Real-world NOx emissions from heavy-duty diesel, natural gas, and diesel hybrid electric vehicles of different vocations on California roadways

Research on real-road NH3 emissions of China-6 heavy-duty natural gas and diesel vehicles

In this paper, ammonia (NH3) emissions of China-5 and three-way catalyst (TWC)-fitted China-6 heavy-duty NG vehicles and SCR-fitted China-6 diesel vehicles were characterized during real-road emission tests. The results showed that, due to side reactions within TWCs, real-world NH3 emissions of China-6 NG vehicles were significantly higher than those of their diesel counterparts and those of China-5 NG vehicles without TWCs. For China-6 NG vehicles, NH3 emissions occurred during the light-off of the TWCs and rich combustion. SCR-fitted China-6 heavy-duty diesel vehicles also had NH3 emissions mainly caused by excessive SCR reductant injection and leakage from ammonia slip catalysts (ASC). China-5 heavy-duty NG vehicles emit little NH3 for they utilized lean-combustion without SCR or TWC, where NH3 forms. Although lean combustion NG engines hardly have NH3 emissions, the extremely high engine-out NOx emissions restricted its further application.

Significant NO2 Formation in Truck Exhaust Plumes and Its Association with Ambient O3: Evidence from Extensive Plume-Chasing Measurements

Vehicle nitrogen oxides (NOx) significantly increase nitrogen dioxide (NO2) exposure in traffic-related environments. The NO2/NOx ratios are crucial for accurate NO2 modeling and are closely linked to public health concerns. In 2020, we used a mobile platform to follow test trucks (plume-chasing) that were installed with a portable emission measuring system (PEMS) on two restricted driving tracts. Six hundred eighteen exhaust plumes were collected through the PEMS-chasing measurements from seven trucks. The NOx emission factors (EFs), and the NO2/NOx ratios, were calculated at distinct stages (i.e., tailpipe and on-road). A significant reduction in NOx EFs (>64%) was observed with normal operating after-treatment devices, except for trucks equipped with diesel particulate filter (DPF). Disparities in tailpipe NO2/NOx ratios were also found, attributed to the after-treatment technologies. The NO2/NOx ratios measured from plume-chasing were significantly higher (3-4 times, p < 0.001) than the tailpipe measurements, providing field evidence of substantial NO2 formation in exhaust plumes. We developed a quantitative relationship between NO2/NOx ratios from tailpipe and plume-chasing measurements and demonstrated a robust correlation (R2 > 0.90). Since the NO2 formation in the exhaust plume is not explicitly accounted for in NO2 modeling, the quantitative relationship (O3-NO2/NOx) could improve the estimation of NO2 exposure when local emission inventory (tailpipe emissions) is available.

A comprehensive OBD data analysis framework: Identification and factor analysis of high-emission heavy-duty vehicles

On-Board Diagnostic (OBD) systems enable real-time monitoring of NOx emissions from heavy-duty diesel vehicles (HDDVs). However, few studies have focused on the root cause analysis of these emissions using OBD data. To address this gap, this study proposes an integrated analysis framework for HDDV NOx emissions that combines data processing, high-emission vehicle identification, and emission cause analysis. The framework employs a fuel-based window method to identify high-emission vehicles, while binning and machine learning techniques trace the causes of NOx emissions. A case study is conducted using data from 32 vehicles sourced from Tianjin On-Board Diagnostic Platform. Of these, five vehicles were identified as high emitters. A machine learning model was trained for each vehicle, with a detailed analysis conducted on three of them. The analysis involves a preliminary investigation of vehicle emissions status, followed by bin analysis to initially identify the causes of emissions. Finally, machine learning analysis is conducted, including the generation of individual conditional expectation (ICE) plots and multivariable partial dependence plots (PDPs), serving as a supplement to bin analysis when it cannot effectively pinpoint the causes of high emissions. This approach effectively uncovers the underlying factors within OBD big data. Using the analysis framework, we discover the identified causes of high NOx emissions were uneven heating of the Selective Catalytic Reduction (SCR) system and prolonged idling and high-power operation, catalyst degradation at 200-250 °C, and SCR system failure before 425 °C. The proposed framework offers a clear approach for identifying the causes of NOx emissions, aiding policymakers in implementing effective NOx control strategies for HDDVs.

