International Journal on Science and Technology

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AI-Enabled Environmental Intelligence System for Urban Air Pollution Mitigation: A Comprehensive Framework with Delhi as a Case Study and Lessons from Beijing

Author(s) Mr. Raghav Ji Dwivedi, Ms. Fariha Kashef, Mr. Dhruv Chaubey
Country India
Abstract Urban air pollution is one of the most severe public health and economic crises facing modern India. Among all cities, Delhi consistently ranks as one of the world’s most polluted megacities, especially during winter inversion months. Pollution in Delhi arises from a complex interplay of seasonal stubble burning, vehicular emissions, industrial discharge, construction dust, poor land management, and unfavorable meteorological patterns.

This research develops a comprehensive AI-powered Environmental Intelligence System (EIS) that integrates satellite monitoring, IoT sensors, computer vision, predictive machine learning models, risk zoning, industrial emission surveillance, and real-time municipal action orchestration. The system focuses on identifying pollution sources instantly, quantifying contributions, forecasting smog movement, generating responsive attack plans, and maintaining economic growth without shutting down industry.

The study further draws lessons from Beijing—a city that successfully reduced pollution by 35–40% in five years through real-time monitoring, strict enforcement, industrial relocation, and predictive analytics. The proposed EIS helps India move toward an evidence-driven, technologically fortified environmental governance model.
Keywords Air pollution, urban smog, Delhi air quality, stubble burning, vehicular emissions, industrial emission monitoring, mandatory stack sensors, construction dust, Environmental Intelligence System (EIS), artificial intelligence, satellite-based pollution detection, IoT air-quality sensors, real-time emission monitoring, plume tracking, smog forecasting, machine learning models, computer vision smoke detection, GIS micro-zoning, predictive environmental analytics, automated municipal response, risk-based zoning, sustainable urban governance, economic-friendly mitigation, AI-generated awareness campaigns, satellite data integration, NDVI green mapping, urban plantation strategies, Beijing pollution control case study, source apportionment, environmental policy reform, climate-resilient urban planning.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-12-09
DOI https://doi.org/10.71097/IJSAT.v16.i4.9840
Short DOI https://doi.org/hbf83j
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