Light-duty Vehicle Emissions Research Articles

Fifteen Years of Community Exposure to Heavy-Duty Emissions: Capturing Disparities over Space and Time.

Disparities in exposure to traffic-related air pollution have been widely reported. However, little work has been done to simultaneously assess the impact of various vehicle types on populations of different socioeconomic/ethnic backgrounds. In this study, we employed an extreme gradient-boosting approach to spatially distribute light-duty vehicle (LDV) and heavy-duty truck emissions across the city of Toronto from 2006 to 2020. We examined associations between these emissions and different marginalization indices across this time span. Despite a large decrease in traffic emissions, disparities in exposure to traffic-related air pollution persisted over time. Populations with high residential instability, high ethnic concentration, and high material deprivation were found to reside in regions with significantly higher truck and LDV emissions. In fact, the gap in exposure to traffic emissions between the most residentially unstable populations and the least residentially unstable populations worsened over time, with trucks being the larger contributor to these disparities. Our data also indicate that the number of trucks and truck emissions increased substantially between 2019 and 2020 whilst LDVs decreased. Our results suggest that improvements in vehicle emission technologies are not sufficient to tackle disparities in exposure to traffic-related air pollution.

Prediction of Real Driving Emission of Light Vehicles in China VI Based on GA-BP Algorithm

In the China VI regulations for light-duty vehicles, the (RDE) test is introduced as a supplementary test procedure. In the actual test process, the RDE results are more significantly influenced by driving behavior and vehicle type. To reduce the test cost, the NOx and PN prediction models are established based on the GA-BP method. The results showed that the coefficients of determination of the GA-BP model for NOx and PN predictions are all greater than 0.9 and are linearly highly correlated at the instantaneous emission level. At the overall emission level, the overall error of the GA-BP model is less than 7% for NOx prediction and less than 6% for PN prediction. The model has high accuracy for both instantaneous and overall emissions of light-duty vehicles. This provides practical engineering value for guiding the RDE test.

Exhaust Emissions of In-Use Euro 6d-TEMP and Euro 6d Vehicles in WLTP and RDE Conditions, a Comparison

<div class="section abstract"><div class="htmlview paragraph">Air pollution is a major threat to the environment and human health being responsible of more than 350 000 premature deaths every year in the European Union (EU). Road transport accounts for circa 40% of the total NOx emissions and 10% of the particulates emissions of the EU. However, these shares increase significantly in urban areas where 75% of the citizens live. In order to curb pollutant emissions from new vehicles entering the European market, the Euro 6 regulation, setting emission limits for cars and vans, was recently updated with the introduction of a new test procedure in the laboratory, the Worldwide Harmonized Light Vehicle Test Procedure (WLTP), and a novel on-road test performed with Portable Emissions Measurement Systems, the Real Driving Emissions regulation (RDE). Both test procedures WLTP and RDE were designed to represent typical driving conditions and thus help characterizing exhaust emissions of light-duty vehicles in real life operation. This manuscript presents NOx, CO, and solid particulate number PN emissions of 53 gasoline and diesel passenger cars type-approved in the EU after the entry into force of the WLTP and RDE (i.e., meeting the Euro 6d-TEMP and Euro 6d standards). Emissions data over WLTP and RDE tests on in-use vehicles was collected by the Joint Research Center (JRC) of the European Commission in the period 2018-2021. Emissions are characterized by powertrain, fuel type, and test procedure. All vehicles comply well with Euro 6 emission limits and no statistically significant differences are found in NOx, CO, and PN emissions measured over the complete WLTP and RDE tests, both for gasoline and diesel vehicles.</div></div>

Characterizing the particle number emissions of light-duty gasoline vehicles under different engine technologies and driving conditions

