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BACKGROUND: There is considerable heterogeneity in fine particulate matter (PM2.5)-mortality associations between studies, potentially due to differences in exposure assessment methods. Our aim was to evaluate associations of PM2.5 predicted from different models with nonaccidental and cause-specific mortality. METHODS: We followed 107,906 participants of the Nurses Health Study cohort from 2001 to 2016. PM2.5 concentrations were estimated from spatiotemporal models developed by researchers at the University of Washington (UW), Pennsylvania State University (PSU), and Harvard TH Chan School of Public Health (HSPH). We calculated 12-month moving average concentrations and we used time-varying Cox proportional hazard ratios (HRs). RESULTS: There were 30,242 nonaccidental deaths in 1,435,098 person-years. We observed high correlations and similar temporal trends between the PM2.5 predictions. We found no associations of UW, PSU, or HSPH PM2.5 with nonaccidental mortality, but suggestive positive associations with cancer, cardiovascular, and respiratory disease mortality. There were small differences in HRs between the PM2.5 predictions. All three predictions showed the strongest associations with cancer mortality: HRs (95% confidence interval, expressed per 5 µg/m3 increase) were 1.06 (1.01, 1.12) for UW, 1.08 (1.03, 1.13) for PSU, and 1.05 (1.00, 1.10) for HSPH. In a subset restricted to participants who were always exposed to PM2.5 below 12 µg/m3, we observed positive associations with nonaccidental mortality. CONCLUSION: We found that differences between PM2.5 exposure assessment methods could lead to minor differences in strengths of associations between PM2.5 and cause-specific mortality in a population of US female nurses.

Existing air pollution metabolomics studies showed inconsistent results, often limited by small sample size and individual air pollutants effects. We conducted a metabolome-wide association study among 1096 women (68.2 ± 5.7 years) who provided blood samples (1998-2001) within the Cancer Prevention Study-II Nutrition Cohort. Annual average individual exposures to particulate matter, nitrogen dioxide, ozone, sulfur dioxide, and carbon monoxide in the year of blood draw were used. Metabolomics profiling was conducted on serum samples by Metabolon. We evaluated the individual air pollutants effects using multiple linear regression and the mixture effect using quantile g-computation, adjusting for confounders and false discovery rate (FDR). Ninety-five metabolites were significantly associated with at least one air pollutant or mixture (FDR < 0.05). These metabolites were enriched in pathways related to oxidative stress, systemic inflammation, energy metabolism, signals transduction, nucleic acid damage and repair, and xenobiotics. Sixty metabolites were confirmed with level 1 or 2 evidence, among which 21 have been previously linked to air pollution exposure, including taurine, creatinine, and sebacate. Overall, our results replicate prior findings in a large sample and provide novel insights into biological responses to long-term air pollution exposure using mixture analysis.

BackgroundCoccidioidomycosis, caused by inhalation of Coccidioides spp. spores, is an emerging infectious disease that is increasing in incidence throughout the southwestern US. The pathogen is soil-dwelling, and spore dispersal and human exposure are thought to co-occur with airborne mineral dust exposures, yet fundamental exposure-response relationships have not been conclusively estimated.ObjectivesWe estimated associations between fine mineral dust concentration and coccidioidomycosis incidence in California from 2000 to 2017 at the census tract level, spatiotemporal heterogeneity in exposure-response, and effect modification by antecedent climate conditions.MethodsWe acquired monthly census tract-level coccidioidomycosis incidence data and modeled fine mineral dust concentrations from 2000 to 2017. We fitted zero-inflated distributed-lag nonlinear models to estimate overall exposure-lag-response relationships and identified factors contributing to heterogeneity in exposure-responses. Using a random-effects meta-analysis approach, we estimated county-specific and pooled exposure-responses for cumulative exposures.ResultsWe found a positive exposure-response relationship between cumulative fine mineral dust exposure in the 1-3 months before estimated disease onset and coccidioidomycosis incidence across the study region [incidence rate ratio (IRR) for an increase from 0.1 to 1.1 μg/m3=1.60; 95% CI: 1.46, 1.74]. Positive, supralinear associations were observed between incidence and modeled fine mineral dust exposures 1 [IRR=1.13 (95% CI: 1.10, 1.17)], 2 [IRR=1.15 (95% CI: 1.09, 1.20)] and 3 [IRR=1.08 (95% CI: 1.04, 1.12)] months before estimated disease onset, with the highest exposures being particularly associated. The cumulative exposure-response relationship varied significantly by county [lowest IRR, western Tulare: 1.05 (95% CI: 0.54, 2.07); highest IRR, San Luis Obispo: 3.01 (95% CI: 2.05, 4.42)]. Season of exposure and prior wet winter were modest effect modifiers.DiscussionLagged exposures to fine mineral dust were strongly associated with coccidioidomycosis incidence in the endemic regions of California from 2000 to 2017. https://doi.org/10.1289/EHP13875.

