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Research Article

National Patterns in Environmental Injustice and Inequality: Outdoor NO2 Air Pollution in the United States

  • Lara P. Clark,

    Affiliation: Department of Civil Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America

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  • Dylan B. Millet,

    Affiliations: Department of Civil Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America, Department of Soil, Water and Climate, University of Minnesota, Minneapolis, Minnesota, United States of America

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  • Julian D. Marshall mail

    julian@umn.edu

    Affiliation: Department of Civil Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America

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  • Published: April 15, 2014
  • DOI: 10.1371/journal.pone.0094431

Abstract

We describe spatial patterns in environmental injustice and inequality for residential outdoor nitrogen dioxide (NO2) concentrations in the contiguous United States. Our approach employs Census demographic data and a recently published high-resolution dataset of outdoor NO2 concentrations. Nationally, population-weighted mean NO2 concentrations are 4.6 ppb (38%, p<0.01) higher for nonwhites than for whites. The environmental health implications of that concentration disparity are compelling. For example, we estimate that reducing nonwhites’ NO2 concentrations to levels experienced by whites would reduce Ischemic Heart Disease (IHD) mortality by ~7,000 deaths per year, which is equivalent to 16 million people increasing their physical activity level from inactive (0 hours/week of physical activity) to sufficiently active (>2.5 hours/week of physical activity). Inequality for NO2 concentration is greater than inequality for income (Atkinson Index: 0.11 versus 0.08). Low-income nonwhite young children and elderly people are disproportionately exposed to residential outdoor NO2. Our findings establish a national context for previous work that has documented air pollution environmental injustice and inequality within individual US metropolitan areas and regions. Results given here can aid policy-makers in identifying locations with high environmental injustice and inequality. For example, states with both high injustice and high inequality (top quintile) for outdoor residential NO2 include New York, Michigan, and Wisconsin.

Introduction

Environmental injustice often places disproportionate health risks on people who are already the most vulnerable or susceptible to those risks. Since the earliest US environmental justice studies [1][6] in the 1960s–1980s, disparities in exposures to environmental risks (e.g., landfills, hazardous waste sites, polluting industries, vehicle traffic) by socioeconomic status (SES) have been widely documented [7][9]. Air pollution is a priority environmental risk in the United States (US): urban outdoor air pollution is one of the top ten causes of death in high-income nations [10]. Low-SES communities are often disproportionately exposed to air pollution [11] and also may be more susceptible to air pollution owing to other underlying disparities in, for example, access to health care [12].

Although relationships between air pollution exposure and SES have been documented in certain US cities, little is known about the broader patterns in ambient air pollution environmental justice within and across US geographies (cities, regions, states, urban versus rural areas). This previous lack of understanding is largely because of the limited coverage and spatial resolution of ambient air pollution data. Recent work exploring air pollution environmental justice in US cities or regions has been based on industrial emissions-based air pollution concentration estimates [13][16], or has focused on people living near regulatory monitor locations [17][19]. Those multi-city and national studies reported differences in environmental injustice by US region [18], metropolitan area [13] and urban form characteristics of metropolitan areas [15][17].

Here, we employ a recently developed ambient air pollution dataset [20] to explore patterns in environmental justice within and across US geographies, including rural and urban populations. The work applies a national land use regression with high spatial resolution (~0.1 km) to examine residential outdoor nitrogen dioxide (NO2) air pollution in the US. NO2, which is one of the six US Environmental Protection Agency criteria pollutants, in the US is mainly emitted (as NOx) from combustion in vehicles and power plants [21]; it is a marker for traffic emissions [22] and has high within-urban variability [23], [24]. NO2 and other traffic emissions are linked to asthma [25] and decreased lung function [26] in children, low birth-weights [27], and cardiovascular and respiratory mortality (e.g., ischemic heart disease mortality) [28], [29]. Previous work in specific US cities suggests that ambient NO2 (and/or NOx) concentrations tend to be higher in low- than in high-SES communities [30][33].

This paper applies a national-scale analysis to quantify US-wide NO2 concentration patterns by SES characteristics. It provides quantitative information for understanding how environmental equality and justice for air pollution vary among communities and regions across the US. A goal of this study is to identify US locations with highest priority environmental justice and equality concerns attributable to NO2 and co-emitted air pollutants.

