Air Quality Commission ropes in top technical institutions to set up a Decision Support System
The System will use Artificial Intelligence(AI) to help improve the air quality over targeted sectors of Delhi /NCR
The Commission for Air Quality Management (CAQM) in NCR and adjoining areashas begun the process of setting up a Decision Support System (DSS) having a web, GIS and multi-model based operational and planning decision support tool.
This tool will help immensely 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 chemical transport model. The system will also be able to handle the source specific interventions with the framework to estimate 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 expert groups from reputed knowledge institutions of the country given belowfor framework development of Air Quality Management DSS for Delhi:-
The Air Quality Management Decision Support Tool (DST) integrates an emissions inventory development application and database; regional, local and source–receptor modelling; 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 over targeted sectors of Delhi / NCR.Identification ofsource specific interventions by the DST is deliberated with the involvement of stake holders.
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 stake holders for identifying interventions related to waste burning, industrial source pollution, respectively.
Upon identification of feasible interventions, the artificial intelligence based expert system which has a hierarchical data base 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 analysed to understand the real-world benefits of such interventio
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