Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression

Cumulative Exposures to Environmental and Socioeconomic Risk Factors in Milwaukee County, Wisconsin

The environmental justice literature demonstrates consistently that low-income and minority communities are disproportionately exposed to environmental hazards. In this case study, we examined cumulative multipollutant, multidomain, and multimatrix environmental exposures in Milwaukee County, Wisconsin for the year 2015. We identified spatial hot spots in Milwaukee County both individually (using local Moran's I) and through clusters (using K-means clustering) across a profile of environmental pollutants that span regulatory domains and matrices of exposure, as well as socioeconomic indicators. The cluster with the highest exposures within the urban area was largely characterized by low socioeconomic status and an overrepresentation of the Non-Hispanic Black population relative to the county as a whole. In this cluster, average pollutant concentrations were equivalent to the 78th percentile in county-level blood lead levels, 67th percentile in county-level NO2, 79th percentile in county-level CO, and 78th percentile in county-level air toxics. Simultaneously, this cluster had an average equivalent to the 62nd percentile in county-level unemployment, 70th percentile in county-level population rate lacking a high school diploma, 73rd percentile in county-level poverty rate, and 28th percentile in county-level median household income. The spatial patterns of pollutant exposure and SES indicators suggested that these disparities were not random but were instead structured by socioeconomic and racial factors. Our case study, which combines environmental pollutant exposures, sociodemographic data, and clustering analysis, provides a roadmap to identify and target overburdened communities for interventions that reduce environmental exposures and consequently improve public health.

Ambient Ultrafine Particulate Matter and Clinical Outcomes in Fibrotic Interstitial Lung Disease

Rationale: Particulate matter with an aerodynamic diameter ⩽2.5 μm is associated with adverse outcomes in fibrotic interstitial lung disease (fILD), but the impact of ultrafine particulates (UFPs; aerodynamic diameter ⩽100 nm) remains unknown. Objective: To evaluate UFP associations with clinical outcomes in fILD. Methods: We conducted a multicenter, prospective cohort study enrolling patients with fILD from the University of Pittsburgh Dorothy P. and Richard P. Simmons Center and the Pulmonary Fibrosis Foundation Patient Registry (PFF-PR). Using a national-scale UFP model, we linked exposures using three approaches in the Simmons cohort (residential address geocoordinates, ZIP code centroid geocoordinates, and ZIP code average) and two in the PFF-PR for which only five-digit ZIP code was available (ZIP code centroid and ZIP code average). We tested UFP associations with transplantation-free survival using multivariable Cox proportional-hazards models, baseline percentage predicted FVC and DlCO using multivariable linear regressions, and decline in FVC and DlCO using linear mixed models adjusting for age, sex, smoking, race, socioeconomic status, site, particulate matter with an aerodynamic diameter ⩽2.5, and nitrogen dioxide. Measurements and Main Results: Annual mean outdoor UFP concentrations for 2017 were estimated for 1,416 Simmons and 1,919 PFF-PR patients. Increased UFP concentration was associated with transplantation-free survival in fully adjusted Simmons residential address models (hazard ratio, 1.08 per 1,000 particles/cm3 [95% confidence interval, 1.01-1.15]; P = 0.02) but not PFF-PR models, which used less precise linkage approaches. Higher UFP exposure was associated with lower baseline FVC and more rapid FVC decline in the Simmons registry. Conclusions: Increased UFP exposure was associated with transplantation-free survival and lung function in the cohort with precise residential location linkage. This work highlights the need for more robust regulatory networks to study the health effects of UFPs nationwide.

Potential Effects of Long-Term Exposure to Air Pollution on Dementia: A Longitudinal Analysis in American Indians Aged 55 Years and Older

(1) Background: American Indians are disproportionately affected by air pollution, an important risk factor for dementia. However, few studies have investigated the effects of air pollution on the risk of dementia among American Indians. (2) Methods: This retrospective cohort study included a total of 26,871 American Indians who were 55+ years old in 2007, with an average follow-up of 3.67 years. County-level average air pollution data were downloaded from land-use regression models. All-cause dementia was identified using ICD-9 diagnostic codes from the Indian Health Service's (IHS) National Data Warehouse and related administrative databases. Cox models were employed to examine the association of air pollution with dementia incidence, adjusting for co-exposures and potential confounders. (3) Results: The average PM2.5 levels in the IHS counties were lower than those in all US counties, while the mean O3 levels in the IHS counties were higher than the US counties. Multivariable Cox regressions revealed a positive association between dementia and county-level O3 with a hazard ratio of 1.24 (95% CI: 1.02-1.50) per 1 ppb standardized O3. PM2.5 and NO2 were not associated with dementia risk after adjusting for all covariates. (4) Conclusions: O3 is associated with a higher risk of dementia among American Indians.

Local exposure misclassification in national models: relationships with urban infrastructure and demographics

BACKGROUND

National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions.

OBJECTIVE

Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics.

METHODS

We compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed "localized difference." We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition.

RESULTS

We find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations.

IMPACT

Comparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Hyper-local to regional exposure contrast of source-resolved PM2.5 components across the contiguous United States: implications for health assessment

BACKGROUND

Improved understanding of sources and processes that drive exposure contrast of fine particulate matter (PM2.5) is essential for designing and interpreting epidemiological study outcomes.

OBJECTIVE

We investigate the contribution of various sources and processes to PM2.5 exposure contrasts at different spatial scales across the continental United States.

METHODS

We consider three cases: exposure contrast within a metro area, nationwide exposure contrast with high spatial resolution, and nationwide exposure contrast with low spatial resolution. Using national empirical model estimates of source- and chemically specific PM2.5 concentration predictions, we quantified the contribution of various sources and processes to PM2.5 exposure contrasts in these three cases.

RESULTS

At the metro level (i.e., metropolitan statistical area; MSA), exposure contrasts of PM2.5 vary between -1.8 to 1.4 µg m-3 relative to the MSA-mean with about 50% of within-MSA exposure contrast of PM2.5 caused by cooking and mobile source primary PM2.5. For the national exposure contrast at low-resolution (i.e., using MSA-average mean concentrations), exposure contrasts (relative to the national mean: -3.9 to 3.2 µg m-3) are larger than within an MSA with ~80% of the variation due to secondary PM2.5. National exposure contrast at high resolution (census block) has the largest absolute range (relative to the national mean: -4.7 to 3.7 µg m-3) due to both regional and intra-urban contributions; on average, 65% of the national exposure contrast is due to secondary PM2.5 with the remaining from the primary PM2.5 (cooking and mobile source 26%, other 9%).

IMPACT

Our study provides a comprehensive analysis of the sources and processes that contribute to exposure contrasts of PM2.5 across different geographic areas in the US. For the first time on a national scale, we used high spatial resolution source-specific exposure estimates to identify the primary contributors to PM2.5 exposure contrasts. The study also highlights the advantages of different study designs for investigating the health impacts of specific PM2.5 components. The findings provide novel insights that can inform public health policies aimed at reducing PM2.5 exposure and advance the understanding of the epidemiological study outcomes.

Environmental exposure disparities in ultrafine particles and PM2.5 by urbanicity and socio-demographics in New York state, 2013-2020

BACKGROUND

The spatiotemporal and demographic disparities in exposure to ultrafine particles (UFP; number concentrations of particulate matter (PM) with diameter ≤0.1 μm), a key subcomponent of fine aerosols (PM2.5; mass concentrations of PM ≤ 2.5 μm), have not been well studied.

OBJECTIVE

To quantify and compare the aerosol pollutant exposure disparities for UFP and PM2.5 by socio-demographic factors in New York State (NYS).

