Draft EJ Technical Guidance: May 1, 2013 – Post-Internal Agency Review Draft

April 2013

Draft Technical Guidance for 

Assessing Environmental 

Justice in Regulatory Analysis
Draft EJ Technical Guidance: April 3, 2013 – Post-Internal Agency Review Draft

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Acronyms and Abbreviations iv

Overview and Background v

What is the Purpose of this Guidance? v

Who is the Audience for this Technical Guidance? vi

How is this Guidance Document Organized? vi

Section 1: Introduction 1

1.1 Overarching Questions and Objectives for Analysis of Potential EJ Concerns 1

1.2 Recommendations to Guide Assessments for Potential EJ Concerns 3

Section 2: Key Analytic Principles and Definitions 5

2.1 Key Analytic Principles 5

2.2 Population Groups of Concern Highlighted in E.O. 12898 6

2.2.1 Minority and Indigenous Populations 6

2.2.2 Low-Income Populations 7

2.2.3 Populations that Principally Subsist on Fish and Wildlife 8

2.3 Selecting a Comparison Group 9

2.4 Disproportionate Impacts Under E.O. 12898 10

2.5 Meaningful Involvement 12

Section 3: Contributors and Drivers of Potential Environmental Justice

Concerns 14

3.1 Social Context and Risk 14

3.2 Contributors to Higher Exposure 15

3.2.1 Proximity to Emission Sources 16

3.2.2 Unique Exposure Pathways 16

3.2.3 Physical Infrastructure 16

Table of Contents
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3.2.4 Exposure to Multiple Stressors 17

3.2.5 Community Capacity to Participate in Decision Making 17

3.3 Contributors to Higher Susceptibility in Response to Exposure 17

Section 4: Considering Environmental Justice when Planning a Human 

Health Risk Assessment 19

4.1 Introduction 19

4.2 Overview of Key Concepts 20

4.3 Considering EJ when Planning a Human Health Risk Assessment 22

4.3.1 Fit for Purpose 23

4.3.2 Planning and Scoping 23

4.3.2.1 Identify Context, Purpose and Scope of the Risk Assessment 24

4.3.2.2 Define Responsibilities for Assessing and Communicating Risks and 

 Allocate Resources 27

4.3.2.3 Identifying Data, Models, Tools and other Technical Resources 27

4.3.2.4 Conducting a Scientific Peer Review 30

4.3.2.5 Ensuring Meaningful Public, Stakeholder and Community Involvement When

 Possible 30

4.3.3 Problem Formulation 31

4.3.3.1 Characterizing the Stressor and its Sources 32

4.3.3.2 Identifying Differences in Exposures that May Lead to Disproportionate Risks 33

4.3.3.3 Population Characteristics 34

4.3.3.4 Building a Conceptual Model 35

4.3.3.5 Developing an Analysis Plan 35

Section 5: Conducting Regulatory Analyses To Assess Potential 

Environmental Justice Concerns 36

5.1 Evaluating the Feasibility of an Assessment of Potential EJ Concerns 37

5.2 Evaluating Baseline and Incremental Changes 40

5.3 Methods to Assess Potential EJ Concerns 41

5.3.1 Summary Statistics 44

5.3.2 Additional Analytic Methods 44

5.3.2.1 Visual Displays 46

5.3.2.2 Proximity-Based Analysis 46

5.3.2.3 More Advanced Methods 48
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5.4 Analytical Considerations 49

5.4.1 Geographic Scope 49

5.4.2 Comparison Group 49

5.4.3 How to Spatially Identify and Aggregate Effects 50

5.4.4 Statistical Significance 50

5.5 Assessing the Distribution of Costs and Other Impacts 51

5.5.1 The Distribution of Costs 51

5.5.2 Other Impacts 52

Section 6: Key Near-Term Research Priorities to Fill Key Data and 

Methodological Gaps 53

Glossary 54

References 57

Appendices

Appendix A: Select Examples of EPA Guidance, Guidelines, and Policy Documents that May Be Helpful When 

Evaluating Potential EJ Concerns for Regulatory Actions

Appendix B: Example Approaches to Address Potential EJ Concerns When Conducting Exposure and DoseResponse Assessments
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ADP – Action Development Process

CAFO – Concentrated Animal Feeding Operation

CDC – Centers for Disease Control and Prevention

CEQ – Council on Environmental Quality

CPI-U – Consumer Price Index 

CRA – Cumulative Risk Assessment

DQO – Data Quality Objectives 

DSW – Definition of Solid Waste

EGU – Electric Generating Utility

EJ – Environmental Justice

EO – Executive Order

EPA – Environmental Protection Agency

GAO – Government Accountability Office 

GHG – Greenhouse Gas

GIS – Geographic Information System

HHRA – Human Health Risk Assessment

HIA – Health Impact Assessment

HUD – Housing and Urban Development 

IQG – Information Quality Guidelines

IRIS – Integrated Risk Information System

MATS – Mercury and Air Toxics Standards

MDI – Mental Development Index 

NAAQS – National Ambient Air Quality Standards

NATA – National Air Toxics Assessment

NEPA – National Environmental Policy Act

NESHAP – National Emission Standards for Hazardous Waste Pollutants

NHANES – National Health and Nutrition Examination Survey

NPDES – National Pollution Discharge Elimination System 

NRC – National Research Council

NYCHANES – New York City Health and Nutrition Examination Survey

OAR – EPA Office of Air and Radiation 

OMB – White House Office of Management and Budget

OPP – EPA Office of Pesticide Programs 

PM – Particulate Matter 

RAF – EPA Risk Assessment Forum 

RCRA – Resource Conservation and Recovery Act 

SES – Socioeconomic Status 

SPM – Supplemental Poverty Measure

Acronyms and Abbreviations
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What is the Purpose of this Guidance?

Executive Order (E.O.) 12898, Federal Actions to Address Environmental Justice in Minority Populations and LowIncome Populations, calls on each Federal agency to make achieving environmental justice (EJ) part of its mission 

“by identifying and addressing, as appropriate, disproportionately high and adverse human health or 

environmental effects of its programs, policies, and activities on minority populations and low-income 

populations.”1,2

In July 2010, the United States Environmental Protection Agency (EPA or the Agency) released its Action 

Development Process: Interim Guidance on Considering Environmental Justice During the Development of an Action

(ADP Interim Process Guide) to assist staff in considering potential EJ concerns during the development of actions.3

The ADP Interim Process Guide aims to “explicitly integrate EJ considerations into the fabric of EPA’s ADP [Action 

Development Process] – through all stages leading to promulgation and implementation.” 

This document, Technical Guidance for Assessing Environmental Justice in Regulatory Analysis (EJ Technical Guidance), 

is a technical complement to the ADP Interim Process Guide and represents an important step toward ensuring that 

Agency regulatory actions appropriately consider EJ issues.4 Specifically, this guidance document will help Agency 

staff conduct analyses to evaluate potential EJ concerns associated with EPA regulatory actions. This document is 

intended for use alongside other relevant Agency guidance on human health risk assessment and economic analysis 

(see Appendix A).5 Together, the ADP Interim Process Guide and the EJ Technical Guidance provide direction on how 

regulatory actions can be responsive to E.O. 12898 as well as EPA’s EJ policies and Plan EJ 2014, the Agency’s 

roadmap for integrating environmental justice into EPA programs and policies (U.S. EPA, 2011b).6

 

1 E.O. 12898 (issued February 11, 1994) is available in full at: http://www.archives.gov/federal-register/executiveorders/pdf/12898.pdf.

2 The term “effects” is typically interpreted within the EPA as a reference to risks, exposures, and outcomes and is sometimes used 

interchangeably with the term, “impacts.”

3 EPA’s Interim Guidance on Considering Environmental Justice During the Development of an Action can be found at 

http://www.epa.gov/compliance/ej/resources/policy/consideringej-in-rulemaking-guide-07-2010.pdf. It applies generally to all “Agency 

actions,” which include rules, policy statements, risk assessments, guidance documents, models that may be used in future rulemakings (for 

more on the ADP see http://intranet.epa.gov/adplibrary). This technical guidance document is designed to apply to analyses conducted 

mainly in direct support of a rule or regulation.

4 E.O. 12866 defines a regulatory action as “any substantive action by an agency (normally published in the Federal Register) that 

promulgates or is expected to lead to the promulgation of a final rule or regulation, including notices of inquiry, advance notices of 

proposed rulemaking, and notices of proposed rulemaking.

5 Also see Plan EJ 2014 Legal Tools for a review of legal authorities under the environmental and administrative statutes administered by 

EPA that may contribute to the effort to advance environmental justice (U.S. EPA, 2011a).

(http://www.epa.gov/compliance/environmentaljustice/resources/policy/plan-ej-2014/ej-legal-tools.pdf).

6 Existing EPA EJ policy is summarized at: http://www.epa.gov/environmentaljustice/resources/policy/. EPA’s historical EJ policies include: 

The EPA’s Environmental Justice Strategy (U.S. EPA, 1995); Environmental Justice Implementation Plan (U.S. EPA, 1996); Environmental Justice: 

Guidance Under the National Environmental Policy Act (Council on Environmental Quality, 1997); Final Guidance for Incorporating 

Environmental Justice Concerns in EPA’s NEPA Compliance Analyses (U.S. EPA,1998a); Toolkit for Assessing Potential Allegations of 

Environmental Justice (U.S. EPA, 2004); Memo from Stephen L. Johnson: Reaffirming the U.S. EPA’s Commitment to Environmental Justice (U.S. 

EPA, 2005); and Memo from Lisa P. Jackson: Next Steps: Environmental Justice and Civil Rights (U.S. EPA, 2009).

Overview and Background
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Who is the Audience for this Technical Guidance?

This guidance document is intended primarily to assist Agency analysts, including risk assessors, economists, and 

other analytic staff, in evaluating potential EJ concerns in the context of rule development (i.e., regulatory actions). 

Senior EPA managers, decision makers, rulewriters, and workgroup chairs will also find this document useful for 

understanding analytic expectations and ensuring that potential EJ concerns are appropriately considered.

How is this Guidance Document Organized? 

The first three sections of this guidance establish the principles, priorities, context, and definitions that drive the 

assessment of potential EJ concerns in support of EPA regulatory actions:

 Section 1: Introduction outlines the main objectives for analysis of potential EJ concerns. In addition, 

the section presents EPA’s six main recommendations to guide assessments of EJ for EPA rules and 

regulations. Appendix A provides a list of relevant additional guidance that may be helpful to the 

analyst when assessing potential EJ concerns

 Section 2: Key Analytic Principles and Definitions reviews the analytic principles that guide the

analysis of potential EJ concerns. The section also introduces and discusses key EJ concepts highlighted 

in Executive Order 12898 (e.g., population groups of concern, disproportionate impacts, meaningful 

involvement) that may interact with analytic considerations. 

 Section 3: Contributors and Drivers of Potential Environmental Justice Concerns identifies important 

human health risk and economic factors that contribute to potential EJ concerns, and highlights the key 

reasons why health risks may be unevenly distributed across population groups.

The main technical sections (Sections 4 and 5) provide guidance for considering EJ in two specific contexts: planning 

for a human health risk assessment (Section 4) and development of a regulatory analysis (Section 5). The final 

section of the document (Section 6) identifies near-term research priorities related to the analysis of EJ.

 Section 4: Considering Environmental Justice when Planning a Human Health Risk Assessment

provides technical guidance on how to incorporate consideration of potential EJ concerns into the 

planning phase of a human health risk assessment. The section describes empirical methodologies and 

tools that are currently available, and discusses how information can be clearly and transparently 

presented to decision makers. Appendix B provides examples of approaches for incorporating EJ into 

the planning stages of exposure and dose-response assessments. 

 Section 5: Conducting Regulatory Analyses To Assess Potential Environmental Justice Concerns

provides technical guidance on how to integrate potential EJ concerns into regulatory analyses. In 

particular, this section discusses how to evaluate what level of analysis is feasible; what types of 

information should be included in an assessment of potential EJ concerns; what methods and tools can 

be used to generate this information; a discussion of other analytic considerations that could affect the 

results of the assessment; and information on when to consider costs and non-health impacts.

 Section 6: Key Near-Term Research Priorities to Fill Key Data and Methodological Gaps provides 

information on longer-term research goals to improve assessment of EJ at the EPA. [NOTE: this section 

will be drafted based on Agency, public, and peer review input and will be added at a later date]

While planning for a human health risk assessment (Section 4) and development of a regulatory analysis (Section 

5) are often carefully coordinated in the rulemaking context, they also may occur in isolation or in parallel. This 

guidance therefore assumes that an analyst may wish to consult only one of the two sections to address a specific 
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context. As a result, the sections present some parallel information about key concepts and methods. This overlap is

by design, and is appropriate given that different analytic experts will access and rely on different sections of the 

document for different purposes within the larger context of EPA regulatory action development.

Disclaimer: This document identifies internal Agency policies and recommended procedures for EPA 

employees. This document is not a rule or regulation and it may not apply to a particular situation based 

upon the circumstances. This guidance does not change or substitute for any law, regulation, or any other 

legally binding requirement and is not legally enforceable. As indicated by the use of non-mandatory 

language such as “guidance,” “recommend,” “may,” “should,” and “can,” it identifies policies and 

provides recommendations and does not impose any legally binding requirements.
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he United States Environmental Protection Agency defines environmental justice as “the fair treatment and 

meaningful involvement of all people regardless of race, color, national origin, or income with respect to the 

development, implementation, and enforcement of environmental laws, regulations, and policies.”7 EPA further 

defines the term “fair treatment” to mean that “no group of people should bear a disproportionate burden of 

environmental harms and risks, including those resulting from the negative environmental consequences of industrial, 

governmental, and commercial operations or programs and policies” (U.S. EPA, 2012a). In implementing its EJrelated efforts, the Agency has expanded the concept of fair treatment to consider not only the distribution of 

burdens across all populations, but also the distribution of reductions in risk from EPA actions. For example, the 

Agency’s ADP Interim Process Guide encourages Agency staff to evaluate the distribution of burdens by paying 

special attention to populations that have historically borne a disproportionate share of environmental harms and 

risk. At the same time, it encourages Agency staff to examine the distribution of positive environmental and health 

outcomes from regulatory and other Agency activities (U.S. EPA, 2010a).

The ADP Interim Process Guide describes the statutory and policy framework for considering EJ, identifies concepts 

central to determining whether an action involves a potential EJ concern, and discusses how they fit into each step 

of the ADP. It provides a limited discussion of possible analytical tools and methodologies available for evaluating 

the impacts of Agency actions on minority, low-income or indigenous populations (U.S. EPA, 2010a). The EJ 

Technical Guidance complements the ADP Interim Process Guide. It outlines particular approaches and methods to 

help Agency analysts, including economists, risk assessors, and other analysts, evaluate potential EJ concerns. 

Together, these documents provide guidance on how regulatory actions can be responsive to E.O. 12898 as well 

as consistent with EPA’s EJ policies and Plan EJ 2014 (U.S. EPA, 2011b). 

It is important to note that this technical guidance will evolve with the state of the science, data, and analytic 

methods available to Agency analysts. In particular, with regard to risk assessment, this technical guidance is 

limited to a discussion of how to integrate EJ into the planning phase of a human health risk assessment. EPA’s Risk 

Assessment Forum (RAF) is currently developing guidance for the Agency on a variety of relevant topics including 

exposure assessment, effects assessment and cumulative risk assessment. The EJ Technical Guidance will be updated 

to integrate these analytical frameworks when they are completed.

1.1 Overarching Questions and Objectives for Analysis of Potential EJ

Concerns

The ADP Interim Process Guide recommends that rule-writers, analysts, and decision makers “respond to three basic 

questions throughout the action development process (U.S. EPA, 2010a): 

1. How did your public participation process provide transparency and meaningful participation for 

minority, low-income and indigenous populations, and Tribes?

 

7 For instance, see Administrator Whitman’s August 9, 2001 memo on EPA’s commitment to environmental justice at 

http://yosemite.epa.gov/opa/admpress.nsf/89745a330d4ef8b9852572a000651fe1/41a2df9798d627a185256aaf0067e435!OpenD

ocument .

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Section 1: Introduction
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2. How did you identify and address existing and new disproportionate environmental and public health 

impacts on minority, low-income and indigenous populations during the rulemaking process?

3. How did actions taken under #1 and #2 impact the outcome or final decision?”

This technical guidance primarily will aid analysts in providing information to Agency decision makers that can be 

used to address Questions 2 and 3 from the ADP Interim Process Guide. Question 1 is expected to be addressed as 

part of broader public outreach during the rulemaking process.8 Senior EPA managers will also find this technical 

guidance useful for understanding analytic expectations and ensuring that potential EJ concerns are appropriately 

considered and, when feasible, addressed in the development of regulatory actions.

The ADP Interim Process Guide introduces and defines the concept of “EJ concerns” as “disproportionate impacts on 

minority, low-income or indigenous populations that exist prior to or that may be created by the proposed action” 

(the term “disproportionate” is discussed in Section 2).9 Analysis of EJ for regulatory actions should therefore 

address the following two questions: 

 Is a potential EJ concern associated with the affected environmental stressors prior to the rulemaking?10

 For each of the regulatory options under consideration, is a potential EJ concern created or mitigated 

for these affected environmental stressors?

To ascertain the extent to which a potential EJ concern is associated with the affected environmental stressors prior 

to the rulemaking, the analyst should, when feasible:

1. Assess exposures, relevant health and environmental outcomes, and other relevant effects by population 

group in the baseline;11 and

2. Assess differences in these exposures, relevant health and environmental outcomes, and other relevant 

effects across population groups in the baseline.

For each regulatory option under consideration, to inform the extent to which a potential EJ concern is created or 

mitigated for the affected stressors, the analyst should, when feasible:

3. Assess exposures, relevant health and environmental outcomes, and other relevant effects by population 

group for each option;

4. Assess differences in these exposures , relevant health and environmental outcomes, and other relevant 

effects across population groups for each option; and

 

8 Note that the EPA’s Toolkit for Assessing Potential Allegations of Environmental Injustice differs from this technical guidance document in that 

it is mainly designed to help investigate allegations of environmental injustice in a particular geographic area, for instance as a result of a 

permitting or enforcement decision that pertains to a particular facility. This technical guidance document has a much broader scope, to aid 

analysts in evaluating potential EJ concerns that may arise due to EPA rulemakings.

9 For analytic purposes, the term “EJ concern” refers to “disproportionate impacts on minority, low-income or indigenous populations that 

exist prior to or that may be created by your proposed action” (U.S. EPA, 2010a). It can also indicate the actual or potential lack of fair 

treatment or meaningful involvement of minority, low-income or indigenous populations in the development, implementation, and 

enforcement of environmental laws, regulations, and policies. The ADP Interim Process Guide (U.S. EPA, 2010a) focuses on identifying 

potential or actual EJ concerns related to the two latter uses of the term, while the Technical Guidance focuses on the former.

10 The general term “environmental stressor” (or “stressor”) is used to encompass the range of chemical, physical or biological agents, 

contaminants, or pollutants that may be subject to a rulemaking.

11 The White House Office of Management and Budget (OMB) defines the baseline as “the best assessment of the way the world would look 

absent the proposed action” (OMB, 2003). See Section 5 of this document for more information. 
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5. Assess how estimated differences in these exposures, relevant health and environmental outcomes, and 

other relevant effects across population groups increase or decrease as a result of each option compared 

to the baseline.12

This document is designed to provide analysts with information on how to address these analytic objectives, and to

identify: (1) which adverse health and environmental outcomes and other relevant effects are associated with the 

regulated stressor or source for the population groups of concern (i.e., minority, low-income or indigenous 

populations)13 relative to a comparison population group; (2) whether these outcomes are differentially distributed 

across population groups; and (3) whether any differences are potentially disproportionate. The extent to which an 

analysis is able to address all of these objectives will vary. Comparison population groups are discussed in greater 

detail in Section 2.3. The term “disproportionate” and the role of an analyst in this regard are discussed in Section 

2.4.

While the main focus of this technical guidance is on tools analysts may use to evaluate differences in health 

outcomes across population groups of concern, the distribution of non-health and environmental outcomes are also 

important to consider. For instance, some population groups may place a higher cultural value on the environmental 

quality or ecological condition of particular places. Likewise, some regulatory options may imply uneven changes in 

access to particular aquatic amenities for recreation across population groups. While the distribution of non-health 

and environmental outcomes may be difficult to quantify, analysts should strive to qualitatively discuss those 

outcomes relevant to the particular regulatory action. 

1.2 Recommendations to Guide Assessments for Potential EJ Concerns

The technical guidance makes six main recommendations designed to ensure consistency across assessments of 

potential EJ concerns for regulatory actions. These recommendations are not intended to be prescriptive and do not 

mandate the use of a specific approach. Rather, they encourage analysts to conduct the highest quality analysis 

feasible, recognizing that data limitations, time and resource constraints, and analytic challenges will vary across 

media and with the specific regulatory context. 

1. For regulatory actions where impacts or benefits will be quantified, some level of quantitative analysis for 

EJ is recommended (see Section 5.1).

 When feasible, analysts should present information on estimated health and environmental risks, 

exposures, outcomes, benefits and other relevant effects disaggregated by race/ethnicity and 

income. 

 When such data are not available, it may still be possible to evaluate risk or exposure using other 

metrics (e.g., prevalence of affected facilities as a function of race/ethnicity or income, evidence 

of unique or unusual (i.e., atypical) consumption patterns or contact rates). 

2. When impacts or benefits will not be quantified or disaggregated by race/ethnicity or income, analysts 

should present information that is insightful with regard to potential EJ concerns (e.g., basic demographic 

information, and evidence of differential exposure) (see Section 5.1).

 Analysts should use their best professional judgement to determine what combination of 

quantitative and qualitative analysis is possible.

3. Analysts should integrate applicable scoping questions during the planning stages of a risk assessment 

when one is being conducted for the regulatory action (see Section 4.3.2).

 

12 Draft Chapter 10 of the Guidelines for Preparing Economic Analyses, which discusses how to evaluate potential EJ concerns when doing 

regulatory analysis for economically significant rules, presents these as three questions (U.S. EPA, 2012b).

13 The term “population groups of concern” is used instead of subpopulations to be inclusive of “population groups that form a relatively 

fixed portion of the population (e.g., groups based on ethnicity).” See http://yosemite.epa.gov/ochp/ochpweb.nsf/content/lifestage.htm.
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4. Analyses should use the same baseline and regulatory option scenarios as other types of regulatory 

analyses (e.g., benefit-cost and economic impact analyses) conducted in support of the rulemaking (see 

Section 5.2).14

5. Analysts should follow identified best practices when feasible and applicable. Text Box 1.1 outlines current 

best practices that may be helpful for evaluating potential EJ concerns (see Sections 4.3 and 5.3)

6. Analysts should consider the distribution of costs associated with implementing a regulatory option from an 

EJ perspective when appropriate and relevant (see Section 5.5.1).15

 

14 Because analyses of potential EJ concerns are often conducted separately from an assessment of benefits and costs (i.e., benefit-cost 

analysis evaluates efficiency, while analyses of potential EJ concerns evaluate whether impacts are distributed differently), it is important 

that the same baseline and options be used to ensure an apples-to-apples comparison when the decision maker weighs various policy 

objectives (e.g., aggregate net benefits and equity).

