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

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3517227

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