Inequalities in cumulative environmental burdens among three urbanized counties in California

Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment

Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.

Environmental Exposure Disparities and Neurodevelopmental Risk: a Review

PURPOSE OF REVIEW

Neurotoxicant exposures are of particular concern in historically marginalized communities. Often a consequence of structural racism, low-income minoritized populations experience a disproportionate burden of hazardous exposures through proximity to industrial facilities, high traffic roads, and suboptimal housing. Here, we summarize reports on exposures and neurodevelopment focused on differences by education, income, race/ethnicity, or immigration status from 2015 to 2022, discuss the importance of such investigations in overburdened communities, and recommend areas for future research.

RECENT FINDINGS

We found 20 studies that investigated exposure disparities and neurodevelopment in children. Most were conducted in the USA, and many focused on air pollution, followed by metal exposures and water contamination. Although several studies showed differences in exposure-outcome associations by income and education, many examining differences by race/ethnicity did not report notable disparities between groups. However, measures of individual race and ethnicity are not reliable measures of discrimination experienced as a consequence of structural racism. Our review supports scientific evidence that the reduction of individual and widespread municipal exposures will improve child development and overall public health. Identified research gaps include the use of better indicators of economic status and structural racism, evaluations of effect modification and attributable fraction of outcomes by these factors, and considerations of multidimensional neighborhood factors that could be protective against environmental insults. Considering that vulnerable populations have disparities in access to and quality of care, greater burden of exposure, and fewer resources to incur associated expenses, such populations should be prioritized.

Health Impacts of Long-Term NO2 Exposure and Inequalities among the Chinese Population from 2013 to 2020

Nitrogen dioxide (NO₂) is associated with mortality and many other adverse health outcomes. In 2021, the World Health Organization established a new NO₂ air quality guideline (AQG) (annual average <10 μg/m³). However, the burden of diseases attributable to long-term NO₂ exposure above the AQG is unknown in China. Nitrogen oxide is a major air pollutant in populous cities, which are disproportionately impacted by NO₂; this represents a form of environmental inequality. We conducted a nationwide risk assessment of premature deaths attributable to long-term NO₂ exposure from 2013 to 2020 based on the exposure-response relationship, high-resolution annual NO₂ concentrations, and gridded population data (considering sex, age, and residence [urban vs rural]). We calculated health metrics including attributable deaths, years of life lost (YLL), and loss of life expectancy (LLE). Inequality in the distribution of attributable deaths and YLLs was evaluated by the Lorenz curve and Gini index. According to the health impact assessments, in 2013, long-term NO₂ exposure contributed to 315,847 (95% confidence interval [CI]: 306,709-319,269) premature deaths, 7.90 (7.68-7.99) million YLLs, and an LLE of 0.51 (0.50-0.52) years. The high-risk subgroup (top 20%) accounted for 85.7% of all NO₂-related deaths and 85.2% of YLLs, resulting in Gini index values of 0.81 and 0.67, respectively. From 2013 to 2020, the estimated health impact from NO₂ exposure was significantly reduced, but inequality displayed a slightly increasing trend. Our study revealed a considerable burden of NO₂-related deaths in China, which were disproportionally frequent in a small high-risk subgroup. Future clean air initiatives should focus not only on reducing the average level of NO₂ exposure but also minimizing inequality.

Reductions in NO2 and emergency room visits associated with California's goods movement policies: A quasi-experimental study

INTRODUCTION

This study examines whether the "Emission Reduction Plan for Ports and Goods Movement" in California reduced air pollution exposures and emergency room visits among California Medicaid enrollees with asthma and/or chronic obstructive pulmonary disease.

METHOD

We created a retrospective cohort of 5608 Medicaid enrollees from ten counties in California with data from 2004 to 2010. We grouped the patients into two groups: those living within 500 m of goods movement corridors (ports and truck-permitted freeways), and control areas (away from the busy truck or car permitted highways). We created annual air pollution surfaces for nitrogen dioxide and assigned them to enrollees' home addresses. We used a quasi-experimental design with a difference-in-differences method to examine changes before and after the policy for cohort beneficiaries in the two groups.

RESULTS

The reductions in nitrogen dioxide exposures and emergency room visits were greater for enrollees in goods movement corridors than those in control areas in post-policy years. We found that the goods movement actions were associated with 14.8% (95% CI, -24.0% to -4.4%; P = 0.006) and 11.8% (95% CI, -21.2% to -1.2%; P = 0.030) greater reduction in emergency room visits for the beneficiaries with asthma and chronic obstructive pulmonary disease, respectively, in the third year after California's emission reduction plan.

CONCLUSION

These findings indicate remarkable health benefits via reduced emergency room visits from the significantly improved air quality due to public policy interventions for disadvantaged and susceptible populations.

