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Recent Advances in Mobile Source Emissions (2nd Edition)
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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: 17 April 2025 | Viewed by 7575

Special Issue Editor

Dr. Mingliang Fu

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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
Special Issues, Collections and Topics in MDPI journals

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

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Published Papers (7 papers)

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23 pages, 8735 KiB  
Article
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 612
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 [...] Read more.
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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Figure 1

Figure 1
<p>Combustion modeling. (<b>a</b>) Zero-dimensional thermodynamic models, (<b>b</b>) Quasi-dimensional models, and (<b>c</b>) Computational fluid dynamics models with chemical reaction—CRFD (Adapted from [<a href="#B31-atmosphere-15-01224" class="html-bibr">31</a>], figure (<b>c</b>) from [<a href="#B32-atmosphere-15-01224" class="html-bibr">32</a>]).</p> Full article ">Figure 2
<p>Python engine solution flowchart.</p> Full article ">Figure 3
<p>Pressure and temperature evolution along eight engine operation cycles at 2500 rpm, fuel injected mass = 0.125 g, mixture M1 (see <a href="#atmosphere-15-01224-t004" class="html-table">Table 4</a> and <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p> Full article ">Figure 4
<p>Predicted engine expansion power (per cylinder) for all the mixtures at all numerical operation conditions simulated in this work. (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p> Full article ">Figure 5
<p>Emissions: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) CH<sub>4</sub> and (<b>d</b>) NOx—Engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p> Full article ">Figure 6
<p>Emissions: (<b>a</b>) Soot precursors—PAH, (<b>b</b>) Soot particles with diameter: 2 nm &lt; d &lt; 10 nm, (<b>c</b>) Soot aggregates with collision diameter: 13 nm &lt; dc &lt; 250 nm—Engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p> Full article ">Figure 7
<p>Emissions: (<b>a</b>) Nonmethane hydrocarbons, (<b>b</b>) unburned hydrocarbons, engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p> Full article ">Figure 8
<p>Relative emission to M1 mixture: (<b>a</b>) Soot particles with diameter: 2 nm &lt; d &lt; 10 nm, (<b>b</b>) Total NOx, engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p> Full article ">Figure 9
<p>Relative emission to M1 mixture: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) CH<sub>4</sub>, (<b>d</b>) NOx, (<b>e</b>) Soot precursors—PAH, (<b>f</b>) Soot particles, (<b>g</b>) Soot aggregates, (<b>h</b>) Non-methane hydrocarbons, (<b>i</b>) Total hydrocarbons—Engine at 1000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p> Full article ">Figure 10
<p>Relative emission to M1 mixture: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) CH<sub>4</sub>, (<b>d</b>) NOx, (<b>e</b>) PAH, (<b>f</b>) Soot particles, (<b>g</b>) Soot aggregates, (<b>h</b>) Non-methane hydrocarbons, (<b>i</b>) Total hydrocarbons—Engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p> Full article ">Figure 11
<p>Relative emission to M1 mixture: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) CH<sub>4</sub>, (<b>d</b>) NOx, (<b>e</b>) PAH, (<b>f</b>) Soot particles, (<b>g</b>) Soot aggregates, (<b>h</b>) Non-methane hydrocarbons, (<b>i</b>) Total hydrocarbons—Engine at 3000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p> Full article ">
26 pages, 25259 KiB  
Article
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
Viewed by 932
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 [...] Read more.
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 NOx 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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Figure 1

