Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: 23 September 2024 | Viewed by 2959

Special Issue Editors

Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
Interests: mobile-source emission prediction; spatiotemporal data; deep learning; intelligent transportation

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Guest Editor
School of Automotive and Transportation Engineering, Hefei University of Technology, Anhui, China
Interests: Outdoor environmental quality; Tunnel ventilation; Built environment simulation; Pollutant dispersion in street canyons; Smoke movement
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Special Issue Information

Dear Colleagues,

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. Additionally, these 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 carry out comprehensive supervision and analysis methods of urban mobile-source emissions, as the results obtained are 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 calculations, 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 variations 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. Summarizing the existing literature, we can find that the focus of mobile-source emission prediction tends to shift from a road segment level to urban region scale, from a single city to multiple cities, from a macro-inventory prediction to fine-grained instantaneous prediction. We propose this Special Issue, “Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources”, to collect state-of-the-art research articles in the field with the hope of sharing views, findings, strategies, and recommendations to achieve equitable access to clean air. 

Dr. Zhenyi Xu
Dr. Changfa Tao
Guest Editors

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Keywords

  • mobile-source emission spatiotemporal analysis at road level
  • relationships of mobile-source emission variations across regions
  • mobile-source emission control management strategies
  • correlation analysis of air pollution and traffic emissions
  • novel analysis method for heavy-duty vehicle OBD measurement data processing

Published Papers (2 papers)

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Research

21 pages, 1583 KiB  
Article
Two-Stream Networks for COPERT Correction Model with Time-Frequency Features Fusion
by Zhenyi Xu, Ruibin Wang, Kai Pan, Jiaren Li and Qilai Wu
Atmosphere 2023, 14(12), 1766; https://doi.org/10.3390/atmos14121766 - 29 Nov 2023
Viewed by 676
Abstract
Emission factors serve as a valuable tool for quantifying the release of pollutants from road vehicles and predicting emissions within a specific time or area. In order to overcome the limitation of the computer program to calculate emissions from the road transport (COPERT) [...] Read more.
Emission factors serve as a valuable tool for quantifying the release of pollutants from road vehicles and predicting emissions within a specific time or area. In order to overcome the limitation of the computer program to calculate emissions from the road transport (COPERT) model in directly obtaining precise emission factors from on-board diagnostic (OBD) data, we propose a novel two-stream network that combines time-series features and time-frequency features to enhance the accuracy of the COPERT model. Firstly, for the instantaneous emission factors of NOx from multiple driving segments provided by heavy-duty diesel vehicles in actual driving, we select the monitored attributes with a high correlation to the emission factor of NOx considering the data scale and employing Spearman rank correlation analysis to obtain the final dataset composed of them and emission factors. Subsequently, we construct an information matrix to capture the impact of past data on emission factors. Each attribute of the time series is then converted into a time-frequency matrix using the continuous wavelet transform. These individual time-frequency matrices are combined to create a multi-channel time-frequency matrix, which represents the historical information. Finally, the historical information matrix and the time-frequency matrix are inputted into a two-stream parallel model that consists of ResNet50 and a convolutional block attention module. This model effectively integrates time-series features and time-frequency features, thereby enhancing the representation of emission characteristics. The reliability and accuracy of the proposed method were validated through a comparative analysis with existing mainstream models. Full article
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25 pages, 17062 KiB  
Article
A Hybrid Autoformer Network for Air Pollution Forecasting Based on External Factor Optimization
by Kai Pan, Jiang Lu, Jiaren Li and Zhenyi Xu
Atmosphere 2023, 14(5), 869; https://doi.org/10.3390/atmos14050869 - 14 May 2023
Cited by 1 | Viewed by 1549
Abstract
Exposure to air pollution will pose a serious threat to human health. Accurate air pollution forecasting can help people to reduce exposure risks and promote environmental pollution control, and it is also an extremely important part of smart city management. However, the current [...] Read more.
Exposure to air pollution will pose a serious threat to human health. Accurate air pollution forecasting can help people to reduce exposure risks and promote environmental pollution control, and it is also an extremely important part of smart city management. However, the current deep-learning-based models for air pollution forecasting usually focus on prediction accuracy improvement without considering the model interpretability. These models usually fail to explain the complex relationships between prediction targets and external factors (e.g., ozone concentration (O3), wind speed, temperature variation, etc.) The relationships between variables in air pollution time series prediction problems are very complex, with intricate relationships between different types of variables, often with nonlinear multivariate dependencies. To address these problems mentioned above, we proposed a hybrid autoformer network with a genetic algorithm optimization to predict air pollution temporal variation as well as establish interpretable relationships between pollutants and external variables. Furthermore, an elite variable voting operator was designed to better filter out more important external factors such as elite variables, so as to perform a more refined search for elite variables. Moreover, we designed an archive storage operator to reduce the effect of neural network model initialization on the search for external variables. Finally, we conducted comprehensive experiments on the Ma’anshan air pollution dataset to verify the proposed model, where the prediction accuracy was improved by 2–8%, and the selection of model influencing factors was more interpretable. Full article
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