Optimized environmental justice calculations for air pollution disparities in Southern California

An Environmental Justice (EJ) analysis was carried out using full Chemical Transport Models (CTMs) over Los Angeles, California, to determine how the combination of domain size and spatial resolution affects predicted air pollution disparities in present day and future simulations when data support from measurements is not available. One set of simulations used the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF/Chem) with spatial resolution ranging from 250 m to 36 km, comparable to census tract sizes, over domains ranging in size from 320 km² to 10,000 km². A second set of simulations used the UCD/CIT CTM with spatial resolution ranging from 4 km to 24 km over domains ranging in size from 98,000 km² to 1,000,000 km². Overall WRF/Chem model accuracy improved approximately 9% as spatial resolution increased from 4 km to 250 m in present-day simulations, with similar results expected for future simulations. Exposure disparity results are consistent with previous findings: the average Non-Hispanic White person in the study domain experiences PM_2.5 mass concentrations 6-14% lower than the average resident, while the average Black and African American person experiences PM_2.5 mass concentrations that are 3-22% higher than the average resident. Predicted exposure disparities were a function of the model configuration. Increasing the spatial resolution finer than approximately 1 km produced diminishing returns because the increased spatial resolution came at the expense of reduced domain size in order to maintain reasonable computational burden. Increasing domain size to capture regional trends, such as wealthier populations living in coastal areas, identified larger exposure disparities but the benefits were limited. CTM configurations that use spatial resolution/domain size of 1 km/10³ km² and 4 km/10⁴ km² over Los Angeles can detect a 0.5 μg m^-3 exposure difference with statistical power greater than 90%. These configurations represent a balanced approach between statistical power, sensitivity across socio-economic groups, and computational burden when predicting current and future air pollution exposure disparities in Los Angeles.

Socioeconomic and Demographic Associations with Wintertime Air Pollution Exposures at Household, Community, and District Scales in Rural Beijing, China

The Chinese government implemented a national household energy transition program that replaced residential coal heating stoves with electricity-powered heat pumps for space heating in northern China. As part of a baseline assessment of the program, this study investigated variability in personal air pollution exposures within villages and between villages and evaluated exposure patterns by sociodemographic factors. We randomly recruited 446 participants in 50 villages in four districts in rural Beijing and measured 24 h personal exposures to fine particulate matter (PM_2.5) and black carbon (BC). The geometric mean personal exposure to PM_2.5 and BC was 72 and 2.5 μg/m³, respectively. The variability in PM_2.5 and BC exposures was greater within villages than between villages. Study participants who used traditional stoves as their dominant source of space heating were exposed to the highest levels of PM_2.5 and BC. Wealthier households tended to burn more coal for space heating, whereas less wealthy households used more biomass. PM_2.5 and BC exposures were almost uniformly distributed by socioeconomic status. Future work that combines these results with PM_2.5 chemical composition analysis will shed light on whether air pollution source contributors (e.g., industrial, traffic, and household solid fuel burning) follow similar distributions.

Mitigating China's Ozone Pollution with More Balanced Health Benefits

China is confronting the challenge of opposite health benefits (OHBs) during ambient ozone (O₃) mitigation because the same reduction scheme might yield opposite impacts on O₃ levels and associated public health across different regions. Here, we used a combination of chemical transport modeling, health benefit assessments, and machine learning to capture such OHBs and optimize O₃ mitigation pathways based on 121 control scenarios. We revealed that, for the China mainland, Beijing-Tianjin-Hebei and its surroundings ("2 + 26" cities), Yangtze River Delta, and Pearl River Delta, there could be at most 2897, 920, 1247, and 896 additional O₃-related deaths in urban areas, respectively, accompanying 21,512, 3442, 5614, and 642 avoided O₃-related deaths in rural areas, respectively, at the same control stage. Additionally, potential disbenefits during O₃ mitigation were "pro-wealthy", that is, residents in developed regions are more likely to afford additional health risks. In order to avoid OHBs during O₃ abatement, we proposed a two-phase control strategy, whereby the reduction ratio of NO_X (nitrogen oxide) to VOCs (volatile organic compounds) was adjusted according to health benefit distribution patterns. Our study provided novel insights into China's O₃ attainment and references for other countries facing the dual challenges of environmental pollution and associated inequality issues.

Both parents matter: a national-scale analysis of parental race/ethnicity, disparities in prenatal PM2.5 exposures and related impacts on birth outcomes

BACKGROUND

Most U.S. studies that report racial/ethnic disparities in increased risk of low birth weight associated with air pollution exposures have been conducted in California or northeastern states and/or urban areas, limiting generalizability of study results. Few of these studies have examined maternal racial/ethnic groups other than Non-Hispanic Black, non-Hispanic White and Hispanic, nor have they included paternal race. We aimed to examine the independent effects of PM_2.5 on birth weight among a nationally representative sample of U.S. singleton infants and how both maternal and paternal race/ethnicity modify relationships between prenatal PM_2.5 exposures and birth outcomes.

METHODS

We used data from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B), a longitudinal nationally representative cohort of 10,700 U.S. children born in 2001, which we linked to U.S.EPA's Community Multi-scale Air Quality (CMAQ)-derived predicted daily PM2.5 concentrations at the centroid of each Census Bureau Zip Code Tabulation Area (ZCTA) for maternal residences. We examined relationships between term birthweight (TBW)_, term low birthweight rate (TLBW) and gestational PM_2.5 pollutant using multivariate regression models. Effect modification of air pollution exposures on birth outcomes by maternal and paternal race was evaluated using stratified models. All analyses were conducted with sample weights to provide national-scale estimates.

RESULTS

The majority of mothers were White (61%). Fourteen percent of mothers identified as Black, 21% as Hispanic, 3% Asian American and Pacific Islander (AAPI) and 1% American Indian and Alaskan Native (AIAN). Fathers were also racially/ethnically diverse with 55% identified as White Non-Hispanic, 10% as Black Non-Hispanic, 19% as Hispanic, 3% as AAPI and 1% as AIAN. Results from the chi-square and ANOVA tests of significance for racial/ethnic differences indicate disparities in prenatal exposures and birth outcomes by both maternal and paternal race/ethnicity. Prenatal PM_2.5 was associated with reduced birthweights during second and third trimester and over the entire gestational period in adjusted regression models, although results did not reach statistical significance. In models stratified by maternal race and paternal race, one unit increase in PM_2.5 was statistically significantly associated with lower birthweights among AAPI mothers, -5.6 g (95% CI:-10.3, -1.0 g) and AAPI fathers, -7.6 g (95% CI: -13.1, -2.1 g) during 3^rd trimester and among births where father's race was not reported, -14.2 g (95% CI: -24.0, -4.4 g).