Real-road NOx and CO2 emissions of city and highway China-6 heavy-duty diesel vehicles

The emission of heavy-duty vehicles has raised great concerns worldwide. The complex working and loading conditions, which may differ a lot from PEMS tests, raised new challenges to the supervision and control of emissions, especially during real-world applications. On-board diagnostics (OBD) technology with data exchange enabled and strengthened the monitoring of emissions from a large number of heavy-duty diesel vehicles. This paper presents an analysis of the OBD data collected from more than 800 city and highway heavy-duty vehicles in China using remote OBD data terminals. Real-world NOx and CO2 emissions of China-6 heavy-duty vehicles have been examined. The results showed that city heavy-duty vehicles had higher NOx emission levels, which was mostly due to longer time of low SCR temperatures below 180°C. The application of novel methods based on 3B-MAW also found that heavy-duty diesel vehicles tended to have high NOx emissions at idle. Also, little difference had been found in work-based CO2 emissions, and this may be due to no major difference were found in occupancies of hot running.

On-road evaluation and regulatory recommendations for NOx and particle number emissions of China VI heavy-duty diesel trucks: A case study in Shenzhen

This research analyzed the real-world NOx and particle number (PN) emissions of 21 China VI heavy-duty diesel trucks (HDDTs). On-road emission conformity was first evaluated with portable emission measurement system (PEMS). Only 76.19 %, 71.43 % and 61.90 % of the vehicles passed the NOx test, PN test and both tests, respectively. The impacts of vehicle features including exhaust gas recirculation (EGR) equipment, mileage and tractive tonnage were then assessed. Results demonstrated that EGR helped reducing NOx emission factors (EFs) while increased PN EFs. Larger mileages and tractive tonnages corresponded to higher NOx and PN EFs, respectively. In-depth analyses regarding the influences of operating conditions on emissions were conducted with both numerical comparisons and statistical tests. Results proved that HDDTs generated higher NOx EFs under low speeds or large vehicle specific powers (VSPs), and higher PN EFs under high speeds or small VSPs in general. In addition, unqualified vehicles generated significantly higher NOx EFs than qualified vehicles on freeways or under speed≥40 km/h, while significant higher PN EFs were generated on suburban roads, freeways or under operating modes with positive VSPs by unqualified vehicles. The reliability and accuracy of on-board diagnostic (OBD) NOx data were finally investigated. Results revealed that 43 % of the test vehicles did not report reliable OBD data. Correlation analyses between OBD NOx and PEMS measurements further demonstrated that the consistency of instantaneous concentrations were generally low. However, sliding window averaged concentrations show better correlations, e.g., the Pearson correlation coefficients on 20s-window averaged concentrations exceeded 0.85 for most vehicles. The research results provide valuable insights into emission regulation, e.g., focusing more on medium- to high-speed operations to identify unqualified vehicles, setting higher standards to improve the quality of OBD data, and adopting window averaged OBD NOx concentrations in evaluating vehicle emission performance.

Regulatory Insights for On-Board Monitoring of Vehicular NOx Emission Compliance

Nitrogen oxide (NOx) emissions from heavy-duty diesel vehicles (HDDVs) have adverse effects on human health and the environment. On-board monitoring (OBM), which can continuously collect vehicle performance and NOx emissions throughout the operation lifespan, is recognized as the core technology for future vehicle in-use compliance, but its large-scale application has not been reported. Here, we utilized OBM data from 22,520 HDDVs in China to evaluate their real-world NOx emissions. Our findings showed that China VI HDDVs had a 73% NOx emission reduction compared with China V vehicles, but a considerable proportion still faced a significant risk of higher NOx emissions than the corresponding limits. The unsatisfactory efficiency of the emission treatment system under disadvantageous driving conditions (e.g., low speed or ambient temperature) resulted in the incompliance of NOx emissions, especially for utility vehicles (sanitation/garbage trucks). Furthermore, the observed intertrip and seasonal variability of NOx emissions demonstrated the need for a long-term continuous monitoring protocol instead of instantaneous evaluation for the OBM. With both functions of emission monitoring and malfunction diagnostics, OBM has the potential to accurately verify the in-use compliance status of large-scale HDDVs and discern the responsibility of high-emitting activities from manufacturers, vehicle operators, and driving conditions.

Characterizing NOx emissions from diesel trucks with tampered aftertreatment systems and its implications for identifying high emitters

Reducing the differences between real-world and certificated NOx emission levels is an important element of in-use emission surveillance programs. Therefore, investigating the characteristics of the vehicles which have much higher NOx emissions (i.e., high-emitters) and determining a reasonable cut-off point to identify high-emitters with a low false detection rate is important. In this study, six diesel trucks were tested under different aftertreatment conditions. The results showed that the discrepancies of fuel-specific NOx emissions between vehicles with functioning and tampered selective catalytic reduction (SCR) systems occur mainly from medium- to high-speed modes. This is because the SCR systems were at low conversion efficiencies when the exhaust temperature was low, including cold-start and urban creep conditions. By using binary classification, we selected fuel-specific NOx cut-off points for high-emitters from China V and China VI diesel trucks. The false detection rate of high-emitters can decrease by 33 % and 95 %, if only NOx emissions from medium- to high-speed modes were used for the chosen cut-off points, respectively. This work highlights the importance of in-use emission compliance programs. It also suggests that high-emitters can be more accurately identified at medium- to high-speed modes if using instantaneous emission data.