Methods

1. Data

Our analysis covers the year-2000 population of the contiguous US (280 million people). The spatial unit of analysis is the Census Block Group (BG), which is the smallest Census geography with demographic data (race-ethnicity, household income, poverty status, education status, and age) reported in the 2000 Census. Of all BGs (n = 207,492), 64% are urban, 14% are rural, and 21% are mixed urban-rural (i.e., contain both urban and rural Census Blocks). The mean BG sizes are 1.1 km2 (urban), 185 km2 (rural), and 45 km2 (mixed); the mean (standard deviation) BG population is 1,350 (890) people.

Air pollution data are year-2006 annual average ground-level NO2 concentration estimates from a recently published national land use regression (LUR) [20]. This LUR predicts NO2 concentrations at the Census Block level for the contiguous US based on satellite- and ground-based measurements of NO2, combined with land use data (e.g., road locations, elevation, tree cover, impervious-surface coverage, population density). To match the Census BG level demographic data, we calculate the mean concentration among all Blocks in each BG. Nationally, the mean NO2 concentration for all BGs is 11.4 ppb.

2. Statistical Analyses

We calculate population-weighted mean NO2 concentrations by race-ethnicity, poverty status, household income, education status, and age, using annual mean BG concentrations (from year-2006 LUR data) and population estimates (from year-2000 Census data). For example, the national population-weighted mean NO2 concentration for nonwhites is the mean of BG mean concentrations weighted by the population of nonwhites in each BG. We then calculate environmental injustice and inequality metrics by US region, state, county, and Urban Area (UA), and rural versus urban location.

Our primary comparison metric for environmental injustice is the difference (ppb) in population-weighted mean NO2 concentration between lower-income nonwhites (LIN; nonwhites in the lowest annual household income quintile [<$20,000]) and higher-income whites (HIW; whites in the highest annual household income quintile [>$75,000]). Our primary comparison metric for environmental inequality is the Atkinson Index (ε = 0.75 [34][38]), which measures the extent to which NO2 concentrations are evenly distributed across the population: Atkinson Index = 0 indicates perfect equality (i.e., concentrations are equal for all people); higher values indicate greater inequality (maximum = 1). The US Census information about race covers 100% of the population, whereas combined race-income categories (e.g., whites with income >$75,000) are only available for 38% of the population (one person per household; “householders”). Our injustice metric includes 10% of the total Census population (26% of householders): lower-income nonwhite householders are 2.9% of the total Census population; higher-income white householders are 7.0%. In contrast, the inequality metric and straightforward white/nonwhite comparisons include 100% of the total Census population. See Supporting Information (Figures S1–S2 and Table S1 in File S1) for sensitivity analyses regarding metric selection.

Results and Discussion

Our results reveal significant disparities in NO2 concentrations for specific socioeconomic groups (Table 1; Table 2). For example, average NO2 concentrations are 4.6 ppb (38%, p<0.01) higher for nonwhites than for whites, 1.2 ppb (10%, p<0.01) higher for people below versus above poverty level, and 3.4 ppb (27%, p<0.01) higher for lower-income nonwhites than for higher-income whites. Likewise, NO2 concentrations are higher for residents with less than a high school education compared to those with a high school education or above (difference: 0.9 ppb [8%], p<0.01). Among urban residents, NO2 concentrations for Black Hispanics (the most exposed race-ethnicity group) are 6.1 ppb (38%, p<0.01) higher than for American Indians (the least exposed race-ethnicity group) and 4.7 ppb (28%, p<0.01) higher than for the total urban population. Urban-rural differences abound: in urban areas, NO2 concentrations are higher for nonwhites than for whites, and higher for low- than for high-income groups; in contrast, NO2 concentrations in rural areas are similar for nonwhites and for whites but are slightly lower for low- than for high-income groups. Urban areas exhibit more low- than high-income communities in NO2-polluted areas (e.g., adjacent to busy roadways), whereas the same trend does not emerge in rural areas. Among race-ethnicity groups, American Indians have the lowest NO2 exposures in urban areas, but the second highest NO2 exposures (after Hispanics) in rural areas. Overall, for seven of the eight nonwhite race-ethnicity groups considered (upper portion of Table 1), NO2 concentrations are higher for that group than for whites.

