A state of the science review of wildfire-specific fine particulate matter data sources, methods, and models.

Abstract

BACKGROUND Despite progress in reducing industrial air pollution, rising wildfire frequency and intensity driven in part by climate change, pose significant health risks. Accurate estimates of wildfire-generated PM2.5 are needed for advancing health research, policymaking, and environmental protection. OBJECTIVE This review evaluates existing methodologies and data sources for estimating wildfire-generated PM2.5, aiming to improving accuracy and accessibility for health research, policy development, and environmental management strategies. METHODS We conducted a systematic literature search across Medline, Scopus, Web of Science, Google Scholar, and Embase (January 2018 to March 2024) using keywords such as "PM2.5 exposure," and "wildfire PM2.5." Studies were included if they were publicly available, focused on North America (primarily the U.S.), and provided wildfire-attributable PM2.5 data. Of 2,757 articles identified, 418 full texts were screened, and 33 met inclusion criteria. Four studies offered wildfire-specific estimates PM2.5, one dataset was excluded due to accessibility issues, leaving three (Aguilera, Childs, Zhang) for analysis. We processes data using R at the Zip Code level for consistency and examined total and wildfire-specific and total PM2.5 estimates for California in 2010 (low fire activity) and 2018 (high fire activity), focusing on Los Angeles (densely monitored) and Modoc (no monitors) counties. Analyses included Pearson correlation, cross-correlation and Granger causality to assess temporal relationships and consistency. RESULTS From the 33 studies included, three main estimation approaches emerged: chemical extraction, thresholding, and integration of satellite and fire-specific data (e.g., smoke plumes, fire perimeters). Most studies combined ground-based monitor data, satellite-derived aerosol optical depth and explanatory data like meteorology and land use. The three public datasets indicated that in California, wildfire-specific PM2.5 contributed 11.2% - 36.9% of total PM2.5 in 2010, and 13.7 - 21.2% in 2018 with stronger agreement in 2018. Correlations were stronger in Modoc County (no monitors) (0.44 - 0.51 in 2010; 0.79 - 0.88 in 2018) than in Los Angeles County (densely populated area, 20 EPA monitors, where correlations ranged from 0.19 - 0.21 in 2010 and 0.54 - 0.79 in 2018). Overall, the datasets estimating total PM2.5 were more consistent than wildfire-specific PM2.5 estimates. CONCLUSIONS We offer a review of current data sources used for wildfire-specific PM2.5 estimation and compare publicly available datasets. As expected, the contribution of wildfire smoke to overall PM2.5 increased with wildfire activity. However, limited publicly available datasets hinders comprehensive comparisons and generalizations for health research and outcomes. https://doi.org/10.1289/EHP15672.