High-emitting Vehicles Research Articles

Energy Policy Scenarios for Carbon Emissions in Road Passenger Transport in Austria up to 2050

The transport sector currently contributes to about 25% of total CO2-emissions in the EU as well as in Austria. The largest share is caused by individual passenger car transport, primarily powered by fossil fuels. It is imperative to replace conventional cars with sustainable alternative technologies and explore alternative modes of transportation, all while reducing the overall vehicle kilometres driven. To meet climate targets, the implementation of effective policies is crucial, and phasing out fossil fuel vehicles is an inevitable step. The core objective of this study is to analyze which policy strategies in road passenger transport in Austria up to 2050 lead to a significant reduction in CO2-emissions. To do so, four scenarios for energy consumption and CO2-emissions, are derived and modelled: a Business-as-Usual Scenario, a Phase-Out-Scenario of fossil fuels, a Tax-Scenario and a Green-Policy-Scenario. Of specific relevance is that our approach is considering flow and embedded CO2-emissions. The major driving factors considered encompass income, vehicle kilometres driven, vehicle power, fuel intensity, and specific CO2-emission factors. The BAU-Scenario continues current policy trends without major changes. The Phase-Out- Scenario looks at gradually stopping the use of fossil fuel vehicles. The Tax-Scenario uses higher financial measures to reduce the use of high-emission vehicles. The Green-Policy-Scenario combines various strategies to encourage public transport and active mobility and improve vehicle efficiency. The major conclusions are: (i) The total vehicle kilometres travelled and overall vehicle stock must be reduced. (ii) Focusing on BEVs is not sufficient; a modal shift towards public transport and active mobility is important. (iii) Public transport and active mobility need to be made more attractive options. These steps will help reduce emissions and create more sustainable urban environments.

Rapid identification method for on-road high-emission vehicle based on deep semi-supervised anomaly detection

This study aims to identify high-emission vehicles in urban traffic management. Existing high-emission vehicle identification models typically overlook the dispersed anomaly data distribution, and machine learning anomaly detection methods face difficulties in learning decision boundaries, leading to reduced detection performance. To address these issues, this study proposes a semi-supervised learning method based on data fusion and deep anomaly detection. This method integrates the chassis and engine dynamometer test data with on-road remote sensing system data to obtain more comprehensive vehicle emission information. Experimental results demonstrate that the proposed method introduces a penalty mechanism for anomaly samples, encouraging the model to increase the dissimilarity in similarity between normal and abnormal data at the latent data distribution level. In vehicle emission datasets from different regions, this method achieves over 95% AUC, demonstrating strong applicability and accuracy.

Emission Control in Expressway Systems: Vehicle Emission Inventory and Policy Scenario Analysis

Expressway systems play a vital role in facilitating intercity travels for both passengers and freights, which are also a significant source of vehicle emissions within the transportation sector. This study investigates vehicle emissions from expressway systems using the COPERT model to develop multi-year emission inventories for different pollutants, covering the past and future trends from 2005 to 2030. Thereinto, an integrated SARIMA-SVR method is designed to portray the temporal variation of vehicle population, and the possible future trends of expressway vehicle emissions are predicted through policy scenario analysis. The Jiang–Zhe–Hu Region of China is taken as the case study to analyze emission control in expressway systems. The results indicate that (1) carbon monoxide (CO) and volatile organic compounds (VOCs) present a general upward trend primarily originating from passenger vehicles, while nitrogen oxides (NOx) and inhalable particles (PM) display a slowing upward trend with fluctuations mainly sourcing from freight vehicles; (2) vehicle population constraint is an effective emission control policy, but upgrading the medium- and long-haul transportation structure is necessary to meet the continuous growth of intercity trips. Expressway vehicle emission reduction effectiveness can be further enhanced by curtailing the update frequency of emission standards, along with the scrapping of high-emission vehicles.

Mobile monitoring reveals the importance of non-vehicular particulate matter sources in London.