Exceedances of Secondary Aerosol Formation from In-Use Natural Gas Heavy-Duty Vehicles Compared to Diesel Heavy-Duty Vehicles

This work, for the first time, assessed the secondary aerosol formation from both in-use diesel and natural gas heavy-duty vehicles of different vocations when they were operated on a chassis dynamometer while the vehicles were exercised on different driving cycles. Testing was performed on natural gas vehicles equipped with three-way catalysts (TWCs) and diesel trucks equipped with diesel oxidation catalysts, diesel particulate filters, and selective catalytic reduction systems. Secondary aerosol was measured after introducing dilute exhaust into a 30 m3 environmental chamber. Particulate matter ranged from 0.18 to 0.53 mg/mile for the diesel vehicles vs 1.4-85 mg/mile for the natural gas vehicles, total particle number ranged from 4.01 × 1012 to 3.61 × 1013 for the diesel vehicles vs 5.68 × 1012-2.75 × 1015 for the natural gas vehicles, and nonmethane organic gas emissions ranged from 0.032 to 0.05 mg/mile for the diesel vehicles vs 0.012-1.35 mg/mile for the natural gas vehicles. Ammonia formation was favored in the TWC and was found in higher concentrations for the natural gas vehicles (ranged from ∼0 to 1.75 g/mile) than diesel vehicles (ranged from ∼0 to 0.4 g/mile), leading to substantial secondary ammonium nitrate formation (ranging from 8.5 to 98.8 mg/mile for the natural gas vehicles). For the diesel vehicles, one had a secondary ammonium nitrate of 18.5 mg/mile, while the other showed essentially no secondary ammonium nitrate formation. The advanced aftertreatment controls in diesel vehicles resulted in almost negligible secondary organic aerosol (SOA) formation (ranging from 0.046 to 2.04 mg/mile), while the natural gas vehicles led to elevated SOA formation that was likely sourced from the engine lubricating oil (ranging from 3.11 to 39.7 mg/mile). For two natural gas vehicles, the contribution of lightly oxidized lubricating oil in the primary organic aerosol was dominant (as shown in the mass spectra analysis), leading to enhanced SOA mass. Heavily oxidized lubricating oil was also observed to contribute to the SOA formation for other natural gas vehicles.

In-use NOx and black carbon emissions from heavy-duty freight diesel vehicles and near-zero emissions natural gas vehicles in California's San Joaquin Air Basin

This study assessed the real-world nitrogen oxide (NOx) and black carbon emissions from six goods movement heavy-duty diesel and compressed natural gas (CNG) vehicles operating in California's San Joaquin Valley and Sacramento regions. The diesel vehicles were all equipped with diesel oxidation catalysts (DOCs) and diesel particulate filters (DPFs), while two diesel vehicles were also equipped with selective catalytic reduction (SCR). All CNG vehicles were equipped with three-way catalysts and fitted with stoichiometric engines meeting the optional ultra-low NOx standard of 0.02 g/bhp-hr. Emissions measurements were conducted with a portable emissions measurement systems (PEMS) during typical goods movement vehicle operation. Black carbon emissions were about 3-7 times higher for the CNG vehicles than those of the DPF-equipped diesel vehicles. NOx emissions for the CNG vehicles were found at or below the optional NOx standard and on average 35 times lower NOx than those of the diesel vehicles. Diesel vehicle NOx hotspots were identified in urban areas and intersections with frequent stop-and-go driving events, whereas the CNG vehicles showed uniform NOx emissions rates along the route. The dispersion modeling results showed elevated NOx and PM emissions exposures to receptors in close proximity to the highway. Our findings suggest that real-time emissions measurements at the tailpipe provide more accurate population exposure assessments near freight corridors compared to utilizing trip-averaged emissions rates values in dispersion models. Under the present test conditions, >70 % of black carbon and NOx were emitted within disadvantaged communities, characterized by low-income minority populations.

