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Recent Advances in Mobile Source Emissions (2nd Edition)

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

Deadline for manuscript submissions: closed (17 April 2025) | Viewed by 14403

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Dr. Mingliang Fu

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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

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Keywords

mobile source
emission factor
emission characteristics
emission inventory
measurement technology
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Published Papers (10 papers)

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Research

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20 pages, 1954 KiB

Open AccessArticle

Analysis of Nitrogen Dioxide Concentration at Highway Toll Stations Based on fsQCA—Data Sourced from Sentinel-5P

by Shenghao Xu and Xinxiang Yang

Atmosphere 2025, 16(5), 517; https://doi.org/10.3390/atmos16050517 - 28 Apr 2025

Viewed by 26

Abstract

The Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method is employed in this study to investigate the combined effects of region area, the number of COVID-19 infections, and the number of family cars on NO₂ concentration at key highway toll stations in Zhejiang Province, China. By selecting and comparing typical cases, the analysis reveals differentiated characteristics in how various factor combinations influence NO₂ concentration. The main findings are as follows: Under COVID-19 lockdown measures, prolonged vehicle waiting times and a shift towards family car usage among the public have led to a significant increase in NO₂ concentration at highway toll stations. Promoting the Electronic Toll Collection (ETC) system and encouraging public transportation are of great importance. The synergistic effects of COVID-19 lockdown policies and urban land area, resulting in the reduction in the number of family cars and the excellent air circulation conditions in large cities, have contributed to the decrease in NO₂ concentration at highway toll stations. Increasing urban green spaces and promoting clean energy vehicles are crucial for advancing urban sustainable development. The combined analysis of the region area and the number of family cars indicates that a higher proportion of large vehicles contributes to improving transportation efficiency, but also results in elevated NO₂ concentration at highway toll stations due to diesel emissions. Optimizing the transportation structure and reducing reliance on large vehicles are of significant importance. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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28 pages, 9704 KiB

Open AccessArticle

Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification

by Afaq Khattak, Badr T. Alsulami and Caroline Mongina Matara

Atmosphere 2025, 16(3), 303; https://doi.org/10.3390/atmos16030303 - 5 Mar 2025

Viewed by 469

Abstract

Traffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Training (PBT)–ResNet framework for classifying traffic-related PM2.5 levels into hazardous exposure (HE) and acceptable exposure (AE), based on the World Health Organization (WHO) guidelines. The framework integrates ResNet architectures (ResNet18, ResNet34, and ResNet50) with PBT-driven hyperparameter optimization, using data from Open-Seneca sensors along the Nairobi Expressway, combined with meteorological and traffic data. First, analysis showed that the PBT-tuned ResNet34 was the most effective model, achieving a precision (0.988), recall (0.971), F1-Score (0.979), Matthews Correlation Coefficient (MCC) of 0.904, Geometric Mean (G-Mean) of 0.962, and Balanced Accuracy (BA) of 0.962, outperforming alternative models, including ResNet18, ResNet34, and baseline approaches such as Feedforward Neural Networks (FNN), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Gene Expression Programming (GEP). Subsequent feature importance analysis using a permutation-based strategy, along with SHAP analysis, revealed that humidity and hourly traffic volume were the most influential features. The findings indicated that medium to high humidity values were associated with an increased likelihood of HE, while medium to high traffic volumes similarly contributed to the occurrence of HE. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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15 pages, 1462 KiB

Open AccessArticle

Gasoline Vehicle Emissions at High Altitude: An Exploratory STATIS Study in Guaranda, Ecuador

by Alejandro Sebastián Sánchez-Mendoza, Mariuxi Vinueza-Morales, Javier Alexander Alcázar-Espinoza, Giovanny Vinicio Pineda-Silva and Iván Patricio Aucay-García

Atmosphere 2025, 16(3), 281; https://doi.org/10.3390/atmos16030281 - 27 Feb 2025

Viewed by 553

Abstract

Vehicle emissions pose significant environmental challenges, particularly in high-altitude regions, where atmospheric conditions amplify pollutant concentrations. This study evaluates CO₂ and hydrocarbon (HC) emissions from 79 gasoline-powered vehicles in Guaranda, Ecuador (2668 m.a.s.l.), by using STATIS, a multivariate statistical method. The vehicles were classified into six model year intervals and tested under idle and dynamic conditions, measuring idle CO₂ and HC (ICD and IHC) and dynamic CO₂ and HC (DCD and DHC). The results showed that vehicles manufactured before 2000 exhibited the highest emissions, with ICD of 3.18% vol. and IHC of 414 ppm, while vehicles produced after 2020 showed significantly lower values (ICD of 0.27% vol. and IHC of 101.44 ppm). Additionally, Chevrolet was the most represented brand, accounting for 41.78% of the analyzed sample, while 34.18% of the vehicles were from the 2010–2015 interval. The STATIS model revealed structural similarities among the 2000–2005, 2016–2019, and post-2020 models, whereas pre-2000 vehicles differed markedly from the 2010–2015 models. Outliers, including older vehicles with low emissions and newer models with unexpectedly high emissions, highlighted the role of maintenance and operational conditions. These findings demonstrate the effectiveness of STATIS in analyzing complex emission patterns and underscore the need for future studies that incorporate variables such as mileage and environmental factors to refine emission mitigation strategies. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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23 pages, 8735 KiB

