CAQM In Delhi To Set Artificial Intelligence For Air Quality Improvement

The Commission for Air Quality Management (CAQM) in Delhi-NCR and adjoining areas has roped in India's top technical institutions to set up a decision support system, which will use Artificial Intelligence to help improve the air quality over targeted sectors of the city.

The Commission for Air Quality Management has begun the process of setting up a Decision Support System (DSS) having a web, Geographical Information System, and multi-modal based operational and planning decision support tool.

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This tool will help in capturing the static and dynamic features of the emissions from various sources. It will have an integrated framework to handle both primary and secondary pollutants using a chemical transport model.

The system will also be able to handle the "source-specific interventions" with the framework to estimate the benefits of interventions and will focus on presenting the best results in a comprehensive user-friendly and simple format for different users.

The Commission has entrusted the task to IMD Delhi, IITM Pune, IIT Delhi, National Environmental Engineering Research Institute (NEERI), The Energy and Resources Institute (TERI), and Centre for Development of Advanced Computing for framework development of Air Quality Management Decision Support System for Delhi.

The Air Quality Management Decision Support Tool (DST) integrates an emissions inventory development application and database; regional, local, and source-receptor modeling; and Geographical Information System (GIS) based visualization tools in a software framework so as to build a robust system to formulate and implement source-specific interventions to improve the air quality.

The sources covered will include industries, transport, power plants, residential, DG sets, road dust, agricultural burning, refuse burning, construction dust, ammonia, volatile organic compounds, landfill, etc. For instance, municipalities, industrial associations, industrial development authorities, etc. would be the stakeholders for identifying interventions related to waste burning, industrial source pollution, respectively.

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Upon identification of feasible interventions, the artificial intelligence-based expert system has a hierarchical database of simulated scenarios, potentially assessing the impact of the identified feasible intervention which would be implemented by the regulatory organization such as CPCB and state PCBs.

The on-field implementation is monitored by credible citizen watch groups and professional NGOs independently. Finally, air quality data collected in the vicinity of the area where intervention is implemented will be analyzed to understand the real-world benefits of such intervention. (IANS)