Environmental inequality in exposures to airborne particulate matter components in the United States

Heavy-metal associated breast cancer and colorectal cancer hot spots and their demographic and socioeconomic characteristics

PURPOSE

Cancer registries offer an avenue to identify cancer clusters across large populations and efficiently examine potential environmental harms affecting cancer. The role of known metal carcinogens (i.e., cadmium, arsenic, nickel, chromium(VI)) in breast and colorectal carcinogenesis is largely unknown. Historically marginalized communities are disproportionately exposed to metals, which could explain cancer disparities. We examined area-based metal exposures and odds of residing in breast and colorectal cancer hotspots utilizing state tumor registry data and described the characteristics of those living in heavy metal-associated cancer hotspots.

METHODS

Breast and colorectal cancer hotspots were mapped across Kentucky, and area-based ambient metal exposure to cadmium, arsenic, nickel, and chromium(VI) were extracted from the 2014 National Air Toxics Assessment for Kentucky census tracts. Among colorectal cancer (n = 56,598) and female breast cancer (n = 77,637) diagnoses in Kentucky, we used logistic regression models to estimate Odds Ratios (ORs) and 95% Confidence Intervals to examine the association between ambient metal concentrations and odds of residing in cancer hotspots, independent of individual-level and neighborhood risk factors.

RESULTS

Higher ambient metal exposures were associated with higher odds of residing in breast and colorectal cancer hotspots. Populations in breast and colorectal cancer hotspots were disproportionately Black and had markers of lower socioeconomic status. Furthermore, adjusting for age, race, tobacco and neighborhood factors did not significantly change cancer hotspot ORs for ambient metal exposures analyzed.

CONCLUSION

Ambient metal exposures contribute to higher cancer rates in certain geographic areas that are largely composed of marginalized populations. Individual-level assessments of metal exposures and cancer disparities are needed.

Socioeconomic Disparities in Women's Cardiovascular Health in the United States and Canada

Cardiovascular disease has been the leading cause of death in the United States and Canada for decades. Although it affects millions of people across a multitude of backgrounds, notable disparities in cardiovascular health are observed among women and become more apparent when accounting for race and socioeconomic status. Although intrinsic sex-specific physiologic differences predispose women to poorer outcomes, social determinants of health (SDOH) and biases at both the individual provider and the larger health care system levels play an equal, if not greater, role. This review examines socioeconomic disparities in women compared with men regarding cardiovascular risk factors, treatments, and outcomes. Although various at-risk subpopulations exist, we highlight the impact of SDOH in specific populations, including patients with disabilities, transgender persons, and South Asian and Indigenous populations. These groups are underrepresented in studies and experience poorer health outcomes owing to structural barriers to care. These findings emphasise the significance of understanding the interplay of different socioeconomic factors and how their stacking can negatively affect women's cardiovascular health. To address these disparities, we propose a multipronged approach to augment culturally sensitive and patient-centred care. This includes increased cardiovascular workforce diversity, inclusion of underrepresented populations into analyses of cardiovascular metrics, and greater utilisation of technology and telemedicine to improve access to health care. Achieving this goal will necessitate active participation from patients, health care administrators, physicians, and policy makers, and is imperative in closing the cardiovascular health gap for women over the coming decades.

Lessons from Mortality Reviews: Nonbiologic Contributors to Maternal Deaths

Based on years of review and analysis of severe maternal morbidity and maternal mortality cases, it is clear that the high rates of maternal mortality in this country are due to more than obstetrical emergencies gone awry. Many nonmedical factors contribute to these poor outcomes including complex and ineffectual health care systems, poor coordination of care, and structural racism. In this article we discuss what physicians can and cannot accomplish on their own, the role of race and racism, and barriers built into the manner in which health care is delivered. We conclude that while obstetricians must continue to focus on the area where their expertise lies, reducing deaths by educating and training physicians to deal with the downstream consequences of upstream events, they must also focus increased attention on educating themselves and their trainees about the effect of racism, social disadvantage, and poor coordination of care on health, as well as their role in resolving these issues. Physicians must also reach out to their representatives in government to partner with them. Those leaders must recognize that when they hear about disparities in maternal mortality, focusing only on events in hospitals ignores the more dispositive issues that put Black women at risk in the first instance. KEY POINTS: · Structural racism contributes to maternal deaths.. · Coordination of postpartum care is critically important.. · U.S. health care system is complex and not patient friendly..

Understanding the Cardiovascular and Metabolic Health Effects of Air Pollution in the Context of Cumulative Exposomic Impacts

Poor air quality accounts for more than 9 million deaths a year globally according to recent estimates. A large portion of these deaths are attributable to cardiovascular causes, with evidence indicating that air pollution may also play an important role in the genesis of key cardiometabolic risk factors. Air pollution is not experienced in isolation but is part of a complex system, influenced by a host of other external environmental exposures, and interacting with intrinsic biologic factors and susceptibility to ultimately determine cardiovascular and metabolic outcomes. Given that the same fossil fuel emission sources that cause climate change also result in air pollution, there is a need for robust approaches that can not only limit climate change but also eliminate air pollution health effects, with an emphasis of protecting the most susceptible but also targeting interventions at the most vulnerable populations. In this review, we summarize the current state of epidemiologic and mechanistic evidence underpinning the association of air pollution with cardiometabolic disease and how complex interactions with other exposures and individual characteristics may modify these associations. We identify gaps in the current literature and suggest emerging approaches for policy makers to holistically approach cardiometabolic health risk and impact assessment.

Exponential growth of private coastal infrastructure influenced by geography and race in South Carolina, USA

Homeowners in coastal environments often augment their access to estuarine ecosystems by building private docks on their personal property. Despite the commonality of docks, particularly in the Southeastern United States, few works have investigated their historical development, their distribution across the landscape, or the environmental justice dimensions of this distribution. In this study, we used historic aerial photography to track the abundance and size of docks across six South Carolina counties from the 1950s to 2016. Across our roughly 60-year study period, dock abundance grew by two orders of magnitude, mean length of newly constructed docks doubled, and the cumulative length of docks ballooned from 34 to 560 km. Additionally, we drew on census data interpolated into consistent 2010 tract boundaries to analyze the racial and economic distribution of docks in 1994, 1999, 2011, and 2016. Racial composition, measured as the percentage of a tract's population that was White, positively correlated with dock abundance in each year. Median household income and dock abundance were only correlated in 2011. Taken together, these metrics indicate the growing desire for direct estuary access, however, that access does not appear to be equally spread across racial groups. Because docks enhance estuarine access and demarcate private property, our study provides longitudinal insights into environmental justice concerns related to disparate private property ownership. We found a persistent correlation between the racial characteristics of an area and dock abundance, strongly indicating that White South Carolinians have had disproportionately greater private water access for the past two decades.

Is residential exposure to oil refineries a novel contextual risk factor for coronary heart disease?

