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. 2025 May 16;11(20):eads4360.
doi: 10.1126/sciadv.ads4360. Epub 2025 May 14.

Global disparities in indoor wildfire-PM2.5 exposure and mitigation costs

Dongjia Han  1   2 , Yongxuan Guo  3   4 , Jianghao Wang  3   4   5 , Bin Zhao  1   2
Affiliations

Global disparities in indoor wildfire-PM2.5 exposure and mitigation costs

Dongjia Han et al. Sci Adv. .

Abstract

Wildfires have become more frequent and severe, and evidence showed that exposure to wildfire-caused PM2.5 (fire-PM2.5) is associated with adverse health effects. Fire-PM2.5 exposure occurs mainly indoors, where people spend most of their time. As an effective and timely approach of mitigating indoor PM2.5 pollution, air purifiers incur notable associated costs. However, the long-term global population exposure to indoor fire-PM2.5 and the economic burden of using air purifiers remain unknown. Here, we estimated the indoor fire-PM2.5 concentration and the cost of reducing indoor PM2.5 exposure, along with the extra cost incurred because of fire-PM2.5, at a resolution of 0.5° by 0.5° globally during 2003 to 2022. Our findings revealed 1009 million individuals exposed to at least one substantial indoor wildfire-air pollution day per year. We identified pronounced socioeconomic disparities in the costs of mitigating indoor PM2.5 exposure, with low-income countries bearing a disproportionately higher economic burden, emphasizing the critical need for addressing these disparities.

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Figures

Fig. 1.
Fig. 1.. Global maps of indoor fire-PM2.5 concentration and population exposure to SIWAP days.
(A) Map of annual indoor fire-PM2.5 concentration of 2003 to 2022. (B) Population-weighted average indoor and outdoor fire-PM2.5 contributions to total PM2.5 concentration of different continents. The percentages represent the proportion of fire-PM2.5 to all-source PM2.5. The error bars represent the 95% confidence intervals of indoor all-source PM2.5 concentrations. (C) Map of annual population exposure to SIWAP days of 2003 to 2022. (D) Total person-days exposed to SIWAP of six continents between 2003 and 2022.
Fig. 2.
Fig. 2.. Annual costs under the three intervention scenarios.
(A) 2003 to 2022 annual cost per capita of controlling indoor PM2.5 to target levels (S1: targeting 25 μg/m3; S2: targeting 15 μg/m3; S3: targeting 5 μg/m3). (B) 2003 to 2022 annual extra cost per capita of controlling indoor fire-PM2.5 under different scenarios (S1, S2, and S3). (C and D) Proportion of population of different continents for different cost (C) or extra cost (D).
Fig. 3.
Fig. 3.. Global and continental trends of extra cost and seasonal patterns.
(A) Trends of population-weighted average (PWA) extra cost to control fire-PM2.5 for the globe under different scenarios. (B) Trends of population-weighted average extra cost to control fire-PM2.5 for different continents in scenario S3 (targeting 5 μg/m3). (C) Population-weighted average annual per capita extra cost of 2003 to 2012 and 2013 to 2022 for the globe and six continents in scenario S3. The numerical values above each pair of bars denote the decadal change in annual extra cost. (D) Seasonal pattern of population-weighted average per capita extra costs for the globe and six continents in scenario S3. The error bars in (A) and (C) and the shaded areas in (D) represent the 95% confidence interval.
Fig. 4.
Fig. 4.. Socioeconomic disparities in intervention cost between countries.
(A) Proportion of population-weighted average annual cost to GNI in scenario S3. (B) Population-weighted average annual cost and proportions to GNI of different HDI groups. (C) Gini index of S3 cost and proportion of cost to GNI during 2003 to 2022. The shaded areas represent the 95% confidence interval for the trend. (D) Proportion of population-weighted average annual extra cost to GNI in scenario S3. (E) Population-weighted average annual extra cost and proportions to GNI of different HDI groups. (F) Gini index of S3 extra cost and proportion of extra cost to GNI during 2003 to 2022.
Fig. 5.
Fig. 5.. Top-ranked countries with the greatest cost and extra cost in scenario S3.
(A) Top 10 countries with the highest population-weighted average indoor PM2.5 concentration, intervention cost, and proportion of cost to GNI. (B) Top 10 countries with the highest population-weighted average indoor fire-PM2.5 concentration, extra intervention cost, and proportion of extra cost to GNI.
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