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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.
Wang et al. (Mon,) studied this question.