Special Issue "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: 31 August 2023 | Viewed by 177

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
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
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • 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

This special issue is now open for submission.
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