Despite a multi-decade decrease in cardiovascular disease, geographic disparities have widened, with excess mortality concentrated within the United States (U.S.) South. Petroleum production and refining, a major contributor to climate change, is concentrated within the U.S. South and emits multiple classes of atherogenic pollutants. We investigated whether residential exposure to oil refineries could explain variation in self-reported coronary heart disease (CHD) prevalence among adults in southern states for the year 2018, where the majority of oil refinery activity occurs (Alabama, Mississippi, Louisiana, Arkansas, Texas, New Mexico, and Oklahoma). We examined census tract-level association between oil refineries and CHD prevalence. We used a double matching method to adjust for measured and unmeasured spatial confounders: one-to-n distance matching and one-to-one generalized propensity score matching. Exposure metrics were constructed based on proximity to refineries, activities of refineries, and wind speed/direction. For all census tracts within 10 km of refineries, self-reported CHD prevalence ranged from 1.2% to 17.6%. Compared to census tracts located at ≥5 km and <10 km, one standard deviation increase in the exposure within 5 km of refineries was associated with a 0.33 (95% confidence interval: 0.04, 0.63) percentage point increase in the prevalence. A total of 1119.0 (123.5, 2114.2) prevalent cases or 1.6% (0.2, 3.1) of CHD prevalence in areas within 5 km from refineries were potentially explained by exposure to oil refineries. At the census tract-level, the prevalence of CHD explained by exposure to oil refineries ranged from 0.02% (0.00, 0.05) to 47.4% (5.2, 89.5). Thus, although we cannot rule out potential confounding by other personal risk factors, CHD prevalence was found to be higher in populations living nearer to oil refineries, which may suggest that exposure to oil refineries can increase CHD risk, warranting further investigation.

Spatiotemporal patterns of surface ozone exposure inequality in China

Rising surface ozone (O3) levels in China are increasingly emphasizing the potential threats to public health, ecological balance, and economic sustainability. Using a 1 km × 1 km dataset of O3 concentrations, this research employs subpopulation demographic data combined with a population-weighted quality model. Its aim is to evaluate quantitatively the differences in O3 exposure among various subpopulations within China, both at a provincial and urban cluster level. Additionally, an exposure disparity indicator was devised to establish unambiguous exposure risks among significant urban agglomerations at varying O3 concentration levels. The findings reveal that as of 2018, the population-weighted average concentration of O3 for all subgroups has experienced a significant uptick, surpassing the average O3 concentration (118 μg/m3). Notably, the middle-aged demographic exhibited the highest O3 exposure level at 135.7 μg/m3, which is significantly elevated compared to other age brackets. Concurrently, there exists a prominent positive correlation between educational attainment and O3 exposure levels, with the medium-income bracket showing the greatest susceptibility to O3 exposure risks. From an industrial vantage point, the secondary sector demographic is the most adversely impacted by O3 exposure. In terms of urban-rural structure, urban groups in all regions had higher levels of exposure to O3 than rural areas, with North and East China having the most significant levels of exposure. These findings not only emphasize the intricate interplay between public health and environmental justice but further highlight the indispensability of segmented subgroup strategies in environmental health risk assessment. Moreover, this research furnishes invaluable scientific groundwork for crafting targeted public health interventions and sustainable air quality management policies.

Assessing the effects of air pollution and residential greenness on frailty in older adults: a prospective cohort study from China

Many studies have established a correlation between air pollution and green space with age-related diseases, yet the relationship between air pollution, green space, and frailty among older adults is not fully understood. The primary objective of this investigation is to examine the longitudinal association among air pollution, green space, and frailty in older adults, as well as the potential interaction and mediating effect. Analyzed data were obtained from the multi-wave CLHLS investigation (2008-2018). The participants' environmental exposure was evaluated using six air pollutants (PM1, PM2.5, PM10, PM10-2.5, O3, and NO2), and normalized difference vegetation index (NDVI). Annual ambient air pollutants were estimated using satellite-based spatiotemporal models. Time-varying Cox proportional risk models were employed to investigate the longitudinal relationships between air pollutants, greenness, and the onset of frailty in the elderly population. We conducted a variety of subgroup analyses, sensitivity analyses, and assessed potential interaction and causal mediating effects. A total of 6953 eligible elderly individuals were enrolled in our study. In the fully adjusted model, per IQR uptick in levels of PM1, PM2.5, PM10, PM10-2.5, O3, and NO2 corresponded to a 17% (95% CI 1.10-1.24), 25% (95% CI 1.17-1.34), 29% (95% CI 1.20-1.39), 35% (95% CI 1.24-1.47), 12% (95% CI 1.04-1.20), and 11% (95% CI 1.05-1.18) increase in frailty risk, respectively. For NDVI, increased IQR was significantly negatively associated with the risk of frailty (aHR 0.82, 95% CI 0.77-0.87). Our results revealed a significant interaction effect among O3, NO2, and residential greenness. PM1, PM2.5, PM10, and PM10-2.5 play a mediating role in the estimated relationship between residential greenness and frailty. In summary, our study reveals that PM1, PM2.5, PM10, PM10-2.5, O3, and NO2 correspond to elevated risks of frailty in the elderly. Residential greenness is associated with a lower risk of frailty in the elderly. Residential greenness can exert a positive impact on frailty by reducing particulate matter concentrations.

Integrating Clinical and Air Quality Data to Improve Prediction of COPD Exacerbations

Several studies have found associations between air pollution and respiratory disease outcomes. However, there is minimal prognostic research exploring whether integrating air quality into clinical prediction models can improve accuracy and utility. In this study, we built models using both logistic regression and random forests to determine the benefits of including air quality data with meteorological and clinical data in prediction of COPD exacerbations requiring medical care. Logistic models were not improved by inclusion of air quality. However, the net benefit curves of random forest models showed greater clinical utility with the addition of air quality data. These models demonstrate a practical and relatively low-cost way to include environmental information into clinical prediction tools to improve the clinical utility of COPD prediction. Findings could be used to provide population level health warnings as well as individual-patient risk assessments.

The Built Environment and Pediatric Health

Buildings, parks, and roads are all elements of the "built environment," which can be described as the human-made structures that comprise the neighborhoods and communities where people live, work, learn, and recreate (https://www.epa.gov/smm/basic-information-about-built-environment). The design of communities where children and adolescents live, learn, and play has a profound impact on their health. Moreover, the policies and practices that determine community design and the built environment are a root cause of disparities in the social determinants of health that contribute to health inequity. An understanding of the links between the built environment and pediatric health will help to inform pediatricians' and other pediatric health professionals' care for patients and advocacy on their behalf. This technical report describes the range of pediatric physical and mental health conditions influenced by the built environment, as well as historical and persistent effects of the built environment on health disparities. The accompanying policy statement outlines community design solutions that can improve pediatric health and health equity, including opportunities for pediatricians and the health care sector to incorporate this knowledge in patient care, as well as to play a role in advancing a health-promoting built environment for all children and families.

Multi-scale spatiotemporal trends and corresponding disparities of PM2.5 exposure in China

Despite the effectiveness of targeted measures to mitigate air pollution, China-a developing country with high PM2.5 concentration and dense population, faces a high risk of PM2.5-related mortality. However, existing studies on long-term PM2.5 exposure in China have not reached a consensus as to which year it peaked during the "initially pollution, then mitigation" process. Furthermore, analyses in these studies were rarely undertaken from multi-spatial scales. In this study, a piecewise linear regression model was employed to detect the turning point of population-weighted exposure (PWE) to PM2.5 for the period 2000-2020. Multi-scale spatiotemporal patterns of PM2.5 exposure were evaluated during upward and downward periods at the province, city and county levels, and their corresponding disparities were estimated using the Gini index. The results showed that 2013 was the breakpoint year for PM2.5 PWE across China from 2000 to 2020. Cities and counties where PM2.5 PWE displayed increasing trends during the mitigation stage (2013-2020) basically became the heaviest PM2.5 exposure regions in 2020. High PM2.5 exposure was observed in Beijing-Tianjin-Hebei, Central China, and the Tarim Basin in Xinjiang, whereas lower PM2.5 exposure regions were mainly concentrated in Hainan Province, the Hengduan Mountains, and northern Xinjiang. These cross-provincial patterns might have been overlooked when conducting macro-scale analyses. Province-level PM2.5 exposure inequality was less than the city- and county-levels estimations, and regional inequalities were high in eastern and western China. In this study, multi-scale PM2.5 exposure trends and their disparities over a prolonged period were investigated, and the findings provide a reference for pollution mitigation and regional inequality reduction.

