India’s Maiden air quality forecasting framework for megacities of divergent environments: The SAFAR-project

• Published in: Environmental modelling & software : with environment data news Vol. 145; p. 105204
• Main Authors: Beig, Gufran, Sahu, S.K., Anand, V., Bano, S., Maji, S., Rathod, A., Korhale, N., Sobhana, S.B., Parkhi, N., Mangaraj, P., Srinivas, R., Peshin, S.K., Singh, S., Shinde, R., Trimbake, H.K.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2021; Elsevier Science Ltd
• Subjects: Air pollution forecasting; Air quality; Decision making; Economic forecasting; Environment; Forecasting model; Mathematical models; Megacities; Meteorology; Numerical models; Outdoor air quality; Particulate matter; Particulate matters; Pollutants; Population density; SAFAR; Topography and health; Weather forecasting; Megacities; Particulate matters; Topography and health; Air quality; SAFAR; Environment; Forecasting model; Meteorology
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2021.105204

Air quality is a strong health driver, its accurate assessment and forecast are important in densely populated megacities to take preventive steps. We describe the first Indian operational air quality framework, SAFAR (System of Air Quality and Weather Forecasting And Research), meant for decision-makers and a research tool with a capability of three days advance forecast in four Indian megacities of distinct environment and topography. The framework includes six different components from observations and modelling to outreach. To evaluate the performance of the forecast, we focus on particulate pollutants which largely define air quality of Indian metropolis. The model prediction skill is tested for the pilot year 2019-20 which is found to be reasonable. The Normalized Gross error of PM2.5 for Delhi is found to be highest (35%) whereas for other cities it is ∼13–20%. The Model Output Statistics (MOS) application enhanced operational forecast ability of numerical model which resulted in improving the accuracy for specific seasons (winter). [Display omitted] •Indian air quality forecasting framework for divergent environment developed.•Performance of the model found reasonable against observations.•Normalized Gross error (NGE) of PM2.5 for Delhi is found to be highest (35%).•The NGE for Mumbai, Ahmedabad and Pune are found ∼13–20%.•Good prediction skills for weather parameters driving air quality also developed.