United States Office Of Air Quality EPA-453/R-01-003

Environmental Protection Planning And Standards January 2001

Agency Research Triangle Park, NC 27711

 Air

 

 

National-Scale Air Toxics 

Assessment for 1996

 

NOTE: THIS DOCUMENT IS A PRELIMINARY DRAFT. 

It has not been formally released by the U.S. Environmental 

Protection Agency and should not at this stage be construed to 

represent Agency policy or factual conclusions. This 

document is being provided now for review to EPA's Science 

Advisory Board. It should not be cited or referred to as EPA's 

final National-Scale Air Toxics Assessment.
EPA-453/R-01-003

National-Scale Air Toxics Assessment for 1996

EPA Office of Air Quality

Planning and Standards

DRAFT for EPA Science Advisory Board Review: January 18, 2001 
i

Table of Contents

Table of Contents............................................................................................................................................. i

List of Tables................................................................................................................................................. iv

List of Figures................................................................................................................................................. v

List of Appendices......................................................................................................................................... vi

1 Introduction ............................................................................................................................................ 1

1.1 EPA’s National Air Toxics Program .............................................................................................. 3

1.1.1 Background on the Air Toxics Program ................................................................................. 3

1.1.2 Air Toxics Program Goals...................................................................................................... 4

1.2 Overview of National Air Toxics Assessment (NATA) Activities................................................. 5

1.2.1 Scope of NATA Activities...................................................................................................... 5

1.2.2 Purpose of NATA Activities................................................................................................... 6

1.2.3 Links between NATA Activities and Other Program Elements ............................................. 6

1.3 The National-Scale Assessments.................................................................................................... 7

2 The Initial National-Scale Assessment ................................................................................................... 8

2.1 Uses and Limitations ...................................................................................................................... 8

2.2 The EPA Risk Assessment Paradigm ............................................................................................. 9

2.3 Application of the EPA Risk Assessment Paradigm..................................................................... 10

2.4 Conceptual Model......................................................................................................................... 10

2.4.1 Scope and Resolution............................................................................................................ 10

2.4.2 Time-Frame for Exposure..................................................................................................... 11

2.4.3 Rationale for Aggregating Air Toxics and Sources.............................................................. 11

2.4.4 Details of the Conceptual Model .......................................................................................... 11

2.4.4.1 Sources.............................................................................................................................. 12

2.4.4.2 Stressors............................................................................................................................ 12

2.4.4.3 Pathways/Media................................................................................................................ 12

2.4.4.4 Exposure Routes............................................................................................................... 12

2.4.4.5 Subpopulations ................................................................................................................. 13

2.4.4.6 Non-Human Receptors ..................................................................................................... 13

2.5 Stakeholder Involvement .............................................................................................................. 13

2.6 Peer Review Activities for the Initial National-Scale Assessment ............................................... 14

2.6.1 Past Reviews of National-Scale Assessment Components................................................... 14

2.6.1.1 List of Urban Air Toxics................................................................................................... 14

2.6.1.2 1996 National Toxics Inventory ....................................................................................... 15

2.6.1.3 ASPEN National Dispersion Model ................................................................................. 15

2.6.1.4 Hazardous Air Pollutant Exposure Model (HAPEM)....................................................... 15

2.6.1.5 EPA Risk Assessment Guidelines..................................................................................... 15

2.6.1.6 Microenvironment Factors................................................................................................ 16

2.6.1.7 ASPEN Results................................................................................................................. 16

2.6.2 Review of Planning and Scoping Document for the Initial National-Scale Assessment ...... 16

3 Methods................................................................................................................................................ 17

3.1 Introduction .................................................................................................................................. 17

3.2 Exposure Assessment ................................................................................................................... 20

3.2.1 Source Characterization: Emission Inventories................................................................... 20

3.2.1.1 Approach........................................................................................................................... 21

3.2.1.2 Scope ................................................................................................................................ 25

3.2.1.3 Sources of Data................................................................................................................. 27

3.2.1.3.1 Point Source Emissions.............................................................................................. 27

3.2.1.3.2 Non-Point Emissions.................................................................................................. 29

3.2.1.3.3 Mobile Sources .......................................................................................................... 29

3.2.2 Environmental Fate and Transport Characterization ............................................................ 30

3.2.2.1 Overview of the ASPEN Dispersion Model ..................................................................... 31

3.2.2.2 Application of ASPEN for the Initial National-Scale Assessment ................................... 32

3.2.3 Estimating Population Exposure........................................................................................... 33
ii

3.2.3.1 Overview of HAPEM4 ..................................................................................................... 33

3.2.3.2 Application of HAPEM4 for the Initial National Scale Assessment ................................ 36

3.3 Dose-Response Assessment.......................................................................................................... 37

3.3.1 Introduction........................................................................................................................... 37

3.3.2 Cancer................................................................................................................................... 38

3.3.2.1 Hazard Identification ........................................................................................................ 38

3.3.2.2 Dose-Response Assessment for Carcinogens ................................................................... 39

3.3.3 Effects other than cancer....................................................................................................... 40

3.3.3.1 Hazard Identification ........................................................................................................ 40

3.3.3.2 Dose-Response Assessment for Non-Carcinogens........................................................... 41

3.4 Risk Characterization.................................................................................................................... 41

3.4.1 Introduction........................................................................................................................... 41

3.4.2 Cancer................................................................................................................................... 43

3.4.3 Effects Other Than Cancer.................................................................................................... 44

3.4.4 Discussion of Uncertainties in the Dose-Response Assessment........................................... 49

3.4.4.1 Uncertainties in the Unit Risk Estimate............................................................................ 49

3.4.4.2 Uncertainties in Reference Concentrations....................................................................... 50

4 Results and Discussion of Exposure Assessment ................................................................................. 52

4.1 Introduction .................................................................................................................................. 52

4.2 Source Characterization: Emission Inventories........................................................................... 52

4.2.1 Summary and Discussion of 1996 Emission Inventory Results ........................................... 53

4.2.2 Discussion of Inventory Uncertainties.................................................................................. 56

4.2.2.1 Uncertainties in Completeness of Point Source Universe................................................. 57

4.2.2.2 Uncertainties Due to the Dynamic Nature of Emission Inventories................................. 60

4.2.2.3 Uncertainties in Emission Locations................................................................................. 60

4.2.2.4 Uncertainty Due to Stack Parameter Defaults .................................................................. 63

4.2.2.5 Variations in Reported Emissions..................................................................................... 64

4.2.2.6 Uncertainties Due to Pollutant Groupings........................................................................ 64

4.2.2.7 Particle Size and Reactivity Assignments......................................................................... 64

4.3 Environmental Fate and Transport Characterization .................................................................... 65

4.3.1 Summary of ASPEN Modeling Results................................................................................ 65

4.3.2 Discussion of Results............................................................................................................ 68

4.3.3 Comparison of ASPEN Modeling System to Monitoring Data ............................................ 69

4.3.4 Discussion of ASPEN Dispersion Modeling Uncertainties.................................................. 74

4.3.4.1 Emission Characterization Uncertainties.......................................................................... 75

4.3.4.2 Meteorological Characterization Uncertainties................................................................. 76

4.3.4.3 Model Formulation and Methodology Uncertainties........................................................ 77

4.3.4.4 Uncertainty Due to Background ....................................................................................... 78

4.3.4.5 Summary of Model Uncertainty Investigations................................................................ 79

4.4 Estimating Population Exposure................................................................................................... 79

4.4.1 HAPEM4 Census Tract Level Exposure Estimates.............................................................. 80

4.4.2 National-Scale Assessment Exposure Estimates .................................................................. 82

4.4.3 Comparison of HAPEM4 Exposure Concentrations to ASPEN Ambient Concentrations... 85

4.4.4 Discussion of HAPEM4 Limitations and Uncertainties ....................................................... 87

5 Risk Characterization............................................................................................................................ 90

5.1 Introduction .................................................................................................................................. 90

5.2 Cancer Risks................................................................................................................................. 93

5.2.1 Pollutant-Specific Cancer Risks to Individuals .................................................................... 93

5.2.2 Pollutant-Specific Cancer Risks to Populations.................................................................... 95

5.2.3 Aggregate Cancer Risks of Multiple Pollutants to Individuals............................................. 97

5.2.4 Aggregate Cancer Risks of Multiple Pollutants to Populations............................................ 97

5.3 Non-Cancer Hazard ...................................................................................................................... 98

5.3.1 Pollutant-Specific Hazard Quotient for Individuals.............................................................. 98

5.3.2 Pollutant-Specific Hazard Quotient for Populations............................................................. 99

5.3.3 Aggregate Target Organ Specific Hazard Index of Multiple Pollutants to Individuals........ 99

5.3.4 Aggregate TOSHI of Multiple Pollutants to Populations ................................................... 100
iii

5.4 Discussion of the Risk of Diesel Exhaust ................................................................................... 101

5.5 Uncertainty and Variability Analysis for the NATA National- Scale Assessment..................... 103

5.5.1 Introduction......................................................................................................................... 103

5.5.2 Source Characterization...................................................................................................... 104

5.5.2.1 Data Sources................................................................................................................... 104

5.5.2.2 Emission Locations......................................................................................................... 105

5.5.2.2.1 Point Sources ........................................................................................................... 105

5.5.2.2.2 County-Level Emissions Sources............................................................................. 105

5.5.2.3 Stack Parameter Defaults................................................................................................ 105

5.5.2.4 Particle Size and Reactivity Assignments....................................................................... 105

5.5.2.5 Chemical Speciation Data............................................................................................... 105

5.5.3 Ambient Concentration Estimation..................................................................................... 106

5.5.3.1 Temporal Resolution of Emissions................................................................................. 106

5.5.3.2 Simplifying Assumptions................................................................................................ 106

5.5.3.3 Meteorological Characterization Uncertainties............................................................... 106

5.5.3.4 Model Formulation and Methodology Uncertainties...................................................... 106

5.5.3.4.1 Deposition and Dispersion Algorithms.................................................................... 106

5.5.3.4.2 Atmospheric Transformation Algorithms................................................................ 107

5.5.3.4.3 Interpolation Between Census Tract Centroids........................................................ 107

5.5.3.4.4 Summary.................................................................................................................. 107

5.5.3.5 Illustration of Uncertainty and Variability Associated With Ambient Concentration 

Estimates 108

5.5.4 Personal Exposure Assessment........................................................................................... 110

5.5.4.1 Microenvironment Factors.............................................................................................. 110

5.5.4.1.1 Population Cohorts.................................................................................................. 110

5.5.4.2 Activity Pattern Sequence............................................................................................... 110

5.5.4.3 Illustration of Uncertainty and Variability Associated with Exposure Estimates........... 111

5.5.5 Illustration of Uncertainty and Variability for Dose-Response Assessment....................... 112

5.5.5.1 Unit Risk Estimates (UREs) ........................................................................................... 112

5.5.5.2 Reference Concentrations (RfCs) ................................................................................... 113

5.5.6 Illustration of Propagation of Uncertainty and Variability ................................................. 114

5.5.7 Aggregation of Risk Across Pollutants............................................................................... 116

5.5.8 Recommendations for Further Characterization of Uncertainty ......................................... 117

5.5.8.1 Role of Uncertainty Analysis.......................................................................................... 117

5.5.8.2 Technical Issues in Further Characterization of Uncertainty.......................................... 118

5.5.8.3 Future Plans.................................................................................................................... 120

6 Summary and Recommendations ....................................................................................................... 121

6.1 Perspective on the National-Scale Assessment for 1996 ............................................................ 121

6.2 Summary of Initial Results of National Scale Assessment......................................................... 122

6.3 Recommendations....................................................................................................................... 124

6.3.1 Identifying Air Toxics of Greatest Concern ....................................................................... 124

6.3.2 Prioritizing Efforts to Reduce Emissions............................................................................ 125

6.3.3 Characterizing Contributions of Sources............................................................................ 125

6.3.4 Tracking Trends and Progress ............................................................................................ 126

6.3.5 Setting Data Collection and Research Priorities................................................................. 126

7 References .......................................................................................................................................... 130
iv

List of Tables

Table 3-1. Pollutant set for the initial national-scale assessment................................................................. 18

Table 3-2. Urban air toxics believed to present risks from multipathway exposure.................................... 20

Table 3-3. Spatial Allocation Factors (SAF) Developed for EMS-HAP ..................................................... 24

Table 3-4. Non-Point Source Stationary Categories with EPA-Derived Emissions.................................... 29

Table 3-5. Hazard identification and dose-response information for carcinogenic effects.......................... 45

Table 3-6. Non-Cancer Dose-Response Information................................................................................... 47

Table 3-7. Grouping of compounds by target organ and uncertainty factor for aggregation of effects other 

than cancer........................................................................................................................................... 49

Table 4-1. Summary of Inventories Used as Input to EMS-HAP................................................................ 53

Table 4-2. Comparison of EMS-HAP and NTI emission totals. ................................................................. 55

Table 4-3. Summary of Data Sources to the NTI. ....................................................................................... 56

Table 4-4. 1990 CEP and 1996 NTI National Emission Totals.................................................................... 58

Table 4-5. Facility Count Summary. ........................................................................................................... 59

Table 4-6. Summary of Defaulted Sources and Emissions for Lead, Chromium, and Cadmium................ 61

Table 4-7. Stack Parameter Default Statistics.............................................................................................. 63

Table 4-8. Pollutants grouped by the dominant source sector affecting their national average 

concentrations....................................................................................................................................... 66

Table 4-9. Number of Counties of Each Dominant Source Sector for the 10 Highest County Median 

Concentrations...................................................................................................................................... 66

Table 4-10. Comparison of the Measurement Data to Modeled Concentration. .......................................... 71

Table 4-11. Maximum Modeled Concentration Compared to Monitored Value.......................................... 72

Table 4-12. Source location uncertainties for point source inventory for three metals. .............................. 76

Table 4-13. HAPEM4 to ASPEN Average Ratio1....................................................................................... 86

Table 5-1. Illustration: Parameters for lognormal distributions fitted to monitor-to-model ratios for seven 

pollutants. ........................................................................................................................................... 109

Table 5-2. Illustration: Calculated percentiles for monitor:model ratio distribution................................. 109

Table 5-3. Illustration: Percentiles for uncertainty and variability in the personal: ambient ratio 

distribution.......................................................................................................................................... 112

Table 5-4. Illustration: Percentiles for variability in the benzene URE...................................................... 113

Table 5-5. Illustration: Combined uncertainty and variability, in terms of the risk ratio (i.e., the ratio of 

“true” risk to estimated risk)............................................................................................................... 116

Table 5-6. Sources of uncertainty for the National-Scale Assessment. ..................................................... 119
v

List of Figures

1-1. Overview of National Air Toxics Assessment (NATA) Activities. .................................... 134

1-2. Links Between NATA Activities and Other Program Elements. . ...................................... 135

2-1. NAS Risk Assessment/Risk Management Paradigm. .......................................................... 136

2-2. National-Scale Air Toxics Health Assessment: Conceptual Model. . ................................ 137

2-3. Diagram of the HAPEM4 40 Cohort Groups (2x4x5=40). . . .............................................. 138

3-1. 1996 NTI State and Local Agency Data Summary. . ........................................................... 139

3-2. Example Demographic Groups, Microenvironment, and Activities. . ................................ 140

3-3. Example of a Daily Exposure Scenario for a Cohort. . .........................................................141

4-1. Summary of 1996 NTI Emissions for 33 Air Toxics by Source Sector. . ............................ 142

4-2. Summary of 1996 NTI Emissions of 33 Air Toxics by Urban and Rural Designations. ..... 142

4-3. 1996 NTI - Aldehydes Emission Densities. . ....................................................................... 143

4-4. 1996 NTI - Metals Emission Densities. . ............................................................................. 143

4-5. 1996 NTI -Halides Emission Densities. . ............................................................................. 144

4-6. 1996 NTI -POM and Hydrocarbons Emission Densities. . .................................................. 144

4-7. Estimated Annual Average Concentrations (ug/m3) for Benzene. ...................................... 145

4-8. Percent Contribution to the Statewide Annual Average Ambient 

Benzene Concentration Estimates........................................................................................ 146

4-9. Comparison of Annual Average Model Concentrations for 10 Pollutants. ......................... 147

4-10. Annual Average Concentrations for Urban and Rural Census Tracts. ................................ 148

4-11. Relative contribution of major, area and mobile sources. ................................................... 149

4-12. Model-to-monitor scatter plot for benzene. ......................................................................... 150

4-13. Ratio box plot showing distribution of model/monitor ratios for each pollutant. ............. 151

4-14. POM Exposure Concentration Distribution Among Cohorts in an Urban NY Census ....... 152

4-15. Exposure Results - Summary Table Example (Partial Table) ............................................. 153

 4-16. 1996 Modeled Exposure Concentrations for Acetaldehyde - Statewide Concentration Distribution 

Estimates .......................................................................................................................154

4-17. 1996 Modeled Exposure Concentrations for Acetaldehyde - Statewide Source Sector Contribution 

Estimates........................................................................................................................155

4-18. 1996 Modeled Median Exposure Concentrations for North Carolina................................. 156

4-19. Example State Exposure Concentration Map....................................................................... 157

4-20. Benzene Exposure Variability within a County. .................................................................. 158

5-1. 1996 Risk Characterization - Distribution of lifetime cancer risk for the US population, based on

1996 exposure to all source sectors and background combined. .................................. 159

5-2. 1996 Risk Characterization - Population whose 1996 exposure exceeded set cancer risk levels

based on all source sectors and background. ................................................................ 160

5-3. 1996 Risk Characterization - Distribution of lifetime cancer risk for the US population, based on

1996 exposure to multiple carcinogens. ....................................................................... 161

5-4. 1996 Risk Characterization - Population whose 1996 exposure* exceeded set risk levels of risk for 

carcinogens combined. ................................................................................................. 162

5-5. 1996 Risk Characterization - Distribution of non-cancer hazard quotient for the US population,

based on 1996 exposure* to all source sectors and background combined. ................ 163

5-6. 1996 Risk Characterization - Adult population whose 1996 exposure* exceeded set non-cancer 

hazard quotient levels based on all source sectors and background combined. ........... 164

5-7. 1996 Risk Characterization - Children population whose 1996 exposure* exceeded set non-cancer 

hazard quotient levels based on all source sectors and background combined.............. 165

5-8. 1996 Risk Characterization - Distribution of non-cancer target organ-specific hazard index

(TOSHI) for effects to the respiratory system, based on 1996 multiple-pollutant exposure to

adults in the US population. ..........................................................................................166

5-9. Illustration: Distribution of monitor-to-model ratios for stable gases, developed from ratios for

benzene and perchloroethylene. .................................................................................... 167

5-10. Illustration: Distribution of monitor-to-model ratios for reactive gases, developed from ratios for

formaldehyde and acetaldehyde..................................................................................... 167
vi

List of Figures

5-11. Illustration: Distribution of monitor-to-model ratios for particulate species, developed from ratios

for lead, chromium, and cadmium. ............................................................................... 167

5-12. Illustration: Distribution of ambient-to-personal concentration ratios for ozone, assumed to apply 

for “typical” gaseous pollutants .................................................................................... 168

5-13. Illustration: Distribution of ambient-to-personal concentration ratios for particulate matter, 

assumed to apply for “typical” particulate pollutants. .................................................. 168

5-14. Illustration: Uncertainty and variability surrounding the URE for benzene, in terms of the ratio 

between the estimated URE and the “true” URE. ......................................................... 169

5-15. Illustration: Uncertainty and variability surrounding a typical RfC, in terms of the ratio between 

the estimated RfC and the “true” RfC. ........................................................................ 169

5-16. Cancer – risk ratio for stable gas. .........................................................................................170

5-17. Cancer – risk ratio for reactive gas....................................................................................... 170

5-18. Cancer – risk ratio for particulate......................................................................................... 170

5-19. Noncancer – risk ratio for stable gas.....................................................................................171

5-20. Noncancer – risk ratio for reactive gas. ................................................................................171

5-21. Noncancer – risk ratio for particulate. ..................................................................................171

List of Appendices

A. Summary of July 2000 Peer Review of the Draft Document “Planning and Scoping the Initial 

National-Scale Assessment: An Element of the EPA National Air Toxics Program”

B. HAPEM4 User’s Guide, with Microenvironment Factor Report

C. EMS-HAP User’s Guide 

D. Development of the Emission Inventory

E. ASPEN User’s Guide 

F. Estimation of Background Concentrations for Diesel Particulate Matter

G. Health Effects Information Used In Cancer and Noncancer Risk Characterization for the NATA 1996 

National-Scale Assessment 

H. Estimating Carcinogenic Potency for Mixtures of Polycyclic Organic Matter (POM) for the 1996 

National-Scale Assessment

I. Model-to-Monitor Comparison Methods

J. Comparison of ASPEN Results to Monitored Concentrations

K. HAPEM4 Results

L. Risk Characterization Results
1

1 Introduction 

This document describes the EPA’s National-Scale Air Toxics Assessment, based on 

emissions data for 1996 (called the “national-scale assessment”). The national-scale 

assessment is a nationwide study of potential inhalation exposures and health risks 

associated with 32 hazardous air pollutants (hereinafter called air toxics) and diesel 

particulate matter (diesel PM), based on 1996 data, because 1996 emission inventories 

were the most complete and up-to-date available. The initial national-scale assessment is 

one component of the National Air Toxics Assessment (NATA), the technical support 

component of EPA’s National Air Toxics Program Specifically, NATA includes 

activities such as expanding air toxics monitoring, improving and periodically updating 

emissions inventories, periodically conducting national- and local-scale air quality, multimedia and exposure modeling, characterizing risks associated with air toxics exposures, 

and continuing research on health and environmental effects and exposures to both 

ambient and indoor sources. 

The initial national-scale assessment is the first in an anticipated series of national-scale 

assessments. These national-scale assessments may be repeated every 3 years, to track 

trends in the reduction of emissions of air toxics, as well as progress in reducing risks 

from air toxics exposure. The purpose of the national-scale assessment is to gain a better 

understanding of the air toxics problem. It was not designed, and is not appropriate 

specifically, for identifying local- or regional-scale air toxics “hot spots,” nor is it 

appropriate for identifying localized risks or individual risks from air toxics. Further 

analyses on a national scale, and additional assessments on other scales (e.g., urban air 

toxics assessments and residual risk assessments) are being performed in order to fully 

characterize risks, especially disproportionate and cumulative risks.

Given the uncertainties and limitations associated with performing this national-scale 

assessment, the EPA is seeking EPA Science Advisory Board (SAB) review on the 

appropriateness of the methodologies and tools, as applied in this assessment, and 

guidance on ways to improve future national-scale assessments. This document was 

prepared for that SAB review.

The goals of this initial national-scale assessment are to assist in:

• Identifying air toxics of greatest potential concern, in terms of contribution to 

population risk; 

• Characterizing the relative contributions to air toxics concentrations and 

population exposures from different types of air toxics emission sources;

• Setting priorities for the collection of additional air toxics data (e.g., emission 

data, ambient monitoring data, data from personal exposure monitoring) for use in 

local-scale and multipathway modeling and assessments, and for future research 

to improve estimates of air toxics concentrations and their potential public health 

impacts;
2

• Establishing a baseline for tracking trends over time in modeled ambient 

concentrations of air toxics; and,

• Establishing a baseline for measuring progress toward meeting goals for 

inhalation risk reduction from ambient air toxics. 

 

The results of the initial national-scale assessment will provide information to guide EPA 

in developing and implementing various aspects of the national air toxics program. 

However, the results will not be used to make specific regulatory decisions for air toxics. 

While regulatory priority-setting will be informed by this and any future national-scale 

assessments, risk-based regulations will be based on more refined and source-specific 

data and assessments.

There are several other important limitations in the scope of the national-scale 

assessment, and they are as follows:

• It is based on 1996 data and does not reflect significant reductions in air toxics 

emissions that have occurred since that time

• It focuses only on 33 selected air toxics (32 air toxics of greatest concern in urban 

areas and diesel particulate matter), and does not address the other 156 air toxics 

listed in section 112(b) of the Clean Air Act

• It does not include risks due to non-inhalation exposure pathways (e.g., 

ingestion), which have been shown to be significant for some air toxics (e.g., 

mercury, dioxins)

• It does not include exposure from indoor sources of air toxics

• It focuses on average population risks, rather than individual extremes (i.e., does 

not identify “hot spots”)

• It does not reliably capture localized impacts and risks

• It currently includes estimates of background levels that are only approximate; 

more research is needed to treat background contributions more explicitly

• Current model evaluation efforts indicate a tendency to under-predict ambient 

concentrations, which may contribute toward an underestimation of risk

For these reasons, the initial national-scale assessment provides only a partial indication 

of the total risk due to all air toxics. Its results should always be interpreted keeping this 

perspective in mind.
3

Section 1 of this document provides background information by describing EPA’s 

national air toxics program and by explaining the goals of NATA and the national-scale 

assessments. Section 2 describes the purpose and goals of the initial national-scale 

assessment, while Section 3 explains the methodologies used for the initial national-scale 

assessment. Section 4 provides a summary of the results of that initial assessment. 

Section 5 of the document presents the risk characterization, which includes an 

uncertainty and variability analysis, and Section 6 provides a summary of results and 

recommendations for future actions. 

1.1 EPA’s National Air Toxics Program

1.1.1 Background on the Air Toxics Program

The air toxics program was authorized under the 1970 Clean Air Act (CAA), and 

redesigned and reauthorized under the 1990 CAA Amendments. The program is 

designed to characterize, prioritize, and address, in an equitable manner (i.e., across 

racial, cultural, and economic groups), the serious impacts of air toxics (also known as 

hazardous air pollutants, or air toxics) on public health and the environment through a 

strategic combination of regulatory approaches, voluntary partnerships, ongoing research 

and assessments, and education and outreach.

Since 1990, EPA has made considerable progress in reducing emissions of air toxics 

through regulatory, voluntary and other programs. To date, the overall air toxics program 

has focused on reducing emissions of air toxics from major stationary sources through the 

implementation of technology-based emissions standards, as required in section 112(d) of 

the CAA. These actions have resulted in, or are projected to result in, substantial 

reductions in air toxics emissions. Additionally, actions to address mobile sources under 

other CAA programs have achieved significant reductions in air toxics emissions (e.g., 

the phase-out of lead from gasoline). Many motor vehicle and fuel emission control 

programs of the past have reduced air toxics. Several current EPA programs further 

reduce air toxics emissions from a wide variety of mobile sources. These include the 

reformulated gasoline (RFG) program, the national low emission vehicle (NLEV) 

program, Tier 2 motor vehicle emission standards and gasoline sulfur control 

requirements, and recently finalized heavy-duty engine and vehicle standards and onroad 

diesel fuel sulfur control requirements. In addition, certain other mobile source control 

programs have been specifically aimed at reducing toxics emissions. These actions are 

projected to reduce emissions substantially. 

EPA expects, however, that the emission reductions that will result from these actions 

may only be part of what is necessary to protect public health and the environment from 

air toxics. In addition, section 112(f) of the CAA specifically directs EPA to assess the 

risk remaining after implementation of technology-based standards (i.e., the residual risk) 

in order to evaluate the need for additional stationary source standards to protect public 

health and the environment. Under section 112(k), EPA will be performing 

comprehensive local-scale assessments for several urban areas. In identifying 

appropriate additional steps, EPA will use a risk-based focus to develop, implement and 

facilitate additional federal, state and local regulatory and voluntary measures, if 

necessary. In considering additional steps toward protecting human health and the 
4

environment, EPA will identify and focus on issues of highest priority. 

1.1.2 Air Toxics Program Goals

EPA has ten long-range strategic goals [1] which establish the focus for the Agency’s 

work in the years ahead. One of these goals, EPA’s Clean Air Goal, states that the air in 

every American community will be safe and healthy to breathe. In particular, children, 

the elderly, and people with respiratory ailments will be protected from health risks of 

breathing polluted air. Reducing air pollution will also protect the environment, resulting 

in many benefits, such as restoring life in damaged ecosystems and reducing health risks 

to those whose subsistence depends directly on those ecosystems. The specific air toxics 

objective under this goal is, by 2020, to eliminate unacceptable risks of cancer and other 

significant health problems from air toxic emissions for at least 95% of the population, 

with particular attention to children and other sensitive sub-populations, and substantially 

reduce or eliminate adverse effects on our natural environment. Further, by 2010, the 

tribes and EPA will have the information and tools to characterize and assess trends in air 

toxics in Indian country. 

EPA will progress toward meeting its air toxics program goals through a combination of

statutory authorities, regulatory activities and voluntary initiatives. EPA’s overall 

approach to reducing air toxics consists of the following four key components, which are 

discussed in greater detail in the July 19, 1999 Federal Register notice for the National 

Air Toxics Program: [2]:

1. Source-specific standards and sector-based standards. Section 112 of the CAA 

specifies emission standards (technology-based standards) and residual risk 

standards (risk-based standards), as well as area source standards identified by the 

Integrated Urban Air Toxics Strategy. Additionally, section 129 of the CAA 

requires standards for solid waste incineration and section 202(l) requires EPA to 

promulgate reasonable requirements to control air toxics from motor vehicles and 

their fuels.

2. National, regional, and community-based initiatives to focus on multi-media and 

cumulative risks. Section 112(k)(4) of the CAA requires EPA to “encourage and 

support area wide strategies developed by the state or local air pollution control 

agencies.” EPA’s risk initiatives will include state, local and tribal program 

activities consistent with the Integrated Urban Air Toxics Strategy on the local 

level, as well as federal and regional activities associated with the multimedia 

aspects of air toxics (e.g., the Great Waters program [Section 112(m)] and 

Agency initiatives concerning mercury and other persistent and bioaccumulative 

toxics [PBTs]). These Agency initiatives include collaboration between the air 

and water programs on the impact of air deposition on water quality (e.g., by 

accounting for the contribution of air deposition to the total maximum daily load 

(TMDL) of pollutants to a water body), and collaboration between offices within 

EPA’s air program to assess the risks from exposures to air toxics from indoor 

sources.

3. National air toxics assessment (NATA) activities. NATA activities will help EPA 
5

identify areas of concern, characterize risks, and track progress toward meeting 

the overall air toxics program goals, as well as the risk-based goals of the various 

activities and initiatives within the program. The NATA activities include 

expansion of air toxics monitoring, improving and periodically updating 

emissions inventories, national- and local-scale air quality, multi-media and

exposure modeling (including modeling which considers stationary and mobile 

sources), continued research on health effects and exposures to both ambient and 

indoor air, and use and improvement of exposure and risk assessment tools. 

These activities will provide EPA with improved characterizations of air toxics 

risk and risk reductions resulting from emissions control standards and initiatives 

for both stationary and mobile source programs.

4. Education and outreach. In light of the scientific complexity inherent in air 

toxics issues, EPA recognizes that the success of the overall air toxics program 

depends, in part, on the Agency’s ability to communicate effectively with the 

public about air toxics risks and activities necessary to reduce those risks. This 

includes education and outreach efforts on air toxics in the ambient as well as 

indoor environments.

Under the CAA, EPA provides leadership and technical and financial assistance for the 

development of cooperative federal, state, local, and tribal programs to prevent and 

control air pollution. A strong partnership with and among the different governments that 

each play a key role in air quality protection is critical to achieving clean air, because it is 

the sum of these collective efforts that constitutes the national air quality program. 

The Agency is committed to working with cities, communities, state, local and tribal 

agencies, and other groups and organizations that can help implement activities to reduce 

air toxics emissions. For example, EPA has been working with its regulatory partners 

and interested stakeholders on activities related to NATA. In addition, the Agency will 

continue to work with its regulatory partners and stakeholders on regulation development. 

EPA also expects to involve local communities and industries in the development of local 

risk initiatives such as the urban community-based pilot projects (i.e., local-scale 

assessments).

1.2 Overview of National Air Toxics Assessment (NATA) 

Activities

As described in section 1.1, the overall air toxics program consists of four central 

elements: (1) source-specific and sector-based standards, (2) national, regional and 

community-based initiatives, (3) NATA activities, and (4) education and outreach. This 

section focuses on the third of these elements, the NATA activities.

1.2.1 Scope of NATA Activities

EPA includes, within the scope of the NATA activities, all data gathering, analyses, 

assessments, characterizations, and related research needed to support the air toxics 

program, as shown in Figure 1-1. The figure depicts two levels of assessments. 

National-scale assessments are shown centrally in boldface, and refined local-scale 

assessments are depicted underneath. Other NATA activities that support each type of 
6

assessment (e.g., emission characterization, meteorological data collection) are shown in 

boxes at the top of the figure, with arrows depicting the points in the assessment where 

the information is used. The information from the NATA activities flows to the right side 

of the figure, where the most important uses for the outputs are shown.

1.2.2 Purpose of NATA Activities

EPA envisions the NATA activities as an ongoing, permanent part of the air toxics 

program. EPA intends to use NATA products to inform the entire air toxics program 

with a body of coordinated information that expands and evolves to fit the needs of each 

part of that program.

EPA’s ongoing data collection and research will allow the Agency to continue to improve 

its understanding of air toxics emissions, ambient concentrations (with a multi-media 

focus, where appropriate), outdoor and indoor exposures (by inhalation and other 

pathways, where appropriate), and health and environmental effects. These efforts will 

also help to improve the tools and methods for assessing and characterizing public health, 

environmental hazards, and cumulative risks associated with exposures to air toxics. 

EPA is currently working to ensure stakeholder involvement in the planning and conduct 

of these activities, and to ensure appropriate peer review of the underlying science and 

assessment methods.

As shown in Figure 1-1, these NATA activities will serve several purposes. The 

information and assessments developed by NATA activities will help EPA: 

1. Determine priorities for regulatory programs as well as for national, regional, and 

community-based initiatives; 

2. Assess progress toward CAA goals and EPA's long-range strategic goals; 

3. Inform state, local, and tribal programs and support public right-to-know 

initiatives regarding risks associated with exposure to air toxics; and, 

4. Support prospective assessments of the benefits estimated to result from 

implementation of statutory air toxics mandates (as required by section 812 of the 

CAA).

1.2.3 Links between NATA Activities and Other Program Elements

Figure 1-2 displays important examples of regulatory programs, risk-based initiatives, 

and special studies that will utilize information from current and future NATA activities. 

National-scale information produced by these NATA activities will specifically support

setting priorities and estimating progress by each of these elements of the air toxics 

program. In turn, it is anticipated that the regulatory programs, initiatives, and studies 

shown on Figure 1-2 will result not only in continued reductions in air toxics emissions 

and risk, but will also lead to enhanced knowledge and tools to improve EPA’s ability to 

characterize air toxics risk. The risk reduction progress that is made, and the improved 

risk characterization information and tools that become available, will then be reflected in 

future NATA activities.
7

1.3 The National-Scale Assessments

The purpose of the national-scale assessments is to gain a better understanding of the air 

toxics problem on a national scale by compiling and analyzing existing air toxics data. 

The goal of the national-scale assessments is to assist in: 1) identifying air toxics of 

greatest potential concern in terms of contribution to population risk; 2) characterizing 

the relative contributions of various types of emission sources to air toxics concentrations 

and population exposures; 3) setting priorities for collection of additional air toxics data 

and research to improve estimates of air toxics concentrations and their potential public 

health impacts; 4) tracking trends in modeled ambient air toxics concentrations over time; 

and, 5) measuring progress toward meeting goals for inhalation risk reduction from 

ambient air toxics. To accomplish these goals, the national-scale assessment will be 

repeated every three years, with the next national-scale assessment being performed in 

2003, with 1999 air toxics data.

EPA particularly notes that, while the initial national-scale assessment (i.e., an 

assessment which is national in scale, has low resolution, and represents only one part of 

the overall NATA activities) can help set general programmatic priorities and provide 

direction for the design of local-scale initiatives and more refined special studies, it is not 

intended to serve as the basis for setting standards or addressing specific local concerns 

or community environmental justice issues. EPA will use more refined and local-scale 

information and assessment tools developed within NATA activities as the basis for riskbased standard setting (e.g., residual risk standards) and for local initiatives. 
8

2 The Initial National-Scale Assessment

2.1 Uses and Limitations

The results of the initial national-scale assessment presented in this document provide 

important information to help EPA continue to develop and implement various aspects of 

the national air toxics program. However, it is important to note that these results will 

not be used directly to regulate sources of air toxics emissions. Although the nationalscale assessments (both the initial assessment and future assessments) will inform the 

regulatory priority-setting process, risk-based regulations will be supported by more 

refined and source-specific data and assessment tools.

More specifically, the national-scale assessment results will assist in:

• Identifying air toxics of greatest potential concern, in terms of contribution to 

population risk; 

• Characterizing the relative contributions by different types of air toxics emission 

sources to air toxics concentrations and population exposures (i.e., major, area 

and other, on-road and non-road mobile, and background sources);

• Setting priorities for the collection of additional air toxics data (e.g., emission 

data, ambient monitoring data, data from personal exposure monitoring) and 

research to improve estimates of air toxics concentrations and their potential 

public health impacts;

• Establishing a baseline for tracking trends over time in modeled ambient 

concentrations of air toxics; and,

• Establishing a baseline for measuring progress toward meeting goals for 

inhalation risk reduction from ambient air toxics. 

The results of the initial national-scale assessment will help identify areas of the country 

and pollutants where additional investigation is needed, and will help target locations 

where more refined, regional- and local-scale analyses should be done.

There are several other important limitations in the scope of the national-scale 

assessment, and they are as follows:

• It is based on 1996 data and does not reflect significant reductions in air toxics 

emissions that have occurred since that time

• It focuses only on 33 selected air toxics (32 air toxics of greatest concern in urban 

areas and diesel particulate matter), and does not address the other 156 air toxics 

listed in section 112(b) of the Clean Air Act
9

• It does not include risks due to non-inhalation exposure pathways (e.g., 

ingestion), which have been shown to be significant for some air toxics (e.g., 

mercury, dioxins)

• It does not include exposure from indoor sources of air toxics

• It focuses on average population risks, rather than individual extremes (i.e., does 

not identify “hot spots”)

• It does not reliably capture localized impacts and risks

• It currently includes estimates of background levels that are only approximate; 

more research is needed to treat background contributions more explicitly

• Current model evaluation efforts indicate a tendency to under-predict ambient 

concentrations, which may contribute toward an underestimation of risk

For these reasons, the initial 1996 national-scale assessment should be considered to 

provide only a partial indication of the total risk due to all air toxics. Its results should 

always be interpreted with this perspective in mind.

The initial national-scale assessment is the first step in an iterative and evolving process 

to assess and characterize risks from exposures to air toxics, measure progress in meeting 

goals, and inform future directions for EPA’s national air toxics program. While there 

continue to be significant uncertainties and gaps in methods, models, and data that limit 

EPA’s ability to assess risks to public health and the environment associated with 

exposures to air toxics, continued research will enable future assessment activities, both 

at the national level and at more refined levels, to yield improved assessments of 

cumulative air toxics risks. An important component of future NATA activities will be to 

repeat the national-scale assessment every three years, with the next national-scale 

assessment being performed in 2003 with 1999 air toxics data.

2.2 The EPA Risk Assessment Paradigm

Because cancer and noncancer health impacts generally cannot be directly isolated and 

measured with respect to environmental exposures, EPA and others have spent more than 

two decades developing an extensive set of risk assessment methods, tools, and data that 

serve the purpose of estimating health risks for many Agency programs. Although 

significant uncertainties remain, EPA’s risk assessment science has been extensively 

peer-reviewed, is widely used and understood by the scientific community, and continues 

to expand and evolve as scientific knowledge advances. All NATA risk assessments will 

be based on the most current and appropriate risk estimation methods.

EPA’s framework for assessing and managing risks reflects the risk assessment and risk 

management paradigm set forth by the National Academy of Sciences (NAS) in 1983 [3], 

shown in Figure 2-1. This figure identifies research, risk assessment, and risk 

management as three separate but connected elements. The NAS concluded that risk 
10

assessment and risk management are “two distinct elements” between which agencies 

should maintain a clear conceptual distinction. The 1983 NAS report identified four 

steps integral to any risk assessment: 1) hazard identification, 2) dose-response 

assessment, 3) exposure assessment, and 4) risk characterization. As described in the 

next section, the NAS paradigm for risk assessment serves as the basis for the NATA 

national-scale assessment.

2.3 Application of the EPA Risk Assessment Paradigm

As described in more detail in section 3, the initial national-scale assessment includes 

four major components: 

(1) compiling a 1996 national emissions inventory of air toxics emissions 

from outdoor sources; 

(2) estimating 1996 air toxics ambient air concentrations for 33 air toxics (32 

urban air toxics and diesel particulate matter (diesel PM)) nationwide; 

(3) estimating 1996 population inhalation exposures to these air toxics; and,

(4) characterizing potential public health risks associated with these 

exposures, including both cancer and noncancer effects.

The following sections describe the elements of the initial national-scale assessment.

2.4 Conceptual Model

2.4.1 Scope and Resolution

The initial national-scale assessment was national in scope, covering the contiguous 

United States, Puerto Rico, and the Virgin Islands. The assessment excluded Alaska, 

Hawaii, and U.S. territories other than Puerto Rico and the Virgin Islands because the 

data needed to support the models (e.g., census tract and meteorological information) 

were not readily available for these areas. 

The pollutants identified and peer-reviewed for inclusion in this initial national-scale 

assessment were the air toxics identified as priority pollutants in EPA's Integrated Urban 

Air Toxics Strategy (IUATS)[2]. These 33 air toxics are a subset of EPA's list of 188 air 

toxics, under CAA section 112(b), and have been identified as those pollutants that 

present the greatest threat to public health in the largest number of urban areas. This 

assessment also included diesel particulate matter, an indicator of diesel exhaust. EPA 

has recently listed this likely human carcinogen as a mobile source air toxic and is 

addressing this pollutant in several regulatory actions.1 Because the current dioxin

exposure and human health reassessment is undergoing review, the initial national-scale 

assessment did not include the class of compounds known as dioxins. Since the most 

significant exposure route for dioxin is ingestion rather than inhalation, dioxin’s relative 

 

1

 In the Mobile Source Air Toxics rule (December 2000), under section 202(l)(2) of the CAA, EPA listed 

diesel particulate matter plus diesel exhaust organic gases, collectively, as a mobile source air toxic. 
11

contribution to this study’s inhalation risk estimates would likely not have been great. It 

is expected that dioxins will be included in future national-scale assessments where 

ingestion and inhalation exposures are assessed. In summary, 32 of the priority 

pollutants in EPA’s Integrated Urban Air Toxics Strategy, as well as diesel PM, make up 

the 33 air toxics that were included in the initial national-scale assessment. These 33 air 

toxics will hereinafter be referred to as “the pollutant set.” 

The national-scale assessment included a risk characterization based on estimates of 

inhalation exposure concentrations determined at the census-tract level. Because of 

uncertainties associated with the accuracy of the results at the census-tract level, the 

results of the risk characterization have been aggregated and presented at the county level 

or higher. EPA strongly cautions that census-tract level estimates are not reliable, and 

results for individual census tracts are not presented. EPA chose the county level of 

resolution for the assessment for three reasons. First, the inventory data for some 

pollutants and source sectors was only available at a county level resolution. Second, 

uncertainties inherent in other model input data (e.g., terrain, meteorology) and the 

simplifying assumptions made in the models (e.g., transformation chemistry) themselves 

rendered estimates at the census-tract level highly uncertain. Third, census-tract level 

exposure estimates included additional variability (e.g., microenvironment data, human 

activity pattern data) that is introduced only at local levels. For these reasons, EPA’s 

confidence in the accuracy of estimates for any given census tract is low. 

2.4.2 Time-Frame for Exposure

The initial national-scale assessment focused on average yearly exposures for effects 

other than cancer, and assumed lifetime exposures based on annual averages for 

carcinogenic effects. Subchronic and acute exposures were not included in the initial 

national-scale assessment. The national-scale assessment excluded acute and subchronic 

exposures because of the nature of the emissions data, which contained only yearly total 

emissions. If the emission inventories are later expanded to cover short-term (e.g., 

hourly, daily) emission rates, EPA may incorporate shorter exposure times into future 

national-scale assessments.

2.4.3 Rationale for Aggregating Air Toxics and Sources

Exposure to air toxics from all sources is determined by a multiplicity of interactions 

among complex factors, including the location and nature of the emissions, the existence 

of multiple sources, local climate, location of receptor populations, and the specific 

behaviors and physiology of those populations. Risks associated with these exposures 

are influenced by the particular combination of air toxics that people actually inhale, and 

the chemical and biological interactions among those air toxics. 

Because of this high level of complexity, the magnitude of risks associated with 

inhalation of air toxics can be usefully depicted by aggregating risk across both 

substances and sources. Given the goals of this assessment, and the purposes for which 

EPA intends to use it, currently available risk assessment data, tools, and guidance were 

judged sufficient.

2.4.4 Details of the Conceptual Model
12

The following subsections include summary descriptions of the risk dimensions and 

elements of the national-scale assessment, as recommended by EPA’s Cumulative Risk 

Assessment Guidance [4]. The conceptual model for the national-scale assessment

appears in Figure 4.

2.4.4.1 Sources

The dispersion modeling, from which the exposure assessment and risk characterization 

were developed, included stationary and mobile source emissions for the contiguous US, 

Puerto Rico, and the Virgin Islands. 

By limiting the exposure assessment and risk characterization of the initial national-scale 

assessment to sources having data within the available emission inventories, EPA 

excluded all non-inventoried sources. This limitation effectively excluded releases (1) 

from many natural processes, (2) from indoor sources (e.g., paints, carpets, etc.), and (3) 

from surface water, groundwater, or soil. While EPA takes these releases and their 

potential to cause adverse health effects seriously, EPA lacked adequate model inputs 

(i.e., data on substance identities and release rates) needed to quantify them in the 

assessment. 

2.4.4.2 Stressors

The initial national-scale assessment encompassed the pollutant set described in section 

2.5.1. Later national-scale assessments may expand to cover additional Clean Air Act air 

toxics, to the extent that available emission and toxicity data allow doing so. EPA chose 

to limit the initial national-scale assessment to this pollutant set for two reasons. First, 

these air toxics, in aggregate, appeared highly likely to encompass most of the total air 

toxics-related risk to human populations [2]. Second, the initial national-scale 

assessment, in combination with similar future assessments, will serve as an important 

vehicle to fulfill EPA’s assessment commitments under the Integrated Urban Air Toxics 

Strategy, which is focused specifically on these air toxics.

2.4.4.3 Pathways/Media

The dispersion modeling step of the assessment included evaluation of the transport of 

particles and gases through the air to receptors within 50km of sources. Atmospheric 

transformation and losses from the air by deposition were included in the modeling, 

where data permit. For 13 pollutants with available ambient monitoring data, background 

concentration data due to sources located more than 50 km away were included. These 

background estimates for each pollutant were assumed constant across the US. The 

assessment excluded accretion in water, soil, or food associated with deposition from air, 

and bioaccumulation of air toxics in tissues. Although EPA takes potential transport of 

air toxics into other media very seriously, refined tools to model multipathway 

concentrations on the national scale are not yet readily available for use for many 

pollutants. Future local- or urban-scale assessments will include multipathway 

calculations, which may be added to national-scale assessments when adequate models 

and input data become available. 

2.4.4.4 Exposure Routes
13

The national-scale assessment focused on exposures due to inhalation of ambient air. 

Human receptors were modeled as they moved within 37 separate microenvironments 

such as residences, offices, schools, outdoor work sites, and automobiles. The exposure 

assessment estimated air concentrations of each substance within each microenvironment, 

using the outdoor concentration, proximity of the microenvironment to sources, time of 

day, air exchange rate, and other. Human activities (e.g., exercising, sleeping) were 

reflected in the assessment by the amount of time individuals spend in each 

microenvironment. 

The national-scale assessment excluded human exposures via ingestion or dermal 

contact. This was a consequence of the lack of multipathway models suitable for 

calculations at the national scale. As modeling tools become available to estimate 

transfers of substances from air to other media on a national scale, future national-scale 

assessments may include dermal and ingestion exposures.

2.4.4.5 Subpopulations

The national-scale assessment characterized risks to 40 distinct human subpopulations, 

divided into five life stage cohorts, two genders, and four racial/ethnic cohorts. Figure 2-

3 shows the 40 possible cohorts chosen for this assessment. These cohorts were selected 

to mirror tract-level demographic census data, to support selection of a representative 

model population for each census tract. Life stages that were separately assessed 

included children aged 5 or less, children aged 6-11, children aged 12-17, adults aged 18-

65, and adults aged 65 or greater. Racial/ethnic groups included African American, 

Caucasian, Hispanic, and Asian and other. Exposures and risks are estimated separately 

for each of the 40 cohorts in each census tract. Within each census tract, the proportion 

of the total population from each cohort was tailored to demographic census data for that 

particular tract and presented as the “average cohort” for that tract. Graphs of estimated 

exposure and risk provide various percentiles only at the county level or higher, for the 

general population. 

2.4.4.6 Non-Human Receptors

The initial national-scale assessment excluded non-human receptors (e.g., wildlife and 

native plants). This limitation resulted from the extreme complexity of considering 

potential adverse ecological impacts to the multiplicity of ecosystems that exist within 

such a large area. Future local- and urban-scale assessments may be expanded to include 

non-human receptors, contingent on the availability of necessary resources, data, and 

methodologies. However, EPA does not envision including non-human receptors in 

future national-scale assessments unless greatly improved models and tools become 

available.

2.5 Stakeholder Involvement

EPA has worked with the following groups as stakeholders for NATA activities in 

general, and for the initial national-scale assessment in particular: state, local, and tribal 

governments; industry; small businesses; and public interest groups.

As part of the outreach efforts on NATA and the national-scale assessment, EPA has held 

informal discussions with several stakeholder groups, including representatives from the 
14

state and Territorial Air Pollution Program Administrators and the Association of Local 

Air Pollution Control Officials (STAPPA/ALAPCO), the National Environmental Justice 

Advisory Council (NEJAC), the Clean Air Act Advisory Committee (CAAAC), the U.S. 

Conference of Mayors, Congressional representatives, tribal air contacts, and industry 

groups. 

