Risk and Technology Review - 

Analysis of Socio-Economic Factors for Populations 

Living Near Steel Pickling Facilities 

Prepared by: 

EC/R Incorporated 

501 Eastowne Drive, Suite 250 

Chapel Hill, NC 27514 

EPA Contract No. EP-D-06-119 

Work Assignment No. 3-14 

Prepared for: 

Terri Hollingsworth, Work Assignment Manager 

Sector Based Analysis Group 

Office of Air Quality Planning and Standards 

U.S. Environmental Protection Agency 

Research Triangle Park, North Carolina 27514 

August 10, 2010 
ii

Disclaimer

Although the analysis described in this document has been funded 

wholly or in part by the United States Environmental Protection 

Agency contract EP-D-06-119 to EC/R Incorporated, it has not 

been subject to the Agency's review and therefore does not 

necessarily reflect the views of the Agency, and no official 

endorsement should be inferred.
iii

Contents 

1. Introduction 1 

2. Census Data 2 

3. Calculation Methods 4 

3.1 Racial and Ethnic Categories and the Total Population 4 

3.2 Age Categories 5 

3.3 Level of Education 6 

3.4 Household Income 6 

3.5 Poverty Level 7 

4. Results 8 

5. Uncertainty Discussion 10
iv
1

1. Introduction 

 This document describes the approach used to evaluate the potential chronic noncancer 

risks that may affect different social, demographic and economic groups within the populations 

living near steel pickling facilities associated with inhalation exposures to hazardous air 

pollutants (HAP) emitted by these facilities. This work was carried out in support of the U.S. 

Environmental Protection Agency’s Residual Risk and Technology Review (RTR) for steel 

pickling emissions subject to Maximum Available Control Technology (MACT) requirements 

under 40 CFR 63 Subpart CCC. 

 In the RTR analysis, risks due to the inhalation of HAP were modeled for the populations 

residing within 50 kilometers of each steel pickling facility using the Human Exposure Model, 

Version 3 (HEM3).1,2 HEM3 estimates cancer and noncancer risks at the level of census blocks 

using the AERMOD state-of-the-art air dispersion model developed under the direction of the 

American Meteorological Society (AMS) / EPA Regulatory Model Improvement Committee 

(AERMIC). Each census block typically includes about 50 people. Additional information on 

the risk analysis is available in the docket for the proposed National Emission Standards for 

Hazardous Air Pollutant Emissions: Hard and Decorative Chromium Electroplating and 

Chromium Anodizing Tanks; Group 1 Polymers and Resins; Marine Tank Vessel Loading 

Operations; Pharmaceuticals Production; the Printing and Publishing Industry; and Steel 

Pickling – HCl Process Facilities and Hydrochloric Acid Regeneration Plants rulemaking 

where a memo to the docket is provided, for the steel pickling source category, covering the 

inputs and specific assumptions, and addressing uncertainties specific to the category. 

 In the current analysis, cancer risk predictions from the steel pickling HEM3 modeling 

effort were linked to detailed census data in order to evaluate the distribution of risks for 

different social, demographic and economic groups. The following population categories were 

studied: 

 Total population 

 White 

 African American (or Black) 

 Native Americans 

 Other races and multiracial 

 Hispanic or Latino 

 Children 18 years of age and under 

 Adults 19 to 64 years of age 

 

1. EC/R. 2006. Modeling for the Residual Risk and Technology Review Using the Human Exposure Model 

3 – AERMOD Version. Prepared by EC/R Incorporated for the U.S. Environmental Protection Agency, 

Research Triangle Park, NC. 

2 EC/R. 2008. HEM-3 User’s Guide. Prepared by EC/R Incorporated for the U.S. Environmental Protection 

 Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/fera/human_hem.html#guide
2

 Adults 65 years of age and over 

 Adults without a high school diploma 

 Households earning under the national median income 

 People living below the poverty line 

The HEM3 results for a particular census block reflect the level of risk that would be 

experienced by an individual residing in the block for 70 years. In addition, the HEM3 risk 

estimates are not adjusted for commuting patterns or for the difference between indoor and 

outdoor pollutant concentrations. 

