Light-duty Vehicle Emissions Research Articles

Light-duty gasoline vehicle emission deterioration insights from large-scale inspection/maintenance data: The synergistic impact of usage characteristics

Accurately estimating vehicle emissions is crucial for effective air quality management. As key data for emission inventory construction, emission factors (EFs) are influenced by vehicle usage characteristics and experience deterioration. Current deterioration models often employ single-factor approaches based on vehicle age or accumulated mileage, which fail to capture the effects of varying usage intensities within the same mileage or age intervals. This study addressed this limitation by developing a novel emission deterioration model that incorporates multi-dimensional usage characteristics and that utilizes a large-scale inspection and maintenance (I/M) dataset for light-duty gasoline vehicles (LDGVs). The modeling results reveal distinct deterioration patterns for different pollutants and highlight the synergistic effects of the usage duration and intensity: natural aging significantly impacts HC and NOx emissions, while CO emissions are more strongly affected by intensive use. Specifically, China V LDGVs that were driven 4 × 104 km/yr exhibited HC, CO, and NOx deterioration rates per mile that were approximately 4.1 % lower, 10.3 % higher, and 1.1 % higher, respectively, than those of vehicles driven 2 × 104 km/yr as the mileage increased from 5 × 104 km to 10 × 104 km. By leveraging timely emission data and explicitly accounting for usage intensity, this study corrected biases in local emission estimates by 5–85 % with respect to estimates from commonly used models. This framework enables the development of more effective control strategies and refinements to policy evaluations in megacities with I/M programs.

Analysis and Exploration of Carbon Emission Dataset using Machine Learning Techniques

One of the primary environmental climate issues in metropolitan cities is that most people are faced with low air quality. Climate variance is impacting the world, triggering drought, storms, and extreme weather events. The primary contributor to climate change is affected greenhouse carbon gas emissions, of which carbon dioxide (CO2) and carbon monoxide (CO) make up the majority. The carbon emissions are expected to rise steadily worldwide. Many factors, including the burning of fossil fuels in transportation and manufacturing sectors, cause climate variance. Fast urbanization has used a high rate of motor vehicles as compared to a rural area. In metropolitan cities across the world, automobiles are the primary cause of air pollution. The use rate of vehicles keeps increasing, and that results in traffic congestion. This work is focusing on collecting two real-time vehicle emission datasets from two different devices to predict and compare the emissions of different light-duty vehicles by using the machine learning techniques. This research also analyzes the Canadian government emission dataset It shows the results of emission by fuel type and vehicle model. It shows the comparison graph of, mean square error, root mean square error and accuracy comparison of different machine learning algorithms. The work suggests some policies for reducing the carbon emissions. Even the government can adapt certain policies for mitigating the carbon emissions of individual vehicles. In this research work issues for reducing the emission of individual vehicles have been addressed. This work helps the automobile sector to reduce the emission and play a part to save the environment.

NOx reduction scenarios under real-world driving conditions for light-duty diesel vehicles

This study analyze analyzes the impact of stringent emissions regulations on future NOx reductions. Using scenario analysis, NOx emissions of diesel light-duty vehicles from 2019 to 2040 are estimated using emission factors from real driving emissions data. The baseline scenario assumes current regulations will be maintained, while other scenarios assess the impact of implementing specific Euro standards as final regulations. If Euro 5 regulations are implemented by 2040, cumulative emissions will increase by 229.1 % compared to the baseline scenario. Implementing Euro 6 a/b/c regulations will increase by 156.2 %, while Euro 6 d-temp and Euro 6 d regulations will increase by 8.4 % and 0.7 %, respectively. These findings highlight the significant contribution of real driving emission test introduced with Euro 6 d-temp to NOx reduction. Furthermore, they indicate that Euro 6 d regulation has already achieved emission levels similar to those of the upcoming Euro 7 regulation.