15 See the Economic Guidelines (U.S. EPA, 2010b) for information on defining costs.

Text Box 1.1: Current Best Practices that May be Helpful for Evaluating Potential EJ Concerns

 Use the latest demographic data available.

 Show changes in potential differences in impacts (i.e., analyze and compare effects in

baseline and across policy scenarios).

 Carefully select and justify the choice of comparison group (discussed further in Sections 2 and 

5).

 Present summary metrics (e.g. risk ratios and measures of statistical significance) for relevant 

population groups of concern and the comparison group, not just data on each population 

group or area.

 Characterize the distribution of risks, exposures, or outcomes across individuals, lifestages, 

gender, or other relevant categories within each population group when feasible, not just 

average impacts.

 Disaggregate data to reveal important spatial differences (e.g., demographic information for 

each facility/place), not just in aggregate, when feasible and appropriate.

 Conduct sensitivity analysis for key assumptions or parameters that may affect findings when 

feasible.

 Present available evidence on other environmental stressors that may contribute to increased 

vulnerability or susceptibility for population groups of concern.
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he purpose of a regulatory analysis is to “anticipate and evaluate the likely consequences of rules” in 

a way that informs the public and decision makers (OMB, 2003).16,17 For this reason, an assessment 

for EJ is most meaningful and useful to the decision maker when it is consistent with the basic 

assumptions underlying other parts of the regulatory analysis that may be conducted (for instance, the 

benefit-cost analysis, economic impact analysis, or risk assessment). This section reviews the key analytic 

principles and concepts that are central to assessing EJ concerns.

2.1 Key Analytic Principles

The basic principles that guide analysis of potential EJ concerns are the same as those used to guide all 

aspects of regulatory analysis at the Agency. In general, analyses should:

 Be designed to inform the pending decision;

 Rely on generally accepted procedures for conducting risk assessment and economic analysis;

 Integrate consideration of EJ into existing analytical efforts;

 Use existing frameworks and data from other parts of the regulatory analysis, supplemented as 

appropriate;

 Be transparent with regard to data sources, assumptions, analytic techniques, and results; and

 Use the best available science and data.

In addition to adhering to these general principles, analyses of potential EJ concerns should also aim to:

 Identify EJ objectives early in the process: Decision makers are encouraged to communicate early in 

the process whether certain aspects of E.O. 12898 or applicable EPA policies or statutes are 

particularly important when evaluating EJ within the context of the regulatory action. 

 

16 “ Important goals of regulatory analysis are (1) to establish whether federal regulation is necessary and justified to achieve a 

social goal and (2) to clarify how to design regulations in the most efficient, least burdensome, and most cost-effective manner.”

(OMB, 2003).

17 Note that E.O. 12866, Regulatory Planning and Review (1993), expects agencies to consider “distributive impacts” and “equity” 

when choosing among alternative regulatory approaches, unless prohibited by statute. OMB’s Circular A-4 also states that 

“regulatory analysis should provide a separate description of distributional effects (i.e., how both benefits and costs are 

distributed among sub-populations of particular concern) so that decision makers can properly consider them along with the effects 

of economic efficiency...Where distributive effects are thought to be important, the effects of various regulatory alternatives

should be described quantitatively to the extent possible, including the magnitude, likelihood, and severity of impacts on particular 

groups” (OMB, 2003). However, Circular A-4’s focus is on benefits and costs, while the focus of E.O. 12898 is on human health or 

environmental effects, which is generally at least one step prior to monetization of benefits and precludes certain other benefit 

categories covered in the EPA’s Economic Guidelines.

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Section 2: Key Analytic Principles and Definitions
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 Understand the root causes and contributors to potential EJ concerns early in the process: Recognizing 

the underlying causes and contributors that may lead to EJ concerns (see Section 3) is important for 

properly assessing them and can aid in the design of regulatory options.

 Identify data, methods and analytical needs early in the process: Decisions about data and 

methodological needs and how to meet these needs are made early on in the planning phase of 

every regulatory analysis. Data and methods availability influences the scope and complexity of 

an assessment, and may even determine the extent to which potential EJ concerns are considered 

in the decision-making process. Analysts should evaluate data and methodological needs for an 

analysis of potential EJ concerns early in the planning process to ensure that such needs are duly 

considered and reasonably accommodated. 

 Identify the population groups of concern early in the process: E.O. 12898 identifies relevant 

population groups of concern. An early priority of analysts should be to define these population 

groups within the context of a particular regulatory action, so that this definition can inform data 

collection and analysis, and the development of reliable inferences from the results of the 

assessment. 

 Identify the comparison group early in the process: The selection of a comparison group should 

allow for a credible evaluation of whether disproportionate impacts are borne by minority, lowincome, or indigenous populations relative to other demographic groups. Like the population 

groups of concern, the comparison population for the analysis of potential EJ concerns should be 

identified early in the process because it can inform decisions related to data collection and 

analytic approaches (see Sections 2.3 and 5.4.2).

 Use baseline and regulatory options consistent with other parts of the regulatory analysis: As stated in 

Recommendation 4 (Section 1.2), use of a consistent baseline and regulatory option scenarios

ensures that analysis of potential EJ concerns remains relevant in the decision making process.

2.2 Population Groups of Concern Highlighted in E.O. 12898

Executive Order 12898 identifies a number of population groups of concern, including: minority, lowincome and indigenous populations. It also mentions “populations who principally rely on fish and/or 

wildlife for subsistence,” a group that may overlap with minority, low-income or indigenous populations by 

virtue of unique exposure pathways. This section provides information for analysts on how to define the 

population groups of concern specifically mentioned in the Executive Order.18

It may be useful in some contexts to analyze these categories in combination – for example, low-income 

minority populations – or to evaluate these categories based on diversity within the population groups of 

concern (e.g., lifestages, gender) when some individuals are at greater risk for experiencing adverse 

effects (i.e., are particularly vulnerable or susceptible). Analysts should rely on the White House Office of 

Management and Budget (OMB) or other official Federal agencies (e.g., United States Census Bureau) for 

definitions of these population groups when available. Note that analysis of additional population groups 

is not a substitute for examining the populations explicitly mentioned in the Executive Order. 

2.2.1 Minority and Indigenous Populations

OMB provides minimum standards for “maintaining, collecting, and presenting data on race and ethnicity 

for all Federal reporting purposes...The standards have been developed to provide a common language 

for uniformity and comparability in the collection and use of data on race and ethnicity by Federal 

agencies” (OMB, 1997). OMB defines six race and ethnic categories:

 

18 This section borrows extensively from Chapter 10 of EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2012b).
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 American Indian or Alaska Native; 

 Asian; 

 Black or African American; 

 Native Hawaiian or Other Pacific Islander; 

 White; and

 Hispanic or Latino.

Note that these categories are not all additive; for example, Hispanic or Latino is an ethnic category and, 

as such, may overlap with several categories based on race. Statistical data collected by the Federal 

government, such as the Census Bureau, adhere to this classification system.19 

While OMB does not define the term indigenous, it defines someone who identifies as an American Indian 

or Alaska Native as a person “having origins in any of the original peoples of North and South America 

(including Central America) and who maintains tribal affiliation or community attachment” (OMB, 1997).

OMB also does not define what constitutes a minority population. The Council on Environmental Quality’s 

(CEQ) Environmental Justice: Guidance Under the National Environmental Policy Act (EJ Guidance for NEPA) 

provides guidance on how to do so based on the Federal race and ethnicity classifications (CEQ, 1997). It 

defines the term “minority” as “individual(s) who are members of the following population groups: 

American Indian or Alaskan Native; Asian or Pacific Islander; Black, not of Hispanic origin; or Hispanic.” A 

population is identified as minority if “either (a) the minority population of the affected area exceeds 50 

percent or (b) the minority population percentage of the affected area is meaningfully greater than the 

minority population percentage in the general population or other appropriate unit of geographic 

analysis.” The term “meaningfully greater” is not defined. CEQ’s EJ Guidance for NEPA also notes that a 

minority population exists “if there is more than one minority group present and the minority percentage, 

as calculated by aggregating all minority persons, meets one of the above-stated thresholds.” Finally, the 

guidance states that analysts ”may consider as a community either a group of individuals living in 

geographic proximity to one another, or a geographically dispersed/transient set of individuals (such as 

migrant workers or Native Americans), where either type of group experiences common conditions of 

environmental exposure or effect.” 

2.2.2 Low-Income Populations

OMB has designated the Census Bureau’s annual poverty measure, produced since 1964, as the official 

metric for program planning and analysis by all Executive branch Federal agencies in Statistical Policy 

Directive No. 14, though it does not preclude the use of other measures (OMB, 1978). CEQ’s EJ Guidance 

for NEPA also suggests analysts use “annual statistical poverty thresholds from the Bureau of the Census’ 

Current Population Reports, Series P-60 on Income and Poverty” to define low-income populations. As with 

minority populations, CEQ’s EJ Guidance for NEPA allows low-income populations to be defined as a 

geographically dispersed group of individuals that experience common environmental exposure conditions 

or effects.

The Census Bureau’s annual poverty measure uses a set of income thresholds that vary by family size and 

composition to determine the households that live in poverty. If a family's total income falls below the 

 

19 See http://www.whitehouse.gov/omb/fedreg_1997standards/ for the OMB definitions. Beginning with the 2000 Census, the 

Federal government began to collect more detailed information on race. Respondents could select more than one category. OMB 

provides guidance on how to aggregate from 63 different race categories to a smaller subset to yield the first five categories 

listed above and four frequently-reported double race categories (OMB, 2000).
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threshold, then that family and every individual in it is defined as being in poverty. This measure of 

poverty has remained essentially unchanged since its inception.20 It does not vary geographically though it 

is updated for inflation using the Consumer Price Index (CPI-U). It also does not take into account capital 

gains or non-cash benefits (such as public housing, Medicaid, and food stamps) (U.S. Census, 2011a). 

The ability of the official poverty measure to adequately capture differences in economic well-being has 

been the subject of ongoing debate. In particular, the National Research Council (NRC) recommended that 

the official measure be revised because “it no longer provides an accurate picture of the differences in the 

extent of economic poverty among population groups or geographic areas of the country, nor an accurate 

picture of trends over time” (Citro and Michael, 1995). In response, OMB convened an interagency group 

in 2009 to define a supplemental poverty measure based on the NRC recommendations. A Supplemental 

Poverty Measure (SPM) was included in the Current Population Reports, Series P-60, for the first time in 

2010 (U.S. Census, 2011b). Unlike the official poverty measure, it accounts for “co-resident unrelated 

children (such as foster children) and any cohabiters and their children,” and uses a broader resource 

measure to account for out-of-pocket medical expenses and in-kind benefits, for example. It also improves 

on the traditional measure of poverty by adjusting for differences in housing prices by metropolitan 

statistical area, as well as family size and composition.21 

Unlike its treatment of poverty, the Census Bureau does not provide an official definition of “low income.” 

An analyst may characterize low-income more broadly than just those that fall below the poverty threshold

(e.g., to include families whose income is above the poverty threshold but still below the average 

household income for the U.S.). Additional socioeconomic characteristics typically collected by U.S. 

statistical agencies (e.g., Census Bureau), such as educational attainment, baseline health status, and health 

insurance coverage, may also be useful for characterizing low-income populations. Another possible 

measure is the percent of people who are chronically poor versus those that experience poverty on a more 

episodic basis (Iceland, 2003).22 

Finally, cross-tabulations often are available through the Census Bureau between many of these poverty 

measures and other socioeconomic characteristics of concern such as race, ethnicity, age, sex, education, 

and work experience.

2.2.3 Populations that Principally Subsist on Fish and Wildlife

E.O. 12898 also identifies the need to analyze the human health risks of “populations with differential 

patterns of subsistence consumption of fish and wildlife” when practical and appropriate. This category 

identifies populations based on particular pathways of exposure, and may overlap with those defined on 

the basis of income and race/ethnicity.

23

CEQ’s EJ Guidance for NEPA describes the two main components of this definition: differential patterns 

and subsistence consumption. “Differential patterns” are “differences in rates and/or patterns of 

subsistence consumption by minority populations, low-income populations, and Indian tribes as compared to 

 

20 The U.S. Census Bureau produces single-year estimates of median household income and poverty by state and county, and 

poverty by school district as part of its Small Area Income and Poverty Estimates. It also provides estimates of health insurance 

coverage by state and county as part of its Small Area Health Insurance Estimates. These data are broken down by race at the 

state level and by income categories at the county level.

21 The NRC recognizes that income-based measures such as the official or supplemental poverty thresholds are not necessarily the 

best measure of relative poverty since it does not account for differences in accumulated assets across households. The SPM tries 

to capture inflows of income and outflows of expenses, which are likely correlated with short-term poverty since many assets are 

not easily convertible to cash in the short run (Short, 2012).

22 This type of measure is reported in the U.S. Census Bureau’s Survey of Income and Program Participation.

23 The overlap between populations that principally subsist on fish and wildlife and minority or low-income populations is an 

important consideration in an evaluation of potential EJ concerns in a risk assessment. As part of a risk assessment, analysts are 

expected to evaluate as appropriate all consumption/contact patterns and rates that are relevant from an EJ perspective, 

including those associated with populations that subsist on fish and wildlife. 
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rates and patterns of consumption of the general population.” The term “subsistence consumption” is 

defined as “dependence by a minority population, low-income population, Indian tribe or subgroup of such 

populations on indigenous fish, vegetation and/or wildlife, as the principal portion of their diet.” 

While Federal statistical agencies do not specifically track individuals and population groups who subsist 

on fish or wildlife, EPA has conducted consumption surveys in specific geographic areas to inform policy 

formulation. If fish and wildlife consumption is a substantial concern for a particular regulatory action, 

analysts should refer to existing EPA guidance on fish and wildlife consumption surveys when collecting

these data (U.S. EPA, 1998b). Analysts may also investigate whether these types of survey data are 

available from other Federal agencies, or state or local governments. However, they should verify that 

any survey data used in an EJ analysis is in accordance with the appropriate parameters and 

methodology (U.S. EPA, 1998b).

2.3 Selecting a Comparison Group

To assess whether a potential EJ concern exists, an analyst should compare impacts experienced by the 

population groups of concern to those of a comparison group (sometimes referred to as a reference 

group). From the perspective of E.O. 12898 and EPA EJ policies, a between-group comparison (i.e., 

comparison to population groups with different socioeconomic characteristics) is generally the most relevant

to policy assessment. It is unlikely, however, that the same comparison group will be appropriate in every 

instance. For example, it may make sense in some contexts to define the comparison group at a subnational level to reflect differences in socioeconomic composition across geographic regions (see Text Box 

2.1 for a few examples from recent rulemakings). 

The comparison group definition can have important implications for evaluating the distribution of risk, 

exposure, and health outcomes across population groups of concern in the baseline and under various 

policy options. In selecting a comparison group, an analyst should evaluate how the use of different 

comparison groups affects the way information is conveyed to the decision maker.24 Analysts should also 

 

24 For example, a comparison group of all minorities in the United States, while informative about the burden of risk among 

minorities, will not directly provide information about whether this burden is higher among minorities relative to non-minorities.

Text Box 2.1: Choosing a Comparison Group – Recent Examples

There are a variety of ways to define a comparison group. For the final Mercury and Air Toxics 

Standards (MATS) Rule (U.S. EPA, 2011c), analysts examined mortality risk associated with fine 

particulate matter by race, income, and poverty level for people living in high risk counties (i.e., in the top 

5 percent). The comparison group was defined as people living in counties not facing a high mortality risk.

For the proposed (but now withdrawn) Reporting Rule for Concentrated Animal Feeding Operations (U.S. 

EPA, 2011d), analysts began by comparing the socioeconomic characteristics of census tracts with 

concentrated animal feeding operations (CAFOs) to an average U.S. census tract without a CAFO. 

However, “data on minority and low-income populations were heavily dominated by populations in urban 

census tracts.” Because CAFOs are located in rural areas of the country, EPA decided it was appropriate 

to exclude urban census tracts and instead compared the socioeconomic characteristics of each census 

tract with a CAFO to the characteristics of the average rural census tract. 

Sources: Regulatory Impact Analysis for the Final Rule for the Mercury and Air Toxics Standard (U.S. EPA, 2011c), and 

Analysis to Address Environmental Justice for National Pollution Discharge Elimination System (NPDES) Concentrated

Animal Feeding Operation (CAFO) Rule (U.S. EPA, 2011d). 
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Text Box 2.2: Distinguishing between Difference 

in and Disproportionate Impacts

For this guidance document, the terms “differential 

risks” or “differences in impacts” refer to a distinct 

concept from “disproportionate” impacts or risks. 

Specifically, the terms difference or differential 

are used to indicate an analytically measurable 

distinction in impacts or risks across population 

groups. The term disproportionate is used to refer 

to differences in impacts or risks that are 

substantial enough that they may merit Agency 

action. 

carefully document the criteria used to choose the comparison group for a particular regulatory action. 

When appropriate and practicable, an analyst may wish to conduct sensitivity analysis using alternate 

definitions of the comparison group (see Section 5.4.2 for further discussion).

2.4 Disproportionate Impacts Under E.O. 12898

E.O. 12898 calls for each Federal agency to “make achieving environmental justice part of its mission by 

identifying and addressing, as appropriate, disproportionately high and adverse human health or 

environmental effects...of its policies, programs, and activities.” In the context of regulatory analysis two 

steps are important in this regard: (1) identifying differences in impacts, and (2) determining whether there 

are potentially disproportionate impacts that may merit Agency action (see Text Box 2.2). 

The term “disproportionate” is not specifically 

defined in E.O. 12898, and it is not defined in EJ 

guidance developed to implement the Executive 

Order. However, CEQ’s EJ Guidance for NEPA 

discusses several factors that the decision maker may 

consider when determining whether human health 

effects are disproportionately high and adverse 

(CEQ, 1997):25

 “Whether the health effects, which may be 

measured in risks and rates, are significant 

(as employed by NEPA), or above generally 

accepted norms;”

 “Whether the risk or rate of hazard 

exposure by a minority population, lowincome population, or Indian tribe to an environmental hazard is significant (as employed by 

NEPA) and appreciably exceeds or is likely to appreciably exceed the risk or rate to the general 

population or other appropriate comparison group;” and

 “Whether health effects occur in a minority population, low-income population, or Indian tribe 

affected by cumulative or multiple adverse exposures from environmental hazards.”

These factors may also be useful to consider in an EPA rulemaking context (though what constitutes

“significant” may differ from how it is defined under NEPA). An analyst should provide the decision maker 

with the best available information (both quantitative and qualitative) for the baseline and policy options

to aid in evaluating whether there is a disproportionate impact that may merit Agency action. What data 

are available and relevant will vary with the specific circumstances of the regulatory action; analysts 

should be transparent about data limitations, time and resource constraints, and analytic challenges.26 

Examples of the kinds of information that may be useful to provide to decision makers include:

 

25 The EJ guidance for NEPA (CEQ, 1997) also discusses similar factors to consider when evaluating the potential for 

disproportionately high and adverse environmental effects.

26 In addition, the decision maker also may need other kinds of information to aid the selection of a regulatory option, including:

The extent to which available options reduce exposure or risk differences, 

How the regulatory options produce benefits (usually in the form of reduced health impacts) for each of the relevant 

population groups, and

When relevant, how the economic costs of these options are borne by each relevant population. 
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 The severity and nature (i.e., biological significance) of the health consequences for which 

differences between population groups have been estimated.

 The magnitude of the estimated differences in impacts between population groups of concern and 

the appropriately defined comparison group (e.g., a measure of statistical significance when 

relevant and appropriate).

 Mean or median exposures or risks to relevant population groups (or acceptable surrogates when 

such data are not available).

 Distributions of exposure or risk to relevant population groups – while average exposure or risk 

estimates are helpful, it may be the case that differences between population groups only occur in 

the tail of the distribution. 

 Characterization of the uncertainty surrounding various aspects of the analysis.

 A discussion of factors that may make population groups of concern more vulnerable to exposure 

(e.g., unique pathways, cumulative exposure, behavioral or biological factors).

The relative weight of these or additional criteria will likely vary with the specific attributes of the 

rulemaking under consideration. It is important to note that the role of an analyst is to assess and present 

differences in anticipated impacts across population groups of concern to the decision maker and the 

public. The determination of whether there is a disproportionate impact that may merit Agency action is 

ultimately a policy judgment informed by analysis, and is the responsibility of the decision maker.27 See 

Text Box 2.3 for examples of the way in which differences in impacts are characterized in a rulemaking to 

allow the decision maker discretion regarding a finding of disproportionate impacts. 

Additionally, as EPA’s Interim ADP Process Guide notes, “the Agency’s statutory and regulatory authorities 

provide a broader basis for protecting human health and the environment and do not require a 

demonstration of disproportionate impacts in order to protect the health or environment of any population, 

including minority, low-income and indigenous populations. Thus, consistent with its mission, the Agency may 

address adverse impacts in the context of developing an action without the need for showing that the 

impacts are disproportionate” (U.S. EPA, 2010a).

 

27 This is not meant to prevent analysts from using their expert judgment to determine whether enough of a risk exists to merit 

moving from one stage of the analysis to the next.
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2.5 Meaningful Involvement

The term “meaningful involvement” means that “1) potentially affected community members have an 

appropriate opportunity to participate in decisions about a proposed activity that will affect their 

environment and/or health; 2) the public’s contribution can influence the regulatory agency’s decision; 3) 

the concerns of all participants involved will be considered in the decision-making process; and 4) the 

decision makers seek out and facilitate the involvement of those potentially affected” (U.S. EPA, 2010a;

U.S. EPA, 2012a).

Ensuring meaningful involvement in the rulemaking process as a whole is not the focus of this guidance 

document, but intersects with analytic considerations in several key respects. First, if the analysis of

potential EJ concerns is explained in plain language, then key assumptions, methods, and results will be 

Text Box 2.3: Characterizing Differences in Impacts for a Rule or Regulation 

Phrases that have been used to characterize differences in impacts (e.g., in the baseline or as a result of a 

rule) in recent rulemakings include “the potential for disproportionate impacts,” “overrepresentation of 

minority, low-income or indigenous populations near sources,” and ”notably higher.” 

