Publication Type
Journal Article
Version
publishedVersion
Publication Date
3-2022
Abstract
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%.
Keywords
Air Pollution, Water Spraying
Discipline
Databases and Information Systems | Environmental Sciences
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume
6
Issue
1
First Page
1
Last Page
29
ISSN
2474-9567
Identifier
10.1145/3517227
Publisher
ACM
Citation
CHENG, Yun; ZHOU, Zimu; and THIELE, Lothar.
iSpray: Reducing urban air pollution with intelligent water spraying. (2022). Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 6, (1), 1-29.
Available at: https://ink.library.smu.edu.sg/sis_research/7221
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3517227