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.

Effects of in-utero personal exposure to PM2.5 sources and components on birthweight

In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-h personal PM2.5 exposure monitoring sub-study. The mass contributions of six major sources of personal PM2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions evaluated the association between personal PM2.5 sources/components and birthweight, adjusting for gestational age, maternal age, race, infant sex, parity, diabetes status, temperature, maternal education, and smoking history. Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3295.8 g (484.1) and mean PM2.5 exposure was 21.3 (14.4) µg/m3. A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2 g decrease in birthweight (95% CI - 197.7, - 0.6), and aged sea salt was associated with a 70.1 g decrease in birthweight (95% CI - 141.7, 1.4). Magnesium, sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM2.5 mass. This study found evidence that major sources of personal PM2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls.

Racial and ethnic and socioeconomic disparities in childhood cancer incidence trends in the United States, 2000-2019

BACKGROUND

Population-based surveillance of pediatric cancer incidence trends is critical to determine high-risk populations, drive hypothesis generation, and uncover etiologic heterogeneity. We provide a comprehensive update to the current understanding of pediatric cancer incidence trends by sex, race and ethnicity, and socioeconomic status (SES).

METHODS

The Surveillance, Epidemiology, and End Results 22 data (2000-2019) was used to summarize age-adjusted incidence rates for children and adolescents aged 0-19 years at diagnosis. The annual percentage change (APC) and 95% confidence interval (CI) were estimated to evaluate incidence trends by sex, race and ethnicity, and SES overall and for cancer subtypes. Tests of statistical significance were 2-sided.

RESULTS

Substantial variation was observed overall and for several histologic types in race and ethnicity- and SES-specific rates. Overall, we observed a statistically significant increase in incidence rates (APC = 0.8%, 95% CI = 0.6% to 1.1%). All race and ethnic groups saw an increase in incidence rates, with the largest occurring among non-Hispanic American Indian and Alaska Native children and adolescents (APC = 1.7%, 95% CI = 0.5% to 2.8%) and the smallest increase occurring among non-Hispanic White children and adolescents (APC = 0.7%, 95% CI = 0.5% to 1.0%). The lowest SES quintiles saw statistically significant increasing trends, while the highest quintile remained relatively stable (quintile 1 [Q1] APC = 1.6%, 95% CI = 0.6% to 2.6%; quintile 5 [Q5] APC = 0.3%, 95% CI = -0.1% to 0.7%).

CONCLUSIONS

Childhood cancer incidence is increasing overall and among every race and ethnic group. Variation by race and ethnicity and SES may enable hypothesis generation on drivers of disparities observed.

Ethnoracial Disparities in Nitrogen Dioxide Pollution in the United States: Comparing Data Sets from Satellites, Models, and Monitors

In the United States (U.S.), studies on nitrogen dioxide (NO2) trends and pollution-attributable health effects have historically used measurements from in situ monitors, which have limited geographical coverage and leave 66% of urban areas unmonitored. Novel tools, including remotely sensed NO2 measurements and estimates of NO2 estimates from land-use regression and photochemical models, can aid in assessing NO2 exposure gradients, leveraging their complete spatial coverage. Using these data sets, we find that Black, Hispanic, Asian, and multiracial populations experience NO2 levels 15-50% higher than the national average in 2019, whereas the non-Hispanic White population is consistently exposed to levels that are 5-15% lower than the national average. By contrast, the in situ monitoring network indicates more moderate ethnoracial NO2 disparities and different rankings of the least- to most-exposed ethnoracial population subgroup. Validating these spatially complete data sets against in situ observations reveals similar performance, indicating that all these data sets can be used to understand spatial variations in NO2. Integrating in situ monitoring, satellite data, statistical models, and photochemical models can provide a semiobservational record, complete geospatial coverage, and increasingly high spatial resolution, enhancing future efforts to characterize, map, and track exposure and inequality for highly spatially heterogeneous pollutants like NO2.

Demographic and Psychosocial Characteristics, Air Pollution Exposure, and Housing Mobility of Mexican Immigrant Families

OBJECTIVE

People of color and lower socioeconomic status groups in the USA, including those of Mexican origin, are exposed to higher concentrations of air pollution, including fine particulate matter (PM2.5). Associations were examined between neighborhood air pollution levels and the psychosocial and demographic characteristics of linguistically isolated Mexican-origin immigrant families. Housing mobility and changes in air pollution levels due to changes in residence were also examined.

METHODS

A sample of 604 linguistically isolated Mexican-origin families in central TX provided data on demographic and psychosocial experiences. Outdoor air pollution concentrations at participants' home addresses were based on high-resolution estimates of fine particulate matter (PM2.5) and its constituents. Movers were identified as families whose residential addresses changed during the study period; these participants were further grouped and compared based on the change in their residential PM2.5 concentration from before to after their move.

RESULTS

Lower PM2.5 concentrations were associated with reports of more ethnic discriminatory experiences, higher socioeconomic status, and higher perceived neighborhood safety. Among the 23% of families who changed residences, PM2.5 concentrations were generally lower at the new family address. Families with mothers reporting a greater sense of neighborhood safety or acculturation levels tended to move from one area low in air pollutants to another, and mothers reporting the lowest levels of neighborhood safety or acculturation tended to move from one area high in air pollutants to another.

CONCLUSION

There are limits to assimilation for Mexican immigrant families. Living in more advantaged neighborhoods is associated with experiencing better air quality, but this advantage may come at the cost of experiencing more ethnic discrimination.

Examining the geographical distribution of air pollution disparities across different racial and ethnic groups: Incorporating workplace addresses

BACKGROUND

Most previous studies on air pollution exposure disparities among racial and ethnic groups in the US have been limited to residence-based exposure and have given little consideration to population mobility and spatial patterns of residences, workplaces, and air pollution. This study aimed to examine air pollution exposure disparities by racial and ethnic groups while explicitly accounting for both the work-related activity of the population and localized spatial patterns of residential segregation, clustering of workplaces, and variability of air pollutant concentration.

METHOD

In the present study, we assessed population-level exposure to air pollution using tabulated residence and workplace addresses of formally employed workers from LEHD Origin-Destination Employment Statistics (LODES) data at the census tract level across eight Metropolitan Statistical Areas (MSAs). Combined with annual-averaged predictions for three air pollutants (PM2.5, NO2, O3), we investigated racial and ethnic disparities in air pollution exposures at home and workplaces using pooled (i.e., across eight MSAs) and regional (i.e., with each MSA) data.

RESULTS

We found that non-White groups consistently had the highest levels of exposure to all three air pollutants, at both their residential and workplace locations. Narrower exposure disparities were found at workplaces than residences across all three air pollutants in the pooled estimates, due to substantially lower workplace segregation than residential segregation. We also observed that racial disparities in air pollution exposure and the effect of considering work-related activity in the exposure assessment varied by region, due to both the levels and patterns of segregation in the environments where people spend their time and the local heterogeneity of air pollutants.

CONCLUSIONS

The results indicated that accounting for workplace activity illuminates important variation between home- and workplace-based air pollution exposure among racial and ethnic groups, especially in the case of NO2. Our findings suggest that consideration of both activity patterns and place-based exposure is important to improve our understanding of population-level air pollution exposure disparities, and consequently to health disparities that are closely linked to air pollution exposure.

Pan-cancer mutational signature surveys correlated mutational signature with geospatial environmental exposures and viral infections

Background

Cancer has been disproportionally affecting minorities. Genomic-based cancer disparity analyses have been less common than conventional epidemiological studies. In the past decade, mutational signatures have been established as characteristic footprints of endogenous or exogenous carcinogens.

