamination sample in block NA NA 7002028 6 0.049 0.020 189 Regression equation from EPA soil measurements vs. distance 31 100 6012021 6 0.019 8.0E-03 53 Regression equation from EPA soil measurements vs. distance 12 50 6014039 5 0.297 0.124 294 Re-contamination sample in block NA NA 16014046 5 0.202 0.084 129 Re-contamination sample in block NA NA 6015012 5 0.072 0.030 63 Re-contamination sample in block NA NA 6015019 5 0.043 0.018 176 Re-contamination sample in block NA NA 6012051 5 0.041 0.017 89 Regression equation from EPA soil measurements vs. distance 26 64 2001058 5 0.010 4.0E-03 34 Regression equation from EPA soil measurements vs. distance 7 44 6012013 5 0.012 5.0E-03 42 Regression equation from EPA soil measurements vs. distance 8 46 8001006 5 0.017 7.0E-03 87 Regression equation from EPA soil measurements vs. distance 11 63 8001049 4 0.039 0.016 585 Regression equation from EPA soil measurements vs. distance 25 244 8001045 4 0.037 0.015 376 Regression equation from EPA soil measurements vs. distance 24 168 

D-63 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

7002031 4 0.048 0.020 237 Regression equation from EPA soil measurements vs. distance 31 117 6012014 4 0.011 4.0E-03 39 Regression equation from EPA soil measurements vs. distance 7 45 2001029 4 7.0E-03 3.0E-03 38 Regression equation from EPA soil measurements vs. distance 4 45 2001015 4 5.0E-03 2.0E-03 26 Regression equation from EPA soil measurements vs. distance 3 41 3001065 4 5.0E-03 2.0E-03 28 Regression equation from EPA soil measurements vs. distance 3 42 2001023 4 3.0E-03 1.0E-03 20 Regression equation from EPA soil measurements vs. distance 2 39 7002011 3 0.043 0.017 556 Regression equation from EPA soil measurements vs. distance 27 234 7002012 3 0.057 0.023 519 Regression equation from EPA soil measurements vs. distance 37 220 6014018 3 0.070 0.029 400 Regression equation from EPA soil measurements vs. distance 45 177 8001000 3 0.030 0.012 461 Regression equation from EPA soil measurements vs. distance 19 199 8001044 3 0.036 0.014 373 Regression equation from EPA soil measurements vs. distance 23 167 6012057 3 0.039 0.016 124 Regression equation from EPA soil measurements vs. distance 25 76 2001059 3 0.016 6.0E-03 36 Regression equation from EPA soil measurements vs. distance 10 44 

D-64 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

6012049 3 0.034 0.014 84 Regression equation from EPA soil measurements vs. distance 21 62 2001056 3 9.0E-03 4.0E-03 35 Regression equation from EPA soil measurements vs. distance 6 44 8001034 3 0.033 0.013 112 Regression equation from EPA soil measurements vs. distance 21 72 8001032 3 0.029 0.012 141 Regression equation from EPA soil measurements vs. distance 19 83 8001029 3 0.030 0.012 129 Regression equation from EPA soil measurements vs. distance 19 78 2001057 3 9.0E-03 4.0e-03 35 Regression equation from EPA soil measurements vs. distance 6 44 6012044 3 0.024 0.010 68 Regression equation from EPA soil measurements vs. distance 15 56 6012030 3 0.020 9.0E-03 62 Regression equation from EPA soil measurements vs. distance 13 54 6012019 3 0.014 6.0E-03 46 Regression equation from EPA soil measurements vs. distance 9 48 8001042 3 0.020 8.0E-03 106 Regression equation from EPA soil measurements vs. distance 13 70 6012022 3 0.014 6.0E-03 51 Regression equation from EPA soil measurements vs. distance 9 50 2001030 3 0.010 4.0E-03 48 Regression equation from EPA soil measurements vs. distance 7 49 6014043 2 0.170 0.071 150 Re-contamination samples nearby NA NA 

D-65 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

6014028 2 0.182 0.076 179 Re-contamination samples nearby NA NA 6015015 2 0.102 0.043 98 Re-contamination samples nearby NA NA 6014021 2 0.099 0.041 95 Re-contamination sample in block NA NA 6015018 2 0.067 0.028 160 Re-contamination samples nearby NA NA 8001047 2 0.040 0.016 447 Regression equation from EPA soil measurements vs. distance 26 194 6012065 2 0.055 0.023 136 Regression equation from EPA soil measurements vs. distance 35 81 7002014 2 0.048 0.020 276 Regression equation from EPA soil measurements vs. distance 31 132 8001019 2 0.039 0.016 230 Regression equation from EPA soil measurements vs. distance 25 115 6012062 2 0.033 0.014 108 Regression equation from EPA soil measurements vs. distance 21 70 8001023 2 0.034 0.014 158 Regression equation from EPA soil measurements vs. distance 22 89 2001051 2 8.0E-03 3.0E-03 32 Regression equation from EPA soil measurements vs. distance 5 43 6012041 2 0.020 9.0E-03 60 Regression equation from EPA soil measurements vs. distance 13 53 2001060 2 9.0E-03 3.0E-03 37 Regression equation from EPA soil measurements vs. distance 5 45 

D-66 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

6012005 2 0.011 4.0E-03 38 Regression equation from EPA soil measurements vs. distance 7 45 6012006 2 0.010 4.0E-03 38 Regression equation from EPA soil measurements vs. distance 7 45 3001017 2 0.018 7.0E-03 87 Regression equation from EPA soil measurements vs. distance 12 63 3001055 2 4.0E-03 2.0E-03 24 Regression equation from EPA soil measurements vs. distance 3 40 6014042 1 0.326 0.136 129 Re-contamination samples nearby NA NA 16014052 1 0.125 0.052 216 Re-contamination sample in block NA NA 6014032 1 0.295 0.123 162 Re-contamination samples nearby NA NA 16014033 1 0.312 0.130 162 Re-contamination samples nearby NA NA 16014049 1 0.147 0.061 167 Re-contamination sample in block NA NA 6014029 1 0.167 0.070 135 Re-contamination sample in block NA NA 6014050 1 0.121 0.051 171 Re-contamination sample in block NA NA 6015013 1 0.076 0.032 53 Re-contamination samples nearby NA NA 6015011 1 0.060 0.025 123 Re-contamination samples nearby NA NA 

D-67 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

7002006 1 0.056 0.023 703 Regression equation from EPA soil measurements vs. distance 36 287 7002009 1 0.044 0.018 958 Regression equation from EPA soil measurements vs. distance 28 380 6014006 1 0.068 0.028 153 Regression equation from EPA soil measurements vs. distance 43 87 7002017 1 0.056 0.023 323 Regression equation from EPA soil measurements vs. distance 35 149 6014007 1 0.071 0.030 200 Regression equation from EPA soil measurements vs. distance 45 104 3001019 1 0.045 0.018 169 Regression equation from EPA soil measurements vs. distance 29 93 2001066 1 0.012 5.0E-03 41 Regression equation from EPA soil measurements vs. distance 8 46 7002025 1 0.031 0.012 179 Regression equation from EPA soil measurements vs. distance 20 97 6012031 1 0.019 8.0E-03 60 Regression equation from EPA soil measurements vs. distance 12 53 8001003 1 0.020 8.0E-03 109 Regression equation from EPA soil measurements vs. distance 13 71 6012018 1 0.012 5.0E-03 43 Regression equation from EPA soil measurements vs. distance 8 47 6012004 1 0.010 4.0E-03 38 Regression equation from EPA soil measurements vs. distance 7 45 3001015 1 0.012 5.0E-03 70 Regression equation from EPA soil measurements vs. distance 7 57 

D-68 
Attachment D-8. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Predicted Indoor Du

Concentrations (μg/

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations From 

Recent 

Air a 

From 

Other a T

2001003 1 3.0E-03 1.0E-03 17 Regression equation from EPA soil measurements vs. distance 2 37 3001063 1 6.0E-03 2.0E-03 30 Regression equation from EPA soil measurements vs. distance 4 42 3001009 1 0.012 5.0E-03 60 Regression equation from EPA soil measurements vs. distance 8 53 3001066 1 6.0E-03 2.0E-03 30 Regression equation from EPA soil measurements vs. distance 4 42 2001104 1 0.010 4.0E-03 56 Regression equation from EPA soil measurements vs. distance 7 52 2001101 1 0.010 4.0E-03 58 Regression equation from EPA soil measurements vs. distance 6 52 2001022 1 3.0E-03 1E-03 22 Regression equation from EPA soil measurements vs. distance 2 39 

a “Other” refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air) and “recent air” ref

associated with outdoor ambient air. The H5 model does not separate out recent air from other air. Therefore, “NA” is indicated in thesmodel. 

D-69 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

4 46 

D

s (μ

T

u

g/

7006031 737 6.0E-03 2.2E-03 40 7009003 254 5E-03 1.8E-03 51 Regression equation from EPA soil measurements vs. distance 3 50 

7008004 197 0.016 6.3E-03 186 Regression equation from EPA soil measurements vs. distance 10 99 

7006052 187 3.0E-03 1.0E-03 51 Regression equation from EPA soil measurements vs. distance 2 50 

7006013 176 0.027 0.0112 231 Regression equation from EPA soil measurements vs. distance 17 115 

7001044 164 3.0E-03 1.2E-03 30 Regression equation from EPA soil measurements vs. distance 2 42 

7010001 145 3.0E-03 1.3E-03 37 Regression equation from EPA soil measurements vs. distance 2 45 

7008007 141 0.010 4.1E-03 105 Regression equation from EPA soil measurements vs. distance 6 70 

7006053 139 6.0E-03 2.1E-03 91 Regression equation from EPA soil measurements vs. distance 4 64 

7009001 120 8.0E-03 3.0E-03 85 Regression equation from EPA soil measurements vs. distance 5 62 

7008005 104 0.012 4.7E-03 132 Regression equation from EPA soil measurements vs. distance 7 79 

6015002 95 0.024 9.9E-03 282 Regression equation from EPA soil measurements vs. distance 15 134 

7008002 92 0.011 4.4E-03 100 Regression equation from EPA soil measurements vs. distance 7 68 

D-70 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

5 65 

D

s (μ

T

u

g/

7009002 86 8.0E-03 3.0E-03 91 

6012052 79 0.016 6.9E-03 107 Regression equation from EPA soil measurements vs. distance 10 70 

7007003 77 0.015 6.0E-03 195 Regression equation from EPA soil measurements vs. distance 9 102 

7007005 74 6.0E-03 2.5E-03 73 Regression equation from EPA soil measurements vs. distance 4 58 

7008003 72 8.0E-03 3.3E-03 83 Regression equation from EPA soil measurements vs. distance 5 62 

7007001 70 0.010 3.9E-03 111 Regression equation from EPA soil measurements vs. distance 6 72 

7006054 63 8.0E-03 3.1E-03 139 Regression equation from EPA soil measurements vs. distance 5 82 

7006051 62 6.0E-03 2.1E-03 55 Regression equation from EPA soil measurements vs. distance 4 51 

7008006 58 0.010 4.0E-03 112 Regression equation from EPA soil measurements vs. distance 6 72 

7007004 49 0.011 4.7E-03 146 Regression equation from EPA soil measurements vs. distance 7 84 

7002029 46 0.023 9.6E-03 222 Regression equation from EPA soil measurements vs. distance 15 112 

7006011 45 0.018 7.4E-03 185 Regression equation from EPA soil measurements vs. distance 11 99 

2001044 34 5.0E-03 1.8E-03 44 Regression equation from EPA soil measurements vs. distance 3 47 

D-71 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

11 160 

D

s (μ

T

u

g/

7002016 29 0.017 6.8E-03 354 

7002033 23 0.022 8.8E-03 245 Regression equation from EPA soil measurements vs. distance 14 120 

8001017 22 0.010 4.2E-03 120 Regression equation from EPA soil measurements vs. distance 7 75 

6014015 15 0.033 0.0139 277 Regression equation from EPA soil measurements vs. distance 21 132 

6014027 14 0.079 0.0331 223 Re-contamination sample in block NA NA 

8001030 14 0.014 5.5E-03 145 Regression equation from EPA soil measurements vs. distance 9 84 

6014025 13 0.039 0.0164 116 Re-contamination samples nearby NA NA 

7002032 13 0.019 7.7E-03 242 Regression equation from EPA soil measurements vs. distance 12 119 

7002021 12 0.018 7.3E-03 211 Regression equation from EPA soil measurements vs. distance 11 108 

6012003 12 4.0E-03 1.6E-03 38 Regression equation from EPA soil measurements vs. distance 2 45 

6015001 11 0.030 0.0126 42 Re-contamination sample in block NA NA 

3001003 11 6.0E-03 2.3E-03 43 Regression equation from EPA soil measurements vs. distance 4 47 

3001000 11 2.0E-03 8.0E-04 27 Regression equation from EPA soil measurements vs. distance 1 41 

D-72 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

9 74 

D

s (μ

T

u

g/

8001036 10 0.013 5.4E-03 117 6012053 9 0.016 6.6E-03 97 Regression equation from EPA soil measurements vs. distance 10 67 

2001050 9 3.0E-03 1.3E-03 32 Regression equation from EPA soil measurements vs. distance 2 43 

6015016 8 0.042 0.0176 105 Re-contamination sample in block NA NA 

8001035 8 0.013 5.0E-03 119 Regression equation from EPA soil measurements vs. distance 8 75 

8001031 8 0.012 4.8E-03 144 Regression equation from EPA soil measurements vs. distance 8 84 

8001037 8 0.011 4.5E-03 113 Regression equation from EPA soil measurements vs. distance 7 72 

2001041 8 2.0E-0E 9.0e-04 28 Regression equation from EPA soil measurements vs. distance 1 42 

6012016 8 5.0E-03 1.9E-03 42 Regression equation from EPA soil measurements vs. distance 3 46 

7002030 7 0.020 8.3E-03 205 Regression equation from EPA soil measurements vs. distance 13 106 

6012001 7 6.0E-03 2.3E-03 43 Regression equation from EPA soil measurements vs. distance 4 47 

6014051 6 0.078 0.0327 184 Re-contamination sample in block NA NA 

6014044 6 0.042 0.0177 159 Re-contamination samples nearby NA NA 

D-73 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Re-contamination sample in 

block 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

NA NA 

D

s (μ

T

u

g/

6015017 6 0.039 0.0163 153 7002028 6 0.019 7.9E-03 189 Regression equation from EPA soil measurements vs. distance 12 100 

6012021 6 7.0E-03 3.1E-03 53 Regression equation from EPA soil measurements vs. distance 5 50 

6014039 5 0.119 0.0498 294 Re-contamination sample in block NA NA 

6014046 5 0.081 0.0338 129 Re-contamination sample in block NA NA 

6015012 5 0.029 0.0120 63 Re-contamination sample in block NA NA 

6015019 5 0.017 7.2E-03 176 Re-contamination sample in block NA NA 

6012051 5 0.017 6.9E-03 89 Regression equation from EPA soil measurements vs. distance 11 64 

2001058 5 4.0E-03 1.6E-03 34 Regression equation from EPA soil measurements vs. distance 3 44 

6012013 5 5.0E-03 2.1E-03 42 Regression equation from EPA soil measurements vs. distance 3 46 

8001006 5 7.0E-03 2.7E-03 87 Regression equation from EPA soil measurements vs. distance 4 63 

8001049 4 0.015 6.2E-03 585 Regression equation from EPA soil measurements vs. distance 10 244 

8001045 4 0.015 6.0E-03 376 Regression equation from EPA soil measurements vs. distance 9 168 

D-74 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

12 117 

D

s (μ

T

u

g/

7002031 4 0.019 7.9E-03 237 

6012014 4 4.0E-03 1.8E-03 39 Regression equation from EPA soil measurements vs. distance 3 45 

2001029 4 3.0E-03 1.1E-03 38 Regression equation from EPA soil measurements vs. distance 2 45 

2001015 4 2.0E-03 7.0E-04 26 Regression equation from EPA soil measurements vs. distance 1 41 

3001065 4 2.0E-03 8.0E-04 28 Regression equation from EPA soil measurements vs. distance 1 42 

2001023 4 1.0E-03 4.0E-04 20 Regression equation from EPA soil measurements vs. distance 1 39 

7002011 3 0.017 7.0E-03 556 Regression equation from EPA soil measurements vs. distance 11 234 

7002012 3 0.023 9.3E-03 519 Regression equation from EPA soil measurements vs. distance 15 220 

6014018 3 0.028 0.0117 400 Regression equation from EPA soil measurements vs. distance 18 177 

8001000 3 0.012 4.8E-03 461 Regression equation from EPA soil measurements vs. distance 8 199 

8001044 3 0.014 5.8E-03 373 Regression equation from EPA soil measurements vs. distance 9 167 

6012057 3 0.016 6.6E-03 124 Regression equation from EPA soil measurements vs. distance 10 76 

2001059 3 7.0E-03 2.6E-03 36 Regression equation from EPA soil measurements vs. distance 4 44 

D-75 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

9 62 

D

s (μ

T

u

g/

6012049 3 0.013 5.6E-03 84 

2001056 3 4.0E-03 1.5E-03 35 Regression equation from EPA soil measurements vs. distance 2 44 

8001034 3 0.013 5.4E-03 112 Regression equation from EPA soil measurements vs. distance 9 72 

8001032 3 0.012 4.7E-03 141 Regression equation from EPA soil measurements vs. distance 7 83 

8001029 3 0.012 4.9E-03 129 Regression equation from EPA soil measurements vs. distance 8 78 

2001057 3 4.0E-03 1.5E-03 35 Regression equation from EPA soil measurements vs. distance 2 44 

6012044 3 9.0E-03 4.0E-03 68 Regression equation from EPA soil measurements vs. distance 6 56 

6012030 3 8.0E-03 3.4E-03 62 Regression equation from EPA soil measurements vs. distance 5 54 

6012019 3 6.0E-03 2.4E-03 46 Regression equation from EPA soil measurements vs. distance 4 48 

8001042 3 8.0E-03 3.2E-03 106 Regression equation from EPA soil measurements vs. distance 5 70 

6012022 3 6.0E-03 2.3E-03 51 Regression equation from EPA soil measurements vs. distance 4 50 

2001030 3 4.0E-03 1.6E-03 48 Regression equation from EPA soil measurements vs. distance 3 49 

6014043 2 0.068 0.0285 150 Re-contamination samples nearby NA NA 

D-76 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Re-contamination samples 

nearby 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

NA NA 

D

s (μ

T

u

g/

6014028 2 0.073 0.0305 179 6015015 2 0.041 0.0171 98 Re-contamination samples nearby NA NA 

6014021 2 0.040 0.0165 95 Re-contamination sample in block NA NA 

6015018 2 0.027 0.0112 160 Re-contamination samples nearby NA NA 

8001047 2 0.016 6.5E-03 447 Regression equation from EPA soil measurements vs. distance 10 194 

6012065 2 0.022 9.3E-03 136 Regression equation from EPA soil measurements vs. distance 14 81 

7002014 2 0.019 7.9E-03 276 Regression equation from EPA soil measurements vs. distance 12 132 

8001019 2 0.016 6.3E-03 230 Regression equation from EPA soil measurements vs. distance 10 115 

6012062 2 0.013 5.5E-03 108 Regression equation from EPA soil measurements vs. distance 8 70 

8001023 2 0.014 5.5E-03 158 Regression equation from EPA soil measurements vs. distance 9 89 

2001051 2 3.0E-03 1.2E-03 32 Regression equation from EPA soil measurements vs. distance 2 43 

6012041 2 8.0E-03 3.4E-03 60 Regression equation from EPA soil measurements vs. distance 5 53 

2001060 2 3.0E--03 1.4E-03 37 Regression equation from EPA soil measurements vs. distance 2 45 

D-77 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

3 45 

D

s (μ

T

u

g/

6012005 2 4.0E-03 1.8E-03 38 

6012006 2 4.0E-03 1.7E-03 38 Regression equation from EPA soil measurements vs. distance 3 45 

3001017 2 7.0E-03 2.9E-03 87 Regression equation from EPA soil measurements vs. distance 5 63 

3001055 2 2.0E-03 7.0E-04 24 Regression equation from EPA soil measurements vs. distance 1 40 

6014042 1 0.130 0.0546 129 Re-contamination samples nearby NA NA 6014052 1 0.050 0.0208 216 Re-contamination sample in block NA NA 

6014032 1 0.118 0.0493 162 Re-contamination samples nearby NA NA 

6014033 1 0.125 0.0522 162 Re-contamination samples nearby NA NA 6014049 1 0.059 0.0245 167 Re-contamination sample in block NA NA 

6014029 1 0.067 0.0280 135 Re-contamination sample in block NA NA 

6014050 1 0.048 0.0202 171 Re-contamination sample in block NA NA 

6015013 1 0.030 0.0128 53 Re-contamination samples nearby NA NA 

6015011 1 0.024 0.0101 123 Re-contamination samples nearby NA NA 

D-78 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

14 287 

D

s (μ

T

u

g/

7002006 1 0.023 9.2E-03 703 

7002009 1 0.017 7.1E-03 958 Regression equation from EPA soil measurements vs. distance 11 380 

6014006 1 0.027 0.0114 153 Regression equation from EPA soil measurements vs. distance 17 87 

7002017 1 0.022 9.1E-03 323 Regression equation from EPA soil measurements vs. distance 14 149 

6014007 1 0.028 0.0118 200 Regression equation from EPA soil measurements vs. distance 18 104 

3001019 1 0.018 7.0E-03 169 Regression equation from EPA soil measurements vs. distance 12 93 

2001066 1 5.0E-03 2.0E-03 41 Regression equation from EPA soil measurements vs. distance 3 46 

7002025 1 0.012 5.0E-03 179 Regression equation from EPA soil measurements vs. distance 8 97 

6012031 1 8.0E-03 3.2E-03 60 Regression equation from EPA soil measurements vs. distance 5 53 

8001003 1 8.0E-03 3.3E-03 109 Regression equation from EPA soil measurements vs. distance 5 71 

6012018 1 5.0E-03 2.1E-03 43 Regression equation from EPA soil measurements vs. distance 3 47 

6012004 1 4.0E-03 1.7E-03 38 Regression equation from EPA soil measurements vs. distance 3 45 

3001015 1 5.0E-03 1.8E-03 70 Regression equation from EPA soil measurements vs. distance 3 57 

D-79 
Attachment D-9. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-month

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor 

Concentration

From 

Recent 

Air a 

From 

Other a 

1 37 

D

s (μ

T

u

g/

2001003 1 1.0E-03 4.0E-04 17 

3001063 1 3.0E-03 1.0e-03 30 Regression equation from EPA soil measurements vs. distance 2 42 

3001009 1 5.0E-03 1.9E-03 60 Regression equation from EPA soil measurements vs. distance 3 53 

3001066 1 2.0E-03 9.0E-04 30 Regression equation from EPA soil measurements vs. distance 1 42 

2001104 1 4.0E-03 1.6E-03 56 Regression equation from EPA soil measurements vs. distance 3 52 

2001101 1 4.0E-03 1.6E-03 58 Regression equation from EPA soil measurements vs. distance 3 52 

2001022 1 1.0E-03 5.0E-04 22 Regression equation from EPA soil measurements vs. distance 1 39 

a “Other” refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air) and “recent air” refe

associated with outdoor ambient air. The H5 model does not separate out recent air from other air. Therefore, “NA” is indicated in thesmodel. 

D-80 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

1 46 

s (μ

us

T

g/g

7006031 737 1.4E-03 6.0E-04 40 

7009003 254 1.2E-03 4.0E-04 51 Regression equation from EPA soil measurements vs. distance 1 50 

7008004 197 3.9E-03 1.6E-03 186 Regression equation from EPA soil measurements vs. distance 2 99 

7006052 187 7.0E-04 3.0E-04 51 Regression equation from EPA soil measurements vs. distance 0 50 

7006013 176 6.7E-03 2.8E-03 231 Regression equation from EPA soil measurements vs. distance 4 115 7001044 164 8.0E-04 3.0E-04 30 Regression equation from EPA soil measurements vs. distance 0 42 

7010001 145 9.0E-04 3.0E-04 37 Regression equation from EPA soil measurements vs. distance 1 45 

7008007 141 2.5E-03 1.0E-03 105 Regression equation from EPA soil measurements vs. distance 2 70 

7006053 139 1.4E-03 5.0E-04 91 Regression equation from EPA soil measurements vs. distance 1 64 

7009001 120 2.0E-03 8.0E-04 85 Regression equation from EPA soil measurements vs. distance 1 62 

7008005 104 2.9E-03 1.2E-03 132 Regression equation from EPA soil measurements vs. distance 2 79 

6015002 95 5.9E-03 2.5E-03 282 Regression equation from EPA soil measurements vs. distance 4 134 7008002 92 2.7E-03 1.1E-03 100 Regression equation from EPA soil measurements vs. distance 2 68 

D-81 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

1 65 

s (μ

us

T

g/g

7009002 86 2.0E-03 7.0E-04 91 6012052 79 4.1E-03 1.7E-03 107 Regression equation from EPA soil measurements vs. distance 3 70 

7007003 77 3.7E-03 1.5E-03 195 Regression equation from EPA soil measurements vs. distance 2 102 7007005 74 1.5E-03 6.0E-04 73 Regression equation from EPA soil measurements vs. distance 1 58 

7008003 72 2.1E-03 8.0E-04 83 Regression equation from EPA soil measurements vs. distance 1 62 

7007001 70 2.4E-03 1.0E-03 111 Regression equation from EPA soil measurements vs. distance 2 72 

7006054 63 2.1E-03 8.0E-04 139 Regression equation from EPA soil measurements vs. distance 1 82 

7006051 62 1.4E-03 5.0E-04 55 Regression equation from EPA soil measurements vs. distance 1 51 

7008006 58 2.5E-03 1.0E-03 112 Regression equation from EPA soil measurements vs. distance 2 72 

7007004 49 2.9E-03 1.2E-03 146 Regression equation from EPA soil measurements vs. distance 2 84 

7002029 46 5.9E-03 2.4E-03 222 Regression equation from EPA soil measurements vs. distance 4 112 7006011 45 4.4E-03 1.8E-03 185 Regression equation from EPA soil measurements vs. distance 3 99 

2001044 34 1.2E-03 5.0E-04 44 Regression equation from EPA soil measurements vs. distance 1 47 

D-82 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

3 160 

s (μ

us

T

g/g

7002016 29 4.2E-03 1.7E-03 354 

7002033 23 5.4E-03 2.2E-03 245 Regression equation from EPA soil measurements vs. distance 3 120 8001017 22 2.6E-03 1.1E-03 120 Regression equation from EPA soil measurements vs. distance 2 75 

6014015 15 8.3E-03 3.5E-03 277 Regression equation from EPA soil measurements vs. distance 5 132 6014027 14 0.020 8.3E-03 223 Re-contamination sample in block NA NA 8001030 14 3.4E-03 1.4E-03 145 Regression equation from EPA soil measurements vs. distance 2 84 

6014025 13 0.010 4.1E-03 116 Re-contamination samples nearby NA NA 

7002032 13 4.7E-03 1.9E-03 242 Regression equation from EPA soil measurements vs. distance 3 119 7002021 12 4.5E-03 1.8E-03 211 Regression equation from EPA soil measurements vs. distance 3 108 6012003 12 1.0E-03 4.0E-04 38 Regression equation from EPA soil measurements vs. distance 1 45 

6015001 11 8E-03 3.2E-03 42 Re-contamination sample in block NA NA 

3001003 11 1.5E-03 6.0E-04 43 Regression equation from EPA soil measurements vs. distance 1 47 

3001000 11 5E-04 2.0E-04 27 Regression equation from EPA soil measurements vs. distance 0 41 

D-83 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

2 74 

s (μ

us

T

g/g

8001036 10 3.3E-03 1.3E-03 117 

6012053 9 4.0E-03 1.7E-03 97 Regression equation from EPA soil measurements vs. distance 3 67 

2001050 9 8.0E-04 3.0E-04 32 Regression equation from EPA soil measurements vs. distance 1 43 

6015016 8 0.0105 4.4E-03 105 Re-contamination sample in block NA NA 8001035 8 3.1E-03 1.3E-03 119 Regression equation from EPA soil measurements vs. distance 2 75 

8001031 8 3.0E-03 1.2E-03 144 Regression equation from EPA soil measurements vs. distance 2 84 

8001037 8 2.8E-03 1.1E-03 113 Regression equation from EPA soil measurements vs. distance 2 72 

2001041 8 6.0E-04 2.0E-04 28 Regression equation from EPA soil measurements vs. distance 0 42 

6012016 8 1.2E-03 5.0E-04 42 Regression equation from EPA soil measurements vs. distance 1 46 

7002030 7 5.1E-03 2.1E-03 205 Regression equation from EPA soil measurements vs. distance 3 106 6012001 7 1.4E-03 6.0E-04 43 Regression equation from EPA soil measurements vs. distance 1 47 

6014051 6 0.020 8.2E-03 184 Re-contamination sample in block NA NA 

6014044 6 0.011 4.4E-03 159 Re-contamination samples nearby NA NA 

D-84 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Re-contamination sample in 

block 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

NA NA 

s (μ

us

T

g/g

6015017 6 .010 4.1E-03 153 7002028 6 4.9E-03 2.0E-03 189 Regression equation from EPA soil measurements vs. distance 3 100 6012021 6 1.9E-03 8.0E-04 53 Regression equation from EPA soil measurements vs. distance 1 50 

6014039 5 0.0297 0.0124 294 Re-contamination sample in block NA NA 

6014046 5 0.0202 8.4E-03 129 Re-contamination sample in block NA NA 

6015012 5 7E-03 3.0E-03 63 Re-contamination sample in block NA NA 

6015019 5 4E-03 1.8E-03 176 Re-contamination sample in block NA NA 

6012051 5 4.1E-03 1.7E-03 89 Regression equation from EPA soil measurements vs. distance 3 64 

2001058 5 1.0E-03 4.0E-04 34 Regression equation from EPA soil measurements vs. distance 1 44 

6012013 5 1.2E-03 5.0E-04 42 Regression equation from EPA soil measurements vs. distance 1 46 

8001006 5 1.7E-03 7.0E-04 87 Regression equation from EPA soil measurements vs. distance 1 63 

8001049 4 3.9E-03 1.6E-03 585 Regression equation from EPA soil measurements vs. distance 2 244 28001045 4 3.7E-03 1.5E-03 376 Regression equation from EPA soil measurements vs. distance 2 168 

D-85 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

3 117 

s (μ

us

T

g/g

7002031 4 4.8E-03 2.0E-03 237 

6012014 4 1.1E-03 4.0E-04 39 Regression equation from EPA soil measurements vs. distance 1 45 

2001029 4 7.0E-04 3.0E-04 38 Regression equation from EPA soil measurements vs. distance 0 45 

2001015 4 5.0E-04 2.0E-04 26 Regression equation from EPA soil measurements vs. distance 0 41 

3001065 4 5.0E-04 2.0E-04 28 Regression equation from EPA soil measurements vs. distance 0 42 

2001023 4 3.0E-04 1.0E-04 20 Regression equation from EPA soil measurements vs. distance 0 39 

7002011 3 4.3E-03 1.7E-03 556 Regression equation from EPA soil measurements vs. distance 3 234 27002012 3 5.7E-03 2.3E-03 519 Regression equation from EPA soil measurements vs. distance 4 220 26014018 3 7.0E-03 2.9E-03 400 Regression equation from EPA soil measurements vs. distance 4 177 8001000 3 3.0E-03 1.2E-03 461 Regression equation from EPA soil measurements vs. distance 2 199 28001044 3 3.6E-03 1.4E-03 373 Regression equation from EPA soil measurements vs. distance 2 167 6012057 3 3.9E-03 1.6E-03 124 Regression equation from EPA soil measurements vs. distance 3 76 

2001059 3 1.6E-03 6.0E-04 36 Regression equation from EPA soil measurements vs. distance 1 44 

D-86 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

2 62 

s (μ

us

T

g/g

6012049 3 3.4E-03 1.4E-03 84 2001056 3 9.0E-04 4.0E-04 35 Regression equation from EPA soil measurements vs. distance 1 44 

8001034 3 3.3E-03 1.3E-03 112 Regression equation from EPA soil measurements vs. distance 2 72 

8001032 3 2.9E-03 1.2E-03 141 Regression equation from EPA soil measurements vs. distance 2 83 

8001029 3 3.0E-03 1.2E-03 129 Regression equation from EPA soil measurements vs. distance 2 78 

2001057 3 9.0E-04 4.0E-04 35 Regression equation from EPA soil measurements vs. distance 1 44 

6012044 3 2.4E-03 1.0E-03 68 Regression equation from EPA soil measurements vs. distance 2 56 

6012030 3 2.0E-03 9.0E-04 62 Regression equation from EPA soil measurements vs. distance 1 54 

6012019 3 1.4E-03 6.0E-04 46 Regression equation from EPA soil measurements vs. distance 1 48 

8001042 3 2.0E-03 8.0E-04 106 Regression equation from EPA soil measurements vs. distance 1 70 

6012022 3 1.4E-03 6.0E-04 51 Regression equation from EPA soil measurements vs. distance 1 50 

2001030 3 1.0E-03 4.0E-04 48 Regression equation from EPA soil measurements vs. distance 1 49 

6014043 2 0.0170 7.1E-03 150 Re-contamination samples nearby NA NA 

D-87 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Re-contamination samples 

nearby 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

NA NA 

s (μ

us

T

g/g

6014028 2 0.0182 7.6E-03 179 

6015015 2 0.0102 4.3E-03 98 Re-contamination samples nearby NA NA 

6014021 2 0.010 4.1E-03 95 Re-contamination sample in block NA NA 

6015018 2 7E-03 2.8E-03 160 Re-contamination samples nearby NA NA 

8001047 2 4.0E-03 1.6E-03 447 Regression equation from EPA soil measurements vs. distance 3 194 6012065 2 5.5E-03 2.3E-03 136 Regression equation from EPA soil measurements vs. distance 4 81 

7002014 2 4.8E-03 2.0E-03 276 Regression equation from EPA soil measurements vs. distance 3 132 8001019 2 3.9E-03 1.6E-03 230 Regression equation from EPA soil measurements vs. distance 2 115 6012062 2 3.3E-03 1.4E-03 108 Regression equation from EPA soil measurements vs. distance 2 70 

8001023 2 3.4E-03 1.4E-03 158 Regression equation from EPA soil measurements vs. distance 2 89 

2001051 2 8.0E-04 3.0E-04 32 Regression equation from EPA soil measurements vs. distance 0 43 

6012041 2 2.0E-03 9.0E-04 60 Regression equation from EPA soil measurements vs. distance 1 53 

2001060 2 9.0E-04 3.0E-04 37 Regression equation from EPA soil measurements vs. distance 1 45 

D-88 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

1 45 

s (μ

us

T

g/g

6012005 2 1.1E-03 4.0E-04 38 6012006 2 1.0E-03 4.0E-04 38 Regression equation from EPA soil measurements vs. distance 1 45 

3001017 2 1.8E-03 7.0E-04 87 Regression equation from EPA soil measurements vs. distance 1 63 

3001055 2 4.0E-04 2.0E-04 24 Regression equation from EPA soil measurements vs. distance 0 40 

6014042 1 0.033 0.0136 129 Re-contamination samples nearby NA NA 26014052 1 0.012 5.2E-03 216 Re-contamination sample in block NA NA 

6014032 1 0.029 0.0123 162 Re-contamination samples nearby NA NA 6014033 1 0.031 0.0130 162 Re-contamination samples nearby NA NA 6014049 1 0.015 6.1E-03 167 Re-contamination sample in block NA NA 

6014029 1 0.017 7.0E-03 135 Re-contamination sample in block NA NA 

6014050 1 0.012 5.1E-03 171 Re-contamination sample in block NA NA 

6015013 1 7.6E-03 3.2E-03 53 Re-contamination samples nearby NA NA 

6015011 1 6.0E-03 2.5E-03 123 Re-contamination samples nearby NA NA 

D-89 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

4 287 

s (μ

us

T

2

g/g

7002006 1 5.6E-03 2.3E-03 703 

7002009 1 4.4E-03 1.8E-03 958 Regression equation from EPA soil measurements vs. distance 3 380 36014006 1 6.8E-03 2.8E-03 153 Regression equation from EPA soil measurements vs. distance 4 87 

7002017 1 5.6E-03 2.3E-03 323 Regression equation from EPA soil measurements vs. distance 4 149 6014007 1 7.1E-03 3.0E-03 200 Regression equation from EPA soil measurements vs. distance 5 104 3001019 1 4.5E-03 1.8E-03 169 Regression equation from EPA soil measurements vs. distance 3 93 

2001066 1 1.2E-03 5.0E-04 41 Regression equation from EPA soil measurements vs. distance 1 46 

7002025 1 3.1E-03 1.2E-03 179 Regression equation from EPA soil measurements vs. distance 2 97 

6012031 1 1.9E-03 8.0E-04 60 Regression equation from EPA soil measurements vs. distance 1 53 

8001003 1 2.0E-03 8.0E-04 109 Regression equation from EPA soil measurements vs. distance 1 71 

6012018 1 1.2E-03 5.0E-04 43 Regression equation from EPA soil measurements vs. distance 1 47 

6012004 1 1.0E-03 4.0E-04 38 Regression equation from EPA soil measurements vs. distance 1 45 

3001015 1 1.2E-03 5.0E-04 70 Regression equation from EPA soil measurements vs. distance 1 57 

D-90 
Attachment D-10. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3 max-mont

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Soil 

Concentration 

(μg/g) 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA 

soil measurements vs. distance 

Predicted Indoor D

Concentration

From 

Recent 

Air a 

From 

Other a 

0 37 

s (μ

us

T

g/g

2001003 1 3.0E-04 1.0E-04 17 3001063 1 6.0E-04 2.0E-04 30 Regression equation from EPA soil measurements vs. distance 0 42 

3001009 1 1.2E-03 5.0E-04 60 Regression equation from EPA soil measurements vs. distance 1 53 

3001066 1 6.0E-04 2.0E-04 30 Regression equation from EPA soil measurements vs. distance 0 42 

2001104 1 1.0E-03 4.0E-04 56 Regression equation from EPA soil measurements vs. distance 1 52 

2001101 1 1.0E-03 4.0E-04 58 Regression equation from EPA soil measurements vs. distance 1 52 

2001022 1 3.0E-04 1.0E-04 22 Regression equation from EPA soil measurements vs. distance 0 39 

a “Other” refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air) and “recent air” ref

associated with outdoor ambient air. The H5 model does not separate out recent air from other air. Therefore, “NA” is indicated in thesmodel. 

D-91 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

2.7E-03 

Soil 

Concentration 

(μg/g) 

40 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

4 

ed Indoor Dust 

tration

From 

Other a 

46 

s (μ

To

5

g/g) 

7006031 737 7.0E-03 7009003 254 6.0E-03 2.2E-03 51 Regression equation from EPA soil measurements vs. distance 4 50 5

7008004 197 0.019 7.8E-03 186 Regression equation from EPA soil measurements vs. distance 12 99 11

7006052 187 3.0E-03 1.3E-03 51 Regression equation from EPA soil measurements vs. distance 2 50 5

7006013 176 0.033 0.0139 231 Regression equation from EPA soil measurements vs. distance 21 115 13

7001044 164 4.0E-03 1.5E-03 30 Regression equation from EPA soil measurements vs. distance 2 42 4

7010001 145 4.0E-03 1.6E-03 37 Regression equation from EPA soil measurements vs. distance 3 45 4

7008007 141 0.012 5.0E-03 105 Regression equation from EPA soil measurements vs. distance 8 70 7

7006053 139 7.0E-03 2.6E-03 91 Regression equation from EPA soil measurements vs. distance 4 64 6

7009001 120 0.01 3.7E-03 85 Regression equation from EPA soil measurements vs. distance 6 62 6

7008005 104 0.014 5.8E-03 132 Regression equation from EPA soil measurements vs. distance 9 79 8

D-92 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

0.0122 

Soil 

Concentration 

(μg/g) 

282 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

19 

ed Indoor Dust 

tration

From 

Other a 

134 

s (μ

To

15

g/g) 

6015002 95 0.029 7008002 92 0.013 5.4E-03 100 Regression equation from EPA soil measurements vs. distance 9 68 7

7009002 86 0.01 3.6E-03 91 Regression equation from EPA soil measurements vs. distance 6 65 7

6012052 79 0.02 8.5E-03 107 Regression equation from EPA soil measurements vs. distance 13 70 8

7007003 77 0.018 7.3E-03 195 Regression equation from EPA soil measurements vs. distance 11 102 11

7007005 74 7.0E-03 3.0E-03 73 Regression equation from EPA soil measurements vs. distance 5 58 67008003 72 0.01 4.1E-03 83 Regression equation from EPA soil measurements vs. distance 7 62 6

7007001 70 0.012 4.8E-03 111 Regression equation from EPA soil measurements vs. distance 8 72 7

7006054 63 0.01 3.9E-03 139 Regression equation from EPA soil measurements vs. distance 7 82 8

7006051 62 7.0E-03 2.6E-03 55 Regression equation from EPA soil measurements vs. distance 4 51 5

7008006 58 0.012 5.0E-03 112 Regression equation from EPA soil measurements vs. distance 8 72 8

7007004 49 0.014 5.8E-03 146 Regression equation from EPA soil measurements vs. distance 9 84 9

D-93 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

0.0118 

Soil 

Concentration 

(μg/g) 

222 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

18 

ed Indoor Dust 

tration

From 

Other a 

112 

s (μ

To

13

g/g) 

7002029 46 0.029 7006011 45 0.022 9.1E-03 185 Regression equation from EPA soil measurements vs. distance 14 99 11

2001044 34 6.0E-03 2.3E-03 44 Regression equation from EPA soil measurements vs. distance 4 47 57002016 29 0.021 8.4E-03 354 Regression equation from EPA soil measurements vs. distance 13 160 17

7002033 23 0.027 0.0108 245 Regression equation from EPA soil measurements vs. distance 17 120 13

8001017 22 0.013 5.2E-03 120 Regression equation from EPA soil measurements vs. distance 8 75 8

6014015 15 0.041 0.0172 277 Regression equation from EPA soil measurements vs. distance 26 132 15

6014027 14 0.098 0.0409 223 Re-contamination sample in block NA NA 45

8001030 14 0.017 6.8E-03 145 Regression equation from EPA soil measurements vs. distance 11 84 9

6014025 13 0.048 0.0203 116 Re-contamination samples nearby NA NA 277002032 13 0.023 9.5E-03 242 Regression equation from EPA soil measurements vs. distance 15 119 13

7002021 12 0.022 9.0E-03 211 Regression equation from EPA soil measurements vs. distance 14 108 12

D-94 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

2.0E-03 

Soil 

Concentration 

(μg/g) 

38 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

3 

ed Indoor Dust 

tration

From 

Other a 

45 

s (μ

To

4

g/g) 

6012003 12 5.0E-03 6015001 11 0.037 0.0156 42 Re-contamination sample in block NA NA 22

3001003 11 7.0E-03 2.8E-03 43 Regression equation from EPA soil measurements vs. distance 5 47 5

3001000 11 3.0E-03 1.0E-03 27 Regression equation from EPA soil measurements vs. distance 2 41 4

8001036 10 0.017 6.6E-03 117 Regression equation from EPA soil measurements vs. distance 11 74 8

6012053 9 0.02 8.2E-03 97 Regression equation from EPA soil measurements vs. distance 12 67 7

2001050 9 4.0E-03 1.6E-03 32 Regression equation from EPA soil measurements vs. distance 3 43 4

6015016 8 0.052 0.0217 105 Re-contamination sample in block NA NA 28

8001035 8 0.015 6.2E-03 119 Regression equation from EPA soil measurements vs. distance 10 75 8

8001031 8 0.015 6.0E-03 144 Regression equation from EPA soil measurements vs. distance 9 84 9

8001037 8 0.014 5.5E-03 113 Regression equation from EPA soil measurements vs. distance 9 72 8

2001041 8 3.0E-03 1.1E-03 28 Regression equation from EPA soil measurements vs. distance 2 42 4

D-95 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

2.4E-03 

Soil 

Concentration 

(μg/g) 

42 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

4 

ed Indoor Dust 

tration

From 

Other a 

46 

s (μ

To

5

g/g) 

6012016 8 6.0E-03 7002030 7 0.025 0.0103 205 Regression equation from EPA soil measurements vs. distance 16 106 12

6012001 7 7.0E-03 2.8E-03 43 Regression equation from EPA soil measurements vs. distance 4 47 56014051 6 0.096 0.0404 184 Re-contamination sample in block NA NA 44

6014044 6 0.052 0.0219 159 Re-contamination samples nearby NA NA 28

6015017 6 0.048 0.0202 153 Re-contamination sample in block NA NA 27

7002028 6 0.024 9.8E-03 189 Regression equation from EPA soil measurements vs. distance 15 100 11

6012021 6 9.0E-03 3.8E-03 53 Regression equation from EPA soil measurements vs. distance 6 50 5

6014039 5 0.147 0.0614 294 Re-contamination sample in block NA NA 60

6014046 5 0.100 0.0416 129 Re-contamination sample in block NA NA 45

6015012 5 0.035 0.0148 63 Re-contamination sample in block NA NA 216015019 5 0.021 8.9E-03 176 Re-contamination sample in block NA NA 15

D-96 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

8.5E-03 

Soil 

Concentration 

(μg/g) 

89 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

13 

ed Indoor Dust 

tration

From 

Other a 

64 

s (μ

To

7

g/g) 

6012051 5 0.02 2001058 5 5.0E-03 2.0E-03 34 Regression equation from EPA soil measurements vs. distance 3 44 4

6012013 5 6.0E-03 2.5E-03 42 Regression equation from EPA soil measurements vs. distance 4 46 5

8001006 5 8.0E-03 3.4E-03 87 Regression equation from EPA soil measurements vs. distance 5 63 6

8001049 4 0.019 7.7E-03 585 Regression equation from EPA soil measurements vs. distance 12 244 25

8001045 4 0.018 7.4E-03 376 Regression equation from EPA soil measurements vs. distance 12 168 18

7002031 4 0.024 9.7E-03 237 Regression equation from EPA soil measurements vs. distance 15 117 13

6012014 4 5.0E-03 2.2E-03 39 Regression equation from EPA soil measurements vs. distance 3 45 4

2001029 4 3.0E-03 1.4E-03 38 Regression equation from EPA soil measurements vs. distance 2 45 4

2001015 4 2.0E-03 9.0E-04 26 Regression equation from EPA soil measurements vs. distance 1 41 4

3001065 4 3.0E-03 1.0E-03 28 Regression equation from EPA soil measurements vs. distance 2 42 4

2001023 4 1.0E-03 5.0E-04 20 Regression equation from EPA soil measurements vs. distance 1 39 3

D-97 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

8.6E-03 

Soil 

Concentration 

(μg/g) 

556 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

13 

ed Indoor Dust 

tration

From 

Other a 

234 

s (μ

To

24

g/g) 

7002011 3 0.021 7002012 3 0.028 0.0115 519 Regression equation from EPA soil measurements vs. distance 18 220 23

6014018 3 0.034 0.0144 400 Regression equation from EPA soil measurements vs. distance 22 177 19

8001000 3 0.015 5.9E-03 461 Regression equation from EPA soil measurements vs. distance 9 199 20

8001044 3 0.018 7.1E-03 373 Regression equation from EPA soil measurements vs. distance 11 167 17

6012057 3 0.019 8.1E-03 124 Regression equation from EPA soil measurements vs. distance 12 76 8

2001059 3 8.0E-03 3.2E-03 36 Regression equation from EPA soil measurements vs. distance 5 44 5

6012049 3 0.017 6.9E-03 84 Regression equation from EPA soil measurements vs. distance 11 62 7

2001056 3 5.0E-03 1.8E-03 35 Regression equation from EPA soil measurements vs. distance 3 44 4

8001034 3 0.016 6.6E-03 112 Regression equation from EPA soil measurements vs. distance 11 72 8

8001032 3 0.014 5.8E-03 141 Regression equation from EPA soil measurements vs. distance 9 83 9

8001029 3 0.015 6.1E-02 129 Regression equation from EPA soil measurements vs. distance 10 78 8

D-98 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

1.9E-03 

Soil 

Concentration 

(μg/g) 

35 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

3 

ed Indoor Dust 

tration

From 

Other a 

44 

s (μ

To

4

g/g) 

2001057 3 5.0E-03 6012044 3 0.012 4.9E-03 68 Regression equation from EPA soil measurements vs. distance 7 56 6

6012030 3 0.01 4.2E-03 62 Regression equation from EPA soil measurements vs. distance 6 54 6

6012019 3 7.0E-03 2.9E-03 46 Regression equation from EPA soil measurements vs. distance 4 48 5

8001042 3 0.01 3.9E-03 106 Regression equation from EPA soil measurements vs. distance 6 70 7

6012022 3 7.0E-03 2.9E-03 51 Regression equation from EPA soil measurements vs. distance 4 50 5

2001030 3 5.0E-03 2.0E-03 48 Regression equation from EPA soil measurements vs. distance 3 49 5

6014043 2 0.084 0.0351 150 Re-contamination samples nearby NA NA 406014028 2 0.090 0.0376 179 Re-contamination samples nearby NA NA 426015015 2 0.050 0.0211 98 Re-contamination samples nearby NA NA 28

6014021 2 0.049 0.0204 95 Re-contamination sample in block NA NA 27

6015018 2 0.033 0.0138 160 Re-contamination samples nearby NA NA 20

D-99 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

8.0E-03 

Soil 

Concentration 

(μg/g) 

447 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

13 

ed Indoor Dust 

tration

From 

Other a 

194 

s (μ

To

20

g/g) 

8001047 2 0.02 6012065 2 0.027 0.0115 136 Regression equation from EPA soil measurements vs. distance 17 81 9

7002014 2 0.024 9.7E-03 276 Regression equation from EPA soil measurements vs. distance 15 132 14

8001019 2 0.019 7.7E-03 230 Regression equation from EPA soil measurements vs. distance 12 115 12

6012062 2 0.016 6.8E-03 108 Regression equation from EPA soil measurements vs. distance 10 70 8

8001023 2 0.017 6.8E-03 158 Regression equation from EPA soil measurements vs. distance 11 89 10

2001051 2 4.0E-03 1.5E-03 32 Regression equation from EPA soil measurements vs. distance 2 43 4

6012041 2 0.01 4.2E-03 60 Regression equation from EPA soil measurements vs. distance 6 53 5

2001060 2 4.0E-03 1.7E-03 37 Regression equation from EPA soil measurements vs. distance 3 45 4

6012005 2 5.0E-03 2.2E-03 38 Regression equation from EPA soil measurements vs. distance 3 45 4

6012006 2 5.0E-03 2.2E-03 38 Regression equation from EPA soil measurements vs. distance 3 45 4

3001017 2 9.0E-03 3.5E-03 87 Regression equation from EPA soil measurements vs. distance 6 63 6

D-100 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

8.0E-04 

Soil 

Concentration 

(μg/g) 

24 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

1 

ed Indoor Dust 

tration

From 

Other a 

40 

s (μ

To

4

g/g) 

3001055 2 2.0E-03 6014042 1 0.161 0.0674 129 Re-contamination samples nearby NA NA 646014052 1 0.061 0.0257 216 Re-contamination sample in block NA NA 32

6014032 1 0.145 0.0609 162 Re-contamination samples nearby NA NA 606014033 1 0.154 0.0644 162 Re-contamination samples nearby NA NA 626014049 1 0.072 0.0303 167 Re-contamination sample in block NA NA 36

6014029 1 0.083 0.0345 135 Re-contamination sample in block NA NA 40

6014050 1 0.060 0.0250 171 Re-contamination sample in block NA NA 31

6015013 1 0.038 0.0157 53 Re-contamination samples nearby NA NA 226015011 1 0.030 0.0125 123 Re-contamination samples nearby NA NA 197002006 1 0.028 0.0113 703 Regression equation from EPA soil measurements vs. distance 18 287 30

7002009 1 0.022 8.8E-03 958 Regression equation from EPA soil measurements vs. distance 14 380 39

D-101 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

0.014 

Soil 

Concentration 

(μg/g) 

153 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

21 

ed Indoor Dust 

tration

From 

Other a 

87 

s (μ

To

10

g/g) 

6014006 1 0.034 7002017 1 0.027 0.0112 323 Regression equation from EPA soil measurements vs. distance 17 149 16

6014007 1 0.035 0.0146 200 Regression equation from EPA soil measurements vs. distance 22 104 12

3001019 1 0.022 8.7E-03 169 Regression equation from EPA soil measurements vs. distance 14 93 102001066 1 6.0E-03 2.4E-03 41 Regression equation from EPA soil measurements vs. distance 4 46 5

7002025 1 0.015 6.2E-03 179 Regression equation from EPA soil measurements vs. distance 10 97 10

6012031 1 0.01 4.0E-03 60 Regression equation from EPA soil measurements vs. distance 6 53 5

8001003 1 0.01 4.1E-03 109 Regression equation from EPA soil measurements vs. distance 6 71 7

6012018 1 6.0E-03 2.5E-03 43 Regression equation from EPA soil measurements vs. distance 4 47 5

6012004 1 5.0E-03 2.1E-03 38 Regression equation from EPA soil measurements vs. distance 3 45 4

3001015 1 6.0E-03 2.2E-03 70 Regression equation from EPA soil measurements vs. distance 4 57 6

2001003 1 1.0E-03 5.0E-03 17 Regression equation from EPA soil measurements vs. distance 1 37 3

D-102 
Attachment D-11. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 max-quarte

for the Primary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual 

Average Air 

Concentration 

(μg/m3) 

Annual 

Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

1.2E-03 

Soil 

Concentration 

(μg/g) 

30 

Method of Estimating Soil 

Concentrations 

Regression equation from EPA soil 

measurements vs. distance 

Predict

Concen

From 

Recent 

Air a 

2 

ed Indoor Dust 

tration

From 

Other a 

42 

s (μ

To

4

g/g) 

3001063 1 3.0E-03 3001009 1 6.0E-03 2.3E-03 60 Regression equation from EPA soil measurements vs. distance 4 53 5

3001066 1 3.0E-03 1.1E-03 30 Regression equation from EPA soil measurements vs. distance 2 42 4

2001104 1 5.0E-03 2.0E-03 56 Regression equation from EPA soil measurements vs. distance 3 52 5

2001101 1 5.0E-03 2.0E-03 58 Regression equation from EPA soil measurements vs. distance 3 52 5

2001022 1 2.0E-03 6.0E-04 22 Regression equation from EPA soil measurements vs. distance 1 39 4

a “Other” refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air) and “recent air” ref

associated with outdoor ambient air. The H5 model does not separate out recent air from other air. Therefore, “NA” is indicated in thesmodel. 

D-103 

Appendix E: Media Concentrations for the Secondary Pb Smelter Case Study 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

Table of Contents 

Table of Contents ................................................................................................................. E-i 

List of Exhibits .................................................................................................................... E-ii 

List of Attachments............................................................................................................E-iii 

E. MEDIA CONCENTRATIONS FOR THE SECONDARY PB SMELTER CASE
 

STUDY................................................................................................................................. E-1
 

E.1. SPATIAL TEMPLATE ............................................................................................... E-2 

E.2. AIR ............................................................................................................................. E-6 

E.2.1. Air Dispersion Modeling ................................................................................. E-6 

E.2.2. Air Concentration and Total Deposition Results............................................. E-7 

E.2.3. Inhalation Exposure Concentrations.............................................................. E-10 

E.2.4. Air Modeling Performance Assessment ........................................................ E-12 

E.3. OUTDOOR SURFACE SOIL/DUST ....................................................................... E-17 

E.4. INDOOR DUST ........................................................................................................ E-19 

REFERENCES ................................................................................................................... E-22 

E-i 
 

List of Exhibits 

Exhibit E-1. Ratios of the Maximum-to-Mean Block-level Annual Average Air 

Concentrations in each Block Group.....................................................................E-4 

Exhibit E-2. Spatial Template for the Secondary Pb Smelter Case Study (Including 

U.S. Census Blocks with Children Less than 7 Years of Age)..............................E-5 

Exhibit E-3. Annual Average Population-weighted Air Concentrations for the 

Exhibit E-4. Annual Average Total Deposition of Pb across the Study Area for the 

Exhibit E-5. Annual Average Air Concentration Isopleths for the Current 

Exhibit E-6. Ratios of Inhalation Exposure Concentrations to Ambient Air 

Exhibit E-7. Annual Average Population-weighted Inhalation Exposure Concentrations 

Exhibit E-8. Modeled Annual Average Air Pb Concentrations Compared to 

Exhibit E-9. Wind Rose of Meteorological Data Used for Secondary Pb Smelter 

Exhibit E-13. Summary of Surface Soil Pb Concentrations for the Current Conditions
 

Residing in Areas Associated with Different Estimates of Indoor Dust Pb 

Exhibit E-15. Annual Average Population-weighted Indoor Dust Pb Exposure 

Secondary Pb Smelter Case Study........................................................................E-8 

Current Conditions Scenario..................................................................................E-8 

Conditions Scenario for the Secondary Pb Smelter Case Study............................E-9 

Concentrations from the NATA National-scale Air Toxics Assessment ............E-11 

for the Secondary Pb Smelter Case Study ...........................................................E-11 

Monitored Annual Average Air Pb Concentrations ............................................E-13 

Case Study (Direction from which Wind is Blowing).........................................E-14 

Exhibit E-10. Air Monitor Locations near the Secondary Pb Smelter .....................................E-15 

Exhibit E-11. Pb Deposition Fluxes from Studies in the United States....................................E-17 

Exhibit E-12. Summary of Soil Pb Concentration Factors with Distance................................E-19 

Scenario ...............................................................................................................E-19 

Exhibit E-14. Number of U.S. Census Blocks and Number of Children Ages 0 to 7 

Concentrations .....................................................................................................E-20 

Concentrations for the Secondary Pb Smelter Case Study..................................E-21 

E-ii 
 

 

 

List of Attachments 

Attachment E-1. Emission Parameters for All Sources for the Secondary Pb 

Smelter Case Study .......................................................................................E-25 

Attachment E-2. Building Downwash Parameters for the Secondary Pb Smelter 

Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario 

Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3
 

Attachment E-5. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3
 

Attachment E-6. Estimated Media Concentrations in Alternative NAAQS (0.05 μg/m3
 

Attachment E-7. Estimated Media Concentrations in Alternative NAAQS (0.2 μg/m3 

Attachment E-8. Comparison of Monitored to Modeled Pb Air Concentrations for the 

Attachment E-9. Input Parameters for Secondary Pb Smelter Case Study Soil Model 

Case Study.....................................................................................................E-26 

for the Secondary Pb Smelter Case Study ....................................................E-27 

Max-Monthly) Scenario for the Secondary Pb Smelter Case Study.............E-38 

Max-Monthly) Scenario for the Secondary Pb Smelter Case Study.............E-49 

Max-Monthly) Scenario for the Secondary Pb Smelter Case Study.............E-60 

Max-Quarterly) Scenario for the Secondary Pb Smelter Case Study ...........E-71 

Secondary Pb Smelter Case Study ................................................................E-82 

Calculations...................................................................................................E-83 

E-iii 

 

E. MEDIA CONCENTRATIONS FOR THE SECONDARY PB SMELTER 

CASE STUDY 

This appendix discusses methods, results, limitations, and uncertainties associated with 

the estimation of environmental media concentrations for the secondary lead (Pb) smelter case 

study included in the human exposure and health risk assessments. These media concentrations 

were estimated using a combination of modeling approaches and the estimated concentrations for 

the current conditions scenario were compared to available measurement data to evaluate the 

performance of the approaches. Estimates presented in this appendix are specified with regard to 

number of decimal places, which results in various numbers of implied significant figures. This 

is not intended to convey greater precision for some estimates than others; it is simply an 

expedient and initial result of the software used for the calculation. Greater attention is given to 

significant figures in the presentation of estimates in the main body of the report. 

•	 For this analysis, five air quality scenarios were evaluated, including current conditions, 

in which the current National Ambient Air Quality Standard (NAAQS) is met and four 

possible alternative NAAQS, as described below: 

•	 Meeting an air concentration of 0.2 μg/m3

, based on a maximum calendar quarter 

averaging period; 

•	 Meeting an air concentration of 0.5 μg/m3, based on a maximum monthly averaging 

period; 

•	 Meeting an air concentration of 0.2 μg/m3

, based on a maximum monthly averaging 

period; and 

•	 Meeting an air concentration of 0.05 μg/m3, based on a maximum monthly averaging 

period. 

This analysis focused on three primary environmental media and their exposure 

concentrations: ambient air, indoor dust, and outdoor surface soil/dust. Estimated inhalation and 

indoor dust exposure concentrations differed for the five air quality scenarios because they each 

were based, at least in part, on the estimated ambient air concentrations, which varied across 

scenarios. The outdoor surface soil/dust exposure concentrations estimated for the current 

conditions scenario were also used for the alternative NAAQS scenarios (i.e., it was assumed 

that reductions in ambient air concentrations associated with the alternative NAAQS scenarios 

E-1
 
 

 

 

 

did not have a significant impact on soil concentrations).1 The approaches used and the 

estimated exposure concentrations for air, outdoor soil, and indoor dust are described in the 

remainder of this appendix. 

E.1. SPATIAL TEMPLATE 

The study area extent was defined using geographic information system (GIS) software to 

identify U.S. Census block groups that fall predominantly within 10 kilometers (km) of the 

facility; 12 U.S. Census block groups were identified. Because of the irregular shape of U.S. 

Census block groups, not all of the U.S. Census block groups with area within 10 km were 

included, and some that were included have area outside 10 km. Block groups falling along the 

10 km radius from the source were generally included if most of their area fell within the radius. 

Model receptors were placed at all U.S. Census block centroids within the 12 U.S. Census block 

groups of interest. This resulted in 665 U.S. Census block centroid points being modeled. The 

U.S. Census blocks with no children less than 7 years of age were included in the modeling 

simulations to aid in understanding the patterns of air concentrations in the study area. These 

locations were not included in this assessment and are not included in exhibits summarizing 

modeling results (with the exception of isopleths diagrams), because this assessment focuses on 

the health risk for Pb in children less than 7 years of age. The remaining 298 U.S. Census blocks 

with children less than 7 years of age as of the 2000 U.S. Census were included in the exposure 

assessment and are the basis for all of the exhibits (with the exception of isopleths diagrams) in 

this appendix. 

1

 Derivation of the outdoor surface soil/dust estimates for the current conditions scenario is further 

discussed in Section E.3. 

E-2
 
 

The spatial template for this case study was developed in the pilot assessment and 

includes all U.S. Census blocks within the extent of the study area. As was done for the primary 

Pb smelter case study, an analysis was performed to investigate whether it would be appropriate 

to reduce the number of individual locations within the template to gain modeling efficiency by 

replacing some sets of individual blocks with the corresponding block group. This analysis 

involved comparing the maximum U.S. Census block level modeled air concentration to the 

mean annual average air concentration for the U.S. Census block group to identify occurrences 

where this difference was less than a factor of two, and the U.S. Census block group might be 

substituted for the individual U.S. Census blocks. For this case study, although five U.S. Census 

block groups had maximum-to-average ratios less than 2.0, the individual U.S. Census blocks 

within these five U.S. Census block groups were included in the spatial template because of the 

small size of the U.S. Census block groups and their proximity to the facility (see Exhibit E-1). 

That is, based on the analysis performed for the pilot assessment, the spatial template for this 

assessment also included all individual U.S. Census blocks within the study area (see Exhibit E2). 

In addition, two air total suspended particulate matter (TSP) monitors from the U.S. EPA 

Air Quality System (AQS) database that measured Pb concentrations were identified between 

400 and 700 meters (m) of the facility (USEPA, 2007). The locations of these two monitors 

were modeled as discrete receptors and the results at these locations were used to directly 

compare estimated concentrations from the current conditions scenario modeling to the available 

monitoring data. 

E-3
 
 

Exhibit E-1. Ratios of the Maximum-to-Mean Block-level Annual Average 

Air Concentrations in each Block Group 

E-4
 
 

Exhibit E-2. Spatial Template for the Secondary Pb Smelter Case Study (Including U.S. 

Census Blocks with Children Less than 7 Years of Age) 

E-5
 
 

E.2. AIR 

The air concentrations and total (dry + wet) deposition fluxes of Pb for the secondary Pb 

smelter case study were modeled using the AERMOD 07026 air dispersion model, and the air 

concentrations were compared to the air concentrations from nearby monitors (USEPA, 2004; 

2004). The emissions used for the air quality modeling are described in Appendix B. 

E.2.1. Air Dispersion Modeling 

The meteorological data used for the AERMOD air dispersion model includes 5 

consecutive years (1998 to 2002) of nearby measurements. Surface-level and upper air 

meteorological data were obtained for weather stations located in Montgomery, Alabama, and 

Centerville, Alabama (National Oceanic and Atmospheric Administration (NOAA), 1997; 1997), 

respectively, and processed using the meteorological pre-processor, AERMET 06341 (USEPA, 

2002). These stations represent locations close in proximity and geography to Troy, Alabama, 

and for which 5 consecutive years of surface and upper air meteorological data were available. 

Obtaining 5 consecutive years of weather observations for use in AERMOD was desirable 

because it allowed for the natural variability in weather conditions to be captured in the air 

modeling. 

All 5 years of meteorological data (1998 to 2002) were simulated individually using 

AERMOD with the same emissions. There were no modeled differences in emissions between 

the different simulation years because the available emissions data were not necessarily 

representative of any particular year. Instead, they were compiled to represent current 

conditions, given the available emissions data. The estimates for process emissions for the 

secondary Pb smelter analyzed in this assessment were calculated from Pb emissions measured 

during stack tests performed in 2005 and 2006 (URS Corporation, 2005; 2005; 2006). Fugitive 

emissions for four fugitive sources (associated with the smelter building, materials handling, 

loader traffic, and truck traffic) were estimated based on 1987 Prevention of Significant 

Deterioration (PSD) data (URS Corporation, 2006), which were the most recent available data on 

fugitive emissions from the facility. Due to the relatively flat terrain in the study area, terrain 

calculations were not included in this application. All of the inputs for these modeling 

simulations are provided in Attachments E-1 and E-2. Monthly average air concentrations and 

total deposition fluxes for each simulation year and receptor location (i.e., U.S. Census blocks 

and monitor locations) were output from the air dispersion model at each receptor (i.e., U.S. 

Census block) and monitor location, as described in Section E.1. 

E-6
 
 

E.2.2. Air Concentration and Total Deposition Results 

The monthly average air concentration model results for the current conditions scenario 

were calculated at the centroid of each U.S. Census block and monitor receptor point as 

described in Section E.2.1. The concentrations were also averaged quarterly and compared to 

the current NAAQS (1.5 μg/m3

) to confirm that the estimated air concentrations for this current 

conditions scenario were at or below the current NAAQS. This comparison indicated that none 

of the U.S. Census block-level air concentrations exceeded the current NAAQS. The monthly 

averages were then averaged over each year of the modeling period to generate annual averages. 

To take into account variations in meteorological data, the annual average concentrations and 

total depositions for each of the 5 years were averaged to generate one set of representative 

annual average concentration estimates for the current conditions scenario. 

Monthly and quarterly averages were also compared to four alternative NAAQS 

scenarios including: monthly maximum NAAQS scenarios of 0.5 μg/m3

, 0.2 μg/m3, and 

0.05 μg/m3

; and one quarterly maximum NAAQS scenario of 0.2 μg/m3. For these alternative 

scenarios there were several modeled U.S. Census blocks which did not meet the alternative 

NAAQS, in which case a ratio was developed from the maximum monthly or quarterly averaged 

value and the alternative NAAQS level. This roll-back factor was then applied to scale down the 

concentrations at each of the locations and a new combined annual average was calculated from 

the scaled data set (i.e., a proportional rollback of all modeled locations was implemented). 

Attachments E-3 to E-7 present the annual average air Pb concentration estimates for the 

298 U.S. Census blocks with at least one child less than 7 years of age for all scenarios. Exhibit 

E-3 presents a summary of the annual average population-weighted air Pb data for the 298 U.S. 

Census blocks with at least one child less than 7 years of age for the current conditions scenario 

and the four alternative NAAQS scenarios. Population-weighted ambient air concentrations 

were calculated by first sorting the block/block groups in increasing ambient air concentration 

order. Then the percentage of children living in block/block groups less than or equal to the 

maximum ambient air concentration of those block/block groups was calculated. The ambient 

air concentration of the block/block group associated with the minimum, 5th, median, 95th, and 

maximum percentile was selected. 

E-7
 
 

 

Exhibit E-3. Annual Average Population-weighted Air Concentrations for the Secondary 

Pb Smelter Case Study 

Annual Average Pb Air Concentration (μg/m3) a 

Alternative NAAQS Scenario 

Statistic b Current 

Conditions 1 

0.2 μg/m3 , 

Max Quarterly 

2 

0.5 μg/m3 , 

Max Monthly 

3 

0.2 μg/m3 , 

Max Monthly 

4 

0.05 μg/m3 , 

Max Monthly 

Maximum 0.126 0.034 0.071 0.028 0.007 

95th Percentile 0.015 0.004 0.008 0.003 0.001 

Median 0.003 0.001 0.002 0.001 < 0.001 

5th Percentile 0.001 < 0.001 < 0.001 < 0.001 < 0.001 

Minimum < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 

a

 The 298 U.S. Census blocks with children less than 7 years of age selected for analysis were used to create 

this summary. 

b

 The statistic (e.g., 95th percentile, median) may not be at the same location for each of the data results 

presented here.
 

As described in Section E.2.1, wet and dry Pb deposition was also modeled and a 

summary of the total deposition flux estimates are presented in Exhibit E-4. 

Exhibit E-4. Annual Average Total Deposition of Pb across the 

Study Area for the Current Conditions Scenario 

Statistic a Annual Average Total Deposition of Pb 

(g/m2

/year) 

Maximum 0.05

95th Percentile 5.4E-03 

Median 1.0E-03 

5th Percentile 1.3E-04 

Minimum 3.8E-05 

a

 The statistic (e.g., 95th percentile, median) may not be at the same location for 

each of the data results presented here. 

Exhibit E-5 shows the isopleths of the U.S. Census block-level modeled annual average 

air concentration results for the current conditions scenario. 

E-8
 
 

Exhibit E-5. Annual Average Air Concentration Isopleths for the Current Conditions 

Scenario for the Secondary Pb Smelter Case Study 

E-9
 
 

E.2.3. Inhalation Exposure Concentrations 

Inhalation exposure concentrations of Pb were estimated for the population of interest 

(young children) from the estimated annual average ambient air concentrations using age groupand location-specific relationships for Pb developed from modeling performed for the U.S. EPA 

1999 National-scale Air Toxics Assessment (USEPA, 2006), one of the U.S. EPA’s National Air 

Toxics Assessment (NATA) activities. These relationships account for air concentration 

differences indoors and outdoors, as well as for mobility or time spent in different locations (e.g., 

outdoors at home, inside at home) for the population of interest. 

The NATA national-scale assessment produced air concentrations of Pb (and other 

hazardous air pollutants [HAPs]) for each U.S. Census tract (using the Assessment System for 

Population Exposure Nationwide model [ASPEN]), and corresponding exposure concentrations 

of Pb for each of 5 age groups at each U.S. Census tract (using the Hazardous Air Pollutant 

Exposure Model [HAPEM]). The relationships (or ratios) between the Pb inhalation exposure 

concentrations and the ambient Pb air concentrations from the NATA national-scale assessment 

for the 0 to 4 age group (the closest age group to the age group of interest for this assessment for 

which ASPEN and HAPEM outputs were available) ranged from 0.44 to 0.46 for the U.S. 

Census tracts in the study area for the secondary Pb smelter case study. The ratios are presented 

in Exhibit E-6. It was assumed that these U.S. Census tract-specific ratios provided a reasonable 

approximation of the ratios for the U.S. Census blocks and block groups contained within each 

tract. 

The resulting inhalation exposure estimates for each scenario and U.S. Census block with 

at least one child less than 7 years of age are provided in Attachments E-3 to E-7. A summary of 

the distribution of annual average inhalation exposure concentrations associated with the five air 

quality scenarios is presented in Exhibit E-7. Population-weighted annual average inhalation 

exposure concentrations were calculated by first sorting the block/block groups in increasing 

inhalation exposure concentration order. Then the percentage of children living in block/block 

groups less than or equal to the maximum annual average inhalation exposure concentration of 

those block/block groups was calculated. The annual average inhalation exposure concentration 

of the block/block group associated with the minimum, 5th, median, 95th, and maximum 

percentile was selected. 

Use of ratios for the 0 to 4 age group (rather than for 0 to 7) contributes some uncertainty 

in the estimate of children’s inhalation exposure concentrations. In addition, there is some 

uncertainty in the magnitude of the air concentrations generated using the ASPEN model for the 

NATA national-scale assessment (USEPA, 2006). In a comparison to monitoring data across the 

E-10
 
 

 

country, the ASPEN-modeled air concentrations generally underestimated monitored 

concentrations (USEPA, 2006; Section on Comparison to Monitored Values). However, the 

relationship between ambient air concentrations and inhalation exposure concentrations (i.e., the 

comparison used here) is not expected to be affected by underestimated ambient air 

concentrations from the NATA national-scale assessment (see Exhibit E-6). In addition, some of 

the exposure modeling inputs used in the NATA simulations were not specific to Pb and thus 

may introduce additional uncertainties. For example, the penetration factor, which is used to 

estimate the fraction of the pollutant in outdoor air that reaches indoor air, used for Pb in the 

NATA assessment, is based on a study that examined the penetration of hexavalent chromium 

particles, which are generally more reactive than Pb particles (Long et al., 2004). 

Exhibit E-6. Ratios of Inhalation Exposure Concentrations to Ambient 

Air Concentrations from the NATA National-scale Air Toxics Assessment 

U.S. Census Tract ID Ratio of Inhalation Exposure Concentration: 

Ambient Air Concentration 

01109988900 0.46

01109989100 0.45 

01109989200 0.45 

01109989000 0.44 

Exhibit E-7. Annual Average Population-weighted Inhalation Exposure Concentrations 

for the Secondary Pb Smelter Case Study 

Annual Average Pb Inhalation Exposure Concentration (μg/m3) a 

Alternative NAAQS Scenario 

Statistic b Current 

Conditions 

Scenario 

1 

0.2 μg/m3 , 

 Max Quarterly 

2 

0.5 μg/m3 , 

Max Monthly 

3 

0.2 μg/m3 , 

Max Monthly 

4 

0.05 μg/m3 , 

Max Monthly 

Maximum 0.056 0.015 0.031 0.013 0.003 

95th Percentile 0.007 0.002 0.004 0.002 < 0.001 

Median 0.001 < 0.001 0.001 < 0.001 < 0.001 

5th Percentile < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 

Minimum < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 

a

 The 298 U.S. Census blocks/block groups with at least one child less than 7 years of age were used to create this 

summary. 

b

 The statistic (e.g., 95th percentile, median) may not be at the same location for each of the data results presented 

here. 

E-11
 
 

 

 

 

E.2.4. Air Modeling Performance Assessment 

The monitoring data at the two air monitor locations near the facility were compared to 

modeled concentrations at the same locations (see Exhibit E-8). For this comparison, air 

monitoring measurements from 1998 through 2002 were compared to the modeled air 

concentrations. These years of monitoring data were selected to correspond to the years of 

meteorological data used in the air modeling.2 Overall, the modeled combined annual average 

concentrations at the monitor locations (located to the northwest of the facility) are slightly lower 

than the weighted annual average values at the monitor3 closest to the facility and approximately 

a factor of two to three lower at the monitor slightly farther from the facility. Because the 

meteorological data used for the modeling were not site-specific, there is likely some uncertainty 

with use of these data to estimate air concentrations at specific points. It is possible that the local 

predominant wind direction is different from that of the meteorological data. Therefore, the 

weighted annual average monitored air concentrations were also compared to the combined 

annual average modeled air concentrations within similar distances to the facility, in all 

directions modeled on a radial grid (see Exhibit E-8). When compared to concentrations in all 

directions, the monitored values fall within the range of modeled results. A more detailed 

comparison is presented in Attachment E-8. 

2

 Note that the emissions data used in this modeling represent stack testing performed in 2005 and 2006 and 

fugitive emission estimates from 1987 (Alabama Department of Environmental Management (ADEM), 2007). 

Given that these emissions data, when used together, are not clearly representative of any specific time period, the 

decision was made to use monitoring data corresponding to the years of meteorological data used in the modeling 

(i.e., 1998 to 2002). 

3

Annual averages were calculated from the monthly composite data from the U.S. EPA AQS database and 

weighted by the number of days in a month (USEPA, 2007). 

E-12
 
 

Exhibit E-8. Modeled Annual Average Air Pb Concentrations Compared to Monitored 

Annual Average Air Pb Concentrations 

Monitor Values a Modeled Results b 

U.S. EPA 

AQS 

Monitor 

Distance 

from 

Midpoint of 

Facility (m) 

Range of Annual 

Average Monitor Air 

Concentrations from 

the U.S. EPA AQS 

Database from 1998 

to 2002 (μg/m3) 

Range of 

Modeled 

Distances for 

Comparison 

Range of Annual 

Average Modeled 

Concentrations 

(μg/m3) 

Annual Average 

Modeled 

Concentration at 

Monitor Location 

(μg/m3) c 

11090003 400 0.275 to 0.467 300 to 500 m (108 

Points) 0.04 to 2.5 0.260 

11090006 680 0.139 to 0.204 600 to 800 m (108 

Points) 0.02 to 0.2 0.059 

a Annual average monitor air concentrations were created from the monthly composite data from the U.S. EPA AQS 

database (USEPA, 2007). Each average was weighted based on the number of days in the month. 

b

 The modeled concentrations presented here were generated from a model run with a radial receptor grid. This 

summary is not from U.S. Census block centroid results. 

c These values are the annual average concentrations for the specific receptor location from the model run.
 

A wind rose created from 5 years of Montgomery, Alabama, wind data (see Exhibit E-9) 

shows that the predominant directions from which the wind is blowing are east, east south-east, 

and northwest. Both monitors are located northwest of the facility. The potential difference 

between actual site meteorological data and the meteorological data used in the modeling may 

help explain why the modeled concentrations are not closer to the monitored concentrations at 

the exact monitor locations, but modeled concentrations in all directions are within the range of 

monitored concentrations at similar distances. Because the monitors are both located northwest 

of the facility (see Exhibit E-10), it cannot be determined from the available data whether all 

modeled air concentrations and deposition rates could potentially be underestimated or the 

degree of over- or under-prediction by the model is dependent on direction (or neither or both). 

A directional difference between modeled and actual air concentrations can impact risk results 

(either under- or over-predicting) because the number of modeled children varies spatially for the 

U.S. Census blocks located near the facility. 

E-13
 
 

 

Exhibit E-9. Wind Rose of Meteorological Data Used for Secondary Pb Smelter Case Study 

(Direction from which Wind is Blowing) 

Note: Wind rose derived from 5 years (1998 to 2002) of meteorological data (41,766 hours of data). 

E-14
 
 

Exhibit E-10. Air Monitor Locations near the Secondary Pb Smelter 

E-15
 
 

No local measurements of Pb deposition (dry, wet, or total) were found for comparison to 

the model predicted deposition results. In the U.S. EPA Pb Criteria Document (2006), the U.S. 

EPA summarized studies that provided ranges of Pb total deposition fluxes in various locations 

across the United States. None of these studies were specifically for total deposition near a 

secondary Pb smelter, but they provided a range of total deposition values for comparison. 

Exhibit E-11 summarizes this range of total deposition values. 

The ranges of annual average deposition fluxes from the secondary Pb smelter emissions 

modeled at a nearby U.S. Census block centroid with children less than 7 years of age are 3.8E05 to 4.9E-02 gram per square meter per year (g/m2

/yr) and 0 to 5.7E-04 g/m2/yr for dry and wet 

deposition, respectively. These ranges are slightly larger than those deposition fluxes presented 

in the studies in Exhibit E-11, which is expected because none of the studies presented in Exhibit 

E-11 measured deposition directly next to a secondary Pb smelter facility. The lower modeled 

dry deposition fluxes are comparable to those at the low end of the majority of the measured 

ranges from the studies in Exhibit E-11, which is expected given that the locations of those 

deposition fluxes could be described as urban background. The lower modeled fluxes for wet 

deposition (median: 2.4E-05) may also be explained by urban background not included in the 

modeling. The median modeled dry deposition flux (1.03E-03 g/m2/year) falls within the range 

of some of the measurements presented in Exhibit E-11 (i.e., New York City, Detroit, and sites 

near Lake Michigan). Comparison of the modeled total deposition fluxes to the study 

measurements throughout the United States provides some confidence that the modeled total 

deposition is within the expected range. 

E-16
 
 

 

 

Exhibit E-11. Pb Deposition Fluxes from Studies in the United States 

Location Mean Value or Range 

(g Pb/m2

/year) Source 

Total Deposition Fluxes 

New York City, building exterior plate collecting total 

deposition (weekly values from 2003 to 2005 averaged) 9.8E-03 (Caravanos et al., 2006) 

Dry Deposition Fluxes 

Two sites on Chesapeake Bay in 1990 to 1991 3.7E-04 to 1E-03 (Wu et al., 1994) 

New York-New Jersey Harbor Bight area 1.5E-04 to 7.6E-04 (Gao et al., 2002) 

Urban site in metropolitan Detroit 1982 to 1991 4E-04 to 4E-03 (Pirrone et al., 1995) 

Sites near Lake Michigan 1993 to 1995 8.4E-03 to 1.4E-02 (Yi et al., 2001) 

Lake Michigan 9.5E-04 (Sweet et al., 1998) 

Lake Superior 9.2E-04 (Sweet et al., 1998) 

Lake Erie 7.8E-04 (Sweet et al., 1998) 

Wet Deposition Fluxes 

Reston, Virginia 4.4E-04 (Conko et al., 2004) 

Delaware Bay and Chesapeake Bay 3.9E-04 to 5.1E-04 (Kim et al., 2000) 

Western Maryland 6.4E-04 (Lawson and Mason, 

2001) 

North-central Maryland 3.0E-04 to 6.0E-04 (Scudlark et al., 2005) 

Great Lakes Region 5.5E-04 to 1.0E-03 (Sweet et al., 1998) 

E.3. OUTDOOR SURFACE SOIL/DUST 

Outdoor surface soil/dust concentrations of Pb were estimated by defining the spatial 

pattern of surface soil/dust concentrations around the secondary Pb smelter facility using air and 

surface soil/dust model results and then adjusting the magnitude of the concentrations based on 

measured concentrations from a different secondary Pb smelter facility for which there were 

soil/dust Pb measurements. 

The spatial pattern of the outdoor soil/dust concentrations were estimated using the 

AERMOD total deposition estimates and the U.S. EPA’s Multiple Pathways of Exposure (MPE) 

methodology (USEPA, 1998). The MPE methodology represents the update of the Indirect 

Exposure Methodology (IEM) (USEPA, 1990) and consists of a set of multimedia fate and 

transport algorithms developed by the U.S. EPA’s Office of Research and Development (ORD), 

including a soil mixing model. In the MPE soil mixing model algorithms, cumulative soil 

concentrations were calculated as a function of total particle deposition, soil mixing depth, bulk 

density, and a soil loss constant. The soil loss constant (in this case) was defined as a function of 

loss due to leaching, erosion, and runoff processes. Concentration in the soil was calculated in 

the top 1 centimeter (cm) of soil assuming constant total deposition of Pb for the entire operating 

E-17
 
 

period of the facility (37 years). All input parameters used for the soil mixing model are listed in 

Attachment E-9. Site-specific input parameters were used when feasible, but assumptions were 

made for some parameters, in many cases based on suggested values in the database of input 

parameters included with the U.S. EPA's Human Health Risk Assessment Protocol (HHRAP) 

(2005). 

As the total deposition rate estimates used in the soil mixing model were those derived 

from the AERMOD simulations using current emissions estimates, without additional historical 

emissions, it is recognized that the resultant cumulative deposition and associated estimate of soil 

concentration will be an underestimate of current soil concentrations (and this is supported by 

comparison to concentrations near other secondary Pb smelters). Consequently, the AERMODMPE generated results were only used to produce a spatial pattern for the soil concentrations. 

This base pattern of concentrations was then scaled up using soil measurements available for 

another secondary Pb smelter facility. The measurements of Pb in surface soil samples located 

100 to 1000 m from the other secondary Pb smelter facility (Kimbrough and Suffet, 1995) were 

up to 13 times higher than the AERMOD-MPE generated base concentrations, depending on the 

distance from the facility. Distance-specific scaling factors, presented in Exhibit E-12, were 

developed by averaging the concentrations from the Kimbrough and Suffet (1995) data within 

different distance rings around the facility and comparing these average concentrations to the 

averages within the same distance rings from the modeled soil concentrations. This scaling 

preserves the overall pattern of soil concentrations estimated using the modeling approach 

(which takes into account site-specific inputs such as meteorological data and facility 

characteristics) and adjusts the magnitude of the concentrations to better correspond with 

measured values at a surrogate location. 

The surface soil concentrations estimated for the current conditions scenario using this 

approach for each U.S. Census block are summarized in Exhibit E-13 and provided in 

Attachment E-3. These surface soil concentrations for the current conditions scenario were also 

used for the alternative NAAQS scenarios (i.e., it was assumed that reductions in ambient air 

concentrations associated with the alternative NAAQS scenarios did not have a significant 

impact on soil concentrations). The individual U.S. Census block surface soil concentrations for 

the alternative NAAQS scenarios are presented in Attachments E-4 to E-7. 

E-18
 
 

Exhibit E-12. Summary of Soil Pb Concentration Factors with Distance 

Distance (m) Factor 

0 to 200 1 

200 to 400 2 

400 to 600 4 

600 to 800 6 

800+ 13 

Exhibit E-13. Summary of Surface Soil Pb Concentrations for the 

Current Conditions Scenario 

Statistic 

Average Surface Soil Pb 

Concentration: 

Model Output (mg/kg) a 

Average Soil Pb 

Concentration: Scaled 

(mg/kg) a Distance from Main Stack 

(m) b 

Maximum 52.5 315.3 680 

95th Percentile 5.0 65.6 1,600 

Median 0.9 12.0 3,300 

5th Percentile 0.1 1.4 8,500 

Minimum 0.03 0.4 16,000 

a

 Surface soil concentrations were calculated to a depth of 1 cm. 

b

 Some U.S. Census blocks greater than 10 km from the facility were included in the spatial template because of the 

irregular shape of U.S. Census block groups (see Section E.1).
 

E.4. INDOOR DUST 

Indoor dust Pb sampling data were not available for the secondary Pb smelter case study, 

necessitating the use of modeling to characterize indoor dust Pb levels within the study area. A 

version of the air-only regression-based model (USEPA, 1989) that uses ambient air Pb levels 

for predicting dust levels was chosen. This is a similar model as used for the primary Pb smelter 

case study at distances greater than 1.5 km from the source; however, in the case of the 

secondary Pb smelter, an “air-only” version of the model was employed reflecting the reduced 

overall confidence associated with soil characterization for this case study. For a more detailed 

explanation of the air-only regression-based model see Appendix G. 

Exhibit E-14 shows the number of U.S. Census blocks associated with different estimates 

of indoor dust Pb concentration. Exhibit E-14 also shows the number of children ages 0 to 7 

residing in areas associated with different estimates of indoor dust Pb concentration. 

E-19
 
Exhibit E-14. Number of U.S. Census Blocks and Number of Children Ages 0 to 7 Residing i

Associated with Different Estimates of Indoor Dust Pb Concentrations 

Number of U.S. Census Blocks with Indoor Dust Pb 

Concentrations 

Greater than Value in First Column a 

Number of Children Living in Area w

Concentrations Greater than Value 

Alternative NAAQS Scenario Alternative NAAQ

Indoor Dust Pb 

Concentration 

(μg/g) Current Conditions 

Scenario 

1 

0.2 μg/m3 , 

Max 

Quarterly 

2 

0.5 μg/m3 , 

Max 

Monthly 

3 

0.2 μg/m3 , 

Max 

Monthly 

4 

0.05 μg/m3 , 

Max 

Monthly 

Current 

Conditions 

Scenario 

1 

0.2 μg/m3 , 

Max 

Quarterly 

2 

0.5 μg/m3 , 

Max 

Monthly 

0

60 298 298 298 298 298 1698 1698 1698 70 27 3 6 1 0 121 8 17 80 4 1 3 1 0 9 1 8 

100 3 0 1 0 0 8 0 1 120 1 0 0 0 0 1 0 0 

a The 298 U.S. Census blocks with children ages 0 to 7 in the 2000 U.S. Census (U.S. Census Bureau, 2005) were used to develop this 

blocks without children were excluded. 

b Number of children ages 0 to 7 from the 2000 U.S. Census were used in this analysis (U.S. Census Bureau, 2005). 

E-20
 
 

 

Exhibit E-15 presents a summary of the population-weighted Pb indoor dust 

concentrations generated in the secondary Pb smelter case study for the 298 U.S. Census 

blocks/block groups with at least one child less than 7 years of age for the current conditions 

scenario and the four alternative NAAQS scenarios. Population-weighted indoor dust 

concentrations were calculated by first sorting the block/block groups in increasing inhalation 

exposure concentration order. Then the percentage of children living in block/block groups less 

than or equal to the maximum indoor dust concentration of those block/block groups was 

calculated. The indoor dust concentration of the block/block group associated with the 

minimum, 5th, median, 95th, and maximum percentile was selected. All estimated indoor dust 

Pb concentrations for residences with at least one child less than 7 years of age in the secondary 

Pb smelter case study are presented in Attachments E-3 to E-7. 

Exhibit E-15. Annual Average Population-Weighted Indoor Dust Pb Exposure 

Concentrations for the Secondary Pb Smelter Case Study 

Annual Average Indoor Dust Pb Exposure Concentrations (μg/g) a 

Alternative NAAQS Scenario 

Statistic b Current 

Conditions 

Scenario 

1 

0.2 μg/m3 , 

Max Quarterly 

2 

0.5 μg/m3 , 

Max Monthly 

3 

0.2 μg/m3 , 

Max Monthly 

4 

0.05 μg/m3 , 

Max Monthly 

Maximum 166.2 89 120 84 66 

95th Percentile 72.6 63 67 63 61 

Median 62.6 61 61 61 60 

5th Percentile 60.4 60 60 60 60 

Minimum 60.2 60 60 60 60 

a

 The 298 U.S. Census blocks/block groups with at least one child less than 7 years of age were used to create 

this summary. 

b

 The statistic (e.g., 95th percentile, median) may not be at the same location for each of the data results 

presented here.
 

Studies summarized in the 1990 review of the Pb NAAQS contained measurements of 

indoor house dust ranging from 10 to 35,000 parts per million (ppm), and a high value of 

100,000 ppm for one home within 2 km of a Pb smelting facility (USEPA, 1989). The indoor 

dust Pbconcentrations for the secondary Pb smelter case study fall within the range presented by 

the U.S. EPA (1989), although at the low-end of the range. The fact that this facility is a 

secondary Pb smelter and the summarized literature was inclusive of primary Pb smelters may 

explain some of the difference. 

E-21
 
 

 

 

 

 

 

 

 

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U.S. Environmental Protection Agency (USEPA). (2004a) Addendum User's Guide for the AMS/EPA Regulatory 

Mode - AERMOD. Office of Air Quality Planning and Standards. Available online at: Available at 

http://www.epa.gov/ttn/scram/7thconf/aermod/aerguide_addm.pdf. 

E-23
 
 

 

 

 

 

 

U.S. Environmental Protection Agency (USEPA). (2004b) User's Guide for the AMS/EPA Regulatory Model ­

AERMOD. EPA-454/B-03-001. Office of Air Quality Planning and Standards; September. 

U.S. Environmental Protection Agency (USEPA). (2005) Human Health Risk Assessment Protocol (HHRAP) for 

Hazardous Waste Combustion Facilities. EPA530-R-05-006. Office of Solid Waste and Emergency 

Response; September. Available online at: http://www.epa.gov/epaoswer/hazwaste/combust/risk.htm. 

U.S. Environmental Protection Agency (USEPA). (2006a) 1999 National-Scale Air Toxics Assessment. Available 

online at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html. 

U.S. Environmental Protection Agency (USEPA). (2006b) Air Quality Criteria for Lead (Final). Volume I and II. 

Research Triangle Park, NC: National Center for Environmental Assessment; EPA/600/R-05/144aF-bF. 

Available online at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=158823. 

U.S. Environmental Protection Agency (USEPA). (2007) Air Quality System (AQS) Database. Available online at: 

http://www.epa.gov/ttn/airs/airsaqs/aqsweb/aqswebwarning.htm. 

Wu, Z. Y.; Han, M.; Lin, Z. C.; Ondov, J. M. (1994) Chesapeake Bay Atmospheric Deposition Study, Year 1: 

Sources and Dry Deposition of Selected Elements in Aerosol Particles. Atmospheric Environment. 28: 

1471-1486. (as cited in EPA 2006, criteria document). 

Yi, S. M.; Shahin, U.; Sivadechathep, J.; Sofuoglu, S. C.; Holsen, T. M. (2001) Overall Elemental Dry Deposition 

Velocities Measured Around Lake Michigan. Atmospheric Environment. 35: 1133-1140. (as cited in EPA 

2006, criteria document). 

E-24
 
Attachment E-1. Emission Parameters for All Sources for the Secondary Pb Smelter Case Stu

Emission 

Point ID 

Location 

Source Type 

(point,area) 

Point Source Area Sour

UTMx (m) UTMy (m) Elevation (m) 

Actual 

Annual 

Average 

Emission 

Rate (g/s) 

Release 

Height (m) 

Stack Gas Exit 

Temperature 

(K) 

Stack Gas 

Exit 

Veolcity 

(m/s) 

Stack 

Diameter 

(m) 

Actual 

Annual 

Average 

Emission 

Rate 

(g/(s*m2) 

Release 

Height (m) 

Length of 

x-side of 

area (m) 

Leng

y-sid

area 

Stack1 596705 3517220 0 POINT 1.22E-02 54.9 360 37.5 1.2 - - - 

Stack4 596810 3517275 0 POINT 1.07E-02 27.4 340 30.4 0.9 - - - Stack5 596715 3517220 0 POINT 2.02E-02 54.9 356 29.9 1.2 - - - Stack10 596766 3517210 0 POINT 6.93E-04 9.1 304 18.3 1.1 - - - Area1 596647 3517376 0 AREAPOLY - - - - - 3.93E-06 0 7 

Area2 596831 3517404 0 AREA - - - - - 1.00E-05 0 27 4Area3 596742 3517510 0 AREAPOLY - - - - - 1.34E-06 0 8 

E-25
 
Attachment E-2. Building Downwash Parameters for the Secondary Pb Smelter Case 

Emission Building Building Downwash Parameters (categorized in 10's of degrees) 

Point ID Parameter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 

BUILDHGT 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 

BUILDWID 80.41 73.38 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 66.71 118.46 123.69 128.49 83.11 86.37 87.01 85.00 80.41 73.38 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 66.71 118.4

Stack1 BUILDLEN 73.98 66.71 60.44 69.19 77.33 83.11 86.37 87.01 85.00 80.41 73.38 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 66.71 60.44 69.19 77.33 83.11 86.37 87.01 85.00 80.41 73.38 65.23 

XBADJ -1.92 8.22 15.09 13.15 10.80 8.13 5.22 2.14 -1.00 -4.11 -7.10 -9.87 -16.26 -28.83 -40.52 -50.99 -59.90 -67.00 -72.06 -74.93 -75.52 -82.34 -88.13 -91.25 -91.59 -89.15 -84.00 -76.30 -66.28 -55.3YBADJ -36.09 -29.59 -22.75 -17.56 -8.50 0.82 10.11 19.09 27.50 35.07 41.57 16.29 20.49 23.88 49.69 48.40 45.64 41.50 36.09 29.59 22.75 17.56 8.50 -0.82 -10.11 -19.09 -27.50 -35.07 -41.57 -16.2

BUILDHGT 14.00 14.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 14.00 14.00 BUILDWID 111.64 88.90 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 170.27 168.20 164.18 164.63 163.45 157.30 146.37 131.00 111.64 88.90 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 170.27 168.2

Stack4 BUILDLEN 167.18 170.27 60.44 69.19 77.33 83.11 86.37 87.01 85.00 80.41 88.90 68.16 76.12 91.53 107.20 128.55 145.99 159.00 167.18 170.27 60.44 69.19 77.33 83.11 86.37 87.01 85.00 80.41 88.90 68.16 

XBADJ -120.83 -133.98 -85.04 -96.48 -104.98 -110.30 -112.26 -110.81 -106.00 -97.96 -92.06 -73.30 -69.83 -71.06 -70.13 -67.07 -61.98 -55.00 -46.35 -36.29 24.61 27.29 27.66 27.19 25.89 23.81 21.00 17.56 3.16 5.13 YBADJ 56.92 47.61 40.68 27.52 16.86 5.68 -5.66 -16.84 -27.50 -37.33 -48.85 -58.97 -68.88 -73.84 -74.86 -73.61 -70.12 -64.50 -56.92 -47.61 -40.68 -27.52 -16.86 -5.68 5.66 16.84 27.50 37.33 48.85 58.97 BUILDHGT 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 BUILDWID 80.41 73.38 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 66.71 118.46 69.19 77.33 83.11 86.37 87.01 85.00 80.41 73.38 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 66.71 118.4

Stack5 BUILDLEN 73.98 66.71 60.44 69.19 77.33 83.11 86.37 87.01 85.00 80.41 73.38 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 66.71 60.44 69.19 77.33 83.11 86.37 87.01 85.00 80.41 73.38 65.23 

XBADJ -3.66 4.80 10.09 6.72 3.14 -0.53 -4.18 -7.71 -11.00 -13.96 -16.49 -18.53 -23.92 -35.26 -45.52 -54.41 -61.64 -67.00 -70.32 -71.51 -70.52 -75.91 -80.47 -82.59 -82.19 -79.30 -74.00 -66.45 -56.88 -46.7YBADJ -26.25 -20.19 -14.09 -9.90 -2.07 5.82 13.53 20.83 27.50 33.33 38.15 11.29 41.31 41.81 41.03 39.01 35.80 31.50 26.25 20.19 14.09 9.90 2.07 -5.82 -13.53 -20.83 -27.50 -33.33 -38.15 -11.2BUILDHGT 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 17.00 BUILDWID 80.41 73.38 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 66.71 60.44 69.19 77.33 83.11 86.37 87.01 85.00 80.41 73.38 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 66.71 60.44 

Stack10 BUILDLEN 73.98 66.71 60.44 69.19 77.33 83.11 86.37 87.01 85.00 80.41 73.38 65.23 67.63 74.66 79.42 81.76 81.62 79.00 73.98 66.71 60.44 69.19 77.33 83.11 86.37 87.01 85.00 80.41 73.38 65.23 

XBADJ -2.66 -3.25 -6.75 -18.40 -29.50 -39.69 -48.68 -56.20 -62.00 -65.92 -67.84 -67.69 -69.41 -75.70 -79.68 -81.25 -80.35 -77.00 -71.32 -63.46 -53.68 -50.79 -47.83 -43.42 -37.69 -30.81 -23.00 -14.49 -5.54 2.46 

YBADJ 25.72 31.15 35.08 35.60 38.37 39.98 40.37 39.54 37.50 34.33 30.11 23.47 16.19 9.17 1.86 -5.50 -12.69 -19.50 -25.72 -31.15 -35.08 -35.60 -38.37 -39.98 -40.37 -39.54 -37.50 -34.33 -30.11 -23.4

E-26
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

9003026 71 2.1E-03 1.0E-03 7.1 1.8 60 

9001004 63 4.6E-03 2.1E-03 14.9 3.9 60 0003048 53 4.2E-03 1.9E-03 17.8 3.6 60 2001012 42 5.8E-04 2.6E-04 1.3 0.5 60 1004004 38 6.7E-04 3.0E-04 2.7 0.6 60 1002001 35 7.9E-03 3.6E-03 28.2 6.7 60 9001007 35 4.0E-03 1.9E-03 14.4 3.4 60 0003040 31 6.8E-03 3.0E-03 29.7 5.7 60 2001009 31 4.9E-04 2.2E-04 1.4 0.4 60 0001002 30 3.0E-03 1.3E-03 11.4 2.5 60 0002023 29 0.02 6.8E-03 79.6 12.9 60 9003043 26 3.0E-03 1.4E-03 11.1 2.5 60 9004000 24 8.8E-04 4.1E-04 1.4 0.7 60 2001037 22 5.2E-04 2.3E-04 1.4 0.4 60 9003012 21 1.4E-03 6.4E-04 5.1 1.2 60 1004092 21 8.1E-04 3.6E-04 3.7 0.7 60 1004014 21 7.2E-04 3.3E-04 3.0 0.6 60 0003121 19 1.4E-03 6.4E-04 6.0 1.2 60 2001005 19 7.5E-04 3.4E-04 2.2 0.6 60 9001011 18 4.4E-03 2.1E-03 16.2 3.7 60 0003061 18 3.3E-03 1.4E-03 14.8 2.7 60 2001004 17 7.3E-04 3.3E-04 2.0 0.6 60 0001023 16 5.4E-03 2.4E-03 25.4 4.6 60 1004031 16 3.1E-03 1.4E-03 10.7 2.6 60 0003080 16 3.0E-03 1.3E-03 14.1 2.5 60 9003051 16 2.9E-03 1.3E-03 11.5 2.4 60 2001039 16 4.4E-04 1.9E-04 1.0 0.4 60 2001001 15 9.8E-04 4.4E-04 2.8 0.8 60 9002026 14 8.1E-03 3.7E-03 25.2 6.8 60 

E-27
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

0001000 14 3.0E-03 1.3E-03 10.5 2.6 60 

0002024 12 0.01 5.9E-03 66.2 11.3 60 0001015 12 5.1E-03 2.3E-03 22.2 4.3 60 2001068 12 9.1E-04 4.0E-04 2.6 0.8 60 1004068 11 4.6E-03 2.1E-03 25.8 3.9 60 0002027 10 0.01 6.0E-03 58.0 11.3 60 1002015 10 9.0E-03 4.1E-03 28.1 7.6 60 0001029 10 5.2E-03 2.3E-03 19.9 4.4 60 1004041 10 3.5E-03 1.6E-03 12.9 3.0 60 2001026 10 5.9E-04 2.6E-04 1.8 0.5 60 1002014 9 0.01 4.7E-03 34.1 8.8 60 1003025 9 8.2E-03 3.7E-03 40.5 6.9 60 0003051 9 2.8E-03 1.3E-03 9.7 2.4 60 9003023 9 2.0E-03 9.5E-04 5.3 1.7 60 2001010 9 6.4E-04 2.9E-04 1.7 0.5 60 0002038 8 0.02 8.5E-03 65.5 16.2 60 0001026 8 7.0E-03 3.1E-03 26.4 5.9 60 1003000 8 6.9E-03 3.1E-03 23.7 5.8 60 1003006 8 6.1E-03 2.7E-03 27.0 5.1 60 0001009 8 4.2E-03 1.9E-03 14.7 3.5 60 9003041 8 3.9E-03 1.8E-03 13.4 3.3 60 1004050 8 3.6E-03 1.6E-03 15.4 3.1 60 1004036 8 3.1E-03 1.4E-03 10.8 2.7 60 0003068 8 2.8E-03 1.2E-03 12.8 2.3 60 0003007 8 1.3E-03 5.6E-04 4.8 1.1 60 1004025 8 1.0E-03 4.7E-04 4.3 0.9 60 9003003 8 8.3E-04 3.8E-04 2.0 0.7 60 1004098 8 6.9E-04 3.1E-04 2.3 0.6 60 0003089 7 9.5E-03 4.2E-03 41.7 8.0 60 

E-28
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

1003008 7 8.1E-03 3.6E-03 36.1 6.8 60 

9002023 7 5.9E-03 2.8E-03 19.1 5.0 60 9002015 7 5.6E-03 2.6E-03 21.7 4.7 60 9001010 7 5.5E-03 2.6E-03 15.5 4.7 60 9001005 7 4.7E-03 2.2E-03 17.5 4.0 60 1004059 7 3.4E-03 1.5E-03 16.0 2.8 60 0003114 7 2.9E-03 1.3E-03 10.1 2.4 60 0003037 7 2.5E-03 1.1E-03 10.1 2.1 60 0003042 6 0.05 2.3E-02 256.0 44.1 60 9003027 6 2.9E-03 1.4E-03 9.7 2.5 60 0003155 6 2.7E-03 1.2E-03 11.5 2.3 60 1004058 6 2.4E-03 1.1E-03 11.2 2.1 60 1004096 6 6.0E-04 2.7E-04 2.4 0.5 60 1004007 6 5.6E-04 2.5E-04 2.0 0.5 60 2001051 6 3.1E-04 1.4E-04 0.6 0.3 60 0002050 5 0.02 1.0E-02 101.5 18.9 60 0002036 5 0.02 8.1E-03 65.1 15.3 60 1003013 5 0.02 7.9E-03 57.6 14.7 60 0002026 5 0.02 7.7E-03 91.1 14.6 60 1003016 5 0.01 6.2E-03 45.1 11.7 60 0003138 5 0.01 5.0E-03 69.5 9.5 60 1002003 5 0.01 4.9E-03 35.3 9.1 60 0003140 5 8.5E-03 3.8E-03 53.5 7.2 60 0003083 5 8.5E-03 3.8E-03 49.0 7.2 60 1003007 5 5.4E-03 2.4E-03 23.8 4.5 60 1004047 5 4.1E-03 1.9E-03 15.6 3.5 60 0001006 5 3.3E-03 1.5E-03 11.9 2.8 60 1004037 5 2.6E-03 1.2E-03 9.0 2.2 60 0003071 5 2.5E-03 1.1E-03 11.3 2.1 60 

E-29
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

9004022 5 1.4E-03 6.6E-04 3.8 1.2 60 

0003004 5 1.2E-03 5.2E-04 4.5 1.0 60 1004028 5 1.1E-03 4.9E-04 3.7 0.9 60 1004019 5 9.3E-04 4.2E-04 3.3 0.8 60 1004013 5 8.6E-04 3.9E-04 3.5 0.7 60 9003002 5 8.2E-04 3.8E-04 1.9 0.7 60 2001006 5 6.2E-04 2.8E-04 1.6 0.5 60 2001007 5 5.9E-04 2.6E-04 1.4 0.5 60 1002018 4 0.02 6.8E-03 51.9 12.7 60 0002018 4 0.01 5.0E-03 47.6 9.4 60 1002012 4 0.01 4.6E-03 34.9 8.6 60 0002022 4 9.1E-03 4.1E-03 36.4 7.7 60 1003023 4 8.7E-03 3.9E-03 49.5 7.3 60 1002016 4 8.6E-03 3.9E-03 26.7 7.2 60 9002021 4 7.2E-03 3.4E-03 25.6 6.1 60 9002030 4 7.0E-03 3.2E-03 27.8 5.9 60 1003028 4 5.5E-03 2.5E-03 28.8 4.7 60 0003144 4 5.2E-03 2.3E-03 27.7 4.4 60 9002000 4 4.6E-03 2.1E-03 14.5 3.9 60 0001012 4 4.3E-03 1.9E-03 15.6 3.6 60 9002006 4 4.3E-03 2.0E-03 15.0 3.6 60 0003060 4 4.0E-03 1.8E-03 16.3 3.4 60 0003079 4 3.9E-03 1.7E-03 17.7 3.3 60 1004051 4 3.6E-03 1.6E-03 13.4 3.0 60 1004048 4 3.4E-03 1.5E-03 12.9 2.9 60 9001013 4 3.4E-03 1.6E-03 8.3 2.8 60 9001002 4 3.3E-03 1.5E-03 9.8 2.8 60 0003107 4 3.3E-03 1.5E-03 10.6 2.8 60 1004049 4 3.1E-03 1.4E-03 12.8 2.6 60 

E-30
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

1004054 4 2.3E-03 1.0E-03 8.0 1.9 60 

1004057 4 2.3E-03 1.0E-03 9.1 1.9 60 9003044 4 2.1E-03 9.8E-04 7.6 1.8 60 1004055 4 2.1E-03 9.3E-04 7.6 1.7 60 0003128 4 2.0E-03 8.7E-04 7.5 1.7 60 2001028 4 1.3E-03 5.9E-04 4.3 1.1 60 1004011 4 7.0E-04 3.1E-04 2.5 0.6 60 0002047 3 0.02 1.0E-02 76.6 19.2 60 0002039 3 0.02 9.2E-03 73.6 17.4 60 0002028 3 0.02 7.3E-03 77.2 13.9 60 1002020 3 0.01 6.5E-03 50.6 12.2 60 1002002 3 8.8E-03 4.0E-03 35.2 7.4 60 0003093 3 8.2E-03 3.7E-03 34.2 7.0 60 0003082 3 7.7E-03 3.4E-03 42.4 6.5 60 0002017 3 6.9E-03 3.1E-03 29.4 5.8 60 9002011 3 6.7E-03 3.1E-03 27.1 5.7 60 1003004 3 6.6E-03 3.0E-03 27.1 5.5 60 0001032 3 6.6E-03 2.9E-03 24.9 5.5 60 0001027 3 6.1E-03 2.7E-03 25.2 5.2 60 9002001 3 4.9E-03 2.3E-03 18.2 4.2 60 0003078 3 4.9E-03 2.2E-03 21.0 4.2 60 1003001 3 4.5E-03 2.0E-03 15.9 3.8 60 1004060 3 4.4E-03 2.0E-03 20.1 3.7 60 1004046 3 4.1E-03 1.8E-03 15.0 3.5 60 0001013 3 4.0E-03 1.8E-03 14.5 3.3 60 9001012 3 3.5E-03 1.6E-03 8.8 3.0 60 1004052 3 3.3E-03 1.5E-03 14.8 2.8 60 9001014 3 3.1E-03 1.5E-03 8.9 2.7 60 0003070 3 2.9E-03 1.3E-03 9.9 2.5 60 

E-31
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

0003036 3 2.9E-03 1.3E-03 12.1 2.4 60 

9004015 3 2.6E-03 1.2E-03 6.5 2.2 60 9004014 3 2.5E-03 1.2E-03 7.1 2.1 60 0003073 3 2.5E-03 1.1E-03 11.3 2.1 60 0001003 3 2.4E-03 1.1E-03 8.0 2.1 60 0003115 3 2.2E-03 9.8E-04 7.5 1.9 60 9004016 3 2.0E-03 9.2E-04 5.4 1.7 60 1004072 3 2.0E-03 8.9E-04 5.1 1.7 60 1004056 3 1.8E-03 8.3E-04 7.1 1.6 60 9003017 3 1.4E-03 6.7E-04 4.5 1.2 60 9004017 3 1.4E-03 6.4E-04 3.7 1.2 60 9004006 3 1.3E-03 5.9E-04 3.2 1.1 60 0003020 3 1.3E-03 5.6E-04 5.1 1.1 60 9003013 3 1.2E-03 5.7E-04 4.2 1.0 60 0003006 3 1.2E-03 5.3E-04 4.8 1.0 60 9004008 3 1.2E-03 5.6E-04 3.0 1.0 60 1004089 3 1.1E-03 4.8E-04 4.8 0.9 60 2001011 3 6.5E-04 2.9E-04 1.9 0.6 60 1004100 3 5.7E-04 2.6E-04 1.5 0.5 60 2001008 3 5.6E-04 2.5E-04 1.8 0.5 60 2001013 3 4.7E-04 2.1E-04 1.0 0.4 60 1003012 2 0.01 6.8E-03 59.6 12.6 60 1002019 2 0.01 6.3E-03 50.2 11.7 60 0002025 2 0.01 6.0E-03 71.9 11.4 60 0003087 2 0.01 5.1E-03 56.2 9.7 60 1003009 2 0.01 4.8E-03 50.4 9.0 60 0003088 2 9.6E-03 4.3E-03 43.5 8.1 60 0002019 2 9.4E-03 4.2E-03 39.7 7.9 60 9002029 2 8.2E-03 3.8E-03 28.4 6.9 60 

E-32
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

0001035 2 7.8E-03 3.5E-03 30.3 6.6 60 

0003085 2 7.2E-03 3.2E-03 47.3 6.1 60 0001025 2 6.8E-03 3.0E-03 27.5 5.7 60 9002012 2 6.1E-03 2.8E-03 23.2 5.1 60 0001031 2 5.9E-03 2.6E-03 23.0 5.0 60 9002017 2 5.8E-03 2.7E-03 19.7 4.9 60 0003142 2 5.2E-03 2.3E-03 29.5 4.4 60 0003077 2 4.8E-03 2.1E-03 19.8 4.0 60 0001024 2 4.7E-03 2.1E-03 19.4 4.0 60 0003055 2 4.1E-03 1.8E-03 14.3 3.5 60 0003056 2 4.0E-03 1.8E-03 16.0 3.4 60 9003042 2 3.8E-03 1.8E-03 14.2 3.2 60 9003050 2 3.8E-03 1.8E-03 14.0 3.2 60 0001008 2 3.7E-03 1.6E-03 13.9 3.1 60 1004045 2 3.6E-03 1.6E-03 12.8 3.0 60 0003065 2 3.5E-03 1.6E-03 11.9 3.0 60 0003052 2 3.3E-03 1.5E-03 11.3 2.8 60 1004033 2 3.1E-03 1.4E-03 10.3 2.6 60 1004040 2 2.9E-03 1.3E-03 10.2 2.4 60 0003050 2 2.7E-03 1.2E-03 9.4 2.3 60 1004038 2 2.7E-03 1.2E-03 9.1 2.2 60 0003069 2 2.6E-03 1.2E-03 11.9 2.2 60 0003049 2 2.6E-03 1.1E-03 10.9 2.2 60 0003031 2 2.5E-03 1.1E-03 10.5 2.1 60 9001001 2 2.1E-03 9.9E-04 5.5 1.8 60 9004018 2 1.9E-03 8.8E-04 4.9 1.6 60 0003122 2 1.9E-03 8.3E-04 5.9 1.6 60 0003123 2 1.8E-03 7.9E-04 5.6 1.5 60 0003160 2 1.7E-03 7.7E-04 8.6 1.5 60 

E-33
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

1004030 2 1.7E-03 7.7E-04 5.5 1.4 60 

9003033 2 1.6E-03 7.3E-04 4.1 1.3 60 0003110 2 1.5E-03 6.8E-04 5.8 1.3 60 2001031 2 1.5E-03 6.5E-04 4.7 1.2 60 2001027 2 1.2E-03 5.3E-04 4.3 1.0 60 9004023 2 1.2E-03 5.4E-04 3.3 1.0 60 0003127 2 1.2E-03 5.1E-04 5.5 1.0 60 0003001 2 9.1E-04 4.1E-04 2.8 0.8 60 2001002 2 8.8E-04 3.9E-04 2.5 0.7 60 1004094 2 8.1E-04 3.6E-04 3.4 0.7 60 1004018 2 8.0E-04 3.6E-04 2.5 0.7 60 1004010 2 6.0E-04 2.7E-04 2.0 0.5 60 2001038 2 5.8E-04 2.6E-04 1.7 0.5 60 2001036 2 5.6E-04 2.5E-04 1.4 0.5 60 1004000 2 5.0E-04 2.2E-04 1.5 0.4 60 1004003 2 4.8E-04 2.2E-04 1.4 0.4 60 2001042 2 3.8E-04 1.7E-04 1.1 0.3 60 2001053 2 3.5E-04 1.6E-04 0.8 0.3 60 2001047 2 3.5E-04 1.6E-04 0.9 0.3 60 2001059 2 3.0E-04 1.3E-04 0.5 0.3 60 0002042 1 0.13 5.6E-02 315.3 106.2 60 0003046 1 0.05 2.3E-02 141.9 44.3 60 0002041 1 0.03 1.4E-02 141.8 26.4 60 0002029 1 0.02 8.2E-03 66.9 15.5 60 0002037 1 0.02 7.3E-03 57.0 13.9 60 1003014 1 0.02 7.0E-03 49.5 13.2 60 0003137 1 0.01 6.7E-03 99.0 12.7 60 1003011 1 0.01 6.6E-03 64.2 12.3 60 1002007 1 0.01 5.9E-03 45.2 11.1 60 

E-34
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

1002013 1 0.01 5.2E-03 39.4 9.7 60 

1002017 1 0.01 5.1E-03 36.0 9.6 60 1003010 1 0.01 5.1E-03 49.7 9.6 60 0003090 1 9.6E-03 4.3E-03 42.0 8.1 60 0003091 1 9.5E-03 4.2E-03 36.9 8.0 60 1003022 1 9.2E-03 4.1E-03 51.0 7.7 60 0002015 1 9.0E-03 4.0E-03 45.4 7.6 60 0003094 1 8.1E-03 3.6E-03 33.4 6.8 60 1001017 1 7.2E-03 3.3E-03 24.1 6.1 60 9002031 1 6.6E-03 3.0E-03 25.3 5.5 60 1003005 1 6.5E-03 2.9E-03 29.5 5.5 60 9002022 1 6.0E-03 2.8E-03 22.2 5.0 60 9002014 1 5.8E-03 2.7E-03 22.5 4.9 60 9002013 1 5.7E-03 2.6E-03 21.3 4.8 60 9002020 1 5.6E-03 2.6E-03 20.5 4.7 60 9002016 1 5.3E-03 2.4E-03 19.1 4.4 60 1003003 1 5.2E-03 2.4E-03 23.1 4.4 60 9001009 1 5.0E-03 2.3E-03 11.6 4.2 60 1001016 1 4.9E-03 2.2E-03 16.1 4.1 60 0003058 1 4.5E-03 2.0E-03 18.5 3.8 60 9002007 1 4.5E-03 2.1E-03 18.6 3.8 60 1004043 1 4.0E-03 1.8E-03 14.5 3.4 60 0001010 1 4.0E-03 1.8E-03 13.7 3.3 60 0003054 1 3.9E-03 1.7E-03 13.5 3.3 60 0003053 1 3.9E-03 1.7E-03 13.1 3.3 60 0003064 1 3.5E-03 1.6E-03 13.2 3.0 60 0001011 1 3.5E-03 1.6E-03 12.9 3.0 60 0001018 1 3.4E-03 1.5E-03 14.7 2.9 60 9001015 1 3.4E-03 1.6E-03 8.2 2.8 60 

E-35
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

0001007 1 3.3E-03 1.4E-03 12.7 2.8 60 

0003063 1 3.2E-03 1.4E-03 10.8 2.7 60 0003066 1 3.2E-03 1.4E-03 10.9 2.7 60 0001020 1 3.1E-03 1.4E-03 14.7 2.7 60 0003067 1 3.0E-03 1.3E-03 10.3 2.6 60 0003109 1 3.0E-03 1.3E-03 15.2 2.5 60 0003076 1 3.0E-03 1.3E-03 12.5 2.5 60 0001019 1 2.6E-03 1.2E-03 12.9 2.2 60 1004039 1 2.5E-03 1.1E-03 8.8 2.1 60 0003072 1 2.4E-03 1.0E-03 10.6 2.0 60 0001005 1 2.1E-03 9.4E-04 8.2 1.8 60 0003152 1 1.7E-03 7.6E-04 8.0 1.4 60 0003159 1 1.7E-03 7.5E-04 6.5 1.4 60 9004021 1 1.6E-03 7.2E-04 5.1 1.3 60 2001029 1 1.3E-03 5.7E-04 4.6 1.1 60 0003015 1 1.3E-03 5.6E-04 3.9 1.1 60 0003112 1 1.2E-03 5.6E-04 4.8 1.1 60 9003020 1 1.2E-03 5.8E-04 2.8 1.1 60 9003016 1 1.2E-03 5.5E-04 2.7 1.0 60 0003002 1 1.1E-03 4.8E-04 4.4 0.9 60 9003021 1 1.1E-03 5.0E-04 2.6 0.9 60 0003003 1 1.1E-03 4.7E-04 4.3 0.9 60 9003010 1 1.0E-03 4.7E-04 3.0 0.9 60 2001000 1 9.5E-04 4.2E-04 3.6 0.8 60 1004081 1 9.1E-04 4.1E-04 4.1 0.8 60 1004024 1 9.1E-04 4.1E-04 3.1 0.8 60 1004091 1 8.4E-04 3.8E-04 3.7 0.7 60 0003129 1 8.2E-04 3.6E-04 2.3 0.7 60 1004093 1 8.1E-04 3.7E-04 3.4 0.7 60 

E-36
 
Attachment E-3. Estimated Media Pb Concentrations in the Current Conditions Scenario f

Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concentra

From Recent Air a From Other b 

1004017 1 7.1E-04 3.2E-04 2.4 0.6 60 

1004015 1 6.9E-04 3.1E-04 2.7 0.6 60 1004005 1 5.2E-04 2.3E-04 1.7 0.4 60 1004006 1 4.9E-04 2.2E-04 1.4 0.4 60 2001070 1 4.3E-04 1.9E-04 1.3 0.4 60 2001052 1 3.4E-04 1.5E-04 0.7 0.3 60 2001062 1 3.3E-04 1.5E-04 0.6 0.3 60 2001058 1 2.7E-04 1.2E-04 0.4 0.2 60 

a Recent air refers to contributions associated with recent outdoor ambient air.
 b Other refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air).
 