CONCLUSIONS

These data suggest that paternal characteristics should be used, in addition to maternal characteristics, to describe the risks of adverse birth outcomes. Additionally, our study suggests that serious consideration should be given to investigating environmental and social mechanisms, such as air pollution exposures, as potential contributors to disparities in birth outcomes among AAPI populations.

Exploring the distributional environmental justice implications of an air quality monitoring network in Los Angeles County

Non-governmental air quality monitoring networks include low-cost, networked air pollution sensors hosted at homes and schools that display real-time pollutant concentration estimates on publicly accessible websites. Such networks can empower people to take health-protective actions, but their unplanned organization may produce an uneven spatial distribution of sensors. Barriers to acquiring sensors may disenfranchise particular social groups. To test this directly, we quantitatively examine if there are social inequalities in the distribution of sensors in a non-governmental air quality monitoring network (PurpleAir) in Los Angeles County, California. We paired sociodemographic data from the American Community Survey and estimates of PM_2.5 concentrations from the USEPA's Downscaler model at the census tract level (n = 2203) with a sensors per capita (SPC) variable, which is based on population proximity to PurpleAir sensors (n = 696) in Los Angeles County. Findings from multivariable generalized estimating equations (GEEs) controlling for clustering by housing age and value reveal patterns of environmental injustice in the distribution of PurpleAir sensors across Los Angeles County census tracts. Tracts with higher percentages of Hispanic/Latino/a and Black residents and lower median household income had decreased SPC. There was a curvilinear (concave) relationship between the percentage of renter-occupants and SPC. Sensors were concentrated in tracts with greater percentages of adults and seniors (vs. children), higher occupied housing density, and higher PM_2.5 pollution. Results reveal social inequalities in the self-organizing PurpleAir network, suggesting another layer of environmental injustice such that residents of low-income and minority neighborhoods have reduced access to information about local air pollution.

Rethinking of Environmental Health Risks: A Systematic Approach of Physical-Social Health Vulnerability Assessment on Heavy-Metal Exposure through Soil and Vegetables

In the field of environmental health risk assessment and management research, heavy metals in soil are a constant focus, largely because of mining and metallurgical activities, and other manufacturing or producing. However, systematic vulnerability, and combined research of social and physical vulnerability of the crowd, have received less attention in the research literature of environmental health risk assessment. For this reason, tentative design modelling for comprehensive environmental health vulnerability, which includes the index of physical and social vulnerability, was conducted here. On the basis of experimental data of heavy-metal pollution in soil and vegetables, and population and societal survey data in Daye, China, the physical, social, and comprehensive environmental health vulnerabilities of the area were analyzed, with each village as an evaluation unit. First, the polluted and reference areas were selected. Random sampling sites were distributed in the farmland of the villages in these two areas, with two sampling sites per village. Then, 204 vegetable samples were directly collected from the farmland from which the soil samples had been collected, composed of seven kinds of vegetables: cowpea, water spinach, amaranth, sweet potato leaves, tomato, eggplant, and pepper. Moreover, 400 questionnaires were given to the local residents in these corresponding villages, and 389 valid responses were obtained. The results indicated that (1) the average physical vulnerability values of the population in the polluted and reference areas were 3.99 and 1.00, respectively; (2) the village of Weiwang (WW) had the highest physical vulnerability of 8.55; (3) vegetable intake is exposure that should be paid more attention, as it contributes more than 90% to physical vulnerability among the exposure pathways; (4) arsenic and cadmium should be the priority pollutants, with average physical vulnerability value contributions of 63.9% and 17.0%, respectively; (5) according to the social vulnerability assessment, the village of Luoqiao (LQ) had the highest social vulnerability (0.77); (6) for comprehensive environmental health vulnerability, five villages near mining activities and two villages far from mine-affected area had high physical and social vulnerability, and are the urgent areas for environmental risk management. In order to promote environmental risk management, it is necessary to prioritize identifying vulnerable populations in the village-scale dimension as an innovative discovery.

Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010

BACKGROUND

Few studies have investigated air pollution exposure disparities by race/ethnicity and income across criteria air pollutants, locations, or time.

OBJECTIVE

The objective of this study was to quantify exposure disparities by race/ethnicity and income throughout the contiguous United States for six criteria air pollutants, during the period 1990 to 2010.

METHODS

We quantified exposure disparities among racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic (any race), non-Hispanic Asian) and by income for multiple spatial units (contiguous United States, states, urban vs. rural areas) and years (1990, 2000, 2010) for carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ; excluding year-1990), particulate matter with aerodynamic diameter ≤ 10 μ m (PM 10 ), and sulfur dioxide (SO 2 ). We used census data for demographic information and a national empirical model for ambient air pollution levels.