 The distributions of source category risks across the various demographic groups were 

compared to: 1) those associated with HAP at the facilities containing the source category (i.e., 

“facility-wide”); and 2) nationwide distributions of each of the demographic groups. The 

“facility-wide” and nationwide inhalation risks were obtained from the final National-scale Air 

Toxics Assessment (NATA) for 2005, which estimates the risks associated with HAP emissions 

from all stationary sources, onroad and nonroad mobile sources, dispersed area sources, and the 

background due to long-range transport and natural emissions.3

 Two separate analyses of the 

risk distributions were performed. One focused on the demographics of those individuals 

projected to experience a risk of 1 in a million or greater. The other focused on the 

demographics of those individuals living within 5 kilometers of a steel pickling facility, 

regardless of the projected risk. 

 The census data used in this analysis is described in Section 2. The algorithms used to 

compute the distributions of risk and exposure are presented in Section 3. The results of this 

analysis are presented in Section 4. 

2. Census Data 

 Table 1 summarizes the census data used in this analysis, showing the source of each 

dataset and the level of geographic resolution. All of the data are from the 2000 Decennial 

census. Race and ethnicity data were obtained at the census block level. Age distributions, data 

on educational status, and economic data were obtained at the block group level. A census block 

contains about 50 people on average; and a block group contains about 26 blocks on average, or 

about 1,350 people. (For comparison, a census tract is larger than a block group, with each tract 

containing an average of 3 block groups, or about 4,300 people.) 

 

3. EPA. 2009. National Air Toxics Assessments. U.S. Environmental Protection Agency, Research Triangle 

Park, NC. http://www.epa.gov/ttn/atw/natamain/
3

Table 1. Summary of Census Data used to Analyze Risks for Different Socioeconomic Groups 

Type of population category Source of data 

Level of 

geographic 

resolution 

Racial and ethnic categories Landview® Census block 

Age groups SF3 Table P8 Block group 

Level of education - adults without a high 

school diploma 

SF3 Table 37 Block group 

Households earning below the national 

median income 

SF3 Table 52 Block group 

People living below the poverty line SF3 Table P87 Block group 

 Data on race and ethnicity were obtained primarily from the Landview® database 

compiled by the Census Department.4

 Landview® gives a breakdown for the population of each 

census block among different racial classifications, including: White, African American or 

Black, American Indian or Native Alaskan, Asian, Native Hawaiian or other South Pacific 

Islander, other race, and two or more races. In the current analysis, the Asian, Native Hawaiian 

or other South Pacific Islander, and other race categories were combined into a single category. 

The Landview® database also indicates the number of people in each tract that are of Hispanic 

or Latino ethnicity. Landview® covers the 50 states, the District of Columbia, and Puerto Rico, 

but does not cover the Virgin Islands. Race and ethnicity data on the Virgin Islands were 

obtained from the Virgin Islands Summary File.5

 Data on age distributions, poverty status, household incomes, and education level in the 

U.S. and Puerto Rico were obtained from the 2000 Census of Population and Housing Summary 

File 3 (SF3) Long Form. For the U.S. this file was accessed on a DVD version prepared by 

GeoLytics.6 SF3 data for Puerto Rico were obtained from the Census Department website,7 and 

data for the Virgin Islands were retrieved from similar tables in the Virgin Islands Summary 

File.5 

The SF3 data set consists of over 800 separate tables, each providing information on a 

different subject. For the current analysis, data were obtained from Tables P8, P37, P52, and 

P87. Table P8 gives the estimated numbers of men and women in different age categories for 

 

4. Census. 2002. LandView 5 on DVD [electronic resource] : a viewer for EPA, Census and USGS data and 

maps. U.S. Census Bureau, Washington, D.C. 

5. Census. 2008. Virgin Islands Summary File. U.S. Census Bureau, Washington, D.C. 

www.factfinder.census.gov

6. Census. 2004. Census DVD 2000 Long Form SF3, Release 2.2. Geolytics, Inc., East Brunswick, NJ. 

www.geolytics.com

7. Census. 2008. SF3 Data for Puerto Rico. U.S. Census Bureau, Washington, D.C. 

www.factfinder.census.gov
4

each census block group. Table P37 analyzes the level of education attained by men and women 

over 25 years of age (e.g. some high school but no high school diploma, high school graduate, 

some college, etc.). Table P52 gives information on household income in 1999, and Table P87 

estimates the number of people living below the poverty line in each block group. 

