Global PM_(2.5) Prediction and Associated Mortality to 2100 under Different Climate Change Scenarios

Wanying Chen; Xingcheng Lu; Dehao Yuan

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Global PM_(2.5) Prediction and Associated Mortality to 2100 under Different Climate Change Scenarios

机译：Global PM_(2.5) Prediction and Associated Mortality to 2100 under Different Climate Change Scenarios

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Ambient fine particulate matter (PM_(2.5)) has severe adverse health impacts, making it crucial to reduce PM_(2.5) exposure for public health. Meteorological and emissions factors, which considerably affect the PM_(2.5) concentrations in the atmosphere, vary substantially under different climate change scenarios. In this work, global PM_(2.5) concentrations from 2021 to 2100 were generated by combining the deep learning technique, reanalysis data, emission data, and bias-corrected CMIP6 future climate scenario data. Based on the estimated PM_(2.5) concentrations, the future premature mortality burden was assessed using the Global Exposure Mortality Model. Our results reveal that SSP3-7.0 scenario is associated with the highest PM_(2.5) exposure, with a global concentration of 34.5 μg/m~3 in 2100, while SSP1-2.6 scenario has the lowest exposure, with an estimated of 15.7 μg/m~3 in 2100. PM_(2.5)-related deaths for individuals under 75 years will decrease by 16.3 and 10.5 under SSP1-2.6 and SSP5-8.5, respectively, from 2030s to 2090s. However, premature mortality for elderly individuals (>75 years) will increase, causing the contrary trends of improved air quality and increased total PM_(2.5)-related deaths in the four SSPs. Our results emphasize the need for stronger air pollution mitigation measures to offset the future burden posed by population age.

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《Environmental Science & Technology: ES&T》 |2023年第27期|10039-10052|共14页
作者
Wanying Chen; Xingcheng Lu; Dehao Yuan;
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作者单位

Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong SAR 999077, China;

Department of Geography and Resource Management, Chinese University of Hong Kong Hong Kong SAR 999077, China;

Department of Computer Science, University of Maryland, College Park, Maryland 20742, United States;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
中图分类环境科学、安全科学;
关键词
climate change; global; PM_(2.5); mortality; deep learning;

Global PM_(2.5) Prediction and Associated Mortality to 2100 under Different Climate Change Scenarios

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