For instance, the proposed rulemaking for the Definition of Solid Waste (U.S. EPA, 2011e) states that, “In 

general, some communities will have a higher percentage [of minority and/or low-income members] than 

the comparison population and some will have a lower percentage. As long as these differences have a 

regular distribution, they would not indicate disproportionate impact. However, if the number of 

communities with a higher percentage of minority and/or low-income population is greater than that of the 

comparison populations, then there is a potential for disproportionate impact. The higher the average 

difference between the potentially affected communities and the comparison group, the greater the 

potential disproportionality.”

The proposed National Emission Standards for Hazardous Air Pollutants (NESHAP) for Polyvinyl Chloride

(U.S. EPA, 2011f) describes its analysis as follows: “An analysis of demographic data shows that the 

average percentage of minorities, percentages of the population below the poverty level, and the 

percentages of the population 17 years old and younger, in close proximity to the sources, are similar to 

the national averages, with percentage differences of 3, 1.8, and 1.7, respectively, at the 3-mile radius of 

concern. These differences in the absolute number of percentage points from the national average 

indicate a 9.4-percent, 14.4-percent, and 6.6-percent over-representation of minority populations, 

populations below the poverty level, and the percentages of the population 17 years old and younger, 

respectively.” 

Another example comes from the Advanced Notice of Proposed Rulemaking on Lead Emissions from PistonEngine Aircraft Using Leaded Aviation Gasoline (U.S. EPA, 2010c): “Demographic factors that can affect 

risk of lead-related effects in children include residential location, poverty, and race. As noted in previous 

EPA actions on lead, situations of elevated exposure, such as residing near sources of ambient lead, as 

well as socioeconomic factors, such as reduced access to health care or low socioeconomic status can also 

contribute to increased blood lead levels and increased risk of associated health effects from air-related 

lead. Additionally, as described in the National Ambient Air Quality Standard (NAAQS) for Lead, 

children in poverty and black, non-Hispanic children have notably higher blood lead levels than do 

economically well-off children and white children, in general.”

Sources: Environmental Justice Analysis of the Definition of Solid Waste Rule (U.S. EPA, 2011e); National Emission 

Standards for Hazardous Air Pollutants for Polyvinyl Chloride and Copolymers Production (U.S. EPA, 2011f), and 

Advance Notice of Proposed Rulemaking on Lead Emissions From Piston-Engine Aircraft Using Leaded Aviation Gasoline; 

Proposed Rule (U.S. EPA, 2010c).
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more transparent and easier to understand. This can further a clear understanding of the potential EJ 

implications of a regulatory action and allow for more substantive engagement by community members 

and other interested parties during public comment periods. Second, it may be possible for analysts to 

request information early on in the process (for instance, by asking for public comment in the proposal) 

regarding unique exposure pathways or end points of concern, as well as data sources that could improve 

the analysis of potential EJ concerns. Text Box 2.4 highlights several examples of how activities taken to 

ensure meaningful involvement have been described in the analysis of EJ issues for recent rulemakings.

See EPA’s Public Involvement Policy (U.S.EPA 2013b) and EPA’s ADP Interim Process Guide (U.S. EPA, 

2010a) for more examples and guidance on how to meaningfully engage environmental justice 

stakeholders in the rulemaking process. Section 4.3.2.5 also discusses meaningful involvement in the context 

of risk communication.

Text Box 2.4: Meaningful Involvement 

As part of the proposed rulemaking for National Emission Standards for Hazardous Waste Pollutants 

(NESHAP) in the chromium electroplating industry (U.S. EPA, 2011f), EPA asked for public comment on the 

analysis of EJ issues: 

“The EPA offers the demographic analyses in this rulemaking as examples of how such analyses might be 

developed to inform such consideration and invites public comment on the approaches used and the 

interpretations made from the results, with the hope that this will support the refinement and improve 

utility of such analyses for future rulemakings.”

The regulatory impact analysis for the Final Mercury and Air Toxics Standards (MATS) Rule (U.S. EPA, 

2011c) also describes activities taken to ensure meaningful involvement: 

“The EPA defines ’environmental justice’ to include meaningful involvement of all people regardless of 

race, color, national origin, or income with respect to the development, implementation, and enforcement 

of environmental laws, regulations, and policies. To promote meaningful involvement, the EPA publicized 

the rulemaking via newsletters, EJ listserves, and the internet, including the Office of Policy’s Rulemaking 

Gateway Web site (http://yosemite.epa.gov/opei/RuleGate.nsf/). During the comment period, the EPA 

discussed the proposed rule via a conference call with communities, conducted a community-oriented

webinar on the proposed rule, and posted the webinar presentation on-line. The EPA also held three 

public hearings to receive additional input on the proposal.”

“Once this rule is finalized, affected electric-generating utilities (EGUs) will need to update their Title V 

operating permits to reflect their new emission limits, any other new applicable requirements, and the

associated monitoring and recordkeeping from this rule. The Title V permitting process provides that when 

most permits are reopened (for example, to incorporate new applicable requirements) or renewed, there 

must be opportunity for public review and comments. In addition, after the public review process, the EPA 

has an opportunity to review the proposed permit and object to its issuance if it does not meet CAA 

[Clean Air Act] requirements.”

Sources: National Emission Standards for Hazardous Air Pollutants for Polyvinyl Chloride and Copolymers Production

(U.S. EPA 2011f, and Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards (U.S. EPA, 2011c).
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he purpose of this section is to briefly highlight the key reasons why health risks may be unevenly 

distributed across population groups. In addition, this section provides an overview of select 

contributors to and drivers of the uneven distribution of health risks that often occur among 

communities with potential environmental justice concerns. 

3.1 Social Context and Risk

Minority, low-income and indigenous populations experience greater exposure and disease burdens that 

can increase their risk of adverse health effects from environmental stressors.

28 For example, many studies 

have established that sources of environmental hazards tend to be located and concentrated in areas that 

are dominated by minority, low-income or indigenous populations (Bullard et al., 2007; Faber and Krieg, 

2002; Faber and Krieg, 2005; GAO, 1983; Maantay, 2001; United Church of Christ, 1987; and Wilson 

et al., 2002). In addition, studies show that these population groups experience higher exposures to 

environmental hazards associated with where they live, work and play (Apelberg et al., 2005; Marshall, 

2008; Morello-Frosch and Jesdale, 2006; Morello-Frosch et al., 2001; Sexton et al., 2007; Thompson et 

al., 2003; and Woodruff et al., 2003). These population groups also tend to be most burdened with 

adverse health conditions such as cardiovascular disease, preterm birth, low birth weight, and asthma that 

either have environmental triggers or affect similar target physiological systems as environmental pollution

(Akinbami, 2006; Akinbami et al., 2012; Glover et al., 2005; Keenan and Rosendorf, 2011; Lara et al., 

2006; Martin, 2011; and Martin et al., 2010). Pre-existing disease and adverse health conditions likely 

increase susceptibility in response to exposure to environmental hazards (Schwartz et al., 2011a).

29

Both high exposures to environmental stressors and increased individual susceptibility can drive higher 

responses to environmental hazards (Schwartz et al. 2011). Therefore, the combination of higher 

exposures and pre-existing disease among minority, low-income or indigenous population groups may 

lead to a predisposition to higher health risks. As a result, in an assessment of potential EJ concerns it is 

important to assess both the potential for higher exposures to a given environmental stressor, and the 

potential for higher susceptibility in response to exposure for population groups of concern. Potential 

contributors to disproportionate health risk and adverse health impacts can thus be identified based on

how they may increase exposure or increase susceptibility in response to exposure.

Differences in exposure to stressors, and resulting adverse health outcomes among certain population 

groups (e.g., low income, minority) underscores the importance of social context. Social context refers 

broadly to all social and political mechanisms that generate, configure and maintain social hierarchies. 

These mechanisms can include the labor market, the educational system, political institutions and cultural 

and societal values (Solar and Irwin, 2010). Social context has been recognized as a critical root cause of 

societal stratification into different social positions (e.g., race/ethnicity, income, occupation). Social 

 

28 The term “racial/ethnic minority” is often used in the literature upon which this section is based to define what is referred to in 

Executive Order 12898 as “minority populations.”

29 An individual who is susceptible is one who is more responsive to exposure (Schwartz et al., 2011a).

T

Section 3: Contributors and Drivers of Potential

Environmental Justice Concerns
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stratification in turn results in differential exposure and vulnerability to stressors, and differential 

consequences (Solar and Irwin 2010). Social context and social stratification together can shape 

determinants of health such as:

 material circumstances (e.g., neighborhood and housing conditions/quality, green space, 

walkability, access to fresh foods, and the work environment); 

 behavioral and biological factors (e.g., nutrition, smoking, genetic factors); 

 the health care system (e.g., access to and interaction with it); and 

 psychosocial circumstances (e.g., stressful living conditions and relationships, availability of coping 

and support mechanisms) (Solar and Irwin, 2003). 

The literature has proposed a number of conceptual frameworks to explicitly integrate social context 

drivers and contributors to disproportionate health risks/impacts into the exposure-disease paradigm, and 

highlight potential pathways through which these drivers and contributors may interact with environmental 

exposures to yield health differences (Gee and Payne-Sturges, 2004; Morello-Frosch and Jesdale, 2006). 

Though the proposed pathways in these frameworks have not all been tested, they are insightful and offer 

integrated approaches for considering pathways through which multiple factors may increase exposure or 

susceptibility. 

3.2 Contributors to Higher Exposure

The steps for performing an exposure assessment are to: (1) identify the source of the environmental 

stressor and the media that transports it; (2) determine the contaminant concentration; (3) determine the 

exposure scenarios, and pathways and routes of exposure; (4) determine the exposure factors related to 

human behaviors that define time, frequency, and duration of exposure; and (5) identify the exposed 

population. Exposure factors are related to human behavior and characteristics, and determine an 

individual's exposure to an agent (U.S. EPA, 2011g). 

The terms “exposure” and “dose” are very closely related and are therefore often confused (Zartarian et 

al., 2007). Dose is the amount of a chemical substance or agent that enters a target (e.g., an organ of the 

body) in a specified period of time after crossing a contact boundary (e.g., the skin or lining of the 

intestine). An exposure does not necessarily lead to a dose, but there cannot be a dose without a 

corresponding exposure (U.S. EPA, 2011g, and Zartarian et al., 2007).

Contributors to higher exposure status among minority, low-income or indigenous populations include:

 Proximity to emission sources;

 Unique exposure pathways; 

 Physical infrastructure (e.g., housing conditions, water infrastructure); 

 Exposure to multiple stressors/cumulative exposures; and 

 Community capacity to participate in decision making (Nweke et al., 2011; U.S. EPA, 2007). 
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3.2.1 Proximity to Emission Sources

Proximity to emission sources is the most studied indicator of high exposure in the environmental justice 

literature. As noted earlier in this section, several studies have found that the location of pollution emission 

sources correlates positively with the location’s composition of minority, low-income or indigenous 

populations. For instance, studies have shown that disproportionate numbers of low-income and/or minority 

populations are located in close proximity to emission sources like high traffic roadways (Apelberg et al., 

2005; Guinier et al., 2003). Additionally, residents in these areas may experience increased 

concentrations of contaminants due to damaged structural and building conditions and inadequate 

ventilation systems. Residential proximity does not, however, imply that exposures and health risks are 

occurring but only that the potential for exposure is increased (NRC, 1991).

Proximity to an emission source is not a complete surrogate exposure measure because it does not 

incorporate key exposure determinants such as time-activity patterns. Nonetheless, several studies have 

found positive associations between residents living near a pollution emissions source such as a hazardous 

waste site, high traffic roadway, or industrial site and adverse health outcomes (Brender et al., 2011).

3.2.2 Unique Exposure Pathways

Unique exposure pathways are non-traditional pathways through which exposure to a given stressor 

occurs. Exploring unique exposure pathways means identifying behaviors that are unique to a specific 

group of individuals who have shared ideas, values, learned traditions and life experiences that are 

embedded in socially grounded processes. The social constructs of culture and ethnicity may to varying 

extents capture shared learned traditions and/or life experiences, and therefore provide a window for 

viewing how exposure pathways may vary across social groups (NRC, 2002). Specific examples of 

exposure pathways for environmental stressors that relate to cultural context or ethnicity are documented 

in the academic literature (Anderson and Rice, 1993; Ernst, 2002a, b; Ernst and Thompson Coon, 2001; 

McKelvey et al., 2011; Peterson et al., 1994). In addition, a detailed review of unique exposure pathways 

and a conceptual model to aid the identification of such pathways are discussed in detail elsewhere

(Burger and Gochfeld, 2011; Gochfeld and Burger, 2011). Examples of shared behavior that may yield 

atypical pathways of exposure to environmental stressors and potentially higher exposures include 

subsistence fishing, consumption of ayurvedic (i.e., alternative) medicines among Asians, and sweat baths 

among Native Americans (Gochfeld and Burger, 2011).

30 

Unique exposure pathways can also be identified based on other factors, such as behavioral and 

physiological stages of growth and development which may occur during a particular lifestage (U.S. EPA,

2011g). For example, individuals in all populations alter their eating patterns as they grow older, (e.g., 

infants’ diets consists primarily of milk products). Breastfeeding, object-to-mouth behavior, and crawling 

are examples of behaviors that are associated with infants and toddlers (U.S. EPA, 2013a). Such behavior 

increases exposure to environmental stressors that may exist in higher concentrations in breast milk, in toys, 

and in contaminants that accumulate on floors or carpets, for example. 

3.2.3 Physical Infrastructure

Physical infrastructure is an important source of environmental stressors. Housing, in particular, has been 

well studied as a potential contributor to environmental exposure. Housing in the United States built before 

1978 may contain lead-based paint, exposure to which can impair cognitive function in children and lead 

to lower IQ. In addition, substandard housing conditions such as water leaks, poor ventilation, dirty carpets 

and pest infestation can lead to an increase in mold, dust mites and other allergens associated with poor 

health (Commission to Build a Healthier America, 2008; U.S. Housing and Urban Development, 2001; 

Thorne et al., 2009). A higher proportion of minorities live in substandard housing (Jacobs, 2011). 

 

30 Ayurvedic medicines are taken as part of a Hindu traditional medicine practice of the same name.
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Therefore, examining how housing may increase exposure to a given stressor is helpful for uncovering 

whether particular minority, low-income or indigenous populations may experience higher exposures. 

Other types of infrastructure such as transportation and drinking water infrastructure may also be 

associated with increased exposure to environmental stressors. For example, in Southern California, 

minority and low-income neighborhoods have twice the traffic density of the rest of region, meaning that 

their potential for greater exposure to hazards from traffic is higher than in other neighborhoods (Houston 

et al., 2004). Differential exposure related to drinking water infrastructure is less examined. However, 

there is some evidence of the potential for differential exposure burdens for some stressors based on 

access to piped water and shared water systems and residence in older housing, which may have lead 

pipes (VanDerslice, 2011). When to examine exposure through the lens of physical infrastructure will 

depend on the stressors. 

3.2.4 Exposure to Multiple Stressors

Exposures to multiple stressors from one or more sources or pathways over time can result in one or 

multiple effects (U.S. EPA, 2003a). Exposure to a specific stressor from a given source when viewed in 

isolation can inaccurately characterize the potential for health risks if the populations for which risk is being 

estimated are also exposed to a stressor from multiple sources. For example, each source might emit low 

levels of a stressor, but when considered across all sources to which a population is exposed emissions may

pose a significant health risk. Emission sources for environmental pollution have been found to be 

concentrated in locations dominated by minority, low-income or indigenous populations (Bullard et al., 

2007; United Church of Christ, 1987). This is further complicated when non-chemical stressors are present, 

such as crime, which may exacerbate the effects of chemical exposures (e.g., changes in immunological 

response due to increased presence of stress hormones (Gee and Payne-Sturges, 2004)).

3.2.5 Community Capacity to Participate in Decision Making

Community capacity is a multidimensional concept that includes factors such as leadership, participation, 

skills, resources, community power, and social and organizational networks (Freudenberg et al., 2011). 

ICommunities with a relatively high proportion of minority, low-income or indigenous populations may have 

lower community capacity, and this may contribute to EJ concerns. The capacity of communities to 

participate in the decision making process is a crucial determinant of the success of civic engagements in 

terms of preventing high burdens of emitting sources and exposure to environmental stressors. Political 

mechanisms, for instance, can influence the potential for exposure to environmental stressors at the 

community level, given the role of such mechanisms in facility siting and permitting. Political mechanisms 

give rise to opportunities for civic engagement such as zoning meetings, which provide communities with 

opportunities to participate in decisions pertaining to the quality of their environment. When communities 

are unable to participate effectively in decision making, they may be more likely to be the recipients of 

negative environmental consequences, including emission sources.

Though meaningful involvement is related to the community’s capacity to participate in the decision making 

process, these topics are not discussed in depth in this guidance document. Some additional information 

about meaningful involvement can be found in Section 2.5, Section 4.3.2.5, and Text Box 2.4 (see also U.S. 

EPA, 2010a).

3.3 Contributors to Higher Susceptibility in Response to Exposure 

An individual’s susceptibility to an environmental stressor is an important determinant of the occurrence of 

and severity of an adverse effect. Some factors that may influence susceptibility include genetics, diet, 

nutritional status, pre-existing disease, psychological stress, co-exposure to similarly acting toxics, and 

cumulative burden of disease resulting from exposure to all stressors throughout the course of life

(Schwartz et al., 2011). Also known as risk or effect-modifiers, these factors influence the outcome of 
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exposure through biological interactions at the individual level. Another noteworthy risk-modifier is 

socioeconomic status, which in and of itself does not elicit a biological interaction. Socioeconomic status has 

a complex and robust association with many health states (Schwartz et al., 2011b), and may operate by 

influencing factors such as diet, nutrition, and access to health care, hence health status. Several examples 

of how these risk modifiers interact with environmental exposures to increase risk are discussed in papers 

by Schwartz et al. (2011a, b).

Some individuals within minority, low-income or indigenous populations may also experience higher 

susceptibility given exposure to stressors compared to other individuals in these population groups. This 

greater susceptibility often reflects age and the stages of physiological and behavioral growth and 

development, referred to as lifestages (U.S. EPA, 2011g). Highly susceptible individuals can include 

children, the elderly, pregnant women and/or women of childbearing age, as well as workers in certain 

occupations. To determine if an individual may experience higher susceptibility, one should consider both 

the lifestage in question and the target health endpoint. For example, unborn babies, infants and children 

under the age of six are more likely to experience adverse neurological health effects if exposed to 

certain levels of lead (U.S. EPA, 2013a). As previously stated, these individuals may also have unique 

exposure pathways (e.g., breast-fed infants) or may be exposed to multiple exposure sources (e.g., 

workers that are both exposed occupationally and also reside in neighborhoods with high ambient 

concentrations of air pollution) that when combined with higher susceptibility can lead to increased risk for 

adverse health effects. Text Box 3.1 gives an example of how the concepts of susceptibility and 

vulnerability can be used to identify population groups of concern. Further discussions of considering 

susceptibility and exposure factors in risk assessments for EJ analyses can be found in Section 4.3 and 

Appendix B.

Text Box 3.1: Identifying Population Groups Based on Varying Definitions of Susceptibility and 

Vulnerability

The concepts of susceptibility and vulnerability can be used to identify population groups consistent 

with dose-response functions found in the literature. Fann et al. (2011) note, “to the extent that certain 

attributes contributing to either susceptibility or vulnerability can be detected among the population, 

then a profile of susceptible and vulnerable individuals can be constructed and the geographic 

distribution of these populations determined.” These profiles combine available data on baseline 

health and demographic information to identify susceptible population groups and then use various 

combinations of demographic, education, poverty, and air quality data to describe them. 
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4.1 Introduction

he purpose of this section is to provide guidance to Agency analysts on integrating the consideration 

of potential EJ concerns into the planning phase of a human health risk assessment (HHRA) conducted 

to support a regulatory action. Senior EPA managers and decision makers may also use this section to 

help establish expectations for information that analysts should provide about potential EJ concerns, and to 

ensure that appropriate provisions are made to evaluate potential EJ concerns in the development of 

regulatory actions. 

This guidance recommends that, to the extent possible, evaluation of potential EJ concerns be integrated 

into an HHRA rather than conducted as an add-on or separate analysis of potentially disproportionate 

risks across population groups of concern. This integration ensures that EPA can more effectively consider 

differential health risks for minority, low-income or indigenous populations that are associated with 

differences in exposure, vulnerability and susceptibility to risk. This recommendation is also consistent with 

EPA’s Draft Framework for Human Health Risk Assessment to Inform Decision Making (U.S. EPA, 2012c), 

which identifies EJ as one of several overarching considerations for which “early consideration and 

discussion ... can enhance the utility of the risk assessment.”31

An analyst planning an HHRA in support of a regulatory action should seek information early in the process 

relevant to the five analytic objectives outlined in Section 1.1 (and repeated here), to the extent feasible. 

To ascertain the extent to which a potential EJ concern is associated with the affected environmental 

stressors prior to the rulemaking, the analyst should, when feasible:

1. Assess exposures, relevant health and environmental outcomes, and other relevant effects by 

population group in the baseline; and

2. Assess differences in these exposures, relevant health and environmental outcomes, and other 

relevant effects across population groups in the baseline.

For each regulatory option under consideration, to inform the extent to which a potential EJ concern is 

created or mitigated for the affected stressors, the analyst should, when feasible:

3. Assess exposures, relevant health and environmental outcomes, and other relevant effects by 

population group for each option;

4. Assess differences in these exposures, relevant health and environmental outcomes, and other 

relevant effects across population groups for each option; and

 

31 EPA’s Draft Framework for Human Health Risk Assessment to Inform Decision Making has not yet been finalized. 

T

Section 4: Considering Environmental Justice when 

Planning a Human Health Risk Assessment
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5. Assess how estimated differences in these exposures, relevant health and environmental 

outcomes , and other relevant effects across population groups increase or decrease as a 

result of each option compared to the baseline.

These objectives help an analyst evaluate whether a potential EJ concern already exists and whether, for 

each of the regulatory options under consideration, a potential EJ concern is likely to be created or 

mitigated by the affected stressors. The role of an analyst is to plan and conduct an HHRA that presents

results – and the appropriate context for those results – in a transparent manner so that the decision maker 

can incorporate consideration of differential risks across population groups into risk management decisions 

(see Section 2.2.4). 

This technical guidance focuses on the planning phase of an HHRA, but is designed to allow analysts to 

incorporate new information on risk assessment as it becomes available through EPA research efforts. EPA’s 

Risk Assessment Forum (RAF) is currently developing guidance on key topics such as cumulative risk 

assessment, dose-response assessment, and exposure assessment that will expand the tools and 

approaches available for analyses of potential EJ concerns. EPA is also involved in ongoing research 

activities designed to advance risk assessment; these efforts are specifically focused on better 

understanding the impact of susceptibility and variability on dose-response and how various risk factors 

beyond chemical exposures (e.g., poor nutrition, stress, access to health care, lower socioeconomic status) 

may be utilized in HHRA to improve the scientific basis for estimating risks at the community level. Once 

these guidance documents are completed, this technical guidance will be updated to integrate these 

analytical frameworks.