How does adoption of electric vehicles reduce carbon emissions? Evidence from China

We investigate the effect of the adoption of electric vehicles (EVs) on CO2 emissions using spatial econometric models and have three findings. First, there are spatial spillover effects of EV adoption on CO2 emissions, implying that the CO2 mitigation of a city depends on local sales of EVs and sales of EVs in neighboring cities. A 1% increase in the sale of EVs in a city can reduce CO2 emissions locally by 0.096% and by 0.087% in a nearby city. Second, EVs indirectly impact CO2 emissions through the substitution effect, energy consumption effect, and technological effect. The overall impact of EV adoption on CO2 emissions is negative. Finally, we demonstrate the moderating effect of urban energy structure on EVs' CO2 emissions mitigation. A 1% increase in the proportion of renewable energy generation increases the decarbonization of EVs by 0.036%. These findings provide policy implications for the coordinated development of EV market and energy system.

A review of regeneration mechanism and methods for reducing soot emissions from diesel particulate filter in diesel engine

With the global emphasis on environmental protection and the proposal of the climate goal of "carbon neutrality," countries around the world are calling for reductions in carbon dioxide, nitrogen oxide, and particulate matter pollution. These pollutants have severe impacts on human lives and should be effectively controlled. Engine exhaust is the most serious pollution source, and diesel engine is an important contributor to particulate matter. Diesel particulate filter (DPF) technology has proven to be an effective technology for soot control at the present and in the future. Firstly, the exacerbating effect of particulate matter on human infectious disease viruses is discussed. Then, the latest developments in the influence of key factors on DPF performance are reviewed at different observation scales (wall, channel, and entire filter). In addition, current soot catalytic oxidant schemes are presented in the review, and the significance of catalyst activity and soot oxidation kinetic models are highlighted. Finally, the areas that need further research are determined, which has important guiding significance for future research. Current catalytic technologies are focused on stable materials with high mobility of oxidizing substances and low cost. The challenge of DPF optimization design is to accurately calculate the balance between soot and ash load, DPF regeneration control strategy, and exhaust heat management strategy.

Inferred vehicular emissions at a near-road site: Impacts of COVID-19 restrictions, traffic patterns, and ambient air temperature

Vehicles are a major source of anthropogenic emissions of carbon monoxide (CO), nitrogen oxides (NO_x), and black carbon (BC). CO and NO_x are known to be harmful to human health and contribute to ozone formation, while BC absorbs solar radiation that contributes to global warming and also has negative impacts on human health and visibility. Travel restrictions implemented during the COVID-19 pandemic provide researchers the opportunity to study the impact of large, on-road traffic reductions on local air quality. Traffic counts collected along Interstate-95, a major eight-lane highway in Maryland (US), reveal a 60% decrease in passenger car totals and an 8.6% (combination-unit) and 21% (single-unit) decrease in truck traffic counts in April 2020 relative to prior Aprils. The decrease in total on-road vehicles led to the near-elimination in stop-and-go traffic and a 14% increase in the mean vehicle speed during April 2020. Ambient near-road (NR) BC, CO, NO_x, and carbon dioxide (CO₂) measurements were used to determine vehicular emission ratios (ΔBC/ΔCO, ΔBC/ΔCO₂, ΔNO_x/ΔCO, ΔNO_x/ΔCO₂, and ΔCO/ΔCO₂), with each ratio defined as the slope value of a linear regression performed on the concentrations of two pollutants within an hour. A decrease of up to a factor of two in ΔBC/ΔCO, ΔBC/ΔCO₂, ΔNO_x/ΔCO₂, and in the fraction of on-road diesel vehicles from weekdays to weekends shows diesel vehicles to be the dominant source of BC and NO_x emissions at this NR site. We estimate up to a 70% reduction in BC emissions in April 2020 compared to earlier years, and attribute much of this to lower diesel BC emissions resulting from improvements in traffic flow and fewer instances of acceleration and braking. Future efforts to reduce vehicular BC emissions should focus on improving traffic flow or turbocharger lag within diesel engines. Inferred BC emissions from the NR site also depend on ambient temperature, with an increase of 54% in ΔBC/ΔCO from -5 to 20 °C during the cold season, similar to previous studies that reported increasing BC emissions with rising temperature. The default setting of MOVES3, the current version of the mobile emission model used by the US EPA, does not adjust hot-running BC emissions for ambient temperature. Future work will focus on improving the accuracy of mobile emissions in air quality modeling by incorporating the effects of temperature and traffic flow in the system used to generate mobile emissions input for commonly used air quality models.

