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Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM 2.5 pollution.

Authors :
Li Y
Huang T
Lee HF
Heo Y
Ho KF
Yim SHL
Source :
The Science of the total environment [Sci Total Environ] 2024 Nov 20; Vol. 952, pp. 175632. Date of Electronic Publication: 2024 Aug 19.
Publication Year :
2024

Abstract

Air pollution has been recognized as a global issue, through adverse effects on environment and health. While vertical atmospheric processes substantially affect urban air pollution, traditional epidemiological research using Land-use regression (LUR) modeling usually focused on ground-level attributes without considering upper-level atmospheric conditions. This study aimed to integrate Doppler LiDAR and machine learning techniques into LUR models (LURF-LiDAR) to comprehensively evaluate urban air pollution in Hong Kong, and to assess complex interactions between vertical atmospheric processes and urban air pollution from long-term (i.e., annual) and short-term (i.e., two air pollution episodes) views in 2021. The results demonstrated significant improvements in model performance, achieving CV R <superscript>2</superscript> values of 0.81 (95 % CI: 0.75-0.86) for the long-term PM <subscript>2.5</subscript> prediction model and 0.90 (95 % CI: 0.87-0.91) for the short-term models. Approximately 69 % of ground-level air pollution arose from the mixing of ground- and lower-level (105 m-225 m) particles, while 21 % was associated with upper-level (825 m-945 m) atmospheric processes. The identified transboundary air pollution (TAP) layer was located at ~900 m above the ground. The identified Episode one (E1: 7 Jan-22 Jan) was induced by the accumulation of local emissions under stable atmospheric conditions, whereas Episode two (E2: 13 Dec-24 Dec) was regulated by TAP under instable and turbulent conditions. Our improved air quality prediction model is accurate and comprehensive with high interpretability for supporting urban planning and air quality policies.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2024 Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1879-1026
Volume :
952
Database :
MEDLINE
Journal :
The Science of the total environment
Publication Type :
Academic Journal
Accession number :
39168320
Full Text :
https://doi.org/10.1016/j.scitotenv.2024.175632