 

The remainder of this section is organized into two parts. Section 4.2 provides an overview of key 

concepts. Section 4.3 reviews the specific processes involved in considering EJ when planning an HHRA. 

Additional information on this topic can be found in Appendix B, which provides examples of ways to 

incorporate potential EJ concerns into the planning stages of exposure and dose-response assessments.

4.2 Overview of Key Concepts

Figure 4.1 depicts EPA’s analytic risk assessment process. A careful planning phase informs each of the first 

three steps (hazard identification, dose-response assessment and exposure assessment). The final step in 

the process, risk characterization, provides the basis for communicating the results to decision makers to 

inform policy. Using the same basic analytic process, an HHRA can be used to characterize the nature, 

probability and magnitude of current or future risks of adverse human health effects related to exposure 

to environmental stressors (e.g., chemical, physical or biological agents; contaminants; or pollutants). An 

HHRA can include both quantitative and qualitative expressions of risk (NRC, 1983; U.S. EPA, 2011h). 
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Figure 4.1: The Four Step Risk Assessment Process

Source: http://www.epa.gov/risk_assessment/health-risk.htm

A traditional risk assessment estimates risks associated with exposure to stressors using a “bottom-up” 

approach. This approach employs a source-to-effects paradigm, and typically begins with exposure 

information (Figure 4.2). This approach is often used to assess the risks associated with exposure to one 

stressor at a time. However, addressing EJ is particularly challenging when using a bottom-up approach 

because this approach does not capture joint effects of a population group’s exposure to multiple stressors. 

While not the focus of this technical guidance, Text Box 4.1 discusses a multiple stressor or cumulative risk 

assessment approach.32

 

32 While this broader definition of cumulative risk considers multiple agents or stressors (both chemical and non-chemical), it is 

important to acknowledge that the Food Quality Protection Act also requires EPA to evaluate aggregate risks of one chemical 

from multiple sources and/or cumulative exposures to multiple chemicals with similar mechanisms of toxicity (U.S. EPA, 2002a).

Textbox 4.1: Cumulative Risk Assessment

Multi-stressor or cumulative risk assessment (CRA) is important for characterizing how risks may 

disproportionately affect one group relative to another. EPA defines CRA as the evaluation of the 

combined risks from aggregate exposure to multiple agents or stressors (both chemical and nonchemical) (U.S. EPA, 2003a). Though current applications of CRA focus largely on chemical mixtures

and/or single chemicals from multiple sources, several key CRA concepts may be more broadly 

applicable in evaluating the range of both chemical and non-chemical stressors relevant to EJ concerns. 

A “top-down” approach using epidemiological data to focus first on health outcomes may better enable 

analysts to examine the potential impacts of such exposures. While epidemiology studies may not isolate

individual effects of numerous co-occurring stressors, an analyst can use these studies, when available, to 

characterize the cumulative impacts of multiple stressors (Levy, 2008). The use of stratification in 

epidemiology to identify effect modification is an important illustration of this concept; it can provide 

data on the risk of an adverse outcome from exposure to a regulated stressor, given co-exposure to 

another stressor that may be of concern when evaluating potential EJ concerns. 
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To ensure that an HHRA sufficiently identifies and characterizes differential and potentially 

disproportionate risks, an analyst should: 

1. Identify those types of individuals or population groups likely to experience higher risks relative to 

the average or comparable individuals in the general population; 

2. Clearly state the reasons why an identified population group (or lifestage within a population 

group) may potentially experience higher risk than the average person; 

3. Estimate and characterize the potential for differences in risk for at-risk groups; and 

4. Present the results to decision makers in a complete and transparent manner. 

Figure 4.2: The Source to Effects Paradigm 

Source: http://www.epa.gov/heasd/basic.html

4.3 Considering EJ when Planning a Human Health Risk Assessment

To implement E.O. 12898 and EPA’s EJ policies, it is important that HHRAs conducted in support of 

regulatory actions explicitly consider health risks that may disproportionately accrue within minority, lowincome or indigenous populations since these demographic attributes may reflect underlying vulnerability 

and susceptibility to environmental stressors. Also, the burden of health problems and potentially 

disproportionate environmental exposures associated with race/ethnicity and income may overlap with 

other susceptibility factors such as lifestage, genetic predisposition, or pre-existing health conditions (see 

Section 3 for further discussion).33

 

33 For example, the burden of environmental exposures and resulting health problems is often borne disproportionately by 

children from low-income communities and minority communities (Israel et al., 2005).
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The risk assessment planning process (also referred to as the “planning and scoping” process or phase) 

encompasses three key priorities that occur in an iterative process. The “fit for purpose” principle governs 

the development of an HHRA methodology. Central to the planning phase is the “planning and scoping” 

process itself; this provides a structure to the analysis and ensures that data collection and the approach 

are appropriate (“fit”) for the intended purpose of the risk assessment, considering the context and use of 

the final results (NRC 2009).34 Finally, the “problem formulation” step builds on planning and scoping and 

focuses on the specific hypotheses and technical approach of the HHRA. The effective interaction of these 

three priorities is essential to producing a sound risk assessment that serves its intended purpose (U.S. EPA,

2012c). As discussed below, the consideration of EJ in each part of the risk assessment planning process is 

important to ensuring an effective assessment of potential EJ concerns. 

4.3.1 Fit for Purpose 

An overarching principle guiding the planning process is ensuring that the risk assessment results will be fit 

for the purpose of informing a specific policy decision, for example addressing local environmental health 

concerns. Consistent with E.O. 12898 and EPA’s EJ policies, one part of the “fit for purpose” planning 

discussion should be ensuring that the analysis will provide useful information about how the policy options 

under consideration might affect distribution of risks across population groups of concern. 

Addressing the “fit for purpose” question early and revisiting it throughout the planning and scoping 

process and the HHRA itself helps ensure that the assessment adequately addresses community concerns 

and provides data to decision makers regarding the distribution of risk across population groups. The 

specific risk assessment methods used to consider EJ will vary with the environmental problem being 

addressed (e.g., a local or national environmental issue). The scope of the HHRA also will be affected by 

statutory mandates and any limitations in data, methods, time and resources. 

4.3.2 Planning and Scoping

The following discussion outlines important elements of this process, and identifies the issues relevant to 

integrating EJ into each of them. Consistent with EPA guidance, the elements of planning and scoping 

include the following:

 Identify the Context, Purpose, and Scope of the Risk Assessment (Section 4.3.2.1)

 Define Responsibilities for Assessing and Communicating Risks and Allocate Resources (Section 

4.3.2.2)

 Identify Data, Models, Tools and Other Technical Resources (Section 4.3.2.3)

 Conduct a Scientific Peer Review (Section 4.3.2.4)

 Ensure Meaningful Public, Stakeholder and Community Involvement When Possible (Section 4.3.2.5)

Each step of planning and scoping for an HHRA is discussed briefly here. Risk assessors and other analysts 

should consult EPA guidance documents on risk assessment for more information (see Appendix A, and U.S. 

EPA, 2012c; U.S. EPA, 1997).

 

34 There are other assessment approaches for determining differential risks across population groups apart from a HHRA, such as 

a health impact assessment (HIA). HIA is an approach for evaluating the potential public health consequences of a project, plan, 

or policy. It can encompass a traditional HHRA approach, but also typically considers a broad spectrum of health determinants 

such as the quality of housing, access to services and social cohesion, and exposure to contaminants (NRC, 2011). 
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4.3.2.1 Identify Context, Purpose, and Scope of the Risk Assessment

For HHRA planning and scoping, an analyst should consider how to proceed in assessing risks in a way 

appropriate to the context of the analysis. Specifically, analysts should identify the regulatory context and 

objectives, and scientific objectives that are driving and defining the analysis. In addition, in this step 

analysts should develop a high-level review of data needs and limitations to ensure that the results will 

adequately inform decision makers (NRC, 2009). Context, risk management and analytic objectives, and 

scope are key elements in this step.

Regulatory, Risk, and Social Context

EPA risk assessments occur in specific policy contexts that inform the scope, purpose and risk management 

objectives. In some cases, the purpose, scope, and risk management objectives should be linked: the 

purpose of a risk assessment is often articulated through risk management objectives, and the related 

scope of the assessment is then articulated through analytical objectives that support the risk management 

objectives (U.S. EPA, 2002b). Early attention to context enables an analyst to make timely decisions about 

the scope of the risk assessment to ensure that potential EJ concerns are appropriately addressed during 

the rulemaking process.

An analyst should identify any complementarities between the triggering statutory authority and E.O. 

12898 regarding identifying and addressing potentially disproportionate risks. In addition to the policy 

context, other contexts may help frame an evaluation of potential EJ concerns within an HHRA. For 

example, background exposure from multiple sources to the environmental stressor being regulated or 

background risks for a relevant adverse health outcome are important contexts for assessing differential 

risk. Similarly, communities with potential EJ concerns may experience differential risks due to higher 

susceptibility (e.g., due to lifestage or pre-existing health conditions) to the stressor being regulated. 

Finally, historical and social contexts may reveal drivers or contributors to differential exposures that 

contribute to potentially disproportionate risks in some locations and among some population groups (e.g., 

behavioral patterns or diet). See Section 3 for additional discussion of social and risk context.

Defining Risk Management and Analytic Objectives 

While risk management objectives typically reflect regulatory and decision-making contexts and focus on 

the pending decision, the related analytical objectives of an HHRA employ contextual information to 

identify the technical and scientific actions that are required to meet regulatory objectives. As risk 

managers and analysts develop risk management and analytic objectives for assessing potential EJ 

concerns, it is important to frame them so they generate responses to the main EJ objectives from Section 

1.1 (see Text Box 4.2 for an example).

To ensure that an HHRA generates useful information, risk managers and analysts should develop concise 

statements of risk management and analytical objectives that incorporate potential EJ concerns. The risk 

management objectives should present EPA’s perspective on how the pending decision will affect 

potentially disproportionate human health risks among minority, low-income or indigenous populations. 

Related analytical objectives for evaluating potential EJ concerns within an HHRA should clearly identify 

anticipated outputs of the assessment. Analytical objectives should concisely identify the nature of evidence 

to be collected; the direction and structure of the planned evaluation for potential EJ concerns; the 

analytical methods to be employed (e.g., between socioeconomic group comparisons); the type of data 

required; and the scope of the analysis (e.g., national versus local scale).
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Key Scoping Questions for Analysis of Potential EJ Concerns

Scoping is an important step in the planning process for a risk assessment. It refers to establishing the 

boundaries of the assessment (e.g., determining what population groups, health effects, chemicals, and 

exposure pathways will be included in the assessment). The scope, in combination with the context and 

purpose, is a major factor in developing a detailed plan for an HHRA (U.S. EPA 2012c). 

Analysts should integrate applicable scoping questions during the planning stages of a risk assessment when 

one is being conducted for the regulatory action. (Recommendation 3 in Section 1.2.) 

Text Box 4.2: Incorporating Potential EJ Concerns for the Definition of Solid Waste Rule; Examples 

of Risk Management and Analytic Objectives (U.S. EPA, 2011e).

Regulatory Context: The Resource Conservation and Recovery Act (RCRA) gives EPA the authority to regulate 

hazardous wastes (see, e.g., RCRA, 1976, sections 3001–3004). Hazardous wastes may (1) cause, or 

significantly increase mortality or serious irreversible or incapacitating reversible illness, or (2) pose a substantial 

present or potential hazard to human health or the environment when improperly managed (see RCRA, 1976,

section 1004(5)). Hazardous wastes are a subset of solid wastes; materials that are not solid wastes are not 

subject to regulation as hazardous wastes. 

Thus, the definition of “solid waste” plays a key role in defining the scope of EPA’s authority under RCRA. EPA 

has historically interpreted “solid waste” to include certain materials that are destined for recycling (see U.S. 

EPA, 1980). Under the 2008 RCRA Hazardous Waste Definition of Solid Waste Rule (DSW rule), EPA sought to 

clarify how the definition of solid waste applies to hazardous secondary material recycling in a way that both 

encourages recycling, and is protective of human health and the environment (U.S. EPA, 2008a). 

Risk Management Objective for EJ Concerns: Review the 2008 DSW rule to determine the potential for 

increased risk to human health and the environment from discarded hazardous secondary materials intended for 

recycling.

Translating Risk Management Objective to Questions: (1) What hazards could pose risks to human health and 

the environment from recycling of hazardous secondary materials, including accidental releases of hazardous 

secondary materials resulting in potentially disproportionate risks to minority, low-income or indigenous 

populations? and (2) What is the likelihood of such hazards occurring under the requirements of the 2008 DSW 

rule as compared to the pre-2008 DSW hazardous waste regulations?

Analytical Objectives for EJ Concerns: (1) Evaluate whether the populations potentially affected by the 2008 

DSW rule have different socioeconomic characteristics (i.e., minority and/or low-income) than the general 

population; (2) Evaluate whether other factors that affect the potential for disproportionate risk to minority 

and/or low-income communities are present under the 2008 DSW rule.

Translating Analytical Objectives to Questions: (1) Do communities surrounding facilities potentially affected 

by the 2008 DSW rule have a higher percentage of minority and/or low-income population compared to the 

comparison population (i.e., national or state population)? (2) Are the communities potentially affected by the 

2008 DSW rule also affected by other potential sources of pollution (e.g., industrial facilities, landfills, 

transportation-related air emissions, lead-based paint, leaking underground storage tanks, pesticides, 

incompatible land uses)? (3) Are there other factors that may contribute to higher susceptibility (e.g., lifestages, 

nursing mothers) among minority and/or low-income populations? (4) Does the 2008 DSW rule reduce the ability 

for potentially impacted communities to participate in the decision making process? 
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Examples of questions that can aid in scoping for EJ are provided below (also see Text Box 4.3). 

 Which population groups, as defined by attributes such as geographic location, ethnicity or 

race, gender, or baseline health status, should be part of the assessment? While an evaluation 

of EJ focuses on minority, low-income and indigenous populations, in some instances diversity within 

these population groups due to the presence of effect-modifying factors (i.e., factors that alter an

individual’s reaction to exposure such as pre-existing disease conditions or lifestage) may mean 

that some types of individuals are at greater risk for experiencing adverse effects. In identifying 

target population groups for the assessment of potentially disproportionate risks, an analyst

should consider the extent to which effect-modifying factors may explain demographically defined 

differences. If an analyst decides to assess population groups defined by effect-modifying factors, 

the rationale for this decision and the associated methods should be transparently documented. 

 What health endpoints are to be addressed by the assessment? Defining health endpoints 

clearly in the planning phase of the HHRA focuses the risk assessment and increases the 

transparency of the process. When selecting health endpoints, an analyst should consider whether 

specific health endpoints may be significant in population groups of concern. In making this 

selection, it is important to evaluate whether health endpoints for a given exposure differ across 

population groups. This type of information is most often found in epidemiology and toxicology 

studies, such as those focused on the modifying effects of social context on environmental risk. It 

may not be possible to identify all health endpoints upfront. Some information found in toxicity 

assessments may only define the potential for an adverse health outcome for specific stressors. 

 What exposure routes and pathways are relevant, are there specific exposure pathways that 

may lead to specific effects, and what exposure scenarios should be modeled? In determining 

the scope of the evaluation for potential EJ concerns, an analyst should evaluate whether 

population groups of concern may have different exposure routes, pathways, or contact scenarios 

from the general population. Scoping for an exposure assessment should include timing of 

exposure, both historical and current. Unique exposure pathways based on lifestages and other 

relevant categories may also be considered. Different pathways of exposure (e.g., inhalation, 

dermal, ingestion) may produce different effects with varying levels of severity.

Depending on the nature of the assessment, it can be helpful to consult with representatives from affected 

population groups and other stakeholders when identifying exposure routes, pathways and other 

information for constructing exposure scenarios for an HHRA.

35 Community and stakeholder knowledge 

may provide information not known to an analyst or undocumented in the literature (e.g., unusual pathways 

or unique behavior patterns that may alter exposure to an environmental stressor, and may affect

estimates of intake or pathways to be examined from a source to the exposed population). EPA has 

developed extensive guidance on community and stakeholder involvement for this purpose (U.S. EPA, 

2013b). In addition, Section 2.5 and the Interim Process Guide also discuss how to effectively engage the 

community in the rulemaking process.

At the completion of the scoping step, analysts will have a set of boundaries for the HHRA that can be 

incorporated into problem formulation (see Section 4.3.3) to produce a detailed plan for the assessment.

 

35 The Paperwork Reduction Act requires that an Information Collection Request (ICR) be submitted for collecting information (e.g., 

surveys) from more than nine people (44 U.S.C. 3501).
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4.3.2.2 Define Responsibilities for Assessing and Communicating Risks and Allocate Resources

The HHRA planning phase includes assigning responsibilities for analysis and clarifying how analysts will 

interact with decision makers and stakeholders. This process also includes describing or establishing the 

available and required resources, including staffing, budget, and time needed for the assessment.

Consideration of EJ is cross-disciplinary in nature due to its cultural, economic, and demographic elements. 

Early identification of skill sets needed for the assessment enables managers to identify the most 

appropriate analytical team at the outset of the planning process. Areas of expertise that may be 

pertinent to EJ (but not necessarily to a more general HHRA) include social epidemiologists and experts on 

cumulative risk. 

4.3.2.3 Identify Data, Models, Tools and Other Technical Resources

A central challenge for an analyst in the HHRA planning process is identifying the data, tools and models 

available or that need to be generated to complete an assessment for EJ. Data selection should be based 

on the context, risk management and analytic objectives, and scope of the analysis. (Appendix B provides 

sample questions to help identify data and model needs when planning for exposure assessment and 

dose-response assessment.)

Text Box 4.3: Example of Scoping Questions for Integrating EJ Considerations into Exposure and 

Dose-Response Assessments

To explicitly consider EJ in exposure assessment, risk assessors should consider scoping questions such as:

 Based on the use and release patterns of the environmental stressor of concern, are there population 

groups that might be more highly exposed?

 Are exposure variabilities predominantly a spatial phenomenon (e.g., due to contaminant hot spots)? Is 

proximity to source a reasonable proxy for estimating exposure to stressors of concern? 

 Can exposure variability be estimated using ambient contaminant concentrations, either measured or 

modeled? Are data available or can data be modeled at a reasonable spatial scale appropriate for 

available demographic data?

 Are bio-monitoring data available for the population groups of concern, including those with potentially 

elevated exposure? 

 Do the physical and/or chemical properties of the stressor indicate a potential for long range transport 

(e.g., volatile, persistent), especially stressors that may also bioaccumulate?

 Are there population groups that may experience greater exposure to stressors because of their unique 

food consumption patterns, behaviors or use of certain cosmetics?

To explicitly consider EJ in dose-response assessment based on available epidemiological data, risk assessors should 

consider scoping questions such as:

 What demographic and population groups are most relevant from a risk perspective for the stressor in 

question?

 Do population-specific dose-response functions exist for particular minority, low-income or indigenous 

populations?

 Are the spatial and temporal scales of the studies supplying the dose-response function comparable to the 

spatial and temporal scales of the assessment of potential EJ concerns, from both an exposure and 

outcome perspective?
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Data Identification. As previously mentioned, a key planning element for identifying data relevant to EJ

analyses is consultation with stakeholders, including communities that may have access to data useful for 

improving the characterization of exposure and risk. Other data that can be used to evaluate EJ within an 

HHRA include exposure data, epidemiological data, toxicity including susceptibility data, and fate and 

transport data. Relevant data can be location-specific or population group-specific, or, ideally, both. 

Relevant data may also include ambient concentration data (e.g. from air monitoring stations and water 

quality measures), or public health data such as disease incidence. 

Exposure data may include intake data such as consumption or contact rates, routes of exposure, behavior 

data for estimating contact rates, concurrent exposures to other stressors that are of toxicological 

relevance, biomonitoring data, or emissions data. Extensive discussion about use of exposure data in the EJ 

context is available in the peer reviewed literature. Burger and Gochfeld (2011), for example, discuss the 

types of unique exposure pathways that may occur in population groups of concern, and suggest that the 

first step in improving risk methodology is to recognize and account for unique exposure sources (e.g., 

native tattoos and sweat baths, culturally significant toys, mercury used in religious practices) and the 

corresponding exposure pathways. If a chemical bioaccumulates in fish, for example, it would pose 

greater risks to populations who eat more local fish for subsistence or cultural reasons (see Fitzgerald et 

al., 2005 for another example). 

Health risk data could include incidence data specific to populations with potential EJ concerns, historical 

population specific disease or illness rates, toxicological data, such as that which can be found in EPA’s 

Integrated Risk Information System (IRIS) database. 

Model and Tool Identification. Risk assessment employs a range of models and tools to estimate ambient 

concentrations of stressors, exposure, amounts of 

stressors likely to reach the target organ (e.g., 

effective dose), risks for a specific health 

endpoint, locational vulnerability to health 

impacts, and other key factors. EPA’s traditional 

models for estimating exposure and risk remain 

relevant for an evaluation of potential EJ 

concerns within an HHRA. 

The challenge in incorporating EJ into an HHRA

is to evaluate input parameters for these models 

to ensure that they are representative of population groups of concern. Traditional defaults used for inputs 

in HHRAs may not adequately reflect the demographic characteristics of these population groups. Within 

the research community and among state and local agencies, several new tools and models reflect recent 

methodological advances for addressing potential EJ concerns. EPA also has developed improved models 

and tools with a specific focus on EJ, such as Environmental Benefits Mapping and Analysis Program 

(BenMAP). BenMAP is designed to provide the type of input that is particularly useful in a regulatory 

analysis and can be adjusted to highlight particular population groups. More recently, the Agency has 

been developing EJSCREEN, a Census tract-level mapping tool that organizes demographic and 

environmental data that could prove useful to HHRA planning for evaluating potential EJ concerns. Text 

Box 4.4 identifies several recent tools that can be used to support EJ planning within an HHRA. Section 5 

also discusses analytic approaches that can be used to address data needs.

Identifying Data Quality and Data Gaps. Because assessing potential EJ concerns relies on rapidly

developing data and tools, it is essential that the HHRA planning process pair data and tool identification 

with a clear and integrated discussion of data available to characterize key uncertainties, data quality, 

and lack of data that may affect methodology development and/or affect results. 