RESULTS

For all years and pollutants, the racial/ethnic group with the highest national average exposure was a racial/ethnic minority group. In 2010, the disparity between the racial/ethnic group with the highest vs. lowest national-average exposure was largest for NO 2 [54% (4.6  ppb )], smallest for O 3 [3.6% (1.6  ppb )], and intermediate for the remaining pollutants (13%-19%). The disparities varied by U.S. state; for example, for PM 2.5 in 2010, exposures were at least 5% higher than average in 63% of states for non-Hispanic Black populations; in 33% and 26% of states for Hispanic and for non-Hispanic Asian populations, respectively; and in no states for non-Hispanic White populations. Absolute exposure disparities were larger among racial/ethnic groups than among income categories (range among pollutants: between 1.1 and 21 times larger). Over the period studied, national absolute racial/ethnic exposure disparities declined by between 35% (0.6 6 μ g / m 3 ; PM 2.5 ) and 88% (0.35  ppm ; CO); relative disparities declined to between 0.99 × (PM 2.5 ; i.e., nearly zero change) and 0.71 × (CO; i.e., a ∼ 29 % reduction).

DISCUSSION

As air pollution concentrations declined during the period 1990 to 2010, absolute (and to a lesser extent, relative) racial/ethnic exposure disparities also declined. However, in 2010, racial/ethnic exposure disparities remained across income levels, in urban and rural areas, and in all states, for multiple pollutants. https://doi.org/10.1289/EHP8584.

Socioeconomic disparity is associated with faster retinal neurodegeneration in multiple sclerosis

Disease course in multiple sclerosis is notably heterogenous, and few prognostic indicators have been consistently associated with multiple sclerosis severity. In the general population, socioeconomic disparity is associated with multimorbidity and may contribute to worse disease outcomes in multiple sclerosis. Herein, we assessed whether indicators of socioeconomic status (SES) are associated with disease progression in people with multiple sclerosis using highly sensitive imaging tools like optical coherence tomography (OCT) and determined if differential multiple sclerosis management or comorbidity mediate any observed SES-associated effects. We included 789 participants with longitudinal OCT and low contrast letter acuity (LCLA; at 1.25% and 2.5%) in whom neighborhood- (derived via 9-digit postal codes) and participant-level SES indicators were available ≤10 years of MS symptom onset. Sensitivity analyses included participants with SES indicators available ≤3years of symptom onset (n = 552). Neighborhood-level indicators included state and national area deprivation indices (ADI), median household income, and the Agency for Healthcare Research and Quality (AHRQ) SES Index. Participant-level indicators included education level. Bi-annual OCT scans were segmented to quantify thickness of the composite macular ganglion cell+inner plexiform layer (GCIPL). We assessed the association between SES indicators and GCIPL atrophy or LCLA loss using mixed models adjusting for demographic (including race and ethnicity) and disease-related characteristics. We also assessed SES indicators in relation to multiple sclerosis therapy changes and comorbidity risk using survival analysis. More disadvantaged neighborhood-level and patient-level SES indicators were associated with faster retinal atrophy. Differences in rate of GCIPL atrophy for individuals in the top quartile (most disadvantaged) relative to the bottom quartile (least) for state ADI were -0.12 µm/year faster (95%CI: -0.19, -0.04; p = 0.003), for national ADI were -0.08 µm/year faster (95%CI: -0.15, -0.005; p = 0.02), for household income were -0.11 µm/year faster (95%CI: -0.19, -0.03; p = 0.008), for AHRQ SES Index were -0.12 µm/year faster (95% CI: -0.19, -0.04) and for education level were -0.17 µm/year faster (95%CI: -0.26, -0.08; p = 0.0002). Similar associations were observed for SES indicators and LCLA loss. Lower SES was associated with higher risk of incident comorbidity during follow-up. Low SES individuals had faster rates of therapy escalation, suggesting the association between SES and GCIPL atrophy may not be explained by differential contemporaneous multiple sclerosis therapy management. In conclusion, socioeconomic disparity is associated with faster retinal neurodegeneration in multiple sclerosis. As low SES was associated with a higher risk of incident comorbidities that may adversely affect multiple sclerosis outcomes, comorbidity prevention may mitigate some of the unfavorable SES-associated consequences.

Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.

Impact of COVID-19 lockdown on NO2 and PM2.5 exposure inequalities in London, UK

Amid the COVID-19 pandemic, a nationwide lockdown was imposed in the United Kingdom (UK) on March 23, 2020. These sudden control measures led to radical changes in human activities in the Greater London Area (GLA). During this lockdown, transportation use was significantly reduced and non-key workers were required to work from home. This study aims to understand how population exposure to PM2.5 and NO2 changed spatially and temporally across London, in different microenvironments, following the lockdown period relative to the previous three-year average in the same calendar period. Our research shows that population exposure to NO2 declined significantly (52.3% ± 6.1%), while population exposure to PM2.5 showed a smaller relative reduction (15.7% ± 4.1%). Changes in population activity had the strongest relative influence on exposure levels during morning rush hours, when prior to the lockdown a large percentage of people would normally commute or be at the workplace. In particular, a very high exposure decrease was observed for both pollutants (approximately 66% for NO2 and 19% for PM2.5) at 08:00am, consistent with the radical changes in population commuting. The infiltration of outdoor air pollution into housing modifies the degree of exposure change both temporally and spatially. Moreover, this study shows that the impacts on air pollution exposure vary across groups with different socioeconomic status (SES), with a disproportionate positive effect on the areas of the city home to more economically deprived communities.

