Input data and analysis codes for "Reversal of trends in global fine particulate matter air pollution"
Authors/Creators
- 1. Washington University in St. Louis
- 2. U.S. Environmental Protection Agency
- 3. University of Washington
- 4. Spadaro Environmental Research Consultants
- 5. Health Effects Institute
- 6. University of California, Berkeley
- 7. George Washington University
- 8. University of British Columbia
- 9. Washington University in St. Louis, St. Louis
Description
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.
Files
GlobalAirPollutionTrend.zip
Files
(135.6 MB)
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