AUTHOR=Liu Jing , Li Shenxin , Xiong Ying , Liu Ning , Zou Bin , Xiong Liwei 

TITLE=Uncertainty Analysis of Premature Death Estimation Under Various Open PM2.5 Datasets

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.934281

DOI=10.3389/fenvs.2022.934281

ISSN=2296-665X

ABSTRACT=Assessments of premature deaths caused by PM2.5 exposure have important scientific significance and provide valuable information for future human health-oriented air pollution prevention. PM2.5 concentration data is particularly vital and may cause great uncertainty in premature deaths assessments. This study constructed an index of deviation frequency to compare differences in premature deaths assessed by five sets of extensively used PM2.5 concentration remote sensing datasets. Then proposed a preferred combination project of PM2.5 dataset by selecting relatively high-accuracy PM2.5 concentration datasets in areas with significant differences. Based on this project, an index of uncertainty was constructed to quantify effects of using different PM2.5 datasets on premature deaths assessments. Results showed that there were significant differences in PM2.5 attributable premature deaths assessed by different datasets from 2000 to 2016, differences were most obvious in 2004. Spatially, differences were most significant in Jilin, Fujian, Liaoning, Hebei, Shanxi, Hubei, Sichuan and Yunnan. The differences were caused by PM2.5 concentration, therefore, in order to reduce uncertainty in subsequent premature deaths assessments because of using different PM2.5 concentration data, CGS3 dataset was recommended for Jilin, Sichuan, Yunnan and Fujian, CHAP dataset was recommended for Liaoning, Hebei, Shanxi and Hubei, for other regions, CGS3, CHAP or PHD datasets were more applicable. CHAP dataset was the best selection for premature deaths assessments in the whole area. Based on the preferred combination project of PM2.5 dataset, uncertainty in annual premature deaths assessments could be reduced by 31% and 159% in the whole and local area, respectively. The research results will provide a scientific basis for reasonable selection of PM2.5 concentration remote sensing datasets in air pollution premature deaths assessments in China.