Optimizing air quality monitoring device deployment: a strategy to enhance distribution efficiency

Precise and efficient air quality monitoring is a pivotal step in combating the harmful effects of pollution. Our research addresses the challenges in obtaining accurate air quality data and identifying pollution sources. It introduces an algorithm to optimize the deployment of air quality monitoring devices, enhancing distribution efficiency. The algorithm considers spatial distribution to minimize capital and operational costs. Utilizing an extensive 300 days dataset covering Durgapur (80 km \(^2\) ${}^2$ ), it achieves an above 90% accuracy rate in predicting air quality. By strategically selecting monitoring locations, it maximizes data coverage while minimizing costs. This advancement supports cost-effective pollution control and resource allocation decisions in affected regions.