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

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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