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. 2025 Jun;133(6):66001.
doi: 10.1289/EHP15672. Epub 2025 Jun 11.

A State of the Science Review of Wildfire-Specific Fine Particulate Matter Data Sources, Methods, and Models

Ava Orr  1   2 , Claire E Adam  1 , Jon Graham  1   3 , Zachary A Holden  4 , Lu Hu  5 , Zeina Jaffar  2 , Cindy Leary  1 , Christopher T Migliaccio  2 , Katrina Mullan  1   6 , Curtis Noonan  1 , Erin O Semmens  1 , Shawn Urbanski  4 , Ethan Walker  1 , Erin L Landguth  1
Affiliations

A State of the Science Review of Wildfire-Specific Fine Particulate Matter Data Sources, Methods, and Models

Ava Orr et al. Environ Health Perspect. 2025 Jun.

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 fine particulate matter with an aerodynamic diameter <2.5μm (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 US), 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 of PM2.5, 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 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 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 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 hinder comprehensive comparisons and generalizations for health research and outcomes. https://doi.org/10.1289/EHP15672.

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Figures

Figure 1 depicts a scientific illustration of a workflow plan for modeling daily particulate matter begin subscript 2.5 end subscript and wildfire-specific particulate matter begin subscript 2.5 end subscript with multi-source data. The figure is divided into two major portions, each surrounded in a dashed box: Section 1: All-Source Particulate Matter Begin Subscript 2.5 End subscript observations and data for estimating daily particulate matter begin subscript 2.5 End Subscript: Inputs: 1.1 Monitoring Data: Includes stacked maps of ground monitoring stations located throughout the western United States. 1.2 Satellite Data: Satellite imagery depicts regional air quality and atmospheric conditions. 1.3 Additional Explanatory Information: Includes information about the landscape, weather, human disturbance, geography, and land type. These inputs go into a pink box named “Model: Particulate Matter Prediction” and maps for particle matter are displayed below this box. Section 2: Additional wildfire-specific data sources for estimating wildfire smoke particle matter begin subscript 2.5 end subscript: Inputs: 2.1 Chemical Extraction: Uses particulate particles composition data (for example, proportion of nitrate ion or H A Ps). 2.2 Thresholding: Uses seasonal indicators and thresholds to classify wildfire influence. 2.3 Additional Wildfire Data: Includes information on fire proximity, fire area, H M S (Hazard Mapping System), and fire intensity. These inputs are directed into a second box called “Model: Wildfire-Specific Particulate Matter Predictions,” which produces maps forecasting wildfire smoke-specific particulate matter begin subscript 2.5 end subscript.
Figure 1.
A conceptual diagram to explain the data sources used in estimating spatial and temporal patterns of PM2.5 (the three basic data types of all-source PM2.5 estimates) and additional data included for estimating spatiotemporal wildfire-specific PM2.5 (the three basic approaches for extracting wildfire-specific PM2.5 estimates). Blue boxes (Ground-based PM2.5 monitoring data and Satellite data for PM2.5 estimates) provide the observational PM2.5 data, and gray boxes (Chemical extraction, Thresholding for wildfire-specific PM2.5) illustrate additional techniques applied to PM2.5 layers to extract the additional explanatory data and wildfire-specific data (green boxes and diamonds). Red boxes illustrate the PM predictions for all sources (the three basic data types of all-source PM2.5 estimates) and wildfire-specific sources (the three basic approaches for extracting wildfire-specific PM2.5 estimates). Note: HAPS, Health and Air Pollution Surveillance System; PM2.5, fine particulate matter with an aerodynamic diameter <2.5μm.
Figure 2 depicts a flowchart with three primary steps: identification, screening, and included. Step 1: Identify studies using databases and registrations. Databases discovered 2,757 records, with 1,655 duplicates eliminated before to screening. Step 2: Screening: There were 1,102 titles and abstracts reviewed, with 684 records being removed based on the title and abstract screen. There are 418 full-text papers that have been evaluated for eligibility, including publicity available, a focus on North America, including California, publication after January 2018, and easy access in cv or common format. Step 3: Included: There are a total of 33 papers included in the review, with 29 included in the categorization part and 4 retrieved for data analysis. One of the four studies was eliminated owing to problems converting data into a similar format with other research, while the other three were included in the data analysis.
Figure 2.