Research on Particle Emissions of Light-duty Hybrid Electric Vehicles in Real Driving

Real driving emission (RDE) assessments were conducted on light-duty hybrid electric vehicles (HEVs) compliant with China-VI standards across various altitudes, including Kaiyuan, Jianshui, and Kunming. Portable Emission Measurement Systems (PEMS) were utilized to analyze instantaneous particulate number (PN) emissions. At the same time, a particle trap was used to collect primary particulate matter (PM). High-resolution Transmission Electron Microscopy (TEM) was employed to capture the micro-morphology and nano-structure of PM. The findings revealed a positive correlation between PN emission factors and altitude, suggesting more stringent regulations are necessary in high-altitude regions. Cold-start conditions exhibited heightened PN emissions, surpassing regulatory thresholds (primarily when the coolant temperature is below 30°C). Notably, the PN emission rate increases when the vehicle is under light acceleration (0 ≤ a ≤ 0.5 m/s2) and the coolant temperature is within the range of 20∼30°C. The PM emitted by HEVs exhibited an unimodal distribution across different driving conditions, with cold-start yielding finer particles. In contrast, urban and rural driving conditions showed enhanced PM aggregation due to more complete fuel combustion. Urban driving conditions, characterized by coolant temperatures exceeding 70°C and speeds below 60 km/h, produced PM with the largest average particle size (32.68 nm) and the broadest size distribution range (8.99 nm to 77.60 nm). Furthermore, increased engine speed and load resulted in elevated average outer shell fringe lengths of primary nanoparticles. The average inner core fringe lengths initially increased, peaked in urban environments (1.04 nm), and then decreased. Fringe separation distances gradually increased, predominantly between 0.2 nm and 0.8 nm. The increase in engine load is the primary factor influencing the growth of microcrystal tortuosity. The study underscores the significance of HEVs in PM emissions, which pose potential risks to both human health and environmental sustainability, emphasizing the need for effective mitigation strategies.

PM2.5 exposure disparities persist despite strict vehicle emissions controls in California.

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from California's aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity.

Impact of Mid-to-Low-Ash, Low-Viscosity Lubricants on Aftertreatment Systems after 210,000-Kilometer Real-World Road Endurance Trials

Engine lubricants globally face the challenge of meeting the demands of new engine technologies while enhancing energy efficiency and reducing emissions. Lubricants must enhance their performance and sustainability, improve reliability in complex and harsh environments, and minimize environmental impact and health risks. This study explores the influence of two different formulations of low viscosity lubricants, tested through actual road endurance trials, on a hybrid vehicle’s aftertreatment system performance and overall emission levels. The study includes 120,000 km of endurance testing in four different challenging environments in China, as well as 90,000 km of endurance testing in a typical urban and highway driving cycle in a large city. Results indicate that emissions from the test vehicles during the 120,000 km and 210,000 km durable Worldwide harmonized Light vehicle Test Cycles (WLTCs) meet China’s Stage 6 light-duty vehicle emission standards, with the 210,000 km Real Driving Emission test (RDE) results also conforming to these standards. Relative to fresh TWC, the light-off temperature increased by a mere 60 °C, and the oxygen storage capacity declined by around 19% following endurance testing. Additionally, the GPF exhibited satisfactory performance after 210,000 km of endurance testing, showing lower backpressure values compared to the fresh-coated samples, with no notable ash buildup observed in the substrate. Drawing on the outcomes of actual road endurance testing, this study illustrates that employing low-to-mid-ash-content, low-viscosity lubricants is both compatible and reliable for aftertreatment systems in present or advanced hybrid technologies. Premium lubricants facilitate vehicles in sustaining compliant and stable emission performance, even amid harsh environments and complex operating conditions. Furthermore, the tested lubricants effectively inhibit excessive aging of the aftertreatment system over prolonged mileage. Moreover, this study discusses the feasibility of rapid aging evaluation methods for aftertreatment systems based on engine test benches, juxtaposed with actual road endurance testing. These findings and conclusions offer crucial references and guidance for enhancing lubricant performance and sustainability. Subsequent studies can delve deeper into the correlation between lubricant performance and environmental impact, alongside optimization strategies for lubricants across various vehicle models and usage scenarios.

Effective Modeling of CO2 Emissions for Light-Duty Vehicles: Linear and Non-Linear Models with Feature Selection

Predictive modeling is important for assessing and reducing energy consumption and CO2 emissions of light-duty vehicles (LDVs). However, LDV emission datasets have not been fully analyzed, and the rich features of the data pose challenges in prediction. This study aims to conduct a comprehensive analysis of the CO2 emission data for LDVs and investigate key prediction model characteristics for the data. Vehicle features in the data are analyzed for their correlations and impact on emissions and fuel consumption. Linear and non-linear models with feature selection are assessed for accuracy and consistency in prediction. The main behaviors of the predictive models are analyzed with respect to vehicle data. The results show that the linear models can achieve good prediction performance comparable to that of nonlinear models and provide superior interpretability and reliability. The non-linear generalized additive models exhibit enhanced accuracy but display varying performance with model and parameter choices. The results verify the strong impact of fuel consumption and powertrain attributes on emissions and their substantial influence on the prediction models. The paper uncovers crucial relationships between vehicle features and CO2 emissions from LDVs. These findings provide insights for model and parameter selections for effective and reliable prediction of vehicle emissions and fuel consumption.

