Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources
Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological en...
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Online Access: | ONIX_20260416T142754_9783725863303_24 |
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| description | Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological environment. Therefore, it is necessary to study the comprehensive supervision and analysis methods of urban mobile source emissions, which is of great significance for protecting public health and improving rational urban planning as well as traffic conditions. Meanwhile, the temporal and spatial distribution of urban mobile source emissions is affected by many complex factors. On the one hand, from the perspective of long-term vehicle emission inventory calculation, it mainly depends on the city's total vehicle volume and vehicle type composition. On the other hand, in terms of short-term and real-time variation in traffic emissions, it is mainly influenced by urban road network topology, traffic flow conditions, and external meteorological factors. This series of factors has led to great challenges in achieving full-time monitoring and comprehensive supervision of urban mobile source emissions. By summarizing the existing literature, we find that the focus of mobile source emissions prediction tends to shift from the road segment level to urban region scale, from a single city to cross cities, and from macro inventory prediction to fine-grained instantaneous prediction. |
| format | Online |
| id | doab-20.500.12854ir-175219 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
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| spelling | doab-20.500.12854ir-1752192026-04-16T19:39:10Z Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources Xu, Zhenyi Tao, Changfa Air pollution forecasting External factor optimization Genetic algorithm Autoformer Temporal variation Multivariate dependencies Relationships analysis Model interpretability OBD Emission factors COPERT Two-stream network Time-frequency features Atmospheric pollutants Wavelet decomposition Multi-layer perceptron model Atmospheric carbonyls Sampling methods Offline analytical methods Online analytical methods Problems and challenges Prospect Air pollution Ozone Houston Forecasting Quantum machine learning Quantum neural network Graph neural network Surface plasmon resonance (SPR) Particulate matter (PM) Sensors Machine learning (ML) Deep learning (DL) Artificial intelligence (AI) Environmental monitoring Air quality Deep learning WRF Weather forecasting Computational fluid dynamics Artificial intelligence Spatiotemporal convolution Air quality prediction Generative CNN Multi-scale fusion Feature correlation Digital twins Machine learning calibration Urban air pollution Graph neural networks (GNNs) N A thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological environment. Therefore, it is necessary to study the comprehensive supervision and analysis methods of urban mobile source emissions, which is of great significance for protecting public health and improving rational urban planning as well as traffic conditions. Meanwhile, the temporal and spatial distribution of urban mobile source emissions is affected by many complex factors. On the one hand, from the perspective of long-term vehicle emission inventory calculation, it mainly depends on the city's total vehicle volume and vehicle type composition. On the other hand, in terms of short-term and real-time variation in traffic emissions, it is mainly influenced by urban road network topology, traffic flow conditions, and external meteorological factors. This series of factors has led to great challenges in achieving full-time monitoring and comprehensive supervision of urban mobile source emissions. By summarizing the existing literature, we find that the focus of mobile source emissions prediction tends to shift from the road segment level to urban region scale, from a single city to cross cities, and from macro inventory prediction to fine-grained instantaneous prediction. 2026-04-16T19:39:04Z 2026-04-16T19:39:04Z 2026 book ONIX_20260416T142754_9783725863303_24 9783725863303 9783725863310 https://directory.doabooks.org/handle/20.500.12854/175219 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/12131 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-6331-0 10.3390/books978-3-7258-6331-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725863303 9783725863310 260 CH open access |
| spellingShingle | Air pollution forecasting External factor optimization Genetic algorithm Autoformer Temporal variation Multivariate dependencies Relationships analysis Model interpretability OBD Emission factors COPERT Two-stream network Time-frequency features Atmospheric pollutants Wavelet decomposition Multi-layer perceptron model Atmospheric carbonyls Sampling methods Offline analytical methods Online analytical methods Problems and challenges Prospect Air pollution Ozone Houston Forecasting Quantum machine learning Quantum neural network Graph neural network Surface plasmon resonance (SPR) Particulate matter (PM) Sensors Machine learning (ML) Deep learning (DL) Artificial intelligence (AI) Environmental monitoring Air quality Deep learning WRF Weather forecasting Computational fluid dynamics Artificial intelligence Spatiotemporal convolution Air quality prediction Generative CNN Multi-scale fusion Feature correlation Digital twins Machine learning calibration Urban air pollution Graph neural networks (GNNs) N A thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources |
| title | Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources |
| title_full | Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources |
| title_fullStr | Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources |
| title_full_unstemmed | Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources |
| title_short | Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources |
| title_sort | applying deep learning technology for spatiotemporal prediction of air pollution from urban mobile sources |
| topic | Air pollution forecasting External factor optimization Genetic algorithm Autoformer Temporal variation Multivariate dependencies Relationships analysis Model interpretability OBD Emission factors COPERT Two-stream network Time-frequency features Atmospheric pollutants Wavelet decomposition Multi-layer perceptron model Atmospheric carbonyls Sampling methods Offline analytical methods Online analytical methods Problems and challenges Prospect Air pollution Ozone Houston Forecasting Quantum machine learning Quantum neural network Graph neural network Surface plasmon resonance (SPR) Particulate matter (PM) Sensors Machine learning (ML) Deep learning (DL) Artificial intelligence (AI) Environmental monitoring Air quality Deep learning WRF Weather forecasting Computational fluid dynamics Artificial intelligence Spatiotemporal convolution Air quality prediction Generative CNN Multi-scale fusion Feature correlation Digital twins Machine learning calibration Urban air pollution Graph neural networks (GNNs) N A thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| topic_facet | Air pollution forecasting External factor optimization Genetic algorithm Autoformer Temporal variation Multivariate dependencies Relationships analysis Model interpretability OBD Emission factors COPERT Two-stream network Time-frequency features Atmospheric pollutants Wavelet decomposition Multi-layer perceptron model Atmospheric carbonyls Sampling methods Offline analytical methods Online analytical methods Problems and challenges Prospect Air pollution Ozone Houston Forecasting Quantum machine learning Quantum neural network Graph neural network Surface plasmon resonance (SPR) Particulate matter (PM) Sensors Machine learning (ML) Deep learning (DL) Artificial intelligence (AI) Environmental monitoring Air quality Deep learning WRF Weather forecasting Computational fluid dynamics Artificial intelligence Spatiotemporal convolution Air quality prediction Generative CNN Multi-scale fusion Feature correlation Digital twins Machine learning calibration Urban air pollution Graph neural networks (GNNs) N A thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| url | ONIX_20260416T142754_9783725863303_24 |