METHODS

Ambient atmospheric UFP and PM2.5 were quantified using a global three-dimensional model of chemical transport with state-of-the-science aerosol microphysical processes validated extensively with observations. We matched these to U.S. census demographic data for varied spatial scales (state, county, county subdivision) and derived population-weighted aerosol exposure estimates. Aerosol exposure disparities for each demographic and socioeconomic (SES) indicator, with a focus on race-ethnicity and income, were quantified for the period 2013-2020.

RESULTS

The average NYS resident was exposed to 4451 #·cm-3 UFP and 7.87 μg·m-3 PM2.5 in 2013-2020, but minority race-ethnicity groups were invariably exposed to greater daily aerosol pollution (UFP: +75.0% & PM2.5: +16.2%). UFP has increased since 2017 and is temporally and seasonally out-of-phase with PM2.5. Race-ethnicity exposure disparities for PM2.5 have declined over time; by -6% from 2013 to 2017 and plateaued thereafter despite its decreasing concentrations. In contrast, these disparities have increased (+12.5-13.5%) for UFP. The aerosol pollution exposure disparities were the highest for low-income minorities and were more amplified for UFP than PM2.5. DISCUSSION: We identified large disparities in aerosol pollution exposure by urbanization level and socio-demographics in NYS residents. Jurisdictions with higher proportions of race-ethnicity minorities, low-income residents, and greater urbanization were disproportionately exposed to higher concentrations of UFP and PM2.5 than other NYS residents. These race-ethnicity exposure disparities were much larger, more disproportionate, and unabating over time for UFP compared to PM2.5 across various income strata and levels of urbanicity.

Multilayer network associations between the exposome and childhood brain development

Growing up in a high poverty neighborhood is associated with elevated risk for academic challenges and health problems. Here, we take a data-driven approach to exploring how measures of children's environments relate to the development of their brain structure and function in a community sample of children between the ages of 4 and 10 years. We constructed exposomes including measures of family socioeconomic status, children's exposure to adversity, and geocoded measures of neighborhood socioeconomic status, crime, and environmental toxins. We connected the exposome to two structural measures (cortical thickness and surface area, n = 170) and two functional measures (participation coefficient and clustering coefficient, n = 130). We found dense connections within exposome and brain layers and sparse connections between exposome and brain layers. Lower family income was associated with thinner visual cortex, consistent with the theory that accelerated development is detectable in early-developing regions. Greater neighborhood incidence of high blood lead levels was associated with greater segregation of the default mode network, consistent with evidence that toxins are deposited into the brain along the midline. Our study demonstrates the utility of multilayer network analysis to bridge environmental and neural explanatory levels to better understand the complexity of child development.

Effect of environmental variables on mercury accumulation in sediments of an anthropogenically impacted tropical estuary (Buenaventura Bay, Colombian Pacific)

Estuaries are the main entry areas of mercury to the marine environment and are important to understand the effect of this contaminant on marine organisms, since it accumulates in the sediments becoming available to enter the food trophic chain. This study aims to determine the environmental variables that mainly influence the spatiotemporal dynamics of total mercury accumulation in sediments of tropical estuaries. Sediment samples were collected from interior and exterior areas of the estuary during the dry and rainy seasons, representing the spatiotemporal gradients of the estuary. The grain size, organic matter content (OM), and total mercury concentration (THg) of the sediment samples were determined. In addition, salinity, temperature, dissolved oxygen, and pH of the water column associated with each sediment sample were assessed. The variations in environmental conditions, OM and THg in sediment were in accordance with a gradient which goes from conditions influenced by fresh water in the inner estuary to conditions influenced by sea water in the outer part of the estuary. The OM and THg in sediments presented similar variation patterns; they were higher in the rainy season than in the dry season and in the interior area of the estuary than in the exterior area. Despite the complex dynamic observed in the distribution and accumulation processes of mercury in sediments, these processes could be modeled from OM and salinity parameters. Due to the correlations found, in the process of accumulation of mercury in sediments the OM could represents the pathway of transport and accumulation of THg, and salinity could represent the influence of the hydroclimatic variations and environmental gradients of the estuary.

Changes in Socioeconomic Disparities for Traffic-Related Air Pollution Exposure During Pregnancy Over a 20-Year Period in Texas

Importance

Air pollution presents clear environmental justice issues. However, few studies have specifically examined traffic-related air pollution (TRAP), a source driven by historically racist infrastructure policies, among pregnant individuals, a population susceptible to air pollution effects. How these disparities have changed over time is also unclear but has important policy implications.

Objective

To examine changes in TRAP exposure by sociodemographic characteristics among recorded pregnancies over a 20-year period.

Design, Setting, and Participants

This population-based birth cohort study used descriptive analysis among pregnant individuals in Texas from 1996 to 2016. All pregnant individuals with valid residential address, socioeconomic, and demographic data were included. Individual-level race and ethnicity, education, and maternal birthplace data were extracted from birth certificates and neighborhood-level household income and historical neighborhood disinvestment (ie, redlining) data were assessed via residential addresses. Data analysis occurred between June 2022 and June 2023.

Main Outcomes and Measures

The main outcome, TRAP exposure at residential addresses, was assessed via traffic levels, represented by total and truck-specific vehicle miles traveled (VMT) within 500 m; nitrogen dioxide (no2) concentrations from a spatial-temporal land use regression model (ie, vehicle tailpipe emissions); and National Air Toxic Agency cancer risk index from on-road vehicle emissions. TRAP exposure differences were assessed by sociodemographic indicators over the 1996 to 2016 period.

Results

Among 7 043 598 pregnant people (mean [SD] maternal age, 26.8 [6.1] years) in Texas from 1996 to 2016, 48% identified as Hispanic or Latinx, 4% identified as non-Hispanic Asian or Pacific Islander, 12% identified as non-Hispanic Black, and 36% identified as non-Hispanic White. There were differences in TRAP for pregnant people by all sociodemographic variables examined. The absolute level of these disparities decreased from 1996 to 2016, but the relative level of these disparities increased: for example, in 1996, non-Hispanic Black pregnant individuals were exposed to a mean (SD) 15.3 (4.1) ppb of no2 vs 13.5 (4.4) ppb of no2 for non-Hispanic White pregnant individuals, compared with 2016 levels of 6.7 (2.4) ppb no2 for Black pregnant individuals and 5.2 (2.4) ppb of no2 for White pregnant individuals. Large absolute and relative differences in traffic levels were observed for all sociodemographic characteristics, increasing over time. For example, non-Hispanic Black pregnant individuals were exposed to a mean (SD) of 22 836 (32 844) VMT within 500 m of their homes, compared with 12 478 (22 870) VMT within 500 m of the homes of non-Hispanic White pregnant individuals in 2016, a difference of 83%.

Conclusions and Relevance

This birth cohort study found that while levels of air pollution disparities decreased in absolute terms over the 20 years of the study, relative disparities persisted and large differences in traffic levels remained, requiring renewed policy attention.

Structural racism, air pollution and the association with adverse birth outcomes in the United States: the value of examining intergenerational associations

Structurally racist policies and practices of the past are likely to be a driving factor in current day differences in exposure to air pollution and may contribute to observed racial and ethnic disparities in adverse birth outcomes in the United States (U.S.). Non-Hispanic Black women in the U.S. experience poorer health outcomes during pregnancy and throughout the life course compared to non-Hispanic White women. This disparity holds even among non-Hispanic Black women with higher socioeconomic status. Reasons for this finding remain unclear, but long-term environmental exposure, either historical exposure or both historical and ongoing exposure, may contribute. Structural racism likely contributes to differences in social and environmental exposures by race in the U.S. context, and these differences can affect health and wellbeing across multiple generations. In this paper, we briefly review current knowledge and recommendations on the study of race and structural racism in environmental epidemiology, specifically focused on air pollution. We describe a conceptual framework and opportunities to use existing historical data from multiple sources to evaluate multi-generational influences of air pollution and structurally racist policies on birth and other relevant health outcomes. Increased analysis of this kind of data is critical for our understanding of structural racism's impact on multiple factors, including environmental exposures and adverse health outcomes, and identifying how past policies can have enduring legacies in shaping health and well-being in the present day. The intended purpose of this manuscript is to provide an overview of the widespread reach of structural racism, its potential association with health disparities and a comprehensive approach in environmental health research that may be required to study and address these problems in the U.S. The collaborative and methodological approaches we highlight have the potential to identify modifiable factors that can lead to effective interventions for health equity.