Vehicle particle number (PN) emissions have attracted increasing public attention due to their severe influence on human health. In this study, we selected 35 light-duty gasoline vehicles (LDGVs) with gasoline direct injection (GDI) and multi-port fuel injection (MPFI) engines to elucidate the main factors influencing PN emissions. Via real driving emission (RDE) and chassis dynamometer tests, we quantified the impact of engine technology, emission standards, engine-start conditions and engine load on vehicle PN emissions. The RDE test results indicated that GDI vehicles generated higher PN emissions than those of MPFI vehicles under hot-running conditions. MPFI vehicle PN emissions were greatly affected by rapidly changing driving conditions, especially vehicles equipped with automatic start-stop systems. In regard to China 6 GDI vehicles equipped with a gasoline particle filter (GPF), their PN emissions were usually low, and peak PN emissions could mainly be attributed to GPF regeneration. Engine manufacturers should optimize GPF regeneration conditions to further reduce particulate emissions. Furthermore, the analysis results of PN emissions for different road types indicated that PN emissions were related to vehicle driving conditions. The vehicle specific power (VSP) could be used as an important explanatory variable to characterize the PN emission rate when distinguishing different engine technologies and emission standards. A real-world LDGV VSP-based PN emission rate was suggested based on the RDE test dataset. The VSP-based emission rate could be considered to more accurately quantify vehicle PN emissions and support the formulation of urban vehicle particle emission control policies.

The role of pickup truck electrification in the decarbonization of light-duty vehicles

Electrification can reduce the greenhouse gas (GHG) emissions of light-duty vehicles. Previous studies have focused on comparing battery electric vehicle (BEV) sedans to their conventional internal combustion engine vehicle (ICEV) or hybrid electric vehicle (HEV) counterparts. We extend the analysis to different vehicle classes by conducting a cradle-to-grave life cycle GHG assessment of model year 2020 ICEV, HEV, and BEV sedans, sports utility vehicles (SUVs), and pickup trucks in the United States. We show that the proportional emissions benefit of electrification is approximately independent of vehicle class. For sedans, SUVs, and pickup trucks we find HEVs and BEVs have approximately 28% and 64% lower cradle-to-grave life cycle emissions, respectively, than ICEVs in our base case model. This results in a lifetime BEV over ICEV GHG emissions benefit of approximately 45 tonnes CO2e for sedans, 56 tonnes CO2e for SUVs, and 74 tonnes CO2e for pickup trucks. The benefits of electrification remain significant with increased battery size, reduced BEV lifetime, and across a variety of drive cycles and decarbonization scenarios. However, there is substantial variation in emissions based on where and when a vehicle is charged and operated, due to the impact of ambient temperature on fuel economy and the spatiotemporal variability in grid carbon intensity across the United States. Regionally, BEV pickup GHG emissions are 13%–118% of their ICEV counterparts and 14%–134% of their HEV counterparts across U.S. counties. BEVs have lower GHG emissions than HEVs in 95%–96% of counties and lower GHG emissions than ICEVs in 98%–99% of counties. As consumers migrate from ICEVs and HEVs to BEVs, accounting for these spatiotemporal factors and the wide range of available vehicle classes is an important consideration for electric vehicle deployment, operation, policymaking, and planning.

Analysing the potential of a simulation-based method for the assessment of CO2 savings from eco-innovative technologies in light-duty vehicles

Mandatory targets are set in Europe for Carbon Dioxide (CO2) emissions of light-duty vehicles. EU law recognises the potential of certain innovative technologies to contribute to reducing CO2 emissions. Vehicle systems and innovations are becoming increasingly complex, and the accurate quantification of their benefits increasingly difficult. The study investigates the potential of the CO2MPAS simulator to serve this purpose. Two innovative technologies were studied, Light-emitting diode (LED) lighting systems, efficient alternators (EA), and their combination. The model was validated on detailed test results from eight vehicles. A total of 452 passenger cars, for which test data were available, were subsequently simulated using CO2MPAS simulator. The mean simulated CO2 savings was 0.91gCO2/km (LED lights), 0.98 gCO2/km (EA), and 1.78 gCO2/km (combined). Results show that simulated CO2 savings were comparable to those calculated using the existing standardised method. For gasoline and diesel vehicles respectively, the difference in CO2 savings between simulated and existing method was 2.8% and 0.14% in the LED lights case, and 0.6% and 0.67% in the alternator case. In the combined case, the difference was calculated to be 1.7% and 0.34%. Similar approaches could be used in the future for accurately capturing the benefits of more complex technologies.

