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Atmosphere
Special Issues
Traffic Related Emission and Control
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Traffic Related Emission and Control

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 7401

Special Issue Editor

Prof. Dr. Ali Alahmer

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA
Interests: human comfort; air conditioning system; renewable energy; combustion; alternative fuel
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Road transport emissions in urban areas have attracted more attention recently. The traffic-related air pollution is a primary source of exposure in urban areas and has detrimental health impacts. Urban vehicle traffic contributes significantly to CO2, NOx, VOC, PM, and non-combustion emissions worldwide. The purpose of the current special issue is to provide a platform for the dissemination to cover promising, contemporary, and novel technologies and application designs for reducing traffic-related emissions. Original research, review papers, and theoretical studies validated with experimental studies are welcomed. The following area of interest include, but are not limited to:

  • Optimization methods and control strategies to reduce exhaust and traffic emissions
  • Machine or deep learning to predict or manage the emission flow of transportation systems
  • Alternative fuels, novel combustion technologies, and emission-reduction techniques
  • Control of exhaust and traffic emissions
  • Carbon footprint life-cycle assessment
  • Assessing the impact of policy, regulations, and infrastructure on vehicle or traffic emissions
  • Measurement, prediction, and simulation techniques related to exhaust or traffic emissions
  • Remote sensing emission testing
  • Trade-offs between air pollutants and greenhouse gases
  • Emissions associated with conventional, hybrid, and electric automobiles in real-world driving cycles
  • Assessing the impact of driving volatility on emission rates
  • Environmental impacts of hybrid and electric vehicles 

Prof. Dr. Ali Alahmer
Guest Editor

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

  • exhaust emission control
  • alternative fuel
  • exhaust measurements
  • control of traffic-related emissions
  • machine learning
  • optimizing traffic control
  • vehicle emission regulations
  • life-cycle assessment
  • emission prediction
  • remote emission testing

Published Papers (4 papers)