Antimony Compounds Associate with Atopic Dermatitis and Influence Models of Itch and Dysbiosis

Compared to the myriad of known triggers for rhinitis and asthma, environmental exposure research for atopic dermatitis (AD) is not well established. We recently reported that an untargeted search of U.S. Environmental Protection Agency (EPA) databases versus AD rates by United States (U.S.) postal codes revealed that isocyanates, such as toluene diisocyanate (TDI), are the pollutant class with the strongest spatiotemporal and epidemiologic association with AD. We further demonstrated that (di)isocyanates disrupt ceramide-family lipid production in commensal bacteria and activate the thermo-itch host receptor TRPA1. In this report, we reanalyzed regions of the U.S. with low levels of diisocyanate pollution to assess if a different chemical class may contribute. We identified antimony compounds as the top associated pollutant in such regions. Exposure to antimony compounds would be expected from brake dust in high-traffic areas, smelting plants, bottled water, and dust from aerosolized soil. Like TDI, antimony inhibited ceramide-family lipid production in Roseomonas mucosa and activated TRPA1 in human neurons. While further epidemiologic research will be needed to directly evaluate antimony exposure with surrounding AD prevalence and severity, these data suggest that compounds which are epidemiologically associated with AD, inhibit commensal lipid production, and activate TRPA1 may be causally related to AD pathogenesis.

The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS)

BACKGROUND

Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution.

METHODS

We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant's address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures.

RESULTS

Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E-2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E-3).

SIGNIFICANCE

While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases.

SIGNIFICANCE AND IMPACT STATEMENT

The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease.

Life Expectancy and Built Environments in the U.S.: A Multilevel Analysis

INTRODUCTION

The purpose of this study is to examine the associations between built environments and life expectancy across a gradient of urbanicity in the U.S.

METHODS

Census tract‒level estimates of life expectancy between 2010 and 2015, except for Maine and Wisconsin, from the U.S. Small-Area Life Expectancy Estimates Project were analyzed in 2022. Tract-level measures of the built environment included: food, alcohol, and tobacco outlets; walkability; park and green space; housing characteristics; and air pollution. Multilevel linear models for each of the 4 urbanicity types were fitted to evaluate the associations, adjusting for population and social characteristics.

RESULTS

Old housing (built before 1979) and air pollution were important built environment predictors of life expectancy disparities across all gradients of urbanicity. Convenience stores were negatively associated with life expectancy in all urbanicity types. Healthy food options were a positive predictor of life expectancy only in high-density urban areas. Park accessibility was associated with increased life expectancy in all areas, except rural areas. Green space in neighborhoods was positively associated with life expectancy in urban areas but showed an opposite association in rural areas.

CONCLUSIONS

After adjusting for key social characteristics, several built environment characteristics were salient risk factors for decreased life expectancy in the U.S., with some measures showing differential effects by urbanicity. Planning and policy efforts should be tailored to local contexts.

Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the association between "exposome"-the totality of environmental exposures and the risk of PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified environmental risk factors for 23 PASC symptoms and conditions from nearly 200 exposome factors. The three domains of exposome include natural environment, built environment, and social environment. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each exposome factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) exposome characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, blood, circulatory, endocrine, and other organ systems. Specific environmental risk factors for each PASC condition and symptom were different across the New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular exposome characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

Social Inequities in Exposure to Traffic-Related Air and Noise Pollution at Public Schools in Texas

Although children are particularly vulnerable to the adverse impacts of vehicular pollution and spend significant portions of their time at school, previous studies have not examined or compared school-level social inequities in exposure to both traffic-related air and noise pollution in the same study area. We addressed this gap through a case study in Texas-the second-largest US state based on total population and number of children. Vehicular pollution exposure was measured using: (1) outdoor concentrations of nitrogen dioxide (NO₂), a widely used proxy for traffic-related air pollution; and (2) road noise estimates from the US Department of Transportation's National Transportation Noise Mapping Tool. These variables were linked to data on locations and sociodemographic characteristics of children enrolled in Texas public schools. We found children attending schools with the highest exposure to both NO₂ and road noise (top 25%) were significantly more likely to be Black, Hispanic, and eligible for free/reduced lunches (socioeconomically deprived). Results from multivariable generalized estimating equations that control for spatial clustering and other relevant factors revealed that schools with greater NO₂ exposure were significantly more likely to serve racial/ethnic minority and younger students, while schools with greater exposure to road noise were significantly more likely to serve socioeconomically deprived and older students. These findings underscore the urgent need to reduce both air pollution and noise exposure at school locations, especially in schools attended by higher proportions of socially disadvantaged children that are often additionally burdened with other challenges.

Spatial Decomposition of Air Pollution Concentrations Highlights Historical Causes for Current Exposure Disparities in the United States

Racial-ethnic disparities in exposure to air pollution in the United States (US) are well documented. Studies on the causes of these disparities highlight unequal systems of power and longstanding systemic racism-for example, redlining, white flight, and racial covenants-which reinforced racial segregation and wealth gaps and which concentrated polluting land uses in communities of color. Our analysis is based on empirical estimates of ambient concentrations for two important pollutants (NO2 and PM2.5). We show that spatially decomposed concentrations can be used to infer and quantify types of root causes for local- to national-scale disparities. Urban-scale segregation is important yet reflects less than half of the overall national disparities. Other historical causes of national exposure disparities include those that led current populations of Black, Asian, and Hispanic Americans to live in larger cities; those outcomes are consistent with, for example, greater economic opportunity in large cities, land-takings from non-White farmers, and racism in homesteading and between-state migration. Our results suggest that contemporary national exposure disparities in the US reflect a broad set of historical local- to national-scale mechanisms-including racist laws and actions that include, but also extend beyond, urban-scale aspects-and offer a first attempt to quantify their relative importance.

Nitrogen dioxide increases the risk of disease progression in idiopathic pulmonary fibrosis

BACKGROUND AND OBJECTIVE

Air pollution affects clinical course and prognosis of idiopathic pulmonary fibrosis (IPF). However, the effect of individual exposure to air pollutants on disease progression is unclear. We aimed to identify the effect of individual exposure to nitrogen dioxide (NO₂ ) and particulate matter (aerodynamic diameter ≤ 10 μm [PM₁₀ ]) on disease progression in patients with IPF.

METHODS

The serial lung function data of 946 IPF patients (mean age: 65.4 years, male: 80.9%) were analysed. Individual-level long-term exposures to NO₂ and PM₁₀ at the residential addresses of patients were estimated using a national-scale exposure prediction model, constructed based on air quality regulatory monitoring data. Progression was defined as a relative decline (≥10%) in forced vital capacity. Individual- and area-level covariates were adjusted in the primary analysis model.

RESULTS

Overall, 547 patients (57.8%) experienced progression during a median follow-up of 1.0 year (interquartile range: 0.4-2.6 years). In the primary model, a 10-ppb increase in NO₂ concentration was associated with a 10.5% increase in the risk of progression (hazard ratio [HR] = 1.105; 95% CI = 1.000-1.219) in patients with IPF. There was also an increasing trend of progression in patients with IPF according to the second to fourth quartiles of NO₂ (Q2 [HR = 1.299; 95% CI = 0.972-1.735], Q3 [1.409; 1.001-1.984], Q4 [1.598; 1.106-2.310]) compared to the first quartile. We found no association between PM₁₀ and progression in IPF patients.

