PM2.5 Population Exposure in New Delhi Using a Probabilistic Simulation Framework

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• 作者：Arvind Saraswat ; Milind Kandlikar ; Michael Brauer ; Arun Srivastava
• 刊名：Environmental Science & Technology
• 出版年：2016
• 出版时间：March 15, 2016
• 年：2016
• 卷：50
• 期：6
• 页码：3174-3183
• 全文大小：466K
• ISSN：1520-5851

文摘

This paper presents a Geographical Information System (GIS) based probabilistic simulation framework to estimate PM_2.5 population exposure in New Delhi, India. The framework integrates PM_2.5 output from spatiotemporal LUR models and trip distribution data using a Gravity model based on zonal data for population, employment and enrollment in educational institutions. Time-activity patterns were derived from a survey of randomly sampled individuals (n = 1012) and in-vehicle exposure was estimated using microenvironmental monitoring data based on field measurements. We simulated population exposure for three different scenarios to capture stay-at-home populations (Scenario 1), working population exposed to near-road concentrations during commutes (Scenario 2), and the working population exposed to on-road concentrations during commutes (Scenario 3). Simulated annual average levels of PM_2.5 exposure across the entire city were very high, and particularly severe in the winter months: ∼200 μg m^–3 in November, roughly four times higher compared to the lower levels in the monsoon season. Mean annual exposures ranged from 109 μg m^–3 (IQR: 97—120 μg m^–3) for Scenario 1, to 121 μg m^–3 (IQR: 110—131 μg m^–3), and 125 μg m^–3 (IQR: 114—136 μ gm^–3) for Scenarios 2 and 3 respectively. Ignoring the effects of mobility causes the average annual PM_2.5 population exposure to be underestimated by only 11%.