E-37
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

9003026 71 1.2E-03 5.6E-04 7.1 1.0 60 

9001004 63 2.6E-03 1.2E-03 14.9 2.2 60 0003048 53 2.4E-03 1.1E-03 17.8 2.0 60 2001012 42 3.3E-04 1.5E-04 1.3 0.3 60 1004004 38 3.8E-04 1.7E-04 2.7 0.3 60 1002001 35 4.4E-03 2.0E-03 28.2 3.7 60 9001007 35 2.2E-03 1.0E-03 14.4 1.9 60 0003040 31 3.8E-03 1.7E-03 29.7 3.2 60 2001009 31 2.8E-04 1.2E-04 1.4 0.2 60 0001002 30 1.7E-03 7.4E-04 11.4 1.4 60 0002023 29 8.6E-03 3.8E-03 79.6 7.2 60 9003043 26 1.7E-03 7.7E-04 11.1 1.4 60 9004000 24 4.9E-04 2.3E-04 1.4 0.4 60 2001037 22 2.9E-04 1.3E-04 1.4 0.2 60 9003012 21 7.7E-04 3.6E-04 5.1 0.7 60 1004092 21 4.5E-04 2.0E-04 3.7 0.4 60 1004014 21 4.1E-04 1.8E-04 3.0 0.3 60 0003121 19 8.1E-04 3.6E-04 6.0 0.7 60 2001005 19 4.2E-04 1.9E-04 2.2 0.4 60 9001011 18 2.5E-03 1.2E-03 16.2 2.1 60 0003061 18 1.8E-03 8.1E-04 14.8 1.5 60 2001004 17 4.1E-04 1.8E-04 2.0 0.3 60 0001023 16 3.0E-03 1.4E-03 25.4 2.6 60 1004031 16 1.8E-03 8.0E-04 10.7 1.5 60 0003080 16 1.7E-03 7.5E-04 14.1 1.4 60 9003051 16 1.6E-03 7.6E-04 11.5 1.4 60 2001039 16 2.5E-04 1.1E-04 1.0 0.2 60 2001001 15 5.5E-04 2.5E-04 2.8 0.5 60 

E-38
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

9002026 14 4.5E-03 2.1E-03 25.2 3.8 60 

0001000 14 1.7E-03 7.6E-04 10.5 1.4 60 0002024 12 7.5E-03 3.3E-03 66.2 6.3 60 0001015 12 2.9E-03 1.3E-03 22.2 2.4 60 2001068 12 5.1E-04 2.3E-04 2.6 0.4 60 1004068 11 2.6E-03 1.2E-03 25.8 2.2 60 0002027 10 7.5E-03 3.4E-03 58.0 6.4 60 1002015 10 5.1E-03 2.3E-03 28.1 4.3 60 0001029 10 2.9E-03 1.3E-03 19.9 2.5 60 1004041 10 2.0E-03 9.0E-04 12.9 1.7 60 2001026 10 3.3E-04 1.5E-04 1.8 0.3 60 1002014 9 5.9E-03 2.7E-03 34.1 5.0 60 1003025 9 4.6E-03 2.1E-03 40.5 3.9 60 0003051 9 1.6E-03 7.0E-04 9.7 1.3 60 9003023 9 1.2E-03 5.3E-04 5.3 1.0 60 2001010 9 3.6E-04 1.6E-04 1.7 0.3 60 0002038 8 0.01 4.8E-03 65.5 9.1 60 0001026 8 3.9E-03 1.7E-03 26.4 3.3 60 1003000 8 3.9E-03 1.7E-03 23.7 3.3 60 1003006 8 3.4E-03 1.5E-03 27.0 2.9 60 0001009 8 2.4E-03 1.0E-03 14.7 2.0 60 9003041 8 2.2E-03 1.0E-03 13.4 1.8 60 1004050 8 2.0E-03 9.2E-04 15.4 1.7 60 1004036 8 1.8E-03 8.0E-04 10.8 1.5 60 0003068 8 1.5E-03 6.9E-04 12.8 1.3 60 0003007 8 7.1E-04 3.2E-04 4.8 0.6 60 1004025 8 5.8E-04 2.6E-04 4.3 0.5 60 9003003 8 4.6E-04 2.2E-04 2.0 0.4 60 

E-39
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

1004098 8 3.9E-04 1.8E-04 2.3 0.3 60 

0003089 7 5.4E-03 2.4E-03 41.7 4.5 60 1003008 7 4.5E-03 2.1E-03 36.1 3.8 60 9002023 7 3.3E-03 1.5E-03 19.1 2.8 60 9002015 7 3.1E-03 1.5E-03 21.7 2.6 60 9001010 7 3.1E-03 1.4E-03 15.5 2.6 60 9001005 7 2.6E-03 1.2E-03 17.5 2.2 60 1004059 7 1.9E-03 8.5E-04 16.0 1.6 60 0003114 7 1.6E-03 7.2E-04 10.1 1.4 60 0003037 7 1.4E-03 6.3E-04 10.1 1.2 60 0003042 6 0.03 1.3E-02 256.0 24.8 60 9003027 6 1.6E-03 7.6E-04 9.7 1.4 60 0003155 6 1.5E-03 6.9E-04 11.5 1.3 60 1004058 6 1.4E-03 6.2E-04 11.2 1.2 60 1004096 6 3.4E-04 1.5E-04 2.4 0.3 60 1004007 6 3.2E-04 1.4E-04 2.0 0.3 60 2001051 6 1.8E-04 7.8E-05 0.6 0.1 60 0002050 5 0.01 5.6E-03 101.5 10.6 60 0002036 5 0.01 4.5E-03 65.1 8.6 60 1003013 5 9.8E-03 4.4E-03 57.6 8.3 60 0002026 5 9.7E-03 4.3E-03 91.1 8.2 60 1003016 5 7.8E-03 3.5E-03 45.1 6.6 60 0003138 5 6.4E-03 2.8E-03 69.5 5.4 60 1002003 5 6.1E-03 2.8E-03 35.3 5.1 60 0003140 5 4.8E-03 2.1E-03 53.5 4.1 60 0003083 5 4.8E-03 2.1E-03 49.0 4.0 60 1003007 5 3.0E-03 1.4E-03 23.8 2.6 60 1004047 5 2.3E-03 1.0E-03 15.6 2.0 60 

E-40
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

0001006 5 1.9E-03 8.3E-04 11.9 1.6 60 

1004037 5 1.5E-03 6.6E-04 9.0 1.2 60 0003071 5 1.4E-03 6.2E-04 11.3 1.2 60 9004022 5 8.0E-04 3.7E-04 3.8 0.7 60 0003004 5 6.6E-04 2.9E-04 4.5 0.6 60 1004028 5 6.1E-04 2.8E-04 3.7 0.5 60 1004019 5 5.2E-04 2.4E-04 3.3 0.4 60 1004013 5 4.8E-04 2.2E-04 3.5 0.4 60 9003002 5 4.6E-04 2.2E-04 1.9 0.4 60 2001006 5 3.5E-04 1.6E-04 1.6 0.3 60 2001007 5 3.3E-04 1.5E-04 1.4 0.3 60 1002018 4 8.5E-03 3.8E-03 51.9 7.2 60 0002018 4 6.3E-03 2.8E-03 47.6 5.3 60 1002012 4 5.7E-03 2.6E-03 34.9 4.8 60 0002022 4 5.1E-03 2.3E-03 36.4 4.3 60 1003023 4 4.9E-03 2.2E-03 49.5 4.1 60 1002016 4 4.8E-03 2.2E-03 26.7 4.1 60 9002021 4 4.1E-03 1.9E-03 25.6 3.4 60 9002030 4 3.9E-03 1.8E-03 27.8 3.3 60 1003028 4 3.1E-03 1.4E-03 28.8 2.6 60 0003144 4 2.9E-03 1.3E-03 27.7 2.5 60 9002000 4 2.6E-03 1.2E-03 14.5 2.2 60 0001012 4 2.4E-03 1.1E-03 15.6 2.0 60 9002006 4 2.4E-03 1.1E-03 15.0 2.0 60 0003060 4 2.2E-03 1.0E-03 16.3 1.9 60 0003079 4 2.2E-03 9.8E-04 17.7 1.9 60 1004051 4 2.0E-03 9.1E-04 13.4 1.7 60 1004048 4 1.9E-03 8.6E-04 12.9 1.6 60 

E-41
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

9001013 4 1.9E-03 8.8E-04 8.3 1.6 60 

9001002 4 1.9E-03 8.7E-04 9.8 1.6 60 0003107 4 1.9E-03 8.3E-04 10.6 1.6 60 1004049 4 1.7E-03 7.8E-04 12.8 1.5 60 1004054 4 1.3E-03 5.8E-04 8.0 1.1 60 1004057 4 1.3E-03 5.8E-04 9.1 1.1 60 9003044 4 1.2E-03 5.5E-04 7.6 1.0 60 1004055 4 1.2E-03 5.2E-04 7.6 1.0 60 0003128 4 1.1E-03 4.9E-04 7.5 0.9 60 2001028 4 7.4E-04 3.3E-04 4.3 0.6 60 1004011 4 3.9E-04 1.8E-04 2.5 0.3 60 0002047 3 0.01 5.7E-03 76.6 10.8 60 0002039 3 0.01 5.2E-03 73.6 9.8 60 0002028 3 9.3E-03 4.1E-03 77.2 7.8 60 1002020 3 8.1E-03 3.7E-03 50.6 6.9 60 1002002 3 5.0E-03 2.2E-03 35.2 4.2 60 0003093 3 4.6E-03 2.1E-03 34.2 3.9 60 0003082 3 4.3E-03 1.9E-03 42.4 3.7 60 0002017 3 3.9E-03 1.7E-03 29.4 3.3 60 9002011 3 3.8E-03 1.8E-03 27.1 3.2 60 1003004 3 3.7E-03 1.7E-03 27.1 3.1 60 0001032 3 3.7E-03 1.6E-03 24.9 3.1 60 0001027 3 3.5E-03 1.5E-03 25.2 2.9 60 9002001 3 2.8E-03 1.3E-03 18.2 2.3 60 0003078 3 2.8E-03 1.2E-03 21.0 2.3 60 1003001 3 2.5E-03 1.1E-03 15.9 2.1 60 1004060 3 2.5E-03 1.1E-03 20.1 2.1 60 1004046 3 2.3E-03 1.0E-03 15.0 1.9 60 

E-42
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

0001013 3 2.2E-03 9.9E-04 14.5 1.9 60 

9001012 3 2.0E-03 9.2E-04 8.8 1.7 60 1004052 3 1.9E-03 8.4E-04 14.8 1.6 60 9001014 3 1.8E-03 8.2E-04 8.9 1.5 60 0003070 3 1.6E-03 7.3E-04 9.9 1.4 60 0003036 3 1.6E-03 7.2E-04 12.1 1.4 60 9004015 3 1.5E-03 6.8E-04 6.5 1.2 60 9004014 3 1.4E-03 6.6E-04 7.1 1.2 60 0003073 3 1.4E-03 6.1E-04 11.3 1.2 60 0001003 3 1.4E-03 6.1E-04 8.0 1.2 60 0003115 3 1.2E-03 5.5E-04 7.5 1.0 60 9004016 3 1.1E-03 5.2E-04 5.4 0.9 60 1004072 3 1.1E-03 5.0E-04 5.1 0.9 60 1004056 3 1.0E-03 4.7E-04 7.1 0.9 60 9003017 3 8.1E-04 3.8E-04 4.5 0.7 60 9004017 3 7.8E-04 3.6E-04 3.7 0.7 60 9004006 3 7.1E-04 3.3E-04 3.2 0.6 60 0003020 3 7.1E-04 3.2E-04 5.1 0.6 60 9003013 3 7.0E-04 3.2E-04 4.2 0.6 60 0003006 3 6.7E-04 3.0E-04 4.8 0.6 60 9004008 3 6.7E-04 3.1E-04 3.0 0.6 60 1004089 3 6.0E-04 2.7E-04 4.8 0.5 60 2001011 3 3.7E-04 1.6E-04 1.9 0.3 60 1004100 3 3.2E-04 1.5E-04 1.5 0.3 60 2001008 3 3.1E-04 1.4E-04 1.8 0.3 60 2001013 3 2.7E-04 1.2E-04 1.0 0.2 60 1003012 2 8.4E-03 3.8E-03 59.6 7.1 60 1002019 2 7.8E-03 3.5E-03 50.2 6.6 60 

E-43
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

0002025 2 7.6E-03 3.4E-03 71.9 6.4 60 

0003087 2 6.5E-03 2.9E-03 56.2 5.5 60 1003009 2 6.0E-03 2.7E-03 50.4 5.1 60 0003088 2 5.4E-03 2.4E-03 43.5 4.6 60 0002019 2 5.3E-03 2.4E-03 39.7 4.5 60 9002029 2 4.6E-03 2.1E-03 28.4 3.9 60 0001035 2 4.4E-03 1.9E-03 30.3 3.7 60 0003085 2 4.0E-03 1.8E-03 47.3 3.4 60 0001025 2 3.8E-03 1.7E-03 27.5 3.2 60 9002012 2 3.4E-03 1.6E-03 23.2 2.9 60 0001031 2 3.3E-03 1.5E-03 23.0 2.8 60 9002017 2 3.3E-03 1.5E-03 19.7 2.8 60 0003142 2 2.9E-03 1.3E-03 29.5 2.5 60 0003077 2 2.7E-03 1.2E-03 19.8 2.3 60 0001024 2 2.7E-03 1.2E-03 19.4 2.2 60 0003055 2 2.3E-03 1.0E-03 14.3 2.0 60 0003056 2 2.3E-03 1.0E-03 16.0 1.9 60 9003042 2 2.1E-03 9.9E-04 14.2 1.8 60 9003050 2 2.1E-03 9.9E-04 14.0 1.8 60 0001008 2 2.1E-03 9.3E-04 13.9 1.8 60 1004045 2 2.0E-03 9.1E-04 12.8 1.7 60 0003065 2 2.0E-03 8.8E-04 11.9 1.7 60 0003052 2 1.9E-03 8.4E-04 11.3 1.6 60 1004033 2 1.7E-03 7.8E-04 10.3 1.5 60 1004040 2 1.6E-03 7.3E-04 10.2 1.4 60 0003050 2 1.5E-03 6.8E-04 9.4 1.3 60 1004038 2 1.5E-03 6.7E-04 9.1 1.3 60 0003069 2 1.5E-03 6.5E-04 11.9 1.2 60 

E-44
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

0003049 2 1.4E-03 6.4E-04 10.9 1.2 60 

0003031 2 1.4E-03 6.3E-04 10.5 1.2 60 9001001 2 1.2E-03 5.6E-04 5.5 1.0 60 9004018 2 1.1E-03 4.9E-04 4.9 0.9 60 0003122 2 1.0E-03 4.7E-04 5.9 0.9 60 0003123 2 1.0E-03 4.4E-04 5.6 0.8 60 0003160 2 9.7E-04 4.3E-04 8.6 0.8 60 1004030 2 9.6E-04 4.3E-04 5.5 0.8 60 9003033 2 8.8E-04 4.1E-04 4.1 0.7 60 0003110 2 8.6E-04 3.8E-04 5.8 0.7 60 2001031 2 8.2E-04 3.7E-04 4.7 0.7 60 2001027 2 6.7E-04 3.0E-04 4.3 0.6 60 9004023 2 6.6E-04 3.1E-04 3.3 0.6 60 0003127 2 6.5E-04 2.9E-04 5.5 0.5 60 0003001 2 5.1E-04 2.3E-04 2.8 0.4 60 2001002 2 4.9E-04 2.2E-04 2.5 0.4 60 1004094 2 4.5E-04 2.0E-04 3.4 0.4 60 1004018 2 4.5E-04 2.0E-04 2.5 0.4 60 1004010 2 3.4E-04 1.5E-04 2.0 0.3 60 2001038 2 3.2E-04 1.4E-04 1.7 0.3 60 2001036 2 3.2E-04 1.4E-04 1.4 0.3 60 1004000 2 2.8E-04 1.3E-04 1.5 0.2 60 1004003 2 2.7E-04 1.2E-04 1.4 0.2 60 2001042 2 2.1E-04 9.5E-05 1.1 0.2 60 2001053 2 2.0E-04 8.8E-05 0.8 0.2 60 2001047 2 2.0E-04 8.8E-05 0.9 0.2 60 2001059 2 1.7E-04 7.6E-05 0.5 0.1 60 0002042 1 0.07 3.1E-02 315.3 59.8 60 

E-45
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

0003046 1 0.03 1.3E-02 141.9 24.9 60 

0002041 1 0.02 7.8E-03 141.8 14.9 60 0002029 1 0.01 4.6E-03 66.9 8.7 60 0002037 1 9.3E-03 4.1E-03 57.0 7.8 60 1003014 1 8.8E-03 4.0E-03 49.5 7.4 60 0003137 1 8.4E-03 3.8E-03 99.0 7.1 60 1003011 1 8.2E-03 3.7E-03 64.2 6.9 60 1002007 1 7.4E-03 3.3E-03 45.2 6.2 60 1002013 1 6.5E-03 2.9E-03 39.4 5.5 60 1002017 1 6.4E-03 2.9E-03 36.0 5.4 60 1003010 1 6.4E-03 2.9E-03 49.7 5.4 60 0003090 1 5.4E-03 2.4E-03 42.0 4.6 60 0003091 1 5.3E-03 2.4E-03 36.9 4.5 60 1003022 1 5.2E-03 2.3E-03 51.0 4.4 60 0002015 1 5.1E-03 2.3E-03 45.4 4.3 60 0003094 1 4.6E-03 2.0E-03 33.4 3.9 60 1001017 1 4.1E-03 1.8E-03 24.1 3.4 60 9002031 1 3.7E-03 1.7E-03 25.3 3.1 60 1003005 1 3.6E-03 1.6E-03 29.5 3.1 60 9002022 1 3.4E-03 1.6E-03 22.2 2.8 60 9002014 1 3.3E-03 1.5E-03 22.5 2.8 60 9002013 1 3.2E-03 1.5E-03 21.3 2.7 60 9002020 1 3.2E-03 1.5E-03 20.5 2.7 60 9002016 1 3.0E-03 1.4E-03 19.1 2.5 60 1003003 1 2.9E-03 1.3E-03 23.1 2.5 60 9001009 1 2.8E-03 1.3E-03 11.6 2.4 60 1001016 1 2.7E-03 1.2E-03 16.1 2.3 60 0003058 1 2.5E-03 1.1E-03 18.5 2.1 60 

E-46
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

9002007 1 2.5E-03 1.2E-03 18.6 2.1 60 

1004043 1 2.2E-03 1.0E-03 14.5 1.9 60 0001010 1 2.2E-03 9.9E-04 13.7 1.9 60 0003054 1 2.2E-03 9.8E-04 13.5 1.9 60 0003053 1 2.2E-03 9.7E-04 13.1 1.8 60 0003064 1 2.0E-03 8.9E-04 13.2 1.7 60 0001011 1 2.0E-03 8.8E-04 12.9 1.7 60 0001018 1 1.9E-03 8.5E-04 14.7 1.6 60 9001015 1 1.9E-03 8.8E-04 8.2 1.6 60 0001007 1 1.8E-03 8.2E-04 12.7 1.5 60 0003063 1 1.8E-03 8.1E-04 10.8 1.5 60 0003066 1 1.8E-03 7.9E-04 10.9 1.5 60 0001020 1 1.8E-03 7.9E-04 14.7 1.5 60 0003067 1 1.7E-03 7.6E-04 10.3 1.4 60 0003109 1 1.7E-03 7.5E-04 15.2 1.4 60 0003076 1 1.7E-03 7.4E-04 12.5 1.4 60 0001019 1 1.5E-03 6.5E-04 12.9 1.2 60 1004039 1 1.4E-03 6.2E-04 8.8 1.2 60 0003072 1 1.3E-03 5.9E-04 10.6 1.1 60 0001005 1 1.2E-03 5.3E-04 8.2 1.0 60 0003152 1 9.6E-04 4.3E-04 8.0 0.8 60 0003159 1 9.5E-04 4.2E-04 6.5 0.8 60 9004021 1 8.8E-04 4.1E-04 5.1 0.7 60 2001029 1 7.2E-04 3.2E-04 4.6 0.6 60 0003015 1 7.1E-04 3.2E-04 3.9 0.6 60 0003112 1 7.0E-04 3.1E-04 4.8 0.6 60 9003020 1 7.0E-04 3.3E-04 2.8 0.6 60 9003016 1 6.7E-04 3.1E-04 2.7 0.6 60 

E-47
 
Attachment E-4. Estimated Media Concentrations in Alternative NAAQS (0.5 μg/m3 Max-Monthly

the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other 

0003002 1 6.0E-04 2.7E-04 4.4 0.5 60 

9003021 1 6.0E-04 2.8E-04 2.6 0.5 60 0003003 1 6.0E-04 2.7E-04 4.3 0.5 60 9003010 1 5.7E-04 2.6E-04 3.0 0.5 60 2001000 1 5.3E-04 2.4E-04 3.6 0.4 60 1004081 1 5.1E-04 2.3E-04 4.1 0.4 60 1004024 1 5.1E-04 2.3E-04 3.1 0.4 60 1004091 1 4.7E-04 2.1E-04 3.7 0.4 60 0003129 1 4.6E-04 2.1E-04 2.3 0.4 60 1004093 1 4.6E-04 2.1E-04 3.4 0.4 60 1004017 1 4.0E-04 1.8E-04 2.4 0.3 60 1004015 1 3.9E-04 1.7E-04 2.7 0.3 60 1004005 1 2.9E-04 1.3E-04 1.7 0.2 60 1004006 1 2.8E-04 1.3E-04 1.4 0.2 60 2001070 1 2.4E-04 1.1E-04 1.3 0.2 60 2001052 1 1.9E-04 8.5E-05 0.7 0.2 60 2001062 1 1.8E-04 8.2E-05 0.6 0.2 60 2001058 1 1.5E-04 6.7E-05 0.4 0.1 60 

a Recent air refers to contributions associated with recent outdoor ambient air.
 b Other refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air).
 

E-48
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

9003026 71 4.8E-04 2.2E-04 7.1 0.4 60 9001004 63 1.0E-03 4.8E-04 14.9 0.9 60 0003048 53 9.5E-04 4.2E-04 17.8 0.8 60 2001012 42 1.3E-04 5.8E-05 1.3 0.1 60 1004004 38 1.5E-04 6.9E-05 2.7 0.1 60 1002001 35 1.8E-03 8.0E-04 28.2 1.5 60 9001007 35 9.0E-04 4.2E-04 14.4 0.8 60 0003040 31 1.5E-03 6.8E-04 29.7 1.3 60 2001009 31 1.1E-04 4.9E-05 1.4 0.1 60 0001002 30 6.7E-04 3.0E-04 11.4 0.6 60 0002023 29 3.4E-03 1.5E-03 79.6 2.9 60 9003043 26 6.7E-04 3.1E-04 11.1 0.6 60 9004000 24 2.0E-04 9.2E-05 1.4 0.2 60 2001037 22 1.2E-04 5.3E-05 1.4 0.1 60 9003012 21 3.1E-04 1.4E-04 5.1 0.3 60 1004092 21 1.8E-04 8.2E-05 3.7 0.2 60 1004014 21 1.6E-04 7.4E-05 3.0 0.1 60 0003121 19 3.2E-04 1.4E-04 6.0 0.3 60 2001005 19 1.7E-04 7.6E-05 2.2 0.1 60 9001011 18 1.0E-03 4.6E-04 16.2 0.8 60 0003061 18 7.3E-04 3.3E-04 14.8 0.6 60 2001004 17 1.6E-04 7.4E-05 2.0 0.1 60 0001023 16 1.2E-03 5.4E-04 25.4 1.0 60 1004031 16 7.1E-04 3.2E-04 10.7 0.6 60 0003080 16 6.7E-04 3.0E-04 14.1 0.6 60 9003051 16 6.5E-04 3.0E-04 11.5 0.5 60 2001039 16 9.8E-05 4.4E-05 1.0 0.1 60 2001001 15 2.2E-04 9.9E-05 2.8 0.2 60 9002026 14 1.8E-03 8.4E-04 25.2 1.5 60 

E-49
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

0001000 14 6.8E-04 3.0E-04 10.5 0.6 60 0002024 12 3.0E-03 1.3E-03 66.2 2.5 60 0001015 12 1.2E-03 5.1E-04 22.2 1.0 60 2001068 12 2.0E-04 9.1E-05 2.6 0.2 60 1004068 11 1.0E-03 4.7E-04 25.8 0.9 60 0002027 10 3.0E-03 1.3E-03 58.0 2.5 60 1002015 10 2.0E-03 9.2E-04 28.1 1.7 60 0001029 10 1.2E-03 5.2E-04 19.9 1.0 60 1004041 10 8.0E-04 3.6E-04 12.9 0.7 60 2001026 10 1.3E-04 6.0E-05 1.8 0.1 60 1002014

9 2.4E-03 1.1E-03 34.1 2.0 60 

1003025

9 1.8E-03 8.3E-04 40.5 1.6 60 

0003051

9 6.3E-04 2.8E-04 9.7 0.5 60 

9003023

9 4.6E-04 2.1E-04 5.3 0.4 60 

2001010

9 1.4E-04 6.4E-05 1.7 0.1 60 

0002038

8 4.3E-03 1.9E-03 65.5 3.6 60 

0001026

8 1.6E-03 7.0E-04 26.4 1.3 60 

1003000

8 1.6E-03 7.0E-04 23.7 1.3 60 

1003006

8 1.4E-03 6.2E-04 27.0 1.2 60 

0001009

8 9.4E-04 4.2E-04 14.7 0.8 60 

9003041

8 8.7E-04 4.1E-04 13.4 0.7 60 

1004050

8 8.2E-04 3.7E-04 15.4 0.7 60 

1004036

8 7.1E-04 3.2E-04 10.8 0.6 60 

0003068

8 6.2E-04 2.8E-04 12.8 0.5 60 

0003007

8 2.8E-04 1.3E-04 4.8 0.2 60 

1004025

8 2.3E-04 1.1E-04 4.3 0.2 60 

9003003

8 1.9E-04 8.6E-05 2.0 0.2 60 

1004098

8 1.6E-04 7.0E-05 2.3 0.1 60 

0003089

7 2.1E-03 9.5E-04 41.7 1.8 60 

E-50
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

1003008

7 1.8E-03 8.2E-04 36.1 1.5 60 

9002023

7 1.3E-03 6.2E-04 19.1 1.1 60 

9002015

7 1.3E-03 5.8E-04 21.7 1.1 60 

9001010

7 1.2E-03 5.8E-04 15.5 1.1 60 

9001005

7 1.1E-03 4.9E-04 17.5 0.9 60 

1004059

7 7.6E-04 3.4E-04 16.0 0.6 60 

0003114

7 6.5E-04 2.9E-04 10.1 0.6 60 

0003037

7 5.7E-04 2.5E-04 10.1 0.5 60 

0003042

6 0.01 5.2E-03 256.0 9.9 60 

9003027

6 6.6E-04 3.1E-04 9.7 0.6 60 

0003155

6 6.2E-04 2.7E-04 11.5 0.5 60 

1004058

6 5.5E-04 2.5E-04 11.2 0.5 60 

1004096

6 1.3E-04 6.1E-05 2.4 0.1 60 

1004007

6 1.3E-04 5.7E-05 2.0 0.1 60 

2001051

6 7.0E-05 3.1E-05 0.6 0.1 60 

0002050

5 5.0E-03 2.2E-03 101.5 4.3 60 

0002036

5 4.1E-03 1.8E-03 65.1 3.4 60 

1003013

5 3.9E-03 1.8E-03 57.6 3.3 60 

0002026

5 3.9E-03 1.7E-03 91.1 3.3 60 

1003016

5 3.1E-03 1.4E-03 45.1 2.6 60 

0003138

5 2.5E-03 1.1E-03 69.5 2.1 60 

1002003

5 2.4E-03 1.1E-03 35.3 2.1 60 

0003140

5 1.9E-03 8.5E-04 53.5 1.6 60 

0003083

5 1.9E-03 8.5E-04 49.0 1.6 60 

1003007

5 1.2E-03 5.5E-04 23.8 1.0 60 

1004047

5 9.3E-04 4.2E-04 15.6 0.8 60 

0001006

5 7.5E-04 3.3E-04 11.9 0.6 60 

1004037

5 5.9E-04 2.7E-04 9.0 0.5 60 

0003071

5 5.5E-04 2.5E-04 11.3 0.5 60 

E-51
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

9004022

5 3.2E-04 1.5E-04 3.8 0.3 60 

0003004

5 2.6E-04 1.2E-04 4.5 0.2 60 

1004028

5 2.4E-04 1.1E-04 3.7 0.2 60 

1004019

5 2.1E-04 9.4E-05 3.3 0.2 60 

1004013

5 1.9E-04 8.7E-05 3.5 0.2 60 

9003002

5 1.9E-04 8.6E-05 1.9 0.2 60 

2001006

5 1.4E-04 6.3E-05 1.6 0.1 60 

2001007

5 1.3E-04 5.9E-05 1.4 0.1 60 

1002018

4 3.4E-03 1.5E-03 51.9 2.9 60 

0002018

4 2.5E-03 1.1E-03 47.6 2.1 60 

1002012

4 2.3E-03 1.0E-03 34.9 1.9 60 

0002022

4 2.1E-03 9.1E-04 36.4 1.7 60 

1003023

4 2.0E-03 8.8E-04 49.5 1.7 60 

1002016

4 1.9E-03 8.7E-04 26.7 1.6 60 

9002021

4 1.6E-03 7.6E-04 25.6 1.4 60 

9002030

4 1.6E-03 7.3E-04 27.8 1.3 60 

1003028

4 1.2E-03 5.6E-04 28.8 1.1 60 

0003144

4 1.2E-03 5.2E-04 27.7 1.0 60 

9002000

4 1.0E-03 4.8E-04 14.5 0.9 60 

0001012

4 9.7E-04 4.3E-04 15.6 0.8 60 

9002006

4 9.7E-04 4.5E-04 15.0 0.8 60 

0003060

4 9.0E-04 4.0E-04 16.3 0.8 60 

0003079

4 8.9E-04 3.9E-04 17.7 0.7 60 

1004051

4 8.1E-04 3.6E-04 13.4 0.7 60 

1004048

4 7.6E-04 3.4E-04 12.9 0.6 60 

9001013

4 7.6E-04 3.5E-04 8.3 0.6 60 

9001002

4 7.5E-04 3.5E-04 9.8 0.6 60 

0003107

4 7.4E-04 3.3E-04 10.6 0.6 60 

1004049

4 7.0E-04 3.1E-04 12.8 0.6 60 

E-52
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

1004054

4 5.2E-04 2.3E-04 8.0 0.4 60 

1004057

4 5.1E-04 2.3E-04 9.1 0.4 60 

9003044

4 4.7E-04 2.2E-04 7.6 0.4 60 

1004055

4 4.6E-04 2.1E-04 7.6 0.4 60 

0003128

4 4.4E-04 2.0E-04 7.5 0.4 60 

2001028

4 3.0E-04 1.3E-04 4.3 0.3 60 

1004011

4 1.6E-04 7.1E-05 2.5 0.1 60 

0002047

3 5.1E-03 2.3E-03 76.6 4.3 60 

0002039

3 4.6E-03 2.1E-03 73.6 3.9 60 

0002028

3 3.7E-03 1.7E-03 77.2 3.1 60 

1002020

3 3.3E-03 1.5E-03 50.6 2.7 60 

1002002

3 2.0E-03 8.9E-04 35.2 1.7 60 

0003093

3 1.9E-03 8.2E-04 34.2 1.6 60 

0003082

3 1.7E-03 7.7E-04 42.4 1.5 60 

0002017

3 1.6E-03 6.9E-04 29.4 1.3 60 

9002011

3 1.5E-03 7.0E-04 27.1 1.3 60 

1003004

3 1.5E-03 6.7E-04 27.1 1.2 60 

0001032

3 1.5E-03 6.6E-04 24.9 1.2 60 

0001027

3 1.4E-03 6.2E-04 25.2 1.2 60 

9002001

3 1.1E-03 5.2E-04 18.2 0.9 60 

0003078

3 1.1E-03 4.9E-04 21.0 0.9 60 

1003001

3 1.0E-03 4.6E-04 15.9 0.9 60 

1004060

3 1.0E-03 4.5E-04 20.1 0.8 60 

1004046

3 9.2E-04 4.2E-04 15.0 0.8 60 

0001013

3 8.9E-04 4.0E-04 14.5 0.8 60 

9001012

3 7.9E-04 3.7E-04 8.8 0.7 60 

1004052

3 7.5E-04 3.4E-04 14.8 0.6 60 

9001014

3 7.1E-04 3.3E-04 8.9 0.6 60 

0003070

3 6.6E-04 2.9E-04 9.9 0.6 60 

E-53
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

0003036

3 6.5E-04 2.9E-04 12.1 0.5 60 

9004015

3 5.8E-04 2.7E-04 6.5 0.5 60 

9004014

3 5.7E-04 2.6E-04 7.1 0.5 60 

0003073

3 5.5E-04 2.5E-04 11.3 0.5 60 

0001003

3 5.5E-04 2.4E-04 8.0 0.5 60 

0003115

3 4.9E-04 2.2E-04 7.5 0.4 60 

9004016

3 4.4E-04 2.1E-04 5.4 0.4 60 

1004072

3 4.4E-04 2.0E-04 5.1 0.4 60 

1004056

3 4.1E-04 1.9E-04 7.1 0.3 60 

9003017

3 3.3E-04 1.5E-04 4.5 0.3 60 

9004017

3 3.1E-04 1.4E-04 3.7 0.3 60 

9004006

3 2.9E-04 1.3E-04 3.2 0.2 60 

0003020

3 2.8E-04 1.3E-04 5.1 0.2 60 

9003013

3 2.8E-04 1.3E-04 4.2 0.2 60 

0003006

3 2.7E-04 1.2E-04 4.8 0.2 60 

9004008

3 2.7E-04 1.3E-04 3.0 0.2 60 

1004089

3 2.4E-04 1.1E-04 4.8 0.2 60 

2001011

3 1.5E-04 6.6E-05 1.9 0.1 60 

1004100

3 1.3E-04 5.8E-05 1.5 0.1 60 

2001008

3 1.3E-04 5.6E-05 1.8 0.1 60 

2001013

3 1.1E-04 4.7E-05 1.0 0.1 60 

1003012

2 3.4E-03 1.5E-03 59.6 2.8 60 

1002019

2 3.1E-03 1.4E-03 50.2 2.6 60 

0002025

2 3.0E-03 1.4E-03 71.9 2.6 60 

0003087

2 2.6E-03 1.2E-03 56.2 2.2 60 

1003009

2 2.4E-03 1.1E-03 50.4 2.0 60 

0003088

2 2.2E-03 9.6E-04 43.5 1.8 60 

0002019

2 2.1E-03 9.4E-04 39.7 1.8 60 

9002029

2 1.8E-03 8.5E-04 28.4 1.6 60 

E-54
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

0001035

2 1.8E-03 7.8E-04 30.3 1.5 60 

0003085

2 1.6E-03 7.2E-04 47.3 1.4 60 

0001025

2 1.5E-03 6.8E-04 27.5 1.3 60 

9002012

2 1.4E-03 6.4E-04 23.2 1.2 60 

0001031

2 1.3E-03 5.9E-04 23.0 1.1 60 

9002017

2 1.3E-03 6.1E-04 19.7 1.1 60 

0003142

2 1.2E-03 5.2E-04 29.5 1.0 60 

0003077

2 1.1E-03 4.8E-04 19.8 0.9 60 

0001024

2 1.1E-03 4.7E-04 19.4 0.9 60 

0003055

2 9.3E-04 4.1E-04 14.3 0.8 60 

0003056

2 9.0E-04 4.0E-04 16.0 0.8 60 

9003042

2 8.6E-04 4.0E-04 14.2 0.7 60 

9003050

2 8.5E-04 4.0E-04 14.0 0.7 60 

0001008

2 8.3E-04 3.7E-04 13.9 0.7 60 

1004045

2 8.1E-04 3.6E-04 12.8 0.7 60 

0003065

2 7.9E-04 3.5E-04 11.9 0.7 60 

0003052

2 7.5E-04 3.3E-04 11.3 0.6 60 

1004033

2 6.9E-04 3.1E-04 10.3 0.6 60 

1004040

2 6.5E-04 2.9E-04 10.2 0.5 60 

0003050

2 6.1E-04 2.7E-04 9.4 0.5 60 

1004038

2 6.0E-04 2.7E-04 9.1 0.5 60 

0003069

2 5.9E-04 2.6E-04 11.9 0.5 60 

0003049

2 5.8E-04 2.6E-04 10.9 0.5 60 

0003031

2 5.7E-04 2.5E-04 10.5 0.5 60 

9001001

2 4.8E-04 2.2E-04 5.5 0.4 60 

9004018

2 4.3E-04 2.0E-04 4.9 0.4 60 

0003122

2 4.2E-04 1.9E-04 5.9 0.4 60 

0003123

2 4.0E-04 1.8E-04 5.6 0.3 60 

0003160

2 3.9E-04 1.7E-04 8.6 0.3 60 

E-55
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

1004030

2 3.8E-04 1.7E-04 5.5 0.3 60 

9003033

2 3.5E-04 1.6E-04 4.1 0.3 60 

0003110

2 3.4E-04 1.5E-04 5.8 0.3 60 

2001031

2 3.3E-04 1.5E-04 4.7 0.3 60 

2001027

2 2.7E-04 1.2E-04 4.3 0.2 60 

9004023

2 2.6E-04 1.2E-04 3.3 0.2 60 

0003127

2 2.6E-04 1.2E-04 5.5 0.2 60 

0003001

2 2.1E-04 9.1E-05 2.8 0.2 60 

2001002

2 2.0E-04 8.8E-05 2.5 0.2 60 

1004094

2 1.8E-04 8.2E-05 3.4 0.2 60 

1004018

2 1.8E-04 8.1E-05 2.5 0.2 60 

1004010

2 1.4E-04 6.1E-05 2.0 0.1 60 

2001038

2 1.3E-04 5.8E-05 1.7 0.1 60 

2001036

2 1.3E-04 5.7E-05 1.4 0.1 60 

1004000

2 1.1E-04 5.0E-05 1.5 0.1 60 

1004003

2 1.1E-04 4.9E-05 1.4 0.1 60 

2001042

2 8.5E-05 3.8E-05 1.1 0.1 60 

2001053

2 7.9E-05 3.5E-05 0.8 0.1 60 

2001047

2 7.9E-05 3.5E-05 0.9 0.1 60 

2001059

2 6.8E-05 3.0E-05 0.5 0.1 60 

0002042 1 0.03 1.3E-02 315.3 23.9 60 0003046 1 0.01 5.2E-03 141.9 10.0 60 0002041 1 7.0E-03 3.1E-03 141.8 5.9 60 0002029 1 4.1E-03 1.8E-03 66.9 3.5 60 0002037 1 3.7E-03 1.7E-03 57.0 3.1 60 1003014 1 3.5E-03 1.6E-03 49.5 3.0 60 0003137 1 3.4E-03 1.5E-03 99.0 2.8 60 1003011 1 3.3E-03 1.5E-03 64.2 2.8 60 1002007 1 3.0E-03 1.3E-03 45.2 2.5 60 

E-56
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

1002013 1 2.6E-03 1.2E-03 39.4 2.2 60 1002017 1 2.6E-03 1.2E-03 36.0 2.2 60 1003010 1 2.6E-03 1.2E-03 49.7 2.2 60 0003090 1 2.2E-03 9.6E-04 42.0 1.8 60 0003091 1 2.1E-03 9.5E-04 36.9 1.8 60 1003022 1 2.1E-03 9.3E-04 51.0 1.7 60 0002015 1 2.0E-03 9.0E-04 45.4 1.7 60 0003094 1 1.8E-03 8.1E-04 33.4 1.5 60 1001017 1 1.6E-03 7.4E-04 24.1 1.4 60 9002031 1 1.5E-03 6.9E-04 25.3 1.2 60 1003005 1 1.5E-03 6.6E-04 29.5 1.2 60 9002022 1 1.3E-03 6.2E-04 22.2 1.1 60 9002014 1 1.3E-03 6.1E-04 22.5 1.1 60 9002013 1 1.3E-03 5.9E-04 21.3 1.1 60 9002020 1 1.3E-03 5.9E-04 20.5 1.1 60 9002016 1 1.2E-03 5.5E-04 19.1 1.0 60 1003003 1 1.2E-03 5.3E-04 23.1 1.0 60 9001009 1 1.1E-03 5.3E-04 11.6 1.0 60 1001016 1 1.1E-03 5.0E-04 16.1 0.9 60 0003058 1 1.0E-03 4.5E-04 18.5 0.9 60 9002007 1 1.0E-03 4.7E-04 18.6 0.9 60 1004043 1 9.0E-04 4.1E-04 14.5 0.8 60 0001010 1 8.9E-04 4.0E-04 13.7 0.8 60 0003054 1 8.8E-04 3.9E-04 13.5 0.7 60 0003053 1 8.7E-04 3.9E-04 13.1 0.7 60 0003064 1 8.0E-04 3.5E-04 13.2 0.7 60 0001011 1 8.0E-04 3.5E-04 12.9 0.7 60 0001018 1 7.6E-04 3.4E-04 14.7 0.6 60 9001015 1 7.6E-04 3.5E-04 8.2 0.6 60 

E-57
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

0001007 1 7.3E-04 3.3E-04 12.7 0.6 60 0003063 1 7.3E-04 3.2E-04 10.8 0.6 60 0003066 1 7.1E-04 3.2E-04 10.9 0.6 60 0001020 1 7.1E-04 3.1E-04 14.7 0.6 60 0003067 1 6.8E-04 3.0E-04 10.3 0.6 60 0003109 1 6.8E-04 3.0E-04 15.2 0.6 60 0003076 1 6.7E-04 3.0E-04 12.5 0.6 60 0001019 1 5.8E-04 2.6E-04 12.9 0.5 60 1004039 1 5.5E-04 2.5E-04 8.8 0.5 60 0003072 1 5.3E-04 2.4E-04 10.6 0.4 60 0001005 1 4.8E-04 2.1E-04 8.2 0.4 60 0003152 1 3.8E-04 1.7E-04 8.0 0.3 60 0003159 1 3.8E-04 1.7E-04 6.5 0.3 60 9004021 1 3.5E-04 1.6E-04 5.1 0.3 60 2001029 1 2.9E-04 1.3E-04 4.6 0.2 60 0003015 1 2.9E-04 1.3E-04 3.9 0.2 60 0003112 1 2.8E-04 1.2E-04 4.8 0.2 60 9003020 1 2.8E-04 1.3E-04 2.8 0.2 60 9003016 1 2.7E-04 1.2E-04 2.7 0.2 60 0003002 1 2.4E-04 1.1E-04 4.4 0.2 60 9003021 1 2.4E-04 1.1E-04 2.6 0.2 60 0003003 1 2.4E-04 1.1E-04 4.3 0.2 60 9003010 1 2.3E-04 1.1E-04 3.0 0.2 60 2001000 1 2.1E-04 9.5E-05 3.6 0.2 60 1004081 1 2.1E-04 9.3E-05 4.1 0.2 60 1004024 1 2.1E-04 9.3E-05 3.1 0.2 60 1004091 1 1.9E-04 8.5E-05 3.7 0.2 60 0003129 1 1.8E-04 8.2E-05 2.3 0.2 60 1004093 1 1.8E-04 8.3E-05 3.4 0.2 60 

E-58
 
Attachment E-5. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Mont

for the Secondary Pb Smelter Case Study 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concen

From Recent Air a From Other b 

1004017 1 1.6E-04 7.2E-05 2.4 0.1 60 1004015 1 1.5E-04 7.0E-05 2.7 0.1 60 1004005 1 1.2E-04 5.3E-05 1.7 0.1 60 1004006 1 1.1E-04 5.0E-05 1.4 0.1 60 2001070 1 9.8E-05 4.4E-05 1.3 0.1 60 2001052 1 7.6E-05 3.4E-05 0.7 0.1 60 2001062 1 7.4E-05 3.3E-05 0.6 0.1 60 2001058 1 6.0E-05 2.7E-05 0.4 0.1 60 

a Recent air refers to contributions associated with recent outdoor ambient air.
 b Other refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air).
 

E-59
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

9003026 71 1.2E-04 5.6E-05 7.1 0.1 60 9001004 63 2.6E-04 1.2E-04 14.9 0.2 60 0003048 53 2.4E-04 1.1E-04 17.8 0.2 60 2001012 42 5.0E-05 2.2E-05 1.3 0.0 60 1004004 38 5.0E-05 2.3E-05 2.7 0.0 60 1002001 35 4.4E-04 2.0E-04 28.2 0.4 60 9001007 35 2.2E-04 1.0E-04 14.4 0.2 60 0003040 31 3.8E-04 1.7E-04 29.7 0.3 60 2001009 31 5.0E-05 2.2E-05 1.4 0.0 60 0001002 30 1.7E-04 7.4E-05 11.4 0.1 60 0002023 29 8.6E-04 3.8E-04 79.6 0.7 60 9003043 26 1.7E-04 7.7E-05 11.1 0.1 60 9004000 24 5.0E-05 2.3E-05 1.4 0.04 60 2001037 22 5.0E-05 2.2E-05 1.4 0.04 60 9003012 21 7.7E-05 3.6E-05 5.1 0.1 60 1004092 21 5.0E-05 2.3E-05 3.7 0.0 60 1004014 21 5.0E-05 2.3E-05 3.0 0.0 60 0003121 19 8.1E-05 3.6E-05 6.0 0.1 60 2001005 19 5.0E-05 2.2E-05 2.2 0.04 60 9001011 18 2.5E-04 1.2E-04 16.2 0.2 60 0003061 18 1.8E-04 8.1E-05 14.8 0.2 60 2001004 17 5.0E-05 2.2E-05 2.0 0.04 60 0001023 16 3.0E-04 1.4E-04 25.4 0.3 60 1004031 16 1.8E-04 8.0E-05 10.7 0.1 60 0003080 16 1.7E-04 7.5E-05 14.1 0.1 60 9003051 16 1.6E-04 7.6E-05 11.5 0.1 60 2001039 16 5.0E-05 2.2E-05 1.0 0.04 60 2001001 15 5.5E-05 2.5E-05 2.8 0.05 60 9002026 14 4.5E-04 2.1E-04 25.2 0.4 60 0001000 14 1.7E-04 7.6E-05 10.5 0.1 60 

E-60
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0002024 12 7.5E-04 3.3E-04 66.2 0.6 60 0001015 12 2.9E-04 1.3E-04 22.2 0.2 60 2001068 12 5.1E-05 2.3E-05 2.6 0.04 60 1004068 11 2.6E-04 1.2E-04 25.8 0.2 60 0002027 10 7.5E-04 3.4E-04 58.0 0.6 60 1002015 10 5.1E-04 2.3E-04 28.1 0.4 60 0001029 10 2.9E-04 1.3E-04 19.9 0.2 60 1004041 10 2.0E-04 9.0E-05 12.9 0.2 60 2001026 10 5.0E-05 2.2E-05 1.8 0.04 60 1002014

9 5.9E-04 2.7E-04 34.1 0.5 60 

1003025

9 4.6E-04 2.1E-04 40.5 0.4 60 

0003051

9 1.6E-04 7.0E-05 9.7 0.1 60 

9003023

9 1.2E-04 5.3E-05 5.3 0.1 60 

2001010

9 5.0E-05 2.2E-05 1.7 0.04 60 

0002038

8 1.1E-03 4.8E-04 65.5 0.9 60 

0001026

8 3.9E-04 1.7E-04 26.4 0.3 60 

1003000

8 3.9E-04 1.7E-04 23.7 0.3 60 

1003006

8 3.4E-04 1.5E-04 27.0 0.3 60 

0001009

8 2.4E-04 1.0E-04 14.7 0.2 60 

9003041

8 2.2E-04 1.0E-04 13.4 0.2 60 

1004050

8 2.0E-04 9.2E-05 15.4 0.2 60 

1004036

8 1.8E-04 8.0E-05 10.8 0.1 60 

0003068

8 1.5E-04 6.9E-05 12.8 0.1 60 

0003007

8 7.1E-05 3.2E-05 4.8 0.1 60 

1004025

8 5.8E-05 2.6E-05 4.3 0.05 60 

1004098

8 5.0E-05 2.3E-05 2.3 0.04 60 

9003003

8 5.0E-05 2.3E-05 2.0 0.04 60 

0003089

7 5.4E-04 2.4E-04 41.7 0.5 60 

1003008

7 4.5E-04 2.1E-04 36.1 0.4 60 

9002023

7 3.3E-04 1.5E-04 19.1 0.3 60 

E-61
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

9002015

7 3.1E-04 1.5E-04 21.7 0.3 60 

9001010

7 3.1E-04 1.4E-04 15.5 0.3 60 

9001005

7 2.6E-04 1.2E-04 17.5 0.2 60 

1004059

7 1.9E-04 8.5E-05 16.0 0.2 60 

0003114

7 1.6E-04 7.2E-05 10.1 0.1 60 

0003037

7 1.4E-04 6.3E-05 10.1 0.1 60 

0003042

6 2.9E-03 1.3E-03 256.0 2.5 60 

9003027

6 1.6E-04 7.6E-05 9.7 0.1 60 

0003155

6 1.5E-04 6.9E-05 11.5 0.1 60 

1004058

6 1.4E-04 6.2E-05 11.2 0.1 60 

1004096

6 5.0E-05 2.3E-05 2.4 0.04 60 

1004007

6 5.0E-05 2.3E-05 2.0 0.04 60 

2001051

6 5.0E-05 2.2E-05 0.6 0.04 60 

0002050

5 1.3E-03 5.6E-04 101.5 1.1 60 

0002036

5 1.0E-03 4.5E-04 65.1 0.9 60 

1003013

5 9.8E-04 4.4E-04 57.6 0.8 60 

0002026

5 9.7E-04 4.3E-04 91.1 0.8 60 

1003016

5 7.8E-04 3.5E-04 45.1 0.7 60 

0003138

5 6.4E-04 2.8E-04 69.5 0.5 60 

1002003

5 6.1E-04 2.8E-04 35.3 0.5 60 

0003140

5 4.8E-04 2.1E-04 53.5 0.4 60 

0003083

5 4.8E-04 2.1E-04 49.0 0.4 60 

1003007

5 3.0E-04 1.4E-04 23.8 0.3 60 

1004047

5 2.3E-04 1.0E-04 15.6 0.2 60 

0001006

5 1.9E-04 8.3E-05 11.9 0.2 60 

1004037

5 1.5E-04 6.6E-05 9.0 0.1 60 

0003071

5 1.4E-04 6.2E-05 11.3 0.1 60 

9004022

5 8.0E-05 3.7E-05 3.8 0.1 60 

0003004

5 6.6E-05 2.9E-05 4.5 0.1 60 

1004028

5 6.1E-05 2.8E-05 3.7 0.1 60 

E-62
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

1004019

5 5.2E-05 2.4E-05 3.3 0.04 60 

1004013

5 5.0E-05 2.3E-05 3.5 0.04 60 

9003002

5 5.0E-05 2.3E-05 1.9 0.04 60 

2001006

5 5.0E-05 2.2E-05 1.6 0.04 60 

2001007

5 5.0E-05 2.2E-05 1.4 0.04 60 

1002018

4 8.5E-04 3.8E-04 51.9 0.7 60 

0002018

4 6.3E-04 2.8E-04 47.6 0.5 60 

1002012

4 5.7E-04 2.6E-04 34.9 0.5 60 

0002022

4 5.1E-04 2.3E-04 36.4 0.4 60 

1003023

4 4.9E-04 2.2E-04 49.5 0.4 60 

1002016

4 4.8E-04 2.2E-04 26.7 0.4 60 

9002021

4 4.1E-04 1.9E-04 25.6 0.3 60 

9002030

4 3.9E-04 1.8E-04 27.8 0.3 60 

1003028

4 3.1E-04 1.4E-04 28.8 0.3 60 

0003144

4 2.9E-04 1.3E-04 27.7 0.2 60 

9002000

4 2.6E-04 1.2E-04 14.5 0.2 60 

0001012

4 2.4E-04 1.1E-04 15.6 0.2 60 

9002006

4 2.4E-04 1.1E-04 15.0 0.2 60 

0003060

4 2.2E-04 1.0E-04 16.3 0.2 60 

0003079

4 2.2E-04 9.8E-05 17.7 0.2 60 

1004051

4 2.0E-04 9.1E-05 13.4 0.2 60 

1004048

4 1.9E-04 8.6E-05 12.9 0.2 60 

9001013

4 1.9E-04 8.8E-05 8.3 0.2 60 

9001002

4 1.9E-04 8.7E-05 9.8 0.2 60 

0003107

4 1.9E-04 8.3E-05 10.6 0.2 60 

1004049

4 1.7E-04 7.8E-05 12.8 0.1 60 

1004054

4 1.3E-04 5.8E-05 8.0 0.1 60 

1004057

4 1.3E-04 5.8E-05 9.1 0.1 60 

9003044

4 1.2E-04 5.5E-05 7.6 0.1 60 

1004055

4 1.2E-04 5.2E-05 7.6 0.1 60 

E-63
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0003128

4 1.1E-04 4.9E-05 7.5 0.1 60 

2001028

4 7.4E-05 3.3E-05 4.3 0.1 60 

1004011

4 5.0E-05 2.3E-05 2.5 0.04 60 

0002047

3 1.3E-03 5.7E-04 76.6 1.1 60 

0002039

3 1.2E-03 5.2E-04 73.6 1.0 60 

0002028

3 9.3E-04 4.1E-04 77.2 0.8 60 

1002020

3 8.1E-04 3.7E-04 50.6 0.7 60 

1002002

3 5.0E-04 2.2E-04 35.2 0.4 60 

0003093

3 4.6E-04 2.1E-04 34.2 0.4 60 

0003082

3 4.3E-04 1.9E-04 42.4 0.4 60 

0002017

3 3.9E-04 1.7E-04 29.4 0.3 60 

9002011

3 3.8E-04 1.8E-04 27.1 0.3 60 

1003004

3 3.7E-04 1.7E-04 27.1 0.3 60 

0001032

3 3.7E-04 1.6E-04 24.9 0.3 60 

0001027

3 3.5E-04 1.5E-04 25.2 0.3 60 

9002001

3 2.8E-04 1.3E-04 18.2 0.2 60 

0003078

3 2.8E-04 1.2E-04 21.0 0.2 60 

1003001

3 2.5E-04 1.1E-04 15.9 0.2 60 

1004060

3 2.5E-04 1.1E-04 20.1 0.2 60 

1004046

3 2.3E-04 1.0E-04 15.0 0.2 60 

0001013

3 2.2E-04 9.9E-05 14.5 0.2 60 

9001012

3 2.0E-04 9.2E-05 8.8 0.2 60 

1004052

3 1.9E-04 8.4E-05 14.8 0.2 60 

9001014

3 1.8E-04 8.2E-05 8.9 0.1 60 

0003070

3 1.6E-04 7.3E-05 9.9 0.1 60 

0003036

3 1.6E-04 7.2E-05 12.1 0.1 60 

9004015

3 1.5E-04 6.8E-05 6.5 0.1 60 

9004014

3 1.4E-04 6.6E-05 7.1 0.1 60 

0003073

3 1.4E-04 6.1E-05 11.3 0.1 60 

0001003

3 1.4E-04 6.1E-05 8.0 0.1 60 

E-64
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0003115

3 1.2E-04 5.5E-05 7.5 0.1 60 

9004016

3 1.1E-04 5.2E-05 5.4 0.1 60 

1004072

3 1.1E-04 5.0E-05 5.1 0.1 60 

1004056

3 1.0E-04 4.7E-05 7.1 0.1 60 

9003017

3 8.1E-05 3.8E-05 4.5 0.1 60 

9004017

3 7.8E-05 3.6E-05 3.7 0.1 60 

9004006

3 7.1E-05 3.3E-05 3.2 0.1 60 

0003020

3 7.1E-05 3.2E-05 5.1 0.1 60 

9003013

3 7.0E-05 3.2E-05 4.2 0.1 60 

0003006

3 6.7E-05 3.0E-05 4.8 0.1 60 

9004008

3 6.7E-05 3.1E-05 3.0 0.1 60 

1004089

3 6.0E-05 2.7E-05 4.8 0.1 60 

2001011

3 5.0E-05 2.2E-05 1.9 0.04 60 

2001008

3 5.0E-05 2.2E-05 1.8 0.04 60 

1004100

3 5.0E-05 2.3E-05 1.5 0.04 60 

2001013

3 5.0E-05 2.2E-05 1.0 0.04 60 

1003012

2 8.4E-04 3.8E-04 59.6 0.7 60 

1002019

2 7.8E-04 3.5E-04 50.2 0.7 60 

0002025

2 7.6E-04 3.4E-04 71.9 0.6 60 

0003087

2 6.5E-04 2.9E-04 56.2 0.5 60 

1003009

2 6.0E-04 2.7E-04 50.4 0.5 60 

0003088

2 5.4E-04 2.4E-04 43.5 0.5 60 

0002019

2 5.3E-04 2.4E-04 39.7 0.4 60 

9002029

2 4.6E-04 2.1E-04 28.4 0.4 60 

0001035

2 4.4E-04 1.9E-04 30.3 0.4 60 

0003085

2 4.0E-04 1.8E-04 47.3 0.3 60 

0001025

2 3.8E-04 1.7E-04 27.5 0.3 60 

9002012

2 3.4E-04 1.6E-04 23.2 0.3 60 

0001031

2 3.3E-04 1.5E-04 23.0 0.3 60 

9002017

2 3.3E-04 1.5E-04 19.7 0.3 60 

E-65
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0003142

2 2.9E-04 1.3E-04 29.5 0.2 60 

0003077

2 2.7E-04 1.2E-04 19.8 0.2 60 

0001024

2 2.7E-04 1.2E-04 19.4 0.2 60 

0003055

2 2.3E-04 1.0E-04 14.3 0.2 60 

0003056

2 2.3E-04 1.0E-04 16.0 0.2 60 

9003042

2 2.1E-04 9.9E-05 14.2 0.2 60 

9003050

2 2.1E-04 9.9E-05 14.0 0.2 60 

0001008

2 2.1E-04 9.3E-05 13.9 0.2 60 

1004045

2 2.0E-04 9.1E-05 12.8 0.2 60 

0003065

2 2.0E-04 8.8E-05 11.9 0.2 60 

0003052

2 1.9E-04 8.4E-05 11.3 0.2 60 

1004033

2 1.7E-04 7.8E-05 10.3 0.1 60 

1004040

2 1.6E-04 7.3E-05 10.2 0.1 60 

0003050

2 1.5E-04 6.8E-05 9.4 0.1 60 

1004038

2 1.5E-04 6.7E-05 9.1 0.1 60 

0003069

2 1.5E-04 6.5E-05 11.9 0.1 60 

0003049

2 1.4E-04 6.4E-05 10.9 0.1 60 

0003031

2 1.4E-04 6.3E-05 10.5 0.1 60 

9001001

2 1.2E-04 5.6E-05 5.5 0.1 60 

9004018

2 1.1E-04 4.9E-05 4.9 0.1 60 

0003122

2 1.0E-04 4.7E-05 5.9 0.1 60 

0003123

2 1.0E-04 4.4E-05 5.6 0.1 60 

0003160

2 9.7E-05 4.3E-05 8.6 0.1 60 

1004030

2 9.6E-05 4.3E-05 5.5 0.1 60 

9003033

2 8.8E-05 4.1E-05 4.1 0.1 60 

0003110

2 8.6E-05 3.8E-05 5.8 0.1 60 

2001031

2 8.2E-05 3.7E-05 4.7 0.1 60 

2001027

2 6.7E-05 3.0E-05 4.3 0.1 60 

9004023

2 6.6E-05 3.1E-05 3.3 0.1 60 

0003127

2 6.5E-05 2.9E-05 5.5 0.1 60 

E-66
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0003001

2 5.1E-05 2.3E-05 2.8 0.04 60 

1004094

2 5.0E-05 2.3E-05 3.4 0.04 60 

1004018

2 5.0E-05 2.3E-05 2.5 0.04 60 

2001002

2 5.0E-05 2.2E-05 2.5 0.04 60 

1004010

2 5.0E-05 2.3E-05 2.0 0.04 60 

2001038

2 5.0E-05 2.2E-05 1.7 0.04 60 

1004000

2 5.0E-05 2.3E-05 1.5 0.04 60 

1004003

2 5.0E-05 2.3E-05 1.4 0.04 60 

2001036

2 5.0E-05 2.2E-05 1.4 0.04 60 

2001042

2 5.0E-05 2.2E-05 1.1 0.04 60 

2001047

2 5.0E-05 2.2E-05 0.9 0.04 60 

2001053

2 5.0E-05 2.2E-05 0.8 0.04 60 

2001059

2 5.0E-05 2.2E-05 0.5 0.04 60 

0002042 1 7.1E-03 3.1E-03 315.3 6.0 60 0003046 1 3.0E-03 1.3E-03 141.9 2.5 60 0002041 1 1.8E-03 7.8E-04 141.8 1.5 60 0002029 1 1.0E-03 4.6E-04 66.9 0.9 60 0002037 1 9.3E-04 4.1E-04 57.0 0.8 60 1003014 1 8.8E-04 4.0E-04 49.5 0.7 60 0003137 1 8.4E-04 3.8E-04 99.0 0.7 60 1003011 1 8.2E-04 3.7E-04 64.2 0.7 60 1002007 1 7.4E-04 3.3E-04 45.2 0.6 60 1002013 1 6.5E-04 2.9E-04 39.4 0.5 60 1002017 1 6.4E-04 2.9E-04 36.0 0.5 60 1003010 1 6.4E-04 2.9E-04 49.7 0.5 60 0003090 1 5.4E-04 2.4E-04 42.0 0.5 60 0003091 1 5.3E-04 2.4E-04 36.9 0.5 60 1003022 1 5.2E-04 2.3E-04 51.0 0.4 60 0002015 1 5.1E-04 2.3E-04 45.4 0.4 60 0003094 1 4.6E-04 2.0E-04 33.4 0.4 60 

E-67
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

1001017 1 4.1E-04 1.8E-04 24.1 0.3 60 9002031 1 3.7E-04 1.7E-04 25.3 0.3 60 1003005 1 3.6E-04 1.6E-04 29.5 0.3 60 9002022 1 3.4E-04 1.6E-04 22.2 0.3 60 9002014 1 3.3E-04 1.5E-04 22.5 0.3 60 9002013 1 3.2E-04 1.5E-04 21.3 0.3 60 9002020 1 3.2E-04 1.5E-04 20.5 0.3 60 9002016 1 3.0E-04 1.4E-04 19.1 0.3 60 1003003 1 2.9E-04 1.3E-04 23.1 0.2 60 9001009 1 2.8E-04 1.3E-04 11.6 0.2 60 1001016 1 2.7E-04 1.2E-04 16.1 0.2 60 0003058 1 2.5E-04 1.1E-04 18.5 0.2 60 9002007 1 2.5E-04 1.2E-04 18.6 0.2 60 1004043 1 2.2E-04 1.0E-04 14.5 0.2 60 0001010 1 2.2E-04 9.9E-05 13.7 0.2 60 0003054 1 2.2E-04 9.8E-05 13.5 0.2 60 0003053 1 2.2E-04 9.7E-05 13.1 0.2 60 0003064 1 2.0E-04 8.9E-05 13.2 0.2 60 0001011 1 2.0E-04 8.8E-05 12.9 0.2 60 0001018 1 1.9E-04 8.5E-05 14.7 0.2 60 9001015 1 1.9E-04 8.8E-05 8.2 0.2 60 0001007 1 1.8E-04 8.2E-05 12.7 0.2 60 0003063 1 1.8E-04 8.1E-05 10.8 0.2 60 0003066 1 1.8E-04 7.9E-05 10.9 0.2 60 0001020 1 1.8E-04 7.9E-05 14.7 0.1 60 0003067 1 1.7E-04 7.6E-05 10.3 0.1 60 0003109 1 1.7E-04 7.5E-05 15.2 0.1 60 

E-68
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0003076 1 1.7E-04 7.4E-05 12.5 0.1 60 0001019 1 1.5E-04 6.5E-05 12.9 0.1 60 1004039 1 1.4E-04 6.2E-05 8.8 0.1 60 0003072 1 1.3E-04 5.9E-05 10.6 0.1 60 0001005 1 1.2E-04 5.3E-05 8.2 0.1 60 0003152 1 9.6E-05 4.3E-05 8.0 0.1 60 0003159 1 9.5E-05 4.2E-05 6.5 0.1 60 9004021 1 8.8E-05 4.1E-05 5.1 0.1 60 2001029 1 7.2E-05 3.2E-05 4.6 0.1 60 0003015 1 7.1E-05 3.2E-05 3.9 0.1 60 0003112 1 7.0E-05 3.1E-05 4.8 0.1 60 9003020 1 7.0E-05 3.3E-05 2.8 0.1 60 9003016 1 6.7E-05 3.1E-05 2.7 0.1 60 0003002 1 6.0E-05 2.7E-05 4.4 0.1 60 9003021 1 6.0E-05 2.8E-05 2.6 0.1 60 0003003 1 6.0E-05 2.7E-05 4.3 0.1 60 9003010 1 5.7E-05 2.6E-05 3.0 0.05 60 2001000 1 5.3E-05 2.4E-05 3.6 0.04 60 1004081 1 5.1E-05 2.3E-05 4.1 0.04 60 1004024 1 5.1E-05 2.3E-05 3.1 0.04 60 1004091 1 5.0E-05 2.3E-05 3.7 0.04 60 1004093 1 5.0E-05 2.3E-05 3.4 0.04 60 1004015 1 5.0E-05 2.3E-05 2.7 0.04 60 1004017 1 5.0E-05 2.3E-05 2.4 0.04 60 0003129 1 5.0E-05 2.2E-05 2.3 0.04 60 1004005 1 5.0E-05 2.3E-05 1.7 0.04 60 1004006 1 5.0E-05 2.3E-05 1.4 0.04 60 2001070 1 5.0E-05 2.2E-05 1.3 0.04 60 

E-69
 
Attachment E-6. Estimated Media Pb Concentrations in Alternative NAAQS (0.05 μg/m3 Max-

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

2001052 1 5.0E-05 2.2E-05 0.7 0.04 60 2001062 1 5.0E-05 2.2E-05 0.6 0.04 60 2001058 1 5.0E-05 2.2E-05 0.4 0.04 60 

a Recent air refers to contributions associated with recent outdoor ambient air.
 b Other refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air).
 

E-70
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

9003026 71 5.8E-04 2.7E-04 7.1 0.5 60 9001004 63 1.2E-03 5.8E-04 14.9 1.0 60 0003048 53 1.1E-03 5.0E-04 17.8 1.0 60 2001012 42 1.6E-04 7.0E-05 1.3 0.1 60 1004004 38 1.8E-04 8.2E-05 2.7 0.2 60 1002001 35 2.1E-03 9.6E-04 28.2 1.8 60 9001007 35 1.1E-03 5.0E-04 14.4 0.9 60 0003040 31 1.8E-03 8.1E-04 29.7 1.5 60 2001009 31 1.3E-04 5.9E-05 1.4 0.1 60 0001002 30 8.0E-04 3.6E-04 11.4 0.7 60 0002023 29 4.1E-03 1.8E-03 79.6 3.5 60 9003043 26 8.0E-04 3.7E-04 11.1 0.7 60 9004000 24 2.4E-04 1.1E-04 1.4 0.2 60 2001037 22 1.4E-04 6.3E-05 1.4 0.1 60 9003012 21 3.7E-04 1.7E-04 5.1 0.3 60 1004092 21 2.2E-04 9.8E-05 3.7 0.2 60 1004014 21 1.9E-04 8.8E-05 3.0 0.2 60 0003121 19 3.9E-04 1.7E-04 6.0 0.3 60 2001005 19 2.0E-04 9.1E-05 2.2 0.2 60 9001011 18 1.2E-03 5.5E-04 16.2 1.0 60 0003061 18 8.8E-04 3.9E-04 14.8 0.7 60 2001004 17 2.0E-04 8.8E-05 2.0 0.2 60 0001023 16 1.5E-03 6.5E-04 25.4 1.2 60 1004031 16 8.4E-04 3.8E-04 10.7 0.7 60 0003080 16 8.1E-04 3.6E-04 14.1 0.7 60 9003051 16 7.8E-04 3.6E-04 11.5 0.7 60 2001039 16 1.2E-04 5.2E-05 1.0 0.1 60 2001001 15 2.6E-04 1.2E-04 2.8 0.2 60 9002026 14 2.2E-03 1.0E-03 25.2 1.8 60 

E-71
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0001000 14 8.1E-04 3.6E-04 10.5 0.7 60 0002024 12 3.6E-03 1.6E-03 66.2 3.0 60 0001015 12 1.4E-03 6.1E-04 22.2 1.2 60 2001068 12 2.4E-04 1.1E-04 2.6 0.2 60 1004068 11 1.2E-03 5.6E-04 25.8 1.1 60 0002027 10 3.6E-03 1.6E-03 58.0 3.0 60 1002015 10 2.4E-03 1.1E-03 28.1 2.1 60 0001029 10 1.4E-03 6.2E-04 19.9 1.2 60 1004041 10 9.5E-04 4.3E-04 12.9 0.8 60 2001026 10 1.6E-04 7.1E-05 1.8 0.1 60 1002014

9 2.8E-03 1.3E-03 34.1 2.4 60 

1003025

9 2.2E-03 9.9E-04 40.5 1.9 60 

0003051

9 7.6E-04 3.4E-04 9.7 0.6 60 

9003023

9 5.5E-04 2.6E-04 5.3 0.5 60 

2001010

9 1.7E-04 7.7E-05 1.7 0.1 60 

0002038

8 5.2E-03 2.3E-03 65.5 4.4 60 

0001026

8 1.9E-03 8.3E-04 26.4 1.6 60 

1003000

8 1.9E-03 8.4E-04 23.7 1.6 60 

1003006

8 1.6E-03 7.4E-04 27.0 1.4 60 

0001009

8 1.1E-03 5.0E-04 14.7 1.0 60 

9003041

8 1.0E-03 4.9E-04 13.4 0.9 60 

1004050

8 9.7E-04 4.4E-04 15.4 0.8 60 

1004036

8 8.5E-04 3.8E-04 10.8 0.7 60 

0003068

8 7.4E-04 3.3E-04 12.8 0.6 60 

0003007

8 3.4E-04 1.5E-04 4.8 0.3 60 

1004025

8 2.8E-04 1.3E-04 4.3 0.2 60 

9003003

8 2.2E-04 1.0E-04 2.0 0.2 60 

1004098

8 1.9E-04 8.4E-05 2.3 0.2 60 

0003089

7 2.6E-03 1.1E-03 41.7 2.2 60 

E-72
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

1003008

7 2.2E-03 9.8E-04 36.1 1.8 60 

9002023

7 1.6E-03 7.4E-04 19.1 1.3 60 

9002015

7 1.5E-03 7.0E-04 21.7 1.3 60 

9001010

7 1.5E-03 6.9E-04 15.5 1.3 60 

9001005

7 1.3E-03 5.9E-04 17.5 1.1 60 

1004059

7 9.0E-04 4.1E-04 16.0 0.8 60 

0003114

7 7.8E-04 3.5E-04 10.1 0.7 60 

0003037

7 6.8E-04 3.0E-04 10.1 0.6 60 

0003042

6 0.01 6.2E-03 256.0 11.9 60 

9003027

6 7.9E-04 3.7E-04 9.7 0.7 60 

0003155

6 7.4E-04 3.3E-04 11.5 0.6 60 

1004058

6 6.6E-04 3.0E-04 11.2 0.6 60 

1004096

6 1.6E-04 7.2E-05 2.4 0.1 60 

1004007

6 1.5E-04 6.8E-05 2.0 0.1 60 

2001051

6 8.4E-05 3.7E-05 0.6 0.1 60 

0002050

5 6.0E-03 2.7E-03 101.5 5.1 60 

0002036

5 4.9E-03 2.2E-03 65.1 4.1 60 

1003013

5 4.7E-03 2.1E-03 57.6 4.0 60 

0002026

5 4.7E-03 2.1E-03 91.1 3.9 60 

1003016

5 3.7E-03 1.7E-03 45.1 3.1 60 

0003138

5 3.0E-03 1.4E-03 69.5 2.6 60 

1002003

5 2.9E-03 1.3E-03 35.3 2.5 60 

0003140

5 2.3E-03 1.0E-03 53.5 1.9 60 

0003083

5 2.3E-03 1.0E-03 49.0 1.9 60 

1003007

5 1.4E-03 6.5E-04 23.8 1.2 60 

1004047

5 1.1E-03 5.0E-04 15.6 0.9 60 

0001006

5 8.9E-04 4.0E-04 11.9 0.8 60 

1004037

5 7.0E-04 3.2E-04 9.0 0.6 60 

0003071

5 6.6E-04 2.9E-04 11.3 0.6 60 

E-73
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

9004022

5 3.8E-04 1.8E-04 3.8 0.3 60 

0003004

5 3.2E-04 1.4E-04 4.5 0.3 60 

1004028

5 2.9E-04 1.3E-04 3.7 0.2 60 

1004019

5 2.5E-04 1.1E-04 3.3 0.2 60 

1004013

5 2.3E-04 1.0E-04 3.5 0.2 60 

9003002

5 2.2E-04 1.0E-04 1.9 0.2 60 

2001006

5 1.7E-04 7.5E-05 1.6 0.1 60 

2001007

5 1.6E-04 7.1E-05 1.4 0.1 60 

1002018

4 4.1E-03 1.8E-03 51.9 3.4 60 

0002018

4 3.0E-03 1.3E-03 47.6 2.5 60 

1002012

4 2.7E-03 1.2E-03 34.9 2.3 60 

0002022

4 2.5E-03 1.1E-03 36.4 2.1 60 

1003023

4 2.3E-03 1.1E-03 49.5 2.0 60 

1002016

4 2.3E-03 1.0E-03 26.7 1.9 60 

9002021

4 1.9E-03 9.0E-04 25.6 1.6 60 

9002030

4 1.9E-03 8.7E-04 27.8 1.6 60 

1003028

4 1.5E-03 6.7E-04 28.8 1.3 60 

0003144

4 1.4E-03 6.2E-04 27.7 1.2 60 

9002000

4 1.2E-03 5.7E-04 14.5 1.0 60 

0001012

4 1.2E-03 5.2E-04 15.6 1.0 60 

9002006

4 1.2E-03 5.4E-04 15.0 1.0 60 

0003060

4 1.1E-03 4.8E-04 16.3 0.9 60 

0003079

4 1.1E-03 4.7E-04 17.7 0.9 60 

1004051

4 9.6E-04 4.3E-04 13.4 0.8 60 

1004048

4 9.1E-04 4.1E-04 12.9 0.8 60 

9001013

4 9.1E-04 4.2E-04 8.3 0.8 60 

9001002

4 8.9E-04 4.1E-04 9.8 0.8 60 

0003107

4 8.9E-04 3.9E-04 10.6 0.8 60 

1004049

4 8.3E-04 3.8E-04 12.8 0.7 60 

E-74
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

1004054

4 6.2E-04 2.8E-04 8.0 0.5 60 

1004057

4 6.1E-04 2.8E-04 9.1 0.5 60 

9003044

4 5.7E-04 2.6E-04 7.6 0.5 60 

1004055

4 5.6E-04 2.5E-04 7.6 0.5 60 

0003128

4 5.3E-04 2.3E-04 7.5 0.4 60 

2001028

4 3.5E-04 1.6E-04 4.3 0.3 60 

1004011

4 1.9E-04 8.5E-05 2.5 0.2 60 

0002047

3 6.1E-03 2.7E-03 76.6 5.2 60 

0002039

3 5.5E-03 2.5E-03 73.6 4.7 60 

0002028

3 4.4E-03 2.0E-03 77.2 3.8 60 

1002020

3 3.9E-03 1.8E-03 50.6 3.3 60 

1002002

3 2.4E-03 1.1E-03 35.2 2.0 60 

0003093

3 2.2E-03 9.9E-04 34.2 1.9 60 

0003082

3 2.1E-03 9.2E-04 42.4 1.8 60 

0002017

3 1.9E-03 8.3E-04 29.4 1.6 60 

9002011

3 1.8E-03 8.4E-04 27.1 1.5 60 

1003004

3 1.8E-03 8.0E-04 27.1 1.5 60 

0001032

3 1.8E-03 7.8E-04 24.9 1.5 60 

0001027

3 1.7E-03 7.4E-04 25.2 1.4 60 

9002001

3 1.3E-03 6.2E-04 18.2 1.1 60 

0003078

3 1.3E-03 5.9E-04 21.0 1.1 60 

1003001

3 1.2E-03 5.5E-04 15.9 1.0 60 

1004060

3 1.2E-03 5.4E-04 20.1 1.0 60 

1004046

3 1.1E-03 5.0E-04 15.0 0.9 60 

0001013

3 1.1E-03 4.7E-04 14.5 0.9 60 

9001012

3 9.5E-04 4.4E-04 8.8 0.8 60 

1004052

3 8.9E-04 4.0E-04 14.8 0.8 60 

9001014

3 8.5E-04 3.9E-04 8.9 0.7 60 

0003070

3 7.9E-04 3.5E-04 9.9 0.7 60 

E-75
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0003036

3 7.7E-04 3.4E-04 12.1 0.7 60 

9004015

3 7.0E-04 3.2E-04 6.5 0.6 60 

9004014

3 6.8E-04 3.1E-04 7.1 0.6 60 

0003073

3 6.6E-04 2.9E-04 11.3 0.6 60 

0001003

3 6.6E-04 2.9E-04 8.0 0.6 60 

0003115

3 5.9E-04 2.6E-04 7.5 0.5 60 

9004016

3 5.3E-04 2.5E-04 5.4 0.4 60 

1004072

3 5.3E-04 2.4E-04 5.1 0.4 60 

1004056

3 4.9E-04 2.2E-04 7.1 0.4 60 

9003017

3 3.9E-04 1.8E-04 4.5 0.3 60 

9004017

3 3.7E-04 1.7E-04 3.7 0.3 60 

9004006

3 3.4E-04 1.6E-04 3.2 0.3 60 

0003020

3 3.4E-04 1.5E-04 5.1 0.3 60 

9003013

3 3.3E-04 1.5E-04 4.2 0.3 60 

0003006

3 3.2E-04 1.4E-04 4.8 0.3 60 

9004008

3 3.2E-04 1.5E-04 3.0 0.3 60 

1004089

3 2.9E-04 1.3E-04 4.8 0.2 60 

2001011

3 1.8E-04 7.9E-05 1.9 0.1 60 

1004100

3 1.5E-04 7.0E-05 1.5 0.1 60 

2001008

3 1.5E-04 6.7E-05 1.8 0.1 60 

2001013

3 1.3E-04 5.7E-05 1.0 0.1 60 

1003012

2 4.0E-03 1.8E-03 59.6 3.4 60 

1002019

2 3.7E-03 1.7E-03 50.2 3.1 60 

0002025

2 3.6E-03 1.6E-03 71.9 3.1 60 

0003087

2 3.1E-03 1.4E-03 56.2 2.6 60 

1003009

2 2.9E-03 1.3E-03 50.4 2.4 60 

0003088

2 2.6E-03 1.2E-03 43.5 2.2 60 

0002019

2 2.5E-03 1.1E-03 39.7 2.1 60 

9002029

2 2.2E-03 1.0E-03 28.4 1.9 60 

E-76
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0001035

2 2.1E-03 9.3E-04 30.3 1.8 60 

0003085

2 1.9E-03 8.6E-04 47.3 1.6 60 

0001025

2 1.8E-03 8.1E-04 27.5 1.5 60 

9002012

2 1.6E-03 7.6E-04 23.2 1.4 60 

0001031

2 1.6E-03 7.1E-04 23.0 1.4 60 

9002017

2 1.6E-03 7.3E-04 19.7 1.3 60 

0003142

2 1.4E-03 6.2E-04 29.5 1.2 60 

0003077

2 1.3E-03 5.7E-04 19.8 1.1 60 

0001024

2 1.3E-03 5.6E-04 19.4 1.1 60 

0003055

2 1.1E-03 4.9E-04 14.3 0.9 60 

0003056

2 1.1E-03 4.8E-04 16.0 0.9 60 

9003042

2 1.0E-03 4.8E-04 14.2 0.9 60 

9003050

2 1.0E-03 4.7E-04 14.0 0.9 60 

0001008

2 1.0E-03 4.4E-04 13.9 0.8 60 

1004045

2 9.6E-04 4.4E-04 12.8 0.8 60 

0003065

2 9.5E-04 4.2E-04 11.9 0.8 60 

0003052

2 9.0E-04 4.0E-04 11.3 0.8 60 

1004033

2 8.3E-04 3.7E-04 10.3 0.7 60 

1004040

2 7.8E-04 3.5E-04 10.2 0.7 60 

0003050

2 7.3E-04 3.3E-04 9.4 0.6 60 

1004038

2 7.1E-04 3.2E-04 9.1 0.6 60 

0003069

2 7.0E-04 3.1E-04 11.9 0.6 60 

0003049

2 6.9E-04 3.1E-04 10.9 0.6 60 

0003031

2 6.8E-04 3.0E-04 10.5 0.6 60 

9001001

2 5.7E-04 2.7E-04 5.5 0.5 60 

9004018

2 5.1E-04 2.4E-04 4.9 0.4 60 

0003122

2 5.0E-04 2.2E-04 5.9 0.4 60 

0003123

2 4.8E-04 2.1E-04 5.6 0.4 60 

0003160

2 4.6E-04 2.1E-04 8.6 0.4 60 

E-77
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

1004030

2 4.6E-04 2.1E-04 5.5 0.4 60 

9003033

2 4.2E-04 2.0E-04 4.1 0.4 60 

0003110

2 4.1E-04 1.8E-04 5.8 0.3 60 

2001031

2 3.9E-04 1.8E-04 4.7 0.3 60 

2001027

2 3.2E-04 1.4E-04 4.3 0.3 60 

9004023

2 3.2E-04 1.5E-04 3.3 0.3 60 

0003127

2 3.1E-04 1.4E-04 5.5 0.3 60 

0003001

2 2.5E-04 1.1E-04 2.8 0.2 60 

2001002

2 2.4E-04 1.1E-04 2.5 0.2 60 

1004094

2 2.2E-04 9.8E-05 3.4 0.2 60 

1004018

2 2.1E-04 9.7E-05 2.5 0.2 60 

1004010

2 1.6E-04 7.3E-05 2.0 0.1 60 

2001038

2 1.6E-04 6.9E-05 1.7 0.1 60 

2001036

2 1.5E-04 6.8E-05 1.4 0.1 60 

1004000

2 1.3E-04 6.0E-05 1.5 0.1 60 

1004003

2 1.3E-04 5.9E-05 1.4 0.1 60 

2001042

2 1.0E-04 4.5E-05 1.1 0.1 60 

2001053

2 9.5E-05 4.2E-05 0.8 0.1 60 

2001047

2 9.4E-05 4.2E-05 0.9 0.1 60 

2001059

2 8.1E-05 3.6E-05 0.5 0.1 60 

0002042 1 0.03 1.5E-02 315.3 28.6 60 0003046 1 0.01 6.3E-03 141.9 11.9 60 0002041 1 8.4E-03 3.7E-03 141.8 7.1 60 0002029 1 5.0E-03 2.2E-03 66.9 4.2 60 0002037 1 4.4E-03 2.0E-03 57.0 3.8 60 1003014 1 4.2E-03 1.9E-03 49.5 3.5 60 0003137 1 4.0E-03 1.8E-03 99.0 3.4 60 1003011 1 3.9E-03 1.8E-03 64.2 3.3 60 1002007 1 3.5E-03 1.6E-03 45.2 3.0 60 

E-78
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

1002013 1 3.1E-03 1.4E-03 39.4 2.6 60 1002017 1 3.1E-03 1.4E-03 36.0 2.6 60 1003010 1 3.1E-03 1.4E-03 49.7 2.6 60 0003090 1 2.6E-03 1.2E-03 42.0 2.2 60 0003091 1 2.5E-03 1.1E-03 36.9 2.2 60 1003022 1 2.5E-03 1.1E-03 51.0 2.1 60 0002015 1 2.4E-03 1.1E-03 45.4 2.0 60 0003094 1 2.2E-03 9.7E-04 33.4 1.8 60 1001017 1 1.9E-03 8.8E-04 24.1 1.6 60 9002031 1 1.8E-03 8.2E-04 25.3 1.5 60 1003005 1 1.7E-03 7.8E-04 29.5 1.5 60 9002022 1 1.6E-03 7.4E-04 22.2 1.4 60 9002014 1 1.6E-03 7.3E-04 22.5 1.3 60 9002013 1 1.5E-03 7.1E-04 21.3 1.3 60 9002020 1 1.5E-03 7.0E-04 20.5 1.3 60 9002016 1 1.4E-03 6.6E-04 19.1 1.2 60 1003003 1 1.4E-03 6.4E-04 23.1 1.2 60 9001009 1 1.4E-03 6.3E-04 11.6 1.1 60 1001016 1 1.3E-03 5.9E-04 16.1 1.1 60 0003058 1 1.2E-03 5.4E-04 18.5 1.0 60 9002007 1 1.2E-03 5.6E-04 18.6 1.0 60 1004043 1 1.1E-03 4.8E-04 14.5 0.9 60 0001010 1 1.1E-03 4.7E-04 13.7 0.9 60 0003054 1 1.1E-03 4.7E-04 13.5 0.9 60 0003053 1 1.0E-03 4.6E-04 13.1 0.9 60 0003064 1 9.5E-04 4.2E-04 13.2 0.8 60 0001011 1 9.5E-04 4.2E-04 12.9 0.8 60 0001018 1 9.1E-04 4.0E-04 14.7 0.8 60 9001015 1 9.1E-04 4.2E-04 8.2 0.8 60 

E-79
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

0001007 1 8.8E-04 3.9E-04 12.7 0.7 60 0003063 1 8.7E-04 3.9E-04 10.8 0.7 60 0003066 1 8.5E-04 3.8E-04 10.9 0.7 60 0001020 1 8.5E-04 3.8E-04 14.7 0.7 60 0003067 1 8.2E-04 3.6E-04 10.3 0.7 60 0003109 1 8.1E-04 3.6E-04 15.2 0.7 60 0003076 1 8.0E-04 3.5E-04 12.5 0.7 60 0001019 1 7.0E-04 3.1E-04 12.9 0.6 60 1004039 1 6.6E-04 3.0E-04 8.8 0.6 60 0003072 1 6.3E-04 2.8E-04 10.6 0.5 60 0001005 1 5.7E-04 2.5E-04 8.2 0.5 60 0003152 1 4.6E-04 2.0E-04 8.0 0.4 60 0003159 1 4.6E-04 2.0E-04 6.5 0.4 60 9004021 1 4.2E-04 1.9E-04 5.1 0.4 60 2001029 1 3.5E-04 1.5E-04 4.6 0.3 60 0003015 1 3.4E-04 1.5E-04 3.9 0.3 60 0003112 1 3.4E-04 1.5E-04 4.8 0.3 60 9003020 1 3.4E-04 1.6E-04 2.8 0.3 60 9003016 1 3.2E-04 1.5E-04 2.7 0.3 60 0003002 1 2.9E-04 1.3E-04 4.4 0.2 60 9003021 1 2.9E-04 1.3E-04 2.6 0.2 60 0003003 1 2.9E-04 1.3E-04 4.3 0.2 60 9003010 1 2.7E-04 1.3E-04 3.0 0.2 60 2001000 1 2.5E-04 1.1E-04 3.6 0.2 60 1004081 1 2.5E-04 1.1E-04 4.1 0.2 60 1004024 1 2.5E-04 1.1E-04 3.1 0.2 60 1004091 1 2.3E-04 1.0E-04 3.7 0.2 60 0003129 1 2.2E-04 9.8E-05 2.3 0.2 60 1004093 1 2.2E-04 9.9E-05 3.4 0.2 60 

E-80
 
Attachment E-7. Estimated Media Pb Concentrations in Alternative NAAQS (0.2 μg/m3 Max-Q

Scenario for the Secondary Pb Smelter Case Study
 

Block ID Children Ages 0 to 7 

Annual Average 

Air 

Concentration 

(μg/m3) 

Annual Average 

Inhalation 

Exposure 

Concentration 

(μg/m3) 

Scaled Soil 

Concentration 

(μg/g) 

Predicted Indoor Dust Pb Concent

From Recent Air a From Other b 

1004017 1 1.9E-04 8.6E-05 2.4 0.2 60 1004015 1 1.8E-04 8.4E-05 2.7 0.2 60 1004005 1 1.4E-04 6.3E-05 1.7 0.1 60 1004006 1 1.3E-04 6.0E-05 1.4 0.1 60 2001070 1 1.2E-04 5.2E-05 1.3 0.1 60 2001052 1 9.1E-05 4.1E-05 0.7 0.1 60 2001062 1 8.8E-05 3.9E-05 0.6 0.1 60 2001058 1 7.2E-05 3.2E-05 0.4 0.1 60 

a Recent air refers to contributions associated with recent outdoor ambient air.
 b Other refers to contributions from indoor paint, outdoor soil/dust and additional sources (including historical air).
 

E-81
 
Attachment E-8. Comparison of Monitored to Modeled Air Pb Concentrations for the Secondary Pb Sm

Monitor ID 

Distance 

from Main 

Stack (km) 

Five Year 

Average 

Modeled 

Air Pb 

Conc 

(μg/m3) 

Average Monitored Pb Concentrations a

1997 1998 1999 2000 2001 

Mean 

Conc 

(μg/m3) 

Ratio 

Monitor to 

Model 

Mean 

Conc 

(μg/m3) 

Ratio 

Monitor to 

Model 

Mean 

Conc 

(μg/m3) 

Ratio 

Monitor to 

Model 

Mean 

Conc 

(μg/m3) 

Ratio 

Monitor to 

Model 

Mean 

Conc 

(μg/m3) 

Ratio 

Monitor to 

Model 

Sanders Pb Data 

11090003 400 0.26 0.40 1.5 0.47 1.8 0.47 1.8 0.38 1.5 0.44 1.7 

11090006 680 0.06 0.13 2.2 0.16 2.7 0.18 3.0 0.19 3.3 0.20 3.5 

a Annual averages were calculated from monthly composite U.S. EPA Air Quality System (AQS) data and weighted by the number of days in a mo

E-82
 
Attachment E-9. Input Parameters for Secondary Pb Smelter Case Study 

Soil Model Calculations
 

Use in Model Parameter Description Value Used Source and Reason a 

Tyd b Yearly total deposition 

rate of contaminant 

Varies by block 

(g/m2

-yr) 

See Attachment 

E-3 to E-7 

AERMOD results – deposition at each 

block was assumed constant for modeling 

period. 

Mixing 

equation 

parameters 

tD 

Total time period over 

which deposition 

occurs 

37 years 

Lifetime of the facility (1969 to present, 

according to Alabama Department of 

Environmental Management (ADEM) 

(2006). 

Zs Soil mixing depth 1 cm 

Human Health Risk Assessment 

Protocol (HHRAP)(USEPA, 2005); 

California Office of Environmental Health 

Hazard Assessment (2000); and for 

consistency with primary Pb smelter soil 

samples. 

BD Bulk density of soil Varies (g/cm3

) 

(Average 1.47) 

From soil survey for Pike county (Alabama 

National Resources Conservation Service 

(NRCS), 2006) Soil type at each block 

centroid was identified. 

Loss equation 

meteorological 

parameters 

My Rainfall 136.7 cm/year 

Annual normal precipitation from 1971 to 

2000 for Troy, AL (National Climatic Data 

Center (NCDC), 2002). 

I Irrigation 0 Assumption. 

Ev Evapotranspiration 82.5 cm/yr 

Midpoint of estimated evapotranspiration 

for Alabama based on hydrologic budget of 

the state (Hanson, 1991). 

RO Average annual 

surface runoff 51.1 cm/yr Value for the south east central United 

States (McKone and Bodnar, 2001). 

Loss equation 

soil and 

contaminant 

properties 

esw 

Volumetric soil water 

content 

0.2 milliliter 

(mL/)cm3 HHRAP default midpoint value. 

Kds Soil-water partitioning 

coefficient 900 mL/g HHRAP default for Pb. 

SD Sediment delivery 

ratio 0.18 MPE default. 

ER Contaminant 

enrichment ratio 1 HHRAP default. 

E-83
 
 

 

Attachment E-9. Input Parameters for Secondary Pb Smelter Case Study 

Soil Model Calculations
 

Use in Model Parameter Description Value Used Source and Reason a 

R Erosivity factor 350 yr-1 Estimated from U.S. Soil Conservation 

Service Map in Schwab et al. (1993). 

Loss equation 

Universal Soil 

Loss Equation 

(USLE) 

additional 

parameters 

K Erodibility factor Varies (ton/acre) 

(Average 0.18) 

From soil survey for Pike county (NRCS, 

2006). Soil type at each block centroid 

was identified. 

LS Topographical or 

slope-length factor 1.5 

HHRAP default that represents a variety of 

distance and slope conditions. Default was 

selected because of the large area used 

relative to the intended design of USLE. 

C Cover management 

factor 0.1 HHRAP value for grass and agricultural crops. 

P Supporting practice 

factor 0 HHRAP conservative assumption that no erosion prevention methods are in place. 

a HHRAP refers to the U.S. EPA (2005) and MPE refers to the U.S. EPA (1998).
 b Dyd (annual dry deposition) and Dyw (annual wet deposition) were pooled to create Tyd (annual total deposition). 

E-84
 
Appendix F: Pb in Outdoor Soil and Dust near Roadways 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

Table of Contents 

Table of Contents ................................................................................................................. F-i 

List of Exhibits .................................................................................................................... F-ii 

F. PB IN OUTDOOR SOIL AND DUST NEAR ROADWAYS ......................................... F-1
 

F.1. INTRODUCTION ........................................................................................................F-1 

F.2. PB CONCENTRATIONS IN SOIL AND DUST NEAR ROADWAYS ....................F-2 

F.3. TRENDS IN PB LEVELS NEAR ROADWAYS........................................................F-9 

REFERENCES ....................................................................................................................F-10 

F-i 
List of Exhibits 

Exhibit F-1. Selected Data – Pb in Surface Soil and Dust Near Roadways and Related 

Urban Measurements..............................................................................................F-3 

Exhibit F-2. Pb Concentrations Measured in Outdoor Soil and Dust Adjacent to United 

States and Canadian Roadways..............................................................................F-8 

F-ii 
 

F. PB IN OUTDOOR SOIL AND DUST NEAR ROADWAYS 

This appendix describes data on concentrations of lead (Pb) in outdoor soil and dust near 

roadways. Section F.1 briefly introduces this topic. Section F.2 summarizes measured Pb 

concentrations in outdoor soil and dust near roadways, as reported in recent literature. Section 

F.3 provides a summary of trends in Pb concentrations in outdoor soil and dust near roadways 

based on this literature review. 

Although dust was not an explicit search term in identifying publications for discussion 

in this appendix, generally speaking, the surface layer of outdoor soil is sometimes referred to as 

outdoor dust. Specifically, the phrase “outdoor dust” refers to particles deposited on any outdoor 

surface, including, for example, roadways (as well as soil). That said, in summarizing literature 

findings in Section F.2, the terms used are consistent with those used in the corresponding 

publication. 

F.1. INTRODUCTION 

Elevated levels of Pb have been observed in roadside soils throughout the United States. 

Although Pb concentrations in air decreased dramatically with the phase-out of Pb in gasoline, 

the persistence and relative immobility of Pb in soils has resulted in elevated concentrations of 

Pb in soils adjacent to roadways. Because the Pb in near-roadway soils is not easily transported 

by erosion, runoff, or other advective processes, it can remain there for relatively long time 

periods (USEPA, 2006). Correlations between current soil concentrations of Pb and air 

concentrations of Pb from periods when leaded gasoline was in use have been observed (Sheets 

et al., 2001). Studies in several cities in the late 1980s and 1990s found high concentrations in 

central sections of each city where traffic and population density are greatest (USEPA, 2006). 

The resuspension of Pb in near-roadway soil and dust is a potential source of airborne Pb 

in some locations (USEPA, 2006). Young et al. (2001; 2002), for example, evaluated Pb levels 

in roadside soils and surface soil samples near facilities to estimate the “potential suspension 

yield” (i.e., the amount of Pb sorbed to particulate matter (PM) less than 10 micrometers (μm) 

that is likely to be subject to resuspension due to wind erosion) and the enrichment ratio of 

suspended Pb (i.e., concentration of Pb in suspended PM versus the measured Pb concentration 

in surface soil). Based on their results, Pb-contaminated soils were found to be a potential source 

of airborne Pb. 

Mass-balance studies performed on urban and metropolitan scales support the hypothesis 

that resuspension of Pb in soil is a source of current levels of airborne Pb. For example, in two 

studies described in the Criteria Document (USEPA, 2006), mass-balance calculations were 

F-1
 
 

conducted for the air emissions of Pb in the California South Coast Air Basin near Los Angeles. 

Lankey et al. (1998) estimated that 40 percent of Pb emitted to air was generated by the 

resuspension of Pb previously deposited on roadways. This mass balance was calculated for 

1989, when some leaded gas was still in use (the authors estimated that direct Pb emitted in car 

exhaust also accounted for 40 percent of the total airborne Pb). Using data collected in 2001, 

Harris and Davidson (2005) estimated that soil contamination subject to resuspension is the 

source of 90 percent of the Pb emitted to air in southern California near Los Angeles. Although 

these studies are based on generalized, mass-balance assumptions and the contribution of nearroadway soils is uncertain, resuspension of soil-bound Pb particles and contaminated road dust is 

considered to be a significant source of airborne Pb (USEPA, 2006). 

F.2. PB CONCENTRATIONS IN SOIL AND DUST NEAR ROADWAYS 

Exhibit F-1 presents a summary of published accounts ordered alphabetically by primary 

author of measured Pb concentrations in outdoor soil and dust near roadways. This summary is 

based on a literature search intended to identify recent studies of Pb in surface soil and dust 

adjacent to roads. Only recent studies that conducted outdoor soil or dust measurements are 

included here, with a focus on those published within the past decade. In many instances, 

additional measurements were collected or investigators completed other analyses using the 

results; these details are not included in this summary. 

This snapshot of the literature reveals that concentrations of Pb in soils or dust near 

roadways have been measured at a wide range of locations. For these studies, Pb levels range 

from typical urban background levels to hundreds or thousands of milligrams per kilogram 

(mg/kg) (Shinn et al., 2000; Sutherland et al., 2000; Turer and Maynard, 2003). Exhibit F-2 

presents the general range of Pb concentrations reported in this subset of the literature for surface 

soil and dust samples taken near United States and Canadian roadways. Note that this chart is 

intended to convey only general information on the levels of total Pb reported in the literature in 

soil and dust near roadways; it should not be interpreted as a representative or comprehensive 

summary of surface soil data for the entire United States nor Canada. 

F-2
 
Exhibit F-1. Selected Data – Pb in Surface Soil and Dust Near Roadways and Related Urban Me

Study Citation Location and Sampling Scheme Reported Pb Concentration(s) (total Pb unless otherwise specified) Other Re

Chirenje et al., 2004 

• Gainesville, Florida (relatively 

undeveloped, low population/traffic 

density) and Miami, Florida (developed, 

high population/traffic density) 

• Locations sampled according to land use 

characterization as residential, 

commercial, public parks, or public 

buildings 

• Sampling depths: 0 to 20 cm 

(centimeters) in Gainesville; 0 to 10 cm in 

Miami 

• In Miami, analyses showed concentrations 

from 0 to 10 cm were no different than 

concentrations from 10 to 20 cm 

• Miami: median 98 parts per million 

(ppm); 55 percent of samples were 51 

to 200 ppm 

• Gainesville: median 15 ppm; 87 

percent of samples <50 ppm 

• Concluded l

due to low

activity, less 

Pb mobility

organic c

conten

• Pb patter

differed b

• Residenti

generally 

Fakayode and OluOwolabi, 2003 

• Osogbo, Orun, Nigeria 

• Samples taken at depths of 0 to 5 cm at 

distances of 5, 15, 30, and 50 meters (m) 

from edge of roads 

• 39 sampling locations; divided into high, 

medium, and low density traffic regions 

• For high traffic density roads: average 

92±21 ppm at 5 m from road; 

reductions in Pb with distance: 37 

percent at 10 m, 62 percent at 30 m, 

81 percent at 50 m 

• For medium traffic density roads: 64, 

42, 27, and 13 ppm, respectively, at 

distance of 5, 10, 30, and 50 m 

• Authors co

based emis

sources a

elevated 

Filippelli et al., 2005 

• Indianapolis, Indiana 

• Sampled at several locations on transects 

along urban and suburban roadways; 10 

to 40 m from road 

• Sampling depth: 0 to 5 cm 

• Urban roadways: 400 to >900 ppm 

• Suburban roadways: 100 to <200 ppm 

• Concent

increasing 

• Also sam

locations to 

(non

• Conducted 

modeling 

F-3
 
Exhibit F-1. Selected Data – Pb in Surface Soil and Dust Near Roadways and Related Urban Me

Study Citation Location and Sampling Scheme Reported Pb Concentration(s) (total Pb unless otherwise specified) Other Re

Gillies et al., 1999 

• Urban locations near Reno, Nevada, and 

surrounding non-urban areas 

• Sampled dust at surface of soil or paved 

road 

• Sampling locations included playas (dry 

lake bed/salt flat), paved roads, and 

• Reported relative abundance of Pb in 

PM 2.5 by weight percent: playa and 

construction site 0.001 to 0.01 percent; 

paved road 0.01 to 0.1 percent 

• Approximate enrichment factors of Pb 

in PM2.5: playa ~1 to 10; paved road 

~30; construction site ~5 to 10 

• Pb enrichment factors slightly lower for 

• Results 

appor

re

construction sites 

• Sampling depth: ~ top 1 cm of soil 

particles in between PM10 and PM2.5 

for playa and paved road; 

approximately same for construction 

site 

Hafen and 

Brinkmann, 1996 

• Tampa, Florida 

• Sampled 32 transects at roadways, 7 

samples per transect; 3 cm to 220 cm 

from road; sampling depth: 0 to 3 cm 

• 224 samples total, 7 samples per transect 

• Range: 40 to 3,360 ppm 

• Mean Pb concentrations by distance 

from road were relatively tightly 

clustered; means ranged from 200 

ppm (>0.8 m) to 440 ppm (0.24 m) 

• Looked for t

distance an

term scale (

negative co

ro

Lejano and Ericson, 

2005 

• Pacoima, California, (near Los Angeles) 

• 210 samples at transects along freeways 

spaced about 1 kilometer (km) apart; 

sampling depth: 0 to 2.54 cm; samples 

collected from within 150 m of the 

roadway 

• Total range not presented; mean 

concentrations of five roadways range 

from 43 to 112 ppm (mean for one 

road up to 232 ppm if one outlier 

included) 

• Mean conc

vehicular” 

• Concluded 

emissions 

most bioava

Li, 2006 

• Burnaby, Canada 

• Three transects across highway; samples 

at 0.1 m intervals from road 

• 139 samples from 17 borehole locations; 

sampling depth: 0 to 10 cm 

• Results for three transects: 7 to 1020 

ppm (lower traffic/speed); 25 to 925 

ppm; 303 to 1650 ppm 

• Sequenti

pe

sorp

Li and Preciado, 

2004 

• British Columbia, Canada, Highway 17 

• Two transects along highway; 0 to 10 m 

from road; 1 m intervals 

• Sampling depth: 0 to 5 cm 

• Also sampled on-road dust and measured 

Pb deposition rates adjacent to roadway 

• Roadside soil results: ~100 ppm for 

samples 0 m from roadside; <50 ppm 

for all samples 1 to 10 m from roadside 

• On-road dust: Pb content ranged from 

51 to 181 mg/kg 

• PM depo

decrease

• Pb depositi

m of road

micrograms 

(μg/m2-day

F-4
 
Exhibit F-1. Selected Data – Pb in Surface Soil and Dust Near Roadways and Related Urban Me

Study Citation Location and Sampling Scheme Reported Pb Concentration(s) (total Pb unless otherwise specified) Other Re

Sanchez-Martin et 

al., 2000 

• Two medium-sized Spanish cities 

(Salamanca and Valladolid) 

• Samples taken at near-roadway, median, 

urban, suburban, park, and natural 

settings 

• Sampling depth: 1 to 10 cm 

• Salamanca: 1 to 3 m from road: 33 to 

353 ppm (mean 122 ppm); 10 m from 

road: 18 to 90 ppm (mean 48 ppm); 

median strip 87 to 1480 ppm (mean 

580 ppm) 

• Valladodid: median strip 51 to 1117 

ppm (mean 96 ppm) 

• Statistica

observed b

and mean d

samp

• Also me

Sheets et al., 2001 

• Springfield, Missouri 

• Multiple sampling locations, including 

three near heavy-traffic streets and two 

more than 30 m from residential street 

• Sampling depth: 0 to 1 cm 

• Averages for surface samples at five 

roadside locations ranged from 18 to 

179 ppm 

• Correlation w

measurem

airborne P

1984 (wh

Shinn et al., 2000 

• Chicago, Illinois 

• Sampled bare soil in four-block urban 

residential area and measured Pb 

• Developed surface plots of Pb levels via 

kriging; analyzed patterns by reviewing 

historical data for potential sources 

• Sampling depth not specified 

• Mean soil Pb: 2180 ppm; median: 

1775 ppm; range: 175 to 7935 ppm 

• Pb distribu

random d

• Pb surfac

existing and 

within stud

street w

Speiran, 1998 

• Interstate 95 (I-95) north of Richmond, 

Virginia (Exit 86 to a moderately traveled, 

two-lane road) 

• 59 soil samples from 19 sites 

• Varying distances from interstate and exit 

ramp 

• Sampling depth: 0 to 7.6 cm 

• Range: 46 to 1200 ppm 

• Spatial va

indicate th

source o

• Concen

increasing 

F-5
 
Exhibit F-1. Selected Data – Pb in Surface Soil and Dust Near Roadways and Related Urban Me

Study Citation Location and Sampling Scheme Reported Pb Concentration(s) (total Pb unless otherwise specified) Other ReSutherland and 

Tolosa, 2001 

• Manoa basin, Oahu, Hawaii 

• Sampled two transects at low speed 

roadways (near park and school) out to 50 

m from road 

• First sample (0 m) from “road deposited 

sediment (RDS)” – curbside area at edge 

of road 

• Sampling depth: 0 to 2.5 cm 

• Park transect: max of 375 ppm (5 m 

from road); RDS 285 ppm 

• School transect: max of 200 ppm in 

RDS; all soil samples 25 to 50 ppm, 

out to 50 m 

• Measurements for both transects drop 

to <50 ppm within 5 to 10 m 

• Local background soil concentrations 

reported as 12 to 13 ppm 

• Concluded 

(sidewalks, g

P

• Pb conce

plotted using 

the 1970

genera

concentrat

• Five sup

collecte

recreati

roadway

sampled fr

Sutherland et al., 

2000 

• Manoa watershed, Oahu, Hawaii 

• Sampled road deposited sediment (in curb 

at roadside) and roadside soils within 2 m 

of road surface; 78 samples 

• Daily traffic volumes: <3200 to 45,200 

vehicles/day 

• Sampling depth: 0 to 2.5 cm 

• Range of total Pb in roadside soil 10 to 

4870 ppm 

• Median Pb concentration 56 ppm 

(includes road deposited sediment, but 

highest levels seen in roadside soil) 

• Enrichme

based on th

influence 

most sign

versus alu

• Enrichment 

four to fi

b

F-6
 
Exhibit F-1. Selected Data – Pb in Surface Soil and Dust Near Roadways and Related Urban Me

Study Citation Location and Sampling Scheme Reported Pb Concentration(s) (total Pb unless otherwise specified) Other Re

Teichman et al., 

1993 

• Alameda County, California, adjacent to 

Interstate 880 (I-880) 

• ~200 samples were taken in residential 

yards and parks/playgrounds in 

communities adjacent to I-880 and within 

1-mile radius of I-880 

• Sampling depth: ranged from surface to 

1.27 to 1.91 cm deep 

• Residential soil measurements: 

average 567.7 ppm; range 195 to 2026 

ppm 

• Parks and playgrounds 

measurements: average 136.5 ppm; 

range 6 to 565 ppm 

• “Gasoline e

Turer and Maynard, 

2003 

• Corpus Christi, Texas; two sampling sites; 

one transect per site 

• Site 1: city center (heavy traffic); 12 

samples; 2 to 12 m from road; 12 m from 

road; sampling depth: 0 to 32.5 cm 

• Site 2: near oil refinery; 10 samples; 0.5 to 

4 m from road; sampling depth: 0 and 0 to 

2.5 cm 

• Site 1: 210 to 770 ppm; Site 2: 140 to 

390 ppm 

• Highest concentrations at both sites 

were observed closest to roadway 

(within 3.5 m) 

• Results wer

Ohio meta

highway soi

determined t

Turer et al., 2001 

• Cincinnati, Ohio Interstate 75 (I-75) 

through city; 58 samples 

• Sampling conducted adjacent to highways 

on median between lanes (within ~50 m of 

road) 

• Sampling depth: 0 to 1 cm; also sampled 

1 to 5 cm 

• Range for 0- to 1-cm samples: 166 to 

942 ppm; range for 1- to 5-cm 

samples: 59 to 1073 ppm 

• Some samples taken at depth of 10 

to15 cm contained total Pb between 

1000 and 2000 ppm 

• Performed 

determine 

historica

currently in 

percent of P

a

• Removal 

proposed as 

mechanis

lesser 

Young et al., 2001 

• California highways; three locations (not 

identified) 

• Samples taken 1.5 m from roadway 

• Sampling depth not specified 

• Pb concentration reported to be 38, 46, 

and 322 ppm 

• Pb content, 

Pb emission 

meas

F-7
 
Exhibit F-2. Pb Concentrations Measured in Outdoor Soil and Dust Adjacent to 

United States and Canadian Roadways 

0 

500 

1000 1500 2000 2500 

Teichman 

et al., 1993 

Li, 2006 Turer et al., 

2001 

Hafen and 

Brinkmann, 

1996 

Turer and 

Maynard, 

2003 

Filippelli et 

al., 2005 

Speiran, 

1998 

Sutherland 

and Tolosa, 

2000 

Young et 

al., 2002 

Lejano and 

Ericson, 

2005 

Sheets et 

al., 2001 

Sutherlan

et al., 200

Total Measured Pb (ppm) 

max: 4,870 ↑

Note: This chart is intended to convey the range of total Pb measured in roadside soils in the United States and Canada in th

studies. For each study, the vertical line represents the approximate range of total Pb reported in surface soil samples taken 

roadside locations; surface sampling depth varies by study. The horizontal hash mark or box represents the "average" total Psamples in that study; this average may be either reported in the study or calculated based on reported data. In some cases, o

average or median concentrations for selected study locations or sample categories were reported; these cases are representeblack box with no vertical line. Refer to cited publications for details on individual studies. 

