India News | CAQM Setting Up Decision Support System for Air Quality for Delhi-NCR

New Delhi [India], January 22 (ANI): The Commission for Air Quality Management (CAQM) in Delhi National Capital Region (NCR) and adjoining areas has begun the process of setting up a Decision Support System (DSS) for air quality having a web, GIS and multi-model based operational and planning decision support tool.

The Ministry of Environment and Forest and Climate Change stated in a release that 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 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 ministry added.

The Commission has entrusted the task to expert groups from reputed knowledge institutions of the country which include IIT Delhi, IMD, C-DAC among others.

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 visualisation 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 of source-specific interventions by the DST is deliberated with the involvement of stakeholders, the ministry stated further.

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.

Upon identification of feasible interventions, the artificial intelligence-based expert system which has a hierarchical database of simulated scenarios, potentially assessing the impact of the identified feasible intervention which would be implemented by the regulatory organisation 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 intervention. (ANI)

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