Methods

Integrating datasets of diverse cancer types from The Cancer Genome Atlas and geospatial environmental risks of the registry hospitals from the United States Environmental Protection Agency, we explored mutational signatures from the aspect of racial disparity concerning pollutant exposures. The raw geospatial environmental exposure data were refined to 449 air pollutants archived and modeled from 2007 to 2017 and aggregated to the census county level. Additionally, hepatitis B and C viruses and human papillomavirus infection statuses were incorporated into analyses for skin cancer, cervical cancer, and liver cancer.

Results

Mutation frequencies of key oncogenic genes varied substantially between different races. These differences were further translated into differences in mutational signatures. Survival analysis revealed that the increased pollution level is associated with worse survival. The analysis of the oncogenic virus revealed that aflatoxin, an affirmed carcinogen for liver cancer, was higher in Asian liver cancer patients than in White patients. The aflatoxin mutational signature was exacerbated by hepatitis infection for Asian patients but not for White patients, suggesting a predisposed genetic or genomic disadvantage for Asians concerning aflatoxin.

Conclusions

Environmental pollutant exposures increase a mutational signature level and worsen cancer prognosis, presenting a definite adverse risk factor for cancer patients.

Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards

Standard environmental hazard exposure assessment methods have been primarily based on residential places, neglecting individuals' hazard exposures due to activities outside home neighborhood and underestimating peoples' overall hazard exposures. To address this limitation, this study proposes a novel mobility-based index for the hazard exposure evaluation. Using large-scale human mobility data, we quantify the extent of population dwell time in high environmental hazard places in 239 US counties for three environmental hazards. We explore how human mobility extends the reach of environmental hazards and leads to the emergence of latent exposure for populations living outside high-hazard areas. Notably, neglect of mobility can lead to over 10% underestimation of hazard exposures. The interplay of spatial clustering in high-hazard regions and human movement trends creates "environmental hazard traps." Poor and ethnic minority residents disproportionately face multiple types of environmental hazards. This data-driven evidence supports the severity of these injustices. We also studied latent exposure arising from visits outside residents' home areas, revealing millions of the population having 5 to 10% of daily activities occur in high-exposure zones. Despite living in perceived safe areas, human mobility could expose millions of residents to different hazards. These findings provide crucial insights for targeted policies to mitigate these severe environmental injustices.

Exposure to Particulate Matter Air Pollution and Age of Menarche in a Nationwide Cohort of U.S. Girls

BACKGROUND

It remains unclear whether in utero and childhood exposure to air pollution affects pubertal development, particularly age of menarche in girls.

OBJECTIVE

The aim of this study was to determine whether residential ambient particulate matter (PM) exposure in utero and during childhood is associated with age of menarche.

METHODS

We studied 5,201 girls in the Growing Up Today Study 2 (2004-present) who were 10-17 y of age at enrollment (47.7% premenarchal; 52.3% postmenarchal). Exposure to three size fractions of PM [fine PM with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ), PM with aerodynamic diameters between  2.5 μ m  and  10 μ m (PM 2.5 - 10 ), and PM with aerodynamic diameter 10 μ m (PM 10 )] was assigned based on maternal residential address, updated every 2 y, using nationwide spatiotemporal models. We estimated average PM exposure in utero, and time-varying windows: annual average exposure in the prior 1 and 2 y and cumulative average from birth. Age of menarche was self-reported on three surveys administered in 2004, 2006, and 2008. We calculated hazard ratios (HR) for menarche for an interquartile range (IQR) increase in PM exposure using Cox proportional hazard models adjusting for potential confounders.

RESULTS

Girls attained menarche at 12.3 y of age on average. In the adjusted model, higher residential exposure to ambient PM 2.5 during all time windows was associated with earlier age of menarche. The HRs of menarche for each IQR (4   μ g / m 3 ) increase in exposure to PM 2.5 during the in utero period, 1 y prior to menarche, and throughout childhood were 1.03 [95% confidence interval (CI): 1.00, 1.06], 1.06 (95% CI: 1.02, 1.10) and 1.06 (95% CI: 1.02, 1.10), respectively. Effect estimates for PM 10 exposure were similar, albeit attenuated, for all time windows. PM 2.5 - 10 exposure was not associated with age of menarche.

DISCUSSION

Among a large, nationwide, prospective cohort of U.S. girls, higher exposure to PM 2.5 and PM 10 in utero and throughout childhood was associated with an earlier age of menarche. Our results suggest that PM 2.5 and PM 10 may have endocrine-disrupting properties that could lead to altered timing of menarche. https://doi.org/10.1289/EHP12110.

Role of neighborhood context in ovarian cancer survival disparities: current research and future directions

Ovarian cancer is the fifth leading cause of cancer-associated mortality among US women with survival disparities seen across race, ethnicity, and socioeconomic status, even after accounting for histology, stage, treatment, and other clinical factors. Neighborhood context can play an important role in ovarian cancer survival, and, to the extent to which minority racial and ethnic groups and populations of lower socioeconomic status are more likely to be segregated into neighborhoods with lower quality social, built, and physical environment, these contextual factors may be a critical component of ovarian cancer survival disparities. Understanding factors associated with ovarian cancer outcome disparities will allow clinicians to identify patients at risk for worse outcomes and point to measures, such as social support programs or transportation aid, that can help to ameliorate such disparities. However, research on the impact of neighborhood contextual factors in ovarian cancer survival and in disparities in ovarian cancer survival is limited. This commentary focuses on the following neighborhood contextual domains: structural and institutional context, social context, physical context represented by environmental exposures, built environment, rurality, and healthcare access. The research conducted to date is presented and clinical implications and recommendations for future interventions and studies to address disparities in ovarian cancer outcomes are proposed.

Data-Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions

There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.

Modeling and Analysis of Air Pollution and Environmental Justice: The Case for North Carolina's Hog Concentrated Animal Feeding Operations

BACKGROUND

Concentrated animal feeding operations (CAFOs) emit pollutants that can cause negative impacts on human health. The concentration of hog production in North Carolina raises concerns regarding the disproportionate exposure of vulnerable communities to air pollution from CAFOs.

OBJECTIVES

We investigated whether exposure to gaseous ammonia (NH 3 ) and hydrogen sulfide (H 2 S ) (in 2019) differs between subpopulations by examining demographics, including race/ethnicity, age, educational attainment, language proficiency, and socioeconomic status.

METHODS

We used an Air Monitoring Station (AMS)/Environmental Protection Agency (EPA) Regulatory Model (AERMOD)-based Human Exposure Model (version 3) to estimate ambient concentrations of NH 3 and H 2 S from hog farms in Duplin County and its surrounding counties in North Carolina and estimate subsequent exposures of communities within 50 km of Duplin County, North Carolina, or the Duplin County Region. We combined estimated exposures with 2016 American Community Summary Census data, at the block group level, using spatial analysis to investigate whether exposures to these pollutants differ by race and ethnicity, age, income, education, and language proficiency. Based on these estimations, we assessed associated exposure risks to the impacted communities and used multivariable regression modeling to evaluate the relationship between average ammonia exposures from Duplin regional hog farms and the presence of vulnerable populations.

RESULTS

The average [± standard deviation  ( SD ) ] annual estimated concentration of NH 3 and H 2 S in the Duplin County Region is 1.75 ± 2.81   μ g / m 3 and 0.0087 ± 0.014   μ g / m 3 , respectively. The maximum average annual ambient concentrations are estimated at 54.27 ± 4.12   μ g / m 3 and 0.54 ± 0.041   μ g / m 3 for NH 3 and H 2 S , respectively. Our descriptive analysis reveals that people of low income, people of color, people with low educational attainment, and the linguistically isolated in the Duplin Region are disproportionately exposed to higher levels of pollutants than the average exposure for residents. Alternatively, our statistical results suggests that after adjusting for covariates, communities of color are associated with 1.70% (95% CI: - 3.79 , 0.44) lower NH 3 concentrations per 1-SD increase. One-standard deviation increases in the adults with low educational attainment and children < 19 years of age is associated with 1.26% (95% CI: - 0.77 , 3.33) and 1.20% (95% CI: - 0.62 , 3.05) higher NH 3 exposure per 1-SD increase, respectively.