In addition, on October 18 and 19, 1999, EPA held two public meetings in Washington 

D.C. which were attended by representatives of regulatory, public interest, and business 

and industry groups and associations. EPA sought input from interested stakeholders as 

to the most appropriate and effective ways to present the results of the initial air toxics 

assessment. Participants discussed the components of EPA’s current air toxics program 

provided input on approaches to presenting initial results from the national-scale air 

toxics assessment activities. The Agency emphasized its desire to clearly communicate 

to the public and its regulatory partners both what can be learned from such nationalscale assessments, as well as the limitations and uncertainties of such information.

Through comments received at the October meetings, and in follow-up comment letters 

received after the meeting, stakeholders provided ideas, cautions, criticisms, and other 

substantive input. EPA has factored this input into plans for the presentation of nationalscale assessment results. During April and May 2000, EPA conducted a six-week 

preview of the results of the ambient concentration modeling step of the national-scale 

assessment with its regulatory partners at the state, local and tribal level. The purpose of 

this preview was to enable EPA’s regulatory partners to provide feedback on the 

appropriate interpretation and communication of the results of the initial national-scale 

assessment and to provide a quality assurance assessment of the results. 

EPA is currently seeking input from these same stakeholder groups on interpretation and 

communication of the exposure assessment and risk characterization results, and on 

quality assurance of these results.

2.6 Peer Review Activities for the Initial National-Scale 

Assessment

Many of the national-scale assessment components described above have undergone 

peer-review and public review, either on their own or as critical elements of other 

analyses. EPA also instituted a peer-review for: (1) the planning and scoping document 

for the initial national-scale assessment, and (2) for this draft NATA national-scale 

assessment report, using procedures recommended by the EPA Science Policy Council 

[5].

2.6.1 Past Reviews of National-Scale Assessment Components

2.6.1.1 List of Urban Air Toxics

In 1997, EPA developed an initial list of potential candidate urban air toxics using a riskbased ranking methodology, and conducted a public review of the national emissions 
15

inventory for those candidate air toxics. During January 1998, a panel of technical 

experts from outside the U.S. EPA reviewed the air toxics ranking methodology and 

analysis. The final methodology used to select the 33 urban air toxics identified in the 

Integrated Urban Air Toxics Strategy incorporated revisions based on comments raised in 

the 1998 peer review. EPA also received public comments on the draft list of urban air 

toxics [6], which led to further modifications of the identification methodology and the 

underlying data inputs.

2.6.1.2 1996 National Toxics Inventory 

EPA made the draft 1996 National Toxics Inventory (NTI) and documentation available

for review and comment between April and August 1999, and received extensive 

comments and revisions from industry, state and local agencies, and others. Before 

incorporation into the final NTI, revisions were subjected to a rigorous review process to 

ensure internal consistency. Further details of the review process are described in section 

3.2.1. In addition to review and comment on the 1996 NTI, methods and assumptions 

used to develop toxic emission estimates for benzene, 1,3-butadiene, formaldehyde, and 

acetaldehyde from on-highway mobile sources were described in an EPA technical report 

[7] and peer reviewed, in addition to undergoing review by industry groups, state 

agencies, and local governments (peer review comments are available at: 

http://www.epa.gov/otaq/toxics.htm).

2.6.1.3 ASPEN National Dispersion Model

In 1996, the EPA Science Advisory Board (SAB) reviewed the Cumulative Exposure 

Project methodology [8], including the underlying scientific basis for the project and 

specific details of the modeling methodology. This review included the use of the 

ASPEN dispersion model in developing estimates of ambient concentrations of air toxics. 

The SAB review found that the overall conceptual framework and underlying scientific 

foundation was sound but stressed the importance of comparing the ASPEN predictions 

with measured ambient concentrations.

2.6.1.4 Hazardous Air Pollutant Exposure Model (HAPEM) 

The EPA Office of Transportation and Air Quality (formerly the Office of Mobile 

Sources) has used earlier versions of HAPEM, the exposure model used for this 

assessment, in analyses of exposure to carbon monoxide and vehicle-related air toxics. 

Two studies of air toxics exposure associated with vehicle emissions in 1993 and 1999 

[9], were peer-reviewed by a panel of independent experts. Details on this version of 

HAPEM and the peer-review comments can be found at: 

http://www.epa.gov/oms/toxics.htm. 

2.6.1.5 EPA Risk Assessment Guidelines

Since cancer and non-cancer health impacts cannot be directly isolated and measured, 

EPA and others have spent more than two decades developing an extensive set of risk 

assessment methods, tools and data that serve the purpose of estimating health risks for 

many Agency programs. EPA develops and publishes its risk assessment methods in the 
16

form of risk assessment guidelines that have been extensively peer-reviewed, are widely 

used and understood by the scientific community, and continue to expand and evolve as 

scientific knowledge advances. 

2.6.1.6 Microenvironment Factors

The microenvironment (ME) factors (described in section 3.2.3) represent the 

relationship between the ambient concentration and the microenvironment of interest. 

The ME factor approach for determining microenvironment concentrations is the only 

computationally feasible way (in lieu of indoor/outdoor mass balance models) for 

predicting exposures on a national scale. To help assure the credibility of the factors, and 

their appropriateness for use in the national-scale assessment, the ME factors underwent a 

peer review consistent with EPA’s peer-review guidance [5].

2.6.1.7 ASPEN Results

Ambient concentration estimates for the pollutant set, calculated by the initial 

Assessment System for Population Exposure Nationwide (ASPEN) model runs, were 

reviewed by state, local, and tribal authorities to find and correct potential input errors, 

and to obtain further input in developing useful presentation formats. The ASPEN model 

was subsequently re-run to incorporate the corrections. 

2.6.2 Review of Planning and Scoping Document for the Initial 

National-Scale Assessment

In July 2000, six technical experts from outside the U.S. EPA completed a peer review of 

the draft planning and scoping document. The reviewers were asked to consider the 

appropriateness of approaches used to (1) process the state-derived National Toxics 

Inventory for dispersion modeling, (2) estimate ambient concentrations using the 

Assessment System for Population Exposure Nationwide (ASPEN) model, (3) estimate 

human inhalation exposures using the Hazardous Air Pollutant Exposure Model version 4 

(HAPEM4), and (4) estimate, aggregate, and interpret associated cancer and non-cancer 

risks. The detailed charge to the reviewers, a summary of the reviewers’ comments, and 

the EPA’s response to comments are provided in Appendix A. 
17

3 Methods 

3.1 Introduction

EPA conducted the initial national-scale assessment to demonstrate an approach for 

characterizing air toxics risks nationwide. This assessment expanded on the approach 

first used by the EPA in the 1998 Cumulative Exposure Project (CEP) [8], in which 

annual ambient concentrations of 155 air toxics were estimated for approximately 61,000 

census tracts in the contiguous US. In the CEP, estimated concentrations, which are 

based on 1990 emission estimates, are assumed to equal human exposure concentrations 

for all persons living in each census tract, and are evaluated, by comparing to currently 

available “health benchmarks,” to assess potential health risks. In this initial nationalscale assessment, EPA augmented the CEP approach by: (1) using a 1996 emission 

inventory compiled by a more rigorous method; (2) incorporating human demographics 

and behavior into the development of exposure estimates; (3) developing a 

comprehensive risk characterization, including estimating cumulative risk associated with 

multiple air toxics; and, (4) expanding geographic coverage to include Puerto Rico and 

the Virgin Islands.

The four major components of the national-scale assessment were as follows: 

1. Compiling a national emissions inventory for 1996 of the pollutant set emissions 

from outdoor sources. The types of emissions sources in the inventory include 

major stationary sources (e.g., large waste incinerators and factories), area sources 

(e.g., dry cleaners, small manufacturers, consumer products), and both onroad and 

nonroad mobile sources (e.g., cars, trucks, and boats). 

2. Estimating 1996 air toxics ambient concentrations. EPA used the ASPEN air 

dispersion model and the 1996 inventory to estimate annual average ambient 

concentrations for the pollutant set (see pollutant names and corresponding CAS 

numbers listed in Table 3-1) across the contiguous United States, Puerto Rico, and 

the Virgin Islands. As part of this modeling exercise, estimated concentrations 

were compared to available ambient monitoring data as a partial evaluation of 

model performance. Details of this comparison are provided in Appendices I & J.

3. Estimating 1996 population exposures. Rather than assume that all people within 

each census tract were exposed to a concentration that was equivalent to the 

ASPEN prediction for that census tract, EPA used an inhalation exposure model 

and the ambient concentrations from item 2 above to estimate human exposure to 

the pollutant set across the contiguous United States (and Puerto Rico and the 

Virgin Islands). Exposure modeling was an important step in this assessment 

because it provided more realistic estimates of population exposures to air toxics 

from outdoor emission sources by accounting for the time people spend indoors 

and in other microenvironments (e.g., in vehicles), patterns of movement (e.g., 

commuting between home and work locations), and activity levels. Detailed 

information about the structure, function, and use of the HAPEM4 exposure 

model is provided in the user’s guide (Appendix B).
18

4. Characterizing potential public health risks. EPA characterized potential 

population health risks associated with inhalation of air toxics, including both 

cancer and noncancer effects, using available information on the health effects of 

each pollutant, current Agency risk assessment and risk characterization 

guidelines, and estimated population exposures. This characterization quantified, 

to the extent possible, potential cumulative risks to public health due to inhalation 

of air toxics from outdoor emission sources, discussed the uncertainties and 

limitations of the assessment, and identified other potential risks to public health 

from exposures that are beyond the scope of this quantitative assessment.

The approach outlined for the national-scale assessment was fundamentally based on 

national-scale modeling techniques to estimate ambient pollutant concentrations and 

Table 3-1. Pollutant set for the initial national-scale assessment.2

Pollutant CAS #

Acetaldehyde 75070 

Acrolein 107028 

Acrylonitrile 107131 

Arsenic compounds

Benzene 71432 

Beryllium compounds

1,3-Butadiene 106990 

Cadmium compounds

Carbon tetrachloride 56235 

Chloroform 67663 

Chromium compounds

Coke Oven Emissions 8007452 

1,3-Dichloropropene 542756 

Diesel particulate matter

Ethylene dibromide (1,2-dibromoethane ) 106934 

Ethylene dichloride (1,2-dichloroethane) 107062 

Ethylene oxide 75218 

Formaldehyde 50000 

Hexachlorobenzene 118741 

Hydrazine, hydrazine sulfate 302012 

Lead compounds

 

2

 This list is the list of 33 urban air toxics, except that dioxins have been removed from the list and diesel 

particulate matter has been added.
19

Pollutant CAS #

Manganese compounds

Mercury compounds

Methylene chloride 75092 

Nickel compounds

Polychlorinated biphenyls (PCBs) 1336363 

Polycyclic Organic Matter (POM) (including PAHs)

Propylene dichloride (1,2-dichloropropane) 78875 

Quinoline 91225 

1,1,2,2-Tetrachloroethane 79345 

Tetrachloroethylene (perchloroethylene) 127184 

Trichloroethylene (TCE) 79016 

Vinyl chloride 75014 

population exposures nationwide. While such computer models necessarily required 

simplifying assumptions and introduced significant uncertainties, they were needed to 

conduct such a large-scale assessment since direct measurements of ambient air toxics 

concentrations are limited, and direct personal exposure measurements are relatively rare. 

Such measurements are available for only a subset of air toxics in relatively few locations 

and for small study populations. Those ambient air toxics data used in the model to 

monitor comparison were taken from the air toxics archive. The air toxics archive is a 

collection of ambient air toxics data that is compiled directly from State and local 

agencies and which is supplemented with any other ambient air toxics data on AIRS. The 

specific data used in the model to monitor comparison can be found at: 

www.epa.gov/ttn/uatw/nata/mtom_pre.html#data. Although EPA is working to expand 

the number and locations of ambient air toxics monitors and the study of personal 

exposures, direct measurement of air toxics concentrations or exposures is not currently 

practical for all air toxics of interest across all areas of the country. As such 

measurement data become available over time, they can and will be used to evaluate the 

models so as to better understand some of the uncertainties in such assessments and to 

improve modeling tools.

In describing what this assessment included, it is also important for the reader to 

recognize potentially important sources and pathways of risks to public health that were 

considered beyond the scope of this assessment, and were therefore not included. First, 

while EPA recognizes that indoor sources of air toxics emissions likely contribute 

substantially to the total exposures that people experience for a number of these air 

toxics, additional work is needed to better develop our tools for assessing these indoor 

sources of exposure on a national scale, that was not a part of this assessment. Further, 

for a subset of air toxics that persist or bioaccumulate in the environment, dietary intake 

(e.g., from eating contaminated fish) likely contributes much more to total exposure than 

does the inhalation pathway that was be addressed in this assessment. Table 3-2 lists 
20

those urban air toxics believed most likely to present important multipathway risks. 

These and other important aspects of total population exposures to air toxics will be 

addressed more fully over time as part of future NATA assessment activities, as more 

comprehensive data and assessment tools become available. As a result of excluding 

these potentially-important contributors to total risk from this assessment, the results of 

this assessment should always be viewed with its limitations in mind, and the risks 

interpreted as only a portion of the total risks which may be associated with these air 

toxics.

Table 3-2. Urban air toxics believed to present risks from multipathway exposure.

Pollutants

Hexachlorobenzene

Lead compounds

Mercury compounds

Polychlorinated biphenyls (PCBs)

Polycyclic Organic Matter (including 7-PAH)

2,3,7,8-TCDD (dioxin)

3.2 Exposure Assessment

EPA’s 1992 guidelines for exposure assessment [10] establish a broad framework for 

exposure assessments by describing the general concepts of exposure assessment, 

including definitions and associated measurement units, and by providing general 

guidance on the planning and conduct of an exposure assessment. The guidelines also

provide information on presenting the results of the exposure assessment and 

characterizing uncertainty.

The guidelines define human exposure as contact with a chemical or agent at the visible 

external boundary of a person, including skin and openings into the body such as mouth 

and nostrils (but not necessarily contact with exchange boundaries where absorption may 

take place, such as skin, lung, and gastrointestinal tract). Therefore, an exposure 

assessment is the quantitative or qualitative evaluation of contact, and includes such 

characteristics as intensity, frequency, and duration of contact. Often, an assessment also 

will evaluate the rate and route at which a chemical crosses the external boundary (dose) 

and the amount absorbed (internal dose). The numerical output of an exposure 

assessment may be either exposure or dose, depending on the purpose of the evaluation 

and availability of appropriate data.

An exposure assessment has three major components: source characterization, 

environmental fate and transport characterization, and characterization of exposure. 

These components are discussed individually below.

3.2.1 Source Characterization: Emission Inventories

In the first step of an exposure assessment for air toxics, the specific air toxics emitted 
21

and the sources of their airborne emissions are determined. Data are collected on the 

emission rates of the pollutants and the release parameters of the source. Knowledge of 

the emission rate and release characteristics enables the pollutant fate and transport to be 

estimated. 

Ideally, the emission estimates are from direct measurements of representative source 

emissions. Although such measurements are likely to provide the most accurate data for 

an emission source, these data are typically not available because such sampling is often 

too time- and resource-intensive. When specific emission measurements are not feasible 

or available, other emission estimation methods, including material balances and 

emission factors, are sometimes used as an alternate method. Emission factors indicate 

the quantity of a pollutant typically released to the atmosphere for a particular source 

operation, and are usually considered to be representative of an industry or emission type 

as a whole. Each approach to estimating emissions, including use of direct measurement 

data, has an inherent level of uncertainty, which adds to the overall uncertainty of a risk 

analysis.

Depending on the analysis, source and emissions data can be derived from broad-scale 

emission inventories, specific data collection efforts with particular industries, or 

information from regional, state, or local agencies. Other information, such as 

geographic location of release points, temporal pattern of emissions (e.g., periodic 

"puffs" vs. constant emission rates), and release height may be necessary, depending on 

the level of detail needed or types of exposure examined in the assessment.

3.2.1.1 Approach 

The majority of emissions information used in the national-scale assessment was 

extracted from EPA’s National Toxics Inventory (NTI), which contains estimates of the 

emissions of the 188 air toxics listed in section 112(b) of the CAA. In addition to the 

NTI, EPA’s National Emission Trends (NET) inventory was also used as a source of 

emissions data for air toxics precursors in order to estimate air toxics generated through 

secondary formation in the atmosphere and for diesel particulate estimates for Puerto 

Rico and the Virgin Islands. Diesel particulate emission estimates for the contiguous 

U.S. were extracted from an inventory developed separately by EPA (see further details 

on this in section 3.2.1.2).

Before the emissions data from the NTI, NET, and diesel particulate inventories could be 

used as input to dispersion modeling, the emissions data required significant preparation. 

Some of this preparation work occurred during the compilation of the inventories and 

some occurred in the Emissions Modeling System for Hazardous Air Pollutants (EMSHAP) which is a series of computer programs that process emission inventory data for 

subsequent air quality modeling. Appendix C provides the EMS-HAP User’s Guide and 

the methodologies used to process the emission data for this assessment. The necessary 

inventory preparation steps are described below:

• Compiling detailed air toxics, air toxics precursors, and diesel PM emissions 

inputs for all known stationary and mobile sources. The sources of this data are 

described in section 3.2.1.3.
22

• Performing quality assurance of the point source inventory location and stack 

parameter data and incorporating defaults for missing or erroneous data where 

possible. In some cases, missing or erroneous information can be found using the 

Toxics Release Inventory database. EMS-HAP defaults missing point source 

locations information to the centroid of the zip code (if zip code information is 

available) or to a census tract within the county. Where possible, stack 

parameters are defaulted based on the source category classification code or 

standard industrial classification code. Where not possible (lack of code in the 

inventory or lack of data for a particular SCC or SIC code, stack parameters) are 

set to conservative defaults (10 meter stack height, 1 meter stack diameter, 1 

meter/second velocity, and a temperature of 295 Kelvin).

• Grouping individual pollutant species into compound groups. The NTI contains 

approximately 400 different species representing the 188 air toxics listed in 

section 112(b) of the CAA. Many of the species belong to compound classes. 

Grouping of these species is necessary for many reasons. One reason is that the 

individual chemical species belonging to groups are not geographically 

representative. For example, “lead oxide” may have been reported in just a few 

counties, whereas other counties aggregated their lead oxide emissions into “lead 

compounds.” Grouping allows for pollutants with similar characteristics to be 

modeled together for purposes of efficiency. For example, specific lead species 

and compounds reported as the broad group “lead compound” are grouped to be 

subsequently modeled as “lead compounds-fine” and “lead compounds-coarse.” 

These groups allow ASPEN to distinguish between the different deposition rates 

for fine and coarse lead particulates.

• Assigning each pollutant to a reactivity class (high, medium, low, etc.) or 

particulate size class (fine or coarse particulate) to allow for the ASPEN model to 

perform decay and deposition calculations. These reactivity classes are based on 

the rate of reaction of the pollutant with OH and NO3 radicals and had previously 

been established for the gaseous air toxics in the Cumulative Exposure Project. 

[11] 

• Temporally allocating emission values to eight annual 3-hours emission rates. 

Emissions are temporally allocated based on the type of source using a database 

of temporal profiles by source classification code. The majority of the profiles are 

from a database originally developed for regional emission modeling studies 

under the National Acid Precipitation program. The factors for the National-Scale 

Air Toxics Assessment consolidate the seasonal and day-of-week classes in order 

to reflect hourly activity for an annually averaged day.

• Grouping all source categories into the following four source sectors: 1) major, 2) 

area and other, 3) on-road, 4) non-road. These sectors and the methodology for 

grouping are discussed below. 

All of the raw inventory inputs for this assessment exist as estimates for point 
23

sources, non-point stationary sources, and mobile sources. “Point” sources 

provide emissions data at the facility and sub-facility level and include location 

coordinates (e.g., latitude and longitude). “Non-point” stationary source and 

“mobile” source data exist as emissions estimates for an entire source category 

aggregated to the county level. Inventory data files for these different types of 

sources are generally maintained separately and include different data elements. 

For the purpose of aggregating air toxics emission sources in the national-scale 

assessment, all emissions inventory inputs were grouped into four sectors: 

“major,” “area and other,” “onroad,” and “nonroad.” Dispersion modeling was 

performed for each sector separately, so that concentration estimates of each air 

toxic could be attributed to each sector. Each sector is further defined as follows:

Major sources are large stationary sources that emit more than 10 tons per year of 

any listed air toxic (CAA, section 112(b)) or a combination of listed air toxics of 

25 tons per year or more. Typical examples of major sources include electric 

utility plants, chemical plants, steel mills, oil refineries, and large hazardous waste 

incinerators. These sources may release air toxics from equipment leaks, when 

materials are transferred from one location to another, or during discharge through 

emissions stacks or vents. 

Area and Other sources are smaller stationary sources that emit less than 10 tons 

per year of a single air pollutant or less than 25 tons per year of a combination of 

air toxics. The emission inventory includes facility data for some area sources 

and aggregated emission estimates at the county level for the remaining area 

sources. Typical examples of area sources include neighborhood dry cleaners and 

gas stations. Though emissions from individual area sources are often relatively 

small, collectively their emissions can be of concern particularly where large 

numbers of sources are located in heavily populated areas. “Other” stationary 

sources are sources that may be more appropriately addressed by other programs 

rather than through regulations developed under certain air toxics provisions 

(sections 112 or 129) in the Clean Air Act. Examples of other stationary sources 

include wildfires and prescribed burning, which have emissions that are being 

addressed through the burning policy agreed to by the EPA and the USDA. For 

this assessment, the “area” and “other” sectors have been combined in the 

calculations and presentation of the current national-scale assessment

Onroad mobile sources comprise vehicles used on roads and highways (e.g., cars, 

trucks, buses). 

Nonroad mobile sources are all remaining mobile sources (e.g., trains, 

lawnmowers, construction vehicles, farm machinery).

Major and area source facilities are drawn from the “point” source inventory files, 

meaning those with known geographic locations (i.e., latitude and longitude). 

Area and other source categories that are aggregated as county-level emissions are 

drawn from the “non-point” source inventory files, meaning those stationary 

sources that do not have location coordinates but instead exist as county-wide 
24

total emissions by source category. Onroad and nonroad sources exist as distinct 

sectors in the “mobile” source inventories and are also aggregated to the county 

level.

• Spatially allocating county-level emissions to the census-tract level using 

surrogate data, such as population, industrial land or roadway miles, which are 

available at the census-tract level. A description of the available surrogates for 

use with EMS-HAP is shown in Table 3-3. Tract-level emissions for a source 

category are computed based on the percentage of the matching surrogate in the 

tract for that county. For example, the consumer products usage source category 

is matched to population. If 10 percent of the population of the county is in tract 

A, then tract A gets 10 percent of the county’s consumer products usage 

emissions.

Table 3-3. Spatial Allocation Factors (SAF) Developed for EMS-HAP

Code for 

set of 

SAFs Surrogate Definition Origin of Data How EPA developed 

the set of SAFs

SAF1 Residential land USGS land use categories: Residential, plus 

one-third of mixed urban and built-up land plus 

one-third of other urban and built-up land 

mid-70's to 80's from CEPa,b

SAF2 Commercial land USGS land use categories: Commercial and 

services, plus one-half of industrial and 

commercial complexes, plus one-third of mixed 

urban and built-up land plus one-third of other 

urban and built-up land

mid-70's to 80's from CEPa,b

SAF3 Industrial land USGS land use categories: industrial, plus 

one-half of industrial and commercial complexes, 

plus one-third of mixed urban and built-up land, 

plus one-third of other urban and built-up land

mid-70's to 80's from CEPa,b

SAF4 Utility land USGS land use category: “transportation, 

communications, and utilities”

mid-70's to 80's from CEPa,b

SAF6 Sum of commercial 

land and industrial 

land

Sum of commercial land and industrial land, as 

defined above

mid-70's to 80's land use data from 

developers of CEPa,b, SAF 

recomputed 

SAF7 Farm land USGS land use category: “cropland and pasture” mid-70's to 80's from CEPa,b

SAF8 Orchard land USGS land use category: “orchards, groves, 

vineyards, nurseries, and ornamental horticultural 

areas”

mid-70's to 80's from CEPa,b

SAF9 Confined feeding USGS land use category "confined feeding" mid-70's to 80's from CEPa,b

SAF10 Farm land & confined 

feeding

USGS land use categories "cropland and pasture" 

plus "confined feeding"

mid-70's to 80's from CEPa,b

SAF12 Rangeland USGS land use categories: “herbaceous 

rangeland” plus “scrub and brush” plus “mixed 

rangeland” 

mid-70's to 80's from CEPa,b

SAF13 Forest land USGS land use categories: “deciduous forest” 

plus “evergreen forest” plus “mixed forest land” 

mid-70's to 80's from CEPa,b

SAF14 Rangeland & forest 

land

Sum of rangeland and forest land, as defined 

above

mid-70's to 80's from CEPa,b
25

Code for 

set of 

SAFs Surrogate Definition Origin of Data How EPA developed the set of SAFs

SAF15 Water US Census category: water area 1990 from CEPa,b

SAF17 Mining & quarry land USGS land use category: “strip mines, quarries, 

and gravel pits”

mid-70's to 80's from CEPa,b

SAF18 1/population density Inverse of: census tract population (defined 

above) divided by census tract area. Tracts with 

zero population assigned spatial factors of zero.

1990 from CEPa,b

SAF19 1/population density Inverse of: census tract population (as defined 

above) divided by census tract land area. Tracts 

with zero population assigned tract population of 

one.

1990 population and land area 

data from CEPb, SAF 

recomputed.

SAF20 Population U.S. Census category: 1990 residential 

population 

1990 from CEPa,b

SAF21 Railway miles Total railway miles, as reported in TIGER/Line 1993 from CEPa,b

SAF22 Roadway miles Total miles of all roadway types in each census 

tract, as reported in TIGER/Line 

1993 from CEPa,b

SAF24 50% Population & 

50% roadway miles

Surrogate based equally on population fraction 

and on roadway mile fractions for each of four 

roadway types

1990-93 0.5*SAF20 + 0.5*SAF22

SAF25 25% Population & 

75% roadway miles

Surrogate based on population fraction and 

roadway mile fractions, respectively weighted by 

25% and 75%, for each of four roadway types

1990-93 0.25*SAF20 + 0.75*SAF22

SAF26 Tract area The area of census tracts (including land and 

water)

1990 tract areas computed from 

CEP tract radiib data SAF 

recomputed.

SAF27 Urban: Inverse 

population density

Rural:farmland

Inverse population density (18) for urbanc

counties; farmland (7) for ruralc counties

1990, mid-70's to 80's SAF 18 from CEP,

SAF 7 from CEP,

urban/rural county 

designations from 1990 and 

1996 census data

SAF28 Urban: population

Rural: tract area

Population (20) for urbanc counties; tract area for 

(26) ruralc counties

1990 SAF 20 from CEP,

SAF 26 from CEP,

urban/rural county 

designations from 1990 and 

1996 census data

SAF29 Sum of farmland and 

orchard land

Sum of farmland and orchard land, as defined 

above

mid-70's to 80's land use data from 

developers of CEPa,b, SAF 

recomputed.

a

 except that changes were made to SAFs in Halifax and South Boston in Virginia

b

 except for census tracts in the Virgin Islands and Puerto Rico which were not modeled in the CEP 

c

 the designation of urban and rural counties is based on data from the U.S. Census Bureau as follows: a county is considered ”urban” 

if either: 1) it includes a metropolitan statistical area with a population greater than 250,000; or, 2) the U.S. Census Bureau designates 

more than fifty percent of the population as “urban.”

3.2.1.2 Scope

The NTI was the source of the majority of emissions inputs to the national-scale 

assessment. It contains estimates of air toxics emitted from many anthropogenic source 

categories for 188 hazardous air pollutants, for the 50 U.S. states, District of Columbia, 

Puerto Rico and Virgin Islands. With a few exceptions (e.g., wildfires), the NTI does not 

include emissions of air toxics from natural sources, indoor sources, or accidental 
26

releases. Sources which are not included in the NTI were also excluded from the initial 

national-scale assessment. 

For the purposes of the national-scale assessment, the pollutants were limited to 32 of the 

33 air toxics included in EPA’s Integrated Urban Air Toxics Strategy [2] (dioxins were 

not included) and diesel particulate matter. The geographic domain was limited to the 

contiguous 48 states, District of Columbia, Puerto Rico and Virgin Islands. Background 

documentation on the development of the 1996 NTI are included in Appendix D or can 

be accessed on the EPA web site at http://www.epa.gov/ttn/chief/nti. The detailed 1996 

NTI data files are available at ftp://ftp.epa.gov/EmisInventory/nti_96.

The NET provides EPA’s latest estimates of national emissions for criteria air pollutants: 

carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs 

[excludes certain non-reactive organic compounds]), sulfur dioxide (SO2 ), particulate 

matter less than 10 microns (PM10), particulate matter less than 2.5 microns (PM2.5), 

and lead (Pb). [12]

To account for secondary formation of volatile air toxics species, point, non-point 

stationary and mobile source emission data were input to the ASPEN model for non-air 

toxics VOC species resulting from a speciation of the 1996 NET (Version 3) inventory. 

These data were included to account for secondary formation of volatile air toxics 

species. In addition, NET data were used to estimate diesel particulate emissions for 

Puerto Rico and the Virgin Islands. The raw NET data are available at 

ftp://ftp.epa.gov/EmisInventory/net_96.

The diesel PM emissions data used for the 1996 national-scale assessment were extracted 

from an inventory developed as part of the rulemaking on Heavy-Duty Engine and 

Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements [13]. This 

inventory is based on Federal Highway Administration estimates of truck operation, 

estimates of the distribution of fuel type and weight classes of truck from the EPA Office 

of Transportation Air Quality (OTAQ), and emission factors provided by OTAQ. The 

nonroad emissions in this inventory were derived from OTAQ’s draft June 2000 

NONROAD model run for 1996. Both of these PM inventories reflect changes in 

methods and data sources since the release of versions used for the 1996 NET and 1996 

NTI. Time did not allow for estimates of other pollutants from diesel vehicles and 

equipment to be revised accordingly, but an exploratory analysis indicated that the effect 

on estimates of other air toxics would not have been large. The diesel particulate 

emissions are all found in the onroad and nonroad source sectors. More details on the 

diesel PM inventories can be found at http://www.epa.gov/otaq/hdmodels.htm.

The NET, diesel particulate, and early versions of the 1996 NTI inventories all contain 

emissions estimates for the 50 states. The geographic domain of the national-scale 

assessment included the contiguous United States, Puerto Rico, and the Virgin Islands. 

In all cases, emissions for the territories included in the assessment were derived in part 

or in total via extrapolation of emissions estimates from surrogate U.S. locations.
27

3.2.1.3 Sources of Data 

EPA prepared the 1996 NTI using various sources of data. The five primary sources of 

1996 NTI data are: (1) state and local air toxics inventories developed by state and local 

air pollution control agencies, (2) existing databases related to EPA’s Maximum 

Achievable Control Technology (MACT) program which requires emission standards 

under Section 112(d) of the CAA. (www.epa.gov/ttn/uatw/eparules.html) (3) Toxics 

Release Inventory (TRI) data (www.epa.gov/tri/), (4) emissions estimated by using 

mobile source methodologies developed by experts in EPA’s Office of Transportation 

and Air Quality, and (5) emission estimates for 30 of 500 non-point source categories 

generated using emission factors and activity data. Much of the state/local, TRI, and 

MACT emissions data may have been generated by the sources themselves. The 1996 

NTI is the first national modeling emission inventory constructed using state and local air 

toxic inventory data and containing stationary and mobile source data. 

To improve the quality of the draft 1996 NTI, EPA requested comments on the inventory 

from state and local agencies, industry and others. The draft 1996 NTI and 

documentation were available to all parties for review and comment from April 30, 1999 

- August 13, 1999. A state-only preview of the ASPEN concentration results from March 

31, 2000 to May 12, 2000 resulted in additional state/local revisions to the NTI. The 

EPA received extensive comments and revisions from industry, state and local agencies, 

and others located in 42 states, Puerto Rico, and the Virgin Islands. Figure 3-1 shows the 

states and local agencies that ultimately provided inventory data, comments, and 

revisions to the EPA. Forty-one states provided point source revisions, 27 states 

provided non-point stationary source revisions and 18 states provided mobile source 

revisions. All requested revisions were evaluated and incorporated as appropriate into 

the NTI prior to final ASPEN modeling. Documentation for all emissions estimates in 

the 1996 NTI is available in Appendix D.

3.2.1.3.1 Point Source Emissions

In compiling point source emissions information for the NTI, preference was given to 

state- and locally-generated information where available except for utility mercury 

emissions and municipal waste combustion emissions. While agencies are neither 

required to compile air toxics emissions data nor to submit such data to EPA, most states 

did respond to EPA’s invitation to do so. EPA did not apply many quality checks on 

state/local data before incorporating it into the NTI, but in the course of three rounds of 

state/local review of the draft NTI and national-scale assessment results, many anomalies 

were noted by EPA or the state/local agencies and resolved by corrections or deletions. 

Where state or local agency data were not provided, existing emissions data from EPA’s 

regulatory development (MACT) databases were utilized. If these databases differed in 

pollutant coverage, MACT data were used to add any missing emissions in order to be as 

comprehensive in pollutant coverage as possible. If neither of state/local or MACT 

sources contained information for a known stationary source, the NTI used estimates 

based on information from EPA’s TRI. 

Most state agencies participated in the development of the 1996 NTI. Forty-seven of the 

50 states, Puerto Rico, the Virgin Islands, and the District of Columbia participated in the 

development of the 1996 NTI either by providing emissions data or by reviewing the 
28

draft inventory. Thirty-six states initially provided draft 1996 air toxics emissions 

inventories. The data collected from the state or local agencies varied significantly in 

terms of completeness, coverage, format, and quality. The majority of the 36 states 

provided data for point sources (primarily large industrial sources which would be 

defined as major sources). The number of air toxics included in the state or local agency 

inventories varied. Some state and local agencies compile emission inventories for fewer 

than the 188 air toxics (e.g., the RAPIDS inventory of Great Lakes states), while other 

states such as California and Louisiana compile emission inventories for more than the 

188 air toxics.

In addition to the 14 states that did not provide draft air toxics inventory data, several 

data gaps were identified in the state databases provided. Data gaps in the state 

inventories included: (1) emissions from entire counties missing from the state databases; 

(2) missing emission sources; (3) lack of stack parameters; and (4) lack of facility 

location data (latitude/longitude or Universal Transverse Mercator (UTM) coordinates).

In order to find missing facilities for entire states, counties, or individual facilities, EPA 

compared existing state air toxics inventories first to MACT data, then to TRI data, and 

then to NET data. For most facilities that appeared in both the state database and in 

either the MACT or TRI database, EPA assumed that the state databases were more 

accurate and, thus, no further revisions were made. This assumption of the data quality 

hierarchy was necessary due to resource limitations that prohibited EPA from comparing 

various emission estimates for a given facility, obtaining documentation for the disparate 

estimates, and choosing the most appropriate information across data bases. In at least 

one case, this hierarchy resulted in the omission of significant emissions in the nationalscale assessment. A major source of lead, a lead smelter in Missouri, does not show any 

lead emissions in the version of the 1996 NTI modeled for this assessment. This resulted 

because the state agency’s emissions estimates for this facility were used but erroneously 

did not include the lead emissions. Since the facility was already present in the NTI from 

the state’s inventory, TRI estimates were not added. Later, after ASPEN modeling, it 

was discovered that TRI contained nearly 100 tons of lead emissions for this facility. It is 

possible that other facilities and emissions could have been omitted from this assessment. 

Similarly, it is possible that a facility could have been double counted if it appeared in 

two or more inventories, but with different facility names. These and other potential 

sources of error are discussed in subsequent sections of this report.

Because the ASPEN model requires a model-ready inventory, the association of stack 

parameters and location data to each facility’s emission estimate was required. If this 

information was found to be missing, incorrect, or out of range, it was corrected with 

defaults. Defaulting schemes were performed in both the NTI development and in the 

subsequent processing in EMS-HAP. Default stack parameters associated with Standard 

Industrial Classification (SIC) codes were used for emissions reported at the SIC code 

level. Also default stack parameters associated with source classification codes (SCCs) 

were employed for emissions reported at the SCC level. Where default parameters were 

available by either SCC or SIC, the SCC took precedence. 

In order to develop appropriate defaults for missing location data, first comparisons were 
29

made to other inventories (e.g., TRI, NET, or the Ozone Transport Assessment Group 

(OTAG) inventory). Where latitude/longitude coordinates were found they were added 

to the NTI. Where the facilities could not be matched to other databases, defaulting 

schemes were employed to place facilities within known zip codes or counties. These 

schemes are explained in the NTI documentation for major sources (Appendix D) and in 

the EMS-HAP User’s Guide (Appendix C). EPA assigned locations to all facilities 

except for 87 facilities that were defined as “portable” in Colorado and Idaho. These 

were not included in the assessment. 

3.2.1.3.2 Non-Point Emissions

Where possible, EPA compiled the 

non-point stationary source emissions 

(which are included in the “area and 

other” source sector) from the 1996 

state emission data sets. The majority 

of the 36 states that initially supplied 

draft 1996 air toxics emission 

inventories did not provide non-point 

source data. EPA evaluated and 

supplemented the state data sets with 

non-point source data gathered during 

the development of MACT standards 

and with TRI data. EPA then 

generated non-point emission 

estimates for 30 remaining source 

categories (listed in Table 3-4) by 

using activity data and emission 

factors and then allocating the 

estimates from the national, state, or 

regional level to the individual 

counties. EPA was careful to avoid 

duplicating emissions (e.g., emissions 

from large dry cleaners included in 

point source files would be subtracted 

from non-point source calculations). 

For example, if the non-point source 

estimates were based on raw material 

usage, the point source fraction 

already accounted for in this fashion 

was subtracted prior to calculating the 

non-point source emissions.

3.2.1.3.3 Mobile Sources

The EPA’s Office of Transportation 

and Air Quality (OTAQ) provided 

direction and advice on which 

emission factors and speciation profiles should be used in the development of air toxics 

Table 3-4. Non-Point Source Stationary 

Categories with EPA-Derived Emissions.

Categories 

Animal Cremation

Asphalt Paving: Cutback Asphalt

Auto body Refinishing Paint Application

Aviation Gasoline Distribution: Stage I & II

Consumer Products Usage

Dental Preparation and Use

Drum and Barrel Reclamation

Fluorescent Lamp Recycling

Food and Agricultural Products: Cotton Ginning

Gasoline Distribution Stage II

General Laboratory Activities

Geothermal Power

Hospital Sterilizers

Human Cremation

Lamp Breakage

Miscellaneous Organic Chemical Processes

Open Burning: Forest and Wildfires

Open Burning: Prescribed Burnings

Open Burning: Scrap Tires

Pesticide Application

Residential Fuel Use: Anthracite Coal

Residential Fuel Use: Bituminous and Lignite Coal

Residential Fuel Use: Distillate Oil

Residential Fuel Use: Natural Gas

Residential Fuel Use: Wood/Wood Residue

Softwood Drying Kilns

Structure Fires

Surface Coating Operations: Architectural

Surface Coating Operations: Traffic Markings

Surface Coating Operations: Industrial Maintenance
30

emission estimates for mobile sources. The mobile source estimates were determined 

using a combination of methods. For highway mobile sources, emission factors for 

benzene, 1,3-butadiene, formaldehyde and acetaldehyde were modeled for 10 urban areas 

and 16 geographic regions, using OTAQ’s MOBTOX5b model [14]. The urban areas 

and geographic regions modeled were selected to encompass a broad range of inspection 

and maintenance (I/M) programs, fuel parameters, and temperature regimes. The intent 

of the selection process was to best characterize the different combinations needed to 

perform accurate nationwide toxic emissions estimates. Every county in the U.S. was 

then “mapped” to one of these modeled areas or regions (i.e., the emission factor for the 

modeled area was also used for the area “mapped” to it). Mapping was done based on a 

combination of geographic proximity, I/M program, and fuel control programs. The 

resulting county level emission factors were then multiplied by county-level VMT 

estimates. To estimate emissions of other air toxics from highway mobile sources, data 

from speciation profiles were applied to VOC emissions for gaseous air toxics and POM, 

and to PM emissions estimates for metal emissions and dioxins (although dioxin 

emissions were subsequently removed from this study). Where emissions of gaseous 

compounds were impacted by the use of reformulated gasoline or winter oxygenated 

gasoline, these impacts were accounted for at the county level.

The 1996 NTI contains nonroad mobile emission estimates for 2- and 4-stroke gasolinepowered engines, diesel engines, aircraft, locomotives and commercial marine vessels. 

For gasoline-powered engines and diesel engines, data from speciation profiles were 

applied to county level VOC and PM estimates generated by the April, 1999 draft version 

of the EPA NONROAD model. Again, impacts of gasoline fuel control programs on 

emissions were accounted for at the county level. Emissions of aircraft, locomotives, and 

commercial marine vessels were estimated by applying data from speciation profiles to 

national VOC and PM estimates, then using activity data to allocate nationwide 

emissions to the county level.

3.2.2 Environmental Fate and Transport Characterization 

After the pollutants of interest and their sources and emission rates are defined, the 

exposure assessment process continues with estimation of pollutant fate and transport in 

the atmosphere. This step describes how the pollutant is transported, dispersed, and 

transformed over the area of interest. Initially, the fate of the emitted pollutants is largely 

determined by the source release characteristics. After pollutants are released to the 

atmosphere, their transport, dispersion, and transformation are governed by 

meteorological principles, terrain characteristics, wet and dry deposition rates, and certain 

chemical properties of the air toxics (e.g., aqueous solubility, vapor pressure, molecular 

diffusivity, melting point, and adsorption characteristics). For a limited subset of air 

toxics, it may be important to consider deposition from air to soil, vegetation, or water 

bodies. For others, such deposition is not important.

Various mathematical models (e.g., Gaussian puff models, Gaussian plume models) [15], 

each with specific data needs, have been developed or are under development to describe 

the transport and fate of pollutants released to the atmosphere. The model chosen must 

be appropriate for the intended application, which may range from estimates of shortterm peak concentrations immediately adjacent to a facility, to long-term concentrations 
31

over a citywide area or deposition over thousands of miles. The reactivity and 

persistence of each air pollutant will also influence its fate, and these factors can be 

important in estimating exposure for certain pollutants. Additionally, secondary 

transformation products of some air toxics may need to be identified for consideration in 

risk assessment. Any available air toxics monitoring data can be used either to check the 

validity of modeled concentration estimates or as a primary or supplemental source of 

information for the exposure assessment itself. 

For a limited subset of air toxics, greater exposures occur through non-inhalation 

exposures than through inhalation exposures. These air toxics typically are persistent in 

the environment and have a strong tendency to bioaccumulate. Exposure assessments 

can consider exposures that occur through routes other than inhalation by using 

multipathway models. The simplest multipathway exposure assessments require 

chemical-specific data (e.g., octanol-water partition coefficient) to model the partitioning 

of the chemical in the environment, and uptake rates (e.g., consumption rate of drinking 

water) to predict intakes. Combining this information yields general predictions of noninhalation exposure. EPA’s current national-scale exposure models do not have the 

capability to quantify non-inhalation exposures, so they were not included in this initial 

assessment, although this may become possible in the future.

3.2.2.1 Overview of the ASPEN Dispersion Model

To develop national-scale estimates of annual average ambient concentrations of air 

toxics, EPA used the Assessment System for Population Exposure Nationwide (ASPEN) 

model that was developed and used in EPA’s Cumulative Exposure Project (CEP)[16]. 

In general, ASPEN uses a Gaussian model formulation and climatological data to 

estimate annual average pollutant concentrations at the centroid of each census tract 

within the modeling domain. Specifically, for each source, the model calculates sets of 

eight 3-hour ground-level concentrations as a function of radial distance and direction 

from the source at a set of receptors laid out in a radial grid pattern. These concentrations 

represent the steady-state concentrations that would occur with constant emissions and 

meteorological parameters. For each grid receptor, concentrations are calculated for each 

of a standard set of stability class/wind speed/wind direction combinations. These 

concentrations are averaged together using the annual frequency of occurrence of each 

combination (i.e., the climatology) as weightings.

These meteorological frequency distributions are typically prepared for the entire 

simulation period, usually one or more years. For ASPEN, however, meteorological data 

are stratified by time of day into eight 3-hour time blocks, to preserve any characteristic 

diurnal patterns that might be important in subsequent estimation of population exposure. 

In addition to the climatology, other inputs to ASPEN that are specified by time block 

include emission rate, mixing height, and reactive decay rates. The resulting output of 

ASPEN is a grid of annual average concentration estimates for each source/pollutant 

combination by time block.

The ASPEN model takes into account important determinants of pollutant concentrations, 

such as: rate of release, location of release, the height from which the pollutants are 
32

released, wind speeds and directions from the meteorological stations nearest to the 

release, breakdown of the pollutants in the atmosphere after being released (i.e., reactive 

decay) settling of pollutants out of the atmosphere (i.e., deposition), transformation of 

one pollutant into another (i.e., secondary formation). 

For all pollutants outdoor concentrations include a “background” component. 

Background is an essential part of the total air quality concentrations. Background 

includes concentrations due to natural sources, sources not in the emissions inventory, 

and long-range transport. In this study, except for diesel PM, background concentrations 

are represented by concentration values measured at “clean air locations” where 

available. Non-zero background values for 13 pollutants, identified from the technical 

literature and reported in the CEP study [16] were used to sum with the ASPENestimated concentrations in each census tract. Except for diesel PM, the background 

value is assumed to be constant for all census tracts due to insufficient data for 

assessment of geographic variations. Where background concentration values were not 

identified in the technical literature and reported in the CEP study for other air toxics, 

their background concentrations are assumed to be zero. This result may be an 

underestimate of outdoor concentrations in some cases.

Annual average concentration estimates for grid receptors surrounding each emission 

source are spatially interpolated to the census tract centroids within the 50 kilometers 

impact zone, and contributions from all modeled sources are summed to give estimates of 

cumulative ambient concentration increments in each census tract [17]. By accounting 

for all identified source categories (including background concentrations), the sum of the 

concentration increments should yield an estimate of the overall concentration of each air 

toxic within each census tract. These estimates are designed to represent populationweighted concentration averages (each census tract generally represents between 2,500 

and 8,000 people). More detailed information on ASPEN is provided in the ASPEN 

User’s Guide (Appendix E).

3.2.2.2 Application of ASPEN for the Initial National-Scale Assessment

For this assessment, the ASPEN model was run in the same manner (e.g., reactivity class 

assignments, wet and dry deposition for particulates, secondary formation) as was done in 

the CEP, with some exceptions:

1. The input inventories were different in data year, approach and some 

emissions processing techniques. For example, location and stack parameter 

defaulting techniques were different. Changes were also made to spatial 

allocation and temporal allocation factors for some emission sources. See section 

3.2.1 for a description of inventories and how they were prepared for the ASPEN 

model. 

2. In 1996, there was 75 percent completeness of meteorological data at 357 

National Weather Service surface stations versus 214 in 1990. Data from the 357 

stations were used for this assessment. Due to the use of an increased number of 

stations, the average distance between the emission source and the meteorological 

station improved (decreased) in the 1996 ASPEN run. 
33

3. The modeling domain was extended to Puerto Rico and the Virgin Islands.

4. For diesel PM only, instead of using monitored air quality data to establish 

background concentrations, a modeling-based approach was developed to provide 

a rough approximation of concentrations due to transport from sources located 

between 50 km and 300 km from the receptor. See Appendix F for a more 

complete discussion of this approach.

To evaluate the quality of ASPEN outputs as a check on input and model execution 

accuracy, predicted concentrations of toxic pollutants were examined graphically to 

establish a framework from which “unusual” data values might be identified. The most 

effective inspection method proved to be the preparation of matrix scatter plots in which 

predicted concentrations of each pollutant were plotted against the concentrations of all 

other pollutants at that location. Generally, these plots showed high correlation among 

pollutants that had common sources (e.g. benzene, toluene, ethyl benzene) and low 

correlation between pollutants that did not generally share common sources (e.g. benzene 

and vinyl chloride). Plots were developed separately for each state using the total 

predicted concentration from ASPEN, but also for each major source component 

including mobile onroad, mobile nonroad, point and non-point source contributors. 

Using these plots along with sorted rankings of pollutant concentrations, predicted values 

were identified that either appeared very large and/or inconsistent with other pollutant 

levels. These suspect values were then subjected to verification and/or correction 

through discussion with emission inventory and modeling experts. From this process, a 

significant number of errors were corrected before the final ASPEN modeling was 

performed.

3.2.3 Estimating Population Exposure

In the third step of the initial national-scale assessment, ambient air toxics concentrations 

derived from ASPEN modeling were used to estimate human exposures to air toxics. 

Inhalation was the only exposure route considered for this initial assessment. For 

characterization of personal exposure, a model was needed that would allow for 

consideration of inhalation exposures to various population groups, who may have 

different levels of exposure as a result of differences in proximity to sources of exposure 

(due to location of residence, occupational setting, etc.). 

An inhalation exposure model, the Hazardous Air Pollutant Exposure Model, Version 4 

(HAPEM4) was selected for use to estimate personal exposure and account for 

differences in exposures among the population. 

3.2.3.1 Overview of HAPEM4 

The HAPEM4 is an exposure model that is capable of assessing average long-term 

inhalation exposures of the general population, or a specific sub-population, over spatial 

scales ranging from urban to national. HAPEM4 utilizes a relatively transparent set of 

exposure assumptions and approximations (Appendix B). HAPEM4 uses the general 

exposure modeling approach of tracking representatives of specified demographic groups 

as they move among indoor and outdoor microenvironments and between geographic 
34

locations performing various activities. Figure 3-2 shows example demographic groups, 

microenvironments, and activities. The estimated pollutant concentrations in each 

microenvironment visited are combined into a time-weighted average exposure 

concentration, which is assigned to members of the demographic group.

HAPEM4 uses four primary sources of information: ambient air concentration data, 

population data, population activity data, and microenvironmental data. HAPEM4 also 

contains several special features to improve its exposure prediction capabilities. These 

data sources and features are described in more detail below.

Ambient Air Concentration Data

The HAPEM4 requires annual-averaged, diurnally-distributed air quality data. In 

addition, HAPEM4 can also evaluate the contributions of sub-sets of the air quality data 

(e.g., air concentration values for specific source sectors such as point source, area 

source, mobile source). While the air concentration data for HAPEM4 must be in a 

specific format (e.g., annual average and diurnally distributed), the source of the data 

could be either from an air dispersion model or an ambient monitor. The most common 

form of ambient air concentration data for HAPEM4 is output data in the ISCLT format. 

Population Data

The U.S. Census Bureau is the primary source of most population demographic data. 

The U.S. Census Bureau collects information on where people live, their demographic 

makeup (e.g., age, gender, ethnic group), and employment. The HAPEM4 model uses 

1990 U.S. Census data reported at the spatial resolution of census tracts, which are small, 

relatively permanent statistical subdivisions of a county. Census tracts usually contain 

between 2,500 and 8,000 residents.

Population Activity Data

HAPEM4 uses two types of population activity data: activity pattern data and 

commuting pattern data. An activity pattern is a series of discrete events of varying time 

intervals describing an individual’s lifestyle and routine. An activity pattern typically 

includes the amount of time spent in each of a set of microenvironments (e.g., at home, at 

work, in an automobile, etc.), and a description of what the individual was doing in each 

microenvironment (e.g., sleeping, eating, exercising, etc.). EPA’s Consolidated Human 

Activity Database (CHAD) [18], containing more than 22,000 person-days of activity 

pattern records from 12 studies, is incorporated into HAPEM4. 

Because activity data are not available at a high enough resolution to estimate the 

exposure of each individual in the population, HAPEM4 groups activity pattern data 

together for people with similar demographic characteristics that are expected to 

influence exposure to air pollutants (e.g., age, gender, race), and HAPEM4 estimates 

exposures for these demographic groups. 

Annual average activity pattern sequences are built by randomly selecting (with 

replacement) 365 daily diary entries. Day of the week, as well as season type, are 

considered in this selection process. It is important to note that construction of an annual 
35

average activity pattern in this manner results in the loss of day-to-day correlations in 

activity patterns.

The commuting data contained in HAPEM4 have been derived from a special 1990 US 

Census database that specifies for each US Census tract the number of residents that work 

in each US Census tract, i.e., the population associated with each home tract/work tract 

pair.

Microenvironment Data

 In order to calculate the exposure concentration for each demographic group, an estimate 

is required of the concentration in each microenvironment (ME) specified by the activity 

pattern. In HAPEM4, these ME concentration estimates are derived from the ambient 

concentration estimate for the census tract (obtained from ASPEN) and a set of 3 ME 

factors: PEN, PROX, and ADD. The penetration factor, PEN, is an estimate of the ratio 

of ME concentration to the concurrent outdoor concentration in the immediate vicinity of 

the ME. These pollutant-specific estimates are derived from reported measurement 

studies. The proximity factor, PROX, is an estimate of the ratio of the outdoor 

concentration in the immediate vicinity of the ME to the outdoor concentration 

represented by the air concentration data. ADD is an additive factor that accounts for 

emission sources within or near a particular microenvironment, i.e., indoor emission 

sources.

In HAPEM4, these ME concentration estimates are derived from the ambient 

concentration estimate for the census tract (obtained from ASPEN) and a set of 3 ME 

factors as follows:

C(i,k,t) = [ASPEN]i x PEN x PROX + [ADD]

Where:

C(i,k,t) = concentration predicted within exposure district i and microenvironment k 

in time step t

[ASPEN]i = ambient concentration estimated from ASPEN in district i

PEN = penetration factor

PROX= proximity factor

ADD = additive factor accounting for sources within the microenvironment

Features of HAPEM4 

It is important to note that the HAPEM4 model has been designed to predict inhalation 

exposures for population groups, not individuals within these groups. However, the 

HAPEM4 model contains a stochastic feature to allow the exposure modeler to try and 

capture some of the variability in activity patterns within these groups. The stochastic 

feature predicts the annual exposure concentrations for a randomly selected set of 30 

estimates for each demographic group. 

The HAPEM4 contains a commuting feature that allows the movement of people 

between home and work locations. In general, the user can specify whether a specific 

demographic group is a "commuting" group or a "non-commuting" group. For work 
36

related activities, the "commuting" group is then placed in a different census tract 

location. A default file, developed from 1990 US Census data, that depicts tract-to-tract 

commuting patterns is included with the HAPEM4 model. The model can be run either 

with or without the commuting feature.

Details on both of these features can be found in the HAPEM4 User’s Guide (Appendix 

B). 

3.2.3.2 Application of HAPEM4 for the Initial National Scale Assessment

For this assessment, the model was applied on the national scale. HAPEM4 estimated 

personal inhalation exposures from ambient air toxics concentrations by defining the 

exposed population in terms of geographic and demographic distributions and then 

accounting for the various microenvironments to which people may be exposed. These 

microenvironments were addressed by considering the time people may spend in each of 

these microenvironments and the air toxics concentration in those microenvironments 

relative to the ambient air toxics concentrations predicted by ASPEN. 

Ambient Air Concentration Data

For the national-scale assessment, annual average ambient concentrations for each US 

Census tract were estimated with the ASPEN model. In order to preserve any 

characteristic diurnal patterns in ambient concentrations that might be important in the 

estimation of population exposure, ASPEN annual average concentration estimates are 

stratified by time of day, with an annual average for each of the (8) 3-hour time blocks 

(e.g., midnight to 3am, 3am to 6am). ASPEN air quality files were also provided by each 

of the 4 major source sectors (i.e., major, area, mobile onroad, mobile nonroad). Thus, 

the results of HAPEM4 can be summarized for each of the 4 groups or a combination of 

them. 

Population Data

For this application, HAPEM4 divided the population into 40 demographic groups, based 

on combinations of age (5 categories), race (4 categories), and gender. Figure 2-3 depicts 

the 40 demographic groups identified and used for the initial assessment.

Population Activity Data

For each demographic group, 365 activity patterns were selected randomly (with 

replacement) and then combined to develop the average fraction of time in each of the 37 

microenvironments for each of the (8) 3-hour time blocks. One hundred such annual 

activity patterns were constructed for each demographic group. Then, for each US Census 

tract, 30 of the 100 annual patterns were randomly selected (with replacement) to 

represent typical annual time allocations for group members in that tract. The result was 

a set of 30 annual exposure concentrations estimates for each demographic group in each 

census tract. Figure 3-3 shows an example of a daily inhalation exposure scenario and 

calculations performed to derive a daily exposure value. Because each component of the 

national-scale assessment has been designed to predict more "typical" or populationbased estimates rather than extremes or individuals, the HAPEM4 inhalation 

concentrations presented for this study, as well as that employed for the subsequent risk 
37

characterization, were derived and aggregated using the median exposure concentration 

from this group of 30 concentrations.

Microenvironment Data

For most MEs, HAPEM4 uses a PROX value of 1.0. However, when assessing exposure 

to motor vehicle emissions, for MEs near roadways (e.g., in-vehicle), the pollutant 

concentration contribution in the immediate vicinity of the ME is generally higher than 

the average pollutant concentration contribution over the census tract. Thus, PROX 

values of greater than 1.0 are used. For this application, which addresses only exposure 

resulting from outdoor emission sources, ADD was uniformly set equal to zero. A 

complete listing of the ME factors employed in the national scale assessment for each 

pollutant are presented in the report "Development of Microenvironmental Factors for the 

HAPEM4 in Support of the National Air Toxics Assessment (NATA) - External Review 

Draft Report"[19]. A summary of the peer review comments on this report and how they 

were addressed in this assessment is included in Appendix B. 

3.3 Dose-Response Assessment

3.3.1 Introduction

Within EPA’s paradigm for risk assessment (Figure 2-1, outer circle), the dose-response 

assessment phase of a risk assessment is based on two sequential analyses. The first 

analysis is the hazard identification, which identifies contaminants that may pose health 

hazards at environmentally relevant concentrations and qualitatively describes the effects 

that may occur in humans. The second analysis is the human health dose-response 

assessment, which characterizes the relationship between the concentration, exposure, or 

dose of a pollutant and the resultant health effects. 

The types of effects relevant to each chemical (e.g., cancer, or effects other than cancer) 

are determined as part of the hazard identification. Factors such as the experimental 

route of exposure, the type and quality of the effects, the biological plausibility of 

findings, and the consistency of findings across studies, all contribute to the nature of the 

hazard identification statement.

The nature of quantitative dose-response assessment typically varies among pollutants. 

Sufficient data often exist for criteria air pollutants, such as ozone or carbon monoxide, 

so that relatively complete dose-response relationships can be characterized. In such 

cases, there is no need for extrapolation to lower doses because adequate human health 

effects data are available at environmentally relevant levels. However, this has not been 

the case for most air toxics. Epidemiologic and toxicologic data on air toxics have 

typically resulted from exposure levels that were high relative to environmental levels.

Generally, dose-response assessment methods for air toxics consist of two parts. First is

the evaluation of data in the observable range, and second is the extrapolation from the 

observable range to low doses/risks. Recent terminology refers to the result of analysis in 

the observable range as the “point of departure” from which extrapolation begins. The 

approaches used for evaluation in the observable range are similar for all types of effects, 
38

but EPA’s current extrapolation methods differ considerably for cancer and noncancer 

effects. 

3.3.2 Cancer 

3.3.2.1 Hazard Identification

The EPA’s 1986 Guidelines for Carcinogen Risk Assessment [20] provide guidance on 

hazard identification for carcinogens in this assessment. This approach recognizes three 

broad categories of data: (1) human data (primarily epidemiological); (2) results of longterm experimental animal bioassays; and (3) supporting data, including a variety of shortterm tests for genotoxicity and other relevant properties, pharmacokinetic and metabolic 

studies, physio-chemical properties, and structure-activity relationships. In hazard 

identification of carcinogens under the 1986 guidelines, human data, animal data, and 

supporting evidence are combined to characterize the weight-of-evidence (WOE) 

regarding the agent’s potential as a human carcinogen into one of several hierarchic 

categories:

• Group A ! Carcinogenic to Humans: Agents with adequate human data to 

demonstrate the causal association of the agent with human cancer (typically 

epidemiologic data).

• Group B ! Probably Carcinogenic to Humans: Agents with sufficient evidence 

(i.e., indicative of a causal relationship) from animal bioassay data, but either 

limited (i.e., indicative of a possible causal relationship, but not exclusive of 

alternative explanations) human evidence (Group B1), or with little or no human 

data (Group B2).

• Group C ! Possibly Carcinogenic to Humans: Agents with limited animal 

evidence and little or no human data.

• Group D ! Not Classifiable as to Human Carcinogenicity: Agents without 

adequate data either to suggest or refute the suggestion of human carcinogenicity.

• Group E ! Evidence of Non-carcinogenicity for Humans: Agents that show no 

evidence for carcinogenicity in at least two adequate animal tests in different 

species or in both adequate epidemiologic and animal studies [20].

It is important to note that the WOE categories under the 1986 cancer guidelines express 

only a relative level of certainty that these agents may cause cancer in humans. The 

categories specifically do not connote relative level of hazard, or degree of conservatism 

applied in developing a dose-response assessment. For example, a substance in group C 

(possible human carcinogen) may very well impart a greater cancer risk to more people 

than another substance in group A (known human carcinogen). EPA has classified 

substances as “possible” carcinogens only because the amount and quality of evidence 

was insufficient to place them in a higher group, not because EPA believes they 

necessarily present less risk.
39

EPA’s 1986 carcinogen risk assessment guidelines were the product of nearly two 

decades of experience and scientific consensus building. EPA has since gained 

considerable experience in applying cancer risk assessment approaches. Likewise, the 

science of risk assessment and toxicological testing has continued to evolve while EPA 

has had to address situations not explicitly discussed in the 1986 guidelines, e.g., 

children’s risk assessment. The revision of EPA’s carcinogen risk assessment guidelines 

that is currently underway will consolidate the Agency’s experience, provide more 

comprehensive and transparent guidance on topics not fully developed in the original 

guidelines, and provide flexibility to accommodate anticipated advances in the science. 

In a 1996 Federal Register notice (61 FR No. 123, 32799- 32801) EPA announced that, 

pending publication of the final revised guidelines, the principles and procedures of the 

draft revised guidelines would be applied in part or in whole on a case-by-case basis for 

new assessments. Further, the 1996 proposed guidelines represent the evolution of risk 

assessment methods rather than a “sea change” in those methods. Application of these 

approaches is felt to be reflective of EPA’s accumulated experience and in keeping with 

advancing knowledge on cancer assessment and, therefore, provides the Agency with 

more experience to draw upon in finalizing the guidelines. Accordingly, substances in 

the current assessment that have EPA hazard identification and dose-response 

information developed since 1996 reflect the proposed cancer guidelines, using the most 

recent 1999 guidelines draft [21].

3.3.2.2 Dose-Response Assessment for Carcinogens

EPA’s 1986 cancer risk assessment guidelines [20] adopted a default assumption that 

chemical carcinogens would exhibit risks at any dose. Extrapolation of cancer risk using 

the linearized multistage model, which results in a linear extrapolation of risk in the low 

dose region, was proposed as a reasonable upper-bound on risk (i.e., the true value of the 

risk is unknown, and may be as low as zero), and this approach has been used for most 

chemicals with adequate data since then. Extrapolation of cancer risk using other linear 

extrapolation models, such as have been used with human data available for some known 

human carcinogens (e.g., benzene, hexavalent chromium) results in estimates which, 

although conceivably surrounded by less uncertainty, are still characterized by the 

Agency as plausible upper bound estimates (i.e., the risk is likely to be lower but may be 

greater). Although the 1986 guidelines also supported the use of non-linear low-dose 

extrapolations (given adequate mechanistic data), until recently the low-dose linearity 

assumption has been used without exception in estimating carcinogenic potency. 

Since the publication of EPA’s original cancer guidelines, considerable new knowledge 

has been developed regarding the processes of chemical carcinogenesis and the 

evaluation of human cancer risk. The revision of the cancer guidelines currently in 

progress [21] departs substantially from the original guidelines by distinguishing between 

linear and nonlinear modes of action. The cancer data in the observable range are to be 

analyzed using a dose-response model similar to the models used for noncancer effects. 

The method of extrapolation to lower doses from the point of departure may vary 

depending on whether the assessment of the available data on the mode of action of the 

chemical indicates a linear or nonlinear mode of action.
40

Under the proposed guidelines, a linear extrapolation will remain appropriate when the 

evidence supports a mode of action of gene mutation due to direct deoxyribonucleic acid 

(DNA) reactivity or another mode of action that is thought to be linear in the low dose 

region. A linear mode of action also will serve as a default when available evidence is 

not sufficient to support a nonlinear extrapolation procedure, even if there is no evidence 

for DNA reactivity. The linear extrapolation method has also been revised and simplified 

from that employed under the 1986 guidelines. Nonlinear methods are to be used if there 

is sufficient evidence to support a nonlinear mode of action. 

EPA’s process of estimating cancer risk is based on the unit risk estimate (URE). A URE 

represents an estimate of the increased cancer risk from a lifetime (generally assumed to 

be 70 years) exposure to a concentration of one unit of exposure. The URE for inhalation 

exposures is typically expressed as risk per microgram of pollutant per cubic meter of air. 

The URE is a plausible upper-bound estimate of the risk (i.e., the risk is likely to be 

lower, but may be greater). EPA defines an upper bound as a plausible upper limit to the 

true value of a quantity. Because UREs reflect unquantifiable assumptions about effects 

at low doses, their upper bounds are usually not true statistical confidence limits. 

Available data were insufficient to support assumptions of threshold or sublinear doseresponse for the substances in this assessment, so estimates of cancer risk were developed 

by linear extrapolation of the URE (i.e., by multiplying the estimated lifetime average 

daily exposure in micrograms per cubic meter by the URE). UREs used in this 

assessment were developed by EPA and by the California EPA, and selected for use by a 

priority system described in Appendix G. 

3.3.3 Effects other than cancer

3.3.3.1 Hazard Identification

Due to the wide variety of endpoints, hazard identification procedures for effects other 

than cancer have not been described as completely in EPA guidance as procedures for the 

identification of carcinogens. However, the EPA has published guidelines for assessing 

several specific types of noncancer effects, including mutagenicity [22], developmental 

toxicity [23], neurotoxicity [24], and reproductive toxicity [25]. 

Under these guidelines for identification of long-term (chronic) hazards other than 

cancer, scientists from EPA (and from other agencies that assess dose-response 

relationships) review the health effects literature and characterize its strengths and 

weaknesses, using a narrative approach rather than a formal classification scheme. 

Available data on different endpoints are arrayed and discussed, and the effects (and their 

attendant dose/exposure levels) described. Particular attention is given to the critical 

effect (defined as the first adverse effect, or its known precursor, that occurs to the most 

sensitive species as the dose rate of an agent increases) in well-designed studies. 

Information is presented in a narrative description that discusses factors such as the 

methodological strengths and weaknesses of individual studies (as well as the overall 

database), the length of time over which the studies were conducted, routes of exposure, 

and possible biological modes of action. Assessors consider the severity of effects, 

which may range from severe frank effects that can cause incapacitation or death to 
41

subtle effects that may occur at the cellular level but are early indicators of toxic effects. 

Not all effects observed in laboratory studies are judged to be adverse. The distinction 

between adverse and non-adverse effects is not always clear-cut, and considerable 

professional judgment is applied to identify adverse effects. All of these observations are 

integrated into a presentation that gives a concise profile of the toxicological properties of 

the pollutant. 

3.3.3.2 Dose-Response Assessment for Non-Carcinogens

The inhalation reference concentration (RfC) is the primary Agency consensus 

quantitative toxicity metric for use in noncancer risk assessments for chronic inhalation 

exposure. The RfC is an estimate (with uncertainty spanning perhaps an order of 

magnitude) of a continuous inhalation exposure to the human population (including 

sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects 

during a lifetime. The RfC is derived after a thorough review of the health effects data 

base for an individual chemical, and identification of the most sensitive and relevant 

endpoint (i.e., the critical effect) and the principal study(ies) demonstrating that endpoint. 

Inhalation RfCs are derived according to the Agency’s Methods for Derivation of 

Inhalation Reference Concentrations and Application of Inhalation Dosimetry [26]. The 

evaluation of and choice of data on which to base the RfC derivation are critical aspects 

of the assessment and require scientific judgment.

Derivation of the RfC typically begins with identification of the critical effect from the 

available valid human and animal study data, followed by identification of a lowestobserved-adverse-effect level (LOAEL) or, preferably, a no-observed-adverse-effect 

level (NOAEL). Some assessments model the dose-response relationship to interpolate a 

benchmark dose (BMD), usually the dose at which 5 percent of the organisms are 

predicted to respond. BMDs are used interchangeably with NOAELs. The LOAELs or 

NOAELs from animal studies are converted to human equivalent concentrations (HECs) 

using dosimetric methods [26]. The NOAEL[HEC] or LOAEL[HEC] from one or a few 

well-conducted studies are the key values gleaned from evaluation of the dose-response 

data. The RfC is then derived by consistent application of uncertainty factors (UFs) to 

account for recognized uncertainties in the extrapolation from the experimental data and 

exposure conditions to an estimate (the RfC) appropriate to the assumed human lifetime 

exposure scenario [26]. RfCs (and similar dose-response values) used in this assessment 

were developed by EPA, the US Agency for Toxic Substances and Disease Registry, and 

the California EPA. They were selected for use by a priority system described in 

Appendix G. 

3.4 Risk Characterization

3.4.1 Introduction 

Under EPA’s risk assessment paradigm, the final product in the risk assessment process 

is the risk characterization, in which the information from the previous steps is integrated 

and overall conclusions about risk are synthesized and presented in a way that is 

appropriate and informative for decision-makers. In general, the nature of a risk 

characterization will depend on the information available, the intended use of the risk 

information, and the resources (including time) available. In all cases, however, major 
42

issues associated with determining the nature and extent of the risk should be identified 

and discussed. Further, the EPA Administrator’s March 1995 Policy for Risk 

Characterization [27] specifies that a risk characterization “be prepared in a manner that 

is clear, transparent, reasonable, and consistent with other risk characterizations of similar 

scope prepared across programs in the Agency.” The 1995 Guidance for Risk 

Characterization [28] lists several guiding principles for defining risk characterization in 

the context of risk assessment. The three principles with respect to the information 

content and uncertainty aspects of risk characterization are as follows:

1. The risk characterization integrates the information from the exposure and doseresponse assessments, using a combination of qualitative information, quantitative 

information, and information regarding uncertainties. A good characterization 

should include different kinds of information from all portions of the foregoing 

assessment, carefully selected for reliability and relevance.

2. The risk characterization includes a discussion of uncertainty and variability. The 

risk assessor must distinguish between variability (arising from true 

heterogeneity) and uncertainty (resulting from a lack of knowledge).

3. Well-balanced risk characterizations present risk conclusions and information 

regarding the strengths and limitations of the assessment for other risk assessors, 

EPA decision-makers, and the public. “Truth in advertising” is an integral part of 

the characterization, discussing all noteworthy limitations while taking care not to 

become mired in analyzing factors that are not significant. 

Risk assessments are intended to address or provide descriptions of risk to: (1) 

individuals exposed at average levels and those in the high-end portions of the risk 

distribution; (2) the exposed population as a whole; and (3) important subgroups of the 

population such as highly susceptible demographic groups or life stages, if known. 

Because cancer and noncancer dose-response assessment methods are currently quite 

different, risk characterizations also differ and are discussed separately.

Given the goals of the initial national-scale assessment, the risk characterization’s 

purposes are best served by an analysis of broad geographic scale, recognizing several 

significant limitations. First, the resolution is coarse. Quantitative estimates of cancer 

risk and non-cancer hazard are calculated at the census-tract level, but presented only 

using statistics which summarize their distributions at the county level or higher. Second, 

the risk characterization limits itself to potential human exposures and health effects, 

including limited information on variations in exposure and risk among specific 

subpopulations. Third, the risk characterization includes only inhalation exposure, and 

excludes estimates of air toxics uptake by ingestion and dermal contact. Fourth, the risk 

characterization includes only inventoried major, area, and mobile sources of the study 

pollutants. For these reasons, the results of this assessment represent only a portion of 

the true risks associated with these air toxics. Interpretation and use of these risk results 

should thus bear in mind these limitations and focus on their relative aspects rather than 

their absolute magnitudes.
43

3.4.2 Cancer

In this assessment, cancer risk is defined as the probability of contracting cancer 

following exposure to a pollutant over a 70-year period (assumed human lifespan) at the 

estimated exposure concentration. This estimate of risk focuses on the additional lifetime 

risk of cancer predicted from the exposure being analyzed, beyond that due to any other 

factors, and utilizes cancer potency factors which the Agency considers to be plausible 

upper-bounds (i.e., the true risk potencies are likely to be lower, but may be greater). It is 

noted that in this assessment, the estimated exposure concentrations are not considered to 

be upper-bound. Rather, they represent central tendency estimates of exposure 

concentrations for each demographic group at the geographic unit of analysis (e.g., 

census tract, county, etc.). Estimates of risk are expressed as a probability, usually 

represented in scientific notation as a negative exponent of 10. For example, an 

additional lifetime risk of contracting cancer of 1 chance in 10,000 (or one additional 

person in 10,000) is written as 1x10-4 or 1e-4.

The distribution of individual exposures and risks within a given population can also be 

presented, providing an estimate of the number of people exposed to various predicted 

levels of risk. The Agency’s risk characterization guidelines recommend that risk 

assessments describe individual risk, population risk, and risk to important subgroups of 

the population such as highly exposed or highly susceptible groups [28]. Quantitative 

individual cancer risks are calculated by multiplying the corresponding exposure estimate 

by the URE. 

People are typically exposed to multiple chemicals rather than a single chemical. In rare 

cases where WOE classifications and UREs are available for the chemical mixture of 

concern or for a similar mixture, risk characterization can be conducted on the mixture 

using the same procedures used for a single compound. However, cancer dose-response 

assessments and UREs are usually available only for individual compounds within a 

mixture. Consequently, in assessments of carcinogens for which there is an assumption 

of a linear dose-response, the cancer risks predicted for individual chemicals are typically 

added to estimate cumulative risk associated with groups of chemicals, as recommended 

by EPA’s guidelines for assessment of mixtures [29].

For the NATA national-scale assessment the risk estimates for cancer have been 

expressed in terms of the probability of contracting cancer from a lifetime of exposure. 

For substances for which UREs have been developed by linear extrapolation to low doses 

(including all the carcinogenic air toxics in this assessment), probabilities were calculated 

by multiplying the URE by the estimated lifetime average daily exposure.

Lifetime cancer risks were calculated and aggregated as follows, in order to focus the 

assessment on those air toxics that drive the assessment:

1. Air toxics-specific cancer risks for each substance having a URE in Table 3-5 

were calculated for the median exposure estimates within each census tract. Plots 

were prepared showing the frequency distribution of risk for each air toxic across 

all census tracts, and population sizes living in tracts where the median cancer risk 

estimates exceeded fixed levels.
44

2. Air toxics for which estimated cancer risk exceeded 1e-6 (1 in 1 million) in the 

99th percentile census tract were grouped by WOE (as per the 1986 cancer 

guidelines), using information shown in Table 3-5. The development of the UREs 

shown in Table 3-5 for total particulate organic matter (POM) and 7-PAH 

(polycyclic aromatic hydrocarbon (PAH)) is described in Appendix H. In the 

spirit of the 1996 proposed classification of “known” and “likely” carcinogens, 

risks of different air toxics were combined only within two groups: the category A 

pollutants, and across all category B and C pollutants.

3. Air toxics for which estimated cancer risk did not exceed 1e-6 (1 in 1 million) in 

the 99th percentile census tract were not included in aggregate risk estimates of 

multiple air toxics because their relatively small contribution to the risk sum 

would be within rounding error.

4. In combining risks across multiple carcinogens, this assessment did not consider 

information supporting non-additive aggregation (as recommended in EPA’s draft 

mixtures assessment guidelines), because such information was not available. 

Accordingly, the assessment used the mixtures guidelines default assumption of 

additivity of risks, and combined risks in the manner described above by 

summing them, using the independence formula in the mixtures guidelines. 

3.4.3 Effects Other Than Cancer 

Unlike linear dose-response assessments for cancer, in most cases, noncancer risks 

generally are not expressed as a probability of an individual suffering an adverse 

effect. Instead, “risk” for noncancer effects typically is quantified by comparing the 

exposure to the reference level as a ratio. The “hazard quotient” (HQ) is the exposure 

divided by the reference level (e.g., the RfC or other similar value). For a given air 

toxic, exposures below the reference level (HQ<1) are not likely to be associated with 

adverse health effects. With exposures increasingly greater than the reference level 

(i.e., HQs increasingly greater than 1), the potential for adverse effects increases. The 

HQ, however, should not be interpreted as a probability of adverse effects.

While some risk assessments may involve significant exposure to only a single 

compound, exposure to a mixture of compounds that may produce similar or 

dissimilar health effects more accurately reflects “real” conditions. In a few cases, 

reference levels may be available for a chemical mixture of concern or for a similar 

mixture. In such cases, risk characterization can be conducted on the mixture using 

the same procedures used for a single compound. However, noncancer health effects 

data are usually available only for individual compounds within a mixture. In 

screening assessments for such cases, a conservative “hazard index” (HI) approach, in 

which all the HQs for individual contaminants are summed, is sometimes used. This 

approach assumes that even when individual pollutant levels are lower than the 

corresponding reference levels,
45

Table 3-5. Hazard identification and dose-response information for carcinogenic effects.

 

3

 Upper confidence limit URE; (assessments that did not specify method were assumed to use the UCL).

4

 Maximum likelihood URE.

5

 Higher of 2 recommended UREs was selected.

6

 Assessment consistent with 1996 proposed cancer guidelines.

7

 Advanced draft of IRIS assessment, expected to be finalized shortly.

8

 Value includes assumptions on speciation of emissions; details will be provided in report text.

9

 WOE varies among individual compounds.

10 Note that the California EPA estimates for various polycyclic organic compounds are based on a toxic equivalency approach, where 

the potency of individual compounds is estimated based on relative activity rather than individual assessments of bioassay data. 

Weight ofEvidence

Air Toxics

EPA IARC

Unit Risk 

Estimate

(per ug/m3)

Source

Acetaldehyde B2 2B 2.2E-06 IRIS3

Acrylonitrile B1 2A 6.8E-05 IRIS3

Arsenic compounds A 1 4.3E-03 IRIS4

Benzene A 1 7.8E-06 IRIS4,5,6

Beryllium compounds B1 1 2.4E-03 IRIS3,6

1,3-Butadiene B2 2A 1E-05 EPA NCEA4,6,7

Cadmium compounds B1 1 1.8E-03 IRIS3

Carbon tetrachloride B2 2B 1.5E-05 IRIS3

Chloroform B2 2B 2.3E-05 IRIS3

Chromium compounds A 1 4.1E-03 IRIS4,6,8

Coke Oven Emissions A - 6.2E-04 IRIS3

1,3-Dichloropropene B2 2B 4.0E-06 IRIS3,6

Ethylene dibromide (1,2-dibromoethane) B2 2A 2.2E-04 IRIS3

Ethylene dichloride (1,2-dichloroethane) B2 2B 2.6E-05 IRIS3

Ethylene oxide B1 1 8.8E-05 CAL EPA

Formaldehyde B1 2A 1.3E-05 IRIS3

Hexachlorobenzene B2 2B 4.6E-04 IRIS3

Hydrazine, hydrazine sulfate B2 2B 4.9E-03 IRIS3

Lead compounds B2 2B 1.2E-05 CAL EPA

Methylene chloride B2 2B 4.7E-07 IRIS3

Nickel compounds A 2B 1.2E-04 3,8

Polychlorinated biphenyls (PCBs) B2 2A 1.1E-04 IRIS3

Polycyclic Organic Matter (total) 9 95.5E-05 10

Polycyclic Organic Matter (7-PAH) B2 92.0E-04 10

Propylene dichloride (1,2-dichloropropane) B2 - 1.9E-05 CONV ORAL3

Quinoline C - 3.4E-03 CONV ORAL3

1,1,2,2-Tetrachloroethane C 3 5.8E-05 IRIS3

Tetrachloroethylene (perchloroethylene) B2-C 2A 5.9E-06 CAL EPA

Trichloroethylene (TCE) B2-C 2A 2.0E-06 CAL EPA

Vinyl chloride A 1 8.8E-06

EPA 

NCEA3,6,7,11
46

some pollutants may work together such that their potential for harm is additive and the 

combined exposure to the group of chemicals poses greater likelihood of harm. This 

assumption of dose additivity is most appropriate to compounds that induce the same 

effect by similar modes of action [29]. As with the HQ, the HI should not be interpreted 

as a probability of adverse effects, or as strict delineation of “safe” and “unsafe” levels 

[29]. Rather, the HI is a rough measure of potential for risk and needs to be interpreted 

carefully.

Although the HI approach encompassing all chemicals in a mixture may be appropriate 

for a screening-level study, it is important to note that application of the HI equation to 

compounds that may produce different effects, or that act by different mechanisms, could 

overestimate the potential for effects. Consequently, EPA generally prefers a more 

refined approach of calculating a separate HI for each noncancer endpoint of concern for 

which modes of action are known to be similar [29]. 

For the NATA national-scale assessment, the risk characterization for effects other than 

cancer has been expressed in terms of the hazard quotient (HQ) for inhalation. As 

discussed in section 3.3.3.2 above, many RfCs incorporate protective assumptions in the 

face of uncertain data, so that an HQ greater than one does not necessarily indicate a 

likelihood of adverse effects. The HQ cannot be translated to a probability that adverse 

effects will occur, and it is unlikely to be proportional to risk.

Different pollutants may cause completely different adverse health effects or act via 

completely different modes of action, so it is often inappropriate to aggregate HQs 

associated with different substances. EPA has drafted revisions to Agency guidelines on 

assessing the impact of mixtures [30], which recognize combining effects of different 

substances in specific and limited ways. The national-scale assessment has aggregated 

non-cancer HQs of air toxics that act by similar toxic modes of action, or (where this 

information is not incorporated in the dose-response assessment) that affect the same 

target organ. Aggregation in this way produced a “target-organ-specific hazard index” 

(TOSHI), defined as the sum of hazard quotients for individual air toxics that affect the 

same organ or organ system. 

Non-cancer HQs were calculated and aggregated as follows, in order to focus the 

assessment on those non-carcinogenic air toxics that drive the assessment:

1. The HQ for each air toxic having an RfC or similar value in Table 3-6 was 

calculated for the median exposure within each census tract. Plots were prepared 

showing the frequency distribution of HQ for each air toxic across all census 

tracts, and population sizes living in tracts where the median HQ exceeded fixed 

levels.

2. Air toxics for which estimated HQ exceeded 0.01 in the 99th percentile census 

tract were grouped by target organ, as shown in Table 3-7. Information on target 

 

The development of UREs for total particulate organic matter (POM) and 7-PAH (polycyclic aromatic hydrocarbon (PAH)) are 

described in Appendix H.

11 URE based on whole life exposure was selected over a URE based on adult exposure only.
47

organs for each pollutant was obtained from dose-response assessments and from 

the scientific literature. To avoid aggregating HQs with widely divergent levels 

of uncertainty, HQs for different air toxics were combined only within two 

groups: those with “high certainty” RfCs (i.e., whose total uncertainty factor was 

100 or less) and those with “low certainty” RfCs (whose total uncertainty factor 

was greater than 100). 

3. Air toxics for which HQ did not exceed 0.01 in the 99th percentile census tract 

were not included in aggregate TOSHIs for multiple air toxics because their 

relatively small contribution to the TOSHI sum would be within rounding error.

4. For each of the target organs shown in Table 3-7, the HQ for each air toxic was 

summed to create the TOSHI (data permitting). 

Table 3-6. Non-Cancer Dose-Response Information.

Urban Air Toxics CAS #

RfC 

(or Equivalent)12

(mg/m3)

UFxMF13

Target Organ 

for Chronic 

Critical Effect14

Target Organs 

for Other 

Chronic 

Effects

Source

Acetaldehyde 75070 9.0E-03 1000 Nasal epithelium

Growth rate, 

blood, and kidney IRIS

Acrolein 107028 2.0E-05 1000 Nasal epithelium

Mucous 

membranes 

(irritation) IRIS

Acrylonitrile 107131 2.0E-03 100/10

Nasal epithelium, 

brain

Central nervous 

system 

(depression) IRIS

Arsenic compounds 3.0E-05 1000

Skeleton (fetal 

malformation)

Skin and mucous 

membranes 

(irritation) CAL EPA

Benzene 71432 6.0E-02 10

Blood, bone 

marrow

Central nervous 

system 

(depression) CAL EPA

Beryllium compounds 2.0E-05 10 Lung Immune system IRIS

1,3-Butadiene 106990 8.0E-03 300

Reproductive 

system

Cardiovascular 

system, blood CAL EPA

Cadmium compounds 2.0E-05 30 Kidney Lung CAL EPA

Carbon tetrachloride 56235 4.0E-02 300 Liver Kidney CAL EPA

Chloroform 67663 9.8E-02 100 Liver, kidney

Central nervous 

system 

(depression) ATSDR

Chromium compounds 1.0E-04 90

Respiratory tract 

(necrosis)

Liver, kidney, GI 

tract, immune 

system IRIS

1,3-Dichloropropene 542756 2.0E-02 30 Nasal epithelium Urinary bladder IRIS

 

12 Includes EPA reference concentrations (RfCs), California EPA reference exposure levels (RELs), 

ATSDR minimum risk levels (MRLs), and HEAST inhalation reference doses (RfDs) converted to 

concentrations in air.

13 Modifying factors of 1 are not shown.

14 Critical effect listed is the adverse effect upon which the RfC or equivalent health-based value is based.
48

Urban Air Toxics CAS #

RfC 

(or Equivalent)12

(mg/m3)

UFxMF13

Target Organ 

for Chronic 

Critical Effect14

Target Organs 

for Other 

Chronic 

Effects

Source

Ethylene dibromide (1,2-dibromoethane) 106934 8.0E-04 100

Reproductive 

system

Liver, kidney, 

testes CAL EPA

Ethylene dichloride (1,2-dichloroethane) 107062 2.4E+00 90 Kidney Liver ATSDR

Ethylene oxide 75218 3.0E-02 100 Blood

Eyes, mucous 

membranes, 

central nervous 

system CAL EPA

Formaldehyde 50000 9.8E-03 30

Respiratory 

epithelium

Immune system 

(sensitization) ATSDR

Hexachlorobenzene 118741 3.0E-03 100

Liver 

(developmental)

Immune system, 

kidney, blood CAL EPA

Hydrazine, hydrazine sulfate 302012 2.0E-04 300 Liver, thyroid

Respiratory 

system, spleen CAL EPA

Lead compounds15 PB_CMPDS 1.5E-03 1

Central nervous 

system 

(neurobehavioral 

effects)

Blood, 

cardiovascular 

system, kidney NAAQS

Manganese compounds MN_CMPDS 5.0E-05 1000

Central nervous 

system 

(neurobehavioral 

effects)

Respiratory 

system IRIS

Mercury compounds16 HG_CMPDS 3.0E-04 30

Central nervous 

system - IRIS

Methylene chloride 75092 1.0E+00 30 Liver

Kidney, 

cardiovascular 

system ATSDR

Nickel compounds NI_CMPDS 2.0E-04 30

Respiratory 

system, immune 

system - ATSDR

Propylene dichloride (1,2-

dichloropropane) 78875 4.0E-03 300 Nasal epithelium Blood IRIS

Tetrachloroethylene (perchloroethylene) 127184 2.7E-01 100

Central nervous 

system 

(depression)

Heart, liver, 

kidney ATSDR

Trichloroethylene (TCE) 79016 6.0E-01 100

Central nervous 

system 

(depression) Liver, kidney IRIS 

Vinyl chloride 75014 1.0E-01 300 Liver

Kidney, central 

nervous system 

(depression) CAL EPA

 

15 EPA has not developed an RfC for lead. The National-Scale Assessment uses the National Ambient Air 

Quality Standard for lead, which was developed using the EPA Integrated Exposure, Uptake, Biokinetic 

Model, and did not use the UF/MF method. Because sensitive human subpopulations were modeled, the 

effective UF is 1.

16 Hazard calculations for mercury compounds were based on the RfC for elemental mercury.
49

Table 3-7. Grouping of compounds by target organ and uncertainty factor for aggregation 

of effects other than cancer.

Target Organs/Systems for Chronic 

Effects UF x MF Contaminant Groupings

1-100 Beryllium, cadmium, chromium VI, 1,3-dichloropropene, 

formaldehyde, nickel

Respiratory system (including nasal 

epithelium, mucous membranes, and 

lung) 101 – 1000 Acetaldehyde, acrolein, acrylonitrile, arsenic, propylene dichloride, 

ethylene oxide, hydrazine, manganese

Blood (including bone marrow and 1-100 Benzene, ethylene oxide, hexachlorobenzene, lead

spleen) 101 – 1000 Arsine, 1,3-butadiene, propylene dichloride, hydrazine

1-100 Benzene, chloroform, ethylene oxide, lead, manganese, mercury, 

tetrachloroethylene, trichloroethylene

Central nervous system (including 

neurobehavioral effects and CNS 

depression) 101 – 1000 Acrylonitrile, vinyl chloride

1-100 Cadmium, chloroform, chromium VI, ethylene dibromide, ethylene 

dichloride, hexachlorobenzene, lead, methylene chloride, 

tetrachloroethylene, trichloroethylene, 

Liver and kidney17

101 – 1000 Acetaldehyde, carbon tetrachloride, hydrazine, vinyl chloride

Cardiovascular system 1-100 Lead, methylene chloride, tetrachloroethylene

101 – 1000 1,3-Butadiene

1-100 Beryllium, chromium VI, formaldehyde, hexachlorobenzene, 

nickel

Immune system (including sensitization)

101 – 1000 --

3.4.4 Discussion of Uncertainties in the Dose-Response Assessment

3.4.4.1 Uncertainties in the Unit Risk Estimate

The process of Unit Risk Estimate (URE) development includes the following important 

sources of uncertainty:

1. Many of the air toxics included in this assessment were classified as probable 

carcinogens, which means that data were not sufficient to prove these substances 

definitely cause cancer in humans. It is possible that some are not human 

carcinogens at environmentally relevant doses, and that true risk associated with 

these air toxics is zero. 

2. All UREs used in this assessment were based on linear extrapolation from high to 

low doses. To the extent that true dose-response relationships for some air toxics 

are less than linear, this assumption may result in significant overestimates of risk. 

3. UREs for most of these substances were developed from animal data using 

conservative methods to extrapolate between species. Actual human responses 

may differ from the predicted ones. 

 

17 Liver and kidney effects were combined because most HAPs that affect either, affect both.
50

4. Most UREs used in this assessment (typically those from assessments based on 

animal data) were based on the statistical upper confidence limit (UCL) of the 

fitted dose-response curve, but a few (typically those from assessments based on 

human data) were based on the statistical best fit (“maximum likelihood 

estimate,” or MLE). The reader should be aware that MLE estimates for some 

known carcinogens are somewhat less conservative than UCL estimates. 

Nevertheless, because of the combination of assumptions used in the face of all four 

sources of uncertainty described above, EPA considers all its UREs to be upper-bound 

estimates. True risk would probably be less, but could be greater.

For the NATA national-scale assessment, hazard identification and dose-response 

assessments for carcinogenic effects (Table 3-5) were obtained from peer-reviewed 

sources and prioritized according to (1) applicability, (2) conceptual consistency with 

EPA risk assessment guidance, and (3) level of review received. A discussion of sources 

and details of the prioritization process are presented in Appendix G.

3.4.4.2 Uncertainties in Reference Concentrations

In the development of reference concentrations (RfCs), uncertainty factors (UFs) are 

applied as appropriate for the following extrapolations or areas of uncertainty:

• Laboratory animal data to humans;

• Average healthy humans to sensitive humans;

• Subchronic to chronic exposure duration;

• LOAEL to NOAEL; and

• Incomplete database.

In addition to UFs (which may be 10, 3 or 1), modifying factors (MFs) may also be 

applied. A modifying factor is a factor used in the derivation of a reference dose or 

reference concentration. The magnitude of the MF reflects the scientific uncertainties of 

the study and database not explicitly treated with standard uncertainty factors (e.g., the 

completeness of the overall database). A MF is greater than zero and less than or equal to 

10, and the default value for the MF is 1.

The composite UF depends on the number of extrapolations required. RfCs have been 

derived for various substances using composite UFs that range from 10 to 3,000, with 

most RfCs using factors of 100 to 1,000. The use of order-of-magnitude uncertainty 

factors for RfCs and the definition of the RfC as having “uncertainty spanning perhaps an 

order of magnitude” are indications of the general lack of precision in the estimates. 

It is important to note that the composite UF expresses only our relative certainty about 

the dose at which these agents may not cause adverse non-cancer effects in humans and 

not the absolute hazard when the RfC is exceeded. This is because the magnitude of the 

UF provides no information on the relative level of hazard or degree of conservatism 

applied in developing a dose-response assessment. For example, one substance’s RfC 

that is based on a composite UF of 3000 may be no further beneath the true threshold for 

effects than the RfC for a different substance that is based on a UF of 10. The higher UF 
51

was assigned to the first substance only because the amount and quality of evidence was 

insufficient to support a lower UF. Thus, RfCs developed using high UFs should not be 

assumed to be more conservative than those using low UFs.

It should also be noted that exposures above an RfC do not necessarily imply 

unacceptable risk or that adverse health effects are expected. Because of the inherent 

conservatism of the RfC methodology, the significance of exceedances must be evaluated 

on a case-by-case basis, considering such factors as the confidence level of the 

assessment, the size of UFs used, the slope of the dose-response curve, the magnitude of 

the exceedance, and the number or types of people exposed at various levels above the 

RfC.

For the NATA national-scale assessment, hazard identification and dose-response 

information for non-cancer health effects (3- 5) were obtained from peer-reviewed 

sources and prioritized according to (1) applicability, (2) conceptual consistency with 

EPA risk assessment guidance, and (3) level of review received. A discussion of sources 

and details of the prioritization process are presented in Appendix G.
52

4 Results and Discussion of Exposure Assessment

4.1 Introduction 

It is important to note that the ambient air quality predictions, and subsequent exposure 

and risk assessment results for this initial national scale assessment are being derived at a 

broad geographical scale. Because of limitations associated with the emission inventory, 

dispersion modeling and exposure modeling approaches, local scale “hot spots” (i.e., 

fence line impacts, complex terrain issues, persistent flow situations, fumigation events, 

etc.) are not predicted with this assessment. Further, the assessment includes only a 

single exposure media (i.e., air inhalation). Thus, extreme care should be taken in 

interpreting the results of this assessment. The results are best used in combination with 

other media assessments, as well as local scale assessments, to better evaluate the full 

potential health implications of a particular pollutant. Finally, it is equally important to 

note that this assessment is still undergoing scientific peer review and the results should 

be considered draft until that process is complete.

The discussion of results follows the sequence of the four major components of the 

assessment: emission inventory development, fate and transport characterization, 

population exposure estimation, and risk characterization. The first three components, 

the exposure assessment components, are discussed in this section, and the last 

component, risk characterization, is discussed in section 5. The discussion of each 

component includes a presentation of important results and a discussion of the major 

sources of uncertainty associated with that component. The fate and transport section 

includes a comparison of model performance which utilizes a comparison between 

modeled and monitored ambient concentrations. In each section, the results presented 

and discussed represent only a portion of the full results for that component; remaining 

results for emission inventory and dispersion modeling portions of the assessment are 

available on the NATA website (http://www.epa.gov/ttn/uatw/nata), and results for the 

exposure modeling portion of the assessment are provided in Appendix K. 

This assessment is the first national-scale assessment, and the results should be 

considered to be preliminary. The national-scale assessments may be repeated every 3 

years, and these future national-scale assessments will expand in scope and will make use 

of improved data and assessment tools.

4.2 Source Characterization: Emission Inventories

This section provides an overview of the emission estimates in the NTI, which provide 

the majority of the emissions data inputs to the national-scale assessment. The NATA 

website contains a complete set of tables, maps and graphics that portray the emission 

inputs to the national-scale assessment (http://www.epa.gov/ttn/uatw/nata/natsa1.html). 

EPA prepared emission inventory ASPEN-input files for 1) direct emissions of air toxics, 

2) direct emissions of diesel particulate matter (PM), and 3) pollutants that can transform 

in the atmosphere to produce air toxics. These inventories are described in section 

3.2.1.2. The majority of the emissions data used in the national-scale assessment were 
53

from EPA’s 1996 NTI. In addition to the NTI, emissions were used from a diesel PM 

inventory from mobile sources, and VOC air toxics precursor emissions were derived 

from the NET criteria database. Table 4-1 summarizes the inventories used as input to 

EMS-HAP to prepare ASPEN inputs.

Table 4-1. Summary of Inventories Used as Input to EMS-HAP

Stationary 

Source 

Inventory

Mobile 

Source 

Inventory

Directly emitted air toxics from the 1996 NTI X X

Air toxics precursors from the 1996 NTI and the 1996 

NET speciated for particular VOC’s X X

Diesel PM from a mobile source diesel PM inventory 

(48 states) as discussed in 3.2.1.2 and the 1996 NET 

(Puerto Rico and the Virgin Islands) for PM-10

X

The NET, diesel PM, and early versions of the 1996 NTI inventories all contained 

emissions estimates for the 50 states. The geographic domain of the national-scale 

assessment included the 48 contiguous states, District of Columbia, Puerto Rico, and 

Virgin Islands. In all of the inventories, emissions for the territories were derived in part 

or in total via extrapolation of emissions estimates from surrogate U.S. locations. More 

information regarding how emissions from all inventories were prepared for ASPEN 

dispersion model input can be found in Appendix C.

4.2.1 Summary and Discussion of 1996 Emission Inventory Results

There are a number of ways that the 1996 NTI data can be summarized. The NATA 

website (http://www.epa.gov/ttn/uatw/nata/natsa1.html) provides various summaries of 

the emissions data used in the national scale assessment. Available on the site are county 

emissions totals by source sector and pollutant, emissions density maps, and pie and bar 

charts. Figure 4-1 shows that the national total of the 33 urban air toxics (this includes 

dioxins and excludes diesel PM) emitted from the four source sectors is 1.1 million tons 

(for 1996). This pie chart illustrates that approximately half of the air toxics in this 

assessment come from mobile sources and the other half from stationary sources. The 

majority of these stationary source emissions are from the area and other source sector. 

This would be expected since these pollutants were selected, in part, based on their 

presence in urban areas and from area sources.

Another way the data can be examined is by their distribution between urban and rural 

counties. Counties are defined as urban or rural based on population data provided by the 

Bureau of the Census. For purposes of developing EPA’s Integrated Urban Air Toxics

Strategy, a county is considered ”urban” if either: 1) it includes a metropolitan statistical 

area with a population greater than 250,000; or, 2) the U.S. Census Bureau designates 

more than fifty percent of the population as “urban.” The Integrated Urban Air Toxics 

Strategy (online at www.epa.gov/ttn/uatw/urban/urbanpg.html) is an important part of 

EPA's national air toxics program. Please note that the definition of “urban” does not 

necessarily apply for regulatory or implementation purposes. 

Figure 4-2 shows a summary of NTI emissions of 33 urban air toxics by urban and rural 
54

designations. As in the previous pie chart (Figure 4-1), the “area and other” category 

dominates the total emissions, as would be expected for this pollutant set. 

Figures 4-3 to 4-6 are maps that show emissions densities by groups of air toxics (metals, 

semi-volatile organic compounds (SVOCs), etc.) across the U.S., based on EMS-HAP 

processed emissions. In some cases, sharp contrasts are obvious along state or county 

lines. This is likely primarily an artifact of variations in state and local agency-reported 

data. The maps illustrate that the greatest emission densities tend to be in metropolitan 

areas and in the eastern U.S. This is to be expected since these pollutants were identified 

based on their presence in urbanized locations. This result is similar across the pollutant 

groups shown. A complete set of emission density maps for the all the pollutants is 

available at http://www.epa.gov/ttn/uatw/nata/natsa1.html#emission. 