● ● ●

"An exposure pathway is the course a chemical 

takes from its source to the person being 

contacted." (U.S. EPA, 1992)

● ● ●
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Text Box 4.4: Examples of Models, Tools and Technical Resources for Evaluating Potential EJ 

Concerns within a Human Health Risk Assessment

Data Resources

 Geospatial Platform http://www.geoplatform.gov

 U.S. Census American Fact Finder http://factfinder2.census.gov/

 EPA Report on the Environment http://www.epa.gov/roe/

 America's Children and the Environment Report, Third Edition http://www.epa.gov/ace/

 Centers for Disease Control (CDC) Tracking Program-Funded State and Local Health and Environmental 

Tracking http://ephtracking.cdc.gov/showStateTracking.action

 CDC Environmental Public Health Indicators http://ephtracking.cdc.gov/showIndicatorsData.action

 National Air Toxics Assessment (NATA) (EPA Office of Air and Radiation (OAR)) 

http://www.epa.gov/ttn/atw/natamain/

 EPA's Air Quality System http://www.epa.gov/ttn/airs/airsaqs/

 EPA’s IRIS Database http://www.epa.gov/IRIS/

 National Library of Medicine, Toxicology and Environmental Health Information Program 

https://www.nlm.nih.gov/pubs/factsheets/tehipfs.html

 State or county public health and environmental databases

 County Health Ranking and Roadmaps http://www.countyhealthrankings.org/

 Superfund site information http://www.epa.gov/superfund/sites/cursites/

 RCRAInfo http://www.epa.gov/enviro/facts/rcrainfo/search.html

 State databases for state-regulated facilities

Guidance and References

 EPA Risk Assessment Portal http://epa.gov/risk/

 EPA Community Action for a Renewed Environment http://www.epa.gov/care/

 Air Toxics Risk Assessment Reference Library http://www.epa.gov/ttn/fera/risk_atra_main.html

 Recent state legislation on a broad range of environmental issues http://www.ncsl.org/issuesresearch/energyhome/energy-environment-legislation-tracking-database.aspx

 Recent state legislation on environmental justice http://www.uchastings.edu/public-law/docs/ejreportfourthedition1.pdf

 Cal EPA Cumulative Impacts Assessment Methodology http://oehha.ca.gov/ej/cipa123110.html

 CDC Health Disparities and Inequalities Report: http://www.cdc.gov/minorityhealth/CHDIReport.html

Models and Tools

 Office of Pesticide Programs (OPP) Tools - see http://www.epa.gov/oppt/exposure/

 BenMAP (OAR) http://www.epa.gov/air/benmap/

 Community-Focused Exposure and Risk Screening Tool (C-FERST) (under development by Office of 

Research and Development) http://www.epa.gov/heasd/c-ferst/

 EJSCREEN (under development by Office of Policy; currently for internal use only)

 Community Cumulative Assessment Tool (under development by Office of Research and Development) 

http://www.epa.gov/research/healthscience/health-ccat.htm

 Council of Regulatory and Environmental Modeling (CREM) tools http://www.epa.gov/crem/index.html
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In some cases, lack of data may prompt a decision to limit the scope of an evaluation for EJ within an 

HHRA. Any decisions to limit an evaluation of potential EJ concerns for data availability reasons should be 

clearly documented. Documentation is particularly important in an EJ context because stakeholders often 

provide comments about how to proceed when data needs are identified. 

To further promote the quality of data used in planning risk assessments, risk analysts should review EPA’s 

Information Quality Guidelines (IQG) and Data Quality Objectives (DQO) (U.S. EPA, 2012d). IQGs and 

DQOs help increase the integrity, objectivity, and quality of data when analyzing potential EJ concerns.36

4.3.2.4 Conduct a Scientific Peer Review 

EPA encourages and expects independent peer review of scientific and technical information intended to 

inform or support Agency decisions.37 When an HHRA that incorporates potential EJ concerns is subject to 

scientific peer review, it is important to identify key expertise needed and potential experts, including 

community representatives with technical expertise and public health scientists with community and EJ 

experience. This step also involves planning the peer review process so that it is interactive to ensure that 

stakeholders have the opportunity to participate effectively in the review process.

4.3.2.5 Ensure Meaningful Public, Stakeholder and Community Involvement When Possible

Stakeholder involvement (http://www.epa.gov/stakeholders/) is integral to both the HHRA process and to 

the broader consideration of potential EJ concerns. As previously mentioned, engaging stakeholders in the 

HHRA process may help analysts identify stressor sources, highlight adverse health effects, and address 

risk perception issues. To foster meaningful participation of members of communities with potential EJ 

concerns in the highly technical HHRA process, EPA should recognize and work to overcome conditions that 

reduce or hinder a community’s ability to participate fully in the rulemaking process, such as time and 

resource constraints, lack of trust, lack of information, language barriers, and difficulty in accessing and 

understanding complex scientific, technical, and legal resources. See Section 2.5 and the ADP Interim 

Process Guide (U.S. EPA, 2010a) for more details on meaningful involvement.

A key element of successful public involvement is effective risk communication. EPA's Seven Cardinal Rules of 

Risk Communication begins with two basic tenets: people and communities have a right to participate in 

decisions that affect their lives; and the goal of risk communication is to produce an informed public that is 

involved, interested, reasonable, thoughtful, solution-oriented, and collaborative (U.S. EPA, 1998c). 

Effective risk communication is essential to identify and address potential EJ concerns, and to ensure that 

relevant information is accessible to affected communities and population groups of concern, who may not 

be familiar with the data and analyses used by EPA to evaluate public health risks.

The Presidential/Congressional Commission on Risk Assessment and Risk Management suggests using the 

following questions to identify potential stakeholders:38

 Who might be affected by the assessment?

 Who has information and expertise that might be helpful?

 Who has been involved in similar risk situations before?

 Who has expressed interest in being involved in similar decisions before?

 

36 For more information on IQGs and DQOs , visit http://epa.gov/quality/informationguidelines/index.html and 

http://www.epa.gov/QUALITY/qs-docs/g4-final.pdf.

37 Guidelines for the peer review process are available in EPA’s Peer Review Handbook: http://www.epa.gov/peerreview/.

38 See http://www.riskworld.com/riskcommission/default.html .
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 Who might reasonably or unreasonably feel they should be included?

It is recommended that analysts and risk managers consult The Framework Implementing EPA’s Public 

Involvement Policy (U.S. EPA, 2003b) for general guidance for scoping a public involvement process.39 

When EPA actions or decisions may affect tribes, EPA has instituted a Tribal Consultation Policy that 

provides clear guidance for when, how and on what issues consultations with tribal governments should 

occur (U.S. EPA, 2011i). To ensure that stakeholders participate meaningfully in the HHRA the approach 

for soliciting information should be specific, involve interactive dialogue that is designed to elicit specific 

responses, and include accommodations for population groups with limited English proficiency. Elements of 

such a dialogue could include specific questions about the types of data or models that are needed for 

analysis of potential EJ concerns.

4.3.3 Problem Formulation

Problem formulation, a key step in risk assessment planning, is a process for generating and evaluating 

preliminary hypotheses about why health effects may occur due to specific stressors. Through problem 

formulation an analyst systemically identifies the major factors to be considered in the risk assessment. 

Problem formulation therefore provides the foundation for the entire risk assessment and also provides a 

process for refining risk assessment objectives. It draws from the regulatory, decision-making, and policy 

contexts to inform the technical approach of the HHRA. Effective problem formulation helps ensure that an 

analyst develops a clear set of goals and endpoints for the HHRA and that all relevant risks are identified 

and addressed in the assessment (U.S. EPA, 2012c). 

In considering EJ, problem formulation focuses on determining whether minority, low-income or indigenous 

populations experience potentially disproportionate risks relative to the general population or other 

appropriate comparison groups (see Sections 2.3, 2.4, and 5.4.2). Specifically, this involves: 1) clarifying 

the source and characteristics of the stressors that are relevant to potential disproportionate risks, 2) 

identifying factors that may influence exposures that contribute to those risks, and 3) characterizing 

susceptibilities or vulnerabilities of the populations with potential EJ concerns that may exacerbate 

differences in exposure or risk. Key products of problem formulation are the assessment endpoints, a 

conceptual model, and an analysis plan. Since planning and scoping is an interactive, nonlinear process, 

substantial re-evaluation is an anticipated step in the development of all problem formulation products. 

Text Box 4.5 presents examples of questions that may be raised during problem formulation in the context 

of proximity to sources of pollution. For additional sample problem formulation questions, see EPA’s Lessons 

Learned in Planning and Scoping for Environmental Risk Assessments (U.S. EPA, 2002b).

 

39 EPA’s public involvement website (www.epa.gov/publicinvolvement) provides guidance documents, case studies, and resources 

helpful in planning outreach activities. EPA also provides specific recommendations regarding outreach to Tribes at: 

www.epa.gov/tribalportal/consultation/index.htm. 
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4.3.3.1 Characterizing the Stressor and its Sources

The properties of the stressor and its sources and their relationships to differential risks are important 

inputs to the HHRA. Although HHRAs typically collect information on the general characteristics of stressors 

and their sources, analysts also should incorporate relevant information specific to potential EJ concerns 

(e.g., the likelihood that the source of the stressor is located in areas where minority, low-income or 

indigenous populations live relative to areas where other population groups live). Analysts should also 

identify the distribution of any additional sources of the stressor that are not the focus of the regulatory 

action, because these sources may contribute to potentially disproportionate risks. For example, a stressor 

may be present in environmental media due to background concentrations (e.g., historical or past industrial 

activity, or naturally occurring) in areas with minority, low-income or indigenous populations. 

Text Box 4.5: Examples of Questions to Consider During Problem Formulation 

Characteristics Related to Proximity to a Stressor or Source

 What are the sources of the stressor?

 Is the source being potentially regulated located in geographic areas with greater minority, low-income

or indigenous populations?

 Are other sources of the stressor more prevalent in geographic areas with greater minority, low-income

or indigenous populations?

 Are there historical releases or uses of the stressor in such areas?

 Is the concentration of the stressor in the relevant ambient media higher in geographic areas with 

greater minority, low-income or indigenous populations?

Potentially Disproportionate Exposures to a Stressor

 Do minority, low-income or indigenous populations have higher body burdens of the contaminant?

 Are these population groups more likely to experience current or historically higher exposures to the 

stressor from sources other than the one under consideration?

 Are there particular lifestages within these population groups that may be more at risk to higher 

exposure to the stressor?

 Are there products/consumer goods that contain the stressor?

 Are these products/consumer goods used at disproportionately higher rates among minority, low-income

or indigenous populations?

 Are there cultural practices that are unique to these population groups versus the general population?

 What is the frequency of occurrence of the cultural practice and its duration?

 What is the frequency of occurrence of an atypical activity and its duration?

 Is proximity to the emitting source an important factor in the assessment?

 What geographic scale is important to highlight different exposures between demographic groups for 

the pollutant in question (e.g., U.S. Census tract, block, or county)?

Population Characteristics

 What are the rates of the adverse health outcome of concern among minority, low-income or indigenous

populations?

 Are the rates of the adverse health outcome of concern higher among these population groups?

 What factors or conditions are known to modify the effect of the regulated contaminant?

 How are these modifying factors or conditions distributed across demographic groups?

 Do minority, low-income or indigenous populations have a higher prevalence of modifying effects or 

conditions?

 Are there more members of these population groups employed in specific professions known to have 

higher risks of the adverse health outcome?
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4.3.3.2 Identifying Differences in Exposures that May Lead to Disproportionate Risks

Differential exposures can be an important indicator of potentially disproportionate risks. Differences in 

exposures across population groups may arise from many causes, including those described earlier such as

proximity to pollution sources, employment in certain occupations, or exposures to multiple sources of a 

specific stressor (Brender et al., 2011; Burger and Gochfeld, 2011). For example, if other sources tend to 

be co-located with the source in question, it may contribute to important differences in patterns of 

exposure to the stressor. Even in situations where a regulated source of the stressor is not 

disproportionately located in geographic areas primarily consisting of minority, low-income or indigenous 

populations, other sources of the stressor may contribute to differential exposures, and ultimately, to 

potentially disproportionate risks.

Patterns of exposure can be location-specific or population group-specific depending on the scale of the 

assessment and the types of data available. Analysts identifying differences in exposure should investigate 

issues such as relevant cultural practices, consumer products use, group differences in body burdens of the 

contaminant, and co-exposures to multiple stressors that may affect the body’s ability to detoxify a 

particular contaminant (e.g. factors that may influence metabolism). Social patterns related to exposure 

could also be evaluated across other characteristics of population groups of concern such as lifestage or 

gender or within multiple social strata (e.g., low income minority) to yield unique and important 

perspectives on population groups most at risk. For example, exposure patterns for blood lead show that 

non-Hispanic black children between the ages of 1 and 5 who live below poverty level have the highest 

median blood lead concentration in the United States (U.S. EPA 2013a).

Background exposure may be evaluated using bio-monitoring data, when available. In the United States, 

the primary source of national bio-monitoring data on chemical hazards is the National Health and 

Nutrition Examination Survey (NHANES). NHANES is designed to collect data on the health and nutritional 

status of the U.S. population. The NHANES is a representative sample of the civilian, non-institutionalized 

population in the U. S. based on age, gender, and race/ethnicity (Centers for Disease Control (CDC),

2009). Due to its sample design, NHANES cannot be used to provide exposure data for small geographic 

units or co-located individuals (U.S. EPA, 2003c). Nevertheless, it is an important information resource for 

identifying differences in exposure.40 For more detailed information on using bio-monitoring data to 

evaluate exposure differences, see the exposure assessment examples in Appendix B.

For some stressors that are dispersed locally in ambient media (e.g., air toxics), exposure may be 

effectively captured using proximity to the source.41 Section 5 discusses use of proximity methods for 

evaluating potential EJ concerns.

In some cases, a screening analysis using measured or estimated concentrations of a stressor in ambient 

media that are correlated with race/ethnicity or income can identify differential exposures. For example,

analysts may have information from ambient air quality monitors or estimated ambient air concentration

data averaged over a period of time. However, in some cases monitoring data may not be adequate to 

evaluate differences in exposure for small geographic units (e.g., Census tracts). See Appendix B for an 

example of estimating exposure using ambient concentration data.

In some cases, states, Tribes, and local governments may have relevant monitoring data. Case studies may 

also offer some insight into potential impacts when data are not available for all areas affected by the 

regulatory action. 

 

40 Some limitations of data available through NHANES can be addressed by locale-specific surveys such as the New York City 

Health and Nutrition Examination Survey (NYCHANES) and other site- and population specific surveys that may be conducted for 

reasons other than EJ considerations. Some limitations to the availability of primary site- and population-specific surveys are cost

and the amount of time required for to conduct these surveys.

41 Methods for estimating exposure using the concept of proximity are well developed and are extensively reviewed in 

Chakraborty et al., 2011.
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In the problem formulation step, it is essential to clearly articulate how population groups of concern may 

be exposed to a stressor. Unique exposure pathways are often important in assessing potential EJ 

concerns.42 In some cases, new pathways can be identified during or after planning as new data become 

available. For example, bio-monitoring data acquired during scoping and problem formulation may 

suggest the presence of unexpected differences, resulting in a focused inquiry.

Alternatively, analysts may seek new

information about unique exposure 

pathways to ensure a comprehensive 

evaluation of the range of exposures in the 

population groups of concern. Conceptual 

frameworks of the type discussed in section 3 

may be useful for identifying and collecting 

data on these exposure pathways. Examples 

of questions that are helpful for extracting 

information about unique exposure 

pathways also are presented in Text Box 

4.5.

43

4.3.3.3 Population Characteristics 

Population characteristics refer to those 

attributes shared by individuals within a 

population group that influence the 

likelihood of exposure to the stressor and the 

risk of adverse health outcome that may 

result from this exposure. These factors or 

conditions range from those with direct 

effects such as pre-existing disease conditions, chronic disease, age, medication status, and immune status 

to those with more indirect influences such as a lack of access to resources (e.g., health care), negative 

social conditions, age of housing as a function of race/ethnicity and income, a specific type of occupation, 

income status, access to transportation, and poor educational status.

Understanding population characteristics is an important step toward identifying factors that may affect 

an individual’s resilience, i.e., their ability to withstand or recover from exposure to a stressor. Such 

information also highlights how these characteristics are distributed in the population groups of concern 

from an EJ perspective. Appendix B provides examples of integrating these characteristics into a doseresponse assessment.

Information on population characteristics can be identified in the literature, including epidemiological and 

toxicological studies of effect-modifying factors. For example, if education modifies an adverse health 

outcome from exposure to a stressor such that population groups with lower educational status have higher 

risk, this information could be used in the risk assessment to identify potentially disproportionate risks. 

Sample questions to guide collection of information on population characteristics are presented in Text Box 

4.5.

 

42 Examples of documented unique exposure pathways include exposure to heavy metals from the use of non-traditional medicines 

(Ernst and Thompson Coon, 2001; Ernst, 2002a, b), exposure to mercury from high consumption rates of fish (Anderson and Rice, 

1993; Peterson et al., 1994), exposure to pesticides tracked into homes by family members from their places of work (Simcox et 

al., 1995), and exposure to inorganic mercury from the use of contaminated cosmetic products for body maintenance purposes 

(McKelvey et al., 2011). 

43 The Exposure Factors Handbook also has exposure factors data stratified by race/ethnicity (U.S. EPA, 2011g).

● ● ●

“Populations who face environmental inequities may be 

identified in national exposure databases but may not 

be located in discrete spatial communities. Such 

databases might identify [population groups] who face 

a disproportionate adverse health outcome, but unless 

they live in a community that is spatially identified, it is 

difficult to address common exposures using 

conventional risk assessment approaches... Broad-scale 

surveys, site-specific surveys, and national databases 

are beneficial, and can be used to identify 

environmental inequities among [groups] that are not 

spatially related” (Burger and Gochfeld, 2011).

● ● ●
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4.3.3.4 Building a Conceptual Model

Information from the problem formulation process should assist in the development of a conceptual model. 

The model should explain how and to what degree identified risk factors contribute to differences in 

exposure and/or risk; communicate the strength and direction of relationships between these factors and 

exposure and/or risk; identify data needs by characterizing relationships as low, medium and high 

uncertainty; and ultimately inform the scope of an assessment for EJ given current scientific understanding. 

A conceptual model includes both a written description and a visual representation of the stressor(s), the 

exposed population(s), actual or predicted relationships between population groups of concern and the 

regulated stressor to which they may be exposed, and the endpoint(s) that will be addressed in the risk 

assessment as well as the relationships among them (U.S. EPA, 2012c). The specific challenges of 

integrating EJ into the risk assessment should be addressed in the conceptual model.

4.3.3.5 Developing an Analysis Plan

The analysis plan is the final stage of problem formulation. It describes decisions made during the planning 

process and provides details on incorporating EJ into the scope of the assessment, including: (a) the 

assessment design and rationale for which relationships are addressed; (b) a description of the data and 

information, methods and models to be used in the analyses (including uncertainty analyses); and (c) the 

associated data gaps, needs, and limitations.
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his section discusses how to assess whether there are existing EJ concerns or are likely to be EJ 

concerns based on information generated from a human health risk, exposure, or other assessments.

44

In particular, it discusses methods that may be useful for answering the analytic objectives from 

Section 1.1 of this document, which are repeated here: 

To ascertain the extent to which a potential EJ concern is associated with the affected environmental 

stressors prior to the rulemaking, the analyst should, when feasible:

1. Assess exposures, relevant health and environmental outcomes, and other relevant effects by 

population group in the baseline; and

2. Assess differences in these exposures, relevant health and environmental outcomes, and other 

relevant effects across population groups in the baseline.

For each regulatory option under consideration, to inform the extent to which a potential EJ concern is 

created or mitigated for the affected stressors, the analyst should, when feasible:

3. Assess exposures, relevant health and environmental outcomes, and other relevant effects by 

population group for each option;

4. Assess differences in these exposures, relevant health and environmental outcomes, and other 

relevant effects across population groups for each option; and

5. Assess how estimated differences in these exposures, relevant health and environmental outcomes, 

and other relevant effects across population groups increase or decrease as a result of each 

option compared to the baseline.

This section is organized as follows.45 Section 5.1 discusses how to evaluate the feasibility of conducting a

quantitative or qualitative assessment of potential EJ concerns; Section 5.2 defines baseline and 

incremental changes for an analysis of potential EJ concerns; Section 5.3 summarizes a number of methods 

for assessing differences in impacts across population groups; Section 5.4 discusses analytic issues, 

including how to define comparison groups and geographic issues relevant for analyses where the source 

of emissions is identifiable and health effects are fairly localized and spatially distinguishable; and Section 

5.5 discusses costs and non-health impacts. 

 

44 While the focus in this section is on population groups mentioned in E.O. 12898, the methods discussed may be applied to any 

population group of concern. 

45 The material discussed in this section is generally consistent with draft Chapter 10 of the Guidelines for Preparing Economic 

Analysis, though there are a few key differences. First, the Economic Guidelines apply to regulatory analyses for economically 

significant rules (i.e., rules with benefits or costs in excess of $100 million in any year); the EJ Technical Guidance applies to a 

broader array of regulatory actions. Second, Chapter 10 says little about the generation of underlying information, such as from 

a risk assessment (U.S. EPA., 2012b). 

T

Section 5: Conducting Regulatory Analyses To 

Assess Potential Environmental Justice Concerns
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5.1 Evaluating the Feasibility of an Assessment of Potential EJ 

Concerns

For policies that strengthen an environmental standard, EPA regulatory analyses have often assumed there 

are no EJ concerns because the regulation is expected to reduce overall environmental burden. However, 

this assumption may lead to erroneous conclusions. A basic analysis should support conclusions with regard 

to potential distributional effects to improve the transparency of the rulemaking process and provide the 

decision maker and public with more complete information regarding the expected effects of the policy. 

The main purpose of analyzing the effects of a regulation on population groups of concern is to examine 

how changes in risk to human health or indicators of environmental quality are distributed across and, when 

relevant and feasible, within (e.g., lifestages, gender) population groups. A quantitative analysis is 

generally preferred to a qualitative assessment since it allows for more rigorous evaluation of the way in 

which emissions, risk, and/or health effects are distributed among population groups. 

For regulatory actions where impacts or benefits will be quantified, some level of quantitative analysis 

for EJ is recommended. (Recommendation 1 in Section 1.2.) 

When information on risk and incidence by groups is available, an analyst may be able to characterize 

the baseline and likely response to a change in exposure quantitatively for each policy option. When 

feasible, analysts should present benefits information disaggregated by race/ethnicity and income.46 

When direct quantitative information on risk is unavailable, analysts may consider using surrogate 

quantitative information, such as prevalence or concentration of affected facilities as a function of 

race/ethnicity or income. For instance, the distribution of ambient environmental quality indicators (e.g., 

pollutant concentrations) or stressors from regulated sources may be a useful proxy. In other cases, 

proximity-based analysis may provide some insight into distributional impacts. However, analysts should 

be aware that the conclusions that can be drawn from these analyses will necessarily be limited. There 

may be considerable uncertainty regarding how a reduction in emissions from a given source translates 

into ambient environmental quality and how it, in turn, translates into the human health impacts that are the 

ultimate objective of the analysis. This is particularly problematic if these uncertainties differ across 

population groups. For instance, if an overexposed population group is also more susceptible – meaning, it 

experiences greater health effects per unit of exposure – then using exposure as a proxy will 

underestimate the actual health risk posed by a stressor to that group. If, on the other hand, local 

proximity to a pollutant source is used as a proxy for risk that is much more widely distributed, it could 

overestimate potential differences in risk.