DISCUSSION

Exposures to NH 3 and H 2 S differed by race and ethnicity, educational attainment, language proficiency, and socioeconomic status. The observed associations between exposure to CAFO-generated pollutants and sociodemographic indicators differed among demographics. The disproportionate distribution of hog facilities and resulting pollutant exposures among communities may have adverse environmental and human health impacts, raising environmental justice concerns. https://doi.org/10.1289/EHP11344.

How Federal and State Regulatory Systems Perpetuate Environmental Injustice in the United States: Industrial Ethylene Oxide Emissions as a Case Study

Background

Ethylene oxide (EtO), a known human carcinogen, is emitted from facilities across the United States. A 2018 assessment by the Environmental Protection Agency (EPA) showed that areas around EtO-emitting facilities had cancer risk levels up to 24 times the national average. The EPA notified the state Department of Environmental Protection (DEP) about the high cancer risk to their residents. Our aim was to analyze actions and implementation equity at the federal, state, and community levels since the EPA notification.

Methods

Using publicly available data, we identified U.S. emitters of EtO and then analyzed community, state, and federal actions since the EPA notification through content analysis of internet data using the lens of the environmental inequality formation (EIF) theory.

Results

Thirty-one of a total 654 EtO-emitting facilities have an estimated cancer risk of over 100 in a million in neighboring census tracts and are located in 13 states and Puerto Rico, representing 7 EPA regions. Content analysis identified themes of community outcry, agency involvement, and legislative action and found no action without community outcry. By January 2021, 2 facilities had closed, 5 facilities had cut emissions, and 24 facilities in 9 states and 5 EPA regions had taken no action.

Discussion

Wealthier white neighborhoods saw facilities close or cut emissions. Differences in state response correlated with differences in community pressure and state priority setting, resulting in over 1 million people having continued significant EtO exposure for years.

Conclusions

The impotence of the federal and state regulatory framework perpetuates environmental injustice in the United States.

Exposure to ambient air pollutants and acute respiratory distress syndrome risk in sepsis

PURPOSE

Exposures to ambient air pollutants may prime the lung enhancing risk of acute respiratory distress syndrome (ARDS) in sepsis. Our objective was to determine the association of short-, medium-, and long-term pollutant exposures and ARDS risk in critically ill sepsis patients.

METHODS

We analyzed a prospective cohort of 1858 critically ill patients with sepsis, and estimated short- (3 days), medium- (6 weeks), and long- (5 years) term exposures to ozone, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), particulate matter < 2.5 μm (PM2.5), and PM < 10 μm (PM10) using weighted averages of daily levels from monitors within 50 km of subjects' residences. Subjects were followed for 6 days for ARDS by the Berlin Criteria. The association between each pollutant and ARDS was determined using multivariable logistic regression adjusting for preselected confounders. In 764 subjects, we measured plasma concentrations of inflammatory proteins at presentation and tested for an association between pollutant exposure and protein concentration via linear regression.

RESULTS

ARDS developed in 754 (41%) subjects. Short- and long-term exposures to SO2, NO2, and PM2.5 were associated with ARDS risk (SO2: odds ratio (OR) for the comparison of the 75-25th long-term exposure percentile 1.43 (95% confidence interval (CI) 1.16, 1.77); p < 0.01; NO2: 1.36 (1.06, 1.74); p = 0.04, PM2.5: 1.21 (1.04, 1.41); p = 0.03). Long-term exposures to these three pollutants were also associated with plasma interleukin-1 receptor antagonist and soluble tumor necrosis factor receptor-1 concentrations.

CONCLUSION

Short and long-term exposures to ambient SO2, PM2.5, and NO2 are associated with increased ARDS risk in sepsis, representing potentially modifiable environmental risk factors for sepsis-associated ARDS.

Global air pollution exposure and poverty

Air pollution is one of the leading causes of health complications and mortality worldwide, especially affecting lower-income groups, who tend to be more exposed and vulnerable. This study documents the relationship between ambient air pollution exposure and poverty in 211 countries and territories. Using the World Health Organization's (WHO) 2021 revised fine particulate matter (PM2.5) thresholds, we show that globally, 7.3 billion people are directly exposed to unsafe average annual PM2.5 concentrations, 80 percent of whom live in low- and middle-income countries. Moreover, 716 million of the world's lowest income people (living on less than $1.90 per day) live in areas with unsafe levels of air pollution, especially in Sub-Saharan Africa. Air pollution levels are particularly high in lower-middle-income countries, where economies tend to rely more heavily on polluting industries and technologies. These findings are based on high-resolution air pollution and population maps with global coverage, as well as subnational poverty estimates based on harmonized household surveys.

Sociodemographic Disparities in Mercury Exposure from United States Coal-Fired Power Plants

Hazardous air pollutants emitted by United States (U.S) coal-fired power plants have been controlled by the Mercury and Air Toxics Standards (MATS) since 2012. Sociodemographic disparities in traditional air pollutant exposures from U.S. power plants are known to occur but have not been evaluated for mercury (Hg), a neurotoxicant that bioaccumulates in food webs. Atmospheric Hg deposition from domestic power plants decreased by 91% across the contiguous U.S. from 6.4 Mg in 2010 to 0.55 Mg in 2020. Prior to MATS, populations living within 5 km of power plants (n = 507) included greater proportions of frequent fish consumers, individuals with low annual income and less than a high school education, and limited English-proficiency households compared to the US general population. These results reinforce a lack of distributional justice in plant siting found in prior work. Significantly greater proportions of low-income individuals lived within 5 km of active facilities in 2020 (n = 277) compared to plants that retired after 2010, suggesting that socioeconomic status may have played a role in retirement. Despite large deposition declines, an end-member scenario for remaining exposures from the largest active power plants for individuals consuming self-caught fish suggests they could still exceed the U.S. Environmental Protection Agency reference dose for methylmercury.

Long-term air pollution exposure and markers of cardiometabolic health in the National Longitudinal Study of Adolescent to Adult Health (Add Health)

BACKGROUND

Air pollution exposure is associated with cardiovascular morbidity and mortality. Although exposure to air pollution early in life may represent a critical window for development of cardiovascular disease risk factors, few studies have examined associations of long-term air pollution exposure with markers of cardiovascular and metabolic health in young adults.

OBJECTIVES

By combining health data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) with air pollution data from the Fused Air Quality Surface using Downscaling (FAQSD) archive, we: (1) calculated multi-year estimates of exposure to ozone (O3) and particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5) for Add Health participants; and (2) estimated associations between air pollution exposures and multiple markers of cardiometabolic health.

METHODS

Add Health is a nationally representative longitudinal cohort study of over 20,000 adolescents aged 12-19 in the United States (US) in 1994-95 (Wave I). Participants have been followed through adolescence and into adulthood with five in-home interviews. Estimated daily concentrations of O3 and PM2.5 at census tracts were obtained from the FAQSD archive and used to generate tract-level annual averages of O3 and PM2.5 concentrations. We estimated associations between average O3 and PM2.5 exposures from 2002 to 2007 and markers of cardiometabolic health measured at Wave IV (2008-09), including hypertension, hyperlipidemia, body mass index (BMI), diabetes, C-reactive protein, and metabolic syndrome.