This study uses mobile monitoring to gain a better understanding of particulate matter (PM) sources in two areas of Central and Outer London, UK. We find that, unlike emissions of nitrogen oxides (NO + NO2 = NOx), which are elevated in Central London due to the high number of diesel vehicles and congestion, fine particulate matter (PM2.5) emissions are well-controlled. This finding provides evidence for the effectiveness of vehicle particulate filters, supporting the view that their widespread adoption has mitigated PM2.5 emissions, even in the highly dieselized area of Central London. However, mobile monitoring also reveals infrequent elevated PM2.5 concentrations caused by malfunctioning vehicles. These events were confirmed through simultaneous measurements of PM2.5 and sulfur dioxide (SO2), the latter being a strong tracer of engine lubricant combustion. A single event from a gasoline car, representing just 0.15% of the driving distance in Outer London, was responsible for 7.4% of the ΔPM2.5 concentration above background levels, highlighting the ongoing importance of addressing high-emission vehicles. In a novel application of mobile monitoring, we demonstrate the ability to identify and quantify non-vehicular sources of PM. Among the sources unambiguously identified are construction activities, which result in elevated concentrations of coarse particulate matter (PMcoarse = PM10 - PM2.5). The mobile measurements clearly highlight the spatial extent of the influence of such sources, which would otherwise be difficult to determine. Furthermore, these sources are shown to be weather-dependent, with PMcoarse concentrations reduced by 62.1% during wet conditions compared to dry ones.

On-road high-emitting vehicle identification by an automatic hyperparameter optimization model based on a remote sensing system

Optical remote sensing systems (RSSs) are ideal for monitoring and identifying high-emitting vehicles on roads, as they can be installed on any road for non-contact measurements. In general, an on-road vehicle is considered a high-emitting vehicle when its instantaneous emissions exceed the specified cut-points, as monitored by RSSs. However, RSS measurements of vehicle emissions are easily influenced by transient operating conditions of passing vehicles and multiple environmental factors, resulting in variable results for the same vehicle, further interfering with the screening of high-emitting vehicles. In this paper, an automatic hyperparametric optimization model is established in an RSS to identify high-emitting vehicles by fusing multi-feature data on environmental factors and vehicle operating conditions obtained from the RSS with the chassis and engine dynamometer test results provided by vehicle inspection stations (VISs). Qualitative and quantitative experimental results show that our model exhibits better recognition performance for high-emitting vehicles in RSSs of different times and sites, which reflects the good self-adaptability of the model. Moreover, the hyperparameters of the model do not need to be manually adjusted, so the model can be automatically trained to meet the requirements of real-time recognition scenarios for high-emitting vehicles on the road.

Assessment of identification performance for high emission heavy-duty diesel vehicles by means of remote sensing

To improve the accuracy of detecting high NO (nitric oxide) emissions from heavy-duty diesel vehicles (HDDV) by remote sensing (RS), the emissions of one HDDV complied with China V regulation and one HDDV complied with China VI regulation at constant speeds, with and without after-treatment devices, are tested by a portable emission measurement system (PEMS) and RS. The optimized measurement procedures for detecting high NO emissions from China V and China VI HDDVs by RS are summarized. The correlation of RS and PEMS data shows that the ratio of NO to CO2 (carbon dioxide) is a more appropriate RS measurement than NO concentration alone for identifying high emitters, although NO concentrations of 600 ppm and 100 ppm can be used as a basis for distinguishing between China V and China VI HDDVs, respectively. When the NO/CO2 ratio is >200 × 10−4 and 25 × 10−4, identifying China V and China VI HDDV high emitters, respectively, is possible. Additionally considering the vehicle speed can reduce the high emitter identification error rate, and excluding data where vehicle acceleration is less than −0.1 m/s2 can further improve identification accuracy. Four new high-emitter identification methods based on different combinations of measurements are shown to improve identification efficiency with only small increases in identification error. This study provides evidence to support the future development of high-precision RS methodologies for identifying high-emission vehicles.

