ISpray : Reducing Urban Air Pollution with IntelligentWater Spraying

Despite regulations and policies to improve city-level air quality in the long run, there lack precise control measures to protect critical urban spots from heavy air pollution. In this work, we propose iSpray, the first-of-its-kind data analytics engine for fine-grained PM_2.5 and PM₁₀ control at key urban areas via cost-effective water spraying. iSpray combines domain knowledge with machine learning to profile and model how water spraying affects PM₂₅ and PM₁₀ concentrations in time and space. It also utilizes predictions of pollution propagation paths to schedule a minimal number of sprayers to keep the pollution concentrations at key spots under control. In-field evaluations show that compared with scheduling based on real-time pollution concentrations, iSpray reduces the total sprayer switch-on time by 32%, equivalent to 1, 782 m³ water and 18, 262 kWh electricity in our deployment, while decreasing the days of poor air quality at key spots by up to 16%. © 2022 ACM.