3. Calculation Methods 

 The HEM3 models the cancer risk at a point near the geographic center of each census 

block.8

 For the current analysis, this risk estimate was assumed to apply to all individuals 

residing in the block. We used block identification codes to link the HEM3 modeling results for 

each block to the appropriate census statistics. This allowed us to estimate the numbers of 

people falling into different population subcategories within each block. We then analyzed the 

distribution of estimated inhalation risks within each population subcategory, giving the numbers 

of people within the subcategory that are exposed to different risk levels. Each distribution 

involved a tabulation of all the census blocks modeled for the steel pickling source category. We 

also computed the average risk for all individuals in each population subcategory. 

 Distributions of risk and average risks were computed for the raw HEM3 model results 

for steel pickling. For comparison, distributions of risk and average risks were also computed 

for all emission sources at the facility (including steel pickling) (e.g., “facility-wide”), and in the 

country as a whole. 

 Section 3.1 describes the calculation method used for categories where block-level data 

were available -- racial and ethnic categories and the total population. Sections 3.2 through 3.5 

describe calculation methods for age categories, education status, household income, and poverty 

status, respectively. 

3.1 Racial and Ethnic Categories and the Total Population 

 Since race and ethnicity data were available at the census block level, the calculation of 

risk distributions for these categories involved a simple block-by-block accumulation of the 

people in each subcategory. We began by identifying a set of bins reflecting the level of risk. 

The population of each block was then assigned to the appropriate risk bin based on the modeled 

risk level in the block. The numbers of people in each risk bin were then added together for all 

of the blocks modeled for the steel pickling source category: 

 H(Rab,s) = ∑i

(Ra≤Ri<Rb) [N(s,i)] (1) 

where: 

 H(Rab,s) = the population count for risk bin Rab, which is between Ra and Rb for population 

subgroup “s” 

 

8

 HEM3 generally uses the coordinates given by the census for the internal point, or “centroid” of each 

 block. However, when the footprint of an industrial facility includes the block centroid, the model is 

 designed to identify the highest-risk point outside of the facility’s footprint. 
5

 Ri = the modeled risk level in block “í” (estimated lifetime cases of cancer per 

million population) 

∑i

(Ra≤Ri<Rb) refers to the summation over all blocks i where Ri falls in bin Rab, between Ra

and Rb 

 N(s,i) = the number of people within population subcategory s, in block i 

The same approach was used for the total population. The average risk for a given population 

subcategory or for the total population was calculated using the following equation: 

 A(S) = ∑i [N(s,i) × Ri] ⁄ ∑i [N(s,i)] (2) 

where: 

 A(s) = the average risk for population subgroup “s” (estimated lifetime cases of cancer 

per million population) 

∑i refers to the summation over all blocks “í” modeled for the emission source 

category 

 N(s,i) and Ri were defined above 

It must be noted that in the overall NATA risk analysis, only stationary sources were 

modeled at the census block level. Risks due to onroad and nonroad mobile emissions sources, 

dispersed area sources, and ambient background levels of HAP were analyzed at the census tract 

level instead of the block level. EPA chose this larger scale of analysis for these categories for 

two reasons. First, the locations of these sources are not known definitively. Rather, the 

geographic distribution of emissions for these categories has been estimated from county level 

emissions data. Second, emissions from these categories are believed to be more uniformly 

distributed within a given county or census tract. Therefore, in the current analysis, we have 

assumed that the NATA risks for mobile sources, area sources, and background pollutant levels 

are the same for all blocks within a given census tract. As noted above, stationary source risk 

estimates were available at the census block level. 

3.2 Age Categories 

 Age data were retrieved from the Table P8 of the census SF3 Table, which contains data 

on the numbers of males and females of different ages in each census block group. In processing 

the age data, we began by aggregating the categories in the census to the broader age groups 

studied in this analysis. For instance, the total number of children 18 years of age and under was 

calculated by adding together the number of girls under 1 year, the number of boys under 1 year, 

the number of 1-year-old girls, the number of 1-year-old boys, and so on up to and including 18-

year-old girls and boys. In this way, we calculated the number of children age 18 and under, the 

number of adults from 18 to 64, and the number of adults 65 and older in each census block 

group. 

