Real-time data, source identification, measurable outcomes: Govt set to develop AI-enabled data system with IIT Kanpur (TO COMBAT AIR POLLUTION), Pg3

Key Highlights:

• Delhi government is collaborating with IIT Kanpur to develop an AI-enabled Decision Support System (DSS) to combat air pollution.
• The focus is on real-time data, source identification, and measurable outcomes for targeted action against pollution hotspots.
• The system aims to identify pollution sources at a granular level and enable timely interventions across sectors.
• Current system relies on Indian Institute of Tropical Meteorology Pune’s (IITM), whose data reliability has been questioned in the past.

Detailed Insights:

• The proposed system will use dynamic source apportionment to identify contributions from dust, transport, industry, biomass burning, and regional factors.
• The goal is to move away from blanket restrictions towards targeted action at pollution hotspots using a 365-day action framework.
• A study by CEEW pointed out that Delhi’s Air Quality Early Warning System (AQEWS), while having high forecast accuracy, is limited by outdated emission inventories.
• Delhi's clean-air action plan has not been updated for seven years, even after a new source-apportionment study was completed in 2023.
• The Delhi government is also focusing on vehicular emissions, road dust, polluting industries, and waste management to combat air pollution.

Key Concepts Involved:

• Source Apportionment: Identifying the contribution of different pollution sources to overall air pollution levels.
• Air Quality Index (AQI): A measure of air quality based on concentrations of various pollutants.
• Decision Support System (DSS): A computer-based system that aids in decision-making by providing relevant data and analysis.