Analysis and Prediction Model of Fuel Consumption and Carbon Dioxide Emissions of Light-Duty Vehicles

Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications.

Rapid assessments of light-duty gasoline vehicle emissions using on-road remote sensing and machine learning

In-time and accurate assessments of on-road vehicle emissions play a central role in urban air quality and health policymaking. However, official insight is hampered by the Inspection/Maintenance (I/M) procedure conducted in the laboratory annually. It not only has a large gap to real-world situations (e.g., meteorological conditions) but also is incapable of regular supervision. Here we build a unique dataset including 103,831 light-duty gasoline vehicles, in which on-road remote sensing (ORRS) measurements are linked to the I/M records based on the vehicle identification numbers and license plates. On this basis, we develop an ensemble model framework that integrates three machining learning algorithms, including neural network (NN), extreme gradient boosting (XGBoost), and random forest (RF). We demonstrate that this ensemble model could rapidly assess the vehicle-specific emissions (i.e., CO, HC, and NO). In particular, the model performs quite well for the passing vehicles under normal conditions (i.e., lower VSP (<18 kw/t), temperature (6–32 °C), relative humidity (<80%), and wind speed (<5 m/s)). Together with the current emission standard, we identify a large number of the ‘dirty’ (2.33%) or ‘clean’ (74.92%) vehicles in the real world. Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions. This approach framework provides a valuable opportunity to reform the I/M procedures globally and mitigate urban air pollution deeply.

Effects from Ambient Temperature and Humidity on Exhaust Emissions of Light Duty Vehicles

Effects from Ambient Temperature and Humidity on Exhaust Emissions of Light Duty Vehicles

The use of alternative fuels to mitigate climate change impacts in the transportation sector in Rio de Janeiro, Brazil

This paper assesses how increased use of alternative fuels (ethanol and electricity) contributes to reduce carbon dioxide (CO2) emissions of light-duty vehicles in Rio Janeiro state (RJ), Brazil, a region of a developing country – most such analysis focus at the aggregate country level. Using an energy systems model (OSeMOSYS), we analyze scenarios that consider an improvement in the quality of the ethanol (in gCO2eq/MJ) consumed in RJ due to the development of the RenovaBio program, greater uptake of electric vehicles, carbon pricing, and a combination of these policies. We also analyze a scenario with the new RenovaBio targets, which the Brazilian government scaled down because of the COVID-19 pandemic. We find that electric vehicles are the most cost-effective policy to reduce CO2 emissions (by 1.9%) when no carbon pricing is considered. When it is, however, CO2 emissions are reduced the most (between 47% and 56%), regardless of the vehicle technology being used, but it is the costlier policy (between 5.4% and 15.3%). In scenarios with carbon pricing, flex-fuel vehicles switch to ethanol, an important result for a region where this technology already dominates the vehicle fleet. Greater uptake of electric vehicles leads to lower overall transportation costs (by 2.5%), but when a CO2 price is considered, because the electricity system still relies on fossil fuels, the reduction in CO2 emissions is compromised. In such a setting, increased ethanol quality reduces carbon emissions more than electric vehicle adoption (by 16%). The RenovaBio targets’ reduction due to the COVID-19 pandemic, however, jeopardizes this result. Such findings convey important evidence to policymakers and the research community: decarbonization of transportation in a region of a developing country, where economic resources are scarcer, can start before electric vehicles become affordable by increasing and maintaining continued access to lower-carbon fuels.

Research on the influence of different calculation methods of deterioration factors on light-duty vehicle emissions

The calculation method of deterioration factor of durability test in China 6 emission regulation of light-duty vehicle is studied. The difference between multiplication and addition in the implementation of the regulations is obtained. The results show that the additive DF can keep the actual deterioration level of the car, while the multiplication DF will change the deterioration level of the car and enlarge the error; With the decrease of emission limit, multiplication DF makes it more difficult for vehicles to pass through emission limit value than add DF; For emission consistency inspection, the two degradation factors have advantages in different regions.

