Overview of Environmental Just

(EJ) Analysis and Analysis 

Conducted for the Clean Pow

Plan 

Public Briefing for 

WeAct for Environmental Justice 

September 17, 2014
General EJ Analysis Overview

► The OAQPS addresses EJ in rulemakings by using a variety of di

methods and tools of varying degrees of sophistication, dependinavailability of information and the results of preliminary evaluation

1. Qualitative / Narrative Statements – Used when unable to quantify affected pop

due to unknown or uncertain source locations, unclassifiable areas and/or impra

• Examples: Reciprocating Internal Combustion Engines (RICE), Grain Elevators 

2. Demographic Proximity Analyses – Identify populations and their socioeconomi

specified distance (i.e., 3 miles) to a facility with comparative analysis to the nat

• Tools: EJView and EJSCREEN (ArcGIS based)• Examples: Polyvinyl Chloride (PVC), Pulp and Paper

3. Risk Based Demographic Analyses – Identify the socioeconomic makeup of the 

different cancer and/or noncancer risk levels due to hazardous air pollutant expfacilities subject to a specific NESHAP.

• Examples: RTR analyses such as Chromium Electroplating, Secondary Lead, Wool Fiberglass

4. Risk Characterization (NAAQS pollutants) – Identify populations susceptible an

criteria air pollutant health impacts in the baseline; characterize the change in rpopulations; approach first applied in the Detroit Multi-pollutant Pilot Project and received favorable peer review in the journal Risk Analysis; methods are generathose described by Joel Schwartz in his recent American Journal of Public Hea

• Tools: BenMAP and CMAQ• Example: Detroit multi-pollutant pilot project; CSAPR, MATs
Clean Power Plan EJ Analysis

► EPA conducted a qualitative EJ analysis for this rulem

► We conducted a qualitative analysis because the EPA c

predict how emissions from specific EGUs would changoutcome of the proposed rule−due to state led impleme

► Overall we found that this guideline would result in reg

national pollutant reductions

► There may be some locations with more times during th

relatively higher concentrations of pollutants−this is due that some EGUs may become dispatched more intensivthe past because they become more energy efficient

► We have considered the potential for such increases an

environmental justice implications of such increase

s
Appendix
Demographic Proximity Analysis

► Goal: to compare demographics of 

populations near emissions sources to the demographics of the whole U.S.

► Census blocks - smallest areas for which 

demographics are available (about 50 people on average).

► Issue: Which census blocks are “near” 

emissions sources? (currently using 3 mile radius)

► General Strategy:

► Choose a “proximity radius” within 

which nearby residents may be affected by local emissions sources. 

► Count “near source” populations in 

census blocks whose distance from one or more emissions sources is less than the proximity radius.

► Determine the demographic 

composition (e.g., race, income, etc.) of these census blocks and compare them to the corresponding compositions nationally. Example: Census blocks which wou

this proximity analysis are highlighte
Pulp and Paper (Proximity Results)

The demographic data in the table below shows that while most demographic categbelow or within 10 percent of their corresponding national averages, the African Ampopulation percentage within 3 miles of any source affected by this rulemaking excenational average by 3 percentage points (15 percent versus 12 percent), or +25 perthose age 65+ exceed the national average by 2 percentage points (14 percent verspercent) or +17 percent.

Demographic Summary

Near Source 

Total

% of Near Source 

Total

National Total

% of National 

Total

Population 2,437,688 285,339,128

White 1,929,157 79% 214,539,706 75%

African American 365,077 15% 35,043,873 12%

Native American 20,449 1% 2,489,515 1%

Other or Multiracial 123,005 5% 33,265,937 12%

Minority 508,531 21% 70,799,422 25%

Hispanic or Latino 78,804 3% 39,083,760 14%

Age 0-17 604,248 25% 77,245,364 27%

Age 65+ 343,439 14% 35,491,274 12%

No High School Diploma 314,843 13% 36,660,464 13%

Below Poverty Line 353,183 14% 37,181,029 13%
Residual Risk Decision Framework

Goals

► Limit Maximum Individual Risk (MIR) for cancer to no higher th

100 in one million (MIR is the person exposed to maximum life

concentrations).