CONCLUSION

Our data suggest that increased individual exposure to NO₂ can increase the risk of progression in patients with IPF.

Role of social determinants of health in differential respiratory exposure and health outcomes among children

BACKGROUND

Attributes defining the Social Determinants of Health (SDoH) are associated with disproportionate exposures to environmental hazards and differential health outcomes among communities. The dynamics between SDoH, disproportionate environmental exposures, and differential health outcomes are often specific to micro-geographic areas.

METHODS

This study focused on children less than 20 years of age who lived in Douglas County, Nebraska, during 2016-2019. To assess the role of SDoH in differential exposures, we evaluated the association between SDoH metrics and criteria pollutant concentrations and the association between SDoH and pediatric asthma exacerbations to quantify the role of SDoH in differential pediatric asthma outcomes. The Bayesian Poisson regression model with spatial random effects was used to evaluate associations.

RESULTS

We identified significant positive associations between the annual mean concentration of criteria pollutants (carbon monoxide, particulate matter_2.5, nitrogen dioxide, sulfur dioxide) with race (Non-Hispanic Black and Hispanic/Latino), financial stability, and literacy. Additionally, there were significant positive associations between higher rates of pediatric asthma emergency department visits and neighborhoods with more Non-Hispanic Black children, children without health insurance coverage, and households without access to a vehicle.

CONCLUSIONS

Non-Hispanic Black and Hispanic/Latino children living in Douglas County, NE experience disproportionately higher exposure to criteria pollutant concentrations. Additionally, higher rates of asthma exacerbations among Non-Hispanic Black children could be due to reduced access to respiratory care that is potentially the result of financial instability and vehicle access. These results could inform city planners and health care providers to mitigate respiratory risks among these higher at-risk populations.

The changing food environment and neighborhood prevalence of type 2 diabetes

In this ecological study, we used longitudinal data to assess if changes in neighborhood food environments were associated with type 2 diabetes mellitus (T2DM) prevalence, controlling for a host of neighborhood characteristics and spatial error correlation. We found that the population-adjusted prevalence of fast-food and pizza restaurants, grocery stores, and full-service restaurants along with changes in their numbers from 1990 to 2010 were associated with 2015 T2DM prevalence. The results suggested that neighborhoods where fast-food restaurants have increased and neighborhoods where full-service restaurants have decreased over time may be especially important targets for educational campaigns or other public health-related T2DM interventions.

Methodological Challenges in Spatial and Contextual Exposome-Health Studies

The concept of the exposome encompasses the totality of exposures from a variety of external and internal sources across an individual's life course. The wealth of existing spatial and contextual data makes it appealing to characterize individuals' external exposome to advance our understanding of environmental determinants of health. However, the spatial and contextual exposome is very different from other exposome factors measured at the individual-level as spatial and contextual exposome data are more heterogenous with unique correlation structures and various spatiotemporal scales. These distinctive characteristics lead to multiple unique methodological challenges across different stages of a study. This article provides a review of the existing resources, methods, and tools in the new and developing field for spatial and contextual exposome-health studies focusing on four areas: (1) data engineering, (2) spatiotemporal data linkage, (3) statistical methods for exposome-health association studies, and (4) machine- and deep-learning methods to use spatial and contextual exposome data for disease prediction. A critical analysis of the methodological challenges involved in each of these areas is performed to identify knowledge gaps and address future research needs.

Assessing microbial manipulation and environmental pollutants in the pathogenesis of psoriasis

The cutaneous microbiome is increasingly recognized as a contributor to skin diseases like atopic dermatitis (AD) and psoriasis. Although traditionally AD and psoriasis have been viewed as having opposing immunologic findings, recent evidence suggests an overlap in ceramide-family lipid production in the protection against symptoms. We recently identified that specific environmental pollutants may drive dysbiosis through direct suppression of ceramide-family lipids produced by health-associated skin bacteria in atopic dermatitis (AD). We further demonstrated that one such bacteria, Roseomonas mucosa, generated significant clinical improvement in AD lasting beyond active treatment via lipid-mediated modulation of tumor necrosis factor (TNF) signaling. To assess the potential preclinical benefit of R. mucosa in psoriasis we assessed for direct effects on surface TNF signaling in cell cultures and identified direct effects on the TNF axis. We also identified preclinical efficacy of R. mucosa treatment in the imiquimod mouse model of psoriasis. Finally, we expanded our previous environmental assessment for psoriasis to include more traditional markers of air quality and found a strong association between disease rates and ambient carbon monoxide (CO), nitrogen dioxide (NO₂), and particulate matter (PM). At the current stage this work is speculative but does support consideration of further preclinical models and/or clinical assessments to evaluate any potential for therapeutic benefit through microbial manipulation and/or environmental mitigation.

Concentrated poverty, ambient air pollution, and child cognitive development

Why does growing up in a poor neighborhood impede cognitive development? Although a large volume of evidence indicates that neighborhood poverty negatively affects child outcomes, little is known about the mechanisms that might explain these effects. In this study, we outline and test a theoretical model of neighborhood effects on cognitive development that highlights the mediating role of early life exposure to neurotoxic air pollution. To evaluate this model, we analyze data from a national sample of American infants matched with information on their exposure to more than 50 different pollutants known or suspected to harm the central nervous system. Integrating methods of causal inference with supervised machine learning, we find that living in a high-poverty neighborhood increases exposure to many different air toxics during infancy, that it reduces cognitive abilities measured later at age 4 by about one-tenth of a standard deviation, and that about one-third of this effect can be attributed to disparities in air quality.

A population-based cohort study of traffic congestion and infant growth using connected vehicle data

More than 11 million Americans reside within 150 meters of a highway, an area of high air pollution exposure. Traffic congestion further contributes to environmental pollution (e.g., air and noise), but its unique importance for population health is unclear. We hypothesized that degraded environmental quality specifically from traffic congestion has harmful impacts on fetal growth. Using a population-based cohort of births in Texas (2015-2016), we leveraged connected vehicle data to calculate traffic congestion metrics around each maternal address at delivery. Among 579,122 births, we found consistent adverse associations between traffic congestion and reduced term birth weight (8.9 grams), even after accounting for sociodemographic characteristics, typical traffic volume, and diverse environmental coexposures. We estimated that up to 1.2 million pregnancies annually may be exposed to traffic congestion (27% of births in the United States), with ~256,000 in the highest congestion zones. Therefore, improvements to traffic congestion may yield positive cobenefits for infant health.

National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data

This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (R² 0.63 for the COA model and 0.62 for the HOA model). Extensive cross-validation suggests that the models are robust with reasonable transferability. The models predict large urban-rural and intra-urban variability with hotspots in urban areas and along the road corridors. The predicted national concentration surfaces show reasonable spatial correlation with source-specific national chemical transport model (CTM) simulations (R²: 0.45 for COA, 0.4 for HOA). Our measured data, empirical models, and CTM predictions all show that COA concentrations are about two times higher than HOA. Since COA and HOA are important contributors to the intra-urban spatial variability of the total PM_2.5, our results highlight the potential importance of controlling commercial cooking emissions for air quality management in the United States.