Accounting for Health Risk Inequality in Regulatory Impact Analysis: Barriers and Opportunities

There has been increasing interest in accounting for inequality in health risks and benefits within regulatory impact analyses, both given more general interest in the distributions of benefits and growing concerns about inequity (defined as those inequalities deemed unjust or unfair) and environmental injustice (in this context, those health risk inequalities that are correlated with race/ethnicity and certain other sociodemographic factors). Although there has been growing literature on this topic, there has been limited progress in practice, and the lack of quantification limits consideration of inequality in the policy process. Controversy remains regarding the best approaches to formally incorporate inequality, when these approaches should be used, and even whether it makes sense to quantify inequality in this context. The objective of this article is to review the literature on approaches for incorporating estimates of, and concerns for, inequality into regulatory impact analyses, especially those where environmental justice considerations are relevant, and consider the interpretation of these approaches and the implications for decision making. Using the case example of the Transportation and Climate Initiative, a collaboration among Northeast and Mid-Atlantic states to reduce carbon emissions from the transportation sector, multiple strategies are described that could be used to shed light on health risk inequality and inequity, consider them in pending policy decisions, and evaluate their implications for the policy or instrument choice. Given appropriate contextualization and acknowledgment of the multidimensionality of equity, quantitative inequality indicators can provide meaningful insight about both inequality and inequity in health risks.

Inferential challenges when assessing racial/ethnic health disparities in environmental research

Numerous epidemiologic studies have documented environmental health disparities according to race/ethnicity (R/E) to inform targeted interventions aimed at reducing these disparities. Yet, the use of R/E under the potential outcomes framework implies numerous underlying assumptions for epidemiologic studies that are often not carefully considered in environmental health research. In this commentary, we describe the current state of thinking about the interpretation of R/E variables in etiologic studies. We then discuss how such variables are commonly used in environmental epidemiology. We observed three main uses for R/E: i) as a confounder, ii) as an effect measure modifier and iii) as the main exposure of interest either through descriptive analysis or under a causal framework. We identified some common methodological concerns in each case and provided some practical solutions. The use of R/E in observational studies requires particular cautions in terms of formal interpretation and this commentary aims at providing a practical resource for future studies assessing racial/ethnic health disparities in environmental research.

Airborne Particulates Affect Corneal Homeostasis and Immunity

Purpose

To determine the effects of airborne particulate matter (PM) <2.5 µm in vitro and on the normal and Pseudomonas aeruginosa (PA)-infected cornea.

Methods

An MTT viability assay tested the effects of PM_2.5 on mouse corneal epithelial cells (MCEC) and human corneal epithelial cells (HCET). MCEC were tested for reactive oxygen species using a 2′,7′-dichlorodihydrofluorescein assay; RT-PCR determined mRNA levels of inflammatory and oxidative stress markers in MCEC (HMGB1, toll-like receptor 2, IL-1β, CXCL2, GPX1, GPX2, GR1, superoxide dismutase 2, and heme oxygenase 1) and HCET (high mobility group box 1, CXCL2, and IL-1β). C57BL/6 mice also were infected and after 6 hours, the PM_2.5 was topically applied. Disease was graded by clinical score and evaluated by histology, plate count, myeloperoxidase assay, RT-PCR, ELISA, and Western blot.

Results

After PM_2.5 (25–200 µg/mL), 80% to 90% of MCEC and HCET were viable and PM exposure increased reactive oxygen species in MCEC and mRNA expression levels for inflammatory and oxidative stress markers in mouse and human cells. In vivo, the cornea of PA+PM_2.5 exposed mice exhibited earlier perforation over PA alone (confirmed histologically). In cornea, plate counts were increased after PA+PM_2.5, whereas myeloperoxidase activity was significantly increased after PA+PM_2.5 over other groups. The mRNA levels for several proinflammatory and oxidative stress markers were increased in the cornea in the PA+PM_2.5 over other groups; protein levels were elevated for high mobility group box 1, but not toll-like receptor 4 or glutathione reductase 1. Uninfected corneas treated with PM_2.5 did not differ from normal.

Conclusions

PM_2.5 triggers reactive oxygen species, upregulates mRNA levels of oxidative stress, inflammatory markers, and high mobility group box 1 protein, contributing to perforation in PA-infected corneas.

National NO2 exposure models for measuring its impact on vulnerable people in the US metropolitan areas

Epidemiological research requires accurate prediction of the concentrations of air pollutants. In this study, satellite-based estimates (OMI NO₂), distance-weighted models (DWMs), and universal kriging (UK) are applied to land use regression (LUR) to predict annually and monthly averaged NO₂ concentrations in the continental United States. In addition, to assess environmental risk, the relationship between NO₂ concentrations and people potentially exposed to NO₂ within urban areas is explored in 377 metropolitan statistical areas (MSAs). The results of this study show that the application of a combination of OMI NO₂, UK, and DWMs to LUR yielded the highest cross-validated (CV) R² values and the lowest root mean square error of prediction (RMSEP): 82.9% and 0.392 on a square root scale of ppb in the annual model and 70.4-83.5% and 0.408-0.518 on square root scale of ppb in the monthly models, respectively. Moreover, the model presented a spatially unbiased distribution of CV error terms. Models based on LUR provided more accurate NO₂ predictions with lower RMSEP in urban areas than in rural areas. In addition, this study finds that the people living in the urban areas of MSAs, with larger populations and a higher percentage of children under 18 years of age, are likely to be exposed to higher NO₂ concentrations. By contrast, people living in the urban areas of MSAs with a higher percentage of the elderly over 65 years of age are likely to be exposed to lower NO₂ concentrations.

