Monitoring of air pollutants such as fine particulate matter (PM2.5) supports improved understanding of trends, assessment of exposure, and development of strategies to protect public health. However, determining the optimal placement of pollutant sensors is an ongoing challenge, as different sensor configurations may yield varied insights into regional air quality. In this study, we propose a quantitative framework that integrates a stochastic advection-diffusion (SAD) model with the value of information (VoI) to design a sensor placement strategy for regional PM2.5 monitoring. Predictions from the SAD model are used to compute the VoI based on a cost function that penalizes misclassifications between model-assigned and observed air quality index values. We apply the framework to the Coastal Bend Region of Texas, where increased development is creating new air quality concerns. We compare three weighting strategies: (1) combined weighting, which accounts for both population density and social vulnerability; (2) vulnerability-focused weighting, which emphasizes socially vulnerable communities regardless of population density; and (3) unweighted VoI, which solely focuses on minimizing assignment error without demographic weighting. With a 10-sensor installation, the combined strategy covered areas with 22%–88% higher population density than the other schemes, whereas the vulnerability-focused strategy reduced uncovered social vulnerability by 6%–19%. Prediction accuracy differed by less than 1.5% across strategies. While differences in outcomes are expected given the distinct objectives, the framework adds value by quantifying these trade-offs and enabling a systematic sensor placement strategy that best aligns with policy goals. The proposed framework is scalable to different sensor types and capable of balancing information gain, population exposure, and social vulnerability, providing a versatile tool to guide sensor network design under resource constraints.

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