Open AccessArticle

Fossil Diesel, Soybean Biodiesel and Hydrotreated Vegetable Oil: A Numerical Analysis of Emissions Using Detailed Chemical Kinetics at Diesel Engine Like Conditions

by Leonel R. Cancino, Jessica F. Rebelo, Felipe da C. Kraus, Eduardo H. de S. Cavalcanti, Valéria S. de B. Pimentel, Decio M. Maia and Ricardo A. B. de Sá

Atmosphere 2024, 15(10), 1224; https://doi.org/10.3390/atmos15101224 - 14 Oct 2024

Viewed by 1154

Abstract

Nowadays, emissions from internal combustion engines are a relevant topic of investigation, taking into account the continuous reduction of emission limits imposed by environmental regulatory agencies around the world, obviously as the result of earnest studies that have pointed out the impact on the human health of high levels of contaminants released into the environment. Over recent years, the use of biofuels has contributed to attenuating this environmental issue; however, new problems have been raised, such as NOx emissions tend to increase as the biofuel percentage in the fuel used in engines increases. In this research, the emissions of a compression ignition internal combustion engine modeled as a variable volume reactor with homogeneous combustion were numerically investigated. To analyze the combustion process, a detailed kinetics model tailored specifically for this purpose was used. The kinetics model comprised 30,975 chemical reactions involving 691 chemical species. Mixtures of fuel surrogates were then created to represent the fuel used in the Brazilian fuel marketplace, involving (i) fossil diesel—“diesel A”, (ii) soybean diesel—“biodiesel”, and (iii) hydrotreated vegetable oil— “HVO”. Surrogate species were then selected for each of the aforementioned fuels, and blends of those surrogates were then proposed as mixture M1 (diesel A:biodiesel:HVO—90:10:0), mixture M2 (diesel A:biodiesel:HVO—85:15:0), and mixture M3 (diesel A:biodiesel:HVO—80:15:5). The species allowed in the kinetics model included all the fuel surrogates used in this research as well as the target emission species of this study: total hydrocarbons, non-methane hydrocarbons, carbon monoxide, methane, nitrogen oxides, carbon dioxide, soot, and soot precursors. When compared to experimental trends of emissions available in the literature, it was observed that, for all the proposed fuel surrogates blends, the numerical approach performed in this research was able to capture qualitative trends for engine power and the target emissions in the whole ranges of engine speeds and engine loads, despite the CO and NOx emissions at specific engine speeds and loads. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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26 pages, 25259 KiB

Open AccessArticle

Coupling MATSim and the PALM Model System—Large Scale Traffic and Emission Modeling with High-Resolution Computational Fluid Dynamics Dispersion Modeling

by Janek Laudan, Sabine Banzhaf, Basit Khan and Kai Nagel

Atmosphere 2024, 15(10), 1183; https://doi.org/10.3390/atmos15101183 - 30 Sep 2024

Cited by 1 | Viewed by 1724

Abstract

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NO_x concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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10 pages, 1839 KiB

Open AccessArticle

Emission Characteristics of Nitrous Oxide (N₂O) from Conventional Gasoline and Hybrid Vehicles

by Guobin Miao, Xiaohu Wang, Guangyin Xuan, Jin Liu, Wenhai Ma and Lili Zhang

Atmosphere 2024, 15(9), 1142; https://doi.org/10.3390/atmos15091142 - 23 Sep 2024

Cited by 1 | Viewed by 1482

Abstract

Considering the potential warming potential and long lifetime of nitrous oxide (N₂O) as a greenhouse gas, exploring its emission characteristics is of great significance for its control and the achievement of sustainable development goals. As vehicles are a significant source of N₂O emissions, in this study we conducted a detailed investigation of N₂O in the exhaust of light-duty vehicles using a chassis dynamometer. We selected one conventional gasoline vehicle and two hybrid electric vehicles. We found that the N₂O emissions from all the tested vehicles complied with the China 6 emission regulation, with emission factors of 7.7 mg/km, 6.8 mg/km, and 17.1 mg/km, respectively, for the three vehicles. Driving conditions played a crucial role in N₂O emissions, with emissions generated primarily during extra-high-speed conditions, possibly due to the higher driving speed and greater number of acceleration/deceleration events. Furthermore, while hybrid electric vehicles emitted less NOx compared to conventional gasoline vehicles, their N₂O emissions were closely tied to their engine operating conditions. Surprisingly, we discovered that hybrid electric vehicles emitted more N₂O during frequent engine start–stop cycles, which could be related to the mechanisms of N₂O generation. These findings contribute to a better understanding of the N₂O emission characteristics of vehicles and will inform the development of emission control strategies to better promote global sustainable development. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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16 pages, 2217 KiB