Sustainable selection of waste collection trucks considering feasible future scenarios by applying the stratified best and worst method

Municipal solid waste (MSW) management is vital in achieving sustainable development goals. It is a complex activity embracing collection, transport, recycling, and disposal; and whose management depends on proper strategic decision-making. The use of decision support methods such as multi-criteria decision-making (MCDM) is widespread in MSW management. However, their application mainly focuses on selecting plant locations and the best technologies for waste treatment. Despite the critical role played by transport in promoting sustainability, MCDM has seldom been applied for the selection of sustainable transport alternatives in the field of MSW management. There are a few MCDM studies about choosing waste collection vehicles, but none that include the most recent green vehicles among the options or consider feasible future scenarios. In this article, different engine technologies for collection trucks (diesel, compressed natural gas (CNG), hybrid CNG-electric, electric, and hydrogen) are evaluated under sustainability criteria in a Spanish city by applying the stratified best and worst method (SBWM). This method enables considering the uncertainty associated with future events to establish various feasible scenarios. The results show that the best-valued options are electric and diesel trucks, in that order, followed by CNG and hybrid CNG-electric, and with hydrogen-powered trucks coming last. The SBWM has proven helpful in defining a comprehensive framework for selecting the most suitable engine technology to support long-term MSW collection. Considering sustainability among the criteria and feasible future scenarios in waste management collection decision-making provides more comprehensive and conclusive results that help managers and policymakers make better informed and more reliable decisions.

Effects off hydrogenated vegetable oil (HVO) and HVO/biodiesel blends on the physicochemical and toxicological properties of emissions from an off-road heavy-duty diesel engine

In this study, the regulated emissions, gaseous toxics, and the physical, chemical, and toxicological properties of particulate matter (PM) emissions from a legacy off-road diesel engine operated on hydrogenated vegetable oil (HVO) and HVO blends with biodiesel were investigated. This is one of the very few studies currently available examining the emissions and potential health effects of HVO and its blends with biodiesel from diesel engines. Extended testing was conducted over the nonroad transient cycle (NRTC) and the 5-mode D2 ISO 8718 cycle. Nitrogen oxide (NOx) emissions showed statistically significant reductions for HVO compared to diesel, whereas the biodiesel blends statistically significant increases in NOx emissions. PM and solid particle number reductions with pure HVO and the biodiesel blends were also observed. Low-molecular weight polycyclic aromatic hydrocarbons (PAHs) were the dominant species in the exhaust for all fuels, with pure HVO and the biodiesel blends showing lower concentrations of these pollutants compared to diesel fuel. Our results showed that the oxidative stress and cytotoxicity in PM emissions decreased with the use of biofuels. Notable correlations were observed between PM emissions and oxidative stress and cytotoxicity, especially elemental carbon and particle-phase PAH emissions.

How can new energy vehicles become qualified relays from the perspective of carbon neutralization? Literature review and research prospect based on the CiteSpace knowledge map

Analyzing the feasibility of fuel vehicle transition will be conducive to the realization of the carbon neutralization goal. However, at present, there are few studies specifically aimed at the transition of fuel vehicles. Therefore, this study first analyzed the necessity for the transition of fuel vehicles and then used CiteSpace to analyze 2081 articles in the core Web of Science database in the past decade with "fuel vehicle emission reduction" as the search keyword. After clarifying the research context and development frontier of fuel vehicle emission reduction, we found that most of the literature with the research theme on this topic ends with the research of electric vehicles. Therefore, we took new energy vehicles represented by electric vehicles as the starting point to explore the realization path of carbon neutralization by analyzing the development dilemma and residents' feedback on electric vehicles. Finally, the research review and research prospects were carried out. The study found that although the development of new energy vehicles has made obvious progress at this stage, there are still some problems in comprehensively promoting electric vehicles, such as battery power, charging facilities, and the weak willingness of consumers to accept electric vehicles. Therefore, improving the usage efficiency of new energy vehicles can more effectively force fuel vehicles and new energy vehicles to complete the relay from the perspective of market attraction. This study will provide a more scientific solution and implementation path for the transition of fuel vehicles in various countries.

Emissions from a Modern Euro 6d Diesel Plug-In Hybrid

Plug-in hybrid electric vehicles (PHEVs) are promoted as an alternative to conventional vehicles to meet European decarbonisation and air quality targets. However, several studies have shown that gasoline PHEVs present similar criteria and particulate emissions as their conventional gasoline counterparts. In the present work, we investigate the environmental performance of a modern plug-in hybrid Diesel-fuelled vehicle meeting the Euro 6d standard under a large variety of driving patterns, ambient temperatures, and battery states of charge (SOC). Emissions of regulated pollutants, currently unregulated pollutants, and CO2 were measured in the laboratory and following various on-road routes. The vehicle, whose electric range was 82 km, presented emissions below the Euro 6 regulatory limits in all the different driving cycles performed at 23 °C and all the on-road tests at the different battery SOC. The emissions were lower than the average of the conventional Diesel vehicles tested at JRC in 2020–2021 for all the SOC tested, the exception being solid particle number emissions >23 nm (SPN23) emissions that were comparable at all SOC. Moreover, the emissions obtained with the high voltage battery fully charged during on-road tests were comparable to those obtained with the battery at the minimum SOC for the entire test (ca. 91 km) as well as for the urban section (ca. 36 km). Overall, NOx and SPN23 emissions increased at lower temperatures, showing that at very low temperatures, there is no benefit in terms of particulate emissions from the electric range. Finally, it is shown that the emissions of N2O, the only unregulated pollutant presenting relevant emissions for this vehicle, and which are of catalytic nature, were proportional to the utilisation of the internal combustion engine. The scope of the manuscript is thus to deepen the knowledge on the emission performances of Diesel PHEVs through the systematic testing of a modern representative of this class of vehicles in a wide range of driving and environmental conditions.