INTRODUCTION: Exposure to unfavorable environmental conditions during pregnancy, such as extreme heat and air pollution, has been linked to increased risk of stillbirth, defined as fetal mortality at or after 20 weeks gestation, however no studies have examined its association with social vulnerability. We examined associations between county-level stillbirth rates, environmental risk factors for stillbirth, and social vulnerability in the United States. METHODS: This ecologic study linked county-level data from three nationwide datasets on stillbirths (National Vital Statistics System), environmental conditions (North American Land Data Assimilation System and Environmental Protection Agency), and social vulnerability (Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry Social Vulnerability Index). Poisson and negative binomial models were fit to the variables and produced rate ratios to estimate associations among stillbirth rates, environmental risk factors, and social vulnerability. RESULTS: Social vulnerability was positively associated withn stillbirth rates, annual average number of extreme heat days, and ambient concentration of particulate matter ≤ 2.5 μm in diameter (PM2.5). The average number of days that ozone and PM2.5 each exceeded regulatory standards were not associated with stillbirth rates or social vulnerability. A positive association between average annual PM2.5 concentration and stillbirth rates was detected; no other significant associations between environmental risk factors and stillbirth rates were observed. DISCUSSION: We found evidence of associations between social vulnerability and stillbirth rates, and between social vulnerability and environmental risk factors for stillbirth at the county level. Further research could inform understanding of how social vulnerability impacts the relationship between environmental exposures and stillbirth risk.

Fine particulate matter (PM2.5) exposure can induce immune system pathology via epigenetic modification, affecting pregnancy outcomes. Our study investigated the association between PM2.5 exposure and immune response, as well as epigenetic changes using high-dimensional epigenetic landscape profiling using cytometry by time-of-flight (EpiTOF) at the single cell. We found statistically significant associations between PM2.5 exposure and levels of certain cytokines [interleukin-1RA (IL-1RA), IL-8/CXCL8, IL-18, and IL-27)] and histone posttranslational modifications (HPTMs) in immune cells (HPTMs: H3K9ac, H3K23ac, H3K27ac, H2BK120ub, H4K20me1/3, and H3K9me1/2) among pregnant and nonpregnant women. The cord blood of neonates with high maternal PM2.5 exposure showed lower IL-27 than those with low exposure. Furthermore, PM2.5 exposure affects the co-modification profiles of cytokines between pregnant women and their neonates, along with HPTMs in each immune cell type between pregnant and nonpregnant women. These modifications in specific histones and cytokines could indicate the toxicological mechanism of PM2.5 exposure in inflammation, inflammasome pathway, and pregnancy complications.

California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989-2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively. Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations. To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)-NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions. Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

The adoption of zero-emission vehicles (ZEVs) offers multiple benefits for the climate, air quality, and public health by reducing tailpipe emissions. However, the environmental justice implications of the nonexhaust emissions from future ZEV fleets for near-roadway communities remain unclear. Here, we model the on-road fine particulate matter (PM2.5) emissions across all California counties and assess the near-roadway exposure disparities at the census block group level in the Los Angeles County in 2050, when almost all passenger vehicles are projected to be ZEVs. We found that promoting zero-emission heavy-duty trucks generates more air quality benefits for disadvantaged communities than light-duty passenger vehicles. Persistent disparities in near-roadway PM2.5 levels, however, exist due to the remaining brake and tire wear emissions and increased truck traffic in disadvantaged communities. We recommend implementing fleet-specific ZEV policies to address brake and tire wear emissions and optimizing freight structures to address these persistent environmental justice issues in California.