<p>The escalating concerns regarding air quality necessitate innovative approaches for accurate prediction and timely mitigation. This research addresses the imperative need for an AI-driven surface-level air quality prediction system, leveraging a synergistic blend of Internet of Things (IoT) and satellite data. By amalgamating real-time sensor data from IoT devices with satellite observations, the proposed model aims to enhance spatial and temporal resolution, providing a comprehensive understanding of air quality dynamics. The integration of artificial intelligence algorithms ensures precise predictions, enabling proactive measures to mitigate pollution. This research contributes to the evolving field of environmental monitoring, offering a sophisticated tool for policymakers and communities to combat the escalating challenges posed by deteriorating air quality.</p>

Smart air quality prediction: AI-driven model fusing IoT and satellite data for accurate real-time monitoring.

AI-based Air Quality Prediction with IoT and Satellite Data

Review of weather-affected urban air pollution forecast models

Comparing the significance of new data products from satellites containing night-time light and day-time aerosol information for air quality prediction

Title	DOI	Published date
Review of weather-affected urban air pollution forecast models	10.4018/978-1-6684-3981-4.ch015	2022
Comparing the significance of new data products from satellites containing night-time light and day-time aerosol information for air quality prediction	10.13140/RG.2.2.35682.20163	2021-11-15
A comparative study of various community detection algorithms in the mobile social network	10.1109/NUICONE.2015.7449651	2016

Ankit Didwania

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