If the available scientific literature and data do not allow an analyst to characterize how risk/exposure or

health outcomes are distributed across population groups of concern, an analyst should qualitatively 

characterize the issue and discuss any evidence, key limitations and sources of uncertainty highlighted in 

the published literature (U.S. EPA, 2010b). See Text Box 5.1 for an example of a qualitative discussion of 

potential EJ concerns from a recent EPA rulemaking. 

When impacts or benefits will not or cannot be quantified or disaggregated by race/ethnicity or 

income, analysts should present information that is insightful with regard to potential EJ concerns 

(e.g., basic demographic information, evidence of differential exposure). (Recommendation 2 in 

Section 1.2.) 

 

46 As discussed in greater detail in Section 4, analysts should engage risk assessors early on in the process to identify data needed 

for evaluating potential EJ concerns that could be generated by the risk assessment. 
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Analysts should use their best judgement when determining the appropriate use of quantitative and 

qualitative information for analysis of potential EJ concerns. 

Text Box 5.1: Qualitative Assessment of Unique Mercury Exposure Pathway in the Final Mercury 

and Air Toxics Standards (MATS) (U.S. EPA, 2011c)

In addition to the quantitative assessments described in Text Box 5.5, analysts for MATS include a 

qualitative assessment of the effects of the rule on population groups with a high potential risk of 

mercury exposure due to a unique exposure pathway – high rates of fish consumption. 

The analysis reviews the literature to identify which populations groups are likely to fall into the high 

risk category for mercury exposure based on higher than average daily fish consumption. Six high risk 

population groups are identified (e.g., Laotian subsistence fishers, low-income African-American 

recreational and subsistence fishers) and the results of the studies summarized, including sample size, 

mean and median fish consumption, and 90th and 95th percentiles. 

The analysis then uses projections of county-level growth to estimate the number of individuals in each 

population group at highest risk for mercury exposure in 2016 absent the rule (i.e., in the baseline) and 

presents this information in a series of maps, one of which is presented below.

Source: Figure 7-1 in the Regulatory Impact Analysis for the Final Mercury and Air Toxics Standard (U.S. EPA, 

2011c).
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Analysts could also consider using case studies, anecdotal information about unique exposure pathways, 

and qualitative relationships (e.g., some low-income populations are known to rely on subsistence fishing), 

information on impacts of facility closures, and general information about demographics to describe the 

expected distributional effects of outcomes when quantitative data are unavailable or would require 

significant resources to collect. Text Box 5.2 provides another example of a qualitative discussion when 

stressors are dispersed widely. 

Text Box 5.2: Qualitative Discussion of EJ Concerns from Final Rule to Establish Greenhouse Gas 

(GHG) Emission Standards for Medium-Heavy Duty Trucks (U.S. EPA, 2011j)

Climate Change Discussion: “Within communities experiencing climate change, certain parts of the 

population may be especially vulnerable; these include the poor, the elderly, those already in poor 

health, the disabled, those living alone, and/or indigenous populations dependent on one or a few 

resources. In addition, the U.S. Climate Change Science Program stated as one of its conclusions: ‘The 

United States is certainly capable of adapting to the collective impacts of climate change. However, 

there will still be certain individuals and locations where the adaptive capacity is less and these 

individuals and their communities will be disproportionally impacted by climate change.’ Therefore, 

these specific population groups may receive benefits from reductions in GHGs. For non-GHG copollutants such as ozone, PM2.5, and toxics, EPA has concluded that it is not practicable to determine 

whether there would be disproportionately high and adverse human health or environmental effects 

on minority and/or low income populations from these rules.”

Non-GHG Air Pollution Discussion: “There is a large population in the United States living in close 

proximity of major roads. According to the Census Bureau’s American Housing Survey for 2007, 

approximately 20 million residences in the United States, 15.6 percent of all homes, are located 

within 300 feet (91 meters) of a highway with 4+ lanes, a railroad, or an airport. Therefore, at 

current population of approximately 309 million, assuming that population and housing are similarly 

distributed, there are over 48 million people in the United States living near such sources. The HEI 

[Health Effects Institute] report also notes that in two North American cities, Los Angeles and Toronto, 

over 40 percent of each city’s population live within 500 meters of a highway or 100 meters of a 

major road. It also notes that about 33 percent of each city’s population resides within 50 meters of 

major roads. Together, the evidence suggests that a large U.S. population lives in areas with elevated 

traffic-related air pollution. People living near [major] roads are often socioeconomically 

disadvantaged. According to the 2007 American Housing Survey, a renter-occupied property is over 

twice as likely as an owner-occupied property to be located near a highway with 4+ lanes, railroad 

or airport. In the same survey, the median household income of rental housing occupants was less than 

half that of owner-occupants ($28,921/$59,886). Numerous studies in individual urban areas report 

higher levels of traffic-related air pollutants in areas with high minority or poor populations.

Students may also be exposed in situations where schools are located near major roads. In a study of 

nine metropolitan areas across the United States, Appatova et al. (2008) found that on average 

greater than 33 percent of schools were located within 400 meters of an Interstate, U.S., or state 

highway, while 12 percent were located within 100 meters. The study also found that among the 

metropolitan areas studied, schools in the Eastern United States were more often sited near major 

roadways than schools in the Western United States. Demographic studies of students in schools near 

major roadways suggest that this population is more likely than the general student population to be 

of non-white race or Hispanic ethnicity, and more often live in low socioeconomic status locations. 

There is some inconsistency in the evidence, which may be due to different local development patterns 

and measures of traffic and geographic scale used in the studies.”
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5.2 Evaluating Baseline and Incremental Changes 

To address the analytic objectives listed above, an analyst needs to isolate the effects of the regulatory 

action from other changes that could occur in the future. To accomplish this, the incremental or additional 

effects are measured relative to a baseline. OMB (2003) defines the baseline as “the best assessment of 

the way the world would look absent the proposed action.” In particular, OMB notes that an analyst may 

need to consider the evolution of the market, compliance with other regulations, and the future effect of 

current government programs and policies, as well as other relevant external factors. 

Analyses should use the same baseline and regulatory option scenarios as other types of regulatory 

analyses (e.g., benefit-cost, economic impact analyses) conducted in support of the rulemaking 

(Recommendation 4 in Section 1.2.).

Defining a consistent baseline allows an analyst to determine how aggregate effects are distributed across 

population groups of concern and to assess whether some groups are more or less affected. See Chapter 

5 of EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010b) for a more detailed discussion of 

baseline issues. 

When evaluating how incremental impacts are distributed across population groups of concern, it is 

important to understand the difference between how a regulatory action changes the baseline distribution 

of health and environmental outcomes across population groups and the distribution of changes in outcomes 

across population groups. For example, consider a regulatory action that reduces emissions of an asthmacausing stressor. An analysis of the change in the distribution of outcomes compares the baseline distribution 

of outcomes across population groups with the projected distribution of outcomes when the regulation is in 

place. In other words, the analysis tries to identify differences in the baseline incidence of asthma induced 

by the stressor and determine if the distribution increases or decreases differences. In contrast, an analysis 

of the distribution of change identifies the total reduction in asthma cases due to the regulation and then 

determines how this reduction in cases is distributed across population groups.47

The first type of comparison is more relevant for evaluating potential EJ concerns. For example, suppose 

that minority populations have a higher number of asthma cases per capita compared to non-minority 

populations in the baseline. Now suppose that the proposed new policy results in a 10 percent reduction in 

asthma cases for both population groups. While the distribution of the change in asthma incidence may 

appear neutral from an EJ standpoint (i.e., both see an equal percentage reduction), it would be incorrect 

to conclude based on this information that there is no potential difference in asthma rates among minority 

and non-minority populations. An analyst needs to know the distribution of outcomes under the regulation 

as well as the distribution of baseline incidence to understand whether this is actually the case. Since 

minority populations have a higher number of asthma incidences than non-minority populations in the 

baseline, reducing asthma incidence by 10 percent actually results in a larger reduction in the number of 

asthma cases for minority populations than for non-minority populations, which represents a decrease in the 

difference in asthma incidence.48 

The difference between these two measures – the distribution of change in health and environmental 

outcomes, and the change in the distribution of health and environmental outcomes – also can affect 

analysis of the distribution of monetized benefits. Unlike physical environmental indicators such as health 

risk or ambient pollutant concentrations, it is not possible to estimate each affected individual’s total 

monetized welfare in the baseline and under each regulatory option. Instead, economists often estimate 

society’s willingness to pay for a change in environmental quality. Thus, although the distribution of the 

 

47 See Maguire and Sheriff (2011) for more information.

48 As discussed in Section 2.4, how to use the information on differences and making a determination of a disproportionate impact 

that merits Agency action is ultimately a policy decision and the responsibility of the decision maker. 
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change in monetized benefits across groups may be of concern in its own right, in isolation it does not 

inform the question of whether the policy increases or reduces pre-existing differences.

Analysis of non-monetized physical indicators when there are multiple health and environmental outcomes 

brings challenges of aggregation, particularly if various policy options affect end points to different 

degrees. It is not appropriate to sum over physical units because it necessarily entails normative value 

judgments regarding the weight to be given to each component (e.g., how many asthma cases are 

equivalent to a heart attack), which implicitly requires use of a social welfare function (see Chapter 10 of 

the Economic Guidelines for more information; U.S. EPA, 2012b). 

While the use of a social welfare function that explicitly weighs one individual’s preferences relative to 

another’s is generally not recommended,49 in some circumstances it may be possible to multiply the 

incidence of each outcome for each population group by the value of society’s willingness to pay to avoid 

the outcome and sum up across all outcomes in each policy scenario.50

5.3 Methods to Assess Potential EJ Concerns 

This section describes a variety of transparent, scientifically sound approaches for presenting EJ 

information on regulatory actions for the public and decision makers: specifically, the evaluation of 

whether there is a potential pre-existing EJ concern and whether any new concerns are created for 

population groups of concern. 

In general, the scope and complexity of the information presented hinges upon the quality and specificity 

of the input data available. Text Box 5.3 gives an example of how data quality may affect the level of 

analysis in an air quality context. Such input data may include:

 Demographic characteristics (e.g., age/sex/race/ethnicity);

 Baseline health data (e.g., hospital and emergency admissions; race/ethnicity-stratified mortality 

rates; race/ethnicity/age-stratified asthma prevalence rates);

 Income data (e.g., median income or population count below poverty level);

 Risk coefficients stratified by socio-economic variables (e.g., education, race/ethnicity);

 Distribution of health effects (or the available proxy such as emissions, ambient concentrations, 

biomarkers, proximity) in the baseline and under each regulatory option; and

 Distribution of costs, when relevant (see Section 5.5.1). 

When data are available, an analyst should present basic summary information for the baseline and each 

regulatory option for the relevant endpoints for the population groups of concern relative to a comparison 

 

49 The use of a social welfare function requires decisions regarding the ranking of alternative outcomes (i.e., society’s preferences 

for more or less equal distributions). There is no consensus regarding such rankings. Hence a universally accepted social welfare 

function does not exist. 

50 The EPA's current approach to valuing damages associated with carbon emissions may make it difficult to conduct a quantitative 

analysis of how domestic effects associated with the pollutant are distributed across population groups of concern. Difficulty 

parsing the effects across particular groups is due to estimation of a comprehensive measure of climate change damages resulting 

from a marginal change in emissions as opposed to the typical outcome-by-outcome approach employed by EPA, and the use of a 

global instead of domestic value of willingness to pay.
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group (see Sections 2. 3 and 5.4.2). This basic information can be supplemented with other approaches 

described below when a screening analysis indicates that a more careful evaluation is needed.51

Regardless of the analytic methods used, information should be presented in a transparent way so that the 

decision maker can determine whether any identified differences across population groups constitute a 

disproportionate impact. Analysts should clearly present:

 Who is affected by the regulatory action;

 Main exposure pathways and expected health and environmental outcomes;

 Evidence for why risk, exposure, or outcomes may vary by population group;

 Relevant geographic scale;

 Main methods of analysis used;

 Summary statistics for the baseline and each regulatory option;

 An easy-to-understand description of what the summary statistics show;

 Conclusions based on the information available; 

 Robustness of results across options presented, and

 Data quality and limitations that affect conclusions regarding potential disparate impacts.

In cases where limited quantitative data are available, an analyst should discuss any information that 

sheds light on whether minority, low-income or indigenous populations may be more susceptible to the 

regulated stressors, may be exposed through unique pathways, and how that may change with the 

regulation.52 There may be cases where an analyst is also able to provide information on how population

groups of concern are distributed in relation to where baseline emissions occur.

 

51 The Interim Guidance on Considering Environmental Justice During the Development of an Action (U.S. EPA, 2010a) suggests 

conducting a screening analysis for determining when an action may require detailed evaluation. 

52 The terms susceptible and vulnerable are discussed in Section 3.
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Text Box 5.3: Data Quality and Spatial Resolution in the Context of Air Regulations

An analyst’s ability to address how a regulation changes the distribution of risk across population groups 

of concern hinges directly on the quality and spatial resolution of the data available. Finer-scale air 

quality, health and socioeconomic data allow one to assess the distribution of air pollution impacts across 

key population groups of concern and to have greater confidence in the conclusions drawn from these 

data. When air quality data are lacking or only available at a coarse-level, the ability to assess change 

in risk across populations and other conclusions are more limited. 

An example in limited data environments: Using race-stratified county-level mortality and morbidity, one 

can calculate population-weighted mortality rates by county. One can then utilize a highly aggregated 

baseline air quality modeling projection (e.g., 12 or 36 km) to identify population groups most exposed 

to air pollution. Using GIS, it is possible to combine the two sources of data. The coarse geographic scale 

of air quality information may inhibit the ability to detect meaningful differences in impacts among and 

between groups. When risk coefficients are unavailable, it is not possible to estimate health impacts 

separately for each population group. 

An example in data rich environments: Using finely resolved air quality data, one can identify at a highly 

disaggregated level (e.g., 1 km) population groups that experience the highest exposure to air pollution. 

One can also identify population groups who exhibit the highest baseline incidence or prevalence rates 

for air pollution health impacts at the zip code-level. Using geographic information system (GIS) 

modeling tools, one can join the two data sources. Using race-specific or standard risk coefficients one 

can then estimate health impacts for each population group. 
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5.3.1 Summary Statistics

Simple summary measures are a useful way to characterize potential differences in baseline and 

regulatory options across population groups of concern relative to appropriate comparison groups. A 

variety of measures can be used to characterize a regulatory action’s health and environmental effects for 

population groups of concern (e.g., averages, ratios). This information should be sufficiently disaggregated 

so that the public can discern how risk, exposure, and/or health effects vary for different types of 

individuals within a population group, to the extent that such a detailed presentation is supported by 

underlying data. For instance, exposure or health outcomes can be presented for income quantiles in 

addition to presenting this information for those above or below a particular poverty threshold. Likewise, 

information on risk, exposure, and/or health effects can be presented for the average-exposed individual 

in each population group as well as a maximally exposed individual (for example, see Text Box 5.4). If 

particular communities are substantially affected, summary statistics can be shown at a locally 

disaggregated level as well as for the nation as a whole.

Analysts should follow identified best practices when feasible and applicable. (Recommendation 5 in 

Section 1.2.)

Current practices – previously outlined in Text Box 1.1 - that may be helpful for evaluating potential EJ 

concerns include using the latest demographic data available; presenting information that shows the 

change in the difference in impacts across population groups; careful selection and justification of 

comparison group, presenting summary metrics for relevant population groups of concern and the 

comparison group; and characterizing the distribution of risks, exposures, or outcomes across individuals, 

lifestages or other relevant categories within each population group, not just average impacts. It is also 

recommended that analysts present disaggregate data to reveal important spatial differences (e.g., 

demographic information for each facility/ place), not just in aggregate, when feasible and appropriate. 

Analysts should conduct sensitivity analysis for important assumptions and parameters (e.g., resolution of 

demographic data) that may affect findings, when feasible and appropriate, and present available 

evidence on other relevant environmental stressors that may contribute to increased vulnerability or 

susceptibility for population groups of concern. 

5.3.2 Additional Analytic Methods

As previously mentioned, the appropriate way to capture differences in health effects is to use information 

on risk and incidence disaggregated by groups to characterize the baseline and likely response to a 

change in exposure. Analysts should engage risk assessors early in the process to identify data needed for 

assessing potential EJ concerns that could be generated as part of the risk assessment (see Section 4 for a 

discussion of how to integrate EJ into a risk assessment). For an example of an analysis of potential EJ 

concerns that makes use of information on differences in mortality risk across population groups in the air 

quality context, see Text Box 5.4. The remainder of Section 5.3 discusses several possible analytic methods 

for evaluating impacts across population groups of concern: visual displays, proximity-based analysis, and 

methods that use exposure or risk information. 
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Text Box 5.4: Quantitative Approaches Used in the Final Rule for the Mercury and Air Toxics Standard 

(MATS) (U.S. EPA, 2011c)

Overall, this regulation will reduce emissions of hazardous and criteria air pollutants from electric generating 

units (EGUs). But because of possible shifts in electricity generation across EGUs, some communities could 

experience small increases in emissions. To evaluate how these changes in emissions and associated human 

health risks are distributed among population groups, the analysis uses two quantitative approaches.

First, the analysis utilizes a proximity-based approach to compare the aggregate average demographic 

composition (i.e., by ethnicity/race, age, and education) of communities within a 5 kilometer radius of specific 

EGUs covered by the rule to the national average. This approach is used as a proxy for exposure to 

hazardous air pollutants such as nickel and chromium, for which the health effects are expected to be 

localized.

Second, the analysis characterizes the distribution of mortality risk associated with fine particulate matter 

(PM2.5) in the baseline and after implementation of the final rule. Proximity-based analysis is not a good 

proxy in this case because criteria air pollutants often travel long distances from sources, and because the 

formation of PM2.5 is governed by a series of complex reactions in the atmosphere. Using methods generally 

consistent with those used in other parts of the regulatory analysis, mortality risk associated with PM2.5 in the 

baseline is examined across population groups by county. There are two differences in methodology: First, 

the baseline mortality rates differ from what is used in the regulatory analysis in order to stratify by race. 

Second, mortality risk coefficients differ to allow for variation by education level (instead of applying the 

same risk coefficient across all socioeconomic groups).

Using a photochemical grid model and BenMAP (a GIS-based tool that estimates health impacts based on air 

pollution concentrations), EPA estimates changes in mortality risk in the baseline and after implementation of 

the final rule for individuals living in counties with the highest PM2.5 mortality risk in 2005 (defined as the top 

5 percent), stratified by race, income, and educational attainment. Finally, the analysis compares the change 

in risk for people living in “high risk” counties to that for people living in other counties. An example of a 

table used to present this information is included below. It shows the estimated change in the percentage of 

all deaths attributable to PM2.5 before and after implementation of MATS in 2015 for each population, by 

race. Populations that are living in “high risk” counties experience reductions in mortality risk that are at least 

as great as for those populations living in other counties, and this occurred regardless of race. 

Estimated Change in the Percentage of All Deaths Attributable to PM2.5 Before and After Implementation 

of MATS by 2016 for Each Populations, Stratified by Race

Race

Year Asian Black Native 

American

White

Among populations at greater risk

2016 (pre-MATS rule) 4.3% 4.4% 4.4% 4.5%

2016 (post-MATS rule) 4.1% 4.1% 4.2 4.3%

Among all other populations

2016 (pre-MATS rule) 3.2% 3.1% 3.1% 3.3%

2016 (post-MATS rule) 3.0% 2.9% 2.9% 3.1%

Source: Table 7-17 in the Regulatory Impact Analysis for the Final Mercury and Air Toxics Standard (U.S. EPA, 2011c)
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5.3.2.1 Visual Displays

Visual displays such as maps, charts, and graphs are commonly used as a way to present information when 

the stressor and demographic groups are geographically distributed. Visual displays can be helpful in 

displaying baseline levels of air pollutants or clusters of hazardous waste sites, and then overlaying the 

demographic profile and baseline health status of various population groups of concern. In this way, 

analysts can identify any potential “hot spots” where high levels of pollution are found in communities with 

minority, low-income or indigenous populations. 

Visual displays are a useful way to communicate large amount of information in an easily digestible form 

(see Text Boxes 5.1 and 5.5 for examples). However, these displays have been criticized for leading to 

erroneous conclusions regarding impacts. For instance, it is difficult to discern differences between baseline 

and regulatory options unless differences are large. However, smaller differences – for instance, those not 

discernible on a map - could still be important.53 For this reason, visual displays are only suggestive of 

potential effects and should be combined with more precise analytic techniques to further refine 

conclusions. 

5.3.2.2 Proximity-Based Analysis

Proximity- or distance-based analysis is an approach commonly used in the literature as a surrogate for a 

direct measure of risk or exposure when such information is not available (United Church of Christ, 1987; 

Baden and Coursey, 2002; Cameron and Crawford, 2003; Wolverton, 2009). Using this approach, it is 

possible to compare the demographic and socioeconomic characteristics of population groups affected by 

a particular source (e.g., a waste site or permitted facility) to the demographic and socioeconomic 

characteristics of population groups unaffected by the source. Text Boxes 5.4 and 5.6 illustrate how it has 

been used in a rulemaking context at EPA. It is important to note that proximity-based approaches should 

not be used if the risks associated with the stressor of concern are not reasonably correlated with the 

geographic location of its source.

For practical reasons, the boundary of an “affected” versus “unaffected” area is usually based on a 

Census-defined geographic area (e.g., census tract) or a GIS-defined concentric buffer (e.g., a specified 

radius around a site that reflects the distance a particular pollutant may travel). When mapping the 

location of polluting sources, it is clearly paramount to have accurate spatial information on sources. 

Analysts should decide what distance from the facility most accurately reflects the community’s exposure to 

a stressor. Buffer-based approaches around an emissions source can be chosen such that they more closely 

approximate actual risk and exposure, though it must be uniform around each source. It is also possible to 

use more continuous measures of distance such as distance to the nearest polluting site or, when additional 

information is available, an emission-weighted distance measure. In some cases, it may be possible to use 

dispersion models to select a buffer that approximates the effect of atmospheric conditions (for instance, 

wind direction and weather patterns) on exposure, though these types of models are data-intensive.54

Regardless of how the boundary is defined, the proximity-based approach assumes that the effects of the 

stressor only occur within the designated boundary (i.e., people located outside the boundary do not suffer 

ill effects from the stressor) and that all individuals residing within the boundary are equally exposed.55 

 

53 See Chakraborty and Maantay (2011) for further discussion of the limitations of using GIS for EJ analyses.

54 For an overview of proximity analysis, including a discussion of various spatial analysis techniques used in the literature, see 

Chakraborty and Maantay (2011) and Mohai and Saha (2007).