► Protect the greatest number of persons to less than one in one 

lifetime cancer risk.

Step One

► Determine what additional emission standards are needed to a

“acceptable risk” of approximately 100 in one million cancer ris

including uncertainty and without consideration of costs.

Step Two

► Set standard to provide “ample margin of safety" considering a

information and all other relevant factors (including chronic nonrisks, potential acute impacts of concern, costs/feasibility).
Risk and Technology Review (RTR) 

Risk-based Demographic Analyses

► Identify the socioeconomic makeup of the population with significant can

noncancer risks due to HAP exposures near the facilities subject to a speNESHAP (case by case basis).

Secondary lead smelting source category example:► Populations living around the 14 secondary lead smelters in the U.S

percent minority than the nation as a whole 

► Baseline cancer risks

• Of the 84,000 people currently exposed to cancer risks greater than one in a million from sec

smelting emissions, about 40% are minorities (the general minority population nationwide is a

total population)

• Compared to the national population, this at-risk group is also a higher percent Hispanic *(56

and a higher percent below the poverty level (22% versus 13%)

► Baseline noncancer risks

• The analysis estimates that 200 people live in areas near 3 secondary lead smelters that may 

experience ambient lead concentrations above the National Ambient Air Quality Standard (NA

– However, only 7% of this group is comprised of minority populations

► After implementation of the proposed controls:

• Proposed controls will result in nearly 60,000 fewer people exposed to cancer risks greater th

and no one experiencing ambient levels above the NAAQS, thus providing benefits to a popuhigher percent minorities than the national average
Characterizing Risk from NAAQS Pollutants

► National Ambient Air Quality Standards are set to protect public h

including the health of at-risk populations, with an adequate margsafety.

► Recent NAAQS reviews identified children, older adults, persons with pre-exis

and/or persons of lower social economic status (SES) as “at-risk” populations. 

► We use exposure factors and concentration-response functions that character

these specific populations, to the extent that relevant information is available.

► We can assess the health risks from criteria pollutants on suscep

vulnerable populations in support of the NAAQS as well as those help implement the NAAQS.

► For rules designed to help implement the NAAQS, EJ and distrib

assessments generally try to answer two key questions:

1. What is the baseline distribution of air pollution risk by race, et

income?

2. To what extent does the rule deliver air quality improvements a

populations?
Example Criteria Pollutant Distributional Assess

Cross-State Air Pollution Rule

Among populations living in counties at greatest risk* of air pollution

Among populations living counties

The Cross-State Rule reduces the mortality burden the greatest among highly susceptible populations, irrespective of race
The Office of Transportation and Air Quality 

► Regulatory office establishing emission standards and cert

for:

► Motor vehicles► Nonroad engines► Locomotive, ship, and airplane engines

► OTAQ programs also include

► Voluntary retrofit programs (National Clean Diesel Campaign)► SmartWay Transport Partnership recognition program for carriers an

that reduce emissions
Environmental Justice in the Office of 

Transportation and Air Quality (OTAQ)► Over last 10-15 years, hundreds of health studies on near

pollution have been published.

► In the 2009 American Housing Survey, 17% of U.S. housi

were within 300 feet of a highway with 4 or more lanes, raairport.

► Over 20 million homes

► Populations living near roads, rail yards, and marine ports

schools near major roads are disproportionately nonwhite in income.
OTAQ Rulemaking Analysis Examples

► Literature reviews► Statistical analyses using 

geographic information systems (GIS)

► Demographic analyses of U.S. 

government databases

► 2000 and 2010 Census► American Housing Survey 

(Census Bureau)

► Common Core of Data (Dept. 

of Ed.)

► Air quality modeling

► Roadways► Rail yards► Ports (example shown)► Airports

Diesel PM Concentration Around Po

Locomotive / Marine Rule Analysis