RESULTS

The final sample size was 11,259 individual participants. The average age of participants at Wave IV was 28.4 years (range: 24-34 years). In models adjusting for age, race/ethnicity, and sex, long-term O3 exposure (2002-07) was associated with elevated odds of hypertension, with an odds ratio (OR) of 1.015 (95% confidence interval [CI]: 1.011, 1.029); obesity (1.022 [1.004, 1.040]); diabetes (1.032 [1.009,1.054]); and metabolic syndrome (1.028 [1.014, 1.041]); PM2.5 exposure (2002-07) was associated with elevated odds of hypertension (1.022 [1.001, 1.045]).

CONCLUSION

Findings suggest that long-term ambient air pollution exposure, particularly O3 exposure, is associated with cardiometabolic health in early adulthood.

Exposure Disparities by Income, Race and Ethnicity, and Historic Redlining Grade in the Greater Seattle Area for Ultrafine Particles and Other Air Pollutants

BACKGROUND

Growing evidence shows ultrafine particles (UFPs) are detrimental to cardiovascular, cerebrovascular, and respiratory health. Historically, racialized and low-income communities are exposed to higher concentrations of air pollution.

OBJECTIVES

Our aim was to conduct a descriptive analysis of present-day air pollution exposure disparities in the greater Seattle, Washington, area by income, race, ethnicity, and historical redlining grade. We focused on UFPs (particle number count) and compared with black carbon, nitrogen dioxide, and fine particulate matter (PM 2.5 ) levels.

METHODS

We obtained race and ethnicity data from the 2010 U.S. Census, median household income data from the 2006-2010 American Community Survey, and Home Owners' Loan Corporation (HOLC) redlining data from the University of Richmond's Mapping Inequality. We predicted pollutant concentrations at block centroids from 2019 mobile monitoring data. The study region encompassed much of urban Seattle, with redlining analyses restricted to a smaller region. To analyze disparities, we calculated population-weighted mean exposures and regression analyses using a generalized estimating equation model to account for spatial correlation.

RESULTS

Pollutant concentrations and disparities were largest for blocks with median household income of < $ 20,000 , Black residents, HOLC Grade D, and ungraded industrial areas. UFP concentrations were 4% lower than average for non-Hispanic White residents and higher than average for racialized groups (Asian, 3%; Black, 15%; Hispanic, 6%; Native American, 8%; Pacific Islander, 11%). For blocks with median household incomes of < $ 20,000 , UFP concentrations were 40% higher than average, whereas blocks with incomes of > $ 110,000 had UFP concentrations 16% lower than average. UFP concentrations were 28% higher for Grade D and 49% higher for ungraded industrial areas compared with Grade A. Disparities were highest for UFPs and lowest for PM 2.5 exposure levels.

DISCUSSION

Our study is one of the first to highlight large disparities with UFP exposures compared with multiple pollutants. Higher exposures to multiple air pollutants and their cumulative effects disproportionately impact historically marginalized groups. https://doi.org/10.1289/EHP11662.

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.

The neighborhood built environment and COVID-19 hospitalizations

Research on the associations between the built environment and COVID-19 outcomes has mostly focused on incidence and mortality. Also, few studies on the built environment and COVID-19 have controlled for individual-level characteristics across large samples. In this study, we examine whether neighborhood built environment characteristics are associated with hospitalization in a cohort of 18,042 individuals who tested positive for SARS-CoV-2 between May and December 2020 in the Denver metropolitan area, USA. We use Poisson models with robust standard errors that control for spatial dependence and several individual-level demographic characteristics and comorbidity conditions. In multivariate models, we find that among individuals with SARS-CoV-2 infection, those living in multi-family housing units and/or in places with higher particulate matter (PM2.5) have a higher incident rate ratio (IRR) of hospitalization. We also find that higher walkability, higher bikeability, and lower public transit access are linked to a lower IRR of hospitalization. In multivariate models, we did not find associations between green space measures and the IRR of hospitalization. Results for non-Hispanic white and Latinx individuals highlight substantial differences: higher PM2.5 levels have stronger positive associations with the IRR of hospitalization for Latinx individuals, and density and overcrowding show stronger associations for non-Hispanic white individuals. Our results show that the neighborhood built environment might pose an independent risk for COVID-19 hospitalization. Our results may inform public health and urban planning initiatives to lower the risk of hospitalization linked to COVID-19 and other respiratory pathogens.

Effects of In-Utero Personal Exposure to PM2.5 Sources and Components on Birthweight

Background

In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-hour personal PM2.5 exposure monitoring sub-study.

Methods

The mass contributions of six major sources of personal PM2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions were used to evaluate the association between personal PM2.5 sources and birthweight. Additionally, high-loading components were evaluated with birthweight individually and in models further adjusted for PM2.5 mass.

Results

Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3,295.8g (484.1) and mean PM2.5 exposure was 21.3 (14.4) μg/m3. A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2g decrease in birthweight (95% CI: -197.7, -0.6), while aged sea salt was associated with lower birthweight (β =-70.1; 95% CI: -141.7, 1.4). Magnesium sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM2.5 mass.

Conclusions

This study found evidence that major sources of personal PM2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48-69 % and 63-71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

Combining Satellite-Derived PM2.5 Data and a Reduced-Form Air Quality Model to Support Air Quality Analysis in US Cities

Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra-urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city-scale decision-making. To reduce InMAP's biases and increase its relevancy for urban-scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite-derived speciated PM2.5 from Washington University and ground-level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground-monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <±10% for most of the PM2.5 components it simulates (pSO4: -48%, pNO3: 8%, pNH4: 69%), but with city-specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model (pSO4: 53%, pNO3: 52%, pNH4: 80%) but is met with the city-scaling approach (15%-27%). The city-specific scaling method also improves the R 2 value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36-0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non-EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide -6%).

Economic and racial disparities of the weather impact on air quality in Brazil

Several studies have quantified the air pollution exposure disparities across racial and income groups. However, there is still a lack of investigations assessing disparities related to the impacts of weather on air pollution, which could indicate target air pollution reduction strategies under different climate scenarios. Our study aims to address this gap by estimating the economic and racial disparities of the weather impact on air quality in Brazil between 2003 and 2018. First, we used a generalized additive approach to estimate the weather-related changes in PM_2.5. This framework derived "weather penalty", which a positive penalty suggests that an increase in PM_2.5 was associated with long-term weather changes in the study period. Then, we estimated the population-weighted weather penalty for racial and income groups. Average penalty for the White population (the most-exposed group) was 31% higher than that of the Pardo population (the least-exposed group, mainly people of light brown skin color) in Brazil. In the stratification analysis by region, the Midwest and South were the regions where the black population was the most-exposed group. For the income group, our results indicate that the high-income population group was the most-exposed group in all analyses, including the national and the regional analyses. These findings are somewhat surprising, as previous studies have shown that minority and low-income populations tend to be more exposed to air pollution, than white and higher-income populations. However, our study suggests that disparities in exposure to air pollution may be more complex and nuanced than previously thought. Further research is needed to better understand the underlying drivers of these environmental disparities, and to develop targeted interventions to reduce exposures.