Scenario simulation of CO2 emissions from light-duty passenger vehicles under land use-transport planning: A case of Shenzhen International Low Carbon City

• CO 2 emissions from light-duty passenger vehicles were simulated. • Light-duty passenger vehicle emissions were calculated based on travel demand. • Travel demand were simulated under different planning policy scenarios. • A case study was conducted in a project of international cooperation in China. • This study can offer a framework for evaluating land use-transport planning. Mitigating transport emissions acts a significant role in coping with global climate change and building sustainable cities. Since light-duty passenger vehicles (LDPVs) significantly contribute to transport CO 2 emissions, it is essential to evaluate their emissions for the policy-making of sustainable transport strategies. However, related studies concerning integrated land use-transport planning are limited. Therefore, this study simulated and evaluated CO 2 emissions from LDPVs at the community level using an integrated model, where the mixed-use development (MXD) model was adopted to estimate travel demand based on mixed land-use and transport accessibility. A vehicle emission calculation model was then employed to calculate CO 2 emissions with the estimated travel demand. With this model, the policy impacts on CO 2 emissions from LDPVs were simulated under different planning policy scenarios of land use-transport planning. As a leading development project of international cooperation on sustainable urbanization in China, Shenzhen International Low Carbon City was chosen as the study case. The results indicated the significance of the spatial equalized patterns of both mixed land-use and transport facilities. This new framework can be used to evaluate and promote low-carbon transport policy options for integrated spatial planning in the sustainable urbanization of developing countries.

Regulating light-duty vehicle emissions: an overview of US, EU, China and Brazil programs and its effect on air quality

This paper reviews the progress and effectiveness of Programs to Control Vehicle Emissions (PCVEs), comparing the experiences in the United States (US), European Union (EU), China, and Brazil. We present a timeline comparison of updates and differences in standards for light-duty vehicle (LDV) compliance. We then review the benefits of controlling LDV emissions on air quality, derived from previous relevant studies. Emission standards have been increasingly restricted in all evaluated PCVEs. However, some technical aspects such as dynamometer test cycles, re-testing structure of environmental protection agency, homogeneity of new and in-use vehicles inspection and maintenance, on-board diagnostics requirements are more consolidated in the US. Previous studies at different scales show the success of PCVEs in reducing vehicle emissions and air pollutant concentrations in the US, EU, China, and Brazil. Despite PCVEs has been achieving relative success, vehicular emissions are still a major threat to air quality around the world, especially in developing countries or ascending economies whose fleet grows dramatically. In places where the air quality standards recommended by the World Health Organization (WHO) are violated, it would be required the implementation of more stringent regulations with a well-designed, and homogeneous compliance policy over regional and national territories. This work contributes to clarifying the current challenges and successful experiences on regulating vehicular emissions worldwide.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s10098-021-02238-1.

Comprehensive US database and model for ethanol blend effects on regulated tailpipe emissions