Cradle-to-grave life cycle assessment of buffalo mozzarella cheese supply chain in central Italy

Public opinion about environmental aspects is generating a widespread concern, which contributes to lead consumers to choose sustainable products. Livestock systems are responsible for the emissions of different pollutants and, as a consequence, this is reflected in the environmental sustainability of animal-based products.This study was aimed at evaluating the environmental impacts of buffalo mozzarella cheese with a cradle-to-grave life cycle assessment approach from the primary production of milk to the end-of-life of buffalo mozzarella cheese. The functional units were 1 kg of buffalo Fat and Protein Corrected Milk (FPCM) evaluated at farm gate and 1 kg of consumed buffalo mozzarella cheese evaluated at consumers’ home.The study enrolled 9 buffalo dairy farms, 2 dairy plants and 4 markets where 243 customers were surveyed. Processes of raw milk production, mozzarella cheese making, transports, retail and home consumption were considered. The accounted environmental impacts were: climate change (CC), terrestrial acidification (TA), freshwater eutrophication (FE), marine eutrophication (ME), ozone depletion (OD), ecotoxicity (EC), human toxicity (HT), land use (LU) and fossil depletion (FD). Buffalo farming was the primary hotspot accounting for more than 50% throughout all impact categories and having a major role in CC, TA, FE, ME, OD and LU. The energy consumption of cheese making had a contribution of 29% in HT, whereas the use of high-emitting vehicles by consumers accounted for 23% and 17% in EC and FD, respectively. To reduce several sources of pollution and mitigate the environmental impacts of buffalo mozzarella cheese, the LCA hotspots identification can lead to the development of specific mitigating strategies to improve sustainability along the supply chain. The development of farm-based models may better assess environmental impacts, the mitigation achieved by the implemented best practices and the “pollution swapping” effect to evaluate the effectiveness of the mitigation strategy.

Taxation of fuel and vehicles when emissions are constrained

A tax on fuel combined with tax exemptions or subsidies for low- and zero emission vehicles is implemented in many countries to fulfill the Paris agreement and to curb mileage-related externalities from road traffic. The present study however shows that a tax on fuel should be combined with tax exemptions for high-emission vehicles to curb mileage-related externalities and to fulfill emission targets within the transport sector. The emission target is fulfilled by adjusting the CO2-tax component on fuel. The road user charge on fuel is designed to curb mileage-related externalities. The heavier tax on low- and zero emission vehicles prevent motorists from avoiding the road user charge on fuel by purchasing low- and zero emission vehicles.

Development of point sampling technology for identifying high-emitting vehicles in narrow and deep street canyons

Side road measurements of fine particle (FP) concentrations were performed in two narrow and deep street canyons in the historic center of Naples using point sampling technology (PS). The FP concentration in the range of 20–103 nm was measured using a condensation particle number counter (CPC) with a 1 Hz frequency. Video recording was used to detect passing vehicles classified into three categories: motorcycles, cars, and light-duty vehicles. The two monitored streets were adjacent, with similar geometrics and orientation but different high- and low-traffic levels. FP concentration time series data were processed to identify concentration peaks generated by the exhausts of passing vehicles. A sensitivity analysis study was carried out to determine how data processing parameters could affect high-emitting vehicle (high emitter) identification. Once the optimum parameters were defined, the contribution of high emitters exhaust emissions to local air quality was assessed. The results showed that PS technology offered viable opportunities for application in narrow street canyons where deploying more common crossroad and top-down monitoring can be troublesome. However, some issues remain to be solved to accurately identify high emitterss in high-traffic cases. PS technology can be useful for developing effective air quality management policies in historical centres.