Ambient fine particulate exposure and subcortical gray matter microarchitecture in 9- and 10-year-old children across the United States

Neuroimaging studies showing the adverse effects of air pollution on neurodevelopment have largely focused on smaller samples from limited geographical locations and have implemented univariant approaches to assess exposure and brain macrostructure. Herein, we implement restriction spectrum imaging and a multivariate approach to examine how one year of annual exposure to daily fine particulate matter (PM_2.5), daily nitrogen dioxide (NO₂), and 8-h maximum ozone (O₃) at ages 9-10 years relates to subcortical gray matter microarchitecture in a geographically diverse subsample of children from the Adolescent Brain Cognitive Development (ABCD) Study^℠. Adjusting for confounders, we identified a latent variable representing 66% of the variance between one year of air pollution and subcortical gray matter microarchitecture. PM_2.5 was related to greater isotropic intracellular diffusion in the thalamus, brainstem, and accumbens, which related to cognition and internalizing symptoms. These findings may be indicative of previously identified air pollution-related risk for neuroinflammation and early neurodegenerative pathologies.

Making the invisible visible: Using a qualitative system dynamics model to map disparities in cumulative environmental stressors and children's neurodevelopment

BACKGROUND

The combined effects of multiple environmental toxicants and social stressor exposures are widely recognized as important public health problems, likely contributing to health inequities. However, US policy makers at state and federal levels typically focus on one stressor exposure at a time and have failed to develop comprehensive strategies to reduce multiple co-occurring exposures, mitigate cumulative risks and prevent harm. This research aimed to move from considering disparate environmental stressors in isolation to mapping the links between environmental, economic, social and health outcomes as a dynamic complex system using children's exposure to neurodevelopmental toxicants as an illustrative example. Such a model can be used to support a broad range of child developmental and environmental health policy stakeholders in improving their understanding of cumulative effects of multiple chemical, physical, biological and social environmental stressors as a complex system through a collaborative learning process.

METHODS

We used system dynamics (SD) group model building to develop a qualitative causal theory linking multiple interacting streams of social stressors and environmental neurotoxicants impacting children's neurodevelopment. A 2 1/2-day interactive system dynamics workshop involving experts across multiple disciplines was convened to develop the model followed by qualitative survey on system insights.

RESULTS

The SD causal map covered seven interconnected themes: environmental exposures, social environment, health status, education, employment, housing and advocacy. Potential high leverage intervention points for reducing disparities in children's cumulative neurotoxicant exposures and effects were identified. Workshop participants developed deeper level of understanding about the complexity of cumulative environmental health risks, increased their agreement about underlying causes, and enhanced their capabilities for integrating diverse forms of knowledge about the complex multi-level problem of cumulative chemical and non-chemical exposures.

CONCLUSION

Group model building using SD can lead to important insights to into the sociological, policy, and institutional mechanisms through which disparities in cumulative impacts are transmitted, resisted, and understood.

Prenatal exposure to persistent and non-persistent chemical mixtures and associations with adverse birth outcomes in the Atlanta African American Maternal-Child Cohort

BACKGROUND

African Americans (AAs) experience higher rates of preterm birth and fetal growth restriction relative to other pregnant populations. Differential in utero exposure to environmental chemicals may partially explain these health disparities, as AAs are disproportionately exposed to environmental hazards.

OBJECTIVE

We examined the individual and mixture effects of non-persistent chemicals and persistent organic pollutants (POPs) on gestational age at birth and birthweight for gestational age z-scores within a prospective cohort of pregnant AAs.

METHODS

First-trimester serum and urine samples obtained from participants within the Atlanta African American Maternal-Child cohort were analyzed for 43 environmental chemicals, including per-and polyfluoroalkyl substances (PFAS), polybrominated diphenyl ethers (PBDEs), organochlorine pesticides, pyrethroid insecticides, phthalates, bisphenol A, nicotine, and the primary metabolite of delta-9-tetrahydrocannabinol. Linear regression was used to estimate individual associations between chemicals and gestational age and birthweight z-scores (N ranging from 107 to 523). Mixture associations were estimated using quantile g-computation, principal component (PC) analyses, and hierarchical Bayesian kernel machine regression among complete cases (N = 86).

RESULTS

Using quantile g-computation, increasing all chemical exposures by one quantile was modestly associated with a reduction in gestational age (mean change per quartile increase = -0.47, 95% CI = -1.56, 0.61) and birthweight z-scores (mean change per quartile increase = -0.49, 95% CI = -1.14, 0.15). All PCs were associated with a reduction in birthweight z-scores; associations were greatest in magnitude for the two PCs reflecting exposure to combined tobacco, insecticides, PBDEs, and phthalates. In single pollutant models, we observed inconsistent and largely non-significant associations.

SIGNIFANCE

We conducted multiple targeted exposure assessment methods to quantify levels of environmental chemicals and leveraged mixture methods to quantify their joint effects on gestational age and birthweight z-scores. Our findings suggest that prenatal exposure to multiple classes of persistent and non-persistent chemicals is associated with reduced gestational age and birthweight z-scores in AAs.

IMPACT

African Americans (AAs) experience higher rates of preterm birth and fetal growth restriction relative to other pregnant populations. Differential in utero exposure to environmental chemicals may partially explain these health disparities, as AAs are disproportionately exposed to environmental hazards. In the present study, we analyzed serum and urine samples for levels of 43 environmental chemicals. We used quantile g-computation, principal component analysis, and BKMR to assess associations between chemical exposure mixtures and adverse birth outcomes. Our findings suggest that prenatal exposure to multiple classes of chemicals is associated with reduced birthweight z-scores, a proxy for fetal growth, in AAs.

Multiscale Analysis of the Relationship between Toxic Chemical Hazard Risks and Racial/Ethnic and Socioeconomic Groups in Texas, USA

Although quantitative environmental (in)justice research demonstrates a disproportionate burden of toxic chemical hazard risks among racial/ethnic minorities and people in low socioeconomic positions, limited knowledge exists on how racial/ethnic and socioeconomic groups across geographic spaces experience toxic chemical hazards. This study analyzed the spatial non-stationarity in the associations between toxic chemical hazard risk and community characteristics of census block groups in Texas, USA, for 2017 using a multiscale geographically weighted regression. The results showed that the percentage of Black or Asian population has significant positive associations with toxic risk across block groups in Texas, meaning that racial minorities suffered more from toxic risk wherever they are located in the state. By contrast, the percentage of Hispanic or Latino has a positive relationship with toxic risk, and the relationship varies locally and is only significant in eastern areas of Texas. Statistical associations between toxic risk and socioeconomic variables are not stationary across the state, showing sub-state patterns of spatial variation in terms of the sign, significant level, and magnitude of the coefficient. Income has a significant negative association with toxic risk around the Dallas-Fort Worth-Arlington Metropolitan Statistical Area. Proportions of people without high school diploma and the unemployment rate both have positive relationships with toxic risk in the eastern area of Texas. Our findings highlight the importance of identifying the spatial patterns of the association between toxic chemical hazard risks and community characteristics at the census block group level for addressing environmental inequality.

On the supra-linearity of the relationship between air pollution, mortality and hospital admission in 18 French cities

PURPOSE

Understanding the relationship between an environmental determinant and a given health outcome is key to inform public health policies. The short-term mortality and morbidity responses to outdoor air pollutants are traditionally assessed as a log-linear relationship, but few studies suggest a possible deviation from linearity. This paper investigates the shape of the relationship between ozone, NO₂ and fine particulate matter (PM₁₀ and PM_2.5), mortality and hospital admissions in 18 French cities between 2000 and 2017.

METHOD

A multi-centric time series design, using quasi-Poisson generalized additive models, was used. Four approaches were compared to model concentration-response curves (log-linear, piecewise-linear with a priori defined breakpoints, piecewise-linear with no a priori breakpoint and cubic spline).

RESULTS

All the models indicated evidence of supra-linearity between PM₁₀, PM_2.5, NO₂, mortality and hospital admissions. For instance, with a log-linear model, a 10 µg/m³ increase in PM_2.5 was associated with a 0.4% [95% CI 0.2; 0.7] increase in non-accidental mortality. When using a piecewise model with a priori set breakpoint at 10 µg/m³, the mortality increase was 3.8% [4.4; 6.3] below 10 µg/m³, and 0.3% [0; 0.6] above. Non-significant impacts of ozone were found for concentrations below 90 µg/m³ to 120 µg/m³, with some variability in the identified threshold across the heath indicator studied.