Identifying Contextual and Spatial Risk Factors for Post-Acute Sequelae of SARS-CoV-2 Infection: An EHR-based Cohort Study from the RECOVER Program

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the contextual and spatial risk factors for PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified contextual and spatial risk factors from nearly 200 environmental characteristics for 23 PASC symptoms and conditions of eight organ systems. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each contextual and spatial factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) contextual and spatial characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), criteria air pollutants (e.g., sulfur dioxide), particulate matter (PM _2.5 ) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, respiratory, blood, circulatory, endocrine, and other organ systems. Specific contextual and spatial risk factors for each PASC condition and symptom were different across New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular contextual and spatial characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

Disparities in exposure to fine particulate air pollution for people with disabilities in the US

Although chronic air pollution has been found to be disproportionately distributed with respect to race/ethnicity and socioeconomic status in the US, previous research on social disparities in air pollution exposure has not focused on persons with disabilities (PwDs). This gap is addressed here by conducting the first national-scale study of the relationship between outdoor exposure to fine particulate matter (PM_2.5) and disability status in the continental US. Census tract-level data on average annual PM_2.5 concentrations (2011-2015) were linked with relevant variables from the 2015 American Community Survey five-year estimates. Statistical analyses were based on bivariate and multivariable generalized estimating equations that account for spatial clustering of tracts within counties. Results indicated that the overall percentage of civilian noninstitutionalized persons with a disability and multiple types of disability are higher in neighborhoods with greater PM_2.5 exposure, after controlling for race/ethnicity, poverty, renter occupancy, older age, population density, and metropolitan status. The percentages of PwDs with cognitive and independent living difficulties indicated stronger positive associations with PM_2.5 exposure, compared to those with other types of difficulties. These findings represent an important starting point for more detailed research investigations and policy interventions that seek to mitigate disproportionate air pollution exposure for this vulnerable group.

National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations

Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO₂ models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO₂. Our results suggest that street view imagery alone may provide sufficient information to explain NO₂ variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10-fold cross-validation R² of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)─a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intra-urban variation of NO₂ pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

National and Intraurban Air Pollution Exposure Disparity Estimates in the United States: Impact of Data-Aggregation Spatial Scale

Air pollution exposure disparities by race/ethnicity and socioeconomic status have been analyzed using data aggregated at various spatial scales. Our research question is this: To what extent does the spatial scale of data aggregation impact the estimated exposure disparities? We compared disparities calculated using data spatially aggregated at five administrative scales (state, county, census tract, census block group, census block) in the contiguous United States in 2010. Specifically, for each of the five spatial scales, we calculated national and intraurban disparities in exposure to fine particles (PM_2.5) and nitrogen dioxide (NO₂) by race/ethnicity and socioeconomic characteristics using census demographic data and an empirical statistical air pollution model aggregated at that scale. We found, for both pollutants, that national disparity estimates based on state and county scale data often substantially underestimated those estimated using tract and finer scales; in contrast, national disparity estimates were generally consistent using tract, block group, and block scale data. Similarly, intraurban disparity estimates based on tract and finer scale data were generally well correlated for both pollutants across urban areas, although in some cases intraurban disparity estimates were substantially different, with tract scale data more frequently leading to underestimates of disparities compared to finer scale analyses.

Associations between Neighborhood Disadvantage and Dog Walking among Participants in the Dog Aging Project

Although neighborhood socioeconomic disadvantage is negatively related to overall physical activity, prior studies reveal a complex relationship between disadvantage and particular walking behaviors. While disadvantage is associated with reduced recreational walking through a hypothesized "fear-of-crime" mechanism, the built environment in disadvantaged neighborhoods may encourage utilitarian walking. To date, no study has assessed how disadvantage relates to dog walking, a distinct walking behavior that is neither strictly recreational nor utilitarian but represents a key mechanism through which pet ownership may affect human health. We employ a large (n = 19,732) dataset from the Dog Aging Project to understand how neighborhood disadvantage is associated with dog walking when controlling for individual-, household-, and environmental-level factors. We find that dog owners in more disadvantaged neighborhoods report less on-leash walking activity compared to owners in advantaged neighborhoods and discuss the possibility of a fear-of-crime mechanism underlying this association. These findings improve our understanding of the relationship between neighborhood disadvantage and physical function and highlight the need for public health interventions that encourage dog ownership to consider neighborhood disadvantage.

Associations between estimates of perinatal industrial pollution exposures and intellectual disability in Utah children

While heavy metals exposure is associated with intellectual disability (ID), little is known about associations between industrial pollution and ID. The objective of this analysis is to assess associations between estimated perinatal industrial pollution exposures from the US Environmental Protection Agency's Risk Screening Environmental Indicators Microdata and children's ID risk. We conducted a case-control study of children born in Utah from 2000 to 2008 (n = 1679). Cases were identified through the Center for Disease Control's Autism and Developmental Disabilities Monitoring Network's Utah site and matched with controls based on birth year, sex, and birth county. We used multivariable generalized estimating equations to examine associations between estimated perinatal industrial pollution exposures and ID risk. The fourth quartile of industrial pollution exposure was associated with increased odds of ID relative to the first (Odds Ratio [OR]: 1.73, 95% Confidence Interval [CI]: 1.23-2.44) and second (OR: 1.67, CI: 1.19-2.35) quartiles. Similarly, the third quartile was associated with increased odds of ID relative to the first (OR: 1.47, CI: 1.06-2.03) and second (OR: 1.41, CI: 1.02-1.96) quartiles. Findings were robust to varied model specifications. Maternal residential exposures to industrial pollution were associated with increased ID prevalence in Utah. Since environmental correlates of ID are understudied, additional research is needed.

Gentrification, Mobility, and Exposure to Contextual Social Determinants of Health

This study uses individual level consumer trace data for 2006 residents of low- and moderate-income neighborhoods for the principal cities of the 100 largest metropolitan regions in the US using their location in 2006 and 2019 to examine exposure to the following four cSDOH: healthcare access (Medically Underserved Areas), socioeconomic condition (Area Deprivation Index), air pollution (NO2, PM 2.5 and PM10), and walkability (National Walkability Index). The results control for individual characteristics and initial neighborhood conditions. Residents of neighborhoods classified as gentrifying were exposed to more favorable cSDOH as of 2006 relative to residents of low- and moderate-income neighborhoods that were not gentrifying in terms of likelihood to be in a MUA, and level of local deprivation and walkability while experiencing similar level of air pollution. As a result of changes in neighborhood characteristics and differential mobility pattern, between 2006 and 2019, individuals who originally lived in gentrifying neighborhoods experienced worse changes in MUAs, ADI, and Walkability Index but a greater improvement in exposure to air pollutants. The negative changes are driven by movers, while stayers actually experience a relative improvement in MUAs and ADI and larger improvements in exposure to air pollutants. The findings indicate that gentrification may contribute to health disparities through changes in exposure to cSDOH through mobility to communities with worse cSDOH among residents of gentrifying neighborhoods although results in terms of exposure to health pollutants are mixed.

Variation in Population Attributable Fraction of Dementia Associated With Potentially Modifiable Risk Factors by Race and Ethnicity in the US

IMPORTANCE

Identifying modifiable risk factors that are associated with dementia burden across racial and ethnic groups in the population can yield insights into the potential effectiveness of interventions in preventing dementia and reducing disparities.

OBJECTIVE

To calculate the population attributable fraction (PAF) of dementia associated with 12 established modifiable risk factors for all US adults, as well as separately by race and ethnicity.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used survey data from nationally representative samples of US adults. PAFs were calculated using relative risks and prevalence estimates for 12 risk factors. Relative risks were taken from meta-analyses, as reported in a 2020 systematic review. Prevalence estimates for risk factors were derived from nationally representative cross-sectional survey data collected between 2011 and 2018. Combined PAFs were adjusted for risk factor communality using weights derived from the Atherosclerosis Risk in Communities (ARIC) study (1987-2018). Analyses were conducted May through October 2021.

EXPOSURES

Low education, hearing loss, traumatic brain injury, hypertension, excessive alcohol consumption, obesity, smoking, depression, social isolation, physical inactivity, diabetes, and air pollution.