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Table 1. Population-weighted mean NO2 concentration in ppb (percent of total population1).

doi:10.1371/journal.pone.0094431.t001
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Table 2. Comparisons between population-weighted mean NO2 concentrations for specific populations.

doi:10.1371/journal.pone.0094431.t002

Young children and the elderly are especially vulnerable to air pollution. We find that NO2 concentrations for these groups correlate with SES. Population-weighted mean NO2 concentrations are similar (within 3% [0.3 ppb]) for those two subpopulations (elderly: greater than 65 years; young: less than 5 years) as for other age groups (5 to 65 years). However, for below-poverty level nonwhite individuals, NO2 concentrations are notably higher for young children (3.0 ppb; 23%, p<0.01) and elderly people (3.1 ppb; 24%, p<0.01) than for the rest of the population (age 5 to 65 years, including whites and nonwhites).

An important issue is whether the NO2 disparities described above are relevant to public health. To investigate that question, we consider here one illustrative example: ischemic heart disease (IHD) annual deaths associated with NO2 concentration disparities between nonwhites and whites. Assuming a 6.6% change in IHD mortality rate per 4.1 ppb NO2 [39] and US-average IHD annual mortality rates (109 deaths per 100,000 people [40]), reducing NO2 concentrations to levels experienced by whites (a 4.6 ppb [38%] reduction) for all nonwhites (87 million people) would be associated with a decrease of ~7,000 IHD deaths per year. For comparison, interventions with a similar benefit (a decrease in ~7,000 IHD deaths per year) include: 16 million people increasing physical activity level from inactive (0 h/wk) to sufficiently active (>2.5 h/wk)[41]; 25 million people increasing physical activity level from insufficiently active (<2.5 h/wk) to sufficiently active (>2.5 h/wk); or, 3.2 million fewer adults (age 30–44) beginning smoking [42]. Calculations in this paragraph (details in Table S2 in File S1) may underestimate true health impacts because we ignore here differences in vulnerability and susceptibility to air pollution and differences in underlying IHD mortality rates; also, the analysis above considers only one health outcome (IHD mortality) and one pollutant (outdoor NO2).

Within individual urban areas, even after controlling for urban area size and household income group, nonwhites are generally more exposed to residential outdoor NO2 air pollution than whites. Figure 1 presents regression models predicting population-weighted mean NO2 concentration as a function of household income for all 16 Census-defined household income categories and for the 4 largest race-ethnicity groups (Whites, Hispanics, Blacks, Asians) by urban area size (small; medium; large; defined by urban population tertiles). Each within-urban model reveals an inverse relationship between population-weighted NO2 concentration and household income with high statistical significance (R2>0.86; model p-value<0.01; Tables S3–S18 in File S1). Across household income groups, urban NO2 concentrations are often highest for Asians or Hispanics and lowest for Whites.

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Figure 1. Within-urban and within-rural population-weighted mean NO2 concentrations (105 million householders) by Census household income category, race, and urban category (large UA population tertile, medium UA population tertile, small UA population tertile, or rural).

Concentrations shown are modeled by UA population tertile (linear regressions: R2>0.98 [large UAs], >0.96 [medium UAs], >0.86 [small UAs], >0.47 [rural]; all models are statistically significant at p<0.01; see Tables S3–S18 in File S1). For visual display, plots use the population-weighted mean UA-specific dummy variable for each UA population tertile. Error bars show the 95% confidence intervals on linear regression model predictions. AD = average difference, UA = Urban Area. AD values shown are for interquartile range incomes ($25k, $75k) and for race-ethnicity groups with highest and lowest concentrations for that panel.