Open AccessEditor’s ChoiceArticle

In-Vehicle Air Pollutant Exposures from Daily Commute in the San Francisco Bay Area, California

by Reshmasri Deevi and Mingming Lu

Atmosphere 2024, 15(9), 1130; https://doi.org/10.3390/atmos15091130 - 18 Sep 2024

Viewed by 1413

Abstract

With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM_2.5, PM₁₀, and CO₂) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM_2.5 varied from 5.07 µg/m³ to 6.55 µg/m³ during morning rush hours and from 4.38 µg/m³ to 4.47 µg/m³ during evening rush hours. In addition, the impacts of local PM_2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO₂ buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO₂ concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO₂ concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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14 pages, 3571 KiB

Open AccessArticle

Real-World Emission Characteristics of Diesel Pallet Trucks under Varying Loads: Using the Example of China

by Ye Zhang, Yating Song and Tianshi Feng

Atmosphere 2024, 15(8), 956; https://doi.org/10.3390/atmos15080956 - 11 Aug 2024

Cited by 2 | Viewed by 1349

Abstract

Diesel pallet trucks, a type of heavy-duty diesel trucks (HDDTs), have historically been a vital component in logistics and transport due to their high payload capacity. However, they also present significant challenges, particularly in terms of emissions which contribute substantially to urban air pollution. Traditional HDDTs emission measurement methods, such as engine bench tests and those used in laboratory settings, often fail to capture real-world emission behaviors accurately. This study specifically examines the real-world emission characteristics of diesel pallet trucks exceeding 30 t under varying loads (unloaded, half loaded, and fully loaded) and different road conditions (urban, suburban, and high-speed). Considering that data quality is the key to the accuracy of the scheme, this research utilized a portable emission measurement system (PEMS) to capture real-time emissions data of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_X), and total hydrocarbons (THC). Key findings demonstrate a direct correlation between vehicle load and emission factors, with the emission factors for CO₂, CO, and NO_X increasing by 39.5%, 105.4%, and 22.7%, respectively, from unloaded to fully loaded states under comprehensive operating conditions. Regression analyses further provide an emission factor prediction model for HDDPTs, underscoring the continuous relationship between speed, load, and emission rates. These findings provide a scientific basis for pollution control strategies for diesel trucks. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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19 pages, 3044 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 2093

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 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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Review

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14 pages, 1481 KiB

Open AccessEditor’s ChoiceReview

Recent Advances in SCR Systems of Heavy-Duty Diesel Vehicles—Low-Temperature NOx Reduction Technology and Combination of SCR with Remote OBD

by Zhengguo Chen, Qingyang Liu, Haoye Liu and Tianyou Wang

Atmosphere 2024, 15(8), 997; https://doi.org/10.3390/atmos15080997 - 20 Aug 2024

Cited by 5 | Viewed by 3725

Abstract

Heavy-duty diesel vehicles are a significant source of nitrogen oxides (NOx) in the atmosphere. The Selective Catalytic Reduction (SCR) system is a primary aftertreatment device for reducing NOx emissions from heavy-duty diesel vehicles. With increasingly stringent NOx emission regulations for heavy-duty vehicles in major countries, there is a growing focus on reducing NOx emissions under low exhaust temperature conditions, as well as monitoring the conversion efficiency of the SCR system over its entire lifecycle. By reviewing relevant literature mainly from the past five years, this paper reviews the development trends and related research results of SCR technology, focusing on two main aspects: low-temperature NOx reduction technology and the combination of SCR systems with remote On-Board Diagnostics (OBD). Regarding low-temperature NOx reduction technology, the results of the review indicate that the combination of multiple catalytic shows potential for achieving high conversion efficiency across a wide temperature range; advanced SCR system arrangement can accelerate the increase in exhaust temperature within the SCR system; solid ammonium and gaseous reductants can effectively address the issue of urea not being able to be injected under low-temperature exhaust conditions. As for the combination of SCR systems with remote OBD, remote OBD can accurately assess NOx emissions from heavy-duty vehicles, but it needs algorithms to correct data and match the emission testing process required by regulations. Remote OBD systems are crucial for detecting SCR tampering, but algorithms must be developed to balance accuracy with computational efficiency. This review provides updated information on the current research status and development directions in SCR technologies, offering valuable insights for future research into advanced SCR systems. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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