MAIN OUTCOMES AND MEASURES

PAF for each dementia risk factor, a combined PAF, and the decrease in the number of prevalent dementia cases in 2020 that would be expected given a 15% proportional decrease in each exposure.

RESULTS

Among all US adults, an estimated 41.0% (95% CI, 22.7%-55.9%) of dementia cases were attributable to 12 risk factors. A 15% proportional decrease in each risk factor would reduce dementia prevalence in the population by an estimated 7.3% (95% CI, 3.7%-10.9%). The estimated PAF was greater for Black and Hispanic than it was for White and Asian individuals. The greatest attributable fraction of dementia cases was observed for hypertension (PAF, 20.2%; 95% CI, 6.3%-34.4%), obesity (PAF, 20.9%; 95% CI, 13.0%-28.8%), and physical inactivity (PAF, 20.1%; 95% CI, 9.1%-29.6%). These factors were also highest within each racial and ethnic group, although the proportions varied.

CONCLUSIONS AND RELEVANCE

A large fraction of dementia cases in the US were associated with potentially modifiable risk factors, especially for Black and Hispanic individuals. Targeting and reducing these risk factors may curb the projected rise in dementia cases over the next several decades.

Air pollution exposure disparities in US public housing developments

Fine particulate matter 2.5 microns or less in diameter (PM_2.5) is widely recognized to be a major public health concern. While ethnic/racial minority and lower socioeconomic status individuals in the US experience higher PM_2.5 exposure, previous research on social disparities in PM_2.5 exposure has not examined residents of federally-assisted public housing developments (PHDs). Here we present the first national-scale analysis of the relationship between outdoor PM_2.5 exposure and PHD residency in the US, as well as exposure disparities within the population of households residing in PHDs. We integrated data on average annual PM_2.5 concentrations (2011–2015) with US Department of Housing and Urban Development data on PHDs (2015), and socio-demographic information from the 2011–2015 American Community Survey. Results from multivariable generalized estimating equations indicated that PHD locations, units, and residents are significantly overrepresented in neighborhoods with greater PM_2.5 exposure, after accounting for clustering, urbanization, and other socio-demographic factors. Additionally, significantly higher percentages of Black, Hispanic, disabled, and extremely low-income households reside in PHDs with greater PM_2.5 exposure. Findings represent an important starting point for future research and emphasize the urgent need to identify gaps in environmental, public health, and housing policies that contribute to disproportionate air pollution exposures among PHD residents.

Mortality risk associated with greenness, air pollution, and physical activity in a representative U.S. cohort

Several cohort studies suggest greenness is associated with decreased mortality risk. Potential confounding by or interactions between physical activity and air pollution remains unclear. This study evaluates associations of greenness, air pollution, and physical activity with mortality risk and investigates confounding and effect modification across these key risk factors. National Health Interview Survey (NHIS) data covering 1997-2014 were linked to the National Death Index to generate a cohort of 403,748 individuals with 39,528 deaths. Greenness, represented by census-tract Normalized Difference Vegetation Index (NDVI) for the seasonal period of May-October, was averaged over the years 2003-2016. Air pollution was estimated by census-tract level PM_2.5 concentrations from 1999 to 2015. Cox Proportional Hazard Models were used to estimate hazard ratios (HR) for differences in greenness, air pollution, and physical activity. Alternative models that evaluated potential confounding and stratified models that evaluated effect modification were examined. Mortality risks were associated with PM_2.5 (HR = 1.14, 95% CI: 1.09-1.19 per 10 μg/m³) and physical inactivity (1.49, 1.44-1.54 relative to sufficiently active), but not with greenness (1.01, 0.99-1.03 per IQR). The PM_2.5-mortality association was mitigated at high levels of greenness (1.05, 0.91-1.22). There was no strong evidence of confounding between air pollution, physical activity, and greenness. However, stratified analysis suggested effect modification for PM_2.5 and NDVI by physical activity. A significant protective greenness-mortality association was observed for only highly active individuals (0.91, 0.86-0.96). Also, relatively high PM_2.5-mortality HRs were observed for more physically active individuals (1.25, 1.12-1.40). PM_2.5 air pollution and physical inactivity are robustly associated with mortality risk. Greenness may be most beneficial and air pollution relatively harmful to highly active individuals. This analysis provides evidence that, in addition to not smoking, being physically active and living in a clean, green environment contributes to improved health and reduced risk of mortality.

Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort

The purpose of this study was to estimate cardiopulmonary mortality associations for long-term exposure to PM_2.5 species and sources (i.e., components) within the U.S. National Health Interview Survey cohort. Exposures were estimated through a chemical transport model for six species (i.e., elemental carbon (EC), primary organic aerosols (POA), secondary organic aerosols (SOA), sulfate (SO₄), ammonium (NH₄), nitrate (NO₃)) and five sources of PM_2.5 (i.e., vehicles, electricity-generating units (EGU), non-EGU industrial sources, biogenic sources (bio), "other" sources). In single-pollutant models, we found positive, significant (p < 0.05) mortality associations for all components, except POA. After adjusting for remaining PM_2.5 (total PM_2.5 minus component), we found significant mortality associations for EC (hazard ratio (HR) = 1.36; 95% CI [1.12, 1.64]), SOA (HR = 1.11; 95% CI [1.05, 1.17]), and vehicle sources (HR = 1.06; 95% CI [1.03, 1.10]). HRs for EC, SOA, and vehicle sources were significantly larger in comparison to those for remaining PM_2.5 (per unit μg/m³). Our findings suggest that cardiopulmonary mortality associations vary by species and source, with evidence that EC, SOA, and vehicle sources are important contributors to the PM_2.5 mortality relationship. With further validation, these findings could facilitate targeted pollution regulations that more efficiently reduce air pollution mortality.

Ambient Air Pollution and Socioeconomic Status in China

BACKGROUND

Air pollution disparities by socioeconomic status (SES) are well documented for the United States, with most literature indicating an inverse relationship (i.e., higher concentrations for lower-SES populations). Few studies exist for China, a country accounting for 26% of global premature deaths from ambient air pollution.

OBJECTIVE

Our objective was to test the relationship between ambient air pollution exposures and SES in China.

METHODS

We combined estimated year 2015 annual-average ambient levels of nitrogen dioxide (NO 2 ) and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] with national demographic information. Pollution estimates were derived from a national empirical model for China at 1 -km spatial resolution; demographic estimates were derived from national gridded gross national product (GDP) per capita at 1 -km resolution, and (separately) a national representative sample of 21,095 individuals from the China Health and Retirement Longitudinal Study (CHARLS) 2015 cohort. Our use of global data on population density and cohort data on where people live helped avoid the spatial imprecision found in publicly available census data for China. We quantified air pollution disparities among individual's rural-to-urban migration status; SES factors (education, occupation, and income); and minority status. We compared results using three approaches to SES measurement: individual SES score, community-averaged SES score, and gridded GDP per capita.

RESULTS

Ambient NO 2 and PM 2.5 levels were higher for higher-SES populations than for lower-SES population, higher for long-standing urban residents than for rural-to-urban migrant populations, and higher for the majority ethnic group (Han) than for the average across nine minority groups. For the three SES measurements (individual SES score, community-averaged SES score, gridded GDP per capita), a 1-interquartile range higher SES corresponded to higher concentrations of 6 - 9   μ g / m 3 NO 2 and 3 - 6   μ g / m 3 PM 2.5 ; average concentrations for the highest and lowest 20th percentile of SES differed by 41-89% for NO 2 and 12-25% for PM 2.5 . This pattern held in rural and urban locations, across geographic regions, across a wide range of spatial resolution, and for modeled vs. measured pollution concentrations.