Figure 1
<p>Data flow of the model integration: Traffic emissions calculated based on output of a MATSim simulation are converted into a PALM chemistry driver. The resulting driver is part of the input for a subsequent PALM-4U simulation.</p> Full article ">Figure 2
<p>Example of emission calculation in the MATSim emission extension. A vehicle traverses three links on the simulation’s street network, resulting in three emission events in the general event log. For simplicity, only NO<sub>x</sub> (Nitrogen Oxides) emissions are depicted. The emission extension is capable of calculating all the pollutants HBEFA provides emission factors for.</p> Full article ">Figure 3
<p>Data flow of the coupling pipeline. With traffic emissions generated from MATSim output data, a chemistry driver for a PALM-4U simulation is generated. The four main stages of the pipeline are executed in consecutive order: (1) mapping of detailed link geometries, (2) temporal aggregation of traffic emissions, (3) rasterization of traffic emissions, and (4) writing of the driver file.</p> Full article ">Figure 4
<p>Example of a simplified street geometry in a MATSim network (blue) and its corresponding original geometry from OSM (orange).</p> Full article ">Figure 5
<p>Aggregation step of emission events. Emission events from the event log are aggregated by time period. Within each time period, emissions of the same pollutant are aggregated by link. Links in red shades indicate that emissions were produced on those links during the respective time period. Darker shades indicate a higher amount of emissions. Blue links indicate that no emissions were produced on that link during the respective time period.</p> Full article ">Figure 6
<p>Rasterization step of aggregated emissions: A separate raster is produced for each time period of the simulation. Within each time period, the accumulated emissions of each link are distributed onto raster cells by the means of Bresenahm’s line drawing algorithm. Red shades indicate the amount of emissions produced on a link within the respective time period, with darker colors indicating higher values. Yellow shades indicate the amount of emissions distributed on each raster cell. Darker shades indicate higher values.</p> Full article ">Figure 7
<p>(<b>a</b>) Rastered emission flows in g/m<sup>2</sup> for the period between 8 am and 9 am. Emission flows in (<b>a</b>) are the output of the MATSim emission model and the raster pipeline; NO<sub>x</sub> has already been split into NO and NO<sub>2</sub>. (<b>b</b>) shows the corresponding NO<sub>2</sub> concentrations simulated with PALM-4U for the same time period. The area of the rectangle in (<b>b</b>) corresponds to <a href="#atmosphere-15-01183-f008" class="html-fig">Figure 8</a>; letters A and B highlight the effects of the wind direction explained in <a href="#sec4dot3dot3-atmosphere-15-01183" class="html-sec">Section 4.3.3</a>.</p> Full article ">Figure 8
<p>NO<sub>2</sub> concentrations on a continuous scale. The area depicted corresponds to the rectangle in <a href="#atmosphere-15-01183-f007" class="html-fig">Figure 7</a>b. Effects of the building layout and the street layout are highlighted by letters A–D.</p> Full article ">Figure 9
<p>The city boundaries of Berlin (blue), as well as the PALM-4U-Domain boundaries (red). Within the city boundaries, a detailed road network (gray) is included. For the remaining MATSim domain, only major roads are included. The MATSim traffic simulation setup stretches beyond the depicted area.</p> Full article ">Figure 10
<p>Comparison of simulated and monitored NO<sub>x</sub> concentrations at urban background monitoring station 010. Simulated hourly averages of NO<sub>x</sub> concentration values are depicted in red. Hourly averages of monitored NO<sub>x</sub> concentration values at urban background station 010 for days with low wind speeds in blue tones. The emission inflow from the closest link is depicted in yellow. One clearly sees that the PALM output (red) is not just a rescaled version of its input (yellow) but that there are quite strong non-linear processes at work.</p> Full article ">Figure 11
<p>NO<sub>2</sub> concentrations for the time period between 8 am and 9 am, showing areas with exceptionally high pollutant concentrations due to artifacts in the simulation setup. The coloring uses equal intervals to highlight outliers on the long tale of the concentration value distribution.</p> Full article ">Figure 12
<p>Two methods to identify emission hot spots: (<b>a</b>) uses a threshold approach showing NO<sub>2</sub> concentrations below and above 80 μg/m<sup>3</sup> and two curbside monitoring stations. (<b>b</b>) shows exposure to traffic emissions at activity locations.</p> Full article ">
10 pages, 1839 KiB  
Article
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
Viewed by 775
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 [...] Read more.
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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Figure 1

Figure 1
<p>N<sub>2</sub>O and NOx emission factors of the tested gasoline vehicles and hybrid electric vehicles during the WLTC protocol.</p> Full article ">Figure 2
<p>The line chart denotes the N<sub>2</sub>O emission concentrations, NOx emission concentrations, and tailpipe flows of the tested gasoline vehicle #1. The stacked chart shows the vehicle speed of the tested gasoline vehicle #1. The pie chart represents the mass percentage of N<sub>2</sub>O emissions during the different speed phases in the WLTC protocol of the tested gasoline vehicle #1.</p> Full article ">Figure 3
<p>N<sub>2</sub>O emission rate under different vehicle-specific power modes of the tested gasoline vehicle #1. The box–whisker plots give the median, the 75th and 25th percentiles, and 1.5 times the Inter-Quartile Range (IQR). The circles show the mean values of N<sub>2</sub>O emissions.</p> Full article ">Figure 4
<p>The line chart denotes the N<sub>2</sub>O emission concentrations, NOx emission concentrations, and tailpipe flows of the tested hybrid vehicles. The stacked chart shows the vehicle speed of the tested hybrid vehicles. The pie chart represents the mass percentage of N<sub>2</sub>O emissions during the different speed phases in the WLTC protocol of the tested hybrid vehicles. Panel (<b>A</b>) represents hybrid vehicle #2 and Panel (<b>B</b>) represents hybrid vehicle #3.</p> Full article ">
16 pages, 2217 KiB  
Article
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 817
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 [...] Read more.
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 (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 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 PM2.5, 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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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map of the study area with red points indicating the selected four monitoring stations for local PM<sub>2.5</sub>.</p> Full article ">Figure 2
<p>Monthly variation of in-vehicle PM concentrations (<b>a</b>) morning rush hours; (<b>b</b>) evening rush hours. In both graphs error bars represent 95% CI.</p> Full article ">Figure 3
<p>PM distributions from Sunnyvale to Walnut Creek on 25 May 2023 for morning rush hours (<b>a</b>) PM<sub>2.5</sub> (<b>b</b>) PM<sub>10</sub>.</p> Full article ">Figure 4
<p>Regression for Y′ vs. local PM<sub>2.5</sub> concentrations based on Temtop M2000 data.</p> Full article ">Figure 5
<p>Seasonal in-vehicle CO<sub>2</sub> concentrations during morning (am) and evening (pm) rush hours with RC on and RC off.</p> Full article ">Figure 6
<p>Temporal plots of CO<sub>2</sub> and PM<sub>2.5</sub> with (<b>a</b>) RC off; and (<b>b</b>) RC on conditions.</p> Full article ">Figure 7
<p>Speed and PM concentrations on 8 December 2023—evening rush hour.</p> Full article ">
14 pages, 3571 KiB  
Article
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
Viewed by 871
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 [...] Read more.
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 (NOX), and total hydrocarbons (THC). Key findings demonstrate a direct correlation between vehicle load and emission factors, with the emission factors for CO2, CO, and NOX 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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Figure 1

