iSpray: Reducing urban air pollution with intelligent water spraying

• Main Authors: CHENG, Yun, ZHOU, Zimu, THIELE, Lothar
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Air Pollution; Water Spraying; Databases and Information Systems; Environmental Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/7221; https://ink.library.smu.edu.sg/context/sis_research/article/8224/viewcontent/ubicomp22_cheng.pdf
• Institution: Singapore Management University

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 PM2.5 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%.