AI-Powered Air Quality Monitoring and Pollution Control Strategies
Abstract
Rising air pollution levels pose significant threats to human health and environmental sustainability. This paper investigates machine learning applications in air quality monitoring, real-time pollution prediction, and emissions reduction. Techniques such as deep learning, sensor fusion, and spatial-temporal analysis are employed to analyze air pollution data from satellite imagery and IoT-based air quality sensors. The study presents AI-driven policy recommendations and pollution mitigation strategies, demonstrating how machine learning enhances efforts to improve air quality and combat climate change.
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