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Fine particulate matter (PM2.5) exposure has been linked to diverse human health impacts. Little is known about the potential heterogeneous impacts of PM2.5generated from different indoor fuel sources and how exposure differs between personal and indoor environments. Therefore, we used PM2.5collected by one stationary sampler in a kitchen and personal samplers (female and male participants), in homes (n= 24) in Kheri, India, that used either biomass or liquified petroleum gas (LPG) as primary fuel sources. PM2.5samples (pooled by fuel type and monitor placement) were analyzed for oxidative potential and chemical composition, including elements and 125 organic compounds. Zebrafish (Danio rerio) embryos were acutely exposed to varying concentrations of PM2.5and behavioral analyses were conducted. We found relatively high PM2.5concentrations (5–15 times above World Health Organization daily exposure guidelines) and varied human health-related chemical composition based on fuel type and monitor placement (up to 15% carcinogenic polycyclic aromatic hydrocarbon composition). Altered biological responses, including changes to mortality, morphology, and behavior, were elicited by exposure to all sample types. These findings reveal that although LPG is generally ranked the least harmful compared to biomass fuels, chemical characteristics and biological impacts were still present, highlighting the need for further research in determining the safety of indoor fuel sources.

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 NO2measurements and estimates of NO2estimates from land-use regression and photochemical models, can aid in assessing NO2exposure gradients, leveraging their complete spatial coverage. Using these data sets, we find that Black, Hispanic, Asian, and multiracial populations experience NO2levels 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 NO2disparities 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.

Accurate data concerning historical fine particulate matter (PM2.5) concentrations are needed to assess long-term changes in exposure and associated health risks. We estimated historical PM2.5concentrations over North America from 1981 to 2016 for the first time by combining chemical transport modeling, satellite remote sensing, and ground-based measurements. We constrained and evaluated our estimates with direct ground-based PM2.5measurements when available and otherwise with historical estimates of PM2.5from PM10measurements or total suspended particle (TSP) measurements. The estimated PM2.5concentrations were generally consistent with direct ground-based PM2.5measurements over their duration from 1988 onward (R2= 0.6 to 0.85) and to a lesser extent with PM2.5inferred from PM10measurements from 1985 to 1998 (R2= 0.5 to 0.6). The collocated comparison of the trends of population-weighted annual average PM2.5from our estimates and ground-based measurements was highly consistent (RMSD = 0.66 μg m–3). The population-weighted annual average PM2.5over North America decreased from 22 ± 6.4 μg m–3in 1981, to 12 ± 3.2 μg m–3in 1998, and to 7.9 ± 2.1 μg m–3in 2016, with an overall trend of −0.33 μg m–3yr–1(95% CI: −0.35, −0.31).

National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO2, PM2.5, O3, CO, PM10, SO2) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10-fold spatial cross-validation (CV) R2increased 0.02–0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R2= 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.