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Atmosphere
Special Issues
Characteristics and Control of Traffic-Related Emissions
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Characteristics and Control of Traffic-Related Emissions

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 3949

Special Issue Editors

Dr. Rencheng Zhu

E-Mail Website
Guest Editor
School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
Interests: vehicle emission; VOCs; particulate matter; fuel quality; air pollution
Dr. Nan Jiang

E-Mail Website
Guest Editor
School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
Interests: emission factors; source profile; source apportionment; PM pollution; PAHs

Special Issue Information

Dear Colleagues,

The aim of this Special issue is to gather recent advances in the field of characteristics and control of traffic-related emissions. With the explosive growth of motor vehicle ownership, traffic-related emissions have become a major source of urban air pollution, especially in large and medium-sized cities. The control of the traffic sector has become increasingly essential in urban air pollution management. However, characteristics of traffic-related emissions are still unclear, especially for unregulated pollutants with new technologies or alternative fuels under real driving conditions. With the recent expansion of research showing that both traditional and electric vehicles face problems with non-tailpipe particulate emissions, this Special Issue is also an appropriate venue for papers that deal with new pollutants from electric vehicles. Ultimately, this Special Issue aims to showcase the most recent research on the control of traffic-related emissions.

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

  • Regulated and unregulated emissions from the traffic sector;
  • Characteristics of vehicular emissions under real driving conditions;
  • Evaporative emissions;
  • Non-tailpipe particulate matter emissions;
  • Impacts of alternative fuels, new technology, etc.;
  • Control of traffic-related emissions.

Dr. Rencheng Zhu
Dr. Nan Jiang
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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

Keywords

  • traffic-related emissions
  • particulate matter
  • VOCs
  • unregulated emissions
  • emission control
  • evaporation
  • non-tailpipe emissions

Published Papers (2 papers)

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Research

12 pages, 4282 KiB  
Article
A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving
by Rongshuo Zhang, Yange Wang, Yujie Pang, Bowen Zhang, Yangbing Wei, Menglei Wang and Rencheng Zhu
Atmosphere 2022, 13(9), 1466; https://doi.org/10.3390/atmos13091466 - 09 Sep 2022
Cited by 12 | Viewed by 2004
Abstract
On-road carbon dioxide (CO2) emissions from light-duty diesel trucks (LDDTs) are greatly affected by driving conditions, which may be better predicted with the sequence deep learning model as compared to traditional models. In this study, two typical LDDTs were selected to [...] Read more.
On-road carbon dioxide (CO2) emissions from light-duty diesel trucks (LDDTs) are greatly affected by driving conditions, which may be better predicted with the sequence deep learning model as compared to traditional models. In this study, two typical LDDTs were selected to investigate the on-road CO2 emission characteristics with a portable emission measurement system (PEMS) and a global position system (GPS). A deep learning-based LDDT CO2 emission model (DL-DTCEM) was developed based on the long short-term memory network (LSTM) and trained by the measured data with the PEMS. Results show that the vehicle speed, acceleration, VSP, and road slope had obvious impacts on the transient CO2 emission rates. There was a rough positive correlation between the vehicle speed, road slope, and CO2 emission rates. The CO2 emission rate increased significantly when the speed was >5 m/s, especially at high acceleration. The correlation coefficient (R2) and the root mean square error (RMSE) between the monitored CO2 emissions with PEMS and the predicted values with the DL-DTCEM were 0.986–0.990 and 0.165–0.167, respectively. The results proved that the model proposed in this study can predict very well the on-road CO2 emissions from LDDTs. Full article
(This article belongs to the Special Issue Characteristics and Control of Traffic-Related Emissions)
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16 pages, 3883 KiB  
Article
Effects of Winter Heating on Urban Black Carbon: Characteristics, Sources and Its Correlation with Meteorological Factors
by Xinyu Liu, Yangbing Wei, Xinhui Liu, Lei Zu, Bowen Wang, Shenbo Wang, Ruiqin Zhang and Rencheng Zhu
Atmosphere 2022, 13(7), 1071; https://doi.org/10.3390/atmos13071071 - 06 Jul 2022
Cited by 2 | Viewed by 1560
Abstract
Coal combustion for winter heating is a major source of heavy atmospheric pollution in China, while its impacts on black carbon (BC) are not yet clear. A dual-spot Aethalometer was selected to monitor the atmospheric BC concentration in Zhengzhou, China, during the heating [...] Read more.
Coal combustion for winter heating is a major source of heavy atmospheric pollution in China, while its impacts on black carbon (BC) are not yet clear. A dual-spot Aethalometer was selected to monitor the atmospheric BC concentration in Zhengzhou, China, during the heating season, which is from 15 November through 15 March of the following year, and the non-heating season (days other than heating season). The characteristics and sources of BC were analyzed, and a concentration weight trajectory (CWT) analysis was conducted. The results showed that the BC concentrations in the heating season were generally higher than those in the non-heating season. The diurnal variation in BC concentrations during heating season was bimodal, and that during the non-heating season was unimodal. The α-values in the heating and non-heating seasons indicated that combustion of coal and biomass and vehicle emissions were the major BC sources for the heating season and non-heating season, respectively. BC concentrations were positively correlated with PM2.5, PM10, CO, and NOX. There was a strong negative correlation between wind speed and BC concentrations, and that for relative humidity was the opposite. BC concentration during heating season was mainly influenced by the northwestern areas of China and the eastern part of Henan, and that in the non-heating season was mainly from the northeastern areas of China and southern Henan. Full article
(This article belongs to the Special Issue Characteristics and Control of Traffic-Related Emissions)
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