The Application of Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources (2nd Edition)
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 219
Special Issue Editors
Interests: mobile source emissions prediction; spatio-temporal data; deep learning; intelligent transportation
Special Issues, Collections and Topics in MDPI journals
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,
This Special Issue is the second volume in a series of publications dedicated to the following topic: “Apply Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources” (https://www.mdpi.com/journal/atmosphere/special_issues/JP5O8A1X0L).
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 pollution; moreover, they have become the main source of this pollution, causing serious damage to socio-ecological environments. Therefore, it is necessary to study the comprehensive supervision and analysis methods of urban mobile source emissions, as doing so will be aid significantly in protecting public health and improving rational urban planning as well as traffic conditions. Moreover, the temporal and spatial distribution of urban mobile source emissions is affected by many complex factors. On the one hand, inventory calculations of long-term vehicle emissions mainly rely on a city's total vehicle volume and vehicle types. On the other hand, short-term and real-time variations in traffic emissions are mainly influenced by urban road network topology, traffic flow conditions, and external meteorological factors. This series of factors has led to significant challenges in achieving the full-time monitoring and comprehensive supervision of urban mobile source emissions. In the existing literature, we find that the focus of mobile source emissions prediction tends to shift from the road segment level to the urban/regional scale, from single cities to multiple cities combined, and from macro-inventory prediction to small-scale instantaneous prediction. We propose a Special Issue titled “The Application of Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources” with the aim of collecting state-of-the-art research articles in order to share views, findings, strategies, and recommendations and ultimately help to achieve equitable access to clean air for all.
Dr. Zhenyi Xu
Dr. Changfa Tao
Guest Editors
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Keywords
- mobile source emissions spatiotemporal analysis at the road level
- relationships and variations of mobile source emissions across regions
- mobile source emissions control management strategies
- correlation analysis of air pollution and traffic emissions
- novel analysis method for heavy-duty vehicles’ OBD data processing
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