High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery
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High Resolution Image Download MS PowerPoint Slide 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 (PM 1.0, PM 2.5, and PM 10, 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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Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks have been extensively employed for temporal data analysis, enabling the capture of dependencies and patterns over time.
“Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks have been extensively employed for temporal data analysis, enabling the capture of dependencies and patterns over time [42–44].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [44]
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- Authors
- Liu, X., Zhang, X., Wang, R., Liu, Y., Hadiatullah, H., Xu, Y., Wang, T., Bendl, J., Adam, T., Schnelle-Kreis, J., & Querol, X.
- Affiliations
- Beijing Technology and Business University (CN)
- Tianjin University (CN)
- Qingdao University of Technology (CN)
- Fudan University (CN)
- Universität der Bundeswehr München (DE)
- Helmholtz Munich (DE)
- Joint Mass Spectrometry Center, Cooperation Group Comprehensive Molecular Analytics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, Neuherberg 85764, Germany
- Institute of Environmental Assessment and Water Research (ES)
- Corresponding author
- Liu, Xiansheng
- Zhang, Xun
- ORCID
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-02-14
- Volume
- 58
- Issue
- 8
- Pages
- 3869-3882
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- PMC
- PMC10902834
- OpenAlex
- W4391842695
- Cited by
- 16
- References
- 40
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 2
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Environmental Science & Technology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by-nc-nd)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, other-oa)
- Funders
- European Commission
- Natural Science Foundation of Xinjiang Uygur Autonomous Region (2023D01A57)
- Bundesministerium f?r Verkehr und Digitale Infrastruktur (19F2003B)
- Horizon 2020 Framework Programme (101036245)
- National Natural Science Foundation of China (42101470, 72242106)
- Chunhui Project Foundation of the Education Department of China (HZKY20220053)
- Social Science Foundation of Xinjiang Uygur Autonomous Region (2023BTY128)
- Digitalization and Technology Research Center of the Bundeswehr
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Air Quality Monitoring and Forecasting
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Keywords
- particulate matter prediction, air quality estimation, street view images, long short-term memory neural network, HSV color features, mobile air quality monitoring, black carbon, brown carbon, lung deposited surface area, urban air pollution, model generalization
- MeSH terms
- Air Pollutants, Air Pollution, Carbon, Environmental Monitoring, Neural Networks, Computer, Particulate Matter
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