 The next step was to estimate the numbers of people in each age group at the block level. 

To make this calculation, we assumed that the fraction of people in each age group was the same 

for all blocks in a given block group. Thus, the number of people in a particular age group and 

within a particular census block was estimated as follows: 
6

 N(a,b/bg) = N(t,b/bg) × N(a,bg) ⁄ N(t,bg) (3) 

where: 

 N(a,b/bg) = number of people within age group “a”, in block “b” of block group “bg” 

 N(t,b/bg) = total number of people in block “b” of block group “bg” 

 N(a,bg) = number of people within age group “a” in block group “bg” 

 N(t,bg) = total number of people in block group “bg” 

Equation 1 was then applied to the block-level population estimates to generate risk distributions 

for different age groups, and Equation 2 was used to compute the average risk for each age 

group. 

3.3 Level of Education 

Table P37 of the SF3 dataset specifies the education status for men and women age 25 

and older for each census block group, based on the last grade completed. To obtain the total 

number of adults without a high school degree, we added together the numbers who had 

completed grades below a high school senior. Thus, the number of people without a high school 

degree equals the sum of the number of males with no schooling, the number of females with no 

schooling, the numbers of males and females who have completed nursery school through 4th

grade, up to the numbers of males and females who have completed some high school but not 

received a high school degree. 

The number of adults without a high school degree as a fraction of the total population 

was assumed to be the same for each block in the block group. Thus, the number of adults 

without a high school degree in each block was computed as follows: 

 N(nhs,b/bg) = N(t,b/bg) × N(nhs,bg) ⁄ N(t,bg) (4) 

where: 

 N(nhs,b/bg) = number of adults without a high school diploma, in block “b” of block group 

“bg” 

 N(t,b/bg) = total number of people in block “b” of block group “bg” 

 N(nhs,bg) = number of adults without a high school diploma in block group “bg” 

 N(t,bg) = total number of people in block group “bg” 

Equation 1 was then used to generate risk distributions based on the block-level results, and 

Equation 2 was used to compute the average risk for adults without a high school diploma. 

3.4 Household Income 

 Table P52 of the SF3 dataset estimates the numbers of households in each block group 

with income for the year 1999 in various ranges, generally divided into $5000 increments (e.g. 

$10,000 to $14,999, $15,000 to $19,999, etc.). The median national income for 1999 was about 

$42,000 per year. Therefore, in order to determine the number of households with incomes 
7

under the median income, we added the estimates for the ranges below that level. We assumed 

that the household incomes in the $40,000 to $44,999 increment were evenly distributed over 

this range. Therefore, 40% of the households in the $40,000 to $49,000 income range were 

assumed to be below the national median income of about $42,000. The following equation was 

used to estimate the fraction of households below the national median income within each census 

block group: 

 F(sm,bg) = [C<10 + C10-15 + .... + C35-40 + (0.4×C40-45)] ⁄ CT (5) 

where: 

 F(sm,bg) = fraction of households in block group “bg” with incomes below the median 

national income 

 C<10 = number of households with incomes under $10,000 

 C10-15 = number of households with incomes from $10,000 to $14,999 

 C35-40 = number of households with incomes from $35,000 to $39,999 

 C40-45 = number of households with incomes from $40,000 to $44,999 

 CT = total number of households in block group “bg” 

 The fraction of people living in households below the median income for each block 

within the block group was assumed to the same as the fraction of households below the median 

income for the block group. 

 N(sm,b/bg) = F(sm,bg) × N(t,b/bg) (6) 

where: 

 N(sm,b/bg) = number of people in block “b” of block group “bg” living in households below 

the national median income 

 F(sm,bg) = fraction of households in block group “bg” below the national median income 

 N(t,b/bg) = total number of people in block “b” of block group “bg” 

Equation 1 was then used to generate risk distributions based on the block-level results, and 

Equation 2 was used to compute the average risk for adults without a high school diploma. It 

must be noted that this approach neglects any potential relationship between household size and 

income level within a particular block group. However, it is expected to provide a reasonable 

indication of the risk level of people living below the national median income, relative to the 

population as a whole. 

