Vehicle Exhaust Emissions Research Articles

Characteristics of particulate-bound n-alkanes indicating sources of PM2.5 in Beijing, China

Abstract. The characteristics of n-alkanes and the contributions of various sources of fine particulate matter (PM2.5) in the atmosphere in Beijing were investigated. PM2.5 samples were collected at Minzu University of China between November 2020 and October 2021, and n-alkanes in the samples were analyzed by gas chromatography mass spectrometry. A positive matrix factorization analysis model and source indices (the main carbon peaks, carbon preference indices, and plant wax contribution ratios) were used to identify the sources of n-alkanes, to determine the contributions of different sources, and to explain the differences. The n-alkane concentrations were 4.51–153 ng m−3 (mean 32.7 ng m−3), and the particulate-bound n-alkane and PM2.5 concentrations varied in parallel. There were marked seasonal and diurnal differences in the n-alkane concentrations (p<0.01). The n-alkane concentrations in the different seasons decreased in the order of winter > spring > summer > fall. The mean concentration of each homolog was higher at night than in the day in all seasons. Particulate-bound n-alkanes were supplied by common anthropogenic and biogenic sources, and fossil fuel combustion was the dominant contributor. The positive matrix factorization model results indicated five sources of n-alkanes in PM2.5, which were coal combustion, diesel vehicle emissions, gasoline vehicle emissions, terrestrial plant release, and mixed sources. Vehicle emissions were the main sources of n-alkanes, contributing 57.6 %. The sources of PM2.5 can be indicated by n-alkanes (i.e., using n-alkanes as organic tracers). Vehicle exhausts strongly affect PM2.5 pollution. Controlling vehicle exhaust emissions is key to controlling n-alkanes and PM2.5 pollution in Beijing.

Emission ratio determination from road vehicles using a range of remote emission sensing techniques

The development of remote emission sensing techniques such as plume chasing and point sampling has progressed significantly and is providing new insight into vehicle emissions behaviour. However, the analysis of remote emission sensing data can be highly challenging and there is currently no standardised method available. In this study we present a single data processing approach to quantify vehicle exhaust emissions measured using a range of remote emission sensing techniques. The method uses rolling regression calculated over short time intervals to derive the characteristics of diluting plumes. We apply the method to high time-resolution plume chasing and point sampling data to quantify gaseous exhaust emission ratios from individual vehicles. Data from a series of vehicle emission characterisation experiments conducted under controlled conditions is used to demonstrate the potential of this approach. First, the method is validated through comparison with on-board emission measurements. Second, the ability of this approach to detect changes in NOx / CO2 ratios associated with aftertreatment system tampering and different engine operating conditions is shown. Third, the flexibility of the approach is demonstrated by varying the pollutants used as regression variables and quantifying the NO2 / NOx ratios for different vehicle types. A higher proportion of total NOx is emitted as NO2 when the selective catalytic reduction system of the measured heavy duty truck is tampered. In addition, the applicability of this approach to urban environments is illustrated using mobile measurements conducted in Milan, Italy in 2021. Emissions from local combustion sources are distinguished from a complex urban background and the spatiotemporal variability in emissions is shown. The mean NOx / CO2 ratio of 1.61 ppb/ppm is considered representative of the local vehicle fleet. It is envisaged that this approach can be used to quantify emissions from a range of mobile and stationary fuel combustion sources, including non-road vehicles, ships, trains, boilers and incinerators.

Distribution characteristics and optical properties of carbonaceous aerosol: brown carbon and black carbon in Nanchang, inland China

PM2.5 samples were collected from October 31, 2019, to November 10, 2019, in Nanchang for 10 consecutive days. The mass concentrations of PM2.5 and carbon components were measured, and the distribution characteristics of carbonaceous aerosols were discussed. The optical properties of carbonaceous aerosols were quantified by the thermal-optical method and solvent extraction method, respectively. The results showed that the average concentrations of organic carbon (OC) and elemental carbon (EC) in aerosols in Nanchang were 11.79 ± 3.86 and 3.51 ± 1.59 μg m−3, respectively. The average OC/EC was 3.62, and OC2, OC3, OC4 and EC1 accounted for relatively large proportions (19.55, 24.96, 22.37 and 17.38%). Carbonaceous aerosols in Nanchang were greatly affected by coal combustion, vehicle exhaust emissions and secondary organic carbon (SOC) formation. For particulate brown carbon (BrC), the average light absorption coefficient (babs) and mass absorption efficiency (MAE) at 405 nm were 16.87 Mm−1 and 1.49 m2g−1, respectively. The average Absorption Ångström exponent (AAE) was 8.58. For water-soluble organic carbon (WSOC) and methanol-soluble organic carbon (MSOC), the average babs,365,WSOC and babs,365,MSOC were 14.79 and 22.51 Mm−1, respectively. The mean values of AAE300-600,WSOC and AAE300-600,MSOC were 4.17 and 4.84, respectively. The mean MAE365,WSOC and MAE365,MSOC were 1.83 and 2.18 m2 g−1, respectively. The mean value of MAEBC at 405 nm was 10.28 m2 g−1. The optical properties of particulate BrC and dissolved BrC were similar at 405 nm. The light absorption of BrC cannot be ignored, although BC was the main light absorption component.