F-8
 
 

F.3. TRENDS IN PB LEVELS NEAR ROADWAYS 

 Pb concentrations are typically higher in roadside soils located in highly developed 

urban areas than in non-urban environments (Chirenje et al., 2004; Shinn et al., 2000; Turer and 

Maynard, 2003). Generalizing beyond this observation, however, is difficult. Although Pb 

concentrations in soils have been positively correlated with traffic volume on adjacent roadways 

in some cases (see, e.g., Sanchez-Martin et al. [2000] and Fakayode and Olu-Owolabi [2003]), 

other analyses have suggested that that relationship may be confounded by variables such as 

microclimate turbulence, near-roadway topography, and human construction and landscaping 

activities (Hafen and Brinkmann, 1996). Although Pb is generally higher in soils near heavilytraveled roadways, determining the specific relationship with traffic volume can be difficult, in 

part because traffic density for previous time periods can be difficult to determine. Also, other 

site-specific factors can affect Pb mobility; for example, lower soil pH and organic carbon and 

clay content have been correlated with increased Pb mobility (i.e., lower retention rates) in 

roadside soils (Chirenje et al., 2004). Pb concentrations tend to be highest in the upper-most 

layer of soil (i.e., first several cm). Some exceptions have been reported; for example, Turer et 

al. (2001) observed concentrations of total Pb in soil adjacent to an interstate highway in 

Cincinnati, Ohio of 1,000 to 2,000 mg/kg at a depth 10 to 15 cm (compared to concentrations up 

to about 1,000 mg/kg in the top 5 cm of soil). 

Substantial evidence indicates that Pb concentrations in surface soil decrease rapidly with 

distance from the roadway. Sutherland and Tolosa (2001) reported that the relationship for 

measurements taken adjacent to roadways (out to 50 m) in Hawaii is approximately linear when 

the log of concentration is plotted against the log of distance from the roadway. Similarly, 

Filippelli et al. (2005) have reported an exponential decay in Pb concentration with increasing 

distance from the roadside based on transects at 10 and 40 m from roadways in Indianapolis, 

Indiana. Hafen and Brinkmann (1996) surveyed results from several studies and observed a 

generally exponential decrease in Pb concentration with distance from the road. Other 

investigators have observed an overall decrease in Pb in surface soil but were unable to 

determine a mathematical relationship (Li and Preciado, 2004; Shinn et al., 2000). In general, 

however, based on the conclusions of these studies, Pb concentrations adjacent to roads appear to 

decrease to local background levels within 50 m of the roadway. 

F-9
 
 

 

 

 

REFERENCES 

Chirenje, T.; Ma, L. Q.; Reeves, M.; Szulczewski, M. (2004) Lead Distribution in Near-Surface Soils of Two 

Florida Cities: Gainesville and Miami. Geoderma. 119(2): 113-120. 

Fakayode, S. O.and Olu-Owolabi, B. I. (2003) Heavy Metal Contamination of Roadside Topsoil in Osogbo, Nigeria: 

Its Relationship to Traffic Density and Proximity to Highways. Environmental Geology. 44: 150-157. 

Filippelli, G. M.; Laidlaw, M. A.; Latimer, J. C.; Raftis, R. (2005) Urban Lead Poisoning and Medical Geology: An 

Unfinished Story. GSA Today. 15(1): 4-11. 

Gillies, J. A.; O'Connor, C. M.; Mamane, Y.; Gerler, A. W. (1999) Chemical Profiles for Characterizing Dust 

Sources in an Urban Area, Western Nevada, USA. In: Livingstone, I., Ed. Aeolian Geomorphology: Papers 

From the 4th International Conference on Aeolian Research 1998, Oxford, UK. Zeitschrift fuer 

Geomorphologie. 116(S): 19-44. 

Hafen, M. R. and Brinkmann, R. (1996) Analysis of Lead in Soils Adjacent to an Interstate Highway in Tampa, 

Florida. Environmental Geochemistry and Health. 18(4): 171-179. 

Harris, A. R.and Davidson, C. I. (2005) The Role of Resuspension in Lead Flows in the California South Coast Air 

Basin. Environ Sci Technol. 39: 7410-7415. 

Lankey, R. L.; Davidson, C. I.; McMichael, F. C. (1998) Mass Balance for Lead in the California South Coast Air 

Basin: an Update. Environmental Research. A 78: 86-93. 

Lejano, R. P.and Ericson, J. E. (2005) Tragedy of the Temporal Commons: Soil-Bound Lead and the Anachronicity 

of Risk. Journal of Environmental Planning and Management. 48(2): 301-320. 

Li, L. Y. and Preciado, H. (2004) Air, Runoff and Soil Monitoring of Highway Pollution by Metals Along Highway 

Corridors. In Brebbia, C.A., ed. Air Pollution XII (Air Pollution 2004). United Kingdom: Wessex Institute 

of Technology. 

Li, L. Y. (2006) Retention Capacity and Environmental Mobility of Pb in Soils Along Highway Corridor. Water, 

Air, and Soil Pollution. 170: 211-227. 

Sanchez-Martin, M. J.; Sanchez-Camazano, M.; Lorenzo, L. F. (2000) Cadmium and Lead Contents in Suburban 

and Urban Soils From Two Medium-Sized Cites of Spain: Influence of Traffic Intensity. Bulletin of 

Environmental Contamination and Toxicology. 64: 250-257. 

Sheets, R. W.; Kryger, J. R.; Biagioni, R. N.; Probst, S.; Boyer, R.; Barke, K. (2001) Relationship Between Soil 

Lead and Airborne Lead Concentrations at Springfield, Missouri, USA. Science of Total Environment. 271: 

79-85. 

Shinn, N. J.; Bing-Canar, J.; Cailas, M.; Peneff, N.; Binns, H. J. (2000) Determination of Spatial Continuity of Soil 

Lead Levels in an Urban Residential Neighborhood. Environmental Research. 82 (Section A): 46-52. 

F-10
 
 

 

 

Speiran, G. K. (1998) Selected Heavy Metals and Other Constituents in Soil and Stormwater Runoff at the Interstate 

95 Interchange Near Atlee, Virginia, April 1993-May 1997. Water-Resources Investigations Report 98­

4115, 39p. U.S. Geological Survey. 

Sutherland, R. A.; Tolosa, C. A.; Tack, F. M. G.; Verloo, M. G. (2000) Characterization of Selected Element 

Concentrations and Enrichment Ratios in Background and Anthropogenically Impacted Roadside Areas. 

Archives of Environmental Contamination and Toxicology. 38: 428-438. 

Sutherland, R. A.and Tolosa, C. A. (2001) Variation in Total and Extractable Elements With Distance From Roads 

in an Urban Watershed, Honolulu, Hawaii. Water, Air, and Soil Pollution. 127(4): 315-338. 

Teichman, J.; Coltrin, D.; Prouty, K.; Bir, W. A. (1993) A Survey of Lead Contamination in Soil Along Interstate 

880, Alameda County, CA. American Industrial Hygiene Association Journal. 54(9): 557-559. 

Turer, D.; Maynard, J. B.; Sansalone, J. J. (2001) Heavy Metal Contamination in Soils of Urban Highways: 

Comparison Between Runoff and Soil Concentrations at Cincinnati, Ohio. Water, Air, and Soil Pollution. 

132: 293-314. 

Turer, D. G. and Maynard, J. B. (2003) Heavy Metal Contamination in Highway Soils. Comparison of Corpus 

Christi, Texas and Cincinnati, Ohio Shows Organic Matter Is Key to Mobility. Clean Technologies and 

Environmental Policy. 4(4): 235-245. 

U.S. Environmental Protection Agency (USEPA). (2006) Air Quality Criteria for Lead (Final). Volume I and II. 

Research Triangle Park, NC: National Center for Environmental Assessment; EPA/600/R-05/144aF-bF. 

Available online at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=158823. 

Young, T. M.; Heeraman, G.; Sirin, G.; Ashbaugh, L. (2001) Resuspension of Contaminated Soil As a Source of 

Airborne Lead. Contract No. 97-325. Final Project Report from Air Quality Group, Crocker Nuclear Lab to 

the Research Division of the Air Resources Board; August. 

Young, T. M.; Heeraman, G.; Sirin, G.; Ashbaugh, L. (2002) Resuspension of Soil As a Source of Airborne Lead 

Near Industrial Facilities and Highways. Sci. Technol. 36: 2484-2490. 

F-11
 

Appendix G: Approaches for Estimating Indoor Dust Pb Concentrations 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

Table of Contents 

Table of Contents .................................................................................................................G-i 

List of Exhibits .................................................................................................................. G-iii 

List of Attachments............................................................................................................G-iv 

G. APPROACHES FOR ESTIMATING INDOOR DUST PB CONCENTRATIONS ....G-1
 

G.1. INDOOR DUST PB CONCENTRATION ALGORITHMS FOR DIFFERENT 

CASE STUDIES..........................................................................................................G-1 

G.1.1. General Urban Case Study............................................................................... G-1 

G.1.2. Point Source Case Studies ............................................................................... G-2 

G.1.2.1. Primary Pb Smelter Case Study........................................................G-2 

G.1.2.2. Secondary Pb Smelter Case Study....................................................G-3 

G.2. BACKGROUND INFORMATION ON RELATIONSHIPS BETWEEN INDOOR 

DUST PB AND AIR AND OTHER VARIABLES .................................................... G-3 

G.3. FOUNDATION FOR THE GENERAL URBAN CASE STUDY INDOOR DUST 

ALGORITHMS ........................................................................................................... G-7 

G.3.1. Investigation of an Empirical Model for the General Urban Case Study........G-7 

G.3.1.1. Lanphear et al. 1996 Data Set for Rochester, New York.................. G-7 

G.3.1.2. HUD National Survey Data Set ...................................................... G-11 

G.3.2. Development of a Mechanistic Air Model for the General Urban 

Case Study ..................................................................................................... G-11 

G.3.2.1. Physical Processes and Derivation of an Equation for 

Steady-state Pb Floor Loading........................................................ G-11 

G.3.2.2. Input Values for the Mechanistic Model ........................................ G-17 

G.3.3. Combining the Mechanistic Air Model with Empirical Data to Derive an 

Indoor Dust Pb Loading Estimate from Other Sources ................................. G-20 

G.3.4. Converting Indoor Dust Pb Loadings to Indoor Dust Pb Concentrations ..... G-21 

G.3.4.1. Estimating Vacuum Pb Loadings from Wipe Pb Loadings............G-22 

G.3.5. Specification of the General Urban Case Study Indoor Dust Algorithms ..... G-22 

G.3.6. Performance Evaluation of the General Urban Case Study Indoor 

Dust Models...................................................................................................G-23 

G-i 
G.3.7. Separating Pb Indoor Dust Concentrations into Recent Air and 

Other Portions................................................................................................G-31 

G.4. FOUNDATION FOR THE PRIMARY PB SMELTER CASE STUDY INDOOR 

DUST ALGORITHMS..............................................................................................G-32 

G.4.1. Site-specific Regression Model ..................................................................... G-32 

G.4.1.1. Overview of Methods ..................................................................... G-33 

G.4.1.2. Data Sources ................................................................................... G-33 

G.4.1.3. Data Manipulation .......................................................................... G-34 

G.4.1.3.1. Data Set Based on Spatial-temporal “Windows”......................... G-35 

G.4.1.3.2. Data Set Based on Indoor Dust Sampling Locations................... G-36 

G.4.1.4. Results of the Statistical Analysis................................................... G-36 

G.4.1.4.1. Exploratory Analysis ................................................................... G-36 

G.4.1.4.2. Regression Modeling of Indoor Dust Pb Concentrations ............ G-38 

G.4.1.4.3. Comparison of Predicted to Observed Indoor Dust Pb 

Concentrations in Primary Pb Smelter Case Study......................... G-41 

G.4.1.5. Primary Pb Smelter Case Study: Indoor Dust Modeling Approach 

Used Near Facility .......................................................................... G-43 

G.4.2. Primary Pb Smelter Case Study: Indoor Dust Modeling Approach Used at 

Distance from Facility ................................................................................... G-44 

G.4.3. Separating Indoor Dust Pb Concentrations into Recent Air and 

Other Portions................................................................................................G-45 

G.5. FOUNDATION FOR THE SECONDARY PB SMELTER CASE STUDY 

INDOOR DUST ALGORITHMS ............................................................................. G-45 

G.5.1. Separating Pb Indoor Dust Concentrations into Recent Air and Other 

Portions .......................................................................................................... G-46 

REFERENCES ................................................................................................................... G-47 

G-ii 
List of Exhibits 

Exhibit G-1. Correlation Coefficients, Number of Samples, and p Values for Variables 

Significantly Correlated with Indoor Dust ......................................................... G-10 

Exhibit G-2. Comparison of Housing Vintage Percentages in the Rochester Data and the HUD 

Exhibit G-4. Input Parameters Selected for the Mechanistic Model for Urban EnvironmentsG-18 

National Survey .................................................................................................. G-11 

Exhibit G-3. Mechanistic Indoor Dust Model Schematic........................................................ G-12 

Exhibit G-5. Comparison of the Hybrid Mechanistic-empirical Model and the Air-only 

Regression-based Model Indoor Dust Pb Concentration Predictions for a Given 

Ambient Air Pb Concentration ........................................................................... G-23 

Exhibit G-6. Summary of Performance Evaluation Performed on General Urban Case Study 

Models ................................................................................................................ G-25 

Exhibit G-7. Primary Pb Smelter Case Study: Summary of Pb Concentrations in Residential 

Exhibit G-8. Primary Pb Smelter Case Study: Relationship between Indoor Dust Pb 

Exhibit G-9. Primary Pb Smelter Case Study: Relationship between Road Dust Pb 

Exhibit G-10. Indoor Dust Regression Models Tested and Summary of Regression Analysis 

Exhibit G-12. Comparison of Three Best "Windows" Models with EPA Air+Soil RegressionExhibit G-13. Comparison of Best-fitting "House" Models with the EPA Air+Soil RegressionExhibit G-14. Ratio of Indoor Dust Pb Concentrations Predicted by the H5 and Air+Soil 

Soil and House and Road Dust ........................................................................... G-34 

Concentrations and Distance from Facility ........................................................ G-37 

Concentrations and Nearby Indoor Dust Pb Concentrations.............................. G-38 

Results for the “Windows” Data Set................................................................. G-39 

Exhibit G-11. Summary of Regression Analysis Results for the “House” Data Set ............... G-41 

based Model and “Windows” Data................................................................... G-42 

based Model and the "Windows" Indoor Dust Data......................................... G-42 

Regression-based Models versus Distance from the Facility ........................... G-44 

G-iii 
List of Attachments 

Attachment G-1. Method Used to Convert Indoor Pb Loadings to Concentrations................ G-50 

G-iv 
 

 

 

G. APPROACHES FOR ESTIMATING INDOOR DUST PB 

CONCENTRATIONS 

Indoor dust concentrations of Pb were estimated using empirically derived relationships 

between indoor dust and other media concentrations, mechanistic models that directly model the 

accumulation of indoor dust due to physical processes, or a combination of the two. The 

following sections present an overview of the algorithms used to calculate indoor dust Pb 

concentrations in each case study followed by a more detailed discussion of the development and 

selection of the algorithms. 

G.1. INDOOR DUST PB CONCENTRATION ALGORITHMS FOR DIFFERENT CASE 

STUDIES 

Different approaches were used to calculate indoor dust concentrations of Pb for different 

case studies. This section provides an overview of the equations used to calculate the indoor 

dust concentrations in each case study. Justification for using these equations appears in the 

subsequent sections. 

G.1.1. General Urban Case Study 

In recognition of the model uncertainty associated with this key analytical step of the risk 

assessment, the general urban case study uses two different models to estimate indoor dust Pb 

concentration given an ambient air concentration. The first is a hybrid model that relies on the 

steady state solution for a mechanistic model to determine the ambient air-derived indoor dust Pb 

loading and an empirical value for the indoor dust Pb loading from other sources (e.g, indoor 

paint, outdoor soil/dust and additional sources including historical air). The mechanistic model 

was developed using a mass-balance equation relating outdoor ambient air Pb to indoor air Pb 

and deposition of Pb to indoor surfaces in typical residences. The indoor dust Pb loading from 

other sources was derived using the U.S. Department of Housing and Urban Development 

(HUD) National Survey of Lead-Based Paint in Housing (USEPA, 1995) average indoor dust Pb 

loadings and subtracting out the air-related indoor dust from the mechanistic model. Both pieces 

of this hybrid model are described more fully in Section G.3. The equation for this model is: 

0.931 PbDUST = EXP[4.92 + 0.52×ln(0.185×(104.2× PbAIR +1.15) )] 

where: 

PbDUST = concentration of Pb in indoor dust (microgram [μg] 

per gram [g]) 

PbAIR = concentration of Pb in the ambient air 

(μg/cubic meter [m3

]) 

G-1 
The second indoor dust estimation algorithm for this case study uses a U.S. EPA 

developed regression model (USEPA, 1989). For the general urban case study, the air-only 

regression-based model is used: 

PbDUST = 60 + (844 × PbAIR) 

where: 

PbDUST = concentration of Pb in indoor dust (μg/g), 

PbAIR = concentration of in the ambient air (μg/m3

) 

G.1.2. Point Source Case Studies 

G.1.2.1. Primary Pb Smelter Case Study 

The primary Pb smelter case study included a remediated zone, where measurements of 

site-specific outdoor soil/dust and indoor dust Pb concentrations were available, and an 

unremediated zone, where no Pb measurements were available. To best capture the outdoor 

soil/dust and indoor dust Pb concentrations at this particular site, a site-specific regression 

equation was developed for all U.S. Census blocks within 1.5 kilometer (km) of the facility (the 

remediated zone): 

ln(PbDUST) = 7.7892 + 0.72 x ln(PbAIR) 

where: 

PbDUST = concentration of Pb in indoor dust (μg/g) 

PbAIR = concentration of Pb in the ambient air (μg/m3

) 

For the remainder of the U.S. Census blocks, a U.S. EPA air+soil regression-based model 

was used to estimate indoor dust Pb concentrations (USEPA, 1989). This equation was 

developed using data from primary smelters, including the primary smelter included in this 

assessment. The relationship specifies that: 

PbDUST = 31.3 + (638 x PbAIR) + (0.364 x PbSOIL) 

where: 

PbDUST = concentration of Pb in indoor dust (μg/g) 

PbAIR = concentration of Pb in the ambient air (μg/m3

) 

PbSOIL = concentration of Pb in outdoor soil/dust (mg/kg) 

For a more complete discussion of the development and selection of these models, see 

Section G.4. 

G-2 
G.1.2.2. Secondary Pb Smelter Case Study 

Unlike the primary Pb smelter case study, no site-specific indoor dust concentration 

observations were available for the secondary Pb smelter case study area. As a result, the 

following air-only regression-based model was used to characterize indoor dust concentrations: 

PbDUST = 60 + (844 × PbAIR) 

where: 

PbDUST = concentration of Pb in indoor dust (μg/g), 

PbAIR = concentration of Pb in the ambient air (μg/m3) 

This model is further described in Section 0. 

G.2. BACKGROUND INFORMATION ON RELATIONSHIPS BETWEEN INDOOR 

DUST PB AND AIR AND OTHER VARIABLES 

Pb in indoor dust, which collects on surfaces and may be ingested by children, typically 

has three major sources: (1) outdoor ambient air-suspended particles, which infiltrate the indoor 

environment and become deposited as indoor dust; (2) outdoor soil/dust, which is tracked into 

the home from the yard or from the wider community; and (3) interior Pb paints, which chip or 

chalk and contribute to indoor dust (e.g., Adgate et al., 1998). Many literature studies have 

examined one or more of these contributors to determine their absolute or relative contribution to 

indoor dust Pb levels. However, this analysis is confounded by the fact that the outdoor ambient 

air contains resuspended outdoor soil/dust that may have been transported over significant 

distances, and that outdoor soil/dust contains signatures of other numerous sources, including 

exterior Pb paint. Thus, determining the exact sources of Pb in indoor dust at a single location is 

a complex exercise. 

Published studies have examined indoor dust Pb loadings or concentrations in both pointsource and urban environments. In general, exposure to Pb near point sources includes both a 

current component due to active emissions and a historical component due to the accumulation in 

outdoor soil/dust of previously emitted Pb and Pb from Pb paint (Hilts, 2003). In point-source 

environments where emission controls have been imposed, current emissions may be reduced, 

but these environments will retain a higher signal of Pb in indoor dust relative to background 

locations away from point sources due to the presence of previously contaminated outdoor 

soil/dust (von Lindern et al., 2003). In a generalized urban environment away from any historic 

Pb point-source emission source, increased Pb exposure is dominated by historical sources of Pb 

only, including the past deposition of Pb in outdoor soil/dust from leaded gasoline, which was 

G-3 
available until the 1980s, and by historic use of Pb paint (Mielke et al., 1997). Because of the 

deposition of Pb from leaded gasoline, urban locations near historically congested roadways tend 

to have higher outdoor soil/dust concentrations than those away from major roadways (see 

Appendix F). In both urban and point-source locations, the residence time of Pb in outdoor 

soil/dust can be up to 700 years in the absence of remediation (Laidlaw et al., 2005), indicating 

that accumulated Pb in outdoor soil/dust can have a long temporal footprint on indoor dust. 

Several studies have attempted to determine the relative contributions of ambient air, 

outdoor soil/dust, and Pb paint to indoor dust Pb levels. Using an isotopic analysis of various 

elements in particulate matter, Adgate et al. (1998) found that air contributed approximately 17 

percent, Pb paint contributed approximately 34 percent, and outdoor soil/road dust contributed 

approximately 49 percent to indoor dust Pb levels by mass. This study was conducted in an 

urban environment in Jersey City, New Jersey. However, the homes in the study were all built 

before 1960, and most of the homes were built prior to 1940 (Adgate et al., 1998); thus, the 

portion of dust arising from Pb paint may be high compared with homes of a younger vintage 

where Pb paint is not as prevalent. A similar study in Christchurch, New Zealand, found that 45 

percent of indoor dust came from paint, three to five percent came from outdoor soil, 15 to 20 

percent came from outdoor road dust, and 15 to 25 percent came from air-related sources 

(Fergusson and Schroeder, 1985). Gwiazda and Smith (2000) found that, in children with the 

highest blood Pb (PbB) levels in Santa Cruz county (> 15 μg/deciliter [dL]), indoor dust 

exposure was usually due to paint ingestion or past exposure due to residing outside the United 

States. Thus, while these studies are useful in suggesting that outdoor soil/dust and Pb paint are 

the strongest contributors to indoor dust, the relative contributions are highly dependent on the 

underlying media concentrations themselves; these factors can be applied only to an urban or 

point-source environment if the underlying media concentrations are similar to those 

encountered in the original study. In addition, because the ambient air may contain resuspended 

outdoor soil/dust particles, the high outdoor soil/dust contribution may actually be delivered via 

the ambient air infiltration, rather than during direct outdoor soil/dust-tracking events. 

Other studies have attempted to develop direct regression relationships between indoor 

dust and one or more of the underlying contributing media. For example, von Lindern et al. 

(2003) developed a structured equation model relating the log-transformed indoor dust Pb and 

outdoor soil/dust (community-wide and neighborhood-wide averages) and air concentrations. 

While the resulting correlations were highly significant, outdoor soil/dust and air contributions 

only accounted for approximately 20 percent of the indoor dust Pb variance. This result suggests 

high house-to-house variability that is related to other confounding variables (cleaning habits, 

carpet versus hard floor, parental occupation, etc.) rather than the media concentrations 

G-4 
themselves. In the absence of regression relationships, other studies have provided 

measurements of a combination of indoor dust, outdoor soil/dust, and air central tendencies in 

urban or point-source environments. Again, both the regression study and the relative indoor 

dust-outdoor soil/dust-air measurements provide a framework for understanding the 

contributions of the underlying sources to Pb in indoor dust, but these data can be applied only 

within the parameter space they define. 

Physically-based mechanistic models offer a potential advantage over regression models 

or empirical observations because they potentially can be used across a wider range of parameter 

values, provided the inputs are selected carefully. No studies were identified that have attempted 

to build a fully mechanistic Pb indoor dust model that simultaneously simulates the contribution 

to Pb indoor dust from ambient air, outdoor soil/dust, and paint to indoor air and indoor floor 

dust Pb levels. However, mass-balance models are available that model the infiltration of 

ambient air into the indoor environment, including the loss of particles through deposition (e.g., 

Ferro et al., 2004; Nazaroff, 2004; Thatcher and Layton, 1995). These mass-balance models 

have been used to infer air exchange rates (the rate at which outdoor air infiltrates the indoor 

environment), penetration efficiencies (the fraction of particulate material that enters the indoor 

environment in a given size class), deposition rates, and resuspension rates for generic particles 

of given size ranges from measured indoor and ambient concentrations. These models may be 

applied to Pb indoor dust in so far as the assumptions made in the modeling studies are relevant 

to particles containing Pb. 

Typically, authors have measured outdoor soil/dust, indoor dust, and ambient air 

contaminant concentrations at a single home, assuming that the dominant influences on indoor 

dust derive from the media in the immediate vicinity. However, some attempts have been made 

to explore the spatial footprint across which media may influence indoor dust. For example, von 

Lindern et al. (2003) calculated correlation coefficients between indoor dust and outdoor 

soil/dust concentrations of Pb averaged over the yard, averaged over the neighborhood (defined 

as within 200 foot [ft]), and averaged over the community (an entire town) in a remediation zone 

near the Bunker Hill Superfund site. In general, indoor dust was most strongly correlated with 

community-level outdoor soil/dust averages, indicating that outdoor soil/dust from a wide spatial 

footprint affects indoor dust levels at a single location. This observation may reflect the fact that 

outdoor soil/dust is tracked from wider areas than those adjacent to a home or that transport of 

airborne outdoor soil/dust particles occurs across large distances. 

In addition to spatial variations in indoor dust concentrations, Pb in indoor dust will also 

vary temporally, particularly when remediation practices are used to reduce media (outdoor 

G-5 
soil/dust or indoor dust) concentrations or when intervention occurs to educate home owners of 

the dangers of Pb exposure. Hilts (2003) measured the changes in air, outdoor soil/dust, and 

indoor dust concentrations after emissions reduction efforts at a Pb smelter in Trail, British 

Columbia. Air and outdoor soil Pb concentrations both decreased (air from 1.1 μg/m3

 to 0.03 

μg/m3

 and soil from 844 parts per million [ppm] to 750 ppm), and indoor dust concentrations 

were observed to decrease as well (758 ppm to 580 ppm) from 1996 to 1999. In addition, von 

Lindern (2003) traced the changes in soil and the concurrent changes in indoor dust after soil 

remediation at the Bunker Hill smelter site. Geometric mean (GM) outdoor soil/dust 

concentrations decreased from 1715 to 1507 ppm, and GM indoor dust concentrations also 

decreased from 1435 to 897 ppm. In addition to changes in indoor dust due to intervention, 

normal seasonal fluctuations in indoor dust are expected; Laidlaw et al. (2005) showed that 

fluctuations in humidity and wind speed can be associated with changes in the mobilization of 

Pb-containing outdoor soil/dust into the air. These changes were subsequently found to be 

associated with changes in PbB concentrations. Thus, climatic variables may affect the amount 

of Pb contained in the ambient air environment and the amount of Pb that subsequently infiltrates 

the indoor environment. 

Although indoor dust Pb concentrations are known to depend on ambient air, outdoor 

soil/dust, and Pb paints, a high degree of uncertainty surrounds the physical processes that 

govern this dependence. In particular, the importance of tracking outdoor soil/dust into a home 

as a source of Pb contamination is poorly constrained by lack of studies in the literature. The 

accumulation of outdoor soil/dust particles on doormats has been measured in several studies 

(Thatcher and Layton, 1995; von Lindern et al., 2003), and these studies found similar overall 

particulate matter accumulation rates in very different environments (urban versus rural). 

However, little information is available about the relative amount that collects on a doormat 

versus the amount that is subsequently tracked throughout the house. Also, the amount of 

tracked dirt highly depends on the type of floor (hard floor or carpet), with carpeted sources 

collecting more tracked material. The contribution of paint flaking is also poorly characterized. 

Pb paints can have widely variable Pb concentrations, and in general the relative contribution of 

paint to indoor dust Pb loading is the most variable among outdoor soil/dust, air, and paint 

(Adgate et al., 1998). Finally, other practices in the home (e.g., cleaning practices), occupation, 

socio-economic status, and other climatic variables (e.g., humidity, wind speed) tend to confound 

the relationship between these media concentrations and the total Pb indoor dust, implying that 

indoor dust concentrations will vary substantially in homes exposed to the exact same media 

concentrations. 

G-6 
 Because of the complex relationship among Pb in air, outdoor soil/dust, paint, and indoor 

dust, regression models based on observed simultaneous measurements are useful tools in 

predicting indoor dust Pb concentrations. However, these models will be relevant only if the 

underlying study from which they were developed included homes similar to those for which 

indoor dust Pb concentrations need to be modeled. Also, mechanistic models may be useful 

tools in modeling the accumulation of dust in the indoor environment; in particular, the air 

component has been relatively well-explored. However, as noted above, the processes governing 

the contribution of paint and outdoor soil/dust to indoor dust have not been extensively studied in 

the literature. Also, mechanistic models based on central tendency household and exposure 

concentration values will not capture any household to household dust concentration variability 

stemming from atypical household practices or exposure concentrations. For these reasons, the 

various case studies rely on different indoor dust prediction techniques, depending on the 

underlying data available in the literature and the extent to which a mechanistic model can be 

reasonably applied. The following sections describe efforts to build indoor dust prediction 

models for each case study. 

G.3. FOUNDATION FOR THE GENERAL URBAN CASE STUDY INDOOR DUST 

ALGORITHMS 

G.3.1. Investigation of an Empirical Model for the General Urban Case Study 

Attempts were made to generate an empirical model relating indoor dust Pb 

concentrations or loadings to measurements of ambient air Pb concentrations, outdoor soil/dust 

concentrations, and indoor paint concentrations for the general urban case study. Two data sets 

were identified as candidates for this activity. The first was a study conducted by Lanphear et al. 

(1996) in Rochester, New York. Data were provided for 205 children with simultaneous 

measurements of indoor dust Pb loadings (in multiple areas of the house), indoor dust 

concentrations (in multiple areas of the house), outdoor soil/dust concentrations (in both the play 

yard and the dripline), and interior paint concentrations (in the form of X-ray fluorescence [XRF] 

measurements), along with PbB measurements and potentially confounding socioeconomic and 

other variables. The second data set included data from the HUD National Survey of LeadBased Paint in Housing (USEPA, 1995), which provided indoor dust Pb concentrations and 

loadings and measurements of outdoor soil/dust and Pb paint for a sample of homes chosen to be 

representative of the national population. 

G.3.1.1. Lanphear et al. 1996 Data Set for Rochester, New York 

The Lanphear et al. (1996) study data (hereafter referred to as the “Rochester data”) were 

collected in an urban environment and contain nearly all the primary variables of interest except 

G-7 
for site-specific ambient air Pb concentrations, which are an integral part of the risk assessment. 

Attempts were made to find an appropriate spatial distribution of ambient air concentrations to 

use with this data set to generate relationships between ambient air, outdoor soil/dust, exterior or 

interior paint, and indoor dust, as described below. Unfortunately, no such appropriate spatial 

distribution could be identified. While this data gap handicapped the ability to develop an 

empirical model relating indoor dust Pb levels to ambient air Pb levels, the data set was analyzed 

to examine relationships between indoor dust Pb and the other key variables that could be 

applied to the general urban case study. 

The data set was prepared to include both arithmetic and GM values for the entire house 

(i.e., averaging across the different sampling rooms in the house: living room, bedroom, play 

yard, and entry way) to provide single indoor dust Pb loading and concentration estimates for 

each child’s residence (205 children in all). The play yard and perimeter outdoor soil 

concentrations, which typically differed by an order of magnitude, were analyzed separately to 

determine which was most strongly correlated with the indoor dust concentrations. 

To approximate the air Pb concentrations, data from three U.S. EPA Air Quality System 

(AQS) air monitors were available that were within 50 km of the study homes (USEPA, 2007). 

The first monitor, monitor 360550014 (Monitor 1), measured Pb in total suspended particulate 

matter (TSP) and is an average of 37 km from the homes included in the study. The other two 

monitors, monitors 360556001 (Monitor 2) and 360551007 (Monitor 3), are PM2.5 monitors (for 

which the Pb concentration is available) and are located an average of 2.8 km and 4.5 km from 

the homes in the study, respectively. In general, the Pb measurements from the TSP monitors 

are an order of magnitude higher than those from the PM2.5 monitors. Data provided for Monitor 

1 spanned January 1993 to June 1996, which includes the time the Rochester data were collected. 

Monitor 2 data spanned May 2004 to November 2006 and Monitor 3 data spanned January 2001 

to March 2004. All three monitors have distinct latitude and longitude coordinates. 

Because the TSP and PM2.5 monitors measure the Pb content in different particle size 

ranges, all three monitors could not be combined. Indoor dust Pb concentrations likely reflect 

the total Pb content of atmospheric particles, rather than a specific size range, since all size 

ranges appear to penetrate at least to some degree into the indoor environment ((e.g., Layton and 

Thatcher, 1995). However, in order to create a spatial distribution of air Pb concentrations that 

correspond to the study homes, at least two monitors were needed, implying the PM2.5 monitors 

had to be used as a proxy for total Pb content in the ambient air. To create this spatial 

distribution, the air concentrations at each of the PM2.5 monitor locations were averaged over the 

longest possible measuring time that included full annual cycles (the data were averaged only 

G-8 
over full years to avoid any artificial variations due to seasonal cycles). The Rochester data 

included zip code information, and these zip codes were converted to latitudes and longitudes 

using the centroid for each zip code area. Then, the distances between the two PM2.5 monitors 

and the zip code of the home in question were calculated, and the two monitor concentrations 

were distance-weighted-averaged. Unfortunately, the two monitors did not take measurements 

during overlapping time periods, so this analysis implicitly assumes that no major emission or 

climatological shifts occurred between the two time periods. These air data were then combined 

with the indoor dust and outdoor soil/dust data in the Rochester data to build a regression model. 

In doing this, however, it was recognized that there were limitations of the spatial coverage for 

this measurement and that the PM2.5-Pb underestimates Pb that may contribute to indoor dust Pb. 

To investigate the correlations among the different study variables, correlation 

coefficients between both the arithmetic and GM of indoor dust concentrations measured on the 

floor and other variables in the data set were calculated. The following variables were explored: 

the exterior XRF paint concentrations, the interior XRF paint concentrations, the play yard soil 

concentrations, the house perimeter soil concentrations, the first-draw water concentrations, 

exterior dust concentrations, porch concentrations, arithmetic and GM window sill 

concentrations, arithmetic and GM window well concentrations, two hand-wipe samples from 

each child, air concentrations, and housing vintage. 

Of these variables, only those shown in Exhibit G-1 were significantly correlated with the 

GM indoor floor dust concentrations, where significance was set at p<0.05. The number of 

points used in each correlation (N) is different for each variable due to missing values. In 

general, the arithmetic means tended to have weaker correlations, so the GM across rooms in 

each house was selected as the primary indoor dust metric. Play yard outdoor soil/dust is weakly 

correlated with indoor dust, although house perimeter soil is not significantly correlated. All 

correlation coefficients are weak, suggesting that variability in other house-to-house practices 

significantly influence the indoor dust load. Correlations (r) between the natural log (ln) of the 

dust concentrations and each of these variables were also calculated, along with correlations 

between the dust concentrations and the natural log of each variable. These calculations were 

designed to identify non-linear relationships between the variables, but the correlations did not 

significantly improve under either of these efforts. 

G-9 
Exhibit G-1. Correlation Coefficients, Number of Samples, and p Values for Variables 

Significantly Correlated with Indoor Dust 

GM of 

Window Sill 

Pb 

Concentration 

(μg/g) 

Exterior Paint 

XRF Reading 

(milligram 

[mg] per 

square foot 

[ft2]) 

Average 

Interior Paint 

XRF Reading 

(mg/ft2) 

Average Play 

Yard Soil 

Concentration, 

ppm 

Exterior Dust 

Concentration 

(μg/g) 

r=0.314 r=0.2498 r=0.2808 r=0.252 r=0.1724 

N=194 N=200 N=204 N=86 N=143 

p=<0.0005 p=<0.0005 P=<0.0005 p=.019 p=.040 

Porch Dust 

Concentration 

(μg/g) 

Window Well 

Dust 

Concentration 

(μg/g) 

Hand Wipe 1 

(μg) 

Hand Wipe 2 

(μg) 

Housing 

Vintage 

(year) 

r=0.1944 r=0.1698 r=0.2199 r=0.1703 r=-0.1566 

N=122 N=187 N=196 N=195 N=204 

p=.032 p=.020 p=.002 p=.017 p=.025 

As expected given the inadequate characterization of airborne Pb near the study residences, no 

correlation was found between air Pb concentrations and indoor dust Pb concentrations. 

The most significant correlations were found between the window sill Pb concentrations, 

which likely have similar sources to the indoor dust concentrations, and the exterior and interior 

paint XRF measurements. Outdoor soil is also significantly correlated with indoor dust 

concentration, although the low correlation coefficient suggests limited predictive power. The 

fact that paint correlations with indoor dust Pb concentration are significant suggests that paint is 

playing a major role in determining indoor dust concentrations. 

To understand why paint may be contributing so strongly to indoor dust, Exhibit G-2 

compares the percentage of study homes in each housing vintage in the Rochester data compared 

with the HUD National Survey. More than 85 percent of the homes are in the oldest vintage in 

the Rochester data, compared with only 27 percent in the HUD survey. These older homes have 

a higher tendency to contain Pb paint and the indoor dust Pb loadings may retain a larger paintderived fraction than in a typical urban environment. Because (1) Pb in ambient air near study 

residences could not be adequately characterized; (2) the correlations among outdoor soil/dust, 

paint, and indoor dust are weak; and (3) because the Rochester data are likely influenced more 

strongly by the presence of Pb paint than in typical urban environments, no empirically derived 

model was obtained from this data set. 

G-10 
Exhibit G-2. Comparison of Housing Vintage Percentages in the Rochester Data and the 

HUD National Survey 

Percent of Homes in Vintage Category 

100% 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

Rochester Data 

HUD National Survey 

Pre 1940 1940 to 1959 1960 to 1979 

Housing Vintage 

G.3.1.2. HUD National Survey Data Set 

Data from the HUD National Survey of Lead in Housing (USEPA, 1995) were also 

evaluated to examine relationships among ambient air Pb concentrations, outdoor soil/dust and 

indoor dust Pb concentrations, and indoor dust Pb loading. The methods and results of this 

analysis are described in detail in Attachment G-1 and are not discussed further here. 

G.3.2. Development of a Mechanistic Air Model for the General Urban Case Study 

G.3.2.1. Physical Processes and Derivation of an Equation for Steady-state Pb Floor 

Loading 

The mechanistic model captures the physical transfer of Pb from one medium to another, 

rather than capturing the interaction between the media in a statistical relationship. As discussed 

in Section G.2, the accumulation of indoor dust depends on the relative contributions of outdoor 

ambient air, outdoor soil/dust, and Pb paint to the interior environment. The tracking of outdoor 

soil/dust and the flaking/chipping of interior Pb paint are both highly variable and poorly studied 

processes. However, the infiltration of outdoor ambient air into the indoor environment and the 

subsequent settling of particles have been extensively studied and have been characterized in 

mass-balance physical models (e.g., Ferro et al., 2004; Nazaroff, 2004; Thatcher and Layton, 

G-11 
 

1995). For this reason, a mechanistic model was derived for the contribution of Pb in outdoor 

ambient air to Pb in dust in the interior environment; then, a non-air component was empirically 

derived, as described in Section G.3.3. 

Exhibit G-3 shows a schematic of the mechanistic indoor dust model. Two separate Pb 

“compartments” accumulate Pb over time: the indoor air Pb compartment and the indoor dust Pb 

compartment. Mass balance dictates that in both of these compartments, the change in Pb mass 

over time depends on the flux of Pb mass into the compartment minus the flux of Pb out of the 

compartment: 

d[Mass] 

= Flux of Mass In − Flux of Mass Out dt 

where: 

 d[Mass]/dt = change over time of the Pb mass (μg/hour [h]) 

Flux of Mass In = flux of Pb into the compartment (μg/h) 

Flux of Mass Out = flux of Pb out of the compartment (μg/h) 

Exhibit G-3. Mechanistic Indoor Dust Model Schematic 

For the indoor air compartment (INAIR), the fluxes include penetration of air and 

particles from outdoors, ventilation of indoor air back to the outdoor environment, deposition of 

Pb out of the air, resuspension of accumulated Pb on the floor back into the air, and filtration 

associated with re-circulating air due to the presence of an HVAC system: 

dINAIR = Penetration Flux −Ventilation Flux − Deposition Flux + Re - suspension Flux − Filtration Flux

dt 

where: 

G-12 
 

 

 

 

 

 

dINAIR/dt = change in time of the indoor air Pb mass (μg/h) 

 Penetration Flux = penetration of air containing particles from outdoors (μg/h)

 Ventilation Flux = ventilation of indoor air back to the outdoor environment (μg/h) 

Deposition Flux = deposition of Pb out of the air (μg/h) 

 Resuspension Flux = resuspension of accumulated Pb on the floor back into the air 

(μg/h) 

 Filtration Flux = filtration associated with re-circulating air due to the presence of 

an HVAC system (μg/h) 

Each flux is parameterized as the mass of the "donor" compartment multiplied by the rate 

(expressed in reciprocal time) of the physical exchange process. In some cases, an efficiency 

factor is also included to account for any filtration of Pb associated with the process: 

Penetration Flux = AER × P × PbAIR ×V 

where: 

 Penetration Flux = penetration of air containing particles from outdoors (μg/h) 

AER = air exchange rate (h-1) 

P = penetration efficiency (unitless)

 PbAIR = concentration of Pb in ambient air (μg/m3) 

V = volume of the house (m3) 

Because the air exchange rate (AER) specifies the number of times the indoor air is 

replaced by outdoor air in a given hour, it represents both the rate of penetration in and 

ventilation out. The ventilation flux out of the house is equal to the AER multiplied by the 

indoor mass of Pb in air (INAIR): 

Ventilation Flux = AER × INAIR 

where: 

 Ventilation Flux = ventilation of indoor air back to the outdoor environment (μg/h)

 AER = air exchange rate (h-1) 

INAIR = indoor mass of Pb in air (μg) 

G-13 
 

The deposition flux (Deposition Flux) is defined as the amount of Pb in the air times a 

deposition rate: 

Deposition Flux = D × INAIR 

where: 

 Deposition Flux = deposition of Pb out of the air (μg/h) 

D = deposition rate (h-1) 

INAIR = indoor mass of Pb in air (μg) 

For resuspension, the amount of resuspended material depends on the total available mass 

of Pb on the floor. Because the current model only traces air-derived floor Pb (and other sources 

of Pb not transported via outdoor to indoor air), resuspension cannot be accurately modeled. In 

addition, resuspension rates have not been extensively studied in field studies. Thus, similar to 

other mass balance models, resuspension is neglected in the current mechanistic model (Riley et 

al., 2002); this assumption will tend to underestimate the Pb in the air compartment and 

overestimate the Pb in the floor compartment. 

Finally, the presence of an HVAC system will tend to re-circulate indoor air, passing the 

air through a filter with each circulation. This system will tend to remove Pb from the indoor 

environment (both in the air and on the floor). Because many urban families do not have HVAC 

systems and because the circulation rate and filtration efficiency of such systems has not been 

comprehensively described in the literature, removal of Pb during recirculation is not included in 

the mechanistic model. 

So, using the penetration, ventilation, and deposition fluxes, the equation for the change 

in time of the indoor air Pb mass is: 

dINAIR = AER × P × PbAIR ×V − AER × INAIR − D × INAIR (Equation 1) dt 

where: 

dINAIR/dt = change in time of the indoor air Pb mass (μg/h) 

AER = air exchange rate (hour1) 

P = penetration efficiency (unitless)

 PbAIR = concentration of Pb in ambient air (μg/m3) 

V = volume of the house (m3)

 D = deposition rate (h-1) 

INAIR = indoor mass of Pb in air (μg) 

G-14 
 

 

 

 

 

For the indoor floor dust compartment (FLOOR), the fluxes include deposition of Pb 

from the air onto the floor, resuspension of Pb from the floor into the air, and removal of Pb due 

to routine cleaning: 

dFLOOR = Deposition Flux − Resuspension Flux − Cleaning Flux dt 

where: 

dFLOOR/dt = change in time of the indoor floor dust Pb mass (μg/h) 

 Deposition Flux = deposition of Pb out of the air onto the floor (μg/h)

 Resuspension Flux = resuspension of Pb from the floor into the air (μg/h)

 Cleaning Flux = removal of Pb due to routine cleaning (μg/h) 

The deposition flux (Deposition Flux) retains the same form as in the INAIR equation, 

and the resuspension flux (Resuspension Flux) is again neglected. The cleaning flux (Cleaning 

Flux) is parameterized assuming a cleaning efficiency (CE) and cleaning frequency (CF) and 

multiplying these by the mass of Pb on the floor (FLOOR): 

Cleaning Flux = CE ×CF × FLOOR 

where: 

 Cleaning Flux = removal of Pb due to routine cleaning (μg/h)

 CE = cleaning efficiency (unitless) 

CF = cleaning frequency (cleanings/h) 

FLOOR = mass of Pb on the floor (μg) 

In this parameterization, discrete cleaning episodes occurring with a given frequency are 

assumed to be captured by assuming continuous cleaning with the same frequency (rate) and 

efficiency. Combining the floor fluxes then gives: 

G-15 
 

dFLOOR = D × INAIR − CE ×CF × FLOOR (Equation 2) dt 

where: 

dFLOOR/dt = change in time of the indoor floor dust Pb mass (μg/h) 

D = deposition rate (h-1) 

INAIR = indoor mass of Pb in air (μg) 

CE = cleaning efficiency (unitless) 

CF = cleaning frequency (cleanings/h) 

FLOOR = mass of Pb on the floor (μg) 

To obtain the steady-state solution for each compartment, the derivative terms are set to 

zero, so that nothing is changing in time. Using equations (1) and (2) and rearranging gives: 

(D + AER) × INAIR = AER × P × PbAIR ×V 

CE ×CF × FLOOR = D × INAIR 

The ambient air concentration (PbAIR), is known, so the upper equation can be solved for 

INAIR to give: 

AER × P ×V INAIR = × PbAIR 

(D + AER) 

Then, substituting into the second equation gives: 

G-16 
 

 

Thus, this equation yields the mass of Pb on the floor, and the Pb loading can be found by 

dividing by the floor area and noting that the house volume divided by the floor area is the 

ceiling height (H): 

D × AER × P × H FLOOR LOADING = × 0.09 × PbAIR (Equation 3) CE ×CF × (D + AER) 

where: 

 FLOOR LOADING = Pb loading on the floor (μg/ft2)

 D = deposition rate (h-1) 

AER = air exchange rate (h-1) 

P = penetration efficiency unitless) 

H = ceiling height (meter [m]) 

CE = cleaning efficiency (unitless) 

CF = cleaning frequency (cleanings/h) 

PbAIR = concentration of Pb in the ambient air (μg/m3) 

The 0.09 term is included in the equation to change the loading units from μg/m2

 to μg/ft2 

(where PbAIR is in μg/m3

). This final equation gives the floor Pb loading accumulated under 

steady-state conditions from air-derived sources, assuming that none of the underlying ambient 

air concentrations or process rates varies over time. In reality, the AER will vary seasonally 

(especially if windows are open), cleaning rates likely are not constant, and other rates may vary; 

in addition, several of the parameters (e.g., deposition rate and penetration efficiency) may vary 

by particle size. Thus, the steady-state solution represents the average floor loading if the inputs 

are selected to be representative of time-averaged and particle-size-averaged rates and 

concentrations. 

G.3.2.2. Input Values for the Mechanistic Model 

To implement the mechanistic model for the general urban case study, representative 

input parameters applicable to urban environments had to be specified. Exhibit G-4 gives the 

input parameter values chosen and the source of the values. 

G-17 
 

Exhibit G-4. Input Parameters Selected for the Mechanistic Model for Urban 

Environments 

Variable Variable 

Name Units Value Source 

D Deposition 

Rate h-1 1.11 (Layton and Thatcher, 1995) 

AER 

Air 

Exchange 

Rate 

h-1 0.5 (USEPA, 1997; Riley et al., 

2002; Vette et al., 2001) 

P Penetration 

Efficiency unitless 1 (Layton and Thatcher, 1995) 

H Ceiling 

Height m 2.44 (USEPA, 1997) 

CE Cleaning 

Efficiency unitless 0.25 (Battelle Memorial Institute, 1997) 

CF Cleaning 

Frequency cleanings/h 0.003 Professional Judgement 

The deposition rate (D) was set to 1.11 h-1. This value was derived from the only Pbspecific estimate of deposition velocity that was found in the literature, obtained from a massbalance modeling analysis of homes near a Pb smelter in Arnhem, Netherlands (Layton and 

Thatcher, 1995). The deposition velocity was converted to a deposition rate by dividing the 

velocity by the assumed ceiling height (8 ft, or 2.44 m). This value tended to be within the range 

of literature values reported for generic particles of differing size distributions (e.g., Riley et al. 

(2002) Figure 3]: 0.04 to 7.2 h-1 for 0.1 to 10 micrometer [μm]; Vette et al. (2001) Figure 7: 0.5 

to 4 h-1 for 0.01 to 2 μm). 

The AER values were consistently reported to have central tendency values near 0.5 

exchanges per h (USEPA, 1997; Riley et al., 2002; Vette et al., 2001). For example, Table 17-10 

of the Exposure Factors Handbook (USEPA, 1997) indicates a GM near 0.5 for all regions of the 

country, with only the north central region having a somewhat lower AER (0.39). 

The penetration efficiency (P) has been modeled for particles of various size classes and 

has been measured in a few field studies to be less than one (e.g., Dockery D.W. and Spengler 

J.D., 1981; Freed et al., 1983; Liu and Nazaroff, 2001). However, unlike the above studies, in a 

field study that simultaneously controlled for penetration and deposition, the penetration 

efficiency (P) was found to be near 1 for all size classes (Thatcher and Layton, 1995); a similar 

result was also reported for PM2.5 for homes in California (Ozkaynak et al., 1996). Thus, the 

penetration efficiency (P) was set to 1 for the mechanistic model. The ceiling height (H) was set 

to 8 ft (2.44 m) based on the typical ceiling height in the United States (USEPA, 1997). 

G-18 
 

 

 

The two cleaning variables (efficiency and frequency) likely represent the most poorly 

characterized parameters. Cleaning efficiency (CE) has been found to vary according to the type 

of flooring (carpeting versus hard floor) and the total amount of Pb on the floor (lower 

efficiencies for very low Pb loadings, due to electrostatic forces attracting the particles to the 

floor or burial of Pb deep into carpet, and higher efficiencies for higher Pb loadings). The 

Environmental Field Sampling Study (EFSS), Volume I: Table 8D-3 (Battelle Memorial 

Institute, 1997) provides pre- and post-cleaning Pb loading estimates from a house with hard 

floors that was subject to a renovation activity and post-activity cleaning. Thus, these estimates 

likely are higher than routine cleaning efficiencies in a house where no renovation (and no 

associated elevated Pb loading ) has occurred. The selected value for CE (25 percent removal 

with each cleaning) is typical of the cases in the lowest Pb loading range in the study. These 

values are similar to values found by Ewers et al. (1994) and Clemson Environmental 

Technologies Laboratory (2001) for cleaning efficiencies on a carpeted floor after a renovation 

activity and after three previous cleaning iterations (so that much of the renovation-related Pb 

loading had already been removed and the cleaning was similar to a routine cleaning). 

The cleaning frequency (CF) is expected to be highly variable from household to 

household, and no information could be located in the literature for urban houses. A 

representative value of one cleaning every two weeks (0.003 cleanings per h) was selected using 

professional judgment. 

Based on these inputs, the final equation for the steady-state air-derived indoor dust Pb 

loading is: 

FLOOR LOADING =104.2× PbAIR (Equation 4) 

where: 

 FLOOR LOADING = Pb loading on the floor (μg/ft2)

 PbAIR = concentration of Pb in the ambient air (μg/m3

)
 

This equation is meant to capture all Pb mass that falls on the floor from air-derived 

sources, so it is more consistent with wipe-based Pb loading measurements rather than vacuumbased Pb loading measurements. This steady-state answer applies to the extent to which the 

inputs can be assumed to represent time averages. With the given inputs, solving this equation 

dynamically indicates that the modeled system will require one year to reach steady-state 

conditions (although the modeled floor Pb loading is within 90 percent of the steady-state 

solution after 129 days). 

G-19 
 

 

G.3.3. Combining the Mechanistic Air Model with Empirical Data to Derive an Indoor 

Dust Pb Loading Estimate from Other Sources 

Equation (4) gives the estimated steady-state indoor dust Pb loading from recent airderived sources. This value must be combined with another estimate of indoor dust Pb loading 

that incorporates all other sources of Pb to indoor dust (e.g, indoor paint, outdoor soil/dust and 

additional sources including historical air). To do so, the median indoor dust Pb loading value 

from the HUD National Survey of Lead-Based Paint in Housing (USEPA, 1995) was selected as 

a representative total indoor dust Pb loading. The HUD survey selected study homes such that 

the overall survey estimates are weighed by population to be nationally representative. Although 

the survey does not focus on urban homes, these homes are likely dominating the signal because 

urban areas represent the population centers in the country. The median wipe indoor dust Pb 

loading in the survey was 5.32 μg/ft2

. 

In order to derive the “other” component from the HUD median Pb loading value, the 

associated recent air component was estimated using an air Pb concentration derived to 

correspond to the HUD survey indoor dust survey. The HUD survey was conducted during late 

1989 and early 1990. To derive a representative air Pb concentration, data for all U.S. EPA AQS 

air monitors operating in 1989 and 1990 were averaged into a single air concentration estimate of 

0.04 μg/m3

 (USEPA, 2007). This average was calculated separately using all monitors and using 

only those monitors in urban locations, but the differences in the concentrations estimated by the 

two methods was minimal; so the all monitors value was used. This air value was then 

substituted into the mechanistic model to give a recent air-derived Pb loading of 4.17 μg/ft2

. By 

subtracting this recent air-derived portion from the total background Pb loading, a Pb indoor dust 

loading estimate of 1.15 μg/ft2

 was derived for other source contributions. Thus, the final hybrid 

mechanistic-empirical model equation is: 

TOTAL FLOOR LOADING = 104.2 × PbAIR +1.15 (Equation 5) 

where: 

TOTAL FLOOR LOADING = total Pb loading on the floor (μg/ft2)

 PbAIR = concentration of Pb in the ambient air (μg/m3

) 

The HUD survey was selected because it is the same data set that was used to derive the 

indoor dust Pb loading to indoor dust Pb concentration conversion equation (see Section G.3.4). 

Because the HUD survey was conducted in 1989 and 1990, it has the potential to introduce an 

upward bias in estimating contributions from sources other than recent air to indoor dust Pb 

levels for the current housing stock. Reductions in Pb paint and outdoor soil/dust Pb 

G-20 
concentrations may have occurred since 1990, due to education about the dangers of indoor Pb 

exposure and because some of the more heavily contaminated older homes have been 

demolished. Furthermore, household habits may have changed (e.g., cleaning behavior) due to 

increased education. 

The picture is less clear for Pb in outdoor soil/dust. As discussed above, in the absence 

of direct remediation, the half-life of Pb in outdoor soil may be up to 700 years (Laidlaw et al., 

2005), suggesting that the outdoor soil levels probably have not dropped significantly since the 

HUD survey. One last limitation of the HUD survey is that it focuses on homes built before 

1980 and does not include any built between 1990 and the present. However, because the focus 

of the hybrid model is on urban homes that tend to be of earlier vintage, using the HUD survey 

data as the basis for estimating background indoor dust loading allows for reasonable estimates 

of overall indoor dust Pb loading to be generated that are typical of current urban housing stock. 

This indoor dust estimate is applicable in “typical” urban environments with outdoor soil and 

paint contributions to indoor dust Pb loading which do not differ strongly from those observed in 

the HUD survey data. For situations with high paint or outdoor soil signals, or atypical 

household habits, the model may not adequately capture the total indoor dust Pb loadings. 

G.3.4. Converting Indoor Dust Pb Loadings to Indoor Dust Pb Concentrations 

Once the indoor dust Pb loadings are calculated, indoor dust concentrations must be 

estimated from these loadings for input into the PbB model. To do so, a regression equation was 

developed based on empirical data. Data on the relationship between indoor dust Pb loading and 

concentration were gathered as part of the HUD National Survey of Lead-Based Paint in 

Housing (USEPA, 1995). 

The equation for the concentration to loading regression was found to be: 

ln(PbCONC) = 4.92 + 0.52 × ln(PbVAC) 

where: 

 PbCONC = indoor dust concentration (μg/g)

 PbVAC = vacuum indoor dust Pb loading (μg/ft2

) 

For more information on the derivation of this equation, see Attachment G-1. Because this 

model was derived using log-transformed variables, small changes in the slope or intercept 

transfer to large changes in the predicted dust concentration; thus, this conversion introduces 

considerable uncertainty into the dust model. 

G-21 
 

 

G.3.4.1. Estimating Vacuum Pb Loadings from Wipe Pb Loadings 

The equation that converts dust Pb loading to dust Pb concentration (see Section G.3.4) 

requires that the dust Pb loading estimates be for vacuum Pb loading . This section describes the 

equation used to convert wipe Pb loadings (from the hybrid model) to vacuum Pb loadings. To 

do so, the following equation developed to convert wipe samples to blue nozzle vacuum samples 

for hard floors is used (USEPA, 1997): 

0.921 PbVAC = 0.185× PbWIPE 

where: 

PbVAC = vacuum indoor dust Pb loading (μg/ft2)

 PbWIPE = indoor wipe Pb loading (μg/ft2

) 

G.3.5. Specification of the General Urban Case Study Indoor Dust Algorithms 

Converting the hybrid model wipe Pb loading to vacuum Pb loadings and using the 

conversion equation to convert from Pb loading to concentration gives the final form of the 

hybrid model for the general urban case study: 

0.931 PbDUST = EXP[4.92 + 0.52 × ln(0.185× (104.2 × PbAIR +1.15) )] (Equation 6) 

where: 

PbDUST = indoor dust Pb loading (μg/ft2)

 PbAIR = concentration of Pb in the ambient air (μg/m3

) 

In contrast, the air-only regression-based model is: 

PbDUST = 60 + 844 × PbAIR (Equation 7) 

where: 

PbDUST = indoor dust Pb loading (μg/ft2) 

PbAIR = concentration of Pb in the ambient air (μg/m3) 

Exhibit G-5 shows a comparison of indoor dust Pb concentrations estimated using the 

hybrid model and the air-only regression based model for a given ambient air Pb concentration. 

The two models have similar intercepts at zero air Pb concentrations. The air-only regressionbased model is linear and tends to predict higher indoor dust Pb concentration than the hybrid 

model for air Pb concentrations between 0 and 0.3 μg/m3

. The average difference between the 

models in this range of air concentrations is 20 percent. Above 0.3 μg/m3

, the slope of the hybrid 

G-22 
model decreases, and the air-only regression-based model then predicts higher indoor dust Pb 

concentrations for a given ambient air level (with an average difference of 61 percent between 

0.3 and 1.5 μg/m3

). 

The hybrid mechanistic-empirical model and the air-only regression-based model 

represent two distinct options for converting ambient air concentrations to indoor dust 

concentrations, one that is strictly empirical and one that combines empirical background 

measurement with a mechanistic air-dust model. Indoor dust calculations are performed using 

both models for the general urban case study to allow for the characterization of uncertainty 

associated with the selection of the indoor dust modeling approach. 

Exhibit G-5. Comparison of the Hybrid Mechanistic-empirical Model and the Air-only 

Regression-based Model Indoor Dust Pb Concentration Predictions for a Given Ambient 

Air Pb Concentration 

1000
 

900
 

800
 

700
 

600
 

500
 

400
 

300
 

200
 

100
 

0 

Hybrid Mechanistic ­

Empirical Model 

Air-Only Regression-Based 

Model 

0 0.2 0.4 0.6 0.8 1 

Ambient Air Pb Concentration (μg/m3

) 

G.3.6. Performance Evaluation of the General Urban Case Study Indoor Dust Models 

Various data sources are available to evaluate the performance of the mechanistic portion 

of the model, the full hybrid model, and the air-only regression-based model in urban or smelter 

environments. Evaluations that have been performed are shown in Exhibit G-6. In general, no 

data set provides the ideal set of data for performance evaluation, which would include Indoor Dust Pb Concentration ( 

μg/g) 

G-23 
simultaneous measurements of ambient air concentrations, indoor air concentrations, indoor dust 

wipe Pb loadings, indoor dust vacuum Pb loadings, and indoor dust Pb concentrations in multiple 

houses in multiple urban environments. However, the available data do provide insights into the 

performance of the models in specific urban environments. 

G-24 
Exhibit G-6. Summary of Performance Evaluation Performed on General Urban Case Study Mo

Study 

Location 

and Year of 

Study 

Study Parameters 

Relevant to Model 

Evaluation 

Evaluation Performed Results of Evaluation Conc

Air-only Regression-based Model Deposition Fluxes 

Caravanos et 

al., 2006 

Manhattan, 

New York 

City, New 

York; 2003 

to 2005 

Median Pb deposition 

flux on a glass plate near 

a window open 1 inch 

(in); Upper limit of 

deposition flux on a glass 

plate near a closed 

window. Glass plates 

were located in a 

stairwell with no Pb paint 

Compare Pb deposition 

fluxes to weekly deposition 

flux in the mechanistic aironly model; mechanistic 

model is run without 

cleaning and at an air 

exchange rate of 0.5 

exchanges per h 

(appropriate for a closedwindow environment); 

Caravanos, window open 

1 in: 4.8 μg/ft2/week; 

Caravanos, window 

closed: < 1.6 μg/ft2/week; 

Mechanistic model: 0.35 

μg/ft2/week. 

The mechanistic mfluxes lower than ththe window open bthe case with a win

and no foot traffic, so the 

deposition is due to air 

contributions only. 

ambient air is assumed to be 

consistent with the 2005 

national value of 0.025 

μg/m3 . 

Ratio of Indoor Air and Ambient Air Pb Concentrations 

Roy et al., 

2003 

NHEXAS 

Region 5: 

Minnesota, 

Wisconsin, 

Michigan, 

Illinois, 

Indiana, and 

Ohio 

25th, Median, and 75th 

percentile indoor and 

ambient air Pb 

concentrations. 

Compare the ratio of indoor 

to ambient air 

concentrations in each 

percentile to the ratio in the 

air-only mechanistic model 

run with an air exchange 

rate of 0.5. 

Roy, 0.62, 0.73, 0.93 

(25th, Median, 75th 

percentile); Mechanistic 

Model: 0.31. 

Assuming that the 2and ambient conceto the same house median and 75th Pstudy indicates that ratio increases for iconcentrations. In tmodel, this ratio is cincreasing ambient The mechanistic mratios, potentially duresuspension. Alsopattern in each of thunknown; open winexchange rate and ambient air concen

G-25 
Exhibit G-6. Summary of Performance Evaluation Performed on General Urban Case Study Mo

Study 

Location 

and Year of 

Study 

Study Parameters 

Relevant to Model 

Evaluation 

Evaluation Performed Results of Evaluation Conc

Riley et al., 

2002 Modeled 

Modeled indoor air to 

ambient air 

concentrations created 

by combining empirical 

data and a mass balance 

model. 

Compare the range of 

predicted ratios with the aironly mechanistic model, 

where both models use the 

same air exchange rate. 

Riley: 0.2 to 0.8 for urban 

scenarios with typical 

ventilation (the range is 

for different particle size 

classes); Mechanistic 

Model: 0.31. 

The modeled indooconsistent with the mass balance modcloser to the modelcoarse mode particParticles less than than 10 μm tend to the Riley study. 

Percent Contribution of Air Pb to Indoor Dust Pb 

Adgate et al., 

1998 

Jersey City, 

New Jersey; 

1992 to 

1994 

Mean percent 

contribution from air, 

paint, and crustal 

materials to indoor dust; 

these are ascertained 

using isotopic ratios of 

multiple elements and 

assuming the indoor dust 

is comprised of Pb from 

these three sources only. 

Compare the percent 

contribution from air in the 

study to the percent 

contribution in the hybrid 

mechanistic-empirical model 

and in the air-only 

regression-based model, 

assuming an air 

concentration of 0.04 μg/m3 

(consistent with national air 

values in 1990). 

Adgate: 17.2 percent 

from air; Hybrid Model: 

78 percent from air; Aironly Regression-based 

Model: 36 percent from 

air. 

Both the hybrid moregression-based mpercentage air contassumed air conceseen in the Adgate percentages tend to decreasing ambienboth the air-only regand hybrid model Testimate of air contbiased low since thlargely < 1940 hompaint dust contributcontribution is also the outdoor soil/duswhich may also be historical presence these older homes. 

Loading to Concentration Regression 

Tang et al., 

2004 

Manhattan, 

New York 

City; 2002 

Mean vacuum Pb 

loadings and 

concentrations, 

assuming the nondetects are 0 (ND=0) and 

the non-detects are the 

detection limit (ND=DL). 

Compare the actual 

concentrations with the 

concentrations predicted 

using the loading to 

concentration regression 

equation with the mean Pb 

loadings. 

Vacuum Loadings: 0.5 

and 3 μg/ft2; Measured 

Indoor Dust Pb 

Concentrations: 130 and 

130 μg/g; Predicted 

Concentrations: 96 and 

243 μg/g (ND=0 and 

ND=DL). 

The indoor dust Pb predicted with the hand lower bounds, non-detects are eithdetection limit) boumeasured mean co

G-26 
Exhibit G-6. Summary of Performance Evaluation Performed on General Urban Case Study Mo

Study 

Location 

and Year of 

Study 

Study Parameters 

Relevant to Model 

Evaluation 

Evaluation Performed Results of Evaluation Conc

Roy et al., 

2003 

NHEXAS 

Region 5: 

Minnesota, 

Wisconsin, 

Michigan, 

Illinois, 

Indiana, and 

Ohio 

25th percentile, Median, 

and 75th percentile 

vacuum Pb loadings and 

wipe Pb concentrations. 

Compare the actual 

concentrations with the 

concentrations predicted 

using the Pb loading to 

concentration regression 

equation for each percentile. 

Vacuum Loadings: 4.77, 

10.44, 22.86 μg/ft2; 

Measured 

Concentrations: 68, 129, 

303 μg/g; Predicted 

Concentrations: 309, 

464, 697 μg/g (25th, 

Median, and 75th 

percentile). 

In general, the Pb lconcentration equaindoor dust concenmeasured values a(where the assump25th percentile Pb to the 25th percentisimilarly for the mepercentile). This reexposure media cothe relative importacontributing media and paint) are diffestudy compared to which the regressio

Predicted Indoor Dust Pb Loadings in the Hybrid Mechanistic-empirical Model 

Tang et al., 

2004 

Manhattan, 

New York 

City, New 

York; 2002 

Mean wipe and vacuum 

Pb loadings and mean 

indoor air Pb 

concentrations. 

Compare the predicted total 

Pb loadings from the hybrid 

model with the mean wipe 

and vacuum Pb loadings; 

the empirical model is run 

using indoor air 

concentrations provided in 

the study. Thus, the model 

equations are altered to 

solve for the floor Pb loading 

as a function of indoor air 

instead of ambient air. Two 

cases are analyzed: one 

assuming the Pb loading 

and indoor air Pb 

concentration non-detects 

are zero (ND=0) and one 

assuming the Pb loading 

and indoor air Pb 

concentration non-detects 

are the detection limits 

(ND=DL). 

Tang Indoor Air 

Concentrations: 0.002 

and 0.05; Tang Wipe 

Loadings: 0.5 and 1.0 

μg/ft2; Tang Vacuum 

Loadings: 0.9 and 3.0 

μg/ft2; Hybrid Model 

Loading: 1.8 and 17.8 

μg/ft2 (ND=0 and 

ND=DL). 

The hybrid model gshould be consistenloadings. The hybrhigher indoor dust Pobserved in both thcases, although the close the actual valwipe Pb loadings aloadings for the NDlikely includes high-outdoor soil/dust traPb levels may be loground-floor homesvacuum Pb loadingwipe Pb loadings, cexpectations. 

G-27 
Exhibit G-6. Summary of Performance Evaluation Performed on General Urban Case Study Mo

Study 

Location 

and Year of 

Study 

Study Parameters 

Relevant to Model 

Evaluation 

Evaluation Performed Results of Evaluation Conc

Roy et al., 

2003 

NHEXAS 

Region 5: 

Minnesota, 

Wisconsin, 

Michigan, 

Illinois, 

Indiana, and 

Ohio 

25th percentile, Median, 

and 75th percentile wipe 

Pb loadings and ambient 

air concentrations. 

Compare the hybrid model 

Pb loadings using the 

measured ambient air 

concentrations to the 

measured wipe Pb loadings. 

Roy Ambient Air: 

0.00599, 0.00863 

0.0123 μg/m3; Roy Wipe 

Loading: 1.5, 5.35, 17.73 

μg/ft2; Predicted Loading: 

1.77, 2.05, 2.43 μg/ft2 

(25th, Median, 75th 

percentile). 

The hybrid model oloading at low air cunderpredicts the Pair concentrations, 25th percentile air mcorrespond to the 2loadings (and similaand 75th percentileloading percentiles than average outdoand/or household-sto indoor dust, whicthe empirical portio(which assumes methe HUD survey). 

Lanphear et 

al., 1996 

Rochester, 

New York; 

1993 

GM indoor dust Pb 

loadings (wipe) averaged 

over all surfaces. 

Compare the predicted total 

Pb loadings from the hybrid 

model with the measured 

indoor dust Pb loading, 

assuming an ambient air Pb 

concentration of 0.04 

(nationally representative 

1990 value) 

Lanphear indoor Pb dust 

loading; 106 μg/ft2; 

hybrid model loading: 5.3 

μg/ft2 . 

The hybrid model gdust Pb level compmeasured Pb loadipercent of the study were constructed bsuggesting a very sis not captured in thLanphear value is hurban indoor dust Pother data sources, survey. 

G-28 
Exhibit G-6. Summary of Performance Evaluation Performed on General Urban Case Study Mo

Study 

Location 

and Year of 

Study 

Study Parameters 

Relevant to Model 

Evaluation 

Evaluation Performed Results of Evaluation Conc

Predicted Concentrations in the Hybrid Mechanistic-Empirical Model and Air-only Regression-based Model 

Tang et al., 

2004 

Manhattan, 

New York 

City, New 

York; 2002 

Mean Pb indoor dust 

concentrations and mean 

indoor air Pb 

concentrations. 

Compare the predicted 

concentrations using the 

hybrid model with the 

measured indoor air 

concentrations to the actual 

indoor dust concentrations; 

compare the air-only 

regression-based model 

predicted concentrations 

assuming that ambient air = 

indoor air to the measured 

indoor dust concentrations. 

Cases using the air 

concentrations assuming the 

non-detects are zero (ND=0) 

and the non-detects are the 

detection limit (ND=DL) are 

both analyzed. 

Tang Indoor Air 

Concentrations: 0.002 

and 0.05 μg/m3; Tang 

Indoor Dust 

Concentrations: 130 and 

130 μg/g; Hybrid Model 

Indoor Dust 

Concentrations: 76 and 

226 μg/g; Air-only 

Regression-based Model 

Indoor Dust 

Concentrations: 62 and 

102 μg/g (ND=0 and 

ND=DL). 

The hybrid model inconcentrations usinND=DL cases bounmeasured concentrair-only regression-both predict lower iconcentrations than The ambient air conequal to indoor air cair-only regression-ambient air concenthan likely actual valow bias to the air-omodel predictions in 

Rasmussen 

et al., 2001 

Ottawa, 

Canada; 

1993 

Arithmetic mean, GM, 

median, minimum, 

maximum, 90th 

percentile and 95th 

percentile indoor dust 

concentrations. 

Compare the hybrid model 

indoor dust concentrations 

and the air-only regressionbased model concentrations 

using an ambient air 

concentration consistent 

with national values in the 

United States in 1990 with 

the measured indoor dust 

concentrations. 

Rasmussen Indoor Dust 

Concentrations: 406, 

233, 222, 50, 3226, 969, 

1312 μg/g (arithmetic 

mean, GM, median, 

minimum, maximum, 

90th percentile, 95th 

percentile); Hybrid Model 

Indoor Dust 

Concentration: 128 μg/g; 

Air-only Regressionbased Model Indoor Dust 

Concentration: 94 μg/g. 

Assuming the ambiis representative of 

hybrid model and thbased model both tthe mean and mediconcentration. This the background Unconcentration used empirical portion of adequately capture concentrations in O

G-29 
Exhibit G-6. Summary of Performance Evaluation Performed on General Urban Case Study Mo

Study 

Location 

and Year of 

Study 

Study Parameters 

Relevant to Model 

Evaluation 

Evaluation Performed Results of Evaluation Conc

Hilts 2003 

Trail, British 

Columbia 

(smelter 

site); 1996 to 

1999 

GM ambient air 

concentrations and 

indoor dust 

concentrations in 1999 

(after the opening of a 

new Pb smelter, which 

reduced ambient air 

levels in the community). 

Compare the hybrid model 

indoor dust concentrations 

and the air-only regressionbased model concentrations 

using the measured ambient 

air concentrations to the 

measured Pb 

concentrations. 

Hilts Ambient Air 

Concentration: 0.3 

μg/m3; Hilts Measured 

Indoor Dust 

Concentration: 583 μg/g; 

Hybrid Model Indoor 

Dust Concentration: 301 

μg/g; Air-only 

Regression-based Model 

Indoor Dust 

Concentration: 313 μg/g. 

The ambient air conthis study is close twhere the hybrid mregression-based mgive very similar esconcentration. Bottend to somewhat uindoor dust concendue to the fact that soil/dust concentratthe smelter are playdetermining the indconcentrations than environment. 

Adgate et al. 

1998 

Jersey City, 

New Jersey; 

1992 to 

1994 

Mean Pb indoor dust 

concentration for the 

coarse size fraction 

(particle size of 2.5 μm to 

10 μm). 

Compare the indoor dust 

concentration in the hybrid 

model and in the air-only 

regression-based model, 

assuming an air 

concentration of 0.04 μg/m3 

(consistent with national air 

values in 1990). 

Adgate: 857 μg/g; Hybrid 

Model: 128 μg/g; Air-only 

Regression-based 

Model: 94 μg/g. 

Both the hybrid moregression-based mthe actual mean indconcentration. This fact that the Jersey in the Adgate study vintage and include that was not capturempirical data or in the air-only regressderived. 

G-30 
The different studies mentioned above allow testing of various portions of the hybrid 

mechanistic-empirical model and the air-only regression-based model. Comparison of the ratio 

of ambient air Pb concentrations to indoor Pb concentrations in the mechanistic portion of the 

hybrid model indicate that the hybrid model ratios are lower than those in the Roy et al. (2003) 

study; however, this ratio will vary depending on whether windows are open or closed, and no 

such information is available for the Roy et al. (2003) study. In addition, the portion of indoor 

dust Pb arising from ambient air contributions is lower in the hybrid model than in the Adgate et 

al. (1998) study. However, most of the Adgate et al. (1998) study homes were built before 1940, 

indicating that Pb paint likely plays a larger role in setting the dust Pb loading than in an urban 

environment including homes from a later vintage. The equation for converting Pb loadings to 

Pb concentrations was tested using both the Tang et al. (2004) study and the Roy et al. (2003) 

study. In general, the Pb concentrations estimated from the Pb loadings were within range for 

the Tang et al. (2004) study, but biased high for the Roy et al. (2003) study, indicating the Roy et 

al. (2003) study may include data that differs significantly from the HUD study from which the 

conversion equation was derived. The final predicted concentrations from the hybrid model 

were compared with the Pb concentrations measured in Manhattan, New York City, New York 

in the Tang et al. (2004) study, and the predicted values bounded the measured mean value. The 

hybrid values underpredicted the indoor dust Pb concentrations in the Hilts (2003) study and the 

Adgate et al. (1998) study. However, the Hilts (2003) study was performed at a Pb smelter site 

and the Adgate et al. (1998) study included homes built before 1940, both of which suggest these 

homes are different from a typical urban home. In general, the hybrid model predicts Pb 

concentrations within the wide range of values available in the literature for urban (and Pb 

smelter) environments. 

G.3.7. Separating Pb Indoor Dust Concentrations into Recent Air and Other Portions 

For the general urban Pb smelter case study, after the Pb indoor dust concentrations have 

been estimated using both the hybrid model and the air-only regression-based model, these 

estimates are also separated into the portion of Pb in indoor dust derived from recent air and the 

portion derived from other sources (e.g, indoor paint, outdoor soil/dust and additional sources 

including historical air). For the air-only regression-based model, the concentration equation is 

linear with respect to the air Pb concentration. Thus, the recent air-derived portion of Pb in 

indoor dust is the air slope multiplied by the air concentration, and the proportion of indoor dust 

Pb from the “other sources” portion is equal to the intercept. 

G-31 
For the hybrid model, the Pb indoor dust concentration equation is non-linear with 

respect to the air concentration. Conversely, the loading equation (including both recent airderived and other sources) is linear with respect to the air concentration and has the format: 

PbDustLoading = a + b * PbAir 

The fraction of total indoor dust from recent air-derived sources is then equal to 

b * PbAir Air − Dust Loading = a + b * PbAir 

This fraction is then applied to the total Pb indoor dust concentration to give the recent 

air-derived portion of total indoor dust. The “other sources” portion is then the remaining Pb 

indoor dust concentration after subtracting the recent air portion. 

G.4. FOUNDATION FOR THE PRIMARY PB SMELTER CASE STUDY INDOOR 

DUST ALGORITHMS 

For estimating indoor dust concentrations for residences in the primary Pb smelter case 

study, two indoor dust prediction models were used: 

•	 For locations within 1.5 km of the facility: a site-specific regression model (referred to as 

the H5 model); and 

•	 For receptors more than 1.5 km away from the facility: a pooled analysis model (referred 

to as the air+soil regression-based model) identified from the literature, which predicts Pb 

indoor dust concentrations given outdoor soil/dust and ambient air Pb levels based on 

data from a variety of industrial and urban studies (USEPA, 1989). 

The site-specific model is based on data collected within the residential remediation zone 

characterizing yard outdoor soil/dust Pb levels (post-remediation) and indoor dust levels. The 

air+soil regression-based model, or non-site-specific model, was selected for zones outside of the 

remediation area because available outdoor soil/dust and indoor dust Pb data did not extend to 

these more distant areas and the site-specific model derived for the remediated zone was deemed 

not representative for the non-remediated zone. 

G.4.1. Site-specific Regression Model 

The objective of the indoor dust analysis for the primary Pb smelter case study was to 

derive a statistical model that could be used to estimate Pb concentrations in indoor dust from Pb 

concentrations in other media at locations where the media concentrations had not been directly 

measured. The models derived were used to estimate total indoor dust Pb concentrations for the 

U.S. Census blocks closest to the primary Pb smelter. 

G-32 
G.4.1.1. Overview of Methods 

The primary approach taken in this analysis was to derive regression-type models that 

describe the relationships among the environmental media concentrations at the primary Pb 

smelter case study location. This approach was informed by previous analysis completed by the 

U.S. EPA and other researchers with similar data. More complex approaches (e.g., structural 

equation modeling) might also be used to explore and/or confirm the relationships among the 

variables examined. Based on preliminary analyses of the data, however, the regression analyses 

were best justified by the quality and quantity of available data. 

G.4.1.2. Data Sources 

All data used in the analyses were obtained electronically from the U.S. EPA Region 7 

(USEPA, 2006) and are presented in Appendix B. Pb concentrations in residential outdoor soil 

and indoor and road dust were obtained from samples taken by EPA contractors as part of 

Superfund investigations conducted in the area around the primary Pb smelter from March 2003 

to May 2006 (see Exhibit G-7). The data set also contained Pb loading information related to 

indoor floor dust, dust obtained from wipe samples, and total dust. 

Universal Transverse Mercator (UTM) coordinates were provided for all of the samples 

and were used in the analysis of the spatial patterns of soil and dust contamination. From March 

2002 to May 2006, concentrations of Pb in both indoor dust and residential soil were measured at 

only 17 locations (homes) near the primary Pb smelter. Pb concentrations in residential soil only 

were measured at 12 other residential locations, for which no accompanying Pb indoor dust 

measurements were available (see Exhibit G-7). Note that the soil measurements were taken 

post-remediation; thus, the effect of the historic facility operations on soil Pb concentrations 

(from stack emissions or road dust) are expected to be greatly attenuated compared to the soil Pb 

concentrations that existed prior to remediation. 

G-33 
 

 

 

 

 

Exhibit G-7. Primary Pb Smelter Case Study: Summary of Pb Concentrations in 

Residential Soil and House and Road Dust 

Data Field Sampling 

Locations a Sampling Dates 

Samples per 

Location Mean 

(Range) 

Total 

Samples 

Distances to 

Main Stack 

Mean (Range) 

(m) b 

Pb Concentration 

Mean (Range) 

mg/kg 

Indoor Dust 17 March 2002 to 

May 2006 

9 

(3 to 20) 159 898 (395 to 1,594) 

1,544 

(348 to 3,812) 

Residential 

Outdoor Soil 17 March 2002 to 

May 2006 

13 

(4 to 23) 215 898 

(395 to 1,594) 

81 

(31 to 139) 

Road Dust 21 c May 2002 to 

April 2006 

42 

(14 to 139) 891 609 

(161 to 1,693) 

28,300 

(1,570 to 111,000) 

a

 Number of locations includes both indoor dust and residential outdoor soil Pb data. 

b

 The main stack location is included as a point of reference only (not intended to imply it is the main contributor to
 

the observed Pb concentrations). 

c

 Sampling locations with the same UTM coordinates were combined. 

Anecdotal evidence suggested that road dust may be a major source of Pb in the air and 

in indoor dust at residences around the primary Pb smelter; therefore, an analysis was performed 

to identify the relationships between road dust Pb concentrations and indoor dust Pb 

concentrations. EPA contractors analyzed almost 900 road dust samples from May 2002 to April 

2006. The road dust samples were taken from 21 locations ranging from 161 to about 1,700 m 

from the main stack. Pb sampling locations for road dust differed from the residential outdoor 

soil and indoor dust sample locations; the distance between road dust sampling locations and the 

17 residential soil and indoor dust sampling locations ranged from 52 to 1328 m (average 280 

m). 

In the absence of residence-specific ambient air Pb concentration monitoring data, the 

indoor dust Pb levels were fit to modeled air concentrations developed as part of the pilot 

assessment. Long-term average air Pb concentrations predicted in the Industrial Source 

Complex (ISC-PRIME) current NAAQS scenario runs for U.S. Census block and block group 

centroids located near the residential indoor dust sampling locations were used (ICF, 2006). The 

centroids were not precisely co-located with any of the indoor dust sampling locations. 

G.4.1.3. Data Manipulation 

Developing indoor dust prediction models for the primary Pb smelter case study 

presented a number of challenges. Primary among these challenges was that the indoor dust, 

residential outdoor soil, and road dust measurements were not taken at the same time. Also, as 

noted above, the road dust and air modeling input data were spatially removed from the 

G-34 
residential indoor dust sampling locations. For these reasons, two approaches were taken to 

develop data sets for the regression analyses. 

G.4.1.3.1. Data Set Based on Spatial-temporal “Windows” 

The first approach involved identifying observations from each of the various 

environmental media that were “close” together in time and space, and using these data to create 

composite data points. Each data point represented the arithmetic or GM value of all 

observations in each medium within defined spatial and temporal “windows” of the nearest 

residential indoor dust observation. The indoor dust observations were used as the centers of the 

“windows” because fewer observations were available for indoor dust than for any other medium 

(and because indoor dust was the “dependent” variable for which values were being predicted). 

The dimensions of the windows were defined for two purposes: 

•	 Maintain, to the extent possible, the temporal and spatial relationships between the indoor 

dust measurements and the measured/estimated concentrations in the other media; and 

•	 Include as many input data points as possible per window. 

After looking at a number of possible approaches to stratify the data, window 

“dimensions” were chosen with the following spatial and temporal boundaries: 

•	 Indoor dust measurements from the same location occurring within ± 30 days of each 

other. 

•	 Residential soil measurements within ± 30 days of the nearest indoor dust sampling date 

for the same residence (soil and indoor dust measurements were taken from the same 

locations, so no spatial window was necessary). 

•	 Road dust Pb measurements from all of the sampling locations within 300 m, or the 

closest road dust sampling location, taken within ± 60 days of the indoor dust sample. If 

no road dust sampling location within 300 m was available, the measurements from the 

nearest road dust sampling locations were used. For five homes, no road dust samples 

were taken within approximately 60 days of any indoor dust sampling events. In these 

cases, all road dust results from within 300 m, or from the closest road dust sampling 

location, were averaged as above, and associated with the indoor dust sampling dates in 

the database. 

•	 Average long-term air Pb concentrations estimated for U.S. Census block centroids 

within 200 m of each indoor dust Pb measurement (ICF, 2006). Most indoor dust 

sampling locations had several centroids less than 200 m away, but averaging the air Pb 

levels within 200 m produced the highest correlations with the indoor dust samples. 

Because no specific date is associated with the estimated air Pb concentrations, the same 

air concentration values were used for all “windows” for each indoor dust location. 

G-35 
The resulting data set contained 125 records comprised of ambient air, residential outdoor 

soil, and indoor and road dust data, along with several other auxiliary variables relating to 

location, distance from the main stack (as a surrogate location for the facility), and sampling 

dates. 

G.4.1.3.2. Data Set Based on Indoor Dust Sampling Locations 

The number of samples (and therefore the amount of information) combined into the 

observations for the individual “windows” varied greatly. The “house” data set, which combines 

all data for each indoor dust sampling location, was developed to avoid giving undue weight to 

points with few observational data. The “house” data set includes 17 values for each variable. 

Each value corresponds to the arithmetic mean or geometric mean of all values for that variable 

for all “windows” associated with a given indoor dust sampling location. As described below, 

the modeling results obtained using the “windows” and the “house” data sets are quite similar. 

G.4.1.4. Results of the Statistical Analysis 

G.4.1.4.1. Exploratory Analysis 

Several exploratory analyses were conducted to confirm the general relationships within 

the data set, and to rule out the potential for omitted variables to affect the regression analysis 

results. The exploratory analyses included graphical summaries and calculation of simple 

correlation coefficients among the variables and their log-transformed values. 

For House 3, two indoor dust Pb measurements (5,230 and 23,640 milligram per 

kilogram [mg/kg]) differed markedly from other measurements taken at that house (mean = 

1,190 mg/kg, 15 samples). The two measurements were the last two samples taken at House 3 

(in April and October 2005). The two measurements were omitted from the analysis on the 

grounds that some factor maybe have been affecting indoor dust Pb concentrations during this 

period that had not been operating previously. After removing these two data points, the indoor 

dust Pb concentrations in the “windows” data set were well-represented by a lognormal 

distribution, and thus both the untransformed and log-transformed indoor dust Pb values were 

included in the regression analyses, as discussed below. 

As expected, average indoor dust Pb concentrations were found to be highly (inversely) 

correlated with distance to the main stack, when the “windows” data set was used (see Exhibit 

G-8). Pb in air is believed to be a major contributor to indoor dust Pb levels, and thus these 

results are to be expected. A weak, but significant, inverse correlation between indoor dust Pb 

concentrations and residential soil Pb was found. The reason for this correlation was not clear, 

G-36 
 

and the significance of the correlation declines in some, but not all, regression models when 

measures of air Pb are also included. Average and log-transformed road dust Pb concentrations 

were weakly correlated with similarly expressed indoor dust Pb statistics, but the correlations 

lost significance when residential soil and air Pb were included in the models (see Exhibit G-9). 

Exhibit G-8. Primary Pb Smelter Case Study: Relationship between Indoor Dust Pb 

Concentrations and Distance from Facility 

y = -1.2552x + 2451.5 

R2

 = 0.1544 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

300 600 900 1,200 1,500 1,800 

Distance from the Main Stack (m) 

Indoor Dust Pb Concentration

(μg/g) 

Note: The main stack location is included as a point of reference only (not intended to imply it is the main 

contributor to the observed Pb concentrations). 

G-37 
Exhibit G-9. Primary Pb Smelter Case Study: Relationship between Road Dust Pb 

Concentrations and Nearby Indoor Dust Pb Concentrations 

y = 0.0126x + 1167.2 

R2

 = 0.0129 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

0 10,000 20,000 30,000 40,000 50,000 

Road Dust Pb Concentration (μg/g) 

Indoor Dust Pb Concentration

(μg/g) 

G.4.1.4.2. Regression Modeling of Indoor Dust Pb Concentrations 

A systematic search for multiple regression models was conducted to maximize the 

proportion of explained variance (R2

) in indoor dust (DustPb) and the log-transformed indoor 

dust (lnDustPb) values. Forwards and backwards stepwise regression methods were used, with 

contribution to the F-statistic as the inclusion/removal criterion for untransformed and logtransformed variables. Residential soil and road dust Pb were “forced” back into well-fitted 

models to determine their effects on R2

 and on the coefficients for other variables. Probability 

plots of residuals and other diagnostics were used to evaluate the quality of the fit and to 

determine failures in assumptions required to produce unbiased estimates. Results of the best 

regressions derived from the “windows” data set are summarized Exhibit G-10. 

G-38 
 

 

 

Exhibit G-10. Indoor Dust Regression Models Tested and Summary of Regression 

Analysis Results for the “Windows” Data Set 

Model a Independent 

Variable b 

DustPb 

Dependent 

Variable(s) 

Intercept 

Estimated 

Values (m) 

685.7 

Coefficient pValue(s) 

0.000 

Adjusted R2 

W1 0.322

AIR_200 1625.2 0.000 

Intercept 1012.5 0.000 

W2 DustPb SoilAvg -4.699 0.002 0.367

AIR_200 1687.2 0.000 

W3 DustPb 

Intercept 2285.6 0.000 

0.343 

lnAIR200 791.0 0.000 

Intercept 2863.2 0.000 

0.426 

W4 DustPb SoilAvg -6.317 0.000 

lnAIR200 874.7 0.000 

W5 LnDustPb 

Intercept 6.4540 0.000 

0.268 

AIR_200 1.2361 0.000 

Intercept 6.6725 0.000 

W6 LnDustPb SoilAvg -0.0031 0.020 0.294

AIR_200 1.2777 0.000 

W7 LnDustPb 

Intercept 7.7366 0.000 

0.336 

lnAIR200 0.6520 0.000 

Intercept 8.1506 0.000 

W8 LnDustPb SoilAvg -0.0045 0.000 0.395

lnAIR200 0.7120 0.000 

a

 Models labeled “W” were developed considering media concentrations within a particular spatial distance 

and temporal period of the nearest indoor dust observation. 

b

 Abbreviations: DustPb = Pb concentration in indoor dust; LnDustPb = log-transformed value; AIR_200 = 

ambient air concentration within 200 m of indoor dust sampling locations; lnAIR200 = log-transformed
 

concentration; and SoilAvg = average residential soil Pb concentration. 

For all of the regressions, variables representing ambient air Pb concentrations at 

monitors within 200 m of indoor dust sampling locations (AIR200, lnAIR200) accounted for the 

bulk of explained variance in indoor dust Pb levels (see Exhibit G-10). The only other variable 

related to environmental concentrations that retained significance and/or resulted in increases in 

G-39 
explained variance was the average residential soil Pb (SoilAvg). Surprisingly, the sign of the 

coefficient for residential soil Pb was consistently negative in those regressions where it was 

statistically significant. When the natural log of indoor dust Pb concentration (LnDustPb) was 

used as the “independent” variable, the R2

 values for regressions including air and residential soil 

Pb levels were reduced slightly compared to the results obtained for the analogous regressions 

using the untransformed DustPb values. However, the pattern of regression residuals was 

considerably improved (more nearly normal) when the log-transformed (as opposed to 

untransformed) indoor dust values were fit. No variables representing road dust Pb 

concentration were found to retain statistical significance when air-related variables were 

included in the regression models. 

Similar results were found when regressions were fit using the “house” data set, as shown 

in Exhibit G-11. Similar coefficient values are observed for analogous regressions based on the 

two data sets. One difference from the results obtained using the “windows” data was that, when 

Air200 was included in the regression, SoilAvg became statistically insignificant. Residential 

soil was significant in the other variants of the model shown in Exhibit G-11. As with the 

“windows” data set, the road dust Pb was never a significant predictor of indoor dust Pb levels. 

Also, patterns of residuals were again superior when the models were fit to LnDustPb, rather 

than DustPb. The results (coefficients and significance) did not significantly change when 

regressions were conducted that were weighted by the numbers of observations at each house, 

rather than uniformly weighted. 

G-40 
 

Exhibit G-11. Summary of Regression Analysis Results for the “House” Data Set 

Model a Independent 

Variable b 

DustPb 

Dependent 

Variable(s) 

Intercept 

Estimated 

Values (m) 

701.2 

Coefficient pValue(s) 

0.008 

Adjusted R2 

H1 0.489

Air200 1573.1 0.001 

H2 DustPb 

Intercept 2447.1 0.000 

0.609 

LnAir200 883.4 0.000 

H3 DustPb 

Intercept 3313.2 0.000 

SoilAvg -11.349 0.019 0.722

LnAir200 946.9 0.000 

H4 LnDustPb 

Intercept 6.3928 0.000 

0.447 

Air200 1.2185 0.002 

H5 LnDustPb 

Intercept 7.7892 0.000 

0.625 

LnAir200 0.7200 0.000 

H5 LnDustPb 

Intercept 8.3884 0.000 

SoilAvg -0.0079 0.045 0.701

LnAir200 0.73639 0.000 

a

 Models labeled “H” were created considering all of the data for each indoor dust sampling
 

location.
 b

 Abbreviations: DustPb = Pb concentration in indoor dust; LnDustPb = log-transformed value;
 

AIR_200 = ambient air concentration within 200 meters (m) of indoor dust sampling locations;
 

LnAIR200 = log-transformed concentration; and SoilAvg = average residential soil Pb
 

concentration.
 

G.4.1.4.3. Comparison of Predicted to Observed Indoor Dust Pb Concentrations in Primary 

Pb Smelter Case Study 

To evaluate potential approaches for estimating indoor dust Pb levels in the primary Pb 

smelter case study, the estimated indoor dust Pb concentrations derived using several of the 

better fitting models (as judged by adjusted R2

 values) were compared based on the “windows” 

data (see Exhibit G-12). 

G-41 
Exhibit G-12. Comparison of Three Best "Windows" Models with EPA Air+Soil 

Regression-based Model and “Windows” Data
 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

0.0 0.5 1.0 1.5 2.0 

Ambient Air Pb Concentration (μg/m3

) 

Predicted Indoor Dust Pb Concentration (μg/g)

Model W4 

Model W8 

Model W2 

Air+Soil Reg.­

Based Model 

"Windows" Data 

Exhibit G-13 shows the indoor dust concentrations predicted by the three best fitting 

models derived using the “house” data. For models that included coefficients for residential soil 

Pb (all except H5), the assumed residential soil Pb concentration was held constant at its mean 

value. In both cases, the predictions are compared to those derived using EPA’s air+soil 

regression-based model (USEPA, 1989). 

Exhibit G-13. Comparison of Best-fitting "House" Models with the EPA Air+Soil 

Regression-based Model and the "Windows" Indoor Dust Data 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

0.0 0.5 1.0 1.5 2.0 

Ambient Air Pb Concentration (μg/m3

) 

Predicted Indoor Dust Pb Concentration (μg/g)

Model H3 

Model H6 

"Windows" Data 

Air+Soil Reg.­

Based Model 

Model H5 

G-42 
 

 

The models derived from the “windows” and “house” data sets have generally the same 

form. The relationships were highly curved, and negative indoor dust values were predicted at 

low air concentrations, when the models were fit to untransformed indoor dust data (W4, H3). 

For the “windows” models, predicted indoor dust Pb concentration values were very similar 

when the model was fit using untransformed air concentrations (W2) or log-transformed values 

(W8.) Also, predicted indoor dust Pb levels were very similar for the two log-log “house” 

models when soil concentration was included (H6) or excluded (H5) from the model. 

All models predicted substantially higher indoor dust Pb concentrations than the air+soil 

regression-based model. Also, the air+soil regression-based model predicts indoor dust levels 

that are far below the observed values. 

G.4.1.5. Primary Pb Smelter Case Study: Indoor Dust Modeling Approach Used Near 

Facility 

The availability of site-specific indoor dust and residential soil concentration data from 

the primary Pb smelter case study location led to the development of a site-specific model as 

described above. Soil and indoor dust samples from which the site-specific models were 

developed were available only to a distance of about 1,600 m from the facility’s main stack, 

leading to greater uncertainty associated with use of the site-specific model to predict indoor dust 

Pb concentrations at greater distances. Thus, the site-specific H5 model was used to predict 

indoor dust Pb concentrations at centroids to a distance of 1.5 km from the site, and the air+soil 

regression-based model was used to predict indoor dust Pb levels for centroids at greater 

distances. The format for the H5 model is: 

ln(PbDUST) = 7.7892 + 0.72 x ln(PbAIR) 

where: 

PbDUST = concentration of Pb in indoor dust (μg/g) 

PbAIR	 = concentration of Pb modeled in the ambient 

air (μg/m3

) 

As shown in Exhibit G-14, the H5 model predicted much higher indoor dust 

concentrations at centroids closer to the facility than the air+soil regression-based model, but at 

longer distances, the predictions became more similar. For centroids around 1,500 m (1.5 km) 

from the facility, the average H5 model predicted indoor dust Pb concentrations of 344 μg/g, 

while the average air + soil regression-based model prediction was approximately 267 μg/g. At 

5,000 m (5 km), the average predictions from the H5 and air + soil regression-based model were 

146 μg/g and 79 μg/g, respectively. 

G-43 
Exhibit G-14. Ratio of Indoor Dust Pb Concentrations Predicted by the H5 and Air+Soil 

Regression-based Models versus Distance from the Facility 

0.0 

1.0 

2.0 

3.0 

4.0 

5.0 

0 5,000 10,000 15,000 

U.S. Census Block Centroid Distance from the Main Stack (m) 

Ratio of H5 to Air+Soil Regression-Based Model

Indoor Dust Predictions 

Note: The main stack location is included as a point of reference only (not intended to imply it is the main 

contributor to the observed Pb concentrations). 

G.4.2. Primary Pb Smelter Case Study: Indoor Dust Modeling Approach Used at Distance 

from Facility 

For the portion of the study area outside the 1.5 km radius from the primary Pb smelter, 

the pooled analysis air+soil regression-based model based on data collected in the past at several 

active primary Pb smelters, including the primary smelter analyzed here, was used (USEPA, 

1989). The air+soil regression-based model predicts indoor dust Pb based on both outdoor 

soil/dust and ambient air Pb levels. The model is appropriate for the non-remediation portion of 

the primary Pb smelter case study area because this area has not been subjected to extensive 

remediation and is therefore likely to resemble the locations included in the pooled analysis used 

in deriving this model (i.e., areas not having undergone extensive outdoor soil remediation). 

Furthermore, because the non-remediation portion of the study area is likely to have outdoor 

surface soil Pb gradients reflecting long-term atmospheric deposition of Pb, indoor dust would 

likely be partially dependent on outdoor soil Pb. Therefore, the air+soil regression-based model 

presented here was selected for this portion of the study area: 

G-44 
 

PbDUST = 31.3 + (638 x PbAIR) + (0.364 x PbSOIL) 

where: 

PbDUST = concentration of Pb in indoor dust (μg/g) 

PbAIR = concentration of Pb in ambient air (μg/m3) 

PbSOIL = concentration of Pb in outdoor surface soil (mg/kg). 

G.4.3. Separating Indoor Dust Pb Concentrations into Recent Air and Other Portions 

For the primary Pb smelter case study, the concentration of Pb in indoor dust was 

estimated using the site-specific H5 model and the air+soil regression-based model. Unlike in 

the general urban and secondary Pb smelter case studies, the indoor dust Pb is not separated out 

into “recent air” and “other” for the primary Pb smelter case study. This is a result of limitations 

of the site-specific H5 model, which is used to calculate the concentration of Pb in indoor dust in 

the primary Pb smelter case study. The site-specific H5 model cannot separate indoor dust into 

“recent air” and “other,” therefore the total indoor dust contribution is determined for the 

primary Pb smelter case study. 

G.5. FOUNDATION FOR THE SECONDARY PB SMELTER CASE STUDY INDOOR 

DUST ALGORITHMS 

Indoor dust sampling data were not available for the secondary Pb smelter case study, 

necessitating the use of modeling to characterize indoor dust Pb levels within the study area. 

The air+soil regression based model (USEPA, 1989) that uses ambient air Pb levels for 

predicting indoor dust levels was chosen. This model is similar to the one used for the primary 

Pb smelter case study at distances greater than 1.5 km from the source; however, in the case of 

the secondary Pb smelter, an “air-only” version of the model was used reflecting the reduced 

overall confidence associated with soil characterization for this case study. 

The air-only regression-based model does reflect (implicitly) some consideration for the 

soil-to-indoor dust mechanism in the air signal. Specifically, the larger air factor for the air-only 

model (relative to the air+soil regression model’s air factor) reflects the fact that, in this version 

of the model, air measurements are used to represent both the direct loading of indoor dust Pb 

from air and the loading of outdoor soil/dust Pb by air with subsequent impacts of that outdoor 

soil/dust on indoor dust through other mechanisms (USEPA, 1989). The air-only regressionbased model used for the secondary Pb smelter was based on a number of studies focusing 

mainly on primary Pb smelters. This introduces uncertainty into the indoor dust predictions 

generated using this model associated with potential differences between primary and secondary 

Pb smelters that may affect indoor dust Pb loading (e.g., particle size profiles and nature of the 

G-45 
 

 

entrained Pb compounds). The air-only regression-based model used in this analysis is presented 

below: 

PbDUST = 60 + (844 × PbAIR) 

where: 

PbDUST = concentration of Pb in indoor dust (μg/g) 

PbAIR = concentration of Pb in the ambient air (μg/m3) 

G.5.1. Separating Pb Indoor Dust Concentrations into Recent Air and Other Portions 

The total Pb indoor dust concentration was separated into the component associated with 

recent air and that associated with other sources (e.g, indoor paint, outdoor soil/dust and 

additional sources including historical air). The Pb indoor dust concentration equation is linear 

with respect to air Pb concentration, so the recent air contribution to indoor dust Pb concentration 

is the slope multiplied by the air concentration, and the other sources contribution is the 

intercept. 

G-46 
 

 

 

 

 

 

 

 

 

 

 

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of Background Concentrations in Upper Manhattan, New York Apartments for Select Contaminants 

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Rulemaking. EPA 747-R-96-012. Office of Pollution, Prevention, and Toxics. Available online at: 

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U.S. Environmental Protection Agency (USEPA). (1998) Risk Analysis to Support Standards for Lead in Paint, 

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U.S. Environmental Protection Agency (USEPA). (2007) Air Quality System (AQS) Database. Available online at: 

http://www.epa.gov/ttn/airs/airsaqs/aqsweb/aqswebwarning.htm. 

G-48 
 

Vette, A. F.; Rea, A. W.; Lawless, P. A.; Rodes, C. E.; Evans, G.; Highsmith, V. R.; Sheldon, L. (2001) 

Characterization of Indoor-Outdoor Aerosol Concentration Relationships During the Fresno PM Exposure 

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Remediation on Lead in House Dust. Sci. Total Environ. 303(1-2): 59-78. 

G-49 
ATTACHMENT G-1. METHOD USED TO CONVERT INDOOR PB 

LOADINGS TO CONCENTRATIONS 

This attachment describes the method used to convert Pb loadings to concentrations for 

the hybrid mechanistic-empirical model in the general urban case study. Section G-1.1 describes 

the data used to derive the indoor dust loading-indoor dust concentration models. Sections G-1.2 

and G-1.3 describe data and correlation analyses. Section G-1.4 discusses the types and design 

of the regression models, and Section G-1.5 discusses the limitations of the data set used and 

uncertainties in the indoor dust Pb concentration models. Section G-1.6 provides detailed 

regression results. 

G-1.1. SOURCE OF INDOOR DUST PB LOADING AND INDOOR DUST 

CONCENTRATION DATA 

Data on the relationship between indoor dust Pb loading and concentration were gathered 

as part of the HUD National Survey of Lead-Based Paint in Housing conducted between 

November 1989 and 1990 (USEPA, 1995). This survey provides the largest data set the 

document’s authors are aware of that contains simultaneous measurements of indoor dust loading 

and indoor dust concentration from the same households. In addition, the survey was designed 

to include a nationally representative sample of houses of varying age, and thus could be used to 

evaluate temporal trends in Pb occurrence and concentration. 

The goal of the survey was to obtain information on the presence and condition of Pbbased paint, Pb in soil, indoor dust Pb loadings, and concentrations as well as other household 

data, from a representative national sample of 300 private homes and 100 public housing 

facilities (USEPA, 1995). The data used to derive relationships between indoor dust loading and 

Pb concentration in this approach came from the 284 private households that were ultimately 

sampled during the survey. The data are tabulated in Appendix C of EPA’s 1998 “Section 403” 

risk analysis (USEPA, 1998). The data elements include: 

•	 Building construction date (vintage) in three ranges (<1940, 1940 to 1959, and 1960 to 

1979); 

•	 Vacuum [Blue Nozzle (BN)] floor indoor dust Pb loading, micrograms (μg) per square 

feet (ft2

); 

•	 Blue nozzle indoor dust Pb concentration, μg per gram (g); 

•	 Vacuum window sill indoor dust loading, μg/ft2

; 

•	 Average yard outdoor soil/dust Pb concentration, μg/g; and 

•	 Maximum interior and exterior X-ray fluorescence (XRF) Pb concentration, milligrams 

(mg) per square centimeter (cm2). 

G-50
 
The data set also included a set of sampling weights developed by EPA designed for 

extrapolation of the survey sample results to United States private residences as a whole. Floor 

indoor dust Pb loading and concentration values were household averages, generally of three 

samples taken at different locations in the sampled household. The Pb concentration values in 

samples with low tap weights (indoor dust loading derived using sampling weights) were 

corrected for systematic bias (USEPA, 1995); this correction affected relatively few samples. 

Because wipe samples have become the preferred technique to measure Pb indoor dust 

loading, EPA also calculated equivalent wipe sample loading estimates for each household based 

on the vacuum sample results. The conversion was accomplished using regression results 

derived from several previous studies of relative sampling method performance (USEPA, 

1997a). Owing to the added level of uncertainty introduced by the vacuum-wipe sample 

conversion, the wipe sample results were not used in this analysis. Instead, as described below, 

regression models were developed that related the vacuum indoor dust loading results from the 

HUD National Survey to indoor dust Pb concentrations. 

G-1.2. PRELIMINARY DATA ANALYSIS 

Data analyses were focused primarily on vacuum indoor dust Pb loading and Pb 

concentration data, but other variables were also examined for possible correlations with indoor 

dust Pb concentration. Data from the 1998 Risk Analysis were imported into Excel 2003TM and 

StatisticaTM Version 7. Reported values for individual variables were examined graphically (e.g., 

histograms, stem-and-leaf plots) for outliers and discrepant values. Probability plots and 

goodness-of-fit tests were used to test individual variable distributions for normality. 