CONCLUSIONS

Multiple analyses here reveal that in China, ambient NO 2 and PM 2.5 concentrations are higher for high-SES than for low-SES individuals; these results are robust to multiple sensitivity analyses. Our findings are consistent with the idea that in China's current industrialization and urbanization stage, economic development is correlated with both SES and air pollution. To our knowledge, our study provides the most comprehensive picture to date of ambient air pollution disparities in China; the results differ dramatically from results and from theories to explain conditions in the United States. https://doi.org/10.1289/EHP9872.

Characterization factors and other air quality impact metrics: Case study for PM2.5-emitting area sources from biofuel feedstock supply

In this paper, we develop a framework and metrics for estimating the impact of emission sources on regulatory compliance and human health for applications in air quality planning and life cycle impact assessment (LCIA). Our framework is based on a pollutant's characterization factor (CF) and three new metrics: Available Regulatory Capacity for Incremental Emissions (ARCIE), Source CF Ratio, and Activity Health Impact (AHI) Ratio. ARCIE can be used to assess whether a receptor location has capacity to accommodate additional source emissions while complying with regulatory limits. We present CF as a midpoint indicator of health impacts per unit mass of emitted pollutant. Source CF Ratio enables comparison of potential new-source locations based on human health impacts. The AHI Ratio estimates the health impacts of a pollutant in relation to the utilization of the source for each unit of product or service. These metrics can be applied to any pollutant, energy source sector (e.g., agriculture, electricity), source type (point, line, area), and spatial modeling domain (nation, state, city, region). We demonstrate these metrics through a case study of fine particulate (PM_2.5) emissions from U.S. corn stover harvesting and local processing at various scales, representing steps in the biofuel production process. We model PM_2.5 formation in the atmosphere using a novel reduced-complexity chemical transport model called the Intervention Model for Air Pollution (InMAP). Through this case study, we present the first area-source PM_2.5 CFs that address the recommendations of several LCIA studies to establish spatially explicit CFs specific to an energy source sector or type. Overall, the framework developed in this work provides multiple new ways to consider the potential impacts of air emissions through spatially differentiated metrics.

Historical Redlining Is Associated with Present-Day Air Pollution Disparities in U.S. Cities

Communities of color in the United States are systematically exposed to higher levels of air pollution. We explore here how redlining, a discriminatory mortgage appraisal practice from the 1930s by the federal Home Owners' Loan Corporation (HOLC), relates to present-day intraurban air pollution disparities in 202 U.S. cities. In each city, we integrated three sources of data: (1) detailed HOLC security maps of investment risk grades [A ("best"), B, C, and D ("hazardous", i.e., redlined)], (2) year-2010 estimates of NO₂ and PM_2.5 air pollution levels, and (3) demographic information from the 2010 U.S. census. We find that pollution levels have a consistent and nearly monotonic association with HOLC grade, with especially pronounced (>50%) increments in NO₂ levels between the most (grade A) and least (grade D) preferentially graded neighborhoods. On a national basis, intraurban disparities for NO₂ and PM_2.5 are substantially larger by historical HOLC grade than they are by race and ethnicity. However, within each HOLC grade, racial and ethnic air pollution exposure disparities persist, indicating that redlining was only one of the many racially discriminatory policies that impacted communities. Our findings illustrate how redlining, a nearly 80-year-old racially discriminatory policy, continues to shape systemic environmental exposure disparities in the United States.

Historical Redlining Is Associated with Present-Day Air Pollution Disparities in U.S. Cities

Communities of color in the United States are systematically exposed to higher levels of air pollution. We explore here how redlining, a discriminatory mortgage appraisal practice from the 1930s by the federal Home Owners' Loan Corporation (HOLC), relates to present-day intraurban air pollution disparities in 202 U.S. cities. In each city, we integrated three sources of data: (1) detailed HOLC security maps of investment risk grades [A ("best"), B, C, and D ("hazardous", i.e., redlined)], (2) year-2010 estimates of NO₂ and PM_2.5 air pollution levels, and (3) demographic information from the 2010 U.S. census. We find that pollution levels have a consistent and nearly monotonic association with HOLC grade, with especially pronounced (>50%) increments in NO₂ levels between the most (grade A) and least (grade D) preferentially graded neighborhoods. On a national basis, intraurban disparities for NO₂ and PM_2.5 are substantially larger by historical HOLC grade than they are by race and ethnicity. However, within each HOLC grade, racial and ethnic air pollution exposure disparities persist, indicating that redlining was only one of the many racially discriminatory policies that impacted communities. Our findings illustrate how redlining, a nearly 80-year-old racially discriminatory policy, continues to shape systemic environmental exposure disparities in the United States.

Air Pollutants over Industrial and Non-Industrial Areas: Historical Concentration Estimates

Only a few researchers have addressed the issue of lifetime exposure related to air pollutant concentration. This study aims to develop a methodology to obtain the most reliable estimates of historical concentrations of air pollutants, which would be further applied to the long-term exposure evaluation. In particular, PM10, PM2.5, NO2, SO2, benzene, and B(a)P concentrations have been obtained. Data of monitored concentrations, model calculations, and subsequent implementation of several corrections based on previous work on temporal and spatial correlations of these substances in the air have been deployed. This work makes an original contribution to the field of meteorology and epidemiology because of this innovative technique to estimate the most reliable historical concentrations of air pollutants. The novelty of our work lies in the additional implications of this study because historical concentration data serve as input data for the construction of epidemiological associations. The approach is based primarily on the availability of monitoring results of air pollutants.

Children's exposure to vehicular pollution: Environmental injustice in Texas, USA

Distributive environmental justice research on children's exposure to vehicular pollution is underdeveloped and few empirical studies have been conducted in the US. This study seeks to address this gap by examining if socially disadvantaged children are disproportionately located in public school districts burdened by higher vehicular pollution in Texas-the second largest US state based on population size. Vehicular pollution exposure is measured using two variables: (1) an index developed by the US Environmental Protection Agency that combines traffic proximity and volume; and (2) outdoor concentrations of nitrogen dioxide (NO₂), a widely used proxy for traffic-related air pollution. These variables are linked to school district level data on socio-demographic characteristics of children obtained from the latest American Community Survey. Statistical analysis is based on multivariable generalized estimating equations that account for spatial clustering of school districts. Results reveal significantly greater traffic proximity and NO₂ exposure in Texas school districts with higher percentages of children, after controlling for clustering, population density, and other socio-demographic factors. Districts exposed to higher levels of traffic proximity and NO₂ exposure also contain significantly greater proportions of racial/ethnic minority, foreign-born, disabled, and socioeconomically vulnerable children. These findings highlight the urgent need to develop mitigation strategies for reducing vehicular pollution exposure, especially in districts with higher proportions of socially disadvantaged students that could be additionally burdened with limited resources. School districts represent a policy relevant analytic unit since school district boards can act as advocates for the environmental health of children and implement mitigation strategies for reducing pollution exposure.

Exposure to Outdoor Particulate Matter Air Pollution and Risk of Gastrointestinal Cancers in Adults: A Systematic Review and Meta-Analysis of Epidemiologic Evidence

BACKGROUND

Outdoor air pollution is a known lung carcinogen, but research investigating the association between particulate matter (PM) and gastrointestinal (GI) cancers is limited.

OBJECTIVES

We sought to review the epidemiologic literature on outdoor PM and GI cancers and to put the body of studies into context regarding potential for bias and overall strength of evidence.

METHODS

We conducted a systematic review and meta-analysis of epidemiologic studies that evaluated the association of fine PM [PM with an aerodynamic diameter of ≤2.5μm (PM2.5)] and PM10 (aerodynamic diameter ≤10μm) with GI cancer incidence or mortality in adults. We searched five databases for original research published from 1980 to 2021 in English and summarized findings for studies employing a quantitative estimate of exposure overall and by specific GI cancer subtypes. We evaluated the risk of bias of individual studies and the overall quality and strength of the evidence according to the Navigation Guide methodology, which is tailored for environmental health research.

