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

Arvind Saraswat1, Milind Kandlikar2, Michael Brauer3

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This study developed a GIS framework to estimate PM2.5 exposure in New Delhi, revealing severe winter pollution. Mobility effects only slightly underestimated average annual exposure.

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Area of Science:

  • Environmental Science
  • Public Health
  • Geographic Information Systems (GIS)

Background:

  • Particulate Matter (PM2.5) pollution poses a significant public health risk, especially in densely populated urban areas.
  • Accurate estimation of population exposure to PM2.5 is crucial for developing effective mitigation strategies.
  • Previous exposure assessments often overlook the impact of daily mobility patterns.

Purpose of the Study:

  • To develop and apply a GIS-based probabilistic simulation framework for estimating PM2.5 population exposure in New Delhi.
  • To quantify population exposure under different mobility scenarios, including stay-at-home and commuting populations.
  • To assess the impact of mobility on overall PM2.5 exposure estimates.

Main Methods:

  • Integrated spatiotemporal Land Use Regression (LUR) models with a Gravity model for trip distribution using zonal data.
  • Incorporated time-activity patterns from a survey of 1012 individuals and in-vehicle exposure data from field measurements.
  • Simulated population exposure for three scenarios: stay-at-home, near-road commute exposure, and on-road commute exposure.

Main Results:

  • Simulated annual average PM2.5 exposure levels in New Delhi were consistently high, with winter months showing significantly elevated concentrations (∼200 μg m⁻³ in November).
  • Mean annual exposures varied across scenarios: 109 μg m⁻³ (Scenario 1), 121 μg m⁻³ (Scenario 2), and 125 μg m⁻³ (Scenario 3).
  • Excluding mobility effects led to an underestimation of average annual PM2.5 population exposure by only 11%.

Conclusions:

  • The developed GIS framework provides a robust method for assessing PM2.5 population exposure in urban environments.
  • High PM2.5 levels, particularly during winter, underscore the urgent need for air quality interventions in New Delhi.
  • While mobility significantly influences exposure patterns, its exclusion results in a relatively small underestimation of average annual exposure.