3.5 Poverty Level 

 Table P87 of the SF3 dataset estimates the total number people in each block group living 

below the poverty level, as well as the numbers of people below the poverty level in different age 

groups. The current study did not include an analysis of poverty status by age group, only of the 

total population below the poverty line. The fraction of people below the poverty line was 

assumed to be the same for each block in the block group. Thus, the population below the 

poverty line in each block was computed as follows: 
8

 N(p,b/bg) = N(T,b/bg) × N(p,bg) ⁄ N(T,bg) (7) 

where: 

 N(p,b/bg) = number of people below the poverty line in block “b” of block group “bg” 

 N(T,b/bg) = total number of people in block “b” of block group “bg” 

 N(p,bg) = number of people below the poverty line in block group “bg” 

 N(T,bg) = total number of people in block group “bg” 

Equation 1 was then used to generate risk distributions based on the block-level results, and 

Equation 2 was used to compute the average risk for adults without a high school diploma. 

4. Results 

 The pollutants emitted from the steel pickling process are hydrogen chloride and 

chlorine. Neither of these pollutants have a cancer dose-response value, so estimated lifetime 

inhalation cancer risks were not calculated for the source category, so these demographic 

analyses are based on noncancer risks, or hazard indices (HI). The distribution of maximum HIs 

above 1 for different racial and ethnic groups among the population living near steel pickling 

facilities is shown in Table 2. For comparison, Table 2 provides the distributions of HIs for the 

population exposed to emissions from all source categories at the steel pickling facility (i.e., 

facility-wide) and for the nationwide demographic breakdown (i.e., nationwide). The facilitywide and nationwide HIs were obtained from the final NATA for 2005, which estimates the risks 

associated with HAP emissions from all stationary sources, onroad and nonroad mobile sources, 

dispersed area sources, and the background due to long-range transport and natural emissions. 

For the detailed demographic analysis used to create Table 2, see Appendix A of this document. 

 The results of the demographic analysis presented in Table 2 show that for those with HI 

greater than 1 due to the steel pickling source category, HIs may be lower for all demographic 

groups, with the exception of the “Ages 19 to 64” and the “Ages 65 and up” demographic 

groups, than we would normally expect, based on the typical distribution of those demographic 

groups across the U.S. The results of the demographic analysis for the facility-wide emissions 

show that for those with HI greater than 1 due to the facility-wide emissions, HIs may be 

significantly higher for the “African American” demographic group and somewhat higher for the 

“Minority” demographic group than we would normally expect based on the typical distribution 

of those demographic groups across the U.S. 
9

Table 2. Summary of Demographic Assessment Results for the Steel Pickling Source Cate

Population With Respiratory Hazard Index Greater Than 1 

Emissions 

Basis

Maximum 

Respiratory 

Hazard 

Index

Total Minority 

African 

American 

Other and 

Multiracial 

Hispanic 

or Latino 

Native 

American 

Ages 0 

to 18 

Ages 19 

to 64 

Source Category 2 45 0% 0% 0% 9% 0% 26% 63% Facility-wide 10 1,657 41% 34% 6% 1% 0.2% 25% 62% Nationwide Demographic Breakdown 

n/a 174,737,524 32% 16% 15% 16% 0.6% 27% 61% 

Notes:Source Category, Facility-wide, and Nationwide emissions are based on the final 2005 NATA, dated May 2010. Minority population is the total population minus the white population. Population figures are for the population residing within 5 km of these facilities. The percentage of the minority population experiencing HI greater than 1 from the source category could be greater than 0% as 4 hispanic/latithis analysis. 
10

 

5. Uncertainty Discussion 

 Our analysis of the distribution of risks across various demographic groups is subject to 

the typical uncertainties associated with census data (e.g., errors in filling out and transcribing 

census forms), which are generally thought to be small, as well as the additional uncertainties 

associated with the extrapolation of census-block group data (e.g., income level and education 

level) down to the census block level. 

 The uncertainties in these risk estimates include the same uncertainties in emissions data 

sets, in air dispersion modeling, in inhalation exposure, and in dose response relationships that 

are associated with our source category risk estimates. The degree of uncertainty associated with 

facility-wide risk estimates, based on the 2005 NATA, dated May 2010, is generally greater than 

for source categories because emissions data for source categories not currently undergoing an 

RTR review may be incomplete. 