RESULTS

Twenty studies met inclusion criteria and included participants from 14 countries; nearly all were of cohort design. All studies identified positive associations between PM exposure and risk of at least one GI cancer, although in 3 studies these relationships were not statistically significant. Three of 5 studies estimated associations with PM10 and satisfied inclusion criteria for meta-analysis, but each assessed a different GI cancer and were therefore excluded. In the random-effects meta-analysis of 13 studies, PM2.5 exposure was associated with an increased risk of GI cancer overall [risk ratio (RR)=1.12; 95% CI: 1.01, 1.24]. The most robust associations were observed for liver cancer (RR=1.31; 95% CI: 1.07, 1.56) and colorectal cancer (RR=1.35; 95% CI: 1.08, 1.62), for which all studies identified an increased risk. We rated most studies with "probably low" risk of bias and the overall body of evidence as "moderate" quality with "limited" evidence for this association. We based this determination on the generally positive, but inconsistently statistically significant, effect estimates reported across a small number of studies.

CONCLUSION

We concluded there is some evidence of associations between PM2.5 and GI cancers, with the strongest evidence for liver and colorectal cancers. Although there is biologic plausibility for these relationships, studies of any one cancer site were few and there remain only a small number overall. Studies in geographic areas with high GI cancer burden, evaluation of the impact of different PM exposure assessment approaches on observed associations, and investigation of cancer subtypes and specific chemical components of PM are important areas of interest for future research. https://doi.org/10.1289/EHP9620.

How do single-family homeowners value residential and commercial density? It depends

This paper develops estimates of the relationship between local density and single-family home values across five U.S. metropolitan areas using 2017 transactions in Chicago, Los Angeles, Minneapolis, Philadelphia, Seattle. Proposals to build new commercial and residential development projects that would increase local density commonly face opposition from local homeowners. Academic literature links the response from homeowners to concerns that higher density is associated with lower property values but there is limited empirical evidence establishing this relationship at the local level. We find a positive and significant relationship between density and house value in the core area of the five metropolitan regions we analyze. For outlying areas, the estimates are smaller and even negative in several cases. We instrument density based on topographic and soil characteristics and find similar results. These findings point to the need for a more nuanced discussion of the relationship between local density and housing values.

An open science study of ageing in companion dogs

The Dog Aging Project is a long-term longitudinal study of ageing in tens of thousands of companion dogs. The domestic dog is among the most variable mammal species in terms of morphology, behaviour, risk of age-related disease and life expectancy. Given that dogs share the human environment and have a sophisticated healthcare system but are much shorter-lived than people, they offer a unique opportunity to identify the genetic, environmental and lifestyle factors associated with healthy lifespan. To take advantage of this opportunity, the Dog Aging Project will collect extensive survey data, environmental information, electronic veterinary medical records, genome-wide sequence information, clinicopathology and molecular phenotypes derived from blood cells, plasma and faecal samples. Here, we describe the specific goals and design of the Dog Aging Project and discuss the potential for this open-data, community science study to greatly enhance understanding of ageing in a genetically variable, socially relevant species living in a complex environment.

Residential proximity to hydraulically fractured oil and gas wells and adverse birth outcomes in urban and rural communities in California (2006-2015)

Supplemental Digital Content is available in the text.

Background:

Prenatal exposure to hydraulic fracturing (HF), a chemically intensive oil and gas extraction method, may be associated with adverse birth outcomes, but no health studies have been conducted in California.

Methods:

We conducted a retrospective cohort study of 979,961 births to mothers in eight California counties with HF between 2006 and 2015. Exposed individuals had at least 1 well hydraulically fractured within 1 km of their residence during pregnancy; the reference population had no wells within 1 km, but at least one oil/gas well within 10 km. We examined associations between HF and low birth weight (LBW), preterm birth (PTB), small for gestational age birth (SGA), and term birth weight (tBW) using generalized estimating equations and assessing urban-rural effect modification in stratified models.

Results:

Fewer than 1% of mothers (N = 1,192) were exposed to HF during pregnancy. Among rural mothers, HF exposure was associated with increased odds of LBW (odds ratio [OR] = 1.74; 95% confidence interval [CI] = 1.10, 2.75), SGA (OR = 1.68; 95% CI = 1.42, 2.27) and PTB (OR = 1.17; 95% CI = 0.64, 2.12), and lower tBW (mean difference: –73 g; 95% CI = –131, –15). Among urban mothers, HF exposure was positively associated with SGA (OR = 1.23; 95% CI = 0.98, 1.55), inversely associated with LBW (OR = 0.83; 95% CI = 0.63, 1.07) and PTB (OR = 0.65; 95% CI = 0.48, 0.87), and not associated with tBW (mean difference: –2 g; 95% CI = –35, 31).

Conclusion:

HF proximity was associated with adverse birth outcomes, particularly among rural Californians.

Greenness, air pollution, and mortality risk: A U.S. cohort study of cancer patients and survivors

BACKGROUND

Several studies suggest that living in areas of high surrounding greenness may be associated with a lower cardiopulmonary mortality risk. However, associations of greenness with specific causes of death in cancer patients and survivors has not been examined and it is unknown whether this relationship is affected by area levels of fine particulate matter air pollution (PM_2.5). This study evaluated associations between greenness and PM_2.5 on causes of death in a large, U.S.-based cohort of cancer patients and survivors.

METHODS

Surveillance, Epidemiology and End Results (SEER) data were used to generate a cohort of 5,529,005 cancer patients and survivors from 2000 to 2016. Census-tract Normalized Difference Vegetation Index (NDVI) during May-October from 2003 to 2016 was population-weighted to act as a county-level greenness measure. County-level PM_2.5 exposure was estimated from annual concentrations averaged from 1999 to 2015. Cox Proportional Hazards models were used to estimate the association between greenness, PM_2.5, and cause-specific mortality while controlling for age, sex, race, and other individual and county level variables.

FINDINGS

An IQR increase in greenness was associated with a decrease in cancer mortality for cancer patients (Hazard ratio of 0.94, 95% CI: 0.93-0.95), but not for cardiopulmonary mortality (0.98, 95% CI: 0.96-1.00). Inversely, an increase in 10 μg/m³ PM_2.5 was associated with increased cardiopulmonary mortality (1.24, 95% CI: 1.19-1.29), but not cancer mortality (0.99, 95% CI: 0.97-1.00). Hazard ratios were robust to inclusion of PM_2.5 in models with greenness and vice versa. Although exposure estimates were constant over most stratifications, greenness seemed to benefit individuals diagnosed with high survivability cancers (0.92, 95% CI: 0.90-0.95) more than those with low survivability cancers (0.98. 95% CI: 0.96-0.99).

INTERPRETATION

Higher levels of greenness are associated with lower cancer mortality in cancer patients. The evidence suggests minimal confounding between greenness and PM_2.5 exposures and risk of mortality.

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 (NO2), ozone (O3), particulate matter with aerodynamic diameter ≤2.5μm (PM2.5; excluding year-1990), particulate matter with aerodynamic diameter ≤10μm (PM10), and sulfur dioxide (SO2). 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 NO2 [54% (4.6 ppb)], smallest for O3 [3.6% (1.6 ppb)], and intermediate for the remaining pollutants (13%-19%). The disparities varied by U.S. state; for example, for PM2.5 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% (0.66μg/m3; PM2.5) and 88% (0.35 ppm; CO); relative disparities declined to between 0.99× (PM2.5; 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.

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO₂, PM_2.5, O₃, CO, PM₁₀, SO₂) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10-fold spatial cross-validation (CV) R² increased 0.02-0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R² = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.