As is commonly the case with environmental sampling data, the distributions of indoor 

dust Pb loading and Pb concentrations were both highly skewed (Attachment G-1-1 and 

Attachment G-1-2). Normal probability plots of the log-transformed data appeared to be 

approximately normal (Attachment G-1-3 and Attachment G-1-4), except that there appeared to 

be outliers in both the low and high “tails” of the log-transformed indoor dust Pb concentration 

data (Attachment G-1-3). As discussed below, the majority of observations in the tails came 

from houses constructed between 1960 and 1979. 

G-51
 
 

 

Attachment G-1-1. Distribution of Pb Concentration Data, 

HUD National Housing Survey 

Note: One data point was omitted at 50,400 μg/g. 

Source: USEPA, 1995
 

Goodness-of-fit tests suggested that the log-transformed Pb loading and concentration 

data from the data set taken as a whole were nearly, but not perfectly, lognormal. The relatively 

less sensitive single-sample Kolgmorgorov-Smirnov (K-S) test tended to give p-values indicating 

consistency with the normal distribution of the log-transformed indoor dust loading and Pb 

concentration data; however, the more sensitive Lilliefors and Shapiro-Wilks W tests gave low 

p-values, indicating the lack of a good “fit” to the normal distribution (Attachment G-1-5, top 

panels). 

G-52
 
Attachment G-1-2. Distribution of Vacuum Dust Pb Loading, 

HUD National Housing Survey 

Source: USEPA, 1995 

Attachment G-1-3. Normal Probability Plot of Log-Transformed 

Dust Pb Concentration Data 

G-53
 
 

Attachment G-1-4. Normal Probability Plot of Log-Transformed 

Vacuum Dust Pb Loading Data 

The distributions of the indoor dust loading and indoor dust concentration data were also 

evaluated separately by vintage because of the possible differences in the distributions of indoor 

dust loading and indoor dust concentration data across the three building vintage strata. Of the 

284 valid observations, 77 were obtained from houses constructed prior to 1940, 87 came from 

houses constructed between 1940 and 1959, and 120 came from houses constructed between 

1960 and 1979. 

It can be seen from the goodness-of fit test results in the lower panels of Attachment G-1­

5 that stratifying the data resulted in more normal distributions of both log-transformed indoor 

dust Pb concentration and indoor dust loading. Some of the apparent improvement is due to the 

smaller number of observations in the stratified data sets. However, the improvement in 

normality is also apparent in the increased linearity of the probability plots of the two variables. 

Removal of the two extreme (outlying) values from the Pb concentration data sets (the very low 

value from the prior to 1940 data and the very high value from the 1960 to 1979 stratum) also 

resulted in additional improvements to the normality of the data (see Attachment G-1-6). These 

values were, however, retained in the following evaluation of multivariate correlations. 

G-54
 
 

 

Attachment G-1-5. Goodness-of-Fit Test Results (p-values) for Log-Transformed 

Dust Loading and Dust Concentration Data
 

Variable K-S Lilliefors ShapiroWilks W 

Combined Data 

LNVAC > 0.20 > 0.20 0.01 

LNPBCONC < 0.10 < 0.01 0.000 

Combined Data ( minus outlying values) 

LNVAC > 0.20 > 0.20 0.03 

LNPBCONC < 0.20 < 0.01 0.02 

<1940 

LNVAC > 0.20 > 0.20 0.66 

LNPBCONC < 0.20 < 0.01 0.000 

<1940 (minus outlying value) 

LNVAC > 0.20 > 0.20 0.69 

LNPBCONC > 0.20 > 0.20 0.71 

1940 - 1959 

LNVAC > 0.20 > 0.20 0.75 

LNPBCONC > 0.20 > 0.20 0.35 

1960 to 1979 

LNVAC > 0.20 > 0.20 0.04 

LNPBCONC > 0.20 < 0.01 0.000 

1960 to 1979 (minus outlying value) 

LNVAC > 0.20 > 0.20 0.17 

LNPBCONC > 0.20 < 0.15 0.000 

Note: Low p-values indicate poor fit to the normal (Gaussian) distribution. 

G-55
 
 Attachment G-1-6. Probability Plots of Log-Transformed Pb Concentration Data for the 

Three Building Vintage Strata (Outliers Removed) 

Observations on other variables (window sill vacuum indoor dust loading, outdoor soil 

Pb concentration, and interior and exterior XRF results) also tended to be skewed, and were 

therefore log-transformed prior to exploration of multivariate correlations. 

G-1.3. CORRELATION ANALYSIS 

In preparation for model building, correlations between potential explanatory variables 

and indoor dust Pb concentration were examined. While the intent was to construct a model that 

predicts indoor dust Pb concentrations from indoor dust loading, it is important to know if any 

other variables in the data are also highly correlated with indoor dust concentration or loading. 

Attachment G-1-7 summarizes the simple product moment correlation coefficients seen in the 

combined data set with indoor dust Pb concentration and log-transformed indoor dust Pb 

concentration. 

G-56
 

 

Attachment G-1-7. Correlations Between Potential Explanatory Variables, Dust Pb 

Concentration (PBCONC), and Log-Transformed Dust Concentration (LNPBCONC) 

Variable PBCONC LNPBCONC 

AGEGRP 0.00 -0.34* 

Pb paint 0.05 0.24* 

VACLOAD 0.49* 0.54* 

LNVAC 0.26* 0.66* 

SILLVAC 0.03 0.15* 

LNSVAC 0.04 0.32* 

YARD 0.03 0.32* 

LNYARD 0.03 0.45* 

INTXRF 0.02 0.34* 

LNINTXRF -0.02 0.36* 

EXTXRF 0.02 0.28* 

Note: A* indicates simple correlation coefficients significant at 

p < 0.05. See text for explanations of variable names. 

It is clear that a number of variables, in addition to vacuum indoor dust loading 

(VACLOAD), are highly correlated with indoor dust Pb concentration when the data set is 

examined as a whole. The correlations are generally much higher when the log-transformed 

variables are used. This is to be expected, since log-transformation reduces the impact of the 

skew in the variables as described earlier, and allows underlying relationships to be more clearly 

seen. 

It is important to note that building vintage (AGEGRP) is negatively correlated with 

indoor dust Pb concentration, as would be expected if the extent of Pb paint usage decreased, and 

the overall state of repair improved, with more recent construction. A dummy variable for the 

observed presence of Pb paint, log-transformed sill vacuum indoor dust Pb loading (LNSVAC), 

log-transformed average yard soil Pb concentration (LNYARD), and interior and exterior XRF 

readings were also found to be correlated with house indoor dust Pb concentration. These latter 

variables were also highly correlated with housing vintage, raising the question as to whether 

there was actually an independent effect of building age that was not already captured by 

differences in sill indoor dust loadings, soil Pb concentrations, and XRF readings. 

Omitting the extreme high and low indoor dust Pb concentration values from the data set 

resulted in a substantial increase in the magnitude of the correlation coefficient between the logtransformed Pb indoor dust concentration (LNPBCONC) and building vintage (AGE GRP) from 

-0.34 to -0.47. Omitting these outlying values also slightly increased the magnitude of the 

correlations between LNPBCONC and most of the other variables in Attachment G-1-7. The 

correlation between LNPBCONC and log-transformed vacuum indoor dust loading (LNVAC) 

G-57
 
 

remains strong within each of the individual building vintage strata (Attachment G-1-8). Most of 

the other variables retain their significant correlations to the log-transformed Pb concentration 

within the individual vintage strata, but the magnitude of the correlations varies. Correlations 

with LNPBCONC are generally weaker in the 1960 to 1979 data than in the other strata. 

Attachment G-1-8. Correlations with Log-Transformed Pb Concentration (LNPBCONC) 

Within Individual Building Vintage Strata 

Variable <1940 1940 to 

1959 

1960 to 

1979 

Pb paint 0.04 0.24* 0.20* 

VAC 

LOAD 0.45* 0.54* 0.58* 

LNVAC 0.62* 0.70* 0.57* 

SILLVAC 0.16 -0.12 0.08 

LNSVAC 0.30* 0.23* 0.25* 

YARD 0.24 0.36* 0.15 

LNYARD 0.41* 0.45* 0.16 

INT XRF 0.30* 0.36* 0.13 

LNINTXRF 0.35* 0.27* 0.13 

EXT XRF 0.15 0.42* 0.14 

Note: A * indicates simple correlation coefficients significant at p < 0.05. 

Removing the low value from the <1940 Pb indoor dust concentration data increases the 

magnitude of the correlation between LNVAC and LNPBCONC (from 0.62 to 0.73). Removing 

the high Pb concentration value from the 1960 to 1979 data, in contrast, reduces this correlation 

from 0.57 to 0.49. 

G-1.4. REGRESSION MODELING 

Correlation coefficients between log-transformed indoor dust Pb concentration and logtransformed vacuum indoor dust loading (Attachment G-1-9) suggested that a linear regression 

model (in this case, log-log) might provide a good fit to the data. Data for the three building 

vintage strata cluster fairly tightly, with data from newer age strata having slightly lower values 

of both LNPBCONC and LNVAC than the data from <1940 houses. Pb concentration values 

from the newer houses (1960 to 1979) also appear to be somewhat more variable than the values 

for the other age strata. 

G-58
 
 

Attachment G-1-9. Correlation Coefficients between Log-Transformed Dust Pb 

Concentration and Log-Transformed Vacuum Dust Pb Loading 

Variable <1940 1940 to 

1959 

1960 to 

1979 

Pb paint 0.04 0.24* 0.20* 

VAC 

LOAD 0.45* 0.54* 0.58* 

LNVAC 0.62* 0.70* 0.57* 

SILLVAC 0.16 -0.12 0.08 

LNSVAC 0.30* 0.23* 0.25* 

YARD 0.24 0.36* 0.15 

LNYARD 0.41* 0.45* 0.16 

INT XRF 0.30* 0.36* 0.13 

LNINTXRF 0.35* 0.27* 0.13 

EXT XRF 0.15 0.42* 0.14 

Note: A * indicates simple correlation coefficients significant at p < 0.05. 

As noted above, it has already been demonstrated that two values in the Pb concentration 

data set (at the upper right and lower left corners of Attachment G-1-9) appear to be “outliers,” 

that is, they seem to fall outside the distribution of the other Pb concentration values. As part of 

the model development, these (and other) data points were tested to determine if these would be 

disproportionately influential in determining the results of a linear regression. 

In a univariate regression of LNPBCONC on LNVAC, the two outlying data points 

appeared to be quite influential; Cook’s distances for these data points were 0.20 and 0.19, 

respectively, more than three times the next highest value, compared to a median value across 

the data points of 0.003. However, these values are not extreme in and of themselves; Cook’s 

distances greater than 1.0 are generally considered to be an indication of undue influence of 

single data points (Kleinbaum et al., 1998). 

When the data are stratified, however, the low and high outlying points are found to be 

very influential in determining regression results. In a LNPBCONC – LNVAC linear regression 

for the <1940 data, the Cook’s distance for the lowest Pb indoor dust concentration value was 

1.05, compared to a next highest value of 0.05. In the univariate regression on the 1960 to 1979 

data, the calculated Cook’s distance for the highest indoor dust Pb concentration data point was 

1.19, compared to a next highest value of 0.19. These results indicate that in both cases the 

overall result of the regression is being strongly influenced by the outlying values. Thus, these 

data points are omitted from the regressions discussed below. 

G-59
 
 

G-4.1.1 Univariate Models 

Log-log regression models were first run in which LNPBCONC was fit to LNVAC only. 

Models were run for the combined data set and for the stratified data sets. Results of the models 

are summarized in Attachment G-1-10. Detailed regression outputs are provided in Section G1.6. 

Attachment G-1-10. Univariate Regression Results: LNPBCONC 

as a Function of LNVAC 

Model 

Data Set Variable Coefficient SE Coefficient 

tstatistic pvalue F-Statistic, plevel Adjusted 

R2 

All 

Vintages 

Combined 

Intercept 5.37 0.05 111.2 0.000 F(1,272)=230.40 

p<0.000 0.46 

LNVAC 0.49 0.03 15.2 0.000 

<1940 Intercept 6.34 0.05 127.4 0.000 F(1,187)=210.06 

p<0.000 0.53 

LNVAC 0.45 0.03 14.5 0.000 

1940 to 

1959 

Intercept 5.30 0.05 104.2 0.000 F(1,189)=175.82 

p<0.000 0.48 

LNVAC 0.44 0.03 13.3 0.000 

1960 to 

1979 

Intercept 4.74 0.05 102.6 0.000 F(1,344)=87.771 

p<.000 0.20 

LNVAC 0.35 0.04 9.37 0.000 

Note: Regressions were performed using the national weight values from the HUD survey data (USEPA 1998); 

LNVAC (log-transformed vacuum Pb loading) values were centered at their means. 

In all cases, the regression results (F-statistics) are highly significant. The LNVAC 

coefficients are likewise significant. Both the intercept and LNVAC coefficients decrease with 

newer building vintages. The 1960 to 1979 model explains a considerably smaller proportion of 

the variance in LNPBCONC (R2

 of 0.20) than the models derived from older houses and from 

the data set as a whole (R2 on order of 0.5). This suggests a weaker and less consistent 

relationship between indoor dust loading and concentration in newer houses, perhaps because of 

a decreased contribution from interior Pb paint and higher contributions from exterior sources. 

G-4.2.1 Multivariate Models 

A number of multivariate models were also tested to determine which, if any, of the other 

variables in the data set might also explain significant proportions of the variance in the logtransformed indoor dust Pb concentration data. Forward and backward stepwise procedures 

were used to identify variables for which regression coefficients retained significance in the 

presence of other covariates, and which appeared to explain appreciable proportions of the 

variance in LNPBCONC in multivariate models. The results of these analyses are summarized 

in Attachment G-1-11. 

G-60
 
 

 Attachment G-1-11. Multivariate Regression Results: LNPBCONC 

as a Function of LNVAC and Other Variables 

Model/Data 

Set Variableb 

Intercept 

Coefficient 

4.43 

SE of 

Coefficient 

0.17 

tstatistic 

26.6 

pvalue 

0.00 

F-Statistic, plevel 

F(3,257)=108.17 

p<0.0000 

Adjusted 

R2 

0.55 All Vintages 

Combined 

LNALL CNT 0.39 0.03 11.9 0.00 

LNYARD 0.20 0.04 5.71 0.00 

LNINTXRF 0.12 0.05 2.30 0.02 

<1940 

Intercept 5.00 0.25 20.1 0.00 

F(3,177)=132.13 

p<0.0000 0.69 LNV1 CNT 0.45 0.03 17.3 0.00 

LNYARD 0.19 0.04 4.92 0.00 

LNINTXRF 0.22 0.03 6.59 0.00 

1940 to 

1959 

Intercept 4.03 0.19 21.0 0.00 

F(2,180)=134.08 

p<0.0000 LNV2 CNT 0.39 0.03 12.3 0.00 0.59

LNYARD 0.28 0.04 6.84 0.00 

1960 to 

1979 

Intercept 4.24 0.17 24.34 0.00 

F(2,343)=49.323 

p<0.0000 LNV3 CNT 0.34 0.04 9.15 0.00 0.22

LNYARD 0.14 0.05 2.98 0.00 

Note: Regressions were performed using the national weight values from the HUD survey data (USEPA 1998). 

a Variables: LNALL CNT = centered LNVAC for combined data set, LNYARD = log-transformed average yard soil 

Pb concentration (μg/g); LNINTXRF = log-transformed interior paint XRF Pb concentration (mg/cm2

); LNV1(2,3) 

CNT = centered LNVAC for each building vintage stratum. 

When analyzing the combined data set, the inclusion of two additional variables (logtransformed yard soil Pb and log-transformed interior XRF Pb concentration) results in an 

increase in R2 to 0.55, compared to 0.46 for the model containing vacuum indoor dust loading 

alone. Similar increases in R2 are achieved with the inclusion of additional variables into the 

models for the stratified data. The R2

 value for the <1940 model increases from 0.53 to 0.69 

when log-transformed soil Pb and interior XRF readings are included. In the 1940 to 1959 

regression, only log-transformed outdoor soil retains significance when LNVAC is also included, 

resulting in an increase in R2

 from 0.48 to 0.59. Including LNYARD in the regression on the 

1960 to 1979 data increases R2

 only from 0.20 to 0.22, and no other variable retains significance 

in this model. 

These results are consistent with a situation where both outdoor soil Pb levels and indoor 

Pb paint concentrations influence the observed indoor dust Pb concentrations in the HUD survey 

data, where the influence of indoor Pb paint concentration is weaker in homes built more 

recently. As always, however, care should be taken in drawing causal inferences from this type 

of analysis. The physical mechanisms responsible for the observed correlations cannot be 

inferred with any degree of certainty based on the regression analysis alone. 

G-61
G-4.3.1 Selection of Models for the Prediction of Dust Pb Concentrations 

The preceding analyses provide the basis for selecting indoor dust Pb concentration 

model(s). The question arises as to whether the univariate (indoor dust loading only) or 

multivariate models should be used. Arguably, the multivariate models explain a larger 

proportion of the variance in Pb concentration, and could thus, in theory, provide more reliable 

and precise predictions. However, to use the multivariate models, it is necessary to have 

information not only on the indoor dust Pb loading levels, but also to have values for the other 

variates (soil Pb concentrations and, for the two older strata, maximum interior XRF readings). 

Estimates of these values are not available from the data sources used to derive indoor dust 

loading estimates in the approach. While it would be defensible to use the mean values of the 

missing variates (from the HUD survey data) when generating predictions, doing so might (1) 

introduce additional bias into the indoor dust concentration estimates and/or (2) provide a 

deceptively precise estimate of indoor dust Pb concentration, since the statistical prediction 

limits for the multivariate models are narrower than those for the univariate models. 

G-4.4.1 Dust Pb Concentration Model Equations and Prediction Limits 

Attachment G-1-12 summarizes the prediction equations and their coefficients derived 

from the HUD National Survey data. The models predict LNPBCONC based solely on LNVAC. 

For each data set (combined, <1940, 1940 to 1959, and 1959-1970), coefficients are provided for 

predicting the geometric mean indoor dust Pb concentration and for estimating the upper and 

lower 95 percent statistical prediction limits. The prediction limits provide an estimate of the 

expected precision of the predicted indoor dust Pb concentrations, given the assumptions 

embodied in the regression models. Note that the coefficients in Attachment G-1-12 are 

different from those in Attachment G-1-10 because the regressions in Attachment G-1-10 were 

conducted using centered Pb loading data. 

G-62
 
 

 

 

Attachment G-1-12. Dust Pb Concentration Prediction Equations and Prediction Limits 

Building 

Vintage Estimate Model Coefficientsa 

Intercept Slope 

Combined 

Data Set 

Predicted Dust Concentration 4.92 0.52 

95% Upper Prediction Limit 6.58 0.52 

95% Lower Prediction Limit 3.26 0.52 

Pre - 1940 

Predicted Dust Concentration 5.51 0.45 

95% Upper Prediction Limit 6.87 0.45 

95% Lower Prediction Limit 4.16 0.45 

1940 - 1959 

Predicted Dust Concentration 4.93 0.44 

95% Upper Prediction Limit 6.33 0.44 

95% Lower Prediction Limit 3.54 0.44 

1960 - 1979 

Predicted Dust Concentration 4.70 0.35 

95% Upper Prediction Limit 6.40 0.35 

95% Lower Prediction Limit 3.01 0.35 a

 Prediction equation: LNPBCONC, μg/g = Intercept + Slope * LNVAC, μg/ft2. 

While the prediction equations are linear in “log-space,” they are not linear in terms of 

the predicted concentration of indoor dust Pb as a function of indoor dust Pb loading. 

Attachment G-1-13 shows the prediction equations derived from the combined data and from 

each age stratum. 

Attachment G-1-13. Predicted Geometric Mean Dust Pb Concentrations as a Function of 

Dust Pb Loading; Models Derived from Different Building Vintage Strata 

Predicted Dust Lead Concentration, μg/g

1,500 

1,000 

500 

0 

Combined Data 

Pre-1940 

1940-1959 

1960-1979 

0 10 20 30 40 50 

Vacuum Dust Loading, μg/ft2 

G-63
 
It can be seen that the range of indoor dust Pb concentration predictions generated by the 

different models becomes increasingly divergent with increasing indoor dust Pb loading. For an 

indoor dust loading of 5 μg/ft2, the predicted indoor dust concentrations range from 195 μg/g 

(1960 to 1979 data) to 515 μg/g (<1940 data). For an indoor dust loading input of 55 μg/ft2

, the 

range of predicted indoor dust concentrations is 440 to 1450 μg/g, with the models derived from 

the newest and oldest subsets of the data again generating the lowest and highest predictions, 

respectively. 

 Statistical prediction limits provide another indication of the expected degree of 

uncertainty associated with the indoor dust Pb concentration estimates. Note that in all cases 

(Attachment G-1-12) the log-transformed models and their prediction limit equations have the 

same slope, and differ only in their intercepts. That is, the width of the log-transformed 

prediction limits is constant, as shown in Attachment G-1-14. This is equivalent to saying that 

the ratio of the upper to lower prediction limits remains constant across the range of indoor dust 

loading inputs. 

Attachment G-1-14. Prediction Equation and Prediction Limits Derived from the 

Combined HUD Survey Data (USEPA 1995) (Log-Transformed) 

Because of the log-transformation of the data, the width of the prediction limits (upper 

minus lower limit) varies with the input indoor dust loading concentrations. At low indoor dust 

G-64
 
loading, the indoor dust Pb concentration limits are relatively narrow, increasing at higher indoor 

dust loading (Attachment G-1-15). 

 Attachment G-1-15. Dust Concentration Prediction Limits As a 

Function of Dust Loading (μg/g) 

Data Set Prediction 

Limit 

Dust Loading, μg/ft2 

0.14 0.37 1.0 2.7 7.4 20.1 54.6 

All Vintages Upper 257 416 674 1,096 1,786 2,918 4,780 

Combined Lower 11 18 29 47 76 123 199 

<1940 Upper NAa

 617 965 1,515 2,384 3,763 5,955 

Lower NAa 40 64 101 159 250 392 

1940 to 1959 Upper 232 358 556 866 1,351 2,116 3,325 

Lower 14 22 34 54 84 129 200 

1960 to 1979 Upper 298 423 601 858 1,229 1,766 NAa 

Lower 10 14 20 29 41 58 NAa 

These values provide a rough guide for judging the uncertainty associated with estimates 

of indoor dust concentrations from indoor dust loading. Ratios of the upper to lower prediction 

limits range from about 15 (<1940 vintage) to approximately 30 (1960 to 1979 vintage), 

reflecting the varying level of variability in the data used to derive the models. Another way of 

expressing the width of the prediction limits is to say that the upper and lower limits are within 

approximately 3.9- to 5.4-fold of the predicted geometric mean indoor dust concentrations 

depending upon which subset of data are included. 

Note that the prediction limits do not capture all of the uncertain in the indoor dust 

loading-concentration models. As discussed below, the overall uncertainty in the indoor dust Pb 

concentration predictions also depends on assumptions regarding the quality and 

representativeness of the data. 

G-1.5. LIMITATIONS AND UNCERTAINTY IN DUST PB CONCENTRATION 

MODELS 

G-5.1.1 Limitations of the Data Set 

As noted at the beginning of this appendix, the HUD National Survey provides the largest 

publicly available data set containing simultaneous measurements of vacuum indoor dust loading 

and indoor dust Pb concentration, along with other environmental Pb measurements, from a 

nationally representative sample of private residences. There are enough (284) observations to 

support the development of indoor dust loading-concentration models both for the data set as a 

whole and for the individual building vintage strata <1940, 1940 to 1959, and 1960 to 1979 (77, 

87, and 120, respectively). Sample collection and analysis techniques were consistent across the 

G-65
 
survey, and laboratory quality assurance procedures were stringent and fully documented. 

Potential biases in indoor dust Pb concentration measurements in “low tap weight” samples were 

identified and suspect samples were eliminated from the data set (USEPA, 1996). Nonetheless, 

the data set has some limitations as the basis for predicting indoor dust Pb concentrations. 

Potential uncertainties associated with the representativeness of the data cannot be 

quantified, but may be substantial. There is no guarantee that the Pb hazard characteristics of 

current urban houses will necessarily be the same as those in the HUD survey. For example, the 

HUD survey was conducted in 1989 to 1990, and the physical characteristics of houses with Pb 

paint hazards surviving to the present may be different from those surveyed 18 years ago 

(perhaps a result of better upkeep and maintenance). In addition, there may be other (unknown) 

reasons why the characteristics of current urban houses are systematically different from those in 

the 30 counties sampled by HUD. On the other hand, there is no reason to suspect that such 

differences would substantially bias the relationship between indoor dust Pb loading and 

concentration. 

As noted above, the technical quality of the data set appears to be quite good. The data 

on the whole are reasonably “well-behaved,” in that log-transformation results in symmetric, 

near-Gaussian distributions for most variables. Two observations, one with a very low indoor 

dust Pb concentration (0.1 μg/g) and one with a very high value (50,400 μg/kg) were identified 

as “outliers” and were found to be unduly influential in the regression models for the <1940 and 

1960 to 1979 data sets, respectively. These observations were omitted from the regression 

models, which had the effect (in both cases) of reducing the estimated regression coefficients for 

LNVAC by about 10 percent, while improving the regularity of the regression residuals. 

The issue of potential errors in the measurements of indoor dust loading has been raised 

in past analyses of indoor dust Pb sampling studies (USEPA, 1997a). If measurement errors are 

significant, there is the potential that the estimated regression coefficients and standard errors 

may be biased and inaccurate. While there are a number of approaches that can be used to 

address errors in variables, it was not necessary to employ any special methods in this approach. 

The major justification for not doing so is the assumption that the indoor dust loading for the 

general urban case study will be subject to roughly the same errors as the loading estimates on 

which the regression models were based. To the extent that the errors in these two sets of 

measurements are systematically different, then the regression coefficients may be biased. 

G-5.2.1 Limitations and Uncertainties in Dust Pb Models 

The most important choices with regard to model design were the decisions to logtransform the variables and employ log-log regression as the primary analytical technique. As 

G-66
 
noted above, log-transformation resulted in much more symmetrical, nearly Gaussian 

distributions for all (non-categorical) variables. The least well-behaved of the important 

explanatory variables was LNPBCONC, where there appeared to still be a slight deviation from 

(log) normality in the extreme “tails” of the data. 

No other simple model form was found that provided better qualitative or quantitative fits 

to the indoor dust loading-concentration data than the log-log multiple regression approach. 

Plots of regression residuals (Section G-1.6) showed little evidence of deviations from linearity 

or heteroscedasticity (non-uniformity of residual variance). The coefficient of determination 

(R2

) values were quite high (>0.46) for all of the univariate regressions, except that derived from 

the 1960 to 1979 subset of the data (0.20). 

All of the models are sufficient to develop reasonably reliable estimates of indoor dust 

concentration from indoor dust loading inputs, although the statistical confidence limits for these 

predictions are quite wide. A higher degree of scatter in the data from buildings built between 

1960 and 1979 is reflected in broader prediction limits for that regression. Also, the statistical 

confidence limits do not capture the full extent of uncertainty associated with potential nonrepresentativeness of data or other data limitations. 

Detailed model outputs and residuals plots are provided in Attachment G-1-16 through 

G-1-19 in Section G-1.6. 

G-1.6. DETAILED REGRESSION RESULTS 

G-67
 
Attachment G-1-16. Regression Results for Combined Data Set 

Combined Data Set Dust Loading Only, Weighted 

Regression Summary for Dependent Variable: LNPBCONC (New HUD Data 

With Weights.sta) 

R = 0.69437119 R2= 0.48215135 Adjusted R2 = 0.48143609 

F(1,724)=674.09 p<0.0000 SE of estimate: 0.84431 

Beta SE of 

Beta B SE of B t(280) p-level 

Intercept 4.920573 0.034640 142.0480 0.00 

LNVAC 0.694371 0.026744 0.517568 0.019935 25.9633 0.00 

G-68
 
 G-69
 
 

Attachment G-1-17. Regression Results for <1940 Data 

<1940 Data, Weighted 

Regression Summary for Dependent Variable: LNPBCONC (New HUD Data 

With Weights.sta) 

R = 0.72734822 R2 = 0.52903543 Adjusted R2 = .52651690 

F(1,187) = 210.06 p<0.0000 SE of estimate: 0.68462 

Include condition: v2 = 1 

Beta SE of 

Beta B SE of B t(187) p-level 

Intercept 5.513770 0.075486 73.04334 0.000000 

LNVAC 0.727348 0.050185 0.454319 0.031347 14.49336 0.000000 

G-70
 
 G-71
 
 

Attachment G-1-18. Regression Results for Data from 1940 to 1959 

1940 to 1959 Data, Weighted 

Regression Summary for Dependent Variable: LNPBCONC (New HUD Data 

With Weights.sta) 

R = 0.69421417 R2 = 0.48193331 Adjusted R2 = 0.47919222 

F(1,189) = 175.82 p<0.0000 SE of estimate: 0.70271 

Include condition: v2 = 2 

Beta SE of 

Beta B SE of B t(189) p-level 

Intercept 4.930233 0.058076 84.89214 0.000000 

LNVAC 0.694214 0.052355 0.443382 0.033438 13.25963 8.49E-29 

G-72
 
 G-73
 
 

Attachment G-1-19. Regression Results from 1960 to 1979 Data 

1960 to 1979 Data, Weighted 

Regression Summary for Dependent Variable: LNPBCONC (New HUD Data 

With Weights.sta) 

R = 0.45086819 R2 = 0.20328213 Adjusted R2 = 0.20096609 

F(1,344) = 87.771 p<.00000 SE of estimate: 0.86020 

Include condition: v2 = 3 

Beta SE of 

Beta B SE of B t(344) p-level 

Intercept 4.704796 0.046407 101.3816 0.000000 

LNVAC 0.450868 0.048125 0.354631 0.037853 9.3686 0.000000 

G-74
 
 G-75
 

Appendix H: Blood Lead (PbB) Prediction Methods, Models, and Inputs 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

 

Table of Contents 

Table of Contents .................................................................................................................H-i 

List of Exhibits .................................................................................................................. H-iii 

List of Attachments............................................................................................................H-iv 

H. BLOOD PB PREDICTION METHODS, MODELS, AND INPUTS ............................H-1
 

H.1. OVERVIEW OF BLOOD PB ESTIMATION APPROACH...................................... H-1 

H.2. DESCRIPTION OF BLOOD PB MODELS ............................................................... H-3 

H.2.1. The IEUBK Model........................................................................................... H-3 

H.2.1.1. Exposure and Uptake Modules of the IEUBK Model ...................... H-3 

H.2.1.2. Biokinetic Module of the IEUBK Model.......................................... H-4 

H.2.2. Leggett Model..................................................................................................H-6 

H.2.3. Lanphear Model...............................................................................................H-8 

H.3. APPLICATION OF BLOOD PB MODELS...............................................................H-9 

H.3.1. Adaptation of the IEUBK Model.....................................................................H-9 

H.3.2. Adaptation of the Leggett Model...................................................................H-10 

H.3.3. Adaptation of the Lanphear Model................................................................H-11 

H.3.3.1. Development of a Dust Pb Loading-Dust Pb Concentration 

Regression Model ........................................................................... H-11 

H.3.3.2. Estimation of Equivalent Dust Pb Concentrations and a Bivariate 

PbB Model ...................................................................................... H-12 

H.3.3.3. Estimation of PbB from Indoor Dust Loadings .............................. H-13 

H.4. INPUTS TO THE BLOOD PB MODELS ................................................................ H-14 

H.4.1. Exposure Concentration Estimates for Inhalation, Outdoor Soil/Dust and 

Indoor Dust .................................................................................................... H-14 

H.4.2. Policy-Relevant Background Exposure Pathway Concentrations and Pb 

Intake Estimates.............................................................................................H-14 

H.4.3. Behavioral, Physical, and Chemicals Factors Affecting Pb Exposure, 

Intake, and Uptake ......................................................................................... H-15 

H.4.4 Inter-Individual Variability............................................................................H-20 

H-i 
 

H.5. LIMITATIONS AND UNCERTAINTIES IN THIS ASSESSMENT AND 

BLOOD PB MODELING ......................................................................................... H-27 

REFERENCES ................................................................................................................... H-28 

H-ii 
 

List of Exhibits 

Exhibit H-1. Structure of the IEUBK Model............................................................................. H-5 

Exhibit H-2. Structure of the Leggett Model............................................................................. H-8 

Exhibit H-3. Ages for the IEUBK-Derived PbB Estimates....................................................... H-9 

Exhibit H-4. Predicted PbB Levels Associated with Combinations of Outdoor Soil/Dust and 

Indoor Dust Pb Loading and Indoor Pb Concentration ...................................... H-13 

Exhibit H-5. Summary of Non-Water Dietary Pb Intake Estimates........................................ H-15 

Exhibit H-6. Input Parameter Values for the PbB Modeling................................................... H-17 

Exhibit H-7. Summary of Children’s PbB Studies .................................................................. H-23 

Exhibit H-8. Time Trend in Children’s PbB GSD Values....................................................... H-24 

Exhibit H-9. GSD Estimates from Seven Studies Used to Derive the Lanphear et al. (2005) 

PbB-IQ Model .................................................................................................... H-26 

H-iii 
 

List of Attachments 

Attachment H-1. Respiratory Deposition and Absorption Fraction – Input for the 

IEUBK Model .............................................................................................. H-32 

H-iv 
 

 

H. BLOOD PB PREDICTION METHODS, MODELS, AND INPUTS 

This appendix describes the approaches and methods that were used to predict the 

changes in individual children's blood lead (PbB) levels and population PbB distributions 

associated with air, outdoor soil/dust, indoor dust, diet, and drinking water exposures. 

H.1. OVERVIEW OF BLOOD PB ESTIMATION APPROACH 

As discussed in Appendices C through E, exposure concentrations of lead (Pb) in air, 

outdoor soil/dust, and indoor dust have been estimated for each of the case studies. For the two 

point source case studies, these estimates are provided for each of the U.S. Census blocks or 

block groups in the assessment. For the general urban case study, a single estimate for each of 

the media is provided to capture the entire urban area. In addition to these exposure media, 

physiological and behavioral inputs are generated for each case study, as described below in 

Section H.4.3. These exposure concentrations and other variables related to exposure patterns, 

and pathway-specific absorption serve as inputs to the Integrated Exposure Uptake Biokinetic 

(IEUBK) Model for Children (hereafter referred to as the “IEUBK model”) to generate PbB 

estimates. Outputs from the IEUBK model take the form of PbB profiles (from 6 to 84 months 

of age) of a child receiving that combination of exposures for the entire exposure period. Two 

PbB metrics have been derived from this lifetime PbB profile. The first metric is the “lifetime” 

average, where “lifetime” is defined as the period from 6 to 84 months. The second metric is an 

estimate of “concurrent” PbB concentration, which has been defined as the average at ages 75 

and 81 months of age in the seventh year of life.1 

The PbB models yield central tendency estimates of a child’s PbB concentrations for 

specified simulation periods (with the temporal precision varying depending on the specific 

model) and for specific patterns of exposure. Unless the graphing option of the IEUBK is used, 

these estimates for a typical child (representing central tendency exposure) do not provide 

information about how individual responses to Pb exposure might vary among the exposed 

children or how changes in an individual’s PbB levels would affect the population’s levels for a 

given case study. Thus, a probabilistic approach has been implemented to capture both the 

effects of inter-individual variability in PbB levels and the population distribution of exposures 

on the resultant population distribution of PbB statistics. 

1

 The rationale for defining the average PbB at 75 and 81 months of age in the seventh year of life as 

concurrent reflects the fact that the average age of the intelligence quotient (IQ) testing in the Lanphear et al. (2005) 

study of PbB-IQ relationships was approximately seven years (see Appendix K for a more detailed discussion). 

H-1 
For the two point source case studies, development of population distributions of PbB 

levels involved the following steps: 

Step 1.	 PbB models were used to generate central tendency estimates of PbB per U.S. 

Census block or block group. 

Step 2.	 The number of children (birth up to 7 years of age) residing in each block and block 

group was determined from U.S. Census Bureau data (2005). 

Step 3.	 Population-weighted random sampling was used to select a PbB level from the 

results of Step 1. The probability for sampling each U.S. Census block or block 

group was set proportional to the number of young children (birth up to 7 years of 

age) residing in each block (obtained from Step 2). The data set generated in Step 1 

was sampled 50,000 times in this way. 

Step 4.	 For the central tendency estimate corresponding to a specific U.S. Census block or 

block group chosen in each iteration of Step 3, a lognormal distribution reflecting 

inter-individual variability in both behavior and biokinetics related to Pb exposure 

was developed using a geometric standard deviation (GSD) obtained from the 

literature. A random number was generated for each of the 50,000 iterations; this 

number corresponded to a cumulative probability value of the cumulative 

distribution function for the lognormal distribution defined by the chosen central 

tendency and the GSD. The Excel function LOGINV was then used to find the 

specific PbB value corresponding to that cumulative distribution function value. In 

this way, the central tendency values were adjusted to reflect specific patterns of 

behavior and biokinetics in children related to Pb exposure. Data related to the 

selection of the GSD values were provided in Section H.4. 

Step 5.	 These 50,000 simulated child PbB levels were then used to characterize (via 

percentiles) the distribution of PbB levels in the population. 

Steps (3) through (5) result in a distribution of predicted PbB levels in the exposed 

population that reflects variability contributed by both the population-weighted distributions of 

exposure concentrations and by the inter-individual variations in response to Pb exposures. 

For the general urban case study, no population-specific differences in central tendency 

PbB levels were available, since only a single representative PbB was generated for the entire 

urban area. Thus, Steps (2) and (3) were skipped, and the same central tendency value was 

always used to generate a lognormal distribution with the specified GSD in Step (4). However, 

as in the other case studies, 50,000 PbB values were selected to reflect the inter-individual 

variability associated with the GSD. 

H-2 
The following sections discuss in detail the PbB models used for this assessment to 

generate the central tendency estimates, the selected model inputs, and how the models were 

implemented to estimate case study-specific PbB levels for children (6 to 84 months of age). 

H.2. DESCRIPTION OF BLOOD PB MODELS 

Two biokinetic models and one empirical (regression-based) model were considered for 

use in this assessment. The two biokinetic models are the IEUBK model described in Section 

H.2.1 and the International Commission for Radiation Protection (ICRP) model (hereafter 

referred to as the “Leggett model”), described in Section H.2.2. Both are well documented, are 

widely used, and have been subject to a range of testing and calibration exercises (see Section 

4.4 of USEPA [2006a]). The empirical model was developed by Lanphear et al. (1998) 

(hereafter referred to as the “Lanphear model”) and is described in Section H.2.3. 

Based on the performance evaluation described in Appendix J, the IEUBK model was 

selected for use in this assessment. However, PbB predictions generated using the Leggett 

biokinetic model are included in the sensitivity analysis for comparison purposes (see Appendix 

L for more details). 

H.2.1. The IEUBK Model 

The U.S. EPA IEUBK model (USEPA, 2005) consists of three main modules: the 

exposure module, the uptake module, and the biokinetic module (see Exhibit H-1). The IEUBK 

model also has a graphing module that estimates a plausible distribution of PbB concentrations 

for a given GSD. The distribution is centered on the geometric mean (GM) PbB concentration 

calculated by the biokinetic module. Each of the main modules is described below. Full 

documentation of the IEUBK module structure and the basis for the suggested default parameter 

values can be found in U.S. EPA (1994b; 2002b). 

H.2.1.1. Exposure and Uptake Modules of the IEUBK Model 

The exposure module accepts inputs related to six exposure media: air, diet (excluding 

drinking water), drinking water, outdoor soil/dust, indoor dust, and other. The IEUBK model 

provides default values for the various model input parameters, which the user can adjust for 

specific applications. These parameters include those used by the model to estimate Pb uptake, 

including absorption fraction and inhalation rate, water intake, dietary intakes of specific food 

classes, and outdoor soil/dust and indoor dust ingestion rates. The selection of model input 

parameter values for this assessment is discussed in more detail in Section H.4. 

H-3 
The exposure module also includes default age-specific estimates of time spent outdoors, 

as well as estimates of outdoor and indoor air Pb concentrations, age-specific inhalation rate, and 

respiratory tract absorption fraction, all of which are used to estimate age-specific Pb inhalation 

uptakes. The respiratory tract absorption fraction implicitly reflects both deposition of inhaled 

Pb in the respiratory tract and absorption of deposited Pb, either from the respiratory tract or 

from the gastrointestinal (GI) tract. The model also contains an option for calculating indoor 

dust Pb concentrations based on an empirical relationship among air, outdoor soil/dust, and 

indoor dust Pb levels (a variation of the air and outdoor soil/dust regression based models 

discussed in Appendix G). Ingestion uptake is calculated using absorption fractions that are 

specific to the ingested medium (diet, drinking water, outdoor soil/dust, or indoor dust). 

In the uptake module, total GI Pb uptake is modeled as being composed of a saturable 

and an unsaturable component using the IEUBK default parameters describing the relative 

importance of these two pathways as a function of Pb intake. The outputs of the uptake module 

are estimates of the masses of Pb absorbed into the body over time as a function of 

concentrations in the various exposure media. 

H.2.1.2. Biokinetic Module of the IEUBK Model 

In the biokinetic module of the model, absorbed Pb (from ingestion and inhalation) is 

assumed to appear immediately in the plasma-extracellular fluid (ECF) compartment. The 

plasma-ECF compartment constitutes the central compartment in the biokinetic model from 

which exchange to all other compartments occurs. Trabecular and cortical bone (which are not 

directly coupled in the IEUBK model) constitute the main long-term storage compartments, with 

the estimated turnover in other compartments being more rapid. The binding capacity of the red 

blood cell (RBC) compartment is modeled as being saturable, simulating the limited capacity of 

aminolevulinate dehydratase (ALAD) and other Pb-binding proteins. Pb excretion occurs 

through a urine pathway (distinct from the kidney compartment); hepatobiliary secretion is 

coupled with the liver compartment, with a minor component of excretion from “other soft 

tissues” (i.e., skin, hair, and nails). A more complete description of the derivation and structure 

of the IEUBK model can be found in U.S. EPA (2006a) and White et al. (1998). 

H-4 
Exhibit H-1. Structure of the IEUBK Model 

Source: Adapted from (USEPA, 2006a). 

H-5 
H.2.2. Leggett Model 

The Leggett model (Leggett, 1993) differs from the IEUBK model in that data from 

short-term studies (on the time-scale of hours to days) are used to estimate parameter values for 

the most rapid uptake and exchange processes, and thus the time resolution of the Leggett model 

is much finer than that of the IEUBK model. The user may specify step length, depending on the 

degree of time resolution required in the PbB predictions. Unlike in the IEUBK model, Pb 

absorption is a linear function of Pb intake, and the known nonlinearity of PbB responses is 

modeled through concentration-dependent variation in Pb binding by RBCs. 

The biokinetic component of the Leggett model is more technically sophisticated than the 

IEUBK model, but the model lacks a built-in facility to convert exposure concentrations to Pb 

uptake and to integrate uptakes from multiple exposure media. 

Other key differences between the structures of the Leggett model and the IEUBK model 

include (Pounds and Leggett, 1998; USEPA, 2006a): 

•	 The published version of the Leggett model lacks the multipathway exposure module of 

the IEUBK model. The Leggett model accepts total respiratory and ingestion intakes 

(administered doses) as inputs and calculates Pb uptake using age-specific absorption 

factors. 

•	 The Leggett model lacks a “probabilistic” component; all predictions are deterministic 

for a single individual receiving a given set of exposures, with no capability for 

generating graphical outputs. 

•	 The central exchange compartment in the Leggett model is “diffusible plasma,” rather 

than the plasma-ECF compartment used in the IEUBK model. Extra-vascular fluid, 

RBCs, and a bound plasma fraction are the other blood/fluid compartments that exchange 

directly with plasma in the Leggett model, with different transfer rates reflecting 

differences in estimated exchange rates. 

•	 The trabecular and cortical bone compartments in the Leggett model are each divided 

into three subcompartments, bone surface and exchangeable and “non-exchangeable” 

bone volume. Pb in the “non-exchangeable” compartments of both types of bone can be 

remobilized, but only relatively slowly as a result of bone remodeling, whereas in the 

IEUBK model bone Pb stores are represented by only two (trabecular and cortical) 

compartments. 

•	 Another major difference between the models in the turn-over of Pb in bone. In the 

IEUBK model, the half-time for transfer from bone to plasma is 8.5 days (at 2 years of 

age). In the Leggett model, approximately 98 percent of bone Pb resides in exchangeable 

and non-exchangeable bone volume, with half-times out of these compartments being 

approximately 40 and 300 days, respectively (at 2 years of age). This difference in bone 

retention while not evident from quasi-steady state bone or blood estimates of the two 

H-6 
models, yields very different bone Pb kinetics in response to change in exposure 

(Leggett, 1993; USEPA, 1994a; 2006a: Section 4.4). 

•	 Urinary excretion is modeled in the Leggett model as part of a kidney subcompartment 

that receives Pb from blood plasma and rapidly transfers it to urine, rather than as a 

distinct compartment as in the IEUBK model. 

•	 In the Leggett model, the liver is modeled as two compartments one with rapid and one 

with moderately rapid Pb exchange. Other soft tissues are modeled as having three 

compartments with differing exchange rates. Pb in brain tissue is explicitly modeled in 

the Leggett model. The IEUBK model, in contrast, simulates three soft tissue 

compartments (kidney, liver and other), and does not specifically model Pb levels in the 

brain. 

The Leggett model predictions have been compared with the deterministic predictions of 

PbB levels generated by the IEUBK model, using the IEUBK default inputs (Pounds and 

Leggett, 1998). In that comparison, the Leggett model predictions were substantially higher than 

those from the IEUBK model. 

Like the IEUBK model, the Leggett model is biokinetic, and exchange among 

compartments is modeled using first-order transfer coefficients (equivalent to first-order rate 

constants). The Leggett model implements values for the transfer rates that are based on a range 

of data from adult human radioactive tracer studies, autopsy data from adults and children, and 

data from animal studies related to the absorption, deposition, and excretion of Pb and 

chemically similar elements (Leggett, 1993). Exhibit H-2 depicts the compartmental structure of 

the Leggett model. These transfer coefficients were estimated during the development of the 

Leggett model and provided as default values for six age categories: newborn (birth to 100 

days), 1 year, 5 years, 10 years, 15 years, and 25 years and older, with age-specific transfer 

parameters for children estimated by interpolation between the nearest values. Transfer factors 

for children were adjusted to take into account the more rapid bone turnover (calcium [Ca] and 

Pb addition and resorption) in children compared with adults. All of the Leggett model’s default 

transfer factors were used without modification in the performance evaluation described in 

Appendix J. 

H-7 
Exhibit H-2. Structure of the Leggett Model 

Source: Leggett (1993). 

H.2.3. Lanphear Model 

Lanphear et al. (1998) reported the results of an analysis of the relationship among 

residential outdoor soil Pb levels, indoor dust Pb, Pb paint hazards, and PbB levels in 12 cohorts 

of urban children in the United States. The study controlled for socioeconomic and family 

variables and exposure to Pb in drinking water. A major result of that effort was a model that 

predicted PbB concentrations as a function of indoor dust Pb loading (the amount of Pb per unit 

area of flooring) and residential outdoor soil Pb concentrations. It is important to reiterate that 

the Lanphear model estimates PbB concentrations for children 16 months of age, so the results 

from this model cannot be directly compared to the lifetime average and concurrent PbB 

predictions developed from outputs of the IEUBK and Leggett models. 

H-8 
 

H.3. APPLICATION OF BLOOD PB MODELS 

H.3.1. Adaptation of the IEUBK Model 

The IEUBK model was used in batch mode to generate PbB estimates at different ages 

for children exposed from 6 to 84 months of age in each block or block group for each case 

study. Inputs to the IEUBK model included exposure parameters and intake and uptake factor 

values (see Section H.4) and the inhalation, outdoor soil/dust and indoor dust exposure 

concentrations of Pb for each block or block group. The input data also included age-specific Pb 

exposure concentrations for policy-relevant background pathways (e.g., drinking water and diet), 

which were assumed to be the same for all children. 

As described in Section H.1, lifetime average and concurrent PbB estimates were derived 

for each (hypothetical) exposed child. Lifetime average is defined as the average PbB level of 

model outputs for the exposure interval 6 to 84 months, and concurrent PbB is defined as the 

average PbB level at 75 and 81 months in the seventh year of life. To derive these metrics, 

IEUBK PbB estimates were first generated for nine specific age ranges (see Exhibit H-3) for 

each block or block group (point source case studies) or for the case study as a whole (the 

general urban case study); these estimates represented the central-tendency PbB levels 

experienced by children of those ages in each block or block group or the general urban 

environment. The lifetime average PbB metric was derived as the time-weighted average of the 

PbB values for the nine ages. The concurrent PbB metric was derived as the average of the last 

two ages (75 and 81 months). 

Exhibit H-3. Ages for the IEUBK-Derived PbB Estimates 

Mid-point of IEUBK Age Ranges 

(Months) 

Age Range Represented by IEUBK 

PbB Estimates 

(Months) 

9 7 to 12 

15 13 to 18 

21 19 to 24 

31 25 to 36 

43 37 to 48 

55 49 to 60 

67 61 to 72 

76 73 to 78 

82 79 to 84 

Note: Modeling periods run from the first day of the first month to the last day of the 

last month. 

H-9 
The nine age periods for which the point estimates were obtained using the IEUBK 

model were selected to capture those periods of childhood exposure expected to produce 

significant variability in PbB (i.e., exposures occurring less than 2 years of age). Consequently, 

exposure intervals covering the first two years of life (i.e., 7 to 12 months, 13 to 18 months, and 

19 to 24 months) were six months long, while the remainder of the simulation periods (up to the 

last year) were simulated with year-long exposure intervals. 

The lifetime average and concurrent estimates were stored in Microsoft Excel® 

spreadsheets to serve as inputs to the probabilistic population PbB model (see Sections H.1 and 

H.4). Each time the Monte Carlo sampling algorithm chose a particular U.S. Census block or 

block group, the appropriate lifetime average and concurrent PbB levels served as the GM values 

for the block or block group from which the individual PbB estimates were derived. 

H.3.2. Adaptation of the Leggett Model 

To evaluate its potential use in these assessments, two adaptations were made to the 

Leggett model code, which Dr. Joel Pounds provided (Pounds, 2005). First, an external 

spreadsheet model (hereafter referred to as the “Leggett uptake calculation model”) was 

developed for converting multimedia exposure concentrations to age-specific Pb uptake 

estimates. This model was constructed using the same exposure factors and absorption fraction 

values for the air, drinking water, diet, outdoor soil/dust, and indoor dust exposure pathways as 

were used in the IEUBK model runs. This approach ensured that the age-specific masses of Pb 

entering the biokinetic module of the Leggett model would be identical to those entering the 

IEUBK model at the same exposure Pb concentrations for a child of the same age. Input values 

for the PbB modeling are provided in Exhibit H-6 in Section H.4.3. 

 In addition to the Leggett uptake calculation model, a FORTRAN “wrapper” was 

developed that allowed the model to be run in the batch mode (hereafter referred to as the “batch 

Leggett model”), generating PbB profiles for multiple children based on the Leggett uptake 

calculation model estimates described above. The outputs of the batch Leggett model were daily 

age profiles of PbB estimates for each exposed child, from which the concurrent and lifetime 

PbB metrics were derived by averaging over the same age ranges as described in Section H.3.1 

for the IEUBK model. 

PbB predictions from the Leggett uptake calculation model and the batch Leggett model 

were compared to results obtained by the U.S. EPA and other investigators for the same 

exposure scenarios. Predicted PbB levels were found to be very similar (nearly identical) to the 

results obtained in earlier model comparisons (USEPA, 2007b). 

H-10 
The Leggett model iteration time step was set at 0.1 day throughout the modeling period. 

Test runs indicated that modeled daily, concurrent, and lifetime average PbB concentrations from 

six months of age and older were identical to those obtained using much shorter time steps. Just 

as was described in Section H.3.1 for the IEUBK model, outputs from the PbB modeling 

(lifetime average and concurrent PbB estimates for each U.S. Census block or block group) were 

saved and stored in Microsoft Excel® spreadsheets to serve as inputs to the probabilistic 

population PbB model described in Section H.1. 

H.3.3. Adaptation of the Lanphear Model 

Two technical issues needed to be addressed in order to apply the Lanphear model to 

estimate PbB levels in this type of assessment. First, because the Lanphear model accepts dust 

Pb loading rather than dust Pb concentration as its input, a method was needed to develop a 

model describing the relationship between the indoor dust concentration estimates generated in 

the primary Pb smelter case study and estimates of indoor dust loading. The second problem 

was how to apply the Lanphear model to the specific combinations of indoor dust and outdoor 

soil Pb exposures in each case study block or block group. Sections H.3.3.1 and H.3.3.2 explain 

how these two issues were addressed. 

H.3.3.1. Development of a Dust Pb Loading-Dust Pb Concentration Regression Model 

The biokinetic models used to predict PbB concentrations use as their inputs the 

concentrations of Pb in outdoor soil/dust and indoor dust. However, the Lanphear model used to 

estimate PbB levels generates outputs from inputs of indoor dust Pb loading. Thus, developing 

approaches for estimating dust Pb concentration based on dust Pb loading is necessary. The 

relationship between indoor dust loading and Pb concentration was investigated using a data set 

developed as part of the U.S. Department of Housing and Urban Development’s (HUD’s) 1997 

National Survey. The data set was used because it appeared to be the largest, most nationally 

representative source of both indoor dust loading and concentration data taken simultaneously 

from the same households. To the extent that these data do not reflect the dust loading-dust 

concentration relationship in the primary Pb smelter case study, the PbB estimates will be biased. 

See Attachment G-1 for a more detailed discussion of the dust Pb loading-dust Pb concentration 

Regression Model. 

 The HUD data comprises 307 wipe sample and dust concentration measurements taken 

from 284 households (USEPA, 1998; Appendix C). The data were stratified into four vintage 

ranges from pre-1940 to post-1979. The data from all four ranges were pooled for the analysis. 

Log-log regression provided the best fit and regression diagnostics. Two dust concentration data 

points, one with a value about five-fold below the next lowest and one with a value more than 

H-11 
 

 

10-fold above the next highest concentration, were excluded from the analysis. The dust 

concentration model derived in this manner was as follows: 

LnHouseDustPb = 4.920573 + 0.517568 × LnDustPbLoading 

where: 

LnHouseDustPb = log-transformed indoor dust Pb concentration (micrograms per 

grams [μg/g]) 

LnDustPbLoading = log-transformed dust Pb loading (vacuum samples) (μg/square 

feet [ft2

]) 

Details of the derivation of the dust Pb loading-dust Pb concentration regression model 

can be found in Attachment G-1. 

H.3.3.2. Estimation of Equivalent Dust Pb Concentrations and a Bivariate PbB Model 

In the second part of the analysis, linear regression was again used to estimate PbB 

concentrations from the dust loading measurements in the Lanphear et al. (1998) analysis. 

Exhibit H-4 reproduces Table 4 from Lanphear et al. (1998) with an added column of estimated 

dust Pb concentrations. The table entries contain covariate-adjusted estimates of PbB for 16­

month-old children associated with specified combinations of indoor dust loading and outdoor 

soil/dust Pb concentrations. In Exhibit H-4, the relationship is also specified for indoor dust Pb 

concentrations. 

To estimate PbB values for individual U.S. Census blocks or in general urban 

environments, data from Exhibit H-4 were used to derive a bivariate model for predicting PbB as 

a continuous function of outdoor soil/dust and indoor dust Pb concentrations. The REGRESS 

module from Mathematica® version 5.2 was used to fit a nonlinear model to the natural log of 

outdoor soil/dust and indoor dust Pb concentrations, as follows: 

BloodPb = -9.1138 + 2.03554 × LnDustPb + 0.66657 × LnSoilPb 

where: 

BloodPb = concentration of Pb in blood (μg/deciliter [dL]) 

LnDustPb = log-transformed indoor dust Pb concentration (μg/g) 

LnSoilPb = log-transformed outdoor soil/dust Pb concentration (μg/g) 

All the coefficients were significant at p < 10-6 and the F Ratio for the fit model was 

960.3. To test the model, the fitted coefficients were used to reproduce the estimated PbB values 

in Exhibit H-4. The resulting PbB values matched those in the table within an average of 0.4 

percent. The maximum difference between any of the values in Exhibit H-4 and those in 

Lanphear’s original Table 4 was 1.6 percent. 

H-12 
 

Exhibit H-4. Predicted PbB Levels Associated with Combinations of Outdoor Soil/Dust 

and Indoor Dust Pb Loading and Indoor Pb Concentration 

Indoor 

Dust Pb 

Loading 

(μg/ft2

) 

Estimated 

Equivalent 

Indoor Dust 

Concentration 

(mg/kg) 

Outdoor Soil/Dust Pb (mg/kg) a 

10 72 100 500 1000 1500 2000 4000 

1 56 2.3 2.8 2.9 3.5 3.8 4 4.1 4.4 

5 150 3.2 4 4.1 4.9 5.3 5.5 5.7 6.1 

10 228 3.7 4.6 4.7 5.6 6.1 6.3 6.5 7.1 

15 292 4 5 5.1 6.1 6.6 6.9 7.1 7.7 

20 348 4.2 5.3 5.4 6.5 7 7.3 7.6 8.1 

25 398 4.4 5.5 5.7 6.8 7.3 7.7 7.9 8.5 

40 530 4.9 6.1 6.3 7.5 8.1 8.4 8.7 9.4 

55 643 5.2 6.5 6.7 8 8.6 9 9.3 10 

70 745 5.5 6.8 7 8.4 9.1 9.5 9.8 10.5 

100 925 5.9 7.3 7.6 9 9.7 10.2 10.5 11.3 

a

 Table adapted from Table 4 in Lanphear et al. (1998). 

Note that for equivalent indoor dust Pb concentrations outside of the range of the model 

(greater than 925 μg/g), the same degree of model fit cannot be expected. However, only 17 

U.S. Census blocks/block groups in the primary Pb smelter case study, with less than two percent 

of the exposed child population, have predicted indoor dust Pb concentrations above this value. 

H.3.3.3. Estimation of PbB from Indoor Dust Loadings 

The adapted version of Table 4 from the Lanphear model (see Exhibit H-4) predicts the 

PbB concentrations in young children as a function of outdoor soil/dust Pb concentration and 

indoor dust Pb loading. Thus, a log-log model of PbB concentration based on these variables can 

be derived directly from the values given in Exhibit H-4. Multiple regression of LnBloodPb on 

LnSoilPb and LnDustPbLoading 2

 yields the following: 

2

 The Lanphear et al. (1998) model is based on wipe loading measurements. 

H-13 
LnBloodPb = 0.578371 + .205290 × LnDustPbLoading + 0.108972 × LnSoilPb 

where: 

LnBloodPb = log-transformed concentration of Pb in blood (μg/dL)

 LnDustPbLoading = log-transformed indoor dust Pb loading (wipe samples) (μg/ft2

) 

LnSoilPb = log-transformed outdoor soil/dust Pb concentration (μg/g) 

 adjusted R2 = adjusted variance, set to 0.9997 

Like the model based on indoor dust Pb concentration, the model fit the data within 

rounding error (R2 = 0.9997, the F Ratio = 1691, and p< 10-6). When the indoor dust estimation 

models were used, which provided indoor dust Pb loading as their outputs, the above equation 

was used to predict PbB levels based on the Lanphear model. 

H.4. INPUTS TO THE BLOOD PB MODELS 

H.4.1. Exposure Concentration Estimates for Inhalation, Outdoor Soil/Dust and Indoor 

Dust 

Exposure concentrations for inhalation, outdoor soil/dust and indoor dust were estimated 

for each U.S. Census block or block group in each case study as described in Appendices C, D, 

and E. The values used for each air quality scenario modeled are presented in Appendix C for 

the general urban case study, in Appendix D for the primary Pb smelter case study, and in 

Appendix E for the secondary Pb smelter case study. 

H.4.2. Policy-Relevant Background Exposure Pathway Concentrations and Pb Intake 

Estimates 

As noted above, the exposure Pb concentrations and Pb intake from policy-relevant 

background pathways (drinking water and diet) were also parameter inputs to the PbB models. 

All exposed populations were assigned the same Pb concentration in drinking water. While the 

literature contains abundant data, in many cases the data are from “first-draw” samples, nonrandom (“priority”) samples, or from communities where Pb levels were known to be elevated. 

After reviewing the literature, the average drinking water concentration was estimated to be 4.61 

μg/liter (L), based on data from two recent studies of residential water concentrations in homes 

and apartments in the United States and Canada (Clayton et al., 1999; Moir et al., 1996). The 

range of values seen in these studies (0.84 to 16 μg/L) was considered to be representative of 

randomly sampled residential water in houses constructed since Pb pipe and solder were banned 

from residential use. The selected value is close to the “default” value (4.0 μg/L) provided with 

the IEUBK model (USEPA, 1994b). Much higher values have been encountered in homes with 

H-14 
 

 

Pb piping and/or very corrosive water. Lower average drinking water Pb concentrations (on the 

order of 0.9 μg/L) have been reported in some recent studies (Ryan et al., 2000). 

In addition to drinking water, young children are expected to be exposed to Pb in the 

foods they consume. In this assessment, all exposed children were assumed to receive the agespecific estimates of dietary Pb intake developed by the U.S. EPA Office of Solid Waste and 

Emergency Response (USEPA, 2006c). The U.S. EPA developed these estimates by analyzing 

food consumption data from the third National Health and Nutrition Examination Survey 

(NHANES III), conducted by the National Center for Health Statistics (CDC, 1997), and food 

residue data from the U.S. Food and Drug Administration’s (FDA) Total Dietary Study 

(USFDA, 2001). The daily intake values shown in Exhibit H-5 are considerably lower than 

those developed using the same methodology in the 1980s and 1990s. Pb concentrations in food 

have decreased dramatically since the prohibition of Pb solder in food containers in 1982 

(USEPA, 2006a, Section 3.4). 

Exhibit H-5. Summary of Non-Water Dietary Pb Intake Estimates 

Age Category 

(months) 

Updated Dietary Pb Intake 

Estimates (μg/day) 

0 to 11 3.16 

12 to 23 2.6 

24 to 35 2.87 

36 to 47 2.74 

48 to 59 2.61 

60 to 71 2.74 

72 to 84 2.99 

The potential exists for double-counting of drinking water and dietary Pb intake because 

some diet categories (e.g., baby formula, soup) may be prepared using domestic drinking water. 

Such double counting is likely to be minimal because the Total Dietary Survey data are limited 

to “direct” drinking water intake (USFDA, 2001). 

The assumption that all children in all exposed populations experience the same 

background exposure concentrations may result in a substantial underestimation of the overall 

variation in Pb uptake in these populations. 

H.4.3. Behavioral, Physical, and Chemicals Factors Affecting Pb Exposure, Intake, and 

Uptake 

As discussed previously, a number of model inputs govern how absorbed dose (uptake) 

estimates are calculated from exposure concentrations. These factors represent the physiological 

H-15 
and behavioral characteristics of the exposed population and the chemical and physical 

properties of the exposure media that govern exposure and absorption by inhalation and 

ingestion. 

Because substantial data have become available since the IEUBK default values were last 

updated, a literature review was conducted to identify and evaluate recent information related to 

Pb exposures, absorption, and bioavailability (USEPA, 2006b). Experts in the U.S. EPA were 

also consulted in an effort to derive exposure, intake, and uptake values for this assessment. 

Exhibit H-6 presents the parameter values that were selected as inputs to the PbB prediction 

models used in this assessment. The same (or equivalent) values were used, as described above, 

to calculate Pb inputs to the Leggett model during the sensitivity analysis 

Several values in Exhibit H-6 differ from the suggested default values in the most current 

version of the IEUBK model (USEPA, 2005). Children’s daily ventilation rate estimates were 

derived from values in the International Commission on Radiological Protection (ICRP) report 

(2002). The child respiratory absorption fraction values used in this assessment were 0.27 for 

the primary and secondary Pb smelter case studies and 0.24 for the general urban case study. 

U.S. EPA staff estimated these values based on multiple analyses of respiratory particulate 

deposition and Pb absorption, assuming a mass median particle diameter (MMAD) of 4.8 

micrometers (μM), with a GSD of 8.29, for areas affected by point sources and 0.5 μM, with 

GSD of 3.94, for urban areas not affected by specific point sources, such as Pb smelters 

(USEPA, 2007a). See Attachment H-1 for more details. 

H-16 
Exhibit H-6. Input Parameter Values for the PbB Modeling 

Parameter Parameter Name a 

Parameter Value 

Basis/De

IEUBK Default Age Ranges (Years) 

0.5 to 1 

1 to 2 

2 to 3 

3 to 4 

4 to 5 

5 to 6 

6 to 7 

Inhalation 

Daily ventilation rate 

(cubic meters 

[m3]/day) 

Ventilation rate 4 5.1 6 6.8 7.8 8.8 10 ICRP (2002), with interpo

ages. 

Absolute inhalation 

absorption fraction 

(unitless) 

• Lung absorption 

(IEUBK) 

• Absolute respiratory 

absorption fraction (Leggett) 

0.27 (Primary, secondary Pb smelter case studies), 

0.24 (general urban case study) 

U.S. EPA analysis of mudeposition and Pb absorp

Indoor air Pb 

concentration 

Indoor air Pb 

concentration 

(percentage of outdoor) 

100 percent Time spent indoors/outdo

when using either the IEUbecause the input air conlong-term weighted averaconcentrations (see Appe

Time spent outdoors Time spent outdoors (hours/day) Not used 

Drinking Water Ingestion 

Water consumption 

(L/day) 

Water consumption 

(L/day) 0.34 0.31 0.31 0.33 0.36 0.39 0.42 

Based on value for infant10-year olds (with trend lintermediate age ranges) 

Water Pb 

concentration 

(μg/L) 

Pb concentration in 

drinking water 

(μg/L) 

4.61 

GM of values reported in and Canadian population(Clayton et al., 1999; MoUSEPA, 2006a, Section 

H-17
 
Exhibit H-6. Input Parameter Values for the PbB Modeling 

Parameter Parameter Name a 

Parameter Value 

Basis/De

IEUBK Default Age Ranges (Years) 

0.5 to 1 

1 to 2 

2 to 3 

3 to 4 

4 to 5 

5 to 6 

6 to 7 

Absolute absorption 

(unitless) 

• Total percent 

accessible (IEUBK) 

• Absolute GI 

absorption fraction (Leggett) 

50 percent 

(Single value used across all age ranges) 

Assumed similar to dietapercent accessible" unde

Diet 

Dietary Pb intake 

(μg/day) 

Daily Pb intake 

(μg/day) 3.16 2.6 2.87 2.74 2.61 2.74 2.99 

Estimates based on the f

• Pb food residue dat

Drug Administration Study (USFDA, 200

• food consumption d

(CDC, 1997). 

Absolute absorption 

(unitless) 

• Total percent 

accessible (IEUBK) 

• Absolute GI 

absorption fraction (Leggett) 

50 percent 

Alexander et al. (1974) acited in U.S. EPA (2006atwo dietary balance studipercent of ingested Pb is weeks to 8 years of age)

Outdoor Soil/Dust and Indoor Dust Ingestion 

Outdoor soil/dust and 

indoor dust weighting 

factor 

(unitless) 

• Outdoor soil/dust 

and indoor dust ingestion weighting factor (percent outdoor soil/dust) (IEUBK) 

• Outdoor soil/dust 

and indoor dust ingestion rates calculated separately using same proportion of outdoor soil/dust ingestion (Leggett) 

45 percent 

This is the percent of totasoil/dust. Value reflects bconsideration (results pual. (1990), as cited in (USWijnen et al. study examingestion rates for rainy dIt was assumed that rainy with all outdoor soil/dust days were associated witoutdoor soil/dust and indrepresenting outdoor soil

H-18
 
Exhibit H-6. Input Parameter Values for the PbB Modeling 

Parameter Parameter Name a 

Parameter Value 

Basis/De

IEUBK Default Age Ranges (Years) 

0.5 to 1 

1 to 2 

2 to 3 

3 to 4 

4 to 5 

5 to 6 

6 to 7 

Total indoor dust + 

outdoor soil/dust 

ingestion 

(mg/day) 

Amount of outdoor 

soil/dust and indoor dust 

ingested daily 

(mg) 

85 135 135 135 100 90 85 U.S. EPA (1989), which w

studies focusing on child

Absolute 

gastrointestinal 

absorption (outdoor soil/dust and indoor 

dust) 

(unitless) 

Total percent 

accessible(IEUBK) 

Absolute GI absorption 

fraction (Leggett) 

Primary Pb smelter case study: 0.48 for outdoor 

soil/dust and 0.26 for indoor dust 

Secondary Pb smelter and general urban case 

study: 0.30 for both outdoor soil/dust and indoor 

dust 

Site-specific absorption fand indoor dust were dersmelter case study using (RBA) estimates generatinvolving outdoor soil/duscollected in the study areThese RBAs were convebioavailability factors (i.evalues) by applying the afactor for the control matesolution also fed to the aSecondary Pb smelter anstudy values: (USEPA, 1Pb in indoor dust and outaccessible as dietary Pb outdoor soil/dust ingestiomealtimes (resulting in enrelative to exposure durin

Other 

Maternal PbB (μg/dL) 

Maternal PbB 

concentration at childbirth, 

μg/dL 

1.94 NHANES IV, national GM 

nationalities (CDC, 2004)

a Where variable names or interpretations differ between the two models, it is specified within the Exhibit. 

H-19 
 

Estimates of children’s direct water ingestion were interpolated from values in the 

U.S. EPA Children-Specific Exposure Factors Handbook (USEPA, 2002a); the GI 

absorption fraction of Pb from water (and diet) was retained at the IEUBK default value 

of 50 percent, and is consistent with the U.S. EPA OAQPS previous analyses of Pb 

uptake (USEPA, 1989). As noted above, age-specific dietary intake values for Pb were 

revised to reflect the latest analyses of the U.S. FDA and NHANES III data on food 

consumption pattern and Pb residue levels (USEPA, 2006c). 

Age-specific outdoor soil/dust and indoor dust ingestion rates for the PbB models 

were left at the IEUBK default values. Similarly, the weighting factor for outdoor 

soil/dust and indoor dust ingestion was also left at 45 percent outdoor soil/dust, despite 

limited data supporting this specific value (USEPA, 1989; 1994b). The impacts of 

changes in the weighting factor and other variables related to outdoor soil/dust and indoor 

dust ingestion were investigated through the sensitivity analysis, which is discussed in 

more detail in Appendix L. 

Casteel et al. (2005) evaluated the GI absorption of Pb and other metals from 

outdoor soil/dust samples taken from the primary Pb smelter study area in juvenile swine. 

Results of these experiments (relative bioavailability estimates) were used to derive 

estimates of absolute GI absorption fractions (the IEUBK inputs are called “Percent 

Available”) of 0.48 (48 percent) for outdoor soil/dust and 0.26 (26 percent) for indoor 

dust. Note that these values, based on site-specific data, should not be considered 

representative of patterns of Pb uptake at other Pb smelter sites. For the other case 

studies, the IEUBK generic default value for GI absorption of Pb from outdoor soil/dust 

and indoor dust (0.30, or 30 percent) was used. This value is generally consistent with 

more recently reported values, although estimates vary widely. As was the case with the 

outdoor soil/dust-indoor dust weighting factor, the impacts of changes in absorption 

fractions for outdoor soil/dust and indoor dust were investigated in the sensitivity 

analyses, which is discussed in more detail in Appendix L. 

For the case study PbB modeling, the IEUBK default value for maternal PbB 

level was updated using data from the most recent NHANES survey. NHANES III data 

from 1988 to 1994 indicate that the GM PbB value for women of reproductive age has 

dropped to about 1.94 μg/deciliter (dL) (Maddaloni et al., 2005). 

H.4.4 Inter-Individual Variability 

The final major input to the probabilistic PbB model that needs to be defined is 

the estimated GSD. The GSD is a measure of the extent to which an individual’s 

H-20
 
 

 

 

simulated PbB level varies from the mean of the PbB levels for all individuals within a 

defined area.3 The selected GSD value determines the shapes of the population 

distributions of PbB levels generated by the probabilistic model within each of the 

defined areas. Larger GSD estimates will stretch the upper “tails” of the distribution, 

resulting in a larger proportion of children having higher estimated PbB values for a 

given set of exposures. As part designing this analysis, a review was conducted of recent 

literature characterizing variability in populations of Pb-exposed children to support the 

GSD values selected for each case study. 

Note that the appropriateness of using the GSD as an indicator of PbB intervariability presupposes that the population distributions of PbB levels are, or are close to, 

lognormal. With a few exceptions, numerous studies of PbB distributions in moderate to 

large populations have shown that lognormal models generally provide a good fit to the 

data. As discussed below, this appears to be the case even in populations where Pb 

exposures are relatively homogeneous. 

Many PbB studies are available, dating to the 1970s, which report PbB GSD 

values or present data from which GSD values can be estimated. These studies include 

large population surveys (such as the NHANES), as well as studies of smaller 

populations, often in limited geographic areas. A substantial proportion of the smaller 

studies are of children residing near smelting or mining operations where point source 

emissions and/or historical outdoor soil/dust contamination are dominant sources of 

exposure. Two objectives of the literature review were to (1) identify trends in GSD 

values over time in both the large population surveys and the smaller cohort studies, and 

(2) determine whether any systematic differences were evident between the PbB GSD 

values for the large and the small studies, and between the smelter and other small cohort 

analyses. The expectation was that the variability in studies of large populations with 

3 These defined areas are designed to delineate portions of the study area expected to have 

relatively uniform Pb media concentrations (for the two point source case studies, these areas are U.S. 

Census blocks and/or block groups). Consequently, the GSD used to cover inter-individual variability in 

PbB levels within each of these defined areas reflects primarily differences in behavior and biokinetics 

related to Pb exposure (i.e., delineation of these areas to include portions of the study area with similar Pb 

media concentrations has controlled for significant differences in Pb exposure concentrations, although 

some variability within these areas is still likely and is covered by the GSD). Note, that the GSD is applied 

to the entire urban case study area because this is a single exposure zone assumed to have uniform Pb 

media concentrations (and is not further differentiated as is the case with the two point source case studies). 

H-21
 
 

very heterogeneous exposure patterns should be greater than the variability in studies of 

small populations, where exposures are less variable. 

Exhibit H-7 lists the studies that were reviewed, and provides details related to the 

study methodologies, populations, and dates of blood sampling. 

H-22
 
Exhibit H-7. Summary of Children’s PbB Studies 

Study, Authors Study Population Age (months) Dates of PbB Measurement GM PbB (μg/dL

New York Screening Study (Billick et al., 1979) New York State NA 1970 to 1976 18 to 25 

NHANES II (Marcus, 1990) National, 6 to 60 6 to 60 1976 to 1980 12.8 

Midvale, Utah (smelter) NA 1980s NA 

Baltimore, Maryland Urban Soil 

Pb Abatement Demonstration 

Project 

NA NA NA 

(White et al., 1998) review 

(see article for full references) 

Butte, Montana (smelter) NA NA NA 

Kellogg, Idaho (smelter) NA 1974, 1983 14.8, 8.0 

E. Helena, Montana (smelter) NA 1983 8.8 

Leadville, Colorado (smelter) NA 1987 8.7 

Telluride, Colorado (smelter) NA 1988 6.1 

Midvale, Utah (smelter) NA 1990 5.1 

(Griffin et al., 1999) Bingham Creek, Utah (smelter) NA 1993 3.1 

Sandy, Utah (smelter) NA 1994 NA 

(Lanphear et al., 1998) Five urban studies 12 to 30 1985 to 1998 5.1 

Seven Pb smelter studies 12 to 30 1989 to 1994 4 

(Lanphear et al., 2005) Seven cohort studies (one smelter, three foreign) 6 to 60 1979 to 2000 11.70 

7.50 

Males 2.7 

Females 2.8 

(Pirkle et al., 1998) NHANES III Urban 12 to 60 1991 to 1994 2.8 

Non-Urban 2.7 

13 Socioeconomic groups -- 

1988 to 1991 3.6 

NHANES III, IV (CDC, 2007) National 12 to 60 1991 to 1994 2.7 

1999 to 2000 2.2 

Arizona 1.8 

NHEXAS, Age 12 to 60 months (USEPA, 2004) Baltimore, Maryland 12 to 60 1997 2.3 

Region 5 1.8 

New York Seasonality (Haley and Talbot, 2004) New York State 12 to 24 1994 to 1997 4 

NHANES IV, Age 12 through 24 months 

 (CDC, 2004) 

National males 12 to 24 1999 to 2000 2.3 

National females 12 to 24 2.4 

a GSD values were estimated from reported GM values and proportions of PbB measurements above 10 μg/dL. b GSD values were estimated from reported GM, 5th and 95th percentiles. 

H-23 
 

These studies illustrate the decline in children’s PbB levels over the past three decades. 

They also suggest that the level of inter-individual variability in PbB levels, as indicated by 

GSDs, has increased. Exhibit H-8 shows the temporal trend in reported and calculated GSD 

values from the studies listed in Exhibit H-7, with midpoint dates assigned to studies where 

sampling took place over more than one year. 

Exhibit H-8. Time Trend in Children’s PbB GSD Values 

1 

1.2 

1.4 

1.6 

1.8 

2 

2.2 

1970 1975 1980 1985 1990 1995 2000 2005 

Approximate Mid-Point of Blood Sampling 

Reported/Estimated PbB GSD (μg/dL)

Large-scale and national studies, in particular, show a dramatic increase in children’s 

PbB GSDs. GSD estimates from the two pre-1980 studies are both approximately 1.4 μg/dL for 

New York State and National populations. In contrast, children’s PbB GSDs in all post-1990 

large population surveys were greater than 1.7 μg/dL. All studies based on the NHANES from 

1991 onward estimate PbB GSDs of between 2.0 and 2.2 μg/dL for children ages 6 to 60 months 

or subgroups of that population. 

Potential time trends in GSD estimates from studies of smaller populations are more 

difficult to discern from data presented in Exhibit H-8. Studies of populations living near 

smelting and mining sites, most of which were conducted between 1970 and the mid-1990s, 

show relatively constant GSDs of between 1.5 and 1.8 μg/dL across this time period. However, 

the non-smelter studies, most of which were conducted more recently (1985 to 2000), indicate 

that PbB GSD values increased over this period, although the trend is less pronounced than for 

Large Population Surveys 

Mining/Smelting Sites 

Urban, Non-Smelting Sites 

H-24
 
 

the large-population survey data. Uncertainties about the exact dates when PbB levels were 

sampled, differences in sampling and averaging methods, and differences in the populations 

studied prevent concluding that this apparent increase in GSD values is “real,” even though such 

a trend would be consistent with that shown by in the national survey data. 

Collectively, the mean GSD value estimated from all the small studies (smelter and nonsmelter) is 1.73 μg/dL. The average GSD derived from studies of smelter populations is 

1.67 μg/dL; the average GSD for studies of non-smelter populations is 1.80 μg/dL. The average 

GSD for all of the small-population studies where blood sampling occurred after 1990 is 

1.76 μg/dL. For large-population surveys where sampling was conducted during the same period 

the average GSD is 2.01 μg/dL. These results generally support the idea that PbB variability in 

small populations with relatively homogeneous exposure patterns is, in fact, less than that for the 

United States population as a whole, where exposure is much less homogeneous. Because of 

methodological differences among these various studies, however, the differences in variability 

should be interpreted cautiously. 

One major difficulty in comparing GSD estimates from the various populations in Exhibit 

H-8 is that the PbB data were collected and interpreted differently from study to study. The 

number of samples taken from each child can strongly affect the overall inter-individual 

variability in PbB levels. Also, the timing and numbers of multiple samples, and how they are 

combined to generate PbB metrics, can strongly influence the reported “GSD” values. As noted 

above, different levels of variability in exposures will also affect the observed variability in PbB 

levels. Differences in analytical methods and levels of detection may also play a role in 

differences in GSD. 

In this assessment, these issues were addressed by basing risk estimates on two different 

PbB metrics, which capture PbB variability over different time periods (i.e., “concurrent” and 

“lifetime” PbB metrics as defined by Lanphear et al. (2005)]). The PbB-IQ model Lanphear et 

al. (2005) was developed based on PbB data from seven cohort studies of Pb-exposed children, 

where multiple PbB measurements had been taken over the age range of 6 months to at least 60 

months. The data from these studies was also helpful in estimating appropriate GSD values for 

use in this assessment; using similar assumptions about PbB variability helped to ensure that the 

risk estimates evaluated were consistent with those that might be derived for the populations 

from which the risk model was developed. 

Exhibit H-9 summarizes the data Lanphear et al. (2005) used in the development of their 

PbB-IQ models. GSD values for each of the seven studies were estimated based on the GM, 5th, 

and 95th percentile values presented in Lanphear et al.’s Table 2 (2005), assuming log normality. 

H-25
 
 

 

In the exhibit, concurrent PbB refers to the PbB measurement closest to the age at which IQ 

testing was performed, which was 6 to 7 years of age for the bulk of the cohorts studied. 

Lifetime PbB levels refer to the average of all PbB samples taken between six months of age and 

the concurrent sample. Because lifetime PbB levels are estimated based on many measurements 

per child, the average GSD value (1.58 μg/dL) for lifetime average PbB levels is lower than the 

average GSD for concurrent PbB (1.72 μg/dL) across the seven studies. The pattern is very 

consistent; the estimated concurrent GSDs are greater than the estimated lifetime GSDs for all of 

the studies evaluated. 

Exhibit H-9. GSD Estimates from Seven Studies Used to Derive the Lanphear et al. (2005) 

PbB-IQ Model 

Study Location 

Lifetime PbB 

(μg/dL) a Concurrent PbB 

(μg/dL) b 

GM GSD c GM GSD c 

(Bellinger et al., 1992) Boston, 

Massachusetts 7.6 1.55 5.4 1.68 

(Dietrich et al., 1993) Cincinnati, Ohio 11.7 1.56 7.5 1.70 

(Ernhart et al., 1989) Cleveland, Ohio 14.5 1.41 14.2 1.53 

(Schnaas et al., 2000) Mexico 10.6 1.60 7.0 1.68 

(Baghurst et al., 1992) Port Pirie, South 

Australia 18.6 1.37 13.0 1.52 

(Canfield et al., 2003) Rochester, New 

York 5.5 1.66 4.0 1.88 

(Wasserman et al., 

1997) Yugoslavia 15.8 1.94 15.9 2.02 

Mean of All Studies 12.04 1.58 9.57 1.72 

Median of All Studies 11.70 1.56 7.50 1.68 a Lifetime PbB levels refer to the average of all PbB samples taken between six months of age and the 

"concurrent" sample. 

b Concurrent PbB refers to the PbB measurement closest to the age at which IQ testing was performed, which 

was 6 to 7 years of age for all of the cohorts studied, except the Boston and Cleveland cohorts. Blood samples 

taken at the age of 5 years and an average age of 4.8 years were used to estimate "concurrent" PbB levels in the 

Boston and Cleveland cohorts, respectively. 

c

 GSD values were calculated from GM, 5th, and 95th percentile in Lanphear et al. (2005). 

The values in Exhibit H-9, along with those in Exhibit H-7 and Exhibit H-8, helped provide the 

basis for selecting appropriate GSD values for this assessment. The IEUBK default GSD value 

(intended to represent variability for children across the 7 year age range) was 1.6 μg/dL. 

H-26
 
 

H.5. LIMITATIONS AND UNCERTAINTIES IN THIS ASSESSMENT AND BLOOD PB 

MODELING 

A number of factors affect the degree of uncertainty associated with this assessment and 

PbB modeling. These factors include the estimated exposure Pb concentrations associated with 

policy-relevant sources and policy-relevant background; the exposure, intake, and uptake factor 

values; the differences in the PbB models themselves; the approach used to characterize interindividual variability; and the demographics of the exposed population. The relative impacts of 

these factors on PbB estimates and health impacts are discussed in Appendix M. 

H-27
 
 

 

 

 

 

 

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H-31
 
 

 

 

ATTACHMENT H-1. RESPIRATORY DEPOSITION AND ABSORPTION 

FRACTION – INPUT FOR THE IEUBK MODEL 

One of the inputs to the Integrated Exposure Uptake Biokinetic Model for Lead in 

Children (IEUBK model) is an estimate of the fraction of lead (Pb) in air that deposits in the 

respiratory system and is absorbed into the blood (either from the respiratory tract or from the 

gastrointestinal tract following mucocilliary clearance from the respiratory system).4 

Throughout this discussion, this parameter is termed respiratory deposition-absorption fraction. 

To estimate appropriate values for the respiratory deposition-absorption fraction for use 

in the case studies for this assessment, the basis for previously used values (i.e., those developed 

for the 1990 U.S. EPA Staff Paper [USEPA, 1990]) and currently available information and 

methodologies were considered. The bases for the value used in the case study assessments 

described in the 1990 Staff Paper and the default value used in the IEUBK model were described 

by Cohen (1987). The value for the 1990 case study assessments was considered ambient air 

near Pb point sources,5

 while the value used as the IEUBK model default was for “general 

atmospheres.” Different analyses, with some commonality, underlie these two values. The 

analyses differ in derivation of the estimates of fractional deposition in the respiratory tract 

regions, due to different aerosol size distributions for the Pb particles in the ambient air in the 

two types of environments (i.e., near point source or general populations). Subsequent steps for 

both analyses relied on estimates of fractional absorption associated with the different regions of 

the respiratory tract, and estimated differences in particle deposition between an adult and a 2­

year-old child. 

Consistent with the 1987 analysis, and given the two types of case studies included in this 

assessment (i.e., point sources and the general urban case study), two estimates of the respiratory 

deposition-absorption fraction pertaining to the two different environments were developed 

again. In addition to the aspects considered in the 1987 analysis, this assessment involved the 

use of publicly available particle dosimetry models and explicitly considered particle 

inhalability. Addressing inhalability, which was not done in the 1987 analysis, has a larger effect 

on the estimate for the point source environment due to a greater preponderance of larger 

particles. 

4

 Among the model parameters for the IEUBK model (windows based version), this is termed “lung absorption” and 

is entered as a percentage (USEPA, 2002b). 

H-32
 
 

 

 

 

 

 

 

 

In the current analysis, the Pb-laden aerosol size distributions for the two types of 

environments were described in terms of their mass median aerodynamic diameter (MMAD) and 

GSD based on information on Pb particle size distributions described in the U.S. EPA Criteria 

Document for Pb (USEPA, 2006a) and other available information (Cohen, 1987; Singh et al., 

2006).6,7 Regional deposition (with consideration to inhalability) for the aerosols was estimated 

using two publicly available mathematical models: 1) the Multiple Path Particle Dosimetry 

(MPPD) model, Version 2.0, and 2) the Lung Dose Evaluation Program (LUDEP), Version 2.07, 

software. The MPPD model was developed by the CIIT Centers for Health Research (CIIT), 

USA, in collaboration with the National Institute of Public Health and the Environment (RIVM), 

the Netherlands, and the Ministry of Housing, Spatial Planning and the Environment, the 

Netherlands (Asgharian et al., 2004; CIIT and RIVM, 2002). The LUDEP model is an 

implementation of the Human Respiratory Tract Model for Radiological Protection model 

developed by the International Commission on Radiological Protection (ICRP, 1994). LUDEP 

(Version 2.07) only allows simulations for adult males, and not for females or children. 

For the adult simulations, the MPPD was run using the (Yeh and Schum, 1980) airway 

model. The adult simulations used the normal augmenter breathing route and similar values for 

functional residual capacity (FRC) (3,300 milliliters [ml]) and head volume (50 ml). Tidal 

volume and breathing frequency values for each activity level were those from (ICRP, 2002), as 

were hours associated with each activity level used in deriving daily regional deposition 

estimates. For the child simulations, the MPPD symmetric airway model (Asgharian et al., 

2004) for age 23 months was run. The FRC, head volume, and activity-dependent values of tidal 

volume and breathing frequency were obtained by a curve fit to the data for three or more ages 

e.g., 0.25, 1, and 5 years of age (see Table 15 [(ICRP, 1994)). 

To create the average daily estimates needed for the IEUBK model, a daily respiratory 

volume-weighted average was derived for each region of the respiratory tract8

 using estimates of 

5 The case studies included in the 1990 U.S. EPA Staff Paper analysis were populations living near two secondary 

Pb smelters, a primary Pb smelter, and a battery recycling plant (USEPA, 1990). 6 For use with the models, the particle size distribution for the smelter environments was assumed to be lognormal 

with MMAD of 4.8 μm and GSD of 8.29. 7 For use with the models, the particle size distribution for the downtown urban site was assumed to be lognormal 

with MMAD of 0.5 μm and GSD of 3.94. 8 The MPPD model truncates calculations at MMAD values above 20 μm. For the point source scenario, assuming a 

lognormal distribution; approximately 30 percent of the particle mass falls into this part of the distribution.
 

Deposition of these particles, assumed to occur in the head, was estimated based on their inhalability (Menache et 

al., 1995). 

H-33
 
 

daily time spent at each activity level and the associated cumulative ventilation volume. The 

estimates of average daily fractional deposition were then combined with estimates of 

absorption. Estimates of fractional absorption of Pb associated with deposition in different 

regions of the respiratory tract used in this analysis were the same as in the Cohen (1987) 

analysis, which are consistent with information presented in the U.S. EPA Criteria Document for 

Pb (USEPA, 2006a). Absorption was estimated to be complete (100 percent) for particles 

depositing in the alveolar region, while absorption was estimated at 40 percent for particles 

depositing in the head or tracheobronchial region and were assumed to clear to the GI tract for 

absorption. 

The adult estimates of total and regional average daily respiratory tract deposition derived 

using the two different models are generally similar (see Attachment H-1-1). The adult estimates 

of total deposition are not that dissimilar from those for children. However, the regional 

deposition values for children relative to adults were lower for the pulmonary region and higher 

for the tracheobronchial and head regions. This finding is consistent with observations in the 

current literature (Phalen and Oldham, 2001; USEPA, 2006a; pages 4-4 and 4-5). Consistent 

with Cohen (1987), the current analysis for the general urban environment showed greater 

deposition in the tracheobronchial and head regions of children as compared to adults. The 

relatively lesser pulmonary deposition of children in both environments, while similar to 

observations in the literature, differs from Cohen (1987), in which factors of 1.3 to 1.5 were 

assigned to calculate estimates of pulmonary deposition for children from estimates for adults. 

Attachment H-1-1. Estimates of Average Daily Respiratory 

Deposition Fraction – Current Analysis 

Body Region 2-year-old Child 

(MPPD) (MPPD) 

Adult 

(LUDEP) 

Adult 

General Urban Case Study 

 Alveolar Region 0.038 0.119 0.122 

 Tracheobronchial Region 0.020 0.026 0.014 

 Head Region 0.122 0.109 0.093 

Total 0.170 0.254 0.230 

Point Sources/Smelters 

 Alveolar Region 0.015 0.053 0.065 

 Tracheobronchial Region 0.012 0.012 0.010 

 Head Region 0.225 0.230 0.207 

Total 0.252 0.295 0.282 

H-34
 
 

All estimates of respiratory deposition-absorption fraction (i.e., the IEUBK “lung 

absorption” parameter) derived in the current analysis are lower than the previous estimates (see 

Attachment H-1-2) indicating the influence of the newly considered inhalability. 

The regional deposition differences between children and adults discussed above were 

amplified when they were multiplied by the regional Pb absorption estimates of 100 percent for 

the pulmonary region (where deposition is greater for adults) and 40 percent for tracheobronchial 

and head regions (where deposition is greater for children), such that the resultant estimates of 

respiratory deposition-absorption fraction were slightly lower for children than adults. However, 

observations on particle deposition in the different regions of the human respiratory tract are less 

available for children (the target population for this risk assessment) as compared to adults, more 

greatly limiting our ability to evaluate the child-specific deposition estimates and accordingly 

contributing to greater uncertainty. Consequently, rather than assigning a lower respiratory 

deposition-absorption fraction estimate to the target population than the estimates obtained from 

the adult modeling, the estimates chosen for IEUBK modeling were the averages of the values 

obtained from the MPPD and ICRP adult model simulations. That is, 0.27 was selected as the 

respiratory deposition-absorption fraction estimate for the smelter case studies and 0.24 was 

selected as the estimate for the general urban case study. The same values were adopted as 

absolute total absorption fractions in the sensitivity analysis conducted using the Leggett model. 

The Leggett model regional deposition fractions (which determine the rate at which Pb is 

released to the blood stream from the various lung compartments) were not changed from the 

default values. 

H-35
 
 

 

 

 

Attachment H-1-2. Estimates of Respiratory Deposition-Absorption Fraction – Previous 

and Current Analyses 

Source 2-Year-old Child Adult 

General Urban 

 Cohen, 1987 0.25 to 0.45 0.15 to 0.30 

 MPPD (this analysis) 0.17 0.25 

ICRP-LUDEP (this analysis) 0.23 

Point Sources/Smelters 

 Cohen, 1987 0.42 0.38 

 Cohen, 1987 (adjusted for inhalability) 0.32 a 0.27 to 0.28 a

 MPPD (this analysis) 0.22 0.26 

ICRP-LUDEP (this analysis) 0.28 

a

 This value was derived by adjusting the Cohen (1987) estimated fractional deposition for larger particles based on 

inhalability (ICRP, 1994; Menache et al., 1995). Per ICRP (1994), the same adjustment was made for child as 

adults. 

H-36
 
Appendix I: Blood Lead (PbB) Modeling Estimates 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

 

Table of Contents 

Table of Contents .................................................................................................................. I-i 

List of Exhibits ..................................................................................................................... I-ii 

I. BLOOD LEAD MODELING ESTIMATES..................................................................... I-1
 

I.1. CALCULATION OF PATHWAY CONTRIBUTIONS TO BLOOD PB....................I-1 

I.2. GENERAL URBAN CASE STUDY ............................................................................I-2 

I.2.1. PbB Model Scenarios Run for the General Urban Case Study..........................I-2 

I.2.2. PbB Results for the General Urban Case Study.................................................I-5 

I.2.3. Ambient Air to PbB Ratios for the General Urban Case Study.......................I-21 

I.3. PRIMARY PB SMELTER CASE STUDY.................................................................I-25 

I.3.1. Description of PbB Model Scenarios Run for the Primary Pb Smelter Case 

Study ................................................................................................................I-25 

I.3.2. PbB Results for the Primary Pb Smelter Case Study.......................................I-27 

I.3.3. Ambient Air to PbB Ratios for the Primary Pb Smelter Case Study...............I-33 

I.4. SECONDARY PB SMELTER CASE STUDY ..........................................................I-39 

I.4.1. Description of PbB Model Scenarios Run for the Secondary Pb Smelter Case 

Study ................................................................................................................I-39 

I.4.2. PbB Results for the Secondary Pb Smelter Case Study...................................I-40 

I.4.3. Ambient Air to PbB Ratios for the Secondary Pb Smelter Case Study...........I-45 

REFERENCES .....................................................................................................................I-52 

I-i 
 

 

 

 

List of Exhibits 

Exhibit I-1. PbB Model Scenarios Run for the General Urban Case Study ................................I-4 

Exhibit I-2. General Urban Case Study: Current Conditions (95th Percentile) – Estimated 

Exhibit I-3. General Urban Case Study: Current Conditions (Mean) – Estimated PbB 

Exhibit I-4. General Urban Case Study: Current NAAQS (1.5 μg/m3, Maximum Quarterly 

Exhibit I-5. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m3

, Maximum
 

Exhibit I-6. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3, Maximum
 

Exhibit I-7. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum
 

Exhibit I-8. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3, Maximum 

Exhibit I-9. General Urban Case Study: Current Conditions (95th Percentile) – Ambient 

Exhibit I-10. General Urban Case Study: Current Conditions (Mean) – Ambient Air Pb to 

Exhibit I-11. General Urban Case Study: Current NAAQS (1.5 μg/m3, Maximum 

Exhibit I-12. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m3, Maximum 

Exhibit I-13. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3

, Maximum 

Exhibit I-14. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum 

Exhibit I-15. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3, Maximum
 

PbB Levels...............................................................................................................I-7 

Levels.......................................................................................................................I-9 

Average) – Estimated PbB Levels.........................................................................I-11 

Quarterly Average) – Estimated PbB Levels.........................................................I-13 

Monthly Average) – Estimated PbB Levels ..........................................................I-15 

Monthly Average) – Estimated PbB Levels ..........................................................I-17 

Monthly Average) – Estimated PbB Levels ..........................................................I-19 

Air Pb to PbB Ratios..............................................................................................I-22 

PbB Ratios............................................................................................................I-22 

Quarterly Average) – Ambient Air Pb to PbB Ratios..........................................I-23 

Quarterly Average) – Ambient Air Pb to PbB Ratios..........................................I-23 

Monthly Average) – Ambient Air Pb to PbB Ratios............................................I-24 

Monthly Average) – Ambient Air Pb to PbB Ratios............................................I-24 

Monthly Average) – Ambient Air Pb to PbB Ratios............................................I-25 

Exhibit I-16. PbB Model Scenarios Run for the Primary Pb Smelter Case Study ....................I-27 

I-ii 
 

Exhibit I-17. Primary Pb Smelter Case Study: Current NAAQS Scenario (1.5 μg/m3, 

Maximum Quarterly Average) – Estimated PbB Levels......................................I-28 

Exhibit I-18. Primary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3

, 

Maximum Quarterly Average) – Estimated PbB Levels......................................I-29 

Exhibit I-19. Primary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3, 

Maximum Monthly Average) – Estimated PbB Levels .......................................I-30 

Exhibit I-20. Primary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) – Estimated PbB Levels .......................................I-31 

Exhibit I-21. Primary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3

, 

Maximum Monthly Average) – Estimated PbB Levels .......................................I-32 

Exhibit I-22. Primary Pb Smelter Case Study: Current NAAQS Scenario (1.5 μg/m3

, 

Maximum Quarterly Average) – Ambient Air to PbB Ratios..............................I-34 

Exhibit I-23. Primary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3

, 

Maximum Quarterly Average) – Ambient Air to PbB Ratios..............................I-35 

Exhibit I-24. Primary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios ...............................I-36 

Exhibit I-25. Primary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios ...............................I-37 

Exhibit I-26. Primary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios ...............................I-38 

Exhibit I-27. PbB Model Scenarios Run for the Secondary Pb Smelter Case Study ................I-40 

Exhibit I-28. Secondary Pb Smelter Case Study: Current Conditions Scenario – Estimated 

PbB Levels............................................................................................................I-41 

Exhibit I-29. Secondary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3

, 

Maximum Quarterly Average) – Estimated PbB Levels......................................I-42 

Exhibit I-30. Secondary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3

, 

Maximum Monthly Average) – Estimated PbB Levels .......................................I-43 

Exhibit I-31. Secondary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3

, 

Maximum Monthly Average) – Estimated PbB Levels .......................................I-44 

Exhibit I-32. Secondary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3

, 

Maximum Monthly Average) – Estimated PbB Levels .......................................I-45 

Exhibit I-33. Secondary Pb Smelter Case Study: Current Conditions Scenario – Ambient 

Air to PbB Ratios..................................................................................................I-47 

I-iii 
 

Exhibit I-34. Secondary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3, 

Maximum Quarterly Average) – Ambient Air to PbB Ratios..............................I-48 

Exhibit I-35. Secondary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios ...............................I-49 

Exhibit I-36. Secondary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios ...............................I-50 

Exhibit I-37. Secondary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios ...............................I-51 

I-iv 
I. BLOOD LEAD MODELING ESTIMATES 

This appendix presents the blood lead (PbB) estimates for each case study and for all 

National Ambient Air Quality Standards (NAAQS) scenarios considered in this analysis. 

Section I.2 contains the results for the general urban case study, including an overview of the 

scenarios evaluated (see Section I.2), the PbB estimates for several percentiles of the PbB 

distribution (see Section I.2.2), and the ambient air Pb concentration to PbB ratios (see Section 

I.2.3). Similarly, Section I.3 provides the results for the primary Pb smelter case study, including 

an overview of the scenarios evaluated (see Section I.3.1), the PbB results for several percentiles 

(see Section I.3.2), and the ambient air Pb concentration to PbB ratios (see Section I.3.3). 

Finally, Section I.4 presents the results for the secondary Pb smelter case study, including an 

overview of the scenarios evaluated (see Section I.4.1), the PbB results for several percentiles 

(see Section I.4.2), and the ambient air Pb concentration to PbB ratios (see Section I.4.3). 

Estimates presented in this appendix are specified with regard to number of decimal 

places, which results in various numbers of implied significant figures. This is not intended to 

convey greater precision for some estimates than others; it is simply an expedient and initial 

result of the software used for the calculation. Greater attention is given to significant figures in 

the presentation of estimates in the main body of the report. 

I.1. CALCULATION OF PATHWAY CONTRIBUTIONS TO BLOOD PB 

In the subsequent sections of this appendix, the PbB estimates are separated into 

contributions from different pathways (i.e., diet, drinking water, outdoor soil/dust, indoor dust, 

and the inhalation of recent air). These contributions are estimated by calculating the percentage 

of uptake from each pathway and applying the same percentage to the total PbB estimate. To 

calculate the percentage of total Pb uptake arising from the different exposure pathways, the 

intake for each medium is calculated as the total amount consumed of the given medium 

multiplied by the concentration of Pb in that medium. The uptake is then calculated as the intake 

multiplied by the fraction of Pb that is absorbed for that medium. All the relevant input 

parameters needed for this calculation are discussed in Appendix H. For indoor dust and outdoor 

soil/dust, the total ingestion of both media is divided into separate indoor dust and outdoor 

soil/dust contributions by multiplying by the percentage of the total ingestion which arises from 

outdoor soil/dust (as discussed in Appendix H). The intakes are calculated up until a child is 7 

years of age and then a lifetime average intake is calculated for each medium. Finally, these are 

summed to get the total average yearly uptake, and the percentage arising from each pathway is 

calculated as the uptake in a given medium divided by the total. 

I-1
 
Indoor dust is separated into two portions for the general urban and the secondary Pb 

smelter case studies, as described in Appendix G. These are: (1) that derived from "recent air" 

contributions and 2) "other." The PbB contributions arising from these different portions of 

indoor dust Pb ingestion are derived by applying the percentage of the dust Pb concentration 

arising from each of these two sources to the total dust intake percentage. As described in 

Appendix G, how these portions, and their corresponding percentages of total dust Pb 

concentration, are estimated varies with the model used to estimate dust Pb concentration. For 

the hybrid mechanistic-empirical model, the "recent air" percentages of total dust Pb is the 

percent contribution of dust Pb loading from the mechanistic portion of the model and the 

percent from "other" is the percent contribution from the empirical portion. For the regressionbased models, these percentages are estimated as the air slope multiplied by the air concentration 

("recent air") and the intercept ("other" sources) relative to the total estimated indoor dust Pb 

concentration. 

For the primary Pb smelter case study, indoor dust Pb is not separated into “recent air” 

and “other” like in the general urban and secondary Pb smelter case studies. This is a result of 

limitations of the site-specific H5 model, which is used to calculate the concentration of Pb in 

indoor dust in the primary Pb smelter case study. The site-specific H5 model cannot separate 

indoor dust into “recent air” and “other,” therefore the total indoor dust contribution is 

determined for the primary Pb smelter case study. 

I.2. GENERAL URBAN CASE STUDY 

I.2.1. PbB Model Scenarios Run for the General Urban Case Study 

Exhibit I-1 lists the major elements of the modeling approach used in estimating PbB 

distributions in each general urban case study scenario. PbB model inputs for the general urban 

case study were single estimates of the exposure concentrations representing the geometric mean 

(GM) exposure concentrations for the entire child population of the simulated urban 

environment. These concentrations were assumed to remain constant throughout the 7 years of 

exposure modeled in the biokinetic model. As discussed in Appendix G, two distinct dust 

models (the air-only regression-based model and the hybrid mechanistic-empirical model 

[“hybrid model” for short]) were used to generate PbB estimates. Both concurrent (average of 

the results at 75 and 81 months of age in the seventh year of life) and lifetime (average of the 

results between age six and 84 months) PbB metrics are reported. The estimated inter-individual 

variability (i.e., geometric standard deviation [GSD] values) used to generate PbB distributions 

are also shown in Exhibit I-1. 

I-2
 
The age-specific outdoor soil/dust, indoor dust, inhalation exposure, and drinking water 

concentrations and dietary Pb intakes discussed in Appendix H were used to generate PbB 

estimates using the Integrated Exposure Uptake Biokinetic (IEUBK) Model for Children 

(hereafter referred to as the “IEUBK model”) for each dust model and each PbB metric. The 

IEUBK model has been well-documented, is widely used, and has been subject to a range of 

testing and calibration exercises (see Section 4.4 of USEPA (2006)]). These estimates 

represented the GM PbB estimates for each scenario in the general urban case study. To capture 

the inter-individual variability within the urban environment, the GSD values were then applied 

to the GM values for each NAAQS scenario-dust model-PbB metric combination. The 

lognormal distributions created by the GM and GSD were sampled 50,000 times to generate PbB 

distributions, from which percentile estimates were derived, as described in Appendix H. For the 

general urban case study, two GSD values were chosen for each PbB metric to represent high 

and low variability cases, as shown in Exhibit I-1. Data supporting the selection of values for the 

GSDs are provided in Appendix H. 

I-3
 
Exhibit I-1. PbB Model Scenarios Run for the General Urban Case Study 

NAAQS Scenario 

Current conditions 

(95th percentile) 

Dust Model 

(see Appendix G) 

Air-only regression-based model 

GSD (microgram 

per deciliter 

[μg/dL]) 

2.1 

PbB Metric 

Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Current conditions 

(mean) 

Air-only regression-based model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Current NAAQS 

(1.5 microgram per 

cubic meter (μg/m3

), 

max quarterly 

average) 

Air-only regression-based model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 1 

(0.2 μg/m3

, max 

quarterly average) 

Air-only regression-based model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 2 

(0.5 μg/m3

, max 

monthly average) 

Air-only regression-based model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

I-4
 
Exhibit I-1 Continued. PbB Model Scenarios Run for the General Urban Case Study 

NAAQS Scenario 

Alternative NAAQS 3 

(0.2 μg/m3

, max 

monthly average) 

Dust Model 

Air-only regression-based model 

GSD (microgram 

per deciliter 

[μg/dL]) 

PbB Metric 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 4 

(0.05 μg/m3

, max 

monthly average) 

Air-only regression-based model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid model 

2.1 Concurrent 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

I.2.2. PbB Results for the General Urban Case Study 

Exhibit I-2 through Exhibit I-8 summarize the predicted PbB percentiles for scenarios in 

the general urban case study. The exhibits also provide estimated contributions from each 

pathway to total Pb uptake, expressed as percentages. Because there is no specific population in 

the general urban case study (unlike in the two point source case studies), these percentages do 

not vary by PbB percentile. The contribution from the ingestion of indoor dust is separated into 

the contribution derived from recent ambient air and that from other sources (e.g., indoor paint, 

outdoor soil/dust, and additional sources including historical air), as described in Appendix G. 

In general, the concurrent PbB values are lower than the lifetime PbB values for all 

percentiles and in all scenarios. Because the age-specific outdoor soil/dust and indoor dust 

ingestion input parameters are highest for children 1 to 2, 2 to 3, and 3 to 4 years of age; PbB 

tends to be higher during these years and lower for children 0 to 1, 4 to 5, 5 to 6, and 6 to 7 years 

of age. Therefore, the lifetime average PbB value, which includes all ages, is higher than the 

concurrent PbB value, which is the average PbB at 75 and 81 months during the seventh year of 

life. 

 The hybrid mechanistic-empirical dust model predicts higher indoor dust Pb 

concentrations for ambient air Pb concentrations less than 0.28 μg/m3

 than those predicted by the 

air-only regression-based model. In contrast, the hybrid model predicts lower indoor dust Pb 

I-5
 
concentrations for ambient air Pb concentrations greater than 0.28 μg/m3

. Only the current 

NAAQS scenario has an annual-average ambient air Pb concentration above 0.28 μg/m3 (i.e., 

0.6 μg/m3

). Thus in this scenario, the air-only regression-based model predicts higher PbB levels 

than the hybrid model. In all other scenarios, the median PbB values are higher when the hybrid 

model is used to predict indoor dust concentrations, as expected. In general, the higher PbB 

percentiles also follow this trend. However, in the second alternative NAAQS (0.5 μg/m3

, 

maximum monthly average) scenario, the PbB values obtained using the higher GSD (2.1 μg/dL) 

for the concurrent PbB metric are higher for the 95th percentile when the air-only regressionbased model is used than when the hybrid model is used. This unexpected trend is likely due to 

sampling error in the “tails” of the distribution, particularly because it occurs with higher GSDs, 

but not with lower GSDs. 

I-6
 
 

Exhibit I-2. General Urban Case Study: Current Conditions (95th Percentile) – Estimated 

PbB Levels 

PbB 

Percentile 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Indoor Dust

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

95th 4.7 

17.1% 10.0% 36.5% 13.5% 21.8% 1.0% 

90th 3.9 

75th 2.8 

Median 2.0 

25th 1.4 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 6.1 

17.1% 10.0% 36.5% 13.5% 21.8% 1.0% 

90th 5.2 

75th 3.9 

Median 2.8 

25th 2.1 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent) 

95th 5.1 

15.7% 9.1% 33.4% 3.6% 37.2% 0.9% 

90th 4.2 

75th 3.1 

Median 2.1 

25th 1.5 

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime) 

95th 6.7 

15.7% 9.1% 33.4% 3.6% 37.2% 0.9% 

90th 5.6 

75th 4.2 

Median 3.1 

25th 2.2 

I-7
 
 

 

 Exhibit I-2 Continued. General Urban Case Study: Current Conditions (95th Percentile) –
 

Estimated PbB Levels 

PbB 

Percentile 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Indoor Dust

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent) 

95th 6.7 

17.1% 10.0% 36.5% 13.5% 21.8% 1.0% 

90th 5.1 

75th 3.3 

Median 2.0 

25th 1.2 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime) 

95th 8.9 

17.1% 10.0% 36.5% 13.5% 21.8% 1.0% 

90th 6.9 

75th 4.5 

Median 2.8 

25th 1.8 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent) 

95th 7.2 

15.7% 9.1% 33.4% 3.6% 37.2% 0.9% 

90th 5.5 

75th 3.5 

Median 2.1 

25th 1.3 

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime) 

95th 9.6 

15.7% 9.1% 33.4% 3.6% 37.2% 0.9% 

90th 7.5 

75th 4.9 

Median 3.1 

25th 1.9 

a Pathway contributions apply to all percentiles. See text for further discussion. 

b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with 

outdoor ambient air Pb levels). 

I-8
Exhibit I-3. General Urban Case Study: Current Conditions (Mean) – Estimated PbB 

Levels 

Pathway Contribution a 

Ingestion 

PbB Indoor Dust

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

95th 4.2 

90th 3.5 

75th 2.5 

Median 1.8 

25th 1.2 

19.4% 11.3% 41.3% 15.3% 12.1% 0.6% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 5.5 

90th 4.6 

75th 3.5 

Median 2.5 

25th 1.8 

19.4% 11.3% 41.3% 15.3% 12.1% 0.6% 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent) 

95th 4.6 

90th 3.8 

75th 2.8 

Median 1.9 

25th 1.3 

17.7% 10.3% 37.6% 5.6% 28.3% 0.5% 

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime) 

95th 6.0 

90th 5.1 

75th 3.8 

Median 2.8 

25th 2.0 

17.7% 10.3% 37.6% 5.6% 28.3% 0.5% 

I-9
 
 

Exhibit I-3 Continued. General Urban Case Study: Current Conditions (Mean) –
 

Estimated PbB Levels 

PbB 

Percentile 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Indoor Dust

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent) 

95th 6.0 

19.4% 11. 3% 41.3% 15.3% 12.1% 0.6% 

90th 4.5 

75th 2.9 

Median 1.8 

25th 1.1 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime) 

95th 7.8 

19.4% 11.3% 41.3% 15.3% 12.1% 0.6% 

90th 6.1 

75th 4.0 

Median 2.5 

25th 1.6 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent) 

95th 6.5 

17.7% 10.3% 37.6% 5.6% 28.3% 0.5% 

90th 5.0 

75th 3.1 

Median 1.9 

25th 1.2 

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime) 

95th 8.6 

17.7% 10.3% 37.6% 5.6% 28.3% 0.5% 

90th 6.7 

75th 4.4 

Median 2.8 

25th 1.7 

a

 Pathway contributions apply to all percentiles. See text for further discussion. b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with 

outdoor ambient air Pb levels). 