Particulate matter (PM), oxides of nitrogen (NOx), carbon monoxide (CO), and total hydrocarbons (THC) in gasoline exhaust affect atmospheric quality, and hence human health. Ethanol produced from corn grain is a renewable resource with favorable anti-knock properties for gasoline blending. Refiners alter petroleum composition to produce a finished blend that meets specifications. Ethanol blending affects emissions from market fuels both directly and indirectly since aromatics are typically removed from the BOB as ethanol is added to reach a constant octane rating. Numerous studies have been conducted to assess the effect of ethanol blending on light duty vehicle emissions. However, few studies have examined market fuel blends directly and small studies yield insufficient information to be generally applicable. If blending of fuels for a study does not yield gasoline that adequately resembles the composition of a market blend, the generalizability of study results may be impacted by nonlinear blending effects. Most vehicle-based fuel effect studies employed fuel formulations that either facilitate examination of several fuel variables or blend ethanol into a baseline gasoline (splash blending). Such study results do not support direct quantification of emissions inventory effects. To examine real world blending implications on regulated emissions [PM, NOx, CO, THC], we compiled a comprehensive database of US emission studies, developed regression models based on fuel and vehicle properties, and used those models to estimate differences in emissions from expected market fuel compositions. We addressed nonlinear responses to ethanol composition by modeling both low (up to 10% ethanol by volume) and mid blends (split models). We used the Federal Test Procedure (FTP) and Unified Cycle (LA92) driving schedule data, with the cold-start eliciting the highest emissions. PM cold-start emissions were lower with higher ethanol content, and more so at higher blend levels but hot-running emissions showed no differences with respect to ethanol level. For all emissions, the effects differed between port fuel injection (PFI) and gasoline direct injection (GDI) powered vehicles and for NOx, CO and THC there were differences between comphrehensive and split models. NOx results varied over blend levels and THC results were scattered for the higher blends. CO emissions were lower with higher ethanol content in nearly all cases for PFI but only the hot-running GDI. Results did not differ between summer regular and premium fuels. To the extent that PFI and GDI models differ, an emissions inventory calculation should treat them separately. There is uncertainty directly associated with the regression process, and with model inputs since study methods vary and compositions are reported differently between laboratories and test methods. Small changes in modeled emissions should be considered in this light.

Alternative electrification pathways for light-duty vehicles in the European transport sector

• Electrification is an attractive option to reduce CO 2 emissions from transport. • Alternative electrification options differ in terms of impact on the energy system. • Indirect electrification might result in a critical burden on the supply sector. • Policies addressing consumer preferences are crucial to trigger mobility shift. There is a wide consensus that a fundamental technology shift within the light duty vehicles (LDVs) sector is necessary to achieve the emissions reductions required for the Paris Agreement’s targets, but substantial controversy prevails about the most suitable strategy. While some decision makers favor a transition to battery electric vehicles, others advocate for fuel cell vehicles and e-fuels. These strategies differ markedly in terms of consumer acceptance and implications for the energy system. We explore a range of electrification pathways in Europe until 2050. Direct electrification leads to a strong reduction in direct CO 2 emissions of LDVs, with electric vehicles reaching 90% of sales in 2050. Indirect electrification places substantially higher pressure on the supply sector, with almost double the primary energy demand relative to direct electricity use. In addition, the implementation of complementary policies addressing perceived inconvenience markups for alternative mobility is crucial to initiate the mobility transformation.

Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model

This paper presents a road vehicle emission model that integrates an artificial neural network (ANN) model with a vehicle dynamics model to predict the instantaneous carbon dioxide (CO2), nitrogen oxides (NOx) and total hydrocarbon (THC) emissions of diesel light-duty vehicles. Real-world measurement data were used to train a multi-layer feed-forward ANN model. The optimal combination of the various experimental variables was selected as the ANN input through a parametric study considering both practicality and accuracy. For CO2 prediction, two variables (engine speed and engine torque) are enough to develop an accurate ANN model. In order to achieve satisfactory accuracy for CO and NOx prediction, more variables were used for ANN training. The trained ANN model was used to predict road vehicle emissions by integrating the vehicle dynamics model, which was used as a supplementary tool to produce ANN input data. The integrated model is practical because it requires relatively simple data for input such as vehicle specifications, velocity, and road gradient. In the accuracy validation, the proposed model showed satisfactory prediction accuracy for road vehicle emissions.

Traffic-related polycyclic aromatic hydrocarbons (PAHs) occurrence in a tropical environment.

