Modeling Wildland Fire-Specific PM2.5 Concentrations for Uncertainty-Aware Health Impact Assessments

Assessing the 2023 Canadian wildfire smoke impact in Northeastern US: Air quality, exposure and environmental justice

The Canadian wildfires in June 2023 significantly impacted the northeastern United States, particularly in terms of worsened air pollution and environmental justice concerns. While advancements have been made in low-cost sensor deployments and satellite observations of atmospheric composition, integrating dynamic human mobility with wildfire PM2.5 exposure to fully understand the environmental justice implications remains underinvestigated. This study aims to enhance the accuracy of estimating ground-level fine particulate matter (PM2.5) concentrations by fusing chemical transport model outputs with empirical observations, estimating exposures using human mobility data, and evaluating the impact of environmental justice. Employing a novel data fusion technique, the study combines the Weather Research and Forecasting model with Chemistry (WRF-Chem) outputs and surface PM2.5 measurements, providing a more accurate estimation of PM2.5 distribution. The study addresses the gap in traditional exposure assessments by incorporating human mobility data and further investigates the spatial correlation of PM2.5 levels with various environmental and demographic factors from the US Environmental Protection Agency (EPA) Environmental Justice Screening and Mapping Tool (EJScreen). Results reveal that despite reduced mobility during high PM2.5 levels from wildfire smoke, exposure for both residents and individuals on the move remains high. Regions already burdened with high environmental pollution levels face amplified PM2.5 effects from wildfire smoke. Furthermore, we observed mixed correlations between PM2.5 concentrations and various demographic and socioeconomic factors, indicating complex exposure patterns across communities. Urban areas, in particular, experience persistent high exposure, while significant correlations in rural areas with EJScreen factors highlight the unique vulnerabilities of these populations to smoke exposure. These results advocate for a comprehensive approach to environmental health that leverages advanced models, integrates human mobility data, and addresses socio-demographic disparities, contributing to the development of equitable strategies against the growing threat of wildfires.

Neurotoxicity of the air-borne particles: From molecular events to human diseases

Exposure to PM_2.5 is associated with an increased incidence of CNS diseases in humans, as confirmed by numerous epidemiological studies. Animal models have demonstrated that PM_2.5 exposure can damage brain tissue, neurodevelopmental issues and neurodegenerative diseases. Both animal and human cell models have identified oxidative stress and inflammation as the primary toxic effects of PM_2.5 exposure. However, understanding how PM_2.5 modulates neurotoxicity has proven challenging due to its complex and variable composition. This review aims to summarize the detrimental effects of inhaled PM_2.5 on the CNS and the limited understanding of its underlying mechanism. It also highlights new frontiers in addressing these issues, such as modern laboratory and computational techniques and chemical reductionism tactics. By utilizing these approaches, we aim to fully elucidate the mechanism of PM_2.5-induced neurotoxicity, treat associated diseases, and ultimately eliminate pollution.

Quantifying the premature mortality and economic loss from wildfire-induced PM2.5 in the contiguous U.S

Emissions from wildfires worsen air quality and can adversely impact human health. This study utilized the fire inventory from NCAR (FINN) as wildfire emissions, and performed air quality modeling of April-October 2012, 2013, and 2014 using the U.S. Environmental Protection Agency CMAQ model under two cases: with and without wildfire emissions. This study then assessed the health impacts and economic values attributable to PM_2.5 from fires. Results indicated that wildfires could lead annually to 4000 cases of premature mortality in the U.S., corresponding to $36 billion losses. Regions with high concentrations of fire-induced PM_2.5 were in the west (e.g., Idaho, Montana, and northern California) and Southeast (e.g., Alabama, Georgia). Metropolitan areas located near fire sources, exhibited large health burdens, such as Los Angeles (119 premature deaths, corresponding to $1.07 billion), Atlanta (76, $0.69 billion), and Houston (65, $0.58 billion). Regions in the downwind of western fires, although experiencing relatively low values of fire-induced PM_2.5, showed notable health burdens due to their large population, such as metropolitan areas of New York (86, $0.78 billion), Chicago (60, $0.54 billion), and Pittsburgh (32, $0.29 billion). Results suggest that impacts from wildfires are substantial, and to mitigate these impacts, better forest management and more resilient infrastructure would be needed.

Wildfires and the Changing Landscape of Air Pollution-related Health Burden in California

Rationale: Wildfires are a growing source of pollution including particulate matter ⩽2.5 μm in aerodynamic diameter (PM2.5), but associated trends in health burden are not well characterized. Objectives: We investigated trends and disparities in PM2.5-related cardiorespiratory health burden (asthma, chronic obstructive pulmonary disease, and all-cause respiratory and cardiovascular emergency department [ED] visits and hospital admissions) for all days and wildfire smoke-affected days across California from 2008 to 2016. Methods: Using residential Zone Improvement Plan code and daily PM2.5 exposures, we estimated overall and subgroup-specific (age, gender, race and ethnicity) associations with cardiorespiratory outcomes. Health burden trends and disparities were evaluated on the basis of relative risk, attributable number, and attributable fraction by demographic and geographic factors and over time. Measurements and Main Results: PM2.5-attributed burden steadily decreased, whereas the fraction attributed to wildfire smoke varied by fire season intensity, constituting up to 15% of the annual PM2.5-burden. The highest relative risk and PM2.5-attributed burden (92 per 100,000 people) was observed for respiratory ED visits, accounting for 2.2% of the respiratory annual burden. Disparities in overall morbidity in the oldest age, Black, and "other" race groups were also reflected in PM2.5-attributed burden, whereas Asian populations had the highest risk rate in respiratory outcomes and thus the largest fraction of the total burden attributed to the exposure. In contrast, high wildfire PM2.5-attributed burden rates in rural, central, and northern California populations occurred because of differential exposure. Conclusions: In California, wildfires' impact on air quality offset the public health gains achieved through reductions in nonsmoke PM2.5. Disproportionate effects could be attributed to differences in subpopulation susceptibility, relative risk, and differential exposure.

