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 (PM_2.5) 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. PM_2.5 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 PM_2.5 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.

中文翻译：

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尽管浓度下降，但在 SSP 情景下，印度环境 PM2.5 的健康负担在 2100 年之前持续上升


预测环境空气污染的变化以及随之而来的健康影响对于保障公众健康、促进环境可持续性、为经济决策提供信息以及促进适当的政策和监管行动至关重要。然而，预测此类变化提出了巨大的挑战，需要准确的数据、复杂的建模方法以及对多个驱动因素的细致评估。在本研究中，我们计算了 2020 年代（2016-2020 年）至 2100 年代（2095-2100 年）四种不同社会经济和气候情景下印度因环境细颗粒物 (PM _2.5 ) 暴露而导致的过早死亡（SSP）基于四个 CMIP6 模型。除 SSP3–7.0 外，所有 SSP 情景中的 PM _2.5 浓度均有所下降，其中在 SSP1–2.6 中观察到浓度最低。结果表明，所有情景下的五年平均死亡人数均呈上升趋势，从 2020 年代的 101 万人到 2100 年代的 412-544 万人。进一步的分析表明，在所有情况下降低 PM _2.5 浓度的好处在很大程度上会因人口老龄化和增长而减弱。这些发现强调了印度采取积极措施和综合方法的重要性，以改善气候条件下的大气质量并减少老化的脆弱性。