Input data and analysis codes for "Reversal of trends in global fine particulate matter air pollution"

This dataset contains Input data and analysis codes used for the following article: Li, C., A. van Donkelaar, M. S. Hammer, E. E. McDuffie, R. T. Burnett, J. V. Spadaro, D. Chatterjee, A. J. Cohen, J. S. Apte, V. A. Southerland, S. C. Anenberg, M. Brauer, & R. V. Martin, Reversal of trends in gl...

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Hauptverfasser: Li, Chi, Van Donkelaar, Aaron, Hammer, Melanie S., McDuffie, Erin E., Burnett, Richard T., Spadaro, Joseph V., Deepangsu Chatterjee, Cohen, Aaron J., Apte, Joshua S., Southerland, Veronica A., Anenberg, Susan C., Brauer, Michael, Martin, Randall V.
Format: Dataset
Sprache:eng
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Zusammenfassung:This dataset contains Input data and analysis codes used for the following article: Li, C., A. van Donkelaar, M. S. Hammer, E. E. McDuffie, R. T. Burnett, J. V. Spadaro, D. Chatterjee, A. J. Cohen, J. S. Apte, V. A. Southerland, S. C. Anenberg, M. Brauer, & R. V. Martin, Reversal of trends in global fine particulate matter air pollution, submitted, 2023. Input Data Baseline mortality data (204 countries and territories, 17 age groups, 6 diseases, 22 years) Concentration-response functions (GEMM and MRBRT) PM2.5 exposure for 204 territories and 22 years Age-specific population for 204 territories and 22 years Derived Data Age- and disease-specific PM2.5-attributable Mortality estimates for 204 territories and 22 years. Sensitivity of PM2.5-attributable Mortality to marginal PM2.5 reduction for 204 territories and 22 years. Attributable of changes in PM2.5-attributable Mortality (and its sensitivity to marginal PM2.5 reduction) to four driving factors. Code Necessary python scripts to verify and replicate analysis results in the manuscript.
DOI:10.5281/zenodo.7618788