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Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India

Nelson Institute for Environmental Studies, Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, Madison, WI 53726, USA
Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI 53726, USA
International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(1), 60;
Received: 7 November 2018 / Revised: 10 December 2018 / Accepted: 18 December 2018 / Published: 26 December 2018
(This article belongs to the Section Environmental Health)
PDF [2069 KB, uploaded 26 December 2018]


Fine particulate matter (PM2.5, diameter ≤2.5 μm) is implicated as the most health-damaging air pollutant. Large cohort studies of chronic exposure to PM2.5 and mortality risk are largely confined to areas with low to moderate ambient PM2.5 concentrations and posit log-linear exposure-response functions. However, levels of PM2.5 in developing countries such as India are typically much higher, causing unknown health effects. Integrated exposure-response functions for high PM2.5 exposures encompassing risk estimates from ambient air, secondhand smoke, and active smoking exposures have been posited. We apply these functions to estimate the future cause-specific mortality risks associated with population-weighted ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. The loss in statistical life expectancy (SLE) is calculated based on risk estimates and baseline mortality rates. Losses in SLE are aggregated and weighted using national age-adjusted, cause-specific mortality rates. 2030 PM2.5 pollution in India reaches an annual mean of 74 μg/m3, nearly eight times the corresponding World Health Organization air quality guideline. The national average loss in SLE is 32.5 months (95% Confidence Interval (CI): 29.7–35.2, regional range: 8.5–42.0), compared to an average of 53.7 months (95% CI: 46.3–61.1) using methods currently applied in GAINS. Results indicate wide regional variation in health impacts, and these methods may still underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response functions suggests that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality. View Full-Text
Keywords: air pollution; exposure-response; modeling; health impact assessment; India air pollution; exposure-response; modeling; health impact assessment; India

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Limaye, V.S.; Schöpp, W.; Amann, M. Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India. Int. J. Environ. Res. Public Health 2019, 16, 60.

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