doi:10.1371/journal.pone.0094431.g001

Within individual urban areas, on average, NO2 concentration disparities by race (after controlling for income) are more than 2 times greater than NO2 concentration disparities by income (after controlling for race). The relative importance of race versus income for environmental injustice increases with urban area size. For each urban area size category, we compared average differences in NO2 concentrations between the race group (of the 4 largest race groups) with the highest versus the lowest NO2 concentrations (controlling for household income group) to the average differences in NO2 concentrations between the $25,000 versus $75,000 income groups (approximate income interquartile range; controlling for race group; Figure 1). In large urban areas, disparities by race are ~4 times greater than by income. In medium and small urban areas, disparities by race are ~2 times greater than by income. For rural residents, differences by race are ~20 times greater than by income (despite significantly lower average concentrations for rural versus urban residents: 4.4 ppb [rural population-weighted mean] versus 14.2 ppb [urban population-weighted mean]). For rural areas, differences by income are small (0.1 ppb) and in the opposite direction as for the US as a whole (i.e., in rural areas, concentrations are higher for higher- than for lower-income groups).

As an alternative analysis, we developed NO2 regression models for which each observation is a Block Group concentration rather than population-weighted concentration (by location, income and race category; Tables S19–S30 in File S1). Results for the Block Group and population-weighted analyses cannot be compared directly. Block Group analyses indicate a more varied relationship with race and with income, but in general suggest that NO2 concentrations are higher for nonwhites than for whites and are higher for lower-income than for higher-income communities; and, on average, disparities are greater by race (percent white) than by income.

Inequality metrics are presented in Table 3. On a national scale, we find that inequality levels are higher for NO2 (Atkinson Index = 0.11) than for income (Atkinson Index = 0.08), despite the fact that the US has a high degree of income inequality compared to most developed nations [43].

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Table 3. Environmental injustice and inequality metric mean (population-weighted mean) [range].

doi:10.1371/journal.pone.0094431.t003

Figure 2. shows national spatial patterns in environmental injustice and inequality in outdoor NO2 air pollution. States with high levels (top quintile) of both injustice and inequality include New York, Michigan, and Wisconsin. Given previous work documenting inequality and injustice in NO2 concentrations (among other environmental hazards) it is not surprising that we observe injustice and inequality in NO2 concentrations on a national basis. What is unexpected, however, are the spatial patterns in Figure 2. Environmental injustice and inequality do not exhibit clear spatial coherence with respect to regional race or income characteristics. For example, among urban areas, environmental inequality (Atkinson Index) has a low correlation with race (percent nonwhite) and average income [Pearson’s r<0.2]. Understanding the processes driving these spatial distributions of environmental injustice and inequality is thus a priority need for future research.

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Figure 2. Environmental injustice and inequality in residential outdoor NO2 concentrations for US regions, states, counties and urban areas.

The left column shows differences in population-weighted mean NO2 concentrations between low-income nonwhites (LIN) and high-income whites (HIW), with larger positive differences (red colors) indicating higher injustice (larger concentration difference between LIN and HIW). The right column shows the Atkinson Index, with higher values indicating greater inequality.

doi:10.1371/journal.pone.0094431.g002

Inequality and injustice metrics vary by location. NO2 inequality (Atkinson Index) is slightly higher among rural residents than among urban residents, but environmental injustice may be higher for urban residents: NO2 concentration differences between lower-income nonwhites and higher-income whites are an order of magnitude higher and in the opposite direction for urban residents as for rural residents (2.8 ppb versus −0.3 ppb; see Table 1). Across the 448 urban areas in the US, there is variation in injustice (difference range [ppb]: −1.1 to 6.0) and inequality (Atkinson Index range: 0.00008 to 0.04) for NO2 air pollution, consistent with a previous multi-city study [13]. In 426 of 448 urban areas (accounting for 99% of the total US urban population), NO2 concentrations are higher for the lower-income nonwhite group than for the higher-income white group, with injustice and inequality tending to be higher in large urban areas. Supporting Information (File S2) provides environmental injustice and inequality rankings by urban area, county, and state.