For the purposes of the national-scale assessment, several assumptions were made to 

accommodate using the NTI data in computer models. For this reason, the emissions 

input to the ASPEN model, which represents the emissions output from EMS-HAP, is not 

identical to the NTI data from which it originated. The county-level emission summaries 

on the NATA website reflect the EMS-HAP processed inventory and therefore, will not 

match exactly to similar county-level summaries directly from the NTI. National level 

summaries of the EMS-HAP emissions and NTI emissions are shown in Table 4-2, and 

the principle ways in which the two differ are described below:

Differences due to pollutant groupings. Proper grouping is essential for assuring that 

the most accurate deposition and decay characteristics are assigned to air toxics provided 

in the emission inventory. The grouping decisions made for the national-scale assessment 

reflect “downstream” data needs, such as making the resultant concentration estimates 

reflect compounds for which health benchmark information exists. These decisions made 

for dispersion and exposure modeling are not always the same as those used to 

summarize pollutant groupings in the NTI. 

For the NTI emission summaries, emissions of particular metal compound species (e.g. 

lead oxide) belonging to groups were summed with no adjustment. For ASPEN 

modeling, particular metal compound species were adjusted to account only for the moles 

of the metal in the compound (e.g., emissions of the oxide fraction of lead oxide were 

subtracted from the emissions of lead oxide). This resulted in apparently lower metal 

compound emissions in the EMS-HAP summaries than the NTI summaries, but actually 

this effect is merely the result of two different definitions of “metal compounds.” 

Differences due to different processing of emissions records that were reported in 

pounds per hour. For the NTI emission summaries, these records were not included in 

the summary because the operating schedule was unknown. In EMS-HAP, these records 

were converted to tons/year assuming a maximum operating schedule (8760 hours per 

year). This resulted in higher emissions in the EMS-HAP summaries than the NTI 

summaries. 

Differences due to geographic domain. The NTI national summaries include Alaska 

and Hawaii. EMS-HAP national summaries do not.
55

Table 4-2. Comparison of EMS-HAP and NTI emission totals.

Major Area and Other Onroad Mobile Nonroad Mobile Total

Pollutant

EMSHAP 

Emiss. 

(tons/yr)

NTI 

Emiss. 

(tons/yr) Diff.

EMSHAP 

Emiss. 

(tons/yr)

NTI 

Emiss. 

(tons/yr) Diff.

EMSHAP 

Emiss. 

(tons/yr)

NTI 

Emiss. 

(tons/yr) Diff.

EMSHAP 

Emiss. 

(tons/yr)

NTI 

Emiss. 

(tons/yr) Diff.

EMSHAP 

Emiss. 

(tons/yr)

NTI 

Emiss. 

(tons/yr) Diff.

1,1,2,2-Tetrachloroethane 9 9 0% 116 116 0% 0 0 0% 0 0 0% 124 125 0%

1,3-Butadiene 2748 2743 0% 16304 19148 -17% 23596 23488 0% 9460 9864 -4% 52108 55243 -6%

1,3-Dichloropropene 4 4 0% 21350 21178 1% 0 0 0% 0 0 0% 21354 21181 1%

7-PAH 105 105 0% 707 828 -17% 42 42 1% 17 19 -8% 872 994 -14%

Acetaldehyde -

Primary 8815 8799 0% 18826 21691 -15% 28790 28698 0% 40764 40828 0% 97195 100016 -3%

Acrolein -

Primary 233 234 0% 16457 19447 -18% 4996 4960 1% 7342 7376 0% 29029 32017 -10%

Acrylonitrile 1001 1001 0% 258 258 0% 0 0 0% 0 0 0% 1259 1259 0%

Arsenic 

Compounds 316 317 0% 76 77 -2% 0 0 1% 2 2 -5% 395 396 0%

Benzene 13388 13487 -1% 60905 68940 -13% 169060 168212 1% 93623 98703 -5% 336976 349342 -4%

Beryllium 

Compounds 33 33 0% 7 7 0% 0 0 0% 0 0 -6% 40 40 0%

Cadmium 

Compounds 68 68 0% 86 84 2% 0 0 0% 0 0 -6% 154 153 1%

Carbon 

Tetrachloride 380 372 2% 105 104 1% 0 0 0% 0 0 0% 485 476 2%

Chloroform 2752 2693 2% 626 615 2% 0 0 0% 0 0 0% 3378 3308 2%

Chromium 

Compounds 676 680 -1% 423 429 -2% 14 13 1% 34 35 -1% 1147 1157 -1%

Coke Oven 

Emissions 1433 1433 0% 4 4 0% 0 0 0% 0 0 0% 1437 1437 0%

Diesel 

Particulate 

Matter N/A N/A N/A N/A 133556 N/A 341241 N/A 474797 N/A

Ethylene 

Dibromide 8 8 0% 4 4 -4% 0 0 0% 0 0 0% 13 13 -1%

Ethylene 

Dichloride 683 684 0% 94 87 8% 0 0 0% 0 0 0% 777 771 1%

Ethylene Oxide 307 279 9% 1114 1112 0% 0 0 0% 0 0 0% 1421 1391 2%

Formaldehyde -

Primary 15959 15961 0% 142589 160622 -13% 83354 83006 0% 86374 86440 0% 328276 346028 -5%

HexachloroA26benzene 1 1 0% 0 0 -1% 0 0 0% 0 0 0% 1 1 0%

Hydrazine 25 25 0% 2 2 0% 0 0 0% 0 0 0% 28 28 0%

Manganese 

Compounds 1567 1567 0% 1116 1123 -1% 6 6 1% 35 36 0% 2724 2732 0%

Mercury 

Compounds 106 106 0% 51 50 1% 0 0 1% 7 7 1% 163 163 0%

Methylene 

Chloride 32117 30460 5% 52889 52477 1% 0 0 0% 0 0 0% 85006 82937 2%

Nickel 

Compounds 600 601 0% 500 517 -3% 11 11 1% 89 93 -5% 1199 1221 -2%

Perchloroethylene 6403 6411 0% 37510 37092 1% 0 0 0% 0 0 0% 43914 43503 1%

Polychlorinated 

Biphenyls 0 0 0% 0 0 0% 0 0 0% 0 0 0% 0 0 0%

Propylene 

Dichloride 81 81 0% 15 15 0% 0 0 0% 0 0 0% 96 96 0%

Quinoline 10 10 0% 4 4 0% 0 0 0% 0 0 0% 15 15 0%

Trichloroethylene 10354 10354 0% 15361 15077 2% 0 0 0% 0 0 0% 25715 25431 1%

Vinyl Chloride 851 851 0% 384 384 0% 0 0 0% 0 0 0% 1235 1235 0%

*Only HAPs for which concentration results have been posted are shown; NTI totals include Alaska and Hawaii, EMS-HAP totals do not
56

Differences due to facilities with missing or incorrect location data. As mentioned 

previously, locations for some facilities were corrected in EMS-HAP. In addition, 87 

facilities in the NTI could not be associated with county coordinates. Forty-three of these 

sources emit the pollutants targeted in the national-scale assessment, but could not be 

included because of this missing location information. These differences would account 

for variations among county totals where location default schemes placed facilities in 

EMS-HAP in different counties than in the NTI, and would make emissions lower in the 

EMS-HAP inventory where these facilities are missing (Colorado and Idaho).

4.2.2 Discussion of Inventory Uncertainties

Because the 1996 NTI is a composite of emissions estimates generated by state and local 

regulatory agencies, EPA, and industry; and because emission estimation techniques vary 

with the agency providing data, the pollutants and the source categories, it is 

understandable that the uncertainties should vary widely among emissions estimates. In 

some cases, an estimate may be derived from few or only one emissions measurement at 

a similar source. The NTI estimates originated from a variety of sources and estimation 

methods, as well as for differing purposes, and they will in turn vary in quality, number 

of pollutants included, level of detail, and geographic coverage. EPA has not attempted to 

verify estimation methodology from the primary sources submitting data or from other 

EPA databases (e.g., state inventories, TRI). EPA has not undertaken a full QA/QC 

evaluation of the NTI because (1) most estimates submitted by state and local agencies 

did not include supporting documentation, (2) EPA was aware of the uneven quality and 

planned to view the national-scale assessment results in light of that awareness, (3) this 

national-scale assessment was planned as a trial effort, not a definitive result, and (4) 

EPA did not have sufficient time or resources available. Nevertheless, EPA recognizes 

that the lack of such an evaluation represents an important source of uncertainty in the 

assessment. Table 4-3 summarizes the origins of the stationary source emission estimates 

in the NTI.

Table 4-3. Summary of Data Sources to the NTI.

Emissions 

of 188 Air 

Toxics

Percent 

Emissions

Number of 

Facilities or 

Categories

Percent of 

Facilities or 

Categories

All Stationary Sources 2,301,700

All Point Sources 1,174,700

From state or local agencies 401,300 34% of all point 

source emissions

55,411 

facilities 90 %

From MACT 619,000 53% of all point 

source emissions

4,310 

facilities 7 %

From TRI 153,500 13% of all point 

source emissions

1,847 

facilities

3 %

All Non-point stationary 

sources

1,128,610

EPA-generated (via emission 

factors/activity data) 787,100 70% of all nonpoint emissions 30 categories 6 %

From state or local agencies 

and TRI 182,900 16% of all nonpoint emissions

405 

categories 81 %

From MACT 158,600 14% of all nonpoint emissions 65 categories 13 %
57

It should also be noted that toxic emissions data for nonroad equipment are much more 

limited than data for onroad mobile sources. While EPA has basic emission factors for 

VOC and PM for most of the nonroad categories, there is very little VOC speciation data 

for the given categories that would allow EPA to develop good estimates of toxic 

emission rates. Given the large variety of nonroad engine sizes, types and uses, as well 

as the likelihood that this variety will result in some differences in VOC composition, it is 

important that EPA obtain or develop speciated VOC data specific to each nonroad 

category in order to more accurately project nonroad mobile source air toxics (MSAT) 

inventories. In its section 202(l)(2) rulemaking for mobile source air toxics, the Office of 

Transportation and Air Quality outlined a strategy to obtain and evaluate these data.

Some comparisons to other inventory databases and discussion of the primary sources of 

uncertainty in the NTI are discussed in Section 4.2.1.2.1.

4.2.2.1 Uncertainties in Completeness of Point Source Universe

Although there are not any other national-level air toxics inventories that include point 

and non-point stationary and mobile sources available for comparison to the NTI, there 

are other emissions data sets that have been used in other dispersion modeling exercises. 

The only two nationwide air toxics data sets are the one used in the 1990 Cumulative 

Exposure Project (CEP) study and the Toxics Release Inventory. In addition to these air 

toxics inventories, the NET is a nationwide criteria pollutant inventory that includes 

emissions from all source sectors mentioned above, but with different source category 

definitions (e.g., major sources in the NET have 100 tons of criteria pollutant actual 

emissions or other limits selected by the states submitting the data). 

Cumulative Exposure Project (CEP)

The 1990 CEP included 1990 TRI data, but also relied heavily on VOC and PM emission 

estimates from the interim 1990 NET inventory. These criteria pollutant emissions were 

converted to individual pollutant emissions via speciation profiles. In general, speciation 

profiles are industry-specific conversion factors that are used to estimate individual air 

toxics emission rates from criteria pollutant emissions. This method creates an air toxics 

emission inventory with minimal resources but produces an uncertain inventory, 

particularly for stationary sources where industry-specific speciation profile information 

is very limited. In addition much of the NET inventory is “grown” from prior years’ 

estimates and is thought to be less accurate than the year-specific emissions data 

compiled in the NTI. Therefore, the NTI would be deemed to be of superior quality to 

the CEP emissions estimates. A comparison of 1990 CEP inventory to 1996 NTI 

emission totals for selected air toxics is presented in Table 4-4.
58

Table 4-4. 1990 CEP and 1996 NTI National Emission Totals.

1990 CEP 1996 NTI

POLLUTANT NAME Total Total

Benzene 587285 349342

Formaldehyde 412450 346028

Tetrachloroethylene 116435 43503

Methylene Chloride 115340 82937

Acetaldehyde 112639 100016

1,3-Butadiene 87600 55243

Trichloroethylene 82344 25431

POM as 7-PAH 76504 994

Acrolein 72628 32017

Coke Oven Emissions 29127 1437

1,3-Dichloropropene 20732 21181

Chloroform 17520 3308

Ethylene Dichloride 11570 771

Vinyl Chloride 6351 1235

Acrylonitrile 5110 1259

Manganese Compounds 4030 2732

Nickel Compounds 4015 1221

Carbon Tetrachloride 3978 476

Ethylene Oxide 1836 1391

Arsenic Compounds 1095 396

Chromium Compounds 996 1157

1,2-Dichloropropane 735 96.1

Cadmium Compounds 299 153

Mercury Compounds 267 163

Ethylene Dibromide 223 12.8

1,1,2,2-Tetrachloroethane 35.1 125

Beryllium Compounds 33.8 40.2

PCBs 29.2 0.21

Hydrazine 15.4 27.5

Quinoline 13.8 14.8

Hexachlorobenzene 9.96 1.00

Toxics Release Inventory (TRI)

The TRI differs from the NTI in several fundamental ways. Most significantly is the fact 

that the TRI universe of sources is a subset of the NTI universe, although the TRI 

contains many pollutants other than the 188 air toxics. The TRI contains data from only 

industrial sources that are large enough (in terms of chemicals usage, emissions, number 

of employees, etc.) to meet certain reporting requirements. In addition to these TRI 

sources, the NTI also contains emission estimates for smaller sources, sources not 

associated with industry (e.g., wildfires, consumer product usage) and mobile sources. 

The two inventories also differ in the level of detail. For instance, TRI sources report 

“stack” and “fugitive” emissions, while the NTI contains point source process parameter 
59

details necessary for computer modeling (e.g., stack height, flow rate, temperatures, etc.) 

that may include many emission points (stacks) at a given facility. EPA uses information 

from the TRI and other EPA programs, as well as information reported voluntarily by 

state and local agencies, to monitor emissions and emission trends across a broad 

spectrum of source categories.

National Emission Trends (NET) 

The NET contains criteria pollutant emissions for point, non-point stationary, and mobile 

sources, compiled by EPA in conjunction with state and local criteria pollutant inventory 

submittals. The NET and NTI inventories are designed for different purposes and contain 

emissions estimates for different pollutants (criteria pollutants in the NET, air toxics in 

the NTI). However, there may be significant overlap of included point sources, 

particularly among large point facilities. Therefore, the NET provides a good resource 

for checking the NTI’s completeness and for gleaning missing location or facility details. 

Table 4-5 compares the number of major source NTI facilities with major source 

facilities in the TRI and unique facilities in the NET. Note that for states that did not 

provide air toxics inventories to EPA, the 1996 NET includes approximately four times 

the number of facilities as the NTI. This fact suggests that large point source facilities 

may be missing from the NTI in these states.

Table 4-5. Facility Count Summary.

State

Unique 

Facilities in 

NTI

Major 

Source 

Facilities in 

TRI

Unique 

Facilities in 

NET State

Unique 

Facilities in 

NTI

Major 

Source 

Facilities in 

TRI

Unique 

Facilities in 

NET

AK* 16 3 28 MT* 28 10 217

AL 240 151 811 NC 2,369 230 925

AR 301 99 114 ND 56 11 65

AZ 244 24 309 NE 290 40 704

CA 7,416 176 18,870 NH 53 13 184

CO 3,424 20 4,645 NJ 148 62 864

CT 68 49 660 NM 34 6 299

DC* 1 0 14 NV* 12 4 109

DE 48 18 87 NY 3,528 103 1,584

FL 359 102 509 OH* 421 275 1,900

GA* 240 158 417 OK* 94 71 373

HI* 16 3 155 OR 187 61 395

IA* 138 101 63 PA 586 206 977

ID 56 6 21 RI 505 15 110

IL 8,813 220 9,713 SC 406 116 489

IN 1,587 287 1,321 SD 24 16 22

KS 150 75 1,963 TN 502 193 588

KY 250 98 370 TX 2,426 288 1,203

LA 301 89 664 UT 137 24 333

MA 311 55 495 VA 1,460 137 2,299

MD 730 35 439 VT 86 3 126

ME 144 20 223 WA 269 66 276

MI* 341 168 1,966 WI 744 147 1,585

MN 210 103 696 WV 172 39 229

MO 791 116 758 WY 115 8 270

MS 150 104 131 TOTAL 40,997 4,424 61,568

* States that did not provide point source data files to draft NTI
60

4.2.2.2 Uncertainties Due to the Dynamic Nature of Emission Inventories

Like other air pollution inventories, the NTI data set for 1996 is dynamic, and it will 

likely change, as resources permit, with periodic updates as new, more reliable data 

become available. For this reason, future assessments based on the 1996 NTI may 

produce somewhat different results. As the results of the ASPEN and subsequent 

HAPEM models are evaluated, modifications to the emission inventory input will 

continue to surface such as the lead emissions that are known to be missing from the 

Missouri lead smelter discussed in section 3.2.1.3.1. Also, it is likely that some facilities 

were double counted under different names, and that some plants that had closed have not 

been omitted from the 1996 data set. 

The county-level mobile, area and other sources may be based on VMT, activity data, 

and/or emission factors, and these components are updated routinely. For example, the 

nonroad mobile source air toxics emissions are based on VOC and PM estimates from the 

June 2000 version of the NONROAD model. When this model run was complete, the 

VOC and PM nonroad estimates from individual equipment categories were consolidated 

into gasoline 2-stroke and 4-stroke and diesel PM source sectors and used to prepare the 

NTI air toxics estimates. However, subsequently a decision was made to revise inventory 

estimates for recreational equipment. When the revised 1996 NET was developed, these 

estimates were replaced with significantly lower recreational equipment estimates 

originally developed for the 1991 EPA Nonroad Engine and Vehicle Emission Study. 

The change affects both 2 and 4-stroke emissions, but it is most noticeable with 2-strokes, 

since that is what makes up most of the recreation equipment VOC inventory. Additional 

revisions to inventory estimates for recreational equipment and other equipment 

categories are anticipated before a final version of NONROAD is released. (More 

background on the draft NONROAD model, its history and recent developments are 

available at http://www.epa.gov/otaq/nonrdmdl.htm)

4.2.2.3 Uncertainties in Emission Locations

The location of emissions is important for all types of sources, whether expressed at a 

given latitude/longitude or at the county level. Many locations are unknown or 

incomplete, yet for dispersion modeling each facility point source must have an exact 

location. If the location coordinates were missing from point sources, they were placed 

via the default mechanisms in EMS-HAP. How well this default location reflects reality 

will affect the model results and the uncertainty of the overall analysis. To illustrate this 

point, Table 4-6 shows, by state, the percent of point sources, and the corresponding 

percent of emissions of lead, chromium, and cadmium emissions, that were located via 

default algorithms. The table shows the percentages based on sources with default 

locations determined by the facilities’ zip codes or counties, as well as the percentage of 

facilities were not defaulted because their coordinates were within the correct county or 

within reasonable bounds outside the county. Although this comparison does not provide 

a quantitative assessment of uncertainty, the magnitude of sources and emissions 

defaulted in a given state suggests the uncertainty in the results due to potential location 

errors.
61

Table 4-6. Summary of Defaulted Sources and Emissions for Lead, Chromium, and Cadmium.

Percent of Sources Percent of Emissions

State Metal

County 

Defaulted

Zip Code 

Defaulted

Not 

Defaulted, 

Outside 

County

Not 

Defaulted, 

Inside 

County

County 

Defaulted

Zip Code 

Defaulted

Not 

Defaulted, 

Outside 

County

Not 

Defaulted, 

Inside 

County

All All 10.7 0.6 4.4 84.4 10.5 0.2 3.7 85.6

All Lead 10.8 0.5 4.1 84.6 12.8 0.3 3.8 83.1

All Cadmium 10.3 0.4 5.2 84.2 25.0 0.2 6.4 68.4

All Chromium 10.9 0.5 4.7 83.8 3.5 0.1 3.0 93.4

Alabama All 23.3 0.0 3.8 72.9 58.6 0.0 4.1 37.3

Arizona All 1.3 0.0 9.0 89.7 2.0 0.0 29.3 68.7

Arkansas All 9.7 0.0 2.9 87.4 10.5 0.0 5.3 84.2

California All 19.1 0.1 0.7 80.1 9.6 0.0 0.3 90.1

Colorado All 6.4 0.0 0.0 93.6 1.1 0.0 0.0 98.9

Connecticut All 37.5 0.0 4.2 58.3 26.4 0.0 0.3 73.3

Delaware All 0.0 0.0 0.0 100.0 0.0 0.0 0.0 100.0

District of 

Columbia All 0.0 0.0 0.0 100.0 0.0 0.0 0.0 100.0

Florida All 11.3 0.0 4.0 84.8 6.7 0.0 0.6 92.8

Georgia All 17.6 0.0 1.6 80.8 46.0 0.0 1.4 52.7

Idaho All 13.0 0.0 4.3 82.6 0.9 0.0 0.0 99.1

Illinois All 1.7 0.0 0.4 97.8 0.4 0.0 0.0 99.6

Indiana All 14.3 0.3 40.3 45.0 21.2 0.2 32.4 46.2

Iowa All 3.4 1.7 0.0 94.9 0.1 1.3 0.0 98.6

Kansas All 2.0 0.0 0.0 98.0 0.0 0.0 0.0 100.0

Kentucky All 6.6 0.0 0.0 93.4 7.0 0.0 0.0 93.0

Louisiana All 22.5 0.0 2.2 75.3 25.8 0.0 0.1 74.0

Maine All 5.8 1.9 5.8 86.5 0.5 0.2 0.2 99.1

Maryland All 19.5 0.8 2.3 77.4 6.1 0.0 4.1 89.8

Massachusetts All 3.2 0.0 0.0 96.8 0.3 0.0 0.0 99.7

Michigan All 12.7 1.6 0.4 85.3 16.7 0.0 0.1 83.2

Minnesota All 22.7 0.0 0.0 77.3 35.7 0.0 0.0 64.3

Mississippi All 16.3 0.0 2.3 81.4 40.8 0.0 1.0 58.2

Missouri All 14.2 0.0 3.1 82.7 4.5 0.0 0.4 95.1

Montana All 13.6 0.0 0.0 86.4 8.9 0.0 0.0 91.1

Nebraska All 34.9 0.0 0.0 65.1 3.7 0.0 0.0 96.3

Nevada All 12.5 0.0 0.0 87.5 0.3 0.0 0.0 99.7

New Hampshire All 0.0 0.0 0.0 100.0 0.0 0.0 0.0 100.0

New Jersey All 1.5 0.0 3.1 95.4 0.0 0.0 0.1 99.9
62

Table 4-6. Summary of Defaulted Sources and Emissions for Lead, Chromium, and Cadmium.

Percent of Sources Percent of Emissions

State Metal

County 

Defaulted

Zip Code 

Defaulted

Not 

Defaulted, 

Outside 

County

Not 

Defaulted, 

Inside 

County

County 

Defaulted

Zip Code 

Defaulted

Not 

Defaulted, 

Outside 

County

Not 

Defaulted, 

Inside 

County

New Mexico All 0.0 0.0 0.0 100.0 0.0 0.0 0.0 100.0

New York All 1.2 0.1 0.7 97.9 13.7 0.0 0.0 86.3

North Carolina All 2.6 0.1 1.9 95.3 1.5 0.0 0.1 98.4

North Dakota All 16.7 0.0 0.0 83.3 2.0 0.0 0.0 98.0

Ohio All 3.1 0.4 1.3 95.1 21.3 0.1 0.8 77.7

Oklahoma All 8.3 0.0 5.6 86.1 15.1 0.0 2.9 82.0

Oregon All 48.8 3.1 1.6 46.5 28.3 0.7 0.1 70.9

Pennsylvania All 10.2 0.4 1.3 88.1 1.9 0.0 5.1 93.0

Puerto Rico All 20.0 0.0 80.0 0.0 2.8 0.0 97.2 0.0

Rhode Island All 36.4 59.1 0.0 4.5 10.7 88.0 0.0 1.3

South Carolina All 4.8 0.0 5.7 89.6 34.8 0.0 0.1 65.1

South Dakota All 12.5 0.0 0.0 87.5 5.5 0.0 0.0 94.5

Tennessee All 61.1 16.6 0.6 21.7 16.4 6.2 0.0 77.4

Texas All 10.2 0.1 1.6 88.1 24.9 0.1 0.8 74.3

Utah All 12.5 0.0 0.0 87.5 0.8 0.0 0.0 99.2

Vermont All 10.3 0.0 6.9 82.8 1.2 0.0 10.6 88.2

Virginia All 17.7 0.0 1.0 81.3 40.8 0.0 0.6 58.7

Washington All 24.1 0.0 1.7 74.1 39.2 0.0 0.5 60.3

West Virginia All 1.9 0.0 0.0 98.1 0.8 0.0 0.0 99.2

Wisconsin All 5.4 1.5 1.5 91.7 0.5 0.0 0.4 99.1

Wyoming All 0.0 0.0 0.0 100.0 0.0 0.0 0.0 100.0

Nationwide, for primary emissions of pollutants that were modeled (i.e., no reactive 

precursors to secondary formation), the following statistics apply to default locations:

Out of the 48,657 sites (there can be multiple sites at a single facility) from the point 

source inventory simulated via the ASPEN model (i.e., those that contained the 33 urban 

air toxics and were not dropped due to lack of information on locations or zero 

emissions), 4212 were defaulted by county-level defaults (census tract centroids) and 460 

were defaulted by zip-code (zip code centroids). There were also 43 sites that were 

dropped from the EMS-HAP input prior to modeling due to the fact that their locations 

were identified as “portable” and therefore could not be defaulted.

In addition to point sources that required exact location coordinates, all county-level 

emissions needed to be spatially allocated to the census-tract level prior to ASPEN 

modeling. Each source category’s emissions were placed based on an appropriate 

surrogate such as population or land use. In some cases, the initial emission estimate 

may have been made at the national or state level and allocated to counties by the same 
63

surrogate. This type of spatial placement may lead to emissions being too dispersed or 

too concentrated at a given location. For example, landfill emissions were first placed in 

the appropriate counties based on information collected via MACT standard 

development. Subsequently, within EMS-HAP, these emissions were further allocated to 

census tracts based on reciprocal population density. This assumption is based on the 

concept that landfills are placed away from populated areas. In reality, landfills are 

located at specific geographic coordinates rather than spread throughout census tracts 

within a county. Since these exact location data were not available, the surrogate 

approach was used as a best approximation. 

The spatial allocation of county emissions, which takes place within EMS-HAP is 

described above in 3.2.1.1. This necessary approximation of emission location adds

uncertainty to the overall analysis although it represents the best method available. As 

shown in Table 3-3, some of the allocation surrogate information used in this process is 

very old (e.g., 1970's). 

Uncertainty due to spatial allocation schemes is also particularly pronounced for mobile 

source categories, especially within the nonroad sector where, in order to simplify 

computation, the county-level emissions from almost 100 source categories (e.g., 

recreational marine vessels, lawn mowers, construction equipment, etc.) were 

consolidated into three source category groups (2-stroke gas, 4-stroke gas, and diesel 

engines) prior to spatial allocation. 

The spatial allocation methods used in this study were revised for some source categories 

as ASPEN concentration results were reviewed in order to improve the overall analysis.

4.2.2.4 Uncertainty Due to Stack Parameter Defaults

A facility in the NTI can contain many emissions release points. Each release point 

requires several process parameters in order to characterize it sufficiently for dispersion 

modeling. When these parameters were missing from the original inventory data or were 

out of reasonable range, they were replaced with defaults either within the NTI or EMSHAP. Table 4-7 describes the number of vertical emission release points that had 

defaulted process parameters. Of the 97,365 unique vertical stacks, 63,292 contained at 

least one defaulted stack parameter. 

Table 4-7. Stack Parameter Default Statistics.

Parameter Number of Defaults Percent of Total 

Vertical Stacks

in NTI in EMS-HAP

NTI Stack Height 28,550 16,048 45.8

NTI Stack Diameter 32,176 20,722 54.3

NTI Exit Velocity 40,361 20,816 62.8

NTI Exit Temperature 28,890 12,982 43.0
64

4.2.2.5 Variations in Reported Emissions

Since no standardized requirements for collecting or submitting air toxics inventories to 

EPA currently exist to make these databases consistent, and because resources among the 

agencies are variable, the quality, coverage and number of air toxics in these inventories 

vary significantly. For instance, the emissions of polychlorinated biphenyls (PCB) 

appear in only 121 counties in the NTI out of a national total of 3,145 counties. It is 

unclear whether the reported emissions are correct and additional emissions are missing 

from the other counties, or if PCB, a banned substance, is no longer emitted from 

anthropogenic sources.

4.2.2.6 Uncertainties Due to Pollutant Groupings

As discussed previously, many of the air toxics from the NTI were reported either as 

groups of compounds (e.g., lead compounds) or individual chemical species (e.g., lead 

oxide). For subsequent modeling, these pollutants had to be grouped together in order to 

create consistency across the model’s geographic domain. This creates uncertainties 

because the groupings made for the national-scale assessment do not necessarily account 

for the difference in toxicological characteristics among individual species in the group. 

In addition, numerous assumptions were made to establish the pollutant groups and the 

resulting pollutant group characteristics (e.g., deposition). 

Another related uncertainty in grouping metal species stems from the adjustments made 

to the mass of metal species emissions to allow for modeling only the metal portion of the 

compound. First there is an assumption that when emissions for a particular species are 

reported, they are for the entire compound and not just the metal portion. Second, for 

metals reported as broad compound groups or subgroups (e.g., lead & compounds, 

alkylated lead), no adjustments were made since the particular species was not known.

4.2.2.7 Particle Size and Reactivity Assignments 

All of the emissions input for ASPEN modeling must be identified by particle size 

fractions (coarse versus fine) and reactivity. This tells the dispersion model how to 

account for deposition and subsequent chemical reactions. None of the emission 

inventories used in this assessment contained this information, and so it had to be 

assigned by EMS-HAP. Since ASPEN uses different deposition rates for fine and coarse 

particulates, each metal particulate must be classified by its particle size. Except for 

mercury, the percentage of a species assigned to coarse/fine particulate (the coarse/fine 

split) was estimated as the percentage of coarse/fine particulate obtained from the 

inventory used for the Cumulative Exposure Project. These percentages vary among the 

onroad, nonroad and stationary source categories, but do not vary within those broad 

groups. For example, the coarse/fine split for chromium compounds emitted from a 

chromium electroplating operation is the same as for chromium emitted from an 

incinerator. For mercury, it was assumed that all mercury species reported are gaseous 

except for mercury emissions from mobile sources and mercuric chloride. Mercuric 

chloride was assumed to be 100 percent fine particulate. These assignments add to the 

overall uncertainty.
65

4.3 Environmental Fate and Transport Characterization

4.3.1 Summary of ASPEN Modeling Results 

The ASPEN model was used to estimate ambient concentrations at each census tract in 

the contiguous United states, plus Puerto Rico and the Virgin Islands. Based on these 

estimates, annual average modeled concentrations (in μg/m3) were calculated for each 

state and territory, and the statistical distributions described by the 25th, median, and 75th 

percentiles across all census tracts were calculated and displayed graphically for each of 

the 33 air toxics; national averages were similarly calculated. These “statewide estimate” 

charts can be viewed at www.epa.gov/ttn/uatw/nata/chartconc.html. A value for 

background concentration, except for diesel PM, was also displayed. In addition, the 

percent contribution to the statewide annual average ambient concentration estimates 

were calculated and displayed. These “percent contribution” charts can be viewed at 

www.epa.gov/ttn/uatw/nata/chartconc.html. Figures 4-7 and 4-8 provide an example of 

each type of display for benzene. As can be seen in Figure 4-7, benzene is fairly 

ubiquitous, is prevalent in all states, and as expected, the median value is generally higher 

in the more populated or industrial states. As can be seen in Figure 4-8, the percent 

contribution for benzene is higher for mobile sources (both onroad and nonroad) than 

stationary sources. The contribution from background is also appreciable.

When examining the source contribution plots for all of the modeled air toxics (at 

http://www.epa.gov/ttn/uatw/nata/chartconc.html), it can be seen that no single source 

sector is the main contributor to the estimated concentrations. Table 4-8 summarizes the 

dominant contributor for each of the modeled air toxics. The results show that, on a 

national level, about half of the pollutants have “area and other sources” as the dominant 

contributing source sector. As seen in the table, the dominant source sector may be 

different when the source contributions are examined at the state level. For example, 

except for coke oven emissions, all of the pollutants in which major is the dominant 

source sector at the National level show the area and other category to be dominant for a 

number of states. For those pollutants where background is dominant, the next highest 

contributing source sector is the “area and other” sector. 

To further explore the geographic variability of the source sector on the ambient 

concentrations, we examined the dominant source sector for the counties with the 10 

highest median concentrations. Table 4-9 shows the number (out of 10) of these 

concentrations dominated by the various source types. The asterisked pollutants in the 

table are those in which the dominant source type differed from the dominant types at the 

national level and state levels as shown in Table 4-8. Tables 4-8 and 4-9 show that most 

pollutants vary geographically in their dominant source sector.
66

Table 4-8. Pollutants grouped by the dominant source

 sector affecting their national average concentrations.

Area and Other Major

Mobile

(onroad and nonroad 

combined) Background

ArsenicM AcrylonitrileA Acetaldehyde Carbon tetrachloridea

BerylliumM Coke oven emissions AcroleinA Chloroforma

CadmiumM HydrazineA Benzene Ethylene dibromidea

ChromiumMPropylene dichlorideA1,3-Butadiene Ethylene dichloridea

1,3-dichloropropene QuinolineA Formaldehyde Hexachlorobenzenea

Ethylene oxideM Mercurya

ManganeseMPolychlorinated biphenyls 

(PCB) a

Methylene chloride TrichloroethyleneA, a

Nickel

Perchloroethylene

POMM

1,1,2,2-

Tetrachloroethane 

Vinyl chlorideM

M

 = for several states, the dominant sector is major; 

A= for several states, the dominant sector is area and other

A= next highest sector after background is area and other

Table 4-9. Number of Counties of Each Dominant Source Sector

 for the 10 Highest County Median Concentrations.

Pollutant Major Area and 

Other Mobile Background

Acetaldehyde 10

Acrolein 2 3 5

Acrylonitrile 10

Arsenic Compounds 3 7

Benzene 10

Beryllium Compounds 7 3

1,3-Butadiene 7 3

Cadmium Compounds 3 7

Carbon Tetrachloride 10

Chloroform 8 2

Chromium Compounds 7 3

Coke Oven Emissions 10

1,3-Dichloropropene 10

Ethylene Dibromide 10

Ethylene Dichloride 10

Ethylene Oxide 10
67

Pollutant Major Area and 

Other Mobile Background

Formaldehyde 6 4

Hexachlorobenzene 3 7

Hydrazine 7 3

Manganese Compounds 7 3

Mercury Compounds 6 4

Methylene Chloride 8 2

Nickel Compounds 6 4

Perchloroethylene 1 9

Polychlorinated Biphenyls 1 9

Polycyclic Organic Matter 10

Propylene Dichloride 10

Quinoline 7 3

1,1,2,2-Tetrachloroethane 1 9

Trichloroethylene 2 8

Vinyl Chloride 9 1

A comparison of estimated annual average concentration distribution for 10 selected 

pollutants is shown in Figure 4-9. These pollutants were selected to include air toxics 

that are relatively inert (e.g., benzene), air toxics that are subject to chemical 

transformation in the atmosphere (e.g., formaldehyde), and metal particles that are 

subject to deposition. As can be seen in this figure, the distribution of the annual average 

concentrations among pollutants spans several orders of magnitude. Benzene and 

formaldehyde have much higher annual average concentrations than metals. One to two 

orders of magnitude variability exists for each air toxic.

Figure 4-10 provides a comparison of estimated annual average concentration for the 

same 10 selected pollutants with each census tract categorized as urban or rural. Each 

census tract is designated as either urban or rural as part of the dispersion modeling 

process, since dispersion parameters differ for these two types of locations. In general, 

census tracts with population density greater than 750 people/km2 are designated as 

urban, while other census tracts are designated as rural [15]. This results in 

approximately an even split of census tracts into the urban and rural designations. As can 

be seen in this figure, the annual average concentrations are generally higher in the urban 

census tracts than rural tracts; this result is likely due both to higher emission densities 

for many pollutants in urban areas and to a closer proximity of many emission sources to 

the modeled receptors (i.e., census tract centroids).

Figure 4-11 shows, for each State, the relative contributions of pollutants, broken out into 

three categories: mobile source dominated pollutants, metals, and stationary source nonmetals. In each bar of this figure, 49 boxes representing 48 states and the District of 

Columbia are displayed. Concentrations are scaled by a maximum value and sorted from 

minimum to maximum. Thus, each box represents the magnitude of relative average 

concentration for the state. As can be seen from this figure, some pollutants (e.g., 
68

propylene dichloride) have an impact in only one or two states while other pollutants 

(e.g., benzene) appear more uniformly distributed across the country.

4.3.2 Discussion of Results

From an examination of the ASPEN dispersion modeling results, the following general 

observations can be made:

• Concentration estimates are a complex function of a number of factors, including 

emissions density (number of sources in a particular area), meteorology and 

source characteristics, rather than just total emissions. 

• Of the four main source sectors (area and other, major, onroad, nonroad), no one 

type is a dominant contributor to the estimated concentrations. On a national 

level, about half of the pollutants have "area and other sources" as the dominant 

contributing source sector. 

• There is considerable variability among the national, state and the county levels in 

terms of contributions of certain pollutants by source type.

• Both emissions and estimated concentrations are generally higher in urban than in 

rural areas. 

• Because different types of sources contribute to emissions in different areas of the 

country, the highest ambient average concentrations of the individual pollutants 

occur in different states (i.e., no one state has the highest concentrations of all the 

pollutants). 

• Some pollutants (e.g., benzene) are more evenly distributed around the country 

while other pollutants (e.g., vinyl chloride) are more related to isolated areas of 

industrial activity. 

• Most of the stationary non-metal pollutants have higher concentrations in only a 

few states whereas the concentrations from metals and mobile source-dominated 

pollutants are more evenly distributed across the country. Among the 20 modeled 

non-metal pollutants, twelve have impacts on one to three states, and only four are 

widely distributed across states. For metals, two pollutants out of eight have a 

major impact on one or two states, while the other six pollutants have an impact in 

more than ten states. Mobile source-dominated pollutants are generally more 

uniformly distributed across the country.

• Background concentrations are an essential part of the total air quality 

concentrations and include pollutant concentrations due to natural sources, 

sources not in the emissions inventory, and long-range transport. Based on the 

CEP study, non-zero estimates of background concentrations for only 13 

pollutants are available. For seven of these (PCBs, ethylene dibromide, carbon 
69

tetrachloride, hexachlorobenzene, ethylene dichloride, chloroform, and mercury), 

the background dominates the total estimated average concentration. 

4.3.3 Comparison of ASPEN Modeling System to Monitoring Data

The ASPEN system as designed and applied in the initial national scale assessment 

provides broad-scale air quality impacts (census tract resolution). The assessment 

system is not designed to capture more localized areas of high concentration or "hot-spot" 

impacts. The system evaluation presented below (model-to-monitoring comparison 

study) was conducted to evaluate the ASPEN system's ability to predict an air quality 

concentration at a given point in space (i.e., an air quality 

monitor). No detailed evaluation of the ASPEN system's 

inability to predict "hot-spot" impacts was conducted. It is 

anticipated that, following completion of more detailed local 

scale assessments that are to be conducted as part of future 

NATA activities, such an evaluation can be made. However, 

EPA conducted a few crude comparisons to assess the 

potential for the ASPEN system to underestimate "hot spots", 

as predicted by local-scale modeling assessments. One such 

comparison, for a major source in an urban environment 

found local fence line impacts approximately 30 times higher then those predicted at the 

census tract centroid by ASPEN in the initial national scale assessment. A similar 

comparison in a more rural setting found maximum predicted fence line impacts greater 

than 2 orders of magnitude above those predicted by ASPEN at the census tract centroid. 

While these two comparisons are not a complete evaluation of expected differences 

between the ASPEN initial national scale assessment predictions and those from local 

scale "hot spot" assessments, they are simple examples of why other scale assessments 

must be considered to fully understand the air toxic issues in a particular location. 

The remainder of this section compares the modeled air quality estimates with currently 

available, but geographically limited, ambient air monitoring data. A representative 

subset of seven air toxics (benzene, perchloroethylene, formaldehyde, acetaldehyde, 

cadmium, chromium, and lead) was selected for this evaluation. These pollutants were 

selected because they represent impacts by different combinations of mobile, countylevel and point sources; include reactive and non-reactive compounds; and include those 

with both primary emissions and secondary formation in the atmosphere. They also 

include those air toxics with the largest number of monitoring sites. 

For each monitor-pollutant combination, a comparison was made between the monitored 

concentration and the concentration estimated by the ASPEN model at the monitor 

location to get point-to-point comparisons. Steps in this comparison, for each pollutant at 

each monitor, were as follows: 

The ASPEN model was used to estimate concentrations at the exact locations of the 

monitors. Using the monitor latitude and longitude coordinates, the monitor location is 

assumed to represent a census tract with 100 m radius. Concentrations are interpolated to 

The initial National-Scale 

Assessment does not capture 

local-scale or “hot spot” 

impacts. Such impacts could 

be orders of magnitude 

greater than those predicted 

at the census tract resolution 

provided by the ASPEN 

modeling system.
70

this “census tract” using the log-linear interpolation scheme used in ASPEN to get the 

annual average estimate.

For monitored concentrations, an annual average estimate was calculated by averaging all 

of the measured values obtained by a monitor across the course of a year. In calculating 

the average monitored concentration, any individual samples where the concentration 

was found to be below the detection limit were assigned a value of one-half the detection 

limit. Further details are available in the full report (Appendix J, Section II.b.ii.)

Model-monitor ratios are calculated by dividing the modeled concentration by the 

monitored concentration. A ratio of 1.0 means that the modeled concentration equals the 

monitored concentration, while a ratio less than 1.0 means that the model underestimates 

the monitored concentration, and a ratio greater than 1.0 means that the model 

overestimates the monitored concentration. A detailed explanation of the results are 

presented in the full report (Appendix J), Section V.

In general, the modeled estimates for most of the pollutants examined are typically lower 

than the measured ambient annual average concentrations when evaluated at the exact 

location of the monitors. However, when the maximum modeled estimate for distances 

up to 10-20 km from the monitoring location are compared to the measured 

concentrations, the modeled estimates are closer to monitored concentrations. This result 

can be attributed, in part, to spatial uncertainty of the underlying emission and 

meteorological data, and the tendency of current air toxics monitoring networks to 

measure the higher, if not highest, local air pollution impact areas. It also shows that the 

model estimates are more uncertain at the census-tract level but are more reliable for 

larger geographic scales, like county or state. Nevertheless, there are many locations for 

several of the studied air toxics (including the aldehydes and metals) for which the model 

estimates are still significantly lower than the measured concentrations even at distances 

up to 50 km. For these instances, the difference between modeled and monitored 

concentrations might be attributed to underestimated or missing emissions data in the 

model as well as uncertainty in chemical transformation for the aldehydes. The 

limitations of modeled concentrations resulting from isolated point sources using a 

geographically sparse ASPEN receptor network in rural areas may be another 

contributing factor. Yet another reason for the discrepancies may be attributed to 

monitors being sited to find peak concentrations. Often, the ambient concentration 

decays quickly around the peak area. Even under the scenario of a “perfect” model and 

“perfect” monitors, if the monitor is sited exactly at the peak and the emissions (or 

meteorological inputs) inputs are even slightly inaccurate, the model will tend to 

underestimate results. This would be especially likely for pollutants dominated by point 

sources with elevated releases, because any errors in release height, exit velocity, and/or 

emissions location will likely cause the model to find a peak concentration different from 

the true peak level. A more detailed discussion of the uncertainties of ASPEN and the 

monitoring data can be found in Appendix J of this document. A description/basis of the 

methodology used for the model-to-monitor comparison is given in Appendix I, and a 

detailed discussion of the model to monitor comparison results is provided in Appendix J.
71

A summary of the results from the model-to-model comparison are presented below. 

Table 4-10 summarizes the comparisons on a point-to-point basis. The best agreement is 

observed for benzene. The results are within a factor of two for 89 percent of the cases. 

The median ratio of model to monitor comparisons is 0.92. The lack of agreement for the 

other air toxics on a point to point basis can also be seen in Table 4-8, which shows the 

median ratios varying between 0.65 for formaldehyde to 0.17 for lead. The number of 

points with agreement within a factor of 2 (or within 30 percent) are also correspondingly 

lower. These results can also be observed using the ratio box plot graphs, shown in 

Figure 4.12.

Table 4-10. Comparison of the Measurement Data to Modeled Concentration.

Pollutant

Number 

of Sites

Median of 

Ratios*

Percentage 

within factor 

of two**

Percentage 

within factor 

of 30%***

Percentage 

Underestimated

Benzene 87 0.92 89 59 59

Perchloroethylene 44 0.52 55 32 86

Formaldehyde 32 0.65 53 28 88

Acetaldehyde 32 0.60 59 22 91

Lead 242 0.17 18 10 91

Cadmium 20 0.18 15 5 85

Chromium 36 0.15 28 19 83

* Ratio represents (Ambient Measurement Concentration/Modeled Concentration).

** This represents the percentage of sites for which the model estimate is somewhere between half and 

double the monitor average.

*** This represents the percentage of sites for which the model/monitor ratio is between 0.7 and 1.3.

While all the air toxics except benzene show relatively poor agreement on a point-topoint basis (with the modeled estimates being systematically lower than the monitor 

averages), they compare more favorably when the maximum model-estimated 

concentration is examined within 30 km of the monitoring site as shown below in Table 

4-11. 

This technique is referred to as “MAXTOMON” and compares the maximum model 

estimate within x kilometers of the monitor TO the monitor average. All model estimates 

are considered (both estimates at monitor sites as well as the estimates at census tract 

centroids) in computing the maximum values. This is an example of a point-to-range 

tool. This tool is used here to test whether the frequent underestimation by the model at 

monitoring sites was due to location uncertainties or due to systematic underestimation. 

The reader is referred to Appendix J for further details on this technique.
72

Table 4-11. Maximum Modeled Concentration Compared to Monitored Value.

Percent Missing Low at a Radius of:

Pollutant

Number of 

Sites

0 km (Exact 

Monitor Site) 10 km 20 km 30 km

Benzene 87 59 25 20 11

Perchloroethylene 44 86 43 23 9

Formaldehyde 32 88 56 31 31

Acetaldehyde 32 91 56 38 34

Lead 242 91 65 51 40

Cadmium 20 85 60 35 25

Chromium 36 83 39 28 25

The improved comparisons, using the MAXTOMON technique, shown in Table 4-12 can 

be attributed to three reasons:

• Many emission sources are not precisely located. The EMS-HAP model defaults 

locations when they are not provided or when total emissions exist for the county. 

Note, however, that this could contribute to either under or over predicting and is 

a “non-bias” source of uncertainty.

• Many of the monitors were likely sited to find peak concentrations. For the point 

source situations with elevated emission releases, the monitors frequently 

represent hot spot locations where the ambient concentration falls off quickly 

around the peak concentration area.

• Since the emissions inventory is likely missing sources, the modeling assessment 

is under predicting air concentrations.

In the following sections, the model-to-monitor comparisons are discussed for benzene, 

other gases, and for the metals.

Benzene

The relationship between model estimates and monitored values for benzene can be 

described by the scatter plot shown in Figure 4-12. Figure 4-12 shows the point to point 

comparison of modeled and monitored annual average concentrations. As also shown in 

Figure 4-13 and Table 4-10, most of the points in the scatter plot fall between the 2:1 and 

1:2 lines, showing good agreement between model predictions and monitor 

measurements.
73

This result is not surprising given the availability of good monitoring and emissions data 

for this ubiquitous pollutant.

Perchloroethylene, Formaldehyde, and Acetaldehyde

The model-to-monitor relationship on a point-to-point basis is similar for the other three 

gases investigated (perchloroethylene, formaldehyde, and acetaldehyde). In the ratio box 

plots in Figure 4-13, however, one can see that the model’s estimates tend to be lower 

than the monitor averages. The typical values, however, agree well with the median 

ratios all within a factor of 2. Nevertheless, a large percentage of the modeled estimates 

are less than the monitored concentration for these gases on a point-to-point basis (see 

Table 4-10 and Figure 4-13). This can be attributed, in part, to spatial uncertainty in the 

underlying emissions for these pollutants; missing source emissions data; and/or 

underestimated emissions. 

To examine spatial uncertainty in the modeling system for the gases, the monitored 

concentration is compared to the maximum modeled estimate in its vicinity (the 

“MAXTOMON” technique described above). The results for these gases are presented in 

Table 4-11. Table 4-11 shows nearby modeled concentrations which are greater than the 

measured average concentration for many of the monitors.

This is especially true for perchloroethylene. In close vicinity (10 km or less) of most 

monitors, higher modeled concentrations are observed. This result for perchloroethylene 

suggests that uncertainties in the magnitude and location of the nearby area sources may 

at least be partly responsible for the underestimation on a point-to-point basis. In other 

words, the model may be systematically underestimating ambient concentrations or it 

may just be finding the peak concentration in the wrong place.

For the two aldehydes, many monitors also have nearby modeled concentrations which 

are greater than their measured values. However, a large fraction of the aldehyde 

monitors cannot be associated with larger modeled values, even within 50 km. This 

suggests systematic underestimation by the modeling system for the aldehydes, at least 

for some of the areas. This may be attributed, in part, to the nature and treatment of these 

air toxics. It may also be due to underestimated emissions. The two aldehydes are 

mobile-source dominated, but a large fraction of their ambient concentrations are 

secondarily formed. The chemical reactions resulting in their formation are simulated in 

ASPEN. This adds an additional source of uncertainty to the modeling system and 

distinguishes the aldehydes from the other air toxics in this comparison.

Metals

For the metals, the monitored concentrations are typically much higher than the modeled 

concentrations when compared at the same location. The difference is most dramatic for 

source-oriented monitors. Based on the median ratio, the source-oriented lead monitors 

are typically underestimated by a factor of 7.5, and the others are underestimated by a 

factor of 4.9. Only 17 percent of the source-oriented monitors and 18 percent of the other 

monitors are estimated within a factor of two at the exact location of the model estimate.
74

A combination of several factors may be responsible for these discrepancies:

• Missing emissions from the inventory (e.g., missing point sources, lack of 

treatment for possible re-entrainment effects)

• Spatial uncertainty in emission locations due to defaulted locations for point 

sources.

