气候变化
环境科学
持续性
保护
脆弱性(计算)
环境卫生
环境规划
公共卫生
空气质量指数
微粒
空气污染
自然资源经济学
环境资源管理
地理
环境保护
气象学
医学
计算机科学
生态学
化学
护理部
计算机安全
有机化学
经济
生物
作者
Yiyi Wang,Jianlin Hu,Yangyang Wu,Sri Harsha Kota,Hongliang Zhang,Kangjia Gong,Xiaodong Xie,Xu Yue,Hong Liao,Lei Huang
标识
DOI:10.1021/acs.est.4c02264
摘要
Forecasting alterations in ambient air pollution and the consequent health implications is crucial for safeguarding public health, advancing environmental sustainability, informing economic decision making, and promoting appropriate policy and regulatory action. However, predicting such changes poses a substantial challenge, requiring accurate data, sophisticated modeling methodologies, and a meticulous evaluation of multiple drivers. In this study, we calculate premature deaths due to ambient fine particulate matter (PM2.5) exposure in India from the 2020s (2016–2020) to the 2100s (2095–2100) under four different socioeconomic and climate scenarios (SSPs) based on four CMIP6 models. PM2.5 concentrations decreased in all SSP scenarios except for SSP3–7.0, with the lowest concentration observed in SSP1–2.6. The results indicate an upward trend in the five-year average number of deaths across all scenarios, ranging from 1.01 million in the 2020s to 4.12–5.44 million in the 2100s. Further analysis revealed that the benefits of reducing PM2.5 concentrations under all scenarios are largely mitigated by population aging and growth. These findings underscore the importance of proactive measures and an integrated approach in India to improve atmospheric quality and reduce vulnerability to aging under changing climate conditions.
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