Estimating individual vehicle emission factors from near-road measurements in India

Over the years, vehicular emissions have been the main contributor to the deterioration of urban air quality. However, quantification of real-world vehicular emissions is quite limited in low- and middle-income countries like India. Developing real-world vehicle emission factors (EFs) using reference-grade instruments requires a significant amount of resources. This study aims to develop the individual and fleet vehicle EFs and the fraction of high-emitting vehicles using high-time resolution, low-cost sensors from near-road measurements – a first-of-its-kind study in India. Traffic and air pollutant measurements were conducted at the kerbside of a street canyon in Mumbai. The individual vehicle fuel-based EFCO and EFNOx were estimated using the plume-identification technique coupled with the information obtained from the vehicle registration number plates. The fleet mean (±SD) EFCO, EFNO, and EFNO2 were 6.30 (±3.16), 1.38 (±1.17), and 0.43 (±0.32) g/kg, respectively, while for EFPM1, EFPM2.5, EFPM2.5-10, and EFPM10 were 0.70 (±0.34), 1.19 (±0.57), 0.90 (±0.65), and 2.09 (±1.05) g/kg, respectively. The developed individual vehicle EFCO and EFNOx were greatly varied within each vehicle type due to differences in emission control technology, engine size, and the prevalence of “super-emitters”. There was no substantial difference in EFCO and EFNOx among the different BS emission standards across almost all vehicle types. The reconstructed fleet EFCO and EFNOx from the developed single-vehicle EFs were 1.4 and 1.9 times higher than the recorded fleet EFs. Approximately 14% of vehicles in the fleet were identified as super-emitters, responsible for 37–54% of total emissions, primarily from private passenger vehicles such as cars and two-wheelers. The EFCO and EFNOx from these high-emitters were 3–30 times greater than the laboratory-reported emissions. Our study suggests that improving emission standards alone is not enough to decrease tailpipe emissions from vehicles. Proper vehicle inspection and maintenance programs are crucial in controlling these emissions.

Time Series Forecasting Fusion Network Model Based on Prophet and營mproved LSTM

Time series forecasting and analysis are widely used in many fields and application scenarios. Time series historical data reflects the change pattern and trend, which can serve the application and decision in each application scenario to a certain extent. In this paper, we select the time series prediction problem in the atmospheric environment scenario to start the application research. In terms of data support, we obtain the data of nearly 3500 vehicles in some cities in China from Runwoda Research Institute, focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou, Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them. This paper proposes a P-gLSTNet model, and uses Autoregressive Integrated Moving Average model (ARIMA), long and short-term memory (LSTM), and Prophet to predict and compare the emissions in the future period. The experiments are validated on four public data sets and one self-collected data set, and the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are selected as the evaluation metrics. The experimental results show that the proposed P-gLSTNet fusion model predicts less error, outperforms the backbone method, and is more suitable for the prediction of time-series data in this scenario.

Vehicle Emission Changes in China under Different Control Measures over Past Two Decades

Vehicle emissions have become a significant source of air pollution in urban cities, especially in China. Mobile sources account for 45% of local fine particle emissions in the Chinese capital Beijing. The Beijing–Tianjin–Hebei (BTH) area, one of China’s most representative urban clusters, is suffering from severe air pollution. With the rapid growth of vehicle ownership in the past two decades, vehicle emissions in China have also undergone great changes under various management measures. The BTH region is also a place where mobile source emission management was carried out earlier. It is of important research value to understand the evolution trend of the vehicle ownerships in the BTH region and the actual effects of various management measures for the control of vehicle emissions. Due to the imperfect evaluation of the current vehicle emission limitation measures from 2000 to 2019, the vehicle emission inventory of the BTH region was established, and the major control measures in the BTH region were evaluated. Results showed that the vehicle ownership has been increasing year by year over the past 20 years, from 2.39 million in 2000 to 25.32 million in 2019, with an average annual growth rate of 13.24%. However, the pollutants discharged by motor vehicles showed a trend of first rising and then falling due to various measures except CO2. The unsynchronized control measures have resulted in huge differences in vehicle growth trends and emissions among Beijing, Tianjin and Hebei. The emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NOX), and particulate matter (PM10) in Beijing showed a trend of increasing first and then decreasing. The changes in these pollutants in Tianjin were similar to those in Beijing, but there was a secondary increase for NOX and PM10 in the later period. The discharge of all pollutants in Hebei Province showed a growing trend except sulfur dioxide (SO2). The major emission source of CO and VOCs in BTH was PCs, and the contribution rate of PCs to VOCs, reached 86.0–89.6% in 2019. Heavy-duty trucks (HDTs) and buses were the main sources of NOX emissions, contributing 78.2–85.4% of NOX in 2019. Eliminating high emission vehicles was the best control measure in the BTH Region, which had a good emission reduction effect on all pollutants. For sustainable development of the BTH region, it is suggested that Beijing, Tianjin and Hebei province implement vehicle control policies simultaneously and establish a joint management mechanism.

Are the Benefits of a High-Emission Vehicle Driving Area Restriction Policy Greater than the Costs?

China implemented a vehicle driving area restriction policy to control air pollution by delimiting a no-driving area for high-emission vehicles (HEVs). Current academic research does not evaluate the benefits and costs of this policy based on vehicle level and lacks evidence at the regional level. Therefore, a cost-benefit analysis method is developed to evaluate the benefits and the costs of this policy, based on 2.128 million HEVs in the Beijing-Tianjin-Hebei (BTH) region from 2008 to 2015. The benefits, the costs, and net benefits of this policy were CNY 98.49, CNY 5.80 and CNY 92.69 billion. The cost-benefit ratios of the BTH region, Beijing, Tianjin and Hebei were 1:16.98, 1:20.88, 1:14.52 and 1:16.55, respectively. Beijing's cost-benefit ratio was the maximum at the city scale. This work can provide scientific support for promoting driving area restriction policies on older gasoline vehicles and older diesel vehicles, the improvement of cost-benefit analysis and efficient decision-making for the Chinese government.

Evaluating traffic emission control policies based on large-scale and real-time data: A case study in central China

The traffic control policies, including “Odd and Even” (OAE) and “One Day Per Week” (ODPW), were adopted in Zhengzhou, China. In this study, we use the bottom-up policy evaluation framework to capture the temporal-spatial characteristics of traffic conditions and vehicle emissions under various traffic restriction scenarios. Moreover, we use the street-scale simulation model to evaluate the effectiveness of improving air quality. Results showed that the improvements in traffic conditions led to the emission decrease by about 28.3 % for carbon monoxide (CO), 16.2 % for nitrogen oxide (NOx), 21.3 % for particulate matter (PM2.5), and 23.2 % for total hydrocarbon (THC) under OAE. During ODPW, total vehicle emissions decreased by 14.1 % for CO, 10.2 % for NOx, 13.7 % for PM2.5, and 12.4 % for THC. However, the spatial analysis indicates traffic restrictions could not significantly reduce those emissions caused by high traffic volume; besides, buses, middle-duty trucks, and heavy-duty trucks have partly offset the reduction benefit from restrictions. The air quality simulation results reveal no significant concentration decrease of CO and nitrogen dioxide (NO2) in most areas. With the update of vehicles, stricter management of high-emission vehicles, and limited coverage for implementation of policies, the traffic control policies were not as effective as before. The limitations of the restriction policies are gradually prominent, and upgrade policies are urgently needed to continuously improve urban air quality in the future.

Application of Remote Sensing Methodology for Vehicle Emission Inspection

Remote sensing detection of vehicle emissions is an effective supplement to the statutory periodic inspection of in-use vehicle emissions and it is a convenient technical method for real-time screening of high-emission vehicles. The principle of remote sensing detection is to inversely calculate the absolute concentrations of gaseous pollutants in vehicle exhaust according to the relative volume concentration ratio of each exhaust component to carbon dioxide (CO2) in the vehicle exhaust plume. Because the combustion mechanisms of gasoline engines and diesel engines are different, different inversion calculation methods of remote sensing data must be applied. The absolute concentrations of gasoline vehicle gaseous emissions measured by remote sensing can be calculated by the inversion calculation method based on the theoretical air–fuel ratio combustion mechanism. However, the absolute concentrations of diesel vehicle nitrogen oxide (NOx) measured by remote sensing must be calculated by the inversion calculation method based on the correction of the excess air coefficient. For the integrated remote sensing test system of gasoline and diesel vehicles, it is necessary to determine the vehicle category according to the vehicle type and license plate and adopt different inversion calculation methods to obtain the correct remote sensing results of vehicle emissions. The big data statistical analysis method for vehicle emission remote sensing results can quickly screen high-emission vehicles and dynamically determine the remote sensing emission screening threshold of high-emission vehicles as the composition of in-use vehicles changes and the overall emission of vehicles declines, so as to achieve dynamic and accurate screening of high-emission vehicles.

On-road vehicular emission characterization from the road-tunnel measurements in India: Morphology, emission factors, and sources

In India, there is very limited data on vehicular emission characterization in real-world driving conditions and the contribution of non-exhaust vehicular emissions to ambient particulate matter (PM) is still unanswered. Furthermore, there are no real-world emission factors (EFs) for the PM constituents. Thus, this study aims to characterize the trace elements and metals, and black carbon (BC) in PM2.5 and PM10 from the light-duty vehicles (LDVs) and mixed vehicular fleet with significant contribution of heavy-duty vehicles (HDVs) through road-tunnel measurements. Real-world EFs were estimated for the measured PM chemical constituents. Further, source apportionment was carried out to find the plausible sources and their contribution to total PM2.5 and PM10 road traffic emissions. Air pollutant and traffic measurements were conducted at two roadway tunnels: Eastern Freeway tunnel (FT; only LDVs) and Kamshet-I tunnel (KT; 80% LDVs & 20% HDVs) in Mumbai, India covering both peak and off-peak traffic hours. Major elements (Al, Ca, Fe, K, Mg, and Na) constitute 90─93% of total measured elemental concentrations in both PM2.5 and PM10 road traffic emissions. Overall, the elemental concentrations were higher for the HDV-dominant fleet than the LDV-fleet for both PM2.5 and PM10. Similarly, BC was higher for the HDV-dominant fleet which is corroborated by the morphological analysis. The measured BC, trace elements and metals EFs in this study were higher than those reported than previous road tunnel studies with similar vehicle composition indicating the presence of high-emitting vehicles in this study. The dominant proportion of PM2.5 road traffic emissions was from the tailpipe (52%) followed by brake wear (30%) and vehicular driven resuspended road dust (18%). Whilst, resuspended road dust (63%) was identified as the major source of PM10 traffic emissions followed by vehicular exhaust (28%) and brake wear (9%). With the potential increase in the share of electric and hybrid vehicles in the vehicular fleet, the relative contribution of non-exhaust emissions to the airborne PM will be more significant. Hence, there is an imminent need to regulate non-exhaust vehicular emissions.

An integrated Principal Component Analysis and Interpretive Structural Modeling approach for electric vehicle adoption decisions in sustainable transportation systems

The rapid growth of the global economy and advanced technology has modernized our life, but it has taken its toll on ecology and environmental sustainability. Over the last few decades, the world has experienced increased emissions of toxic gases, severe global warming, and ambient air pollution. Mass dependency on internal combustion engine (ICE) vehicles is rightfully criticized for increasing air pollution, jeopardizing societal health outcomes, and perpetuating the use of fossil fuels-all of which threaten sustainable development. Amid this situation, to ensure economic and social growth as well as help to achieve sustainable development goals (SDGs) by improving energy security, electric vehicles (EVs) are the green alternatives to conventional high-emission vehicles. To successfully promote EV adoption and diffusion in emerging economies, the key drivers that may foster the adoption process must be identified and evaluated. However, there exists a conspicuous literature gap in this domain. This study presents an intelligent multi-criteria decision-making (MCDM) approach, integrating Principal Component Analysis (PCA), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) to identify, examine, and classify the drivers of successful adoption of EVs in the emerging economies. Based on the literature review and expert feedback, a total of seventeen drivers were identified and later prioritized by using PCA. The top twelve drivers have been further analyzed using ISM-MICMAC to examine their interrelationships. The findings reveal that vehicle performance and reliability, adequate power and charging infrastructure, and government policies are the most influential factors for EV adoption. Finally, this study offers several managerial implications and prospects, which may aid governments and the automotive sectors in taking strategic measures to capture this booming market and pursue more EV customers for a sustainable future.