Measurement of Light-Duty Vehicle Exhaust Emissions with Light Absorption Spectrometers

Light-duty vehicle emission regulations worldwide set limits for the following gaseous pollutants: carbon monoxide (CO), nitric oxides (NOX), hydrocarbons (HCs), and/or non-methane hydrocarbons (NMHCs). Carbon dioxide (CO2) is indirectly limited by fleet CO2 or fuel consumption targets. Measurements are carried out at the dilution tunnel with “standard” laboratory-grade instruments following well-defined principles of operation: non-dispersive infrared (NDIR) analyzers for CO and CO2, flame ionization detectors (FIDs) for hydrocarbons, and chemiluminescence analyzers (CLAs) or non-dispersive ultraviolet detectors (NDUVs) for NOX. In the United States in 2012 and in China in 2020, with Stage 6, nitrous oxide (N2O) was also included. Brazil is phasing in NH3 in its regulation. Alternative instruments that can measure some or all these pollutants include Fourier transform infrared (FTIR)- and laser absorption spectroscopy (LAS)-based instruments. In the second category, quantum cascade laser (QCL) spectroscopy in the mid-infrared area or laser diode spectroscopy (LDS) in the near-infrared area, such as tunable diode laser absorption spectroscopy (TDLAS), are included. According to current regulations and technical specifications, NH3 is the only component that has to be measured at the tailpipe to avoid ammonia losses due to its hydrophilic properties and adsorption on the transfer lines. There are not many studies that have evaluated such instruments, in particular those for “non-regulated” worldwide pollutants. For this reason, we compared laboratory-grade “standard” analyzers with FTIR- and TDLAS-based instruments measuring NH3. One diesel and two gasoline vehicles at different ambient temperatures and with different test cycles produced emissions in a wide range. In general, the agreement among the instruments was very good (in most cases, within ±10%), confirming their suitability for the measurement of pollutants.

Database of emission factors of volatile organic compound (VOC) species in motor vehicle exhaust in China

The synergistic strategy for fine particulate matter (PM2.5) and O3 pollution prevention and control has emerged as a pivotal approach in combating air pollution. Volatile organic compounds (VOCs) serve as crucial precursors to both O3 and secondary organic aerosols (SOAs), with motor vehicles representing one of their significant sources. In this study, a standard for establishing a database of VOC species emission factors for motor vehicles was developed, and a database containing 134 VOC species was constructed through field tests and literature surveys. The VOC emissions of light-duty gasoline passenger vehicles (LDGPVs) comprised primarily alkanes and aromatics. The VOC emissions of light-duty diesel trucks (LDDTs) comprised mostly alkanes. Regarding low-speed trucks, 3-wheel vehicles, medium-duty diesel trucks (MDDTs) and heavy-duty diesel trucks (HDDTs), their VOC emissions comprised mainly oxygenated volatile organic compounds (OVOCs). The update of emission standards resulted in a reduction in VOC species emission factors while altering the composition of VOCs. Attention should be directed toward isopentane, benzene and dichloromethane emitted by LDGPVs, dodecane, undecane, ethene and propene emitted by LDDTs, and acetaldehyde emitted by HDDTs. VOC species originating from LDGPVs were more dispersed than those originating from LDDTs and HDDTs. In addition, variations in VOC species were observed among motor vehicles with different fuel types. Toluene, ethene, benzene, m,p-xylene, isopentane, hexanal, ethyne and 1,2,4-trimethylbenzene were the predominant VOC species emitted by gasoline vehicles. Diesel vehicles emitted mainly dodecane, formaldehyde, propene, undecane, acetaldehyde, ethene, decane and benzene. The results could enhance our comprehension of the emission characteristics of VOC species originating from motor vehicles and provide data support and a scientific foundation for achieving synergistic PM2.5 and O3 pollution prevention and control.

