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

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 fine particulate matter with an aerodynamic diameter <2.5μm (PM_2.5) are needed for advancing health research, policymaking, and environmental protection. Objective: This review evaluates existing methodologies and data sources for estimating wildfire-generated PM_2.5, 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 “PM_2.5 exposure,” and “wildfire PM_2.5.” Studies were included if they were publicly available, focused on North America (primarily the US), and provided wildfire-attributable PM_2.5 data. Of 2,757 articles identified, 418 full texts were screened, and 33 met inclusion criteria. Four studies offered wildfire-specific estimates of PM_2.5, and one dataset was excluded due to accessibility issues, leaving three for analysis. We processed data using R (version R 4.3.1; R Development Core Team) at the ZIP code level for consistency and examined total and wildfire-specific PM_2.5 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 and 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 PM_2.5 contributed 11.2%-36.9% of total PM_2.5 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 PM_2.5 were more consistent than wildfire-specific PM_2.5 estimates. Conclusions: We offer a review of current data sources used for wildfire-specific PM_2.5 estimation and compare publicly available datasets. As expected, the contribution of wildfire smoke to overall PM_2.5 increased with wildfire activity. However, limited publicly available datasets hinder comprehensive comparisons and generalizations for health research and outcomes.