• Spatial uncertainty of nearby impacts from elevated point sources (i.e., narrow 

plume impact) together with a small number of receptors.

• High coarse particle deposition velocities.

The effects of missing source location data and defaulted emission locations were 

explored. This analysis focused on 30 of the 42 monitors which were underestimated by 

a factor of 10 or greater. This analysis demonstrated that, for the included monitoring 

locations, several nearby lead sources are missing location information (have uncertain 

locations). The effects for spatial uncertainty of lead emissions were also examined using 

the MAXTOMON technique. Because this analysis did not reveal higher predicted 

concentrations within a relatively large region surrounding the monitor, it also suggests 

that emission sources are likely missing or underestimated. However, this analysis may 

be less definitive for point source pollutants in rural areas. Such monitors tend to 

represent small (less than 0.5 km) areas. The surrounding regions also have few census 

tracts and the small number of ASPEN receptors limit the opportunity to find other peak 

concentrations. Regarding deposition velocities, it was estimated that ASPEN has a bias 

to under predict average lead concentrations by 20-30 percent, because of high coarse 

particle deposition velocities.

It appears that the current modeling system is underestimating lead for a large percentage 

of the monitors used in this evaluation. It should be noted that the monitors do not 

represent a random sampling of all census tracts. To attain better modeled results in the 

vicinity of isolated point sources, emissions and source locations should be more 

accurately characterized. In addition, a denser receptor network may be required.

The results for cadmium and chromium also show many locations with low modeled 

concentrations. However, the amount of disagreement between the modeled estimates 

and monitored concentrations appear to be dependent on the different source regions 

represented. This suggests possible differences in the state inventories; however, 

generalizations are difficult because of the limited number of monitoring locations 

included.

4.3.4 Discussion of ASPEN Dispersion Modeling Uncertainties

In this discussion we will summarize the studies and conclusions reached regarding 

uncertainties in the ASPEN modeling results. The main sources for uncertainty in the 

ASPEN dispersion modeling results are 1) emission characterization uncertainties (e.g., 

specification of source location, emission rates and release characteristics); 2) 
75

meteorological characterization uncertainties (e.g., representativeness); 3) model 

formulation and methodology uncertainties (e.g., characterization of dispersion, plume 

rise, deposition, and uncertainties associated with using a “net” of receptors for 

characterizing concentration impacts at particular locations); and 4) uncertainty of 

background concentrations. 

4.3.4.1 Emission Characterization Uncertainties 

When performing comparisons between modeled and monitored concentration values, it 

is of key importance to properly locate sources relative to monitoring locations and 

properly characterize whether the emissions are low-level (i.e., those closer to the ground 

such as fugitive emissions) or elevated stack emissions. Inspection of the Gaussian 

plume formulations reveals that the simulated impact at the surface generally decreases as 

1/(distance)á, where á is on the order of 1 to 2. The low-level emissions affect a receptor 

by a factor of 5 to 8 times more than if the emissions were from an elevated stack (as the 

elevated emissions will be diluted during transport and dispersion to the surface) [31, 32]. 

The National Toxics Inventory (NTI) provides the “raw” emissions data that are 

processed and made "model-ready" for ASPEN by the Emissions Modeling System for 

Hazardous Air Pollutants (EMS-HAP). Annual emission rates are provided for point 

sources. Emissions not allocated to point sources are in the form of “non-point” countylevel summaries. The ASPEN model requires higher resolution both temporally and 

spatially. Temporally, EMS-HAP allocates the annual total emissions equally into days 

and then allocates daily emissions into eight three-hour periods using temporal profiles 

based on the type of emission source. The county-wide area and mobile emissions are 

allocated by EMS-HAP to a specific census tract centroid within the county, based on 

surrogates such as population density, land use, roadway miles, etc., depending on the 

source category. For point source emissions, EMS-HAP processing is required whenever 

latitude/longitude coordinates or source emission characteristics (e.g., stack parameters) 

are missing. When the location coordinates are missing or suspect, EMS-HAP assigns a 

location using the zip code’s centroid (or if this is missing to a census tract centroid 

chosen at random within the source’s county). 

To assess the extent of point source emissions that were assigned coordinates by EMSHAP, the point source inventory for three metals (lead, cadmium, chromium) was 

investigated. Table 4-12 shows the percentage of sources and the corresponding 

percentage of mass for which point source coordinates were deemed suspect. It would 

appear that, although the percentage of sources with suspect locations was approximately 

the same (about 15 percent) for all three metals across the United States, the amount of 

mass differed greatly, ranging from 7 to 32 percent. 

In part to check the accuracy of the stack release heights in the inventory, members of the 

EPA project team visited a lead smelter in Herculaneum, Missouri. According to data 

files available to the modeling team (Richard Daye, EPA Region 7, personnel 

communication), 89.91 tons per year of lead are emitted from the 550-foot stack in the 

center of the facility, and 7.7 tons per year are released as "fugitive" emissions, that is, 

escaping from the facility through open doors, windows, etc. However, the emissions for 
76

an ongoing 2-month study at the facility suggest that the “fugitive” emissions are of order 

50 tons rather than 7.7 tons. EPA cannot generalize to all other sources from one site 

visit, but this does reveal the possibility of the types of emission characterization 

uncertainties that can occur. All other factors being equal, an increase from 7.7 to 50 

tons in low-level emissions would likely increase the modeled annual average 

concentration at the monitoring site by a factor of 3. 

Table 4-12. Source location uncertainties for point source inventory for three metals.

Lead Cadmium Chromium

Percent of sources with suspect 

coordinates 15.4 15.8 16.2

Percent of mass for which sources 

were assigned coordinates 16.9 31.6 6.6

EPA concluded that since surrogates (such as population) are used to allocate countywide emissions to specific locations (rather than placing emissions at their true locations), 

and since almost 15 percent of the point source inventory have uncertain coordinates, 

EPA can anticipate that the ASPEN modeling system will likely not provide good 

correlation with observed concentration values at specific locations. If the modeling 

results are in accord with observations, it will be for those instances where point source 

emissions (location and release height) are well characterized, and where use of a countylevel inventoried source category for the characterization of the other emissions is 

appropriate (i.e., the emissions are ubiquitous with respect to the monitoring location).

4.3.4.2 Meteorological Characterization Uncertainties 

Meteorological data are a critical input for ISC-based Gaussian air quality models like 

ASPEN. EPA analyzed the effects of using 1990 Hourly United States Weather 

Observations (HUSWO) and 1996 International Surface Weather Observations (INSWO) 

databases on the annual average concentrations predictions. The 1990 and 1996 

meteorological inputs differed in three respects: the methods in which the data were 

obtained, the number of stations employed, and differences in meteorological conditions 

between 1990 and 1996. Because the same emissions input and fixed receptor locations 

were used for both model simulations, differences in annual average concentration 

estimates between the model simulations are entirely attributable to the differences 

between the meteorological inputs for these two years.

The number of surface stations in most states increased in going from the 1990 HUSWO 

data set to the 1996 INSWO data set. In the 1990 model simulation, the mean source-tometeorological station separation distance was approximately 70 km; this mean 

separation distance was reduced to under 50 km in the 1996 simulation by the inclusion 

of more meteorological observation locations. The increased density of surface stations 

in 1996 is anticipated to result in a better representation of the overall climate at most 

emission sources. Due to differences in reported variables, number and location of valid 
77

stations, and meteorology itself, statewide annual-averaged concentration estimates were 

found to vary from 16.9 percent lower to 84.4 percent higher in 1996, as compared to 

1990. Greater variability in modeled concentrations was found when comparisons were 

made at the receptor level demonstrating the importance of meteorology in dispersionbased models. 

4.3.4.3 Model Formulation and Methodology Uncertainties 

The ASPEN air quality model employs a Gaussian plume model for the characterization 

of the transport and dispersion. The particular algorithms employed within the model 

were extracted from version 2 of the Industrial Source Complex Long-Term model 

(ISCLT2). ISCLT2 has a history of development that dates back to the 1960's, so there is 

a history of usage of this model that allows EPA to anticipate the uncertainty in its 

estimates (given well-characterized emissions). In general, field study comparisons have 

shown that approximately 90 percent of the estimated annual averages are within a factor 

of two of those observed [33, 34, 35, 36, 37, 38]. These comparisons were for sulfur 

dioxide concentrations at receptors that were generally within 20 km of the sources and 

so transformation and deposition effects would be considered negligible.

To verify that the model formulation algorithms were performing as anticipated, several 

investigations were conducted. In the first, EPA investigated whether the deposition 

algorithms were working properly. In the second, EPA jointly tested whether the 

dispersion characterizations were operating as expected, and whether the use of a “net” of 

receptors might lead to a bias in the modeling results.

The ASPEN model simulates the effect of dry deposition of particulate by adding an 

additional decay term to the emission rate in calculation of ambient concentrations. The 

decay term is a function of the deposition velocity, downwind distance from the source, 

and plume dimensions with respect to the mixing height. Deposition velocity is also a 

function of the particle size, wind speed, and the land-use type. The ASPEN model 

allows different deposition options for fine and coarse particulate and urban/rural 

environments. In order to analyze the effect of these options on the modeled ambient 

concentrations, EPA performed test case simulations using lead emissions from mobile 

nonroad sources in Colorado. Different compositions of fine/coarse fractions were used 

while the total emission rate was held constant. Five different scenarios were used for 

this test case: 10 percent fine and 90 percent coarse, 25 percent fine and 75 percent 

coarse, 50 percent fine and 50 percent coarse, 75 percent fine and 25 percent coarse, and 

90 percent fine and 10 percent coarse. Emissions from 17 pseudo-point sources of 10m 

height, 1m/s exit velocity, and T = 295 K were considered. For fine particles, the 

ASPEN deposition velocities are generally similar to those estimated by ISC and 

scattered around the 1:1 ratio line. The deposition velocities for coarse particles are 

much higher for ASPEN than for ISCST3. The effects of these differences were 

extrapolated to the national scale by estimating the fraction of lead emissions that were 

assigned as coarse particles and as fine particles. For the entire U.S., the total lead 

emissions were 66.5 g/s and the percent contribution from different source categories was 

as follows: 49 percent of all lead emissions were accounted for by major sources, 28 

percent by area sources, less than 0.01 percent by mobile onroad, and 23 percent by 
78

mobile nonroad sources. For the ASPEN simulations this means that about 50 percent of 

all lead emission sources were treated as point sources and about 50 percent - as pseudo 

point sources. By this means, it was estimated that ASPEN would predict average lead 

concentrations in the air 20-30 percent lower than would typically be predicted by 

ISCST3 because of differences in coarse particle deposition velocities between the two 

models. 

Gaussian dispersion models are designed to work with inert (non-reactive) pollutants. 

Although they can be made to handle linear production or removal effects, nonlinear 

chemistry effects are typically not treated. To address this limitation, a simple 

mechanism was built to estimate the concentration from those species that could be 

formed or destroyed in the atmosphere through secondary chemical reactions (secondary 

formation), and within the ASPEN modeling system, this secondary formed 

concentration is added to the concentration attributable to primary emissions. Analysis of 

ASPEN modeled nationwide mean values for formaldehyde and acrolein suggest that 23 

percent and 44 percent (respectively) of the total modeled concentration is attributable to 

secondary formation. Results from a recent study using the research version of the 

EPA’s Ozone Isopleth Plotting Program (OZIPR, a photochemical grid model), suggest 

that on a national scale, secondary formation for formaldehyde and acrolein would 

account for 90 percent and 85 percent (respectively) of the total modeled concentration 

[39]. However, there has not been any direct comparison of ASPEN and OZIPR modeled 

concentrations with equivalent emissions and meteorology, accounting for the fact that 

ASPEN provides values at specific locations while OZIPR provides grid-averaged 

values. Therefore, generalizing current results is problematic. However, these results do 

suggest that the ASPEN modeling system for reactive species may be underestimating (or 

overestimating) the total concentration, because ASPEN may be underestimating the 

component of the concentration that is produced (or destroyed) through nonlinear 

chemical reactions. At this point, EPA cautions that concentration estimates for reactive 

species should be considered more uncertain than for non-reactive species, all other 

factors being equal.

EPA specifically investigated whether the interpolation scheme used within ASPEN 

might be underestimating the modeled impacts. This concern arose because a "net" of 

receptors is employed by ASPEN, and then concentrations at specific points are 

estimated by interpolating within the "net". EPA wondered whether ASPEN might 

underestimate peak ambient concentrations because it might "average out" the peak 

values by combining them with lower concentrations nearby. To do this, EPA simulated 

three types of emissions sources, and compared the ASPEN estimates downwind from 

each source to the estimates derived from a more recent dispersion model, the Industrial 

Source Complex Long-Term Model Version 3 (ISCLT3). The simulations were run 

under a variety of wind speed conditions. The ASPEN estimates are lower than ISCLT3’s 

by about 10 percent in the near distances, with the underestimation increasing to about 25 

percent at 30 km downwind. 

4.3.4.4 Uncertainty Due to Background

For all air toxics except for diesel PM, a uniform “background” concentration was 
79

applied across all geographic areas to account for long-range transport. (The ASPEN 

model does not simulate the transport of any pollutant beyond 50 km from its original 

emission point or for emissions from natural sources.) The use of a uniform background 

concentration is a simplifying assumption because such extrinsic concentrations vary 

geographically for many of these pollutants. 

4.3.4.5 Summary of Model Uncertainty Investigations 

Past studies of the performance of long-term air quality models (for air toxics at more 

localized scales) suggest that 90 percent of the estimated concentrations should be within 

a factor of 2 of those observed, if the emissions are well characterized and the 

meteorological data are representative. If differences between observed and estimated 

concentration values differ greatly from what has historically been seen, one questions 

whether the model formulations are in error, whether the meteorological data is 

unrepresentative, or whether the emission characterization is in error. The investigations 

of the model formulation and methodology characterization uncertainties suggest that the 

differences in the dispersion algorithms between standard long-term algorithms and those 

within ASPEN are minor, contributing to differences of less than 10 percent in the near 

field. Significant differences were seen in the ASPEN deposition velocities for coarse 

particles, which were estimated to result in a bias to underestimate lead concentrations by 

roughly 30 percent. This bias is of importance only for those toxics simulated as having a 

significant fraction of coarse particles, as there was no bias seen in ASPEN deposition 

velocities for fine particles. Preliminary investigation of uncertainties associated with 

chemical reaction effects suggests that we should consider concentration estimates for 

reactive species as more uncertain than for non-reactive species. To improve the 

representativeness of the meteorological data, the INSWO surface weather observations 

data archive was employed. This reduced the average separation distance between source 

locations and observation meteorological sites, and is anticipated to aid in providing more 

representative meteorological data. The spot checks on location uncertainties for the 

three metals suggest that 6 to 30 percent of these emissions were assigned to default 

locations, rather than specified at their true locations. A site visit to one lead smelter 

suggests that close inspection of facilities in the vicinity of monitors might reveal other 

emissions not reported. These emission characterization uncertainties could have a 

greater impact on the model-to-monitor comparison results (resulting in differences of a 

factor of 3 or more) than uncertainties seen elsewhere in the modeling algorithms or the 

meteorological characterizations (which appear to result in differences of 30 to 80 

percent).

4.4 Estimating Population Exposure

In this phase of the national-scale assessment, characterization of population exposure, 

ambient air toxics concentrations derived from ASPEN modeling were used to estimate 

human exposures to the 33 pollutants included in the ambient concentration analysis. 

The exposure assessment was conducted with the HAPEM4 model, which predicts 

annual average inhalation exposure estimates. Under the national-scale assessment, the 

exposure results serve two purposes. First, the results of the exposure assessment are 

directly presented on a pollutant-by-pollutant or geographic area-specific basis, for 

comparative purposes. Second, the inhalation exposure estimates serve as input to the 
80

risk characterization (see section 4.3). 

As applied in the national-scale assessment, the HAPEM4 model predicted a series of 

inhalation exposure concentrations for each pollutant, for 40 demographic groups, at over 

61,000 census tract locations in the contiguous US, as well as Puerto Rico and the Virgin 

Islands. In all, the HAPEM4 model predicts over 74 million exposure estimates as part 

of the national-scale assessment. Therefore, to be useful, these results must be 

aggregated into the geographic areas and exposure demographic groups of concern. The 

aggregation of the 74 million exposure estimates occurs at two levels: at the census-tract 

level and across census tracts. At the census-tract level, aggregation is across the 

predicted exposures within and across different demographic groups. Across census 

tracts, aggregation of results to a larger geographic area (i.e., counties, states) is made to 

match the reliability of exposure estimates with that of the emissions inventory and air 

quality phases of the assessment.

For presentation and comparison of the exposure assessment results over a broad 

geographic area (i.e., counties, states), the study focuses on exposures derived for the 

“general population.” For the risk characterization, an “age-related” breakdown of the 

exposure results is important in defining a lifetime (i.e., 70 years) exposure and because 

health responses to pollutants vary with age. Details on the aggregation of the HAPEM4 

model results for these two purposes, in the national-scale assessment, are presented 

below.

4.4.1 HAPEM4 Census Tract Level Exposure Estimates

The HAPEM4 model is designed to predict inhalation exposure concentrations at the 

census-tract level. As applied in the national-scale assessment, the HAPEM4 model 

predicted a set of 30 annual exposure concentration estimates (i.e., 30 different activity 

pattern scenarios) for each of 40 demographic groups at each census tract, or a total of 

1200 exposure concentration estimates per census tract. 

It is important to note that, although these results from the HAPEM4 assessment are 

derived at the census-tract level, the results are this level are not reliable, due to 

recognized limitations in the emissions inventory and dispersion model results. 

However, to minimize data aggregation errors, the census tract results serve as the basis 

for determining pollutant exposure and risk distributions over larger geographic areas.

In the exposure assessment, certain demographic groups include commuting to other 

census tracts. As applied in the national-scale assessment, the exposure concentrations 

from these groups (for time spent in the home or work tracts) are recorded in their 

“home” census tract. Thus, if a person spends 8-hours in his “work tract” location, the 

exposure concentration predicted during this 8-hour period would be included in the 

person’s “home tract” exposure concentration. In urban settings, the commuting feature 

can tend to “spread” the predicted exposure concentrations from central business districts 

(which generally have higher ambient concentrations) to the suburban areas (which 

generally have lower ambient concentrations). 
81

Finally, for census tracts with “zero population,” the HAPEM4 model will predict an 

exposure estimate of “zero,” even though the tract may have a significant ambient 

concentration.

Variability Within a Census Tract

It is important to note that the predicted range of exposure estimates for each census tract 

(i.e.,1200 exposure concentrations) results from variability in demographic makeup, 

variations in activity patterns, and variations in commuting patterns and are derived from 

an ambient air quality estimate from a single location (at the census tract centroid as 

predicted by the ASPEN model). The modeling exercise did not attempt to quantify the 

variability in exposures across individuals within demographic groups. Thus, the 

HAPEM4 model, as applied in the national-scale assessment, does not predict a 

“complete” distribution of inhalation exposure concentrations across a census tract, only 

the distribution that results from “exposure-related” variations. This portion of the 

distribution is expected to be small compared to the actual variations in air quality across 

the tract. An air quality model applied at a more localized scale would be required to 

predict the “more complete” distribution of exposure concentrations across a census tract. 

Local scale modeling efforts have shown that ambient concentration gradients can easily 

span several orders of magnitude across a small geographic area, such as a census tract. 

The distribution resulting from “exposure-related” variations alone is expected to be far 

less than an order of magnitude at most locations across the study domain.

Figure 4-14 shows an example of this “exposure- related” variation across an example 

urban census tract. In this example, predicted POM exposure concentrations vary by less 

than 5 percent between different demographic groups and less than 1 percent within a 

specific demographic group. In general the youngest and oldest demographic groups 

considered (i.e., 0-5 year olds and 65+ years old) are predicted to have the lowest 

exposure levels. These groups are expected to spend more time indoors, where POM 

concentration levels are about 30 percent lower than outdoor levels (as defined by the 

HAPEM4 model). Thus, EPA can expect the annual average exposure levels for these 

groups to be lower than demographic groups which spend more time outdoors. This 

relatively small variation in exposure concentrations resulting from “exposure-related” 

variations can be found for most of the pollutants and locations included in the 

assessment. 

Aggregation of Exposure Estimates Within a Census Tract

As noted above, the HAPEM4 model predicts 1200 exposure estimates at each census 

tract. These exposure estimates have been aggregated in two different ways. For 

presentation and comparison of the exposure assessment results over a broad geographic 

area, an assessment of exposure estimates to “the general population” is the most feasible 

approach. For the risk characterization, an “age-related” breakdown of the exposure 

results is important. These approaches are discussed further below:

Aggregation of Exposures for County, State, and National Level Estimates

Since the goal of the national-scale assessment is to assess exposure and risk levels across 
82

a wide population distribution, and the variability of exposure concentrations resulting 

from “exposure-related” factors is small, for presentation and area-to-area comparison of 

exposure results (i.e., in the tables and charts discussed below), the 1200 exposure 

concentrations were characterized by a single “median exposure concentration” at the 

census-tract level. This was done by population-weighting the median exposure for each 

of the 40 demographic groups. This population-weighted “median exposure 

concentration” represents a “best estimate” of the population exposure for a given census 

tract. For the national-scale assessment, census-tract level “median exposure 

concentrations” were estimated for the contiguous United States, plus Puerto Rico and 

the Virgin Islands. This population exposure approach should be carefully considered 

when examining and interpreting the results of this national scale assessment. The 

national-scale assessment cannot be used to predict local scale “hot spot” exposures or to 

capture the range of exposures experienced by individuals, including the maximum 

exposed individuals. 

Aggregation of Exposures for the Risk Characterization

Although the variation across different demographic groups was small (about 5 percent or 

less), for defining carcinogenic risks (see section 4.3), the variation across different age 

groups was retained to build a 70-year lifetime exposure period for a “typical” person at 

each census tract. This was done by aggregating the predicted median exposures for each 

of 5 age groups (0 - 5, 6 - 11, 12 - 17, 18 - 64, and 65+ years). For accessing non-cancer 

risks (see section 4.3), population weighted exposures were determined for 2 age groups, 

children (<12 years) and adults (12+ years). 

4.4.2 National-Scale Assessment Exposure Estimates

As previously noted, the exposure results under the national scale assessment serve two 

purposes. First, the results of the exposure assessment are directly presented on a 

pollutant-by-pollutant or geographic area specific basis for comparative purposes. Under 

this assessment, the median census tract exposure concentrations (as described above) 

were used as the basis to derive the statistical distributions at the county, state, and 

national levels. Results of these distributions have been prepared in both a tabular and 

graphical format and are discussed further below. Second, the inhalation exposure 

estimates serve as input to the risk characterization. Results of these exposure estimates 

are presented as part of the risk characterization in section 4-3. 

Presentation of County, State, and National Exposure Estimates

Aggregated exposure information, at the county, state, and national level is available in 

both tabular and graphical formats. The tabular information is summarized on a 

pollutant-by-pollutant basis for the entire study domain. Figure 4-15 shows an example 

of part of the tabular distribution tables (actual tables contain over 3300 lines). 

Graphically, three types of chart presentations are available, “Statewide Exposure 

Concentration Estimates,” “Percent Contribution,” and “Individual State Exposure 

Concentration Estimates.” The “Statewide Exposure Concentration Estimates” and 

“Percent Contribution” charts have been prepared on a pollutant-by-pollutant basis. The 

“Individual State Exposure Concentration Estimates” are presented on a state-by-state 

pollutant basis. In addition, state and national maps of the exposure estimates on a 
83

pollutant-by-pollutant basis have also been prepared. Examples of each chart, and a map, 

are presented in Figures 4-16 through 4-19, respectively. Also attached to each exposure 

assessment chart/map will be an additional text page (called “page 2”) that contains a set 

of caveats that the user is encouraged to review. A copy of “page 2,” along with a 

complete set of charts, is provided in Appendix K. The contents of each presentation 

type are described in further detail below. 

Exposure Distribution Tables:

The tables contain exposure concentration distributions (i.e., as 

percentiles), in addition to a breakdown of exposure concentrations into 

major, area and other, onroad mobile, and nonroad mobile sources, as well 

as background concentrations for each pollutant at the county, state, and 

national levels. Results are also segregated by urban and rural county 

designation.

Statewide Exposure Concentration Estimates Charts:

These charts display a horizontal bar for each state, to represent the central 

part of the distribution of the exposure concentrations in that state (the 

25th to 75th percentile), as well as the median value for the selected 

pollutant. If the air pollutant is a carcinogen, the bar chart also displays a 

line at the exposure concentrations representing the "1 in a Million Cancer 

Risk", the "10 in a Million Cancer Risk" and the "100 in a Million Cancer 

Risk"18. For pollutants with noncancer risks, the bar chart displays a line at 

the exposure concentrations representing a " Hazard Quotient = 1.0" (for 

reference purposes, some charts may contain a line representing a Hazard 

Quotient = 0.1). An example is provided in Figure 4-16.

Percent Contribution Charts:

These charts contain horizontal bars representing the percent contribution 

of four source types (major, area and other, onroad mobile, and nonroad 

mobile) as well as the contribution from the estimated background to the 

statewide average exposure concentration estimate for the selected 

pollutant (see Figure 4-17). 

Individual State Exposure Concentration Estimates Charts:

These charts depict the median exposure concentrations for each pollutant 

in the NATA analysis on a single chart. A chart is provided for each state. 

Pollutant exposure concentration symbols are plotted to give the 

relationship of the state's median exposure concentrations to the cancer or 

noncancer risk level, where applicable. An example is provided in Figure 

4-18.

 

18 Concentrations at various levels of cancer risk and non-cancer hazard were determined using procedures 

described in section 3.3 and assume a continuous lifetime exposure to the concentrations predicted by this 

assessment. It is important to note that none of these risk-based concentrations represent regulatory 

standards.
84

National and State Maps of Modeled Exposure Concentrations:

These maps illustrate the modeled human exposure concentration to air 

toxics by county in 1996. Map colors indicate categories of exposure risk / 

hazard, and the corresponding ranges of inhalation exposure 

concentration. The exposure concentration value displayed in maps is the 

county median. Pollutant exposure concentration is expressed in 

micrograms per cubic meter of air (ìg/m3). Figure 4-19 provides an 

example of a state map with estimated exposure concentrations.

Interpretation of County, State, and National Exposure Estimates

It is important to note that the exposure distributions presented in this part of the national 

scale assessment represent the range of census tract median exposure concentrations 

across a geographic area (i.e., county, state, and national levels). As previously noted, 

the available information about the range of exposures across individuals or, spatially, 

within census tracts, is not reliable. This information is best suited to estimate “trends” 

in exposure or general population level exposures. The results of the exposure 

assessment presented at this level are best used to define broad geographic areas and 

pollutants of initial concern. A relatively low predicted exposure value does not 

necessarily mean that a particular area or pollutant is exempt from health-related air toxic 

issues. In certain situations, a more refined or local scale assessment may be needed to 

assess whether there is an air toxics problem. In areas with a relatively high predicted 

exposure value, the reliability of the estimate, and the data used to derive that estimate 

must be further examined before concluding that concern is warranted. 

The results of the exposure assessment are only meaningful when examined at the 

individual county level or above. Comparison across geographic areas can be made by 

examining the graphical and tabular exposure results. However, it is important to note 

that the confidence in the predictions may be better in some geographic areas than others 

or may be better for certain pollutants. The variability in the quality of the predictions 

can vary from not only state-to-state but county-to-county within the same state. Thus, 

while the presentation materials contain multiple geographic areas (i.e., states compared 

on a single chart), the data are presented in this fashion for convenience and to minimize 

the number of charts/graphs that is required to summarize the assessment. 

For reference, cancer and noncancer health criteria lines have been included on the 

graphical presentations. These have been placed only to compare the presented exposure 

estimates in a general context to the health criteria. It is strongly advised that the risk 

characterization section, as well as the assessment’s limitations, be reviewed before 

assessing a particular health concern in a specific geographic area. A discussion of 

exposure results on a pollutant-by-pollutant basis is made in the risk characterization 

results section. 

Care should be taken when trying to interpret the geographic distribution of exposure 

concentrations across a county. Figure 4-20 presents the range of exposure 

concentrations across tracts within a county. This shows that, for benzene exposure 

estimates, the range of exposures within a county (the tract-to-tract variations) appears to 
85

be directly related to the population of that county. Thus, at least for benzene, the most 

populated counties are predicted to have the largest range in exposure concentrations, and 

thus the greatest uncertainty in the geographic distribution of these estimates. It is not 

clear whether this is a true relationship or an artifact of the assessment, in that a denser 

modeling (both ambient and exposure) grid is utilized in more populated areas (more 

census tracts).

4.4.3 Comparison of HAPEM4 Exposure Concentrations to ASPEN

Ambient Concentrations 

To illustrate the contribution of the HAPEM4 model to the assessment, census tract 

median exposure concentrations were compared to corresponding census tract median 

ambient concentrations as predicted by ASPEN. Table 4-13 shows the average ratio of 

HAPEM4 to ASPEN for each of the study pollutants. This ratio is presented for total 

levels as well as for each of the five source sectors considered in the assessment (i.e., 

major, area and other, onroad mobile, nonroad mobile, and background). In general the 

HAPEM4 exposure predictions are 5-40 percent lower than the corresponding predicted 

air quality values. This reduction likely results from the inability of many pollutants to 

penetrate efficiently into an indoor environment (see section 3.2.3). Exposures resulting 

from emissions originating from major, area, nonroad mobile, and background source 

sectors are seen to be about 20-30 percent lower than the corresponding predicted air 

quality values for most gaseous pollutants, with some metals approaching a 40 percent 

reduction from air quality levels. Exposures resulting from emissions from onroad 

mobile sources range from 40 percent lower than the corresponding predicted air quality 

values for chromium to 24 percent greater than the predicted air quality values for 

benzene. Benzene exposure levels for this source sector are most likely higher than their 

corresponding ambient levels because of the relatively high “proximity term” (see section 

3.2.3) assigned to the “in-vehicle” microenvironment. This “proximity term” is required 

to adjust the ASPEN predicted ambient level, which is assumed representative of the 

census tract centroid, to that which EPA would expect immediately outside of the 

microenvironment. For most microenvironments this term was set to unity. However, 

for the transportation related microenvironments it is presumed that the ambient 

concentrations immediately outside the vehicle (i.e., very close to the pollutant source) 

are considerably higher than the census tract centroid value predicted by ASPEN. Thus 

for pollutants where emissions are significantly dominated by onroad mobile sources an 

appropriate proximity term was developed and applied specific for this assessment. For 

example, for benzene, a proximity term of 6.6 was applied to the ASPEN census tract 

centroid levels to predict the expected ambient concentration immediately outside a 

vehicle.
86

Table 4-13. HAPEM4 to ASPEN Average Ratio1.

Source Sector

Pollutant Major

Area and 

Other

Onroad 

Mobile

Nonroad 

Mobile

Estimated 

Background Total

Acetaldehyde 0.79 0.79 0.93 0.80 0.87

Acrolein 0.82 0.80 1.06 0.82 0.92

Acrylonitrile 0.75 0.75 0.75

Arsenic 0.79 0.79 0.80 0.79 0.79

Benzene 0.85 0.85 1.24 0.85 0.85 0.98

Beryllium 0.79 0.79 0.79 0.79

1,3-Butadiene 0.83 0.83 0.93 0.84 0.90

Cadmium 0.79 0.79 0.79 0.79

Carbon Tetrachloride 0.71 0.71 0.72 0.72

Chloroform 0.82 0.82 0.82 0.82

Chromium 0.64 0.63 0.66 0.66 0.64

Coke Oven Emissions 0.75 0.75 0.75

1,3-Dichloropropene 0.82 0.83 0.83

Diesel Particulate Matter 0.72 0.70 0.71

Ethylene Dibromide 0.79 0.79 0.79 0.79

Ethylene Dichloride 0.87 0.87 0.87 0.87

Ethylene Oxide 0.79 0.79 0.79

Formaldehyde 0.69 0.69 0.89 0.72 0.69 0.75

Hexachlorobenzene 0.81 0.81 0.81 0.81

Hydrazine 0.83 0.83 0.83

Lead 0.84 0.84 0.84 0.84 0.84

Manganese 0.74 0.73 0.75 0.75 0.73

Mercury 0.79 0.79 0.80 0.80 0.79 0.79

Methylene Chloride 0.80 0.81 0.80 0.80

Nickel 0.79 0.79 0.80 0.79 0.79

Polychlorinated biphenyls (PCBs) 0.79 0.79 0.79 0.79

Perchloroethylene 0.75 0.76 0.75 0.75

Polycyclic Organic Matter (7-PAH) 0.75 0.75 0.76 0.78 0.75

Polycyclic Organic Matter (Total) 0.76 0.76 0.76 0.77 0.76

Propylene Dichloride 0.79 0.79 0.79

Quinoline 0.75 0.75 0.75

1,1,2,2-Tetrachloroethane 0.79 0.79 0.79

Trichloroethylene 0.84 0.84 0.84 0.84

Vinyl Chloride 0.75 0.75 0.75

1 Average ratio developed by comparing (HAPEM4 Concentration)/(ASPEN Concentration) at each census tract
87

4.4.4 Discussion of HAPEM4 Limitations and Uncertainties

Like any model, especially when applied on a very broad geographical scale, HAPEM4 

relies on a number of assumptions and approximations in estimating inhalation 

exposures. In general, the accuracy of the results is primarily limited by:

1. The emissions inventory and dispersion modeling results;

2. Reliability of ME factors for various pollutants;

3. Activity pattern information (e.g., patterns of time spent in various 

microenvironments for the populations in the geographic areas modeled); and 

4. The ability of the chosen population cohorts to adequately represent the true 

demographics of every census tract.

More specifically, limitations and uncertainties include:

1. Uncertainty with the emissions inventory and the ASPEN dispersion modeling, 

which was discussed in the previous sections.

2. Indoor emission sources - The exposure estimates do not include exposures 

related to emissions of air toxics from indoor sources (e.g., off-gassing from 

building or consumer products, smoking, internal combustion sources, etc.). 

Indoor sources of some air toxics are likely to be important in assessing total 

inhalation exposures, and will be addressed in the future as additional data are 

obtained and new analyses are conducted.

3. Spatial resolution - Because of the spatial uncertainties associated with air toxics 

emission inventory data and the associated ASPEN model ambient concentration 

estimates, results of the HAPEM4 inhalation exposure model are compiled and 

presented at county level or larger spatial scales.

4. Exposure routes - The HAPEM4 model only estimates inhalation exposures, since 

it is based on ambient air concentration data as input. Thus, it does not address 

exposures through other pathways (e.g., ingestion). This is especially important 

for toxic pollutants that are persistent and bioaccumulate, such as mercury, 

dioxins, and PCBs. Emissions of these pollutants disperse through the 

atmosphere and eventually deposit to land or water bodies. Once deposited, they 

can bioaccumulate up the food chain. For example, mercury bioaccumulates most 

efficiently in the aquatic food chain (e.g., fish). Fish consumption is the primary 

route of exposure to mercury [40]. For dioxins, (which bioaccumulate mainly in 

animal fat tissue) population exposures are primarily due to ingestion of dairy 

products, fish, beef, pork, and poultry [41]. Multimedia exposure models are 

needed to address such multipathway exposures. These will be included in future 

national-scale assessments as appropriate tools and data become available.
88

5. Population exposure estimates - The exposure estimates represent midrange 

estimates of population exposures. Due to a number of factors, some individuals 

may have substantially higher or lower exposures. It is important to note that the 

exposure model, as applied on the national scale, is not designed to quantify these 

extreme values of individual exposures.

6. Representativeness of the ME factors - Exposure models must predict the 

relationship between the ambient air quality (outside) and that in a 

microenvironment (inside or outside). When applied on a local scale, exposure 

models can employ detailed mass balance equations to predict this relationship. 

However, on a national scale, the development of such a detailed relationship is 

not feasible. Thus, the HAPEM4 model, applied on a national scale, relies on 

generalized ME factors, which do not account for variability (e.g., penetration 

affected by air exchange rate, which is a function of ambient temperature, 

heating/cooling system, and open windows). These factors can be represented as 

simple first-order relationships between the outdoor and the indoor air quality. 

For some pollutants and microenvironments this relationship is well-documented 

and well-understood. However, for many pollutants and microenvironments this 

relationship is either not clear or has not been measured. As part of this 

assessment, EPA conducted a detailed study of exposure literature to develop ME 

factors for each air toxic in the study [19]. This ME study has undergone a 

separate technical peer review. The technical reviewers agreed that there is a 

great degree of uncertainty associated with these numbers. They have suggested 

that this uncertainty be assessed either qualitatively or quantitatively as part of 

this study. In future versions of this assessment EPA plans to define these ME 

factors as distributions rather than fixed "best estimate" values as was employed 

for this initial assessment. 

7. Representativeness of the population cohorts - The assessment assumed that the 

40 cohort groups selected can represent the activity patterns of the general 

population in all areas of the country. The groups were selected to represent 

variability in population activity patterns while at the same time maintaining the 

ability to present the exposure assessment results in a manner that will allow for 

an adequate lifetime exposure aggregation. It is also possible that members of the 

same cohort may have significantly different activity patterns in different census 

tracts. The usage of the 40 cohort groups reflects the finest resolution that EPA 

believes is possible with currently-available models.

8. Representativeness of the activity pattern sequence - When selecting multiple 24-

hour activity patterns to construct an annual average pattern, patterns are 

combined that pertain to different individuals, so that day-to-day correlations in 

activities are not preserved. For example, for day 1 the pattern may specify a 

house with an attached garage, and for day 2 a house without an attached garage. 

In this situation, the HAPEM4 model would underestimate the annual average 

exposure for a person residing in a house with an attached garage, and 
89

overestimate the exposure of the person in the house without an attached garage, 

and overestimate the exposure of the person in the house without an attached 

garage. As a result the aggregated activity pattern is more representative of a 

population average pattern for the demographic group, than any individual 

pattern. Thus, the distribution of exposure concentrations for the group estimated 

by HAPEM4 represents the uncertainty in the population average exposure 

concentration, rather than the variability of the individual exposure concentrations 

among members of the group. Uncertainty and variability of input data other than 

activity data are not considered, so that the resulting uncertainty information 

provided by the prediction distributions is an underestimate of the overall 

uncertainty. 
90

5 Risk Characterization 

5.1 Introduction 

As described in Section 2, this assessment was based on EPA’s paradigm for risk 

assessment, a framework to assess and manage risks developed by the National Academy 

of Sciences in 1983. The paradigm divides the risk assessment and risk management 

process into different general phases (as shown in Figure 2-1). The phases that comprise 

risk assessment are (1) exposure assessment (which describes how humans come into 

contact with pollutants), (2) dose-response assessment (which describes the adverse 

health effects the pollutants may produce and at what doses these effects may occur), and 

(3) risk characterization (which combines the exposure and dose-response information to 

draw qualitative or quantitative inferences about risk). The risk characterization section 

presents an interpretation and discussion of the results of the NATA national-scale 

assessment, including the uncertainties associated with each element, and makes specific 

recommendations for future directions of air toxics assessments on the basis of this 

information. Complete results for the risk characterization portion of the assessment are 

provided in Appendix L. 

This risk characterization examines inhalation risks for 32 hazardous air pollutants 

associated with 1996 emissions in urban and rural areas nationwide. This assessment was 

conducted on a coarse spatial resolution (i.e., census tracts) and used many simplifying 

assumptions. Therefore, it should be noted that individuals within a census tract may 

have substantially higher or lower exposures (and concomitant risks) than estimated here. 

The characterization is intended to help identify pollutants of greatest potential concern 

(among these 32), prioritize efforts to reduce emissions, provide a baseline for measuring 

future trends, and to help set research priorities. EPA plans to update this assessment 

every three years. The next assessment will focus on 1999 emissions, concentrations and 

risks. 

As described in Section 3, several risk presentation formats have been used. Cancer risks 

to individuals are presented as lifetime (e.g., 70-year) individual risks. These risks are 

expressed in terms of the estimated “upper-bound” (i.e., likely actually to be lower, but 

may be higher) probability that a person with the median exposure in a census tract will 

contract cancer. Non-cancer hazard to individuals is expressed in terms of the hazard 

quotient, defined as the ratio between the estimated median exposure in an individual’s 

census tract and the reference concentration (or similar value). The reference 

concentration is an exposure that is believed to be without significant risk of adverse noncancer health effects in a chronically-exposed population, including sensitive individuals.

Cancer risks to individuals exposed to multiple pollutants were combined by summing, 

with known human carcinogens kept separate from probable and possible carcinogens. 

Non-cancer hazards for multiple pollutants were combined by summing hazard quotients 

for pollutants affecting the same target organs to create a target organ-specific hazard 

index (TOSHI) for non-carcinogenic effects. Non-carcinogenic pollutants having “high 

certainty” RfCs were summed separately from those with “low certainty” RfCs. Due to 
91

the broad scale of the assessment, the risk characterization focused on results at the 

national level, which is the level at which EPA believes the results are most meaningful.

Estimated census tract median cancer risks and non-cancer hazards to individuals have 

been shown as box plots. The plots include a range extending from the 5th to the 99th

percentile census tracts. Cancer risks and non-cancer hazards to populations have been 

expressed as total numbers of people (i.e., not truncated at the 5th and 99th percentiles) 

who reside in census tracts where the estimated median risk (or hazard quotient) exceeds 

fixed levels within the contiguous US, Puerto Rico, and the Virgin Islands.

For a complete understanding of the risk characterization, it is important to remember 

that the scope and methods of this assessment have imposed the following limitations and 

uncertainties:

1. Risk and hazard quotient levels are not regulatory levels. The determination of 

what is an acceptable or unacceptable risk depends on additional factors and more 

refined information.

2. The risk estimates presented are based on the assumption that pollutant exposures 

would remain at 1996 levels over a lifetime. They did not take into account 

significant reductions that have taken effect since 1996, or future reductions 

expected from: 1) mobile sources; 2) major industrial sources; 3) State or industry 

initiatives; and 4) facility closures. For example, EPA expects exposures to some 

gaseous air toxics from onroad mobile sources to be reduced about 50% by 2007 

and 60% by 2020. While such a “snapshot” type of assessment may not be able 

to provide direct estimates of absolute risk levels, it nonetheless provides a useful 

yardstick against which one may evaluate the risk-reduction potential of 

hypothetical risk reduction scenarios, and which may be used to assess the 

relative contributions of various pollutants and source groupings to national-scale 

risks.

3. The risk estimates for chromium, nickel, and polycyclic organic matter were 

based on conservative assumptions of speciation that were applied uniformly to 

all areas. Actual risks associated with these pollutants may be lower than 

estimated in some areas. 

4. The risk estimates represent risks associated with midrange estimates of 

population exposures within each census tract. Some individuals may have had 

substantially higher or lower exposures and risks. It is important to understand 

that the dispersion and exposure models used by the assessment were not 

designed to quantify inter-individual variability in exposures. 

5. All cancer risk estimates should be considered as conservative, but not worstcase. Because they represent a composite of upper bound UREs and median 

population exposures (which may be underestimated), the true risks would 
92

probably be less, but could be greater. For more highly exposed segments of the 

population, these cancer risk estimates would be correspondingly higher.

6. EPA is currently reassessing the carcinogenic effects of 19 of the air pollutants 

included in this study. Cancer unit risk estimates could change substantially as a 

result of these reassessments.

7. For pollutants that have more than one unit risk estimate (e.g., benzene and vinyl 

chloride), this characterization uses the highest available unit risk. For more 

details about these unit risks, see Appendix G. 

8. This characterization presents non-cancer hazard in terms of the hazard quotient, 

which is the ratio of a given exposure level to the reference concentration (RfC) 

or similar value for a pollutant. The RfC is an estimate of the continuous lifetime 

inhalation exposure that the EPA believes is likely to have no appreciable risk of 

deleterious non-cancer effects. Although hazard quotients below 1.0 (i.e., 

exposures below the RfC) are believed safe, hazard quotients above 1.0 are not 

necessarily harmful. Nevertheless, as the hazard quotient increases above 1, 

potential for adverse effects also increases. 

9. EPA combined non-cancer hazard estimates for multiple pollutants using the 

target-organ-specific hazard index (TOSHI), defined as the sum of hazard 

quotients for individual toxic air pollutants that affect the same organ or organ 

system. This method is a simplified approximation of the potential aggregate 

effect because different substances may affect the same organ in different and 

non-additive ways. As with the hazard quotient, aggregate exposures below a 

hazard index of 1.0 will likely not result in adverse non- cancer health effects over 

a lifetime of exposure. However, a hazard index greater than 1.0 does not 

necessarily suggest a likelihood of adverse effects. 

10. Estimates of the number of people at various cancer risk or non-cancer hazard 

levels were based on census tract population estimates from 1990, the most recent 

available. The total population of the contiguous US, Puerto Rico, and the Virgin 

Islands in 1990 was 251 million, including 207 million adults and 44 million 

children.

11. Exposure pathways other than inhalation, as well as indoor sources of air toxics, 

may contribute substantially to total risks for some pollutants. This assessment 

does not address oral or dermal exposures, or inhalation exposure resulting from 

indoor sources. 

12. The simplifying assumptions necessary for national-scale modeling have 

introduced significant uncertainties into each component of the assessment. 

Because of these uncertainties, EPA will not use the results of this assessment to 

determine source-specific contributions or to set regulatory requirements.
93

These uncertainties are discussed in greater detail in Section 5.4. This section includes 

qualitative descriptions of all major sources of uncertainty and variability in the nationalscale assessment, provides a simple illustration of one method by which the magnitude of 

variability and uncertainty can be estimated, and provides recommendations for future 

efforts for a more complete quantification of variability and uncertainty in the future.

5.2 Cancer Risks

5.2.1 Pollutant-Specific Cancer Risks to Individuals

Figure 5-1 shows the distribution of estimated cancer risks for each of the pollutants 

nationwide, from the 5th to the 99th percentile. Risks were based on the median exposure 

within each of approximately 61,000 census tracts nationwide. Because census tracts are 

intended to include more or less similar population sizes, the distribution of tract median 

risks should be generally representative of the distribution of risks for “typical” 

individuals in the US. It should be noted that this distribution does not represent risks to 

all individuals in the US. Some individuals within a census tract may have higher or 

lower exposures (and concomitant risks) that those shown. (For example, the 5th

percentile risk level on the figure indicates that approximately 5% of the population lives 

in census tracts where the median risks are at that level or lower. The 99th percentile risk 

level indicates that 99% of the population lives in census tracts where the median risks 

are at that level or lower.)

The extremes at either end of the distributions (i.e., less than the 5th and greater than the 

99th percentiles) were not shown because EPA believes that, given the broad scope of this 

assessment, these risk estimates were less reliable than the information shown on the 

graphs. That does not mean these risks are unimportant, however. For example, 

approximately 2.5 million people resided in census tracts where the median estimated 

risks were higher than the 99th percentile risk shown. Because this assessment was 

designed to evaluate average exposure and risk at the national scale, more refined localscale assessments will be needed to adequately characterize exposures and risks at the 

upper end of the national range.

In general, narrow distributions on Figure 5-1 (e.g., for PCBs or carbon tetrachloride) 

suggest that background sources were dominant. Risks associated with these 

background-dominated pollutants were similar in all tracts. Broad risk distributions (e.g., 

for coke oven emissions) suggest dominance by major sources that can strongly affect 

limited areas. Risks associated with these pollutants varied with location by many orders 

of magnitude. Distributions intermediate between narrow and broad (e.g., for benzene or 

perchloroethylene) suggest dominance by mobile or area (and other) sources whose 

impacts were more widespread than major sources but less widespread than background. 

Detailed risk distributions for major, area, mobile, and background sources are shown in 

Appendix L (Figures 1-5).

Based on an examination of Figure 5-1, pollutants can be grouped into scale-related 

categories as follows:
94

National risk drivers: Figure 5-1 shows that, for at least 50% of the US population, 

estimated inhalation cancer risks associated with three pollutants – benzene, carbon 

tetrachloride, and formaldehyde – approached or exceeded 10 in 1 million. Benzene and 

formaldehyde risks were associated primarily with mobile and background sources, 

whereas carbon tetrachloride risks were virtually all from background. 

Regional risk drivers: Four more pollutants – chromium, ethylene oxide, polycyclic 

organic matter, and coke oven emissions – showed estimated cancer risks exceeding 10 in 

1 million for more than 1% of the US population. The first three of these pollutants 

originated primarily from area (and other) sources; the fourth was associated entirely with 

major sources.

Important national contributors: Estimated risks to 50% or more of the US population 

for five more pollutants– acetaldehyde, 1,3-butadiene, ethylene dibromide, ethylene 

dichloride, and perchloroethylene – were near or above 1 in 1 million. Mobile sources 

were most important for emissions of acetaldehyde and 1,3-butadiene, background 

sources for ethylene dibromide and ethylene dichloride, and area sources for 

perchloroethylene.

Important regional contributors: 

Nine more pollutants – arsenic, 

nickel, acrylonitrile, cadmium, 

chloroform, 1,3-dichloropropene, 

hydrazine, quinoline, and 

trichloroethylene – showed estimated 

cancer risks exceeding 1 in 1 million 

for some of the US population. Major 

sources contributed important 

amounts of hydrazine and quinoline, 

although area (and other) sources 

were also important for these 

pollutants. Risks for the remaining 

seven pollutants were dominated by 

area (and other) sources. (Note that 

one pollutant that met this condition, 7-PAH, was omitted from this discussion because it 

is redundant with total POM. Because total POM is a more inclusive group, discussion 

focuses on it.)

For the remaining carcinogenic pollutants in the national-scale assessment – vinyl 

chloride, beryllium, hexachlorobenzene, lead, methylene chloride, PCBs, propylene 

dichloride, and 1,1,2,2-tetrachloroethane – 99% of the US population lived in census 

tracts where the median cancer risk did not exceed 1 in 1 million. It is important to note, 

however, that this result does not rule out potential concerns for these pollutants. First, 

this national-scale assessment lacks the resolution to evaluate all local inhalation risk hot 

spots. Therefore, there may be locations where these substances pose significant risks 

due to concentrations in air, but this assessment lacked the resolution to detect them. 

