Dataset Information

Measuring long-term exposure to wildfire PM_2.5 in California: Time-varying inequities in environmental burden.

ABSTRACT: Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM_2.5) concentrations differ from relatively smoothly varying total PM_2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM_2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM_2.5 exposure based on intermittent and extreme daily wildfire PM_2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM_2.5 < 5 μg/m³; 2) days with non-zero wildfire PM_2.5; 3) mean wildfire PM_2.5 during peak exposure week; 4) smoke waves (≥2 consecutive days with <15 μg/m³ wildfire PM_2.5); and 5) mean annual wildfire PM_2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM_2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM_2.5 exposure for several racial/ethnic groups.

SUBMITTER: Casey JA

PROVIDER: S-EPMC10895344 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Measuring long-term exposure to wildfire PM2.5 in California: Time-varying inequities in environmental burden.

Casey Joan A JA Kioumourtzoglou Marianthi-Anna MA Padula Amy A González David J X DJX Elser Holly H Aguilera Rosana R Northrop Alexander J AJ Tartof Sara Y SY Mayeda Elizabeth Rose ER Braun Danielle D Dominici Francesca F Eisen Ellen A EA Morello-Frosch Rachel R Benmarhnia Tarik T

Proceedings of the National Academy of Sciences of the United States of America 20240213 8

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM2.5) concentrations differ from relatively smoothly varying total PM2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM2.5 exposure based on intermit ...[more]

PMID: 38349877

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Project description:BackgroundElevated homocysteine (Hcy) is a pathogenic mechanism of adverse pregnancy outcomes and PM2.5-induced cardiovascular diseases. We investigated the associations of fine particulate matter (PM2.5) and chemical constituent exposures with maternal circulatory Hcy in early pregnancy.MethodsSerum Hcy and 5-methyltetrahydrofolate in 324 women with pregnancy (162 normal early pregnancy [NEP] and 162 early pregnancy loss [EPL]) were measured by ultra-performance liquid chromatography-tandem triple quadrupole mass spectrometry. Daily exposures to PM2.5 and constituents (black carbon [BC], organic matter, nitrate, ammonium, and sulfate) were accessed using data of Tracking Air Pollution in China platform. Nonlinear and linear associations of average pollutant exposures during the post-conception period with serum Hcy were estimated using generalized additive models and multivariable linear regression models, respectively. Weekly cumulative and distributed lag associations between pollutant exposures within three months before serum collection and Hcy were analyzed by distributed lag nonlinear models combined with multivariable linear regression models. Sensitivity analyses were conducted using constituent residuals instead of constituent concentrations.ResultsThree-month PM2.5 and the five constituent exposures were associated with elevated serum Hcy in all participants, EPL group, and NEP group, with 3-12 weeks before serum collection being the susceptible exposure time windows. Pollutants-related Hcy were generally higher in EPL group than in NEP group. Higher post-conception PM2.5, BC, and sulfate exposures increased serum Hcy in lower but not in higher 5-methyltetrahydrofolate subgroup. Sulfate was the highest risk constituent with residual-related increased Hcy. BC residuals of both post-conception and three-month periods increased Hcy in EPL group but not in NEP group.ConclusionsMaternal circulatory Hcy in early pregnancy increased with PM2.5 and constituent exposures, with sulfate being the highest risk constituent. BC-related increased Hcy may induce EPL.Trial registrationThe study protocol was registered for clinical trials (ChiCTR1900028619) on December 29, 2019.

Project description:IntroductionEstimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.MethodsUsing ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model.ResultsEnsemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers.ConclusionOur method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies.

Project description:Though fine particulate matter (PM2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.

Project description:Reduced complexity tools that provide a representation of both primarily emitted particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), secondarily formed PM2.5, and ozone (O3) allow for a quick assessment of many iterations of pollution control scenarios. Here, a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS), that estimates annual average PM2.5 and seasonal average maximum daily average 8 h (MDA8) O3 for any source location in the United States is described and evaluated. Typically, reduced complexity tools are not evaluated for skill in predicting change in air pollution by comparison with more sophisticated modeling systems. Here, PCAPS was compared against multiple types of emission control scenarios predicted with state-of-the-science photochemical grid models to provide confidence that the model is realistically capturing the change in air pollution due to changing emissions. PCAPS was also applied with all anthropogenic emissions sources for multiple retrospective years to predict PM2.5 chemical components for comparison against routine surface measurements. PCAPS predicted similar magnitudes and regional variations in spatial gradients of measured chemical components of PM2.5. Model performance for capturing ambient measurements was consistent with other reduced complexity tools. PCAPS also did well at capturing the magnitude and spatial features of changes predicted by photochemical transport models for multiple emissions scenarios for both O3 and PM2.5. PCAPS is a flexible tool that provides source-receptor relationships using patterns of air quality gradients from a training data set of generic modeled sources to create interpolated air pollution gradients for new locations not part of the training database. The flexibility provided for both sources and receptors makes this tool ideal for integration into larger frameworks that provide emissions changes and need estimates of air quality to inform downstream analytics, which often includes an estimate of monetized health effects.

Project description:BackgroundPersonal exposure to fine particulate matter (PM2.5) is impacted by different sources each with different chemical composition. Determining these sources is important for reducing personal exposure and its health risks especially during pregnancy.ObjectiveIdentify main sources and their contributions to the personal PM2.5 exposure in 213 women in the 3rd trimester of pregnancy in Los Angeles, CA.MethodsWe measured 48-hr integrated personal PM2.5 exposure and analyzed filters for PM2.5 mass, elemental composition, and optical carbon fractions. We used the EPA Positive Matrix Factorization (PMF) model to resolve and quantify the major sources of personal PM2.5 exposure. We then investigated bivariate relationships between sources, time-activity patterns, and environmental exposures in activity spaces and residential neighborhoods to further understand sources.ResultsMean personal PM2.5 mass concentration was 22.3 (SD = 16.6) μg/m3. Twenty-five species and PM2.5 mass were used in PMF with a final R2 of 0.48. We identified six sources (with major species in profiles and % contribution to PM2.5 mass) as follows: secondhand smoking (SHS) (brown carbon, environmental tobacco smoke; 65.3%), fuel oil (nickel, vanadium; 11.7%), crustal (aluminum, calcium, silicon; 11.5%), fresh sea salt (sodium, chlorine; 4.7%), aged sea salt (sodium, magnesium, sulfur; 4.3%), and traffic (black carbon, zinc; 2.6%). SHS was significantly greater in apartments compared to houses. Crustal source was correlated with more occupants in the household. Aged sea salt increased with temperature and outdoor ozone, while fresh sea salt was highest on days with westerly winds from the Pacific Ocean. Traffic was positively correlated with ambient NO2 and traffic-related NOx at residence. Overall, 76.8% of personal PM2.5 mass came from indoor or personal compared to outdoor sources.ImpactWe conducted source apportionment of personal PM2.5 samples in pregnancy in Los Angeles, CA. Among identified sources, secondhand smoking contributed the most to the personal exposure. In addition, traffic, crustal, fuel oil, fresh and aged sea salt sources were also identified as main sources. Traffic sources contained markers of combustion and non-exhaust wear emissions. Crustal source was correlated with more occupants in the household. Aged sea salt source increased with temperature and outdoor ozone and fresh sea salt source was highest on days with westerly winds from the Pacific Ocean.

Dataset Information

Measuring long-term exposure to wildfire PM_2.5 in California: Time-varying inequities in environmental burden.

Publications

Measuring long-term exposure to wildfire PM<sub>2.5</sub> in California: Time-varying inequities in environmental burden.

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