Combined environmental and social exposures during pregnancy and associations with neonatal size and body composition: the Healthy Start study

BACKGROUND

Prenatal environmental and social exposures have been associated with decreased birth weight. However, the effects of combined exposures in these domains are not fully understood. Here we assessed multi-domain exposures for participants in the Healthy Start study (Denver, CO) and tested associations with neonatal size and body composition.

METHODS

In separate linear regression models, we tested associations between neonatal outcomes and three indices for exposures. Two indices were developed to describe exposures to environmental hazards (ENV) and social determinants of health (SOC). A third index combined exposures in both domains (CE = ENV/10 × SOC/10). Index scores were assigned to mothers based on address at enrollment. Birth weight and length were measured at delivery, and weight-for-length z-scores were calculated using a reference distribution. Percent fat mass was obtained by air displacement plethysmography.

RESULTS

Complete data were available for 897 (64%) participants. Median (range) ENV, SOC, and CE values were 31.9 (7.1-63.2), 36.0 (2.8-75.0), and 10.9 (0.4-45.7), respectively. After adjusting for potential confounders, 10-point increases in SOC and CE were associated with 27.7 g (95%CI: 12.4 - 42.9 g) and 56.3 g (19.4 - 93.2 g) decreases in birth weight, respectively. SOC and CE were also associated with decreases in % fat mass.

CONCLUSIONS

Combined exposures during pregnancy were associated with lower birth weight and % fat mass. Evidence of a potential synergistic effect between ENV and SOC suggests a need to more fully consider neighborhood exposures when assessing neonatal outcomes.

Air Pollution and Cardiovascular Disease: A Focus on Vulnerable Populations Worldwide

Purpose of review

Certain subgroups defined by sociodemographics (race/ethnicity, age, sex and socioeconomic status [SES]), geographic location (rural vs. urban), comorbid conditions and country economic conditions (developed vs. developing) may disproportionately suffer the adverse cardiovascular effects of exposure to ambient air pollution. Yet, previous reviews have had a broad focus on the general population without consideration of these potentially vulnerable populations.

Recent findings

Over the past decade, a wealth of epidemiologic studies have linked air pollutants including particulate matter, oxides of nitrogen, and carbon monoxide to cardiovascular disease (CVD) risk factors, subclinical CVD, clinical cardiovascular outcomes and cardiovascular mortality in certain susceptible populations. Highest risk for poor CVD outcomes from air pollution exist in racial/ethnic minorities, especially in blacks compared to whites in the U.S, those at low SES, elderly populations, women, those with certain comorbid conditions and developing countries compared to developed countries. However, findings are less consistent for urban compared to rural populations.

Summary

Vulnerable subgroups including racial/ethnic minorities, women, the elderly, smokers, diabetics and those with prior heart disease had higher risk for adverse cardiovascular outcomes from exposure to air pollution. There is limited data from developing countries where concentrations of air pollutants are more extreme and cardiovascular event rates are higher than that of developed countries. Further epidemiologic studies are needed to understand and address the marked disparities in CVD risk conferred by air pollution globally, particularly among these vulnerable subgroups.

Spatial Modeling to Identify Sociodemographic Predictors of Hydraulic Fracturing Wastewater Injection Wells in Ohio Census Block Groups

BACKGROUND

Hydraulically fractured wells produce 2-14 million liters of wastewater, which may contain toxic and radioactive compounds. The wastewater is predominantly disposed of using Class II injection wells.

OBJECTIVE

Our objective was to evaluate the relationship between sociodemographic characteristics and injection well locations in Ohio.

METHODS

Using state and federal data sources, we classified Ohio census block groups by presence of injection wells, number of hydraulically fractured wells, sociodemographic factors (median household income, % white, population density, % ≥high school education, median age, voter turnout), and geographic information (land area, water area, situated over shale). We modeled the odds of having at least one injection well within a block group with respect to all covariates using three multivariable models incorporating different spatial components to account for similarities in neighboring block groups.

RESULTS

In bivariate analyses, block groups with injection wells (n=156) compared with those without (n=9,049) had lower population density (71 vs. 2,210 people/mi2 or 27 vs. 854 people/km2), larger median area (43.5 vs. 1.35 km2), higher median age (42.8 vs. 40.2 y), and higher % white (98.1% vs. 92.1%). After adjustment using a spatial logistic regression model, the odds of a block group containing an injection well were 16% lower per $10,000 increase in median income [odds ratio(OR)=0.837; 95% credible interval (CI): 0.719, 0.961] and 97% lower per 1,000 people/mi2 (or per 386 people/km2) increase (OR=0.030; 95% CI=0.008, 0.072). Block groups on shale and those containing fewer hydraulically fractured wells were more likely to include an injection well. Percentage white, median age, % ≥high school education, and % voter turnout were not significant predictors of injection well presence.

CONCLUSION

In Ohio, injection wells were inversely associated with block groups' median incomes after adjusting for other sociodemographic and geographic variables. Research is needed to determine whether residents in census blocks with injection wells face increased risk of chemical exposures or adverse health outcomes. https://doi.org/10.1289/EHP2663.

