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The 17-y spatiotemporal trend of PM 2.5 and its mortality burden in China.
- Source :
-
Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2020 Oct 13; Vol. 117 (41), pp. 25601-25608. Date of Electronic Publication: 2020 Sep 21. - Publication Year :
- 2020
-
Abstract
- Investigations on the chronic health effects of fine particulate matter (PM <subscript>2.5</subscript> ) exposure in China are limited due to the lack of long-term exposure data. Using satellite-driven models to generate spatiotemporally resolved PM <subscript>2.5</subscript> levels, we aimed to estimate high-resolution, long-term PM <subscript>2.5</subscript> and associated mortality burden in China. The multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) at 1-km resolution was employed as a primary predictor to estimate PM <subscript>2.5</subscript> concentrations. Imputation techniques were adopted to fill in the missing AOD retrievals and provide accurate long-term AOD aggregations. Monthly PM <subscript>2.5</subscript> concentrations in China from 2000 to 2016 were estimated using machine-learning approaches and used to analyze spatiotemporal trends of adult mortality attributable to PM <subscript>2.5</subscript> exposure. Mean coverage of AOD increased from 56 to 100% over the 17-y period, with the accuracy of long-term averages enhanced after gap filling. Machine-learning models performed well with a random cross-validation R <superscript>2</superscript> of 0.93 at the monthly level. For the time period outside the model training window, prediction R <superscript>2</superscript> values were estimated to be 0.67 and 0.80 at the monthly and annual levels. Across the adult population in China, long-term PM <subscript>2.5</subscript> exposures accounted for a total number of 30.8 (95% confidence interval [CI]: 28.6, 33.2) million premature deaths over the 17-y period, with an annual burden ranging from 1.5 (95% CI: 1.3, 1.6) to 2.2 (95% CI: 2.1, 2.4) million. Our satellite-based techniques provide reliable long-term PM <subscript>2.5</subscript> estimates at a high spatial resolution, enhancing the assessment of adverse health effects and disease burden in China.<br />Competing Interests: The authors declare no competing interest.
- Subjects :
- Adult
China
Environmental Monitoring
Geographic Information Systems
Humans
Machine Learning
Models, Statistical
Spatio-Temporal Analysis
Air Pollution statistics & numerical data
Environmental Exposure adverse effects
Environmental Exposure statistics & numerical data
Mortality, Premature trends
Particulate Matter analysis
Subjects
Details
- Language :
- English
- ISSN :
- 1091-6490
- Volume :
- 117
- Issue :
- 41
- Database :
- MEDLINE
- Journal :
- Proceedings of the National Academy of Sciences of the United States of America
- Publication Type :
- Academic Journal
- Accession number :
- 32958653
- Full Text :
- https://doi.org/10.1073/pnas.1919641117