SmartSpr: A Physics-Informed Mobile Sprinkler Scheduling System for Reducing Urban Particulate Matter Pollution

Urban particulate pollution presents considerable public health hazards, underscoring the need for effective control measures in various cities. This paper proposes SmartSpr, a physics-informed urban mobile sprinkler scheduling system designed for enhanced efficiency in reducing particulate pollution. SmartSpr incorporates a Physics-Informed Neural Network (PINN)-based model, enriched with Bayesian optimization, to accurately simulate the impact of mobile sprinklers on particulate matter (PM) dispersion. Building on this sprinkling effect model, a selective sprinkling strategy considering the replenish process is proposed. This strategy employs a sparsity-driven decoupling simulated annealing algorithm to refine sprinkler routes, prioritizing areas with substantial environmental benefits. Extensive field experiments and simulations have validated SmartSpr, demonstrating a 64.8% reduction in prediction error of SmartSpr's sprinkling model compared to the leading baseline and an 18% enhancement in pollutant reduction efficiency of the proposed scheduling algorithm.