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Liu, X.* ; Zhang, X.* ; Wang, R.* ; Liu, Y.* ; Hadiatullah, H.* ; Xu, Y.* ; Wang, T.* ; Bendl, J.* ; Adam, T. ; Schnelle-Kreis, J. ; Querol, X.*

High-precision microscale particulate matter prediction in diverse environments using a long short-term memory neural network and street view imagery.

Environ. Sci. Technol. 58, 3869-3882 (2024)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
In this study, we propose a novel long short-term memory (LSTM) neural network model that leverages color features (HSV: hue, saturation, value) extracted from street images to estimate air quality with particulate matter (PM) in four typical European environments: urban, suburban, villages, and the harbor. To evaluate its performance, we utilize concentration data for eight parameters of ambient PM (PM1.0, PM2.5, and PM10, particle number concentration, lung-deposited surface area, equivalent mass concentrations of ultraviolet PM, black carbon, and brown carbon) collected from a mobile monitoring platform during the nonheating season in downtown Augsburg, Germany, along with synchronized street view images. Experimental comparisons were conducted between the LSTM model and other deep learning models (recurrent neural network and gated recurrent unit). The results clearly demonstrate a better performance of the LSTM model compared with other statistically based models. The LSTM-HSV model achieved impressive interpretability rates above 80%, for the eight PM metrics mentioned above, indicating the expected performance of the proposed model. Moreover, the successful application of the LSTM-HSV model in other seasons of Augsburg city and various environments (suburbs, villages, and harbor cities) demonstrates its satisfactory generalization capabilities in both temporal and spatial dimensions. The successful application of the LSTM-HSV model underscores its potential as a versatile tool for the estimation of air pollution after presampling of the studied area, with broad implications for urban planning and public health initiatives.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Lstm ; Pm Metrics ; Air Quality ; Deep Learning ; Exposure Assessment Models; Black Carbon; Land-use; Dispersion Model; Air; Pollution; Pollutants; Augsburg; Nexus
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 0013-936X
e-ISSN 1520-5851
Zeitschrift Environmental Science & Technology
Quellenangaben Band: 58, Heft: 8, Seiten: 3869-3882 Artikelnummer: , Supplement: ,
Verlag ACS
Verlagsort Washington, DC
Begutachtungsstatus Peer reviewed
Institut(e) Cooperation Group Comprehensive Molecular Analytics (CMA)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Environmental Sciences
PSP-Element(e) G-504500-001
Förderungen Germany Federal Ministry of Transport and Digital Infrastructure (BMVI)
European Union-NextGenerationEU
Dtec.b-Digitalization and Technology Research Center of the Bundeswehr
RI-URBANS project (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial Areas, European Union's Horizon 2020 research and innovation program, Green Deal, European Commission)
Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region
Chunhui Project Foundation of the Education Department of China
State Key Laboratory of Resources and Environmental Information System
National Natural Science Foundation of China
DOI 10.1021/acs.est.3c06511
WOS ID 001166454100001
PubMed ID 38355131
Erfassungsdatum 2024-04-22
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