A contribution of this work is that it covers the entire contiguous US population, including both urban and rural populations, with higher spatial precision in urban areas (urban BG-scale: ~1-km; LUR scale: ~0.1-km) relative to previous regional or multi-city air quality environmental equality and/or justice studies (typical air quality model-scale: ~12-km grid or coarser). Although the spatial resolution is higher than in previous work, resolution is still a limitation: because we are using Census demographic data, we are unable to study within-BG variations. As a second limitation, we measure inequality for one pollutant (NO2); inequality may differ for other pollutants (e.g., ozone [44]) or for multi-pollutant cumulative exposure [32]. As a third limitation, we study only ambient pollution; disparities may also exist for indoor NO2 emissions (e.g., owing to indoor sources such as natural gas combustion), for indoor-outdoor pollution relationships (e.g., because low-income households may live in comparatively older, leakier buildings), and for occupational and commute exposures. As a fourth limitation, there is a temporal mismatch between the year-2000 Census data and year-2006 air pollution data. We expect demographic changes during that time to be small compared to the cross-sectional differences explored here.

We investigated environmental injustice and inequality in residential outdoor NO2 air pollution for the contiguous US population. Nationally, inequality in average NO2 concentration is greater than inequality in average income. Nonwhites experience 4.6 ppb (38%) higher residential outdoor NO2 concentrations than whites – an exposure gap that has potentially large impacts to public health. Within individual urban areas, after controlling for income, nonwhites are on average exposed to higher outdoor residential NO2 concentrations than whites; and, after controlling for race, lower-income populations are exposed to higher outdoor residential average NO2 concentrations than higher-income populations. The spatial patterns observed for inequality and injustice nationally (Figure 2) are not predicted by region, race, or income. Our results highlight a need for future work exploring the reasons behind these spatial distributions of environmental injustice and inequality. Results given here provide strong US-wide evidence of ambient NO2 air pollution injustice and inequality, establish a national context for studies of individual metropolitan areas and regions, and enable comprehensive tracking over time. Hopefully results given here will usefully allow policy-makers to identify counties and urban areas with highest priority NO2 air pollution environmental justice and equality concerns.

Supporting Information

File S1.

doi:10.1371/journal.pone.0094431.s002

(PDF)

File S2.

doi:10.1371/journal.pone.0094431.s001

(XLSX)

Acknowledgments

Matthew Bechle calculated Block Group mean NO2 concentrations. The Minnesota Supercomputing Institute provided computational resources.

Author Contributions

Conceived and designed the experiments: LPC DBM JDM. Analyzed the data: LPC DBM JDM. Wrote the paper: LPC DBM JDM.