Traffic-related PAH emissions over the urban area of Natal, Brazil, have shown a significant increase because of automobile usage and have become a major concern due to their potential effects on human health and the environment. Therefore, this research measured PAH contamination on major roads and river compartments in a tropical catchment (Pitimbu River) over an expanding urban area. Road PAH concentrations spanned from 692 to 2098ngg-1 and suggest the predominance of heavy (diesel-powered) and light-duty (gasoline plus alcohol-powered) vehicle emission sources. High concentrations of naphthalene (515ngg-1) and acenaphthylene (145ngg-1) were found in river sediments, indicating oil-related spillage and low-temperature combustion sources. Diagnostic ratios indicated the prevalence of biomass, coal and petroleum combustion processes and refined oil products. The ecological risk assessment indicated an ecological contamination risk ranging between low and moderate because of naphthalene and acenaphthylene concentrations higher than ERL threshold values. Toxicity risks caused by PAHs were assessed by using the BaP-equivalent carcinogenic power (BaPE). Results indicated that both RDS and riverbed sediment samples are at low toxicity risk.

Research on ammonia emissions characteristics from light-duty gasoline vehicles

In this study, ammonia emissions characteristics of typical light-duty gasoline vehicles were obtained through laboratory vehicle bench test and combined with New European Driving Cycle (NEDC) condition and Worldwide Harmonized Light Vehicles Test Cycle (WLTC) condition. The influence of ambient temperature on ammonia emissions is mainly concentrated in the cold start stage. The influence of ambient temperature on ammonia emission is shown that the ammonia emissions of light-duty gasoline vehicles under ambient temperature conditions (14 and 23°C) are lower than those under low ambient temperature conditions (−7°C) and high ambient temperature conditions (35 and 40°C). The influence of TWC on ammonia emission is shown that ammonia is a by-product of the catalytic reduction reaction of conventional gas pollutants in the exhaust gas in the TWC. Under NEDC operating conditions and WLTC operating conditions, ammonia emissions after the catalyst are 45 times and 72 times that before the catalyst, respectively. In terms of ammonia emissions control strategy research, Pd/Rh combination can reduce NH3 formation more effectively than catalyst with a single Pd formula. Precise control of the engine's air-fuel ratio and combination with the optimized matched precious metal ratio TWC can effectively reduce ammonia emissions.

Analysis of the influence of RDE test data processing methods on the emission results of China 6 light duty vehicles

The CO2 moving average window(MAW) method is used to process RDE (real drive emissions) emissions data in China 6 light duty vehicle emissions regulations, while the Euro 6 light duty vehicle emission regulations allow to use both of MAW and power binning(PB) method to deal with RDE emission data. In order to study the difference between the two data processing methods and analyze the differences in the emission results, 10 different types of light duty vehicles are conducted RDE test with PEMS (portable emissions measurement system), and the test data are processed by the two methods separately. The results show that there is a little difference between MAW and PB, while both of them can satisfy the vehicle emission assessment. The PB method calculates the emission factors higher than the MAW method. After removing the cold start and idle condition data, the results of PB is similar to MAW. Besides, reducing the average speed limit of urban working conditions in PB has a greater impact on the urban driving condition emission factor, but less on the whole cycle emission factor.

Analysis on the progress of evaporative emission (type IV) standards for light-duty vehicles in China

This paper briefly introduces the progress of evaporative emission standards for light-duty vehicles in developed countries such as the United States and Europe, and the test procedures specified in the latest evaporative emission standard were concluded. Moreover, the development of evaporative emission standards for light-duty vehicles in China was comparatively analyzed. The evaporative emission test data from 2004 to 2019 was randomly selected for analysis of the trend of evaporative emission performance of vehicles in China with the use of EPR. Affected by the more stringent China 6 Evaporative Emissions standards issued in 2016, the EPR value of the evaporative emission test conducted according to the China 5 had continuously decreased to 41% in 2018. Subsequently, the EPR value increased again to a value of 60% in 2018 and 2019 due to strengthen of the emission limit from 2g to 0.7g and the raise of deterioration factor. Finally, based on the world's latest evaporative emission standards, the development trend of evaporative emission standards for light-duty vehicles in China is forecasted. The application of canister bench aging test, BETP, running loss emission test, and a test cycle with Chinese characteristics may be more conducive to control the light-duty vehicle emissions. Compared with the LEV 3, the evaporative emission limit of 0.7g/test specified in China 6 is still relatively larger. In addition, strengthen the control of durability test and in-use emission performance test would makes the HC emission less during the actual operation of the vehicle.