Cancer risk to individuals:

• National risk drivers: benzene, carbon 

tetrachloride, formaldehyde

• Regional risk drivers: chromium, ethylene 

oxide, polycyclic organic matter, coke oven 

emissions

• Important national contributors: 

acetaldehyde, 1,3-butadiene, ethylene 

dibromide, ethylene dichloride, 

perchloroethylene

• Important regional contributors: arsenic, 

nickel, acrylonitrile, cadmium, chloroform, 

1,3-dichloropropene, hydrazine, quinoline, 

trichloroethylene
95

Additionally, there is the potential for the omission of important sources in the inventory, 

especially since this is the first time such a comprehensive inventory has been attempted. 

Furthermore, beryllium, hexachlorobenzene, lead, and PCBs all tend to accumulate in 

soil and are persistent in the environment. Because this assessment was limited to 

inhalation exposure, these substances may pose important risks via the ingestion pathway 

that were beyond the scope of this assessment.

In general, the most important national-scale “risk drivers” originated predominantly 

from mobile (i.e., benzene, formaldehyde) or background (i.e., carbon tetrachloride, 

formaldehyde) sources. Most of the “second-tier” pollutants originated mostly from area 

or background sources. Pollutants associated with major sources were generally not seen 

to be as important nationally, but were very important in some regions. Given the 

resolution of this national-scale assessment, this result is not very surprising; the 

assessment tools are simply inadequate to quantify localized impacts around isolated 

stationary sources. More rigorous localized assessments will be needed to quantify risks 

associated with such individual sources; this is one of the goals of the assessments being 

carried out as a part of EPA’s residual risk assessment efforts. Finally, it is important to 

realize that “background” in this assessment was really a miscellaneous category, 

attempting to capture sources that were not inventoried or sources beyond 50 km that 

were inventoried. The category included natural background emissions, historic 

emissions of persistent substances, international transport, and interstate transport. The 

first three of these subcategories are not controllable under the Clean Air Act, but 

elements of the fourth – interstate transport – are. Thus, some part of the background 

sources component of risk for each substance may be addressable by the air toxics 

program. This addressable portion likely varies among different substances, and the 

current state of EPA’s dispersion models and databases is insufficient for quantitative 

estimation.

5.2.2 Pollutant-Specific Cancer Risks to Populations

Figure 5-2 shows numbers of people residing in census tracts for which the median 

exposure corresponds with estimated lifetime cancer risks exceeding three fixed levels 

(100, 10, and 1 in one million) for emissions from all sources combined. The same 

information for each source sector (major, area, mobile, and background sources) is 

provided in Appendix L (Figures 13-17). Unlike the individual risk figures (which 

exclude the extreme ends of the distribution), these population figures include the entire 

assessed population in order to increase sensitivity to those potential urban-scale hot 

spots that affect the median exposure estimate at the census-tract level.

Based on an examination of Figure 5-2, pollutants can be grouped into scale-related 

categories as follows:

National risk drivers: Figure 5-2 shows that three pollutants – benzene, formaldehyde, 

and chromium – presented estimated risks of 10 in 1 million or higher to more than 10 

million people. The first two pollutants posed such risks for more than 100 million 

people. Contributions of individual source sectors (shown in Appendix L, Figures 13-17) 

suggest that benzene originated predominantly from a mixture of onroad and nonroad 
96

mobile sources, but formaldehyde was emitted predominantly by nonroad mobile 

sources. Both of these pollutants had an important background component (i.e., greater 

than 1 in 1 million risk to the entire population) as well. In contrast, chromium emissions 

originated predominantly from a mixture of major and area sources. 

Regional risk drivers: Coke oven emissions posed estimated risks of 100 in 1 million or 

higher to more than 100,000 people, and acrylonitrile, arsenic, and hydrazine presented 

such risks to more than 10,000 

people. Arsenic is emitted by both 

major and mobile sources; the other 

three pollutants are emitted 

predominantly or exclusively by 

major sources. These substances 

may be good candidates for more 

refined, local-scale analyses in areas 

where emissions sources exist.

Important national contributors:

Six more pollutants – acetaldehyde, 

carbon tetrachloride, ethylene 

dibromide, ethylene dichloride, 

polycyclic organic matter, and perchloroethylene – presented risks of 1 in 1 million or 

higher to more than 100 million people. Mobile sources were most important for 

emissions of acetaldehyde, background sources for carbon tetrachloride, ethylene 

dibromide, and ethylene dichloride, and area (and other) sources for polycyclic organic 

matter and perchloroethylene.

Important regional contributors: Four more pollutants –1,3-butadiene, cadmium, 1,3-

dichloropropene, and ethylene oxide – posed estimated cancer risks of 1 in 1 million or 

higher to more than 10 million people. Cadmium, 1,3-dichloropropene and ethylene 

oxide originated mostly from area sources, and 1,3-butadiene from mobile sources 

(mostly onroad).

The remaining carcinogenic pollutants in the national-scale assessment – nickel, vinyl 

chloride, beryllium, chloroform, hexachlorobenzene, lead, methylene chloride, PCBs, 

propylene dichloride, quinoline, 1,1,2,2-tetrachloroethane, and trichloroethylene – were 

estimated to exceed risks of 1 in 1 million for fewer than 10 million people and to exceed 

risks of 10 in 1 million for fewer than 1 million people. As noted in the discussion of 

individual risk, this result does not exonerate these pollutants. In fact, Figure 5-2 shows 

that beryllium, chloroform, and quinoline contribute estimated inhalation risks exceeding 

10 in 1 million for more than 100,000 people, making them potentially important urbanscale concerns. Also, as noted previously, potentially important ingestion exposures for 

beryllium, hexachlorobenzene, lead, and PCBs were not included in the assessment.

Cancer risk to populations:

• National risk drivers: benzene, 

formaldehyde, chromium

• Regional risk drivers: arsenic, coke oven 

emissions, acrylonitrile, hydrazine

• Important national contributors: 

acetaldehyde, carbon tetrachloride, ethylene 

dibromide, ethylene dichloride, polycyclic 

organic matter, perchloroethylene

• Important regional contributors: 1,3-

butadiene, cadmium, 1,3-dichloropropene, 

ethylene oxide
97

5.2.3 Aggregate Cancer Risks of Multiple Pollutants to Individuals

Figure 5-3 shows the distribution of estimated cancer risks for multiple pollutants 

combined, from the 5th to the 99th percentile in the US population. The risks are based on 

median exposures within each census tract nationwide. Known human carcinogens were 

aggregated separately from probable and possible carcinogens, and the figure shows risks 

by individual source sector, and for all source sectors combined.

The median aggregate risk estimate for all known carcinogens and source sectors 

combined was 10 in 1 million and the 99th percentile risk was 60 in 1 million. The 

median aggregate risk estimate for all probable and possible carcinogens and source 

sectors combined was 20 in 1 million and the 99th percentile risk was 100 in 1 million. 

While EPA has not combined risk estimates for known and probable/possible 

carcinogens because of their different magnitudes of relative uncertainty, it appears 

reasonable to assume that total risk associated will all carcinogens in this assessment 

could approach 30 in 1 million risk to the median US receptor.

Background sources alone were estimated to provide an aggregate risk of approximately 

3 per million for known carcinogens and 4 per million for probable/possible carcinogens. 

Because background exposures were assumed to be ubiquitous, they effectively 

established the “floor” for the distributions of total risk. Major sources contributed little 

to the central part of the total risk distributions, but made dominant contributions to the 

upper end of the distribution for known carcinogens. Mobile sources were the most 

important contributors to total risk for known carcinogens; area and mobile sources were 

of roughly equal importance in determining total risk for probable and possible 

carcinogens.

5.2.4 Aggregate Cancer Risks of Multiple Pollutants to Populations 

Figure 5-4 shows numbers of people residing in census tracts for which the median 

exposures to all carcinogens combined corresponded with estimated lifetime cancer risks 

exceeding three fixed levels – 100, 10, and 1 in one million. As before, known 

carcinogens were aggregated separately from probable and possible carcinogens. The 

figure shows affected populations by source sector, and for all source sectors combined.

Based on Figure 5-4, approximately 130 million people resided in census tracts where 

cancer risk exceeded 10 in 1 million, and 800,000 lived in tracts where risk exceeded 100 

in 1 million from known carcinogens. For probable and possible carcinogens, 

approximately 200 million people lived in tracts where risks exceeded 10 in 1 million and 

2 million lived in tracts where risks were above 100 in 1 million.

As already noted, the entire assessed population experienced estimated risks exceeding 1 

in 1 million from background sources alone. For known carcinogens, major sources were 

responsible for most risks exceeding 100 in 1 million. For probable/possible carcinogens, 

risks above 100 in 1 million were associated more or less equally with major, area, and 

nonroad mobile sources. The greatest contributors to “intermediate risks” (i.e., between 

10 and 100 in a million) were onroad and nonroad mobile sources.
98

5.3 Non-Cancer Hazard

5.3.1 Pollutant-Specific Hazard Quotient for Individuals

Figure 5-5 shows the distribution of non-cancer hazard quotient (HQ) for 27 pollutants 

nationwide, from the 5th to the 99th percentile. As with the cancer risk figures, HQs were 

based on the median exposure within each census tract nationwide and are therefore 

likely to be similar to the distribution of risks for individuals in the US. The extremes at 

either end of the distributions were truncated at the 5th and 99th percentiles, but readers 

should keep in mind that approximately 2.5 million people reside in census tracts where 

the median HQs are higher than the upper end of the distribution on the graphs.

In general, a very narrow distribution on Figure 5-5 (e.g., chloroform, ethylene 

dibromide) suggests that background sources were dominant. HQs associated with these 

pollutants were similar in all tracts. A very broad risk distribution (e.g., lead) suggests 

dominance by major sources or by a few large area sources. Hazards associated with 

these pollutants varied with location by several orders of magnitude. Distributions in 

between narrow and broad (e.g., benzene, manganese) suggest that dominant sources 

were either mobile or area sources that impacted more areas than major sources but were 

less ubiquitous than background. Detailed risk distributions for major, area, mobile, and 

background sources are shown in Appendix L (Figures 7-11).

National hazard drivers: Figure 5-5 shows that for at least 50% of the US population, the 

inhalation HQ associated with a single pollutant – acrolein – was approximately 4. The 

HQ for the most exposed 1% of the population was approximately 20. No other 

pollutants approached within an order of magnitude of acrolein’s HQ distribution. Most 

of the acrolein exposure was associated with mobile sources, with area (and other) 

sources also contributing.

It is important to note that many reference concentrations incorporate protective 

assumptions in the face of uncertain data. For this reason, an HQ greater than 1.0 does 

not necessarily suggest a likelihood of adverse effects, whereas an HQ less than 1.0 does 

suggest that adverse effects are unlikely. Furthermore, the HQ cannot be translated to a 

probability that adverse effects will occur, and is not likely to be proportional to risk. An 

HQ greater than one can be best described as indicating that a potential may exist for 

adverse health effects, warranting further investigation.

Three more pollutants – formaldehyde, acetaldehyde, and manganese – showed HQs 

exceeding 0.1 for some of the US population. While an HQ of 0.1 does not suggest a 

present potential for adverse health effects, the fact that these HQs are within an order of 

magnitude of 1.0 suggests that some potential may exist for adverse health effects if 

emissions increase in the future. Also, these substances could potentially combine with 

other pollutants to present an aggregate threat, or pose local threats. The remaining 

pollutants in the national-scale assessment were found not to contribute HQs exceeding 

0.1 for 99% of the US population. As with carcinogens, this result does not exonerate 

these pollutants because of the coarse resolution of the assessment, potential omissions in 

the inventory, and the fact that ingestion exposure was not assessed.
99

5.3.2 Pollutant-Specific Hazard Quotient for Populations

Figures 5-6 and 5-7 show numbers of adults and children, respectively, residing in census 

tracts where median exposures exceeded three fixed HQ levels (0.1, 1.0, and 10) for 

emissions from all sources combined. The same information is provided in Appendix L 

(Figures 19-23 and 25-29) for each source sector (major, area, mobile, and background 

sources). Unlike the individual HQ figures (which exclude the extreme ends of the 

distribution), these population figures include the entire assessed population, with the 

goal of increasing sensitivity to identify potential urban-scale areas of concern. (As 

already noted, the national-scale assessment was designed to assess exposure and risk 

levels across large populations. Therefore, this use to identify urban-scale areas may 

produce false negatives.)

Pollutants can be grouped in categories of decreasing importance in the assessment as 

follows:

National hazard drivers: Figures 5-6 and 5-7 show that one pollutant, acrolein, presented 

an HQ exceeding 10 to more than 20 million adults and 4 million children. Virtually all 

adults and children in the US population lived in census tracts where the median HQ 

exceeded 1.0. Similar results were obtained by considering mobile sources alone 

(Appendix L, Figures 21, 22, 27, and 28).

Regional hazard drivers: Three more pollutants – formaldehyde, acetaldehyde, and 

manganese – presented HQs greater than 1.0 to more than 100,000 adults and 20,000 

children. Most of the formaldehyde and acetaldehyde exposures were attributable to 

nonroad mobile sources, while manganese exposures originated primarily from area 

sources. 

Important regional contributors: Two more pollutants – cadmium and arsenic –

presented HQs greater than 1.0 to more than 10,000 people. Arsenic contributions 

originated mostly from a mixture of major and area sources, and cadmium came mostly 

from area sources alone.

5.3.3 Aggregate Target Organ Specific Hazard Index of Multiple 

Pollutants to Individuals

Aggregating hazards of multiple pollutants that have the potential to cause adverse health 

effects other than cancer is inherently more complex than assessing cancer risk because 

(1) it is necessary to consider different toxic effects and mechanisms that may not be 

additive, and (2) it is necessary to consider adults and children separately. 

Regarding the first issue, the most effective way to aggregate hazards for multiple 

pollutants would be to combine hazard quotients for pollutants that cause the same 

adverse effects by the same toxic mechanism. However, because detailed information on 

toxic mechanisms was not available for most of the substances in this assessment, EPA 

used a simpler and more conservative method, the target organ-specific hazard index 

(TOSHI). The TOSHI is the sum of hazard quotients for pollutants that affect the same 

organ or organ system. This assessment calculated TOSHIs for the respiratory system, 
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blood and blood-forming tissues, central nervous system, liver and kidney, cardiovascular 

system, and immune system.

Exposures to children and adults were estimated separately to respect children’s 

potentially greater susceptibility to some toxic effects. This susceptibility may be due to 

physiological differences from adults, different activity patterns that may lead to higher 

exposures, or both. However, dose-response assessments for non-cancer effects 

developed by EPA and other agencies do not currently include separate reference 

concentrations (or similar values) for adults and children. Therefore, adult and child 

hazard quotients all had the same denominators. Also, the HAPEM4 exposure model 

takes into account the different activity patterns of children but does not consider their 

higher mass-specific inhalation rates. Because adults tend to commute to areas of higher 

concentrations more frequently than children do, HAPEM4 systematically produced 

higher inhalation exposure estimates, and higher TOSHIs, for adults than for children. 

For this reason, most of the discussion of individual non-cancer hazards in this section 

will focus on adults.

To avoid mixing hazard quotients for well-understood pollutants with those from 

pollutants for which data are sparse, separate TOSHIs were calculated for two groups –

pollutants for which the total RfC uncertainty factor ranged from 1 to 100, and those for 

which the uncertainty factor exceeded 100.

Figure 5-8 shows distributions of individual TOSHIs for the respiratory system for adults. 

Distributions of individual TOSHIs for six organs or organ systems, for adults and for 

children are included in Appendix L (Figures 33-38 for adults and Figures 45-50 for 

children). Of the six TOSHIs computed, only the respiratory TOSHI for low-certainty 

pollutants exceeded 1.0 for more than 1% of the US population. This result was 

dominated by a single substance, acrolein. The median of this TOSHI distribution was 

approximately 4, and its 99th percentile was approximately 20. More than three quarters 

of the population had a respiratory TOSHI greater than 1.0.

Much of the respiratory TOSHI was contributed by onroad mobile sources, with nonroad 

mobile and area (and other) sources contributing lesser but still important amounts. 

Major sources did not add much to the respiratory TOSHI nationally, and none of the 

pollutants in this TOSHI had an estimated background contribution. 

5.3.4 Aggregate TOSHI of Multiple Pollutants to Populations 

Figures in Appendix L (Figures 39-44 and 51-56) show numbers of adults and children, 

respectively, residing in census tracts for which the median exposures to multiple 

pollutants produced TOSHIs exceeding three fixed levels (0.1, 1.0, and 10) for emissions 

from all sources combined. Unlike the individual TOSHI figures (which exclude the 

extreme ends of the distribution), these population figures include the entire assessed 

population in order to increase sensitivity to potential urban-scale hot spots.

Pollutants can be grouped in categories of decreasing importance in the assessment as 

follows:
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National hazard drivers: Results show that nearly the entire US population resided in 

census tracts where the median TOSHI for respiratory effects exceeded 1.0, and that 

about 25 million adults and 4 million children resided in tracts where the respiratory 

TOSHI exceeded 10 (see Appendix L, Figures 39 and 51). These results are for the lowcertainty group of pollutants alone, and were dominated by acrolein. 

Regional hazard drivers: TOSHIs for four more organs or organ systems – blood and 

blood-forming tissues, central nervous system, liver or kidney, and immune system –

exceeded 1.0 for more than 100,000 adults and 20,000 children. Results for the immune 

system were for high-certainty pollutants; results for the other three target organs were 

for low-certainty pollutants. Of these, the central nervous system TOSHI potentially 

affected the largest populations. Approximately 600,000 adults and 100,000 children 

resided in tracts where the CNS TOSHI exceeded 1.0, and 10,000 adults lived in tracts 

where the CNS TOSHI exceeded 10.

The sixth TOSHI, for the cardiovascular system, did not exceed 1.0 for more than 10,000 

people nationwide.

5.4 Discussion of the Risk of Diesel Exhaust

Although EPA is providing concentration exposure information on diesel particulate 

matter as a surrogate for diesel exhaust, the Agency is unable to provide the same 

quantitative information in this risk characterization as is provided for the other 32 air 

toxics. At the national level, EPA believes that diesel exhaust is one of the air toxics that 

poses the greatest risks to the public based on its potential carcinogenic effects and other 

health effects related to diesel exhaust, especially since diesel engine emissions provide a 

substantial contribution to fine particle emissions.

EPA=s Clean Air Scientific Advisory Committee (CASAC) recently approved 

conclusions that EPA has reached regarding the lung cancer hazard and risk of diesel 

exhaust [42]. In EPA=s draft Health Assessment Document for Diesel Exhaust (HAD), 

the Agency concluded that diesel exhaust is likely to be carcinogenic to humans at 

environmental exposure levels that the public faces (classifying it as a “probable” human 

carcinogen in the scheme used in this NATA report)[43]. However, as stated in the 

"Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control 

Rule" [44], EPA has concluded that the available data are not sufficient to develop a 

confident estimate of cancer unit risk. The Agency concluded in developing its 

perspective on risk in the HAD that there is a reasonable potential that environmental 

lifetime cancer risks (>environmental risk=) from diesel exhaust may exceed one in a 

hundred thousand and could be as high as one in a thousand. The environmental risk 

estimates included in the Agency=s risk perspective are meant only to gauge the possible 

magnitude of risk to provide a means to understand the potential significance of the lung 

cancer hazard. The estimates are not to be construed as cancer unit risk estimates and are 

not suitable for use in analyses which would estimate possible lung cancer cases in the 

exposed populations. 
102

EPA recognizes that, as in all such risk assessments, there are uncertainties in this 

assessment of the environmental risk range including limitations in exposure data, 

uncertainty with respect to the most accurate characterization of the risk increases 

observed in the epidemiological studies, chemical changes in diesel exhaust over time, 

and extrapolation of the risk from occupational to ambient environmental exposures. As 

with any such risk assessment for a carcinogen, despite EPA=s thorough examination of 

the available epidemiologic evidence and exposure information, at this time EPA can not 

rule out the possibility that the lower end of the risk range includes zero.19 However, it 

is the Agency=s best scientific judgment that the assumptions and other elements of this 

analysis are reasonable and appropriate for identifying the risk potential based on the 

scientific information currently available. 

Even the lower end of the risk range (presented in the risk perspectives section of the 

Diesel Exhaust HAD) is above the level that has historically warranted regulatory 

concern at EPA for air toxics. The Agency believes that areas of the U.S. that have 

relatively higher annual exposure levels for diesel exhaust, certainly those counties and 

States with annual exposure average levels above 2 micrograms per cubic meter, should 

consider the scientific judgments that the Agency has made in the risk perspectives 

section of the HAD while considering the important limitations in their efforts to 

compare air toxic risks and set priorities for their programs. At the higher exposure 

levels found in a number of urban areas in NATA, there is an overlap between what the 

occupational levels were in the epidemiological studies that EPA considered and 

environmentally equivalent exposures. 

There is substantial evidence that diesel exhaust alone and as part of mixture of fine 

particles is associated with harmful respiratory and cardiovascular health effects 

including an association with premature mortality. In addition to the direct emissions of 

diesel engines of fine particulate, the NOx, SO2, and VOC emissions from these sources 

are transformed into substantial concentrations of fine particles in the atmosphere (e.g. 

nitrates and sulfates). The Agency provided an assessment of the seriousness of the 

health effects associated with human exposure to fine particles in a Criteria Document 

(CD) in 1996 [45]. Recent major reanalysis of two of the most critical studies regarding 

the health effects of long-term exposure to fine particles examined in that CD confirmed 

the findings of associations between long-term fine particle exposure and mortality [46].

 

19EPA’s scientific judgment (which CASAC has supported) is that diesel exhaust is likely to be 

carcinogenic to humans. Notably, similar scientific judgments about the carcinogenicity of diesel exhaust 

have been recently made by the National Toxicology Program for the Department of Health and Human 

Services, NIOSH, WHO, and OEHA of the State of California. In the risk perspective discussed above, 

EPA recognizes the possibility that the lower end of the environmental risk range includes zero. The risks 

could be zero because (1) some individuals within the population may have a high tolerance level to 

exposure from diesel exhaust and therefore are not susceptible to the cancer risks from environmental 

exposure and (2) although EPA has not seen evidence of this, there could be a threshold of exposure below 

which there is no cancer risk.
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5.5 Uncertainty and Variability Analysis for the NATA NationalScale Assessment

5.5.1 Introduction 

EPA’s guidelines for risk characterization recommend that estimates of health risk be 

presented in the context of uncertainties and limitations in the data and methodology. 

The degrees to which different types of uncertainty and variability need to be quantified, 

and the amount of uncertainty that is considered acceptable, may vary with the scope and 

purpose of the assessment. Because the national-scale assessment is generally intended 

for prioritization, tracking national trends and progress, and setting the research agenda 

for the air toxics program, EPA can accept a higher degree of uncertainty than if the 

assessment were intended for supporting regulatory actions or as a final assessment of 

risk at the local scale. Instead, the national-scale assessment is only a part of the analyses 

that EPA intends to conduct to guide and inform the Air Toxics Program. It is inevitable 

that local details in a nationwide study will be more uncertain than in an optimized local 

study, and that the uncertainty of the nationwide study will be described in more general 

terms.

Here, we refer to “uncertainty” as imperfect knowledge regarding the values of specific 

parameters included in the assessment, and “variability” as real differences in the values 

of specific parameters among places or individuals included in the assessment. 

Generally, uncertainty can be reduced by gathering better data, whereas variability cannot 

be reduced but can be characterized through more refined information or model 

resolution. 

EPA had hoped to undertake a quantitative analysis of uncertainty and variability within 

each component of the national-scale assessment, using a “bottom-up” approach. The 

intent was to methodically estimate the range of possible values (and use frequency 

distributions where supported by more complete data) for each parameter used in the risk 

calculations. These ranges and distributions would then be used as input for separate 

calculations of the propagation of uncertainty and variability for all variables combined. 

However, the EPA technical experts who contributed the various components of this 

assessment could not with confidence place quantitative estimates, or even semiquantitative order-of-magnitude estimates, on uncertainty and variability for many of the 

input parameters. It appears unlikely that a complete “bottom-up” approach to 

characterizing uncertainty will be feasible without significant additional work.

Nevertheless, in order to obtain a minimal estimate of how much higher or lower the risks 

calculated by this assessment are likely to be, we have provided an illustration of a “topdown” approach to estimating some of the uncertainty and variability associated with (1) 

modeling of ambient concentrations, (2) estimation of personal exposure, and (3) doseresponse assessment. This illustration also shows how this uncertainty and variability 

might propagate into the final estimates of risks. 

The illustration has several important limitations that readers should keep in mind. First, 

it was not possible to fully separate variability from uncertainty with this “top-down” 
104

approach. Therefore, the propagated characterization of uncertainty incorporates both. 

Second, the quantitative uncertainty estimates did not include all sources of uncertainty, 

and the combined estimates of uncertainty seem likely to be underestimates. True 

uncertainty and variability may be greater, but are not likely to be less. 

The uncertainty and variability section ends with recommendations to develop a plan for 

a more rigorous and comprehensive assessment of uncertainty and variability in this and 

future national-scale assessments the future, based on more refined information for 

individual components of the assessment.

5.5.2 Source Characterization 

5.5.2.1 Data Sources

As described in Section 4.2.1.3, the 1996 NTI is a composite of emissions estimates 

generated by State and local regulatory agencies and EPA using emission estimation 

techniques determined by agency, pollutant, and source category. These emissions 

estimates differed in quality, number of pollutants included, level of detail, and 

geographic coverage. Furthermore, EPA did not attempt to verify the methods by which 

emissions were estimated or undertake a full quality assurance and quality control 

evaluation of the NTI. 

As discussed in Section 4.2.1.2.1, EPA compared the information in the NTI to the 

emissions in the CEP, and the point sources included in the TRI, and NET data sets, 

which have been used in other dispersion modeling exercises. However, each of these 

comparison data sets was developed for different purposes and had different facility and 

pollutant coverage. Also, their data were compiled by methods likely to be surrounded 

by at least as much uncertainty as the NTI. In general, the NTI compares favorably to 

these data sets considering its inclusion of all anthropogenic source sectors and the level 

of emissions detail required for this study (e.g., location coordinates, stack parameters, 

etc.).

The quality and associated uncertainty in the NTI emissions data varies according to 

source sector and individual source categories and pollutants. For point sources, 

including those categorized as “major sources,” emissions estimates and the associated 

facility/stack details are more certain where more effort has been concentrated on 

improving estimates (e.g., under certain MACT standards development efforts that 

included gathering individual facility emissions data or within states that have gathered 

emissions data for certain facilities for permitting or planning purposes). By their nature, 

nonpoint emissions (area, other, or mobile), that are compiled in the NTI as county-wide 

emissions estimates, rather than at known location coordinates, are less certain than point 

sources. Many of these nonpoint emissions are estimated by using emission factors, that 

may be out dated, and surrogate information (e.g., industry sales, population. Uncertainty 

due to such surrogate allocation schemes are particularly pronounced for mobile source 

categories, especially within the nonroad sector where, in order to simplify computation, 

the county-level emissions from hundreds of nonroad sources (e.g., recreational marine 

vessels, lawn mowers, construction equipment, etc.) were consolidated into three source 
105

category groups (2-stroke gas, 4-stroke gas, and diesel engines) prior to spatial allocation. 

It is important to note that the 1996 NTI is not static. EPA intends to periodically update 

it, as resources allow and as more reliable data (e.g., addition of missing information, 

removal of double-counting, improved emission factors) become available for that year. 

Thus future assessments based on the 1996 NTI may produce different results. These 

factors represent important sources of uncertainty in the assessment.

5.5.2.2 Emission Locations

5.5.2.2.1 Point Sources

Locations for many point sources in the NTI were unknown, and had to be placed via 

default mechanisms (e.g., using zip codes or counties). EPA prepared a State-by-State 

analysis of proportions of emissions of three metals that were located by default. This 

analysis suggested that “area and other” sources dominated the total emissions, as 

expected for these pollutants. Although this comparison did not provide a quantitative 

assessment of uncertainty, the magnitude of sources and emissions defaulted in a given 

State suggests that there is uncertainty in the results due to potential location errors. 

Nationwide, fewer than 10% of point source sites of primary modeled emissions of the 32 

pollutants in this assessment were based on default locations.

5.5.2.2.2 County-Level Emissions Sources

All county-level emissions (i.e., mobile sources and many area and other sources) were 

spatially allocated to the census-tract level prior to ASPEN modeling, using appropriate 

surrogates such as population or land use using methods described in Appendix 3. In 

addition to uncertainties introduced by the use of surrogates for allocation, the surrogate 

information itself was subject to substantial uncertainty due to age or variable quality. 

5.5.2.3 Stack Parameter Defaults

A facility in the NTI may have contained many emissions release points, and each release 

point required several process parameters for dispersion modeling. When these 

parameters were missing or were out of reasonable range they were replaced by defaults. 

EPA compiled a table of the percentage of facilities for which defaults were used for 

eight individual parameters, and found that on average, about one-quarter of the facility 

data had to be augmented by one or more defaults.

5.5.2.4 Particle Size and Reactivity Assignments 

All emissions input for ASPEN modeling had to be categorized according to particle size 

fraction (coarse versus fine) and reactivity to support the model’s accounting for 

deposition and subsequent chemical reactions. None of the emissions inventories used in 

this assessment contained this information; all had to be assigned by EMS-HAP. These 

assignments added to the overall uncertainty.

5.5.2.5 Chemical Speciation Data

The NTI did not include uniform speciation information for HAP groups (e.g., polycyclic 

organic matter, chromium and compounds) uniformly. In the process of preparing the 
106

NTI data for ASPEN modeling, EPA made important assumptions about chemical 

speciation within these groups, creating additional uncertainty in the modeling results. 

5.5.3 Ambient Concentration Estimation

5.5.3.1 Temporal Resolution of Emissions

The NTI provided the raw emissions data, which were processed and made "modelready" for ASPEN by EMS-HAP. In addition to the spatial allocation described in 

section 1.2.1 above, the ASPEN model also required higher temporal resolution. To 

support this, EMS-HAP allocated annual emissions into days and daily emissions into 

three-hour periods. 

5.5.3.2 Simplifying Assumptions

The ASPEN model does not simulate local terrain effects and the local meteorological 

conditions associated with terrain effects, which may have caused local-scale 

inaccuracies in the predicted concentrations. Also, ASPEN does not model transport of 

any pollutant beyond 50 km from its original emission point. For some air toxics in this 

assessment (e.g., reactive volatile organic compounds), this assumption was reasonable, 

while for others (e.g., persistent metal compounds), this assumption represented a 

significant source of uncertainty. Finally, the ambient concentration estimates included 

uniform “background” concentrations for 13 pollutants, applied across all geographic 

areas. This was an important simplifying assumption, because such extrinsic 

concentrations may vary geographically for many of these pollutants. These three 

simplifying assumptions create currently unquantifiable but important uncertainties in the 

ASPEN outputs. 

5.5.3.3 Meteorological Characterization Uncertainties 

Meteorological data are a critical input for the ASPEN model. EPA’s analysis of the 

influence of two different sources of input data showed that ambient concentration 

estimates varied within a range of minus 17% to plus 84%. EPA used the 1996 database, 

which had a lower mean source-to-meteorological station separation distance that was 

more representative, for this assessment.

5.5.3.4 Model Formulation and Methodology Uncertainties

5.5.3.4.1 Deposition and Dispersion Algorithms 

The ASPEN air quality model employs a Gaussian plume model to characterize transport 

and dispersion, using algorithms extracted from version 2 of the Industrial Source 

Complex Long-Term model (ISCLT2). This model has a history of usage and 

development that dates back to the 1960's, suggesting that we can anticipate its level of 

uncertainty. As discussed in Section 4.2.2.4, comparisons have suggested that 

approximately 90% of the model’s estimates are within a factor of two of those observed. 

As described in Section 4.2.2.4, EPA verified that the model formulation algorithms were 

performing as anticipated by using lead emissions to test both the deposition and 

dispersion algorithms were working properly, and whether the use of a “net” of receptors 
107

might lead to a bias in the modeling results. The results suggested that ASPEN may 

predict average ambient lead concentrations 20 to 30% lower than a more refined model, 

because ASPEN uses coarse particle deposition velocities that are higher than typically 

used in local-scale modeling.

5.5.3.4.2 Atmospheric Transformation Algorithms

Although Gaussian dispersion models can be modified to handle zero-order (linear) 

production or removal, they typically do not treat nonlinear chemistry effects. To address 

this limitation EPA developed a mechanism to separately estimate exponential formation 

and added (or subtracted) the secondary formed concentration to that attributable to 

primary emissions. Using this mechanism, ASPEN results for formaldehyde and acrolein 

were 23% and 44% attributable secondary formation. However, results from the more 

refined OZIPR model suggest that secondary formation for formaldehyde and acrolein 

would account for 90% and 85% (respectively) of the total. These results should be 

considered only approximate, because the two models were not directly compared (i.e., 

using equivalent emissions and meteorology), but they do suggest that ASPEN may be 

underestimating changes in concentration due to reactivity.

5.5.3.4.3 Interpolation Between Census Tract Centroids

EPA’s comparison of ASPEN and ISCLT3 outputs (to determine if ASPEN’s 

interpolation scheme might be underestimating actual impacts) found that ASPEN 

estimates were lower than ISCLT3’s by about 10% in the near distances, with the 

underestimation increasing to about 25% at 30 km downwind.

5.5.3.4.4 Summary

As discussed above, the use of ASPEN to estimate ambient concentrations added 

important elements of uncertainty to the national-scale assessment. The simplifying 

assumptions that made the a national-scale dispersion modeling possible at all – lack of 

terrain effects, a 50-km radius for effects, and the application of uniform national 

background concentrations for some pollutants – each have uncertainties that would be 

difficult to quantify but that may be important.

However, it was possible to make some rough estimates of the performance of the entire 

modeling system, and of some of its parts. Past studies with air toxics at the local scale 

have suggested that about 90% of estimated concentrations should be within a factor of 2 

of those observed, assuming well-characterized emissions and representative 

meteorological data. EPA’s comparisons of the formulation and methodology used by 

ASPEN and other dispersion models suggested that differences in dispersion algorithms 

were minor, likely leading to performance differences of less than 10% in the near field. 

ASPEN’s deposition velocities for coarse particles showed larger differences, and were 

estimated to bias concentrations roughly 30% low, a bias that affects only pollutants 

simulated as having coarse particles. Investigations of uncertainties associated with 

chemical reaction effects suggested that concentration estimates for reactive species 

should be considered more uncertain than for non-reactive species.
108

Spot checks on location uncertainties for three metals suggested that 6 to 30% of these 

emissions were assigned to locations rather than modeled at their true locations. A site 

visit to one lead smelter found fugitive emissions that appeared greater than those 

reported in the NTI, suggesting that close inspection of facilities might reveal other 

under-reported non-point emissions. These emission characterization uncertainties could 

have a greater impact on the model-to-monitor comparison results (resulting in 

differences of a factor of 3 or more) than uncertainties seen elsewhere in the modeling 

algorithms or the meteorological characterizations (which appear to result in differences 

of 30 to 80%).

5.5.3.5 Illustration of Uncertainty and Variability Associated With Ambient 

Concentration Estimates

EPA’s statistical comparison of ASPEN model estimates with monitored ambient 

concentrations at the same locations (Section 4.2.2.3) provides an opportunity to estimate 

the uncertainty in the model’s performance for seven substances (benzene, 

perchloroethylene, formaldehyde, acetaldehyde, cadmium, chromium and lead) 

representing different source types, deposition characteristics, and atmospheric reactivity. 

The model-to-monitor comparison showed that modeled estimates for most of the 

pollutants examined were on average lower than measured concentrations. The degree to 

which the model underestimated the measured concentration was greater for reactive 

gases than for stable ones, and greater still for particulates. In addition, the variance of 

the ratios generally appeared to increase with the degree of underestimation.

If there were sufficient monitored values, and these values represented truth, this 

information would be a reasonable approximation of total uncertainty contributed by the 

National Toxics Inventory, all ASPEN model inputs, and dispersion model error. 

Although there is uncertainty regarding the monitored values, the following illustration 

shows how a model-to-monitor comparison approach can be used to estimate model 

system uncertainty. The illustration also provides, for a limited set of pollutants, 

calculated values based on this approach. 

As described in Section 4.2.2.3, the tendency of the model to underestimate measured 

levels decreased when the comparison was made with the highest modeled concentrations 

within 10 and 20 km of monitor locations. This result suggested that some part of the 

underestimation could be attributed to spatial uncertainty of the underlying emission and 

meteorological data and the tendency of air monitoring networks to select sites in highimpact areas. Nevertheless, there were many locations for which maximum model 

estimates were still lower than measured concentrations, even at distances up to 50 km. 

These underpredictions were judged most likely due to underestimated (or missing) 

emissions data or uncertainty in chemical transformation assumptions.

In this illustration, total uncertainty surrounding the modeled estimates of ambient 

concentrations was estimated quantitatively, as follows: (1) The model-to-monitor ratios 

from the original analysis were inverted, to provide an estimate of the factor by which the 

ASPEN model under-predicted each monitored concentration. (2) The populations of 

resulting monitor-to-model ratios for seven pollutants were fitted to lognormal 
109

distributions20 using Crystal Ball 4.0 software. The parameters of the fitted distributions 

are shown in Table 5-1. Variability could not be estimated because only the 1996 NTI 

was modeled. Therefore, comparisons to monitored data for other years were not 

possible.

In addition to expressing the uncertainty of the ASPEN estimates, the fact that the mean 

monitor-to-model ratio for all 

seven pollutants exceeded 1.0 was 

consistent with the reported 

tendency of ASPEN to underpredict ambient concentrations. 

Furthermore, both the degree of 

bias and the total range of 

uncertainty varied between classes 

of pollutants. Accordingly, to 

better support extrapolation of 

these results to pollutants that were 

not monitored in 1996, EPA 

developed composite monitor-tomodel frequency distributions for 

three classes of pollutants: stable gases; reactive gases (i.e., subject to atmospheric 

transformation), and particulates using standard Monte Carlo simulation techniques21. 

Stable gases were represented by the geometric mean of randomly-selected ratios for 

benzene and perchloroethylene; reactive gases were represented by the geometric mean 

of ratios for 

formaldehyde and 

acetaldehyde. 

Particulates were 

represented by the 

geometric mean of 

ratios for lead, 

cadmium, and 

chromium. Results are shown in Table 5-2 and Figures 5-9 to 5-11.

A monitor-to-model ratio of 1.0 indicates perfect agreement. For stable gases, 95% of the 

ratios fell between 0.69 and 2.9. For gases subject to transformation, 95% of the ratios 

fell between 0.76 and 5.0. For particulates, 95% of the ratios fell between 1.2 and 20. 

Because these ranges clearly show different amounts of both bias and variance, these 

three groups of pollutants have been considered separately in the analysis of aggregate 

uncertainty.

 

20 The lognormal distribution was the continuous distribution of best fit for five of the pollutants, and 

provided second-best fit for the other two.

21 Simulation was performed with Crystal Ball 4.0 software, using 10,000 iterations with Monte Carlo 

sampling and an initial random number seed of 0. 

Table 5-1. Illustration: Parameters for lognormal 

distributions fitted to monitor-to-model ratios for 

seven pollutants. 

Pollutant Mean

Standard

Deviation

Benzene 1.19 0.51

Perchloroethylene 2.26 1.56

Formaldehyde 2.28 1.60

Acetaldehyde 2.69 2.25

Lead 15.37 37.23

Cadmium 12.53 24.40

Chromium 6.05 9.51

Table 5-2. Illustration: Calculated percentiles for 

monitor:model ratio distribution.

Monitor:Model 

Ratio for:

2.5% 5% 50% 95% 97.5%

Stable gas 0.69 0.78 1.4 2.6 2.9

Reactive gas 0.76 0.88 2.0 4.3 5.0

Particulate 1.2 1.4 4.9 16 20
110

This characterization of uncertainties in the emissions inventory and dispersion modeling 

portions inherently assumed that monitored ambient concentrations were measured 

without error, and that ASPEN’s divergence from monitored levels was the result of 

either model error or errors in inputs to the model. Of course, ambient concentrations 

cannot be measured without error, and further errors may have occurred when these data 

were compiled and reported. Nevertheless, EPA believes that this approach has the 

potential to provide a fair approximation of the total uncertainty associated with the use 

of ASPEN for this assessment. 

5.5.4 Personal Exposure Assessment

5.5.4.1 Microenvironment Factors

To predict personal exposure, HAPEM4 modeled the relationship between ambient air 

quality and concentrations in microenvironments occupied by human receptor 

populations. Because its use on a national-scale precluded detailed mass balance 

equations, HAPEM4’s microenvironment modeling relied on microenvironment factors 

(simple first-order relationships between outdoor and indoor air quality). However, the 

outdoor-indoor relationship is not well documented or understood for many pollutants 

and technical reviewers of this approach agreed there is a great degree of uncertainty 

associated with it, suggesting that this uncertainty be assessed qualitatively or 

quantitatively. 

5.5.4.1.1 Population Cohorts 

The assessment assumed that information from EPA’s CHAD database for 40 cohort 

groups adequately represented the activity patterns of the general population in all areas 

of the country. Groups were selected to represent variability by age, race, and gender, in 

order to support comparisons of various demographic groups while still allowing 

aggregation of exposure across ages. However, there is no way to measure whether these 

data actually captured the full range of human activity in each tract. For example, it is 

possible and even probable that members of the same cohort behave differently in 

different census tracts. It is also possible that selecting different types of cohorts (e.g., 

economic classes, inside vs. outside workers, etc.) may have encompassed more 

variability. 

5.5.4.2 Activity Pattern Sequence

HAPEM4 constructed annual average activity patterns from multiple 24-hour activity 

patterns by combining patterns from different individuals, so that day-to-day correlations 

in activity were not preserved. These aggregated activity patterns were therefore more 

representative of population averages for that demographic group, rather than individual 

patterns. For this reason, exposure distributions for the each group represented 

uncertainty in the population average rather than variability in individual exposures. 

Uncertainty and variability of input data other than activity pattern were not considered, 

so that the resulting uncertainty information provided by the prediction distributions is an 

underestimate of the overall uncertainty.
111

5.5.4.3 Illustration of Uncertainty and Variability Associated with Exposure 

Estimates

A comparison of HAPEM4 exposure estimates against personal monitoring data 

(analogous to the ASPEN model-to-monitor comparison) would be valuable to quantify 

the aggregate uncertainty of the exposure estimation methods. However, EPA is not 

aware of any body of personal monitoring data that has not already been used in 

developing HAPEM4, making any such comparison invalid. Therefore, it is not possible 

to determine if the HAPEM4 exposure estimates were biased relative to measured 

exposures. Nonetheless, it is possible as an illustration to compare ratios of ambient 

concentrations to personal exposures from modeling and monitoring studies for other 

pollutants to determine if HAPEM4 has captured an appropriate range of variability. For 

this illustration, EPA utilized data collected for two criteria pollutants, ozone and 

particulate matter, to develop frequency distributions for the exposure modeling portion 

of the risk calculation. 

Several simple statistics confirmed that HAPEM4 did not capture inter-individual 

variability in exposure, in that it produced exposure estimates that varied too little among 

people living in the same tract. For example, in no case (considering all tracts and 

pollutants) did the 90th percentile exposure exceed the 50th percentile by more than 2%. 

An analysis of the full range of exposures within several tracts showed that exposures for 

the least and most exposed individuals differed by less than 20%. Additionally, a tractby-tract correlation analysis of ASPEN and HAPEM4 results produced correlation 

coefficients equal to or greater than 0.998. These results contrasted sharply with realworld measurements of personal exposure, which tend to have much greater interpersonal 

variability. 

Databases of matched ambient and personal monitoring data sufficient to support 

estimating uncertainty and variability in personal exposure among people living in the 

same area were not available for the pollutants in this assessment. As a surrogate for this 

illustration, EPA used Spearman correlation coefficients for ozone and particulate matter 

as examples of “typical” gases and particulates. These data may not be directly 

applicable because (1) they were developed from monitoring data at a coarser resolution 

than used in the national-scale assessment, and (2) pollutant characteristics may differ. 

However, they are used here to illustrate data needs for “top down” estimates of 

uncertainty and variability for air toxics.

The correlation coefficients (seasonal averages of 0.49 for ozone and 0.13 for PM, from 

correlation analyses of ambient and personal measurements reported in EPA’s draft 

exposure assessment for particulate matter were input into a Monte Carlo simulation, 

which developed a set of estimated personal-to-ambient ratios by sampling two correlated 

uniform distributions22. 

 

22 Populations of personal-to-ambient ratios were calculated using 10,000 iterations with Crystal Ball 4.0, 

by dividing an personal exposure selected from a uniform distribution between 0.1 and 10 by an ambient 

concentration linked to the exposure level by the appropriate correlation coefficient. 
112

The resulting distributions of ambient-to-personal ratios shown in Table 5-3 and Figures 

5-12 and 5-13. 

For “typical” 

gases, 95% of 

simulated 

ambient-topersonal ratios 

fell between 0.09 

and 13.0. For particulates 95% of the ratios fell between 0.13 and 7.08. The relatively 

weak observed correlation between ambient and personal concentrations for ozone and 

PM results from elements of both uncertainty (e.g., errors in measurement of ambient and 

personal concentrations) and variability (e.g., varying characteristics of different 

microenvironments). Although data were too sparse to support separating variability and 

uncertainty, it appears likely that variability among microenvironments is the dominant 

factor.

While these distributions are obviously limited in their applicability to the use of 

HAPEM4 to predict individual exposures, EPA currently lacks the information on 

correlated ambient and personal data that would be needed to do better. Furthermore, this 

analysis focuses only on the variations in ambient-to-personal ratios, and assumes that 

HAPEM4 contributed no bias. EPA intends to seek better data in the hope of extending 

and improving this part of the uncertainty analysis for future national-scale assessments.

5.5.5 Illustration of Uncertainty and Variability for Dose-Response 

Assessment

Uncertainty in dose-response assessments is amenable at least to partial quantification. 

Specifically, assessments that use statistical methods to determine benchmark doses and 

to fit dose-response relationships to toxicological data often provide confidence intervals 

for the results. This information is available for many RfCs and UREs. In addition, EPA 

generically considers RfCs for effects other than cancer as being surrounded by an 

uncertainty “spanning perhaps one order of magnitude.” These uncertainty 

characterizations clearly incorporate both uncertainty and variability in a manner which 

does not allow them to be separated. Additionally, it is important to realize that the 

confidence intervals surrounding RfCs and UREs include only the statistical uncertainty 

in interpreting the data. Uncertainties inherent in the choice of models to extrapolate 

from animals to humans and from high to low doses are potentially far larger, and cannot 

be quantified.

5.5.5.1 Unit Risk Estimates (UREs)

The UREs used in the national-scale assessment are subject to four major areas of 

variability and uncertainty. First, many of the pollutants were classified as probable 

carcinogens because data were not sufficient to prove causality in humans. It is possible 

that some of these pollutants do not cause cancer at environmentally relevant doses, and 

that true risk associated with these air toxics is zero. Second, all UREs in this study were 

based on linear extrapolation from high to low doses. It is possible that the true doseresponse relationships for some pollutants may be less than linear, resulting in an 

Table 5-3. Illustration: Percentiles for uncertainty and variability in 

the personal: ambient ratio distribution.

Personal:ambient ratio 

for:

2.5% 5% 50% 95% 97.5%

Gas 0.09 0.14 1.0 7.6 13

Particulate 0.13 0.21 1.0 4.5 7.1
113

overestimate of risk. Third, most UREs in this study were developed from animal data 

using conservative methods to extrapolate between species. Human responses may differ 

from the predicted ones. The first three elements are comprised entirely of uncertainty. 

Fourth, most UREs in this study were based on statistical upper confidence limits, though 

some were based on statistical best fits. (While this does not affect overall uncertainty, 

UREs based on best fits should be unbiased, while those based on upper confidence 

limits should be biased high.) This fourth element represents a combination of variability 

(i.e., based on variation responses of different people or animals) and uncertainty (i.e., 

potential errors in the measurement of exposure and response). Because of the aggregate 

treatment all four sources of variability and uncertainty described above, EPA considers 

all its UREs to be upper-bound estimates. 

Of these four areas of uncertainty and variability, only the variability element of the 

fourth is amenable to quantitative analysis for this illustration. Some dose-response 

assessments that determine points-of-departure or fit dose-response curves to data using 

statistical methods also include enough information to support fitting a frequency 

distribution to the URE. To illustrate this approach, EPA developed a frequency 

distribution for benzene (Table 5-4 and Figure 5-14), using the lognormal distribution 

and confidence 

interval reported 

in the recent 

IRIS 

reassessment. 

To be consistent 

with the use of ratios to describe uncertainty in the exposure assessment, the distribution 

was assigned a mean of 1.0. 

It is important to remember that this illustration is based on a single, well-understood 

substance. While aggregated uncertainty illustration at the end of this section assumes 

this distribution is typical of other carcinogens, it is possible that other substances (many 

of which lack distributional information) may have greater statistical error terms. More 

importantly, this frequency distribution represents only the statistical error term in the 

dose-response assessment and does not address the other three important sources of 

uncertainty in dose-response assessment. Information sufficient to quantify these other 

uncertainties does not currently exist, and this estimate of uncertainty and variability 

should be considered a minimum for the dose-response assessment as a whole. The true 

aggregate of uncertainty and variability is likely to be much greater.

5.5.5.2 Reference Concentrations (RfCs)

EPA and other agencies express uncertainty in reference concentrations for effects other 

than cancer using a series of uncertainty and modifying factors (UFs and MFs). UFs are 

assigned for extrapolation (1) between species, (2) to sensitive individuals within a 

species, (3) from subchronic to chronic exposure duration, (4) to estimate no-effect levels 

from lowest-effect levels, and (5) to account for incomplete data. MFs may also be 

assigned to consider other issues not covered by the standard UF categories. The 

aggregate UF/MF depends on the number of extrapolations required, and is best viewed 

Table 5-4. Illustration: Percentiles for variability in the benzene 

URE.