References

  1. 1. Anderson SJ, Gardner BW, Moll BJ, Tribble GL, Webster TF, et al. (1978) Correlation between air pollution and socio-economic factors in Los Angeles County. Atmos Environ 12: 1531–1535. doi: 10.1016/0004-6981(78)90097-5
  2. 2. Council on Environmental Quality (1971) Environmental quality: the second annual report of the council on environmental quality (US Government Printing Office, Washington, DC).
  3. 3. General Accounting Office (1983) Siting of hazardous waste landfills and their correlation with racial and economic status of surrounding communities (Rep. GAO/RCED-83-168, General Accounting Office, Washington, DC).
  4. 4. United Church of Christ Commission for Racial Justice (1987) Toxic wastes and race in the United States: A national report on the racial and socio-economic characteristics of communities surrounding hazardous waste sites (UCCRJ: New York, NY, USA).
  5. 5. Van Arsdol MD (1966) Metropolitan growth and environmental hazards: an illustrative case. Ekistics 21: 48–50.
  6. 6. Van Arsdol MD, Sabagh G, Alexander F (1964) Reality and the perception of environmental hazards. J Health Hum Behav 5: 144–153.
  7. 7. Brown P (1995) Race, class, and environmental health: a review and systematization of the literature. Environ Res 69: 15–30. doi: 10.1006/enrs.1995.1021
  8. 8. Chakraborty J, Maantay JA, Brender JD (2011) Disproportionate proximity to environmental health hazards: methods, models, and measurement. Amer J Pub Health 101: S27–S36. doi: 10.2105/ajph.2010.300109
  9. 9. Mohai PM, Pellow D, Roberts TJ (2009) Environmental justice. Annu Rev Env Resour 34: 405–430. doi: 10.1146/annurev-environ-082508-094348
  10. 10. World Health Organization. Global health risks: mortality and burden of disease attributable to selected major risks. Available: www.who.int/healthinfo/global_burden_dis​ease/GlobalHealthRisks_report_full.pdf. Accessed 2013 April 1.
  11. 11. Schweitzer L, Valenzuela A (2004) Environmental injustice and transportation: the claims and the evidence. J Plan Lit 18: 383–398. doi: 10.1177/0885412204262958
  12. 12. O’Neill MS, Jerrett M, Kawachi I, Levy JI, Cohen AJ, et al. (2003) Health, wealth, and air pollution: advancing theory and methods. Environ Health Persp 111: 1861–1870. doi: 10.1289/ehp.6334
  13. 13. Downey L, Dubois S, Hawkins B, Walker M (2008) Environmental inequality in metropolitan America. Organ Environ 21: 270–294. doi: 10.1177/1086026608321327
  14. 14. Lopez R (2002) Segregation and black/white differences in exposure to air toxics in 1990. Environ Health Persp 110(S2): 289–295. doi: 10.1289/ehp.02110s2289
  15. 15. Morello-Frosch R, Jesdale BM (2006) Separate and unequal: residential segregation and estimated cancer risks associated with ambient air toxics in US metropolitan areas. Environ Health Persp 114: 386–393. doi: 10.1289/ehp.8500
  16. 16. Brooks N, Sethi R (1997) The distribution of pollution: community characteristics and exposure to air toxics. J Environ Econ Manag 32: 233–250. doi: 10.1006/jeem.1996.0967
  17. 17. Schweitzer L, Zhou J (2010) Neighborhood air quality, respiratory health, and vulnerable populations in compact and sprawled regions. J Am Plann Assoc 76: 363–371. doi: 10.1080/01944363.2010.486623
  18. 18. Miranda ML, Edwards SE, Keating MH, Paul CJ (2011) Making the environmental justice grade: the relative burden of air pollution exposure in the United States. Int J Environ Res Public Health 8: 1755–1771. doi: 10.3390/ijerph8061755
  19. 19. Bell ML, Ebisu K (2012) Environmental inequality in exposures to airbourne particulate matter components in the United States. Environ Health Persp 120: 1699–1704. doi: 10.1289/ehp.1205201
  20. 20. Novotny EV, Bechle MJ, Millet DB, Marshall JD (2011) National satellite-based land use regression: NO2 in the United States. Environ Sci Technol 45: 4407–4414. doi: 10.1021/es103578x
  21. 21. U. S. Environmental Protection Agency. Our nation’s air: status and trends through 2010. Available: www.epa.gov/airtrends/2011/report/fullre​port.pdf. Accessed 2013 April 1.
  22. 22. Beckerman B, Jerrett M, Brook JR, Verma DK, Arain MA, et al. (2008) Correlation of nitrogen dioxide with other traffic pollutants near a major expressway. Atmos Environ 42: 275–290. doi: 10.1016/j.atmosenv.2007.09.042
  23. 23. Hewitt CN (1991) Spatial variations in nitrogen dioxide concentrations in an urban area. Atmos Environ B 25: 429–434. doi: 10.1016/0957-1272(91)90014-6
