Integrating Augmented <i>In Situ</i> Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019

Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-based aerosol optical depth before 2000, has resulted in a gap in spatiotemporally resolved PM2.5 data for historical periods. Taking the United Kingdom as an example, we leveraged the light gradient boosting model to capture the spatiotemporal association between PM2.5 concentrations and multi-source geospatial predictors. Augmented PM2.5 from PM10 measurements expanded the spatiotemporal representativeness of the ground measurements. Observations before and after 2009 were used to train and test the models, respectively. Our model showed fair prediction accuracy from 2010 to 2019 [the ranges of coefficients of determination (R2) for the grid-based cross-validation are 0.71–0.85] and commendable back extrapolation performance from 1998 to 2009 (the ranges of R2 for the independent external testing are 0.32–0.65) at the daily level. The pollution episodes in the 1980s and pollution levels in the 1990s were also reproduced by our model. The 4-decade PM2.5 estimates demonstrated that most regions in England witnessed significant downward trends in PM2.5 pollution. The methods developed in this study are generalizable to other data-rich regions for historical air pollution exposure assessment

Economic Growth Facilitates Household Fuel Use Transition to Reduce PM_2.5-Related Deaths in China

Exposure to ambient and indoor particle matter (PM2.5) leads to millions of premature deaths in China. In recent years, indoor air pollution and premature deaths associated with polluting fuel cooking demonstrate an abrupt decline. However, the driving forces behind the mortality change are still unclear due to the uncertainty in household fuel use prediction. Here, we propose an integrated approach to estimate the fuel use fractions and PM2.5-related deaths from outdoor and indoor sources during 2000–2020 across China. Our model estimated 1.67 and 1.21 million premature deaths attributable to PM2.5 exposure in 2000 and 2020, respectively. We find that the residential energy transition is associated with a substantial reduction in premature deaths from indoor sources, with 100,000 (95% CI: 76,000–122,000) for urban and 265,000 (228,000–300,000) for rural populations during 2000–2020. Economic growth is the dominant driver of fuel use transition and avoids 21% related deaths (357,000, 315,000–402,000) from polluting fuel cooking since 2000, which offsets the adverse impact of ambient emissions contributed by economic growth. Our findings give an insight into the coupled impact of socioeconomic factors in reshaping health burden in exposure pathways