Analysis of Learning Algorithms for Predicting Carbon Emissions of Light-Duty Vehicles

Analysis of Learning Algorithms for Predicting Carbon Emissions of Light-Duty Vehicles

Non-exhaust gas and particle emissions of light-duty vehicles – Quantification under driving conditions in a sealed cabin

Non-exhaust emissions have emerged as a pivotal contributor to vehicle emissions, as regulatory efforts persist in curtailing exhaust emissions. This research focused on the running-loss evaporative volatile organic carbon (VOC) and non-exhaust particle (NEP) emission factors (EFs) of a light-duty vehicle in the sealed housing. VOC concentrations demonstrated an upward trend with elevated vehicle temperatures and extended driving durations. Normalized over driving distance, the hot-start WLTC test exhibited the highest VOC EFs, about 617 μg/km, and the highest ozone formation potential (OFP) value of 1.75 mgO3/km. Toluene and xylene were the main pollutants contributing to the OFP value across all tests. Evaluating EFs of various evaporative emission forms in mg/h, the BTEXs EF of running-loss tests was recorded at 10.2 mg/h, comparable to diurnal-breathing tests but significantly lower than hot-soak tests. Additionally, particles within the 10–23 nm range consistently dominated total NEP above 10 nm. Particle number (PN) concentrations of 10–23 nm particles exhibited a sharp increase under intense driving conditions and a slow decrease under other driving conditions. The results suggested a continuous consumption of smaller particles, leading to their coalescence into larger particles. The EF of NEP above 23 nm in the US06_4 test was 1.72 × 1012 #/km, surpassing the current exhaust particle emission limit. Consequently, NEP has been a major vehicle particle source of greater concern than exhaust particles.

Analyzing and Optimizing the Emission Impact of Intersection Signal Control in Mixed Traffic

Signalized intersections are one of the typical bottlenecks in urban transport systems that have reduced speeds and which have substantial vehicle emissions. This study aims to analyze and optimize the impacts of signal control on the emissions of mixed traffic flow (CO, HC, and NOx) containing both heavy- and light-duty vehicles at urban intersections, leveraging high-resolution field emission data. An OBEAS-3000 (Manufacturer: Xiamen Tongchuang Inspection Technology Co., Ltd., Xiamen, China.) vehicle emission testing device was used to collect microscopic operating characteristics and instantaneous emission data of different vehicle types (light- and heavy-duty vehicles) under different operating conditions. Based on the collected data, the VSP (Vehicle Specific Power) model combined with the VISSIM traffic simulation platform was used to quantitatively analyze the impact of signal control on traffic emissions. Heavy-duty vehicles contribute to most of the emissions regardless of the low proportion in the traffic flows. Afterward, a model is proposed for determining the optimal signal control at an intersection for a specific percentage of heavy-duty vehicles based on the conversion of emission factors of different types of vehicles. Signal control is also optimized based on conventional signal timing, and vehicle emissions are calculated. In the empirical analysis, the changes in CO, HC, and NOx emissions of light- and heavy-duty vehicles before and after conventional signal control optimization are quantified and compared. After the signal control optimization, the CO, HC, and NOx emissions of heavy-duty vehicles were reduced. The CO and HC emissions of light-duty vehicles were reduced, but the NOx emissions of light-duty vehicles remained unchanged. The emissions of vehicles after optimized signal control based on vehicle conversion factors are reduced more significantly than those after conventional optimized signal control. This study provides a scientific basis for developing traffic management measures for energy saving and emission reduction in transport systems with mixed traffic.

A Comparison of the Evaporative Emissions Performance of Light-Duty Vehicles under US Tier 3 and China 6 Test Conditions

To further improve air quality in China, especially ozone and fine particulate matter (PM2.5), additional reductions of volatile organic compounds (VOCs) are needed. Evaporative emissions from internal combustion engines (ICE) and hybrid vehicles are known to be an important source of VOCs in urban cities, which can be efficiently reduced through the introduction of more stringent regulatory standards. The most stringent global standards for evaporative emissions are the U.S. Tier 3 light-duty vehicle standards. This study compares the evaporative emissions of light-duty ICE vehicles designed to China 6 and U.S. Tier 3 regulations for both China and U.S. test conditions. Data are analyzed from manufacturer testing of full vehicle evaporative emissions conducted in the laboratory following U.S. Tier 3 and China 6 certification test methods utilizing Sealed Housing for Evaporative Determination (SHED) chambers equipped with Flame Ionization Detection (FID) analyzers. Vehicles designed to U.S. Tier 3 standards are observed to have 64% lower diurnal and hot soak evaporative emissions and 98% lower canister bleed emissions relative to vehicles designed to China 6 standards. U.S. Tier 3, U.S. Tier 2, and China 6 light-duty evaporative emission certification values submitted by manufacturers are also compared. The average of all current U.S. Tier 3 certifications is, on average, 52.4% lower than the average of all China 6 certifications. The results from the testing and data analysis of five vehicles, with comparison to the certification data, suggest that the introduction of standards equivalent in stringency to U.S. Tier 3 in China can significantly reduce evaporative emissions relative to China 6.