55 Chakraborty and Maantay (2011) identify another assumption: that communities with more than one pollutant source are 

treated the same as those with only one source. One can account for this through the use of a count regression technique. 

However, each pollutant source is treated as identical with regard to its effect on the health of the surrounding community. In 

reality, these sources could vary widely in size, age, and production techniques resulting in differing amounts of pollution released 

into the environment. 
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The results of proximity-based analyses also often depend on the choice of the geographic unit of analysis 

(e.g., Ringquist, 2005; Mohai and Saha, 2007). For this reason, an analyst should explore alternative 

geographic units or distances when defining proximity to a source.

The two groups – individuals located near and far from the source – can be compared on the basis of 

simple statistical or regression estimation techniques. Statistical tests on summary data can be used to 

identify whether, on average, there are statistically discernible differences in the characteristics of the two 

Text Box 5.5: Use of Visual Displays to Characterize EJ Concerns in the Proposed Reporting Rule for 

Concentrated Animal Feeding Operations (U.S. EPA, 2011d)

The objective of this proposed rule was to collect facility information from concentrated animal feeding 

operations (CAFOs). This rulemaking addressed reporting requirements rather than technical operations 

at CAFOs, and thus did not include an assessment of water quality impacts deriving from the proposed 

requirements. For this reason, the analysis of EJ concerns only focused on outreach to potentially affected 

communities regarding the content and utility of the new data. (Note: EPA ultimately withdrew this rule.)

In particular, the analysis identified areas in the United States with large numbers of CAFOs as well as 

high concentrations of minority or low-income populations. The location of large livestock operations was 

identified at the county level from custom tabulations of U.S. Census of Agriculture data and used as a 

proxy for CAFOs. The analysis combined this data on CAFOs with information on minority or low-income 

populations at the Census tract level from the U.S. Census of Population and Housing. Because CAFOs are 

located in rural areas, urban census tracts were excluded from the dataset. Using geographic information 

system (GIS) modeling tools, information from these two data sources are overlaid and presented 

visually. Areas circled in red on the map identify communities with both a high number of CAFOs and 

population groups of concern. 

Source: Figure 3 in Analysis to Address Environmental Justice for National Pollution Discharge Elimination System 

(NPDES) Concentration Animal Feeding Operation (CAFO) Rule (U.S. EPA 2011d). Note this proposed rule has been 

withdrawn.
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groups. Regression techniques such as a binary logit also can be used to formally make this comparison 

(where 1 indicates areas where one or more sources are located, and 0 indicates areas with no sources of 

the stressor). A significant coefficient on a demographic variable such as poverty indicates a statistically 

measurable difference in the variable across geographic areas with and without stressor sources. 

5.3.2.3 More Advanced Methods

When data are available, analysts may want to use emissions or ambient concentration data combined 

with fate and transport modeling to examine distributional effects at a disaggregated level. For instance, 

criteria air pollutants are monitored nationally while data on hazardous air pollutants are available 

through EPA’s National Air Toxics Assessment (NATA). These monitoring data can be combined with 

demographic data and dispersion models to generate baseline and regulatory distributions of pollutants 

by population groups of concern. Appendix B discusses this in more detail. 

In cases where disaggregated information is available on the types of activities that result in differences in 

exposure across population groups of concern, it may be possible to characterize differences in various 

Text Box 5.6: Proximity-Based Analysis in the Definition of Solid Waste Proposed Rule (U.S. EPA,

2011e)

This proposed regulation would allow recycling of hazardous secondary materials, and implies that some 

facilities may process higher amounts of hazardous material than was the case prior to the rule. After 

identifying potential hazards that could pose risks to human health from the recycling of hazardous 

materials and the likelihood that they could occur under the new requirements, the analysis uses a 

proximity-based approach to evaluate the potential for EJ concerns. First, the analysis identifies the 

location and type of facilities that may handle secondary recycled materials (both existing and those that 

may come on-line in the future). Using GIS tools, the analysis then draws a 3 kilometer radius boundary 

around each facility to compare the average demographic characteristics of communities within the buffer 

to those outside of it at the state and national level. State-level comparisons are included to account for 

differences in the way Resource Conservation and Recovery Act (RCRA) programs are implemented 

(which is done at the state-level). The demographic characteristics used to make these comparisons are 

minority, American Indian/Alaskan Native, persons below poverty, and children under five years old.

The analysis uses several types of summary statistics for these comparisons. First, it evaluates whether 

communities with a particular type of facility have higher or lower percent minority or low-income 

populations on average, and the degree to which they differ from communities without a facility. Second, 

the analysis generates two types of ratios to determine whether (1) there is a substantially greater 

probability that minority or low-income populations will be affected in comparison to non-minority or nonlow income populations (referred to as an “affected population ratio”), and (2) minority or low-income 

populations comprise a greater proportion of the affected population than the unaffected population 

(referred to as a “demographic ratio”). Each of these metrics is also calculated separately for urban and 

rural populations (based on Census definitions that use population density to determine a rural or urban 

area) to examine whether the propensity for hazardous material recycling facilities to locate in urban 

areas affects the results. The analysis then evaluates whether statistically distinguishable differences exist 

for each metric. 

In addition, the analysis qualitatively discusses factors that may affect the vulnerability of affected 

populations to pollutants associated with recycling of hazardous materials at nearby facilities. The 

analysis ends with a discussion of potential strategies to prevent non-compliance and ways to mitigate the 

potential adverse impacts identified in the analysis.
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health effects due to the regulatory action. In some cases it also may be possible to combine exposure 

data with information on differences in risk across population groups (see Text Box 5.4). 

5.4 Analytical Considerations

Regardless of the analytic approach taken, there are a number of key decisions that an analyst needs to 

make that can have a substantial effect on the results, including: the geographic scope of the analysis; the 

comparison group; and, for regulatory actions where health effects have distinct spatial distributions, how

to spatially identify and aggregate effects across affected and unaffected (i.e., comparison group) 

populations.

5.4.1 Geographic Scope

The geographic scope of analysis for an EPA regulatory action is often the entire country since 

requirements typically apply nationally. However, there may be circumstances under which a regulatory 

action’s effects occur mainly at a sub-national level. For example, some regulatory actions are more 

regional in scope or have effects that are expected to be concentrated in particular regions or states. In 

such cases, it may make sense for an analyst to also analyze and present differences in health and 

environmental outcomes across population groups of concern at a sub-national level. 

5.4.2 Comparison Group

As previously discussed in section 2.3, for decision makers to determine whether impacts on population 

groups of concern are disproportionate, an analyst needs to present them with information that compares 

changes experienced by one group to those of a comparison group. The way the comparison group is 

defined can have important implications for evaluating changes in health, risk, or exposure effects across 

population groups of concern relative to the baseline. 

It is possible to define the comparison group as individuals with similar socioeconomic characteristics 

unaffected by the regulation (i.e., within-group comparison) or as individuals that are also affected by the 

regulation but have different socioeconomic characteristics (i.e., between-group comparison). From the 

perspective of E.O. 12898 and EPA’s EJ policies, a between-group comparison is probably more relevant

because it is the differences across groups that is of primary importance. 

As with geographic scope, some have argued (e.g., Bowen, 2001) that restricting the comparison group to 

a sub-national level may be more defensible than a national level comparison in some instances, given 

heterogeneity in industrial development and economic growth and inherent differences in socioeconomic 

composition across geographic regions (e.g., relatively more Hispanics reside in the Southwest). However, 

Ringquist (2005) notes that placing restrictions on comparison groups may “reduce the power of statistical 

tests by reducing sample sizes” or bias the results against finding disproportionate impacts because such 

restrictions reduce variation in socioeconomic variables of concern. 

Since results often are sensitive to the comparison group chosen, analysts should consider conducting 

sensitivity analysis regarding how the comparison group is defined. 
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5.4.3 How to Spatially Identify and Aggregate Effects

Spatial distribution of health outcomes is a relevant consideration for some regulations, such as those that 

reduce emissions from point sources that have fairly localized effects. In other cases, the regulation’s 

effects may be more wide spread. For instance, the effects of a national regulation on a chemical product

does not hinge on the spatial distribution of health and environmental outcomes but instead on variation in 

the purchase, use, transport, and disposal of this product. Text Box 5.2 provides an example of qualitative 

discussions for disperse stressors. 

When human health outcomes are spatially distributed, analysts need to determine how to spatially 

identify and aggregate affected and unaffected (i.e., comparison group) populations. The nature of the 

stressor should guide an analyst’s choice of the geographic area of analysis. Some air pollutants, for 

example, may travel long distances, affecting individuals hundreds of miles away from the source, making 

it appropriate to choose a relatively large geographic area. In contrast, water pollutants or waste 

facilities may have more localized effects, making it appropriate to select relatively small areas for 

analysis. Likewise, an assessment of local impacts from point sources may call for more spatially resolved 

air quality, demographic and health data than those that affect regional air quality. The quality and type 

of data available also affect the spatial resolution of the analysis. Using more than one geographic area 

of analysis to examine the robustness of results may also be useful since effects are unlikely to be neatly 

contained within geographic boundaries and results may be sensitive to the choice of the geographic area 

of analysis (Mohai and Bryant, 1992; Baden et al., 2007).

Census-based definitions often reflect topographical features such as rivers, highways, and railroads. As a 

result, they may exclude a portion of the affected population who, although separated by some physical 

feature, have the same adverse impacts. While Census-based definitions are easily accessible and offer 

many options with regard to geographic scale, use of geographic information system (GIS) software 

allows for a potentially more flexible approach. GIS-based methods enable analysts to define spatial 

buffers around an emissions source that are more uniform in size and that are easier to customize to reflect 

the appropriate scale and characteristics of the emissions being analyzed (e.g., fate and transport) for a 

given policy action.56 

5.4.4 Statistical Significance

The extent to which a finding of statistical significance can be interpreted as evidence of differential 

impacts depends on a number of factors. First, a statistical difference does not necessarily indicate that the 

difference is meaningful from a policy perspective. For instance, suppose an analyst finds that poor 

households are more likely to locate near a pollution source than wealthier households and that this effect 

is statistically significant. This indicates that the effect is statistically distinguishable from zero (i.e., not due 

to sampling error). However, the difference in likelihood between these types of households could still be 

quite small. Analysts will need to examine what the coefficent estimate implies (e.g., how different is 

poverty across these geographic areas). Second, many of the demographic and socioeconomic 

characteristics that are often included in these types of regressions are highly correlated with each other, 

making it difficult to interpret the meaning of a coefficient on any given variable. Third, it is important that 

analysts consider other factors aside from demographic and socioeconomic characteristics that may have 

influenced the location of stressor sources. Regression techniques are able to partially control for these 

factors, while the use of statistical tests on summary data cannot. Finally, it is important that analysts be 

aware of the biases and limitations that are introduced when proximity or distance is used as a substitute 

for risk and exposure modeling and that these limitations be clearly discussed (see Chakraborty and 

 

56 There are a number of challenges typical of geospatial data. Some statistical techniques rely on assumptions that are regularly 

violated by these types of data (Chakraborty and Maantay, 2011). For instance, when data are spatially autocorrelated –

locations in closer proximity are more highly correlated than those further away – they violate the assumption that underlies some 

regression and statistical techniques that error terms are independently distributed.
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Maantay, 2011). Given the analytic challenges associated with proximity-based analysis, analysts may 

only be able to draw limited conclusions regarding the possibility of differences across populations groups.

 

5.5 Assessing the Distribution of Costs and Other Impacts 

This section covers the following topics: when it may be appropriate to evaluate the distribution of costs 

across population groups of concern; and the evaluation of non-health impacts.

5.5.1 The Distribution of Costs

This technical guidance focuses on providing information to decision makers so that they can assess the 

potential for disproportionate health impacts of regulations on population groups of concern. However, 

certain directives (e.g., E.O. 13175 and OMB Circular A-4) identify the distribution of economic costs as an 

important consideration. While health or environmental improvements may benefit certain population 

groups of concern, their costs may be borne by others. 

Analysts should consider distribution of costs associated with implementing a regulatory option from 

an EJ perspective when appropriate and relevant. (Recommendation 6 in Section 1.2.)

57

Consideration of the distribution of costs in the context of EJ is not always necessary.58 Often, the costs of 

regulation are passed onto consumers as higher prices that are spread fairly evenly across many 

households. When these price increases are small, the effect on an individual household also will likely be 

relatively small and not warrant further analysis. However, there may be situations where further 

exploration of the distribution of costs is warranted. Whether to undertake such an analysis is a case-bycase determination. Examples when such consideration is warranted include: costs to consumers may be 

concentrated among particular types of households such that they impose a noticeable burden (see Text 

Box 5.7, for example); identifiable plant closures or relocation of facilities; or behavioral changes in 

response to a rule or regulation. When this is the case, analysts should consider including an analysis of 

costs when evaluating potential EJ concerns. 

Data or methods may not exist to fully examine the distributional implications of costs across population

groups of concern. For example, an analyst may not have information on whether assistance is available to 

low-income consumers to offset any differences in cost or whether consumption patterns differ substantially 

by race/ethnicity and income. Absent such data, it is difficult to precisely predict the regulatory action’s 

impact on different population groups of concern. In these instances, the issue can be qualitatively 

discussed.59

 

57 Recommendation 5 from Section 1.2 pertains to the use of scoping questions in the planning of a human health risk assessment 

and therefore is referenced in Section 4.3.2.1.

58 By costs we refer to the direct and indirect costs to consumers. There may be other impacts of a rule (e.g., employment effects), 

but understanding how all impacts vary across population groups of concern may not be feasible. For example, data on the 

distribution of changes in employment across low-income and minority populations may be difficult to assess. 

59 Under some environmental statutes, EPA performs “affordability analyses,” the purpose of which is to evaluate how increased 

costs of a regulatory action are passed through in the price of a household good or service and how this affects the purchase 

decision. For instance, the increased costs to a rural water system of new effluent requirements could affect a household’s decision 

to purchase a new home. Because affordability analyses often differentiate purchase decisions by income, they may be a good 

starting point for information useful for evaluating potential EJ concerns. 
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5.5.2 Other Impacts

Non-health endpoints may be unevenly distributed across population groups. Alternatively, some 

population groups may place a higher cultural value on the environmental quality or ecological condition 

of particular places. Data on the distribution of baseline conditions by non-health endpoints are often not 

easily available or are difficult to quantify. For instance, data on ecosystem services in the United States is 

of much poorer quality than data for identifying baseline criteria air pollution levels and mortality 

incidence. Likewise, data and models to assess how various regulatory options affect non-health endpoints 

may not be available.

Given these challenges, analysts should identify any non-health endpoints that may be affected by the 

regulatory action, noting any that are of cultural importance for population groups of concern, and discuss 

how they may be distributed across population groups in the baseline. Conditions under various policy 

options and their effect on non-health endpoints should be qualitatively discussed if feasible. When data 

are available, analysts should use them in the evaluation.

Text Box 5.7: Example of Qualitative Discussion of Costs from Lead Renovation, Remodeling, and 

Painting Final Rule (U.S. EPA 2008)

This rule requires renovators to adhere to certain work practices that reduce risk of exposure to lead 

dust and is expected to raise the cost of renovations. The analysis includes a qualitative assessment of 

how minority or low-income households could be affected by the rule in terms of both cost and human 

health impacts. Excerpts are included below:

“Because these disadvantaged groups are more likely to reside in rental and older housing, they are 

more likely to be affected under the options that emphasize regulating older and/or rental housing. In 

addition, individuals and children with food insecurity (i.e., those who do not have healthy diets or do not 

eat enough because of poverty) are more susceptible to ill health effects from lead dust. Thus, they

stand to accrue greater benefits under all of the options considered.

Following the work practice, cleaning, and cleaning verification steps specified in the rule will increase 

the costs for renovation, repair and painting activities covered by the rule. These additional costs may 

lead some lower income homeowners or some landlords of properties in lower income neighborhoods to 

avoid using certified renovators or recommended practices. The incremental costs of the rule’s work 

practices are typically below $200. These costs are likely to be a small part of the total cost of the 

renovation, repair, and painting projects. EPA believes that these costs are unlikely to result in significant 

changes in consumer behavior. If however, the increased costs result in more projects being undertaken 

by uncertified firms or by do-it yourselfers, the risks in these instances would be the same as in the 

baseline and would not constitute new risks resulting from the rule. EPA believes that the rule would result 

in new risks only if the increased costs caused individuals to delay work such as painting until leadbased paint began peeling and chipping, creating a lead hazard. This is expected to occur infrequently 

given the rule’s low cost per event.”
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This section will be added at a later date and will summarize identified data and methodological gaps in 

conducting analyses of EJ concerns based on internal and external peer review comments.

Section 6: Key Near-Term Research Priorities to 

 Fill Key Data and Methodological Gaps
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Adverse effect: a biochemical change, functional impairment, or pathological lesion that either singly or in 

combination adversely affects the performance of the whole organism or reduces an organism’s ability to 

respond to an additional environmental challenge.

Agency actions: includes rules, policy statements, risk assessments, guidance documents, and models that 

may be used in future rulemakings, and strategies that are related to regulations.

Background exposures: potential exposures due to background levels of both naturally occurring and 

anthropogenic sources. 

Baseline: describes an initial, status quo scenario that is used for comparison with one or more alternative 

scenarios. In typical economic analyses, the baseline is defined as the best assessment of the world absent 

the proposed regulation or policy action. 

Comparison group: see Sections 2.3 and 5.4.2.

Contaminant: a substance that is either present in an environment where it does not belong or is present at 

levels that might cause harmful (adverse) health effects. Also see “stressor.”

Cumulative risk assessment: an analysis, characterization, and possible quantification of the combined 

risks to human health or the environment from multiple agents or stressors.

Disproportionate risk: see Section 2.4.

Dose: the amount of a substance that enters a target in a specified period of time after crossing an 

exposure surface.

Dose-response assessment: a determination of the relationship between the magnitude of an 

administered, applied, or internal dose and a specific biological response. Response can be expressed as 

measured or observed incidence, percent response in groups of subjects (or populations), or as the 

probability of occurrence within a population.

Effects: refers to risks, exposures, and outcomes and is sometimes used interchangeably with the term, 

“impacts.”

Effects modifier: factors that may influence susceptibility, and may include genetics, diet, nutritional status, 

pre-existing disease, psychological stress, co-exposure to similarly acting toxics, and cumulative burden of 

disease resulting from exposure to all stressors throughout the course of life. 

Glossary
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Environmental justice: the fair treatment and meaningful involvement of all people regardless of race, 

color, national origin, or income with respect to the development, implementation, and enforcement of 

environmental laws, regulations, and policies.

Environmental justice concern: In this document, potentially disproportionate impacts on minority, lowincome, or indigenous populations that exist prior to or that may be created by a proposed action. It can 

also indicate the actual or potential lack of fair treatment or meaningful involvement of minority, lowincome, or indigenous populations in the development, implementation, and enforcement of environmental 

laws, regulations, and policies. 

Exposure: human contact with environmental contaminants in media including air, water, soil, and food.

Exposure Assessment: an identification and evaluation of the human population exposed to a toxic 

agent, describing its composition and size, as well as the type, magnitude, frequency, route and duration 

of exposure.

Fair treatment: no group of people should bear a disproportionate burden of environmental harms and 

risks, including those resulting from the negative environmental consequences of industrial, governmental, 

and commercial operations or programs and policies.

Hazard: inherent property of an agent or situation having the potential to cause adverse effects when an 

organism, system, or population is exposed to that agent.

Human health risk assessment (HHRA): the process to estimate the nature and probability of adverse 

health effects in humans who may be exposed to chemicals in contaminated environmental media, now or 

in the future. 

Lifestage: a distinguishable time frame in an individual's life characterized by unique and relatively stable 

behavioral and/or physiological characteristics that are associated with development and growth. 

Low-income: see Section 2.2.2.

Meaningful involvement: occurs when 1) potentially affected community members have an appropriate 

opportunity to participate in decisions about a proposed activity that will affect their environment and/or 

health; 2) the public’s contribution can influence the regulatory agency’s decision; 3) the concerns of all 

participants involved will be considered in the decision-making process; and 4) the decision makers seek 

out and facilitate the involvement of those potentially affected.

Minority: see Section 2.2.1.

Pollutant: an emitted substance that is regulated or monitored for its potential to cause harm to the health 

of individuals or to the environment.

Population groups of concern: in this document, minority, low-income and indigenous populations in the 

United States and its territories and possessions.

Proximity analysis: see section 5; analytical approach using spatial data to estimate a populations’ risk 

or exposure to a stressor when direct measurement of risk or exposure is unavailable.

Reference group: see Sections 2.3 and 5.4.2. (synonym for comparison group).
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Regulatory action: a subset of Agency actions conducted in direct support of a rulemaking; means any 

substantive action by an agency (normally published in the Federal Register) that promulgates or is 

expected to lead to the promulgation of a final rule or regulation, including notices of inquiry, advance 

notices of proposed rulemaking, and notices of proposed rulemaking.

Regulatory analysis: a tool regulatory agencies use to anticipate and evaluate the likely consequences of 

rules.

Risk: the probability of an adverse effect in an organism, system, or population caused under specified 

circumstances by exposure to an agent.

Risk analyst/assessor: plans and conducts a risk assessment. provides a transparent description of all 

aspects of the risk assessment (e.g., default assumptions, data selected and policy choices) to make clear 

the range of plausible risk associated with each risk management option. 

Risk management: in the context of human health, a decision making process that accounts for political, 

social, economic and engineering implications together with risk-related information in order to develop, 

analyze and compare management options and select the appropriate managerial response to a 

potential chronic health hazard.

Source: the origin of potential contaminants.

Susceptibility: increased likelihood of an adverse effect, often discussed in terms of relationship to a 

factor that can be used to describe a population group (e.g., life stage, demographic feature, or genetic 

characteristic). In this document, the term refers to an individual who is more responsive to exposure.

Stressor: a stressor is any physical, chemical, or biological entity that can induce an adverse response. 

Stressors may adversely affect specific natural resources or entire ecosystems, including plants and 

animals, as well as the environment with which they interact. In this document, the term is used to encompass 

the range of chemical, physical, or biological agents, contaminants, or pollutants that may be subject to a 

rulemaking.

Stakeholders: broadly defined as the interested parties who are concerned with the decisions made about 

how a risk may be avoided, mitigated, or eliminated, as well as those who may be affected by regulatory 

decisions.

Summary statistics: see Section 5; descriptive statistics which provide an overview of available data and 

may include the mean, median, mode, interquartile mean, range, and/or standard deviation, etc.

Vulnerability: physical, chemical, biological, social, and cultural factors that result in certain communities 

and population groups being more susceptible or more exposed to environmental toxins, or having 

compromised ability to cope with and/or recover from such exposure. 

.
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from Spray Drift and the Impact of Potential Risk Reduction Measures. Memorandum. Washington, DC: U.S. 