CONCLUSION

The supra-linearity of the relationship between PM₁₀, PM_2.5, NO₂, mortality and hospital admissions supports the need to further reduce air pollution concentrations well below regulatory values.

Effects of prenatal polycyclic aromatic hydrocarbons and childhood material hardship on reading achievement in school-age children: A preliminary study

Background

Children from socioeconomically disadvantaged backgrounds are at elevated risk for reading problems. They are also likely to live in neighborhoods with high levels of air pollution and to experience material hardship. Despite these risk factors, the links between prenatal chemical exposures, socioeconomic adversities, and reading problems in youth from disadvantaged backgrounds remain understudied. Here we examine associations between prenatal exposure to polycyclic aromatic hydrocarbons (PAH), a common air pollutant, and reading skills, and determine if this relationship is exacerbated by material hardship among Black and/or Latinx children who have been followed as part of a longitudinal urban birth cohort.

Methods

Mothers and their children, who were participants in a prospective birth cohort followed by the Columbia Center for Children's Environmental Health, were recruited for the current study. Personal prenatal PAH exposure was measured during the third-trimester of pregnancy using a personal air monitoring backpack. Mothers reported their level of material hardship when their child was age 5 and children completed measures of pseudoword and word reading [Woodcock Johnson III Tests of Achievement (WJ-III) Basic Reading Index] at age 7. We used multiple linear regression to examine the effects of the interaction between prenatal PAH and material hardship on Basic Reading Index, controlling for ethnicity/race, sex, birthweight, presence of a smoker in the home (prenatal), and maternal education (prenatal) (N = 53).

Results

A prenatal PAH × material hardship interaction significantly associated with WJ-III Basic Reading Index scores at age 7 (β = -0.347, t(44) = -2.197, p = 0.033). Exploratory analyses suggested that this effect was driven by untimed pseudoword decoding (WJ-III Word Attack: β = -0.391, t(44) = -2.550, p = 0.014).

Conclusion

Environmental chemical exposures can be particularly toxic during the prenatal period when the fetal brain undergoes rapid development, making it uniquely vulnerable to chemical perturbations. These data highlight the interactive effects of environmental neurotoxicants and unmet basic needs on children's acquisition of reading skill, specifically phonemic processing. Such findings identify potentially modifiable environmental risk factors implicated in reading problems in children from economically disadvantaged backgrounds.

Acute effects of ambient nitrogen dioxide exposure on serum biomarkers of nervous system damage in healthy older adults

Ambient nitrogen dioxide (NO₂)-induced adverse health effects have been studied, but documented evidence on neural systems is limited. This study aimed to determine the acute effect of NO₂ exposure on nervous system damage biomarker levels in healthy older adults. Five rounds of follow-up among 34 healthy retired people were scheduled from December 2018 to April 2019 in Xinxiang, China. The real-time NO₂ concentrations were measured using a fixed site monitor. Serum samples were acquired during each round to measure nervous system damage biomarker levels: brain-derived neurotrophic factor (BDNF), neurofilament light chain (NfL), neuron-specific enolase (NSE), protein gene product 9.5 (PGP9.5), and S100 calcium-binding protein B (S100B). A linear mixed-effect model was incorporated to analyze the association between short-term NO₂ exposure and serum concentrations of the above-mentioned biomarkers. Stratification analysis based on sex, educational attainment, glutathione S-transferase theta 1 gene (GSTT1) polymorphism, and physical activity intensity was conducted to explore their potential modification effect. The NO₂ concentration ranged from 34.7 to 59.0 µg/m³ during the study period. Acute exposure to ambient NO₂ was significantly associated with elevated serum levels of NfL, PGP9.5, and BDNF. In response to a 10 µg/m³ increase in NO₂ concentration, NfL and PGP9.5 levels increased by 76 % (95 % confidence interval [CI]: 12-140 %) and 54 % (95 % CI: 1-107 %) on the lag0 day, respectively, while BDNF levels increased by 49 % (95 % CI: 2-96 %) at lag4 day. The estimated effect of NO₂ on NSE levels in GSTT1-sufficient participants was significantly higher than that in GSTT1-null participants. Intriguingly, the estimation of NO₂ on PGP9.5 levels in females was significantly higher than that in males. Most two-pollutant models showed robust results, except for O₃, which might have had confounding effects on NO₂-induced BDNF stimulation. In summary, acute exposure to NO₂ was associated with increased levels of serum nervous system damage biomarker levels including NFL, PGP9.5, and BDNF. The present study provided insights into NO₂ exposure-induced adverse neural effects.

Public Concern about Air Pollution and Related Health Outcomes on Social Media in China: An Analysis of Data from Sina Weibo (Chinese Twitter) and Air Monitoring Stations

To understand the temporal variation, spatial distribution and factors influencing the public's sensitivity to air pollution in China, this study collected air pollution data from 2210 air pollution monitoring sites from around China and used keyword-based filtering to identify individual messages related to air pollution and health on Sina Weibo during 2017-2021. By analyzing correlations between concentrations of air pollutants (PM_2.5, PM₁₀, CO, NO₂, O₃ and SO₂) and related microblogs (air-pollution-related and health-related), it was found that the public is most sensitive to changes in PM_2.5 concentration from the perspectives of both China as a whole and individual provinces. Correlations between air pollution and related microblogs were also stronger when and where air quality was worse, and they were also affected by socioeconomic factors such as population, economic conditions and education. Based on the results of these correlation analyses, scientists can survey public concern about air pollution and related health outcomes on social media in real time across the country and the government can formulate air quality management measures that are aligned to public sensitivities.

Epidemiology and Risk Factors for Dementia

In this article, the authors discuss primarily what is known about the epidemiology of all-cause dementia. Dementia is caused by a complex interplay of genetics, comorbidities, and lifestyle factors, and drug development has been challenging. However, evidence from large, prospective, observational studies has identified a variety of factors that may prevent or delay the onset of dementia. Several of these factors are modifiable and lend themselves to well to treatments currently available. The authors discuss the state of current evidence on dementia risk factors, the most promising avenues, and future directions for dementia prevention and management.

Reduced inequality in ambient and household PM2.5 exposure in China

The society has high concerns on the inequality that people are disproportionately exposed to ambient air pollution, but with more time spent indoors, the disparity in the total exposure considering both indoor and outdoor exposure has not been explored; and with the socioeconomical development and efforts in fighting against air pollution, it is unknown how the exposure inequality changed over time. Based on the city-level panel data, this study revealed the Concentration Index (C) in ambient PM_2.5 exposure inequality was positive, indicating the low-income group exposed to lower ambient PM_2.5; however, the total PM_2.5 exposure was negatively correlated with the income, showing a negative C value. The low-income population exposed to high PM_2.5 associated with larger contributions of indoor exposure from the residential emissions. The total PM_2.5 exposure caused 1.13 (0.63-1.73) million premature deaths in 2019, with only 14 % were high-income population. The toughest-ever air pollution countermeasures have reduced ambient PM_2.5 exposures effectively that, however, benefited the rich population more than the others. The transition to clean household energy sources significantly affected on indoor air quality improvements, as well as alleviation of ambient air pollution, resulting in notable reductions of the total PM_2.5 exposure and especially benefiting the low-income groups. The negative C values decreased from 2000 to 2019, indicating a significantly reducing trend in the total PM_2.5 exposure inequality over time.

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM_2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM_2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM_2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM_2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).