Decision Support System for Air-quality management in Delhi, India

Delhi, the national capital of India, has been experiencing a polluted environment, especially during the post-monsoon and winter season in the recent decade. The post-harvest burning of paddy-crop residue in the north-western states of India and the ever-rising local anthropogenic emissions of pollutants are known to be primary factors responsible for the poor air quality in the capital city. Responding to air quality degradation, the Indian Institute of Tropical Meteorology, in collaboration with the India Meteorological Department under the guidance of the Ministry of Earth Sciences, India, developed an 'Air Quality Early Warning System' (AQEWS) to inform the citizens and the policymakers about potential severe air-pollution episodes, about one week in advance. However, the policymakers seek more information, mainly on the sources possibly responsible for the degradation of air quality during a forecast air-pollution episode. This information would assist them in making policy-level decisions to manage the air quality. Understanding this requirement, we have now developed a 'Decision Support System' (DSS) for air-quality management in Delhi. The DSS, an extension of AQEWS, assimilates surface observations of PM_2.5 mass concentrations (for more than 150 stations across northern India) and satellite retrievals of aerosol optical depth and active fire counts. It provides quantification of the contribution of a). the emissions from Delhi and the surrounding 19 districts to the air pollution load in Delhi b). the emissions from 8 different emission sectors in Delhi to the air quality of Delhi c). the stubble-burning activities in the neighboring states to the air quality in Delhi. Such information highlights the most significant emission sources for the degraded air quality in Delhi. Additionally, DSS also quantifies the effects of possible source-level interventions on the forecast air-quality event in Delhi. All this information assists the authorities in managing the air quality on time. The DSS has been operational since the post-monsoon season of 2021. This study depicts the underlying methodology employed in DSS, which makes it useful for decision-making purposes. We discuss the performance of DSS in simulating PM_2.5 mass concentration in Delhi for the post-monsoon and winter seasons of 2021-22. We also show the source apportionment for PM_2.5 in Delhi during the study period. We further discuss the various scenarios of emission reductions and their effects on the ambient PM_2.5 in Delhi. With a plethora of quantitative information, the DSS has become a critical tool for the policymakers for air-quality management in Delhi and the surrounding region.