 The methodology for our demographic analyses is still evolving. While this is our best 

attempt to provide useful information now, our thinking is continuously advancing. EPA is in 

the process of developing technical guidance for environmental justice analyses. We present 

these analyses, with their associated uncertainties, to EPA decision makers and the public as 

additional analyses to inform RTR decisions. 
A-1

Appendix A 
Total 

population White

African 

American

Native 

American

Other and 

multiracial

Hispanic or 

Latinoc

0 to 0.2 2,430,795 1,295,667 823,486 10,623 301,019 296,779

0.2 to 1 1,476 1,417 5 3 51 74

1 to 1.5 13 13 0 0 0 0

1.5 to 2 32 32 0004

Total number 2,432,316 1,297,129 823,491 10,626 301,070 296,857

0.0018 0.0025 0.0011 0.0012 0.0008 0.0011

0 to 0.2 2,319,606 1,220,276 803,609 9,837 285,884 276,794

0.2 to 1 111,053 75,869 19,314 785 15,085 20,041

1 to 1.5 1,260 758 400 4 98 17

1.5 to 2 355 215 137035

2 to 5 40 11 29 0 0 0

5 to 10 0 0 0 0 0 0

10 and over 2 0 2000

Total number 2,432,316 1,297,129 823,491 10,626 301,070 296,857

0.04 0.0453 0.0329 0.0464 0.0358 0.0431

Notes:

Numbers of people in different ranges for respiratory hazard indicesb

c

The Hispanic or Latino population is double-counted in this analysis, since different individuals within the 

category may classify themselves as White, African American, Native American, or other.

Table A-1. Distribution of Respiratory Hazard Indices for Racial and Ethnic Groups

a

Modeled hazard indices are for a 70-year lifetime, based on the predicted outdoor concentration and not adjusted 

for exposure factors.

Range of lifetime respiratory 

hazard indexa

Average HI

b

Distributions by race are based on demographic information at the census block level. Risks from steel pickling 

emissions were modeled at the block level, as were other NATA stationary source emissions. Mobile sources, area 

sources, and background risks were analyzed at the census block level in NATA. 

Modeled risk from the steel pickling category

Modeled risk from facilities containing steel 

pickling source category

Average HI

A‐2
Total 

population Ages 0 thru 18

Ages 19 thru 

64

Ages 65 and 

up

0 to 0.2 2,430,795 714,793 1,398,059 317,943

0.2 to 1 1,476 381 923 172

1 to 1.5 13 3 9 1

1.5 to 2 32 8 20 4

Total number 2,432,316 715,186 1,399,011 318,120

0.0018 0.0017 0.0019 0.0018

0 to 0.2 2,319,606 680,226 1,333,166 306,214

0.2 to 1 111,053 34,538 64,825 11,691

1 to 1.5 1,260 329 786 146

1.5 to 2 355 85 209 61

2 to 5 40 9 23 8

5 to 10 0 0 0 0

10 and over 2010

Total number 2,432,316 715,186 1,399,011 318,120

0.04 0.0405 0.0401 0.038

Notes:

Table A-2. Distribution of Respiratory Hazard Indices for Different Age Groups

Modeled risk from the steel pickling category

b

Distributions by age and education level are based on modeling data at the Census block level, 

and age and education data at the block group level. All blocks in a block group are assumed to 

have the same age and income distribution.

Range of lifetime respiratory hazard 

indexa

Numbers of people in different ranges for respiratory hazard 

indicesb

Average HI

a

Modeled hazard indices are for a 70-year lifetime, based on the predicted outdoor concentration 

Modeled risk from facilities containing steel 

pickling source category

Average HI

A‐3
Total 

population

Total number 25 

and older

Number 25 and older 

without a high school 

diploma

0 to 0.2 2,430,795 1,507,852 395,071

0.2 to 1 1,476 1,003 124

1 to 1.5 13 8 1

1.5 to 2 32 22 3

Total number 2,432,316 1,508,885 395,198

0.0018 0.0019 0.0013

0 to 0.2 2,319,606 1,441,017 372,890

0.2 to 1 111,053 66,751 22,095

1 to 1.5 1,260 841 145

1.5 to 2 355 247 59

2 to 5 40 29 8

5 to 10 0 0 0

10 and over 2 1 0

Total number 2,432,316 1,508,885 395,198

0.04 0.0399 0.0453

Notes:

Table A-3. Distribution of Respiratory Hazard Indices for Adults with and without a 

High School Diploma

Modeled risk from the steel pickling category

b

Distributions by age and education level are based on modeling data at the Census block level, 

and age and education data at the block group level. All blocks in a block group are assumed to 

have the same age and income distribution.