Air pollutant strategies to reduce adverse health impacts and health inequalities: a quantitative assessment for Detroit, Michigan

The development of air quality management (AQM) strategies provides opportunities to improve public health and reduce health inequalities. This study evaluates health and inequality impacts of alternate SO2 control strategies in Detroit, MI, a designated non-attainment area. Control alternatives include uniform reductions across sources, ranking approaches based on total emissions and health impacts per ton of pollutant emitted, and optimizations that meet concentration and health goals. Using dispersion modeling and quantitative health impact assessment (HIA), these strategies are evaluated in terms of ambient concentrations, health impacts, and the inequality in health risks. The health burden attributable to SO2 emissions in Detroit falls primarily among children and includes 70 hospitalizations and 6,000 asthma-related respiratory symptom-days annually, equivalent to 7 disability-adjusted life years (DALYs). The health burden disproportionately falls on Hispanic/Latino residents, residents with less than a high school diploma, and foreign-born residents. Control strategies that target smaller facilities near exposed populations provide the greatest benefit in terms of the overall health burden reductions and the inequality of attributable health risk; conventional strategies that target the largest emission sources can increase inequality and provide only modest health benefits. The assessment is novel in using spatial analyses that account for urban scale gradients in exposure, demographics, vulnerability, and population health. We show that quantitative HIA methods can be used to develop AQM strategies that simultaneously meet environmental, public health, and environmental justice goals, advancing AQM beyond its current compliance-oriented focus.

Retooling CalEnviroScreen: Cumulative Pollution Burden and Race-Based Environmental Health Vulnerabilities in California

The California Community Environmental Health Screening Tool (CalEnviroScreen) advances research and policy pertaining to environmental health vulnerability. However, CalEnviroScreen departs from its historical foundations and comparable screening tools by no longer considering racial status as an indicator of environmental health vulnerability and predictor of cumulative pollution burden. This study used conceptual frameworks and analytical techniques from environmental health and inequality literature to address the limitations of CalEnviroScreen, especially its inattention to race-based environmental health vulnerabilities. It developed an adjusted measure of cumulative pollution burden from the CalEnviroScreen 2.0 data that facilitates multivariate analyses of the effect of neighborhood racial composition on cumulative pollution burden, net of other indicators of population vulnerability, traffic density, industrial zoning, and local and regional clustering of pollution burden. Principal component analyses produced three new measures of population vulnerability, including Latina/o cumulative disadvantage that represents the spatial concentration of Latinas/os, economic disadvantage, limited English-speaking ability, and health vulnerability. Spatial error regression analyses demonstrated that concentrations of Latinas/os, followed by Latina/o cumulative disadvantage, are the strongest demographic determinants of adjusted cumulative pollution burden. Findings have implications for research and policy pertaining to cumulative impacts and race-based environmental health vulnerabilities within and beyond California.

Retooling CalEnviroScreen: Cumulative Pollution Burden and Race-Based Environmental Health Vulnerabilities in California

The California Community Environmental Health Screening Tool (CalEnviroScreen) advances research and policy pertaining to environmental health vulnerability. However, CalEnviroScreen departs from its historical foundations and comparable screening tools by no longer considering racial status as an indicator of environmental health vulnerability and predictor of cumulative pollution burden. This study used conceptual frameworks and analytical techniques from environmental health and inequality literature to address the limitations of CalEnviroScreen, especially its inattention to race-based environmental health vulnerabilities. It developed an adjusted measure of cumulative pollution burden from the CalEnviroScreen 2.0 data that facilitates multivariate analyses of the effect of neighborhood racial composition on cumulative pollution burden, net of other indicators of population vulnerability, traffic density, industrial zoning, and local and regional clustering of pollution burden. Principal component analyses produced three new measures of population vulnerability, including Latina/o cumulative disadvantage that represents the spatial concentration of Latinas/os, economic disadvantage, limited English-speaking ability, and health vulnerability. Spatial error regression analyses demonstrated that concentrations of Latinas/os, followed by Latina/o cumulative disadvantage, are the strongest demographic determinants of adjusted cumulative pollution burden. Findings have implications for research and policy pertaining to cumulative impacts and race-based environmental health vulnerabilities within and beyond California.

Examining Joint Effects of Air Pollution Exposure and Social Determinants of Health in Defining "At-Risk" Populations Under the Clean Air Act: Susceptibility of Pregnant Women to Hypertensive Disorders of Pregnancy

Pregnant women are uniquely susceptible to adverse effects of air pollution exposure due to vulnerabilities and health consequences during pregnancy (e.g., hypertensive disorders of pregnancy [HDP]) compared to the general population. Because the Clean Air Act (CAA) creates a duty to protect at-risk groups, the regulatory assessment of at-risk populations has both policy and scientific foundations. Previously, pregnant women have not been specially protected in establishing the margin of safety for the ozone and particulate matter (PM) standards. Due to physiological changes, pregnant women can be at greater risk of adverse effects of air pollution and should be considered an at-risk population. Women with preexisting conditions, women experiencing poverty, and groups that suffer systematic discrimination may be particularly susceptible to cardiac effects of air pollutants during pregnancy. We rigorously reviewed 11 studies of over 1.3 million pregnant women in the United States to characterize the relationship between ozone or PM exposure and HDP. Findings were generally mixed, with a few studies reporting a joint association between ozone or PM and social determinants or pre-existing chronic health conditions related to HDP. Adequate evidence associates exposure to PM with an adverse effect of HDP among pregnant women not evident among non-gravid populations.

