Evaluation of Model-Based PM2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S

Wildland fire smoke adds to disproportionate PM2.5 exposure in the United States

Wildland fire (i.e., prescribed fire and wildfire) smoke exposure is an emerging public health threat, in part due to climate change. Previous research has demonstrated disparities in ambient fine particulate matter (PM2.5) exposure, with Black people, among others, exposed to higher concentrations; yet it remains unclear how wildland fire smoke may contribute to additional disproportionate exposure. Here, we investigate the additional PM2.5 burden contributed by wildland fire smoke in the contiguous United States by race and ethnicity, urbanicity, median household income, and language spoken at home, using modeled total, non-fire, and fire PM2.5 concentrations from 2007 to 2018. Wildland fires contributed 7% to 14% of total population weighted PM2.5 concentrations annually, while non-fire PM2.5 concentrations declined by 24% over the study period. Wildland fires contributed to greater PM2.5 exposure for Black and American Indian or Alaska Native people, and those who live in non-urban areas. Disproportionate mean non-fire PM2.5 concentrations for Black people (9.1 μg/m3, compared to 8.7 μg/m3 overall) were estimated to be further exacerbated by additional disproportionate concentrations from fires (1.0 μg/m3 , compared to 0.9 μg/m3 overall). These results can inform equitable strategies by public health agencies and air quality managers to reduce smoke exposure in the United States.

Evaluating Chemical Transport and Machine Learning Models for Wildfire Smoke PM2.5: Implications for Assessment of Health Impacts

Growing wildfire smoke represents a substantial threat to air quality and human health. However, the impact of wildfire smoke on human health remains imprecisely understood due to uncertainties in both the measurement of exposure of population to wildfire smoke and dose-response functions linking exposure to health. Here, we compare daily wildfire smoke-related surface fine particulate matter (PM2.5) concentrations estimated using three approaches, including two chemical transport models (CTMs): GEOS-Chem and the Community Multiscale Air Quality (CMAQ) and one machine learning (ML) model over the contiguous US in 2020, a historically active fire year. In the western US, compared against surface PM2.5 measurements from the US Environmental Protection Agency (EPA) and PurpleAir sensors, we find that CTMs overestimate PM2.5 concentrations during extreme smoke episodes by up to 3-5 fold, while ML estimates are largely consistent with surface measurements. However, in the eastern US, where smoke levels were much lower in 2020, CTMs show modestly better agreement with surface measurements. We develop a calibration framework that integrates CTM- and ML-based approaches to yield estimates of smoke PM2.5 concentrations that outperform individual approach. When combining the estimated smoke PM2.5 concentrations with county-level mortality rates, we find consistent effects of low-level smoke on mortality but large discrepancies in effects of high-level smoke exposure across different methods. Our research highlights the differences across estimation methods for understanding the health impacts of wildfire smoke and demonstrates the importance of bench-marking estimates with available surface measurements.

Air pollutants contribute to epithelial barrier dysfunction and allergic diseases

Air pollution is a global problem associated with various health conditions, causing elevated rates of morbidity and mortality. Major sources of air pollutants include industrial emissions, traffic-related pollutants, and household biomass combustion, in addition to indoor pollutants from chemicals and tobacco. Various types of air pollutants originate from both human activities and natural sources. These include particulate matter, pollen, greenhouse gases, and other harmful gases. Air pollution is linked to allergic diseases, including atopic dermatitis, allergic rhinitis, allergic conjunctivitis, food allergy, and bronchial asthma. These pollutants lead to epithelial barrier dysfunction, dysbiosis, and immune dysregulation. In addition, climate change and global warming may contribute to the exacerbation and the development of allergic diseases related to air pollutants. Epigenetic changes associated with air pollutants have also been connected to the onset of allergic diseases. Furthermore, these changes can be passed down through subsequent generations, causing a higher prevalence of allergic diseases in offspring. Modulation of the aryl hydrocarbon receptor could be a valuable strategy for alleviating air pollutant-induced epidermal barrier dysfunction and atopic dermatitis. A more effective approach to preventing allergic diseases triggered by air pollutants is to reduce exposure to them. Implementing public policies aimed at safeguarding individuals from air pollutant exposure may prove to be the most efficient solution. A pressing need exists for global policy initiatives that prioritize efforts to reduce the production of air pollutants.

Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study

BACKGROUND

Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA.

METHODS

In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health.

FINDINGS

Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke.

INTERPRETATION

Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades.

FUNDING

National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

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.