Range of lifetime respiratory hazard 

indexa

Numbers of people in different ranges for respiratory 

hazard indicesb

Average HI

a

Modeled hazard indices are for a 70-year lifetime, based on the predicted outdoor concentration 

and not adjusted for exposure factors.

Modeled risk from facilities containing steel pickling 

source category

Average HI

A‐4
Total 

population

People living in 

households below the 

national median 

incomec

People living 

below the poverty 

line

0 to 0.2 2,430,795 1,426,666 432,702

0.2 to 1 1,476 585 96

1 to 1.5 13 4 1

1.5 to 2 32 13 2

Total number 2,432,316 1,427,269 432,801

0.0018 0.0015 0.0012

0 to 0.2 2,319,606 1,359,763 410,439

0.2 to 1 111,053 66,573 22,182

1 to 1.5 1,260 714 146

1.5 to 2 355 196 31

2 to 5 40 22 3

5 to 10 0 0 0

10 and over 2 1 0

Total number 2,432,316 1,427,269 432,801

0.04 0.0405 0.0425

Notes:

Table A-4. Distribution of Respiratory Hazard Indices for People Living in 

Households below the National Median Income and Below the Poverty Line

Range of lifetime respiratory hazard 

indexa

Numbers of people in different ranges for respiratory 

hazard indicesb

Average HI

c

The median income is the national median household income in 1999, about $41,000.

Modeled risk from the steel pickling category

Average HI

a

Modeled hazard indices are for a 70-year lifetime, based on the predicted outdoor 

concentration and not adjusted for exposure factors.

b

Distributions by income are based on modeling data at the Census block level, and income 

data at the block group level. All blocks in a block group are assumed to have the same age 

and income distribution.

Modeled risk from facilities containing steel 

pickling source category

A‐5
A-6

Table A-5. Distribution of Respiratory Hazard Indicesa for Racial and Ethnic Groups, Age Groups, Adu

School Diploma, and People Living in Households Below the Poverty Line 

Total 

population Minorityc

African 

American 

Native 

American 

Other and 

multiracial 

Hispanic 

or Latinod

Ages 0 

thru 18 

Ages 19 

thru 64 

Ages 65 

and up 

Modeled HI from the steel pickling category Total population within 5 km of facilitiesb 2,432,316 1,135,187 823,491 10,626 301,070 296,857 715,186 1,399,011 318,120 Percentage of total 47% 34% 0.4% 12% 12% 29% 58% 13% Average HI 0.0018 0.0010 0.0011 0.0012 0.0008 0.0011 0.0017 0.0019 0.0018 Modeled HI from facilities containing steel pickling source category Total population within 5 km of facilitiesb 2,432,316 1,135,187 823,491 10,626 301,070 296,857 715,186 1,399,011 318,120 Percentage of total 47% 34% 0.4% 12% 12% 29% 58% 13% Average HI 0.040 0.034 0.033 0.046 0.036 0.043 0.041 0.040 0.038 Nationwide demographic breakdown Total population 285,339,128 70,799,422 35,043,873 2,489,515 33,265,937 39,083,760 77,245,364 172,602,490 35,491,274 Percentage of total 25% 12% 0.9% 12% 14% 27% 60% 12% Notes: a Modeled HIs are for a 70-year lifetime, based on the predicted outdoor concentration and not adjusted for exposure factors. b Distributions by race are based on demographic information at the census block level. Distributions of other demographic groups are based on the extrapolation of censu

census block level. Risks from steel pickling emissions were modeled at the block level. c The minority population is the total population minus the white population. d The Hispanic or Latino population is double-counted in this analysis, since different individuals within the category may classify themselves as White, African American