Comparison of the emission factors of air pollutants from gasoline, CNG, LPG and diesel fueled vehicles at idle speed

The emission factor (EF) is a parameter used to assess vehicle emissions. Many studies have reported EFs for vehicles in driving conditions. However, the idling emissions should not be neglected in characterizing actual vehicle emissions in congested large cities, where idling is very common on the road. Whereas, EF data for idling vehicles have scarcely been reported in the literature, let alone comparison of different fuels. In this study, the EFs of passenger cars burning four types of fuels - gasoline, compressed natural gas (CNG), diesel, and liquefied petroleum gas (LPG) were measured and compared. The emissions data for CO, CO₂, unburned hydrocarbon (HC), and NO were recorded to calculate fuel-based EFs in units of g pollutants/kg fuel burned. EFs for CO, HC, and NO were compared for the four fuels. Diesel vehicles had the highest EF for CO, with an average value of 35.12 ± 21.37 g/kg fuel, due to low concentration of CO₂ in lean operation compared to CO emission. CNG vehicles had the highest EF for HC, with an average value of 28.15 ± 11.97 g/kg fuel, due to high concentration of unburned methane gas due to slow CNG flame speed whereas diesel vehicles again had the highest EF for NO due to high temperature and pressure and freezing of NO decomposition reaction, with an average value of 12.07 ± 5.37 g/kg fuel. Further comparison was conducted to analyze the effects of two additional variables on EF: engine displacement volume and model/brand year. Only the gasoline-fueled vehicles showed an increase in EFs (for CO, HC and NO) with the vehicle age according to the model year. However, no clear correlation was observed for CNG, LPG, and diesel-fueled vehicles. Finally, the EF results were compared with those reported in the literature, which have been measured in various countries under both idling and non-idling conditions. Because the idling EFs were not substantially smaller than those under moving conditions, and vehicles spend substantial time idling in large cities, idling emissions should not be ignored in the emission inventories for large cities.

Effect of Exhaust Gas Recirculation Combined with Selective Catalytic Reduction on NO x Emission Characteristics and Their Matching Optimization of a Heavy-Duty Diesel Engine

Exhaust gas recirculation (EGR) and selective catalytic reduction (SCR) have become important technologies to reduce the NO _x emission of heavy-duty diesel engines and meet the increasingly stringent emission regulations. This paper studied the effect of EGR combined with SCR on the NO _x emission characteristics of a heavy-duty diesel engine based on the engine bench test. The results showed that the NO reduction rate of EGR-coupled SCR increased with the increase of engine load, and the effect was no longer significant when the NO reduction rate exceeded a certain limit under the same working conditions. EGR combined with SCR has little effect on NO₂ emission reduction, and the increase of engine speed can significantly improve the efficiency of the NO₂ reduction rate at 75 and 100% load. 25% opening of the EGR valve (OEV) and 50% OEV have very similar effects on the NO _x reduction rate when the engine speed is at a low level. Compared with low engine speeds, increased OEV or ammonia NO _x molar ratio (ANR) had a more obvious effect on the NO _x reduction rate at high engine speeds. SCR combined with low valve-opening EGR had a more significant effect on the NO _x reduction rate. The increase of OEV led to the increase of fuel consumption rate, but the effect on the fuel consumption rate decreased gradually with the increase of diesel engine speed. Meanwhile, this study optimized the matching relationship between OEV and ANR based on the data of the genetic algorithm, which provides a theoretical research method and application basis for diesel engine-matching of EGR and SCR.