Flow diagram of study selection. Initially, 2,757 records were identified through our comprehensive database searches, which included MEDLINE, Web of Science, Google Scholar, EMBASE, and Scopus. After duplicates were removed, 1,102 records were screened, leading to 684 being excluded after title and abstract review based on predefined criteria. The search was conducted using an extensive list of keywords and phrases to ensure comprehensive coverage of all pertinent studies on wildfire-specific PM2.5 exposure. These keywords included: “PM2.5 exposure estimates,” “Estimating PM2.5 exposure,” “PM2.5 pollution,” “PM2.5 estimates,” “wildfires,” “wildfire-specific PM2.5,” “wildfire PM2.5,” “wildfire air quality impacts,” “particulate matter from wildfires,” “smoke pollution,” “wildfire pollution,” “airborne particulates from wildfires,” “wildfire smoke assessment,” “smoke exposure,” “wildfire particulate analysis,” “atmospheric particulates from wildfires,” “fire-related air pollution,” and “forest fire pollution.” These terms were chosen to capture a broad spectrum of research related to PM2.5 emissions from wildfires. Further assessment of 418 full-text articles resulted in 384 exclusions due to not being assessable (71), study covering North America (40), relevant to the study (273), leaving 33 studies included in the final review. Only 4 out of the 33 studies included in this review had publicly available data. In the final dataset comparison analysis, we only include 3 of the 4 publicly available datasets due to accessibility issues; see methods section for detailed inclusion and exclusion criteria. Note: PM2.5, fine particulate matter with an aerodynamic diameter <2.5μm.
Figure 3 is a set of eight maps of California, United States. The first set of two maps depict the locations of Environmental Protection Agency monitoring stations for particulate matter begin subscript 2.5 end subscript in the years 2010 and 2018. A scale depicts the particulate matter begin subscript 2.5 end subscript (micrograms per meter cubed), ranging from 0.0 to 20.0 in increments of 2.5. The second set of two maps depict the Childs et al. wildfire particulate matter begin subscript 2.5 end subscript in the years 2010 and 2018. A scale depicts the particulate matter begin subscript 2.5 end subscript (micrograms per meter cubed), ranging from 0.0 to 20.0 in increments of 2.5. The third set of two maps depict the Aguilera et al. wildfire particulate matter begin subscript 2.5 end subscript in the years 2010 and 2018. A scale depicts the particulate matter begin subscript 2.5 end subscript (micrograms per meter cubed), ranging from 0.0 to 20.0 in increments of 2.5. The fourth set of two maps depict the Zhang et al. wildfire particulate matter begin subscript 2.5 end subscript in the years 2010 and 2018. A scale depicts the particulate matter begin subscript 2.5 end subscript (micrograms per meter cubed), ranging from 0.0 to 20.0 in increments of 2.5.
Figure 3.
Predicted wildfire-specific PM2.5 datasets for California during the years of 2010 (low wildfire year, left column) and 2018 (one of the highest wildfire years, right column). The top row shows the EPA monitoring stations aggregated to the mean for each ZIP code and reflects all-source PM2.5 levels as a reference dataset. All wildfire-specific PM2.5 estimates are summarized to the mean values for each ZIP code. Note: Each dataset, EPA, Aguilera et al., Childs et al., and Zhang et al., is presented at its obtained scales with white values for missing ZIP code data. Graphs created using python. PM2.5, fine particulate matter with an aerodynamic diameter <2.5μm.
Figure 4 is two graphs titled comparison of overall and wildfire-specific particulate matter begin subscript 2.5 end subscript levels in Los Angles 2010 and comparison of overall and wildfire-specific particulate matter begin subscript 2.5 end subscript levels in Modoc 2010, plotting particulate matter begin subscript 2.5 end subscript micrograms per meter cubed, ranging from 0 to 25 in increments of 5 and 0 to 8 in increments of 2 (y-axis) across date, ranging from January 2010 to January 2011 in increments of 2 (x-axis) for Aguilera overall particulate matter, Zhang overall particulate matter, Aguilera wildfire particulate matter, Childs wildfire particulate matter, and Zhang wildfire particulate matter.
Figure 4.
Graphical representation of ambient and wildfire-specific PM2.5 estimates for the year 2010 comparing an area with (Los Angeles) and without a ground monitor (Modoc). Solid lines represent the ambient estimates, and dashed lines show the wildfire-specific estimates. Los Angeles County also shows the EPA monitoring sites average within that county. No EPA data exist for Modoc County. Data sources: EPA, Aguilera et al., Childs et al., and Zhang et al. Graph generated using python matplotlib. Note: PM2.5, fine particulate matter with an aerodynamic diameter <2.5μm.
Figure 5 is two graphs titled comparison of overall and wildfire-specific particulate matter begin subscript 2.5 end subscript levels in Los Angles 2018 and comparison of overall and wildfire-specific particulate matter begin subscript 2.5 end subscript levels in Modoc 2018, plotting particulate matter begin subscript 2.5 end subscript micrograms per meter cubed, ranging from 0 to 35 in increments of 5 and 0 to 80 in increments of 10 (y-axis) across date, ranging from January 2018 to January 2019 in increments of 2 (x-axis) for Aguilera overall particulate matter, Zhang overall particulate matter, Aguilera wildfire particulate matter, Childs wildfire particulate matter, and Zhang wildfire particulate matter.
Figure 5.
Graphical representation of ambient and wildfire-specific PM2.5 estimates for the year 2018 comparing an area with (Los Angeles) and without a ground monitor (Modoc). Solid lines represent the ambient estimates, and dashed lines show the wildfire-specific estimates. Los Angeles County also shows the EPA monitoring sites average within that county. No EPA data exist for Modoc County. Data sources: EPA, Aguilera et al., Childs et al., and Zhang et al. Graph generated using python matplotlib. Note: PM2.5, fine particulate matter with an aerodynamic diameter <2.5μm.
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