The impact of fire-specific PM2.5 calibration on health effect analyses

The quantification of PM_2.5 concentrations solely stemming from both wildfire and prescribed burns (hereafter referred to as 'fire') is viable using the Community Multiscale Air Quality (CMAQ), although CMAQ outputs are subject to biases and uncertainties. To reduce the biases in CMAQ-based outputs, we propose a two-stage calibration strategy that improves the accuracy of CMAQ-based fire PM_2.5 estimates. First, we calibrated CMAQ-based non-fire PM_2.5 to ground PM_2.5 observations retrieved during non-fire days using an ensemble-based model. We estimated fire PM_2.5 concentrations in the second stage by multiplying the calibrated non-fire PM_2.5 obtained from the first stage by location- and time-specific conversion ratios. In a case study, we estimated fire PM_2.5 during the Washington 2016 fire season using the proposed calibration approach. The calibrated PM_2.5 better agreed with ground PM_2.5 observations with a 10-fold cross-validated (CV) R² of 0.79 compared to CMAQ-based PM_2.5 estimates with R² of 0.12. In the health effect analysis, we found significant associations between calibrated fire PM_2.5 and cardio-respiratory hospitalizations across the fire season: relative risk (RR) for cardiovascular disease = 1.074, 95% confidence interval (CI) = 1.021-1.130 in October; RR = 1.191, 95% CI = 1.099-1.291 in November; RR for respiratory disease = 1.078, 95% CI = 1.005-1.157 in October; RR = 1.153, 95% CI = 1.045-1.272 in November. However, the results were inconsistent when non-calibrated PM_2.5 was used in the analysis. We found that calibration affected health effect assessments in the present study, but further research is needed to confirm our findings.

Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event

Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM2.5) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire-related smoke PM2.5 fields and variability in concentration-response parameters from epidemiologic studies of ambient and smoke PM2.5. This analysis, focused on the 2016 Southeastern wildfires, suggests that emissions from these fires had public health consequences in North Carolina. Using several methods based on publicly available monitor data and atmospheric models to represent wildfire-attributable PM2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration-response parameters derived from studies of ambient and wildfire-specific PM2.5 are used to assess health-related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health-related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire-related decision-making.

Estimating the Acute Health Impacts of Fire-Originated PM2.5 Exposure During the 2017 California Wildfires: Sensitivity to Choices of Inputs

Exposure to wildfire smoke increases the risk of respiratory and cardiovascular hospital admissions. Health impact assessments, used to inform decision-making processes, characterize the health impacts of environmental exposures by combining preexisting epidemiological concentration-response functions (CRFs) with estimates of exposure. These two key inputs influence the magnitude and uncertainty of the health impacts estimated, but for wildfire-related impact assessments the extent of their impact is largely unknown. We first estimated the number of respiratory, cardiovascular, and asthma hospital admissions attributable to fire-originated PM2.5 exposure in central California during the October 2017 wildfires, using Monte Carlo simulations to quantify uncertainty with respect to the exposure and epidemiological inputs. We next conducted sensitivity analyses, comparing four estimates of fire-originated PM2.5 and two CRFs, wildfire and nonwildfire specific, to understand their impact on the estimation of excess admissions and sources of uncertainty. We estimate the fires accounted for an excess 240 (95% CI: 114, 404) respiratory, 68 (95% CI: -10, 159) cardiovascular, and 45 (95% CI: 18, 81) asthma hospital admissions, with 56% of admissions occurring in the Bay Area. Although differences between impact assessment methods are not statistically significant, the admissions estimates' magnitude is particularly sensitive to the CRF specified while the uncertainty is most sensitive to estimates of fire-originated PM2.5. Not accounting for the exposure surface's uncertainty leads to an underestimation of the uncertainty of the health impacts estimated. Employing context-specific CRFs and using accurate exposure estimates that combine multiple data sets generates more certain estimates of the acute health impacts of wildfires.

Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification

Satellite-derived aerosol optical depth (AOD) has been widely used to predict ground-level fine particulate matter (PM_2.5) concentrations, although its utility can be limited due to missing values. Despite recent attempts to address this issue by imputing missing satellite AOD values, the uncertainty associated with the AOD imputation and its impacts on PM_2.5 predictions have been understudied. To fill this gap, we developed a missing data imputation model for the AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and PM_2.5 prediction models using several machine learning methods. We also examined how the uncertainty associated with the imputed AOD and a choice of machine learning algorithm were propagated to PM_2.5 predictions. The application of the proposed imputation model to the data from New York State in the U.S. achieved a superior performance than those related studies, with a cross-validated R² of 0.94 and a Root Mean Square Error of 0.017. We also found that there was considerable uncertainty in PM_2.5 predictions associated with the use of imputed AOD values, although it was not as high as the uncertainty from the machine learning algorithms used in PM_2.5 prediction models. We concluded that the quantification of uncertainties for both AOD imputation and its propagation to AOD-based PM_2.5 prediction is necessary for accurate and reliable PM_2.5 predictions.

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields. We investigated whether individuals' routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time-activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual's mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals' routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data ('a multi-sourced exposure model'). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data ('a single-sourced exposure model'). This study showed that there was a significant association between individuals' mobility and the long-term exposure measurement error. However, the effect could be modified by individuals' routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.