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, &amp; R. V. Martin, Reversal of trends in global fine particulate matter air pollution, submitted, 2023. <strong>Input Data</strong> Baseline mortality data (204 countries and territories, 17 age groups, 6 diseases, 22 years) Concentration-response functions (GEMM and MRBRT) PM_2.5 exposure for 204 territories and 22 years Age-specific population for 204 territories and 22 years <strong>Derived Data</strong> Age- and disease-specific PM_2.5-attributable Mortality estimates for 204 territories and 22 years. Sensitivity of PM_2.5-attributable Mortality to marginal PM_2.5 reduction for 204 territories and 22 years. Attributable of changes in PM_2.5-attributable Mortality (and its sensitivity to marginal PM_2.5 reduction) to four driving factors. <strong>Code</strong> Necessary python scripts to verify and replicate analysis results in the manuscript.本数据集包含用于下述学术论文的输入数据与分析代码：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 与 R. V. Martin 合著的《全球细颗粒物空气污染趋势逆转》（Reversal of trends in global fine particulate matter air pollution），2023年投稿。 **输入数据** - 基线死亡率数据（覆盖204个国家及地区、17个年龄组、6类疾病，时间跨度为22年） - 浓度-响应函数（GEMM与MRBRT） - 204个地区22年的细颗粒物（PM₂.₅）暴露数据 - 204个地区22年的分年龄组人口数据 **衍生数据** - 204个地区22年的分年龄、分疾病的细颗粒物（PM₂.₅）归因死亡率估算结果 - 204个地区22年的细颗粒物（PM₂.₅）归因死亡率对边际PM₂.₅减排的敏感性结果 - 细颗粒物（PM₂.₅）归因死亡率变化（及其对边际PM₂.₅减排的敏感性）的四类驱动因素归因结果 **代码** 用于验证并复现论文中分析结果的必要Python脚本。