Ratio of “true” URE to 

the estimated URE

2.5% 5% 50% 95% 97.5%

Benzene 0.14 0.19 1.0 5.3 7.2
114

as an expression of the possible range within which the RfC could change when more 

complete data become available.

Quantitative estimates of the uncertainty associated with each of these factors are not 

available. In general, if sufficient data were available to support a quantitative 

assessment of these factors for any substance, the factors would not be needed in the first 

place. For this reason, it is not possible to develop total uncertainty ranges for RfCs by 

aggregating uncertainties of each UF or MF. However, EPA’s definition for the RfC23

specifically describes it as having “uncertainty spanning perhaps one order of 

magnitude.” Because the variability among test organisms is likely to be only a small 

part of this order-of-magnitude range, this component can be considered to be dominated 

by uncertainty. Although the actual uncertainty in the RfC may vary among substances, 

this order-of-magnitude generic range is probably the most reasonable characterization 

for the purposes of the national-scale assessment. 

Thus, for illustrating the uncertainty and variability surrounding RfCs and similar values, 

EPA assigned the RfC a uniform distribution with upper and lower bounds of 3.0 and 0.3, 

respectively, as shown in Figure 5-15.

5.5.6 Illustration of Propagation of Uncertainty and Variability

As discussed above, EPA has developed an illustration of a “top-down” uncertainty 

assessment that quantitatively estimates a portion of the variability and uncertainty 

associated with three major components of this assessment – the estimation of ambient 

concentrations, the estimation of personal exposures associated with the ambient 

concentrations, and the assessment of dose-response.

The illustration for ambient concentrations inherently includes all the uncertainties in the 

NTI, the other inputs to ASPEN, and modeling error within ASPEN itself. Although the 

relative contributions of each element cannot be distinguished, the method used has the 

potential to fairly portray both bias and uncertainty for the aggregate NTI-ASPEN 

component of the assessment. Because the annual average concentration for a specific 

time and location has no variation, variability should not be an important component in 

this part of the illustration.

The uncertainty and variability analysis for personal exposure characterizes the 

“expected” variation between ambient and personal exposures noted for ozone and 

particulates in other studies. In contrast, HAPEM4 produced essentially perfect 

correlations between ambient levels and personal exposures in the national-scale 

assessment. Therefore, applying an interpersonal variation term developed elsewhere 

may better describe the variability that should be present in the HAPEM4 personal 

exposure estimates, but was not. However, this illustration does not attempt to estimate 

potential bias in the HAPEM4 personal exposure estimates, which would require personal 

monitoring data not already used in the development of HAPEM4. As described, this 

 

23 An estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation 

exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable 

risk of deleterious effects during a lifetime.
115

element of the uncertainty/variability illustration should be dominated by variability, 

most likely among microenvironments.

The uncertainty/variability analysis for dose-response should be considered a generic 

illustration that would need to be repeated on a pollutant-specific basis. For carcinogens, 

the variability surrounding the URE has been fitted to a distribution that should be 

considered a lower estimate of the URE’s plausible variability and uncertainty. However, 

readers should keep in mind that most carcinogens are far less studied than benzene and 

may have correspondingly greater uncertainty. Also, the analysis for benzene includes 

only the statistical error term; other important sources of uncertainty that may be far 

larger are outside the scope of the current analysis.

For noncarcinogens the uncertainty/variability analysis is based on the stated order-ofmagnitude of uncertainty in EPA’s definition for the RfC (which should be dominated by 

uncertainty rather than variability). All values within a 10-fold range were considered 

equally likely. While obviously represents an oversimplification of the uncertainty 

associated with the RfC, it nonetheless provides a quantitative illustration of the stated 

uncertainties associated with the RfC derivation process. Better characterization of the 

distribution of possible RfC values would require significant additional research.

The last step in the “top-down” illustration of a quantitative variability/uncertainty 

assessment approach was the aggregation of uncertainty and variability associated with 

ASPEN, HAPEM4, and the dose-response assessment process were combined by Monte 

Carlo simulation24, as follows:

Where:

Mon2Mod = ratio of monitored to modeled concentration

Pers2Amb = ratio of ambient concentration to personal exposure concentration

DR = of “true” RfC or URE to deterministic estimate

RR = ratio of “true” risk to deterministic estimate

The calculation was repeated 10,000 times, with each of the three input distributions 

sampled randomly and without correlation to the others. The risk ratio was estimated 

separately for carcinogens and noncarcinogens, and for stable gases, transformed gases, 

and particulates, for a total of six aggregate uncertainty estimates (Table 5-5 and Figures 

5-16 to 5-21 below). As discussed, uncertainty and variability could not be separated for 

this demonstration exercise because they were inextricably mixed in some elements. 

 

24 Simulations were conducted with Crystal Ball 4.0 software, using 10,000 iterations, Monte Carlo 

sampling and an initial seed value of 0.

(Mon2Mod)(Pers2Amb)(DR) = RR
116

Because the propagation of variability and uncertainty did not capture important 

uncertainties in the dose-response assessment process, and did not include potential bias 

in the personal 

exposure or 

dose-response 

elements, these 

percentiles 

should be 

considered a low 

estimate of the 

total plausible 

uncertainty and 

variability surrounding the risk characterization. 

For stable gases, 95% of likely cancer risk and noncancer hazard quotient values lay 

within a range from approximately one order of magnitude below and 1.5 orders of 

magnitude above the deterministic risk estimate. For reactive gases, 95% of likely values 

fell between approximately one order of magnitude below and 1.7 orders of magnitude 

above the deterministic value. For particulates, 95% of likely values fell between 

approximately half an order of magnitude below and 2 orders of magnitude above the 

point values. In general, stable gases had the smallest plausible risk range, followed by 

reactive gases, and then by particulates.

This illustration of an approach to estimating combined variability and uncertainty from 

the individual components of the assessment is a relatively crude estimate that is 

nevertheless somewhat useful for putting the risk results for individual census tracts into 

perspective. For example, a typical census tract median risk estimate for benzene was 

about 10 in 1 million. Since benzene is a stable gas, one can use the table to provide a 

minimal 95% confidence interval for benzene-related risks in that tract extending from 

0.6 to 360 in 1 million. Similarly, a typical census tract median cancer risk for chromium 

was about 2 in a million. The illustration provides a 90% confidence interval on this tract 

estimate ranging from 0.8 in a million to about 120 in a million. While these confidence 

interval illustrations are only approximations, EPA believes that they provide an 

appropriate sense of the imprecision of risk estimates that deterministic risk estimates do 

not give.

5.5.7 Aggregation of Risk Across Pollutants

Because the above illustration does not characterize the variability and uncertainty 

associated with all 32 pollutants, it was not logical to finish it with a quantitative 

propagation of uncertainty associated with aggregating risk across pollutants. However, 

some insights about this can be gained from procedures for propagation of statistical 

error. When aggregating risks across multiple pollutants, it is apparent from Table 5-5 

that differences among classes of pollutants will lead to differences in aggregate 

uncertainty/variability. Also, by simple error propagation theory, we can show that if:

Table 5-5. Illustration: Combined uncertainty and variability, in 

terms of the risk ratio (i.e., the ratio of “true” risk to estimated risk).

Risk Ratio for: 2.5% 5% 50% 95% 97.5%

Cancer: stable gas 0.06 0.11 1.4 20 36

Cancer: reactive gas 0.08 0.14 2.0 29 51

Cancer: particulate 0.23 0.41 4.7 61 100

Noncancer: stable gas 0.13 0.22 2.1 19 33

Noncancer: reactive gas 0.16 0.27 2.9 29 48

Noncancer: particulate 0.48 0.76 7.0 57 92
117

where RA is aggregate risk and ri is the risk associated with pollutant i, the uncertainty 

associated with the aggregate risk, UR, can be shown to be:

where Uri is the uncertainty of the risk associated with pollutant i, and the uncertainties 

associated with each pollutant risk are assumed to be independent. If we simplify this 

equation by dividing each side by RA, we obtain:

where the left-hand side of the equation now corresponds to the fractional or relative 

uncertainty in the aggregate risk. Examination of the right-hand side of the equation 

shows that (1) pollutants with larger absolute uncertainties in their risks will contribute 

more to the aggregate uncertainty, and (2) when individual pollutant risk uncertainties are 

comparable, pollutants that dominate or drive the aggregate risk will also tend to 

dominate the uncertainty in that result.

The implications of the partial quantification of uncertainties in this case study are 

multiple. First, the uncertainties were large, limiting the use of the absolute values of the 

risk results in any decision-making process. Second, the magnitudes of the uncertainties 

tended to be similar across pollutants, reinforcing the notion that the relative 

interpretation of risks can be useful in terms of setting priorities for further analysis or 

data gathering. Third, while the uncertainties associated with the dose-response 

component of the assessment were large, there appeared to be equally large uncertainties 

associated with the emissions and dispersion modeling component of the assessment, 

especially for particulates and reactive gases. Finally, mathematical procedures for 

propagation of uncertainties associated with the aggregation of risks across pollutants 

shows that pollutants which dominate or drive aggregate risk levels will also contribute 

the most to the uncertainties in those aggregate risks.

5.5.8 Recommendations for Further Characterization of Uncertainty

5.5.8.1 Role of Uncertainty Analysis 

Although the quantitative estimates of uncertainty described above are the best that can 

currently be developed, they have important limitations. They are based on available 

indicators of uncertainty and variability, which resulted in all the sources of uncertainty 

being aggregated into only three steps and in the use results from one or a few pollutants 

being applied to many other pollutants. For example, the aggregate uncertainty and 

RA r

i

= ∑ i

UR Uri

i

=

















 ∑ 

2

1/2

U

R

U R

R

A

ri

i

A =











∑ 

2 2

1/2
118

variability in the emissions estimates and the dispersion and transformation modeling are 

characterized by the monitor-to-model concentration comparisons, which in turn are 

limited to the relatively few pollutants and locations with monitoring data. Thus, the 

estimates described above likely do not properly capture all the important sources of 

uncertainty in the population risk estimates. To the extent that this or future national 

scale assessments are used in decision making, whether to set priorities among pollutants, 

to decide on control actions, or to judge the progress of the control program, better 

understanding of uncertainties will assist in that decision making process. Also, because 

of the aggregation approach, the uncertainty estimates described above do not indicate 

how the uncertainty in the risk estimates would be reduced if uncertainty in specific 

components of the assessment were reduced. That is, they do not tell us much about how 

to get the best “bang for the buck” in reducing the uncertainty in future assessments. For 

both reasons, we believe that it should be a goal to improve the characterization of the 

uncertainties in the national-scale risk assessment process.

EPA plans to repeat the national scale assessment in 2002-2003, for calendar year 1999 

emissions and meteorological conditions, and we believe that steps already taken or 

underway will lead to improved inputs. For example, we expect more states to provide 

emission inventory data, and both EPA and states will have more experience with such 

inventories than in this assessment for 1996. Because the 1999 national-scale assessment 

will give us risk estimates for a second point in time, and incorporate improvements in 

several important areas, reducing and better understanding the uncertainties the 1999 

assessment will be arguably more important than better understanding the uncertainties in

this 1996 assessment. Nevertheless, EPA will be pursuing better approaches to 

characterizing the uncertainties in this 1996 assessment, both to help focus the efforts to 

improve the 1999 assessment and to test and refine techniques that may ultimately be

applied to the 1999 assessment.

5.5.8.2 Technical Issues in Further Characterization of Uncertainty 

In a formal bottom-up uncertainty analysis, many individual components or input values 

are considered to have uncertainty or variability, and that uncertainty or variability is 

represented by a frequency distribution. Variability is then propagated upwards through 

the assessment calculations using Monte Carlo methods, similar to the general approach 

used in the case study. Given the broad geographic, multi-source, and multi-pollutant 

scope of the national scale assessment, there are very many components or input values 

that affect the final risk estimates, and hence that contribute to the uncertainty of the risk 

estimates. Table 5-6 below is a structured list of components. The list provides a 

framework for considering how a more complete, “bottom-up” uncertainty assessment 

might be conducted. It should be noted that for most of the components listed there are 

actually many individual quantitative input values that could be further distinguished and 

subject to a formal uncertainty analysis. Moreover, many listed components of the 

national scale assessment have “structural” uncertainties that are not directly addressable 

by a Monte Carlo approach. Also, the chain of modeling steps involved in producing one 

“run” from one set of inputs is time consuming. Repeating it hundreds or thousands of 

time to create a distribution of risk estimates would be a demanding approach, and less 

demanding alternatives should be considered. It may be sufficient, for example, to 
119

characterize the uncertainty of each main modeling step separately, and then consider 

how these uncertainties affect the aggregate uncertainty.

Table 5-6. Sources of uncertainty for the national-scale assessment.

Emissions Data

Stationary source emission data sources:

q HAP inventories developed by State and local air agencies

q Databases related to EPA’s MACT program 

q Toxics Release Inventory data

q Emission factors and activity data 

Mobile source emission data sources:

q On-road sources:

q Non-road sources

Preparing Emissions Data for Dispersion Modeling

Use of compound classes to group pollutant species

Assumptions used for other input data 

q Use of default physical release characteristics (i.e., temporal pattern, spatial 

pattern, release height, etc.) by SIC of SCC code

q Estimation of chemical characteristics (e.g., vapor-particle ratio, secondary 

formation, reactivity class, particle size class)

q Use of surrogate data to allocate county emissions to census tracts 

ASPEN Dispersion Modeling

Model error

q Limitations of Gaussian models (e.g., 50-km limit) 

q Use of a single background assumption for selected HAPs

q Use of an assumption of flat terrain

q Use of meteorological data from nearest airport

HAPEM4 Exposure Modeling

q Use of ME factors to extrapolate from census tract to microenvironment 

concentrations

q ME factors assumed independent of geography

q Use of CHAD activity data to represent behavior of entire demographic cohorts

q Annualized behavior of cohorts assembled from daily diaries

Dose-Response Assessment

Carcinogens 

q Probable and possible human carcinogens assumed carcinogenic

q UREs based on linear extrapolation from high to low doses
120

q Most UREs developed from animal data extrapolated to humans

q Most UREs based on the statistical upper confidence limit of dose-response 

curve; some based on maximum likelihood estimate

q Grouping of aggregate cancer risk by weight-of-evidence

Non-Carcinogens

q Laboratory animal data to humans;

q Average healthy humans to sensitive humans;

q Subchronic to chronic exposure duration;

q LOAEL to NOAEL

q Incomplete database.

Risk Characterization

Cancer

q Use of assumption that cancer risks for different pollutants are additive

q Degree to which aggregation of risks based on upper-bound UREs may propagate 

overestimates

Non-Cancer 

q Assumption that effects to the same organ or organ system are additive

q Use of the RfC for the critical effect (i.e., the adverse effect appearing at the 

lowest dose) to all effects (i.e., that may appear at substantially higher doses)

q Grouping of aggregate noncancer hazard by total uncertainty and modifying 

factor 

5.5.8.3 Future Plans

EPA intends to consider over the next year how to formulate an uncertainty analysis that 

is appropriate to the particular nature of the national scale assessment process, with its 

very large number of numerical inputs, particular structural approaches, and multiple 

modeling steps. We will have to consider where to treat critical inputs individually and 

where and how to use a more aggregated approach. We will also have to estimate the 

bias, uncertainty, distributional shape, etc, of the inputs that are treated through frequency 

distributions. This may involve the use of panels of experts in each of the following 

areas: (1) emission inventories, (2) dispersion modeling, (3) exposure modeling, (4) doseresponse assessment, and (5) risk characterization. Additional data collection efforts 

might need to be initiated to provide needed quantitative information to support the 

process. Although it may not be possible to develop true frequency distributions to 

characterize uncertainties surrounding some of the items in Table 5-6 without additional 

data, these panels might be charged with providing bounding estimates at the least and 

more, if possible.
121

6 Summary and Recommendations

6.1 Perspective on the National-Scale Assessment for 1996

The purpose of the national-scale assessment is to gain a better understanding of the air 

toxics problem. The national-scale assessment was not designed, and is not appropriate 

specifically, for identifying local- or regional-scale air toxics “hot spots,” nor is it 

appropriate for identifying localized risks or individual risks from air toxics. Further 

analyses on a national scale, and additional assessments on other scales (e.g., urban air 

toxics assessments and residual risk assessments) are being performed in order to fully 

characterize risks, especially disproportionate and cumulative risks. This means that this 

initial national-scale assessment is not a complete characterization of the exposures and 

risks associated with air toxics. Therefore, before considering the results, it is important 

to understand the context of the assessment. 

This initial application of the nationalscale assessment should be viewed 

more as a step in building the analytical 

framework necessary to estimate risks 

of air toxics on a national scale – not as 

producing a definitive estimate of these 

risks. That is, the individual emissions, 

dispersion/fate, exposure and risk

characterization tools have been 

integrated for the first time and an 

initial evaluation of the limitations and 

uncertainty has been completed. In 

addition, much of the data collection, 

while representing a significant effort 

on the part of many contributors, was 

performed for the first time in this 

initial national-scale assessment. Also, 

some of the pre-existing tools were 

used in their existing form in the 

interest of time, even though 

approximations and simplifications 

could be improved with more effort. 

Accordingly, EPA is continuing to 

develop the process and an understanding of the uncertainties and limitations of the 

national-scale assessment. Nevertheless, this application is complete enough to warrant 

a scientific peer review that will provide a basis for making improvements to any future 

national-scale assessment, through improved data collection, revisions to the tools and 

appropriate research.

It is critical to understand that the national-scale assessment does not include other 

relevant exposure scenarios (or scales). For example, the national-scale assessment does 

Key Limitations of this NationalScale Assessment:

• It is based on 1996 data

• It only includes 33 air toxics

• It only addresses inhalation 

exposures and risks (not 

ingestion, a significant 

exposure pathway for some 

air toxics (e.g., mercury))

• It does not capture localized 

impacts and risks

• It focuses on average 

population risks, rather than 

individual extremes, for 

ambient outdoor exposure 

only
122

not estimate exposures and risks at a local-scale. At best, such high exposures are partly 

reflected in average exposure concentrations calculated in each census tract. The 

national-scale assessment also does not include risks associated with emissions of air 

toxics indoors. In addition, the national-scale assessment does not include exposures and 

risks from ingestion and dermal exposure that may be posed by these pollutants. These 

non-inhalation risks are an important component of the air toxics program and should not 

be ignored in an overall characterization of the risks associated with air toxics. This 

means that this initial national-scale assessment is not a complete characterization of the 

exposures and risks associated with air toxics. Therefore, it is not appropriate to 

determine that a particular air toxic is not significant based solely on this analysis.

As a consequence, the results should neither be viewed as final nor considered definitive 

or in isolation. The assessment does, however represent our best effort to develop a 

process for risk characterization for air toxics on a national scale. Additionally, the 

results provide a picture from which to review and evaluate the technical aspects of a 

national-scale assessment, to gain insight into the areas of greatest uncertainty, to aid in 

identifying research priorities, and to provide an initial sense of relative priorities among 

these first 33 air toxics for the air toxics program.

6.2 Summary of Initial Results of National Scale Assessment

In the risk characterization presented in Section 5, EPA grouped pollutants into four 

categories based on the magnitude of the risk or hazard estimates and the number of 

people potentially affected. Magnitude of risk was expressed by classifying a substance 

as a “driver” (i.e., contributing a relatively large share of the total) or an “important 

contributor” (i.e., contributing a smaller but still important share of the total). The 

number of people affected was expressed by assigning a substance national scope (i.e., 

with potential impacts to millions of people) or regional scope (i.e., with potential 

impacts to tens or hundreds of thousands of people). This categorization scheme

produced four groupings: national drivers, regional drivers, important national 

contributors, and important regional contributors. Twenty-three of the 32 pollutants were 

placed in one of these groups. One pollutant – polycyclic organic matter – was grouped 

both with regional drivers and important national contributors. 

National drivers included acrolein, benzene, carbon tetrachloride, chromium, and 

formaldehyde. Regional drivers included acrylonitrile, arsenic, coke oven emissions, 

ethylene oxide, hydrazine, manganese, and polycyclic organic matter. Important national 

contributors were acetaldehyde, 1,3-butadiene, ethylene dibromide, ethylene dichloride, 

perchloroethylene, and polycyclic organic matter. Important regional contributors were 

cadmium, chloroform, 1,3-dichloropropene, nickel, quinoline, and trichloroethylene. 

In addition, as explained in Section 5.4, EPA believes that diesel exhaust is also one of 

the air toxics that poses the greatest risks to the public based on its potential carcinogenic 

effects and other health effects related to diesel exhaust, especially since diesel engine 

emissions provide a substantial contribution to fine particle emissions. For the nine air 

toxics not found to be important contributors to inhalation risks on a national or regional 

scale, this result does not necessarily mean these pollutants are not important. It could 
123

indicate that their main impacts may be limited to the local or neighborhood scales at 

which we expect the national-scale assessment methodology to under-predict individual 

risks. These pollutants would therefore be better investigated with local-scale data and 

assessment tools. It may also be that the initial national-scale assessment underestimated 

ambient concentrations, and therefore exposures and risks, as appears to be the case with 

many of the metals.

Mobile sources air toxics showed a strong association with national-scale risks, but the 

remaining mobile source pollutants appeared to have limited potential for national- or 

regional-scale risks. Major sources, in contrast, showed a strong association with 

regional risks rather than national risks. Area sources appeared to produce important 

risks on both the national and regional scales. Background sources were associated 

exclusively with nationwide risks, as expected. Because background was assumed to be 

the same in all tracts, exposure to background pollutants varied only with different human 

activity. 

In summary, the results of the national-scale assessment suggest that 23 of the 32 air 

toxics have the potential to present some noteworthy risk to some group of people in the 

U.S. It is important to remember the previously stated limitations when interpreting the 

results of this assessment. 

• Non-inhalation exposures such as ingestion and dermal, are not included. A complete 

picture of risk would include additional pathways for exposure. This is especially 

important for pollutants that persist in the environment and bioaccumulate, such as 

mercury, dioxins, and PCBs. 

• The highest localized exposures and risks are not captured by the national-scale 

approach. As mentioned earlier in this report, two comparisons between the results of 

the 1996 national-scale assessment and results from local-scale refined assessments 

indicate that this limitation can lead to significant underestimation of risks in the 

vicinity of individual point sources. These two comparisons showed an under 

prediction of local-scale risks by a factor of 30 in an urban area and by a factor 

greater than 100 in a rural setting.

• In a direct comparison with ambient monitoring data, the ASPEN model was found to 

consistently underpredict annual average concentrations. While the best agreement 

between the ASPEN model and ambient data was found for benzene (which ASPEN 

underpredicted by about 10%), concentration estimates of some metal s were found to 

be underpredicted by more than a factor of 5.

• Information on dioxins is still under review, and therefore, this pollutant has not been 

included in the risk characterization. Since dioxins are considered to be an important 

potential health threat, efforts will be made to include them in future assessments.

• Indoor sources of pollution are not included. While these are considered outside the 

scope of the current study, it is important to recognize that, for certain hazardous air 
124

pollutants, total long-term human exposures can be significantly influenced and 

sometimes dominated by exposures due to indoor sources.

• Sources, emission estimates, and exposure factors have a high degree of uncertainty.

6.3 Recommendations

As stated in section 2.1, the results of the initial national-scale assessment (in addition to 

other assessments) are intended to help inform EPA as it continues to develop and 

implement various aspects of the national air toxics program. The initial national-scale 

assessment will assist by: 

• Identifying air toxics of greatest potential concern, in terms of contribution to 

population risk; 

• Characterizing the relative contributions to air toxics concentrations and 

population exposures from different types of air toxics emission sources;

• Setting priorities for the collection of additional air toxics data (e.g., emission 

data, ambient monitoring data, data from personal exposure monitoring) for use in 

local-scale and multipathway modeling and assessments, and for future research 

to improve estimates of air toxics concentrations and their potential public health 

impacts;

• Establishing a baseline for tracking trends over time in modeled ambient 

concentrations of air toxics; and,

• Establishing a baseline for measuring progress toward meeting goals for 

inhalation risk reduction from ambient air toxics. 

The recommendations have been categorized below according to the assessment goal that 

they support. 

6.3.1 Identifying Air Toxics of Greatest Concern

Given the limitations and uncertainties of this initial assessment, it is not possible, at this 

time, to identify definitively the air toxics of greatest concern from among these 33. 

Nevertheless, it is possible to identify air toxics that appear to be important; that is, those 

air toxics posing estimated risks at or above levels typically addressed by EPA. 

However, it is not possible to eliminate other air toxics from consideration.

Of the 33 hazardous air pollutants assessed, those that appear to pose the greatest health 

threats to individuals (from inhalation exposure) in all parts of the U.S. are chromium, 

acrolein, benzene, formaldehyde, and carbon tetrachloride. Pollutants having the 

greatest potential to pose health threats to individuals in some regions of the US include 

acrylonitrile, coke oven emissions, hydrazine, ethylene oxide, manganese, and polycyclic 

organic matter. In addition, as explained in Section 5.4, EPA believes that diesel exhaust 

is also one of the air toxics that poses the greatest risks to the public based on its potential 
125

carcinogenic effects and other health effects related to diesel exhaust, especially since 

diesel engine emissions provide a substantial contribution to fine particle emissions. 

Although other pollutants besides these twelve substances may also be important 

contributors of health risk in some areas, these eleven pollutants account for most of the 

total estimated national air toxics-related health risk in this assessment. 

6.3.2 Prioritizing Efforts to Reduce Emissions 

As discussed above, the results of the initial national-scale assessment should be used 

cautiously when planning efforts to reduce air toxics emissions at the national level. The 

national-scale assessment results may be used, in conjunction with other information, for 

planning program activities, assessment activities and emission reduction efforts. For 

example, the program decisions related to the urban air toxics program and priorities 

associated with national technology-based standards for area and mobile sources can be 

informed by results from the initial national-scale assessment. However, these decisions 

should not be based solely on the results of the initial national-scale assessment. Because 

other pollutants included in the initial national-scale assessment may have been 

inadequately characterized or may be important contributors of health risk at a local scale 

or health risk due to non-inhalation exposure (e.g., ingestion), they should not be 

excluded from future consideration or assessments based on results from the initial 

national-scale assessment alone.

Given the limitations and uncertainties, state and local agencies need to be particularly 

careful in interpreting the initial national-scale assessment results. While these results 

can be helpful in planning assessments within a state or local area, they should not be 

used without confirming information to reduce the pollutant scope of an assessment, or to 

decide on major control steps. The results alone should not be used to dismiss local 

complaints about air toxics pollutants.

6.3.3 Characterizing Contributions of Sources

Given the limitations and uncertainties of this initial assessment, it is not possible, at this 

time, to definitively characterize the relative contribution of sources. In particular, the 

emissions for metals (with their corresponding low ambient and exposure concentrations 

and risk estimates) may not be adequately characterized by the models in the assessment, 

particularly at the national scale. Thus, at this time, it is difficult to describe the relative 

risk contribution from different sources. 

Accordingly, characterizing the relative contribution of sources based on the results of 

the initial national-scale assessment can only be done in general terms. The results 

appear to show that mobile and area sources of air toxics are responsible for a majority of 

the health risk concerns that can be identified on a national scale. Nevertheless, certain 

air toxics from major sources may present significantly greater levels of individual risk to 

smaller portions of the population than the risks posed to a broader portion of the 

population from mobile or area sources. Local-scale assessments (such as those being 

performed as part of EPA’s residual risk program and those being anticipated as part of 

future NATA activities under the urban strategy) will be needed to more accurately 

characterize the exposures and risks associated with major sources before a complete 
126

characterization of the relative contribution of all sources can be made.

As discussed in Section 5.4, the Agency is unable to provide the same quantitative 

information for diesel PM in this risk characterization as is provided for the other 32 air 

toxics. At the national level, EPA believes that diesel exhaust is also one of the air toxics 

that poses the greatest risks to the public based on its potential carcinogenic effects and 

other health effects related to diesel exhaust, especially since diesel engine emissions 

provide a substantial contribution to fine particle emissions. Diesel PM emissions result 

mainly from mobile sources and in the national-scale assessment EPA focused on 

estimating the ambient concentrations and exposure from only these sources. EPA spent 

considerable effort estimating the 1966 emissions inventories from onroad sources 

(primarily diesel trucks) and nonroad sources (such as construction and farm equipment) 

for this study. The national-scale assessment results suggest that throughout the country 

most of the population’s exposure to diesel PM emissions results from nonroad sources. 

There is a relatively wide range of exposures concentrations that occurs in various 

counties throughout the country with the highest exposures levels occurring as expected 

in heavily populated urban areas.

6.3.4 Tracking Trends and Progress

EPA plans to update this assessment every three years and the next assessment will occur 

in 2002-2003. The next assessment will focus on 1999 emissions, concentrations and 

risks. The assessment of 1999 data will provide results that can be used to assess relative 

reductions in air toxics emissions, ambient levels, exposures, and risks since 1996 (by 

comparing with results of this assessment). It will also be important to determine how 

effective this tool is in accomplishing that objective, to address the limitations and 

uncertainties in the initial national-scale assessment, as well as the limitations and 

uncertainties in the second national-scale assessment, to provide an adequate basis for 

comparison. EPA will attempt to compare on a national-scale the relative impact of 

reductions of the 33 air toxics since 1996. As previously mentioned, EPA plans to 

develop a more complete approach for assessing uncertainties and variability of nationalscale assessment results over the course of the next year.

However, EPA will also begin to analyze its pilot monitoring results, residual risk 

assessments, other localized efforts in an attempt to measure progress and the 

effectiveness of the air toxics program.

EPA can also utilize the national-scale assessment approach in a predictive mode to 

evaluate potential changes in population-based risks for candidate emission-reduction 

scenarios. Results from such assessments can be used to inform discussions on future 

voluntary or regulatory actions to reduce emissions of air toxics and their associated 

risks.

6.3.5 Setting Data Collection and Research Priorities

The evaluation of the NATA national-scale assessment results is an iterative process. 

The current evaluation has demonstrated the need for better information that, in turn, will 

permit an improved evaluation in the future. As a consequence, EPA’s Office of 
127

Research and Development has drafted an air toxics research strategy. This draft strategy 

is expected to undergo peer review by the EPA’s Science Advisory Board in the spring of 

2001.

The initial national-scale assessment should assist EPA in understanding our risk 

assessment tools (the National Toxics Inventory, dispersion and exposure modeling, and 

dose-response information) the proper uses and limitations of each, and how to most 

effectively improve them. The results of the assessment have shown that the following 

steps should be taken to improve the quality of the next such assessment:

Improve the quality of emission data. EPA has already requested that State, local, and 

tribal authorities that submit emission information to the National Toxics Inventory, for 

the assessment year 1999, make specific improvements in the way they speciate 

polycyclic organic matter, chromium, and nickel. Other important improvements include 

(1) entering location coordinates and stack heights for all point source emissions, (2) 

entering the specific method by which emissions were quantified, and (3) better 

characterizing the spatial nature of mobile source emissions. In addition, it would be 

desirable to conduct a study on a small sample of sources, to see if the emissions are 

accurately located and that their rates are accurately estimated. 

Improve support for urban-scale modeling. EPA plans to evaluate the initial nationalscale assessment by assessing air toxics on urban and local scales using more refined air 

quality modeling tools that factor in specific local information such as terrain and local 

weather patterns. The results of national-, urban-, and local-scale modeling would be 

compared to provide a more complete context for the characterization of air toxics. State, 

local, and tribal authorities have been encouraged to revisit and revise inventories in areas 

of relatively high risk where urban-scale modeling would be valuable. EPA should focus 

on improving spatial allocation methods for area and mobile sources and reducing the 

need to rely on these methods by better characterizing the spatial resolution of its 

emission inventory.

Improve the characterization of background. Background sources of five substances --

benzene, carbon tetrachloride, formaldehyde, ethylene dibromide, and ethylene dichloride 

– were found to contribute substantially to estimated air toxics-associated health risks. 

However, the assumption of a ubiquitous background concentration for each of these 

represents an important simplification that is unlikely to hold true. With the exception of 

diesel PM (see Appendix F), background was defined as that part of ambient 

concentrations not caused by modeled sources within 50 kilometers of the receptor 

population. This definition encompasses natural sources, international transport, 

intranational transport, and persistent historic emissions, all of which may vary with 

location. In addition, one component of background – intranational transport – most 

likely includes sources that EPA has the authority to control and is therefore not properly 

“background” at all. The uncertainties surrounding the treatment of background should 

be reduced by (1) establishing a remote monitoring network for these substances, and (2) 

improving the capabilities of EPA’s national dispersion modeling approach to extend 

beyond the current 50-km range.
128

Provide support for future model-to-monitor comparisons for ambient air toxics 

concentrations. Data from existing State and local air monitoring programs have already 

been compiled to summarize EPA’s current knowledge about ambient air toxics, and to 

serve as a “reality check” on modeling results. As a result of Congressional direction and 

SAB review of EPA’s air toxics monitoring concept paper, an improved, expanded and 

more representative air toxics monitoring network will be available in the near future to 

better support model evaluation. To assist with the development of this network and to 

provide better model evaluation information in the short-term, pilot monitoring studies 

have been initiated which will have multiple monitors in four urban areas (Seattle, 

Tampa, Providence, and Detroit) and also provide information in six smaller 

communities across the country. Information from this assessment will also be used to 

inform the monitor siting process. Limited information on background concentrations 

will also be provided. These new data will provide a wider range of information than the 

current monitoring data set.

Provide support for future model-to-monitor comparisons for exposure. Personal 

exposure monitoring studies are needed to evaluate the ability of the exposure modeling 

approach embodied in HAPEM4 to capture the average exposure levels for air toxics in 

individual census tracts as well as to describe the complete distribution of that can be 

expected across each census tract. Without such information, exposure models cannot be 

improved to capture the full range of risks experienced by the exposed population.

Improve dose-response information. Within the scope of this assessment, most of the 

cancer risk and virtually the entire non-cancer hazard are associated with substances in 

the “lower-certainty” groups. For example, formaldehyde and carbon tetrachloride are 

probable (i.e., less certain) carcinogens, and acrolein has a reference concentration that 

has a total uncertainty factor of more than 100. EPA already has new assessments in 

progress for these and other substances that are important sources of risk in this 

assessment, but may want to consider expediting these assessments and subjecting them 

to review by the EPA‘s SAB in addition to regular internal and external peer review. 

Other areas in which dose-response information can be improved include development of 

(1) organ-specific RfC (to reduce conservatism associated with applying critical-effect 

RfCs to all target organs when combining non-cancer hazard across pollutants) and (2) 

UREs based on maximum-likelihood rather than upper-bound estimates (to reduce 

conservatism associated with combining cancer risks across pollutants).

Extend EPA risk assessment guidelines to be more inclusive of children and other 

vulnerable subpopulations. EPA’s current IRIS assessments for effects other than cancer 

are severely limited by their use of the average inhaled concentration as the exposure 

metric. In this way the IRIS assessments fail to support consideration of the different 

inhalation rates and body weights of children. This prevented the initial national-scale 

assessment from fully differentiating between adults and children. For the next such 

assessment, it will be desirable to either adjust estimated inhalation concentrations to 

children’s equivalence, or to develop separate reference concentrations for children.
129

Improve modeling to include multipathway exposures. One major limitation of this 

assessment is that it does not fully account for exposure pathways, such as ingestion, 

which are important for many persistent, bioaccumulative toxics. There are a variety of 

approaches for accommodating other than inhalation exposures to air pollutants, each 

with attendant strengths and limitations. In the Mercury Study Report to Congress [40], 

non-inhalation mercury exposures (from fish ingestion) were estimated using monitoring 

data (fish tissue), with a recognition that a portion of these exposures result from air 

emissions. For local scale assessments, EPA has been developing an improved multimedia fate and transport model for air pollutants (TRIM.FaTE) which will be getting 

some initial applications in 2001-2002. Additionally, development of a more refined 

ingestion exposure model for air pollutants (ingestion component of TRIM.Expo) is 

planned for 2002. These tools are expected to contribute to our multipathway exposure 

modeling capabilities.
130

7 References

 

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2 U.S. EPA. 1999. Integrated Urban Air Toxics Strategy. 64 FR 38705. Available on-line 

at http://www.epa.gov/ttn/uatw/urban/urbanpg.html

3 National Research Council. 1983. Risk assessment in the federal government. Managing 

the process. National Academy Press, Washington, DC.

4 U.S. EPA. 1997. Cumulative Risk Assessment Guidance – Phase I: Planning and Scoping. 

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http://www.epa.gov/ordntrnt/ORD/spc/cumulrsk.htm.

5 U.S. EPA. 1998. Peer Review. Science Policy Council Handbook. Office of Research and 

Development, Washington, DC. EPA 100-B-98-001. Available on-line at 

http://www.epa.gov/ordntrnt/ORD/spc/prhandbk.pdf.

6 U.S. EPA. 1998. Draft Integrated Urban Air Toxics Strategy to Comply with Section 

112(k), 112(c)(3) and section 202(l) of the Clean Air Act. 63 FR 49240.

7 U.S. EPA. 1999. Analysis of the Impacts of Control Programs on Motor Vehicle Toxic 

Emissions and Exposure Nationwide. Prepared for U. S. EPA, Office of Transportation 

and Air Quality, by Sierra Research, Inc., and Radian International Corporation/Eastern 

Research Group, Report No. EPA420-R-99-029/030.

8 Woodruff, T.J., D.A. Axelrad, J. Caldwell, R. Morello-Frosch, and A. Rosenbaum. 1998. 

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Health. Pers. 106(5): 245-251. Available on-line at 

www.epa.gov/CumulativeExposure/CEPpapers/paperWACMR.html. 

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Urban Areas and Nationwide. Office of Transportation and Air Quality. Available on-line at 

http://www.epa.gov/orcdizux/toxics.htm. 

10 U.S. EPA. 1992. Guidelines for Exposure Assessment. National Center for 

Environmental Assessment, Washington, DC. EPA/600Z-92/001, or FR 57: 22888 - 22938. 

Available on-line at http://www.epa.gov/nceawww1/pdfs/guidline.pdf. 

11 Rosenbaum, A.S.; Ligocki, M.P.; Wei, Y.H. 1998. Pages 5-9 in “Modeling Cumulative 

Outdoor Concentrations of Hazardous Air Pollutants, Volume 1: Text”; SYSAPP-99-96/33r2, 

Prepared for U.S. Environmental Protection Agency, Office of Policy, Planning and 

Evaluation, by Systems Applications International, Inc., San Rafael, CA. 1998.

12 U.S. EPA. 1998. “National Air Pollutant Emission Trends Procedures Document, 1900-

1996.” Office of Air Quality Planning and Standards. May 1998. EPA-454/R-98-008. 

13 U.S. EPA. 2000. Control of Air Pollution From New Motor Vehicles: Heavy-Duty 
131

 

Engine and Vehicle Standards; Highway Diesel Fuel Sulfur Control Requirements; Proposed 

Rules. 65 FR 35429-35478.

14 U.S. EPA. 1999. Analysis of the Impacts of Control Programs on Motor Vehicle Toxics 

Emissions and Exposure in Urban Areas and Nationwide. Prepared for U. S. EPA, Office of 

Transportation and Air Quality, by Sierra Research, Inc., and Radian International 

Corporation/Eastern Research Group. Report No. EPA 420 BR-99-029/030

15 Code of Federal Regulations, Title 40 (Protection of the Environment) Appendix W to 

Part 51-Guideline on Air Quality Models.

16 Rosenbaum, A.S., D.A. Axelrad, T.J. Woodruff, Y.H. Wei, M.P. Ligocki, and J.P. 

Cohen, 1999. National estimates of outdoor air toxics concentrations. Journal of the Air 

& Waste Management Association, Vol.49, pp.1138-1152. 

17 U.S. EPA, 2000. User’s Guide for the Assessment System for Population Exposure 

Nationwide (ASPEN, Version 1.1) Model. EPA-454/R-00-017. U.S. Environmental 

Protection Agency, Research Triangle Park, NC, 108pp.

18 McCurdy, T., Glen, G., Smith, L., and Lakkadi, Y. 2000. The National Exposure 

Research Laboratory’s Consolidated Human Activity Database. J. Exp. Anal. Environ. 

Epidem. (2000) 10, 566-578.

19 U.S. EPA. 2000. Development of Microenvironmental Factors for the HAPEM4 in 

Support of the National Air Toxics Assessment (NATA), External Review Draft. Prepared 

for the U.S. EPA, Office of Air Quality Planning and Standards, by ICF Consulting, Inc., with 

TRJ Environmental, Inc. 

20 U.S. EPA. 1986. Guidelines for Carcinogen Risk Assessment. 51 FR 33992-34003. 

21 US EPA. 1999. Proposed Guidelines for Carcinogen Risk Assessment. NCEA-F-0644, 

available on-line at http://www.epa.gov/ncea/raf/car2sab/preamble.pdf.

22 U.S. EPA. 1986. Guidelines for Mutagenicity Risk Assessment. 51 FR 34006-34012. 

23 U.S. EPA. 1991. Guidelines for Developmental Toxicity Risk Assessment. 56 FR 63798-

63826.

24 U.S. EPA. 1998. Guidelines for Neurotoxicity Risk Assessment; Notice 60. 63 FR 

26926-26954. Available on-line at http://www.epa.gov/ncea/nurotox.htm. 

25 U.S. EPA. 1996. Guidelines for Reproductive Toxicity Risk Assessment. National 

Center for Environmental Assessment. EPA/630/R-96/009. Available on-line at 

http://www.epa.gov/ORD/WebPubs/repro/. 

26 U.S. EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and 

Application of Inhalation Dosimetry. Office of Research and Development, Washington, DC. 
132

 

EPA/600/8-90/066F.

27 U.S. EPA. 1995. Policy for Risk Characterization at the U.S. Environmental Protection 

Agency. Memo from Carol Browner, available on-line at 

http://www.epa.gov/ordntrnt/ORD/spc/rcpolicy.htm. 

28 U.S. EPA. 1995. Guidance for Risk Characterization . Science Policy Council. Available 

on-line at http://www.epa.gov/ordntrnt/ORD/spc/rcguide.htm. 

29 U.S. EPA. 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. 52 FR 

34014-34025.

30 U.S. EPA. 1999. Guidance for Conducting Health Risk Assessment of Chemical 

Mixtures. External Scientific Peer Review Draft, September 1999, NCEA-C-0148. Available 

on-line at http://www.epa.gov/ncea/mixtures.htm. 

31 Gifford, F.A. and Hanna, S.R. 1973. Modeling urban air pollution. Atmospheric 

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32 Hanna, S.A., Briggs, G.A., and Hosker, R.P. 1982. Handbook on Atmospheric 

Diffusion. Available as DE82002045 (DOE/TIC-11223) from the National Technical 

Information Service, U.S. Department of Commerce, Springfield, VA, 22161, 108 pages.

33 Calder, K.L., (1971): A climatological model for multiple source urban air pollution. 

Paper presented at First Meeting of the NATO/CCMS Panel on Modeling. Paper 

published in Appendix D of User’s Guide for the Climatological Dispersion Model. 

EPA-R4-73-024. Office of Research and Development, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 27711, pages 73-105.

34 Martin, D.O., (1971): An urban diffusion model for estimating long term average 

values of air quality. J. of the Air Pollution Control Association. Vol 21(1):16-19.

35 Irwin, J.S. and Brown, T.M., (1985): A sensitivity analysis of the treatment of area 

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36 Pooler, F., (1961): A prediction model of mean urban pollution for use with standard wind 

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38 Turner, D.B., Zimmerman, J.R., and Busse, A.D., (1971): An evaluation of some 

climatological models. Paper presented at Third Meeting of the NATO/CCMS Panel on 

Modeling. Paper published in Appendix E of User’s Guide for the Climatological Dispersion 

Model. EPA-R4-73-024. Office of Research and Development, U.S. Environmental 
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Protection Agency, Research Triangle Park, NC 27711, pages 107-131.

39 U.S. EPA, (1999): A simplified approach for estimating secondary production of 

hazardous air pollutants (HAPS) using the OZIPR model. EPA-454R-99-054. U.S. 

Environmental Protection Agency, Research Triangle Park, NC 27711, 86 pages. 

40 U.S. EPA. 1997. Mercury Study Report to Congress. Office of Air Quality Planning and 

Standards and Office of Research and Development. U.S. Environmental Protection Agency. 

Washington, D.C. December 1997. EPA-452/R-97-007.

41 ATSDR. 1998. Toxicological Profile for Chlorinated Dibenzo-p-Dioxins (Update). U.S. 

Department of Health and Human Services. Agency for Toxic Substances and Disease 

Registry. December 1998.

42 U.S. EPA. 2000. Review of EPA’s Health Assessment Document for Diesel Exhaust. 

Science Advisory Board. Clean Air Scientific Advisory Committee. December 2000. EPA 

600/8-90/057E

43 U.S. EPA. 2000. Draft Health Assessment Document for Diesel Exhaust. July 2000.

44 Federal Register, 2001. USEPA; 40 CFR Parts 69, 80, and 86; [AMS-FRL-6923-7] RIN 

2060-AI69; "Control of Air Pollution from New Motor Vehicles: Heavy-Duty Engine and 

Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements"; 

http://www.epa.gov/fedrgstr/EPA-AIR/2001/January/Day-18/a01a.htm

45 U.S. EPA. 1996. Air Quality Criteria for Particulate Matter. U.S. Environmental 

Protection Agency, Office of Research and Development, Washington, D.C. 20460. 

EPA/600/P-95/001a-c. 

46 Krewski D, Burnett RT, Goldbert MS, Hoover K. Siemiatycki J, Jerrett M, 

Abrahamowicz M, and White WH. 2000. Reanalysis of the Harvard Six Cities Study and 

the American Cancer Society Study of Particulate Air Pollution and Mortality. Special 

Report to the Health Effects Institute, Cambridge MA, July 2000.
Figures
Figure 1-1. Overview of National Air Toxics Assessment (NATA) Activities.

Refined & Local-Scale Modeling

Multimedia Models

Exposure Models

Air Quality Models

Emission

Inventories

(1996 NTI)

Air Dispersion

Modeling

(ASPEN and

 EMS-HAP)

Inhalation

Exposure

Modeling

(HAPEM4)

Risk Assessment/

Characterization

(Nationwide and

 Urban areas)

- State/local/industry

emissions data

- Emission Factors

- Emission

Characterization

- Meteorological and

Topographical Data

- "Background" Air Quality

- Atmospheric Chemistry

and Physics

- Human Activity Data

(CHAD)

- Microenvironment

Factors

- Census Data

- Hazard ID and DoseResponse Assessments

- Ecological

Assessments

- Agency Risk Guidelines

Data Inputs

and Research

Areas

Screening-level,

National-scale

Assessments

(Initial Assessment)

Refined Local-scale

Assessments,

Evaluation of Initial

Assessment, and

Related Activities

Expanded Air

Quality Monitoring

Indoor,

Microenvironmenta

l, and Personal

Exposure

Monitoring

Refined LocalScale

Inventories

Source

Measurement

Programs

Priority Air

Toxics

Priority Setting

(e.g., Federal and

State/local/Tribal

Regulatory Programs,

Initiatives)

Progress Toward

Statutory Risk

Reduction Goals

(e.g., 75% cancer

incidence reduction in

urban areas)

Public Right to

Know

(e.g., Trends Report)

Progress Toward

Future Risk-Based

Goals

CAA Section 812

Prospective

Benefits

Assessment

134
MACT/GACT

Standards

Incineration

Standards

Urban Area

Source

Standards

Other

Regulatory

Programs (e.g.,

Ozone, PM

NAAQS, non-air

programs)

Mobile Source

Studies/

Standards

Urban Strategy:

Local Studies/

Initiatives

Mercury Studies

Great Waters

Studies

Indoor

Environments

Program

PBT Initiatives

Regulatory

Programs

Risk-based

Initiatives and

Special

Studies

Priorities/

Progress from

Current NATA

Activities

Figure 1-2. Links Between NATA Activities and Other Program Elements.

Residual Risk

Assessments/

Standards

Electric Utilities

Study and

Regulatory

Determination

Improved Risk

Characterizations

Ongoing

Reductions in Air

Toxics Emissions

and Risk

Ongoing Input

to Future

NATA

Activities

135

134
Laboratory and

field observations

Information on

extrapolation methods

Field measurements,

characterization of

populations

Toxicity Assessment:

Hazard Identification and

Dose-Response

Assessment

Exposure Assessment

Emissions Characterization

Development of

regulatory options

Evaluation of public

health, economic, social,

political consequences of

regulatory options

Agency decisions

 and actions

Research Risk

Assessment

Risk

Management

Developed from: NAS, 1983

Risk

Characterization

Figure 2-1. NAS Risk Assessment/Risk Management Paradigm

Research needs identified

from risk assessment process

136
Subset of PBTs

Figure 2-2. National-Scale Air Toxics Health Assessment: Conceptual Model.

Heavy lines indicate dimensions/elements included in the Initial National Scale Assessment;

Light lines indicate dimensions/elements that may be included in future assessments

Cardiovascular Hazard Index

Liver and Kidney Hazard Index

CNS Hazard Index

Blood Hazard Index

Stressors

Sources

Routes

Pathways/

Media

Measures

Endpoints

Subpopulations

Subset of PBTs

33 Priority Air Toxics Other Air Toxics

Indoor air

sources

Major

Industrial

Extrinsic

"background" in air

Mobile (onand off-road)Small "area"

sources

General

Population

Inhalation Ingestion Dermal

Outdoor air Water Indoor air

microenvironments

Food Soil

White Asian American

African

American

Adults Elderly

Cancers

(leukemia, lung, others)

Young

Children

Adolescents

CNS Blood (including

marrow & spleen)

CardiovascularLiver &

kidney Respiratory

Respiratory System Hazard Index

Distribution of

estimated

index values

Estimated percent of

population within specified

ranges of index values

Extrinsic "background"

in other media

Possible Carcinogens

Probable Carcinogens

Known Carcinogens

Distribution of

high-end cancer

risk estimates

Estimated percent of

population within specified

cancer risk ranges

Estimated

number of

cancer cases

Pollutant-specific and

cumulative (e.g., by

cancer type, weight of

evidence; by targetorgan-specific hazard

index), by State by county

(also all counties and all

urban counties)

Other health

effects (Specific noncancer target organ

endpoints shown for

example purposes)

Hispanic

137
Gender (2):

Age (5):

Race (4):

Female Male

African

American

Asian &

Other Hispanic Caucasian

0-5 yrs 6-11 yrs 18-64 yrs12-17 yrs 65+ yrs

Figure 2-3. Diagram of the HAPEM4 40 Cohort Groups (2x4x5=40).

