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Area of Science:

  • Atmospheric Science
  • Environmental Science
  • Meteorology

Background:

  • Acute air pollution episodes pose significant risks to public health in Delhi and the National Capital Region (NCR).
  • Existing air quality forecasting systems may lack the necessary resolution to capture localized pollution events effectively.

Purpose of the Study:

  • To develop and evaluate a novel, very high-resolution (400 m grid-spacing) operational air quality forecasting system for Delhi and NCR.
  • To provide timely alerts for acute air pollution episodes to residents and inform decision-making.

Main Methods:

  • Implementation of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in a dynamical downscaling framework.
  • Assimilation of near real-time aerosol observations from in situ and space-borne platforms, including aerosol optical depth and surface PM2.5.
  • Daily generation of 72-hour air quality forecasts.

Main Results:

  • The assimilation of aerosol data significantly improved the initial conditions for surface PM2.5 by approximately 50% (45 µg/m³).
  • Forecast accuracy showed a slight degradation with lead time, with mean bias increasing from +2.5 µg/m³ on day 1 to -17 µg/m³ on day 3.
  • The system demonstrated high skill in forecasting PM2.5 concentrations and unhealthy/very unhealthy Air Quality Index (AQI) categories.

Conclusions:

  • The developed high-resolution air quality forecasting system is a valuable tool for managing air pollution in Delhi and NCR.
  • The system's ability to predict severe pollution events aids decision-makers in implementing mitigation strategies.
  • This represents a pioneering effort in deploying such a high-resolution forecasting system for operational air quality management.