Figure 1
<p>Vehicle exhaust gas collection process.</p> Full article ">Figure 2
<p>Total test route for urban, suburban, and high-speed sections. Red represents the urban route; purple represents the suburban route; green represents the high-speed route. The high-speed and suburban routes have some overlapping lines.</p> Full article ">Figure 3
<p>Test flow.</p> Full article ">Figure 4
<p>Emission factors of CO<sub>2</sub>, CO, NO<sub>X</sub> and THC.</p> Full article ">Figure 5
<p>Emission rate distribution of CO<sub>2</sub> based on speed and acceleration. (<b>a</b>) Emission rate distribution at unloaded state; (<b>b</b>) emission rate distribution at half-loaded state; (<b>c</b>) emission rate distribution at fully loaded state.</p> Full article ">Figure 6
<p>Emission rate distribution of CO based on speed and acceleration. (<b>a</b>) Emission rate distribution at unloaded state; (<b>b</b>) emission rate distribution at half-loaded state; (<b>c</b>) emission rate distribution at fully loaded state.</p> Full article ">Figure 7
<p>Emission rate distribution of NO<sub>X</sub> based on speed and acceleration. (<b>a</b>) Emission rate distribution at unloaded state; (<b>b</b>) emission rate distribution at half-loaded state; (<b>c</b>) emission rate distribution at fully loaded state.</p> Full article ">Figure 8
<p>Emission rate distribution of THC based on speed and acceleration. (<b>a</b>) Emission rate distribution at unloaded state; (<b>b</b>) emission rate distribution at half-loaded state; (<b>c</b>) emission rate distribution at fully loaded state.</p> Full article ">Figure 9
<p>Correlation between speed and emission factors of CO<sub>2</sub>, CO, NO<sub>X</sub> and THC.</p> Full article ">Figure 10
<p>Average vehicle emission rate for each micro-operational mode.</p> Full article ">
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 1268
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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<p>Dynamic spatial–temporal correlations of traffic flows.</p> Full article ">Figure 2
<p>Structure of an STGCN.</p> Full article ">Figure 3
<p>Overall framework of IDG-PSAtt.</p> Full article ">Figure 4
<p>Diagram of the ST-Conv block framework.</p> Full article ">Figure 5
<p>Comparison between the MAE metrics produced on the two datasets.</p> Full article ">Figure 6
<p>Comparison between the MAPE metrics produced on the two datasets.</p> Full article ">Figure 7
<p>Comparison between the RMSE metrics produced on the two datasets.</p> Full article ">Figure 8
<p>Visualizations of the comparisons conducted between different models on the PEMS-BAY dataset.</p> Full article ">

Review

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14 pages, 1481 KiB  
Review
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
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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 [...] Read more.
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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<p>Working principle of urea SCR technology.</p> Full article ">Figure 2
<p>A diesel cc-SCR engine aftertreatment system designed by Southwest Researchers, United [<a href="#B29-atmosphere-15-00997" class="html-bibr">29</a>].</p> Full article ">Figure 3
<p>Comparison of NOx and window average power rate (APR) distribution of PEMS tests using work-based window and fuel-based window methods ((<b>A</b>–<b>D</b>) stand for different vehicle PEMS test cases).</p> Full article ">Figure 4
<p>Schematic diagram of tampering detection of aftertreatment system proposed by Roland et al. [<a href="#B61-atmosphere-15-00997" class="html-bibr">61</a>].</p> Full article ">
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