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High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery.
Liu, Xiansheng; Zhang, Xun; Wang, Rui; Liu, Ying; Hadiatullah, Hadiatullah; Xu, Yanning; Wang, Tao; Bendl, Jan; Adam, Thomas; Schnelle-Kreis, Jürgen; Querol, Xavier.
Afiliación
  • Liu X; Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China.
  • Zhang X; Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain.
  • Wang R; Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China.
  • Liu Y; State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China.
  • Hadiatullah H; Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China.
  • Xu Y; Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China.
  • Wang T; School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China.
  • Bendl J; School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266525, China.
  • Adam T; Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai 200433, China.
  • Schnelle-Kreis J; University of the Bundeswehr Munich, Faculty for Mechanical Engineering, Institute of Chemical and Environmental Engineering, 85577 Neubiberg, Germany.
  • Querol X; University of the Bundeswehr Munich, Faculty for Mechanical Engineering, Institute of Chemical and Environmental Engineering, 85577 Neubiberg, Germany.
Environ Sci Technol ; 58(8): 3869-3882, 2024 Feb 27.
Article en En | MEDLINE | ID: mdl-38355131
ABSTRACT
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Contaminación del Aire Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Contaminación del Aire Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos