On-road Emissions Research Articles

Quantifying on-road vehicle emissions during traffic congestion using updated emission factors of light-duty gasoline vehicles and real-world traffic monitoring big data

Light-duty gasoline vehicles (LDGVs) have made up >90 % of vehicle fleets in China since 2019, moreover, with a high annual growth rate (> 10 %) since 2017. Hence, accurate estimates of air pollutant emissions of these fast-changing LDGVs are vital for air quality management, human healthcare, and ecological protection. However, this issue is poorly quantified due to insufficient reserves of timely updated LDGV emission factors, which are dependent on real-world activity levels. Here we constructed a big dataset of explicit emission profiles (e.g., emission factors and accumulated mileages) for 159,051 LDGVs based on an official I/M database by matching real-time traffic dynamics via real-world traffic monitoring (e.g., traffic volumes and speeds). Consequently, we provide robust evidence that the emission factors of these LDGVs follow a clear heavy-tailed distribution. The top 10 % emitters contributed >60 % to the total fleet emissions, while the bottom 50 % contributed <10 %. Such emission factors were effectively reduced by 75.7–86.2 % as official emission standards upgraded gradually (i.e., from China 2 to China 5) within 13 years from 2004 to 2017. Nevertheless, such achievements would be offset once traffic congestion occurred. In the real world, the typical traffic congestions (i.e., vehicle speed <5 km/h) can lead to emissions 5– 9 times higher than those on non-congested roads (i.e., vehicle speed >50 km/h). These empirical analyses enabled us to propose future traffic scenarios that could harmonize emission standards and traffic congestion. Practical approaches on vehicle emission controls under realistic conditions are proposed, which would provide new insights for future urban vehicle emission management.

Exploring the Role of Shared Mobility in Alleviating Private Car Dependence and On-Road Carbon Emissions in the Context of COVID-19

Shared mobility is becoming increasingly popular worldwide, and travelers show more complex choice preferences during the post-pandemic era. This study explored the role of shared mobility in the context of coronavirus disease (COVID-19) by comparing the travel mode choice behavior with and without shared mobility. Considering the shared mobility services of ride-hailing, ride-sharing, car-sharing, and bike-sharing, the stated preference survey was designed, and the mixed logit model with panel data was applied. The results show that if shared mobility is absent, approximately 50% of motorized mobility users and 84.62% of bike-sharing adopters will switch to using private car and public transport, respectively. The perceived pandemic severity positively affects the usage of car-sharing and bike-sharing, while it negatively affects the ride-sharing usage. Under different pandemic severity levels, the average probabilities of private car choice with and without shared mobility are 38.70 and 57.77%, respectively; thus, shared mobility would alleviate the dependence on private car in post-pandemic future. It also helps to decrease the on-road carbon emissions when the pandemic severity is lower than 53. These findings suggest policymakers to maintain the shared mobility ridership and simultaneously contain the pandemic. Additionally, pricing discount and safety enhancement are more effective than reducing detour time to protect ride-sharing against COVID-19.

On-Road and Laboratory Emissions from Three Gasoline Plug-In Hybrid Vehicles-Part 2: Solid Particle Number Emissions

Plug-in hybrid electric vehicles (PHEVs) are a promising technology for reducing the tailpipe emissions of CO2 as well as air pollutants, especially in urban environments. However, several studies raise questions over their after-treatment exhaust efficiency when their internal combustion engine (ICE) ignites. The rationale is the high ICE load during the cold start in combination with the cold conditions of the after-treatment devices. In this study, we measured the solid particle number (SPN) emissions of two Euro 6d and one Euro 6d-TEMP gasoline direct injection (GDI) PHEVs (electric range 52–61 km) all equipped with a gasoline particulate filter, in the laboratory and on-road with different states of charge of the rechargeable electric energy storage system (REESS) and ambient temperatures. All vehicles met the regulation limits but it was observed that, even for fully charged REESS, when the ICE ignited SPN emissions were similar or even higher in some cases compared to the operation of these vehicles solely with their ICE (discharged REESS) and also when compared to conventional GDI vehicles. On-road SPN emission rate spikes during the first 30 s after a cold start were, on average, 2 to 15 times higher with charged compared to discharged REESS due to higher SPN concentrations and exhaust flow rates. For one vehicle in the laboratory under identical driving conditions, the ICE ignition at high load resulted in 10-times-higher SPN emission rate spikes at cold-start compared to hot-start. At −10 °C, for all tested vehicles, the ICE ignited at the beginning of the cycle even when the REESS was fully charged, and SPN emissions increased from 30% to 80% compared to the cycle at 23 °C in which the ICE ignited. The concentration of particles below 23 nm, which is the currently regulated lower particle size, was low (≤18%), showing that particles larger than 23 nm were mainly emitted irrespective of cold or hot engine operation and ambient temperature.

Assessing heavy-duty vehicles (HDVs) on-road NOx emission in China from on-board diagnostics (OBD) remote report data

Mobile source emissions are some of the major causes of air pollution across the world and are associated with numerous adverse health impacts. China has implemented increasingly stringent emission standards over the past few decades, the most recent one being the sixth emission standard (China VI) first rolled out in 2019. The China VI standard places special emphasis on tightening limits for NOx emission and introduces remote monitoring of vehicles' On-board diagnostics (OBD) data to reduce emissions from diesel and gas-powered heavy-duty vehicles (HDV).This paper aims to establish a methodology to calculate HDV NOx emissions on an extended timescale, and under real-world operations. OBD data was collected and examined from 53 China VI diesel HDVs and 14 China V diesel HDVs, which found that the average NOx emission factors are 1.420 g/km and 3.894 g/km for the two samples respectively; this indicates that the implementation of China VI standard helps to significantly reduce NOx emission from HDVs. Combining the emission factors and calculated travel distances collected by OBD and vehicle sales data, the China VI emission standard is estimated to have resulted in 43,969 tons of NOx emission reduction by the end of 2020. With China announcing country-wide enforcement of the new standard in 2021, >1.7 million tons of NOx emission could be avoided by 2023 annually.

A human exposure-based traffic assignment model for minimizing fine particulate matter (PM2.5) intake from on-road vehicle emissions

An exposure-based traffic assignment (TA) model and accompanying analysis framework have been developed to quantify primary and secondary fine particulate matter (PM2.5) exposure due to modeled on-road vehicle flow on a regional network at a high spatial resolution. The Chicago Metropolitan Area transportation network is used to demonstrate the model’s decision-informing power. The study compares the spatially distributed exposure impacts due to traffic emissions of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a novel system optimal with respect to pollutant intake (SOI). The UET and SOI scenarios are developed through the use of (a) the TA model used for obtaining vehicle flow patterns and characteristics including emissions, (b) a source-receptor matrix for PM2.5 developed through a reduced-complexity air quality model to quantify primary and secondary PM2.5 concentrations across the exposure domain, (c) spatial analysis for assessing exposure profiles at the census tract level, and (d) a health impact model to quantify exposure damages. The SOI scenario yields a 9% – 10% total reduction in exposure damages, with the most impacted census tracts benefiting from up to 20% – 30% of reductions, but leads to a 16% increase in travel time costs. Further reduction to PM2.5 exposure by the SOI is hindered by network constraints, where travel demand in populous areas around the network must still be satisfied. The model can be used to systematically quantify the mitigation potential of different transportation exposure reduction strategies, to assess the exposure impacts of newly developed transportation infrastructure, and to address the equity implications of PM2.5 exposure from traffic, all under realistic system behavior and bounded by actual system constraints.

THE IMPACT OF DIESEL BUS EMISSIONS ON AIR POLLUTION IN ULAANBAATAR AND ATTEMPT TO REDUCE IT

The advantages of diesel engines have led it to become the only solution for heavy-duty vehicles (HDV), including city buses. Exhaust gas from diesel engines (EG) is a common environmental pollutant and carcinogenic to human health. The paper presents the results of measuring the emissions of city buses running on high sulfur fuel with and without diesel particulate filter (DPF). The study was conducted in real traffic conditions along the regular route of the city of Ulaanbaatar. The measurements were carried out using the HORIBA PEMS (Portable Emissions Measurement System) and the gravimetric method. The measured data was used to determine the actual emission levels from city buses. The actual particulate matter (PM) emissions from city buses were determined during the warm and cold seasons on a daily basis. It is found that a bus with average daily mileage of 242 km emits average of 166.155 g of PM into the atmosphere per day. This fluctuates depending on the season - 141.3 g in summer and 175.8 g in winter. The actual PM emissions of a city bus is 0.6866 g/km. The NOx concentration in the exhaust gases is 1410.94 ppm on average. As a result of 6 months of measurements, a total of 346.651 kg of soot was collected from 24 buses. Innovation: Actual on-road emissions from Ulaanbaatar buses and a cassette-type DPF system with “active” outside the bus regeneration, that can reduce conventional diesel engine PM emissions by up to 90% regardless of the sulfur content of the fuels.

Developing and analyzing eco-driving strategies for on-road emission reduction in urban transport systems - A VR-enabled digital-twin approach

A vast number of pollutants are generated from on-road commuting vehicles, and there is an increasing need to explore vehicle emission monitoring and mitigation strategies. Traditionally, vehicle emissions can be monitored and measured directly from on-vehicle devices such as a sensor at the tailpipe, or based on expensive data collection tools such as roadside units; while another mainstream research estimates vehicle emissions by relying on the connection to the vehicle motions, which can approximately calculate vehicle emissions under certain traffic conditions. This paper proposes a virtual reality (VR) enabled digital twin platform for on-road emission monitoring, and it develops and evaluates eco-driving strategies within a specific area. The proposed approach, integrating a VR-based digital environment, a micro-simulation model for background traffic, and a Motor Vehicle Emission Simulator for emission estimation, offers an alternative to collect and examine vehicle emissions such as NOx under various traffic conditions. A case study on a central business area in Melbourne is conducted and eco-driving strategies are tested in two scenarios. The first scenario concerns the impact of hybrid electric vehicles and connected autonomous vehicles, which points to the long-term benefit of having controllable and cleaner modes of transportation as a strategy. Results showed that manipulating the penetration rate of emission-friendly engines or motions could reduce vehicle emissions effectively. The second scenario concerns the real-time eco-routing based on emission-optimum, which points to the short-term benefit of emission control strategies. Human-in-the-loop experiments were conducted to test drivers’ responses toward routing options. Results showed that over 90% of participants would follow the eco-routing recommendations completely. The presented study offers an alternative to data-generating, analyzing, and managing approaches for on-road emissions in urban transportation systems.

A European Regulatory Perspective towards a Euro 7 Proposal

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The implementation of emission standards has brought significant reductions in vehicle emissions in the EU, but road transport is still a major source of air pollution. Future emission standards will aim at making road vehicles as clean as possible under a wide range of driving conditions and throughout their complete lifetime. The current paper presents the methodology followed by the Consortium for ultra LOw Vehicle Emissions (CLOVE) to support the preparation of the Euro 7 proposal.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;As a first step, the emission performance of the latest-technology vehicles under various driving conditions was evaluated. Towards this direction, an emissions database was developed, containing data from a wide range of tests, both within and beyond the current RDE boundaries. The results revealed that harsh accelerations, extreme ambient temperatures, very short trips (particularly at urban conditions), DPF regeneration and uphill driving, or combination of those conditions, can result to high emissions. Next, suitable technology packages to address such high emissions were defined and evaluated, using simulation models. On top of this analysis, additional elements were assessed, namely on-board emissions monitoring, additional species to be regulated and instrumentation for future on-road emission testing.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The overall analysis revealed that existing state-of-the-art emission control technologies can achieve very low emission levels, but not under all driving conditions. Thus, additional improvements and potential new technologies are needed to bring ultra-low emissions. These technologies include larger exhaust aftertreatment devices, optimized engine and aftertreatment thermal management (mainly during cold-start) and further penetration of electrification. Particularly the latter is heavily enforced by the CO&lt;sub&gt;2&lt;/sub&gt;-related measures and can strongly support the limitation of pollutant emissions, as well.&lt;/div&gt;&lt;/div&gt;

Impact from the evolution of private vehicle fleet composition on traffic related emissions in the small-medium automotive city

Understanding the emission characteristics in the evolution of private vehicle fleet composition has become a key issue to be addressed to develop appropriate emission mitigation strategies in transportation sector. In this study, the influence of such evolution on on-road emissions was investigated based on a comprehensive dataset encompassing vehicle fleet composition, demographic, economic, and energy features from a representative small-medium automotive city in North America. The decoupling analysis was carried out to assess the dynamic linkage between environmental pressure exerted by the transportation sector and economic growth at both city level and national level in North America. We also developed an approach that supports the long-term traffic-related air pollutant prediction and investigated the potential influence on urban air quality. A sharp upward trajectory was observed in the quantity of SUVs from 2001 to 2018, gradually replacing the dominance of the quantity of four-door cars. There was a significant shift in the GHG emissions emitted from vehicle types used for passenger transport: emissions from SUVs and trucks rose by 374.0% and 69.3%, respectively, whereas emissions from four-door cars, two-door cars, station wagons, and vans all decreased. The changes in vehicle composition, along with the steady trend in GHG emissions from private fleet and decrease in on-road air pollutant concentrations found in Regina, were a response to the establishment of federal fuel economy standards and improved fuel economy. Relative decoupling was observed in aggregate for Regina and Canada in most of the years while both experienced economic downturns and increases in environmental pressure in the form of emissions from 2014 to 2015. The predicted results also demonstrate the high capability of XGboost machine learning algorithm in predicting on-road air pollutant concentrations of CO, PM2.5, and NOX.

On-road CO2 and NOx emissions of a diesel vehicle in urban traffic

Light-duty diesel vehicles are a significant contributor to urban air pollution. This study aimed to investigate the variation in driving style and emissions based on traffic conditions, route features and route familiarity using 30 drivers. Driving styles were assessed using acceleration, relative positive acceleration and velocity × positive acceleration and it was found that approximately 42% of drivers were aggressive on both trips. The difference between the highest and lowest emitting drivers was observed to be a factor of 5.9 for nitrogen oxides (NO x ) and 1.56 for carbon dioxide (CO 2 ). A linear relationship between cumulative NO x emissions and cumulative CO 2 emissions was found at the individual driver level. The study also detected several emission hotspots resulting from certain route features. This study will contribute to: 1) understand how driver behaviour, traffic conditions, route familiarity and route features contribute to emissions; 2) develop predictive emission models; and 3) optimise route characteristics.

Compilation of a city-scale black carbon emission inventory: Challenges in developing countries based on a case study in Brazil

Black carbon (BC) inventories for cities are scarce, especially in developing countries, despite their importance to tackle climate change and local air pollution. Here, we draw on results from a case study in a Brazilian city to discuss the challenges of compiling a BC inventory for different activity sectors. We included traditionally inventoried sectors, such as industries and on-road transportation, other less reported sectors (food establishments and aviation), and open burning of household solid waste (HSW), typically found in developing countries. We present a machine-learning technique (Random Forest) as a novel approach to obtain HSW burning activity using a set of spatial predictors. The BC inventory was based on PM2.5 emissions weighted by the fraction of PM2.5 emitted as BC and developed for the year 2018. We also reported the disaggregated spatial PM2.5 emissions for the same combustion sources, and documented the databases used for activity data and emission factors (EF). The total estimated BC and PM2.5 emissions amounted to 57.88 and 234.75 tons, respectively, with on-road vehicle exhaust emissions and industrial combustion as the main BC sources (63 and 22%, respectively). For PM2.5 emissions, on-road transportation (exhaust and non-exhaust) contributed 48%, followed by industrial combustion (21%) and food establishments (20%). Population density, number of vacant lots, and property tax values were identified as the most important features to predict the HSW fire activity. A comparison with other inventories revealed that the BC emission profile of Londrina is similar to the profile reported for Greater Mexico City, another Latin American city. Thus, the methodology used in this study could be extended to other cities with similar local BC sources. Finally, we highlight that the lack of local activity data, representative EF, and even methodology may undermine the development of reliable BC inventories, and intensive research should be conducted to characterize the emission sources.

Well-to-wheel emissions and abatement strategies for passenger vehicles in two Latin American cities.

More stringent standards for engines and fuels are progressively implemented as alternatives to reduce on-road vehicle emissions. While electric vehicles appear as a perfect alternative since their engines do not emit pollutants, wear and dust resuspension (W&R) and indirect emissions associated with electricity production remain significant sources of pollution. This work compares well-to-wheel emissions (WTW) and abatement strategies for various types of passenger vehicles in Bogotá and Santiago for different pollutants (CO, PM2.5, SO2, and NOx) and greenhouse gases like CO2 equivalent (CO2-Eq). Results show that WTW baseline emissions are more extensive in Bogotá than in Santiago (i.e., 58 and 30% for PM2.5 and CO2-Eq), mainly due to the higher vehicle activity and older state of Bogotá's fleet. We also evaluated extreme scenarios to assess the potential of a given vehicle technology or energy source to reduce emissions. We assessed, in particular, the replacement of all current vehicles by (1) conventional technologies with stricter emission standards and (2) battery electric vehicles powered with different energy resources. Our results indicate that replacing the current fleet with modern combustion technologies has a lower reduction potential than battery electric vehicles, but these reductions largely depend on the energy mix. Substitution by electric vehicles powered with electricity from renewable energies is the most efficient scenario in both cities. Finally, results also stress the importance of the resuspension of deposited road dust and brake and tire wear emissions in both cities as a crucial source of PM2.5, which must be better controlled.

Effect of Tampering on On-Road and Off-Road Diesel Vehicle Emissions

Illegal manipulation (i.e., tampering) of vehicles is a severe problem because vehicle emissions increase orders of magnitude and significantly impact the environment and human health. This study measured the emissions before and after representative approaches of tampering of two Euro 6 Diesel light-duty passenger cars, two Euro VI Diesel heavy-duty trucks, and a Stage IV Diesel non-road mobile machinery (NRMM) agricultural tractor. With tampering of the selective catalytic reduction (SCR) for NOx, the NOx emissions increased by more than one order of magnitude exceeding 1000 mg/km (or mg/kWh) for all vehicles, reaching older Euro or even pre-Euro levels. The tampering of the NOx sensor resulted in relatively low NOx increases, but significant ammonia (NH3) slip. The particle number emissions increased three to four orders of magnitude, reaching 6–10 × 1012 #/km for the passenger car (one order of magnitude higher than the current regulation limit). The tampered passenger car’s NOx and particle number emissions were one order of magnitude higher even compared to the emissions during a regeneration event. This study confirmed that (i) tampering with the help of an expert technician is still possible, even for vehicles complying with the current Euro standards, although this is not allowed by the regulation; (ii) tampering results in extreme increases in emissions.

Analysis of activity duration-related charging behavioral responses of electric vehicle travelers to charging services

Electric vehicles (EVs) have drawn much attention in recent years due to the advantage of zero on-road carbon emission. The government subsidies and rapid development of electricity charging technologies promote the roll-out of EVs. The boost of EVs brings challenges to the deployment of EV charging services. Given that travelers may need to charge their EVs while conducting out-of-home activities, this study analyzes the activity duration-related charging behavioral responses of private EV travelers to charging services in an equilibrium model. The charging services concern the operating policies related to the spatial allocation of charging opportunities and charging pricing. Considering the trade-offs between travel and activity duration-related charging choices, a boundedly rational dynamic user equilibrium (BR-DUE) model is proposed to evaluate the two operating policies. The case study of the Eindhoven network demonstrates that the operating policies, if appropriately set up, can improve the accessibility to charging services with improved charging service rate and decreased queuing time.

Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China

On-road vehicle emissions play a crucial role in affecting air quality and human exposure, particularly in megacities. In the absence of comprehensive traffic monitoring networks with the general lack of intelligent transportation systems (ITSs) and big-data-driven, high-performance-computing (HPC) platforms, it remains challenging to constrain on-road vehicle emissions and capture their hotspots. Here, we established an intelligent modelling and visualization system driven by ITS traffic data for real-world, on-road vehicle emissions. Based on the HPC platform (named “City Brain”) and an agile Web Geographic Information System (WebGISs), this system can map real-time (hourly), hyperfine (10~1000 m) vehicle emissions (e.g., PM2.5, NOx, CO, and HC) and associated traffic states (e.g., vehicle-specific categories and traffic fluxes) over the Xiaoshan District in Hangzhou. Our results show sharp variations in on-road vehicle emissions on small scales, which even fluctuated up to 31.2 times within adjacent road links. Frequent and widespread emission hotspots were also exposed. Over custom spatiotemporal scopes, we virtually investigated and visualized the impacts of traffic control policies on the traffic states and on-road vehicle emissions. Such results have important implications for how traffic control policies should be optimized. Integrating this system with chemical transport models and air quality measurements would bridge the technical gap between air pollutant emissions, concentrations, and human exposure.

Real time roadway pollution in Indian cities: A comparative assessment with modelled emission

Roadway transport sector is one of the major contributors of atmospheric pollution. The study aims to understand the real time roadway pollution in megacities and medium sized Indian cities; therefore, the present study estimated on-road emission factors (EFs) and compared them with two other regulatory models for Delhi and Guwahati. Both the cities are different in traffic flow management strategies, traffic composition and vehicular density per 1000 population. Emission rate was found higher in Guwahati (17% to 38%) due to frequent stops-and-go patterns of driving profile and poor traffic flow management per vehicle as compared to Delhi, but overall roadway pollution load is higher in Delhi due to higher vehicular density. The real-time on-road EFs were compared with modelled EFs, estimated with two regulatory models International Vehicle Emission (IVE) model and COPERT-IV. The modelled EFs magnitude is lower than the real-time EFs. Pollutants HC, CO and CO2 were about 17% to 30% and 33% to 55% fewer from IVE and COPERT-IV respectively whereas NOx emissions are over predicted by both the models (COPERT-IV: -54% to -95% and IVE: -26% to -35%). Results demonstrate that efficient traffic flow management plays important role in reducing roadway pollution, which is major share of environmental pollution, but uncontrolled vehicular growth quickly offsets the benefit of emission control policies. Therefore, there is need for establishing equilibrium between emission control and vehicle growth rate in control policies for optimum benefits from policies for each city.

Effect of sampling duration on the estimate of pollutant concentration behind a heavy-duty vehicle: A large-eddy simulation

Plume chasing is cost-effective, measuring individual, on-road vehicular emissions. Whereas, wake-flow-generated turbulence results in intermittent, rapid pollutant dilution and substantial fluctuating concentrations right behind the vehicle being chased. The sampling duration is therefore one of the important factors for acquiring representative (average) concentrations, which, however, has been seldom addressed. This paper, which is based on the detailed spatio-temporal dispersion data after a heavy-duty truck calculated by large-eddy simulation (LES), examines how sampling duration affects the uncertainty of the measured concentrations in plume chasing. The tailpipe dispersion is largely driven by the jet-like flows through the vehicle underbody with approximate Gaussian concentration distribution for x ≤ 0.6h, where x is the distance after the vehicle and h the characteristic vehicle size. Thereafter for x ≥ 0.6h, the major recirculation plays an important role in near-wake pollutant transport whose concentrations are highly fluctuating and positively shewed. Plume chasing for a longer sampling duration is more favourable but is logistically impractical in busy traffic. Sampling duration, also known as averaging time in the statistical analysis, thus has a crucial role in sampling accuracy. With a longer sampling (averaging) duration, the sample mean concentration converges to the population mean, improving the sample reliability. However, this effect is less pronounced in long sampling duration. The sampling accuracy is also influenced by the locations of sampling points. For the region x > 0.6h, the sampling accuracy is degraded to a large extent. As a result, acceptable sample mean is hardly achievable. Finally, frequency analysis unveils the mechanism leading to the variance in concentration measurements which is attributed to sampling duration. Those data with frequency higher than the sampling frequency are filtered out by moving average in the statistical analyses.

Effects of Payload on Non-exhaust PM Emissions from Hybrid Electric Vehicle during Braking Sequences

Abstract Vehicles equipped with internal combustion engines are known as one of the most important Particulate Matters (PM) emissions sources. Many countries are aware of this issue and interested in employing more electric vehicles to reduce this emission. However, various past research claims that electric vehicles also emitted PM as conventional vehicles due to their non-exhaust emissions such as brake wear, tyre wear, road surface wear, and road dust resuspension. In addition, substantial evidence showed that there was indeed a positive correlation between the weight of vehicles and amounts of non-exhaust PM emissions. This study aims to measure on-road non-exhaust PM emissions from a hybrid electric vehicle during braking sequences. An onboard PM measuring device is attached to the side of the tested hybrid electric vehicle. PM measurements are monitored during the braking sequence in the electrified vehicle mode. Studies of increasing payloads that might affect the tendency of non-exhaust PM emissions are observed. The PM emission pattern during the braking sequence is captured by the current PM measuring setup as seen in the literature. The braking pattern (hard vs. soft brake) shows distinct amounts in PM emissions by a factor of two. Based on experimental data, it is found out that the additional payload of approximately 70 kg increases the amount of non-exhaust PM emissions by almost 20%.

Gasoline and diesel passenger car emissions deterioration using on-road emission measurements and measured mileage

Modern gasoline and diesel vehicles are equipped with highly effective emission control systems that result in low emissions of pollutants such as nitrogen oxides (NO x ) when new. However, with increasing age or mileage, the emissions performance of vehicles can deteriorate over time, leading to increased emissions. In this work we use comprehensive vehicle emission remote sensing measurements collected over a wide range of conditions, together with individual vehicle measured mileage to quantify vehicle emissions deterioration. A quantile regression modelling approach is used to provide a more complete understanding of the distribution of deterioration effects that is not captured by considering mean changes over time. The approach accounts for factors such as driving conditions and ambient temperature, as well as determining whether deterioration affects whole populations of vehicles or a smaller subset of them. Accounting for these factors, we find that for most pollutants the rate of deterioration of emissions from pre-Euro 5 gasoline passenger cars is highly skewed. Between 5% and 10% of pre-Euro 5 gasoline passenger cars have emissions similar to a Euro 5 diesel car, suggesting that policies should be developed to accelerate their removal from the fleet. Furthermore, we find evidence that there are differences between vehicle manufacturers in the way emissions of NO x deteriorate. • 197,000 vehicle emission remote sensing measurements coupled with individual measured mileage data from vehicle inspections. • Quantile regression framework used to examine distribution of deterioration effects of passenger car NO x and PM emissions. • Rate of deterioration for gasoline NO x emission controlled by small proportion of vehicles. • Large fleet coverage of remote sensing is exploited to examine manufacturer-specific NO x emission deterioration.

Exhaust Emissions from Plug-in and HEV Vehicles in Type-Approval Tests and Real Driving Cycles

The amount of hybrid vehicles and their contribution have increased in the global market. They are a promising aspect for a decrease in emissions. Different tests are used to determine the factors of such emissions. The goal of the present study was to compare the emissions of two hybrid vehicles of the same manufacturer: the plug-in version and the HEV version (gasoline + electric engine). These vehicles were chosen because they comprise the largest market share of hybrid cars in Poland. The exhaust emission tests were conducted in the WLTC tests on a chassis dynamometer and under real traffic conditions. Simultaneous testing on a dyno and under real driving is the most adequate test to assess the environmental aspects of vehicles—especially hybrids. The combustion engines of the tested vehicles were supplied with gasoline containing 5% biocomponents. The emissions, including CO2, CO, NOx, THC and PNs, were measured in accordance with the European Union procedure. According to the latter, the resistance to motion of the chassis dyno was adjusted to the road load, allowing the hybrid vehicles to move in electric mode and allowing the dynamometer to operate in energy recovery mode. The obtained emissions of CO2, CO, NOx and THC in the case of the plug-in hybrid vehicle were lower by 3%, 2%, 25%, and 13%, respectively, compared to the case of HEV. Fuel consumption in the case of the plug-in hybrid vehicle was lower by 3%, and PN was lower by 10% compared to the case of HEV (WLTC). In real driving conditions, the differences were more pronounced in favour of the plug-in vehicle: CO2 emissions in the RDE test were 30% lower, NOx emissions were 50% lower, and PN was 10% lower. An increase in emissions was only observed for CO2 emissions—the plug-in vehicle’s on-road emissions were 6% higher compared to the HEV. The obtained emissions for FC and PN varied with actual velocity values due to competitive driving between a combustion engine and an electric motor, as well as existing acceleration and deceleration events during the test and other factors.