Abstract
Air pollution is a global public health concern, particularly due to PM2.5, which can cause respiratory and cardiovascular diseases. Accurate placement of monitoring sensors is essential to effectively monitor and mitigate PM2.5 effects. However, the complex nature of air pollution, including factors like traffic density, population density, and weather conditions, poses challenges for sensor placement. Additionally, cost and resource constraints further complicate the process. In this study, we propose a novel algorithm that utilizes a multi-criteria optimization approach to identify optimal locations and distribution of PM2.5 monitoring sensors. The algorithm integrates various geographical covariates, such as roads, population density, terrain elevation, and satellite observations of surface PM2.5. By applying the Non-dominated Sorting Genetic Algorithm II (NSGA-II), we optimize sensor placement. Our algorithm is validated through a case study in a metropolitan area, demonstrating its ability to identify optimal sensor locations while reducing their number and maintaining high accuracy. Furthermore, we highlight the value of satellite observations for initial PM2.5 estimates and aiding sensor placement. Our comprehensive algorithm optimizes air quality monitoring, enabling the identification of pollution hotspots, assessment of health risks, and informing policy and mitigation strategies.
Published Version
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