Analysis of Emissions and Fuel Consumption in Freight Transport

Currently in Europe, road freight transport is characterized by the most dynamic advancement. Year after year, we may observe an increase in the amount of transported goods. The paper presents the emissions of gaseous exhaust components such as CO, THC, and NOx as well as fuel consumption in freight transport. The emission analysis was performed for the entire transport cycle covering the handling of the goods with forklifts and carriage with a heavy-duty truck. The investigations were performed under actual conditions of operation using a Portable Emission Measurement System (PEMS). The fuel mileage was determined using the carbon balance method. The test routes were designed so as to reproduce the transport-logistic system typical of small towns. The setting for the tests was a town located in central Poland near the A2 motorway constituting part of the trans-European logistic network with multiple locations of logistic centers. In order to present the real emissions during handling, two test variants were considered: an outdoor variant (on a nearby lot) and inside a warehouse. The test run of the heavy-duty truck involved transporting 24,000 kg of load on urban and extra-urban (local and intercity) roads. The exhaust emissions and fuel mileage were determined for each of the stages as well as for the entire research cycle.

Catalytic Systems in the Reduction of Nitrogen Oxide Emissions in Diesel-Powered Trucks

In recent years, the number of motor vehicles in circulation has increased in proportion to Brazil’s economic growth, resulting in an increase in emissions of toxic gases from combustion, such as nitrogen oxide, particulate matter, carbon dioxide and volatile organic compounds, among other polluting compounds. This type of pollution has its impacts potentiated in large cities, accumulating due to the configuration of streets and buildings in large urban centers, and can even penetrate indoor environments, having harmful effects on the health of residents. To minimize the emission of these gases, catalytic converters can be used in the vehicle exhausts. Catalytic converters are a promising technology used to reduce exhaust emissions from the engine. In this context, this paper presents an overview of the emission of toxic gases by heavy transport powered by diesel oil and the influence of the use of automotive catalysts in reducing the emission of toxic gases. Additionally, a proposal for monitoring the useful life of automotive catalysts is presented through an electronic sensing system, which makes it possible to determine the catalyst efficiency and the appropriate point for its reactivation or replacement.

Prediction of Emission Reduction Potential from Road Diesel Vehicles in the Beijing–Tianjin–Hebei (BTH) Region, China

China has started to focus on the reduction in pollutants from diesel vehicles with high emission intensities in recent years. Therefore, it is essential and valuable to conduct a deep and detailed exploration of the reduction potential from diesel vehicles and compare the abatement effect from different control measures in upcoming decades. This study attempted to estimate the present emissions of four conventional pollutants from diesel vehicles by applying the Computer Program to Calculate Emissions from Road Transport (COPERT) model, and to predict the future emission trends under different scenarios during 2019–2030, taking the Beijing–Tianjin–Hebei (BTH) region as the case study area. In addition, we analyzed the emission reduction potential of diesel vehicles and compared the reduction effects from different control measures. The results showed that the CO and NOX emissions from diesel vehicles in this region could increase by 104.8% and 83.9%, respectively, given no any additional control measures adopted over the next decade. The largest emission reduction effect could be achieved under the comprehensive scenario, which means that vehicular diesel emissions in 2030 could decrease by 74.8–94.0% compared to values in 2018. The effect of emission reduction under the emission standards’ upgrade scenario could cause a gradual increase and achieve a 19.8–82.6% reduction for the four pollutants in 2030. Furthermore, the new energy vehicle promotion scenario could achieve a considerable reduction effect. It could also offer better emission reduction effects under the highway to railway scenario for Tianjin and Hebei provinces. The old vehicle elimination scenario could have a considerable reduction effect, but only in the short term. Furthermore, emission reductions could be mainly influenced by heavy diesel trucks. These results can provide scientific support to formulate effective reduction measures to diesel vehicles for policy makers.

Super-learner model realizes the transient prediction of CO2 and NOx of diesel trucks: Model development, evaluation and interpretation

The transient simulation of CO₂ and NO_X from motor vehicles has essential applications in evaluating vehicular greenhouse gas emissions and pollutant emissions. However, accurately estimating vehicular transient emissions is challenging due to the heterogeneity between different vehicles and the continuous upgrading of vehicle exhaust purification technology. To accurately characterize the transient emissions of motor vehicles, a Super-learner model is used to build CO₂ and NOx transient emission models. The actual onboard test data of 9 China VI N₂ vehicles were used to train the model, and the test data of another China VI N₂ vehicle were selected for further robustness verification. There were significant differences in the emissions between the vehicles, but the constructed transient model could capture the common law of transient emissions from China VI N₂ vehicles. The R² values of CO₂ and NOx emission in the test data of the validation vehicle were 0.71 and 0.82, respectively. In addition, to further prove the model's robustness, the training data were synchronously modelled based on the Moves-method. The Super-learner model has a smaller RMSE on the validation set than the model based on the Moves-method, indicating that the Super-learner model has more transient simulation advantages. The marginal contributions of the model characteristics to the model results were analysed by SHapley Additive exPlanation (SHAP) value interpretation, and the marginal contributions of different pollutant characteristic parameters varied. Therefore, when establishing transient models of different pollutants, the selection of the model parameters demands considering the generation and purification process of different pollutants. The present work provides novel insights into the parameter selection, construction, and interpretation of the transient vehicle emission model.

Nitric oxide detection using principal component analysis spectral structure matching to the UV derivative spectrum

Ultraviolet (UV) spectroscopy is widely applied in real-time environmental monitoring, especially in diesel vehicle nitrogen monoxide (NO) emissions. However, in field experiments, UV absorption spectrum may exist for different degrees of drifts. Spectral jitters may exist for various reasons such as optical power variation, electrical signal drift, and the refractive index jitters of the optical path for an extended period of time, which causes the detection system to be calibrated. And the pulse xenon lamps as the UV source are characterized by specific emission lines that interfere in spectral analysis directly. For these problems, we proposed the spectral structure matching method based on principal component analysis (PCA), which was compared with the conventional polynomial fitting method to observe feasibility and variability. Further, the UV derivative spectrum was applied to the system appropriately, due to the variation of the absorption peak, and was only related to the target gas by using the above method. We validated our method experimentally by performing the NO UV detection system with the calibration and the comparison test. The results suggested that the calibration relative error was less than 9% and the measurement relative error was less than 6% for this wide range by the proposed processes, which optimized the interference of spectral structures and fluctuation to the system and therefore provided better monitoring. This study may provide an alternative spectral analysis method that is unaffected on the specific emission lines of lamps and is not limited to the spectral region and the target gas.

Features Importance Analysis of Diesel Vehicles' NOx and CO2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model

In order to have an accurate and fast prediction of the artificial intelligence (AI) model, the choice of input features is at least as important as the choice of model. The effect of input features selection on the emission models of light diesel vehicles driven on real roads was investigated in this paper. The gradient boosting regression (GBR) model was used to train and to predict the emissions of nitrogen oxide (NO_x), carbon dioxide (CO₂), and the fuel consumption of real driving diesel vehicles in urban scenarios, the suburbs, and on highways. A portable emissions measurement system (PEMS) system was used to collect data of vehicles as well as environmental conditions. The vehicle was run on two routes. The model was trained with the first route data and was used to predict the emissions of the second route. There were ten features related to the NO_x model and nine features associated with the CO₂ model. The importance of each feature was sorted, and a different number of features were used as input to train the models. The best NO_x model had the coefficient of determination (R²) values of 0.99, 0.99, and 0.99 in each driving pattern (urban, suburbs, and highways). Predictions of the second route had the R² values of 0.88, 0.89, and 0.96 respectively. The best CO₂ model had the R² values of 0.98, 0.99, and 0.99 in each driving pattern, respectively. Predictions of the second route had the R² values are 0.79, 0.82, and 0.83, respectively. The most important features for the NO_x model are mass air flow rate (g/s), exhaust flow rate (m³/min), and CO₂ (ppm), while the important features for the CO₂ model are exhaust flow rate (m³/min) and mass air flow rate (g/s). It is noted that the regression models based on the top three features may give predictions very close to the measured data.

NOx Emission from Diesel Vehicle with SCR System Failure Characterized Using Portable Emissions Measurement Systems

Nitrogen oxides (NOx) emissions from diesel vehicles are major contributors to increasing fine particulate matter and ozone levels in China. The selective catalytic reduction (SCR) system can effectively reduce NOx emissions from diesel vehicles and is widely used in China IV and V heavy-duty diesel vehicles (HDDVs). In this study, two China IV HDDVs, one with SCR system failure and the other with a normal SCR system, were tested by using a portable emissions measurement system (PEMS). Results showed that the NOx emission factors of the test vehicle with SCR system failure were 8.42 g/kW∙h, 6.15 g/kW∙h, and 6.26 g/kW∙h at loads of 0%, 50%, and 75%, respectively, which were 2.14, 2.10, and 2.47 times higher than those of normal SCR vehicles. Emission factors, in terms of g/km and g/kW∙h, from two tested vehicles were higher on urban roads than those on suburban and motorways. The NOx emission factor of the vehicle with failed SCR system did not meet the China IV emission standard. The time-weighted results for normal SCR vehicle over the three road types show that, except for NOx emission factor 12.17% higher than the China IV limit at 0% load, the emission values are 16.21% and 27.54% below the China IV standard limit at 50% load and 75% load, respectively. In general, with higher load, NOx emissions (in terms of g/kW∙h) from the tested vehicle decreased. Furthermore, NO/NOx concentrations of both vehicles with normal and failed SCR systems showed a decreasing trend with the increase in load.