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

Identification of Effects of Regulatory Actions on Air Quality in Goods Movement Corridors in California

Few studies have assessed the impact of regulatory actions on air quality improvement through a comprehensive monitoring effort. In this study, we designed saturation sampling of nitrogen oxides (NOX) for the counties of Los Angeles and Alameda (San Francisco Bay) before (2003-2007) and after (2008-2013) implementation of goods movement actions in California. We further separated the research regions into three location categories, including goods movement corridors (GMCs), nongoods movement corridors (NGMCs), and control areas (CTRLs). Linear mixed models were developed to identify whether reductions in NOX were greater in GMCs than in other areas, after controlling for potential confounding, including weather conditions (e.g., wind speed and temperature) and season of sampling. We also considered factors that might confound the relationship, including traffic and cargo volumes that may have changed due to economic downturn impacts. Compared to the pre-policy period, we found reductions of average pollutant concentrations for nitrogen dioxide (NO2) and NOX in GMCs of 6.4 and 21.7 ppb. The reductions were smaller in NGMCs (5.9 and 16.3 ppb, respectively) and in CTRLs (4.6 and 12.1 ppb, respectively). After controlling for potential confounding from weather conditions, season of sampling, and the economic downturn in 2008, the linear mixed models demonstrated that reductions in NO2 and NOX were significantly greater in GMCs compared to reductions observed in CTRLs; there were no statistically significant differences between NGMCs and CTRLs. These results indicate that policies regulating goods movement are achieving the desired outcome of improving air quality for the state, particularly in goods movement corridors where most disadvantaged communities live.

Environmental Health Related Socio-Spatial Inequalities: Identifying "Hotspots" of Environmental Burdens and Social Vulnerability

Differential exposure to multiple environmental burdens and benefits and their distribution across a population with varying vulnerability can contribute heavily to health inequalities. Particularly relevant are areas with high cumulative burdens and high social vulnerability termed as “hotspots”. This paper develops an index-based approach to assess these multiple burdens and benefits in combination with vulnerability factors at detailed intra-urban level. The method is applied to the city of Dortmund, Germany. Using non-spatial and spatial methods we assessed inequalities and identified “hotspot” areas in the city. We found modest inequalities burdening higher vulnerable groups in Dortmund (CI = −0.020 at p < 0.05). At the detailed intra-urban level, however, inequalities showed strong geographical patterns. Large numbers of “hotspots” exist in the northern part of the city compared to the southern part. A holistic assessment, particularly at a detailed local level, considering both environmental burdens and benefits and their distribution across the population with the different vulnerability, is essential to inform environmental justice debates and to mobilize local stakeholders. Locating “hotspot” areas at this detailed spatial level can serve as a basis to develop interventions that target vulnerable groups to ensure a health conducive equal environment.

Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other.

Residential exposure to air toxics is linked to lower grade point averages among school children in El Paso, Texas, USA

Children in low-income neighborhoods tend to be disproportionately exposed to environmental toxicants. This is cause for concern because exposure to environmental toxicants negatively affect health, which can impair academic success. To date, it is unknown if associations between air toxics and academic performance found in previous school-level studies persist when studying individual children. In pairing the National Air Toxics Assessment (NATA) risk estimates for respiratory and diesel particulate matter risk disaggregated by source, with individual-level data collected through a mail survey, this paper examines the effects of exposure to residential environmental toxics on academic performance for individual children for the first time and adjusts for school-level effects using generalized estimating equations. We find that higher levels of residential air toxics, especially those from non-road mobile sources, are statistically significantly associated with lower grade point averages among fourth and fifth grade school children in El Paso (Texas, USA).

Socioeconomic Disparities and Air Pollution Exposure: a Global Review

The existing reviews and meta-analyses addressing unequal exposure of environmental hazards on certain populations have focused on several environmental pollutants or on the siting of hazardous facilities. This review updates and contributes to the environmental inequality literature by focusing on ambient criteria air pollutants (including NOx), by evaluating studies related to inequality by socioeconomic status (as opposed to race/ethnicity) and by providing a more global perspective. Overall, most North American studies have shown that areas where low-socioeconomic-status (SES) communities dwell experience higher concentrations of criteria air pollutants, while European research has been mixed. Research from Asia, Africa, and other parts of the world has shown a general trend similar to that of North America, but research in these parts of the world is limited.

Modeling spatial effects of PM(2.5) on term low birth weight in Los Angeles County

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.

Populations potentially exposed to traffic-related air pollution in seven world cities

Traffic-related air pollution (TRAP) likely exerts a large burden of disease globally, and in many places, traffic is increasing dramatically. The impact, however, of urban form on the portion of population potentially exposed to TRAP remains poorly understood. In this study, we estimate portions of population potentially exposed to TRAP across seven global cities of various urban forms. Data on population distributions and road networks were collected from the best available sources in each city and from remote sensing analysis. Using spatial mapping techniques, we first overlaid road buffers onto population data to estimate the portions of population potentially exposed for four plausible impact zones. Based on a most likely scenario with impacts from highways up to 300meters and major roadways up to 50meters, we identified that the portions of population potentially exposed for the seven cities ranged from 23 to 96%. High-income North American cities had the lowest potential exposure portions, while those in Europe had the highest. Second, we adjusted exposure zone concentration levels based on a literature suggested multiplier for each city using corresponding background concentrations. Though Beijing and Mexico City did not have the highest portion of population exposure, those in their exposure zones had the highest levels of exposure. For all seven cities, the portion of population potentially exposed was positively correlated with roadway density and, to a lesser extent, with population density. These analyses suggest that urban form may influence the portion of population exposed to TRAP and vehicle emissions and other factors may influence the exposure levels. Greater understanding of urban form and other factors influencing potential exposure to TRAP may help inform interventions that protect public health.

National patterns in environmental injustice and inequality: outdoor NO2 air pollution in the United States

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.