Forecasting Carbon Dioxide Emissions of Light-Duty Vehicles with Different Machine Learning Algorithms

Accurate estimation of fuel consumption and emissions is crucial for assessing the impact of materials and stringent emission control techniques on climate change, particularly in the transportation industry, which accounts for a significant portion of global greenhouse gases and hazardous pollutants emissions. To address these concerns, the government of Canada has collected a large sensor-based dataset containing detailed information on 7384 light-duty vehicles from 2017 to 2021, with the goal of reducing CO2 emissions by 40–45% by 2030. To this end, various researchers worldwide have developed vehicle emissions and consumption models to comply with these targets and achieve the Canadian government’s ambitious objectives. In this work, we propose the development of boosting and other regression models to predict carbon dioxide emissions for light-duty vehicle designs, with the aim of creating ensemble learning models that leverage vehicle specifications to forecast emissions. Our proposed boosting model is capable of accurately predicting CO2 emissions, even with only one car attribute as input. Moreover, our regression models, in conjunction with the boosting algorithm, can effectively make predictions from various vehicle inputs. Our proposed technique, categorical boosting (Catboost), provides critical insights into transportation-generated air pollution, offering valuable recommendations for both vehicle users and manufacturers. Importantly, Catboost performs data processing in less time and with less memory than other algorithms proposed in the literature. Future research efforts should focus on developing higher performance models and expanding datasets to further improve the accuracy of predictions.

Chemical composition and potential health risks of tire and road wear microplastics from light-duty vehicles in an urban tunnel in China

Tire and road wear microplastics (TRWMPs) are one of the main non-exhaust pollutants of motor vehicles, which cause serious environmental and health issues. Here, TRWMPs in PM2.5 samples were collected in a tunnel in urban Xi'an, northwest China, during four periods [I: 7:30–10:30, II: 11:00–14:00, III: 16:30–19:30, IV: 20:00–23:00 local standard time (LST)] in summer of 2019. The chemical components of rubbers, benzothiazoles, phthalates, and amines in TRWMPs were quantified, with a total concentration of 6522 ± 1455 ng m−3 (mean ± standard deviation). Phthalates were predominant in TRWMPs, accounting for 64.8% on average, followed by rubbers (33.2%) and benzothiazoles (1.19%). The diurnal variations of TRWMPs showed the highest concentration in Period III (evening rush hour) and the lowest concentration in Period I (morning rush hour), which were not exactly consistent with the variation of the number of light-duty vehicles passed through the tunnel. The result implied that the number of vehicles might not be the most important contributor to TRWMPs concentration, whereas meteorological variables (i.e., precipitation, and relative humidity), vehicle speed, vehicle class, and road cleaning also affected their abundances. The non-carcinogenic risk of TRWMPs in this study was within the international safety threshold, but their carcinogenic risk exceeded the threshold by 2.7–4.6 times, mostly dominated by bis(2-ethylhexyl)phthalate (DEHP). This study provides a new basis for the source apportionment of urban PM2.5 in China. The high concentrations and high potential cancer risks of TRWMPs represent the requirement for more efficient measures to control light-duty vehicle emissions.

Brake Particle PN and PM Emissions of a Hybrid Light Duty Vehicle Measured on the Chassis Dynamometer

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.

Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles

This work focused on demonstrating the capability of unsupervised machine learning techniques in detecting impending anomalies by extracting hidden trends in the datasets of fuel economy and emissions of light-duty vehicles (LDVs), which consist of cars and light-duty trucks. This case study used the vehicles’ fuel economy and emissions testing datasets for vehicle model years 2015 to 2023 with a total of 34,602 data samples on LDVs of major vehicle manufacturers. Three unsupervised techniques were used: principal components analysis (PCA), K-Means clustering, and self-organizing maps (SOM). Results show that there are clusters of data that exhibit trends not represented by the dataset as a whole. Fuel CO vs. Fuel Economy has a negative correlation in the whole dataset (r = −0.355 for LDVs model year 2022), but it has positive correlations in certain sample clusters (e.g., LDVs model year 2022: r = +0.62 in a K-Means cluster where the slope is around 0.347 g−CO/mi/MPG). A time series analysis of the results of clustering indicates that Test Procedure and Fuel Type, specifically Test Procedure 11 and Fuel Type 26 as defined by the US EPA, could be the contributors to the positive correlation of CO and Fuel Economy. This detected peculiar trend of CO-vs.-Fuel Economy is an impending anomaly, as the use of Fuel 26 in emissions testing with Test Procedure 11 of US-EPA has been increasing through the years. With the finding that the clustered data samples with positive CO-vs.-Fuel Economy correlation all came from vehicle manufacturers that independently conduct the standard testing procedures and not data from US-EPA testing centers, it was concluded that the chemistry of using Fuel 26 in performing Test Procedure 11 should be re-evaluated by US-EPA.

Measurement of Gaseous Exhaust Emissions of Light-Duty Vehicles in Preparation for Euro 7: A Comparison of Portable and Laboratory Instrumentation

The European Union’s ambition to reach climate neutrality and a toxic-free environment by 2050 entails, among other things, cleaner road vehicles. The European Commission’s proposal for the next regulatory emissions standard, Euro 7, requires the measurement of pollutants currently not regulated on the road. In this study we compared a prototype portable emissions measurement system (PEMS) measuring CO2, CO, NO, NO2, N2O, NH3, CH4, and HCHO based on infrared laser absorption modulation (IRLAM), and two Fourier transform infrared (FTIR) spectrometers with laboratory grade analyzers. To this end, one Euro 6d Diesel, one Euro 6d gasoline, and one Euro 4 gasoline vehicle were tested at −7 °C and 23 °C with various driving cycles covering traffic conditions to highway dynamic driving. The results demonstrated that the differences among the instruments were small: ±1 mg/km for HCHO, N2O, and CH4, ±2.5 mg/km for NH3, ±10–15 mg/km for NOx, ±50 mg/km or ±15% for CO (whichever was larger), and ±10–15 g/km for CO2. These values corresponded to <10–15% of the proposed Euro 7 limits or the emission levels of the tested vehicles. Our results confirm the feasibility of on-board systems to measure the conventional components including CO2 and the aforementioned additional pollutants.

Design and evaluation of a volatile particle remover combining hot dilution and a thermodenuder

Portable and efficient removal of volatile particles from motor vehicle exhaust is essential for the accurate measurement of involatile solid particle number concentration in on-board emission measurement systems. For this purpose, a volatile particle remover with a combination of hot dilution and a thermodenuder (referred to as VPR-TD) was designed. The flow and temperature fields of the VPR-TD were simulated and analyzed by COMSOL Multiphysics software by which experimental verification and system performance tests were performed. The simulation showed that the effective heating length and residence time of the VPR-TD increased by 6 cm and 25%, respectively, as the dilution ratio increased from 1 to 10 at a total flow of 0.6 L/min at 350 °C. In addition, the experimental results showed that the penetration efficiency of 100 nm sodium chloride (non-volatile particles) in the VPR-TD was higher than 70.4%, and the removal efficiency of 30 nm tetracontane (volatile particles) was higher than 99% at a total flow 0.6 L/min, heating temperature 350 °C, and dilution ratios of 5 and 10. In summary, when the dilution ratio was set to 10, the device met the requirements of the Light Duty Vehicle Emission Limits and Measurement Methods (CHINA 6) for a volatile particle remover. At dilution ratio of 5, VPR-TD may be used as a pre-conditioning unit in portable emissions measurement systems.

Based on a comparison of life-cycle carbon and pollution emissions of fuel-fired light-duty vehicles and new energy vehicles and an analysis of the influencing factors

Using the GREET model and the WTW system, compare and analyse the carbon emissions of fuel-light vehicles (E10, liquefied natural gas) and new energy vehicles (pure electric vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, liquefied hydrogen energy) during the entire life cycle of automobiles and pollutant emissions and compare the carbon emissions of plug-in hybrid vehicles under different power structures. The conclusions of the emissions of the two models, the number of emission factors, the contribution of various links of the two models, upstream emissions, vehicle life, light materials, and power structure are drawn, and relevant suggestions are given based on the conclusions.