AirSense AI: Urban Air Quality Intelligence Platform

Dr, Madhur Jain, Dr, Shilpi Jain, Mr, Nikhil Negi, Mr, Aditya Kumar Jha, Mr and Krishna Tyagi

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 5, 386-401

Abstract: Urban air pollution is one of the most persistent environmental and public-health problems facing Indian cities, and Delhi in particular records some of the highest particulate matter concentrations in the world during the post-monsoon and winter months. Public air-quality portals in India largely display current or historical readings, but they rarely tell a city administrator what the air will look like over the next few days, or where a pollution spike is likely originating from. This gap between “knowing today's AQI” and “deciding what to do about it” motivates AirSense AI, a full-stack urban air-quality intelligence platform designed as an academic prototype for Delhi. The system ingests PM2.5, PM10 and NO₂ station readings together with weather variables, satellite fire detections and land-use information, converts pollutant concentrations into a standardized Air Quality Index using the U.S. EPA methodology, and applies a gradient-boosted decision tree model (XGBoost) to forecast AQI up to 72 hours ahead. In parallel, the platform combines NASA FIRMS active-fire data with OpenStreetMap land-use polygons and Haversine-distance calculations to rank probable pollution hotspots, and uses a large language model (Groq/LLaMA 3) to translate the resulting numerical evidence into readable, bilingual (English/Hindi) advisories and enforcement summaries for administrators and citizens. The results are presented through a React and Leaflet-based interactive dashboard. This paper describes the motivation, related work, and proposed architecture of AirSense AI as an integrated decision-support system rather than a monitoring-only tool. As the platform is at the prototype stage, no experimental forecasting accuracy, source-attribution validation, or user-study results are reported here; these are identified as required additions for the next version of this paper.

Keywords: Air Quality Index; AQI Forecasting; XGBoost; Geospatial Intelligence; Pollution Source Attribution; Large Language Models; Urban Environmental Decision Support (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2612543
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DOI: 10.32628/CSEIT2612543

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