I-10
 
Exhibit I-4. General Urban Case Study: Current NAAQS (1.5 μg/m3

, Maximum Quarterly 

Average) – Estimated PbB Levels 

PbB 

Percentile 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Indoor Dust

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

95th 8.7 

8.7% 5.1% 18.6% 6.9% 58.0% 2.8% 

90th 7.2 

75th 5.2 

Median 3.7 

25th 2.6 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 11.5 

8.7% 5.1% 18.6% 6.9% 58.0% 2.8% 

90th 9.7 

75th 7.3 

Median 5.3 

25th 3.9 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent) 

95th 7.6 

10.4% 6.0% 22.1% 1.1% 57.1% 3.3% 

90th 6.2 

75th 4.5 

Median 3.1 

25th 2.2 

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime) 

95th 9.9 

10.4% 6.0% 22.1% 1.1% 57.1% 3.3% 

90th 8.3 

75th 6.2 

Median 4.5 

25th 3.3 

I-11
 
 

Exhibit I-4 Continued. General Urban Case Study: Current NAAQS (1.5 μg/m3, 

Maximum Quarterly Average) – Estimated PbB Levels 

Pathway Contribution a 

Ingestion 

Indoor Dust

 PbB 

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent) 

Inhalation 

(Recent Air) 

95th 12.3 

8.7% 5.1% 18.6% 6.9% 58.0% 2.8% 

90th 9.4 

75th 6.0 

Median 3.6 

25th 2.2 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime) 

95th 16.5 

8.7% 5.1% 18.6% 6.9% 58.0% 2.8% 

90th 12.8 

75th 8.4 

Median 5.3 

25th 3.3 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent) 

95th 10.6 

10.4% 6.0% 22.1% 1.1% 57.1% 3.3% 

90th 8.1 

75th 5.1 

Median 3.1 

25th 1.9 

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime) 

95th 14.1 

10.4% 6.0% 22.1% 1.1% 57.1% 3.3% 

90th 10.9 

75th 7.2 

Median 4.5 

25th 2.8 

a

 Pathway contributions apply to all percentiles. See text for further discussion. b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with 

outdoor ambient air Pb levels). 

I-12
 
Exhibit I-5. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m3

, Maximum 

Quarterly Average) – Estimated PbB Levels 

Pathway Contribution a 

Ingestion 

PbB Indoor Dust

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

Inhalation 

(Recent Air) 

95th 4.4 

18.4% 10.7% 39.2% 14.5% 16.3% 0.8% 

90th 3.6 

75th 2.7 

Median 1.9 

25th 1.3 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 5.7 

18.4% 10.7% 39.2% 14.5% 16.3% 0.8% 

90th 4.8 

75th 3.7 

Median 2.7 

25th 1.9 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent) 

95th 4.8 

16.7% 9.7% 35.6% 4.5% 32.7% 0.7% 

90th 4.0 

75th 2.9 

Median 2.0 

25th 1.4 

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime) 

95th 6.3 

16.7% 9.7% 35.6% 4.5% 32.7% 0.7% 

90th 5.3 

75th 4.0 

Median 2.9 

25th 2.1 

I-13
 
 

Exhibit I-5 Continued. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m3, 

Maximum Quarterly Average) – Estimated PbB Levels 

Pathway Contribution a 

Ingestion 

Indoor Dust

 PbB 

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent) 

Inhalation 

(Recent Air) 

95th 6.2 

18.4% 10.7% 39.2% 14.5% 16.3% 0.8% 

90th 4.8 

75th 3.1 

Median 1.9 

25th 1.1 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime) 

95th 8.2 

18.4% 10.7% 39.2% 14.5% 16.3% 0.8% 

90th 6.4 

75th 4.3 

Median 2.7 

25th 1.7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent) 

95th 6.9 

16.7% 9.7% 35.6% 4.5% 32.7% 0.7% 

90th 5.3 

75th 3.3 

Median 2.0 

25th 1.2 

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime) 

95th 9.2 

16.7% 9.7% 35.6% 4.5% 32.7% 0.7% 

90th 7.1 

75th 4.7 

Median 2.9 

25th 1.8 

a

 Pathway contributions apply to all percentiles. See text for further discussion. b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with 

outdoor ambient air Pb levels). 

I-14
 
Exhibit I-6. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3

, Maximum 

Monthly Average) – Estimated PbB Levels 

Pathway Contribution a 

Ingestion 

PbB Indoor Dust

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

Inhalation 

(Recent Air) 

95th 4.8 

16.8% 9.8% 35.8% 13.2% 23.3% 1.1% 

90th 3.9 

75th 2.9 

Median 2.0 

25th 1.4 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 6.2 

16.8% 9.8% 35.8% 13.2% 23.3% 1.1% 

90th 5.3 

75th 4.0 

Median 2.9 

25th 2.1 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent) 

95th 5.2 

15.4% 9.0% 32.9% 3.4% 38.3% 1.0% 

90th 4.3 

75th 3.1 

Median 2.2 

25th 1.5 

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime) 

95th 6.8 

15.4% 9.0% 32.9% 3.4% 38.3% 1.0% 

90th 5.7 

75th 4.3 

Median 3.2 

25th 2.3 

I-15
 
 

Exhibit I-6 Continued. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3, 

Maximum Monthly Average) – Estimated PbB Levels 

Pathway Contribution a 

Ingestion 

Indoor Dust

 PbB 

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent) 

Inhalation 

(Recent Air) 

95th 6.8 

16.8% 9.8% 35.8% 13.2% 23.3% 1.1% 

90th 5.2 

75th 3.3 

Median 2.0 

25th 1.2 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime) 

95th 9.1 

16.8% 9.8% 35.8% 13.2% 23.3% 1.1% 

90th 7.0 

75th 4.6 

Median 2.9 

25th 1.8 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent) 

95th 6.7 

15.4% 9.0% 32.9% 3.4% 38.3% 1.0% 

90th 5.2 

75th 3.4 

Median 2.2 

25th 1.4 

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime) 

95th 10.5 

15.4% 9.0% 32.9% 3.4% 38.3% 1.0% 

90th 8.0 

75th 5.1 

Median 3.1 

25th 1.9 

a

 Pathway contributions apply to all percentiles. See text for further discussion. b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with 

outdoor ambient air Pb levels). 

I-16
 
Exhibit I-7. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum 

Monthly Average) – Estimated PbB Levels 

Pathway Contribution a 

Ingestion 

PbB Indoor Dust

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

Inhalation 

(Recent Air) 

95th 4.2 

19.7% 11.5% 41.9% 15.5% 10.9% 0.5% 

90th 3.5 

75th 2.5 

Median 1.7 

25th 1.2 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 5.4 

19.7% 11.5% 41.9% 15.5% 10.9% 0.5% 

90th 4.6 

75th 3.4 

Median 2.5 

25th 1.8 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent) 

95th 4.5 

17.9% 10.4% 38.2% 6.0% 27.0% 0.5% 

90th 3.7 

75th 2.7 

Median 1.9 

25th 1.3 

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime) 

95th 5.9 

17.9% 10.4% 38.2% 6.0% 27.0% 0.5% 

90th 5.0 

75th 3.7 

Median 2.7 

25th 2.0 

I-17
 
 

Exhibit I-7 Continued. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) – Estimated PbB Levels 

Pathway Contribution a 

Ingestion 

Indoor Dust

 PbB 

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent) 

Inhalation 

(Recent Air) 

95th 5.9 

19.7% 11.5% 41.9% 15.5% 10.9% 0.5% 

90th 4.5 

75th 2.9 

Median 1.8 

25th 1.1 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime) 

95th 7.8 

19.7% 11.5% 41.9% 15.5% 10.9% 0.5% 

90th 6.0 

75th 4.0 

Median 2.5 

25th 1.6 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent) 

95th 6.4 

17.9% 10.4% 38.2% 6.0% 27.0% 0.5% 

90th 4.9 

75th 3.1 

Median 1.9 

25th 1.1 

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime) 

95th 8.5 

17.9% 10.4% 38.2% 6.0% 27.0% 0.5% 

90th 6.6 

75th 4.3 

Median 2.7 

25th 1.7 

a

 Pathway contributions apply to all percentiles. See text for further discussion. b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with 

outdoor ambient air Pb levels). 

I-18
 
Exhibit I-8. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3

, Maximum 

Monthly Average) – Estimated PbB Levels 

Pathway Contribution a 

Ingestion 

PbB Indoor Dust

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

Inhalation 

(Recent Air) 

95th 3.9 

21.5% 12.5% 45.8% 17.0% 3.0% 0.1% 

90th 3.2 

75th 2.3 

Median 1.6 

25th 1.1 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 5.0 

21.5% 12.5% 45.8% 17.0% 3.0% 0.1% 

90th 4.2 

75th 3.1 

Median 2.3 

25th 1.7 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent) 

95th 4.1 

20.5% 11.9% 43.7% 11.1% 12.6% 0.1% 

90th 3.4 

75th 2.4 

Median 1.7 

25th 1.2 

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime) 

95th 5.2 

20.5% 11.9% 43.7% 11.1% 12.6% 0.1% 

90th 4.4 

75th 3.3 

Median 2.4 

25th 1.7 

I-19
 
 

Exhibit I-8 Continued. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3, 

Maximum Monthly Average) – Estimated PbB Levels 

Pathway Contribution a 

Ingestion 

Indoor Dust

 PbB 

Percentile 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other b Recent Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent) 

Inhalation 

(Recent Air) 

95th 5.5 

21.5% 12.5% 45.8% 17.0% 3.0% 0.1% 

90th 4.2 

75th 2.7 

Median 1.6 

25th 1.0 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime) 

95th 7.2 

21.5% 12.5% 45.8% 17.0% 3.0% 0.1% 

90th 5.6 

75th 3.7 

Median 2.3 

25th 1.4 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent) 

95th 5.7 

20.5% 11.9% 43.7% 11.1% 12.6% 0.1% 

90th 4.4 

75th 2.8 

Median 1.7 

25th 1.0 

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime) 

95th 7.5 

20.5% 11.9% 43.7% 11.1% 12.6% 0.1% 

90th 5.9 

75th 3.8 

Median 2.4 

25th 1.5 

a

 Pathway contributions apply to all percentiles. See text for further discussion. b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with 

outdoor ambient air Pb levels). 

I-20
 
 

 

 

I.2.3. Ambient Air to PbB Ratios for the General Urban Case Study 

Exhibit I-9 through Exhibit I-15 show the ratio of the annual average ambient air Pb 

concentration to the PbB estimate (where a ratio of 1:2.0 indicates that the PbB, estimated in 

μg/dL, is twice the ambient air concentration, estimated in μg/m3

). The ratios in this section 

were calculated before the application of the GSD to the PbB values to account for interindividual variability. That is, the GM PbB estimates for each NAAQS scenario (i.e., the 

unadjusted IEUBK outputs) are used to determine the ratios. All ratios are presented to one 

decimal place, which results in various numbers of implied significant figures (e.g., 1 to 5).1 

This is not intended to convey greater precision for some ratios than others; it is simply an 

expedient and initial result of the software used for the calculation. Greater attention is given to 

significant figures in the presentation of ratios in the main body of the report. 

For each NAAQS scenario, ratios are provided for different portions of the estimated 

PbB. The first ratio (inhalation [recent air]) is for that portion of PbB estimated to be derived 

from inhalation of ambient air. The second (inhalation+ingestion [recent air]) is for the 

aggregate PbB estimated to result from inhalation of ambient air plus ingestion of the Pb in 

indoor dust that is predicted to be associated with ambient air Pb levels. The third 

(inhalation+ingestion [recent and past air]) is the aggregate PbB resulting from the inhalation of 

ambient air, the ingestion of indoor dust, and the ingestion of outdoor soil/dust. 

As a result of the dust equations used for the general urban case study, the indoor dust Pb 

contributions other than that associated with recent ambient air Pb levels cannot be distinguished. 

This is because indoor paint, outdoor soil/dust or other sources (e.g., historical ambient air 

contributions) are all represented by a single constant intercept in the indoor dust loading 

equation (for the hybrid model) or indoor dust concentration equation (for the air-only 

regression-based model). Therefore, the third ratio includes contributions to PbB from indoor 

paint, as well as recent ambient air Pb levels and past deposition of ambient air Pb to outdoor 

soil/dust. Accordingly, this ratio may be an overestimate of the relationship of ambient air Pb 

concentration to the portion of PbB derived from ambient air Pb. 

1 Similarly, the ambient air annual average Pb concentration estimates are presented to three decimal places, 

resulting in various numbers of implied significant figures (e.g., 1 to 3). No difference in precision is intended to be 

conveyed; this is simply an expedient and initial result of the software used for presentation. 

I-21
 
 

 

 

 

 

 

Exhibit I-9. General Urban Case Study: Current Conditions (95th Percentile) – Ambient 

Air Pb to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model Ambient Air Annual 

Average Pb Concentration 

(μg/m3) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and 

Past Air) a,b 

Concurrent PbB Metric 

Air-only regressionbased 0.114 1 : 0.2 1 : 3.9 1 : 12.6 

Lifetime PbB Metric 

Air-only regressionbased 0.114 1 : 0.3 1 : 5.7 1 : 18.1 

Concurrent PbB Metric 

Hybrid 0.114 1 : 0.2 1 : 7.1 1 : 14.0 

Lifetime PbB Metric 

Hybrid 0.114 1 : 0.3 1 : 10.3 1 : 20.3 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

Exhibit I-10. General Urban Case Study: Current Conditions (Mean) – Ambient Air Pb to 

PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb Concentration 

(μg/m3) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and 

Past Air) a,b 

Concurrent PbB Metric 

Air-only regressionbased 0.056 1 : 0.2 1 : 4.0 1 : 21.9 

Lifetime PbB Metric 

Air-only regressionbased 0.056 1 : 0.3 1 : 5.7 1 : 31.2 

Concurrent PbB Metric 

Hybrid 0.056 1 : 0.2 1 : 9.9 1 : 24.6 

Lifetime PbB Metric 

Hybrid 0.056 1 : 0.3 1 : 14.2 1 : 35.5 a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

I-22
 
 

 

 

 

 

 

Exhibit I-11. General Urban Case Study: Current NAAQS (1.5 μg/m3, Maximum 

Quarterly Average) – Ambient Air Pb to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb Concentration 

(μg/m3

) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and 

Past Air) a,b 

Concurrent PbB Metric 

Air-only regressionbased 0.600 1 : 0.2 1 : 3.7 1 : 5.3 

Lifetime PbB Metric 

Air-only regressionbased 0.600 1 : 0.2 1 : 5.4 1 : 7.6 

Concurrent PbB Metric 

Hybrid 0.600 1 : 0.2 1 : 3.2 1 : 4.4 

Lifetime PbB Metric 

Hybrid 0.600 1 : 0.2 1 : 4.6 1 : 6.3 a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

Exhibit I-12. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m3, Maximum 

Quarterly Average) – Ambient Air Pb to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model Ambient Air Annual Average 

Pb Concentration (μg/m3) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and Past 

Air) a,b 

Concurrent PbB Metric 

Air-only regressionbased 0.080 1 : 0.2 1 : 4.0 1 : 16.4 

Lifetime PbB Metric 

Air-only regressionbased 0.080 1 : 0.3 1 : 5.7 1 : 23.5 

Concurrent PbB Metric 

Hybrid 0.080 1 : 0.2 1 : 8.4 1 : 18.5 

Lifetime PbB Metric 

Hybrid 0.080 1 : 0.3 1 : 12.1 1 : 26.7 a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

I-23
 
 

 

 

 

 

 

Exhibit I-13. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3, Maximum 

Monthly Average) – Ambient Air Pb to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model Ambient Air Annual Average 

Pb Concentration (μg/m3

) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and 

Past Air) a,b 

Concurrent PbB Metric 

Air-only regressionbased 0.125 1 : 0.2 1 : 3.9 1 : 11.8 

Lifetime PbB Metric 

Air-only regressionbased 0.125 1 : 0.3 1 : 5.7 1 : 17.0 

Concurrent PbB Metric 

Hybrid 0.125 1 : 0.2 1 : 6.8 1 : 13.1 

Lifetime PbB Metric 

Hybrid 0.125 1 : 0.3 1 : 9.9 1 : 19.0 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

Exhibit I-14. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3, Maximum 

Monthly Average) – Ambient Air Pb to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model Ambient Air Annual Average 

Pb Concentration (μg/m3) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and 

Past Air) a,b 

Concurrent PbB Metric 

Air-only regressionbased 0.050 1 : 0.2 1 : 4.0 1 : 24.0 

Lifetime PbB Metric 

Air-only regressionbased 0.050 1 : 0.3 1 : 5.7 1 : 34.3 

Concurrent PbB Metric 

Hybrid 0.050 1 : 0.2 1 : 10.4 1 : 27.1 

Lifetime PbB Metric 

Hybrid 0.050 1 : 0.3 1 : 14.9 1 : 38.9 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

I-24
 
 

 

 

Exhibit I-15. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3, Maximum 

Monthly Average) – Ambient Air Pb to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model Ambient Air Annual Average 

Pb Concentration (μg/m3

) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and 

Past Air) a,b 

Concurrent PbB Metric 

Air-only regressionbased 0.013 1 : 0.2 1 : 4.0 1 : 84.9 

Lifetime PbB Metric 

Air-only regressionbased 0.013 1 : 0.2 1 : 5.7 1 : 120.6 

Concurrent PbB Metric 

Hybrid 0.013 1 : 0.2 1 : 17.1 1 : 90.8 

Lifetime PbB Metric 

Hybrid 0.013 1 : 0.2 1 : 24.4 1 : 129.3 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

I.3. PRIMARY PB SMELTER CASE STUDY 

I.3.1. Description of PbB Model Scenarios Run for the Primary Pb Smelter Case Study 

Ambient air and soil Pb concentration estimates for the primary Pb smelter case study 

were estimated as described in Appendix D. Exposure concentrations were assumed to be 

constant throughout the 7-year duration of the exposure scenario. Data from the U.S. Census 

provided estimates of the numbers of children (less than 7 years of age) living in each block or 

block group in the year 2000 (U.S. Census Bureau, 2005). The numbers of exposed children in 

each U.S. Census block or block group were assumed to be constant through the entire 7-year 

exposure period. In- and out-migration to and from the case study areas was not considered. 

PbB levels were modeled for each child as though exposure started at birth and continued 

through 84 months of age. Maternal PbB levels during pregnancy were assumed to be identical 

for all children at a level consistent with nationally representative values for women of 

childbearing age. Thus, all children were assumed to start with the same body burden of Pb at 

birth. Similarly, all exposed children were assumed to receive the same pattern of nationally 

representative policy-relevant background exposures throughout the exposure period. 

Estimates of indoor dust Pb concentrations were generated using the site-specific H5 

model for the U.S. Census blocks and block groups within 1.5 kilometer (km) of the source. 

I-25
 
Dust Pb concentration estimates in more distant U.S. Census blocks and block groups were 

derived using the U.S. EPA air+soil regression-based model, as discussed in Appendix G. Thus, 

unlike in the general urban case study, only a single set of indoor dust concentrations was input 

to the IEUBK model (along with the outdoor soil/dust, inhalation exposure, dietary, and drinking 

water Pb concentrations) to generate GM PbB estimates for each U.S. Census block and block 

group. As in the urban case study, both concurrent (at 75 and 81 months during the seventh year 

of life) and lifetime (ages 6 to 84 months) average PbB metrics were estimated for each NAAQS 

scenario. 

To capture the inter-individual variability and the PbB levels for the whole population, 

random lognormal probability distributions, represented by GSD values, were superimposed on 

the U.S. Census block GM estimates, as discussed in Appendix H. In each iteration of the 

probabilistic model, a single U.S. Census block or block group was randomly selected, where the 

probability of selecting a given block was proportional to the number of children less than 7 

years of age in that block. A random uniform variate was sampled and used as the probability 

(“p”) input to the Excel® LOGINV function, along with the GM value for the block group and 

the GSD value selected for the case study and exposure scenario. The resulting PbB estimate for 

each iteration was therefore a lognormally distributed variate reflecting the GM for the randomly 

chosen U.S. Census block and the specified GSD value. This process was repeated for 50,000 

iterations, and the resultant distribution of PbB estimates was used to generate population PbB 

percentile estimates. For the primary Pb smelter case study, a single set of GSD values was used 

for each PbB metric, as shown in Exhibit I-16. Supporting data for the GSD estimates are 

provided in Appendix H. 

I-26
 
Exhibit I-16. PbB Model Scenarios Run for the Primary Pb Smelter Case Study 

Current NAAQS 

(1.5 μg/m3

, max quarterly average) 

NAAQS Scenario 

1.7 

GSD (μg/dL) 

Concurrent 

PbB Metric 

1.6 Lifetime 

Alternative NAAQS 1 

(0.2 μg/m3

, max quarterly average) 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 2 

(0.5 μg/m3

, max monthly average) 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 3 

(0.2 μg/m3

, max monthly average) 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 4 

(0.05 μg/m3

, max monthly average) 

1.7 Concurrent 

1.6 Lifetime 

I.3.2. PbB Results for the Primary Pb Smelter Case Study 

Exhibit I-17 through Exhibit I-21 summarize PbB distribution percentile estimates for all 

scenarios in the primary Pb smelter case study. In addition, the estimates of the percent 

contribution of each exposure pathway to the overall Pb uptake estimates are given for each 

percentile. Percents less than 0.1 are indicated by <0.1%. The total indoor dust contribution 

were derived for the GM PbB estimates for each U.S. Census block or block group before the 

GSD is applied to generate the PbB distributions. The PbB percentile estimates, however, are 

those after the application of the GSD. Thus, as some of the high percentile PbB values are 

actually associated with U.S. Census blocks (or block groups) with low PbB GMs (and vice 

versa), these exhibits contain some seemingly irregular trends in pathway contributions. 

Also included in Exhibit I-17 through Exhibit I-21 are the estimated numbers of children 

with PbB levels above the various percentiles. As in the general urban case study, the concurrent 

PbB percentile estimates tend to be lower than the corresponding percentiles of lifetime 

estimates under all of the exposure scenarios. 

I-27
 
Exhibit I-17. Primary Pb Smelter Case Study: Current NAAQS Scenario (1.5 μg/m3

, 

Maximum Quarterly Average) – Estimated PbB Levels 

Pathway Contribution 

Ingestion PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent) 

95th 194 4.6 24.4% 14.2% 35.1% 25.6% 0.6% 

90th 388 3.5 24.4% 14.2% 35.5% 25.4% 0.6% 

75th 970 2.3 19.8% 11.5% 40.7% 27.1% 0.8% 

Median 1940 1.5 21.9% 12.8% 33.4% 30.8% 1.2% 

25th 2910 1.0 39.7% 23.1% 16.1% 20.9% 0.2% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime) 

95th 194 6.2 11.1% 6.5% 53.9% 27.7% 0.9% 

90th 388 4.8 9.8% 5.7% 10.7% 72.9% 0.9% 

75th 970 3.2 35.1% 20.4% 20.6% 23.4% 0.4% 

Median 1940 2.1 32.9% 19.1% 22.6% 24.8% 0.6% 

25th 2910 1.4 21.9% 12.8% 33.4% 30.8% 1.2% 

I-28
 
Exhibit I-18. Primary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3

, 

Maximum Quarterly Average) – Estimated PbB Levels 

Pathway Contribution 

Ingestion PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent) 

95th 194 4.0 14.0% 8.2% 55.8% 21.8% 0.3% 

90th 388 3.2 12.2% 7.1% 59.3% 21.2% 0.2% 

75th 970 2.2 35.1% 20.4% 24.2% 20.1% 0.1% 

Median 1940 1.4 23.4% 13.6% 42.3% 20.6% 0.2% 

25th 2910 0.9 35.1% 20.4% 24.2% 20.1% 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime) 

95th 194 5.3 14.5% 8.4% 55.3% 21.5% 0.2% 

90th 388 4.3 36.3% 21.1% 22.9% 19.6% < 0.1% 

75th 970 2.9 36.3% 21.1% 22.9% 19.6% < 0.1% 

Median 1940 1.9 31.5% 18.3% 30.0% 20.0% 0.1% 

25th 2910 1.3 32.5% 18.9% 28.6% 19.9% < 0.1% 

I-29
 
Exhibit I-19. Primary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3

, 

Maximum Monthly Average) – Estimated PbB Levels 

PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent) 

95th 194 4.2 6.3% 3.6% 17.5% 71.5% 1.1% 

90th 388 3.3 13.5% 7.9% 53.7% 24.4% 0.5% 

75th 970 2.2 13.5% 7.9% 53.7% 24.4% 0.5% 

Median 1940 1.4 39.0% 22.7% 18.4% 19.9% 0.1% 

25th 2910 0.9 33.8% 19.7% 25.3% 21.0% 0.2% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime) 

95th 194 5.6 15.6% 9.1% 52.5% 22.4% 0.3% 

90th 388 4.4 13.5% 7.9% 53.7% 24.4% 0.5% 

75th 970 3.0 34.4% 20.1% 23.7% 21.5% 0.3% 

Median 1940 2.0 15.6% 9.1% 52.5% 22.4% 0.3% 

25th 2910 1.3 24.7% 14.4% 38.9% 21.8% 0.3% 

I-30
 
Exhibit I-20. Primary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3

, 

Maximum Monthly Average) – Estimated PbB Levels 

PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent) 

95th 194 4.0 25.3% 14.7% 39.8% 20.1% 0.1% 

90th 388 3.2 32.7% 19.1% 28.9% 19.3% < 0.1% 

75th 970 2.1 25.3% 14.7% 39.8% 20.1% 0.1% 

Median 1940 1.4 35.2% 20.5% 24.3% 19.9% 0.1% 

25th 2910 0.9 20.1% 11.7% 48.1% 20.0% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime) 

95th 194 5.3 22.8% 13.3% 43.5% 20.2% 0.1% 

90th 388 4.2 13.9% 8.1% 38.2% 39.4% 0.3% 

75th 970 2.9 26.3% 15.3% 38.2% 20.1% 0.1% 

Median 1940 1.9 35.2% 20.5% 24.3% 19.9% 0.1% 

25th 2910 1.3 32.7% 19.1% 28.9% 19.3% < 0.1% 

I-31
 
Exhibit I-21. Primary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3

, 

Maximum Monthly Average) – Estimated PbB Levels 

PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent) 

95th 194 3.8 16.8% 9.8% 53.9% 19.5% < 0.1% 

90th 388 3.1 14.9% 8.7% 56.8% 19.6% < 0.1% 

75th 970 2.1 35.6% 20.8% 24.6% 19.0% < 0.1% 

Median 1940 1.4 35.6% 20.8% 24.6% 19.0% < 0.1% 

25th 2910 0.9 35.6% 20.8% 24.6% 19.0% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime) 

95th 194 5.1 20.9% 12.2% 47.6% 19.4% < 0.1% 

90th 388 4.1 14.4% 8.4% 57.4% 19.7% < 0.1% 

75th 970 2.8 23.0% 13.4% 44.3% 19.3% < 0.1% 

Median 1940 1.9 25.6% 14.9% 40.3% 19.2% < 0.1% 

25th 2910 1.3 36.7% 21.3% 23.1% 18.9% < 0.1% 

I-32
 
 

 

I.3.3. Ambient Air to PbB Ratios for the Primary Pb Smelter Case Study 

Exhibit I-22 through Exhibit I-26 show the ratios of the ambient air Pb concentration to 

estimated PbB, where a ratio of 1:2.0 indicates that the PbB is twice the ambient air 

concentration, using ambient air units of μg/m3

 and PbB units of μg/dL. In all of these exhibits, 

the ratios are calculated before the application of the GSD representing inter-individual 

variability to the U.S. Census block or block group GM Pb values. And, the PbB estimates used 

to calculate air to blood ratios come from either the median or 95th percentile U.S. Census blocks 

or block groups (with regard to air concentration), as indicated in the tables. All ratios are 

presented to one decimal place, which results in various numbers of implied significant figures 

(e.g., 1 to 5).2 This is not intended to convey greater precision for some ratios than others; it is 

simply an expedient and initial result of the software used for the calculation. Greater attention 

is given to significant figures in the presentation of ratios in the main body of the report. 

Ratios are provided for different portions of the estimated PbB. The first ratio (inhalation 

[recent air]) is for that portion of PbB estimated to be derived from inhalation of ambient air. 

The second (inhalation+ingestion [total]) is the aggregate PbB resulting from the inhalation of 

ambient air, the ingestion of indoor dust, and the ingestion of outdoor soil/dust. 

2 Similarly, the ambient air annual average Pb concentration estimates are presented to three decimal places, 

resulting in various numbers of implied significant figures (e.g., 1 to 3). No difference in precision is intended to be 

conveyed; this is simply an expedient and initial result of the software used for presentation. 

I-33
 
Exhibit I-22. Primary Pb Smelter Case Study: Current NAAQS Scenario (1.5 μg/m3

, 

Maximum Quarterly Average) – Ambient Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a Inhalation 

+Ingestion (Total) a 

Concurrent PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.093 1 : 0.2 1 : 12.5 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.458 1 : 0.2 1 : 11.8 

Lifetime PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.059 1 : 0.3 1 : 28 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.458 1 : 0.2 1 : 17.1 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

I-34
 
 

Exhibit I-23. Primary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3, 

Maximum Quarterly Average) – Ambient Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a Inhalation +Ingestion (Total) a 

Concurrent PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.017 1 : 0.2 1 : 62.8 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.154 1 : 0.2 1 : 20.5 

Lifetime PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.017 1 : 0.3 1 : 89.2 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.020 1 : 0.3 1 : 232.2 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

I-35
 
 

Exhibit I-24. Primary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a Inhalation +Ingestion (Total) a 

Concurrent PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.033 1 : 0.2 1 : 32.3 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.198 1 : 0.2 1 : 19.6 

 Lifetime PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.033 1 : 0.3 1 : 45.9 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.043 1 : 0.3 1 : 132.6 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

I-36
 
 

Exhibit I-25. Primary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a Inhalation +Ingestion (Total) a 

Concurrent PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.030 1 : 0.2 1 : 34 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.016 1 : 0.2 1 : 193.8 

Lifetime PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.030 1 : 0.3 1 : 48.1 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.016 1 : 0.3 1 : 285.8 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

I-37
 
 

 

Exhibit I-26. Primary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a Inhalation +Ingestion (Total) a 

Concurrent PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.002 1 : 0.2 1 : 464.6 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.007 1 : 0.2 1 : 410.5 

Lifetime PbB Metric 

Air+Soil Regression-Based 

and H5 Median 0.002 1 : 0.3 1 : 654 

Air+Soil Regression-Based 

and H5 

95th 

Percentile 0.007 1 : 0.3 1 : 605.7 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

I-38
 
I.4. SECONDARY PB SMELTER CASE STUDY 

I.4.1. Description of PbB Model Scenarios Run for the Secondary Pb Smelter Case Study 

Ambient air and soil Pb concentration estimates for the secondary Pb smelter case study 

were estimated as described in Appendix E. Exposure concentrations were assumed to be 

constant throughout the 7-year duration of the exposure scenario. As in the primary Pb smelter 

case study, the numbers of exposed children in each U.S. Census block or block group were 

assumed to be constant through the entire 7-year exposure period. In- and out-migration to and 

from the case study areas was not considered. PbB levels were modeled for each child as though 

exposure started at six months and continued through 84 months. Maternal PbB levels during 

pregnancy were assumed to be identical for all children at a level consistent with nationally 

representative values for women of childbearing age. Thus, all children were assumed to start 

with the same body burden of Pb at birth. Similarly, all exposed children were assumed to 

receive the same pattern of nationally representative policy-relevant background exposures 

throughout the exposure period. 

For all of the scenarios evaluated, indoor dust Pb concentrations were estimated using the 

air-only regression-based model. Thus, as for the primary Pb smelter case study, only one set of 

indoor dust concentrations were input to the IEUBK model (along with the outdoor soil/dust, 

inhalation exposure, dietary, and drinking water Pb concentrations) to generate PbB estimates for 

each scenario evaluated. Concurrent and lifetime average PbB metrics were generated for each 

NAAQS scenario. The probabilistic model was then run in the same manner as described in 

I.3.1 for the primary Pb smelter case study. Exhibit I-27 summarizes the various model 

scenarios run for the secondary Pb smelter case study. 

I-39
 
Exhibit I-27. PbB Model Scenarios Run for the Secondary Pb Smelter Case Study 

Current Conditions 

NAAQS Scenario 

1.7 

GSD (μg/dL) 

Concurrent 

PbB Metric 

1.6 Lifetime 

Alternative NAAQS 1 

(0.2 μg/m3

 max quarterly average) 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 2 

(0.5 μg/m3

, max monthly average) 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 3 

(0.2 μg/m3

, max monthly average) 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 4 

(0.05 μg/m3

, max monthly average) 

1.7 Concurrent 

1.6 Lifetime 

I.4.2. PbB Results for the Secondary Pb Smelter Case Study 

Exhibit I-28 through Exhibit I-32 provide the population percentile PbB estimates for the 

secondary Pb smelter case study scenarios, along with estimates of the pathway contributions to 

total Pb uptake. The indoor dust contribution is separated into the contribution derived from 

recent ambient air, and that from other sources (e.g., indoor paint, outdoor soil/dust, and 

additional sources including historical air), as described in Appendix G. These estimates of 

pathway contributions were derived for the GM PbB estimates for the individual U.S. Census 

blocks, before the GSDs for inter-individual PbB variability were applied to generate the PbB 

distributions. The PbB percentile estimates, however, are those after application of the GSD. 

Thus, as some of the high percentile PbB values are actually associated with U.S. Census blocks 

with low PbB GMs (and vice versa), these exhibits contain some seemingly irregular trends in 

pathway contributions. The exhibits also provide estimates of the numbers of children estimated 

to have PbB levels greater than the various percentiles. As in the previous two case studies, the 

concurrent PbB population percentile estimates are less than the lifetime estimates for the 

corresponding percentiles in all cases. 

I-40
 
 

Exhibit I-28. Secondary Pb Smelter Case Study: Current Conditions Scenario – Estimated 

PbB Levels 

Pathway Contribution 

Ingestion 

Indoor Dust PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

95th 85 2.4 41.1% 24.0% 1.9% 32.5% 0.5% 0.0% 

90th 170 2.0 29.4% 17.1% 25.1% 23.2% 5.0% 0.3% 

75th 425 1.4 37.9% 22.1% 8.2% 29.9% 1.9% 0.1% 

Median 849 1.0 39.7% 23.1% 4.5% 31.3% 1.3% 0.1% 

25th 1274 0.7 41.8% 24.3% 0.6% 33.0% 0.3% 0.0% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 85 2.9 41.6% 24.2% 1.0% 32.8% 0.3% 0.0% 

90th 170 2.4 38.7% 22.5% 6.2% 30.5% 2.0% 0.1% 

75th 425 1.8 39.6% 23.0% 4.9% 31.2% 1.2% 0.1% 

Median 849 1.3 41.4% 24.1% 1.3% 32.6% 0.5% 0.0% 

25th 1274 0.9 40.4% 23.5% 3.1% 31.9% 1.0% 0.1% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor 

ambient air Pb levels). 

I-41
 
 

Exhibit I-29. Secondary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3, 

Maximum Quarterly Average) – Estimated PbB Levels 

Pathway Contribution 

Ingestion 

Indoor Dust PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

95th 85 2.3 41.9% 24.4% 0.6% 33.0% 0.1% 0.0% 

90th 170 1.9 41.6% 24.2% 1.2% 32.8% 0.1% 0.0% 

75th 425 1.4 40.7% 23.7% 3.1% 32.1% 0.3% 0.0% 

Median 849 1.0 39.4% 22.9% 6.2% 31.1% 0.5% 0.0% 

25th 1274 0.7 40.1% 23.3% 4.6% 31.6% 0.4% 0.0% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 85 2.8 37.5% 21.9% 10.4% 29.6% 0.5% 0.0% 

90th 170 2.4 40.1% 23.3% 4.5% 31.6% 0.4% 0.0% 

75th 425 1.8 38.7% 22.5% 7.7% 30.5% 0.5% 0.0% 

Median 849 1.3 41.7% 24.3% 0.9% 32.9% 0.1% 0.0% 

25th 1274 0.9 39.9% 23.2% 5.1% 31.4% 0.4% 0.0% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor 

ambient air Pb levels). 

I-42
 
 

Exhibit I-30. Secondary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3, 

Maximum Monthly Average) – Estimated PbB Levels 

Pathway Contribution 

Ingestion 

Indoor Dust PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

95th 85 2.4 35.0% 20.4% 14.9% 27.6% 2.1% 0.1% 

90th 170 2.0 41.4% 24.1% 1.5% 32.7% 0.2% 0.0% 

75th 425 1.4 39.2% 22.8% 6.2% 30.9% 0.8% 0.0% 

Median 849 1.0 31.5% 18.4% 22.5% 24.9% 2.6% 0.1% 

25th 1274 0.7 41.2% 24.0% 2.0% 32.5% 0.4% 0.0% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 85 2.8 39.1% 22.8% 6.2% 30.9% 1.0% 0.1% 

90th 170 2.4 39.0% 22.7% 6.2% 30.8% 1.1% 0.1% 

75th 425 1.8 41.7% 24.3% 0.9% 32.9% 0.2% 0.0% 

Median 849 1.3 41.4% 24.1% 1.5% 32.7% 0.2% 0.0% 

25th 1274 0.9 41.1% 23.9% 2.1% 32.4% 0.3% 0.0% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor 

ambient air Pb levels). 

I-43
 
 

Exhibit I-31. Secondary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) – Estimated PbB Levels 

Pathway Contribution 

Ingestion 

Indoor Dust PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

95th 85 2.4 33.9% 19.7% 18.3% 26.8% 1.2% 0.1% 

90th 170 1.9 42.0% 24.5% 0.3% 33.2% 0.0% 0.0% 

75th 425 1.4 39.4% 23.0% 6.1% 31.1% 0.4% 0.0% 

Median 849 1.0 38.8% 22.6% 7.4% 30.7% 0.4% 0.0% 

25th 1274 0.7 38.8% 22.6% 7.4% 30.7% 0.4% 0.0% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 85 2.8 41.3% 24.1% 1.9% 32.6% 0.1% 0.0% 

90th 170 2.4 33.7% 19.6% 19.4% 26.6% 0.7% 0.0% 

75th 425 1.8 38.6% 22.5% 7.9% 30.5% 0.5% 0.0% 

Median 849 1.3 41.8% 24.4% 0.7% 33.0% 0.1% 0.0% 

25th 1274 0.9 37.0% 21.5% 11.7% 29.2% 0.6% 0.0% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor 

ambient air Pb levels). 

I-44
 
 

 

Exhibit I-32. Secondary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3, 

Maximum Monthly Average) – Estimated PbB Levels 

Pathway Contribution 

Ingestion 

Indoor Dust PbB 

Percentile 

Population 

Above 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent Air 

Inhalation 

(Recent Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent) 

95th 85 2.4 41.9% 24.4% 0.6% 33.1% 0.0% 0.0% 

90th 170 1.9 17.1% 10.0% 58.0% 13.5% 1.3% 0.1% 

75th 425 1.4 39.6% 23.0% 6.1% 31.2% 0.1% 0.0% 

Median 849 1.0 39.8% 23.2% 5.5% 31.4% 0.1% 0.0% 

25th 1274 0.7 41.8% 24.3% 0.8% 33.0% 0.0% 0.0% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime) 

95th 85 2.8 41.9% 24.4% 0.6% 33.1% 0.0% 0.0% 

90th 170 2.4 40.2% 23.4% 4.7% 31.7% 0.1% 0.0% 

75th 425 1.8 39.8% 23.2% 5.5% 31.4% 0.1% 0.0% 

Median 849 1.3 39.5% 23.0% 6.2% 31.2% 0.1% 0.0% 

25th 1274 0.9 40.2% 23.4% 4.7% 31.7% 0.1% 0.0% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

(including historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb 

levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor 

ambient air Pb levels). 

I.4.3. Ambient Air to PbB Ratios for the Secondary Pb Smelter Case Study 

Exhibit I-33 through Exhibit I-37 show the ratio of ambient air Pb concentration to PbB 

estimates, where a ratio of 1:2.0 indicates that the PbB, estimated in μg/dL, is twice the ambient 

air concentration, estimated in μg/m3

. The ratios are calculated before the application of the 

GSD to the GM PbB values to account for inter-individual variability. And, as in the primary Pb 

smelter case study, the PbB estimates come from either the median or 95th percentile U.S. 

Census blocks or block groups (with regard to air concentration). All ratios are presented to one 

decimal place, which results in various numbers of implied significant figures (e.g., 1 to 5).3 

3

 Similarly, the ambient air Pb concentration estimates are presented to three decimal places, resulting in 

various numbers of implied significant figures (e.g., 1 to 3). No difference in precision is intended to be conveyed; 

this is simply an expedient and initial result of the software used for presentation. 

I-45
 
This is not intended to convey greater precision for some ratios than others; it is simply an 

expedient and initial result of the software used for the calculation. Greater attention is given to 

significant figures in the presentation of ratios in the main body of the report. 

Ratios are provided for different pathway contributions to PbB. The first ratio (inhalation 

[recent air]) is for that portion of PbB estimated to be derived from inhalation of ambient air. 

The second (inhalation+ingestion [recent air]) is for the aggregate PbB estimated to result from 

inhalation of ambient air plus ingestion of the Pb in indoor dust that is predicted to be associated 

with ambient air Pb levels. The third (inhalation+ingestion [recent and past air]) is the aggregate 

PbB resulting from the inhalation of ambient air, the ingestion of indoor dust, and the ingestion 

of outdoor soil/dust. 

The indoor dust model used to estimate indoor dust Pb concentrations in this case study 

does not distinguish Pb contributions to indoor dust other than that from recent ambient air Pb 

levels. This is because indoor paint, outdoor soil/dust and other sources are all represented by a 

single constant intercept in the model. Therefore, the third ratio includes contributions to PbB 

from indoor paint as well as recent ambient air Pb levels and recent plus past deposition of 

ambient air Pb to outdoor soil/dust. Accordingly, this ratio may be an overestimate of the 

relationship of ambient air Pb concentration to the portion of PbB derived from recent and past 

air sources. 

I-46
 
 

 

 

Exhibit I-33. Secondary Pb Smelter Case Study: Current Conditions Scenario – Ambient 

Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and Past 

Air) a,b 

Concurrent PbB Metric 

Air-Only Regression-Based Median 0.005 1 : 0.2 1 : 4.5 1 : 73.9 

Air-Only Regression-Based 95th 

Percentile 0.011 1 : 0.2 1 : 4.3 1 : 54.1 

Lifetime PbB Metric 

Air-Only Regression-Based Median 0.003 1 : 0.3 1 : 5.9 1 : 184.8 

Air-Only Regression-Based 95th 

Percentile 0.011 1 : 0.3 1 : 5.8 1 : 73.7 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

I-47
 
 

 

 

Exhibit I-34. Secondary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3, 

Maximum Quarterly Average) – Ambient Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and Past 

Air) a,b 

Concurrent PbB Metric 

Air-Only Regression-Based Median 0.001 1 : 0.2 1 : 4.5 1 : 264.1 

Air-Only Regression-Based 95th 

Percentile 0.005 1 : 0.2 1 : 4.3 1 : 116.7 

Lifetime PbB Metric 

Air-Only Regression-Based Median 0.001 1 : 0.3 1 : 5.9 1 : 344.2 

Air-Only Regression-Based 95th 

Percentile 0.005 1 : 0.3 1 : 5.8 1 : 158.2 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

I-48
 
 

 

 

Exhibit I-35. Secondary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and Past 

Air) a,b 

Concurrent PbB Metric 

Air-Only Regression-Based Median 0.003 1 : 0.2 1 : 4.5 1 : 127.7 

Air-Only Regression-Based 95th 

Percentile 0.010 1 : 0.2 1 : 4.3 1 : 57.8 

Lifetime PbB Metric 

Air-Only Regression-Based Median 0.002 1 : 0.3 1 : 5.9 1 : 238 

Air-Only Regression-Based 95th 

Percentile 0.010 1 : 0.3 1 : 5.8 1 : 78.6 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

I-49
 
 

 

 

Exhibit I-36. Secondary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and Past 

Air) a,b 

Concurrent PbB Metric 

Air-Only Regression-Based Median 0.001 1 : 0.2 1 : 4.5 1 : 315.1 

Air-Only Regression-Based 95th 

Percentile 0.004 1 : 0.2 1 : 4.3 1 : 138.9 

Lifetime PbB Metric 

Air-Only Regression-Based Median 0.001 1 : 0.3 1 : 5.9 1 : 410.7 

Air-Only Regression-Based 95th 

Percentile 0.004 1 : 0.3 1 : 5.8 1 : 188 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

I-50
 
 

 

 

Exhibit I-37. Secondary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3, 

Maximum Monthly Average) – Ambient Air to PbB Ratios 

Air to PbB Ratios (μg/m3 : μg/dL) 

with PbB Contribution from: 

Dust Model 

Ambient Air Annual 

Average Pb 

Concentration (μg/m3) Inhalation 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent Air) a 

Inhalation 

+Ingestion 

(Recent and Past 

Air) a,b 

Concurrent PbB Metric 

Air-Only Regression-Based Median 0.000 1 : 0.2 1 : 4.5 1 : 1780.5 

Air-Only Regression-Based 95th 

Percentile 0.001 1 : 0.2 1 : 4.3 1 : 539.1 

Lifetime PbB Metric 

Air-Only Regression-Based Median 0.000 1 : 0.3 1 : 5.9 1 : 2864.7 

Air-Only Regression-Based 95th 

Percentile 0.001 1 : 0.3 1 : 5.8 1 : 696.6 

a

 These results exclude application of the GSD reflecting inter-individual variability in Pb exposure and 

biokinetics. 

b

 "Past air" includes contributions from outdoor soil/dust contribution to indoor dust, historical air contribution to 

indoor dust, and outdoor soil/dust pathways, and "recent air" refers to contributions associated with outdoor 

ambient air Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be 

associated with outdoor ambient air Pb levels). 

I-51
 
REFERENCES 

U.S. Census Bureau. (2005) United States Census 2000: Summary File 1. Public Information Office. Available 

online at: http://www.census.gov/Press-Release/www/2001/sumfile1.html. 

U.S. Environmental Protection Agency (USEPA). (2006) Air Quality Criteria for Lead (Final). Volume I and II. 

Research Triangle Park, NC: National Center for Environmental Assessment; EPA/600/R-05/144aF-bF. 

Available online at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=158823. 

I-52
 
Appendix J: Performance Evaluation of Blood Pb (PbB) Models 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

 

Table of Contents 

Table of Contents ..................................................................................................................J-i 

List of Exhibits ....................................................................................................................J-iii 

J. PERFORMANCE EVALUATION OF BLOOD PB MODELS .....................................J-1
 

J.1. EVALUATION OF BIOKINETIC MODELS (IEUBK AND LEGGETT): 

BLOOD PB PREDICTIONS FOR INDIVIDUAL CHILDREN ................................. J-1 

J.1.1. Exposure Scenarios........................................................................................... J-1 

J.1.2. Model Setup...................................................................................................... J-2 

J.1.3. Performance Evaluation Results....................................................................... J-3 

J.1.3.1. Scenario 1: Change in Predicted PbB with Increasing Pb 

Uptake ................................................................................................ J-3 

J.1.3.2. Scenario 2: Leggett and IEUBK Model Responses to Episodic 

High Exposure ................................................................................... J-5 

J.1.3.3. Scenario 3: IEUBK Default Multipathway Exposure Scenario........ J-6 

J.1.3.4. Summary of Biokinetic Model Performance on Defined 

Exposure Scenarios............................................................................ J-9 

J.2. EVALUATION OF LANPHEAR ET AL. (1998) EMPIRICAL 

BLOOD PB MODEL.................................................................................................. J-10 

J.2.1. Performance and Limitations of the Lanphear PbB Model ............................ J-11 

J.3. PREDICTION OF BLOOD PB MODELS COMPARED TO POPULATION 

BLOOD PB DATA..................................................................................................... J-11 

J.3.1. Comparison of Biokinetic Model Predictions to NHANES PbB 

Survey Data..................................................................................................... J-11 

J.3.2. Comparison of Predicted PbB Concentrations to Measured PbB 

Values from an Urban Cohort......................................................................... J-14 

J.3.2.1. Overview of the Data Set................................................................. J-14 

J.3.2.2. Model Test Procedures .................................................................... J-15 

J.3.2.3. Model Evaluation Results................................................................ J-16 

J.3.2.4. Explanation for the Discrepancies between Measured and 

Predicted PbB Concentrations ......................................................... J-20 

J-i 
 

J.4. SUMMARY OF BLOOD PB MODEL EVALUATION........................................... J-23 

REFERENCES .................................................................................................................... J-26 

J-ii 
 

List of Exhibits 

Exhibit J-1. Predicted PbB at Age 3 Years versus Pb Intake.......................................................J-4 

Exhibit J-2. FORTRAN Leggett Model Predicted PbB Response to a 1 Year Increase in Pb 

Exhibit J-3. Estimated Age-Specific Pb Intakes and Uptakes Derived Based on the IEUBK 

Exhibit J-4. Comparison of IEUBK PbB Predictions from the Pounds and Leggett (1998) 

Multi-Source Exposure Scenario with Results Obtained in this Analysis Using 

Exhibit J-5. Comparison of Leggett Model-Predicted Annual Average PbB Concentrations 

Obtained Based on the IEUBK Default Pb Intake Estimates with the Results of
 

Exhibit J-6. Comparison of Leggett Model-Predicted Annual Average PbB Concentrations 

Obtained Based on the IEUBK Default Pb Uptake Estimates with the Results 

Intake of 100 μg/day Starting at Age 2 ....................................................................J-5 

Default Input Parameters..........................................................................................J-6 

IEUBKwin32............................................................................................................J-7 

Pounds and Leggett (1998).......................................................................................J-8 

of Pounds and Leggett (1998) ..................................................................................J-9 

Exhibit J-7. Comparison of Biokinetic Model PbB Predictions to PbB Survey Data ...............J-13 

Exhibit J-8. Comparison of Observed and Predicted PbB Concentrations for the 

Rochester, New York, Cohort ................................................................................J-17 

Exhibit J-9. Measured and Predicted PbB Levels for Subsets of the Rochester, 

Exhibit J-10. Comparison of Average PbB Predictions from the IEUBK, Leggett, and 

Exhibit J-11. Correlation between IEUBK PbB Prediction Errors and Measured 

Exhibit J-12. Errors in Lanphear Predicted PbB Concentrations versus Measured 

Exhibit J-13. Errors in Lanphear Predicted PbB Concentrations versus Measured 

New York, Cohort Data..........................................................................................J-18 

Lanphear Models with Measured PbB Levels from the Rochester Cohort............J-19 

Arithmetic Mean Indoor Dust Pb Concentrations..................................................J-21 

Arithmetic Mean Indoor Dust Pb Concentrations..................................................J-22 

PbB Concentrations................................................................................................J-23
 

J-iii 

 

J. PERFORMANCE EVALUATION OF BLOOD PB MODELS 

This appendix presents the results of performance evaluation analyses of the models used 

to estimate blood Pb (PbB) levels in this assessment. Section J.1 describes the relative 

performance of two biokinetic models when applied to a range of exposure scenarios for 

individuals and for populations of children exposed to Pb. The two models are the Integrated 

Exposure Uptake Biokinetic Model for Lead in Children (IEUBK) (hereafter referred to as the 

“IEUBK model”) (USEPA, 1994) and the International Commission for Radiation Protection 

(ICRP) model (hereafter referred to as the “Leggett model”) (Leggett, 1993). Both models are 

well-documented, widely used, and have been subject to a range of testing and calibration 

exercises (see Section 4.4 of USEPA [2006]). Section J.2 describes the performance of the 

“empirical” model (hereafter referred to as the “Lanphear model”), which includes children 6 to 

24 months of age (Lanphear et al., 1998). Section J.3 describes the performance of the biokinetic 

and empirical models when applied to selected populations of children exposed to Pb and 

Section J.4 summarizes the results of the performance analysis. 

J.1.	 EVALUATION OF BIOKINETIC MODELS (IEUBK AND LEGGETT): BLOOD 

PB PREDICTIONS FOR INDIVIDUAL CHILDREN 

The performance of the two biokinetic models, IEUBK and Leggett, was evaluated by 

comparing the PbB predictions from each model to results obtained previously by the U.S. EPA 

and other investigators when the models were tested using specific exposure scenarios. The 

purpose of this evaluation was to ensure that the model results were consistent with previous 

calibration results. 

J.1.1. Exposure Scenarios 

The following three exposure scenarios were used to examine the performance of the two 

biokinetic models: 

•	 Scenario 1: This scenario compared the predicted PbB levels in 2- to 3-year-old children 

in response to a range of constant Pb uptakes from 0.1 to 100 micrograms (μg) per day. 

This scenario is described on pages 4-122 and is illustrated in Figure 4-32 of the U.S. 

EPA Air Quality Criteria Document for Lead (USEPA, 2006). The primary output 

measure from this scenario is the slope of the relationship between estimated PbB at age 

3 years and Pb uptake in the low-dose range (0 to 10 μg/day), where the model responses 

are very nearly linear. Estimates of the daily Pb uptake were also compared, which 

resulted in a predicted average PbB level of 10 μg per deciliter (dL), and a predicted PbB 

level associated with 100 μg/day Pb uptake. This scenario provides a straightforward test 

of the biokinetic components of the models because it bypasses assumptions related to Pb 

absorption from different media. In the Leggett model, Pb was assumed to directly enter 

the blood stream, as described below. In the IEUBK model, Pb uptake occurs through 

J-1
 
 

the ingestion pathway with an assumed ingestion absorption fraction (AFI) value of 1.0 

(or 100 percent absorption), or through the “alternative” pathway, also with 100 percent 

absorption. 

•	 Scenario 2: In this scenario, a constant Pb uptake is assumed to begin at birth, resulting 

in a PbB level of 2.0 μg/dL at 2 years of age. At age two, Pb “exposure” (actually, oral 

intake) is increased by 100 μg/day for 1 year. This scenario is described in the U.S. EPA 

Air Quality Criteria Document for Lead (USEPA, 2006; page 4-127, Figure 4-35). 

Consistent with the description in the legend for Figure 4-32 of the U.S. EPA Air Quality 

Criteria Document for Lead, “default bioavailability assumptions” were used (USEPA, 

2006). The default was interpreted to be the Leggett default age-specific AFI values for 

children from birth through 3 years of age, which is 45 percent from birth through age 

100 days, decreasing linearly to 30 percent by 1 year of age, and remaining at 30 percent 

through childhood (USEPA, 2006). For the IEUBK runs, the default absorption factor 

for outdoor soil/dust and indoor dust (30 percent) was used. 

•	 Scenario 3: Scenario 3 is a multi-pathway exposure scenario, described by Pounds and 

Leggett (1998). This exposure scenario was derived from the IEUBK default exposure 

concentration and exposure/uptake/intake factor values, as defined in the U.S. EPA 1994 

Technical Support Document (USEPA, 1994). In their study, Pounds and Leggett used 

the IEUBK default values to derive annual average Pb intake and uptake estimates for 

seven 1-year age ranges beginning at birth. Exposure sources included diet, drinking 

water, outdoor soil/dust, and indoor dust. Two sets of model inputs were developed for 

the Leggett model: one set was the Pb intake estimates derived from the IEUBK defaults, 

and the other set was the Pb uptake estimates corresponding to the same set of exposures. 

In reproducing these two sets of estimates (see below), the age-specific Pb intakes were 

input to the model using the default age-specific AFI values described in Scenario 2. Pb 

uptake for input to the Leggett models was assumed to occur either directly into the blood 

stream or by ingestion with 100 percent gastrointestinal (GI) absorption. All IEUBK 

model inputs were maintained at their default values, except for indoor dust Pb 

concentration, which was set to 200 μg per gram (g), consistent with the value that 

Pounds and Leggett assumed. 

J.1.2. Model Setup 

Dr. Joel Pounds of Battelle Pacific Northwest Laboratories provided the Leggett model 

FORTRAN code. The code (Pounds, 2000) was imported into the Digital Visual FORTRAN® 

compiler and compiled into an .exe file that could be run from Windows®. The original input 

and output file formats were preserved. A batch version of the model (also in FORTRAN) was 

also created that repeatedly called the original model code as a subroutine, passing results to 

various sets of ingestion and inhalation Pb intake or uptake estimates for each age range. No 

other features were added to the batch version of the model. 

In both FORTRAN versions, the assumption that all ingested Pb was absorbed with the 

same efficiency was maintained (i.e., only a single AFI value applies to all ingested Pb). 

J-2
 
 

Therefore, to evaluate PbB impacts of multi-source scenarios (involving, for example, dietary, 

drinking water, and outdoor soil/dust exposures), calculating Pb uptake (input to the GI tract or 

blood stream) external to the model was necessary, so that a single “ingestion” intake or uptake 

value could be provided for each age interval evaluated. 

For simplicity, age-specific Pb inputs to the Leggett model were specified in one of two 

ways: (1) as ingestion uptake values, assigning a constant value of 100 percent to the GI 

absorption fraction; or (2) by using the “chronic” exposure pathway of the model, in which all 

uptake is assumed to enter the blood/extra-vascular fluid compartment instantaneously. These 

two approaches resulted in nearly identical PbB estimates, except for the first iterations 

following large changes in exposures. In these cases, slightly more rapid increases in PbB levels 

occurred in the “chronic” pathway than in other compartments. All biokinetic modeling 

parameters and age ranges were maintained exactly as in the default input file Dr. Pounds 

provided. In all tests performed, the batch version of the Leggett model generated identical 

results to the off-the-shelf version (Pounds, 2000). 

Also as part of the testing process, the effects of using different simulation time steps in 

the Leggett model were examined. In all scenarios tested, time steps shorter than 0.1 day 

resulted in nearly identical results, except in the first few iterations of each run. The differences 

essentially disappeared for time steps of 0.01 days or less. Therefore, a constant iteration step of 

0.01 days was used for all Leggett model testing. The default time step of 4 hours was used in 

all IEUBK runs. 

To reproduce comparisons with the IEUBK results, the U.S. EPA IEUBKwin32 model 

Version 1.0©, build 261, was used. Both single-run and batch model results were used, with 

input parameter values specified as discussed below. 

J.1.3. Performance Evaluation Results 

J.1.3.1. Scenario 1: Change in Predicted PbB with Increasing Pb Uptake 

The FORTRAN version of the Leggett model, in response to varying Pb uptake levels 

between 1.0 and 100 μg/dL, produced results that were very similar to those presented in the 

U.S. EPA Air Quality Criteria Document for Lead (see Exhibit J-1 and Figure 4-32 from the U.S. 

EPA Air Quality Criteria Document for Lead). In the uptake range of 0.1 to 10 μg/day, an 

increase in Pb uptake of 0.90 μg/dL per 1.0 μg/day was estimated between the ages of 2 and 3 

years, which corresponds to 0.88 μg/dL per μg/day in Pb uptake reported in the U.S. EPA Air 

Quality Criteria Document for Lead (USEPA, 2006). The U.S. EPA Air Quality Criteria 

Document for Lead reported that a 10 μg/dL PbB level would result from a 12 μg/day Pb uptake. 

J-3
 
 

Based on the Leggett modeling results, a value of 11.1 μg/day was calculated. The PbB 

concentration associated with 100 μg/day Pb uptake in Figure 4-31 of the U.S. EPA Air Quality 

Criteria Document for Lead is around 55 μg/dL (the axes of the chart are not labeled clearly); the 

corresponding value predicted by the Leggett model was 55.4 μg/dL. 

Initially, the PbB levels predicted using the IEUBK model differed slightly from the 

results presented in the U.S. EPA Air Quality Criteria Document for Lead (USEPA, 2006), in 

that the results of this assessment show a slight downward curvature with increasing Pb uptake. 

However, essentially identical PbB predictions were obtained if the nonlinear uptake module in 

the IEUBK was bypassed by setting the “Fraction Passive” input value to 1.0 (100 percent). It 

was assumed that the U.S. EPA Office of Research and Development (ORD) also overrode this 

module in their performance analysis, given the lack of curvature demonstrated in the PbB-Pb 

uptake plot in the U.S. EPA Air Quality Criteria Document for Lead (USEPA, 2006; Figure 4­

32), which is reproduced by the results in Exhibit J-1. 

Exhibit J-1. Predicted PbB at Age 3 Years versus Pb Intake 

60
 

50
 

40
 

30
 

20
 

10
 

0 

0 20 40 60 80 100 

Pb Uptake (μg/day) 

Predicted PbB at Age 3 (

μg/dL)

Leggett 

IEUBK 

From the IEUBK runs, a PbB-Pb uptake slope of 0.36 μg/dL per μg/day uptake was 

estimated, which is identical to the value reported in the U.S. EPA Air Quality Criteria 

Document for Lead. A Pb uptake of 27 μg/day corresponded to an estimated PbB level of 10 

μg/dL for a 3-year-old, close to the value of 29 μg/day reported in the Criteria Document for 

Lead (USEPA, 2006). The IEUBK estimated PbB at 100 μg/day uptake was 33.7 μg/dL; the 

corresponding value from the U.S. EPA Air Quality Criteria Document for Lead figure is 

approximately 33 μg/dL. 

J-4
 
 

The results presented here closely agree with the results of the Leggett and IEUBK model 

comparisons reported in the U.S. EPA Air Quality Criteria Document for Lead. The reasons for 

the small differences between these results and those in the U.S. EPA Air Quality Criteria 

Document for Lead are unclear, but they could include minor differences in the specification of 

model inputs, limitations in machine precision, or rounding error. As mentioned above, identical 

results were obtained with the off-the-shelf and batch versions of the FORTRAN version of the 

Leggett model. 

J.1.3.2. Scenario 2: Leggett and IEUBK Model Responses to Episodic High Exposure 

As noted above, the second scenario examined the Leggett and IEUBK model response to 

a sudden increase in Pb exposure beginning at 2 years of age. As shown in Exhibit J-2, the 

results obtained using the FORTRAN version of the Leggett model are indistinguishable from 

those presented in Figure 4-32 of the U.S. EPA Air Quality Criteria Document for Lead. When 

the U.S. EPA ran this scenario through the Leggett model, the peak PbB achieved at age 3 years 

was 23 μg/dL. The corresponding result with the FORTRAN Leggett model was 23.2 μg/dL. 

The maximum PbB predicted by the IEUBK model (10.0 μg/dL) also precisely matched the 

results presented in the U.S. EPA Air Quality Criteria Document for Lead. 

Exhibit J-2. FORTRAN Leggett Model Predicted PbB Response to a 1 Year Increase in Pb 

Intake of 100 μg/day Starting at Age 2 

25 

20 

15 

10 

5 

0 

1 2 3 4 

Age (years) 

Predicted PbB Concentration (

μg/dL)

Leggett 

IEUBK 

J-5
 
 

 

 

J.1.3.3. Scenario 3: IEUBK Default Multipathway Exposure Scenario 

To compare results from the Leggett and IEUBK models, Pounds and Leggett (1998) 

constructed an exposure scenario for children less than 7 years of age based on the default input 

parameter values for the IEUBK model. For each age group, they estimated Pb intake 

(administered dose) and uptake (absorbed dose) using IEUBK default exposure concentrations, 

behavioral variables, and absorption fractions. The IEUBK model was run using the default 

values and the estimated annual average PbB for children less than 7 years of age served as the 

basis for comparison with the Leggett model predictions. 

Pounds and Leggett (1998) ran the Leggett model using two different sets of intakes. 

First, the uptake values were used as direct inputs to the biokinetic algorithms. Second, they 

used the calculated Pb intake values as inputs, apparently applying the Leggett model default 

AFI values to the summed intakes. (Note that the exact methods used to calculate uptake are not 

well documented). Exhibit J-3 displays the intake and uptake estimates from Pounds and 

Leggett (1998). 

Exhibit J-3. Estimated Age-Specific Pb Intakes and Uptakes Derived Based on the IEUBK 

Default Input Parameters 

Source of 

Exposure 

Age Range (months) 

6 to 12 12 to 23 24 to 35 36 to 47 48 to 59 60 to 71 72 to 84 

Default Intake, μg/day 

Air 0.07 0.11 0.19 0.21 0.21 0.29 0.29 

Diet 5.53 5.78 6.49 6.24 6.01 6.34 7.00 

Drinking Water 0.80 2.00 2.08 2.12 2.20 2.32 2.36 

Outdoor Soil/Dust 7.65 12.15 12.15 12.15 9.00 8.10 7.65 

Indoor Dust 9.35 14.85 14.85 14.85 11.00 9.90 9.35 

Pb Paint 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Total Intake 23.40 34.89 35.76 35.57 28.42 26.95 26.65 

Default Uptake, μg/day 

Air 0.02 0.04 0.06 0.07 0.07 0.09 0.09 

Diet 2.54 2.63 2.98 2.90 2.86 3.03 3.36 

Drinking Water 0.37 0.91 0.96 0.99 1.04 1.11 1.13 

Outdoor Soil/Dust 

+ Indoor Dust 4.68 7.36 7.44 7.53 5.69 5.16 4.89 

Pb Paint 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Total Uptake 7.59 10.90 11.38 11.42 9.58 9.30 9.30 

Note: Data extracted from Pounds and Leggett (1998). 

J-6 
 

The Pounds and Leggett (1998) IEUBK exposure scenario estimates were reproduced by 

simply running the IEUBK with its default inputs, which have not changed since the 1994 

Technical Support Document was issued. As noted above, the only input that was adjusted was 

the default indoor dust concentration, which was adjusted from 150 μg/g to 200 μg/g to yield 

intake values consistent with Pounds and Leggett (1998). As shown in Exhibit J-4, resulting 

PbB predictions were essentially identical to those reported by Pounds and Leggett (1998). 

Exhibit J-4. Comparison of IEUBK PbB Predictions from the Pounds and Leggett (1998) 

Multi-Source Exposure Scenario with Results Obtained in this Analysis Using 

IEUBKwin32 

0.0 

2.0 

4.0 

6.0 

1 2 3 4 5 6 7 

Age (years) 

Predicted Average PbB (

μg/dL) 

2.7 

4.1 

4.5 

4.2 4.0 

3.4 

2.9 2.7 

4.1 

4.5 

4.2 

4.0 

3.4 

3.0 

Pounds and Leggett (1998) 

This Analysis 

When the Pb intake values from Exhibit J-3 were used as inputs to the Leggett model in 

this analysis, the results were generally similar to the Leggett model results obtained by Pounds 

and Leggett (see Exhibit J-5). Except for age “1,” which is defined by Pounds and Leggett as 

from birth to the first birthday, results presented here are very close to the values from the 

previous scenario. For infants less than 1-year-old, the average PbB estimate is about 36 percent 

higher than the earlier estimate (8.5 versus 6.2 μg/dL) (Pounds and Leggett, 1998). Possible 

explanations for this rather large difference may be differing assumptions about very early 

exposure patterns and/or assumptions about when the averaging of PbB concentrations was 

initiated. 

J-7
 
 

Exhibit J-5. Comparison of Leggett Model-Predicted Annual Average PbB Concentrations 

Obtained Based on the IEUBK Default Pb Intake Estimates with the 

Results of Pounds and Leggett (1998) 

0.0 

2.0 

4.0 

6.0 

8.0 

10.0 

1 2 3 4 5 6 7 

Age (years) 

Predicted Average PbB (

μg/dL) 

8.5 8.8 

8.6 

7.3 

6.6 6.3 6.9 

6.2 

8.7 9.1 

8.8 

7.6 

6.4 

9.3 

Pounds and Leggett (1998) 

This Analysis 

For older children, predicted PbB levels (based on intake) were very close to, but slightly 

higher than, the corresponding values Pounds and Leggett obtained. For age “2,” the prediction 

is about 7 percent higher than the earlier estimate, and the difference decreases with age until age 

7 when the difference is less than 2 percent. Given the inherent uncertainty in PbB modeling and 

potentially numerous subtle differences in the way the model could have been run, these results 

represent very good agreement. 

When the calculated Pb uptake values from Exhibit J-3 were used as model inputs to the 

Leggett model, results differed substantially from those of Pounds and Leggett, even though they 

(presumably) used the same assumptions (see Exhibit J-6). For all age groups, predicted PbB 

levels in this analysis using the Leggett model are 26 to 43 percent higher than the Pounds and 

Leggett predictions. The reason for these differences is not clear. However, although the agespecific Pb intakes obtained were consistent with the default IEUBK input parameters, the 

pathway-specific or total Pb uptake (Pounds and Leggett, 1998) using the default values from the 

1994 Technical Support Document (USEPA, 1994) were not. A more complete understanding 

of the differences in PbB predictions requires access to documentation of the exact approaches 

Pounds and Leggett used in deriving the intake and uptake estimates and in running the Leggett 

model. Given the close agreement between the intake-based results, however, the differences are 

almost certainly due to differences in model inputs, rather than significant differences in model 

performance. 

J-8
 
 

Exhibit J-6. Comparison of Leggett Model-Predicted Annual Average PbB Concentrations 

Obtained Based on the IEUBK Default Pb Uptake Estimates with the 

Results of Pounds and Leggett (1998) 

0.0 

2.0 

4.0 

6.0 

8.0 

10.0 

12.0 

14.0 

1 2 3 4 5 6 7 

Age (years) 

Predicted Average PbB (

μg/dL) 

9.8 

7.3 7.1 

8.0 

9.1 9.1 8.9 

6.1 

10.4 

11.3 

12.4 12.4 11.8 

7.7 

Pounds and Leggett (1998) 

This Analysis 

J.1.3.4. Summary of Biokinetic Model Performance on Defined Exposure Scenarios 

IEUBK results reported in previous model comparisons were almost exactly replicated 

here using the newest version of the model. The low-dose PbB slope estimate for 3-year-olds 

exactly matched the value reported in the U.S. EPA Air Quality Criteria Document for Lead, as 

did the maximum predicted PbB response to episodic high exposure beginning at age two. 

IEUBK estimates of annual average PbB estimates arising from the Pounds and Leggett (1998) 

multi-source scenario were also identical (within 0.1 μg/dL or less) to the previously reported 

values for all age groups. These results indicate that application of the IEUBK model is 

basically consistent with the approaches used in previous model comparisons. 

In two of the three tests conducted, the FORTRAN version of the Leggett model 

generated PbB predictions that were close or identical to the results obtained in previous 

calibration and comparison exercises. The low-dose PbB slope for 3-year-old children was 

within about two percent (0.90 versus 0.88 μg/dL per μg/day uptake) of the value reported in the 

U.S. EPA Air Quality Criteria Document for Lead (2006). The maximum predicted PbB level in 

response to a sudden increase for 1 year in exposure beginning at 2 years of age (23.2 μg/dL) 

was identical to that reported in the U.S. EPA Air Quality Criteria Document for Lead (23 

μg/dL). Thus, when the exposure scenarios and intake/uptake assumptions are precisely 

duplicated, the FORTRAN version of the Leggett model appears to produce essentially the same 

results as the model when applied by other investigators. 

J-9
 
 

Even when the exposure conditions are less well documented and more difficult to 

duplicate, results presented here for the Leggett model are similar to those obtained in previous 

analyses. As noted above, for similar age patterns of total Pb intake, results matched fairly 

closely (within seven percent, except for the youngest age group) those that Pounds and Leggett 

(1998) obtained in their model comparison. Larger differences from the Pounds and Leggett 

results were observed when uptake estimates were used as the basis for PbB prediction. As 

explained above, these differences are likely related to potential inconsistencies in the way Pb 

uptakes were calculated, rather than to differences in model performance per se. 

Consistent with previous analyses, the Leggett model predicts PbB levels that are 

significantly higher than the IEUBK model levels for similar exposure scenarios. The Pb intakePbB slope estimate derived from the Leggett model for exposure between 2 and 3 years of age 

was approximately 2.5 times higher than that derived using the IEUBK model. This difference is 

entirely due to differences in the biokinetic components of the two models, because PbB uptake 

(the dose entering the biokinetic modules) was the same for both models. Similarly, the Leggett 

prediction for the other two scenarios was 2.1 to 2.6 times greater than the IEUBK-predicted 

response for the same exposures. 

J.2. EVALUATION OF LANPHEAR ET AL. (1998) EMPIRICAL BLOOD PB MODEL 

Lanphear et al.(1998) reported on the results of analyzing the relationships among 

observed PbB levels in young children, socioeconomic and behavioral variables, and several Pb 

exposure metrics in indoor dust, outdoor soil/dust, Pb paint, and drinking water. The model was 

derived based on data from 12 United States epidemiologic studies of approximately 1,300 

children, aged 6 months to 24 months, published between 1985 and 1996. Five of the studies 

focused on children in urban areas while the others focused on children living near Pb smelting 

or mining sites. Geometric mean (GM) PbB levels in the individual studies ranged from 1.92 

μg/dL to 11.17 μg/dL; the GM PbB for the collective study population was 5.02 μg/dL. 

In the best fitting (log-linear) model, wipe-dust Pb loading, outdoor soil/dust Pb 

concentration, exterior sample location, paint condition, race, mouthing behavior, and several 

interaction terms were significantly related to PbB. Lanphear et al. (1998) presented the results 

in look-up tables showing the predicted PbB concentrations, with covariates set to mean values, 

as a function of outdoor soil/dust Pb concentration and indoor dust Pb loading (Lanphear et al., 

1998; Table 4). The results in these tables can easily be interpolated using multiple regressions 

to derive models to predict PbB in 16-month-olds (the mean age in the study population). 

J-10
 
 

J.2.1. Performance and Limitations of the Lanphear PbB Model 

The performance of the Lanphear model has not been compared to that of other PbB 

models to the same extent as the biokinetic models previously discussed. Although several other 

empirical models have been developed to predict children's PbB (USEPA, 2006; Section 4.4.2), 

variations in study populations, model structure, and input variables make model comparisons 

difficult. 

For human exposure and health risk assessment, the Lanphear model has two distinct 

limitations. The first is that the model estimates PbB levels as a function of wipe-dust Pb 

loading, rather than Pb dust concentration, which is the dust Pb metric used by many biokinetic 

models (including the IEUBK and Leggett models). As discussed in Attachment G-1, deriving 

empirical estimates of dust Pb concentrations from dust Pb loading values using the few data sets 

that contain both measurements appears possible, but a substantial degree of uncertainty is 

introduced into the estimates of the exposure metrics. Furthermore, the Lanphear model 

estimates PbB levels for an infant of mean study age 16 months based on point estimates of 

outdoor soil/dust and indoor dust exposure, with no temporal variation. Thus, no dynamic 

component is incorporated, and the model cannot (except by averaging) predict PbB for 

situations where exposures change over time. 

More importantly, the Lanphear model was derived based on data from infants and 

toddlers age 6 to 24 months and thus cannot be used to estimate PbB in older children. The 

Lanphear model predictions are for children near their expected peak PbB levels; these values 

cannot be directly compared to the lifetime and concurrent PbB metrics used in this assessment 

for estimating IQ decrement (presented in Appendix K). These reasons limit use of the Lanphear 

et al. (1998) model as a primary tool in this assessment. However, comparisons of the Lanphear 

model predictions with predictions of the biokinetic models are presented later in this appendix 

for a small cohort of young children with known dust Pb loading, dust Pb concentration, and PbB 

levels as a further check on the performance of the biokinetic models. 

J.3.	 PREDICTION OF BLOOD PB MODELS COMPARED TO POPULATION 

BLOOD PB DATA 

J.3.1. Comparison of Biokinetic Model Predictions to NHANES PbB Survey Data 

The biokinetic models were further evaluated by comparing results (predicted PbB 

levels) to statistics from PbB surveys of large populations. The premise underlying this 

comparison was that, if the exposure factors and exposure concentrations used in the simulations 

were, in fact, representative of recent general population exposures, a finding of predicted ageJ-11
 
 

 

 

 

 

 

PbB profiles that were similar to the reported general population PbB profiles would increase 

confidence in the ability of the models to capture impacts of changes in aggregate Pb intakes. 

The model predictions were compared to data from the NHANES surveys conducted 

from 1999 to 2002 (USEPA, 2006) and data from the National Human Exposure Assessment 

Survey (NHEXAS) (USEPA, 2004) that measured children’s PbB concentrations in three areas 

of the United States in 1994. The biokinetic model simulations relied on the exposure factor 

values, drinking water Pb concentrations and age-specific dietary Pb intake values used in this 

risk assessment (Appendix H). Two sets of model outputs, based on two sets of indoor dust and 

outdoor soil/dust Pb concentrations (see below) were generated for comparison to the PbB 

survey data. Additionally an ambient air concentration of 0.06 microgram per cubic meter (μg/ 

m3

) was assumed for the inhalation exposure pathway, which contributed little to overall Pb 

intake compared to the other pathways. 

Two sets of "typical" indoor dust and outdoor soil/dust Pb concentrations were derived 

for use in the simulations. The first set consisted of the population-weighted GM indoor dust 

and outdoor soil/dust Pb concentrations from the Housing and Urban Development (HUD) 

National Survey data (86 and 200 μg/g, respectively) (USEPA, 1996).1 See Appendix G for a 

more detailed discussion of the HUD Survey data. The second set of outdoor soil/dust and 

indoor dust concentration estimates was derived from data gathered during the NHEXAS. 

Weighted GM soil/dust (56 μg/g) and dust Pb (162 μg/g) concentrations from the combined 

NHEXAS study areas (Arizona; Baltimore, Maryland; and Region 5) were input into the IEUBK 

and Leggett models to simulate typical children's exposures. 

The IEUBK and Leggett models were run using both sets of outdoor soil/dust and 

indoor dust inputs, and the other inputs described above, to generate age profiles of estimated 

PbB concentrations. The results are summarized in Exhibit J-7. 

1

 Data on the relationship between dust Pb loading and Pb concentration was gathered as part of the HUD 

National Survey of Lead-Based Paint in Housing conducted between November 1989 and 1990 (USEPA, 1995). 

The goal of the survey was to obtain information on the presence and condition of Pb paint, outdoor soil/dust and 

indoor dust Pb loading and Pb concentrations, as well as other household data, from a representative national sample 

of 300 private homes and 100 public housing facilities. The data used to estimate outdoor soil/dust and indoor dust 

Pb concentration in this analysis came from 284 private households that were ultimately sampled during the survey. 

J-12
 
 

 

Exhibit J-7. Comparison of Biokinetic Model PbB Predictions to PbB Survey Data 

PbB Levels from Biokinetic Models or Survey Data by Age in Months PbB Levels 

(μg/dL) 

Age 13 to 24 Months 

GM PbB Levels from Survey 

Data NHANES IV 1999 to 2000 a

 2.5 

PbB Levels Predicted by 

Biokinetic Models 

Leggett (NHEXAS, outdoor soil/dust and indoor dust) 6.9 

Leggett (HUD, outdoor soil/dust and indoor dust) 9.4 

IEUBK (NHEXAS, outdoor soil/dust and indoor dust) 2.5 

IEUBK (HUD Survey, outdoor soil/dust and indoor dust) 3.4 

Age 13 to 60 Months 

GM PbB Levels from Survey 

Data 

NHANES IV 1999 to 2000 b

 2.2 

NHANES IV 2001 to 2002 b

 1.7 

PbB Levels Predicted by 

Biokinetic Models 

Leggett (NHEXAS, outdoor soil/dust and indoor dust) 6.7 

Leggett (HUD, outdoor soil/dust and indoor dust) 9.2 

IEUBK (NHEXAS, outdoor soil/dust and indoor dust) 2.2 

IEUBK (HUD Survey, outdoor soil/dust and indoor dust) 3.0 

Age 37 to 84 Months 

GM PbB Levels from Survey 

Data NHEXAS IV 1994 2.1 

PbB Levels Predicted by 

Biokinetic Models 

Leggett (NHEXAS, outdoor soil/dust and indoor dust) 5.9 

Leggett (HUD, outdoor soil/dust and indoor dust) 8.1 

IEUBK (NHEXAS, outdoor soil/dust and indoor dust) 1.7 

IEUBK (HUD Survey, outdoor soil/dust and indoor dust) 2.3 

a

 Data extracted from Hattis (2006). 

b

 Data extracted from U.S. EPA (2006; Table 4.4). 

Exhibit J-7 shows that the IEUBK model PbB concentrations were much closer to the 

NHANES IV GM than those of the Leggett model. Using the lower NHEXAS outdoor soil/dust 

and indoor dust Pb concentration data, the PbB level for the youngest children (ages 13 to 24 

months) predicted by IEUBK matched the NHANES age GM value for the same age group of 

2.5 μg/dL. The predicted PbB levels for children 1 through 5 years of age (2.2 or 3.0 μg/dL, 

depending on the assumed outdoor soil/dust and indoor dust Pb concentrations) were somewhat 

lower than the GM values for children in the same age range (2.2 μg/dL and 1.7 μg/dL) seen in 

the 1999 to 2000 and 2001 to 2002 NHANES data, respectively. The same pattern was seen 

when the age-averaged PbB predictions for older children (age 37 to 84 months) are compared to 

J-13
 
 

survey data. The IEUBK model predictions were very close to the GM values derived from the 

survey data, while the Leggett predictions were much higher. 

When the higher GM indoor dust and outdoor soil/dust Pb concentrations from the HUD 

National Survey are used as inputs to the IEUBK model, the predicted PbB levels for young 

children are higher than the GM values from the NHANES IV. The IEUBK blood predictions 

decrease from 3.4 μg/dL for a 13- to 24-month-old to 2.4 μg/dL for a 49- to 60-month-old, 

compared to NHANES GM PbB estimates for 1- through 5-year-olds of 2.2 μg/dL (1999 to 

2000) and 1.7 μg/dL (2001 to 2002). 

The ratio of Leggett predictions to survey GM PbB values ranges from 2.74 to 5.41 

depending on the age group and assumed indoor dust and outdoor soil/dust Pb concentrations. 

Using the GM indoor dust loading and outdoor soil/dust Pb concentration from the HUD 

national survey as inputs to the Lanphear et al. (1998) empirical model results in a PbB estimate 

for a 16-month-old of 5.1 μg/dL. This estimate is roughly twice the observed GM value from the 

NHANES IV (1999-2000) data for ages 13 to 24 months, but as high as that obtained with the 

Leggett model for that age group. 

J.3.2.	 Comparison of Predicted PbB Concentrations to Measured PbB Values from an 

Urban Cohort 

As the final test of model performance, the predicted PbB levels from the IEUBK, 

Leggett, and Lanphear models were compared to measured PbB levels in a cohort of young 

children for whom Pb outdoor soil/dust and indoor dust exposures have been well characterized. 

J.3.2.1. Overview of the Data Set 

Data relating to PbB levels, outdoor soil/dust Pb concentrations, indoor dust Pb 

concentrations, and loading for a cohort of 204 children who had been the subjects of a previous 

epidemiological investigation were obtained from Dr. Bruce Lanphear (Lanphear et al., 1995; 

Lanphear and Roghmann, 1997). The purpose of the study was to measure the levels of Pb in 

outdoor soil/dust, indoor dust, paint, drinking water and PbB levels among children who had 

lived at the same address in Rochester, New York, since six months of age. PbB and 

environmental sampling were conducted in 1991 through 1994, when the children were between 

12 to 30 months old. Also included in the data set were a number of variables related to 

socioeconomic status, ethnicity, and income level. This cohort was one of the 12 (Lanphear et 

al., 1998) later used to derive the previously discussed empirical model for predicting PbB from 

outdoor soil/dust concentration and indoor dust Pb loading. 

J-14
 
 

Data were obtained as a SAS transport file; relevant variables were extracted to 

spreadsheets. Arithmetic and GM values of outdoor soil/dust Pb concentration, house floor dust 

loading, and house floor dust concentration values were derived for each sampled household. 

Dust loading and concentration values were included in calculations of summary statistics 

irrespective of floor covering type. Missing values were excluded from the calculation of 

average and GM values; all households had at least one outdoor soil/dust and indoor dust sample, 

and most had multiple samples. Outdoor soil/dust samples measured in the play yard, however, 

were available for only 86 of the 204 households. Single PbB measurements (means of triplicate 

analyses of the same sample) were also extracted from the SAS file. 

J.3.2.2. Model Test Procedures 

All three previously discussed models (the IEUBK and Leggett biokinetic models and the 

Lanphear empirical equation) were used to derive PbB estimates for individual children in the 

cohort. Estimates were derived using the outdoor soil/dust Pb, indoor dust loading, or indoor 

dust Pb concentration data reported for the households for each child as model inputs. Reported 

outdoor soil/dust concentrations measured in the play yard and the arithmetic mean indoor dust 

concentrations measured on the floor were used as inputs to the biokinetic models. Outdoor 

soil/dust concentrations measured in the play yard were found to be much more strongly 

correlated with PbB levels than perimeter [drip line] outdoor soil/dust Pb levels. Air 

concentration data were not collected in the study. One U.S. EPA Air Quality System (AQS) 

monitor collected Pb concentrations in total suspended particulate matter (TSP) during the 

sampling time period (January 1993 to June 1996) and within 50 kilometer (km) of the homes 

where indoor dust and outdoor soil/dust samples were collected (USEPA, 2007). Concentrations 

from this monitor were averaged from January 1993 to December 1996 to yield an average Pb air 

concentration of 0.035 μg/m3

. This value was used to approximate concentrations for input into 

the biokinetic models. As in the other PbB estimating exercises, ambient air Pb concentrations 

(used here only for the inhalation exposure pathway) contributed only a very small proportion of 

total Pb intake. Pb exposures from other pathways (diet, drinking water) were also simulated; 

the inputs and values for other exposure factors were described in Appendix H. 

Biokinetic model PbB estimates for each child were the annual average PbB outputs for 

the age group corresponding to the child’s age at the time of the PbB measurement (rounded to 

the nearest year (i.e., age groups 1 to 2 years or 2 to 3 years). Estimates were derived only for 

the 86 of the 204 children for whom play yard outdoor soil/dust Pb concentrations Pb 

concentrations had been measured, because, as noted above, outdoor soil/dust concentration in 

the play yard was found to be much more strongly correlated to measured PbB concentration 

J-15
 
 

than other outdoor soil/dust metrics. The estimates discussed below were derived using 

arithmetic mean indoor dust Pb concentrations, unless otherwise specified. 

PbB estimates were also derived for the Lanphear et al. (1998) empirical model, using 

average play yard and indoor dust Pb loading values for the households where the children lived. 

The Lanphear model provides estimates of PbB concentrations for 16-month-olds (the mean age 

of children in the cohorts used to estimate the model). PbB concentrations from the model were 

not corrected for variation with age (the Lanphear et al. (1998) model results were compared to 

measured levels for all children, irrespective of the age at which PbB was measured) or for other 

covariates. 

J.3.2.3. Model Evaluation Results 

Exhibit J-8 provides a comparison of the relationship between the measured PbB 

concentrations (the x-axis) and PbB concentrations predicted (as described in Section J.3.2.2) for 

the same child (the y-axis). The black line corresponds to equality between the measured and 

predicted PbB levels (i.e., no prediction "error"). The strongest pattern visible is the large 

number of children for which the Leggett PbB predictions were very much higher than the 

measured PbB levels. Only two children had measured PbB levels greater than those predicted 

for them by the Leggett model. 

J-16
 
 

 

Exhibit J-8. Comparison of Observed and Predicted PbB Concentrations for the 

Rochester, New York, Cohort 

0 

10 

20 

30 

40 

50 

60 

70 

0 10 20 30 

Measured PbB (μg/dL) 

Predicted PbB (μg/dL) 

IEUBK 

Leggett 

Lanphear 

Predicted = Measured 

Note: IEUBK and Leggett predictions are for age of child associated with measured PbB value, while Lanphear
 

predictions are based on children, age 16 months
 

A substantial proportion of the PbB levels predicted by the IEUBK model also fell well 

above the measured values. In contrast to the Leggett model, however, a cluster of IEUBK 

predicted PbB concentrations fell near or below the measured values. Finally, the bulk of the 

Lanphear PbB model predictions were near or below the corresponding measured PbB values. 

The slope of the Lanphear model predictions, however, appeared to be very small; compared to 

measured values PbB tends to have been over-predicted at low Pb levels and under-predicted at 

high Pb levels. 

A more detailed breakdown of the PbB predictions for each model is presented in Exhibit 

J-9. In this exhibit, average measured and predicted PbB levels are shown for the entire cohort 

and for the cohort broken down by quintiles with regard to measured PbB levels. For the entire 

data set, the average PbB levels predicted by IEUBK (12.3 μg/dL) and Leggett (22.4 μg/dL) 

were substantially greater than the average measured PbB (7.3 μg/dL). The IEUBK predictions 

were on average about 70 percent greater than the corresponding measured values for the data set 

taken as a whole, while the PbB levels Leggett model predictions were on average 3.1 times 

J-17
 
 

greater. This pattern is consistent with the relative magnitude of IEUBK and Leggett predictions 

based on typical population exposures discussed in Section J.3.1. 

Exhibit J-9. Measured and Predicted PbB Levels for Subsets of 

the Rochester, New York, Cohort Data 

Group Measured IEUBK Leggett Lanphear 

Arithmetic Mean Measured/Estimated PbB (μg/dL) 

Whole Data Set 7.3 12.3 22.4 6.5 

1st Quintile 3.1 13.7 18.3 6.2 

2nd Quintile 4.9 8.6 18.9 5.3 

3rd Quintile 6.7 7.6 21.0 6.5 

4th Quintile 8.5 13.7 23.6 7.0 

5th Quintile 13.5 18.1 30.6 7.5 

Ratio of Prediction to Measured PbB (unitless) 

Whole Data Set -- 1.7 3.1 0.9 

1st Quintile -- 4.4 5.9 2.0 

2nd Quintile -- 1.8 3.8 1.1 

3rd Quintile -- 1.1 3.1 1.0 

4th Quintile -- 1.6 2.8 0.8 

5th Quintile -- 1.3 2.3 0.6 

Note: IEUBK and Leggett predictions are for age of child associated with measured PbB 

value, while Lanphear predictions are based on children, age 16 months 

Exhibit J-10 provides a graphical summary of the data in Exhibit J-9. This exhibit clearly 

illustrates how much greater the average modeled Leggett PbB predictions were across all 

quintiles than the measured PbB levels for the same quintiles. Interestingly, however, the 

“slope” of the Leggett predictions across the quintiles was very similar to that seen in the 

observed average PbB levels. 

J-18
 
 

Exhibit J-10. Comparison of Average PbB Predictions from the IEUBK, Leggett, and 

Lanphear Models with Measured PbB Levels from the Rochester Cohort 

Predicted Average PbB, μg/dL 

35 

30 

25 

20 

15 

10 

5 

0 

Measured 

IEUBK 

Leggett 

Lanphear 

1st Quintile 2nd Quintile 3rd Quintile 4th Quintile 5th Quintile 

In contrast, PbB predictions from the IEUBK model did not increase monotonically 

across the measured PbB quintiles. Predicted PbB values for the two lowest quintiles were 

higher than the observed average PbB levels, but the IEUBK predictions for the three highest 

quintiles increased with a slope not very dissimilar from that seen in the data. As shown in 

Exhibit J-9, the IEUBK model over predicted PbB levels compared to the measured average 

values by between 30 to 60 percent for the two highest quintiles. Finally, it can be seen that the 

Lanphear model gave average PbB predictions that were, on the whole, closest to those seen in 

the Rochester data set. However, the “slope” across the quintiles was much lower than that seen 

in the data set. 

Looking at the performance of the three models in predicting PbB levels for this data set, 

three distinct patterns can be seen. The Leggett model consistently and substantially over 

predicted average PbB relative to the observed data, but the change in predicted PbB levels 

across the quintiles was very close to that seen in the data set. This suggests that the lowexposure “intercept” of the Leggett model was set too high, while the “slope” (response to 

increasing Pb uptake) reproduced the pattern seen in the data quite well. In the case of the 

IEUBK model, it was hard to understand the pattern of PbB predictions that were seen for the 

two lowest quintiles. Based on the pattern shown in Exhibit J-10, it appeared that outdoor 

soil/dust and indoor dust Pb exposure levels associated with relatively low PbB levels in the data 

J-19
 
 

set were consistently being given undue weight in the model’s exposure, intake and uptake 

modules, while at higher exposures, the outdoor soil/dust and indoor dust Pb intake values were 

weighted so as to given similar PbB increments as observed in the data set. Finally, while the 

Lanphear model yielded PbB predictions that most closely matched the observed quintile 

averages, in terms of absolute differences, it appeared that the response to increasing Pb uptake 

from outdoor soil/dust and indoor dust was weaker (the “slope” is shallower) than the pattern 

seen in the Rochester data set. Potential explanations for these patterns of model behavior are 

discussed in Section J.3.2.4. 

J.3.2.4. Explanation for the Discrepancies between Measured and Predicted PbB 

Concentrations 

One issue that effected the evaluation of all of the models was the difficulty of estimating 

the contribution of inter-individual variability in exposures, and responses to Pb exposures, to the 

observed variability in measured PbB levels in the Rochester cohort. When the biokinetic 

models were applied to estimate PbB levels for children in this cohort, Pb exposure 

concentrations inputs were measurements at a single point in time which did not reflect potential 

temporal (e.g., seasonal) variability. Similarly, the uptake and biokinetic module parameters 

were single-valued estimates, and likewise did not reflect inter-individual differences in Pb 

absorption, deposition, and elimination. In the case of the Lanphear empirical model, variability 

in exposure, absorption, and responsiveness were “lumped” into the central tendency estimates 

of the model parameters that were used in this analysis. 

To the extent that the various sources of uncertainty were not accounted for in the PbB 

modeling, the overall variability of predicted PbB values can be expected to be lower than they 

would be if all of these factors could be included in the analysis. Exposure concentrations and 

other input parameters tend to be positively skewed (often lognormal) with long “tails” 

increasing the mean of the distribution. Therefore, it is likely that the overall impact of not 

including all sources of variability in the PbB modeling was to give arithmetic means that are 

somewhat underestimated compared to those that would be obtained if all sources of variability 

could be included. The available data do not allow the extent of this potential bias to be 

estimated. It is not likely that the general patterns of predicted versus measured PbB values 

shown in Exhibit J-9 and Exhibit J-10 depend very strongly on the on the extent to which interindividual variability is accounted for in the PbB modeling. 

Predictions from the IEUBK model seem to match population PbB distributions more 

closely than those from the Leggett model. For the Rochester cohort data, the extent to which 

the IEUBK model overestimated PbB compared to measured Pb was strongly correlated with the 

J-20
 
 

average measured indoor dust Pb concentration (see Exhibit J-11). That is, the IEUBK model 

appeared to be giving a greater influence to the higher dust Pb concentrations than was seen in 

the PbB measurements. 

Exhibit J-11. Correlation between IEUBK PbB Prediction Errors and Measured 

Arithmetic Mean Indoor Dust Pb Concentrations 

-10.0 

10.0 

30.0 

50.0 

0 5,000 10,000 15,000 20,000 25,000 

Indoor Dust Pb Concentration (μg/g) 

IEUBK Predicted - Measured PbB (

μg/dL) 

In contrast, Lanphear model errors (differences from measured values) were somewhat 

more weakly related to indoor dust Pb concentrations than the IEUBK model errors (see Exhibit 

J-12). Also, the correlation between the Lanphear PbB prediction error and play yard soil/dust 

Pb was not significant (R = 0.024, p = 0.82). In contrast, the correlation between the Lanphear 

model prediction error and wipe dust Pb loading was significant (R = 0.53, p < 0.001), but the 

relationship was largely determined by two very high dust Pb loading observations. 

Interestingly, the relationship between the Lanphear PbB model error and the age of the children 

when PbB was measured was not significant (R = 0.12, p = 0.27). 

J-21
 
 

Exhibit J-12. Errors in Lanphear Predicted PbB Concentrations versus Measured 

Arithmetic Mean Indoor Dust Pb Concentrations 

-20 

-15 

-10 

-5 

0 

5 

10 

15 

20 

0 5,000 10,000 15,000 20,000 25,000 

Indoor Dust Pb Concentration, μg/g 

Lanphear Predicted PbB - Measured PbB

(μg/dL) 

In fact, the strongest predictor of the Lanphear model error (predicted - measured PbB) 

was measured PbB itself (see Exhibit J-13). This pattern suggests that, despite its relatively good 

overall accuracy at predicting PbB levels (based on the average ratio of predicted versus measure 

values), the Lanphear model was not adequately capturing the exposure factors that cause PbB 

levels to change in this cohort. Instead, the model was predicting more or less constant, 

relatively low, PbB levels across the entire range of exposures. This behavior may be a function 

of how the model was derived; the equation used in this evaluation exercise was developed using 

data from 12 study cohorts. The result was a rather generic model, based on the averages of 

many covariates, which may not be the best fit to the Rochester cohort. A more detailed, 

multivariate model might perform better. 

J-22
 
 

Exhibit J-13. Errors in Lanphear Predicted PbB Concentrations 

versus Measured PbB Concentrations 

Lanphear Predicted - Measured PbB

Concentrations (

μg/dL) 

20 

10 

0 

-10 

-20 

0 5 10 15 20 25 30 35 

Measured PbB Concentration (μg/dL) 

J.4. SUMMARY OF BLOOD PB MODEL EVALUATION 

The IEUBK and Leggett biokinetic model evaluations established, first of all, that the 

performance of these models was consistent with that reported by previous investigators 

(USEPA, 2006; Lanphear and Roghmann, 1997; Pounds and Leggett, 1998). Tests of the models 

against specific individual exposure scenarios (Section J.1.3) to a very high degree reproduced 

the results of previous model comparisons. 

Age profiles of predicted PbB levels were also compared against PbB data from the 

NHANES IV national survey, under the assumption that children in the sample population 

experienced "typical" pathway-specific Pb exposures as determined from reviews of the recent 

literature (see Section J.3.1). Depending on the assumptions made regarding typical outdoor 

soil/dust and indoor dust Pb concentrations, the IEUBK model either moderately over-predicted 

age-specific GM PbB levels (by two-fold or less) or generated predictions that were very close to 

the NHANES summary statistics. In contrast, age-averaged predictions from the Leggett model 

were between 2.7 and 5.4 times higher than the age-specific NHANES IV GM values. 

J-23
 
 

Comparisons of the model predictions to individual measured PbB values in a small 

urban cohort of urban toddlers (see Section J.3.2) showed similar results. Average PbB 

predictions from the IEUBK model were about 70 percent higher than the average measured PbB 

for the entire study cohort. The differences between IEUBK-predicted and measured PbB levels 

varied, however, for subsets of the study groups with different average measured PbB levels. 

For children in the first quintile of measured PbB, the IEUBK predictions were about four-fold 

higher than the average measured value. For higher PbB quintiles, while the IEUBK predictions 

were still greater than the measured values, the extent of agreement between the IEUBK 

predictions and measured PbB was much better (differences between 10 and 80 percent). The 

increase in PbB levels predicted by the IEUBK model across the three highest quintiles was 

similar to that seen in the data. 

As shown in the comparison to the NHANES data, PbB predictions from the Leggett 

model were all much higher than the corresponding average values in the urban cohort. The 

average ratio of Leggett-predicted PbB to the measured values was 3.1 for the entire study group. 

The increments in predicted average PbB values were very similar to the increments seen in the 

PbB data (see Exhibit J-10). 

The Lanphear empirical equation model predicted steady-state PbB concentrations that 

were quite close to the measured values in the study cohort. The average ratio of Lanphearpredicted PbB to measured PbB values was 0.9 for the entire study population. The average 

predicted PbB was two-fold greater than the measured values for the lowest PbB quintile, 

decreasing to 40 percent below the average measured values for the highest quintile. The 

increments in predicted PbB across the quintiles was much smaller than the increments seen in 

the data, suggesting that the Lanphear model was underestimating the effect of increasing 

exposure on PbB compared to that seen in the data. 

The results of this evaluation suggest that, of the two biokinetic models, the IEUBK 

generates PbB estimates that are most similar to measured values in populations of Pb-exposed 

children, especially for children with higher Pb uptakes. The Leggett PbB predictions are 

consistently much higher than both measured PbB levels and PbB levels predicted by the other 

models that have been tested. 

Although the Lanphear model generated PbB predictions that were relatively close to 

measured values in the small urban cohort, it tends to under-predict the slope of the relationship 

between Pb exposure (i.e., indoor dust Pb and outdoor soil/dust Pb) and PbB. Additionally, the 

potential utility of the Lanphear model in this assessment is limited by the lack of a dynamic 

component and the inability to predict PbB levels for children outside of the age range for which 

J-24
 
 

the model was derived (12 to 30 months). Thus, it cannot be used to calculate the "concurrent" 

or "lifetime" PbB metrics that are the primary inputs to the PbB-IQ modeling. 

Differences between measured PbB levels and the levels predicted by the IEUBK model 

were greatest for children associated with high measured indoor dust Pb levels (and to a lesser 

extent, high outdoor soil/dust Pb concentrations). The IEUBK model would appear to give 

undue weight to these high Pb exposure concentrations compared to the strength of their 

influence on PbB levels in the urban child data set. This may be because the high measured dust 

Pb values are unrepresentative of time-averaged exposures. While the arithmetic mean indoor 

dust Pb concentrations used in the model evaluation may provide the theoretical best estimates of 

the expected values of exposure Pb concentrations, the mean values for some children may be 

highly influenced by high “outlier” values, whose concentrations are not representative of longterm averages. Using the household GM indoor dust Pb concentrations, which reduced the effect 

of "outliers," instead of the arithmetic means as inputs to the IEUBK model, results in PbB 

predictions for the urban cohort that are much closer to the measured values. For the entire study 

population, the average difference between the IEUBK model prediction and measured PbB was 

20 percent. While this argument provides a plausible explanation for some of the difference 

between the observed and predicted PbB values for this cohort, it does not imply that any 

adjustment to the exposure Pb concentration estimates is necessary in this assessment. Unlike 

the test cases evaluated above, the exposure Pb concentration estimates in this assessment were 

intended to be representative of long-term Pb exposures. 

J-25
 
 

 

 

 

 

 

 

 

 

REFERENCES 

 ICF Consulting. (2006). White Paper on Advances in Knowledge Concerning Lead Exposures, Body Burdens, and 

Adverse Effects Since the 1991 Lead and Copper Rule: Options for the Reevaluation of the Drinking Water 

Action Level for Lead. Submitted to the Office of Science and Technology, Office of Water, U.S. 

Environmental Protection Agency; March. 

Lanphear, B. P.; Emond, M.; Jacobs, D. E.; Weitzman, M.; Tanner, M.; Winter, N. L.; Yakir, B.; Eberly, S. (1995) 

A Side-by-Side Comparison of Dust Collection Methods for Sampling Lead-Contaminated House Dust. 

Environ. Res. 68(2): 114-123. 

Lanphear, B. P.; Matte, T. D.; Rogers, J.; Clickner, R. P.; Dietz, B.; Bornschein, R. L.; Succop, P.; Mahaffey, K. R.; 

Dixon, S.; Galke, W.; Rabinowitz, M.; Farfel, M.; Rohde, C.; Schwartz, J.; Ashley, P.; Jacobs, D. E. (1998) 

The Contribution of Lead-Contaminated House Dust and Residential Soil to Children's Blood Lead Levels: 

A Pooled Analysis of 12 Epidemiologic Studies. Environmental Research. 79: 51-68. 

Lanphear, B. P.and Roghmann, K. J. (1997) Pathways of Lead Exposure in Urban Children. Environ. Res. 74(67): 

73 

Leggett, R. W. (1993) An Age-Specific Kinetic Model of Lead Metabolism in Humans. Environ Health Perspect. 

101: 598-616. 

Pounds, J. G. (2000) An Operators Manual for the Leggett Age-Dependent Biokinetic Model for Lead. Version 1.1. 

Pounds, J. G.and Leggett, R. W. (1998) The ICRP Age-Specific Biokinetic Model for Lead: Validations, Empirical 

Comparisons, and Explorations. Environ Health Perspect. 106 Suppl 6: 1505-1511. 

U.S. Environmental Protection Agency (USEPA). (1994) Technical Support Document: Parameters and Equations 

Used in the Integrated Exposure Uptake Biokinetic Model for Lead in Children (v.099d). EPA 540/R94/040. Office of Solid Waste. 

U.S. Environmental Protection Agency (USEPA). (1995) Report on the National Survey of Lead-Based Paint in 

Housing: Appendix I: Design and Methodology. EPA 747-R95-004. Office of Pollution Prevention and 

Toxics. 

U.S. Environmental Protection Agency (USEPA). (1996) Adjustments to the HUD National Survey Dust Data for 

Section 403 Analyses. EPA 747-R-96-011. Office of Pollution, Prevention, and Toxics. 

U.S. Environmental Protection Agency (USEPA). (2004) Exposure Measurements: The National Human Exposure 

Assessment Survey. Available online at: http://www.epa.gov/heasd/edrb/nhexas.htm. 

U.S. Environmental Protection Agency (USEPA). (2006) Air Quality Criteria for Lead (Final). Volume I and II. 

Research Triangle Park, NC: National Center for Environmental Assessment; EPA/600/R-05/144aF-bF. 

Available online at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=158823. 

U.S. Environmental Protection Agency (USEPA). (2007) Air Quality System (AQS) Database. Available online at: 

http://www.epa.gov/ttn/airs/airsaqs/aqsweb/aqswebwarning.htm. 

J-26
 
Appendix K: Risk (IQ Decrement) Estimates 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

 

Table of Contents 

Table of Contents .................................................................................................................K-i 

List of Exhibits ....................................................................................................................K-ii 

K. IQ DECREMENT RESULTS............................................................................................K-1
 

K.1. GENERAL URBAN CASE STUDY .......................................................................... K-1 

K.1.1. Description of Scenarios Analyzed.................................................................. K-1 

K.1.2. IQ Decrement Results for the General Urban Case Study............................... K-3 

K.2. PRIMARY PB SMELTER CASE STUDY............................................................... K-40 

K.2.1. Description of Scenarios Analyzed................................................................ K-40 

K.2.2. IQ Decrement Results for the Primary Pb Smelter Case Study..................... K-40 

K.3. SECONDARY PB SMELTER CASE STUDY ........................................................ K-47 

K.3.1. Description of Scenarios Analyzed................................................................ K-47 

K.3.2. IQ Decrement Results Tables for the Secondary Pb Smelter Case Study ..... K-47 

K-i 
List of Exhibits 

Exhibit K-1. IQ Decrement Scenarios Run for the General Urban Case Study ...................... K-2 

Exhibit K-2. General Urban Case Study: Current Conditions (95th Percentile) 

Estimated IQ Losses ........................................................................................... K-5 

Exhibit K-3. General Urban Case Study: Current Conditions (Mean) Estimated 

IQ Losses .......................................................................................................... K-10 

Exhibit K-4. General Urban Case Study: Current NAAQS (1.5 μg/m3

, Maximum 

Quarterly Average) Estimated IQ Losses ......................................................... K-15 

Exhibit K-5. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m3

, Maximum 

Quarterly Average) Estimated IQ Losses ......................................................... K-20 

Exhibit K-6. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3, Maximum 

Monthly Average) Estimated IQ Losses........................................................... K-25 

Exhibit K-7. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses........................................................... K-30 

Exhibit K-8. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3, Maximum 

Monthly Average) Estimated IQ Losses........................................................... K-35 

Exhibit K-9. IQ Decrement Scenarios Run for the Primary Pb Smelter Case Study ............ K-40 

Exhibit K-10. Primary Pb Smelter Case Study: Current NAAQS (1.5 μg/m3, Maximum 

Quarterly Average) Estimated IQ Losses ......................................................... K-42 

Exhibit K-11. Primary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3

, 

Maximum Quarterly Average) Estimated IQ Losses........................................ K-43 

Exhibit K-12. Primary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3

, 

Maximum Monthly Average) Estimated IQ Losses ......................................... K-44 

Exhibit K-13. Primary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) Estimated IQ Losses ......................................... K-45 

Exhibit K-14. Primary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3, 

Maximum Monthly Average) Estimated IQ Losses ......................................... K-46 

Exhibit K-15. IQ Decrement Scenarios Run for the Secondary Pb Smelter Case Study ........ K-47 

K-ii 
Exhibit K-16. Secondary Pb Smelter Case Study: Current Conditions Estimated 

IQ Losses .......................................................................................................... K-48 

Exhibit K-17. Secondary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3

, 

Maximum Quarterly Average) Estimated IQ Losses........................................ K-49 

Exhibit K-18. Secondary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3

, 

Maximum Monthly Average) Estimated IQ Losses ......................................... K-50 

Exhibit K-19. Secondary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) Estimated IQ Losses ......................................... K-51 

Exhibit K-20. Secondary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3

, 

Maximum Monthly Average) Estimated IQ Losses ......................................... K-52 

K-iii 

 

K. IQ DECREMENT RESULTS 

This appendix presents the estimated distributions of intelligence quotient (IQ) 

decrements for each of the case studies and for all National Ambient Air Quality Standards 

(NAAQS) scenarios considered in this analysis. Section K.1 contains the results for the general 

urban case study, including an overview of the scenarios run (see Section K.1.1) and the 

estimated IQ decrement distributions (see Section K.1.2). Similarly, Section K.2 provides the 

results for the primary lead (Pb) smelter case study, including an overview of the scenarios run 

(see Section K.2.1) and the estimated IQ decrement distributions (see Section K.2.2). Finally, 

Section K.3 presents the results for the secondary Pb smelter case study, including an overview 

of the scenarios run (see Section K.3.1) and the estimated IQ decrement distributions (see 

Section K.3.2). 

Estimates presented in this appendix are specified with regard to number of decimal 

places, which results in various numbers of implied significant figures. This is not intended to 

convey greater precision for some estimates than others; it is simply an expedient and initial 

result of the software used for the calculation. Greater attention is given to significant figures in 

the presentation of estimates in the main body of the report. 

K.1. GENERAL URBAN CASE STUDY 

K.1.1. Description of Scenarios Analyzed 

Exhibit K-1 lists the general urban case study scenarios for which IQ decrement estimates 

were generated for the general urban case study. As discussed in Appendix I, blood Pb (PbB) 

distributions were generated using two different indoor dust Pb concentration models (i.e., the 

air-only regression-based model and the hybrid mechanistic-empirical model [“hybrid model”]) 

and two different PbB metrics (i.e., concurrent [average of the results at 75 and 81 months of age 

in the seventh year of life] and lifetime [average of the results between 6 and 84 months of age]). 

These PbB estimates included a correction to account for inter-individual variability using two 

different geometric standard deviation (GSD) values. These corrections were applied in 50,000 

iterations of a probabilistic model in order to generate a distribution of PbB estimates for each 

NAAQS scenario. Finally, three different IQ functions (i.e., the two-piece linear IQ change 

function [“two-piece linear”], the log-linear IQ change function [“log-linear with cutpoint”], and 

the log-linear IQ change function with low-exposure linearization [“log-linear with 

linearization”]), as described in Section 4.1.1 of the main body of the report, were used to 

estimate IQ loss impacts from the PbB concentration distributions estimated for each scenario. 

K-1
 
 

Exhibit K-1. IQ Decrement Scenarios Run for the General Urban Case Study 

NAAQS Scenario a Dust Model 

GSD 

(microgram 

per deciliter 

[μg/dL]) 

PbB Metric 

Concurrent 

IQ Decrement Models 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.1 

Air-only regression­ 2.0 Lifetime 

based model 1.7 Concurrent 

Current conditions 

(95th percentile) 

1.6 Lifetime 

Hybrid mechanisticempirical model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Current conditions 

(mean) 

Air-only regressionbased model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid mechanisticempirical model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Current NAAQS 

(1.5 μg/m3

, max quarterly 

average) 

Air-only regressionbased model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid mechanisticempirical model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Alternative 1 NAAQS 

(0.2 μg/m3

, max quarterly 

average) 

Air-only regressionbased model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid mechanisticempirical model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Alternative 2 NAAQS 

(0.5 μg/m3

, max monthly 

average) 

Air-only regressionbased model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid mechanisticempirical model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

K-2
 
 

Exhibit K-1. IQ Decrement Scenarios Run for the General Urban Case Study 

NAAQS Scenario a 

Alternative NAAQS 3 

(0.2 μg/m3

, max monthly 

average) 

Dust Model 

Air-only regressionbased model 

GSD 

(microgram 

per deciliter 

[μg/dL]) 

2.1 

PbB Metric 

Concurrent 

IQ Decrement Models 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid mechanisticempirical model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Alternative NAAQS 4 

(0.05 μg/m3

, max monthly 

average) 

Air-only regressionbased model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

Hybrid mechanisticempirical model 

2.1 Concurrent 

Two-piece linear, log-linear 

with cutpoint, and log-linear 

with linearization 

2.0 Lifetime 

1.7 Concurrent 

1.6 Lifetime 

a

 For a more detailed discussion of the NAAQS scenarios see Appendix C. 

K.1.2. IQ Decrement Results for the General Urban Case Study 

Exhibits K-2 through K-8 summarize the distributions of estimated losses in IQ 

associated with each of the scenarios analyzed for the general urban case study. In the exhibits, 

IQ decrements less than 0.1 are reported as “<0.1.” IQ decrements that were exactly zero 

because the estimated PbB was below the cutpoint are reported as “-.” The PbB values 

corresponding to the each IQ percentile are also given. In addition, the approximate contribution 

from each exposure pathway to the overall IQ change is provided. The indoor dust contribution 

is separated into an ambient air contribution (ingestion [recent air]) and a contribution from other 

sources (e.g., indoor paint, outdoor soil/dust, and additional sources [including historical air]), as 

described in Appendix G. The pathway associated with inhalation of policy-relevant air Pb 

concentrations is shown as “inhalation (recent air).” 

The pathway contribution estimates correspond to the fraction of Pb uptake coming from 

each pathway; and, in their presentation in these exhibits, the assumption is made that these 

fractions map linearly to corresponding fractional contributions to PbB and IQ change. Because 

there is no underlying population in the general urban case study (unlike the two point source 

case studies), these percentages do not vary by IQ decrement percentile. 

