Effects of using different exposure data to estimate changes in premature mortality attributable to PM2.5 and O3 in China

The assessment of premature mortality associated with the dramatic changes in fine particulate matter (PM2.5) and ozone (O3) has important scientific significance and provides valuable information for future emission control strategies. Exposure data are particularly vital but may cause great uncertainty in health burden assessments. This study, for the first time, used six methods to generate the concentration data of PM2.5 and O3 in China between 2014 and 2018, and then quantified the changes in premature mortality due to PM2.5 and O3 using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that PM2.5-related premature mortality in China decreases by 263 (95% confidence interval (CI95): 142–159) to 308 (CI95: 213–241) thousands from 2014 to 2018 by using different concentration data, while O3-related premature mortality increases by 67 (CI95: 26–104) to 103 (CI95: 40–163) thousands. The estimated mean changes are up to 40% different for the PM2.5-related mortality, and up to 30% for the O3-related mortality if different exposure data are chosen. The most significant difference due to the exposure data is found in the areas with a population density of around 103 people/km2, mostly located in Central China, for both PM2.5 and O3. Our results demonstrate that the exposure data source significantly affects mortality estimations and should thus be carefully considered in health burden assessments.