Data quality evaluation and calibration of on-road remote sensing systems based on exhaust plumes

In recent years, with rapid increases in the number of vehicles in China, the contribution of vehicle exhaust emissions to air pollution has become increasingly prominent. To achieve the precise control of emissions, on-road remote sensing (RS) technology has been developed and applied for law enforcement and supervision. However, data quality is still an existing issue affecting the development and application of RS. In this study, the RS data from a cross-road RS system used at a single site (from 2012 to 2015) were collected, the data screening process was reviewed, the issues with data quality were summarized, a new method of data screening and calibration was proposed, and the effectiveness of the improved data quality control methods was finally evaluated. The results showed that this method reduces the skewness and kurtosis of the data distribution by up to nearly 67%, which restores the actual characteristics of exhaust diffusion and is conducive to the identification of actual clean and high-emission vehicles. The annual variability of emission factors of nitric oxide decreases by 60% – on average – eliminating the annual drift of fleet emissions and improving data reliability.

Using snapshot measurements to identify high-emitting vehicles

Policy makers have long been interested in detecting ‘high-emitters’, a supposedly smallfraction of vehicles that make disproportionally large contributions to total fleet emissions. However, existing identification schemes often exclusively rely on snapshot measurements (i.e. emissions within less than a second), and thus simply identify vehicles with high instantaneous emissions, instead of vehicles with high average emissions over a driving period as regulated by emission standards. We design a comprehensive scheme to address this challenge by combining fleetwide remote sensing measurements with detailed second-by-second emission measurements from individual vehicles. We first determine the trip-average NO x emission rates of individual vehicles in a Euro-5 diesel fleet measured across European locations; this allows, second, to calculate the fraction and emission contributions of high-emitters based on trip-average emission. We demonstrate that the identification of high-emitters is quite uncertain as long as it is based on single snapshots only; but 80% of the high-emitters can be identified with over 75% precision with five or more repeated measurements of the same vehicle. Compared to the conventional detection schemes, our scheme can increase the identified high-emitters and associated emission reductions by over 140%. Our method is validated and shown to be superior to the conventional interpretation of snapshot measurements.

Detecting candidate high NOx emitting light commercial vehicles using vehicle emission remote sensing

Vehicle emission remote sensing devices have been widely used for monitoring and assessing the real-world emission performance of vehicles. They are also well-suited to identify candidate high emitting vehicles as remote sensing surveys measure the on-road, real-driving emissions (RDE) of a high proportion of the operational vehicle fleet passing through a testing site. This study uses the Gumbel distribution to characterize the fuel-specific NOx emission rates (g·kg−1) from diesel vans (formally referred to as light commercial vehicles or LCVs) and screen candidate high emitting vehicles. Van emission trends of four European countries (Belgium, Sweden, Switzerland and the UK) from Euro 3 to Euro 6a/b have been studied, and the impact of road grade on candidate Euro 6a/b high-emitters is also evaluated. The measurements of Euro 6a/b fleets from four countries are pooled together, and a consistent 4% of candidate high-emitters are found in both class II and class III Euro 6a/b vans, accounting for an estimated 24% and 21% total NOx emissions respectively. The pooled four country data is differentiated by vehicle models and manufacture groups. Engine downsizing of Euro 6a/b class II vans is suspected to worsen the emission performance when vehicles are driven under high engine load. The VW Group is found to be the manufacture with cleanest NOx emission performance in the Euro 6a/b fleets. By distinguishing high-emitters from normally behaving vehicles, a more robust description of fleet behaviour can be provided and high-emitting vehicles targeted for further testing by plume chasing or in an inspection garage. If the vehicle is found to have a faulty, deteriorated or tampered emission after-treatment system, the periodic vehicle inspection safety and environmental performance certificate could be revoked.