K-3
 
 

In general, the two-piece linear IQ function predicts the lowest IQ losses and the loglinear with linearization IQ function predicts the highest IQ losses at the specified percentiles. 

The trends in IQ tend to follow the trends in PbB across the different dust models, GSD values, 

and NAAQS scenarios. In particular, the hybrid model, which tends to predict higher Pb 

concentration than the air-only regression-based model for most NAAQS scenarios, also predicts 

larger losses in IQ. The exception is the current NAAQS scenario. As discussed in Appendix I, 

this is the only NAAQS scenario which predicts ambient air Pb concentrations above 0.28 

micrograms per cubic meter (μg/m3

) (the point at which the hybrid model and air-only 

regression-based model cross) and thus is the only scenario for which the hybrid model predicts 

lower indoor dust concentrations than the air-only regression model. In addition, in the second 

alternative NAAQS (0.5 μg/m3

, maximum monthly average) scenario, the PbB values obtained 

using the higher GSD (2.1 μg per deciliter [dL]) for the concurrent PbB metric are higher for the 

95th percentile when the air-only regression-based model is used than when the hybrid model is 

used. This unexpected trend is likely due to sampling error in the “tails” of the distribution, as 

discussed in Appendix I. 

The IQ results for the log-linear model with linearization and the log linear with cutpoint 

model using the concurrent PbB metric with a GSD of 2.1 and the hybrid dust model are 

presented in Appendix N in Exhibits N-5 through N-12. 

K-4
 
Exhibit K-2. General Urban Case Study: Current Conditions (95th Percentile) Estimated 

IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.1 4.7

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 1.8 3.9

75th 1.3 2.8

Median 0.9 2.0

25th 0.6 1.4

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 4.2 4.7

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 3.7 3.9

75th 2.8 2.8

Median 1.8 2.0

25th 0.9 1.4

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 6.9 4.7

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 6.4 3.9

75th 5.5 2.8

Median 4.5 2.0

25th 3.6 1.4

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.3 6.1

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 2.0 5.2

75th 1.5 3.9

Median 1.1 2.8

25th 0.8 2.1

K-5
 
Exhibit K-2. General Urban Case Study: Current Conditions (95th Percentile) Estimated 

IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 4.4 6.1

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 3.9 5.2

75th 3.1 3.9

Median 2.1 2.8

25th 1.1 2.1

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 7.5 6.1

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 6.9 5.2

75th 6.1 3.9

Median 5.1 2.8

25th 4.2 2.1

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.3 5.1

15.7% 9.1% 33.4% 3.6% 37.2% 0.9%

90th 1.9 4.2

75th 1.4 3.1

Median 1.0 2.1

25th 0.7 1.5

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 4.4 5.1

15.7% 9.1% 43.7% 11.1% 37.2% 0.1%

90th 3.9 4.2

75th 3.0 3.1

Median 2.0 2.1

25th 1.1 1.5

K-6
 
Exhibit K-2. General Urban Case Study: Current Conditions (95th Percentile) Estimated 

IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 7.1 5.1

15.7% 9.1% 33.4% 3.6% 37.2% 0.9%

90th 6.6 4.2

75th 5.7 3.1

Median 4.7 2.1

25th 3.8 1.5

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.5 6.7

15.7% 9.1% 33.4% 3.6% 37.2% 0.9%

90th 2.1 5.6

75th 1.6 4.2

Median 1.2 3.1

25th 0.9 2.2

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 4.7 6.7

15.7% 9.1% 33.4% 3.6% 37.2% 0.9%

90th 4.2 5.6

75th 3.3 4.2

Median 2.3 3.1

25th 1.4 2.2

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 7.7 6.7

15.7% 9.1% 33.4% 3.6% 37.2% 0.9%

90th 7.2 5.6

75th 6.3 4.2

Median 5.4 3.1

25th 4.4 2.2

K-7
 
Exhibit K-2. General Urban Case Study: Current Conditions (95th Percentile) Estimated 

IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3.0 6.7

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 2.3 5.1

75th 1.5 3.3

Median 0.9 2.0

25th 0.5 1.2

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 5.1 6.7

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 4.4 5.1

75th 3.2 3.3

Median 1.8 2.0

25th 0.5 1.2

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 7.8 6.7

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 7.1 5.1

75th 5.9 3.3

Median 4.5 2.0

25th 3.2 1.2

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.4 8.9

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 2.6 6.9

75th 1.7 4.5

Median 1.1 2.8

25th 0.7 1.8

K-8
 
Exhibit K-2. General Urban Case Study: Current Conditions (95th Percentile) Estimated 

IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 5.5 8.9

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 4.8 6.9

75th 3.5 4.5

Median 2.1 2.8

25th 0.7 1.8

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 8.6 8.9

17.1% 10.0% 36.5% 13.5% 21.8% 1.0%

90th 7.8 6.9

75th 6.5 4.5

Median 5.1 2.8

25th 3.7 1.8

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3.3 7.2

15.7% 9.1% 33.4% 3.6% 37.2% 0.9%

90th 2.5 5.5

75th 1.6 3.5

Median 1.0 2.1

25th 0.6 1.3

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.7 9.6

15.7% 9.1% 33.4% 3.6% 37.2% 0.9%

90th 2.8 7.5

75th 1.9 4.9

Median 1.2 3.1

25th 0.7 1.9

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5.8 9.6

15.7% 9.1% 33.4% 3.6% 37.2% 0.9%

90th 5.0 7.5

75th 3.8 4.9

Median 2.3 3.1

25th 0.9 1.9

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 8.8 9.6

15.7% 9.1% 33.4% 3.6% 37.2% 0.9%

90th 8.1 7.5

75th 6.8 4.9

Median 5.4 3.1

25th 4.0 1.9

a

 Pathway contributions apply to all percentiles. See text for further discussion.
 

b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

K-9
Exhibit K-3. General Urban Case Study: Current Conditions (Mean)
 

Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 1.9 4.2

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 1.6 3.5

75th 1.1 2.5

Median 0.8 1.8

25th 0.6 1.2

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 3.9 4.2

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 3.4 3.5

75th 2.5 2.5

Median 1.5 1.8

25th 0.6 1.2

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 6.6 4.2

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 6.1 3.5

75th 5.2 2.5

Median 4.2 1.8

25th 3.3 1.2

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.1 5.5

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 1.7 4.6

75th 1.3 3.5

Median 1.0 2.5

25th 0.7 1.8

K-10
 
Exhibit K-3. General Urban Case Study: Current Conditions (Mean)
 

Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 4.1 5.5

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 3.6 4.6

75th 2.7 3.5

Median 1.7 2.5

25th 0.8 1.8

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 7.1 5.5

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 6.6 4.6

75th 5.7 3.5

Median 4.8 2.5

25th 3.8 1.8

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.1 4.6

17.7% 10.3% 37.6% 5.6% 28.3% 0.5%

90th 1.7 3.8

75th 1.3 2.8

Median 0.9 1.9

25th 0.6 1.3

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 4.1 4.6

17.7% 10.3% 43.7% 11.1% 28.3% 0.1%

90th 3.6 3.8

75th 2.7 2.8

Median 1.8 1.9

25th 0.8 1.3

K-11
 
Exhibit K-3. General Urban Case Study: Current Conditions (Mean)
 

Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 6.8 4.6

17.7% 10.3% 37.6% 5.6% 28.3% 0.5%

90th 6.3 3.8

75th 5.4 2.8

Median 4.5 1.9

25th 3.5 1.3

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.3 6.0

17.7% 10.3% 37.6% 5.6% 28.3% 0.5%

90th 1.9 5.1

75th 1.4 3.8

Median 1.0 2.8

25th 0.8 2.0

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 4.4 6.0

17.7% 10.3% 37.6% 5.6% 28.3% 0.5%

90th 3.9 5.1

75th 3.0 3.8

Median 2.0 2.8

25th 1.0 2.0

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 7.4 6.0

17.7% 10.3% 37.6% 5.6% 28.3% 0.5%

90th 6.9 5.1

75th 6.0 3.8

Median 5.0 2.8

25th 4.1 2.0

K-12
 
Exhibit K-3. General Urban Case Study: Current Conditions (Mean)
 

Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.7 6.0

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 2.1 4.5

75th 1.3 2.9

Median 0.8 1.8

25th 0.5 1.1

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 4.8 6.0

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 4.1 4.5

75th 2.9 2.9

Median 1.5 1.8

25th 0.2 1.1

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 7.5 6.0

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 6.8 4.5

75th 5.6 2.9

Median 4.2 1.8

25th 2.9 1.1

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.0 7.8

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 2.3 6.1

75th 1.5 4.0

Median 1.0 2.5

25th 0.6 1.6

K-13
 
Exhibit K-3. General Urban Case Study: Current Conditions (Mean)
 

Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 5.2 7.8

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 4.4 6.1

75th 3.1 4.0

Median 1.7 2.5

25th 0.3 1.6

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 8.2 7.8

19.4% 11.3% 41.3% 15.3% 12.1% 0.6%

90th 7.4 6.1

75th 6.2 4.0

Median 4.8 2.5

25th 3.4 1.6

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.9 6.5

17.7% 10.3% 37.6% 5.6% 28.3% 0.5%

90th 2.2 5.0

75th 1.4 3.1

Median 0.9 1.9

25th 0.5 1.2

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.3 8.6

17.7% 10.3% 37.6% 5.6% 28.3% 0.5%

90th 2.5 6.7

75th 1.7 4.4

Median 1.0 2.8

25th 0.7 1.7

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5.5 8.6

17.7% 10.3% 37.6% 5.6% 28.3% 0.5%

90th 4.7 6.7

75th 3.4 4.4

Median 2.0 2.8

25th 0.6 1.7

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 8.5 8.6

17.7% 10.3% 37.6% 5.6% 28.3% 0.5%

90th 7.7 6.7

75th 6.5 4.4

Median 5.0 2.8

25th 3.6 1.7

a

 Pathway contributions apply to all percentiles. See text for further discussion.
 

b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

K-14
 
Exhibit K-4. General Urban Case Study: Current NAAQS (1.5 μg/m3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 4.0 8.7

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 3.3 7.2

75th 2.4 5.2

Median 1.7 3.7

25th 1.2 2.6

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 5.9 8.7

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 5.3 7.2

75th 4.5 5.2

Median 3.5 3.7

25th 2.5 2.6

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 8.6 8.7

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 8.0 7.2

75th 7.2 5.2

Median 6.2 3.7

25th 5.2 2.6

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 4.3 11.5

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 3.7 9.7

75th 2.8 7.3

Median 2.0 5.3

25th 1.5 3.9

K-15
 
Exhibit K-4. General Urban Case Study: Current NAAQS (1.5 μg/m3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 6.3 11.5

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 5.8 9.7

75th 5.0 7.3

Median 4.0 5.3

25th 3.0 3.9

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 9.4 11.5

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 8.9 9.7

75th 8.0 7.3

Median 7.0 5.3

25th 6.1 3.9

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3.4 7.6

10.4% 6.0% 22.1% 1.1% 57.1% 3.3%

90th 2.8 6.2

75th 2.0 4.5

Median 1.4 3.1

25th 1.0 2.2

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5.5 7.6

10.4% 6.0% 43.7% 11.1% 57.1% 0.1%

90th 4.9 6.2

75th 4.1 4.5

Median 3.1 3.1

25th 2.1 2.2

K-16
 
Exhibit K-4. General Urban Case Study: Current NAAQS (1.5 μg/m3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 8.2 7.6

10.4% 6.0% 22.1% 1.1% 57.1% 3.3%

90th 7.6 6.2

75th 6.8 4.5

Median 5.8 3.1

25th 4.8 2.2

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.7 9.9

10.4% 6.0% 22.1% 1.1% 57.1% 3.3%

90th 3.1 8.3

75th 2.4 6.2

Median 1.7 4.5

25th 1.3 3.3

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5.9 9.9

10.4% 6.0% 22.1% 1.1% 57.1% 3.3%

90th 5.3 8.3

75th 4.5 6.2

Median 3.5 4.5

25th 2.6 3.3

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 8.9 9.9

10.4% 6.0% 22.1% 1.1% 57.1% 3.3%

90th 8.4 8.3

75th 7.5 6.2

Median 6.5 4.5

25th 5.6 3.3

K-17
 
Exhibit K-4. General Urban Case Study: Current NAAQS (1.5 μg/m3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 5.1 12.3

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 4.3 9.4

75th 2.7 6.0

Median 1.7 3.6

25th 1.0 2.2

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 6.8 12.3

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 6.1 9.4

75th 4.8 6.0

Median 3.5 3.6

25th 2.1 2.2

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 9.5 12.3

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 8.8 9.4

75th 7.5 6.0

Median 6.2 3.6

25th 4.8 2.2

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 5.4 16.5

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 4.9 12.8

75th 3.2 8.4

Median 2.0 5.3

25th 1.3 3.3

K-18
 
Exhibit K-4. General Urban Case Study: Current NAAQS (1.5 μg/m3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 7.4 16.5

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 6.7 12.8

75th 5.4 8.4

Median 4.0 5.3

25th 2.6 3.3

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 10.5 16.5

8.7% 5.1% 18.6% 6.9% 58.0% 2.8%

90th 9.7 12.8

75th 8.4 8.4

Median 7.0 5.3

25th 5.6 3.3

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 4.8 10.6

10.4% 6.0% 22.1% 1.1% 57.1% 3.3%

90th 3.7 8.1

75th 2.3 5.1

Median 1.4 3.1

25th 0.9 1.9

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 5.2 14.1

10.4% 6.0% 22.1% 1.1% 57.1% 3.3%

90th 4.1 10.9

75th 2.7 7.2

Median 1.7 4.5

25th 1.1 2.8

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 6.9 14.1

10.4% 6.0% 22.1% 1.1% 57.1% 3.3%

90th 6.2 10.9

75th 4.9 7.2

Median 3.5 4.5

25th 2.1 2.8

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 10.0 14.1

10.4% 6.0% 22.1% 1.1% 57.1% 3.3%

90th 9.2 10.9

75th 7.9 7.2

Median 6.5 4.5

25th 5.1 2.8

a

 Pathway contributions apply to all percentiles. See text for further discussion.
 

b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

K-19
 
Exhibit K-5. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m 3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.0 4.4

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 1.7 3.6

75th 1.2 2.7

Median 0.8 1.9

25th 0.6 1.3

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 4.0 4.4

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 3.5 3.6

75th 2.6 2.7

Median 1.7 1.9

25th 0.7 1.3

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 6.7 4.4

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 6.2 3.6

75th 5.3 2.7

Median 4.4 1.9

25th 3.4 1.3

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.2 5.7

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 1.8 4.8

75th 1.4 3.7

Median 1.0 2.7

25th 0.7 1.9

K-20
 
Exhibit K-5. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m 3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 4.2 5.7

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 3.7 4.8

75th 2.9 3.7

Median 1.9 2.7

25th 0.9 1.9

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 7.3 5.7

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 6.7 4.8

75th 5.9 3.7

Median 4.9 2.7

25th 4.0 1.9

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.2 4.8

16.7% 9.7% 35.6% 4.5% 32.7% 0.7%

90th 1.8 4.0

75th 1.3 2.9

Median 0.9 2.0

25th 0.6 1.4

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 4.2 4.8

16.7% 9.7% 43.7% 11.1% 32.7% 0.1%

90th 3.7 4.0

75th 2.9 2.9

Median 1.9 2.0

25th 0.9 1.4

K-21
 
Exhibit K-5. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m 3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 6.9 4.8

16.7% 9.7% 35.6% 4.5% 32.7% 0.7%

90th 6.4 4.0

75th 5.6 2.9

Median 4.6 2.0

25th 3.6 1.4

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.4 6.3

16.7% 9.7% 35.6% 4.5% 32.7% 0.7%

90th 2.0 5.3

75th 1.5 4.0

Median 1.1 2.9

25th 0.8 2.1

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 4.5 6.3

16.7% 9.7% 35.6% 4.5% 32.7% 0.7%

90th 4.0 5.3

75th 3.1 4.0

Median 2.2 2.9

25th 1.2 2.1

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 7.5 6.3

16.7% 9.7% 35.6% 4.5% 32.7% 0.7%

90th 7.0 5.3

75th 6.2 4.0

Median 5.2 2.9

25th 4.2 2.1

K-22
 
Exhibit K-5. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m 3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.8 6.2

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 2.2 4.8

75th 1.4 3.1

Median 0.8 1.9

25th 0.5 1.1

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 4.9 6.2

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 4.2 4.8

75th 3.0 3.1

Median 1.7 1.9

25th 0.3 1.1

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 7.6 6.2

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 6.9 4.8

75th 5.7 3.1

Median 4.4 1.9

25th 3.0 1.1

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.1 8.2

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 2.4 6.4

75th 1.6 4.3

Median 1.0 2.7

25th 0.6 1.7

K-23
 
Exhibit K-5. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m 3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 5.3 8.2

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 4.6 6.4

75th 3.3 4.3

Median 1.9 2.7

25th 0.5 1.7

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 8.4 8.2

18.4% 10.7% 39.2% 14.5% 16.3% 0.8%

90th 7.6 6.4

75th 6.4 4.3

Median 4.9 2.7

25th 3.5 1.7

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3.2 6.9

16.7% 9.7% 35.6% 4.5% 32.7% 0.7%

90th 2.4 5.3

75th 1.5 3.3

Median 0.9 2.0

25th 0.6 1.2

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.5 9.2

16.7% 9.7% 35.6% 4.5% 32.7% 0.7%

90th 2.7 7.1

75th 1.8 4.7

Median 1.1 2.9

25th 0.7 1.8

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5.7 9.2

16.7% 9.7% 35.6% 4.5% 32.7% 0.7%

90th 4.9 7.1

75th 3.6 4.7

Median 2.2 2.9

25th 0.7 1.8

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 8.7 9.2

16.7% 9.7% 35.6% 4.5% 32.7% 0.7%

90th 7.9 7.1

75th 6.6 4.7

Median 5.2 2.9

25th 3.8 1.8

a

 Pathway contributions apply to all percentiles. See text for further discussion.
 

b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

K-24
 
Exhibit K-6. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.2 4.8

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 1.8 3.9

75th 1.3 2.9

Median 0.9 2.0

25th 0.6 1.4

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 4.2 4.8

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 3.7 3.9

75th 2.9 2.9

Median 1.9 2.0

25th 0.9 1.4

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 6.9 4.8

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 6.4 3.9

75th 5.6 2.9

Median 4.6 2.0

25th 3.6 1.4

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.4 6.2

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 2.0 5.3

75th 1.5 4.0

Median 1.1 2.9

25th 0.8 2.1

K-25
 
Exhibit K-6. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 4.5 6.2

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 4.0 5.3

75th 3.1 4.0

Median 2.1 2.9

25th 1.2 2.1

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 7.5 6.2

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 7.0 5.3

75th 6.2 4.0

Median 5.2 2.9

25th 4.2 2.1

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.4 5.2

15.4% 9.0% 32.9% 3.4% 38.3% 1.0%

90th 1.9 4.3

75th 1.4 3.1

Median 1.0 2.2

25th 0.7 1.5

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 4.4 5.2

15.4% 9.0% 43.7% 11.1% 38.3% 0.1%

90th 3.9 4.3

75th 3.1 3.1

Median 2.1 2.2

25th 1.1 1.5

K-26
 
Exhibit K-6. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 7.1 5.2

15.4% 9.0% 32.9% 3.4% 38.3% 1.0%

90th 6.6 4.3

75th 5.8 3.1

Median 4.8 2.2

25th 3.8 1.5

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.6 6.8

15.4% 9.0% 32.9% 3.4% 38.3% 1.0%

90th 2.2 5.7

75th 1.6 4.3

Median 1.2 3.2

25th 0.9 2.3

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 4.7 6.8

15.4% 9.0% 32.9% 3.4% 38.3% 1.0%

90th 4.2 5.7

75th 3.4 4.3

Median 2.4 3.2

25th 1.4 2.3

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 7.8 6.8

15.4% 9.0% 32.9% 3.4% 38.3% 1.0%

90th 7.3 5.7

75th 6.4 4.3

Median 5.4 3.2

25th 4.5 2.3

K-27
 
Exhibit K-6. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3.1 6.8

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 2.4 5.2

75th 1.5 3.3

Median 0.9 2.0

25th 0.6 1.2

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 5.2 6.8

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 4.5 5.2

75th 3.2 3.3

Median 1.9 2.0

25th 0.5 1.2

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 7.9 6.8

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 7.2 5.2

75th 5.9 3.3

Median 4.6 2.0

25th 3.2 1.2

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.4 9.1

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 2.7 7.0

75th 1.7 4.6

Median 1.1 2.9

25th 0.7 1.8

K-28
 
Exhibit K-6. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 5.6 9.1

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 4.8 7.0

75th 3.6 4.6

Median 2.1 2.9

25th 0.7 1.8

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 8.7 9.1

16.8% 9.8% 35.8% 13.2% 23.3% 1.1%

90th 7.9 7.0

75th 6.6 4.6

Median 5.2 2.9

25th 3.8 1.8

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3.0 6.7

15.4% 9.0% 32.9% 3.4% 38.3% 1.0%

90th 2.4 5.2

75th 1.6 3.4

Median 1.0 2.2

25th 0.6 1.4

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 4.0 10.5

15.4% 9.0% 32.9% 3.4% 38.3% 1.0%

90th 3.0 8.0

75th 1.9 5.1

Median 1.2 3.1

25th 0.7 1.9

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 6.1 10.5

15.4% 9.0% 32.9% 3.4% 38.3% 1.0%

90th 5.2 8.0

75th 3.9 5.1

Median 2.4 3.1

25th 0.9 1.9

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 9.1 10.5

15.4% 9.0% 32.9% 3.4% 38.3% 1.0%

90th 8.3 8.0

75th 6.9 5.1

Median 5.4 3.1

25th 3.9 1.9

a

 Pathway contributions apply to all percentiles. See text for further discussion.
 

b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

K-29
 
Exhibit K-7. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 1.9 4.2

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 1.6 3.5

75th 1.1 2.5

Median 0.8 1.7

25th 0.6 1.2

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 3.9 4.2

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 3.3 3.5

75th 2.5 2.5

Median 1.5 1.7

25th 0.5 1.2

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 6.6 4.2

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 6.0 3.5

75th 5.2 2.5

Median 4.2 1.7

25th 3.2 1.2

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.1 5.4

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 1.7 4.6

75th 1.3 3.4

Median 0.9 2.5

25th 0.7 1.8

K-30
 
Exhibit K-7. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 4.0 5.4

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 3.5 4.6

75th 2.7 3.4

Median 1.7 2.5

25th 0.7 1.8

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 7.1 5.4

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 6.6 4.6

75th 5.7 3.4

Median 4.7 2.5

25th 3.8 1.8

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.0 4.5

17.9% 10.4% 38.2% 6.0% 27.0% 0.5%

90th 1.7 3.7

75th 1.2 2.7

Median 0.9 1.9

25th 0.6 1.3

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 4.1 4.5

17.9% 10.4% 43.7% 11.1% 27.0% 0.1%

90th 3.6 3.7

75th 2.7 2.7

Median 1.7 1.9

25th 0.7 1.3

K-31
 
Exhibit K-7. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 6.8 4.5

17.9% 10.4% 38.2% 6.0% 27.0% 0.5%

90th 6.3 3.7

75th 5.4 2.7

Median 4.4 1.9

25th 3.4 1.3

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.2 5.9

17.9% 10.4% 38.2% 6.0% 27.0% 0.5%

90th 1.9 5.0

75th 1.4 3.7

Median 1.0 2.7

25th 0.7 2.0

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 4.3 5.9

17.9% 10.4% 38.2% 6.0% 27.0% 0.5%

90th 3.8 5.0

75th 2.9 3.7

Median 1.9 2.7

25th 1.0 2.0

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 7.3 5.9

17.9% 10.4% 38.2% 6.0% 27.0% 0.5%

90th 6.8 5.0

75th 5.9 3.7

Median 5.0 2.7

25th 4.0 2.0

K-32
 
Exhibit K-7. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.7 5.9

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 2.0 4.5

75th 1.3 2.9

Median 0.8 1.8

25th 0.5 1.1

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 4.8 5.9

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 4.1 4.5

75th 2.9 2.9

Median 1.5 1.8

25th 0.2 1.1

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 7.5 5.9

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 6.8 4.5

75th 5.6 2.9

Median 4.2 1.8

25th 2.9 1.1

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.0 7.8

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 2.3 6.0

75th 1.5 4.0

Median 0.9 2.5

25th 0.6 1.6

K-33
 
Exhibit K-7. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 5.2 7.8

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 4.4 6.0

75th 3.1 4.0

Median 1.7 2.5

25th 0.3 1.6

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 8.2 7.8

19.7% 11.5% 41.9% 15.5% 10.9% 0.5%

90th 7.4 6.0

75th 6.2 4.0

Median 4.7 2.5

25th 3.3 1.6

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.9 6.4

17.9% 10.4% 38.2% 6.0% 27.0% 0.5%

90th 2.2 4.9

75th 1.4 3.1

Median 0.9 1.9

25th 0.5 1.1

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3.2 8.5

17.9% 10.4% 38.2% 6.0% 27.0% 0.5%

90th 2.5 6.6

75th 1.6 4.3

Median 1.0 2.7

25th 0.6 1.7

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5.4 8.5

17.9% 10.4% 38.2% 6.0% 27.0% 0.5%

90th 4.6 6.6

75th 3.4 4.3

Median 1.9 2.7

25th 0.5 1.7

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 8.4 8.5

17.9% 10.4% 38.2% 6.0% 27.0% 0.5%

90th 7.7 6.6

75th 6.4 4.3

Median 5.0 2.7

25th 3.6 1.7

a

 Pathway contributions apply to all percentiles. See text for further discussion.
 

b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

K-34
 
Exhibit K-8. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 1.8 3.9

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 1.5 3.2

75th 1.0 2.3

Median 0.7 1.6

25th 0.5 1.1

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 3.7 3.9

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 3.1 3.2

75th 2.3 2.3

Median 1.3 1.6

25th 0.3 1.1

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 6.4 3.9

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 5.8 3.2

75th 5.0 2.3

Median 4.0 1.6

25th 3.0 1.1

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 1.9 5.0

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 1.6 4.2

75th 1.2 3.1

Median 0.9 2.3

25th 0.6 1.7

K-35
 
Exhibit K-8. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 3.8 5.0

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 3.3 4.2

75th 2.4 3.1

Median 1.4 2.3

25th 0.5 1.7

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 6.8 5.0

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 6.3 4.2

75th 5.4 3.1

Median 4.5 2.3

25th 3.5 1.7

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 1.8 4.1

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 1.5 3.4

75th 1.1 2.4

Median 0.8 1.7

25th 0.5 1.2

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 3.8 4.1

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 3.3 3.4

75th 2.4 2.4

Median 1.4 1.7

25th 0.4 1.2

K-36
 
Exhibit K-8. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Hybrid), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 6.5 4.1

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 6.0 3.4

75th 5.1 2.4

Median 4.1 1.7

25th 3.1 1.2

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.0 5.2

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 1.7 4.4

75th 1.3 3.3

Median 0.9 2.4

25th 0.7 1.7

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 3.9 5.2

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 3.4 4.4

75th 2.5 3.3

Median 1.6 2.4

25th 0.6 1.7

Dust Model (Hybrid), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 7.0 5.2

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 6.5 4.4

75th 5.6 3.3

Median 4.6 2.4

25th 3.7 1.7

K-37
 
Exhibit K-8. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.5 5.5

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 1.9 4.2

75th 1.2 2.7

Median 0.7 1.6

25th 0.4 1.0

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 4.6 5.5

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 3.9 4.2

75th 2.7 2.7

Median 1.3 1.6

25th - 0.7

Dust Model (Air-only Regression-based), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 7.3 5.5

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 6.6 4.2

75th 5.4 2.7

Median 4.0 1.6

25th 2.6 1.0

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.7 7.2

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 2.1 5.6

75th 1.4 3.7

Median 0.9 2.3

25th 0.5 1.4

K-38
 
Exhibit K-8. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution a 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other b Recent 

Air 

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Cutpoint) 

95th 4.9 7.2

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 4.1 5.6

75th 2.9 3.7

Median 1.4 2.3

25th - 1.4

Dust Model (Air-only Regression-based), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 8.0 7.2

21.5% 12.5% 45.8% 17.0% 3.0% 0.1%

90th 7.2 5.6

75th 5.9 3.7

Median 4.5 2.3

25th 3.1 1.4

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 2.6 5.7

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 2.0 4.4

75th 1.3 2.8

Median 0.8 1.7

25th 0.5 1.0

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 2.8 7.5

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 2.2 5.9

75th 1.4 3.8

Median 0.9 2.4

25th 0.6 1.5

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5.0 7.5

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 4.3 5.9

75th 3.0 3.8

Median 1.5 2.4

25th 0.1 1.5

Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 8.1 7.5

20.5% 11.9% 43.7% 11.1% 12.6% 0.1%

90th 7.3 5.9

75th 6.0 3.8

Median 4.6 2.4

25th 3.2 1.5

a

 Pathway contributions apply to all percentiles. See text for further discussion.
 

b

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources 

K-39
 
 

K.2. PRIMARY PB SMELTER CASE STUDY 

K.2.1. Description of Scenarios Analyzed 

For the primary Pb smelter case study, Exhibit K-9 lists the NAAQS scenarios, along 

with the PbB metrics and IQ functions that were used to generate IQ estimates for the primary Pb 

smelter case study. As discussed in Appendix I, PbB results were generated using the sitespecific H5 model for the U.S. Census blocks and block groups within 1.5 kilometer (km) of the 

source. Dust concentration estimates in more distant U.S. Census blocks and block groups were 

derived using the U.S. EPA air+soil regression-based model, as discussed in Appendix G. Interindividual variability was incorporated using a single GSD for each PbB metric (i.e., concurrent 

and lifetime). Three different IQ functions (two-piece linear, log linear with cutpoint, and 

loglinear with linearization) were used to estimate the IQ decrements for each for each of the five 

NAAQS scenarios, as summarized in the Exhibit K-9. 

Exhibit K-9. IQ Decrement Scenarios Run for the Primary Pb Smelter Case Study 

NAAQS Scenario 

Current NAAQS 

(1.5 μg/m3

, max 

quarterly average) 

Dust Model 

H5 or air+soil 

regression-based 

model 

GSD 

(μg/dL) 

1.7 

PbB Metric 

Concurrent 

IQ Functions 

Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization 1.6 Lifetime 

Alternative NAAQS 1 

(0.2 μg/m3

, max 

quarterly average) 

H5 or air+soil 

regression-based 

model 

1.7 Concurrent Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization 1.6 Lifetime 

Alternative NAAQS 2 

(0.5 μg/m3

, max 

monthly average) 

H5 or air+soil 

regression-based 

model 

1.7 Concurrent Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization 1.6 Lifetime 

Alternative NAAQS 3 

(0.2 μg/m3

, max 

monthly average) 

H5 or air+soil 

regression-based 

model 

1.7 Concurrent Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization 1.6 Lifetime 

Alternative NAAQS 4 

(0.05 μg/m3

, max 

monthly average) 

H5 or air+soil 

regression-based 

model 

1.7 Concurrent Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization 1.6 Lifetime 

K.2.2. IQ Decrement Results for the Primary Pb Smelter Case Study 

Exhibits K-10 through K-14 summarize the IQ modeling distribution estimates for the 

NAAQS scenarios associated with the primary Pb smelter case study. Just as for the general 

urban case study, IQ decrements less than 0.1 are reported as “<0.1.” IQ decrements that were 

exactly zero because the estimated PbB was below the cutpoint are reported as “-.” The PbB 

values corresponding to the each IQ percentile are also given. The exhibits also present 

estimates of the proportional contribution of each exposure pathway to the total Pb uptake. The 

K-40
 
 

contributions from the policy-relevant air and background pathways are estimated as described in 

Section K.1.2, except, the indoor dust Pb is not separated out into “recent air” and “other” for the 

primary Pb smelter case study. This is a result of limitations of the site-specific H5 model, 

which is used to calculate the concentration of Pb in indoor dust in the primary Pb smelter case 

study. The site-specific H5 model cannot separate indoor dust into “recent air” and “other,” 

therefore the total indoor dust contribution is determined for the primary Pb smelter case study. 

Just as in the general urban case study, because of nonlinearities in the IQ functions, the 

estimated pathway contributions to IQ impacts are only approximate. In addition, use of the 

two-piece linear IQ function results in the lowest estimated IQ losses, while the log-linear model 

with linearization results in the highest IQ losses. The IQ results for the log-linear model with 

linearization and the log linear with cutpoint model using the concurrent PbB metric are 

presented in Appendix N in Exhibits N-18 through N-23. 

K-41
 
Exhibit K-10. Primary Pb Smelter Case Study: Current NAAQS (1.5 μg/m3

, 

Maximum Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Total Indoor 

Dust 

Dust Model (Air+Soil Regression-Based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (TwoPiece Linear) 

95th 194 2.1 4.6 24.4% 14.2% 35.1% 25.6% 0.6% 

90th 388 1.6 3.5 24.4% 14.2% 35.5% 25.4% 0.6% 

75th 970 1.1 2.3 19.8% 11.5% 40.7% 27.1% 0.8% 

Median 1940 0.7 1.5 21.9% 12.8% 33.4% 30.8% 1.2% 

25th 2910 0.5 1.0 39.7% 23.1% 16.1% 20.9% 0.2% 

Dust Model (Air+Soil Regression-Based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (TwoPiece Linear) 

95th 194 2.4 6.2 11.1% 6.5% 53.9% 27.7% 0.9% 

90th 388 1.8 4.8 9.8% 5.7% 10.7% 72.9% 0.9% 

75th 970 1.2 3.2 35.1% 20.4% 20.6% 23.4% 0.4% 

Median 1940 0.8 2.1 32.9% 19.1% 22.6% 24.8% 0.6% 

25th 2910 0.5 1.4 21.9% 12.8% 33.4% 30.8% 1.2% 

Dust Model (Air+Soil Regression-Based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (LogLinear with Cutpoint) 

95th 194 4.5 6.2 11.1% 6.5% 53.9% 27.7% 0.9% 

90th 388 3.7 4.8 9.8% 5.7% 10.7% 72.9% 0.9% 

75th 970 2.4 3.2 35.1% 20.4% 20.6% 23.4% 0.4% 

Median 1940 1.1 2.1 32.9% 19.1% 22.6% 24.8% 0.6% 

25th 2910 - 0.7 34.8% 20.3% 21.9% 22.6% 0.4% 

Dust Model (Air+Soil Regression-Based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (LogLinear with Linearization) 

95th 194 7.5 6.2 11.1% 6.5% 53.9% 27.7% 0.9% 

90th 388 6.7 4.8 9.8% 5.7% 10.7% 72.9% 0.9% 

75th 970 5.5 3.2 35.1% 20.4% 20.6% 23.4% 0.4% 

Median 1940 4.2 2.1 32.9% 19.1% 22.6% 24.8% 0.6% 

25th 2910 3.0 1.4 21.9% 12.8% 33.4% 30.8% 1.2% 

K-42
 
Exhibit K-11. Primary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3

, 

Maximum Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Total Indoor 

Dust 

Dust Model (Air+Soil Regression-Based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (TwoPiece Linear) 

95th 194 1.8 4.0 14.0% 8.2% 55.8% 21.8% 0.3% 

90th 388 1.4 3.2 12.2% 7.1% 59.3% 21.2% 0.2% 

75th 970 1.0 2.2 35.1% 20.4% 24.2% 20.1% 0.1% 

Median 1940 0.6 1.4 23.4% 13.6% 42.3% 20.6% 0.2% 

25th 2910 0.4 0.9 35.1% 20.4% 24.2% 20.1% 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function 

(Two-piece Linear) 

95th 194 2.0 5.3 14.5% 8.4% 55.3% 21.5% 0.2% 

90th 388 1.6 4.3 36.3% 21.1% 22.9% 19.6% < 0.1% 

75th 970 1.1 2.9 36.3% 21.1% 22.9% 19.6% < 0.1% 

Median 1940 0.7 1.9 31.5% 18.3% 30.0% 20.0% 0.1% 

25th 2910 0.5 1.3 32.5% 18.9% 28.6% 19.9% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function 

(Log-linear with Cutpoint) 

95th 194 4.0 5.3 14.5% 8.4% 55.3% 21.5% 0.2% 

90th 388 3.3 4.3 36.3% 21.1% 22.9% 19.6% < 0.1% 

75th 970 2.2 2.9 36.3% 21.1% 22.9% 19.6% < 0.1% 

Median 1940 0.9 1.9 36.3% 21.1% 22.9% 19.6% < 0.1% 

25th 2910 - 1.1 15.1% 8.8% 54.9% 21.0% 0.2% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function 

(Log-linear with Linearization) 

95th 194 7.0 5.3 14.5% 8.4% 55.3% 21.5% 0.2% 

90th 388 6.4 4.3 36.3% 21.1% 22.9% 19.6% < 0.1% 

75th 970 5.2 2.9 36.3% 21.1% 22.9% 19.6% < 0.1% 

Median 1940 4.0 1.9 36.3% 21.1% 22.9% 19.6% < 0.1% 

25th 2910 2.8 1.3 32.5% 18.9% 28.6% 19.9% < 0.1% 

K-43
 
Exhibit K-12. Primary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3

, 

Maximum Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust) 

Total Indoor 

Dust 

Dust Model (Air+Soil Regression-Based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (TwoPiece Linear) 

95th 194 1.9 4.2 6.3% 3.6% 17.5% 71.5% 1.1% 

90th 388 1.5 3.3 13.5% 7.9% 53.7% 24.4% 0.5% 

75th 970 1.0 2.2 13.5% 7.9% 53.7% 24.4% 0.5% 

Median 1940 0.7 1.4 39.0% 22.7% 18.4% 19.9% 0.1% 

25th 2910 0.4 0.9 33.8% 19.7% 25.3% 21.0% 0.2% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (Twopiece Linear) 

95th 194 2.1 5.6 15.6% 9.1% 52.5% 22.4% 0.3% 

90th 388 1.7 4.4 13.5% 7.9% 53.7% 24.4% 0.5% 

75th 970 1.1 3.0 34.4% 20.1% 23.7% 21.5% 0.3% 

Median 1940 0.7 2.0 15.6% 9.1% 52.5% 22.4% 0.3% 

25th 2910 0.5 1.3 24.7% 14.4% 38.9% 21.8% 0.3% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (Loglinear with Cutpoint) 

95th 194 4.2 5.6 15.6% 9.1% 52.5% 22.4% 0.3% 

90th 388 3.4 4.4 13.5% 7.9% 53.7% 24.4% 0.5% 

75th 970 2.2 3.0 34.4% 20.1% 23.7% 21.5% 0.3% 

Median 1940 1.0 2.0 15.6% 9.1% 52.5% 22.4% 0.3% 

25th 2910 - 1.3 23.3% 13.6% 40.2% 22.6% 0.4% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (Loglinear with Linearization) 

95th 194 7.2 5.6 15.6% 9.1% 52.5% 22.4% 0.3% 

90th 388 6.5 4.4 13.5% 7.9% 53.7% 24.4% 0.5% 

75th 970 5.3 3.0 34.4% 20.1% 23.7% 21.5% 0.3% 

Median 1940 4.0 2.0 15.6% 9.1% 52.5% 22.4% 0.3% 

25th 2910 2.8 1.3 24.7% 14.4% 38.9% 21.8% 0.3% 

K-44
 
Exhibit K-13. Primary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3

, 

Maximum Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Total Indoor 

Dust 

Dust Model (Air+Soil Regression-Based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (TwoPiece Linear) 

95th 194 1.8 4.0 25.3% 14.7% 39.8% 20.1% 0.1% 

90th 388 1.4 3.2 32.7% 19.1% 28.9% 19.3% < 0.1% 

75th 970 1.0 2.1 25.3% 14.7% 39.8% 20.1% 0.1% 

Median 1940 0.6 1.4 35.2% 20.5% 24.3% 19.9% 0.1% 

25th 2910 0.4 0.9 20.1% 11.7% 48.1% 20.0% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (Twopiece Linear) 

95th 194 2.0 5.3 22.8% 13.3% 43.5% 20.2% 0.1% 

90th 388 1.6 4.2 13.9% 8.1% 38.2% 39.4% 0.3% 

75th 970 1.1 2.9 26.3% 15.3% 38.2% 20.1% 0.1% 

Median 1940 0.7 1.9 35.2% 20.5% 24.3% 19.9% 0.1% 

25th 2910 0.5 1.3 32.7% 19.1% 28.9% 19.3% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (Loglinear with Cutpoint) 

95th 194 4.0 5.3 22.8% 13.3% 43.5% 20.2% 0.1% 

90th 388 3.3 4.2 13.9% 8.1% 38.2% 39.4% 0.3% 

75th 970 2.2 2.9 26.3% 15.3% 38.2% 20.1% 0.1% 

Median 1940 0.9 1.9 35.2% 20.5% 24.3% 19.9% 0.1% 

25th 2910 - 1.2 35.2% 20.5% 24.3% 19.9% 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (Loglinear with Linearization) 

95th 194 7.0 5.3 22.8% 13.3% 43.5% 20.2% 0.1% 

90th 388 6.3 4.2 13.9% 8.1% 38.2% 39.4% 0.3% 

75th 970 5.2 2.9 26.3% 15.3% 38.2% 20.1% 0.1% 

Median 1940 4.0 1.9 35.2% 20.5% 24.3% 19.9% 0.1% 

25th 2910 2.8 1.3 32.7% 19.1% 28.9% 19.3% < 0.1% 

K-45
 
Exhibit K-14. Primary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3

, 

Maximum Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Total Indoor 

Dust 

Dust Model (Air+Soil Regression-Based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (TwoPiece Linear) 

95th 194 1.7 3.8 16.8% 9.8% 53.9% 19.5% < 0.1% 

90th 388 1.4 3.1 14.9% 8.7% 56.8% 19.6% < 0.1% 

75th 970 0.9 2.1 35.6% 20.8% 24.6% 19.0% < 0.1% 

Median 1940 0.6 1.4 35.6% 20.8% 24.6% 19.0% < 0.1% 

25th 2910 0.4 0.9 35.6% 20.8% 24.6% 19.0% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (Twopiece Linear) 

95th 194 1.9 5.1 20.9% 12.2% 47.6% 19.4% < 0.1% 

90th 388 1.5 4.1 14.4% 8.4% 57.4% 19.7% < 0.1% 

75th 970 1.1 2.8 23.0% 13.4% 44.3% 19.3% < 0.1% 

Median 1940 0.7 1.9 25.6% 14.9% 40.3% 19.2% < 0.1% 

25th 2910 0.5 1.3 36.7% 21.3% 23.1% 18.9% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (Loglinear with Cutpoint) 

95th 194 3.9 5.1 20.9% 12.2% 47.6% 19.4% < 0.1% 

90th 388 3.2 4.1 14.4% 8.4% 57.4% 19.7% < 0.1% 

75th 970 2.1 2.8 23.0% 13.4% 44.3% 19.3% < 0.1% 

Median 1940 0.9 1.9 14.4% 8.4% 57.4% 19.7% < 0.1% 

25th 2910 - 1.0 25.7% 15.0% 40.1% 19.1% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.6), PbB Metric (Lifetime), IQ Function (Loglinear with Linearization) 

95th 194 6.9 5.1 20.9% 12.2% 47.6% 19.4% < 0.1% 

90th 388 6.2 4.1 14.4% 8.4% 57.4% 19.7% < 0.1% 

75th 970 5.1 2.8 23.0% 13.4% 44.3% 19.3% < 0.1% 

Median 1940 3.9 1.9 25.6% 14.9% 40.3% 19.2% < 0.1% 

25th 2910 2.7 1.3 36.7% 21.3% 23.1% 18.9% < 0.1% 

K-46
 
 

K.3. SECONDARY PB SMELTER CASE STUDY 

K.3.1. Description of Scenarios Analyzed 

Exhibit K-15 lists the secondary Pb smelter case study scenarios, along with the PbB 

metrics and IQ functions used to estimate IQ decrements. As discussed in Appendix I, PbB 

results were generated for a single dust model and the GSD for each PbB metric (concurrent and 

lifetime). Three IQ functions (two-piece linear, log linear with cutpoint, and loglinear with 

linearization) were used to estimate the IQ decrements for each of the five NAAQS scenarios, as 

summarized in the Exhibit K-15. 

Exhibit K-15. IQ Decrement Scenarios Run for the Secondary Pb Smelter Case Study 

NAAQS Scenario 

Current Conditions 

Dust Model 

Air-only regressionbased model 

GSD 

(μg/dL) 

1.7 

PbB Metric 

Concurrent 

IQ Functions 

Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization 1.6 Lifetime 

Alternative NAAQS 1 

(0.2 μg/m3

, max 

quarterly average) 

Air-only regressionbased model 

1.7 Concurrent Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization ion 1.6 Lifetime 

Alternative NAAQS 2 

(0.5 μg/m3

, max 

monthly average) 

Air-only regressionbased model 

1.7 Concurrent Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization 1.6 Lifetime 

Alternative NAAQS 3 

(0.2 μg/m3

, max 

monthly average) 

Air-only regressionbased model 

1.7 Concurrent Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization 1.6 Lifetime 

Alternative NAAQS 4 

(0.05 μg/m3

, max 

monthly average) 

Air-only regressionbased model 

1.7 Concurrent Two-piece linear, log-linear with cutpoint, 

and log-linear with linearization 

1.6 Lifetime 

K.3.2. IQ Decrement Results Tables for the Secondary Pb Smelter Case Study 

Exhibits K-16 through K-20 summarize the IQ change distribution estimates for the 

secondary Pb smelter case study. As in the general urban case study and primary Pb smelter case 

study, IQ decrements less than 0.1 are reported as “<0.1.” IQ decrements that were exactly zero 

because the estimated PbB was below the cutpoint are reported as “-.” The PbB values 

corresponding to the each IQ percentile are also given. The exhibits also present estimates of the 

proportional contribution of each exposure pathway to the total Pb uptake, as for the other two 

case studies. The contributions from the policy-relevant air and background pathways are 

estimated as described for the general urban case study in Section K.1.2. Again, these serve as 

proxy estimates of the proportional contribution of each pathway to overall IQ loss. As for the 

other two case studies, use of the two-piece linear IQ function results in the lowest estimated IQ 

losses, while the log-linear model with linearization results in the highest IQ losses. 

K-47
 
Exhibit K-16. Secondary Pb Smelter Case Study: Current Conditions Estimated IQ Losses 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other a Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 85 1.1 2.4 41.1% 24.0% 1.9% 32.5% 0.5% < 0.1% 

90th 170 0.9 2.0 29.4% 17.1% 25.1% 23.2% 5.0% 0.3% 

75th 425 0.6 1.4 37.9% 22.1% 8.2% 29.9% 1.9% 0.1% 

Median 849 0.4 1.0 41.8% 24.3% 0.6% 33.0% 0.2% < 0.1% 

25th 1274 0.3 0.7 41.8% 24.3% 0.6% 33.0% 0.3% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 85 2.4 2.4 41.1% 24.0% 1.9% 32.5% 0.5% < 0.1% 

90th 170 1.8 2.0 29.4% 17.1% 25.1% 23.2% 5.0% 0.3% 

75th 425 0.9 1.4 37.9% 22.1% 8.2% 29.9% 1.9% 0.1% 

Median 849 - 1.0 41.7% 24.3% 0.6% 32.9% 0.4% < 0.1% 

25th 1274 - 1.0 41.7% 24.3% 0.6% 32.9% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 85 5.1 2.4 41.1% 24.0% 1.9% 32.5% 0.5% < 0.1% 

90th 170 4.5 2.0 29.4% 17.1% 25.1% 23.2% 5.0% 0.3% 

75th 425 3.6 1.4 37.9% 22.1% 8.2% 29.9% 1.9% 0.1% 

Median 849 2.7 1.0 41.8% 24.3% 0.6% 33.0% 0.2% < 0.1% 

25th 1274 1.9 0.7 41.8% 24.3% 0.6% 33.0% 0.3% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 85 1.1 2.9 41.6% 24.2% 1.0% 32.8% 0.3% < 0.1% 

90th 170 0.9 2.4 38.7% 22.5% 6.2% 30.5% 2.0% 0.1% 

75th 425 0.7 1.8 39.6% 23.0% 4.9% 31.2% 1.2% 0.1% 

Median 849 0.5 1.3 41.4% 24.1% 1.3% 32.6% 0.5% < 0.1% 

25th 1274 0.4 0.9 40.4% 23.5% 3.1% 31.9% 1.0% 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 85 2.1 2.9 41.6% 24.2% 1.0% 32.8% 0.3% < 0.1% 

90th 170 1.6 2.4 38.7% 22.5% 6.2% 30.5% 2.0% 0.1% 

75th 425 0.7 1.8 39.6% 23.0% 4.9% 31.2% 1.2% 0.1% 

Median 849 - 1.2 41.7% 24.3% 0.6% 32.9% 0.4% < 0.1% 

25th 1274 - 1.2 41.7% 24.3% 0.6% 32.9% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 85 5.2 2.9 41.6% 24.2% 1.0% 32.8% 0.3% < 0.1% 

90th 170 4.6 2.4 38.7% 22.5% 6.2% 30.5% 2.0% 0.1% 

75th 425 3.7 1.8 39.6% 23.0% 4.9% 31.2% 1.2% 0.1% 

Median 849 2.8 1.3 41.4% 24.1% 1.3% 32.6% 0.5% < 0.1% 

25th 1274 2.0 0.9 40.4% 23.5% 3.1% 31.9% 1.0% 0.1% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources (including 

historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb levels (either by 

inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels). 

K-48
Exhibit K-17. Secondary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3

, Maximum 

Quarterly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other a Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 85 1.1 2.3 41.9% 24.4% 0.6% 33.0% 0.1% < 0.1% 

90th 170 0.9 1.9 41.6% 24.2% 1.2% 32.8% 0.1% < 0.1% 

75th 425 0.6 1.4 40.7% 23.7% 3.1% 32.1% 0.3% < 0.1% 

Median 849 0.4 1.0 38.8% 22.6% 7.4% 30.6% 0.5% < 0.1% 

25th 1274 0.3 0.7 40.1% 23.3% 4.6% 31.6% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 85 2.3 2.3 41.9% 24.4% 0.6% 33.0% 0.1% < 0.1% 

90th 170 1.8 1.9 41.6% 24.2% 1.2% 32.8% 0.1% < 0.1% 

75th 425 0.9 1.4 40.7% 23.7% 3.1% 32.1% 0.3% < 0.1% 

Median 849 - 0.5 39.3% 22.9% 6.3% 31.0% 0.5% < 0.1% 

25th 1274 - 0.5 39.3% 22.9% 6.3% 31.0% 0.5% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 85 5.0 2.3 41.9% 24.4% 0.6% 33.0% 0.1% < 0.1% 

90th 170 4.5 1.9 41.6% 24.2% 1.2% 32.8% 0.1% < 0.1% 

75th 425 3.6 1.4 40.7% 23.7% 3.1% 32.1% 0.3% < 0.1% 

Median 849 2.6 1.0 38.8% 22.6% 7.4% 30.6% 0.5% < 0.1% 

25th 1274 1.9 0.7 40.1% 23.3% 4.6% 31.6% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 85 1.1 2.8 37.5% 21.9% 10.4% 29.6% 0.5% < 0.1% 

90th 170 0.9 2.4 40.1% 23.3% 4.5% 31.6% 0.4% < 0.1% 

75th 425 0.7 1.8 38.7% 22.5% 7.7% 30.5% 0.5% < 0.1% 

Median 849 0.5 1.3 40.6% 23.6% 3.5% 32.0% 0.3% < 0.1% 

25th 1274 0.4 0.9 39.9% 23.2% 5.1% 31.4% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 85 2.0 2.8 37.5% 21.9% 10.4% 29.6% 0.5% < 0.1% 

90th 170 1.5 2.4 40.1% 23.3% 4.5% 31.6% 0.4% < 0.1% 

75th 425 0.6 1.8 38.7% 22.5% 7.7% 30.5% 0.5% < 0.1% 

Median 849 - 0.7 39.3% 22.9% 6.3% 31.0% 0.5% < 0.1% 

25th 1274 - 0.7 39.3% 22.9% 6.3% 31.0% 0.5% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 85 5.1 2.8 37.5% 21.9% 10.4% 29.6% 0.5% < 0.1% 

90th 170 4.6 2.4 40.1% 23.3% 4.5% 31.6% 0.4% < 0.1% 

75th 425 3.7 1.8 38.7% 22.5% 7.7% 30.5% 0.5% < 0.1% 

Median 849 2.7 1.3 40.6% 23.6% 3.5% 32.0% 0.3% < 0.1% 

25th 1274 2.0 0.9 39.9% 23.2% 5.1% 31.4% 0.4% < 0.1% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources (including 

historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb levels (either by 

inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels). 

K-49
Exhibit K-18. Secondary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other a Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 85 1.1 2.4 35.0% 20.4% 14.9% 27.6% 2.1% 0.1% 

90th 170 0.9 2.0 41.4% 24.1% 1.5% 32.7% 0.2% < 0.1% 

75th 425 0.6 1.4 39.2% 22.8% 6.2% 30.9% 0.8% < 0.1% 

Median 849 0.4 1.0 35.4% 20.6% 13.9% 27.9% 2.0% 0.1% 

25th 1274 0.3 0.7 41.2% 24.0% 2.0% 32.5% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 85 2.3 2.4 35.0% 20.4% 14.9% 27.6% 2.1% 0.1% 

90th 170 1.8 2.0 41.4% 24.1% 1.5% 32.7% 0.2% < 0.1% 

75th 425 0.9 1.4 39.2% 22.8% 6.2% 30.9% 0.8% < 0.1% 

Median 849 - 0.9 41.8% 24.3% 0.6% 33.0% 0.2% < 0.1% 

25th 1274 - 0.9 41.8% 24.3% 0.6% 33.0% 0.2% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 85 5.0 2.4 35.0% 20.4% 14.9% 27.6% 2.1% 0.1% 

90th 170 4.5 2.0 41.4% 24.1% 1.5% 32.7% 0.2% < 0.1% 

75th 425 3.6 1.4 39.2% 22.8% 6.2% 30.9% 0.8% < 0.1% 

Median 849 2.7 1.0 35.4% 20.6% 13.9% 27.9% 2.0% 0.1% 

25th 1274 1.9 0.7 41.2% 24.0% 2.0% 32.5% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 85 1.1 2.8 39.1% 22.8% 6.2% 30.9% 1.0% 0.1% 

90th 170 0.9 2.4 39.0% 22.7% 6.2% 30.8% 1.1% 0.1% 

75th 425 0.7 1.8 41.7% 24.3% 0.9% 32.9% 0.2% < 0.1% 

Median 849 0.5 1.3 41.4% 24.1% 1.5% 32.7% 0.2% < 0.1% 

25th 1274 0.4 0.9 41.1% 23.9% 2.1% 32.4% 0.3% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 85 2.1 2.8 39.1% 22.8% 6.2% 30.9% 1.0% 0.1% 

90th 170 1.5 2.4 39.0% 22.7% 6.2% 30.8% 1.1% 0.1% 

75th 425 0.7 1.8 41.7% 24.3% 0.9% 32.9% 0.2% < 0.1% 

Median 849 - 1.1 41.8% 24.3% 0.6% 33.0% 0.2% < 0.1% 

25th 1274 - 1.1 41.8% 24.3% 0.6% 33.0% 0.2% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 85 5.1 2.8 39.1% 22.8% 6.2% 30.9% 1.0% 0.1% 

90th 170 4.6 2.4 39.0% 22.7% 6.2% 30.8% 1.1% 0.1% 

75th 425 3.7 1.8 41.7% 24.3% 0.9% 32.9% 0.2% < 0.1% 

Median 849 2.7 1.3 40.1% 23.3% 4.3% 31.6% 0.6% < 0.1% 

25th 1274 2.0 0.9 41.1% 23.9% 2.1% 32.4% 0.3% < 0.1% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources (including 

historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb levels (either by 

inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels). 

K-50
Exhibit K-19. Secondary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3

, Maximum 

Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other a Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 85 1.1 2.4 33.9% 19.7% 18.3% 26.8% 1.2% 0.1% 

90th 170 0.9 1.9 42.0% 24.5% 0.3% 33.2% < 0.1% < 0.1% 

75th 425 0.6 1.4 39.4% 23.0% 6.1% 31.1% 0.4% < 0.1% 

Median 849 0.4 1.0 38.8% 22.6% 7.4% 30.7% 0.4% < 0.1% 

25th 1274 0.3 0.7 38.8% 22.6% 7.4% 30.7% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 85 2.3 2.4 33.9% 19.7% 18.3% 26.8% 1.2% 0.1% 

90th 170 1.8 1.9 42.0% 24.5% 0.3% 33.2% < 0.1% < 0.1% 

75th 425 0.9 1.4 39.4% 23.0% 6.1% 31.1% 0.4% < 0.1% 

Median 849 - 0.9 41.9% 24.4% 0.6% 33.0% 0.1% < 0.1% 

25th 1274 - 0.9 41.9% 24.4% 0.6% 33.0% 0.1% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 85 5.0 2.4 33.9% 19.7% 18.3% 26.8% 1.2% 0.1% 

90th 170 4.5 1.9 42.0% 24.5% 0.3% 33.2% < 0.1% < 0.1% 

75th 425 3.6 1.4 39.4% 23.0% 6.1% 31.1% 0.4% < 0.1% 

Median 849 2.6 1.0 38.8% 22.6% 7.4% 30.7% 0.4% < 0.1% 

25th 1274 1.8 0.7 38.8% 22.6% 7.4% 30.7% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 85 1.1 2.8 41.3% 24.1% 1.9% 32.6% 0.1% < 0.1% 

90th 170 0.9 2.4 33.7% 19.6% 19.4% 26.6% 0.7% < 0.1% 

75th 425 0.7 1.8 38.6% 22.5% 7.9% 30.5% 0.5% < 0.1% 

Median 849 0.5 1.3 41.8% 24.4% 0.7% 33.0% 0.1% < 0.1% 

25th 1274 0.4 0.9 37.0% 21.5% 11.7% 29.2% 0.6% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 85 2.1 2.8 41.3% 24.1% 1.9% 32.6% 0.1% < 0.1% 

90th 170 1.5 2.4 33.7% 19.6% 19.4% 26.6% 0.7% < 0.1% 

75th 425 0.7 1.8 38.6% 22.5% 7.9% 30.5% 0.5% < 0.1% 

Median 849 - 1.1 41.9% 24.4% 0.6% 33.0% 0.1% < 0.1% 

25th 1274 - 1.1 41.9% 24.4% 0.6% 33.0% 0.1% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 85 5.1 2.8 41.3% 24.1% 1.9% 32.6% 0.1% < 0.1% 

90th 170 4.6 2.4 33.7% 19.6% 19.4% 26.6% 0.7% < 0.1% 

75th 425 3.7 1.8 38.6% 22.5% 7.9% 30.5% 0.5% < 0.1% 

Median 849 2.7 1.3 41.8% 24.4% 0.7% 33.0% 0.1% < 0.1% 

25th 1274 2.0 0.9 37.0% 21.5% 11.7% 29.2% 0.6% < 0.1% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources (including 

historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb levels (either by 

inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels). 

K-51
Exhibit K-20. Secondary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3

, 

Maximum Monthly Average) Estimated IQ Losses
 

IQ Loss 

Percentile 

Population 

Above 

Predicted 

IQ Loss 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Inhalation 

Diet (Recent Air) Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other a Recent 

Air 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 85 1.1 2.4 41.9% 24.4% 0.6% 33.1% < 0.1% < 0.1% 

90th 170 0.9 1.9 17.1% 10.0% 58.0% 13.5% 1.3% 0.1% 

75th 425 0.6 1.4 39.6% 23.0% 6.1% 31.2% 0.1% < 0.1% 

Median 849 0.4 1.0 39.8% 23.2% 5.5% 31.4% 0.1% < 0.1% 

25th 1274 0.3 0.7 41.8% 24.3% 0.8% 33.0% < 0.1% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 85 2.3 2.4 41.9% 24.4% 0.6% 33.1% < 0.1% < 0.1% 

90th 170 1.8 1.9 17.1% 10.0% 58.0% 13.5% 1.3% 0.1% 

75th 425 0.9 1.4 39.6% 23.0% 6.1% 31.2% 0.1% < 0.1% 

Median 849 - 1.0 41.9% 24.4% 0.6% 33.1% <0.1% < 0.1% 

25th 1274 - 1.0 41.9% 24.4% 0.6% 33.1% <0.1% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 85 5.0 2.4 41.9% 24.4% 0.6% 33.1% < 0.1% < 0.1% 

90th 170 4.5 1.9 17.1% 10.0% 58.0% 13.5% 1.3% 0.1% 

75th 425 3.6 1.4 39.6% 23.0% 6.1% 31.2% 0.1% < 0.1% 

Median 849 2.6 1.0 39.8% 23.2% 5.5% 31.4% 0.1% < 0.1% 

25th 1274 1.8 0.7 41.8% 24.3% 0.8% 33.0% < 0.1% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 85 1.1 2.8 41.9% 24.4% 0.6% 33.1% < 0.1% < 0.1% 

90th 170 0.9 2.4 40.2% 23.4% 4.7% 31.7% 0.1% < 0.1% 

75th 425 0.7 1.8 39.8% 23.2% 5.5% 31.4% 0.1% < 0.1% 

Median 849 0.5 1.3 39.5% 23.0% 6.2% 31.2% 0.1% < 0.1% 

25th 1274 0.4 0.9 40.2% 23.4% 4.7% 31.7% 0.1% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 85 2.1 2.8 41.9% 24.4% 0.6% 33.1% < 0.1% < 0.1% 

90th 170 1.5 2.4 40.2% 23.4% 4.7% 31.7% 0.1% < 0.1% 

75th 425 0.6 1.8 39.8% 23.2% 5.5% 31.4% 0.1% < 0.1% 

Median 849 - 1.2 41.9% 24.4% 0.6% 33.1% < 0.1% < 0.1% 

25th 1274 - 1.2 41.9% 24.4% 0.6% 33.1% < 0.1% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

95th 85 5.1 2.8 41.9% 24.4% 0.6% 33.1% < 0.1% < 0.1% 

90th 170 4.6 2.4 40.2% 23.4% 4.7% 31.7% 0.1% < 0.1% 

75th 425 3.7 1.8 39.8% 23.2% 5.5% 31.4% 0.1% < 0.1% 

Median 849 2.7 1.3 40.1% 23.3% 4.9% 31.6% 0.1% < 0.1% 

25th 1274 2.0 0.9 40.2% 23.4% 4.7% 31.7% 0.1% < 0.1% 

a

 "Other" refers to contributions to indoor dust Pb from indoor paint, outdoor soil/dust, and additional sources (including 

historical air), and "recent air" refers to pathway contributions associated with outdoor ambient air Pb levels (either by 

inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels). 

K-52
Appendix L. Sensitivity Analysis Approach and Results 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

 

Table of Contents 

Table of Contents ................................................................................................................. L-i 

List of Exhibits .................................................................................................................... L-ii 

L. SENSITIVITY ANALYSIS METHODS AND RESULTS ............................................. L-1
 

L.1. OVERVIEW OF SENSITIVITY ANALYSIS............................................................ L-1 

L.2. BASELINE AND SENSITIVITY ANALYSIS CASES............................................. L-5 

L.3. SENSITIVITY ANALYSIS RESULTS...................................................................... L-9 

L.3.1. Absolute Changes in IQ Loss Estimates Associated with the 

Sensitivity Cases............................................................................................ L-10 

L.3.2. Relative IQ Loss Associated with the Sensitivity Cases ............................... L-12 

L.3.3. Change in IQ Loss Associated with Recent Air Exposures........................... L-14 

REFERENCES ................................................................................................................... L-17 

L-i 
List of Exhibits 

Exhibit L-1. Summary of Sensitivity Analysis - Percent Change in IQ Loss 

Compared to Baseline ............................................................................................L-3 

Exhibit L-2. Summary of Baseline and Sensitivity Analysis Model Inputs and 

Assumptions ...........................................................................................................L-6 

Exhibit L-3. Summary of Sensitivity Analysis IQ Loss Estimates...........................................L-10 

Exhibit L-4. Absolute Differences in IQ Loss Estimates Between the Sensitivity and 

Baseline Cases......................................................................................................L-11 

Exhibit L-5. Percent Difference in IQ Loss Estimates between the Sensitivity and 

Baseline Cases......................................................................................................L-13 

Exhibit L-6. Percentage of IQ Loss Contributed from the Recent Air Pathways 

Associated with the Sensitivity Cases ..................................................................L-15 

Exhibit L-7. Percent Differences in IQ Loss Estimates Between the Sensitivity and 

Baseline Cases - Recent Air Pathways.................................................................L-16 

L-ii 
L. SENSITIVITY ANALYSIS METHODS AND RESULTS 

L.1. OVERVIEW OF SENSITIVITY ANALYSIS 

This appendix describes the results of a series of modeling runs that were performed to 

evaluate the sensitivity of intelligence quotient (IQ) loss estimates to changes in specific models 

and input parameter values. The overall objective of these model runs was to identify specific 

models and inputs that contribute the most uncertainty to the IQ loss estimates and to help 

develop insights concerning the overall level of uncertainty in the estimates. 

This sensitivity analysis is structured to involve “one-at-a-time” variations on given 

models or parameter values. In addition, in order to determine the impact of multiple parameter 

changes on a single model element, several cases involve simultaneous variations in more than 

one modeling assumption and/or parameter value. The results of the sensitivity runs are 

compared to the IQ loss distribution estimated for a “baseline” case, which while not a formal 

“central tendency” estimate, has been derived using models and parameter values which 

experience has demonstrated are reasonable and representative of the exposure patterns and 

receptors for which the analysis is being conducted (see Exhibit L-1). The baseline case 

(described more completely in Section L.1) generally consists of the IEUBK modeled current 

conditions (mean) NAAQS case using the hybrid mechanistic-empirical model, a geometric 

standard deviation (GSD) value of 1.6 μg/dL, the concurrent blood lead (PbB) metric, and the 

two-piece linear IQ change function. 

The baseline case is based on the general urban case study because this case has the 

potential to characterize potential exposures for a larger number of exposed children than either 

the primary or secondary Pb smelter case studies. In addition, analyses of available data suggest 

that exposure patterns for urban children are highly variable and less well-documented than those 

near Pb smelters. In particular, the relative importance of the contribution of recent air Pb to 

indoor dust exposures, compared to historical outdoor soil/dust contamination and Pb paint, is 

not well-defined in the literature (see Appendix G), and a range of alternative assumptions 

regarding indoor dust models are evaluated in the sensitivity analysis, as described in 

Section L.2. 

Exhibit L-1 provides an overview of the results of the sensitivity analysis. This exhibit 

describes the variables varied in the analysis, the percent change in the total IQ loss compared to 

the baseline case for the median and 95th percentile, and the percent change in the IQ loss arising 

from the “recent air" (both inhalation and ingestion) pathways compared to the baseline case for 

the median and 95th percentile. Recent air is used here to refer to Pb exposures in the general 

L-1
 
urban case study that are derived from the estimate of outdoor ambient air Pb concentration (i.e., 

inhalation of ambient Pb and ingestion of indoor dust Pb predicted to be associated with recent 

air Pb concentrations). Analyses are presented with exposure concentration variations near the 

top, with the results progressing through the PbB modeling assumptions and the IQ loss 

modeling assumptions. Further details about all the cases run are provided below. 

L-2
 
Exhibit L-1. Summary of Sensitivity Analysis - Percent Change in IQ Loss Compared to Ba

Variable Description of Sensitivity Analysis Performed 

Total Percent Ch

Compared t

ange in IQ Loss 

o Baseline a 

P

f

C

Air conversion ratio 

Maximum quarterly average to annual average air concentration conversion ratio of 7.6 (95th percentile) 

compared to 2.5 (baseline, mean) 

Median 

(Baseline IQ 

Loss < 1) 

-11% 

95th Percentile 

(Baseline IQ 

Loss = 2.1) 

-11% 

M

(Ba

Lo

Outdoor soil/dust Pb 

concentration 

648 μg/g (95th percentile) compared to 198 microgram per 

gram (μg/g) (baseline, mean) 73% 71% 

Mechanistic portion of the hybrid 

mechanistic-empirical model 

Alternate inputs for key variables (i.e., cleaning frequency, cleaning efficiency, deposition, and air exchange rate [AER]) in the mechanistic portion of the hybrid model compared to the baseline inputs. 

-9 to 45% -9 to 45% -19 

Empirical portion of the hybrid 

mechanistic-empirical model 

Total dust Pb estimate of 12.2 μg/ft2 (75th percentile total 

dust estimate) compared to 5.32 micrograms per square foot (μg/ft2) (baseline, median) 

11% 10% 

Hybrid mechanistic-empirical 

model 

The air-only regression-based model compared to the hybrid mechanistic-empirical model (baseline) -8% -8% 

PbB model 

The International Commission for Radiation Protection (ICRP) model (or Leggett model) compared to the Integrated Exposure Uptake Biokinetic (IEUBK) Model for Children (baseline) 

279% 170% 

Diet and drinking water absorption Diet and drinking water absorption fraction of 40% (lower) (60%) (higher) compared to 50% (baseline) -7 to 6% -7 to 6% 

Outdoor soil/dust and indoor dust 

fraction 

Percentage of soil from outdoor soil/dust+indoor dust ingestion of 58% compared to 45% (baseline) -7% -8% 

Outdoor soil/dust and indoor dust 

absorption 

Percentage of outdoor soil/dust and indoor dust intake that is absorbed of 18% compare to a 30% (baseline) absorption fraction 

-7% -7% 

L-3 
Exhibit L-1. Summary of Sensitivity Analysis - Percent Change in IQ Loss Compared to Ba

Total Percent Change in IQ Loss 

Compared to Baseline a 

P

f

C

Variable Description of Sensitivity Analysis Performed 

Median 

(Baseline IQ 

Loss < 1) 

95th Percentile 

(Baseline IQ 

Loss = 2.1) 

M

(Ba

Lo

PbB metric Lifetime metric compared to concurrent metric (baseline) 20% 9% 

GSD 

Lower-bound (1.6 μg/dL) and upper-bound (2.1 μg/dL) values compared to 1.7 microgram per deciliter (μg/dL) (baseline) 

0% -10 to 40% 

IQ change function Log-linear with cutpoint and log-linear with linearization functions compared to two-piece linear (baseline) function 102 to 412% 97 to 226% 102 

a The baseline case consists of the IEUBK modeled current conditions (mean) NAAQS case using the hybrid mechanistic-empirical modelμg/dL, the concurrent PbB metric, and the two-piece linear IQ change function. b Recent air is used here to refer to Pb exposures in the general urban case study that are derived from the estimate of outdoor ambient air P

inhalation of ambient air Pb and ingestion of indoor dust Pb predicted to be associated with ambient air Pb concentrations).
 

L-4 
 

L.2. BASELINE AND SENSITIVITY ANALYSIS CASES 

The “Baseline Parameter Value” column of Exhibit L-2. summarizes the baseline case 

which served as the basis for comparison for all of the sensitivity case results. As shown in the 

exhibit, the current conditions (mean) general urban case study NAAQS scenario was selected as 

the baseline for comparison of IQ loss estimates. The major models and assumptions associated 

with the baseline case are as follows: 

•	 Exposures were estimated for a single exposed (hypothetical) population cohort, rather 

than for residents of many U.S. Census blocks. Thus, the output distribution of IQ loss 

includes no contribution from explicitly modeled variations in exposure. 

•	 Urban annual average ambient air Pb concentrations were estimated based on analyses of 

maximum quarterly concentration data for 2003 to 2005 from monitors in urban areas 

with more than 1 million population (see Appendix C). The mean ratio of maximum 

quarterly average to annual average concentration of Pb in total suspended particulate 

matter (TSP) was used to convert the maximum quarterly average concentration to an 

annual average equivalent. 

•	 The baseline outdoor soil/dust Pb exposure concentration was the arithmetic mean 

estimated from the interim National Survey of Lead and Allergens in housing (NSLAH) 

data (198 μg/g) (Westat Inc., 2002). 

•	 The indoor dust Pb exposure concentration was estimated using the hybrid model (see 

Appendix G), with the non-air dust Pb concentration based on the median wipe dust 

loading from the Department of Housing and Urban Development (HUD) National 

Survey (USEPA, 1995) and the ambient air Pb contribution estimated using the 

mechanistic portion of the hybrid mechanistic-empirical model. 

•	 PbB levels were estimated using the IEUBK model (USEPA, 2005), with the baseline 

exposure factor values and policy-relevant background pathways (drinking water and 

diet) Pb concentration and intake estimates described in Appendix H. 

•	 The concurrent PbB metric (average of the results at 75 and 81 months in the seventh 

year of life) was used as input to the IQ loss model. 

•	 Distributions of PbB concentrations (percentiles) were derived assuming a lognormal 

distribution of concurrent PbB levels with a GSD of 1.7 (background for this estimate can 

be found in Appendix H). 

•	 IQ loss percentiles were derived by applying a two-piece linear IQ loss model derived 

from the Lanphear et al. (2005) pooled analysis of epidemiological studies of PbB and IQ 

(see Section 4.1.1 of the main body of this report). 

L-5
 
Exhibit L-2. Summary of Baseline and Sensitivity Analysis Model Inputs and Assumptio

Variable Baseline Parameter Value Sensitivity Analysis Vari

Case study/NAAQS scenario General urban case study, current conditions (mean) Unchanged 

Outdoor soil/dust Pb 

concentration 

Arithmetic mean (198 μg/g) from NSLAH (see 

Appendix C) Estimated 95th percentile (648 μg/

Annual average ambient air 

Pb concentration 

Maximum quarterly-averaged Pb concentrations from 

urban TSP monitoring sites converted to equivalent 

annual average concentrations using the mean ambient 

air ratio (2.5) of maximum quarterly average to annual 

average Pb-TSP concentrations (see Appendix C) 

Ambient air ratio varied from the mean rati

average to annual average Pb-TSP concen

percentile ratio of maximum quarterly averag

TSP concentrations (7

Indoor dust Pb concentration 

model 

• Mechanistic portion of the hybrid mechanisticempirical model estimate, using inputs as 

described in Appendix G 

• Empirical portion of the hybrid mechanisticempirical model, using total indoor dust estimate 

based on HUD National Survey median 

• Air-only regression-based model • Empirical portion of the hybrid mechanis

total indoor dust estimate based on the 

percentile (12.5 μg/ft2) 

• Multiple cases, each with variations in th

the mechanistic-empirical model. Each calternate value of a single parameter. Th(1 cleaning per month [m-1]) and high (1 

cleaning frequency, low cleaning efficienPb deposition (0.39 per hour [h-1)], and u

values. An overall upper-bound case wasimultaneously using the low cleaning freefficiency, upper bound AER, and the ba

PbB estimation model IEUBK (batch mode age profile) model Leggett (batch mode) m

Exposure/ intake/uptake 

factors 

Baseline exposure factor values and policy-relevant 

background contributions (see Appendix H) 

• Absolute diet, drinking water pathway ab

from baseline 50% to 40 and 60% 

• Outdoor soil/dust and indoor dust weight

baseline 45% to 58% (von Lindern et al., 

• Outdoor soil/dust and indoor dust absorp

baseline 30% to 18% (von Lindern et al., 

PbB metric Concurrent (average of results at 75 and 81 months in the seventh year of life) Lifetime Average (average of results from 6 to 84 m

Inter-individual PbB variability 

(GSD) 

Central tendency value (1.7 μg/dL) estimated from 

epidemiological studies (see Appendix H) 

Baseline GSD varied to a lower-bound value 

bound value of 2.1 μg/dL, estimated from 

IQ model 

Two-piece linear model (break point = 13 μg/dL), 

derived from Lanphear et al. (2005) as described in 

Section 4.1.1 of the main body of this report 

• Log-linear with cutpoint model • Log-linear with linearization model 

L-6 
 

The “Sensitivity Analysis Variations” column of Exhibit L-2 summarizes the alternative 

modeling assumptions and parameter values that were used as inputs to each of the sensitivity 

analysis cases. Note that the sensitivity analysis covers only a very small portion of the credible 

combinations of modeling assumptions and parameter values that could be tested. A full 

analysis of the uncertainty contributions from each model and parameter would require the use of 

Monte Carlo analysis or a related probabilistic method. However, data and resource limitations 

prevented such a full-scale probabilistic model analysis at this time. 

Instead, credible alternative models and parameter values for each step in the modeling 

process were selected for the sensitivity analysis. The derivation of sensitivity analysis cases 

was informed by the results of the pilot assessment and by additional research conducted in 

support of this assessment. The alternative parameter values were chosen based on professional 

judgment, supported by quantitative data to the extent possible. Where parameters were known 

to be variable, but the range of variability was poorly constrained (e.g., gastrointestinal [GI] 

absorption fractions for Pb in diet and drinking water), reasonable upper and lower values were 

chosen to cover a substantial proportion of the overall variability in long-term average values. 

For the exposure Pb concentrations, alternate values for both the outdoor soil/dust and the 

ambient air Pb concentrations were explored. For example, the alternative ("upper") outdoor 

soil/dust Pb exposure concentration estimate was taken as the estimated 95th percentile (rather 

than the baseline arithmetic mean) from the NSLAH survey (as cited in U.S. EPA (2000)). This 

value was estimated using the geometric mean (GM) and GSD in the NSLAH survey and 

assuming a lognormal distribution. For the ambient air Pb concentration, an alternate value was 

used to convert the maximum quarterly-averaged Pb concentrations from urban TSP monitoring 

sites to equivalent annual average concentrations. Rather than using mean ambient air ratio of 

maximum quarterly to annual average Pb-TSP concentrations (2.5), the sensitivity analysis used 

the 95th percentile ratio of maximum quarterly to annual average Pb-TSP concentrations (7.6). 

That is, the annual ambient air Pb concentration estimate is lower when the 95th percentile ratio 

is used. 

In addition, the method for determining indoor dust Pb concentrations was also 

investigated. Because of the importance of determining the contribution of ambient air Pb to 

indoor dust concentrations, a range of sensitivity analyses were performed wherein various 

aspects of the indoor dust Pb estimation model were varied. Three major alternative models 

were evaluated, with varying assumptions related to input parameters: 

•	 IQ estimates from the hybrid (baseline) model were compared to those obtained when 

indoor dust Pb concentrations were estimated using an empirical (air-only regressionL-7
 
 

based model) derived through analysis of air-indoor dust Pb relationships at Pb smelting 

and mining sites (see Appendix G). 

•	 IQ estimates were developed by applying the hybrid (baseline) model, but using the 75th 

percentile indoor dust Pb loading (12.2 μg/ft2

) from the HUD National Survey (USEPA, 

1995), instead of the survey baseline case median value (5.3 μg/ft2) to derive the non-air 

estimate of Pb loading. 

•	 The hybrid (baseline) model was applied, varying the inputs to the mechanistic portion of 

the model affecting indoor dust Pb deposition and removal rates. The parameter values 

that were varied included cleaning frequency, cleaning efficiency, AER, and the average 

Pb deposition rate. 

For the third bullet above, the mechanistic portion of the hybrid model requires inputs 

(such as the AER, the deposition rate, the cleaning frequency, and the cleaning efficiency) as 

discussed in Appendix G. In the sensitivity analysis, two approaches were taken. First, single 

inputs were varied one at a time to investigate the effects of that parameter on the overall IQ 

change. In general, the parameter values selected were based on alternate values in the literature 

deemed appropriate for urban scenarios, and these values caused the overall dust exposure to 

either increase or decrease, depending on the value chosen. Second, a combination of these 

alternate values was used in which each alternate parameter value caused the dust exposure to 

increase. This second method then represented an overall high-end estimate of dust exposure. 

For the AER, an upper-bound of 1.26 h-1 was used, reflecting the 90th percentile AER for all 

regions of the country (USEPA, 1997; Table 17-10). For the Pb deposition rate, a lower-bound 

value of 0.39 h-1 was used, reflecting an estimate for particulate matter (PM) that is 2.5 

micrometers (μm) or smaller (PM2.5) (USEPA, 1997; Table 17-12). This value is lower than the 

Pb-specific value of 1.11 h-1 used in the baseline case. For the cleaning frequency, both a lower 

value (1 m-1) and an upper value (1 w-1) were compared with the baseline cleaning of 2 cleanings 

m-1. Finally, for the cleaning efficiency, an upper-bound value of 25 percent was compared with 

the baseline cleaning efficiency of 12.5 percent. In each of these sensitivity cases, the 

mechanistic recent air contribution to total indoor dust loading was added to the other sources 

portion to get a total Pb dust loading. To get this other sources portion for the sensitivity 

analysis cases, the ratio of these two portions was calculated for the baseline case. Then, this 

ratio was applied to each of the sensitivity mechanistic model estimates to generate a total indoor 

Pb dust loading estimate for each. 

Alternative PbB estimates were derived using a range of different PbB models and 

parameter values from those used in the baseline case, and these differences were carried through 

to the IQ losses using the two-piece linear model. First, the International Commission for 

Radiation Protection (ICRP) PbB model (hereafter referred to as the “Leggett model”), (Leggett, 

L-8
 
 

1993) (see Appendix H) was applied (instead of the baseline IEUBK model (USEPA, 2005) with 

the same exposure factor and policy-relevant non-air exposure concentrations and intakes as 

those used in the baseline case. The differences in results from the baseline case thus reflect only 

differences in the biokinetic predictions of the two models. In addition, the impacts of varying 

the GI absorption fractions for diet, drinking water, outdoor soil/dust, and indoor dust exposure, 

and the relative amounts of outdoor soil/dust and indoor dust ingestion inputs to the IEUBK 

model were also estimated. The IEUBK model was used to estimate both concurrent and 

lifetime PbB metrics, and the impacts of using these different measures of PbB impacts on 

estimated IQ losses were also evaluated. The effect of applying a low-end and high-end estimate 

of the PbB GSD (1.6 μg/dL and 2.1 μg/dL, instead of the baseline estimate of 1.7) on estimated 

IQ loss percentiles was also evaluated. 

In addition, IQ loss predictions derived using two alternative forms of the IQ loss model 

were compared to the baseline estimates. The derivation of the alternative IQ functions (loglinear with cutpoint and log-linear with linearization) was discussed in Section 4.1.1 of the main 

body of this report. 

L.3. SENSITIVITY ANALYSIS RESULTS 

Exhibit L-3 provides a summary of the sensitivity case outputs. Selected percentile IQ 

loss estimates are presented for each case, with the cases ranked in decreasing order of the 

estimated 95th percentile values, and the baseline case results indicated in bold. 

The estimated median and 95th percentile IQ loss estimates for the baseline case are 

approximately 0.9 and 2.1 points, respectively. Quantitative estimates are presented in this 

appendix in order to support estimates of absolute and relative differences between the baseline 

sensitivity analysis case estimates discussed in the following sections. 

Because more high than low parameter values were tested, the majority of the sensitivity 

analysis runs yielded IQ loss estimates higher than the results from the baseline case. It can be 

seen from the estimates in Exhibit L-2. that the cases resulting in the highest estimated IQ loss 

are those derived using different PbB and/or IQ loss estimation models. Use of the log-linear 

with linearization IQ loss model and the Leggett PbB model yield by a large margin the highest 

median and higher percentile IQ losses among all of the sensitivity cases. Smaller impacts are 

associated with cases assuming the 95th percentile soil concentration estimates and high-end 

mechanistic portion of the indoor hybrid mechanistic-empirical model inputs. 

L-9
 
 

 

 

Exhibit L-3. Summary of Sensitivity Analysis IQ Loss Estimates 

Sensitivity Case 

Percentile IQ Estimate 

95th 90th Median a 

Log-linear with linearization IQ loss model 6.8 6.3 4.5 

Leggett PbB model 5.7 5.3 3.3 

Log-linear with cutpoint IQ loss model 4.1 3.6 1.8 

Urban soil 95th percentile (648 μg/g) 3.6 2.9 1.5 

High-end hybrid model parameters 3.0 2.5 1.3 

Hybrid model with low cleaning frequency (1 m-1) 2.3 1.9 1.0 

Hybrid model with low cleaning efficiency (0.125) 2.3 1.9 1.0 

Hybrid model based on 75th percentile total indoor dust Pb (12.2 μg/ft2) 2.3 1.9 1.0 

Lifetime PbB metric 2.3 1.9 1.0 

High PbB GSD (2.1 μg/dL) 2.9 2.2 0.9 

Hybrid model with high AER (1.26 h-1) 2.3 1.9 0.9 

Diet/drinking water GI absorption fraction (60%) 2.2 1.8 0.9 

Low PbB GSD (1.6 μg/dL) 1.9 1.6 0.9 

Baseline 2.1 1.7 0.9 

Hybrid model with low Pb deposition rate (0.39 h-1) 2.0 1.6 0.8 

Diet/Water GI absorption fraction (40%) 1.9 1.6 0.8 

Outdoor soil/dust, indoor dust GI absorption Fraction (0.18) 1.9 1.6 0.8 

Outdoor soil/dust ingestion weighting factor (58%) 1.9 1.6 0.8 

Air-only regression-based indoor dust model 1.9 1.6 0.8 

Hybrid model with high cleaning frequency (1 w-1) 1.9 1.6 0.8 

95th Percentile ratio of maximum quarterly to annual average Pb-TSP 

concentrations (7.6) 1.9 1.5 0.8 

a

 Values less than 1.0 should be interpreted with caution (see text following this exhibit). 

L.3.1. Absolute Changes in IQ Loss Estimates Associated with the Sensitivity Cases 

This section discusses and compares the absolute changes in IQ loss relative to the 

baseline that are associated with the sensitivity cases. 

Exhibit L-4 summarizes the differences between the percentile IQ loss estimated for the 

baseline case and the analogous percentile losses for the sensitivity analysis. The cases are again 

listed by decreasing order of the estimated differences in the absolute values of the 95th 

percentile IQ estimates relative to baseline. Cases giving the largest differences in the 95th 

percentile estimates compared to the baseline are at the top of the table. 

L-10
 
 

 

Exhibit L-4. Absolute Differences in IQ Loss Estimates Between the 

Sensitivity and Baseline Cases 

Absolute Chang

Percentile Estim

Base

Sensitivity Case 

95th 

Percentile 

(Baseline = 2.1) 

e (IQ Points) in 

ates Relative to 

line 

Median 

(Baseline = 0.9) 

Log-linear with linearization IQ loss model 4.7 3.6 

Leggett PbB model 3.6 2.4 

Log-linear with cutpoint IQ loss model 2.0 0.9 

Urban soil 95th percentile (648 μg/g) 1.5 0.6 

High-end hybrid model parameters 0.9 0.4 

Hybrid model with low cleaning frequency (1 m-1) 0.2 0.1 

Hybrid model with low cleaning efficiency (0.125) 0.2 0.1 

Hybrid model based on 75th percentile total indoor dust Pb (12.2 

μg/ft2

) 0.2 0.1 

Lifetime PbB metric 0.2 0.2 

High PbB GSD (2.1 μg/dL) 0.8 0.0 

Hybrid model with high AER (1.26 h-1) 0.2 0.1 

Diet/drinking water GI absorption fraction (60%) 0.1 0.1 

Low PbB GSD (1.6 μg/dL) -0.2 0.0 

Hybrid model with low Pb deposition rate (0.39 h-1) -0.1 -0.1 

Diet/Water GI absorption fraction (40%) -0.2 -0.1 

Outdoor soil/dust, indoor dust GI absorption Fraction (0.18) -0.2 -0.1 

Outdoor soil/dust ingestion weighting factor (58%) -0.2 -0.1 

Air-only regression-based indoor dust model -0.2 -0.1 

Hybrid model with high cleaning frequency (1 w-1) -0.2 -0.1 

95th Percentile ratio of maximum quarterly to annual average 

Pb-TSP concentrations (7.6) -0.2 -0.1 

As noted in the previous section, the largest "across-the-board" differences from the 

baseline IQ loss estimates come from the use of other than baseline IQ loss estimation models 

(i.e., the log-linear with linearization IQ loss model and the Leggett PbB model) to estimate PbB 

or IQ. Impacts of these model selections on the various percentiles range from 0.9 IQ points (the 

increase in the median associated with the use of the log-linear with cutpoint IQ loss model) to 

4.7 IQ points (increase in the 95th percentile associated with use of the log-linear with 

L-11
 
 

linearization IQ loss model). Application of the log-linear with cutpoint model is associated with 

an estimated increase in IQ loss relative to the baseline of 2.0 points at the 95th percentile, and 

with an increase in estimated median IQ loss of 0.9 points. 

Two cases involving changes to specific exposure concentration or exposure factor 

values generate substantially different percentile IQ loss values at the higher percentiles, but not 

in the median value, compared to the baseline case. Using the 95th percentile soil Pb 

concentration estimate from the NSLAH data (instead of the mean), and applying a combination 

of high input values to the mechanistic portion of the hybrid mechanistic-empirical model results 

in changes in the 95th IQ estimates ranging from 0.9 to 1.5 points. The increases in the predicted 

median IQ values relative to the baseline associated with these two cases were 0.6 and 0.4 points, 

respectively. 

Cases that include a high-end assumption related to inter-individual PbB variability (GSD 

= 2.1 μg/dL) also strongly affect the estimated upper (95th) percentile IQ estimates, but as 

expected, have minimal impact on the estimated medians. When the high-end GSD is applied 

along with the baseline (two-piece linear) IQ model, the estimated 95th percentile IQ estimate is 

0.8 points higher than the corresponding estimate from the baseline (GSD = 1.7 μg/dL) case. 

When the high-end GSD is applied in a case along with the log-linear with linearization model, 

the 95th percentile IQ estimates are 0.9 and 1.5 points higher than the baseline estimates. 

None of the other cases result in IQ percentile estimates that differ by more than 0.6 

points from the baseline estimates, and most of the impacts, even on the higher percentile 

estimates, are much lower. 

L.3.2. Relative IQ Loss Associated with the Sensitivity Cases 

Exhibit L-5 summarizes the relative differences between the IQ percentiles estimated in 

the sensitivity cases and the corresponding estimates from the baseline. This approach 

"normalizes," or scales the differences between the estimated IQ percentiles in terms of the 

baseline values. The cases are arranged in decreasing order according to the absolute values of 

the differences in the 95th percentile values between the sensitivity cases and the baseline case. 

L-12
 
 

 

 

Exhibit L-5. Percent Differences in IQ Loss Estimates between the 

Sensitivity and Baseline Cases 

Sensitivity Case 

Relative Change

Estimate Compar

95th 

(Baseline = 2.1) 

226% 

in Percentile 

ed to Baseline 

Median 

(Baseline = 0.9) 

Log-linear with linearization IQ loss model 412% 

Leggett PbB model 170% 279% 

Log-linear with cutpoint IQ loss model 97% 102% 

Urban soil 95th percentile (648 μg/g) 71% 73% 

High-end hybrid model parameters 45% 45% 

Hybrid model with low cleaning frequency (1 m-1) 11% 12% 

Hybrid model with low cleaning efficiency (0.125) 11% 12% 

Hybrid model based on 75th percentile total indoor dust Pb 

(12.2 μg/ft2

) 10% 11% 

Lifetime PbB metric 9% 20% 

High PbB GSD (2.1 μg/dL) 40% 0% 

Hybrid model with high AER (1.26 h-1) 8% 9% 

Diet/drinking water GI absorption fraction (60%) 6% 6% 

Low PbB GSD (1.6 μg/dL) -10% 0% 

Hybrid model with low Pb deposition rate (0.39 h-1) -7% -6% 

Diet/Water GI absorption fraction (40%) -7% -7% 

Outdoor soil/dust, indoor dust GI absorption Fraction (0.18) -7% -7% 

Outdoor soil/dust ingestion weighting factor (58%) -8% -7% 

Air-only regression-based indoor dust model -8% -8% 

Hybrid model with high cleaning frequency (1 w-1) -9% -9% 

95th Percentile ratio of maximum quarterly to annual 

average Pb-TSP concentrations (7.6) -11% -11% 

As expected, the proportional differences between the sensitivity case and baseline 

estimates closely parallel the pattern of the absolute differences shown in Exhibit L-3. The 

exhibit shows how some of the relatively small absolute changes in the median IQ estimates 

associated with the sensitivity analysis cases correspond to large proportional changes from the 

low baseline value. 

L-13
 
 

L.3.3. Change in IQ Loss Associated with Recent Air Exposures 

In addition to the total predicted IQ loss, an analysis was performed on how changes in 

modeling assumptions and parameters affected the proportions of IQ loss associated with the 

"recent air" exposure pathways. As discussed in Appendix K, the estimated contributions to IQ 

loss associated with specific exposure pathways are estimated from the estimated contributions 

to total Pb intake. Given the nonlinearity of the IQ loss model, the proportional contributions are 

therefore approximate. In addition, because the baseline case involves derivation of IQ loss 

distributions based on a single exposure value, the point estimate of the pathway contribution to 

IQ loss is the same across all the estimated percentiles within each case. 

Exhibit L-6 summarizes the estimated changes in recent air pathway contributions (i.e., 

ingestion of indoor dust Pb predicted to be associated with ambient air Pb concentrations, 

inhalation of ambient air Pb, and the sum of the two) associated with the various sensitivity 

cases. These results indicate the percentage of the total IQ that comes from the recent air 

pathways for each case. The exhibit provides results for only 14 of the 21 sensitivity cases 

because cases that do not involve changes in exposure models or parameter values result in no 

change in the recent air contribution compared to the baseline value. This is true of all the cases 

that assume different PbB GSDs and different PbB and IQ loss models. In these cases, as in the 

baseline, the estimated contribution of recent air pathways to the total IQ loss is 29 percent 

(rounded), 28 percent associated with indoor dust ingestion and 0.5 percent associated with 

inhalation exposures. 

L-14
 
 

 

 

Exhibit L-6. Percentage of IQ Loss Contributed from the Recent Air Pathways Associated 

with the Sensitivity Cases 

Case 

Recent Air a

 Contribution to IQ Loss 

Indoor Dust 

Ingestion 

Ambient Air 

Inhalation 

Total 

Contribution 

High-end hybrid model parameters 47% 0.3% 48% 

Hybrid model with low cleaning efficiency (0.125) 35% 0.5% 35% 

Hybrid model with low cleaning frequency (1 m-1) 35% 0.5% 35% 

Hybrid model with high AER (1.26 h-1) 33% 0.5% 34% 

Diet/Water GI absorption fraction (40%) 30% 0.6% 31% 

Baseline 28% 0.5% 29% 

Diet/drinking water GI absorption fraction (60%) 27% 0.5% 27% 

Hybrid model with low Pb deposition rate (0.39 h-1) 24% 0.6% 25% 

Outdoor soil/dust, indoor dust GI absorption Fraction (0.18) 24% 0.7% 25% 

Hybrid model with high cleaning frequency (1 w-1) 22% 0.6% 23% 

Outdoor soil/dust ingestion weighting factor (58%) 21% 0.5% 22% 

Hybrid model based on 75th percentile total indoor dust Pb (12.2 

μg/ft2

) 18% 0.5% 18% 

95th Percentile ratio of maximum quarterly to annual average PbTSP concentrations (7.6) 16% 0.2% 16% 

Urban soil 95th percentile (648 μg/g) 15% 0.3% 16% 

Air-only regression-based indoor dust model 12% 0.6% 13% a

 Recent air is used here to refer to Pb exposures in the general urban case study that are derived from the estimate of 

outdoor ambient air Pb concentration (i.e., inhalation of ambient air Pb and ingestion of indoor dust Pb predicted to 

be associated with ambient air Pb concentration). 

The data in Exhibit L-6 illustrate that changing parameters in a number of exposure 

models can have a large impact on the proportion of IQ loss attributed to the recent air pathway. 

Assuming high parameter values in the mechanistic portion of the hybrid mechanistic-empirical 

model can substantially increase the estimated recent air contribution relative to baseline. 

Assuming low cleaning efficiency, low cleaning frequency, or higher air exchange rates 

increases the estimated recent air contribution to between 33 and 35 percent from the baseline 

value of 29 percent. Assuming high values for all of these values simultaneously (i.e., the highend indoor dust model) increases the total “recent air” contribution (ingestion of indoor dust plus 

inhalation) to 48 percent of total IQ loss (subject to the limitations noted above). 

Assumptions that significantly reduce the proportion of IQ loss attributed to recent air 

exposure pathways include use of the air-only regression-based model to estimate indoor dust Pb 

concentrations (13 percent), use of the 95th percentile urban outdoor soil/dust Pb concentration or 

95th percentile ratio of maximum quarterly to annual average Pb-TSP concentrations (16 percent 

each), or use of the 75th percentile total indoor dust Pb estimate from the HUD National Survey 

(18 percent). The remaining sensitivity cases have less impact on the estimated proportion of IQ 

loss attributable to recent air exposure pathways. 

L-15
 
 

 

 

 

 

Exhibit L-7 shows the relative changes in the IQ loss from the “recent air” pathways. 

This exhibit is similar to Exhibit L-5, but it shows the change relative to the baseline IQ for the 

IQ derived from recent air pathways only. The rank order in the table is the same as that in 

Exhibit L-5. In some cases, changes that result in an increase in total IQ loss compared to the 

baseline case cause a decrease in recent air-related IQ loss (e.g., the urban soil 95th percentile 

case). Percent changes tend to be larger for the recent air portion of IQ loss, compared with the 

total. However, the recent air portion of the IQ loss tends to be small (usually less than one IQ 

point), and thus the overall effect on IQ is usually small. 

Exhibit L-7. Percent Differences in IQ Loss Estimates Between the 

Sensitivity and Baseline Cases - Recent Air Pathways 

Relative Change in Percentile 

Estimate Compared to Baseline 

for Recent Aira Sensitivity Case Pathways 

95th 

(Baseline = 2.1) 

Median 

(Baseline = 0.9) 

Log-linear with linearization IQ loss model 226% 412% 

Leggett PbB model 170% 279% 

Log-linear with cutpoint IQ loss model 97% 102% 

Urban soil 95th percentile (648 μg/g) -8% -7% 

High-end hybrid model parameters 139% 139% 

Hybrid model with low cleaning frequency (1 m-1) 36% 37% 

Hybrid model with low cleaning efficiency (0.125) 36% 37% 

Hybrid model based on 75th percentile total indoor dust 

Pb (12.2 μg/ft2

) -31% -31% 

Lifetime PbB metric 9% 20% 

High PbB GSD (2.1 μg/dL) 40% 0% 

Hybrid model with high AER (1.26 h-1) 27% 28% 

Diet/drinking water GI absorption fraction (60%) 0% 1% 

Low PbB GSD (1.6 μg/dL) -10% 0% 

Hybrid model with low Pb deposition rate (0.39 h-1) -19% -19% 

Diet/Water GI absorption fraction (40%) -2% -1% 

Outdoor soil/dust, indoor dust GI absorption Fraction 

(0.18) -21% -21% 

Outdoor soil/dust ingestion weighting factor (58%) -31% -30% 

Air-only regression-based indoor dust model -60% -60% 

Hybrid model with high cleaning frequency (1 w-1) -28% -27% 

95th Percentile ratio of maximum quarterly to annual 

average Pb-TSP concentrations (7.6) -50% -49% 

a

 Recent air is used here to refer to Pb exposures in the general urban case study that are 

derived from the estimate of outdoor ambient air Pb concentration (i.e., inhalation of ambient 

air Pb and ingestion of indoor dust Pb predicted to be associated with ambient air Pb 

concentration). 

L-16
 
 

 

 

 

 

 

 

 

 

REFERENCES 

Lanphear, B. P.; Hornung, R.; Khoury, J.; Yolton, K.; Baghurst, P.; Bellinger, D. C.; Canfield, R. L.; Dietrich, K. 

N.; Bornschein, R.; Greene, T.; Rothenberg, S. J.; Needleman, H. L.; Schnaas, L.; Wasserman, G.; 

Graziano, J.; Robe, R. (2005) Low-Level Environmental Lead Exposure and Children's Intellectual 

Function: An International Pooled Analysis. Environmental Health Perspectives. 113(7) 

Leggett, R. W. (1993) An Age-Specific Kinetic Model of Lead Metabolism in Humans. Environ Health Perspect. 

101: 598-616. 

U.S. Environmental Protection Agency (USEPA). (1995) Report on the National Survey of Lead-Based Paint in 

Housing: Appendix I: Design and Methodology. EPA 747-R95-004. Office of Pollution Prevention and 

Toxics. 

U.S. Environmental Protection Agency (USEPA). (1997) Exposure Factors Handbook Vol. III: Activity Factors. 1­

74. USEPA; August. 

U.S. Environmental Protection Agency (USEPA). (2000) Hazard Standard Risk Analysis Supplement - TSCA 

Section 403. Available online at: http://www.epa.gov/lead/pubs/403risksupp.htm. 

U.S. Environmental Protection Agency (USEPA). (2005) Integrated Exposure Uptake Biokinetic Model for Lead in 

Children, Windows® Version (IEUBKwin V1.0 Build 263). Available online at: 

http://www.epa.gov/superfund/lead/products.htm. 

von Lindern, I.; Spalinger, S.; Petrosyan, V.; and von Braun, M. (2003) Assessing Remedial Effectiveness Through 

the Blood Lead:Soil/Dust Lead Relationship at the Bunker Hill Superfund Site in the Silver Valley of 

Idaho. Sci. Total Environ. 303: 139-170. 

Westat Inc. (2002) National Survey of Lead and Allergens in Housing. Volume I: Analysis of Lead Hazards. Final 

Report. Revision 7.1. Washington, D.C.: Office of Health Homes and Lead Hazard Control, U.S. 

Department of Housing and Urban Development. 

L-17
 

Appendix M: Qualitative Discussion of Sources of Uncertainty and Quantitative Analysis of
 

Two Design Features 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

 

Table of Contents 

Table of Contents ................................................................................................................ M-i 

List of Exhibits ...................................................................................................................M-ii 

M. QUALITATIVE SOURCES OF UNCERTAINTY AND DESIGN UNCERTAINTY 

ANALYSES .............................................................................................................................. M-1
 

M.1. QUALITATIVE SOURCES OF UNCERTAINTIES IN THE EXPOSURE 

CONCENTRATIONS AND RISK ANALYSES MODELS ..................................... M-1 

M.2. QUANTITATIVE ANALYSIS OF TWO DESIGN ELEMENTS .......................... M-13 

M.2.1. Stability of the Upper Percentiles in the Probabilistic Model Run............... M-13 

REFERENCES .................................................................................................................. M-15 

M-i 
 

List of Exhibits 

Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this 

Assessment ...........................................................................................................M-2 

Exhibit M-2. Summary of Simulation Uncertainty for IQ Loss Estimates..............................M-13 

M-ii 
 

M. QUALITATIVE SOURCES OF UNCERTAINTY AND DESIGN 

UNCERTAINTY ANALYSES 

This appendix presents an overview of the qualitative uncertainties in the risk analysis. 

For many of the uncertainties discussed, a full quantitative uncertainty analysis is not possible 

because the uncertainty in many of the exposure lead (Pb) concentrations or prediction models is 

not well-quantified. However, where possible, attempts have been made to account for these 

uncertainties by running multiple models and looking at the range of results. For example, for 

the general urban case study, two different indoor dust models were used to estimate indoor dust 

Pb concentrations; two different geometric standard deviations (GSDs) were used to estimate 

inter-individual variability; two different blood Pb (PbB) metrics were used to estimate PbB 

concentrations; and three different intelligence quotient (IQ) change functions were used to 

generate IQ loss estimates. Comparison across all these different cases does, however, provide 

some estimate of the overall uncertainty in the risk results. This appendix further delineates 

individual sources of uncertainty in each step of the risk analyses for each case study. Section 

M.1 presents a summary of the uncertainties in the Pb exposure concentrations and risk analysis 

models, and Section M.2 further discusses uncertainties specific to the design of the risk 

analyses. 

M.1. QUALITATIVE SOURCES OF UNCERTAINTIES IN THE EXPOSURE 

CONCENTRATIONS AND RISK ANALYSES MODELS 

Exhibit M-1 presents a summary of limitations contributing uncertainty to the assessment 

that are associated with the following: 

• The general (vs. specific) case study strategy (i.e., of general urban case study), 

• Emissions characterization, 

• Ambient air Pb concentrations, 

• Roll-back approach for alternative NAAQS scenarios, 

• Inhalation Pb exposure concentrations, 

• Outdoor soil/dust Pb exposure concentrations, 

• Indoor dust Pb exposure concentrations, 

• Other sources of exposure, 

• The PbB estimation model, 

• Biokinetic exposure/intake/uptake factors, 

• The PbB metric, 

• Inter-individual PbB variability (i.e., GSD), 

• The IQ loss model for each case study, and 

• The apportionment of PbB concentrations and IQ loss to different exposure pathways. 

M-1
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

General (vs. specific) 

Case Study Strategy 

Modeling 

Element 

across the United States. Although the approach provides a reasonable approximation of average conditions within urbanized areas in the United States, it 

General Urban 

- In considering the general urban case study, uncertainty results from a reliance on a general approach to characterize conditions in urban areas is unlikely that it could be used to accurately represent individual cities when they are considered outside the 

--

Case Study a 

Primary Pb Smelter 

Emissions 

Characterization 

--

framework of this average across cities. 

- Emission estimates for the current NAAQS scenario reflect the proposed Missouri Department of Natural Resources [MDNR] 2007 State Implementation Plan [SIP] [(2007). The U.S. EPA has not completed its review of this proposed SIP. Further, actual emissions from Pb sources in this case study occurring when the current or alternative NAAQS is met may differ. 

- Pthe obttesestThact

Ambient Air Pb 

Concentrations 

- Although the general approach provides bounds on the current situation by examining mean and 95th percentile 

current conditions, it does not bound the conditions under each alternative NAAQS scenario. The use of single NAAQS values for the alternative NAAQS standards does not allow for consideration of the fact that meeting an alternative NAAQS is likely to result in a non-uniform ambient air surface, including areas with levels below that standard. 

- The spatial pattern of air concentrations predicted from the dispersion modeling for the current NAAQS scenario is used for all scenarios. 

- Tcondisconsce

M-2
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

Modeling 

Element General Urban 

- Mean and 95th percentile ratios of maximum quarterly to 

Case Study a 

Primary Pb Smelter 

- In dispersion modeling used to predict air concentrations for the current NAAQS scenario, 

Ambient Air Pb 

Concentrations 

(Continued) 

annual average Pb-total suspended particulate matter (TSP) concentration estimates for all air monitors were used to convert the quarterly maximum concentration to an annual equivalent for current conditions. The use of these ratios incorporates uncertainty into the analysis. In addition, lowerbound estimates of these ratios were not generated, which limits the ability of this assessment to represent locations with smaller ratios. 

only two years of meteorological data were modeled (as compared to the more traditional five), which limited the ability of the analysis to capture year-to-year variability in meteorological conditions. However, the data that were used were site-specific data, which are generally considered preferable to five years of data from the closest National Weather Service (NWS) station. 

- In precusitwene

Roll-back Approach for 

Alternative NAAQS 

Scenarios 

--

- The roll-back approach used in this assessment assu(relative to the reduction necessary for the maximum coalternative standard) for all locations across the study aexplicitly consider the spatial differences in concentratiocontrol strategies. 

- Concentrations were not modeled using an exposure-event model (e.g., APEX). Instead, this analysis used convers

U.S. EPA’s 1999 National-scale Air Toxics Assessment (USEPA, 2006a) ambient and inhalation Pb exposure concen

estimate of how these Pb concentrations relate to each other for each case study. - The NATA results for the entire United States, rather than those specific to only urban locations, were used. 

- The U.S. Census tract results from NATA were assumambient Pb-exposure relationships for all U.S. Census tract. 

Inhalation Pb Exposure 

Concentrations 

- The NATA age group used to estimate the ambient-to-inhalation Pb exposure concentration conversion was specific 

assessment is focused on children less than 7 years of age. The uncertainty associated with this assumption is depe

activity patterns of 0 to 4 years olds does not represent 0 to 7 year olds. It is unclear whether this uncertainty results inhalation exposure concentrations. - The penetration factor, which was used in the HAPEM modeling for NATA to estimate the fraction of Pb in outdoor a

based on a study that examined the penetration of hexavalent chromium particles, which are generally more reactive 

2004). - The arithmetic mean of ambient-to-inhalation Pb exposure concentration ratios was assumed appropriate for all case capture the variability in this relationship across different individuals. 

M-3
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

Modeling 

Element General Urban 

Case Study a 

Primary Pb Smelter 

Outdoor Soil/Dust Pb 

Exposure 

Concentrations 

Based on time and resource constraints, this analysis used a data point from a readily available interim version of the National Survey of Lead and Allergens in Housing (NSLAH) rather than a point from the final study data, which is contained in a less user-friendly format. The primary difference between the survey versions is that the interim version contains data from 706 housing units (USEPA, 2000b), while the final version uses data from 831 housing units (Westat Inc., 2002). Since the interim soil Pb concentration is calculated using weighting designed to produce a nationally representative value (the same procedure would be used for the calculation from the final version data), it is expected that the concentrations from the 

two versions would differ but the magnitude of the difference is expected to be small. 

- For this case study, post-excavation data were used to characterize soil/dust Pb concentrations within the remediation zone (i.e., within 1.5 kilometers [km] of the facility) and pre-excavation data were used to characterize concentrations outside of the remediation zone. The post-excavation data were collected immediately following excavation, prior to the yards being backfilled with clean soil. It is unclear how these measurements compare to the current, postbackfill soil/dust concentrations. In addition, none of the soil/dust concentration estimates used for this case study include consideration for continuing contamination that has occurred since the measurements were taken. Given the relatively high emissions from this facility, it is expected that these limitations result is an overall underestimate of soil/dust concentrations for this case study. 

- NPb seclocto csoismemconair mesimsoirepconeva

- Current soil measurements were not available for the area outside of the soil cleanup area. Outdoor soil/dust concentrations in this area were estimated using a regression equation of the 

available pre-excavation soil concentrations based on distance to the main stack. Due to the soil - Tcleanup within 1 mile of the stack, the calculated for 

- The interim NSLAH survey is not focused on urban homes, and measured soil Pb concentrations near the outbut is based on a nationally-representative survey of primary Pb smelter were in some cases lower the residential locations, which impacts the ability of these data than the soil concentrations calculated or assto be used to represent urban locations. measured in locations without soil cleanup. This car

likely contributes uncertainty to the risk results con(e.g., underestimating the contribution from the introutdoor soil/dust pathway close to the facility). However, the impact of this limitation on results was likely reduced by the selection of different indoor dust Pb prediction models for the two different parts of the study area. 

M-4
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

Modeling 

Element 

Outdoor Soil/Dust Pb 

General Urban 

- There is a significant amount of variation across cities, with regard to soil Pb levels. There is also significant variation across houses in a given city depending on housing vintage, whether renovation activities occurred on the site, historical usage of the land on which a house is built, etc. A single value (as used in the urban case study) does not capture this inter-city and inter-house variability. Consequently, risk predictions generated using this hypothetical case study could misrepresent exposures and risks for cities where soil Pb levels demonstrate a significantly different trend form the central-tendency value used in this analysis. 

Case Study a 

Primary Pb Smelter 

--

- Sthe PamixfeapabasdatincHe(HHunkade

Exposure 

Concentrations 

(Continued) 

- The yard-wide average used in this analysis, which incorporates samples from throughout the yard, may not be the optimal way to characterize the outdoor soil/dust Pb concentrations to which children may be exposed. Children may spend significantly more time in a particular part of the yard. NSLAH sampled in the play areas for some homes, but play area data were not used in this analysis because these samples were only taken for approximately half of the homes assessed. It is unclear whether the yard-wide averages generally over- or under-estimate soil/dust concentrations to which children are exposed. However, since U.S. EPA (2000a) indicates that NSLAH play area samples are assumed to come from remote areas of the yard, which generally have lower Pb soil concentrations than locations closer to the building, it is expected that the use of yard-wide averages would bias the concentration high. 

--

- Mconon smavasmMPscaundconPb 

M-5
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

Modeling 

Element General Urban 

- The cleaning efficiency and frequency inputs used in the mechanistic portion of the hybrid model (i.e., the part of the model that calculates the contribution to the total indoor dust Pb loading from the ambient air) were developed based on limited data from the available literature and have significant associated uncertainties. Given the relatively high sensitivity of the model to changes in these two inputs, these limitations contribute to the uncertainties in the indoor dust Pb concentration estimates. It is unclear whether these uncertainties result in over- or under-estimates of these concentrations. 

Case Study a 

Primary Pb Smelter 

- For locations within 1.5 km of the primary Pb smelter, a site-specific model was used to generate indoor dust Pb concentration estimates. This model will only capture central tendency indoor dust Pb concentrations and is relatively uncertain for U.S. Census blocks or block groups with atypical exposure patterns. In addition, the model does not explicitly capture the relationships between outdoor soil/dust Pb and indoor dust Pb or road dust Pb and indoor dust Pb because no statistical relationships were identified in the data. These limitations introduce uncertainty into the estimated dust Pb concentrations, although it is unclear whether they result in over- or underestimates. 

