Recent Advances in Mobile Source Emissions (2nd Edition)

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

Deadline for manuscript submissions: 23 August 2024 | Viewed by 638

Special Issue Editor


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Guest Editor
Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Interests: vehicle emission test; emission factors measurement; emission inventory; after-treatment device performance evaluation; emission model development
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Special Issue Information

Dear Colleagues,

This Special Issue is the second volume of the Special Issue entitled "Recent Advances in Mobile Source Emissions”, which was published in Atmosphere in 2023: (https://www.mdpi.com/journal/atmosphere/special_issues/I6AEML1VZN).

Mobile source emissions, especially vehicle emissions, are an significantly contribute to urban atmospheric pollution. With the rapid growth of the economy, the number of vehicles being manufacture is rapidly increasing. Mobile sources emit large amounts of VOC, NOx and PM, which are major precursors to ozone and secondary organic aerosols (SOA). Therefore, the effective monitoring and control of mobile source emissions remains a serious challenge.

In recent decades, various emission measurement technologies have been used to record vehicle emissions, helping us to better understand these emissions in real-world scenarios. Equally, more detailed information about mobile source activity can be obtained using various monitoring approaches. Developing a mobile source emission inventory with a high spatial–temporal resolution has become a popular research topic.

The aim of this Special Issue is to present the most recent advances in the factors and inventories of vehicle and off-road mobile source emissions. The scope of this Special Issue covers emission factors from different measurement technologies, the activity approach of mobile sources, and the emission inventory development method.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Regulated and unregulated pollutants tests;
  • Measurement and control technologies;
  • Exhaust emission and non-exhaust emission;
  • Emission model;
  • Emission inventory;
  • Environmental effect.

Dr. Mingliang Fu
Guest Editor

Manuscript Submission Information

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Keywords

  • mobile source
  • emission factor
  • emission characteristics
  • emission inventory
  • measurement technology
  • policies and recommendations

Published Papers (1 paper)

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Research

20 pages, 3044 KiB  
Article
Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)
by Zijie Ding, Zhuoshi He, Zhihui Huang, Junfang Wang and Hang Yin
Atmosphere 2024, 15(4), 413; https://doi.org/10.3390/atmos15040413 - 26 Mar 2024
Viewed by 541
Abstract
Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on [...] Read more.
Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on an interactive dynamic spatial–temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed. Specifically, the IDG-PSAtt model consists of an interactive dynamic graph convolutional network (IL-DGCN) with a spatial–temporal convolution (ST-Conv) block and a probabilistic sparse self-attention (ProbSSAtt) mechanism. The IL-DGCN divides the time series of a traffic flow into intervals and synchronously and interactively shares the captured dynamic spatiotemporal features. The ST-Conv block is utilized to capture the complex dynamic spatial–temporal characteristics of the traffic flow, and the ProbSSAtt block is utilized for medium-to-long-term forecasting. In addition, a dynamic GCN is generated by fusing adaptive and learnable adjacency matrices to learn the hidden dynamic associations among road network nodes. Experimental results demonstrate that the IDG-PSAtt model outperforms the baseline methods in terms of prediction accuracy. Specifically, on METR-LA, the mean absolute error (MAE) and root mean square error (RMSE) induced by IDG-PSAtt for a 60 min forecasting scenario are reduced by 0.75 and 1.31, respectively, compared to those of the state-of-the-art models. This traffic flow prediction improvement will lead to more precise estimates of the emissions produced by mobile sources, resulting in more accurate air quality forecasts. Consequently, this research will greatly support local environmental management efforts. Full article
(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))
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