Wildfires are thought to be increasing in severity and frequency as a result of climate change^1–5. Air pollution from landscape fires can negatively affect human health^4–6, but human exposure to landscape fire-sourced (LFS) air pollution has not been well characterized at the global scale^7–23. Here, we estimate global daily LFS outdoor fine particulate matter (PM_2.5) and surface ozone concentrations at 0.25° × 0.25° resolution during the period 2000–2019 with the help of machine learning and chemical transport models. We found that overall population-weighted average LFS PM_2.5 and ozone concentrations were 2.5 µg m⁻³ (6.1% of all-source PM_2.5) and 3.2 µg m⁻³ (3.6% of all-source ozone), respectively, in 2010–2019, with a slight increase for PM_2.5, but not for ozone, compared with 2000–2009. Central Africa, Southeast Asia, South America and Siberia experienced the highest LFS PM_2.5 and ozone concentrations. The concentrations of LFS PM_2.5 and ozone were about four times higher in low-income countries than in high-income countries. During the period 2010–2019, 2.18 billion people were exposed to at least 1 day of substantial LFS air pollution per year, with each person in the world having, on average, 9.9 days of exposure per year. These two metrics increased by 6.8% and 2.1%, respectively, compared with 2000–2009. Overall, we find that the global population is increasingly exposed to LFS air pollution, with socioeconomic disparities.

To assess mortality risks and burdens associated with short-term exposure to wildfire-related fine particulate matter with diameter ≤ 2.5 μm (PM_2.5), we collect daily mortality data from 2000 to 2016 for 510 immediate regions in Brazil, the most wildfire-prone area. We integrate data from multiple sources with a chemical transport model at the global scale to isolate daily concentrations of wildfire-related PM_2.5 at a 0.25 × 0.25 resolution. With a two-stage time-series approach, we estimate (i) an increase of 3.1% (95% confidence interval [CI]: 2.4, 3.9%) in all-cause mortality, 2.6% (95%CI: 1.5, 3.8%) in cardiovascular mortality, and 7.7% (95%CI: 5.9, 9.5) in respiratory mortality over 0–14 days with each 10 μg/m³ increase in daily wildfire-related PM_2.5; (ii) 0.65% of all-cause, 0.56% of cardiovascular, and 1.60% of respiratory mortality attributable to acute exposure to wildfire-related PM_2.5, corresponding to 121,351 all-cause deaths, 29,510 cardiovascular deaths, and 31,287 respiratory deaths during the study period. In this study, we find stronger associations in females and adults aged ≥ 60 years, and geographic difference in the mortality risks and burdens.

Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.

Background: Long-term exposure to fine particles ≤2.5 μm in diameter (PM_2.5) has been linked to cancer mortality. However, the effect of wildfire-related PM_2.5 exposure on cancer mortality risk is unknown. This study evaluates the association between wildfire-related PM_2.5 and site-specific cancer mortality in Brazil, from 2010 to 2016. Methods and findings: Nationwide cancer death records were collected during 2010–2016 from the Brazilian Mortality Information System. Death records were linked with municipal-level wildfire- and non-wildfire-related PM_2.5 concentrations, at a resolution of 2.0° latitude by 2.5° longitude. We applied a variant difference-in-differences approach with quasi-Poisson regression, adjusting for seasonal temperature and gross domestic product (GDP) per capita. Relative risks (RRs) and 95% confidence intervals (CIs) for the exposure for specific cancer sites were estimated. Attributable fractions and cancer deaths were also calculated. In total, 1,332,526 adult cancer deaths (age ≥ 20 years), from 5,565 Brazilian municipalities, covering 136 million adults were included. The mean annual wildfire-related PM_2.5 concentration was 2.38 μg/m³, and the annual non-wildfire-related PM_2.5 concentration was 8.20 μg/m³. The RR for mortality from all cancers was 1.02 (95% CI 1.01–1.03, p < 0.001) per 1-μg/m³ increase of wildfire-related PM_2.5 concentration, which was higher than the RR per 1-μg/m³ increase of non-wildfire-related PM_2.5 (1.01 [95% CI 1.00–1.01], p = 0.007, with p for difference = 0.003). Wildfire-related PM_2.5 was associated with mortality from cancers of the nasopharynx (1.10 [95% CI 1.04–1.16], p = 0.002), esophagus (1.05 [95% CI 1.01–1.08], p = 0.012), stomach (1.03 [95% CI 1.01–1.06], p = 0.017), colon/rectum (1.08 [95% CI 1.05–1.11], p < 0.001), larynx (1.06 [95% CI 1.02–1.11], p = 0.003), skin (1.06 [95% CI 1.00–1.12], p = 0.003), breast (1.04 [95% CI 1.01–1.06], p = 0.007), prostate (1.03 [95% CI 1.01–1.06], p = 0.019), and testis (1.10 [95% CI 1.03–1.17], p = 0.002). For all cancers combined, the attributable deaths were 37 per 100,000 population and ranged from 18/100,000 in the Northeast Region of Brazil to 71/100,000 in the Central-West Region. Study limitations included a potential lack of assessment of the joint effects of gaseous pollutants, an inability to capture the migration of residents, and an inability to adjust for some potential confounders. Conclusions: Exposure to wildfire-related PM_2.5 can increase the risks of cancer mortality for many cancer sites, and the effect for wildfire-related PM_2.5 was higher than for PM_2.5 from non-wildfire sources.