- Tmoindcasassoutsturefteroutof tCoindindinteconcas

Indoor Dust Pb 

Exposure 

Concentrations - The Pb deposition rate and air exchange rate (AER) 

used in the mechanistic portion of the hybrid model were fairly well characterized by data in the literature; however, their variability, which is excepted to be fairly high, is not fully captured by the model and may contribute to uncertainties in the indoor dust Pb concentration estimates. 

- For locations greater than 1.5 km from the primary Pb smelter, the air+soil regression-based model was used for this case study. This model was developed primarily using data from the 1980s for Pb smelters in the United States and Canada. The conditions at these smelters in the 1980s may not match those currently existing at the primary Pb smelter case study. It is unclear how these uncertainties may bias the estimated indoor dust Pb concentrations. 

- Tfor primfor and smthosecis umaPb 

- Resuspension is not explicitly modeled in the mechanistic portion of the hybrid model. For higher indoor dust Pb loadings, resuspension may be considerable and its exclusion tends to bias the indoor dust Pb loadings high. Direct quantification of the bias is not possible, however, because resuspension will depend on the total dust Pb loading, not just the portion 

- Any uncertainties in the ambient air Pb concentrations and in the outdoor soil/dust concentrations for locations greater then 1.5 km from the facility will result in uncertainties in the indoor dust Pb concentration estimates. 

- APb unccon

arising from the ambient air Pb, and the mechanistic portion of the model only addresses the latter. 

M-6
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

Indoor Dust Pb 

Exposure 

Concentrations 

(Continued) 

Modeling 

Element 

- The empirical portion of the hybrid model uses estimates of total dust Pb loading from the median values in the Department of Housing and Urban Development (HUD) survey (USEPA, 1995c) as the basis for deriving non-air related indoor dust Pb concentrations. The HUD survey is designed to be representative of housing for the United States’ population and thus does not represent exclusively urban homes. As a result, the variability in the indoor dust Pb loadings across the study homes is large. The median Pb background used for this case study does not capture any variability due to higher ambient Pb air, 

indoor Pb paint, outdoor soil/dust concentrations, or 

atypical cleaning habits. In addition, the HUD study was conducted over a decade ago, and background conditions may have changed between the study time period and today. The limitations in these values introduce uncertainty into the estimated dust Pb concentrations, although it is unclear whether they result in over- or under-estimates. 

General Urban 

--

Case Study a 

Primary Pb Smelter 

- The mechanistic portion of the hybrid model requires input of an Pb ambient air concentration that represents the conditions in the homes in the HUD study (USEPA, 1995b) to ensure that the ambient air and indoor dust loadings used in the model are consistent. The ambient air concentration selected was a national average of all air monitors in urban environments operating during the time of the HUD study. However, this ambient air Pb concentration may not actually correspond to the typical air Pb concentration near the HUD study homes. 

-- 

M-7
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

Indoor Dust Pb 

Exposure 

Concentrations 

(Continued) 

Modeling 

Element 

- In the hybrid model, the total indoor dust Pb loading is converted to a total Pb dust concentration using a regression equation developed from the HUD survey data (USEPA, 1995a). This equation was fit by log transforming both the indoor dust Pb loading and the indoor dust Pb concentration measurements and fitting a linear equation to the data. Because the regression was done in log space, small changes to the intercept result in large changes to the predicted indoor dust Pb concentration. The use of this equation assumes the nature of the indoor dust Pb in the house in question is similar to the composition of indoor dust Pb in a typical HUD study home. Differences in percent contributions 

from indoor Pb paint, outdoor soil/dust, or ambient air could result in different indoor dust Pb concentrations for the same indoor dust Pb loading. Thus, there is a large degree of uncertainty associated with the conversion equation. It is unclear whether this uncertainty results in over- or under-estimates. 

General Urban 

--

Case Study a 

Primary Pb Smelter 

- In the hybrid model, contributions from air-related sources to indoor dust Pb loadings varied across the different NAAQS scenarios. Contributions from other (non-air) sources, however, were constant across NAAQS scenarios. As a result, there are differences in the percent contributions of these sources to indoor dust Pb loadings. These percent contributions are used in the pathway apportionment and result in limitations in the resulting apportionment of PbB and IQ loss, which are discussed below. 

-- 

M-8
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

Indoor Dust Pb 

Exposure 

Concentrations 

(Continued) 

Modeling 

Element 

- As part of the effort to consider uncertainty in key modeling steps, indoor dust Pb concentrations were estimated with both the hybrid model and the air-only regression-based model (same model used for the secondary Pb smelter case study – see above). Several of the locations included in the data used to generate the air-only regression-based model were in urban environments, but the data were dominated by point sources. Thus, this equation’s application in urban environments is limited by the representativeness of the locations included in the original pooled analysis and the extent to which current conditions are represented by conditions in the 1980s when the data were collected. It is unclear how these uncertainties may bias the estimated indoor dust Pb concentrations. 

General Urban 

--

Case Study a 

Primary Pb Smelter 

- Any uncertainties associated with the ambient air Pb concentrations for this case study will be carried through to the indoor Pb dust calculations and will introduce uncertainties there as well. 

-- 

- There is uncertainty associated with estimates of the amounts of foods eaten (by age, ethnicity) used in the generati

children's food consumption and thus potential dietary Pb exposures have changed over time. Limited data were ava

ethnic groups that could identify highly exposed population subgroups which may not be well represented by the mod

terms of background exposures). 

Other Sources of 

Exposure 

- Representative residue levels of Pb in specific foods (commercial and homegrown) for each case study were not obtassumed to receive the age-specific estimates of dietary Pb intake developed by the U.S. EPA Office of Solid Waste a

(OSWER) (U.S. Environmental Protection Agency (USEPA), 2006b). The U.S. EPA developed these estimates by an

from the NHANES III, conducted by the National Center for Health Statistics , and food residue data from the U.S. FD

(USFDA, 2001). These estimates may either over- or under-estimate the actual central tendency dietary Pb intake in - There is uncertainty associated with estimates of the amounts of drinking water consumed. Existing study data were specific consumption for each year modeled in the Integrated Exposure Uptake Biokinetic (IEUBK) model. In additionconsumption was included; any consumption from non-residential sources is not reflected in this analysis. 

M-9
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

Other Sources of 

Exposure (Continued) 

Modeling 

Element 

-The Pb concentration in drinking water used in this assessment was taken from samples using a limited number of chbuilt after Pb piping was banned. Consequently, this analysis does not address elevated background exposures relat

In addition, the central tendency drinking water Pb concentration estimates will necessarily exclude any regional varia

drinking water Pb exposure. Finally, any systematic differences in background Pb water concentrations between the 

urban case study have not been captured. 

Case Study a 

General Urban Primary Pb Smelter 

- Contributions of Pb to indoor dust from indoor paint were not explicitly captured, although they are covered to some dust Pb models used in the analysis. For the primary and secondary Pb smelter case studies, this contribution is impthe indoor dust calculation equations. For the general urban case study hybrid model, the indoor paint contribution is 

empirical non-air portion of the hybrid model. Any regional or temporal changes in the contribution of indoor Pb paint - Folk medicines, toys, enamelware, and other sources are not likely to be major sources of Pb exposure for most chil

exposures were not characterized for this assessment. Specific ethnic or social groups may have high risks of Pb exp

however, the magnitude of these risks for these groups is unknown. 

PbB Estimation Model 

- Of the two biokinetic models considered, the IEUBK model generates PbB estimates that are three times low(1993) when the same Pb uptake assumptions are used. No concrete explanation for this discrepancy currenthe limited data available for performance evaluation, the IEUBK model appears to give estimates close to tho

known Pb exposure concentrations. Because of the wide discrepancy between the models, considerable unc

choice of the PbB model. - As described above, uncertainties are introduced due to the selection of food intake, Pb concentration in food, drinkiPb concentrations (see "Other Sources of Exposure" above). 

Biokinetic 

Exposure/Intake/Uptake 

Factors 

- The defaults for indoor dust and outdoor soil/dust ingestion rates and the fraction of outdoor soil/dust and indoor dus

were retained in this analysis. No urban-specific or Pb smelter-specific values could be determined. Thus, these valuestimate the outdoor soil/dust and indoor dust parameters. - The GI absorption fraction of Pb from drinking water (and diet) was retained at the IEUBK default value. These absofor temporal or inter-individual variations and may either over- or under-estimate the actual GI absorption rate. 

M-10
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

Modeling 

Element 

Case Study a 

General Urban Primary Pb Smelter 

Site-specific absorption factors for outdoor soil/dust and indoor dust were derived for this case study using relative bioavailability (RBA) estimates generated based on swine studies involving outdoor soil/dust and indoor dust samples collected in the study area (Casteel et al., 

- Fge

Biokinetic 

Exposure/Intake/Uptake 

Factors (Continued) 

- For this case study, the IEUBK generic default value for gastrointestinal (GI) absorption of Pb from outdoor soil/dust and indoor dust was used. This value is generally consistent with more recently reported values, although estimates vary widely. Thus, these estimates may either over- or under-estimate the actual GI absorption for a child in these study areas. 

2005). These site-specific absorption factors showed uptake rates that were contrary to the typical pattern seen with outdoor soil/dust and indoor dust given that the estimated GI absorption fraction for outdoor soil/dust Pb (0.48) was higher than that for indoor dust (0.26.) Because the estimated indoor dust PB concentrations were so much higher than the outdoor soil/dust Pb 

absouscorepestestun

concentrations for the same U.S. Census blocks, use of these site-specific values probably resulted in slightly lower estimated Pb uptakes than would have resulted from using the default GI absorption fraction value of 0.30 for both outdoor soil/dust and indoor dust. 

abstu

PbB Metric - In the Lanphear et al. (2005) study, the concurrent metric was shown to provide an empirical relationship with the hig

any errors associated with using one metric over the other are not quantified and introduce uncertainty in the IQ loss e

Inter-Individual PbB 

Variability (i.e., GSD) 

-A range of GSDs were considered for the general urban case study including values reflective of a) a more homogenous population of children (in terms of Pb exposure (GSDs of 1.6 to 1.7 μg/dL) and b) a more heterogeneous population of children (GSDs of 2.0 to 2.1). There is uncertainty in inclusion of the larger values since these are based on the United States’ population and may well overstate variability for any size urban population exposed to a fairly uniform ambient air Pb level (as is the case with the 

- A range of GSDs reflecting a more homogenous (loca1.6 to 1.7) was used for the two point source case studfor both point source case studies is based on applicatistratifies the modeled population prior to the application an over-prediction of PbB level variability. Specifically, in underlying PbB levels (i.e., gradients in air-related maddressed through the spatial template, GSDs would idof variability (e.g., variability in non air-related Pb sourcbehavior related to Pb exposure). 

- Any variations in the inter-individual variability in different age groups, genders, ethnic groups, or other categories weGSD values. Thus, any differences between the population in the data from which the GSD values were derived and 

case studies used in this analysis introduce uncertainty in the GSD estimates. - The GSD was observed to increase in recent years, potentially due to the persistence of a small "tail" of high-exposufalling for the vast majority of children. The effects of this change were not explored in this analysis. 

M-11
 
Exhibit M-1. Summary of Limitations Contributing Uncertainty to Various Aspects of this As

IQ Loss Model 

Modeling 

Element 

- Any effects of covariates on the Lanphear et al. (2005) model predictions are unknown, and the IQ change functions 

from this Lanphear study. Thus, any inherent differences between the Lanphear et al. population of children and the cstudies used in this analysis introduce uncertainty in the IQ estimates. 

Case Study a 

General Urban Primary Pb Smelter 

- Any errors introduced during the estimation of Pb exposure concentrations or PbB levels discussed above will be cauncertainty in the IQ loss predictions. - A key source of uncertainty related to IQ loss modeling is the degree of health decrement associated with lolevels less than 5 μg/dL). The Lanphear pooled analysis did not provide data that pointed to a clear functionaexposure levels and consequently, several candidate functions were included in this analysis. 

Pathway 

Apportionment 

- It was assumed that the central tendency pathway apportionment of PbB levels holds for higher percentiles 

the pattern seen for central-tendency PbB level estimates generated for that same exposure range). In reality

shift as higher exposure percentiles are considered (e.g., Pb paint and/or drinking water exposures may increrelated contributions decreasing as an overall percentage of PbB levels). - As discussed above, the apportionment of PbB and IQ loss from sources of indoor dust Pb loading is based 

air and other sources. This approach leads to estimates of other source contributions to PbB and IQ loss thaNAAQS scenarios, even though the actual sources are the same. This results from a number of factors, inclu

and IQ loss modeling. The limitation generally results in higher contributions from other sources for scenariocontributions. - The percentage of IQ loss arising from different exposure pathways was assumed to be the same as the perpathway. These Pb uptakes are estimated from exposure media concentrations and are used as inputs to the Appendix I. In addition, because of the nonlinearity of the IQ models themselves, there is considerable ambig

proportional pathway contributions to IQ change; using the proportional contribution to total Pb uptake as prwhich introduces uncertainty into these estimates. 

a Those sources of uncertainty anticipated to have a particular significant impact on risk results generated for this analysis (based either on 

of the sensitivity analysis, where applicable, or input from the analysis team) have been bolded. Efforts to enhance the analysis through furesearch would likely be focused on these specific analytical steps/inputs. 

M-12
 
 

 

 

 

 

 

M.2. QUANTITATIVE ANALYSIS OF TWO DESIGN ELEMENTS 

In addition to the uncertainties listed in Section M.1, other uncertainties are introduced to 

the overall analyses due to specific aspects of the design. A key element of the analysis design 

which is subject to uncertainty is the number of times the probabilistic model is run (see Section 

M.2.1). 

M.2.1. Stability of the Upper Percentiles in the Probabilistic Model Run 

All of the IQ loss distributions discussed in this appendix were derived based on the 

probabilistic simulation model described in Appendices H and I. In this model, PbB statistics 

(percentiles) were derived by sampling from log-normal distributions centered on the geometric 

mean (GM) PbB levels estimated for the entire exposed populations (general urban case study) 

or the populations residing in specific U.S. Census blocks or block groups (primary and 

secondary Pb smelter case studies). Then, the IQ loss estimates were generated for each of the 

PbB statistics. 

In addition to the sources of uncertainty mentioned above, the probabilistic modeling 

process itself introduces a degree of uncertainty into the output IQ loss statistics, and that 

contribution can be quantified, as shown in Exhibit M-2. This exhibit summarizes the observed 

variability in estimated IQ loss percentiles produced by repeating each run of the probabilistic 

model (which consists of 50,000 sampling iterations) 100 times. For this analysis, the model was 

run using input data from a general urban case study scenario using the hybrid mechanisticempirical model (“hybrid” model for short), a GSD of 1.7 microgram per deciliter (μg/dL), and 

the concurrent PbB metric. 

Exhibit M-2. Summary of Simulation Uncertainty for IQ Loss Estimates 

Percentile 

Distribution of IQ Loss Estimates from 100 Replicate Model Runs 

5th 

Percentile Median Mean 95th Percentile 

Standard 

Deviation 

Coefficient 

of Variation 

95th 2.1 2.1 2.1 2.1 0.012 0.56% 

90th 1.7 1.7 1.7 1.7 0.008 0.47% 

75th 1.2 1.2 1.2 1.3 0.004 0.34% 

Median <1 <1 <1 <1 0.002 0.27% 

25th <1 <1 <1 <1 0.002 0.30% 

The rows of Exhibit M-2 correspond to the various IQ loss statistics (i.e., 95th to 5th 

percentile) that were estimated from the simulations. The columns of Exhibit M-2 show the 

M-13 
 

 

distribution of the percentile estimates across the 100 repeated model runs. It can be seen that 

the simulation uncertainty throughout the majority of the IQ loss distribution (i.e., 95th to 5th 

percentile) is quite small. The coefficients of variation for the individual estimates (i.e., the ratio 

of the standard deviation to the mean) are on the order of 1 percent or less for the percentiles.1 

The difference between the median and 95th percentile estimates tend to be about the same as the 

differences between the median and 5th percentile estimates. 

Note that the ultimate limits on the degree of accuracy with which the various percentile 

values can be estimated is determined by the total number of iterations and/or replicates; the 

standard errors of the percentile estimates can be reduced to a degree that is proportional to the 

square root of the number of iterations. The above analysis suggests that the existing modeling 

approach and number of iterations can provide IQ loss percentile estimates in which the 

simulation uncertainty will be far less than the uncertainty associated with, for example, the 

selection of PbB models or input parameter values. 

1

 This result can be interpreted to mean that successive estimates of these percentiles generated by 

individual model runs can be expected to vary by approximately these amounts. 

M-14
 
 

 

 

 

 

 

 

 

 

 

 

REFERENCES 

Casteel, S. W.; Tessman R.; Brattin W.J.; Wahlquist A.M. (2005) Relative Bioavailability of Lead in House Dust 

and Soil From the Herculaneum Lead Smelter Site in Herculaneum, Missouri. Prepared for Black & 

Veatch Special Projects Corporation; May. 

Lanphear, B. P.; Hornung, R.; Khoury, J.; Yolton, K.; Baghurst, P.; Bellinger, D. C.; Canfield, R. L.; Dietrich, K. 

N.; Bornschein, R.; Greene, T.; Rothenberg, S. J.; Needleman, H. L.; Schnaas, L.; Wasserman, G.; 

Graziano, J.; Robe, R. (2005) Low-Level Environmental Lead Exposure and Children's Intellectual 

Function: An International Pooled Analysis. Environmental Health Perspectives. 113(7) 

Leggett, R. W. (1993) An Age-Specific Kinetic Model of Lead Metabolism in Humans. Environ Health Perspect. 

101: 598-616. 

Long, T.; Johnson, T.; Laurenson, J.; Rosenbaum, A. (2004) Development of Penetration and Proximity 

Microenvironment Factor Distributions for the HAPEM5 in Support of the 1999 National-Scale Air Toxics 

Assessment (NATA). Memorandum prepared for Ted Palma, U.S. EPA, Office of Air Quality Planning 

and Standards (OAQPS); April 5. 

Missouri Department of Natural Resources (MDNR). (2007) Doe Run - Herculaneum State Implementation Plan 

(SIP) Dispersion Modeling Review. Memorandum From Jeffry D. Bennett to John Rustige. February 12, 

2007. Available online at: http://www.dnr.mo.gov/env/apcp/herculaneumsip.htm. 

U.S. Environmental Protection Agency (USEPA). (1995c) Report on the National Survey of Lead-Based Paint in 

Housing: Appendix I: Design and Methodology. EPA 747-R95-004. Office of Pollution Prevention and 

Toxics. 

U.S. Environmental Protection Agency (USEPA). (1995b) Report on the National Survey of Lead-Based Paint in 

Housing: Appendix I: Design and Methodology. EPA 747-R95-004. Office of Pollution Prevention and 

Toxics. 

U.S. Environmental Protection Agency (USEPA). (1995a) Report on the National Survey of Lead-Based Paint in 

Housing: Appendix I: Design and Methodology. EPA 747-R95-004. Office of Pollution Prevention and 

Toxics. 

U.S. Environmental Protection Agency (USEPA). (1998) Methodology for Assessing Health Risks Associated With 

Multiple Pathways of Exposure to Combustor Emissions. Update to EPA/600/6-90/003, EPA/NCEA (EPA 

600/R-98/137). Cincinnati, OH: National Center for Environmental Assessment (NCEA). Available online 

at: oaspub.epa.gov/eims/eimscomm.getfile?p_download_id=427339. 

U.S. Environmental Protection Agency (USEPA). (2000b) Hazard Standard Risk Analysis Supplement - TSCA 

Section 403: Risk Analysis to Support Standards to Lead in Paint, Dust, and Soil: Supplemental Report. 

EPA 747-R-00-004. Available online at: http://www.epa.gov/lead/pubs/403risksupp.htm. 

U.S. Environmental Protection Agency (USEPA). (2000a) Hazard Standard Risk Analysis Supplement - TSCA 

Section 403: Risk Analysis to Support Standards to Lead in Paint, Dust, and Soil: Supplemental Report. 

EPA 747-R-00-004. Available online at: http://www.epa.gov/lead/pubs/403risksupp.htm. 

U.S. Environmental Protection Agency (USEPA). (2005) Human Health Risk Assessment Protocol (HHRAP) for 

Hazardous Waste Combustion Facilities. EPA530-R-05-006. Office of Solid Waste and Emergency 

Response; September. Available online at: http://www.epa.gov/epaoswer/hazwaste/combust/risk.htm. 

U.S. Environmental Protection Agency (USEPA). (2006a) 1999 National-Scale Air Toxics Assessment. Available 

online at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html. 

M-15
 
 

 

 

 

U.S. Environmental Protection Agency (USEPA). (2006b) Specific Estimates of Dietary Pb Intake Developed by 

EPA's Office of Solid Waste and Emergency Response. Available online at: 

http://www.epa.gov/superfund/lead/ieubkfaq.htm#FDA . 

U.S. Food and Drug Administration (USFDA). (2001) Total Diet Study. Center for Food Safety and Applied 

Nutrition, Office of Plant and Dairy Foods and Beverages; June. Available online at: 

http://www.cfsan.fda.gov/~comm/tds-toc.html. 

Westat Inc. (2002) National Survey of Lead and Allergens in Housing. Volume I: Analysis of Lead Hazards. Final 

Report. Revision 7.1. Washington, D.C.: Office of Health Homes and Lead Hazard Control, U.S. 

Department of Housing and Urban Development. 

M-16
 
Appendix N: Additional General Urban and Primary Pb Smelter Case Study Analyses 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

Table of Contents 

Table of Contents .................................................................................................................N-i 

List of Exhibits ....................................................................................................................N-ii 

N. ADDITIONAL GENERAL URBAN AND PRIMARY PB SMELTER CASE 

STUDY ANALYSES................................................................................................................. N-1
 

N.1. OVERVIEW OF CORE MODELING APPROACH.................................................. N-1 

N.2. CORE MODELING APPROACH RESULTS............................................................ N-4 

N.2.1. General Urban Case Study Results..................................................................N-4 

N.2.1.1. Media Concentrations ....................................................................... N-4 

N.2.1.2. PbB and IQ Change .......................................................................... N-7 

N.2.1.3. Ambient Air to PbB Ratios.............................................................N-16 

N.2.2. Primary Pb Smelter (Full Study Area) Results..............................................N-16 

N.2.2.1. Media Concentrations ..................................................................... N-16
 

N.2.2.2. PbB and IQ Change ........................................................................ N-20
 

N.2.2.3. Ambient Air to PbB Ratios.............................................................N-27
 

N.2.3. Primary Pb Smelter (Subarea) Results........................................................... N-27 

N.2.3.1. Media Concentrations ..................................................................... N-27
 

N.2.3.2. PbB and IQ Change ........................................................................ N-31
 

N.2.3.3. Ambient Air to PbB Ratios.............................................................N-38
 

N.3. SENSITIVITY ANALYSIS OF IQ CHANGE FUNCTIONS..................................N-38 

N.3.1. Absolute IQ Change for Four IQ Change Functions ..................................... N-40 

N.3.2. Relative IQ Change Comparing Baseline Function to Three Alternative 

Functions..................................................................................................... N-40 

N-i 
List of Exhibits 

Exhibit N-1. General Urban Case Study: Estimated Population-weighted Annual Ambient 

Air Concentrations ............................................................................................... N-5 

Exhibit N-2. General Urban Case Study: Estimated Population-weighted Inhalation 

Exhibit N-3. General Urban Case Study: Estimated Population-weighted Outdoor Soil/Dust 

Exhibit N-4. General Urban Case Study: Estimated Population-weighted Indoor Dust 

Exhibit N-5. General Urban Case Study: Current Conditions (Mean) – Estimated IQ 

Exhibit N-6. General Urban Case Study: Current NAAQS (1.5 μg/m3, Maximum Quarterly 

Exhibit N-7. General Urban Case Study: Current Conditions (95th Percentile) – Estimated 

Exhibit N-8. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m3, Maximum 

Exhibit N-9. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3, Maximum 

Exhibit N-10. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3, Maximum
 

Exhibit N-11. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3, Maximum 

Exhibit N-12. General Urban Case Study: Alternative NAAQS 5 (0.02 μg/m3

, Maximum 

Exhibit N-14. Primary Pb Smelter Case Study: Estimated Annual Ambient Air 

Exhibit N-15. Primary Pb Smelter Case Study: Estimated Inhalation Exposure 

Exhibit N-16. Primary Pb Smelter Case Study: Estimated Outdoor Soil/Dust 

Exposure Concentrations...................................................................................... N-5 

Concentrations...................................................................................................... N-6 

Concentrations...................................................................................................... N-6 

Changes ................................................................................................................ N-8 

Average) – Estimated IQ Changes ....................................................................... N-9 

IQ Changes ......................................................................................................... N-10 

Quarterly Average) – Estimated IQ Changes..................................................... N-11 

Monthly Average) – Estimated IQ Changes ...................................................... N-12 

Monthly Average) – Estimated IQ Changes ...................................................... N-13 

Monthly Average) – Estimated IQ Changes ...................................................... N-14 

Monthly Average) – Estimated IQ Changes ...................................................... N-15 

Exhibit N-13. General Urban Case Study: Air to PbB Ratios ................................................ N-16 

Concentrations.................................................................................................... N-18 

Concentrations.................................................................................................... N-18 

Concentrations.................................................................................................... N-19 

Exhibit N-17. Primary Pb Smelter Case Study: Estimated Indoor Dust Concentrations ....... N-19 

N-ii 
Exhibit N-18. Primary Pb Smelter Case Study: Current NAAQS (1.5 μg/m3

, Maximum 

Quarterly Average) – Estimated IQ Changes..................................................... N-21 

Exhibit N-19. Primary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3, 

Maximum Quarterly Average) – Estimated IQ Changes ................................... N-22 

Exhibit N-20. Primary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3, 

Maximum Monthly Average) – Estimated IQ Changes..................................... N-23 

Exhibit N-21. Primary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) – Estimated IQ Changes..................................... N-24 

Exhibit N-22. Primary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3, 

Maximum Monthly Average) – Estimated IQ Changes..................................... N-25 

Exhibit N-23. Primary Pb Smelter Case Study: Alternative NAAQS 5 (0.02 μg/m3

, 

Maximum Monthly Average) – Estimated IQ Changes..................................... N-26 

Exhibit N-24. Primary Pb Smelter Case Study: Air to PbB Ratios ........................................ N-27 

Exhibit N-25. Primary Pb Smelter Case Study (Subarea): Estimated Annual Ambient 

Air Concentrations ............................................................................................. N-29 

Exhibit N-26. Primary Pb Smetler Case Study (Subarea): Estimated Inhalation Exposure 

Concentrations.................................................................................................... N-29 

Exhibit N-27. Primary Pb Smelter Case Study (Subarea): Estimated Outdoor Soil/Dust 

Concentrations.................................................................................................... N-30 

Exhibit N-28. Primary Pb Smelter Case Study (Subarea): Estimated Indoor Dust 

Concentrations.................................................................................................... N-30 

Exhibit N-29. Primary Pb Smelter Case Study (Subarea): Current NAAQS (1.5 μg/m3

, 

Maximum Quarterly Average) – Estimated IQ Changes ................................... N-32 

Exhibit N-30. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 1 

(0.2 μg/m3

, Maximum Quarterly Average) – Estimated IQ Changes................ N-33 

Exhibit N-31. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 2 

(0.5 μg/m3

, Maximum Monthly Average) – Estimated IQ Changes.................. N-34 

Exhibit N-32. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 3 

(0.2 μg/m3, Maximum Monthly Average) – Estimated IQ Changes.................. N-35 

Exhibit N-33. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 4 

(0.05 μg/m3

, Maximum Monthly Average) – Estimated IQ Changes................ N-36 

Exhibit N-34. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 5 

(0.02 μg/m3, Maximum Monthly Average) – Estimated IQ Changes................ N-37 

N-iii 
 

 

Exhibit N-35. Primary Pb Smelter (Subarea) Case Study: Air to PbB Ratios........................ N-38 

Exhibit N-36. Absolute Differences in IQ Loss Estimates Between the Sensitivity and 

Baseline Cases.................................................................................................... N-40 

Exhibit N-37. Percent Difference in IQ Loss Estimates Between the Sensitivity and
 

Baseline Cases.................................................................................................... N-40 

N-iv 
N. ADDITIONAL GENERAL URBAN AND PRIMARY PB SMELTER 

CASE STUDY ANALYSES 

This Appendix details the application of both an updated modeling approach (hereafter 

referred to as the “core” modeling approach) and a new sensitivity analyses to the general urban 

case study. Section N.1 provides a description of the core modeling approach, Section N.2 

presents the results of the application of this modeling approach to the general urban and primary 

Pb smelter case studies, and Section N.3 provides the results of a sensitivity analysis examining 

the effects of different concentration-response (CR) functions used to model intelligence quotient 

(IQ) change as a result of blood-lead (PbB) concentrations. The rationale behind the core 

modeling approach and additional analyses is described in Chapter 5 of Volume I of the Risk 

Assessment. 

N.1. OVERVIEW OF CORE MODELING APPROACH 

The overall modeling procedures used to estimate IQ change associated with different Pb 

National Ambient Air Quality Standards (NAAQS) under the core modeling approach for the 

general urban and primary Pb smelter case studies was generally the same as that described in 

Appendix C, Appendix D, Appendix I, and Appendix K. Estimates of media Pb concentrations 

were developed and used to calculate the PbB concentrations from which IQ change estimates 

were derived. The core modeling approach is described below. 

In the core modeling approach, the primary Pb smelter case study is like it was described 

in Appendix D, except in the core modeling approach the results are presented separately for the 

two areas of the primary Pb smelter case study (i.e., the 10 kilometer (km) full study area and the 

1.5 km subarea. The primary Pb smelter subarea analysis only includes data for the sub-section 

of the primary Pb smelter case study area closest to the facility, where the greatest impacts (e.g., 

highest air and surface soil Pb levels) from the facilities have been found. The subarea for the 

primary Pb smelter facility was defined as those U.S. Census blocks within which soil 

remediation activities have occurred because of elevated soil Pb levels. These blocks extend 

approximately 1.5 km from the facility’s main stack. Appendix P includes further discussion of 

the primary Pb smelter subarea analyses. 

The analyses described in this appendix were performed for the air quality scenarios 

presented in Appendices C, D, I, and K plus an additional alternative NAAQS (0.02 μg/m3

, 

maximum monthly average) scenario not previously included. The reasons for including this 

standard, which is lower than the standards previously considered in the full-scale analysis, are 

covered in Chapter 5 of Volume I of the Risk Assessment. 

N-1
 
Pb concentrations in environmental media under the core modeling approach were 

calculated for the general urban and primary Pb smelter case studies using the methods detailed 

in Appendix C and Appendix D, respectively, with one difference. For the general urban case 

study, indoor dust Pb concentrations were only calculated using the hybrid mechanistic-empirical 

dust model or “hybrid” for short, whereas in previous analyses (Appendices C and D) they were 

calculated using both the hybrid model and the air-only regression-based model (detailed 

descriptions of these models are provided in Appendix G). 

The PbB modeling conducted using the core modeling approach was completed for both 

the general urban and primary Pb smelter case studies as described in Appendix I, with the 

following exceptions: 

•	 Under the core modeling approach, only concurrent PbB metric (average of the results at 

75 and 81 months of age in the seventh year of life) results were calculated. 

•	 For the general urban case study, the concurrent PbB metric results were only modeled 

using an estimated inter-individual variability (i.e., geometric standard deviation [GSD] 

values) value of 2.1. 

For both the general urban and primary Pb smelter case studies, the process used in the 

core modeling approach to estimate IQ change from these estimated PbB results was the same as 

that described in Appendix K, with a few differences. For the core modeling approach, four IQ 

change functions were used, two of which were not included in previous analyses. These 

functions are: 

•	 Population stratified dual linear IQ change function for concurrent PbB, derived from 

the pooled data set stratified at a peak PbB of 10 μg/dL or “dual linear – stratified at 10 

μg/dL peak” for short; 

•	 Log-linear IQ change function for concurrent PbB with cutpoint or “log-linear with 

cutpoint” for short; 

•	 Log-linear IQ change function for concurrent PbB with low-exposure linearization or 

“log-linear with linearization” for short; and 

•	 Population stratified dual linear IQ change function for concurrent PbB, derived from 

the pooled data set stratified at peak PbB of 7.5 μg/dL or “dual linear – stratified at 

7.5 μg/dL peak” for short 

N-2
 
 

 

 

 

 

 

 

Each of these IQ functions is presented below: 

Dual linear – stratified at 10 μg/dL peak: 

For PbB ≤ 5 μg/dL (concurrent): IQ Change = - 0.8 * PbB 

For PbB > 5 μg/dL (concurrent): IQ Change = - 4.0 - 0.13 * (PbB - 5) 

where: 

PbB = PbB levels (micrograms per grams [μg/dL]) 

 IQ Change = Change in IQ 

Log-linear with cutpoint: 

For PbB < 1.0 μg/dL (concurrent): IQ Change = 0 

For PbB ≥ 1.0 μg/dL (concurrent): IQ Change = - 2.70 * ln (PbB / 1.0) 

where: 

PbB = PbB levels (micrograms per deciliter [μg/dL]) 

 IQ Change = Change in IQ 

Log-linear with linearization

 For PbB < 1 μg/dL (concurrent): IQ change = - (2.70 / 1.0) * PbB

 For PbB ≥ 1 μg/dL (concurrent): IQ change = - 2.70 * ln (PbB / 1.0) – (2.70 / 1.0) * 1.0 

where: 

1.0 = Threshold 

2.70 = Beta 

Dual linear – stratified at 7.5 μg/dL peak: 

For PbB ≤ 3.75 μg/dL (concurrent): IQ Change = - 2.94 * PbB

 For PbB > 3.75 μg/dL (concurrent): IQ Change = - 11.03 - 0.16 * (PbB – 3.75) 

where: 

PbB = PbB levels (micrograms per grams [μg/dL]) 

 IQ Change = Change in IQ 

N-3
 
The IQ change results using the concurrent PbB metric presented in Appendix K were 

generated using three IQ change functions, as described in Chapter 4 of Volume I of the Risk 

Assessment. Two of these functions (the log-linear with cutpoint and log-linear with 

linearization) are included in the core modeling approach. The third IQ function from Appendix 

K was a two-piece linear IQ change function. This third function was not included in the core 

modeling approach. 

N.2. CORE MODELING APPROACH RESULTS 

N.2.1. General Urban Case Study Results 

N.2.1.1. Media Concentrations 

Media concentration estimates for the general urban case study are presented in Exhibit 

N-1 through Exhibit N-4. Population-weighted media concentrations were calculated by first 

sorting the block/block groups in increasing media concentration order. Then the percentage of 

children living in block/block groups less than or equal to the maximum media concentration of 

those block/block groups was calculated. The media concentration of the block/block group 

associated with the minimum, 5th, median, 95th, and maximum percentile was selected. 

The ambient air annual average Pb concentration estimates are presented to three decimal 

places, resulting in various numbers of implied significant figures (e.g., 1 to 3). No difference in 

precision is intended to be conveyed; this is simply an expedient and initial result of the software 

used for presentation. 

N-4
 
Exhibit N-1. General Urban Case Study: Estimated Population-weighted Annual Ambient Air Co

Statistic 

Average Annual Air Pb Concentration (μg/m3) 

Current 

Conditions 

Current NAAQS 

Scenario 

Alternative NAAQS Scenario 

1 2 3 4 

0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

NA – Single 

Study Area 

High-end: 0.114 

0.600 0.080 0.125 0.050 0.013 

Mean: 0.056 

Exhibit N-2. General Urban Case Study: Estimated Population-weighted Inhalation Exposure Co

Statistic 

Average Annual Inhalation Exposure Concentration of Pb (μg/m3) 

Current 

Conditions 

Current NAAQS 

Scenario 

Alternative NAAQS Scenario 

1 2 3 4 

0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

NA – Single 

Study Area 

High-end: 0.049 0.258 0.034 0.054 0.021 0.005 

Mean: 0.024 

N-5
 
Exhibit N-3. General Urban Case Study: Estimated 

Population-weighted Outdoor Soil/Dust Concentrations 

Statistic Projected Average Outdoor Soil/Dust Pb Concentration (mg/kg) a 

NA – Single Study Area 198 

a Same for all air quality scenarios. 

Exhibit N-4. General Urban Case Study: Estimated Population-weighted Indoor Dus

Concentrations 

Statistic 

Projected Average Indoor Dust Pb Concentration (mg/kg or ppm) 

Current Current NAAQS 

Alternative NAAQS Scenario 

1 2 3 4 5 

Conditions Scenario 0.2 μg/m3 , 

Max 

Quarterly 

0.5 μg/m3 , 

Max 

Monthly 

0.2 μg/m3 , 

Max 

Monthly 

0.05 μg/m3 , 

Max 

Monthly 

0.02 μg/

Max 

Month

NA – Single High-end: 198 426 169 206 140 88 73 Study Area Mean: 146 

N-6
 
 

N.2.1.2. PbB and IQ Change 

Exhibit N-5 through Exhibit N-12 provide the population percentiles of estimated PbB 

levels and IQ changes, as well as the number of children less than 7 years of age estimated to 

have IQ changes greater than the various percentiles, for the general urban case study. The 

exhibits also present estimates of the proportional contribution of each exposure pathway to the 

total Pb uptake. IQ changes that were exactly zero because the estimated PbB was below the 

cutpoint are reported as “-.” IQ changes that were greater than -0.1 are reported as “>-0.1.” 

The pathway contribution estimates correspond to the fraction of Pb uptake coming from 

each pathway; and, in their presentation in these exhibits, the assumption is made that these 

fractions map linearly to corresponding fractional contributions to PbB and IQ change. The 

indoor dust contribution is separated into an ambient air contribution and a contribution from 

other sources (e.g., indoor paint, outdoor soil/dust, and additional sources [including historical 

air]), as described in Appendix G. Because there is no specific population in the general urban 

case study (unlike the two point source case studies and location-specific urban case studies), 

these pathway contributions do not vary by IQ change percentile. 

N-7
 
 

 

Exhibit N-5. General Urban Case Study: Current Conditions (Mean) – Estimated IQ 

Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Predicted 

IQ Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL 

Peak) 

95th -4.2 6.5 

90th -4.0 5.0 

75th -2.5 3.2 

Median -1.5 1.9 

25th -0.9 1.2 

18% 10% 38% 6% 28% 0.5% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th -5.0 6.5 

90th -4.3 5.0 

75th -3.1 3.2 

Median -1.8 1.9 

25th -0.4 1.2 

18% 10% 38% 6% 28% 0.5% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th -7.7 6.5 

90th -7.0 5.0 

75th -5.8 3.2 

Median -4.5 1.9 

25th -3.1 1.2 

18% 10% 38% 6% 28% 0.5% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL 

Peak ) 

95th -11.5 6.5 

90th -11.2 5.0 

75th -9.3 3.2 

Median -5.6 1.9 

25th -3.4 1.2 

18% 10% 38% 6% 28% 0.5% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and 

additional sources, including historical air, while "recent air" refers to contributions associated with 

recent/current outdoor ambient air, with outdoor ambient air also potentially including resuspended, 

previously deposited Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to 

contributions from ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

N-8
 
 

 

Exhibit N-6. General Urban Case Study: Current NAAQS (1.5 μg/m3, Maximum 

Quarterly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Predicted 

IQ Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL 

Peak) 

95th -4.7 10.6 

90th -4.4 8.1 

75th -4.0 5.1 

Median -2.5 3.1 

25th -1.5 1.9 

10% 6% 22% 1% 57% 3.3% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th -6.4 10.6 

90th -5.6 8.1 

75th -4.4 5.1 

Median -3.1 3.1 

25th -1.7 1.9 

10% 6% 22% 1% 57% 3.3% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th -9.1 10.6 

90th -8.3 8.1 

75th -7.1 5.1 

Median -5.8 3.1 

25th -4.4 1.9 

10% 6% 22% 1% 57% 3.3% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL 

Peak) 

95th -12.1 10.6 

90th -11.7 8.1 

75th -11.2 5.1 

Median -9.2 3.1 

25th -5.6 1.9 

10% 6% 22% 1% 57% 3.3% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and 

additional sources, including historical air, while "recent air" refers to contributions associated with 

recent/current outdoor ambient air, with outdoor ambient air also potentially including resuspended, 

previously deposited Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to 

contributions from ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

N-9
 
 

 

 

Exhibit N-7. General Urban Case Study: Current Conditions (95th Percentile) – Estimated 

IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Predicted 

IQ Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL 

Peak) 

95th -4.3 7.2 

90th -4.1 5.5 

75th -2.8 3.5 

Median -1.7 2.1 

25th -1.0 1.3 

16% 9% 33% 4% 37% 0.9% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th -5.3 7.2 

90th -4.6 5.5 

75th -3.4 3.5 

Median -2.0 2.1 

25th -0.7 1.3 

16% 9% 33% 4% 37% 0.9% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th -8.0 7.2 

90th -7.3 5.5 

75th -6.1 3.5 

Median -4.7 2.1 

25th -3.4 1.3 

16% 9% 33% 4% 37% 0.9% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL 

Peak) 

95th -11.6 7.2 

90th -11.3 5.5 

75th -10.3 3.5 

Median -6.3 2.1 

25th -3.8 1.3 

16% 9% 33% 4% 37% 0.9% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and 

additional sources, including historical air, while "recent air" refers to contributions associated with 

recent/current outdoor ambient air, with outdoor ambient air also potentially including resuspended, 

previously deposited Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to 

contributions from ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

N-10
 
 

 

Exhibit N-8. General Urban Case Study: Alternative NAAQS 1 (0.2 μg/m3, Maximum 

Quarterly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Predicted 

IQ Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL 

Peak) 

95th -4.2 6.8 

90th -4.0 5.2 

75th -2.6 3.3 

Median -1.6 2.0 

25th -1.0 1.2 

17% 10% 36% 5% 33% 0.7% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th -5.2 6.8 

90th -4.4 5.2 

75th -3.2 3.3 

Median -1.9 2.0 

25th -0.5 1.2 

17% 10% 36% 5% 33% 0.7% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th -7.9 6.8 

90th -7.1 5.2 

75th -5.9 3.3 

Median -4.6 2.0 

25th -3.2 1.2 

17% 10% 36% 5% 33% 0.7% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL 

Peak) 

95th -11.5 6.8 

90th -11.3 5.2 

75th -9.7 3.3 

Median -5.9 2.0 

25th -3.6 1.2 

17% 10% 36% 5% 33% 0.7% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and 

additional sources, including historical air, while "recent air" refers to contributions associated with 

recent/current outdoor ambient air, with outdoor ambient air also potentially including resuspended, 

previously deposited Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to 

contributions from ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

N-11
 
 

 

Exhibit N-9. General Urban Case Study: Alternative NAAQS 2 (0.5 μg/m3, Maximum 

Monthly Average) – Estimated IQ Changes 

IQ Change 

Percentile 

Predicted 

IQ Change 

Predicted 

PbB 

(μg/dL) 

Pathway Contribution 

Ingestion 

Inhalation 

(Recent Air) Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other a Recent 

Air a 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL 

Peak) 

95th -4.3 7.4 

15% 9% 33% 3% 38% 1.0% 

90th -4.1 5.6 

75th -2.9 3.6 

Median -1.7 2.2 

25th -1.1 1.3 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th -5.4 7.4 

15% 9% 33% 3% 38% 1.0% 

90th -4.7 5.6 

75th -3.4 3.6 

Median -2.1 2.2 

25th -0.7 1.3 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th -8.1 7.4 

15% 9% 33% 3% 38% 1.0% 

90th -7.4 5.6 

75th -6.1 3.6 

Median -4.8 2.2 

25th -3.4 1.3 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL 

Peak) 

95th -11.6 7.4 

15% 9% 33% 3% 38% 1.0% 

90th -11.3 5.6 

75th -10.5 3.6 

Median -6.4 2.2 

25th -3.9 1.3 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and 

additional sources, including historical air, while "recent air" refers to contributions associated with 

recent/current outdoor ambient air, with outdoor ambient air also potentially including resuspended, 

previously deposited Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to 

contributions from ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

N-12
 
 

 

Exhibit N-10. General Urban Case Study: Alternative NAAQS 3 (0.2 μg/m3, Maximum 

Monthly Average) – Estimated IQ Changes 

IQ Change 

Percentile 

Predicted 

IQ Change 

Predicted 

PbB 

(μg/dL) 

Pathway Contribution 

Ingestion 

Inhalation 

(Recent Air) Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other a Recent 

Air a 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL 

Peak) 

95th -4.2 6.4 

18% 10% 38% 6% 27% 0.5% 

90th -3.9 4.9 

75th -2.5 3.1 

Median -1.5 1.9 

25th -0.9 1.1 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th -5.0 6.4 

18% 10% 38% 6% 27% 0.5% 

90th -4.3 4.9 

75th -3.1 3.1 

Median -1.7 1.9 

25th -0.4 1.1 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th -7.7 6.4 

18% 10% 38% 6% 27% 0.5% 

90th -7.0 4.9 

75th -5.8 3.1 

Median -4.4 1.9 

25th -3.1 1.1 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL 

Peak) 

95th -11.4 6.4 

18% 10% 38% 6% 27% 0.5% 

90th -11.2 4.9 

75th -9.2 3.1 

Median -5.6 1.9 

25th -3.4 1.1 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and 

additional sources, including historical air, while "recent air" refers to contributions associated with 

recent/current outdoor ambient air, with outdoor ambient air also potentially including resuspended, 

previously deposited Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to 

contributions from ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

N-13
 
 

 

Exhibit N-11. General Urban Case Study: Alternative NAAQS 4 (0.05 μg/m3, Maximum 

Monthly Average) – Estimated IQ Changes 

IQ Change 

Percentile 

Predicted 

IQ Change 

Predicted 

PbB 

(μg/dL) 

Pathway Contribution 

Ingestion 

Inhalation 

(Recent Air) Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other a Recent 

Air a 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL 

Peak) 

95th -4.1 5.7 

21% 12% 44% 11% 13% 0.1% 

90th -3.5 4.3 

75th -2.2 2.8 

Median -1.3 1.7 

25th -0.8 1.0 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th -4.7 5.7 

21% 12% 44% 11% 13% 0.1% 

90th -4.0 4.3 

75th -2.8 2.8 

Median -1.4 1.7 

25th > -0.1 1.0 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th -7.4 5.7 

21% 12% 44% 11% 13% 0.1% 

90th -6.7 4.3 

75th -5.5 2.8 

Median -4.1 1.7 

25th -2.8 1.0 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL 

Peak) 

95th -11.3 5.7 

21% 12% 44% 11% 13% 0.1% 

90th -11.1 4.3 

75th -8.2 2.8 

Median -5.0 1.7 

25th -3.0 1.0 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and 

additional sources, including historical air, while "recent air" refers to contributions associated with 

recent/current outdoor ambient air, with outdoor ambient air also potentially including resuspended, 

previously deposited Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to 

contributions from ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

N-14
 
 

 

Exhibit N-12. General Urban Case Study: Alternative NAAQS 5 (0.02 μg/m3, Maximum 

Monthly Average) – Estimated IQ Changes 

IQ Change 

Percentile 

Predicted 

IQ Change 

Predicted 

PbB 

(μg/dL) 

Pathway Contribution 

Ingestion 

Inhalation 

(Recent Air) Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Indoor Dust 

Other a Recent 

Air a 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL 

Peak) 

95th -4.1 5.5 

21% 12% 46% 14% 6% < 0.1% 

90th -3.3 4.2 

75th -2.1 2.7 

Median -1.3 1.6 

25th -0.8 1.0 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th -4.6 5.5 

21% 12% 46% 14% 6% < 0.1% 

90th -3.9 4.2 

75th -2.7 2.7 

Median -1.3 1.6 

25th - 0.5 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th -7.3 5.5 

21% 12% 46% 14% 6% < 0.1% 

90th -6.6 4.2 

75th -5.4 2.7 

Median -4.0 1.6 

25th -2.7 1.0 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL 

Peak) 

95th -11.3 5.5 

21% 12% 46% 14% 6% < 0.1% 

90th -11.1 4.2 

75th -7.9 2.7 

Median -4.8 1.6 

25th -2.9 1.0 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously 

deposited Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions 

from ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

N-15
 
 

N.2.1.3. Ambient Air to PbB Ratios 

Exhibit N-13 shows the air-to-PbB ratios for the general urban case study. Note that 

these ratios are derived in a different manner than the air to PbB ratios presented in Appendix I. 

The air to PbB ratios presented here are derived by comparing changes (deltas) in median total 

PbB levels (concurrent) to associated changes in annual average air Pb levels as one steps to the 

next lowest air quality scenario. The ambient air annual average Pb concentration estimates are 

presented to three decimal places, resulting in various numbers of implied significant figures 

(e.g., 1 to 3). No difference in precision is intended to be conveyed; this is simply an expedient 

and initial result of the software used for presentation. 

Exhibit N-13. General Urban Case Study: Air to PbB Ratios 

Air Scenario Median Total PbB 

(μg/dL) 

Annual Average 

Ambient Air 

Concentration 

(μg/m3

) 

Ratio a 

Current NAAQS (1.5 μg/m3, max 

quarterly average) 3.1 0.600 

1 : 2.0 Alternative NAAQS 2 (0.5 μg/m3 

, 

max monthly average) 

2.2 0.125 

Current Conditions (95th percentile) 

2.1 0.114 1 : 2.8 

Alternative NAAQS 1 (0.2 μg/m3 

, 

max quarterly average) 

2.0 0.080 1 : 3.6 

Current Conditions (mean) 1.9 0.056 1 : 3.9 

Alternative NAAQS 3 (0.2 μg/m3 

, 

max monthly average) 

1.9 0.050 1 : 5.2 

Alternative NAAQS 4 (0.05 μg/m3 

, 

max monthly average) 

1.7 0.013 1 : 5.6 

Alternative NAAQS 5 (0.02 μg/m3 

, 

max monthly average) 

1.6 0.005 1 : 8.6 

a

 A ratio is not presented adjacent to the current NAAQS air quality scenario (for any of the case 

studies) because the air-to-PbB ratios are derived by comparing changes (deltas) in median total PbB 

levels (concurrent) to associated changes in annual average air Pb levels as one steps to the next 

lowest air quality scenario. The first ratio presented for any of the case studies is generated by 

comparing median PbB levels at the current NAAQS level to the median PbB level at the highest of 

the alternative NAAQS levels (i.e., Alternative NAAQS 5 [0.05 μg/m3 max monthly] value). 

N.2.2. Primary Pb Smelter (Full Study Area) Results 

N.2.2.1. Media Concentrations 

Media concentration estimates for the primary case study are presented in Exhibit N-14 

through Exhibit N-17. Population-weighted media concentrations were calculated by first 

sorting the block/block groups in increasing media concentration order. Then the percentage of 

children living in block/block groups less than or equal to the maximum media concentration of 

those block/block groups was calculated. The media concentration of the block/block group 

associated with the minimum, 5th, median, 95th, and maximum percentile was selected. 

October 2007 N-16 Draft- Do Not Quote or Cite 
The ambient air annual average Pb concentration estimates are presented to three decimal 

places, resulting in various numbers of implied significant figures (e.g., 1 to 3). No difference in 

precision is intended to be conveyed; this is simply an expedient and initial result of the software 

used for presentation. 

October 2007 N-17 Draft- Do Not Quote or Cite 
Exhibit N-14. Primary Pb Smelter Case Study: Estimated Annual Ambient Air Concentrations 

Average Annual Air Pb Concentration (μg/m3) 

Alternative NAAQS Scenario 

Statistic Current NAAQS 1 2 3 4 

Scenario 0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

0.02 μ

Mo

Maximum 0.740 0.161 0.326 0.130 0.033 0

95th 

percentile 0.153 0.033 0.067 0.027 0.007 0

Median 0.042 0.009 0.019 0.007 0.002 0

5th percentile 0.015 0.003 0.007 0.003 0.001 < 

Minimum 0.006 0.001 0.003 0.001 < 0.001 < 

Exhibit N-15. Primary Pb Smelter Case Study: Estimated Inhalation Exposure Concentrations 

Average Annual Inhalation Exposure Concentration of Pb (μg/m3) 

Alternative NAAQS Scenario 

Statistic Current NAAQS 1 2 3 4 

Scenario 0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

0.02 μ

Mo

Maximum 0.310 0.067 0.136 0.055 0.014 0

95th 

percentile 0.064 0.014 0.028 0.011 0.003 0

Median 0.017 0.004 0.007 0.003 0.001 < 

5th percentile 0.006 0.001 0.003 0.001 < 0.001 < 

Minimum 0.002 < 0.001 0.001 < 0.001 < 0.001 < 

October 2007 N-18 Draft- D
Exhibit N-16. Primary Pb Smelter Case Study: 

Estimated Outdoor Soil/Dust Concentrations
 

Statistic Projected Average Outdoor Soil/Dust Pb Concentration (mg/kg) a 

Maximum 958 

95th percentile 245 

Median 85 

5th percentile 30 

Minimum 17 

a Same for all air quality scenarios. 

Exhibit N-17. Primary Pb Smelter Case Study: Estimated Indoor Dust Concentration

Statistic 

Projected Average Indoor Dust Pb Concentration (mg/kg or ppm) 

Current NAAQS 

Alternative NAAQS Scenario 

1 2 3 4 

Scenario 0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

Maximum 1944 648 1077 557 383 

95th 

percentile 219 152 172 149 138 

Median 84 68 73 67 63 

5th percentile 53 45 47 44 43 

Minimum 41 38 39 38 38 

October 2007 N-19 Draft- D
N.2.2.2. PbB and IQ Change 

Exhibit N-18 through Exhibit N-23 provide the population percentiles of estimated PbB 

levels and IQ changes, as well as the number of children less than 7 years of age estimated to 

have IQ changes greater than the various percentiles, for the primary Pb smelter case study. The 

exhibits also present estimates of the proportional contribution of each exposure pathway to the 

total Pb uptake. Just as for the general urban case study, IQ changes that were exactly zero 

because the estimated PbB was below the cutpoint are reported as “-.” IQ changes that were 

greater than -0.1 are reported as “>-0.1.” 

The pathway contribution estimates correspond to the fraction of Pb uptake coming from 

each pathway; and, in their presentation in these exhibits, the assumption is made that these 

fractions map linearly to corresponding fractional contributions to PbB and IQ change. Unlike 

the exhibits for the general urban case study, the indoor dust Pb is not separated out into “recent 

air” and “other” for the primary Pb smelter case study. This is a result of limitations of the sitespecific H5 model, which is used to calculate the concentration of Pb in indoor dust in the 

primary Pb smelter case study. The site-specific H5 model cannot separate indoor dust into 

“recent air” and “other,” therefore the total indoor dust contribution is determined for the 

primary Pb smelter case study. Also note that the estimates of pathway contributions were 

derived for the GM PbB estimates for the individual U.S. Census blocks, before the GSDs for 

inter-individual PbB variability were applied to generate the PbB distributions. The PbB and IQ 

change percentile estimates, however, are those after application of the GSD. Thus, as some of 

the high percentile PbB values are actually associated with U.S. Census blocks with low PbB 

GMs (and vice versa), these exhibits contain some seemingly irregular trends in pathway 

contributions. 

October 2007 N-20 Draft- Do Not Quote or Cite 
 

Exhibit N-18. Primary Pb Smelter Case Study: Current NAAQS (1.5 μg/m3, Maximum 

Quarterly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion IQ Change 

Percentile 

Population 

Above 

Predicted IQ 

Change 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified 

at 10 μg/dL Peak) 

95th 194 -3.7 4.6 24% 14% 35% 26% 0.6% 

90th 388 -2.8 3.5 24% 14% 35% 25% 0.6% 

75th 970 -1.9 2.3 20% 12% 41% 27% 0.8% 

Median 1940 -1.2 1.5 31% 18% 27% 24% 0.4% 

25th 2910 -0.8 1.0 40% 23% 16% 21% 0.2% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 194 -4.1 4.6 24% 14% 35% 26% 0.6% 

90th 388 -3.4 3.5 24% 14% 35% 25% 0.6% 

75th 970 -2.3 2.3 20% 12% 41% 27% 0.8% 

Median 1940 -1.1 1.5 31% 18% 27% 24% 0.4% 

25th 2910 - 0.5 35% 20% 22% 23% 0.4% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 194 -6.8 4.6 24% 14% 35% 26% 0.6% 

90th 388 -6.1 3.5 24% 14% 35% 25% 0.6% 

75th 970 -5.0 2.3 20% 12% 41% 27% 0.8% 

Median 1940 -3.8 1.5 31% 18% 27% 24% 0.4% 

25th 2910 -2.7 1.0 40% 23% 16% 21% 0.2% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear- Stratified 

at 7.5 μg/dL Peak) 

95th 194 -11.2 4.6 24% 14% 35% 26% 0.6% 

90th 388 -10.4 3.5 24% 14% 35% 25% 0.6% 

75th 970 -6.9 2.3 20% 12% 41% 27% 0.8% 

Median 1940 -4.4 1.5 31% 18% 27% 24% 0.4% 

25th 2910 -2.9 1.0 40% 23% 16% 21% 0.2% 

N-21
 
 

Exhibit N-19. Primary Pb Smelter Case Study: Alternative NAAQS 1 (0.2 μg/m3, 

Maximum Quarterly Average) – Estimated IQ Changes 

Pathway Contribution 

IQ Change Ingestion 

Percentile 

Population 

Above 

Predicted IQ 

Change 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified 

at 10 μg/dL Peak) 

95th 194 -3.2 4.0 25% 15% 40% 20% < 0.1% 

90th 388 -2.5 3.2 26% 15% 38% 20% 0.2% 

75th 970 -1.7 2.1 25% 15% 40% 20% 0.1% 

Median 1940 -1.1 1.4 23% 14% 42% 21% 0.2% 

25th 2910 -0.7 0.9 35% 20% 24% 20% 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), Pb Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 194 -3.8 4.0 25% 15% 40% 20% < 0.1% 

90th 388 -3.1 3.2 26% 15% 38% 20% 0.2% 

75th 970 -2.1 2.1 25% 15% 40% 20% 0.1% 

Median 1940 -0.9 1.4 23% 14% 42% 21% 0.2% 

25th 2910 - 0.5 25% 15% 40% 20% 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 194 -6.5 4.0 25% 15% 40% 20% < 0.1% 

90th 388 -5.8 3.2 26% 15% 38% 20% 0.2% 

75th 970 -4.8 2.1 25% 15% 40% 20% 0.1% 

Median 1940 -3.6 1.4 23% 14% 42% 21% 0.2% 

25th 2910 -2.5 0.9 35% 20% 24% 20% 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear-Stratified 

at 7.5 μg/dL Peak) 

95th 194 -11.1 4.0 25% 15% 40% 20% < 0.1% 

90th 388 -9.3 3.2 26% 15% 38% 20% 0.2% 

75th 970 -6.3 2.1 25% 15% 40% 20% 0.1% 

Median 1940 -4.2 1.4 23% 14% 42% 21% 0.2% 

25th 2910 -2.7 0.9 35% 20% 24% 20% 0.1% 

N-22
 
 

Exhibit N-20. Primary Pb Smelter Case Study: Alternative NAAQS 2 (0.5 μg/m3, 

Maximum Monthly Average) – Estimated IQ Changes 

IQ Change 

Percentile 

Population 

Above 

Predicted IQ 

Change 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified 

at 10 μg/dL Peak) 

95th 194 -3.4 4.2 16% 9% 52% 23% 0.4% 

90th 388 -2.6 3.3 16% 9% 52% 23% 0.4% 

75th 970 -1.8 2.2 36% 21% 23% 20% 0.2% 

Median 1940 -1.1 1.4 14% 8% 54% 24% 0.5% 

25th 2910 -0.8 0.9 38% 22% 19% 20% 0.2% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 194 -3.9 4.2 16% 9% 52% 23% 0.4% 

90th 388 -3.2 3.3 16% 9% 52% 23% 0.4% 

75th 970 -2.1 2.2 36% 21% 23% 20% 0.2% 

Median 1940 -1.0 1.4 14% 8% 54% 24% 0.5% 

25th 2910 - 0.8 26% 15% 37% 22% 0.3% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 194 -6.6 4.2 16% 9% 52% 23% 0.4% 

90th 388 -5.9 3.3 16% 9% 52% 23% 0.4% 

75th 970 -4.8 2.2 36% 21% 23% 20% 0.2% 

Median 1940 -3.7 1.4 14% 8% 54% 24% 0.5% 

25th 2910 -2.5 0.9 38% 22% 19% 20% 0.2% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear-Stratified 

at 7.5 μg/dL Peak) 

95th 194 -11.1 4.2 16% 9% 52% 23% 0.4% 

90th 388 -9.7 3.3 16% 9% 52% 23% 0.4% 

75th 970 -6.5 2.2 36% 21% 23% 20% 0.2% 

Median 1940 -4.2 1.4 22% 13% 43% 22% 0.3% 

25th 2910 -2.8 0.9 38% 22% 19% 20% 0.2% 

N-23
 
 

Exhibit N-21. Primary Pb Smelter Case Study: Alternative NAAQS 3 (0.2 μg/m3, 

Maximum Monthly Average) – Estimated IQ Changes 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ Change 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified 

at 10 μg/dL Peak) 

95th 194 -3.2 4.0 23% 14% 42% 20% 0.1% 

90th 388 -2.5 3.1 17% 10% 53% 21% 0.1% 

75th 970 -1.7 2.1 15% 8% 56% 21% 0.2% 

Median 1940 -1.1 1.4 17% 10% 53% 21% 0.1% 

25th 2910 -0.7 0.9 35% 21% 24% 20% 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 194 -3.7 4.0 23% 14% 42% 20% 0.1% 

90th 388 -3.1 3.1 17% 10% 53% 21% 0.1% 

75th 970 -2.0 2.1 15% 8% 56% 21% 0.2% 

Median 1940 -0.9 1.4 38% 22% 20% 19% < 0.1% 

25th 2910 - 0.7 26% 15% 40% 20% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 194 -6.4 4.0 23% 14% 42% 20% 0.1% 

90th 388 -5.8 3.1 17% 10% 53% 21% 0.1% 

75th 970 -4.7 2.1 15% 8% 56% 21% 0.2% 

Median 1940 -3.6 1.4 17% 10% 53% 21% 0.1% 

25th 2910 -2.5 0.9 35% 21% 24% 20% 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear-Stratified at 

7.5 μg/dL Peak) 

95th 194 -11.1 4.0 23% 14% 42% 20% 0.1% 

90th 388 -9.3 3.1 17% 10% 53% 21% 0.1% 

75th 970 -6.2 2.1 15% 8% 56% 21% 0.2% 

Median 1940 -4.1 1.4 17% 10% 53% 21% 0.1% 

25th 2910 -2.7 0.9 35% 21% 24% 20% 0.1% 

N-24
 
 

Exhibit N-22. Primary Pb Smelter Case Study: Alternative NAAQS 4 (0.05 μg/m3, 

Maximum Monthly Average) – Estimated IQ Changes 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ Change 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified 

at 10 μg/dL Peak) 

95th 194 -3.1 3.8 32% 19% 23% 26% 0.1% 

90th 388 -2.4 3.1 24% 14% 43% 19% < 0.1% 

75th 970 -1.7 2.1 23% 13% 44% 19% < 0.1% 

Median 1940 -1.1 1.4 33% 19% 29% 19% < 0.1% 

25th 2910 -0.7 0.9 16% 9% 55% 20% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 194 -3.6 3.8 32% 19% 23% 26% 0.1% 

90th 388 -3.0 3.1 24% 14% 43% 19% < 0.1% 

75th 970 -2.0 2.1 23% 13% 44% 19% < 0.1% 

Median 1940 -0.9 1.4 33% 19% 29% 19% < 0.1% 

25th 2910 - 1.0 12% 7% 61% 20% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 194 -6.3 3.8 32% 19% 23% 26% 0.1% 

90th 388 -5.7 3.1 24% 14% 43% 19% < 0.1% 

75th 970 -4.7 2.1 23% 13% 44% 19% < 0.1% 

Median 1940 -3.6 1.4 33% 19% 29% 19% < 0.1% 

25th 2910 -2.5 0.9 16% 9% 55% 20% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear-Stratified at 

7.5 μg/dL Peak) 

95th 194 -11.0 3.8 32% 19% 23% 26% 0.1% 

90th 388 -9.0 3.1 24% 14% 43% 19% < 0.1% 

75th 970 -6.1 2.1 23% 13% 44% 19% < 0.1% 

Median 1940 -4.0 1.4 33% 19% 29% 19% < 0.1% 

25th 2910 -2.7 0.9 16% 9% 55% 20% < 0.1% 

N-25
 
 

Exhibit N-23. Primary Pb Smelter Case Study: Alternative NAAQS 5 (0.02 μg/m3, 

Maximum Monthly Average) – Estimated IQ Changes 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ Change 

Predicted 

PbB (μg/dL) 

Pathway Contribution 

Ingestion 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent Air) 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified 

at 10 μg/dL Peak) 

95th 194 -3.1 3.8 11% 6% 64% 19% < 0.1% 

90th 388 -2.4 3.0 20% 12% 49% 19% < 0.1% 

75th 970 -1.7 2.1 36% 21% 25% 19% < 0.1% 

Median 1940 -1.1 1.4 16% 10% 55% 19% < 0.1% 

25th 2910 -0.7 0.9 22% 13% 46% 19% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Cutpoint) 

95th 194 -3.6 3.8 11% 6% 64% 19% < 0.1% 

90th 388 -3.0 3.0 20% 12% 49% 19% < 0.1% 

75th 970 -2.0 2.1 36% 21% 25% 19% < 0.1% 

Median 1940 -0.9 1.4 16% 10% 55% 19% < 0.1% 

25th 2910 - 0.9 36% 21% 25% 19% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with 

Linearization) 

95th 194 -6.3 3.8 11% 6% 64% 19% < 0.1% 

90th 388 -5.7 3.0 20% 12% 49% 19% < 0.1% 

75th 970 -4.7 2.1 36% 21% 25% 19% < 0.1% 

Median 1940 -3.6 1.4 16% 10% 55% 19% < 0.1% 

25th 2910 -2.5 0.9 22% 13% 46% 19% < 0.1% 

Dust Model (Air+Soil Regression-based and H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear-Stratified at 

7.5 μg/dL Peak) 

95th 194 -11.0 3.8 11% 6% 64% 19% < 0.1% 

90th 388 -9.0 3.0 20% 12% 49% 19% < 0.1% 

75th 970 -6.1 2.1 36% 21% 25% 19% < 0.1% 

Median 1940 -4.1 1.4 16% 10% 55% 19% < 0.1% 

25th 2910 -2.7 0.9 22% 13% 46% 19% < 0.1% 

N-26
 
 

 

N.2.2.3. Ambient Air to PbB Ratios 

Exhibit N-24 shows the air-to-PbB ratios for the primary Pb smelter case study. Note 

that these ratios are derived in a different manner than the air to PbB ratios presented in 

Appendix I. The air to PbB ratios presented here are derived by comparing changes (deltas) in 

median total PbB levels (concurrent) to associated changes in annual average air Pb levels as one 

steps to the next lowest air quality scenario. The ambient air annual average Pb concentration 

estimates are presented to three decimal places, resulting in various numbers of implied 

significant figures (e.g., 1 to 3). No difference in precision is intended to be conveyed; this is 

simply an expedient and initial result of the software used for presentation. 

Exhibit N-24. Primary Pb Smelter Case Study: Air to PbB Ratios 

Air Scenario Median Total PbB 

(μg/dL) 

Annual Average 

Ambient Air 

Concentration 

(μg/m3

) 

Ratio a 

Current NAAQS (1.5 μg/m3, max 

quarterly average) 1.5 0.042 

1 : 2.9 Alternative NAAQS 2 (0.5 μg/m3 

, 

max monthly average) 1.4 0.019 

Alternative NAAQS 1 (0.2 μg/m3 

, 

max quarterly average) 1.4 0.009 1 : 3.3 

Alternative NAAQS 3 (0.2 μg/m3 

, 

max monthly average) 1.4 0.007 1 : 3.9 

Alternative NAAQS 4 (0.05 

μg/m3

, max monthly average) 1.4 0.002 1 : 3.8 

Alternative NAAQS 5 (0.02 

μg/m3

, max monthly average) 1.4 0.001 1 : 7.4 

a

 A ratio is not presented adjacent to the current NAAQS air quality scenario (for any of the case 

studies) because the air-to-PbB ratios are derived by comparing changes (deltas) in median total PbB 

levels (concurrent) to associated changes in annual average air Pb levels as one steps to the next 

lowest air quality scenario. The first ratio presented for any of the case studies is generated by 

comparing median PbB levels at the current NAAQS level to the median PbB level at the highest of 

the alternative NAAQS levels (i.e., Alternative NAAQS 5 [0.05 μg/m3 max monthly] value). 

N.2.3. Primary Pb Smelter (Subarea) Results 

N.2.3.1. Media Concentrations 

Media concentration estimates for the primary case study subarea analyses are presented 

in Exhibit N-25 through Exhibit N-28. Population-weighted media concentrations were 

calculated by first sorting the block/block groups in increasing media concentration order. Then 

the percentage of children living in block/block groups less than or equal to the maximum media 

concentration of those block/block groups was calculated. The media concentration of the 

block/block group associated with the minimum, 5th, median, 95th, and maximum percentile was 

selected. 

N-27
 
 

The ambient air annual average Pb concentration estimates are presented to three decimal 

places, resulting in various numbers of implied significant figures (e.g., 1 to 3). No difference in 

precision is intended to be conveyed; this is simply an expedient and initial result of the software 

used for presentation. 

N-28
 
Exhibit N-25. Primary Pb Smelter Case Study (Subarea): 

Estimated Annual Ambient Air Concentrations 

Statistic 

Average Annual Air Pb Concentration (μg/m3) 

Current NAAQS 

Alternative NAAQS Scenario 

1 2 3 4 

Scenario 0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

0.02 μ

Mo

Maximum 0.740 0.161 0.326 0.130 0.033 0

95th 

percentile 0.675 0.147 0.297 0.119 0.030 0

Median 0.238 0.052 0.105 0.042 0.011 0

5th percentile 0.137 0.030 0.060 0.024 0.006 0

Minimum 0.098 0.021 0.043 0.017 0.004 0

Exhibit N-26. Primary Pb Smetler Case Study (Subarea): Estimated Inhalation Exposure Concentra

Statistic 

Average Annual Inhalation Exposure Concentration of Pb (μg/m3) 

Current NAAQS 

Scenario 

Alternative NAAQS Scenario 

1 2 3 4 

0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

0.02 μ

Mo

Maximum 0.310 0.067 0.136 0.055 0.014 0

95th 

percentile 0.282 0.061 0.124 0.050 0.012 0

Median 0.100 0.022 0.044 0.018 0.004 0

5th percentile 0.057 0.012 0.025 0.010 0.003 0

Minimum 0.041 0.009 0.018 0.007 0.002 0

N-29
 
Exhibit N-27. Primary Pb Smelter Case Study (Subarea): 

Estimated Outdoor Soil/Dust Concentrations
 

Statistic Projected Average Outdoor Soil/Dust Pb Concentration (mg/kg) a 

Maximum 294 

95th percentile 223 

Median 150 

5th percentile 42 

Minimum 42 

a Same for all air quality scenarios. 

Exhibit N-28. Primary Pb Smelter Case Study (Subarea): Estimated Indoor Dust Concentration

Statistic 

Projected Average Indoor Dust Pb Concentration (mg/kg or ppm) 

Current NAAQS 

Scenario 

Alternative NAAQS Scenario 

1 2 3 4 

0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

0.02 

M

Maximum 1944 648 1077 557 205 

95th 

percentile 1819 606 1008 521 192 

Median 860 287 477 246 91 

5th percentile 578 193 320 166 61 

Minimum 453 151 251 130 60 

N-30
 
N.2.3.2. PbB and IQ Change 

Exhibit N-29 through Exhibit N-34 provide the population percentiles of estimated PbB 

levels and IQ changes, as well as the number of children less than 7 years of age estimated to 

have IQ changes greater than the various percentiles, for the primary Pb smelter case study. The 

exhibits also present estimates of the proportional contribution of each exposure pathway to the 

total Pb uptake. Just as for the general urban and primary Pb smelter (full study area) case 

studies, IQ changes that were exactly zero because the estimated PbB was below the cutpoint are 

reported as “-.” IQ changes that were greater than -0.1 are reported as “>-0.1.” 

The pathway contribution estimates correspond to the fraction of Pb uptake coming from 

each pathway; and, in their presentation in these exhibits, the assumption is made that these 

fractions map linearly to corresponding fractional contributions to PbB and IQ change. Unlike 

the exhibits for the general urban case study, the indoor dust Pb is not separated out into “recent 

air” and “other” for the primary Pb smelter case study. This is a result of limitations of the sitespecific H5 model, which is used to calculate the concentration of Pb in indoor dust in the 

primary Pb smelter case study. The site-specific H5 model cannot separate indoor dust into 

“recent air” and “other,” therefore the total indoor dust contribution is determined for the 

primary Pb smelter case study. Also note that the estimates of pathway contributions were 

derived for the GM PbB estimates for the individual U.S. Census blocks, before the GSDs for 

inter-individual PbB variability were applied to generate the PbB distributions. The PbB and IQ 

change percentile estimates, however, are those after application of the GSD. Thus, as some of 

the high percentile PbB values are actually associated with U.S. Census blocks with low PbB 

GMs (and vice versa), these exhibits contain some seemingly irregular trends in pathway 

contributions. 

N-31 
Exhibit N-29. Primary Pb Smelter Case Study (Subarea): Current NAAQS (1.5 

μg/m3

, Maximum Quarterly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ Change 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent 

Air) 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

95th 5 -5.0 12.3 10% 6% 31% 53% 0.6% 

90th 10 -4.6 9.9 5% 3% 11% 79% 1.3% 

75th 26 -4.2 6.8 5% 3% 14% 76% 1.2% 

Median 52 -3.7 4.6 8% 4% 15% 72% 1.0% 

25th 77 -2.4 3.1 8% 4% 15% 72% 1.0% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -6.8 12.3 10% 6% 31% 53% 0.6% 

90th 10 -6.2 9.9 5% 3% 11% 79% 1.3% 

75th 26 -5.2 6.8 5% 3% 14% 76% 1.2% 

Median 52 -4.1 4.6 8% 4% 15% 72% 1.0% 

25th 77 -3.0 3.1 8% 4% 15% 72% 1.0% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -9.5 12.3 10% 6% 31% 53% 0.6% 

90th 10 -8.9 9.9 5% 3% 11% 79% 1.3% 

75th 26 -7.9 6.8 5% 3% 14% 76% 1.2% 

Median 52 -6.8 4.6 8% 4% 15% 72% 1.0% 

25th 77 -5.7 3.1 8% 4% 15% 72% 1.0% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

95th 5 -12.4 12.3 10% 6% 31% 53% 0.6% 

90th 10 -12.0 9.9 5% 3% 11% 79% 1.3% 

75th 26 -11.5 6.8 5% 3% 14% 76% 1.2% 

Median 52 -11.2 4.6 10% 6% 7% 76% 1.0% 

25th 77 -9.0 3.1 8% 4% 15% 72% 1.0% 

N-32 
Exhibit N-30. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 1 

(0.2 μg/m3

, Maximum Quarterly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent 

Air) 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

95th 5 -4.2 6.6 7% 4% 37% 51% 0.6% 

90th 10 -4.0 5.4 20% 12% 15% 53% 0.4% 

75th 26 -3.0 3.7 16% 9% 48% 27% 0.2% 

Median 52 -2.0 2.5 15% 9% 27% 49% 0.4% 

25th 77 -1.3 1.7 15% 9% 30% 46% 0.4% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -5.1 6.6 7% 4% 37% 51% 0.6% 

90th 10 -4.5 5.4 20% 12% 15% 53% 0.4% 

75th 26 -3.6 3.7 16% 9% 48% 27% 0.2% 

Median 52 -2.5 2.5 17% 10% 36% 37% 0.3% 

25th 77 -1.4 1.7 15% 9% 30% 46% 0.4% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -7.8 6.6 7% 4% 37% 51% 0.6% 

90th 10 -7.2 5.4 20% 12% 15% 53% 0.4% 

75th 26 -6.3 3.7 16% 9% 48% 27% 0.2% 

Median 52 -5.2 2.5 17% 10% 36% 37% 0.3% 

25th 77 -4.1 1.7 15% 9% 30% 46% 0.4% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

95th 5 -11.5 6.6 7% 4% 37% 51% 0.6% 

90th 10 -11.3 5.4 20% 12% 15% 53% 0.4% 

75th 26 -11.0 3.7 16% 9% 48% 27% 0.2% 

Median 52 -7.4 2.5 15% 9% 27% 49% 0.4% 

25th 77 -4.9 1.7 15% 9% 30% 46% 0.4% 

N-33 
Exhibit N-31. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 2 

(0.5 μg/m3

, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent 

Air) 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

95th 5 -4.5 8.5 7% 4% 27% 61% 0.8% 

90th 10 -4.2 6.9 7% 4% 27% 61% 0.8% 

75th 26 -3.8 4.8 7% 4% 27% 61% 0.8% 

Median 52 -2.6 3.2 11% 7% 23% 59% 0.6% 

25th 77 -1.7 2.1 11% 7% 23% 59% 0.6% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -5.8 8.5 7% 4% 27% 61% 0.8% 

90th 10 -5.2 6.9 7% 4% 27% 61% 0.8% 

75th 26 -4.2 4.8 7% 4% 27% 61% 0.8% 

Median 52 -3.1 3.2 11% 7% 23% 59% 0.6% 

25th 77 -2.1 2.1 11% 7% 23% 59% 0.6% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -8.5 8.5 7% 4% 27% 61% 0.8% 

90th 10 -7.9 6.9 7% 4% 27% 61% 0.8% 

75th 26 -6.9 4.8 7% 4% 27% 61% 0.8% 

Median 52 -5.8 3.2 15% 9% 16% 60% 0.6% 

25th 77 -4.8 2.1 11% 7% 23% 59% 0.6% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

95th 5 -11.8 8.5 7% 4% 27% 61% 0.8% 

90th 10 -11.5 6.9 7% 4% 27% 61% 0.8% 

75th 26 -11.2 4.8 7% 4% 27% 61% 0.8% 

Median 52 -9.4 3.2 11% 7% 23% 59% 0.6% 

25th 77 -6.3 2.1 11% 7% 23% 59% 0.6% 

N-34 
Exhibit N-32. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 3 

(0.2 μg/m3

, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent 

Air) 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

95th 5 -4.1 6.1 12% 7% 27% 54% 0.6% 

90th 10 -4.0 5.0 11% 6% 34% 48% 0.5% 

75th 26 -2.8 3.5 16% 9% 32% 43% 0.4% 

Median 52 -1.9 2.3 22% 13% 16% 49% 0.4% 

25th 77 -1.3 1.6 12% 7% 32% 49% 0.5% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -4.9 6.1 12% 7% 27% 54% 0.6% 

90th 10 -4.3 5.0 11% 6% 34% 48% 0.5% 

75th 26 -3.4 3.5 16% 9% 32% 43% 0.4% 

Median 52 -2.3 2.3 16% 9% 29% 45% 0.4% 

25th 77 -1.2 1.6 12% 7% 32% 49% 0.5% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -7.6 6.1 12% 7% 27% 54% 0.6% 

90th 10 -7.0 5.0 11% 6% 34% 48% 0.5% 

75th 26 -6.1 3.5 16% 9% 32% 43% 0.4% 

Median 52 -5.0 2.3 16% 9% 29% 45% 0.4% 

25th 77 -3.9 1.6 12% 7% 32% 49% 0.5% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

95th 5 -11.4 6.1 12% 7% 27% 54% 0.6% 

90th 10 -11.2 5.0 11% 6% 34% 48% 0.5% 

75th 26 -10.2 3.5 16% 9% 32% 43% 0.4% 

Median 52 -6.9 2.3 16% 9% 29% 45% 0.4% 

25th 77 -4.7 1.6 12% 7% 32% 49% 0.5% 

N-35 
Exhibit N-33. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 4 

(0.05 μg/m3

, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent 

Air) 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

95th 5 -3.6 4.5 14% 8% 54% 23% 0.2% 

90th 10 -2.9 3.7 22% 13% 44% 22% 0.1% 

75th 26 -2.1 2.6 19% 11% 51% 19% 0.1% 

Median 52 -1.4 1.7 22% 13% 44% 22% 0.1% 

25th 77 -0.9 1.2 18% 11% 41% 30% 0.2% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -4.1 4.5 14% 8% 54% 23% 0.2% 

90th 10 -3.5 3.7 22% 13% 44% 22% 0.1% 

75th 26 -2.6 2.6 19% 11% 51% 19% 0.1% 

Median 52 -1.5 1.7 14% 8% 54% 23% 0.2% 

25th 77 -0.4 1.2 18% 11% 41% 30% 0.2% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -6.8 4.5 14% 8% 54% 23% 0.2% 

90th 10 -6.2 3.7 22% 13% 44% 22% 0.1% 

75th 26 -5.3 2.6 19% 11% 51% 19% 0.1% 

Median 52 -4.2 1.7 22% 13% 44% 22% 0.1% 

25th 77 -3.1 1.2 18% 11% 41% 30% 0.2% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

95th 5 -11.1 4.5 14% 8% 54% 23% 0.2% 

90th 10 -10.8 3.7 22% 13% 44% 22% 0.1% 

75th 26 -7.6 2.6 19% 11% 51% 19% 0.1% 

Median 52 -5.1 1.7 22% 13% 44% 22% 0.1% 

25th 77 -3.5 1.2 18% 11% 41% 30% 0.2% 

N-36 
Exhibit N-34. Primary Pb Smelter Case Study (Subarea): Alternative NAAQS 5 

(0.02 μg/m3

, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB (μg/dL) 

Diet Drinking 

Water 

Outdoor 

Soil/Dust 

Total 

Indoor 

Dust 

Inhalation 

(Recent 

Air) 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

95th 5 -3.3 4.2 18% 10% 50% 21% 0.1% 

90th 10 -2.7 3.4 25% 14% 44% 17% < 0.1% 

75th 26 -1.9 2.4 16% 9% 61% 13% < 0.1% 

Median 52 -1.3 1.6 18% 11% 56% 15% < 0.1% 

25th 77 -0.9 1.1 33% 20% 24% 23% < 0.1% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint 

95th 5 -3.8 4.2 18% 10% 50% 21% 0.1% 

90th 10 -3.3 3.4 25% 14% 44% 17% < 0.1% 

75th 26 -2.4 2.4 16% 9% 61% 13% < 0.1% 

Median 52 -1.3 1.6 20% 12% 52% 15% < 0.1% 

25th 77 -0.3 1.1 33% 20% 24% 23% < 0.1% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -6.5 4.2 18% 10% 50% 21% 0.1% 

90th 10 -6.0 3.4 25% 14% 44% 17% < 0.1% 

75th 26 -5.1 2.4 16% 9% 61% 13% < 0.1% 

Median 52 -4.0 1.6 18% 11% 56% 15% < 0.1% 

25th 77 -3.0 1.1 33% 20% 24% 23% < 0.1% 

Dust Model (H5), GSD (1.7), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

95th 5 -11.1 4.2 18% 10% 50% 21% 0.1% 

90th 10 -10.0 3.4 25% 14% 44% 17% < 0.1% 

75th 26 -7.1 2.4 16% 9% 61% 13% < 0.1% 

Median 52 -4.8 1.6 20% 12% 52% 15% < 0.1% 

25th 77 -3.3 1.1 33% 20% 24% 23% < 0.1% 

N-37 
 

N.2.3.3. Ambient Air to PbB Ratios 

Exhibit N-35 presents the air-to-PbB ratios for the primary Pb smelter (subarea) case 

study. Note that these ratios are derived in a different manner than the air to PbB ratios 

presented in Appendix I. the air to PbB ratios presented here are derived by comparing changes 

(deltas) in median total PbB levels (concurrent) to associated changes in annual average air Pb 

levels as one steps to the next lowest air quality scenario. The ambient air annual average Pb 

concentration estimates are presented to three decimal places, resulting in various numbers of 

implied significant figures (e.g., 1 to 3). No difference in precision is intended to be conveyed; 

this is simply an expedient and initial result of the software used for presentation. 

Exhibit N-35. Primary Pb Smelter (Subarea) Case Study: Air to PbB Ratios 

Air Scenario Median Total PbB 

(μg/dL) 

Annual Average 

Ambient Air 

Concentration 

(μg/m3

) 

Ratio a 

Current NAAQS (1.5 μg/m3, max 

quarterly average) 

4.6 0.238 

1 : 10.3 Alternative NAAQS 2 (0.5 μg/m3 

, 

max monthly average) 

3.2 0.105 

Alternative NAAQS 1 (0.2 μg/m3 

, 

max quarterly average) 

2.5 0.052 1 : 13.2 

Alternative NAAQS 3 (0.2 μg/m3 

, 

max monthly average) 

2.4 0.042 1 : 15.3 

Alternative NAAQS 4 (0.05 μg/m3 

, 

max monthly average) 

1.7 0.011 1 : 19.1 

Alternative NAAQS 5 (0.02 μg/m3 

, 

max monthly average) 

1.6 0.004 1 : 19.0 

a

 A ratio is not presented adjacent to the current NAAQS air quality scenario (for any of the case 

studies) because the air-to-PbB ratios are derived by comparing changes (deltas) in median total PbB 

levels (concurrent) to associated changes in annual average air Pb levels as one steps to the next 

lowest air quality scenario. The first ratio presented for any of the case studies is generated by 

comparing median PbB levels at the current NAAQS level to the median PbB level at the highest of 

the alternative NAAQS levels (i.e., Alternative NAAQS 5 [0.05 μg/m3 max monthly] value). 

N.3. SENSITIVITY ANALYSIS OF IQ CHANGE FUNCTIONS 

A sensitivity analysis was performed using the general urban case study results with the 

core modeling approach to examine the effects of using different IQ change functions relative to 

the population stratified dual linear IQ change function for concurrent PbB, derived from the 

pooled data set stratified at a peak PbB of 10 μg/dL peak (referred to as the “baseline” function). 

The baseline function was compared with three “alternative” IQ change functions: the high- and 

low-bound functions, which were the population stratified dual linear IQ change function for 

concurrent PbB derived from the pooled data set stratified at a peak PbB of 7.5 μg/dL and the 

log-linear IQ change function for concurrent PbB with cutpoint, respectively; and the log-linear 

IQ change function for concurrent PbB with low-exposure linearization. Section N.3.1 presents 

N-38 
the absolute IQ change for the sensitivity cases and Section N.3.2 provides the relative changes 

in IQ change associated with the sensitivity cases. 

N-39 
 

 

 

N.3.1. Absolute IQ Change for Four IQ Change Functions 

Exhibit N-36 presents the absolute IQ change results for the general urban case study for 

the four different IQ change functions included in the core modeling approach. 

Exhibit N-36. Absolute Differences in IQ Loss Estimates 

Between the Sensitivity and Baseline Cases 

Sensitivity Case 

Absolute Change (IQ Points) in 

Percentile Estimate Compared to 

Baseline 

95th 

(Baseline = -4.2) 

Median 

(Baseline = -1.5) 

IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) -7.3 -4.1 

IQ Function (Log-linear with Linearization) -3.6 -2.9 

IQ Function (Log-linear with Cutpoint) -0.9 -0.2 

N.3.2. Relative IQ Change Comparing Baseline Function to Three Alternative Functions 

Exhibit N-37 presents the relative IQ change results for the general urban case study for 

the three alternative IQ change functions compared to the baseline function under the core 

modeling approach. 

Exhibit N-37. Percent Difference in IQ Loss Estimates 

Between the Sensitivity and Baseline Cases 

Sensitivity Case 

Relative Change in Percentile 

Estimate Compared to Baseline 

95th 

(Baseline = -5.2) 

Median 

(Baseline = -1.5) 

IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 173% 268% 

IQ Function (Log-linear with Linearization) 85% 191% 

IQ Function (Log-linear with Cutpoint) 20% 15% 

N-40 
Appendix O: Location-specific Urban Case Study Analyses 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

 

Table of Contents 

Table of Contents.................................................................................................................O-i 

List of Exhibits ....................................................................................................................O-ii 

O. LOCATION-SPECIFIC URBAN CASE STUDY ANALYSES.....................................O-1
 

O.1. SELECTION OF STUDY AREA BOUNDARIES..................................................... O-1 

O.2. MEDIA CONCENTRATION ESTIMATES .............................................................. O-5 

O.2.1. Air .................................................................................................................... O-5 

O.2.1.1. Ambient Air Concentrations............................................................................ O-6 

O.2.1.2. Inhalation Exposure Concentrations.............................................................. O-16 

O.2.2. Outdoor Soil/Dust .......................................................................................... O-18 

O.2.3. Indoor Dust .................................................................................................... O-19 

O.3. BLOOD PB AND IQ CHANGE MODELING......................................................... O-21 

O.3.1. Approach........................................................................................................ O-21 

O.3.2. PbB and IQ Change Results........................................................................... O-21 

O.3.3. Ambient Air to PbB Ratios............................................................................ O-38 

O.3.4. Number of Children with IQ Loss Resulting from Total Pb Exposure ......... O-39 

REFERENCES ................................................................................................................... O-52 

O-i 
 

List of Exhibits 

Exhibit O-1. Descriptive Statistics for the Location-specific Urban Case Studies.................... O-1 

Exhibit O-2. Chicago Study Area .............................................................................................. O-2 

Exhibit O-3. Cleveland Study Area ........................................................................................... O-3 

Exhibit O-4. Los Angeles Study Area ....................................................................................... O-4 

Exhibit O-5. Air Quality Scenarios Included in the Location-specific Urban Case Study 

Analyses................................................................................................................ O-6 

Exhibit O-6. Was a “roll-back” performed in the NAAQS Adjustment Zone?......................... O-8 

Exhibit O-7. Current 3-year Average Air Pb Concentrations (μg/m3) and Max Quarterlyand Max Monthly-to-Annual Conversion Ratios, Chicago Monitor Stations ...... O-9 

Exhibit O-8. Quarterly and Monthly "Rolled" Average Pb Air Concentrations (μg/m3) for 

NAAQS Scenarios, Chicago Monitor Stations................................................... O-10 

Exhibit O-9. Annual "Rolled" Average Pb Air Concentrations (μg/m3

) for NAAQS 

Scenarios, Chicago Monitor Stations.................................................................. O-10 

Exhibit O-10. Current 3-year Average Air Pb Concentrations (μg/m3) and Max Quarterlyand Max Monthly-to-Annual Conversion Ratios, Cleveland Monitor 

Stations.............................................................................................................. O-11 

Exhibit O-11. Quarterly and Monthly "Rolled" Average Pb Air Concentrations (μg/m3) 

for NAAQS Scenarios, Cleveland Monitor Stations ........................................ O-11 

Exhibit O-12. Annual "Rolled" Average Pb Air Concentrations (μg/m3) for NAAQS 

Scenarios, Cleveland Monitor Stations............................................................. O-12 

Exhibit O-13. Current 3-year Average Air Pb Concentrations (μg/m3) and Max Quarterlyand Max Monthly-to-Annual Conversion Ratios, Los Angeles Monitor 

Stations.............................................................................................................. O-12 

Exhibit O-14. Quarterly and Monthly "Rolled" Average Pb Air Concentrations (μg/m3

) 

for NAAQS Scenarios, Los Angles Monitor Stations ...................................... O-13 

Exhibit O-15. Annual "Rolled" Average Pb Air Concentrations (μg/m3

) for NAAQS 

Scenarios, Los Angeles Monitor Stations......................................................... O-13 

Exhibit O-16. Location-Specific Urban Case Studies: Estimated Population-weighted 

Annual Ambient Air Concentrations ................................................................ O-15 

Exhibit O-17. Location-specific Urban Case Studies: Estimated Population-weighted 

Inhalation Exposure Concentrations................................................................. O-17 

O-ii 
 

Exhibit O-18. Location-specific Urban Case Studies: Estimated Population-weighted 

Outdoor Soil/Dust Concentrations.................................................................... O-18 

Exhibit O-19. Location-specific Urban Case Studies: Estimated Population-weighted 

Indoor Dust Concentrations .............................................................................. O-20 

Exhibit O-20. Chicago: Current Conditions Estimated IQ Changes ...................................... O-22 

Exhibit O-21. Chicago: Current NAAQS (1.5μg/m3

, Maximum Quarterly Average) 

Estimated IQ Changes....................................................................................... O-23 

Exhibit O-22. Chicago: Alternative NAAQS 3 (0.2μg/m3, Maximum Monthly Average) 

Estimated IQ Changes....................................................................................... O-24 

Exhibit O-23. Chicago: Alternative NAAQS 4 (0.05μg/m3, Maximum Monthly Average) 

Estimated IQ Changes....................................................................................... O-25 

Exhibit O-24. Chicago: Alternative NAAQS 5 (0.02μg/m3, Maximum Monthly Average) 

Estimated IQ Changes....................................................................................... O-26 

Exhibit O-25. Cleveland: Current Conditions Estimated IQ Changes ................................... O-27 

Exhibit O-26. Cleveland: Current NAAQS (1.5μg/m3, Maximum Quarterly Average) 

Estimated IQ Changes....................................................................................... O-28 

Exhibit O-27. Cleveland: Alternative NAAQS 1 (0.2μg/m3, Maximum Quarterly Average) 

Estimated IQ Changes....................................................................................... O-29 

Exhibit O-28. Cleveland: Alternative NAAQS 2 (0.5μg/m3

, Maximum Monthly Average) 

Estimated IQ Changes....................................................................................... O-30 

Exhibit O-29. Cleveland: Alternative NAAQS 3 (0.2μg/m3, Maximum Monthly Average) 

Estimated IQ Changes....................................................................................... O-31 

Exhibit O-30. Cleveland: Alternative NAAQS 4 (0.05μg/m3

, Maximum Monthly Average) 

Estimated IQ Changes....................................................................................... O-32 

Exhibit O-31. Cleveland: Alternative NAAQS 5 (0.02μg/m3

, Maximum Monthly Average) 

Estimated IQ Changes....................................................................................... O-33 

Exhibit O-32. Los Angeles: Current Conditions Estimated IQ Changes................................ O-34 

Exhibit O-33. Los Angeles: Current NAAQS (1.5μg/m3

, Maximum Quarterly Average) 

Estimated IQ Changes....................................................................................... O-35 

Exhibit O-34. Los Angeles: Alternative NAAQS 4 (0.05μg/m3

, Maximum Monthly 

Average) Estimated IQ Changes....................................................................... O-36 

Exhibit O-35. Los Angeles: Alternative NAAQS 5 (0.02μg/m3

, Maximum Monthly 

Average) Estimated IQ Changes....................................................................... O-37 

O-iii 
 

Exhibit O-36. Location-specific Urban Case Studies: Air to PbB Ratios .............................. O-38 

Exhibit O-37. Chicago: Number of Children with IQ Loss Resulting from Total Pb 

Exposure ........................................................................................................... O-40 

Exhibit O-38. Chicago: Percentage of Children with IQ Loss Resulting from Total Pb 

Exposure ............................................................................................................. O-42 

Exhibit O-39. Cleveland: Number of Children with IQ Loss Resulting from Total Pb 

Exposure ............................................................................................................. O-44 

Exhibit O-40. Cleveland: Percentage of Children with IQ Loss Resulting from Total Pb 

Exposure ........................................................................................................... O-46 

Exhibit O-41. Los Angeles: Number of Children with IQ Loss Resulting from Total Pb 

Exposure ........................................................................................................... O-48 

Exhibit O-42. Los Angeles: Percentage of Children with IQ Loss Resulting from Total Pb 

Exposure ........................................................................................................... O-50
 

O-iv 
 

O. LOCATION-SPECIFIC URBAN CASE STUDY ANALYSES 

This appendix presents the methodology used to estimate the change in children’s IQ 

associated with Pb exposure in the Chicago, Cleveland, and Los Angeles study areas that were 

selected for the location-specific urban case study analyses described in Chapter 5 of Volume I 

of the Risk Assessment. This analysis uses the same modeling approach used for the analyses 

described in Appendix N (referred to as the “core” modeling approach), rather than the full-scale 

analysis approach described in the earlier appendices. 

The first section of this appendix (Section O.1) provides a brief overview of each study 

area. The following three sections step the reader through the calculation of media 

concentrations for each study area (Section O.2), and the estimation of PbB levels and IQ change 

for children (Section O.3). 

O.1. SELECTION OF STUDY AREA BOUNDARIES 

Chapter 5 of Volume I of the Risk Assessment describes the rationale for selecting study 

areas in Chicago, Cleveland, and Los Angeles as the sites for evaluation. Maps of the study 

areas are provided in Exhibit O-2, Exhibit O-3, and Exhibit O-4 for Chicago, Cleveland, and Los 

Angeles, respectively. For each selected city, a set of total suspended particulate matter (TSP) 

monitors was selected for inclusion in the analysis and used to define the boundary of the study 

area and the U.S. Census blocks included in the analysis. For each study area, a set of sourceand/or nonsource-related TSP monitors was selected, as described in Chapter 5 of Volume I of 

the Risk Assessment. The locations of each of these TSP monitors were plotted using ESRI® 

ArcMapTM version 9.2, and then overlaid upon a GIS layer containing U.S. Census blocks in the 

area surrounding these monitors. The outermost TSP monitors in each direction were identified 

and a line was drawn connecting them (see the dark lines in Exhibit O-2, Exhibit O-3, and 

Exhibit O-4, which passes through each of the outermost monitors). A second line was then 

constructed that measured 1 mile from every point along the initial line (see the outer, lightercolored line in Exhibit O-2, Exhibit O-3, and Exhibit O-4). All the U.S. Census blocks falling 

within or crossing the outer line at any point were considered part of the study area. For brief 

statistics describing the three study areas, see Exhibit O-1 (U.S. Census Bureau, 2001). 

Exhibit O-1. Descriptive Statistics for the Location-specific Urban Case Studies 

City Total Population Number of 

Blocks Area (km2

) 

Chicago 396,511 38,807 1,090.70 

Cleveland 13,990 2,180 67.1 

Los Angeles 372,252 21,608 720.7 

O-1
 
 

Exhibit O-2. Chicago Study Area 

O-2
 
 

Exhibit O-3. Cleveland Study Area 

O-3
 
 

Exhibit O-4. Los Angeles Study Area 

O-4
 
 

 

 

 

O.2. MEDIA CONCENTRATION ESTIMATES 

O.2.1. Air 

Ambient air and inhalation exposure Pb concentrations were estimated through a similar 

process for each of the location-specific urban case studies. The process began with the 

establishment of source-oriented exposure zones and non-source-oriented exposure zones within 

each study area. This was done based on the Pb-TSP data set for 2003 to 2005 analyzed in 

Appendix A. Each of the source-oriented exposure zones is associated with a single Pb-TSP 

monitor considered to be source-oriented for this analysis1 and contains all the U.S. Census 

blocks with a centroid that falls within one mile of that monitor. It was assumed that these 

source-oriented monitors provide ambient air Pb concentrations pertinent to these blocks. With 

two and three source-oriented monitors in their study areas, the Chicago and Cleveland study 

areas had two and three source-oriented exposure zones, respectively. The Cleveland study area 

contained two source-oriented monitors in close enough proximity that the centroids of a number 

of U.S. Census blocks fell within one mile of both monitors. For each of these blocks, the 

distances between the block's centroid and both monitors were compared and the block was 

assigned to the zone associated with the nearest monitor. Because the Los Angeles study area 

did not contain any source-oriented monitors, it had no source-oriented exposure zones. 

The non-source-oriented exposure zones, which were each associated with a single nonsource-oriented monitor, encompassed all the remaining U.S. Census blocks that were not 

assigned to a source-oriented monitor. These zones were defined as the areas over which it was 

assumed the non-source-oriented monitors provided representative ambient air Pb 

concentrations. Each of the remaining U.S. Census blocks was assigned to the closest nonsource-oriented monitor. Because the Chicago, Cleveland, and Los Angeles case study areas 

contained nine, three, and seven non-source-oriented monitors, respectively, the areas contained 

nine, three, and seven non-source-oriented exposure zones. 

After assigning each U.S. Census block in a study area to an exposure zone, the ambient 

air data for the monitor in each zone was assumed to be representative of current conditions air 

concentrations in the blocks associated with that zone. Measured ambient air Pb concentrations 

for each monitor in the study area were adjusted to provide representative air concentrations for 

1 With one exception, monitors are identified as source-oriented if so specified in Appendix A (Attachment 

A-2, Table 1). The exception is Chicago CMSA monitor 180892011, for which Pb-TSP values were similar to or 

higher than those at an adjacent monitor that was specified as source-oriented. Consequently, this monitor was 

identified as source-oriented for the purposes of this assessment. 

O-5
 
 

each zone under the current NAAQS and alternative NAAQS scenarios. These ambient air 

concentrations were then modified using data on the relationship between ambient 

concentrations and inhalation exposure concentrations to generate inhalation exposure 

concentrations for each exposure zone (as is discussed in more detail in Section O.2.1.2). 

O.2.1.1. Ambient Air Concentrations 

The ambient air concentrations were estimated under the seven air quality scenarios 

shown in Exhibit O-5. For each exposure zone in a given study area, an arithmetic annual 3-year 

average Pb-TSP concentration for the time period 2003 to 2005 was calculated using data from 

the U.S. EPA Air Quality System [AQS] database (USEPA, 2007). Similarly, a maximum 

monthly average Pb-TSP concentration and a maximum quarterly average Pb-TSP concentration 

were calculated for each monitor over the same time period. The three mean concentration 

values, which are shown respectively for each of the three study areas in Exhibit O-7, Exhibit 

O-10, and Exhibit O-13 formed the basis for estimating ambient air concentrations in each 

exposure zone under the seven air quality scenarios. 

Exhibit O-5. Air Quality Scenarios Included in the Location-specific 

Urban Case Study Analyses 

Air Quality Scenario Level (μg/m3) Averaging Time 

Current Conditions a Varies by block group Calendar Annual (mean) 

Current NAAQS 1.5 Calendar Quarter (maximum) 

Alternative NAAQS 1 0.2 Calendar Quarter (maximum) 

Alternative NAAQS 2 0.5 Monthly (maximum) 

Alternative NAAQS 3 0.2 Monthly (maximum) 

Alternative NAAQS 4 0.05 Monthly (maximum) 

Alternative NAAQS 5 0.02 Monthly (maximum) 

a

 The data used to derive the current conditions concentrations are Pb-TSP monitoring data in the 

U.S. EPA AQS database for 2003 to 2005. 

O-6
 
 

 

The annual mean concentrations were used to represent air concentrations under the 

current conditions scenario. The remainder of the air quality scenarios required adjustment of 

the maximum quarterly mean or maximum monthly mean concentrations, either up (a “roll-up”) 

or down (a “roll-back”) depending on the air quality scenario and estimated current conditions. 

A “roll-up” refers to an upward adjustment of the maximum mean monitor concentrations (either 

monthly or quarterly, depending on whether the air quality scenario is based on a monthly or 

quarterly standard) to represent conditions under a standard that allows higher ambient 

concentrations. The “roll-up” is achieved by identifying the monitor with the highest maximum 

mean concentration out of all the monitors in the “NAAQS adjustment zone,” setting it equal to 

the level of the standard (e.g., 1.5 μg/m3

 maximum quarterly mean for the current NAAQS 

scenario), and increasing the concentrations of the other monitors in the NAAQS adjustment 

zone by the same proportion. Note that for this analysis, the “roll-up” procedure was only 

applied for the current NAAQS scenario to analyze the impacts associated with just meeting the 

current NAAQS. This procedure was not applied for any of the alternative NAAQS scenarios. 

For the Cleveland and Los Angeles study areas, a single NAAQS adjustment zone was 

defined that included all of the exposure zones. For the Chicago study area, each exposure zone 

was assigned to one of two NAAQS adjustment zones, with four exposure zones falling within 

one NAAQS adjustment zone and seven within the other (the rationale for these NAAQS 

adjustment zones is provided in Chapter 5 of Volume I of the Risk Assessment). As a result, the 

“roll-up” was performed separately for the two NAAQS adjustment zones in Chicago. The 

results of the “roll-up” procedure are shown in Exhibit O-8, Exhibit O-11, and Exhibit O-14 for 

the Chicago, Cleveland, and Los Angeles study areas, respectively. 

 The “roll-back” refers to the downward adjustment of the monitor concentrations (either 

monthly or quarterly, depending on whether the air quality scenario is based on a monthly or 

quarterly standard) to represent conditions under a standard that requires lower ambient 

concentrations. A “roll-back” is achieved by identifying the monitor with the highest maximum 

mean concentration out of all the monitors in the NAAQS adjustment zone, setting it equal to the 

level of the standard (e.g., 0.02 μg/m3

 maximum monthly mean for Alternate NAAQS 5), and 

decreasing the concentrations of the other monitors by the same proportion. Just as for the “rollup,” Chicago’s two NAAQS adjustment zones were “rolled-back” separately. The results of the 

“roll-back” procedure are shown in Exhibit O-8, Exhibit O-11, and Exhibit O-14 for the 

Chicago, Cleveland, and Los Angeles study areas, respectively. It is important to note that “rollbacks” to simulate an alternate NAAQS were only performed for a given case study if it 

contained at least one monitor with a current condition concentration (either maximum monthly 

O-7
 
 

or maximum quarterly means, depending on the air quality standard) above the relevant air 

quality standard. For the Chicago case study, which contains two NAAQS adjustment zones, 

roll-backs were only performed for the zone containing the monitor with a concentration above 

the relevant air quality standard. As a result, the exhibits do not show data for cases where 

monitor values (i.e., the current conditions) were below the relevant air quality standard. Exhibit 

O-6 indicates when roll-backs were performed. 

Exhibit O-6. Was a “roll-back” performed in the NAAQS Adjustment Zone? 

Air Quality Scenario Level Averaging Time Chicago Chicago Cleveland Los 

(μg/m Angeles 3

) Zone 1 Zone 2 

Alternative NAAQS 1 0.2 Calendar Quarter (maximum) no no yes no 

Alternative NAAQS 2 0.5 Monthly (maximum) no no yes no 

Alternative NAAQS 3 0.2 Monthly (maximum) yes no yes no 

Alternative NAAQS 4 0.05 Monthly (maximum) yes yes yes yes 

Alternative NAAQS 5 0.02 Monthly (maximum) yes yes yes yes 

Once the “roll-ups” and “roll-backs” were complete, the newly adjusted maximum 

quarterly or monthly averages for each exposure zone were converted into annual averages using 

the associated conversion ratio. Conversion ratios were calculated by dividing the current annual 

3-year average by either the monthly or quarterly 3-year average. Exhibit O-7, Exhibit O-10, 

and Exhibit O-13 show these conversion ratios for each exposure zone-associated monitor for 

Chicago, Cleveland and Los Angeles, respectively. The resulting “rolled” annual average Pb Air 

concentrations for the Chicago, Cleveland and Los Angeles study areas are shown in Exhibit 

O-9, Exhibit O-12, and Exhibit O-15, respectively. 

 The three step process described above for producing annual average “rolled” Pb Air 

concentrations for the monitors in Chicago is shown in Exhibit O-7, Exhibit O-8, and Exhibit 

O-9, while the process for Cleveland is displayed in Exhibit O-10, Exhibit O-11, and Exhibit 

O-12, and the process for Los Angeles is presented in Exhibit O-13, Exhibit O-14, and Exhibit 

O-15. 

O-8
 
 

 

 Exhibit O-7. Current 3-year Average Air Pb Concentrations (μg/m3) and Max Quarterlyand Max Monthly-to-Annual Conversion Ratios, Chicago Monitor Stations 

Monitor ID 

NAAQS 

Adjustment 

Zone 

Monitor Type 

3-year Average (μg/m3

)

a Conversion Ratiosb 

Annual 

Average 

Max 

Quarterly 

Average 

Max 

Monthly 

Average 

Max Quarterlyto-Annual 

Average 

Max Monthly-toAnnual Average 

180890023 1 Source 0.039 0.069 0.091 0.563 0.428 

180892008 1 Nonsource 0.022 0.030 0.059 0.740 0.371 

180892011 1 Source c

 0.037 0.135 0.305 0.273 0.121 

170310022 1 Nonsource 0.027 0.035 0.044 0.764 0.614 

170310001 2 Nonsource 0.014 0.023 0.036 0.625 0.397 

170310026 2 Nonsource 0.040 0.061 0.090 0.660 0.450 

170310052 2 Nonsource 0.021 0.026 0.040 0.824 0.535 

170313103 2 Nonsource 0.015 0.027 0.044 0.549 0.339 

170313301 2 Nonsource 0.031 0.075 0.195 0.411 0.158 

170314201 2 Nonsource 0.011 0.013 0.018 0.844 0.643 

170316003 2 Nonsource 0.030 0.039 0.050 0.785 0.607 

a The data presented here and used for estimates of current conditions concentrations are taken from the 2003 

to 2005 data set of Pb-TSP monitor values presented in Appendix A (Attachment A-2, Table 1). b

 Conversion ratios were calculated by dividing the current annual 3-year average by either the monthly or 

quarterly 3-year average. 

c

 Although monitor 180892011 was not identified as source-oriented in Appendix A (Attachment A-2, Table 

1) its Pb-TSP concentrations were similar to or higher than those at an adjacent monitor that was specified as 

source-oriented. Consequently, this monitor was identified as source-oriented for the purposes of this 

assessment. 

O-9
 

 

 

Exhibit O-8. Quarterly and Monthly "Rolled" Average Pb Air Concentrations (μg/m3) for 

NAAQS Scenarios, Chicago Monitor Stations 

Monitor ID 

NAAQS 

Adjustment 

Zone 

Monitor Type 

Quarterly and Monthly "Rolled" Average Pb Air Concentrations (μg/m3

) a 

Roll-up to current 

NAAQS (1.5 max 

quarterly) 

Roll-back 

to 0.2 

monthly 

Roll-back to 

0.02 monthly 

Roll-back to 0.05 

monthly 

180890023 1 Source 0.767 0.060 0.006 0.015 

180892008 1 Nonsource 0.328 0.039 0.004 0.010 

180892011 1 Source 1.500 0.200 0.020 0.050 

170310022 1 Nonsource 0.392 0.029 0.003 0.007 

170310001 2 Nonsource 0.457 0.004 0.009 

170310026 2 Nonsource 1.227 0.009 0.023 

170310052 2 Nonsource 0.520 0.004 0.010 

170313103 2 Nonsource 0.543 0.005 0.011 

170313301 2 Nonsource 1.500 0.020 0.050 

170314201 2 Nonsource 0.267 0.002 0.004 

170316003 2 Nonsource 0.773 0.005 0.013 

a

 The procedure for performing the “roll-up” and “roll-backs” is described in Section O.2.1.1. 

Exhibit O-9. Annual "Rolled" Average Pb Air Concentrations (μg/m3) for NAAQS 

Scenarios, Chicago Monitor Stations 

Monitor ID 

NAAQS 

Adjustment 

Zone 

Monitor Type 

Annual "Rolled" Average Pb Air Concentrations (μg/m3

) a 

Roll-up to current 

NAAQS (1.5 max 

quarterly) 

Roll-back 

to 0.2 

monthly 

Roll-back to 

0.02 monthly 

Roll-back to 0.05 

monthly 

180890023 1 Source 0.432 0.026 0.003 0.006 

180892008 1 Nonsource 0.243 0.014 0.001 0.004 

180892011 1 Source 0.409 0.024 0.002 0.006 

170310022 1 Nonsource 0.300 0.018 0.002 0.004 

170310001 2 Nonsource 0.286 0.001 0.004 

170310026 2 Nonsource 0.809 0.004 0.010 

170310052 2 Nonsource 0.428 0.002 0.005 

170313103 2 Nonsource 0.298 0.002 0.004 

170313301 2 Nonsource 0.617 0.003 0.008 

170314201 2 Nonsource 0.225 0.001 0.003 

170316003 2 Nonsource 0.607 0.003 0.008 

a The procedure for calculating the annual “rolled” average is described in Section O.2.1.1. 

O-10
 
 

 

 

 

Exhibit O-10. Current 3-year Average Air Pb Concentrations (μg/m3) and Max Quarterlyand Max Monthly-to-Annual Conversion Ratios, Cleveland Monitor Stations 

Monitor ID Monitor 

Type 

3-year Average (μg/m3

)

a Conversion Ratiosb 

Annual 

Average 

Max Quarterly 

Average 

Max Monthly 

Average 

Max Quarterlyto-Annual 

Average 

Max Monthlyto-Annual 

Average 

390350038 Nonsource 0.021 0.030 0.060 0.684 0.342 

390350042 Nonsource 0.017 0.028 0.043 0.605 0.394 

390350049 Source 0.121 0.237 0.450 0.513 0.270 

390350050 Source 0.036 0.055 0.100 0.658 0.362 

390350061 Source 0.048 0.360 0.560 0.132 0.085 

390350069 Nonsource 0.017 0.023 0.047 0.727 0.361 

a

 The data presented here and used for estimates of current conditions concentrations are taken from the 2003 to 

2005 data set of Pb-TSP monitor values presented in Appendix A (Attachment A-2, Table 1). 

b

 The quarterly and monthly conversion ratios were calculated by dividing the current annual 3-year 

averages by the quarterly and monthly 3-year averages, respectively. 