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Page A-1

Topic Area Title

Publication 

Year Weblink

Economics Guidelines for Preparing Economic 

Analyses

2010 http://yosemite.epa.gov/ee/epa/eed.nsf/pages/guideli

nes.html

Action 

Development 

Process

EPA’s Action Development Process: 

Interim Guidance on Considering 

Environmental Justice During the 

Development of an Action

2010 http://www.epa.gov/compliance/ej/resources/policy/co

nsidering-ej-in-rulemaking-guide-07-2010.pdf

Human Health 

Risk Frameworks

Framework for Human Health Risk 

Assessment to Inform Decision Making 

(Draft)

2012 http://www.epa.gov/raf/frameworkhhra.htm

Framework for Assessing Health Risk of 

Environmental Exposures to Children

2006 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=

158363

Other Health 

Risk Guidance

Guideline for Microbial Risk 

Assessment: Pathogenic Microorganisms 

with Focus on Food and Water

2012 http://www.epa.gov/raf/microbial.htm

Supplemental Guidance for Assessing 

Susceptibility from Early-Life Exposure 

to Carcinogens

2005 http://www.epa.gov/ttnatw01/childrens_supplement_fin

al.pdf

Supplementary Guidance for 

Conducting Health Risk Assessment of 

Chemical Mixtures

1986 http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=

20533

Exposure 

Assessment

Exposure Factors Handbook 2011 http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=

236252

Exposure Assessment Guidelines 1992 http://www.epa.gov/raf/publications/guidelines-forexposure-assessment.htm

Risk 

Characterization

Risk Characterization Handbook 2000 http://www.epa.gov/spc/2riskchr.htm

Cumulative Risk 

Assessment

Considerations for Developing a 

Dosimetry-Based Cumulative Risk 

Assessment Approach for Mixtures of 

Environmental Contaminants (Final 

Report)

2009 http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=

172725

Concepts, Methods, and Data Sources 

for Cumulative Health Risk Assessment 

of Multiple Chemicals, Exposures and 

Effects: A Resource Document (Final 

Report) 

2008 http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=

190187

Framework for Cumulative Risk 

Assessment 

2003 http://www.epa.gov/raf/publications/frameworkcra.htm

Cumulative Risk Planning and Scoping 

Guidance 

1997 http://www.epa.gov/spc/2cumrisk.htm

Appendix A: Select Examples of EPA Guidance, Guidelines, and 

Policy Documents that May Be Helpful When Evaluating Potential EJ 

Concerns for Regulatory Actions
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P a g e B - 1

The planning and scoping process provides a key opportunity to ensure that potential EJ concerns are incorporated 

into a risk assessment. This appendix provides several key EJ-specific questions to consider when designing an 

exposure or dose-response assessment, describing the implications of each question for the data and analytic work 

that may be necessary to address it. Also included are examples of analyses from the peer-reviewed literature 

and/or U.S. government analyses that may suggest approaches for an analyst to consider during planning and 

scoping.

Planning for an Exposure Assessment

Patterns of exposure to stressors across population groups of concern may vary for a number of reasons: it may be 

predominantly a spatial phenomenon, if exposure is highest within close proximity to pollution sources and that is 

where the population group of concern is most likely to reside; it may reflect variation in behaviors (e.g., 

subsistence anglers) or exposures due to specific dietary or cultural practices of a population group (e.g., 

exposures to pesticides in reeds used for basket weaving); it may reflect unique aspects of the use or application 

of the chemical (e.g., exposures to pesticide applicators); or it may be due to still other factors that vary by 

population group. Text Box B.1 illustrates how the five scoping questions (below) for integrating EJ into an 

exposure assessment could be posed to evaluate dietary risks from pesticide residues.

Questions and Key Considerations

1. Based on the use and release patterns of the environmental stressors of concern, are there population 

groups that might be more highly exposed?

Environmental stressors may be used and released in a variety of circumstances. However, even when the stressor is 

intended for use in a particular circumstance or location, unintended releases can result. For instance, the stressor 

could migrate to an unintended location. One example of this is spray dust from pesticide applications that result in 

drift falling on “off-target” locations, which may lead to increased exposure to certain populations (e.g., farmers, 

migrant workers, children, sprayers). Text Box B.2 discusses how the potential risk for exposure due to pesticide 

application and residues can be calculated using drift modeling and other methods while accounting for 

evaporation of aerosols (i.e. volatization), and the potential effects to bystanders. Some factors for consideration 

when evaluating the use and release patterns of environmental stressors include evaluating the potential for risks 

due to intended use and potential migration of the stressor, prevalence of use, environmental fate, and the 

toxicological characteristics of the stressor.

2. Are exposure variabilities predominantly a spatial phenomenon (e.g., due to contaminant hot spots)? Is 

proximity to source a reasonable proxy for estimating exposure to stressors of concern?

For environmental stressors that are dispersed locally in ambient media, exposure may be effectively captured 

using proximity to the source as a surrogate measure. Further detail about these methods can be found in the 

recent review by Chakraborty et al. (2011) and Section 5. 

Appendix B: Example Approaches to Address Potential EJ 

Concerns When Conducting Exposure and Dose-Response 

Assessments
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P a g e B - 2

3. Can exposure variability be estimated using ambient contaminant concentrations, either measured or 

modeled? Are data available or can data be modeled at a reasonable spatial scale appropriate for 

available demographic data?

Ambient concentrations can be used to identify and assess spatial variability in exposure that may contribute to 

exposure differences between population groups. Two types of ambient concentration data exist: data from 

ambient air quality monitors and modeled estimates of ambient concentrations averaged over a period of time. 

Monitoring data generally offer a more accurate estimate of the level of exposure to a stressor. However, 

obtaining monitoring data at a level of geospatial resolution that allows for the evaluation of differences may not 

be feasible for a number of reasons, including: (1) some environmental stressors may not be routinely monitored 

(e.g. consumer products); and (2) coverage for routinely monitored stressors is insufficient to provide the level of 

geospatial resolution required to discern differences as most monitoring data are available only down to the 

county level. This lack of detail is problematic given that both racial, ethnic, and income diversity, and differences 

in ambient concentrations could vary widely with the level of geospatial resolution. An example of an alternative 

strategy for evaluating multi-pollutant settings is provided in Text Box B.3.

Modeled data can sometimes serve as a surrogate for monitoring data. Ambient air quality modeling methods 

have been developed to estimate ambient concentrations of a plume beyond its point of release, based on 

relevant factors such as meteorology and chemical characteristics (e.g., reactivity and solubility). However, the

predictive accuracy of models is not comparable across stressors. Important considerations for using modeled data 

should include the predictive accuracy of the model for the stressor in question, and the ability to predict ambient 

concentrations for smaller geospatial units such as Census tracts. Data provided at a larger geospatial scale than 

the census tract may not be able to discern differences in exposure. An analyst may consider the use of screening 

models to highlight concerns about exposure differences, which can be evaluated in greater detail with more 

sophisticated models at a later stage. 

Text Box B.1: Example of Scoping Questions for Integrating EJ Considerations into Assessments of

Dietary Risk from Pesticide Residues

To ensure that EJ considerations are explicitly considered in dietary risk assessments for pesticides, risk assessors could 

consider the following scoping questions when determining whether risk concerns may exist:

 Based on the pesticide use patterns, are there population groups that might be more highly exposed to pesticide 

residues because of their unique consumption patterns (e.g., unusual ethnic diets; subsistence consumers)?

 Is it likely that the pesticide or its metabolites/degradates will bioaccumulate such that increased exposure and 

risk might be expected for certain population groups (e.g., lifestages; subsistence consumers of fish, shellfish, 

game.)?

 Is the pesticide used on, or likely to be found in, foods that are consumed in significantly higher amounts by 

certain ethnic or other population groups (e.g., lemon grass; quinoa grain; broccoli raab)? 

 Does the pesticide have an atypical or unusual use pattern that could result in unusual exposures for certain 

population groups (e.g., use of straw that is woven into baskets by American Indians; use in non-traditional 

agriculture; locally-restricted use)?

 Do the physical and/or chemical properties of the pesticide indicate a potential for long range transport (e.g., 

volatile, persistent), especially pesticides that may also bioaccumulate?

 Are there other groups within the population groups of concern (e.g. based on lifestage) who might be more 

highly exposed through their diet to the pesticide?
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P a g e B - 3

4. Are bio-monitoring data available for the population groups of concern, including those with potentially 

elevated exposures?

Although analysis using bio-monitoring data can be time consuming, it may be the most accurate way to estimate 

exposures for population groups of concern. A literature search for previous assessments of differential exposure 

using survey data should be conducted prior to commencing such an analysis. An important resource to consider is 

the National Report on Human Exposure to Environmental Chemicals generated by the United States Centers for 

Disease Control (CDC, 2009). Human exposure data in this report are presented by lifestage, race/ethnicity and 

income to the extent that such detailed breakouts are possible.

When using exposure biomarkers to draw inferences about exposure differences in the context of source-specific 

regulation, an analyst should carefully consider the extent to which measured levels reflect exposure, and also 

whether biomarkers represent total exposure to an environmental stressor from multiple sources. Comparisons at 

this stage are often focused on point estimates or, at most, deterministic models rather than complex probabilistic 

models. An analyst may use simple, well-established comparative methods such as ratios to examine betweenpopulation group comparisons, or apply more complex approaches such as analysis of variance or regression 

techniques as needed. Comparisons may focus on particular segments of the distribution, (e.g., 95th percentile of 

minorities compared to 95th percentile of non-minorities) or on the percent of a population group represented 

within a percentile group (e.g., percent minorities compared to percent non-minorities in the 10th percentile of the 

population). Sometimes, several years of data may need to be combined to obtain sufficient sample size to conduct 

analysis in the tail of the distribution, though this would be subject to possible resource, analytic, and data 

constraints.

As discussed in Section 4.3.3.2 of this document, use of biomonitoring data has both benefits and limitations. While 

a large population survey (e.g., NHANES) may suggest the existence of exposure difference, locale- or sitespecific surveys (e.g., NYCHANES) can yield more detailed insights into the dimensions of the differences. For 

example, analysis of NHANES data from 1999-2004 demonstrated that total organic blood mercury levels among 

New Yorkers was on average three times higher than the U.S. population, and highest among Asian New Yorkers.

The NYCHANES data provided the additional perspective that among Asians, the levels were highest among 

foreign-born Chinese New Yorkers (Kass 2009).

60 

5. Are there population groups that may experience greater exposure to stressors because of their unique 

food consumption patterns, behaviors or use of certain cosmetics?

When planning for an assessment of potential EJ concerns, an analyst should consider whether the population 

group of concern has higher levels of exposure to a stressor due to food consumption patterns that differ from 

those of the general population (e.g., unique ethnic diets or subsistence living), behaviors (e.g., pica), or through use 

of imported cosmetics (e.g. dyes used for kumkum and bindi body art). Understanding potential exposures from 

these types of sources will allow more accurate estimates of exposures to the stressor of concern. Differences in 

exposures from ingestion may be due to several factors, including regional variation in dietary habits, and cultural, 

ethnic or religious practices. A population group of concern may consume certain foods at higher rates than 

members of other groups or consume parts of animals or plants not commonly consumed by the general population. 

For example, children in tribal communities may consume as much as fifteen times more fish than children in the 

general population (U.S. EPA, 2011g). Additionally, some population groups may eat food predominantly from 

specific locations. Likewise, subsistence fishers may consume fish far more frequently and obtain it only from local 

waterways. If fish from these waterways have higher levels of a contaminant, subsistence fishers may have higher 

exposure levels both due to their increased consumption of fish and their dependence on particular water sources 

(U.S. EPA, 2011g). Similar to foods, some cosmetics may contain lead. An analyst should evaluate the exposure 

pathway (e.g. dermal or inhalation), frequency of use, and identify the populations most likely to use these 

products in unique ways (Burger and Gochfield, 2011). 

 

60 Combining inferences from different surveys should be done with a clear and cautious understanding of the key attributes of each survey, 

including its design, the intended use of the data, how this intended use may bias the sample, statistical characteristics of each survey, and 

use of validated laboratory methods, among other considerations. 
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Text Box B.2: Pesticide Spray Drift Risk Assessment to Bystanders

Farm workers and their families often live near the fields where they work, and can be exposed to 

pesticides in a manner different from other population groups because of this proximity. While direct 

measures of the degree of drift in the vicinity of fields may be difficult or impossible to obtain, potential 

exposure estimates from these residues may be calculated using drift modeling and methods employed for 

typical residential risk assessments.

Spray drift can be characterized as the movement of aerosols and volatile components away from a 

treated area as a result of the application process. Bystanders, defined as those who live on, work in or 

frequent areas adjacent to treated fields, can be exposed to spray drift directly or by contact with resulting 

deposited residues (e.g., children playing on lawns next to treated fields). The degree of such impacts is 

governed by many processes (e.g., application method, nozzles used, release height) and the conditions at 

the time of application (e.g., wind speed and direction). 

To model potential high-end exposure to people living near treated agricultural fields (e.g., via deposition 

on residential turf), EPA used AgDRIFT (V2.1.1) and AgDISP (V8.26) to provide deposition values for 

residential lawns, as a fraction of the application rate, at different distances downwind of a treated field.1

AgDRIFT is used to estimate air concentrations at different distances downwind of a treated field. Analysis of 

spray drifts also includes evaluation of risks from pesticides used on turf because this scenario represents the 

highest potential for exposure associated with spray drift and considers different lifestages, including 

children at different developmental stages. Data from pesticide studies that determined turf residue levels 

and dissipation rates after application are often available, and in the absence of these data, default 

assumptions can be used. This information is used in conjunction with the standard residential methods to 

estimate exposure from treated turf, including exposures from the oral, dermal, and inhalation routes for 

both adults and children. 

Along with these types of exposures, airborne concentrations of pesticides are also defined and used to 

estimate risks from inhalation exposure. For this calculation, concentrations are defined in the breathing zone 

(area around the nose, ~ 3 to 5 feet above ground for this analysis) at the edge of a hypothetical 50 foot 

wide lawn closest to the treated field.

Conceptual Model for Spray Drift Modeling

Figure may be found in Chlorpyrifos, PC Code 059101, DP Barcode 399483 and 399485; Evaluation of the Potential Risks from 

Spray Drift and the Impact of Potential Risk Reduction Measures (U.S. EPA, 2012g).
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Text Box B.3: Detroit Multi-Pollutant Pilot Project Incorporating EJ

EPA conducted a peer-reviewed case study (Fann et al., 2011) to test whether a multi-pollutant, risk-based 

pollution control strategy - designed using spatially resolved air quality, population, and baseline health 

data in the Detroit metropolitan area - represented a viable alternative to a traditional pollutant-bypollutant approach to air quality. The authors performed both within-group and between-group comparisons 

of exposure and risk. The objective of the case study was to demonstrate how states might design air quality 

attainment strategies that met multiple goals: (1) attaining a tighter National Ambient Air Quality Standard 

or NAAQS; (2) maximizing human health benefits of air quality improvements; and (3) achieving a more 

equitable distribution of air pollution-related risk.

The study characterized the costs, benefits and risk inequality implications of two alternative emission control 

scenarios constructed by Wesson et al. (2010) for the Detroit metropolitan area: a more traditional 

approach to pollution reduction for NAAQS compliance, and achieving compliance using a multi-pollutant

strategy that maximized population risk reduction in the area. The assessment for potential EJ concerns 

followed four basic steps: (1) identify and model exposure to population groups susceptible and/or 

vulnerable to PM2.5-related mortality and morbidity impacts in the baseline, based on fine scale air quality 

modeling and population characteristics including education attainment, race and poverty level; (2) design an 

emission control strategy that maximized air quality improvements among these population groups, primarily 

by reducing emissions of directly-emitted PM2.5, which exhibits a strong spatial gradient; (3) compare the

multi-pollutant, risk-based strategy with the traditional pollution control strategy for attainment by modeling 

the air quality impacts of each strategy and comparing the results with the baseline scenario; and (4) 

calculate the change in exposure/risk inequality from the baseline using economic measures to assess whether 

a multi-pollutant risk-based strategy results in a more equal distribution of exposure and risk than a 

traditional pollution control strategy. The findings from this study revealed that the population risk reduction 

approach produced greater net benefits.

Risk-Based, Multi-Pollutant Modeling Framework (Fann et al., 2011)

Additional information about the Detroit multi-stressor project can at: http://www.epa.gov/dears/info.htm
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Planning For a Dose-Response Assessment

As part of the planning process, the dose-response assessment phase presents another opportunity to incorporate 

EJ concerns into a risk assessment. It plays a key role in identifying potential population groups of concern that may 

exhibit particular sensitivity to a stressor and in assessing how demographic characteristics might modify effects 

seen in the general U.S. population. If there are multiple stressors of concern for which a dose-response relationship 

has been drawn, the relationship of this dose-response to any susceptible population will be defined by whether 

there are factors particular to that population that may alter the dose-response relationship in question. For 

example, stress level is a recognized effect-modifer that may alter the dose-response curve for lead. 

Below are a few key questions and sample responses that highlight the types and scale of analytic work that may 

be required to adequately integrate EJ concerns into a dose-response assessment.

Questions and Key Considerations

1. What demographic and population groups are most relevant from a risk perspective for the stressor in 

question? 

The purpose of asking this question is two-fold: (1) defining the susceptible and vulnerable population groups and 

(2) considering what dose-response or concentration-response information is available for those population groups. 

The goal should be to achieve as close a match as possible between the information available in the literature and 

the dose-response function so that the information is not being stretched to fit a population group to which it does 

not apply. The answer to this question may involve stratification by race/ethnicity and income, or it might also 

include factors such as educational level, access to health care, and baseline disease prevalence (e.g., asthma). 

2. Do population-specific dose-response functions exist for particular minority, low-income or indigenous 

populations? 

In answering this question, an analyst should investigate:

Are there known or identified effect modifiers? 

For identified factors that modify risks of interest, how are they distributed among minority, lowincome or indigenous populations? 

Are different lifestages within population groups relevant to the distribution analyses? 

Is the dose-response function the same for all population groups of concern? 

To answer these questions, a thorough review of the relevant literature is necessary to identify potential sources of 

population group-specific dose-response information (see Text Boxes B.4 and B.5). An example of guidelines 

based on dose-response information is also provided in Text Box B.6. 

Text Box B.4: Concentration-Response Functions Stratified by Demographic Factors

The literature on particulate matter (PM) provides examples of concentration-response functions stratified by 

demographic factors that may be indicative of socioeconomic status. In particular, the Regulatory Impact 

Analysis for the proposed PM NAAQS (U.S. EPA, 2012f) includes a distributional analysis of the estimated 

relative risk of PM2.5-related mortality modified by race and educational attainment for counties projected to 

exceed baseline and rolled-back PM2.5 standards. This analysis uses dose-response functions stratified by 

educational level from Krewski et al. (2000). Although dose-response functions modified by race were not 

available, the analysis relied on county-level baseline mortality rates stratified by race in place of racespecific functions. Similarly, Fann et al. (2011) incorporates educational attainment-specific dose-response

functions from Krewski et al. (2009): “Krewski et al. find that educational attainment is inversely related to allcause PM mortality risk, noting that ‘[a]lthough the reasons for this finding are unknown . . . level of education 

attainment may likely indicate the effects of complex and multifactorial socioeconomic processes on mortality 

or may reflect disproportionate pollution exposures” (Fann et al. 2011, pg. 912). 
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Text Box B.5: The Role of Socioeconomic Status as an Effect Modifier of Lead Neurotoxicity (Chari et al. 

2012)

In this study the authors base their approach on a comprehensive literature review of available dose-response

data on the human health effects of lead exposure for children ranging in age from 6 months to 13 years. “To 

identify candidate C[-]R [Concentration-Response] functions we relied on a previously conducted systematic 

review of studies assessing SES [socio-economic status] as an effect modifier of lead toxicity for neurocognitive, 

renal, and cardiovascular (hypertension) outcomes. C-R functions for neurocognitive outcomes were included in 

the present analysis if their associated studies focused on children, measured lead in blood, assessed global 

aspects of cognitive function (Mental Development Index (MDI) or IQ), and reported separate estimates for 

each socioeconomic level,” (Chari et al. 2012). The Figure, reproduced from the Chari et al. paper, illustrates 

how SES modifies the effect of lead on IQ, with low-SES population groups exhibiting a greater response to a 

given level of lead exposure than the general population. Note that this study is only suggestive of the role of 

SES as an effect modifier of lead neurotoxicity due to study limitations (Murphy et al., 2013). 

Central and High-End Estimates of IQ Loss for General Population and Susceptible Groups
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3. Are the spatial and temporal scales of the study or studies supplying the dose-response function comparable 

to the spatial and temporal scales of the assessment of potential EJ concerns, from both an exposure and 

outcome perspective? 

Ideally, the dose-response functions chosen should match 

as closely as possible the geographic scale of the 

proposed analysis for potential EJ concerns. An analyst

may introduce measurement errors if dose-response 

functions from studies conducted over smaller geographic 

areas are applied at a more aggregate scale to 

evaluate potential EJ concerns. For example, if the study 

assigned each subject in the cohort a county-level 

average, the study could underestimate the true 

relationship between exposure and outcome for an 

analysis of potential EJ concerns at a finer spatial scale. 

Likewise, if the exposure in the study is acute, it cannot 

be applied directly to an analysis for potential EJ 

concerns where the exposure of interest is chronic; rather, 

the exposure duration being modeled in the regulatory 

analysis should be considered.

Analysts may consider adjusting the geographic or 

temporal scope of an analysis for potential EJ concerns 

for this reason, and also may need to change the scope if detailed data on factors such as baseline health are 

available only at a certain scales (e.g., at the local urban level or at the acute exposure level).

Text Box B.6: Guidelines Based on Dose-Response Functions for Lead

The U.S. EPA’s 2008 Lead Renovation, Repair, and Painting Program provides specific guidelines for population 

groups (e.g., children) whose dose-response functions for lead makes them exceptionally vulnerable to lead 

exposure: “Under this rule, a child-occupied facility is a building, or a portion of a building, constructed prior to 

1978, visited regularly by the same child, under 6 years of age, on at least two different days within any week 

(Sunday through Saturday period), provided that each day’s visit lasts at least 3 hours and the combined 

weekly visits last at least 6 hours, and the combined annual visits last at least 60 hours. Child-occupied facilities 

may be located in public or commercial buildings or in target housing.” (U.S. EPA, 2008b).

● ● ●

“[T]hese publications used coarse spatial resolution 

exposure measures to develop their concentrationresponse functions, which may result in exposure 

misclassification (and is not aligned with the fine 

spatial resolution of our exposure assessment). An 

analysis of a subset of the American Cancer 

Society cohort using more spatially refined 

exposure characterization... [indicates] that past 

studies may have underestimated the effects of 

PM2.5,” (Levy et al., 2009, p.38).

● ● ●