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Research

16 pages, 7895 KiB  
Article
Brake Particle PN and PM Emissions of a Hybrid Light Duty Vehicle Measured on the Chassis Dynamometer
by Panayotis Dimopoulos Eggenschwiler, Daniel Schreiber and Joel Habersatter
Atmosphere 2023, 14(5), 784; https://doi.org/10.3390/atmos14050784 - 26 Apr 2023
Cited by 5 | Viewed by 1409
Abstract
Brake particle emissions number (PN) and mass (PM) of a light-duty hybrid-electric vehicle have been assessed under realistic driving patterns on a chassis dynamometer. Therefore, the front-right disc brake was enclosed in a specifically designed casing featuring controlled high scavenging air ventilation. The [...] Read more.
Brake particle emissions number (PN) and mass (PM) of a light-duty hybrid-electric vehicle have been assessed under realistic driving patterns on a chassis dynamometer. Therefore, the front-right disc brake was enclosed in a specifically designed casing featuring controlled high scavenging air ventilation. The WLTC cycle was chosen for most measurements. Different scavenging flow rates have been tested assessing their influence on the measured particles as well as on the temperature of the braking friction partners. Particle transport efficiencies have been assessed revealing scavenging flow rates with losses below 10%. During the performed cycle, most brake particle emissions occurred during braking. There were also isolated emission peaks during periods with no brakes in use, especially during vehicle accelerations. Sequential WLTC cycles showed a continuous decrease in the measured PN and PM emissions; however, size-number and size-mass distributions have been very similar. The measured PN emission factors (>23 nm) at the right front wheel over the WLTC cycle lie at 5.0 × 1010 1/km, whereas the PM emission factor lies at 3.71 mg/km for PM < 12 µm and 1.58 mg/km for PM < 2.5 µm. These values need to roughly triple in order to obtain the brake particle emission of all four brakes and wheels of the entire vehicle. Thus, the brake PN emissions factors have been in the same order of magnitude as the tailpipe PN of a Euro 6 light-duty vehicle equipped with a particle filter. Finally, differences between brake particle emissions in hybrid and all-electric operating modes have been assessed by a series of specific measurements, demonstrating the potential of all-electric vehicle operation in reducing brake particles by a factor of two. Full article
(This article belongs to the Special Issue Traffic Related Emission and Control)
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26 pages, 3384 KiB  
Article
Optimal Water Addition in Emulsion Diesel Fuel Using Machine Learning and Sea-Horse Optimizer to Minimize Exhaust Pollutants from Diesel Engine
by Hussein Alahmer, Ali Alahmer, Malik I. Alamayreh, Mohammad Alrbai, Raed Al-Rbaihat, Ahmed Al-Manea and Razan Alkhazaleh
Atmosphere 2023, 14(3), 449; https://doi.org/10.3390/atmos14030449 - 23 Feb 2023
Cited by 21 | Viewed by 2419
Abstract
Water-in-diesel (W/D) emulsion fuel is a potentially viable diesel fuel that can simultaneously enhance engine performance and reduce exhaust emissions in a current diesel engine without requiring engine modifications or incurring additional costs. In a consistent manner, the current study examines the impact [...] Read more.
Water-in-diesel (W/D) emulsion fuel is a potentially viable diesel fuel that can simultaneously enhance engine performance and reduce exhaust emissions in a current diesel engine without requiring engine modifications or incurring additional costs. In a consistent manner, the current study examines the impact of adding water, in the range of 5–30% wt. (5% increment) and 2% surfactant of polysorbate 20, on the performance in terms of brake torque (BT) and exhaust emissions of a four-cylinder four-stroke diesel engine. The relationship between independent factors, including water addition and engine speed, and dependent factors, including different exhaust released emissions and BT, was initially generated using machine learning support vector regression (SVR). Subsequently, a robust and modern optimization of the sea-horse optimizer (SHO) was run through the SVR model to find the optimal water addition and engine speed for improving the BT and lowering exhaust emissions. Furthermore, the SVR model was compared to the artificial neural network (ANN) model in terms of R-squared and mean square error (MSE). According to the experimental results, the BT was boosted by 3.34% compared to pure diesel at 5% water addition. The highest reduction in carbon monoxide (CO) and unburned hydrocarbon (UHC) was 9.57% and 15.63%, respectively, at 15% of water addition compared to diesel fuel. The nitrogen oxides (NOx) emissions from emulsified fuel were significantly lower than those from pure diesel, with a maximum decrease of 67.14% at 30% water addition. The suggested SVR-SHO model demonstrated superior prediction reliability, with a significant R-Squared of more than 0.98 and a low MSE of less than 0.003. The SHO revealed that adding 15% water to the W/D emulsion fuel at an engine speed of 1848 rpm yielded the optimum BT, CO, UHC, and NOx values of 49.5 N.m, 0.5%, 57 ppm, and 369 ppm, respectively. Finally, these outcomes have important implications for the potential of the SVR-SHO approach to minimize engine exhaust emissions while maximizing engine performance. Full article
(This article belongs to the Special Issue Traffic Related Emission and Control)
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16 pages, 2300 KiB  
Article
Maximization of CO2 Capture Capacity Using Recent RUNge Kutta Optimizer and Fuzzy Model
by Ahmed M. Nassef, Hegazy Rezk, Ali Alahmer and Mohammad Ali Abdelkareem
Atmosphere 2023, 14(2), 295; https://doi.org/10.3390/atmos14020295 - 1 Feb 2023
Cited by 12 | Viewed by 1474
Abstract
This study aims to identify the optimal operating parameters for the carbon dioxide (CO2) capture process using a combination of artificial intelligence and metaheuristics techniques. The main objective of the study is to maximize CO2 capture capacity. The proposed method [...] Read more.
This study aims to identify the optimal operating parameters for the carbon dioxide (CO2) capture process using a combination of artificial intelligence and metaheuristics techniques. The main objective of the study is to maximize CO2 capture capacity. The proposed method integrates fuzzy modeling with the RUNge Kutta optimizer (RUN) to analyze the impact of three operational factors: carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio. These factors are considered to maximize the CO2 capture. A fuzzy model was developed based on the measured data points to simulate the CO2 capture process in terms of the stated parameters. The model was then used to identify the optimal values of carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio using RUN. The results of the proposed method are then compared with an optimized performance using the response surface methodology (RSM) and measured data to demonstrate the superiority of the proposed strategy. The results of the study showed that the suggested technique increased the CO2 capture capacity from 6.39 to 6.99 by 10.08% and 9.39%, respectively, compared to the measured and RSM methods. This implies that the proposed method is an effective approach to maximize the CO2 capture capacity. The results of this study can be used to improve the performance of the CO2 capture process in various industrial applications. Full article
(This article belongs to the Special Issue Traffic Related Emission and Control)
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14 pages, 3835 KiB  
Article
A Study on Monitoring and Supervision of Ship Nitrogen-Oxide Emissions and Fuel-Sulfur-Content Compliance
by Zheng Wang, Qianchi Ma, Zhida Zhang, Zichao Li, Cuihong Qin, Junfeng Chen and Chuansheng Peng
Atmosphere 2023, 14(1), 175; https://doi.org/10.3390/atmos14010175 - 13 Jan 2023
Cited by 1 | Viewed by 1370
Abstract
Regulations for the control of air-pollutant emissions from ships within pollutant emission control areas (ECAs) have been issued for several years, but the lack of practical technologies and fundamental theory in the implementation process remains a challenge. In this study, we designed a [...] Read more.
Regulations for the control of air-pollutant emissions from ships within pollutant emission control areas (ECAs) have been issued for several years, but the lack of practical technologies and fundamental theory in the implementation process remains a challenge. In this study, we designed a model to calculate the nitrogen-oxide-emission intensity of ships and the sulfur content of ship fuels using theoretical deduction from the law of the conservation of mass. The reliability and availability of the derived results were empirically evaluated using measurement data for NOx, SO2, and CO2 in the exhaust gas of a demonstration ship in practice. By examining the model and the measured or registered fuel-oil-consumption rates of ships, a compliance-determination workflow for NOx-emission intensity and fuel-sulfur-content monitoring and supervision in on-voyage ships were proposed. The results showed that the ship fuel’s NOx-emission intensity and sulfur content can be evaluated by monitoring the exhaust-gas composition online and used to assist in maritime monitoring and the supervision of pollutant emissions from ships. It is recommended that uncertainties regarding sulfur content should be considered within 15% during monitoring and supervision. The established model and workflow can assist in maritime monitoring. Meanwhile, all related governments and industry-management departments are advised to actively lead the development of monitoring and supervision technology for ship-air-pollutant control in ECAs, as well as strengthening the quality management of ships’ static data. Full article
(This article belongs to the Special Issue Traffic Related Emission and Control)
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