Example Cohort: Female; Hispanic; 18-64yrs

138
Figure 3-1. 1996 NTI State and Local Agency Data Summary.

*

*

*

*

*

*

*

*

*

*

*

*

Gray - states who submitted air toxics inventory data

 - states who submitted revisions

* - local agencies who submitted revisions

139
Figure 3-2. Example Demographic Groups, Microenvironment, and Activities.

Demographic Groups

Microenvironments

Activities

Inhalation Exposure

[e.g., Groups by age,

gender,ethnic background]

[e.g., home, school, car, park]

[e.g., bicycling, sleeping, eating]

140
Figure 3-3. Example of a Daily Exposure Scenario for a Cohort.

1.5 1.2 1.2 2.5 1.7

Midnite 3am 9am6am Noon

1.5 1.1 1.8 3.8 2.8

3pm 6pm 9pm Midnite

1.1

1.2

2.8

4.5

1.5

1.8

Home Census Tract Concentration XH

Work Census Tract Concentration XW

Sleeping @ Home Drive to

Work Work in Office DrivetHome

Eat In

resturant Sleeping @ Home Activity/Location

X1=(XH(0-3)* 3hrs+ XH(3-6)* 1.5hrs) * MEHome

X2=(XH(3-6)* 1.5hrs+ XH(6-9)* 0.5hrs) * MEPark

Jog in Park

X3=(XH(6-9)* 1.2hrs) * MECar

X4=(Xw(6-9)*1.3hrs+ Xw(9-12)* 3hrs)+

Xw(12-3)* 3hrs+ Xw(3-6)* 2hrs)) * MEOffice)

X5=(Xw(3-6)* 1hr+ Xw(6-9)*1hr) * MECar

X6=(XH(6-9)* 2hrs) * MERest

X7=(XH(9-12)* 3hrs) * MEHome

Cohort: Black Male 18-64

Daytype: Weekday

Daily Exposure Concentration:

XDay=(X1 +X2+X3+X4+X5+X6+X7)/24hrs)

141
9%

40%

22%

29%

Major

Area & Other

Non-Road

On-Road

Figure 4-1. Summary of 1996 NTI Emissions for 33 Air Toxics by Source Sector.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Emissions 

(Million 

tons/year)

Major Area &

Other

OnRoadNonRoad

Rural

Urban

Figure 4-2. Summary of 1996 NTI Emissions of 

33 Air Toxics by Urban and Rural Designations.

142
Tons/Year/Sq Mile

0.00032 - 0.04731

0.04731 - 0.10288

0.10288 - 0.17952

0.17952 - 44.41798

Figure 4-3. 1996 NTI - Aldehydes Emission Densities.

Tons/Year/SqMile

0 - 0.00007

0.00007 - 0.00034

0.00034 - 0.00237

0.00237 - 0.70105

Figure 4-4. 1996 NTI - Metals Emission Densities.

143
Tons/Year/Sq Mile

0- 0.01

0.01 - 0.02

0.02 - 0.06

0.06 - 9.28

Figure 4-5. 1996 NTI -Halides Emission Densities.

Tons/Year/Sq Mile

0- 0.05

0.05 - 0.09

0.09 - 0.17

0.17 - 9.1

Figure 4-6. 1996 NTI -POM and Hydrocarbons Emission Densities.

144
0 1 2 3

Alabama 

Arizona 

Arkansas 

California 

Colorado 

Connecticut 

Delaware 

Washington DC 

Florida 

Georgia 

Idaho 

Illinois 

Indiana 

Iowa 

Kansas 

Kentucky 

Lousiana 

Maine 

Maryland 

Massachusetts 

Michigan 

Minnesota 

Mississippi 

Missouri 

Montana 

Nebraska 

Nevada 

New Hampshire 

New Jersey 

New Mexico 

New York 

North Carolina 

North Dakota 

Ohio 

Oklahoma 

Oregon 

Pennsylvania 

Rhode Island 

South Carolina 

South Dakota 

Tennessee 

Texas 

Utah 

Vermont 

Virginia 

Washington 

West Virginia 

Wisconsin 

Wyoming 

Puerto Rico 

Virgin Islands 

National 

Key

Median

75th25th

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

Estimated Annual Average Ambient Concentrations ( μg/m3)

Background

Figure 4-7. Estimated Annual Average Concentrations (ug/m3) for Benzene.

145
Alabama 

Arizona 

Arkansas 

California 

Colorado 

Connecticut 

Delaware 

Washington DC 

Florida 

Georgia 

Idaho 

Illinois 

Indiana 

Iowa 

Kansas 

Kentucky 

Lousiana 

Maine 

Maryland 

Massachusetts 

Michigan 

Minnesota 

Mississippi 

Missouri 

Montana 

Nebraska 

Nevada 

New Hampshire 

New Jersey 

New Mexico 

New York 

North Carolina 

North Dakota 

Ohio 

Oklahoma 

Oregon 

Pennsylvania 

Rhode Island 

South Carolina 

South Dakota 

Tennessee 

Texas 

Utah 

Vermont 

Virginia 

Washington 

West Virginia 

Wisconsin 

Wyoming 

Puerto Rico 

Virgin Islands 

National 

Major

| | |

0 50 100

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

Area and Other

|||

0 50 100

Onroad

| | |

0 50 100

Percent Contribution to the Statewide Annual Average Ambient Concentration Estimates

Nonroad

|||

0 50 100

Background

| | |

0 50 100

Figure 4-8. Percent Contribution to the Statewide Annual Average Ambient Benzene Concentration Estimates

146
10−5

10−4

10−3

10−2

10−1

100

101

Arsenic Benzene Butadiene Formaldehyde Lead Mercury Meth−Chl. Nickel 7−PAH Vinyl−Chl. 

Legend:

90%

10%

50%

Mean

Concentration [ug/m3

]

Distribution of Modeled Concentration from ASPEN, 1996

Figure 4-9. Comparison of Annual Average Model Concentrations for 10 Pollutants.

147
10−5

10−4

10−3

10−2

10−1

100

101

Arsenic Benzene Butadiene Formaldehyde Lead Mercury Meth−Chl. Nickel 7−PAH Vinyl−Chl. 

Legend:

90%

10%

50%

Mean

Concentration [ug/m3

]

Distributions of Modeled Concentrations from ASPEN, 1996 (rural white, urban black)

Figure 4-10. Annual Average Concentrations for Urban and Rural Census Tracts.

148
Concentrations scaled by Cmax(HAP): min − white, max − black

Methylene−Cl

Tetrchl.ethy

1,3−Dichlor.

Trichloroeth

Chloroform..

Tetrchl.etha

(PCBs)......

Ethylene−Dcl

Quinoline...

Hexachl.benz

Ethylene−Dbr

Ethylene−Ox.

Vinyl−Chlor.

Total−POM...

Carbon−Tetrc

Coke−Oven...

Hydrazine...

STATIONARY NON−METALS

Acrylonitril

Propylene−Di

−−−−−−−−−−−−

Manganese−co

Nickel−comp.

Mercury−comp

Cadmium−comp

METALS

Chromium−co.

Arsenic−comp

Beryllium−co

−−−−−−−−−−−−

Benzene.....

Acetaldehyde

Acrolein....

Diesel−PM...

MOBILE

Formaldehyde

1,3−Butadien

Concentrations scaled by Cmax(HAP): min − white, max − black

Methylene−Cl

Figure 4-11. Relative contribution of major, area and mobile sources.
Figure 4-12. Model-to-monitor scatter plot for benzene.

Note: Most points fall within the factor of two wedge, and none are far outside the wedge.

150
Figure 4-13. Ratio box plot showing distribution of model/monitor 

ratios for each pollutant. 

Note: The bottom of each box is the 25th percentile, the top is the 75th percentile, and the horizontal 

line in the middle is the median.

151
0.0215 0.0220 0.0225 0.0230 0.0235

White (111)

Black (0)

Hispanic (9)

Other (8)

White (142)

Black (4)

Hispanic (18)

Other (0)

White (128)

Black (7)

Hispanic (0)

Other (0)

White (928)

Black (5)

Hispanic (6)

Other (6)

White (210)

Black (0)

Hispanic (0)

Other (0)

White (158)

Black (0)

Hispanic (0)

Other (0)

White (167)

Black (0)

Hispanic (6)

Other (0)

White (116)

Black (6)

Hispanic (0)

Other (0)

White (1,011)

Black (5)

Hispanic (19)

Other (11)

White (298)

Black (6)

Hispanic (0)

Other (0)

Male

(0-5)

Male

(6-11)

Male

(12-17)

Male

(18-64)

Male

(65+)

Female

(0-5)

Female

(6-11)

Female

(12-17)

Female

(18-64)

Female

(65+)

POM Exposure Concentration (ug/m3)

Figure 4-14. POM (Total) Exposure Concentration Distribution Among Cohorts in an Urban NY Census 

Tract.

Min Median Max

Cohort (Tract Pop)

152
5th 10th 25th Median Average 75th 90th 95th Major

Area and 

Other

Onroad 

Mobile

Nonroad 

Mobile

Estimated 

Background

National All 99999 - 3.09E-02 6.62E-02 1.67E-01 4.43E-01 6.56E-01 8.44E-01 1.34E+00 1.82E+00 5.23E-03 5.39E-02 3.89E-01 2.08E-01 0.00E+00

National All Rural Counties 99998 R 1.15E-02 2.72E-02 5.87E-02 1.03E-01 1.58E-01 1.76E-01 2.73E-01 3.43E-01 6.02E-03 3.97E-02 8.37E-02 2.86E-02 0.00E+00

National All Urban Counties 99998 U 6.69E-02 1.37E-01 3.07E-01 5.91E-01 7.66E-01 9.56E-01 1.48E+00 2.04E+00 5.05E-03 5.70E-02 4.56E-01 2.48E-01 0.00E+00

Alabama State Total 01000 - 7.37E-02 9.38E-02 1.59E-01 2.97E-01 3.93E-01 5.32E-01 8.96E-01 1.02E+00 4.51E-03 4.53E-02 2.89E-01 5.42E-02 0.00E+00

Alabama State Rural Counties 01000 R 5.98E-02 6.92E-02 9.16E-02 1.31E-01 1.59E-01 1.94E-01 2.59E-01 3.31E-01 2.28E-03 3.24E-02 1.01E-01 2.40E-02 0.00E+00

Alabama State Urban Counties 01000 U 1.43E-01 1.87E-01 2.79E-01 4.56E-01 5.04E-01 6.25E-01 9.81E-01 1.07E+00 5.57E-03 5.15E-02 3.78E-01 6.86E-02 0.00E+00

Alabama Autauga County 01001 U 1.26E-01 1.39E-01 1.62E-01 2.90E-01 2.47E-01 3.09E-01 3.73E-01 3.74E-01 1.66E-02 4.52E-02 1.48E-01 3.68E-02 0.00E+00

Alabama Baldwin County 01003 U 1.54E-01 1.68E-01 1.86E-01 2.16E-01 2.26E-01 2.66E-01 2.99E-01 3.00E-01 3.36E-03 3.60E-02 1.18E-01 6.88E-02 0.00E+00

Alabama Barbour County 01005 U 5.67E-02 6.65E-02 8.22E-02 1.01E-01 1.19E-01 1.80E-01 1.85E-01 1.89E-01 1.38E-04 4.97E-02 4.21E-02 2.71E-02 0.00E+00

Alabama Bibb County 01007 R 1.46E-01 1.51E-01 1.66E-01 2.28E-01 2.40E-01 3.04E-01 3.43E-01 3.56E-01 1.43E-07 4.71E-02 1.66E-01 2.70E-02 0.00E+00

Alabama Blount County 01009 U 1.58E-01 1.64E-01 1.86E-01 2.32E-01 2.20E-01 2.48E-01 2.49E-01 2.50E-01 7.51E-06 2.71E-02 1.68E-01 2.53E-02 0.00E+00

Alabama Bullock County 01011 R 8.40E-02 8.94E-02 1.06E-01 1.19E-01 1.19E-01 1.34E-01 1.52E-01 1.58E-01 4.75E-05 2.85E-02 3.49E-02 5.51E-02 0.00E+00

Alabama Butler County 01013 R 5.08E-02 5.30E-02 6.60E-02 6.85E-02 1.02E-01 1.39E-01 1.66E-01 2.00E-01 0.00E+00 4.66E-02 4.45E-02 1.07E-02 0.00E+00

Alabama Calhoun County 01015 R 2.17E-01 2.28E-01 2.65E-01 3.31E-01 3.05E-01 3.73E-01 4.09E-01 4.15E-01 2.27E-06 3.39E-02 2.28E-01 4.24E-02 0.00E+00

Alabama Chambers County 01017 U 1.10E-01 1.15E-01 1.23E-01 1.78E-01 1.78E-01 2.02E-01 2.49E-01 2.60E-01 4.39E-07 6.00E-02 9.89E-02 1.94E-02 0.00E+00

Alabama Cherokee County 01019 R 1.62E-01 1.62E-01 1.62E-01 1.70E-01 1.76E-01 1.87E-01 2.03E-01 2.08E-01 9.18E-04 3.36E-02 1.24E-01 1.72E-02 0.00E+00

Alabama Chilton County 01021 R 1.10E-01 1.11E-01 1.18E-01 1.39E-01 1.44E-01 1.60E-01 1.70E-01 1.72E-01 7.54E-04 3.61E-02 8.80E-02 1.94E-02 0.00E+00

Alabama Choctaw County 01023 R 7.88E-02 7.93E-02 8.07E-02 8.31E-02 8.41E-02 8.66E-02 8.98E-02 9.09E-02 6.39E-04 4.41E-02 3.33E-02 6.07E-03 0.00E+00

Alabama Clarke County 01025 R 5.93E-02 6.06E-02 6.34E-02 6.75E-02 6.98E-02 7.52E-02 9.12E-02 9.84E-02 4.67E-03 3.15E-02 2.61E-02 7.60E-03 0.00E+00

Alabama Clay County 01027 R 7.78E-02 7.84E-02 8.00E-02 8.31E-02 8.85E-02 9.14E-02 1.02E-01 1.06E-01 2.87E-04 2.93E-02 5.07E-02 8.21E-03 0.00E+00

Alabama Cleburne County 01029 R 1.05E-01 1.10E-01 1.23E-01 1.31E-01 1.28E-01 1.35E-01 1.42E-01 1.44E-01 2.16E-05 3.16E-02 8.32E-02 1.31E-02 0.00E+00

Alabama Coffee County 01031 U 8.71E-02 9.89E-02 1.18E-01 1.31E-01 1.51E-01 1.94E-01 2.00E-01 2.04E-01 5.50E-06 3.35E-02 7.45E-02 4.27E-02 0.00E+00

Alabama Colbert County 01033 U 1.50E-01 1.51E-01 1.79E-01 2.95E-01 2.42E-01 3.55E-01 3.72E-01 3.74E-01 1.38E-03 2.74E-02 1.87E-01 2.60E-02 0.00E+00

Alabama Conecuh County 01035 R 4.85E-02 5.00E-02 5.41E-02 5.89E-02 6.13E-02 6.45E-02 7.61E-02 8.11E-02 6.87E-05 3.22E-02 2.44E-02 4.68E-03 0.00E+00

Alabama Coosa County 01037 R 9.49E-02 9.53E-02 9.67E-02 9.89E-02 1.04E-01 1.09E-01 1.15E-01 1.17E-01 7.62E-04 3.67E-02 5.56E-02 1.04E-02 0.00E+00

Alabama Covington County 01039 R 5.76E-02 5.99E-02 6.58E-02 8.21E-02 8.68E-02 1.16E-01 1.28E-01 1.31E-01 0.00E+00 3.32E-02 4.08E-02 1.28E-02 0.00E+00

Alabama Crenshaw County 01041 R 7.19E-02 7.76E-02 9.28E-02 1.17E-01 1.14E-01 1.31E-01 1.47E-01 1.54E-01 6.52E-05 2.65E-02 4.44E-02 4.25E-02 0.00E+00

Figure 4-15. Exposure Results - Summary Table Example (Partial Table)

Percentile Distribution of Exposure Concentrations Across Census Tracts Contribution to Average from ...

State County FIPS

Urban 

or 

Rural

EPA strongly cautions that these modeling results should not be used to draw conclusions about local concentrations or risk. The results are most meaningful when viewed at the state or national level; for smaller areas, the modeling becomes 

less certain. In addition, these results represent conditions in 1996 rather than current conditions. 

l The emissions used in this assessment do not reflect potentially significant emission reductions that have taken effect since 1996, including those from: 1) mobile source regulations which are being phased in over time; 2) many of the air toxics 

regulations EPA has issued for major industrial sources; 3) State or industry initiatives; and 4) any facility closures.

1996 Modeled Exposure Concentrations for Acetaldehyde (CAS#75070)

Estimated Annual Exposure Concentrations (Fg/m3) for Acetaldehyde (CAS#75070)

l Simplified modeling assumptions may introduce significant uncertainties into each component of the assessment. See the full discussion of these limitations.

l Because of these uncertainties, EPA will not use the results of this assessment to determine source-specific contributions or to set regulatory requirements. However, EPA expects to use these results to inform decisions about the priorities of the air 

toxics program as well as to guide the collection of additional data that could lead to regulatory decisions.

l The exposure estimates presented below are representative of midrange estimates of population exposures. Due to a number of factors, some individuals may have substantially higher or lower exposures. It is important to note that the model, as 

applied on the national scale, is not designed to quantify these extreme values of individual exposures.

Draft for Scientific Peer Review

l Note that for certain chemicals, exposure pathways other than inhalation as well as indoor sources of air toxics may contribute substantially to total exposures of concern. This assessment does not address these other routes of exposure (i.e.,ingestion 

or dermal) or inhalation exposure resulting from indoor sources.

Page 1 of 1 Release Version: August, 2000

153
0.001 0.010 0.100 1.000 10.000 100.000

Wyoming

Wisconsin

West Virginia

Washington

Virginia

Virgin Islands

Vermont

Utah

Texas

Tennessee

South Dakota

South Carolina

Rhode Island

Puerto Rico

Pennsylvania

Oregon

Oklahoma

Ohio

North Dakota

North Carolina

New York

New Mexico

New Jersey

New Hampshire

Nevada

Nebraska

Montana

Missouri

Mississippi

Minnesota

Michigan

Massachusetts

Maryland

Maine

Louisiana

Kentucky

Kansas

Iowa

Indiana

Illinois

Idaho

Georgia

Florida

District Of Columbia

Delaware

Connecticut

Colorado

California

Arkansas

Arizona

Alabama

National

Figure 4-16. 1996 Modeled Exposure Concentrations

Acetaldehyde - Statewide Concentration Distribution Estimates

Inhalation Exposure Concentration (μg/m3)

1 in a Million 

Cancer Risk*

10 in a Million Cancer Risk*

100 in a Million Cancer Risk*

Hazard Quotient Equal 1

25th median 75th

* Upper bound cancer risk assuming lifetime exposure

154
0.0 20.0 40.0 60.0 80.0 100.0

Wyoming

Wisconsin

West Virginia

Washington

Virginia

Virgin Islands

Vermont

Utah

Texas

Tennessee

South Dakota

South Carolina

Rhode Island

Puerto Rico

Pennsylvania

Oregon

Oklahoma

Ohio

North Dakota

North Carolina

New York

New Mexico

New Jersey

New Hampshire

Nevada

Nebraska

Montana

Missouri

Mississippi

Minnesota

Michigan

Massachusetts

Maryland

Maine

Louisiana

Kentucky

Kansas

Iowa

Indiana

Illinois

Idaho

Georgia

Florida

District Of Columbia

Delaware

Connecticut

Colorado

California

Arkansas

Arizona

Alabama

National

0

Figure 4-17. 1996 Modeled Exposure Concentrations

Acetaldehyde - Statewide Source Sector Contribution Estimates

 

0 100

Area & Other

0 100

Major

100

Background

0 100

Onroad

0 100

Nonroad

Percent Contribution to Statewide Annual Average Exposure Concentration Estimates

155
Release Version: September 2000

Figure 4-18. 1996 Modeled Median Exposure Concentrations for North Carolina

0.00000001

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

10

Acetaldehyde

Acrolein

Acrylonitrile

Arsenic

Benzene

Beryllium

1,3-Butadiene

Cadmium

Carbon Tetrachloride

Chloroform

Chromium

Coke Oven Emissions

1,3-Dichloropropene

Diesel PM

Dioxin

Ethylene Dibromide

Ethylene Dichloride

Ethylene Oxide

Formaldehyde

Hexachlorobenzene

Hydrazine

Lead

Manganese

Mercury

Methylene Chloride

Nickel

PCBS

Perchloroethylene

POM (7-PAH)

POM (total)

Propylene Dichloride

Quinoline

1,1,2,2-TCA

Trichloroethylene

Vinyl Chloride

Inhalation Exposure Concentration (ug/m3

)

Median Exposure Concentration Greater than 1 in a Million Cancer Risk*

Median Exposure Concentration Greater than 10 in a Million Cancer Risk*

Median Exposure Concentration Greater than RfC (Non-Cancer)

Median Exposure Concentration Less than Shown Health Criteria

or No Health Criteria Considered

Results Not Available for Dioxin

* Upper bound cancer risk assuming lifetime exposure

Results Not Available for Diesel PM

156
Figure 4-19. Example State Exposure Concentration Map

157
Figure 4-20. Benzene Exposure Variability within a County.

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

1,000 10,000 100,000 1,000,000 10,000,000

County Population

County Level Benzene Exposure Concentration Range (Tract-to-Tract)

158
Figure 5-1. 1996 Risk Characterization

Distribution of lifetime cancer risk for the US population, based on 1996 

exposure* to all source sectors and background combined.

0.001 0.01 0.1 1 10 100

Arsenic

Benzene

Chromium

Coke Oven Emissions

Nickel

Vinyl Chloride

Acetaldehyde

Acrylonitrile

Beryllium

1,3-Butadiene

Cadmium

Carbon Tetrachloride

Chloroform

1,3-Dichloropropene

Ethylene Dibromide

Ethylene Dichloride

Ethylene Oxide

Formaldehyde

Hexachlorobenzene

Hydrazine

Lead

Methylene Chloride

PCBs

POM total

POM 7PAH

Propylene Dichloride

Quinoline

1,1,2,2-Tetrachloroethane

Perchloroethylene

Trichloroethylene

Known Carcinogens Probable or Possible Carcinogens

Upper-Bound Lifetime Cancer Risk per Million

5 25 50 75 95 99

Population Percentile

* Results are based on inhalation exposure to outdoor sources only. Although these results assume continuous exposure to 

1996 levels of air toxics over a lifetime, current and planned control programs are expected to substantially reduce these 

exposures and associated cancer risk for some pollutants. See additional information on the following page.

159
0.01 0.1 1 10 100 1000

Arsenic

Benzene

Chromium

Coke Oven Emissions

Nickel

Vinyl Chloride

Acetaldehyde

Acrylonitrile

Beryllium

1,3-Butadiene

Cadmium

Carbon Tetrachloride

Chloroform

1,3-Dichloropropene

Ethylene Dibromide

Ethylene Dichloride

Ethylene Oxide

Formaldehyde

Hexachlorobenzene

Hydrazine

Lead

Methylene Chloride

PCBs

POM (total)

POM (7-PAH)

Propylene Dichloride

Quinoline

1,1,2,2-Tetrachloroethane

Perchloroethylene

Trichloroethylene

Known Carcinogens Probable or Possible Carcinogens

Millions of People

* Results are based on inhalation exposure to outdoor sources only. Although these results assume continuous exposure to 1996 

levels of air toxics over a lifetime, current and planned control programs are expected to substantially reduce these exposures and 

associated cancer risk for some pollutants. See additional information on the following page.

Total US Population

> 100 in 1 Million

>10 in 1 Million

>1 in 1 Million

Figure 5-2. 1996 Risk Characterization 

Population whose 1996 exposure* exceeded set 

cancer risk levels based on all source sectors and background.

160
Figure 5-3. 1996 Risk Characterization

Distribution of lifetime cancer risk for the US population, 

based on 1996 exposure* to multiple carcinogens.

0.001 0.01 0.1 1 10 100

Major

Area

Mobile On-Road

Mobile Non-Road

Background

Total

Major

Area

Mobile On-Road

Mobile Non-Road

Background

Total

Known Carcinogens Probable or Possible Carcinogens

Upper-Bound Lifetime Cancer Risk per Million

5 25 50 75 95 99

Population Percentile

* Results are based on inhalation exposure to outdoor sources only. Although these results assume continuous exposure to 

1996 levels of air toxics over a lifetime, current and planned control programs are expected to substantially reduce these 

exposures and associated cancer risk for some pollutants. See additional information on the following page.

161
0.01 0.1 1 10 100 1000

Major

Area

Mobile On-Road

Mobile Non-Road

Background

Total

Major

Area

Mobile On-Road

Mobile Non-Road

Background

Total

Known Carcinogens Probable or Possible Carcinogens

Millions of People

Figure 5-4. 1996 Risk Characterization 

Population whose 1996 exposure* exceeded 

set risk levels of risk for carcinogens combined.

* Results are based on inhalation exposure to outdoor sources only. Although these results assume continuous exposure to 1996 levels 

of air toxics over a lifetime, current and planned control programs are expected to substantially reduce these exposures and associated 

cancer risk for some pollutants. See additional information on the following page.

Total US Population

> 100 in 1 Million

>10 in 1 Million

>1 in 1 Million

162
Figure 5-5. 1996 Risk Characterization

Distribution of non-cancer hazard quotient for the US population, based on 

1996 exposure* to all source sectors and background combined.

0.00001 0.0001 0.001 0.01 0.1 1 10 100

Benzene

Beryllium

Cadmium

Chloroform

Chromium

1,3-Dichloropropene

Ethylene Dibromide

Ethylene Dichloride

Ethylene Oxide

Formaldehyde

Hexachlorobenzene

Lead

Mercury

Methylene Chloride

Nickel

Perchloroethylene

Trichloroethylene

Acetaldehyde

Acrolein

Acrylonitrile

Arsenic

1,3-Butadiene

Carbon Tetrachloride

Hydrazine

Manganese

Propylene Dichloride

Vinyl Chloride

High Certainty (UF 1-100) Low Certainty (UF>100)

Hazard Quotient (Exposure/RfC)

* Results are based on inhalation exposure to outdoor sources only. Although these results assume continuous exposure to 

1996 levels of air toxics over a lifetime, current and planned control programs are expected to substantially reduce these 

exposures and associated non-cancer risk for some pollutants. See additional information on the following page.

5 25 50 75 95 99

Population Percentile

163
0.01 0.1 1 10 100 1000

Benzene

Beryllium

Cadmium

Chloroform

Chromium

1,3-Dichloropropene

Ethylene Dibromide

Ethylene Dichloride

Ethylene Oxide

Formaldehyde

Hexachlorobenzene

Lead

Mercury

Methylene Chloride

Nickel

Perchloroethylene

Trichloroethylene

Acetaldehyde

Acrolein

Acrylonitrile

Arsenic

1,3-Butadiene

Carbon Tetrachloride

Hydrazine

Manganese

Propylene Dichloride

Vinyl Chloride

High Certainty (UF 1-100) Low Certainty (UF>100)

Millions of Adults

Total US Adult Population

* Results are based on inhalation exposure to outdoor sources only. Although these results assume continuous exposure to 1996 

levels of air toxics over a lifetime, current and planned control programs are expected to substantially reduce these exposures and

associated non-cancer risk for some pollutants. See additional information on the following page.

Figure 5-6. 1996 Risk Characterization 

Adult population whose 1996 exposure* exceeded set non-cancer 

hazard quotient levels based on all source sectors and background combined.

HQ > 10

HQ > 1

HQ > 0.1

164
0.01 0.1 1 10 100 1000

Benzene

Beryllium

Cadmium

Chloroform

Chromium

1,3-Dichloropropene

Ethylene Dibromide

Ethylene Dichloride

Ethylene Oxide

Formaldehyde

Hexachlorobenzene

Lead

Mercury

Methylene Chloride

Nickel

Perchloroethylene

Trichloroethylene

Acetaldehyde

Acrolein

Acrylonitrile

Arsenic

1,3-Butadiene

Carbon Tetrachloride

Hydrazine

Manganese

Propylene Dichloride

Vinyl Chloride

High Certainty (UF 1-100) Low Certainty (UF>100)

Millions of Children

Figure 5-7. 1996 Risk Characterization 

Children population whose 1996 exposure* exceeded set non-cancer 

hazard quotient levels based on all source sectors and background combined.

* Results are based on inhalation exposure to outdoor sources only. Although these results assume continuous exposure to 1996 

levels of air toxics over a lifetime, current and planned control programs are expected to substantially reduce these exposures and

associated non-cancer risk for some pollutants. See additional information on the following page.

Total US Children Population

HQ > 10

HQ > 1

HQ > 0.1

165
Figure 5-8. 1996 Risk Characterization

Distribution of non-cancer target organ-specific hazard index (TOSHI) for 

effects to the respiratory system, based on 1996 multiple-pollutant exposure* 

to adults in the US population.

0.001 0.01 0.1 1 10 100

Major

Area

Mobile On-Road

Mobile Non-Road

Background

Total

Major

Area

Mobile On-Road

Mobile Non-Road

Background

Total

High Certainty (UF 1-100) Low Certainty (UF>100)

Hazard Quotient (Exposure/RfC)

* Results are based on inhalation exposure to outdoor sources only. Although these results assume continuous exposure to 

1996 levels of air toxics over a lifetime, current and planned control programs are expected to substantially reduce these 

exposures and associated non-cancer risk for some pollutants. See additional information on the following page.

5 25 50 75 95 99

Population Percentile

166
Figure 5-9. Illustration: Distribution of monitor-to-model ratios for stable gases, 

developed from ratios for benzene and perchloroethylene.

Figure 5-10. Illustration: Distribution of monitor-to-model ratios for reactive gases, 

developed from ratios for formaldehyde and acetaldehyde

Figure 5-11. Illustration: Distribution of monitor-to-model ratios for particulate species, developed from 

ratios for lead, chromium, and cadmium.

Frequency Chart

.000

.008

.015

.023

.031

0

77.25

154.5

231.7

309

0.00 0.88 1.75 2.63 3.50

10,000 Trials 82 Outliers

Forecast: Stable Gas -- Mon:Mod Ratio

Frequency Chart

.000

.007

.015

.022

.029

0

73

146

219

292

0.00 1.38 2.75 4.13 5.50

10,000 Trials 152 Outliers

Forecast: Reactive Gas -- Mon:Mod Ratio

Frequency Chart

.000

.008

.016

.023

.031

0

78.25

156.5

234.7

313

0.00 5.00 10.00 15.00 20.00

10,000 Trials 251 Outliers

Forecast: Particulate -- Mon:Mod Ratio

167
Figure 5-12. Illustration: Distribution of ambient-to-personal concentration ratios for ozone, assumed to 

apply for “typical” gaseous pollutants.

Figure 5-13. Illustration: Distribution of ambient-to-personal concentration ratios for particulate matter, 

assumed to apply for “typical” particulate pollutants.

Frequency Chart

.000

.022

.043

.065

.087

0

216.5

433

649.5

866

0.00 3.75 7.50 11.25 15.00

10,000 Trials 195 Outliers

Forecast: Gas – Pers:Amb Ratio

Frequency Chart

.000

.017

.033

.050

.067

0

167

334

501

668

0.00 2.00 4.00 6.00 8.00

10,000 Trials 209 Outliers

Forecast: Particulate – Pers:Amb Ratio

168
Figure 5-14. Illustration: Uncertainty and variability surrounding the URE for benzene, 

in terms of the ratio between the estimated URE and the “true” URE.

Figure 5-15. Illustration: Uncertainty and variability surrounding a typical RfC, in terms 

of the ratio between the estimated RfC and the “true” RfC.

Frequency Chart

.000

.014

.028

.042

.056

0

138.7

277.5

416.2

555

0.00 2.00 4.00 6.00 8.00

10,000 Trials 199 Outliers

Frequency Chart

.000

.003

.007

.010

.014

0

33.75

67.5

101.2

135

0.25 0.94 1.63 2.31 3.00

10,000 Trials 0 Outliers

Forecast: Noncarcinogen -- Pers:Risk Ratio

169
Figure 5-16. Cancer – risk ratio for stable gas.

Figure 5-17. Cancer – risk ratio for reactive gas

Figure5-18. Cancer – risk ratio for particulate

Frequency Chart

.000

.070

.141

.211

.281

0

702.5

2810

0.00 15.00 30.00 45.00 60.00

10,000 Trials 117 Outliers

Forecast: Cancer -- Risk Ratio, Stable Gas

Frequency Chart

.000

.070

.139

.209

.278

0

695.7

2783

0.00 20.00 40.00 60.00 80.00

10,000 Trials 136 Outliers

Forecast: Cancer -- Risk Ratio, Reactive Gas

Frequency Chart

.000

.088

.177

.265

.353

0

883.2

3533

0.00 68.75 137.50 206.25 275.00

10,000 Trials 64 Outliers

Forecast: Cancer -- Risk Ratio, Particulate

170
Figure 5-19. Noncancer – risk ratio for stable gas

Figure 5-20. Noncancer – risk ratio for reactive gas.

Figure 5-21. Noncancer – risk ratio for particulate.

Frequency Chart

.000

.034

.067

.101

.134

0

335

670

1340

0.00 11.25 22.50 33.75 45.00

10,000 Trials 160 Outliers

Forecast: Noncancer -- Risk Ratio, Stable Gas

Frequency Chart

.000

.032

.063

.095

.126

0

315.5

631

946.5

1262

0.00 15.00 30.00 45.00 60.00

10,000 Trials 179 Outliers

Forecast: Noncancer -- Risk Ratio, Reactive Gas

Frequency Chart

.000

.028

.055

.083

.110

0

276

552

828

1104

0.00 30.00 60.00 90.00 120.00

10,000 Trials 165 Outliers

Forecast: Noncancer -- Risk Ratio, Particulate

171
Appendix A

Summary of July 2000 Peer Review of the Draft Document 

“Planning and Scoping the Initial National-Scale Assessment: 

An Element of the EPA National Air Toxics Program”

(Provided in electronic format on Appendices CD)
Appendix A - Summary

Summary of July 2000 Peer Review of the Draft Document “Planning and Scoping the 

Initial National-Scale Assessment: An Element of the EPA National Air Toxics Program”

In July 2000, six non-U.S. EPA scientists completed a peer review of the "Planning and Scoping 

the Initial National-Scale Assessment: An Element of the EPA National Air Toxics Program'". 

The reviewers were asked to focus their review on the main body of the planning and scoping 

document as well as the supporting technical information. Reviewers were asked to consider the 

appropriateness of approaches used to (1) process the State-derived National Toxics Inventory 

for dispersion modeling, (2) estimate ambient concentrations using the Assessment System for 

Population Exposure Nationwide (ASPEN) model, (3) estimate human inhalation exposures 

using the Hazardous Air Pollutant Exposure Model version 4 (HAPEM4), and (4) estimate, 

aggregate, and interpret associated cancer and non-cancer risks. Specific charge questions, the 

reviewer's comments as well as the EPA's response to these comments are contained in this 

appendix
Appendix B

HAPEM4 Documentation

(Provided in electronic format on Appendices CD)
Appendix B - Summary

HAPEM4 Documentation

This appendix contains detailed documentation for the HAPEM4 exposure model. Two items 

are include "The HAPEM4 User’s Guide" and the "Development of Microenvironmental Factors 

for the HAPEM4 in Support of the National Air Toxics Assessment (NATA)"

The HAPEM4 User’s Guide contains:

Chapter 1 - Provides a brief introduction to HAPEM4 modeling fundamentals including

a brief history of the development of HAPEM4.

Chapter 2 - Provides an overview of the various components of HAPEM4 and basic i

information needed to run the model.

Chapter 3 - Provides a description of the format, data, and options for each of HAPEM4 

input files.

Chapter 4 - Provides a description of the format and data associated with each of 

HAPEM4 output files.

Chapter 5 - Provides a description of the purpose and operations, inputs, and outputs, 

including a brief description of the computer code, for each of HAPEM4 computer 

programs.

Chapter 6 - References.

The Microenvironmental Factors Report contains detailed information on:

• Defining the HAPEM4 ME Factors 

• Calculating HAPEM4 ME Concentrations and Estimation of Proximity Factors 

• Estimating Ambient HAP Concentrations Using ASPEN 

• Literature Search 

• Grouping HAPs and Microenvironments 

• A complete listing of the ME factors for the Urban 33 pollutants

• ME Factors for Diesel PM (Provided as an addendum to the Report)

• The Use of a Linear Model for the Initial NATA Assessment 
Appendix C

EMS-HAP User’s Guide

(Provided in electronic format on Appendices CD)
Appendix C - Summary

The Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) User Guide 

Synopsis

1.1 What is EMS-HAP?

The Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) is a series of 

computer programs that process emission inventory data for subsequent air quality modeling. 

EMS-HAP accomplishes two goals. 

1. It processes an emission inventory, such as the 1996 National Toxics Inventory, for use in 

the Assessment System for Population Exposure Nationwide (ASPEN) dispersion model. 

2. It allows you to estimate future emissions resulting from user-designed emission 

reduction scenarios and growth.

To accomplish the first goal, EMS-HAP:

C quality assures point source inventory location and stack parameter data and 

defaults missing or erroneous data where possible, 

C groups individual pollutant species (e.g. lead oxide, lead chromate into lead 

compounds),

C facilitates the selection of pollutants and pollutant groups for modeling,

C spatially allocates area and mobile source emissions from the county level to the 

census tract level using surrogates such as industrial land or roadway miles,

C allocates aircraft emissions to airport locations, 

C temporally allocates annual emission values to average hourly values based on the 

type of source, and,

C produces emission files formatted for direct input into the ASPEN model. 

To accomplish the second goal, EMS-HAP adjusts point, area and mobile emissions to account 

for growth and emission reductions resulting from user-designed scenarios such as the 

implementation of the Maximum Achievable Control Technology (MACT) standards. 

The U.S. Environmental Protection Agency's Office of Air Quality Planning and Standards 

(EPA/OAQPS), developed EMS-HAP to facilitate multiple runs of ASPEN and to analyze 

emission reduction scenarios. ASPEN can be used to estimate annual average ambient air 

quality concentrations of multiple pollutants emitted from a large number of sources at a large 

scale (i.e. nationwide) as part of a national air toxics assessment.
Although we tailored EMS-HAP to process the1996 National Toxics Inventory (NTI), 

you can use it for any emission inventory following the instructions in this guide. The 

1996 NTI is the first comprehensive model-ready national inventory of toxics, containing 

facility-specific estimates of hazardous air pollutants (HAPs).

While other emission models, such as EMS-95 and EPS 2.0, are available, they do not 

address the details of the 1996 NTI or the input requirements of the ASPEN model. 

1.2 How do I use the EMS-HAP User's Guide?

This guide describes the programs that comprise EMS-HAP, and gives instructions on 

how to use them to create ASPEN emission input files for base year or projected year 

inventories of your choice. This manual is not specific to any one input inventory. For 

example, you are not limited to using the 1996 NTI to run EMS-HAP. You need only 

make sure your input inventory meets the requirements described within each program. 

This guide also provides information on how we used EMS-HAP to process 1996 

emissions data for a national screening study. 

We present the programs in the order you may choose to use them. Chapter 2 describes 

the aircraft emissions processing program. Chapters 3 through 7 describe the point 

source processing programs. Chapters 8 through 10 describe the programs for area and 

mobile source processing. Each chapter describes the function of the program, how to 

run the program, all required ancillary input files and emission inventory data 

requirements, and how to evaluate the output to determine if the data were processed 

successfully. In this guide, all ancillary SAS data files are named without their 

extension, since SAS files extension names vary with system and engine type. All 

programs are also named without their extension.

Appendix A presents the file formats of the ancillary input files. Appendix B contains 

sample batch files for running the EMS-HAP programs. Appendix C discusses 

preparation of the point source component of the 1996 NTI for input into EMS-HAP. 

Appendix D presents the methodologies used to prepare emission input files for the 

ASPEN model for a national air toxics assessment. Appendix D also discusses how we 

developed the key ancillary input files, such as the spatial allocation factor files, provided 

with EMS-HAP. The ancillary files provided with EMS-HAP are those we used to 

produce the 1996 ASPEN modeling inventory. 

A separate user's guide is available for the ASPEN model. Users familiar with ASPEN 

model input requirements will have a better understanding of EMS-HAP.
Appendix D

Development of the Emission Inventory

(Provided in electronic format on Appendices CD)
Appendix D - Summary

Development of the Emission Inventory

This appendix includes a technical paper describing the development of the 1996 

National Toxic Inventory (NTI). The following is the abstract for this paper:

The 1990 amendments to the Clean Air Act (CAA) established the need for a 

comprehensive hazardous air pollutant (HAP) emissions inventory effort that can be used 

to track progress by the Environmental Protection Agency (EPA) over time in reducing 

HAPs in ambient air. To estimate risk and HAP emission reductions, the EPA compiled 

the 1996 National Toxics Inventory (NTI) to provide a model-ready emissions inventory. 

The 1996 NTI contains estimates of facility-specific HAP emissions and their 

source-specific parameters necessary for modeling such as location and facility 

characteristics (stack height, exit velocity, temperature, etc.). Complete source category 

coverage is needed for modeling, and the NTI contains estimates of emissions from 

major, area, and mobile source categories. Compiling this huge amount of data presents 

a significant challenge to EPA. To compile the data, the EPA first solicited HAP 

emissions data from states, and 36 states, Puerto Rico and the Virgin Islands delivered 

HAP emissions inventories to the EPA. These state data varied in completeness, format, 

and quality. The EPA evaluated the state data and supplemented it with data gathered 

while developing Maximum Achievable Control Technology (MACT) standards and 

with Toxic Release Inventory (TRI) data. Then the EPA estimated emissions for other 

states and for sources not included in the state data to produce a complete model-ready 

national 1996 inventory. The EPA released the draft 1996 NTI for external comment and 

received revisions from 42 states, industry, and other organizations. The EPA released 

the final 1996 NTI in June 2000. This paper discusses the compilation of the 1996 NTI 

in order to evaluate the success of EPA's national air toxics program and presents 

summary emissions data from the 1996 NTI.

In addition to the paper include in Appendix D, the following are reference files that 

provide further detail on the inventory development. These documents are avaiable at 

http://www.epa.gov/ttn/chief/ei_guide.html#airtoxics

• Documentation for the 1996 Base Year National Toxics Inventory for Aircraft Sources

 

• Documentation for the 1996 Base Year National Toxics Inventory for Area Sources

• Documentation for the 1996 Base Year National Toxics Inventory for Commercial 

Marine Vessel and Locomotive Mobile Sources

• Documentation for the 1996 Base Year National Toxics Inventory for Nonroad Vehicle 

and Equipment Mobile Sources

 • Documentation for the 1996 Base Year National Toxics Inventory for Onroad Sources

 • Documentation for the 1996 Base Year National Toxics Inventory for Point Sources
Appendix E

ASPEN User’s Guide

(Provided in electronic format on Appendices CD)
Appendix E - Summary

This appendix contains the " User’s Guide for the Assessment System for Population 

Exposure Nationwide Model (ASPEN, Version 1.1"

This user’s guide provides documentation for the Assessment System for Population 

Exposure Nationwide (ASPEN, Version 1.1), referred to hereafter as ASPEN. It includes 

a technical description of the ASPEN algorithms, user instructions for running the model 

and a tutorial for getting started. The ASPEN model consists of a dispersion and mapping 

module. The dispersion module is a Guassian formulation for estimating ambient annual 

average concentrations at a set of fixed receptors within the vicinity of the emission 

source. The mapping module produces a concentration at each census tract. Input data 

needed are emissions data, meteorological data and census tract data.
Appendix F

Estimation of Background Concentrations for Diesel Particulate Matter

(Provided in electronic format on Appendices CD)
Appendix F - Summary

Estimation of Background Concentrations for Diesel Particulate Matter

This appendix contains the calculations utilized to determine the background 

concentrations for Diesel Particulate Matter. Background concentrations are an 

essential part of the total air quality concentration to be considered in determining source 

impacts. Background air quality includes pollutant concentrations due to: 1) natural 

sources; 2) nearby sources that are unidentified in the inventory; and 3) long range 

transport into the modeling domain. Typically, monitored air quality data should be used 

to establish background concentrations.
Appendix G

Health Effects Information Used in Cancer and Noncancer Risk Characterization 

for the NATA 1996 National-Scale Assessment

(Provided in electronic format on Appendices CD)
Appendix G - Summary

Health Effects Information Used In Cancer and Noncancer Risk Characterization 

for the NATA 1996 National-Scale Assessment

This appendix contains the hazard identification and dose-response assessment 

information for the NATA national-scale assessment. The criteria for selection of the 

information was obtained from various sources and prioritized according to (1) 

applicability, (2) conceptual consistency with EPA risk assessment guidelines, and (3) 

level of review received. The prioritization process was aimed at incorporating into our 

assessment the best-available science with respect to dose-response information. The 

following sources were used:

• US Environmental Protection Agency (EPA)

• Agency for Toxic Substances and Disease Registry (ATSDR)

• California Environmental Protection Agency (CalEPA)

• International Agency for Research on Cancer (IARC)
Appendix H

Estimating Carcinogenic Potency for Mixtures of Polycyclic Organic Matter (POM) 

for the 1996 National-Scale Assessment

(Provided in electronic format on Appendices CD)
Appendix H - Summary

Estimating Carcinogenic Potency for Mixtures of Polycyclic Organic Matter (POM) 

for the 1996 National-Scale Assessment

The polycyclic organic matter (POM) category within the Clean Air Act’s section 112(b) 

list of hazardous air pollutants comprises a broad spectrum of compounds having widely 

varying toxic potencies. Because all these compounds have been listed as a single 

category under the Act, the 1996 National Toxics Inventory (NTI) also records them only 

as a group for the great majority of sources, usually in terms of total polynuclear aromatic 

hydrocarbons (PAH – one type of POM) or total POM. Most of these entries do not 

include information on the method used to estimate the emission rate.

For this reason, the NTI data could not support modeling of individual POM compounds 

for the initial national-scale assessment. The alternative-modeling modeling POM as a 

group – was a significant simplifying step because different types of emission sources 

may be expected to produce different characteristic mixtures of POM compounds. These 

different mixtures have the potential to vary substantially in toxic potency per unit mass.

The method to aggregate these pollutants for the national scale assessment is presented in 

this appendix.
Appendix I

Model-to-Monitor Comparison Methods

(Provided in electronic format on Appendices CD)
Appendix I - Summary

Protocol for Model-to-Monitor Comparisons for the 

Initial National Scale Assessment

This appendix provides the protocol for "Model-to-Monitor" comparisons for national 

scale Assessment (Protocol) that was reviewed by the EPA Science Advisory Board 

(SAB) on August 18, 2000. The SAB review of the Protocol is available on the internet 

at: http://www.epa.gov/sab/ec0015.pdf

Not all of the methods described in the Protocol were used in the model-to-monitor 

comparison included in the NATA Report; some of the methods that were used were 

modified; and, some new methods were introduced. The decision to diverge somewhat 

from the methods described in the Protocol was based on the recommendations of the 

SAB, as well as our own judgment.
Appendix J

Comparison of ASPEN Results to Monitored Concentrations

(Provided in electronic format on Appendices CD)
Appendix J - Summary

Initial National-Scale Assessment 

Comparison of ASPEN Modeling System Results to Monitored Concentrations

This appendix describes the results of a model-to-monitor comparison we conducted for 

subset of the 33 urban HAPs. We view this comparison as a evaluation of ASPEN and he 

inputs that go into ASPEN, including: emissions data from various sources including the 

National Toxics Inventory (NTI), the Emissions Modeling System for Hazardous Air 

pollutants (EMS-HAP), and meteorological data. For most of the pollutants evaluated, we 

found that ASPEN estimates tended to be lower than the monitor averages at the exact 

locations of the monitors. In general it appears that ASPEN is underestimating monitorbased HAP concentrations. 

Possible reasons for ASPEN to underestimate HAP concentrations include:

1) The National Toxics Inventory (NTI) missing specific emissions sources (for many of 

the sources in the NTI some of the emissions parameters are defaulted or missing)

2) Emission rates being underestimated in many locations. We believe the ASPEN model 

itself is contributing in only a minor way to the underestimation. This is mainly 

due to output from the antecedents of the ASPEN model comparing favorably to 

monitoring data in cases where the emissions and meteorology were accurately 

characterized and the monitors took more frequent readings. In simulations we 

ran, the ASPEN's estimates compared favorably to the estimates derived from a 

more meticulous model.

3) Monitor siting may have also contributed to the underestimation. Sites are normally 

situated to find peak pollutant concentrations, which implies that errors in the 

characterization of sources would tend to make the model underestimate the 

monitor values.

4) Finally, we are not sure of the accuracy of the monitor averages, which have their own 

sources of uncertainty.

Our results suggest that the model estimates are uncertain on a local scale (i.e., at the 

census tract level). We believe that the model estimates are more reliably interpreted as 

being a value likely to be found within 30 km of the census tract location.
Appendix K

HAPEM4 Results

(Provided in electronic format on Appendices CD)
Appendix K - Summary

HAPEM4 Result Charts

This appendix presents the graphical results of the HAPEM4 exposure modeling 

Included are:

• Pollutant specific exposure concentration charts 

• Pollutant specific percent contribution charts 

• State summary exposure concentration estimate charts

In addition, the "Page 2" referred to on each chart has been provided at end of the 

appendix.
Appendix L

Risk Characterization Results

(Provided in electronic format on Appendices CD)
Appendix L - Summary

Risk Characterization Charts

This appendix contains the risk and population summary charts for carcinogens as well as 

noncarcinogens. Charts are also summarized by source sector (i.e., major, area, mobile 

onroad, mobile off-road, background). A set of the Key Risk Assumptions and 

Limitations is also provided.