  24. 24. Jerrett M, Arain MA, Kanaroglou P, Beckerman B, Crouse D, et al. (2007) Modeling the intraurban variability of ambient traffic pollution in Toronto, Canada. J Toxicol Env Heal A 70: 200–212. doi: 10.1080/15287390600883018
  25. 25. Brauer M, Hoek G, Van Vliet P, Meliefste K, Fischer PH, et al. (2002) Air pollution from traffic and the development of respiratory infections and asthmatic and allergic symptoms in children. Am J Respir Crit Care Med 166: 1092–1098. doi: 10.1164/rccm.200108-007oc
  26. 26. Gauderman WJ, Avol E, Gilliland F, Vora H, Thomas D, et al. (2004) The effect of air pollution on lung development from 10 to 18 years of age. N Engl J Med 351: 1057–1067. doi: 10.1056/nejmoa040610
  27. 27. Brauer M, Lencar C, Tamburic L, Koehoorn M, Demers P, et al. (2008) A cohort study of traffic-related air pollution impacts on birth outcomes. Environ Health Persp 116: 680–686. doi: 10.1289/ehp.10952
  28. 28. Chiusolo M, Cadum E, Stafoggia M, Galassi C, Berti G, et al. (2011) Short-term effects of nitrogen dioxide on mortality and susceptibility factors in ten Italian cities: the EpiAir Study. Environ Health Persp 119: 1233–1238. doi: 10.1289/ehp.1002904
  29. 29. Filluel L, Rondeau V, Vandentorren S, Le Moual N, Cantagrel A, et al. (2005) Twenty five year mortality and air pollution: results from the French PAARC survey. Occup Environ Med 62: 453–460. doi: 10.1097/00001648-200611001-00156
  30. 30. Grineski S, Bolin B, Boone C (2007) Criteria air pollution and marginalized populations: environmental inequity in metropolitan Phoenix, Arizona. Soc Sci Quart 88: 535–554. doi: 10.1111/j.1540-6237.2007.00470.x
  31. 31. Stuart AL, Zeager M (2011) An inequality study of ambient nitrogen dioxide and traffic levels near elementary schools in the Tampa area. J Environ Manag 92: 1923–1930. doi: 10.1016/j.jenvman.2011.03.003
  32. 32. Su JG, Jerrett M, Morello-Frosch R, Jesdale BM, Kyle AD (2012) Inequalities in cumulative environmental burdens among three urbanized counties in California. Environ Int 40: 79–87. doi: 10.1016/j.envint.2011.11.003
  33. 33. Yanosky JD, Schwartz J, Suh HH (2008) Associations between measures of socioeconomic position and chronic nitrogen dioxide exposure in Worcester, Massachusetts. J Toxicol Env Heal A 71: 1593–1602. doi: 10.1080/15287390802414307
  34. 34. Levy JI, Chemerynski SM, Tuchman JL (2006) Incorporating concepts of inequality and inequity into health benefits analysis. Int J Equity Health 5: 10.1186/1475–9276-5-2.
  35. 35. Levy JI, Wilson AM, Zwack LM (2007) Quantifying the efficiency and equity implications of power plant and air pollution control strategies in the United States. Environ Health Persp 115: 743–750. doi: 10.1289/ehp.9712
  36. 36. Levy JI, Greco SL, Melly SJ, Mukhi N (2009) Evaluating efficiency-equality tradeoffs for mobile source control strategies in an urban area. Risk Anal 29: 34–47. doi: 10.1111/j.1539-6924.2008.01119.x
  37. 37. Fann N, Roman HA, Fulcher CM, Gentile MA, Hubbell BJ, et al. (2011) Maximizing health benefits and minimizing inequality: incorporating local-scale data in the design and evaluation of air quality policies. Risk Anal 36: 1–15. doi: 10.1111/j.1539-6924.2011.01629.x
  38. 38. Post ES, Belova A, Huang J (2011) Distributional benefit of a national air quality rule. Int J Environ Res Public Health 8: 1872–1892. doi: 10.3390/ijerph8061872
  39. 39. Jerrett M, Burnett RT, Beckerman BS, Turner MC, Krewski D, et al. (2013) Spatial analysis of air pollution and mortality in California. Am J Resp Crit Care 188: 593–599. doi: 10.1164/rccm.201303-0609oc
  40. 40. U. S. Centers for Disease Control. National Vital Statistics Reports: Deaths, Preliminary Data for 2011. Available: http://www.cdc.gov/nchs/data/nvsr/nvsr61​/nvsr61_06.pdf. Accessed 2013 October 1.
  41. 41. World Health Organization. Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attribution to Selected Major Risk Factors. Available: http://www.who.int/publications/cra/chap​ters/volume1/0729-0882.pdf. Accessed 2013 October 1.
  42. 42. Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, et al. (2009) The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLOS Med 6: 10.1371/journal.pmed.1000058. doi: 10.1371/journal.pmed.1000058
  43. 43. U. S. Central Intelligence Agency. World Fact Book 2013–2014: Distribution of Family Income: Gini Index. Available: https://www.cia.gov/library/publications​/the-world-factbook/index.html. Accessed 2014 March 10.
  44. 44. Marshall JD (2008) Environmental inequality: air pollution exposures in California’s south coast air basin. Atmos Environ 42: 5499–5503. doi: 10.1016/j.atmosenv.2008.02.005