Exhibit O-11. Quarterly and Monthly "Rolled" Average Pb Air Concentrations (μg/m3) 

for NAAQS Scenarios, Cleveland Monitor Stations 

Monitor ID Monitor 

Type 

Quarterly and Monthly "Rolled" Average Pb Air Concentrations (μg/m3

) a 

Roll-up to current 

NAAQS (1.5 max 

quarterly) 

Roll-back 

to 0.2 

monthly 

Roll-back 

to 0.02 

monthly 

Roll-back 

to 0.2 

quarterly 

Roll-back 

to 0.05 

monthly 

Roll-back 

to 0.5 

monthly 

390350038 Nonsource 0.125 0.021 0.002 0.017 0.005 0.054 

390350042 Nonsource 0.117 0.015 0.002 0.016 0.004 0.038 

390350049 Source 0.986 0.161 0.016 0.131 0.040 0.402 

390350050 Source 0.229 0.036 0.004 0.031 0.009 0.089 

390350061 Source 1.500 0.200 0.020 0.200 0.050 0.500 

390350069 Nonsource 0.097 0.017 0.002 0.013 0.004 0.042 

a

 The procedure for performing the “roll-up” and “roll-backs” is described in Section O.2.1.1. 

O-11
 
 

 

Exhibit O-12. Annual "Rolled" Average Pb Air Concentrations (μg/m3) for NAAQS 

Scenarios, Cleveland Monitor Stations 

Monitor ID Monitor 

Type 

Annual "Rolled" Average Pb Air Concentrations (μg/m3

) a 

Roll-up to current 

NAAQS (1.5 max 

quarterly) 

Roll-back 

to 0.2 

monthly 

Roll-back 

to 0.02 

monthly 

Roll-back 

to 0.2 

quarterly 

Roll-back 

to 0.05 

monthly 

Roll-back 

to 0.5 

monthly 

390350038 Nonsource 0.085 0.007 0.001 0.011 0.002 0.018 

390350042 Nonsource 0.071 0.006 0.001 0.009 0.002 0.015 

390350049 Source 0.506 0.043 0.004 0.067 0.011 0.108 

390350050 Source 0.151 0.013 0.001 0.020 0.003 0.032 

390350061 Source 0.199 0.017 0.002 0.026 0.004 0.043 

390350069 Nonsource 0.071 0.006 0.001 0.009 0.002 0.015 

a The procedure for calculating the annual “rolled” average is described in Section O.2.1.1. 

Exhibit O-13. Current 3-year Average Air Pb Concentrations (μg/m3) and Max Quarterlyand Max Monthly-to-Annual Conversion Ratios, Los Angeles Monitor Stations 

Monitor ID Monitor 

Type 

3-year Average (μg/m3

)

a Conversion Ratiosb 

Annual 

Average 

Max 

Quarterly 

Average 

Max 

Monthly 

Average 

Max 

Quarterly-toAnnual 

Average 

Max 

Monthly-toAnnual 

Average 

060371103 Nonsource 0.022 0.063 0.146 0.359 0.154 

060371301 Nonsource 0.019 0.031 0.044 0.599 0.427 

060371601 Nonsource 0.019 0.030 0.048 0.619 0.387 

060374002 Nonsource 0.015 0.040 0.096 0.374 0.156 

060374004 Nonsource 0.011 0.094 0.102 0.119 0.109 

060375001 Nonsource 0.022 0.067 0.170 0.333 0.130 

060375005 Nonsource 0.006 0.012 0.015 0.483 0.381 

a

 The data presented here and used for estimates of current conditions concentrations are taken 

from the 2003 to 2005 data set of Pb-TSP monitor values presented in Appendix A 

(Attachment A-2, Table 1). 

b

 The quarterly and monthly conversion ratios were calculated by dividing the current annual 

3-year averages by the quarterly and monthly 3-year averages, respectively. 

O-12
 
 

 

 

Exhibit O-14. Quarterly and Monthly "Rolled" Average Pb Air Concentrations (μg/m3) 

for NAAQS Scenarios, Los Angles Monitor Stations 

Monitor ID Monitor Type 

Quarterly and Monthly "Rolled" Average Pb Air 

Roll-up to current 

NAAQS (1.5 max 

quarterly) 

Roll-back to 0.02 

monthly 

Concentrations (μg/m3

) a 

Roll-back to 0.05 

monthly 

060371103 Nonsource 1.002 0.017 0.043 

060371301 Nonsource 0.501 0.005 0.013 

060371601 Nonsource 0.480 0.006 0.014 

060374002 Nonsource 0.639 0.011 0.028 

060374004 Nonsource 1.500 0.012 0.030 

060375001 Nonsource 1.066 0.020 0.050 

060375005 Nonsource 0.189 0.002 0.004 

a

 The procedure for performing the “roll-up” and “roll-backs” is described in Section O.2.1.1. 

Exhibit O-15. Annual "Rolled" Average Pb Air Concentrations (μg/m3) for NAAQS 

Scenarios, Los Angeles Monitor Stations 

Monitor ID Monitor Type 

Annual "Rolled" Average Pb Air Concen

Roll-up to current 

NAAQs (1.5 max 

quarterly) 

Roll-back to 0.02 

monthly 

trations (μg/m3

) a 

Roll-back to 0.05 

monthly 

060371103 Nonsource 0.360 0.003 0.007 

060371301 Nonsource 0.300 0.002 0.006 

060371601 Nonsource 0.297 0.002 0.005 

060374002 Nonsource 0.239 0.002 0.004 

060374004 Nonsource 0.178 0.001 0.003 

060375001 Nonsource 0.354 0.003 0.007 

060375005 Nonsource 0.091 0.001 0.002 

a The procedure for calculating the annual “rolled” average is described in Section O.2.1.1. 

O-13
 
 

 The resulting distributions of the estimated annual ambient air concentrations in 

each urban case study area for the air quality scenarios are shown in Exhibit O-16. Populationweighted annual ambient air concentrations were calculated by first sorting the block/block 

groups in increasing media concentration order. Then the percentage of children living in 

block/block groups less than or equal to the maximum annual ambient air concentration of those 

block/block groups was calculated. The annual ambient air concentration of the block/block 

group associated with the minimum, 5th, median, 95th, and maximum percentile was selected. 

The ambient air annual average Pb concentration estimates are presented to three decimal 

places, resulting in various numbers of implied significant figures (e.g., 1 to 3). No difference in 

precision is intended to be conveyed; this is simply an expedient and initial result of the software 

used for presentation. 

O-14
Exhibit O-16. Location-Specific Urban Case Studies: Estimated Population-weighted Annual A

Concentrations 

Average Annual Air Pb Concentration (μg/m3) 

Alternative NAAQS Scenario 

1 2 3 4 

Statistic Current Conditions 

Scenario 

Current NAAQS 

Scenario 0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

Chicago 

Maximum 0.040 0.809 0.040 0.010 

95th percentile 0.040 0.809 0.040 0.010 

Median 0.027 0.428 0.021 0.005 

5th percentile 0.014 0.286 0.014 0.004 

Minimum 0.011 0.225 0.011 0.003 

Cleveland 

Maximum 0.121 0.506 0.067 0.108 0.043 0.011 

95th percentile 0.121 0.506 0.067 0.108 0.043 0.011 

Median 0.021 0.085 0.011 0.018 0.007 0.002 

5th percentile 0.017 0.071 0.009 0.015 0.006 0.002 

Minimum 0.017 0.071 0.009 0.015 0.006 0.002 

Los Angeles 

Maximum 0.022 0.360 

0.009 

95th percentile 0.022 0.360 

0.009 

Median 0.019 0.300 

0.007 

5th percentile 0.015 0.239 

0.006 

Minimum 0.006 0.091 

0.002 

O-15
 
 

O.2.1.2. Inhalation Exposure Concentrations 

Inhalation exposure concentrations were calculated from these ambient air concentrations 

for both the source-oriented and non-source-oriented exposure zones using the same procedure 

employed for the general urban case study, as discussed in Appendix C. See Exhibit O-17 for 

population-weighted inhalation exposure concentrations for all of the location-specific urban 

case studies. 

The population-weighted inhalation exposure concentrations were calculated by first 

sorting the block/block groups in increasing inhalation exposure concentration order. Then the 

percentage of children living in block/block groups less than or equal to the maximum inhalation 

exposure concentration of those block/block groups was calculated. The inhalation exposure 

concentration of the block/block group associated with the minimum, 5th, median, 95th, and 

maximum percentile was selected. 

O-16
 
Exhibit O-17. Location-specific Urban Case Studies: Estimated Population-weighted Inhalation 

Concentrations 

Average Annual Inhalation Exposure Concentration of Pb (μg/m3) 

Alternative NAAQS Scenario 

1 2 3 4 

Statistic Current Conditions 

Scenario 

Current NAAQS 

Scenario 0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

Chicago 

Maximum 0.017 0.347 0.017 0.004 

95th percentile 0.017 0.347 0.017 0.004 

Median 0.012 0.184 0.009 0.002 

5th percentile 0.006 0.123 0.006 0.002 

Minimum 0.005 0.097 

0.005 

0.001 

Cleveland 

Maximum 0.052 0.217 0.029 0.047 0.019 0.005 

95th percentile 0.052 0.217 0.029 0.047 0.019 0.005 

Median 0.009 0.037 0.005 0.008 0.003 0.001 

5th percentile 0.007 0.030 0.004 0.006 0.003 0.001 

Minimum 0.007 0.030 0.004 0.006 0.003 0.001 

Los Angeles 

Maximum 0.010 0.154 

0.004 

95th percentile 0.010 0.154 

0.004 

Median 0.008 0.129 

0.003 

5th percentile 0.006 0.103 

0.002 

Minimum 0.002 0.039 

0.001 

O-17
 
O.2.2. Outdoor Soil/Dust 

The outdoor soil/dust Pb concentration estimate for each of the location-specific urban 

case studies is the same value as that employed for the general urban case study in Appendix C. 

The estimated population-weighted outdoor soil/dust concentrations for the location-specific 

case studies are seen in Exhibit O-18. 

The population-weighted outdoor soil/dust Pb concentrations were calculated by first 

sorting the block/block groups in increasing outdoor soil/dust Pb concentration order. Then the 

percentage of children living in block/block groups less than or equal to the maximum outdoor 

soil/dust concentration of those block/block groups was calculated. The outdoor soil/dust Pb 

concentration of the block/block group associated with the minimum, 5th, median, 95th, and 

maximum percentile was selected. 

Exhibit O-18. Location-specific Urban Case Studies: 

Estimated Population-weighted Outdoor Soil/Dust 

Concentrations 

Statistic Projected Average Outdoor Soil/Dust Pb 

Concentration (mg/kg) a 

Chicago 

NA - Full Study Area 198 

Cleveland 

NA - Full Study Area 198 

Los Angeles 

NA - Full Study Area 198 

a

 The projected average outdoor soil/dust concentrations are the same for 

all air quality scenarios. 

O-18
 
O.2.3. Indoor Dust 

The modeling of indoor dust concentrations for the location-specific urban case studies 

was completed using the methods detailed in Appendix C, with one difference. For this analysis, 

indoor dust concentrations were only calculated using the hybrid mechanistic-empirical dust 

model or “hybrid” for short, rather than using both the hybrid model and the air-only regressionbased model (detailed descriptions of these models are provided in Appendix G). The estimated 

population-weighted indoor dust concentrations for the location-specific urban case studies are 

seen in Exhibit O-19. 

The population-weighted indoor dust Pb concentrations were calculated by first sorting 

the block/block groups in increasing indoor dust Pb concentration order. Then the percentage of 

children living in block/block groups less than or equal to the maximum indoor dust 

concentration of those block/block groups was calculated. The indoor dust Pb concentration of 

the block/block group associated with the minimum, 5th, median, 95th, and maximum percentile 

was selected. 

O-19
 
Exhibit O-19. Location-specific Urban Case Studies: Estimated Population-weighted Indoor Dust C

Projected Average Indoor Dust Pb Concentration (mg/kg or ppm) 

Alternative NAAQS Scenario 

1 2 3 4 

Statistic Current Conditions 

Scenario 

Current NAAQS 

Scenario 0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3 , Max 

Monthly 

0.2 μg/m3, Max 

Monthly 

0.05 μg/m3, Max 

Monthly 

Chicago 

Maximum 128 491 

128 

84 

95th percentile 128 491 

128 

84 

Median 111 363 

103 

74 

5th percentile 91 300 91 70 

Minimum 86 269 86 68 

Cleveland 

Maximum 203 392 158 193 132 85 

95th percentile 203 392 158 193 132 85 

Median 101 174 86 98 78 66 

5th percentile 96 161 82 93 75 65 

Minimum 96 161 82 93 75 65 

Los Angeles 

Maximum 104 334 

81 

95th percentile 104 334 

81 

Median 99 307 

78 

5th percentile 92 276 

75 

Minimum 75 179 

67 

O-20
 
 

O.3. BLOOD PB AND IQ CHANGE MODELING 

O.3.1. Approach 

The blood lead (PbB) and intelligence quotient (IQ) change modeling for the locationspecific urban case studies was completed using the core modeling approach, as described in 

Appendix N. 

O.3.2. PbB and IQ Change Results 

Exhibit O-20 through Exhibit O-35 provide the population percentiles of estimated PbB 

levels and IQ changes, as well as the number of children less than 7 years of age estimated to 

have IQ changes greater than the various percentiles, for the location-specific urban case studies. 

The exhibits also present estimates of the proportional contribution of each exposure pathway to 

the total Pb uptake. Exhibit O-20 through Exhibit O-24 provide data for Chicago; Exhibit O-25 

through Exhibit O-31 provide data for Cleveland; and Exhibit O-32 through Exhibit O-35 

provide data for Los Angeles. IQ changes that were exactly zero because the estimated PbB was 

below the cutpoint are reported as “-.” IQ changes that were greater than -0.1 are reported as 

“> -0.1.” 

The pathway contribution estimates correspond to the fraction of Pb uptake coming from 

each pathway; and, in their presentation in these exhibits, the assumption is made that these 

fractions map linearly to corresponding fractional contributions to PbB and IQ change. The 

indoor dust contribution is separated the portion of Pb in indoor dust derived from recent air and 

the portion derived from other sources (e.g., indoor paint, outdoor soil/dust, and additional 

sources [including historical air]), as described in Appendix G. Also note that the estimates of 

pathway contributions were derived for the GM PbB estimates for the individual U.S. Census 

blocks, before the GSDs for inter-individual PbB variability were applied to generate the PbB 

distributions. The PbB and IQ change percentile estimates, however, are those after application 

of the GSD. Thus, as some of the high percentile PbB values are actually associated with U.S. 

Census blocks with low PbB GMs (and vice versa), these exhibits contain some seemingly 

irregular trends in pathway contributions. 

O-21
 
 

Exhibit O-20. Chicago: Current Conditions Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 19826 -4.1 6.0 20% 11% 42% 9% 18% 0.2% 

90th 39651 -3.7 4.6 18% 11% 39% 7% 24% 0.4% 

75th 99128 -2.3 2.9 20% 11% 42% 9% 18% 0.2% 

Median 198256 -1.4 1.8 19% 11% 41% 8% 21% 0.3% 

25th 297383 -0.9 1.1 18% 11% 39% 7% 24% 0.4% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 19826 -4.8 6.0 20% 11% 42% 9% 18% 0.2% 

90th 39651 -4.1 4.6 18% 11% 39% 7% 24% 0.4% 

75th 99128 -2.9 2.9 20% 11% 42% 9% 18% 0.2% 

Median 198256 -1.5 1.8 20% 12% 43% 11% 14% 0.2% 

25th 297383 -0.2 1.1 18% 11% 39% 7% 24% 0.4% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 19826 -7.5 6.0 20% 11% 42% 9% 18% 0.2% 

90th 39651 -6.8 4.6 18% 11% 39% 7% 24% 0.4% 

75th 99128 -5.6 2.9 20% 11% 42% 9% 18% 0.2% 

Median 198256 -4.2 1.8 20% 12% 43% 11% 14% 0.2% 

25th 297383 -2.9 1.1 18% 11% 39% 7% 24% 0.4% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 19826 -11.4 6.0 20% 11% 42% 9% 18% 0.2% 

90th 39651 -11.2 4.6 18% 11% 39% 7% 24% 0.4% 

75th 99128 -8.6 2.9 20% 11% 42% 9% 18% 0.2% 

Median 198256 -5.2 1.8 20% 12% 43% 11% 14% 0.2% 

25th 297383 -3.2 1.1 18% 11% 39% 7% 24% 0.4% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-22
 
 

 

Exhibit O-21. Chicago: Current NAAQS (1.5μg/m3, Maximum 

Quarterly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 19826 -4.7 10.2 9% 5% 20% 1% 60% 4.0% 

90th 39651 -4.4 7.7 9% 5% 20% 1% 60% 4.0% 

75th 99128 -4.0 4.9 13% 7% 27% 2% 49% 2.0% 

Median 198256 -2.4 3.0 10% 6% 22% 1% 57% 3.4% 

25th 297383 -1.4 1.8 10% 6% 22% 1% 57% 3.3% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 19826 -6.3 10.2 9% 5% 20% 1% 60% 4.0% 

90th 39651 -5.5 7.7 9% 5% 20% 1% 60% 4.0% 

75th 99128 -4.3 4.9 13% 7% 27% 2% 49% 2.0% 

Median 198256 -2.9 3.0 10% 6% 22% 1% 57% 3.4% 

25th 297383 -1.6 1.8 10% 6% 22% 1% 57% 3.3% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 19826 -9.0 10.2 9% 5% 20% 1% 60% 4.0% 

90th 39651 -8.2 7.7 9% 5% 20% 1% 60% 4.0% 

75th 99128 -7.0 4.9 13% 7% 27% 2% 49% 2.0% 

Median 198256 -5.6 3.0 10% 6% 22% 1% 57% 3.4% 

25th 297383 -4.3 1.8 10% 6% 22% 1% 57% 3.3% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 19826 -12.1 10.2 9% 5% 20% 1% 60% 4.0% 

90th 39651 -11.7 7.7 9% 5% 20% 1% 60% 4.0% 

75th 99128 -11.2 4.9 13% 7% 27% 2% 49% 2.0% 

Median 198256 -8.8 3.0 9% 5% 20% 1% 60% 4.0% 

25th 297383 -5.3 1.8 10% 6% 22% 1% 57% 3.3% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-23
 
 

 

Exhibit O-22. Chicago: Alternative NAAQS 3 (0.2μg/m3, Maximum 

Monthly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 19826 -4.1 6.0 18% 11% 39% 7% 24% 0.4% 

90th 39651 -3.6 4.5 20% 11% 42% 9% 18% 0.2% 

75th 99128 -2.3 2.9 19% 11% 41% 8% 21% 0.3% 

Median 198256 -1.4 1.8 20% 11% 42% 9% 18% 0.2% 

25th 297383 -0.9 1.1 20% 11% 42% 9% 18% 0.2% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 19826 -4.8 6.0 18% 11% 39% 7% 24% 0.4% 

90th 39651 -4.1 4.5 20% 11% 42% 9% 18% 0.2% 

75th 99128 -2.9 2.9 19% 11% 41% 8% 21% 0.3% 

Median 198256 -1.5 1.8 20% 12% 43% 10% 14% 0.2% 

25th 297383 -0.2 1.1 20% 11% 42% 9% 18% 0.2% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 19826 -7.5 6.0 18% 11% 39% 7% 24% 0.4% 

90th 39651 -6.8 4.5 20% 11% 42% 9% 18% 0.2% 

75th 99128 -5.6 2.9 19% 11% 41% 8% 21% 0.3% 

Median 198256 -4.2 1.8 20% 12% 43% 10% 14% 0.2% 

25th 297383 -2.9 1.1 20% 11% 42% 9% 18% 0.2% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 19826 -11.4 6.0 18% 11% 39% 7% 24% 0.4% 

90th 39651 -11.1 4.5 20% 11% 42% 9% 18% 0.2% 

75th 99128 -8.5 2.9 19% 11% 41% 8% 21% 0.3% 

Median 198256 -5.2 1.8 20% 11% 42% 9% 18% 0.2% 

25th 297383 -3.2 1.1 20% 11% 42% 9% 18% 0.2% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the “other” portion of indoor dust. 

O-24
 
 

 

Exhibit O-23. Chicago: Alternative NAAQS 4 (0.05μg/m3

, Maximum 

Monthly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 19826 -4.1 5.5 21% 12% 46% 14% 6% < 0.1% 

90th 39651 -3.4 4.2 21% 12% 45% 13% 9% < 0.1% 

75th 99128 -2.1 2.7 21% 12% 44% 12% 11% 0.1% 

Median 198256 -1.3 1.6 22% 13% 46% 15% 5% < 0.1% 

25th 297383 -0.8 1.0 21% 12% 45% 14% 7% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 19826 -4.6 5.5 21% 12% 46% 14% 6% < 0.1% 

90th 39651 -3.9 4.2 21% 12% 45% 13% 9% < 0.1% 

75th 99128 -2.7 2.7 21% 12% 44% 12% 11% 0.1% 

Median 198256 -1.3 1.6 22% 13% 46% 15% 5% < 0.1% 

25th 297383 - 0.4 21% 12% 46% 14% 6% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 19826 -7.3 5.5 21% 12% 46% 14% 6% < 0.1% 

90th 39651 -6.6 4.2 21% 12% 45% 13% 9% < 0.1% 

75th 99128 -5.4 2.7 21% 12% 44% 12% 11% 0.1% 

Median 198256 -4.0 1.6 22% 13% 46% 15% 5% < 0.1% 

25th 297383 -2.7 1.0 21% 12% 45% 14% 7% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 19826 -11.3 5.5 21% 12% 46% 14% 6% < 0.1% 

90th 39651 -11.1 4.2 21% 12% 45% 13% 9% < 0.1% 

75th 99128 -7.9 2.7 21% 12% 44% 12% 11% 0.1% 

Median 198256 -4.8 1.6 22% 13% 46% 15% 5% < 0.1% 

25th 297383 -2.9 1.0 21% 12% 45% 14% 7% < 0.1% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the “other” portion of indoor dust. 

O-25
 
 

 

Exhibit O-24. Chicago: Alternative NAAQS 5 (0.02μg/m3, Maximum 

Monthly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 19826 -4.1 5.4 22% 13% 46% 15% 4% < 0.1% 

90th 39651 -3.3 4.1 22% 13% 46% 16% 3% < 0.1% 

75th 99128 -2.1 2.6 22% 13% 46% 15% 4% < 0.1% 

Median 198256 -1.3 1.6 22% 13% 47% 16% 3% < 0.1% 

25th 297383 -0.8 1.0 22% 13% 46% 15% 5% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 19826 -4.6 5.4 22% 13% 46% 15% 4% < 0.1% 

90th 39651 -3.8 4.1 22% 13% 46% 16% 3% < 0.1% 

75th 99128 -2.6 2.6 22% 13% 46% 15% 4% < 0.1% 

Median 198256 -1.3 1.6 22% 13% 47% 16% 3% < 0.1% 

25th 297383 - 0.5 22% 13% 46% 15% 4% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 19826 -7.3 5.4 22% 13% 46% 15% 4% < 0.1% 

90th 39651 -6.5 4.1 22% 13% 46% 16% 3% < 0.1% 

75th 99128 -5.3 2.6 22% 13% 46% 15% 4% < 0.1% 

Median 198256 -4.0 1.6 22% 13% 47% 16% 3% < 0.1% 

25th 297383 -2.6 1.0 22% 13% 46% 15% 5% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 19826 -11.3 5.4 22% 13% 46% 15% 4% < 0.1% 

90th 39651 -11.1 4.1 22% 13% 46% 16% 3% < 0.1% 

75th 99128 -7.8 2.6 22% 13% 46% 15% 4% < 0.1% 

Median 198256 -4.7 1.6 22% 13% 46% 16% 3% < 0.1% 

25th 297383 -2.9 1.0 22% 13% 46% 15% 5% < 0.1% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-26
 
 

 

Exhibit O-25. Cleveland: Current Conditions Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 700 -4.1 6.1 20% 12% 43% 10% 15% 0.2% 

90th 1399 -3.7 4.6 20% 12% 43% 10% 15% 0.2% 

75th 3498 -2.3 2.9 20% 12% 43% 10% 15% 0.2% 

Median 6995 -1.4 1.8 20% 12% 43% 10% 15% 0.2% 

25th 10493 -0.9 1.1 16% 9% 33% 3% 38% 1.0% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 700 -4.9 6.1 20% 12% 43% 10% 15% 0.2% 

90th 1399 -4.1 4.6 20% 12% 43% 10% 15% 0.2% 

75th 3498 -2.9 2.9 20% 12% 43% 10% 15% 0.2% 

Median 6995 -1.5 1.8 20% 12% 43% 10% 15% 0.2% 

25th 10493 -0.2 1.1 16% 9% 33% 3% 38% 1.0% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 700 -7.6 6.1 20% 12% 43% 10% 15% 0.2% 

90th 1399 -6.8 4.6 20% 12% 43% 10% 15% 0.2% 

75th 3498 -5.6 2.9 20% 12% 43% 10% 15% 0.2% 

Median 6995 -4.2 1.8 20% 12% 42% 9% 17% 0.2% 

25th 10493 -2.9 1.1 16% 9% 33% 3% 38% 1.0% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 700 -11.4 6.1 20% 12% 43% 10% 15% 0.2% 

90th 1399 -11.2 4.6 20% 12% 43% 10% 15% 0.2% 

75th 3498 -8.6 2.9 20% 12% 43% 10% 15% 0.2% 

Median 6995 -5.2 1.8 20% 12% 42% 9% 17% 0.2% 

25th 10493 -3.2 1.1 16% 9% 33% 3% 38% 1.0% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-27
 
 

 

Exhibit O-26. Cleveland: Current NAAQS (1.5μg/m3, Maximum 

Quarterly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dLPeak) 

95th 700 -4.3 7.4 17% 10% 36% 5% 31% 0.6% 

90th 1399 -4.1 5.6 17% 10% 35% 4% 34% 0.7% 

75th 3498 -2.8 3.5 17% 10% 36% 5% 31% 0.6% 

Median 6995 -1.7 2.1 17% 10% 35% 4% 34% 0.7% 

25th 10493 -1.0 1.3 17% 10% 35% 4% 34% 0.7% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 700 -5.4 7.4 17% 10% 36% 5% 31% 0.6% 

90th 1399 -4.6 5.6 17% 10% 35% 4% 34% 0.7% 

75th 3498 -3.4 3.5 17% 10% 36% 5% 31% 0.6% 

Median 6995 -2.0 2.1 11% 6% 23% 1% 55% 2.9% 

25th 10493 -0.7 1.3 17% 10% 35% 4% 34% 0.7% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 700 -8.1 7.4 17% 10% 36% 5% 31% 0.6% 

90th 1399 -7.3 5.6 17% 10% 35% 4% 34% 0.7% 

75th 3498 -6.1 3.5 17% 10% 36% 5% 31% 0.6% 

Median 6995 -4.7 2.1 11% 6% 23% 1% 55% 2.9% 

25th 10493 -3.4 1.3 17% 10% 35% 4% 34% 0.7% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 700 -11.6 7.4 17% 10% 36% 5% 31% 0.6% 

90th 1399 -11.3 5.6 17% 10% 35% 4% 34% 0.7% 

75th 3498 -10.3 3.5 17% 10% 36% 5% 31% 0.6% 

Median 6995 -6.3 2.1 17% 10% 35% 4% 34% 0.7% 

25th 10493 -3.8 1.3 17% 10% 35% 4% 34% 0.7% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-28
 
 

 

Exhibit O-27. Cleveland: Alternative NAAQS 1 (0.2μg/m3, Maximum 

Quarterly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 700 -4.1 5.8 21% 12% 44% 11% 12% 0.1% 

90th 1399 -3.5 4.4 21% 12% 44% 11% 12% 0.1% 

75th 3498 -2.2 2.8 21% 12% 44% 12% 10% 0.1% 

Median 6995 -1.4 1.7 21% 12% 44% 12% 10% 0.1% 

25th 10493 -0.8 1.0 21% 12% 44% 12% 10% 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 700 -4.7 5.8 21% 12% 44% 11% 12% 0.1% 

90th 1399 -4.0 4.4 21% 12% 44% 11% 12% 0.1% 

75th 3498 -2.8 2.8 21% 12% 44% 12% 10% 0.1% 

Median 6995 -1.4 1.7 21% 12% 44% 12% 10% 0.1% 

25th 10493 > -0.1 1.0 21% 12% 44% 12% 10% 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 700 -7.4 5.8 21% 12% 44% 11% 12% 0.1% 

90th 1399 -6.7 4.4 21% 12% 44% 11% 12% 0.1% 

75th 3498 -5.5 2.8 21% 12% 44% 12% 10% 0.1% 

Median 6995 -4.1 1.7 21% 12% 44% 12% 10% 0.1% 

25th 10493 -2.8 1.0 21% 12% 44% 12% 10% 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 700 -11.3 5.8 21% 12% 44% 11% 12% 0.1% 

90th 1399 -11.1 4.4 21% 12% 44% 11% 12% 0.1% 

75th 3498 -8.2 2.8 21% 12% 44% 12% 10% 0.1% 

Median 6995 -5.0 1.7 21% 12% 44% 12% 10% 0.1% 

25th 10493 -3.0 1.0 21% 12% 44% 12% 10% 0.1% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-29
 
 

 

Exhibit O-28. Cleveland: Alternative NAAQS 2 (0.5μg/m3, Maximum 

Monthly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 700 -4.1 6.0 18% 11% 39% 6% 25% 0.4% 

90th 1399 -3.7 4.6 20% 12% 43% 10% 14% 0.2% 

75th 3498 -2.3 2.9 20% 12% 43% 10% 16% 0.2% 

Median 6995 -1.4 1.8 20% 12% 43% 10% 14% 0.2% 

25th 10493 -0.9 1.1 20% 12% 43% 10% 14% 0.2% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 700 -4.8 6.0 18% 11% 39% 6% 25% 0.4% 

90th 1399 -4.1 4.6 20% 12% 43% 10% 14% 0.2% 

75th 3498 -2.9 2.9 20% 12% 43% 10% 16% 0.2% 

Median 6995 -1.5 1.8 20% 12% 43% 10% 14% 0.2% 

25th 10493 -0.2 1.1 20% 12% 43% 10% 14% 0.2% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 700 -7.5 6.0 18% 11% 39% 6% 25% 0.4% 

90th 1399 -6.8 4.6 20% 12% 43% 10% 14% 0.2% 

75th 3498 -5.6 2.9 20% 12% 43% 10% 16% 0.2% 

Median 6995 -4.2 1.8 20% 12% 43% 10% 14% 0.2% 

25th 10493 -2.9 1.1 20% 12% 43% 10% 14% 0.2% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 700 -11.4 6.0 18% 11% 39% 6% 25% 0.4% 

90th 1399 -11.2 4.6 20% 12% 43% 10% 14% 0.2% 

75th 3498 -8.6 2.9 20% 12% 43% 10% 16% 0.2% 

Median 6995 -5.2 1.8 16% 9% 34% 4% 37% 0.9% 

25th 10493 -3.1 1.1 20% 12% 43% 10% 14% 0.2% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-30
 
 

Exhibit O-29. Cleveland: Alternative NAAQS 3 (0.2μg/m3, Maximum 

Monthly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 700 -4.1 5.7 21% 12% 45% 14% 7% < 0.1% 

90th 1399 -3.5 4.3 20% 12% 43% 10% 15% 0.2% 

75th 3498 -2.2 2.8 21% 12% 45% 13% 9% < 0.1% 

Median 6995 -1.3 1.7 21% 12% 45% 14% 7% < 0.1% 

25th 10493 -0.8 1.0 21% 12% 45% 13% 9% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 700 -4.7 5.7 21% 12% 45% 14% 7% < 0.1% 

90th 1399 -4.0 4.3 20% 12% 43% 10% 15% 0.2% 

75th 3498 -2.7 2.8 21% 12% 45% 13% 9% < 0.1% 

Median 6995 -1.4 1.7 21% 12% 45% 14% 7% < 0.1% 

25th 10493 > -0.1 1.0 21% 12% 45% 13% 9% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 700 -7.4 5.7 21% 12% 45% 14% 7% < 0.1% 

90th 1399 -6.7 4.3 20% 12% 43% 10% 15% 0.2% 

75th 3498 -5.4 2.8 21% 12% 45% 13% 9% < 0.1% 

Median 6995 -4.1 1.7 20% 12% 43% 10% 15% 0.2% 

25th 10493 -2.7 1.0 21% 12% 45% 13% 9% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 700 -11.3 5.7 21% 12% 45% 14% 7% < 0.1% 

90th 1399 -11.1 4.3 20% 12% 43% 10% 15% 0.2% 

75th 3498 -8.1 2.8 21% 12% 45% 13% 9% < 0.1% 

Median 6995 -4.9 1.7 21% 12% 45% 14% 7% < 0.1% 

25th 10493 -3.0 1.0 21% 12% 45% 13% 9% < 0.1% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the “other” portion of indoor dust. 

O-31
 
 

Exhibit O-30. Cleveland: Alternative NAAQS 4 (0.05μg/m3, Maximum 

Monthly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 700 -4.0 5.4 22% 13% 47% 16% 2% < 0.1% 

90th 1399 -3.3 4.1 22% 13% 47% 16% 2% < 0.1% 

75th 3498 -2.1 2.6 22% 13% 47% 16% 2% < 0.1% 

Median 6995 -1.3 1.6 22% 13% 47% 16% 2% < 0.1% 

25th 10493 -0.8 1.0 22% 13% 47% 16% 2% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 700 -4.5 5.4 22% 13% 47% 16% 2% < 0.1% 

90th 1399 -3.8 4.1 22% 13% 47% 16% 2% < 0.1% 

75th 3498 -2.6 2.6 22% 13% 47% 16% 2% < 0.1% 

Median 6995 -1.3 1.6 22% 13% 47% 16% 3% < 0.1% 

25th 10493 - 0.9 21% 12% 44% 12% 11% 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 700 -7.2 5.4 22% 13% 47% 16% 2% < 0.1% 

90th 1399 -6.5 4.1 22% 13% 47% 16% 2% < 0.1% 

75th 3498 -5.3 2.6 22% 13% 47% 16% 2% < 0.1% 

Median 6995 -4.0 1.6 22% 13% 47% 16% 3% < 0.1% 

25th 10493 -2.6 1.0 22% 13% 47% 16% 2% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 700 -11.3 5.4 22% 13% 47% 16% 2% < 0.1% 

90th 1399 -11.1 4.1 22% 13% 47% 16% 2% < 0.1% 

75th 3498 -7.7 2.6 22% 13% 47% 16% 2% < 0.1% 

Median 6995 -4.7 1.6 22% 13% 47% 16% 3% < 0.1% 

25th 10493 -2.8 1.0 22% 13% 47% 16% 2% < 0.1% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the “other” portion of indoor dust. 

O-32
 
 

Exhibit O-31. Cleveland: Alternative NAAQS 5 (0.02μg/m3

, Maximum 

Monthly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 700 -4.0 5.3 22% 13% 47% 17% 1% < 0.1% 

90th 1399 -3.2 4.1 22% 13% 47% 17% 1% < 0.1% 

75th 3498 -2.1 2.6 22% 13% 47% 17% 1% < 0.1% 

Median 6995 -1.3 1.6 22% 13% 47% 17% 1% < 0.1% 

25th 10493 -0.8 1.0 22% 13% 47% 17% 1% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 700 -4.5 5.3 22% 13% 47% 17% 1% < 0.1% 

90th 1399 -3.8 4.1 22% 13% 47% 17% 1% < 0.1% 

75th 3498 -2.6 2.6 22% 13% 47% 17% 1% < 0.1% 

Median 6995 -1.2 1.6 22% 13% 47% 17% 1% < 0.1% 

25th 10493 - 1.0 22% 13% 47% 17% 1% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 700 -7.2 5.3 22% 13% 47% 17% 1% < 0.1% 

90th 1399 -6.5 4.1 22% 13% 47% 17% 1% < 0.1% 

75th 3498 -5.3 2.6 22% 13% 47% 17% 1% < 0.1% 

Median 6995 -3.9 1.6 22% 13% 47% 17% 1% < 0.1% 

25th 10493 -2.6 1.0 22% 13% 47% 17% 1% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 700 -11.3 5.3 22% 13% 47% 17% 1% < 0.1% 

90th 1399 -11.1 4.1 22% 13% 47% 17% 1% < 0.1% 

75th 3498 -7.7 2.6 22% 13% 47% 17% 1% < 0.1% 

Median 6995 -4.6 1.6 22% 13% 47% 17% 1% < 0.1% 

25th 10493 -2.8 1.0 22% 13% 47% 17% 1% < 0.1% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-33
 
 

 

Exhibit O-32. Los Angeles: Current Conditions Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 14305 -4.1 5.9 20% 11% 42% 9% 18% 0.2% 

90th 28611 -3.6 4.5 20% 11% 42% 9% 18% 0.2% 

75th 71527 -2.3 2.8 20% 11% 42% 9% 18% 0.2% 

Median 143054 -1.4 1.7 20% 12% 42% 10% 16% 0.2% 

95th 214580 -0.8 1.1 20% 12% 42% 10% 16% 0.2% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 14305 -4.8 5.9 20% 11% 42% 9% 18% 0.2% 

90th 28611 -4.0 4.5 20% 11% 42% 9% 18% 0.2% 

75th 71527 -2.8 2.8 20% 11% 42% 9% 18% 0.2% 

Median 143054 -1.5 1.7 20% 11% 42% 9% 18% 0.2% 

25th 214580 -0.1 1.1 20% 12% 42% 10% 16% 0.2% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 14305 -7.5 5.9 20% 11% 42% 9% 18% 0.2% 

90th 28611 -6.7 4.5 20% 11% 42% 9% 18% 0.2% 

75th 71527 -5.5 2.8 20% 11% 42% 9% 18% 0.2% 

Median 143054 -4.2 1.7 20% 12% 42% 10% 16% 0.2% 

25th 214580 -2.8 1.1 20% 12% 42% 10% 16% 0.2% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

95th 14305 -11.4 5.9 20% 11% 42% 9% 18% 0.2% 

90th 28611 -11.1 4.5 20% 11% 42% 9% 18% 0.2% 

75th 71527 -8.4 2.8 20% 11% 42% 9% 18% 0.2% 

Median 143054 -5.1 1.7 20% 11% 42% 9% 18% 0.2% 

25th 214580 -3.1 1.1 20% 12% 42% 10% 16% 0.2% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-34
 
 

 

Exhibit O-33. Los Angeles: Current NAAQS (1.5μg/m3, Maximum 

Quarterly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 14305 -4.5 8.9 13% 7% 27% 2% 49% 2.0% 

90th 28611 -4.2 6.8 13% 7% 27% 2% 49% 2.0% 

75th 71527 -3.5 4.4 13% 7% 27% 2% 49% 2.0% 

Median 143054 -2.1 2.6 13% 7% 27% 2% 49% 2.0% 

25th 214580 -1.3 1.6 13% 7% 27% 2% 49% 2.0% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 14305 -5.9 8.9 13% 7% 27% 2% 49% 2.0% 

90th 28611 -5.2 6.8 13% 7% 27% 2% 49% 2.0% 

75th 71527 -4.0 4.4 13% 7% 27% 2% 49% 2.0% 

Median 143054 -2.6 2.6 13% 7% 27% 2% 49% 2.0% 

25th 214580 -1.3 1.6 13% 7% 27% 2% 49% 2.0% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 14305 -8.6 8.9 13% 7% 27% 2% 49% 2.0% 

90th 28611 -7.9 6.8 13% 7% 27% 2% 49% 2.0% 

75th 71527 -6.7 4.4 13% 7% 27% 2% 49% 2.0% 

Median 143054 -5.3 2.6 13% 7% 27% 2% 49% 2.0% 

25th 214580 -4.0 1.6 13% 7% 27% 2% 49% 2.0% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 14305 -11.8 8.9 13% 7% 27% 2% 49% 2.0% 

90th 28611 -11.5 6.8 13% 7% 27% 2% 49% 2.0% 

75th 71527 -11.1 4.4 13% 7% 27% 2% 49% 2.0% 

Median 143054 -7.7 2.6 13% 7% 27% 2% 49% 2.0% 

25th 214580 -4.7 1.6 13% 7% 27% 2% 49% 2.0% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-35
 
 

 

Exhibit O-34. Los Angeles: Alternative NAAQS 4 (0.05μg/m3, Maximum 

Monthly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 14305 -4.1 5.5 21% 12% 45% 12% 10% < 0.1% 

90th 28611 -3.4 4.2 21% 12% 45% 14% 7% < 0.1% 

75th 71527 -2.2 2.7 21% 12% 45% 13% 8% < 0.1% 

Median 143054 -1.3 1.6 21% 12% 45% 13% 9% < 0.1% 

25th 214580 -0.8 1.0 21% 12% 45% 13% 9% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 14305 -4.6 5.5 21% 12% 45% 12% 10% < 0.1% 

90th 28611 -3.9 4.2 21% 12% 45% 14% 7% < 0.1% 

75th 71527 -2.7 2.7 21% 12% 45% 13% 8% < 0.1% 

Median 143054 -1.3 1.6 21% 12% 45% 13% 9% < 0.1% 

25th 214580 - 0.4 21% 12% 45% 12% 10% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 14305 -7.3 5.5 21% 12% 45% 12% 10% < 0.1% 

90th 28611 -6.6 4.2 21% 12% 45% 14% 7% < 0.1% 

75th 71527 -5.4 2.7 21% 12% 45% 13% 8% < 0.1% 

Median 143054 -4.0 1.6 21% 12% 45% 13% 9% < 0.1% 

25th 214580 -2.7 1.0 21% 12% 45% 13% 9% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 14305 -11.3 5.5 21% 12% 45% 12% 10% < 0.1% 

90th 28611 -11.1 4.2 21% 12% 45% 14% 7% < 0.1% 

75th 71527 -7.9 2.7 21% 12% 45% 13% 8% < 0.1% 

Median 143054 -4.8 1.6 21% 12% 45% 13% 9% < 0.1% 

25th 214580 -2.9 1.0 21% 12% 45% 13% 9% < 0.1% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the “other” portion of indoor dust. 

O-36
 
 

 

Exhibit O-35. Los Angeles: Alternative NAAQS 5 (0.02μg/m3, Maximum 

Monthly Average) Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent Air) 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 10 μg/dL Peak) 

95th 14305 -4.0 5.4 22% 13% 46% 16% 3% < 0.1% 

90th 28611 -3.3 4.1 22% 13% 46% 16% 4% < 0.1% 

75th 71527 -2.1 2.6 22% 13% 46% 16% 3% < 0.1% 

Median 143054 -1.3 1.6 22% 13% 46% 16% 3% < 0.1% 

25th 214580 -0.8 1.0 22% 13% 46% 16% 4% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 14305 -4.5 5.4 22% 13% 46% 16% 3% < 0.1% 

90th 28611 -3.8 4.1 22% 13% 46% 16% 4% < 0.1% 

75th 71527 -2.6 2.6 22% 13% 46% 16% 3% < 0.1% 

Median 143054 -1.3 1.6 22% 13% 46% 16% 3% < 0.1% 

25th 214580 - 0.6 22% 13% 47% 16% 3% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 14305 -7.2 5.4 22% 13% 46% 16% 3% < 0.1% 

90th 28611 -6.5 4.1 22% 13% 46% 16% 4% < 0.1% 

75th 71527 -5.3 2.6 22% 13% 46% 16% 3% < 0.1% 

Median 143054 -4.0 1.6 22% 13% 46% 16% 4% < 0.1% 

25th 214580 -2.6 1.0 22% 13% 46% 16% 4% < 0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear - Stratified at 7.5 μg/dL Peak) 

95th 14305 -11.3 5.4 22% 13% 46% 16% 3% < 0.1% 

90th 28611 -11.1 4.1 22% 13% 46% 16% 4% < 0.1% 

75th 71527 -7.7 2.6 22% 13% 46% 16% 3% < 0.1% 

Median 143054 -4.7 1.6 22% 13% 46% 16% 4% < 0.1% 

25th 214580 -2.9 1.0 22% 13% 46% 16% 4% < 0.1% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional 

sources, including historical air, while "recent air" refers to contributions associated with recent/current 

outdoor ambient air, with outdoor ambient air also potentially including resuspended, previously deposited 

Pb (see Section 2.4.3). In other summary tables, and text, "past air" refers to contributions from ingestion 

of outdoor soil/dust and of the "other" portion of indoor dust. 

O-37
 
 

 

 

O.3.3. Ambient Air to PbB Ratios 

Exhibit O-36 shows the air-to-PbB ratios for each of the location-specific urban case 

studies. Note that these ratios are derived in a different manner than the air to PbB ratios 

presented in Appendix I. The air to PbB ratios presented here are derived by comparing changes 

(deltas) in median total PbB levels (concurrent) to associated changes in annual average air Pb 

levels as one steps to the next lowest air quality scenario. The ambient air annual average Pb 

concentration estimates are presented to three decimal places, resulting in various numbers of 

implied significant figures (e.g., 1 to 3). No difference in precision is intended to be conveyed; 

this is simply an expedient and initial result of the software used for presentation. 

Exhibit O-36. Location-specific Urban Case Studies: Air to PbB Ratios 

Air Scenario 

Median 

Total PbB 

(μg/dL) 

Annual 

Average 

Ambient Air 

Concentration 

(μg/m3

) 

Ratio a 

Chicago: PbB Metric (Concurrent) 

Current NAAQS (1.5 μg/m3

, maximum quarterly average) 3.0 0.428 

Current Conditions 1.8 0.027 1 : 3.0

Alternative NAAQS 3 (0.2 μg/m3

, maximum monthly average) 1.8 0.021 1 : 0.5 

Alternative NAAQS 4 (0.05 μg/m3

, maximum monthly average) 1.6 0.005 1 : 8.0 

Alternative NAAQS 5 (0.02 μg/m3

, max monthly average) 1.6 0.002 1 : 12.9 

Cleveland: PbB Metric (Concurrent) 

Current NAAQS (1.5 μg/m3

, maximum quarterly average) 2.1 0.085 

Alternative NAAQS 2 (0.5 μg/m 1 : 5.4 3

, maximum monthly average) 1.8 0.018 

Current Conditions 1.8 0.021 1 : 1.2 

Alternative NAAQS 1 (0.2 μg/m3

, maximum quarterly average) 1.7 0.011 1 : 10.1 

Alternative NAAQS 3 (0.2 μg/m3

, maximum monthly average) 1.7 0.007 1 : 8.3 

Alternative NAAQS 4 (0.05 μg/m3

, maximum monthly average) 1.6 0.002 1 : 10.8 

Alternative NAAQS 5 (0.02 μg/m3

, max monthly average) 1.6 0.001 1 : 22.5 

Los Angeles: PbB Metric (Concurrent) 

Current NAAQS (1.5 μg/m3

, maximum quarterly average) 2.6 0.300 

Current Conditions 1.7 0.019 1 : 3.2

Alternative NAAQS 4 (0.05 μg/m3

, maximum monthly average) 1.6 0.007 1 : 6.8 

Alternative NAAQS 5 (0.02 μg/m3

, max monthly average) 1.6 0.002 1 : 10.2 

a

 A ratio is not presented adjacent to the current NAAQS air quality scenario (for any of the case studies) 

because the air-to-PbB ratios are derived by comparing changes (deltas) in median total PbB levels 

(concurrent) to associated changes in annual average air Pb levels as one steps to the next lowest air 

quality scenario. The first ratio presented for any of the case studies is generated by comparing median 

PbB levels at the current NAAQS level to the median PbB level at the highest of the alternative NAAQS 

levels (i.e., Alternative NAAQS 5 [0.05 μg/m3

 max monthly] value). 

O-38
 
 

O.3.4. Number of Children with IQ Loss Resulting from Total Pb Exposure 

The following exhibits show the number and percentage of children experiencing IQ loss 

under different NAAQS scenarios for the location-specific case study areas (Exhibit O-37 and 

Exhibit O-38 for the Chicago case study area, Exhibit O-39 and Exhibit O-40 for the Cleveland 

case study area, and Exhibit O-41 and Exhibit O-42 for the Los Angeles case study areas). 

O-39
 
Exhibit O-37. Chicago: Number of Children with IQ Loss Resulting from Total Pb Expo

Number of Children with IQ Change Resulting from Total Pb Exposure per IQ Change 

Air Quality Scenario 

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dLPeak) 

Current conditions (mean) 3600 27756 94124 143854 64679 30159 30825 1301 151 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 508 7264 41324 112712 84195 53125 87280 8105 1443 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 3997 27375 93696 144798 65535 29960 29794 1190 119 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 5218 33886 103632 142236 59992 25297 25242 896 95 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 5638 35512 105496 141118 59588 24266 23894 888 103 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

Current conditions (mean) 106638 17232 36384 79881 68311 46915 25551 10833 3751 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 47264 10325 24869 68057 77637 70936 51768 27732 12696 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 107589 16899 36463 77375 70492 46677 25234 11047 3727 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 116765 17891 37462 77740 67423 43204 22815 9611 2736 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 119604 18287 39326 78914 64814 42332 21229 8525 2791 

O-40
 
Exhibit O-37. Chicago: Number of Children with IQ Loss Resulting from Total Pb Expo

Number of Children with IQ Change Resulting from Total Pb Exposure per IQ Change 

Air Quality Scenario 

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

 Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

Current conditions (mean) 16 428 6312 40135 58398 73212 78247 64171 41927 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 0 56 928 12014 25282 43933 66852 76669 70619 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 8 523 6519 40436 56717 74481 78700 64417 42165 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 24 761 8319 47883 64393 75131 76542 59810 36281 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 48 658 9175 49191 65924 76051 75028 59104 35305 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dLPeak) 

Current conditions (mean) 16 301 4592 33370 51546 52197 47367 38937 31475 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 0 48 666 9366 20730 27478 31943 31602 30278 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 0 365 4988 33537 50634 52649 47621 38668 31983 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 16 476 6447 40341 57058 55448 47399 37946 30674 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 40 397 6899 41832 58945 56194 47058 37740 29588 

a The number of children with IQ loss in each IQ change range for each air quality scenario was calculated by multiplying the n

each IQ change range by a ratio of the number of children in the city in the target age range to the number of iterations. 

O-41
 
Exhibit O-38. Chicago: Percentage of Children with IQ Loss Resulting from Total Pb Exp

Percentage of Children with IQ Loss Resulting from Total Pb Exposure per IQ Loss R

Air Quality Scenario 

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

Current conditions (mean) 0.9% 7% 24% 36% 16% 8% 8% 0.3% <0.1% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 0.1% 2% 10% 28% 21% 13% 22% 2% 0.4% 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 1% 7% 24% 37% 17% 8% 8% 0.3% <0.1% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 1% 9% 26% 36% 15% 6% 6% 0.2% <0.1% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 1% 9% 27% 36% 15% 6% 6% 0.2% <0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

Current conditions (mean) 27% 4% 9% 20% 17% 12% 6% 3% 0.9% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 12% 3% 6% 17% 20% 18% 13% 7% 3% 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 27% 4% 9% 20% 18% 12% 6% 3% 0.9% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 29% 5% 9% 20% 17% 11% 6% 2% 0.7% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 30% 5% 10% 20% 16% 11% 5% 2% 0.7% 

O-42
 
Exhibit O-38. Chicago: Percentage of Children with IQ Loss Resulting from Total Pb Exp

Percentage of Children with IQ Loss Resulting from Total Pb Exposure per IQ Loss R

Air Quality Scenario 

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

Current conditions (mean) <0.1% 0.1% 2% 10% 15% 18% 20% 16% 11% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) <0.1% <0.1% 0.2% 3% 6% 11% 17% 19% 18% 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 10% 14% 19% 20% 16% 11% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) <0.1% 0.2% 2% 12% 16% 19% 19% 15% 9% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) <0.1% 0.2% 2% 12% 17% 19% 19% 15% 9% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

Current conditions (mean) <0.1% 0.1% 1% 8% 13% 13% 12% 10% 8% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) <0.1% <0.1% 0.2% 2% 5% 7% 8% 8% 8% 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) <0.1% 0.1% 1% 8% 13% 13% 12% 10% 8% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 10% 14% 14% 12% 10% 8% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 11% 15% 14% 12% 10% 7% 

a For each air quality scenario and IQ range, the percentage of children with IQ loss resulting from total Pb exposure was calcul

number of children with IQ loss by the total number of children in the city in the target age range. 

O-43
 
Exhibit O-39. Cleveland: Number of Children with IQ Loss Resulting from Total Pb Exp

Number of Children with IQ Change Resulting from Total Pb Exposure per IQ Change 

Air Quality Scenario 

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

Current conditions (mean) 131 983 3349 5027 2288 1050 1106 47 6 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 71 644 2611 4870 2653 1357 1658 108 14 

Alternative NAAQS 1 (0.2 μg/m3 , 

maximum quarterly average) 161 1104 3503 5027 2210 957 978 44 4 

Alternative NAAQS 2 (0.5 μg/m3 , 

maximum monthly average) 146 994 3353 5027 2283 1056 1079 46 6 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 167 1144 3595 4982 2174 944 930 48 5 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 194 1253 3758 4997 2063 879 816 29 2 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 204 1295 3754 4993 2062 851 795 31 3 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

Current conditions (mean) 3611 598 1267 2786 2442 1708 964 424 145 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 2616 495 1110 2679 2637 2123 1299 668 258 

Alternative NAAQS 1 (0.2 μg/m3 , 

maximum quarterly average) 3914 600 1315 2750 2379 1638 867 360 128 

Alternative NAAQS 2 (0.5 μg/m3 , 

maximum monthly average) 3660 591 1274 2762 2438 1718 961 402 141 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 4009 624 1347 2736 2352 1578 828 355 117 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 4303 636 1332 2759 2271 1504 761 306 95 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 4333 627 1362 2747 2286 1489 746 282 91 

O-44
 
Exhibit O-39. Cleveland: Number of Children with IQ Loss Resulting from Total Pb Exp

Number of Children with IQ Change Resulting from Total Pb Exposure per IQ Change 

Air Quality Scenario 

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

 Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

Current conditions (mean) 1 16 228 1420 2072 2572 2740 2268 1462 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 0 8 125 987 1594 2282 2734 2540 1862 

Alternative NAAQS 1 (0.2 μg/m3 , 

maximum quarterly average) 1 22 264 1585 2168 2600 2716 2189 1372 

Alternative NAAQS 2 (0.5 μg/m3 , 

maximum monthly average) 1 18 250 1426 2086 2569 2727 2254 1477 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 0 20 276 1635 2191 2666 2670 2169 1336 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 1 25 322 1762 2311 2686 2680 2074 1243 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 1 28 334 1800 2301 2729 2644 2062 1225 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

Current conditions (mean) 0 11 170 1182 1820 1843 1660 1368 1101 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 0 4 93 798 1391 1543 1525 1337 1160 

Alternative NAAQS 1 (0.2 μg/m3 , 

maximum quarterly average) 0 17 203 1327 1916 1920 1635 1366 1081 

Alternative NAAQS 2 (0.5 μg/m3 , 

maximum monthly average) 1 13 187 1201 1833 1833 1667 1360 1089 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 0 13 219 1369 1939 1978 1662 1340 1046 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 0 17 244 1495 2071 1977 1673 1353 1054 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 0 19 250 1544 2082 1978 1699 1301 1066 

a The number of children with IQ loss in each IQ change range for each air quality scenario was calculated by multiplying the n

each IQ change range by a ratio of the number of children in the city in the target age range to the number of iterations. 

O-45 
t

Exhibit O-40. Cleveland: Percentage of Children with IQ Loss Resulting from Total Pb Ex

Air Quality Scenario 

Percentage of Children with IQ Loss Resulting from Total Pb Exposure per IQ Loss R

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

Current conditions (mean) 0.9% 7% 24% 36% 16% 8% 8% 0.3% <0.1% 

Current NAAQS (1.5 μg/m3 , 

i l ) 

0.5% 5% 19% 35% 19% 10% 12% 0.8% 0.1% 

Alternative NAAQS 1 (0.2 μg/m3 , 

maximum quarterly average) 1% 8% 25% 36% 16% 7% 7% 0.3% <0.1% 

Alternative NAAQS 2 (0.5 μg/m3 , 

maximum monthly average) 1% 7% 24% 36% 16% 8% 8% 0.3% <0.1% 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 1% 8% 26% 36% 16% 7% 7% 0.3% <0.1% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 1% 9% 27% 36% 15% 6% 6% 0.2% <0.1% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 1% 9% 27% 36% 15% 6% 6% 0.2% <0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

Current conditions (mean) 26% 4% 9% 20% 17% 12% 7% 3% 1% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 19% 4% 8% 19% 19% 15% 9% 5% 2% 

Alternative NAAQS 1 (0.2 μg/m3 , 

maximum quarterly average) 28% 4% 9% 20% 17% 12% 6% 3% 0.9% 

Alternative NAAQS 2 (0.5 μg/m3 , 

maximum monthly average) 26% 4% 9% 20% 17% 12% 7% 3% 1% 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) 29% 4% 10% 20% 17% 11% 6% 3% 0.8% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 31% 5% 10% 20% 16% 11% 5% 2% 0.7% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 31% 4% 10% 20% 16% 11% 5% 2% 0.7% 

O-46
 
Exhibit O-40. Cleveland: Percentage of Children with IQ Loss Resulting from Total Pb Ex

Air Quality Scenario 

Percentage of Children with IQ Loss Resulting from Total Pb Exposure per IQ Loss R

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

Current conditions (mean) <0.1% 0.1% 2% 10% 15% 18% 20% 16% 10% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) <0.1% 0.1% 0.9% 7% 11% 16% 20% 18% 13% 

Alternative NAAQS 1 (0.2 μg/m3 , 

maximum quarterly average) <0.1% 0.2% 2% 11% 15% 19% 19% 16% 10% 

Alternative NAAQS 2 (0.5 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 10% 15% 18% 19% 16% 11% 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 12% 16% 19% 19% 16% 10% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) <0.1% 0.2% 2% 13% 17% 19% 19% 15% 9% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) <0.1% 0.2% 2% 13% 16% 20% 19% 15% 9% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

Current conditions (mean) <0.1% 0.1% 1% 8% 13% 13% 12% 10% 8% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) <0.1% <0.1% 0.7% 6% 10% 11% 11% 10% 8% 

Alternative NAAQS 1 (0.2 μg/m3 , 

maximum quarterly average) <0.1% 0.1% 1% 9% 14% 14% 12% 10% 8% 

Alternative NAAQS 2 (0.5 μg/m3 , 

maximum monthly average) <0.1% 0.1% 1% 9% 13% 13% 12% 10% 8% 

Alternative NAAQS 3 (0.2 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 10% 14% 14% 12% 10% 7% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 11% 15% 14% 12% 10% 8% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 11% 15% 14% 12% 9% 8% 

a For each air quality scenario and IQ range, the percentage of children with IQ loss resulting from total Pb exposure was calcul

number of children with IQ loss by the total number of children in the city in the target age range. 

O-47 
Exhibit O-41. Los Angeles: Number of Children with IQ Loss Resulting from Total Pb Exp

Number of Children with IQ Change Resulting from Total Pb Exposure per IQ Change 

Air Quality Scenario 

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

Current conditions (mean) 2827 21469 70136 103765 45514 20617 20823 830 103 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 595 7176 37348 90307 59602 35191 51362 3714 629 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 3679 24411 74622 102558 43465 18374 18242 669 74 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 4074 24971 76316 103582 42241 17739 16548 544 74 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

Current conditions (mean) 76946 12434 26253 57639 49291 33852 18437 7816 2707 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 34104 7450 17945 49107 56020 51185 37354 20010 9161 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 84253 12909 27031 56094 48650 31174 16463 6935 1974 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 86301 13195 28376 56941 46767 30545 15318 6151 2014 

O-48
 
Exhibit O-41. Los Angeles: Number of Children with IQ Loss Resulting from Total Pb Exp

Number of Children with IQ Change Resulting from Total Pb Exposure per IQ Change 

Air Quality Scenario 

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

 Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

Current conditions (mean) 11 338 5047 30579 43311 54057 55951 45279 28868 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 0 57 1104 11667 22700 37863 51963 55751 47168 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 17 532 5900 34596 46046 54315 55144 43591 26185 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 11 538 6500 35512 46464 56432 54761 42470 25481 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

Current conditions (mean) 6 240 3645 25664 38418 38235 34916 27998 22299 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 0 40 795 9207 19129 23919 26614 25052 23180 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 11 349 4635 28931 40913 39900 34173 27438 22454 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 6 372 4990 30333 41074 41159 35008 27815 21441 

a The number of children with IQ loss in each IQ change range for each air quality scenario was calculated by multiplying the n

each IQ change range by a ratio of the number of children in the city in the target age range to the number of iterations. 

O-49
 
Exhibit O-42. Los Angeles: Percentage of Children with IQ Loss Resulting from Total Pb E

Air Quality Scenario Percentage of Children with IQ Loss Resulting from Total Pb Exposure per IQ Loss R

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 10 μg/dL Peak) 

Current conditions (mean) 1% 8% 25% 36% 16% 7% 7% 0.3% <0.1% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 0.2% 3% 13% 32% 21% 12% 18% 1% 0.2% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 1% 9% 26% 36% 15% 6% 6% 0.2% <0.1% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 1% 9% 27% 36% 15% 6% 6% 0.2% <0.1% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

Current conditions (mean) 27% 4% 9% 20% 17% 12% 6% 3% 0.9% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) 12% 3% 6% 17% 20% 18% 13% 7% 3% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) 29% 5% 9% 20% 17% 11% 6% 2% 0.7% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) 30% 5% 10% 20% 16% 11% 5% 2% 0.7% 

O-50
 
Exhibit O-42. Los Angeles: Percentage of Children with IQ Loss Resulting from Total Pb E

Air Quality Scenario Percentage of Children with IQ Loss Resulting from Total Pb Exposure per IQ Loss R

< 0.25 0.25 to 0.5 0.5 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

Current conditions (mean) <0.1% 0.1% 2% 11% 15% 19% 20% 16% 10% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) <0.1% <0.1% 0.4% 4% 8% 13% 18% 19% 16% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) <0.1% 0.2% 2% 12% 16% 19% 19% 15% 9% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) <0.1% 0.2% 2% 12% 16% 20% 19% 15% 9% 

Dust Model (Hybrid), GSD (2.1), PbB Metric (Concurrent), IQ Function (Dual Linear – Stratified at 7.5 μg/dL Peak) 

Current conditions (mean) <0.1% 0.1% 1% 9% 13% 13% 12% 10% 8% 

Current NAAQS (1.5 μg/m3 , 

maximum quarterly average) <0.1% <0.1% 0.3% 3% 7% 8% 9% 9% 8% 

Alternative NAAQS 4 (0.05 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 10% 14% 14% 12% 10% 8% 

Alternative NAAQS 5 (0.02 μg/m3 , 

maximum monthly average) <0.1% 0.1% 2% 11% 14% 14% 12% 10% 7% 

a For each air quality scenario and IQ range, the percentage of children with IQ loss resulting from total Pb exposure was calcul

number of children with IQ loss by the total number of children in the city in the target age range. 

O-51
 
 

 

 

REFERENCES 

U.S. Census Bureau. 2001. U.S. Census 2000 Summary File (Indiana, Illinois, Ohio, and California) prepared by the 

U.S. Census Bureau. 

U.S. Environmental Protection Agency (USEPA). (2007) Air Quality System (AQS) Database. Available online at: 

http://www.epa.gov/ttn/airs/airsaqs/aqsweb/aqswebwarning.htm. 

O-52
 
Appendix P: Primary and Secondary Pb Smelter Case Studies’ Subarea Analyses 

Prepared by: 

ICF International 

Research Triangle Park, NC 

Prepared for: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 

Contract No. EP-D-06-115 

Work Assignment No. 0-4 

 

Table of Contents 

Table of Contents ................................................................................................................. P-i 

List of Exhibits .................................................................................................................... P-ii 

P. PRIMARY AND SECONDARY PB SMELTER CASE STUDIES’ SUBAREA 

ANALYSES ......................................................................................................................... P-1
 

P.1. METHODOLOGY .......................................................................................................P-1 

P.2. RESULTS .....................................................................................................................P-5 

P.2.1. Primary Pb Smelter Case Study........................................................................P-5 

P.2.2. Secondary Pb Smelter Case Study....................................................................P-5 

P.2.2.1. Media Concentrations .......................................................................................P-5 

P.2.2.2. PbB Levels and IQ Change Estimates ..............................................................P-7 

P.2.2.3. Ambient Air Pb to PbB Ratios........................................................................P-17 

P.2.3. Secondary Pb Smelter Case Study (Population-adjusted) ..............................P-17 

P.2.3.1. Media Concentrations .....................................................................................P-18 

P.2.3.2. PbB Levels and IQ-change Estimates.............................................................P-19 

P.2.3.3. Ambient Air Pb to PbB Ratios........................................................................P-29 

REFERENCES ....................................................................................................................P-31 

P-i 
 

List of Exhibits 

Exhibit P-1. Primary Pb Smelter Case Study Area and Subarea ................................................P-3 

Exhibit P-2. Secondary Pb Smelter Case Study Area and Subarea ............................................P-4 

Exhibit P-3. Secondary Pb Smelter Case Study (1.5 km): Estimated Population-weighted 

Annual Ambient Air Concentrations ...................................................................P-5 

Exhibit P-4. Secondary Pb Smelter Case Study (1.5 km): Estimated Population-weighted 

Inhalation Exposure Concentrations....................................................................P-6 

Exhibit P-5. Secondary Pb Smelter Case Study (1.5 km): Estimated Population-weighted 

Outdoor Soil/Dust Concentrations.......................................................................P-6 

Exhibit P-6. Secondary Pb Smelter Case Study (1.5 km): Estimated Population-weighted 

Indoor Dust Concentrations .................................................................................P-6 

Exhibit P-7. Secondary Pb Smelter Case Study (1.5 km): Current Conditions – Estimated 

IQ Changes...........................................................................................................P-7 

Exhibit P-8. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 1 

(0.2 μg/m3

, Maximum Quarterly Average) – Estimated IQ Changes .................P-9 

Exhibit P-9. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 2 

(0.5 μg/m3, Maximum Monthly Average) – Estimated IQ Changes.................P-11 

Exhibit P-10. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 3 

(0.2 μg/m3, Maximum Monthly Average) – Estimated IQ Changes.................P-13 

Exhibit P-11. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 4 

(0.05 μg/m3, Maximum Monthly Average) – Estimated IQ Changes...............P-15 

Exhibit P-12. Secondary Pb Smelter (1.5 km) Case Study: Air to Concurrent PbB Ratios ....P-17 

Exhibit P-13. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Estimated 

Population-weighted Annual Ambient Air Concentrations...............................P-18 

Exhibit P-14. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Estimated 

Population-weighted Inhalation Exposure Concentrations................................P-18 

Exhibit P-15. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Estimated 

Population-weighted Outdoor Soil/Dust Concentrations...................................P-19 

Exhibit P-16. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Estimated 

Population-weighted Indoor Dust Concentrations.............................................P-19 

Exhibit P-17. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Current 

Conditions – Estimated IQ Changes..................................................................P-20 

P-ii 
 

Exhibit P-18. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 1 (0.2 μg/m3, Maximum Quarterly Average) – 

Estimated IQ Changes........................................................................................P-22 

Exhibit P-19. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 2 (0.5 μg/m3, Maximum Monthly Average) – 

Estimated IQ Changes........................................................................................P-24 

Exhibit P-20. Secondary Pb Smelter Population-adjusted Case Study 

(1.5 km): Alternative NAAQS 3 (0.2 μg/m3, Maximum Monthly Average) 

– Estimated IQ Changes.....................................................................................P-26 

Exhibit P-21. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Alternative 

NAAQS 4 (0.05 μg/m3

, Maximum Monthly Average) – Estimated 

IQ Changes.........................................................................................................P-28 

Exhibit P-22. Secondary Pb Smelter (1.5 km) Population Adjusted Case Study: Air to 

Concurrent PbB Ratios ......................................................................................P-30 

P-iii 

 

P.	 PRIMARY AND SECONDARY Pb SMELTER CASE STUDIES’ 

SUBAREA ANALYSES 

This appendix presents analyses of how the distributions of blood lead (PbB) and 

intelligence quotient (IQ) change estimates for the primary Pb and secondary Pb smelter case 

studies are affected by the extent of the study areas examined. These analyses supplement the 

analyses presented in the preceding appendices. In these supplemental analyses, the 

methodology applied to the full study areas [which consisted of locations within approximately 

10 kilometers (km) of each facility] was used to develop estimates of PbB and IQ change for the 

portions of these study areas within approximately 1.5 km of each facility. Consistent with the 

analyses of the full study areas, result’s distributions are presented for exposures associated with 

policy-relevant sources and policy-relevant backgrounds. 

P.1. METHODOLOGY 

In the analyses of the full study areas, the outer boundaries of the study areas were set to 

approximately 10 km from each facility (see Appendices D and E). The analyses described in 

this appendix focused on the portions of the primary and secondary Pb smelter case study areas 

closest to the facilities, where the greatest impacts (e.g., highest air and surface-soil Pb levels) 

from the facilities have been or have been expected to be found. The subarea for the primary Pb 

smelter facility was defined as those U.S. Census blocks within which soil remediation activities 

have occurred because of elevated soil Pb levels. These blocks extend approximately 1.5 km 

from the facility’s main stack. The subarea for the secondary Pb smelter facility was defined as 

those U.S. Census blocks that fall within 1.5 km of the facility’s main stack. For blocks that fall 

partially within 1.5 km of the main stack, the percentage of each block within this radius was 

calculated and only those blocks with 50 percent or more of their area within this radius were 

included in the subarea. 

The subarea for the primary Pb smelter case study contains a total of 25 U.S. Census 

blocks. The subarea and full 10-km study area for the primary Pb smelter case study are shown 

in Exhibit P-1. In the current NAAQS scenario, the estimated annual average air Pb 

concentrations for this subarea range from 0.098 to 0.740 μg/m3

. This subarea includes 103 

children less than 7 years of age, while the full study area includes 3,880 (U.S. Census Bureau, 

2005). 

The subarea for the secondary Pb smelter case study contains a total of 22 U.S. Census 

blocks. The subarea and full 10-km study area for the secondary Pb smelter case study are 

shown in Exhibit P-2. In the current NAAQS scenario, the estimated annual average air Pb 

P-1
 
 

concentrations for this subarea range from 0.005 to 0.056 μg/m3. The total number of children 

younger than 7 years of age is 61 and 1,698 in the subarea and full study area, respectively (U.S. 

Census Bureau, 2005). 

For the primary Pb smelter subarea analysis, the updated modeling approach (hereafter 

referred to as the “core” modeling approach) was applied. This approach focused on the 

concurrent PbB metric, added an additional alternative Pb NAAQS scenario [Alternative 

NAAQS 5 (0.02 μg/m3, Maximum Monthly Average)] to those modeled in the full-scale 

analysis, and estimated IQ change using four IQ functions (two new functions and two which 

were included in the full-scale analysis). Appendix N provides a more in-depth description of 

the core approach and presents each of the IQ functions used. 

For the secondary Pb smelter subarea analysis, the core approach was not used. As a 

result, this analysis did not include the additional alternative NAAQS scenario [Alternative 

NAAQS 5 (0.02 μg/m3, Maximum Monthly Average)] or two of the IQ functions included in the 

primary Pb smelter subarea analysis. It did, however, include the lifetime and concurrent PbB 

metric and one IQ function not included in the core approach. In addition, two different 

population scenarios were analyzed for the secondary Pb smelter subarea analysis: one with the 

actual population in the study area and one with an adjusted population used to analyze the 

impact of including children in blocks with zero children less than 7 years of age (referred to as 

the “population-adjusted” scenario). For the population-adjusted scenario, the average number 

of children less than 7 years of age was calculated for blocks within the subarea with one or more 

children. The result (5 children) was then assigned to all blocks with no children less than 7 

years of age; this change impacted a total of 9 (out of 22) blocks in the subarea. 

P-2
 
 

 Exhibit P-1. Primary Pb Smelter Case Study Area and Subarea 

P-3
 

Exhibit P-2. Secondary Pb Smelter Case Study Area and Subarea 

P-4
 
 

P.2. RESULTS 

P.2.1. Primary Pb Smelter Case Study 

The results for the primary Pb smelter subarea analysis are presented in Appendix N. 

P.2.2. Secondary Pb Smelter Case Study 

This Section presents results of the analysis focused on the 1.5 km-subarea of the 

secondary Pb smelter case study. These results include media concentration estimates 

(Section P.2.2.1), estimates of PbB levels and IQ change estimates (Section P.2.2.2), and ratios 

of estimated ambient air concentrations to PbB levels (Section P.2.2.3). 

P.2.2.1. Media Concentrations 

Population-weighted media concentration estimates for the secondary Pb smelter subarea 

are presented in Exhibit P-3 through Exhibit P-6. Population weighted media concentrations 

were calculated by first sorting the block/block groups in increasing media concentration order. 

Then the percentage of children living in block/block groups less than or equal to the maximum 

media concentration of those block/block groups was calculated. The media concentration of the 

block/block group associated with the minimum, 5th, median, 95th, and maximum percentile was 

selected. 

The ambient air annual average Pb concentration estimates are presented to three decimal 

places, resulting in various numbers of implied significant figures (e.g., one to three). No 

difference in precision is intended to be conveyed; this is simply an expedient and initial result of 

the software used for presentation. 

Exhibit P-3. Secondary Pb Smelter Case Study (1.5 km): 

Estimated Population-weighted Annual Ambient Air Concentrations 

Statistic 

Average Annual Air Pb Concentration (μg/m3

) 

Current 

Conditions 

Alternative NAAQS Scenarios 

1 2 3 4 

0.2 μg/m3 

, 

Max 

Quarterly 

0.5 μg/m3 

, 

Monthly 

0.2 μg/m3 , 

Max Monthly 

0.05 μg/m3 , 

Max Monthly 

Maximum 0.056 0.015 0.031 0.013 0.003 

95th percentile 0.023 0.006 0.013 0.005 0.001 

Median 0.007 0.002 0.004 0.002 < 0.001 

5th percentile 0.005 0.001 0.003 0.001 < 0.001 

Minimum 0.005 0.001 0.003 0.001 < 0.001 

P-5
 
 

Exhibit P-4. Secondary Pb Smelter Case Study (1.5 km): 

Estimated Population-weighted Inhalation Exposure Concentrations 

Statistic 

Average Annual Inhalation Exposure Concentration of Pb (μg/m3

) 

Current 

Conditions 

Alternative NAAQS Scenarios 

1 2 3 4 

0.2 μg/m3 

, 

Max 

Quarterly 

0.5 μg/m3 

, 

Monthly 

0.2 μg/m3 , 

Max Monthly 

0.05 μg/m3 , 

Max Monthly 

Maximum 0.126 0.034 0.071 0.028 0.007 

95th percentile 0.052 0.014 0.029 0.012 0.003 

Median 0.015 0.004 0.009 0.003 0.001 

5th percentile 0.011 0.003 0.006 0.003 0.001 

Minimum 0.011 0.003 0.006 0.002 0.001 

Exhibit P-5. Secondary Pb Smelter Case Study (1.5 km): 

Estimated Population-weighted Outdoor Soil/Dust Concentrations 

Statistic Projected Average Outdoor Soil/Dust Pb Concentration 

(mg/kg) a 

Maximum 315 

95th percentile 256 

Median 80 

5th percentile 60 

Minimum 50 

a

 Same for all air quality scenarios. 

Exhibit P-6. Secondary Pb Smelter Case Study (1.5 km): 

Estimated Population-weighted Indoor Dust Concentrations 

Statistic 

Projected Average Indoor Dust Pb Concentration (mg/kg or ppm) 

Current 

Conditions 

Alternative NAAQS Scenarios 

1 2 3 4 

0.2 μg/m3 , 

Max Quarterly 

0.5 μg/m3 , 

Monthly 

0.2 μg/m3 , 

Max Monthly 

0.05 μg/m3 , 

Max Monthly 

Maximum 166 89 120 84 66 

95th percentile 104 72 85 70 62 

Median 73 63 67 63 61 

5th percentile 70 63 65 62 61 

Minimum 69 62 65 62 61 

P-6
 
 

P.2.2.2. PbB Levels and IQ Change Estimates 

PbB and IQ-change estimates for the secondary Pb smelter subarea are presented in 

Exhibit P-7 through Exhibit P-11. IQ changes that were greater than -0.1 were reported as 

“> -0.1.” IQ changes that were exactly zero because the estimated PbB was below the cutpoint 

are reported as “-.” 

Exhibit P-7. Secondary Pb Smelter Case Study (1.5 km): Current Conditions – 

Estimated IQ Changes
 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3 -1.5 3.3 22% 13% 34% 17% 13% 1.6% 

90th 6 -1.2 2.7 23% 14% 36% 18% 8% 1.0% 

75th 15 -0.9 1.9 27% 16% 29% 21% 7% 0.8% 

Median 31 -0.6 1.3 29% 17% 25% 23% 5% 0.6% 

25th 46 -0.4 0.9 17% 10% 48% 14% 10% 1.3% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.2 3.3 22% 13% 34% 17% 13% 1.6% 

90th 6 -2.7 2.7 23% 14% 36% 18% 8% 1.0% 

75th 15 -1.8 1.9 27% 16% 29% 21% 7% 0.8% 

Median 31 -0.8 1.3 31% 18% 23% 24% 4% 0.5% 

25th 46 - 0.9 29% 17% 25% 23% 5% 0.6% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 3 -5.9 3.3 22% 13% 34% 17% 13% 1.6% 

90th 6 -5.4 2.7 23% 14% 36% 18% 8% 1.0% 

75th 15 -4.5 1.9 27% 16% 29% 21% 7% 0.8% 

Median 31 -3.5 1.3 29% 17% 25% 23% 5% 0.6% 

25th 46 -2.5 0.9 17% 10% 48% 14% 10% 1.3% 

P-7
 
 

Exhibit P-7. Secondary Pb Smelter Case Study (1.5 km): Current Conditions – 

Estimated IQ Changes
 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ Change 

Percentile 

Population 

Above 

Predicted IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3 -1.6 4.2 14% 8% 46% 11% 19% 2.4% 

90th 6 -1.3 3.5 29% 17% 25% 23% 5% 0.6% 

75th 15 -1.0 2.5 29% 17% 25% 23% 5% 0.6% 

Median 31 -0.7 1.8 17% 10% 48% 14% 10% 1.3% 

25th 46 -0.5 1.3 29% 17% 25% 23% 5% 0.6% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.3 4.2 14% 8% 46% 11% 19% 2.4% 

90th 6 -2.7 3.5 29% 17% 25% 23% 5% 0.6% 

75th 15 -1.7 2.5 29% 17% 25% 23% 5% 0.6% 

Median 31 -0.7 1.8 17% 10% 48% 14% 10% 1.3% 

25th 46 - 1.3 29% 17% 25% 23% 5% 0.6% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 3 -6.3 4.2 14% 8% 46% 11% 19% 2.4% 

90th 6 -5.8 3.5 29% 17% 25% 23% 5% 0.6% 

75th 15 -4.8 2.5 29% 17% 25% 23% 5% 0.6% 

Median 31 -3.8 1.8 17% 10% 48% 14% 10% 1.3% 

25th 46 -2.8 1.3 29% 17% 25% 23% 5% 0.6% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P-8
 
 

 

Exhibit P-8. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 1 

(0.2 μg/m3, Maximum Quarterly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3 -1.4 3.1 16% 9% 55% 13% 6% 0.8% 

90th 6 -1.2 2.6 30% 18% 26% 24% 1% 0.2% 

75th 15 -0.8 1.8 30% 18% 26% 24% 1% 0.2% 

Median 31 -0.6 1.3 25% 14% 37% 19% 4% 0.5% 

25th 46 -0.4 0.9 25% 14% 37% 19% 4% 0.5% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.1 3.1 16% 9% 55% 13% 6% 0.8% 

90th 6 -2.5 2.6 30% 18% 26% 24% 1% 0.2% 

75th 15 -1.6 1.8 30% 18% 26% 24% 1% 0.2% 

Median 31 -0.6 1.3 30% 18% 26% 24% 1% 0.2% 

25th 46 - 0.8 30% 18% 26% 24% 1% 0.2% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 3 -5.8 3.1 16% 9% 55% 13% 6% 0.8% 

90th 6 -5.2 2.6 30% 18% 26% 24% 1% 0.2% 

75th 15 -4.3 1.8 30% 18% 26% 24% 1% 0.2% 

Median 31 -3.3 1.3 25% 14% 37% 19% 4% 0.5% 

25th 46 -2.4 0.9 25% 14% 37% 19% 4% 0.5% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3 -1.5 4.0 30% 18% 26% 24% 1% 0.2% 

90th 6 -1.2 3.3 28% 16% 31% 22% 2% 0.2% 

75th 15 -0.9 2.4 30% 18% 26% 24% 1% 0.2% 

Median 31 -0.7 1.7 32% 18% 24% 25% 1% 0.1% 

25th 46 -0.5 1.2 30% 18% 26% 24% 1% 0.2% 

P-9
 
 

 

Exhibit P-8. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 1 

(0.2 μg/m3, Maximum Quarterly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.1 4.0 30% 18% 26% 24% 1% 0.2% 

90th 6 -2.5 3.3 28% 16% 31% 22% 2% 0.2% 

75th 15 -1.6 2.4 30% 18% 26% 24% 1% 0.2% 

Median 31 -0.6 1.7 32% 18% 24% 25% 1% 0.1% 

25th 46 - 1.1 30% 18% 26% 24% 1% 0.2% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 3 -6.1 4.0 30% 18% 26% 24% 1% 0.2% 

90th 6 -5.6 3.3 28% 16% 31% 22% 2% 0.2% 

75th 15 -4.6 2.4 30% 18% 26% 24% 1% 0.2% 

Median 31 -3.6 1.7 34% 20% 18% 27% 1% 0.1% 

25th 46 -2.6 1.2 30% 18% 26% 24% 1% 0.2% 

a

 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P-10
 
 

 

Exhibit P-9. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 2 

(0.5 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ 

Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3 -1.5 3.3 15% 9% 51% 12% 12% 1.5% 

90th 6 -1.2 2.6 28% 16% 30% 22% 4% 0.5% 

75th 15 -0.9 1.9 23% 14% 36% 18% 8% 1.0% 

Median 31 -0.6 1.3 30% 17% 26% 24% 3% 0.4% 

25th 46 -0.4 0.9 28% 16% 30% 22% 4% 0.5% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.2 3.3 15% 9% 51% 12% 12% 1.5% 

90th 6 -2.6 2.6 28% 16% 30% 22% 4% 0.5% 

75th 15 -1.7 1.9 23% 14% 36% 18% 8% 1.0% 

Median 31 -0.7 1.3 30% 17% 26% 24% 3% 0.4% 

25th 46 - 0.9 23% 14% 36% 18% 8% 1.0% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 3 -5.9 3.3 15% 9% 51% 12% 12% 1.5% 

90th 6 -5.3 2.6 28% 16% 30% 22% 4% 0.5% 

75th 15 -4.4 1.9 23% 14% 36% 18% 8% 1.0% 

Median 31 -3.4 1.3 23% 14% 36% 18% 8% 1.0% 

25th 46 -2.4 0.9 28% 16% 30% 22% 4% 0.5% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3 -1.6 4.1 23% 14% 36% 18% 8% 1.0% 

90th 6 -1.3 3.4 31% 18% 23% 25% 2% 0.3% 

75th 15 -0.9 2.5 30% 17% 26% 24% 3% 0.4% 

Median 31 -0.7 1.8 28% 16% 30% 22% 3% 0.3% 

25th 46 -0.5 1.3 30% 17% 26% 24% 3% 0.4% 

P-11
 
 

 

Exhibit P-9. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 2 

(0.5 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ 

Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.2 4.1 23% 14% 36% 18% 8% 1.0% 

90th 6 -2.6 3.4 31% 18% 23% 25% 2% 0.3% 

75th 15 -1.7 2.5 30% 17% 26% 24% 3% 0.4% 

Median 31 -0.6 1.8 30% 17% 26% 24% 3% 0.4% 

25th 46 - 1.1 23% 14% 36% 18% 8% 1.0% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 3 -6.3 4.1 23% 14% 36% 18% 8% 1.0% 

90th 6 -5.7 3.4 31% 18% 23% 25% 2% 0.3% 

75th 15 -4.7 2.5 30% 17% 26% 24% 3% 0.4% 

Median 31 -3.7 1.8 30% 17% 26% 24% 3% 0.4% 

25th 46 -2.7 1.3 30% 17% 26% 24% 3% 0.4% 

a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P-12
 
 

 

Exhibit P-10. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 3 

(0.2 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ 

Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3 -1.4 3.1 34% 20% 18% 27% 1% 0.1% 

90th 16 -1.2 2.5 31% 18% 26% 24% 1% 0.1% 

75th 15 -0.8 1.8 31% 18% 26% 24% 1% 0.1% 

Median 31 -0.6 1.3 25% 14% 38% 20% 3% 0.4% 

25th 46 -0.4 0.9 31% 18% 26% 24% 1% 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.1 3.1 34% 20% 18% 27% 1% 0.1% 

90th 16 -2.5 2.5 31% 18% 26% 24% 1% 0.1% 

75th 15 -1.6 1.8 31% 18% 26% 24% 1% 0.1% 

Median 31 -0.6 1.3 25% 14% 38% 20% 3% 0.4% 

25th 46 - 0.9 31% 18% 26% 24% 1% 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 3 -5.8 3.1 34% 20% 18% 27% 1% 0.1% 

90th 6 -5.2 2.5 31% 18% 26% 24% 1% 0.1% 

75th 15 -4.3 1.8 31% 18% 26% 24% 1% 0.1% 

Median 31 -3.3 1.3 33% 19% 21% 26% 1% 0.2% 

25th 46 -2.4 0.9 31% 18% 26% 24% 1% 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3 -1.5 3.9 19% 11% 52% 15% 2% 0.3% 

90th 6 -1.2 3.3 31% 18% 26% 24% 1% 0.1% 

75th 15 -0.9 2.4 16% 10% 55% 13% 5% 0.6% 

Median 31 -0.7 1.7 31% 18% 26% 24% 1% 0.1% 

25th 46 -0.5 1.2 31% 18% 26% 24% 1% 0.1% 

P-13
 
 

 

Exhibit P-10. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 3 

(0.2 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ 

Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.1 3.9 19% 11% 52% 15% 2% 0.3% 

90th 6 -2.5 3.3 31% 18% 26% 24% 1% 0.1% 

75th 15 -1.6 2.4 16% 10% 55% 13% 5% 0.6% 

Median 31 -0.6 1.7 31% 18% 26% 24% 1% 0.1% 

25th 46 - 0.8 31% 18% 26% 24% 1% 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 3 -6.1 3.9 19% 11% 52% 15% 2% 0.3% 

90th 6 -5.6 3.3 31% 18% 26% 24% 1% 0.1% 

75th 15 -4.6 2.4 16% 10% 55% 13% 5% 0.6% 

Median 31 -3.6 1.7 31% 18% 26% 24% 1% 0.1% 

25th 46 -2.6 1.2 31% 18% 26% 24% 1% 0.1% 

a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P-14
 
 

 

Exhibit P-11. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 4 

(0.05 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ 

Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 3 -1.4 3.1 31% 18% 26% 24% 0.3% < 0.1% 

90th 6 -1.1 2.5 19% 11% 53% 15% 0.6% < 0.1% 

75th 15 -0.8 1.8 31% 18% 26% 24% 0.3% < 0.1% 

Median 31 -0.6 1.2 31% 18% 26% 24% 0.3% < 0.1% 

25th 46 -0.4 0.9 32% 19% 24% 25% 0.2% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.0 3.1 31% 18% 26% 24% 0.3% < 0.1% 

90th 6 -2.5 2.5 19% 11% 53% 15% 0.6% < 0.1% 

75th 15 -1.6 1.8 31% 18% 26% 24% 0.3% < 0.1% 

Median 31 -0.6 1.2 31% 18% 26% 24% 0.3% < 0.1% 

25th 46 - 0.9 31% 18% 26% 24% 0.3% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 3 -5.7 3.1 31% 18% 26% 24% 0.3% < 0.1% 

90th 6 -5.2 2.5 19% 11% 53% 15% 0.6% < 0.1% 

75th 15 -4.3 1.8 31% 18% 26% 24% 0.3% < 0.1% 

Median 31 -3.3 1.2 19% 11% 53% 15% 0.6% < 0.1% 

25th 46 -2.3 0.9 32% 19% 24% 25% 0.2% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 3 -1.5 3.9 19% 11% 53% 15% 0.6% < 0.1% 

90th 6 -1.2 3.2 32% 19% 24% 25% 0.2% < 0.1% 

75th 15 -0.9 2.4 31% 18% 26% 24% 0.3% < 0.1% 

Median 31 -0.6 1.7 31% 18% 26% 24% 0.3% < 0.1% 

25th 46 -0.5 1.2 31% 18% 26% 24% 0.3% < 0.1% 

P-15
 
 

 

Exhibit P-11. Secondary Pb Smelter Case Study (1.5 km): Alternative NAAQS 4 

(0.05 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust 

IQ 

Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 3 -3.0 3.9 19% 11% 53% 15% 0.6% < 0.1% 

90th 6 -2.5 3.2 32% 19% 24% 25% 0.2% < 0.1% 

75th 15 -1.5 2.4 31% 18% 26% 24% 0.3% < 0.1% 

Median 31 -0.5 1.7 31% 18% 26% 24% 0.3% < 0.1% 

25th 46 - 1.3 31% 18% 26% 24% 0.3% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 3 -6.1 3.9 19% 11% 53% 15% 0.6% < 0.1% 

90th 6 -5.5 3.2 32% 19% 24% 25% 0.2% < 0.1% 

75th 15 -4.6 2.4 31% 18% 26% 24% 0.3% < 0.1% 

Median 31 -3.6 1.7 25% 15% 39% 20% 0.8% 0.1% 

25th 46 -2.6 1.2 31% 18% 26% 24% 0.3% < 0.1% 

a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P-16
 
 

 

P.2.2.3. Ambient Air Pb to PbB Ratios 

Exhibit P-12 shows the air-to-PbB ratios for the secondary Pb smelter (1.5 km) case 

study. Note that these ratios are derived in a different manner than the air-to-PbB ratios 

presented in Appendix I. Air-to-PbB ratios were calculated for the secondary Pb smelter based 

on concurrent PbB estimates. This is in keeping with the calculation of air-to-PbB ratios 

completed for the core analysis (see Sections N.2.2.3 and N.2.3.3) and reflects the fact that the 

concurrent PbB metric has been emphasized in the full-scale analysis. The ambient air annual 

average Pb concentration estimates are presented to three decimal places, resulting in various 

numbers of implied significant figures (e.g., one to three). No difference in precision is intended 

to be conveyed; this is simply an expedient and initial result of the software used for 

presentation. 

Exhibit P-12. Secondary Pb Smelter (1.5 km) Case Study: Air to Concurrent 

PbB Ratios 

Air Scenario 

Median 

Total PbB 

(μg/dL) 

Annual 

Average 

Ambient Air 

Concentration 

(μg/m3) 

Ratio a 

Current Conditions 

(1.5 μg/m3

, maximum quarterly average) 1.0 0.007 

1 : 1.4 Alternative NAAQS 2 (0.5 μg/m3

, maximum monthly 

average) 1.0 0.004 

Alternative NAAQS 1 (0.2 μg/m3

, maximum quarterly 

average) 1.0 0.002 1 : 1.3 

Alternative NAAQS 3 (0.2 μg/m3

, maximum monthly 

average) 1.0 0.002 1 : 6.7 

Alternative NAAQS 4 (0.05 μg/m3

, maximum monthly 

average) 1.0 0.000 1 : 4.7 

a

 A ratio is not presented adjacent to the current NAAQS air quality scenario (for any of the case 

studies) because the air-to-PbB ratios are derived by comparing changes (deltas) in median total 

PbB levels (concurrent) to associated changes in annual average air Pb levels as one steps to the 

next lowest air quality scenario. The first ratio presented for any of the case studies is generated by 

comparing median PbB levels for the current conditions scenario to the median PbB level for the 

highest of the alternative NAAQS levels [i.e., Alternative NAAQS 4 (0.05 μg/m3

 max monthly) 

value]. 

P.2.3. Secondary Pb Smelter Case Study (Population-adjusted) 

This Section presents results of the analysis focused on the 1.5 km subarea of the 

secondary Pb smelter case study using the population-adjusted scenario. These results include 

media concentration estimates (Section P.2.3.1), estimates of PbB levels and IQ-change 

estimates (Section P.2.3.2), and ratios of estimated ambient air concentrations to PbB levels 

(Section P.2.3.3). 

P-17
 
 

P.2.3.1. Media Concentrations 

Population-weighted media concentration estimates for the secondary Pb smelter subarea 

using the population-adjusted scenario are presented in Exhibit P-13 through Exhibit P-16. 

Population-weighted media concentrations were calculated by first sorting the block/block 

groups in increasing media concentration order. Then the percentage of children living in 

block/block groups less than or equal to the maximum media concentration of those block/block 

groups was calculated. The media concentration of the block/block group associated with the 

minimum, 5th, median, 95th, and maximum percentile was selected. 

The ambient air annual average Pb concentration estimates are presented to three decimal 

places, resulting in various numbers of implied significant figures (e.g., one to three). No 

difference in precision is intended to be conveyed; this is simply an expedient and initial result of 

the software used for presentation. 

Exhibit P-13. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Estimated 

Population-weighted Annual Ambient Air Concentrations 

Statistic 

Average Annual Air Pb Concentration (μg/m3) 

Current 

Alternative NAAQS Scenarios 

1 2 3 4 

Conditions 0.2 μg/m3 

, 

Max 

Quarterly 

0.5 μg/m3 

, 

Monthly 

0.2 μg/m3 , 

Max Monthly 

0.05 μg/m3 , 

Max Monthly 

Maximum 0.056 0.015 0.031 0.013 0.003 

95th percentile 0.049 0.013 0.027 0.011 0.003 

Median 0.007 0.002 0.004 0.002 < 0.001 

5th percentile 0.005 0.001 0.003 0.001 < 0.001 

Minimum 0.005 0.001 0.003 0.001 < 0.001 

Exhibit P-14. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Estimated 

Population-weighted Inhalation Exposure Concentrations 

Statistic 

Average Annual Inhalation Exposure Concentration of Pb (μg/m3

) 

Current 

Alternative NAAQS Scenarios 

1 2 3 4 

Conditions 0.2 μg/m3 

, 

Max 

Quarterly 

0.5 μg/m3 

, 

Monthly 

0.2 μg/m3 , 

Max Monthly 

0.05 μg/m3 , 

Max Monthly 

Maximum 0.126 0.034 0.071 0.028 0.007 

95th percentile 0.110 0.030 0.062 0.025 0.006 

Median 0.015 0.004 0.009 0.003 0.001 

5th percentile 0.011 0.003 0.006 0.002 0.001 

Minimum 0.010 0.003 0.006 0.002 0.001 

P-18
 
 

 

Exhibit P-15. Secondary Pb Smelter Population-adjusted Case Study 

(1.5 km): Estimated Population-weighted Outdoor Soil/Dust 

Concentrations 

Statistic Projected Average Outdoor Soil/Dust Pb Concentration 

(mg/kg) a 

Maximum 315 

95th percentile 256 

Median 80 

5th percentile 54 

Minimum 50 

a

 Same for all air quality scenarios. 

 Exhibit P-16. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Estimated 

Population-weighted Indoor Dust Concentrations 

Statistic 

Projected Average Indoor Dust Pb Concentration (mg/kg or ppm) 

Current 

Alternative NAAQS Scenarios 

1 2 3 4 

Conditions 0.2 μg/m3

, Max 

Quarterly 

0.5 μg/m3 , 

Monthly 

0.2 μg/m3 , 

Max Monthly 

0.05 μg/m3 , 

Max Monthly 

Maximum 166 89 120 84 66 

95th 

percentile 153 85 112 81 65 

Median 73 63 67 63 61 

5th percentile 69 62 65 62 61 

Minimum 69 62 65 62 60 

P.2.3.2. PbB Levels and IQ-change Estimates 

PbB and IQ-change estimates for the secondary Pb smelter subarea using the populationadjusted scenario are presented in Exhibit P-17 through Exhibit P-21. IQ changes that were 

greater than -0.1 were reported as “> -0.1.” IQ changes that were exactly zero because the 

estimated PbB was below the cutpoint are reported as “-.” 

P-19
 
 

Exhibit P-17. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Current 

Conditions – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 5 -1.5 3.2 31% 18% 20% 25% 5% 0.6% 

90th 11 -1.2 2.6 29% 17% 25% 23% 5% 0.6% 

75th 27 -0.9 1.9 29% 17% 25% 23% 5% 0.6% 

Median 53 -0.6 1.3 32% 19% 19% 26% 4% 0.5% 

25th 80 -0.4 0.9 31% 18% 22% 24% 4% 0.5% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -3.2 3.2 31% 18% 20% 25% 5% 0.6% 

90th 11 -2.6 2.6 29% 17% 25% 23% 5% 0.6% 

75th 27 -1.7 1.9 29% 17% 25% 23% 5% 0.6% 

Median 53 -0.7 1.3 32% 19% 19% 26% 4% 0.5% 

25th 80 - 0.6 29% 17% 25% 23% 5% 0.6% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -5.9 3.2 31% 18% 20% 25% 5% 0.6% 

90th 11 -5.3 2.6 29% 17% 25% 23% 5% 0.6% 

75th 27 -4.4 1.9 29% 17% 25% 23% 5% 0.6% 

Median 53 -3.4 1.3 32% 19% 19% 26% 4% 0.5% 

25th 80 -2.4 0.9 31% 18% 22% 24% 4% 0.5% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 5 -1.5 4.1 31% 18% 20% 25% 5% 0.6% 

90th 11 -1.3 3.4 29% 17% 24% 23% 7% 0.8% 

75th 27 -0.9 2.5 33% 19% 18% 26% 4% 0.5% 

Median 53 -0.7 1.8 29% 17% 25% 23% 5% 0.6% 

25th 80 -0.5 1.3 29% 17% 24% 23% 6% 0.8% 

P-20
 
 

Exhibit P-17. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): Current 

Conditions – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5 -3.2 4.1 31% 18% 20% 25% 5% 0.6% 

90th 11 -2.6 3.4 29% 17% 24% 23% 7% 0.8% 

75th 27 -1.7 2.5 33% 19% 18% 26% 4% 0.5% 

Median 53 -0.6 1.8 29% 17% 25% 23% 5% 0.6% 

25th 80 - 0.8 29% 17% 25% 23% 5% 0.6% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 5 -6.2 4.1 31% 18% 20% 25% 5% 0.6% 

90th 11 -5.6 3.4 29% 17% 24% 23% 7% 0.8% 

75th 27 -4.7 2.5 33% 19% 18% 26% 4% 0.5% 

Median 53 -3.7 1.8 29% 17% 25% 23% 5% 0.6% 

25th 80 -2.7 1.3 29% 17% 24% 23% 6% 0.8% 

a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P-21
 
 

Exhibit P-18. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 1 (0.2 μg/m3, Maximum Quarterly Average) – Estimated IQ 

Changes
 Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 5 -1.4 3.1 26% 15% 27% 21% 9% 1.1% 

90th 11 -1.1 2.5 29% 17% 29% 23% 2% 0.2% 

75th 27 -0.8 1.8 30% 18% 26% 24% 1% 0.2% 

Median 53 -0.6 1.2 28% 16% 31% 22% 2% 0.2% 

25th 80 -0.4 0.9 30% 18% 26% 24% 1% 0.2% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -3.0 3.1 26% 15% 27% 21% 9% 1.1% 

90th 11 -2.5 2.5 29% 17% 29% 23% 2% 0.2% 

75th 27 -1.6 1.8 30% 18% 26% 24% 1% 0.2% 

Median 53 -0.6 1.2 30% 18% 26% 24% 1% 0.2% 

25th 80 - 0.9 30% 18% 26% 24% 1% 0.2% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -5.7 3.1 26% 15% 27% 21% 9% 1.1% 

90th 11 -5.2 2.5 29% 17% 29% 23% 2% 0.2% 

75th 27 -4.3 1.8 30% 18% 26% 24% 1% 0.2% 

Median 53 -3.3 1.2 30% 18% 26% 24% 1% 0.2% 

25th 80 -2.3 0.9 30% 18% 26% 24% 1% 0.2% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 5 -1.4 3.8 28% 16% 31% 22% 2% 0.2% 

90th 11 -1.2 3.2 31% 18% 26% 24% 2% 0.2% 

75th 27 -0.9 2.3 26% 15% 27% 21% 9% 1.1% 

Median 53 -0.6 1.7 30% 18% 26% 24% 1% 0.2% 

25th 80 -0.5 1.2 33% 19% 20% 26% 1% 0.1% 

P-22
 
 

Exhibit P-18. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 1 (0.2 μg/m3, Maximum Quarterly Average) – Estimated IQ 

Changes
 Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5 -3.0 3.8 28% 16% 31% 22% 2% 0.2% 

90th 11 -2.4 3.2 31% 18% 26% 24% 2% 0.2% 

75th 27 -1.5 2.3 26% 15% 27% 21% 9% 1.1% 

Median 53 -0.5 1.7 30% 18% 26% 24% 1% 0.2% 

25th 80 - 0.8 30% 18% 26% 24% 1% 0.2% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 5 -6.0 3.8 28% 16% 31% 22% 2% 0.2% 

90th 11 -5.5 3.2 31% 18% 26% 24% 2% 0.2% 

75th 27 -4.5 2.3 26% 15% 27% 21% 9% 1.1% 

Median 53 -3.5 1.7 30% 18% 26% 24% 1% 0.2% 

25th 80 -2.6 1.2 33% 19% 20% 26% 1% 0.1% 

a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P-23
 
 

Exhibit P-19. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 2 (0.5 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 5 -1.4 3.1 18% 11% 50% 14% 6% 0.7% 

90th 11 -1.1 2.5 31% 18% 23% 25% 2% 0.3% 

75th 27 -0.8 1.8 30% 17% 26% 24% 3% 0.4% 

Median 53 -0.6 1.2 30% 17% 26% 24% 3% 0.4% 

25th 80 -0.4 0.9 30% 17% 26% 24% 3% 0.4% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -3.1 3.1 18% 11% 50% 14% 6% 0.7% 

90th 11 -2.5 2.5 31% 18% 23% 25% 2% 0.3% 

75th 27 -1.6 1.8 30% 17% 26% 24% 3% 0.4% 

Median 53 -0.6 1.2 30% 17% 26% 24% 3% 0.4% 

25th 80 - 0.9 30% 17% 26% 24% 3% 0.4% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -5.8 3.1 18% 11% 50% 14% 6% 0.7% 

90th 11 -5.2 2.5 31% 18% 23% 25% 2% 0.3% 

75th 27 -4.3 1.8 30% 17% 26% 24% 3% 0.4% 

Median 53 -3.3 1.3 30% 17% 26% 24% 3% 0.4% 

25th 80 -2.4 0.9 30% 17% 26% 24% 3% 0.4% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 5 -1.5 3.9 18% 11% 50% 14% 6% 0.7% 

90th 11 -1.2 3.2 18% 11% 50% 14% 6% 0.7% 

75th 27 -0.9 2.4 33% 19% 20% 26% 2% 0.3% 

Median 53 -0.7 1.7 30% 17% 25% 24% 4% 0.5% 

25th 80 -0.5 1.2 15% 9% 51% 12% 12% 1.5% 

P-24
 
 

Exhibit P-19. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 2 (0.5 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5 -3.0 3.9 18% 11% 50% 14% 6% 0.7% 

90th 11 -2.5 3.2 18% 11% 50% 14% 6% 0.7% 

75th 27 -1.6 2.4 33% 19% 20% 26% 2% 0.3% 

Median 53 -0.6 1.7 30% 17% 25% 24% 4% 0.5% 

25th 80 - 1.3 30% 17% 26% 24% 3% 0.4% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 5 -6.1 3.9 18% 11% 50% 14% 6% 0.7% 

90th 11 -5.5 3.2 18% 11% 50% 14% 6% 0.7% 

75th 27 -4.6 2.4 33% 19% 20% 26% 2% 0.3% 

Median 53 -3.6 1.7 30% 17% 25% 24% 4% 0.5% 

25th 80 -2.6 1.2 15% 9% 51% 12% 12% 1.5% 

a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P-25
 
 

Exhibit P-20. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 3 (0.2 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 5 -1.4 3.0 31% 18% 25% 24% 2% 0.2% 

90th 11 -1.1 2.5 32% 19% 22% 26% 1% 0.1% 

75th 27 -0.8 1.8 33% 19% 21% 26% 1% 0.2% 

Median 53 -0.6 1.2 31% 18% 26% 24% 1% 0.1% 

25th 80 -0.4 0.8 31% 18% 26% 24% 1% 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -3.0 3.0 31% 18% 25% 24% 2% 0.2% 

90th 11 -2.4 2.5 32% 19% 22% 26% 1% 0.1% 

75th 27 -1.5 1.8 33% 19% 21% 26% 1% 0.2% 

Median 53 -0.6 1.2 31% 18% 26% 24% 1% 0.1% 

25th 80 - 0.5 29% 17% 29% 23% 1% 0.2% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -5.7 3.0 31% 18% 25% 24% 2% 0.2% 

90th 11 -5.1 2.5 32% 19% 22% 26% 1% 0.1% 

75th 27 -4.2 1.8 33% 19% 21% 26% 1% 0.2% 

Median 53 -3.3 1.2 31% 18% 26% 24% 1% 0.1% 

25th 80 -2.3 0.8 31% 18% 26% 24% 1% 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 5 -1.4 3.8 34% 20% 20% 26% 1% 0.1% 

90th 11 -1.2 3.1 16% 10% 55% 13% 5% 0.6% 

75th 27 -0.9 2.3 31% 18% 25% 25% 1% 0.1% 

Median 53 -0.6 1.7 27% 16% 28% 21% 7% 0.9% 

25th 80 -0.5 1.2 27% 16% 28% 21% 7% 0.9% 

P-26
 
 

Exhibit P-20. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 3 (0.2 μg/m3, Maximum Monthly Average) – Estimated IQ Changes 

Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5 -2.9 3.8 34% 20% 20% 26% 1% 0.1% 

90th 11 -2.4 3.1 16% 10% 55% 13% 5% 0.6% 

75th 27 -1.5 2.3 31% 18% 25% 25% 1% 0.1% 

Median 53 -0.5 1.7 32% 19% 23% 25% 1% 0.1% 

25th 80 - 0.7 29% 17% 29% 23% 1% 0.2% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 5 -6.0 3.8 34% 20% 20% 26% 1% 0.1% 

90th 11 -5.4 3.1 16% 10% 55% 13% 5% 0.6% 

75th 27 -4.5 2.3 31% 18% 25% 25% 1% 0.1% 

Median 53 -3.5 1.7 27% 16% 28% 21% 7% 0.9% 

25th 80 -2.6 1.2 27% 16% 28% 21% 7% 0.9% 

a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P-27
 
 

Exhibit P-21. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 4 (0.05 μg/m3, Maximum Monthly Average) – Estimated IQ Changes
 Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear) 

95th 5 -1.3 2.9 31% 18% 26% 24% 0.3% < 0.1% 

90th 11 -1.1 2.4 29% 17% 30% 23% 2% 0.2% 

75th 27 -0.8 1.7 33% 19% 21% 26% 0.3% < 0.1% 

Median 53 -0.5 1.2 33% 19% 20% 26% 0.2% < 0.1% 

25th 80 -0.4 0.8 31% 18% 26% 25% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Cutpoint) 

95th 5 -2.9 2.9 31% 18% 26% 24% 0.3% < 0.1% 

90th 11 -2.4 2.4 29% 17% 30% 23% 2% 0.2% 

75th 27 -1.5 1.7 33% 19% 21% 26% 0.3% < 0.1% 

Median 53 -0.5 1.2 33% 19% 20% 26% 0.2% < 0.1% 

25th 80 - 0.8 31% 18% 26% 24% 0.3% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Log-linear with Linearization) 

95th 5 -5.6 2.9 31% 18% 26% 24% 0.3% < 0.1% 

90th 11 -5.1 2.4 29% 17% 30% 23% 2% 0.2% 

75th 27 -4.2 1.7 33% 19% 21% 26% 0.3% < 0.1% 

Median 53 -3.2 1.2 33% 19% 20% 26% 0.2% < 0.1% 

25th 80 -2.3 0.8 31% 18% 26% 25% 0.4% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Two-piece Linear) 

95th 5 -1.4 3.7 25% 15% 39% 20% 1% 0.1% 

90th 11 -1.2 3.1 17% 10% 58% 13% 1% 0.2% 

75th 27 -0.9 2.3 31% 18% 26% 24% 0.3% < 0.1% 

Median 53 -0.6 1.6 34% 20% 19% 27% 0.2% < 0.1% 

25th 80 -0.5 1.2 34% 20% 18% 27% 0.2% < 0.1% 

P-28
 
 

Exhibit P-21. Secondary Pb Smelter Population-adjusted Case Study (1.5 km): 

Alternative NAAQS 4 (0.05 μg/m3

, Maximum Monthly Average) – Estimated IQ Changes
 Pathway Contribution 

Ingestion 

Indoor Dust IQ Change 

Percentile 

Population 

Above 

Predicted 

IQ 

Change 

Predicted 

PbB 

(μg/dL) Diet Drinking 

Water 

Outdoor 

Soil/Dust Other a Recent 

Air a 

Inhalation 

(Recent 

Air) 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Cutpoint) 

95th 5 -2.9 3.7 25% 15% 39% 20% 1% 0.1% 

90th 11 -2.3 3.1 17% 10% 58% 13% 1% 0.2% 

75th 27 -1.4 2.3 31% 18% 26% 24% 0.3% < 0.1% 

Median 53 -0.4 1.6 34% 20% 19% 27% 0.2% < 0.1% 

25th 80 - 1.2 31% 18% 26% 24% 0.3% < 0.1% 

Dust Model (Air-only Regression-based), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization) 

95th 5 -5.9 3.7 25% 15% 39% 20% 1% 0.1% 

90th 11 -5.4 3.1 17% 10% 58% 13% 1% 0.2% 

75th 27 -4.5 2.3 31% 18% 26% 24% 0.3% < 0.1% 

Median 53 -3.5 1.6 34% 20% 19% 27% 0.2% < 0.1% 

25th 80 -2.5 1.2 34% 20% 18% 27% 0.2% < 0.1% 

a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, 

including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, 

with outdoor ambient air also potentially including resuspended, previously deposited Pb. (See Section 2.4.3 of 

Volume I of the Risk Assessment.) In other summary tables, and text, "past air" refers to contributions from 

ingestion of outdoor soil/dust and of the "other" portion of indoor dust. 

P.2.3.3. Ambient Air Pb to PbB Ratios 

Exhibit P-22 shows the air-to-PbB ratios for the secondary Pb smelter (1.5 km) 

population-adjusted case study. Note that these ratios are derived in a different manner than the 

air-to-PbB ratios presented in Appendix I. Air-to-PbB ratios were calculated for the secondary 

Pb smelter case study based on concurrent PbB estimates. This is in keeping with the calculation 

of air-to-PbB ratios completed for the core analysis (see Sections N.2.2.3 and N.2.3.3) and 

reflects the fact that the concurrent PbB metric has been emphasized in the full-scale analysis. 

The ambient air annual average Pb concentration estimates are presented to three decimal places, 

resulting in various numbers of implied significant figures (e.g., one to three). No difference in 

precision is intended to be conveyed; this is simply an expedient and initial result of the software 

used for presentation. 

P-29
 
 

Exhibit P-22. Secondary Pb Smelter (1.5 km) Population Adjusted 

Case Study: Air to Concurrent PbB Ratios
 

Air Scenario Median Total 

PbB (μg/dL) 

Annual Average 

Ambient Air 

Concentration 

(μg/m3

) 

Ratio a 

Current Conditions (1.5 μg/m3 , 

maximum quarterly average) 1.3 0.007 

1 : 12.1 Alternative NAAQS 2 (0.5 μg/m3 

, 

maximum monthly average) 1.3 0.004 

Alternative NAAQS 1 (0.2 μg/m3 

, 

maximum quarterly average) 1.2 0.002 1 : 13.0 

Alternative NAAQS 3 (0.2 μg/m3 

, 

maximum monthly average) 1.2 0.002 1 : 10.7 

Alternative NAAQS 4 (0.05 μg/m3 

, 

maximum monthly average) 1.2 0.000 1 : 17.4 

a

 A ratio is not presented adjacent to the current NAAQS air quality scenario (for any of the case 

studies) because the air-to-PbB ratios are derived by comparing changes (deltas) in median total 

PbB levels (concurrent) to associated changes in annual average air Pb levels as one steps to the 

next lowest air quality scenario. The first ratio presented for any of the case studies is generated 

by comparing median PbB levels for the current conditions scenario to the median PbB level for 

the highest of the alternative NAAQS levels [i.e., Alternative NAAQS 4 (0.05 μg/m3

 max 

monthly) value]. 

P-30
 
 

REFERENCES 

U.S. Census Bureau. (2005) United States Census 2000: Summary File 1. Public Information Office. Available 

online at: http://www.census.gov/Press-Release/www/2001/sumfile1.html. 

P-31
 
United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-07-014b 

Environmental Protection Health and Environmental Impacts Division October 2007 Agency Research Triangle Park, NC