Operational and Environmental Assessment of Weaving Section for Urban Roads: Case Study, Aljouf Region, KSA

The urban weaving sections are more complex due to the various disturbing elements within the weaving section, such as lane-change situations. These turbulences reduced road capacity and increased vehicle exhaust emissions. The Highway Capacity Manual (HCM 2010) has a methodology for the analysis of weaving sections for the free highways; the methodology for the analysis of urban roads is not investigated in the HCM. Therefore, the main objective of this research is to present a systematic analysis of the factors that could potentially affect the capacity and exhaust emissions of urban weaving sections. These factors include the main road traffic volume, the weaving section length (WL), the volume ratio (VR), and the percentage of heavy vehicles (HV%). Two weaving sections were selected in the Aljouf Region, KSA; the Sakakah–Dumat Al-Jandal road. The collected data were used in the development of microsimulation using VISSIM models. The results indicated that increasing the volume ratio and percentage of heavy vehicles caused a decrease in capacity and an increase in exhaust emissions. It was discovered that the increase of weaving length resulted in increasing the capacity. However, increasing the weaving length was not beneficial for reducing exhaust emissions. Finally, regression models were developed for capacity estimation and emissions prediction of urban weaving sections based on weaving length and volume ratio, resulting in relatively high R2 values.

Research on the Intelligent Energy Governance of Parallel Hybrid Vehicle Based on Deep Learning

To realize the intelligent energy governance of hybrid vehicles, a Deep-Q-Network energy controller based on the construction of parallel hybrid vehicle model is proposed, which aiming at energy loss problem of parallel hybrid vehicles and combining deep learning with reinforcement learning, and it is simulated through the ADVISOR software platform and compared with the traditional fuzzy logic strategy. The experimental results indicate that the DQN-based control strategy proposed in this paper reduces both the energy consumption and exhaust emissions of parallel hybrid vehicles. Compared with the traditional fuzzy control strategy, fuel consumption is reduced by 0.43L while the fuel economy increases by 10.9%. and exhaust gas such as CO44, CO, NOxx the emission were reduced by 28.9%, 0.2%, and 7.4%, respectively. It shows the feasibility and effectiveness of the proposed methods.

An improved RRT-Connect path planning algorithm of robotic arm for automatic sampling of exhaust emission detection in Industry 4.0

In the time of industrial 4.0 era represented by intelligent manufacturing, as the most widely used equipment in the industrial robots filed, robotic arms can replace people for a large number of exhaustive, repetitive and harmful work. In the process of vehicle exhaust emission detection, manual exhaust gas sampling is required, which is inefficient and easily harmful to the human body. Using a robotic arm in the detection scene can improve the detection efficiency and prevent the exhaust gas from affecting the human body. In this paper, an improved RRT-Connect path planning algorithm is proposed to realize a reliable path design required for automatic sampling control of exhaust emission detection. For the problems of blind expansion, low efficiency in the RRT and its improved algorithm, an improved RRT-Connect algorithm through setting the adaptive step strategy and using the fixed sampling function to construct four random trees from the starting point, the end point and the fixed point for search is proposed in this paper, which can solve the problem of slow expansion and speed up convergence. Experimental results show that compared with the unimproved RRT-Connect algorithm, the improved algorithm decreases the number of iterations by 18.11% and reduces the number of path nodes by 23.02%, which meets the path planning requirements of automatic sampling control of the robotic arm.

Analisis Hubungan Kinerja Ruas Jalan terhadap Emisi Gas Buang Karbon Monoksida (CO) pada Jalan Nasional Lingkar Timur I Kota Jambi

Jambi City moves quickly in all fields including one of its transportation activities. The negative impact of transportation activities is the exhaust emissions emitted by vehicle smoke which are toxic to the surrounding air. Air pollution that has an impact on transportation activities reaches more than 50%. The East Ring Road Section I of Jambi City is one of the national roads, where this road has a relatively high traffic volume, and when viewed visually this road is one of the roads that contribute to the large level of vehicle exhaust emissions. One of the things that trigger vehicles to become more congested due to the presence of several activity centers. The purpose of this study is to determine the relationship between carbon monoxide (CO) exhaust emissions and traffic volume and degree of saturation. The method used in this study is a quantitative descriptive method. The data collection techniques used are surveys and observations. From the results of the study, it was obtained that the road capacity was 2914 smp/hour with the traffic flow on the road section exceeding the road capacity and the average DS value of 0.96 exceeding the standard based on the 1997 MKJI of 0.75. This shows that the congested road capacity cannot accommodate the traffic flow that occurs and the flow of vehicles moves with many obstacles. Based on the analysis with the correlation method and regression method between the degree of saturation and carbon monoxide gas, it was found that the degree of saturation and carbon monoxide gas has a strong relationship with the positive correlation result, meaning that if the degree of saturation increases, there will also be an increase in carbon monoxide gas.

Study on Vehicle Fuel Consumption and Exhaust Emissions Based on a New Viscous Macroscopic Traffic Flow Model

Based on a conserved higher-order traffic flow model (CHO model), we propose a new viscous macroscopic traffic flow model taking into account the diffusion effect in traffic. The model can reasonably smooth the shock wave so that the acceleration is maintained in a reasonable range. To balance the computational efficiency and accuracy, this viscous macroscopic traffic flow model is integrated with the microscopic vehicle fuel consumption and emission models to estimate vehicle fuel consumption and exhaust emissions. The local discontinuous Galerkin (LDG) method is used to solve the viscous model, and the simulation results are inputted into the microscopic models to calculate vehicle fuel consumption and exhaust emissions. Numerical results illustrate that the proposed viscous model is reasonable and that the designed scheme is feasible and effective. Moreover, we provide concrete suggestions for controlling vehicle fuel consumption and exhaust emissions.

Mechanically robust conjugated microporous polymer membranes prepared using polyvinylpyrrolidone (PVP) electrospun nanofibers as a template for efficient PM capture

Air pollution has become a challenging environmental problem worldwide due to rapid industrial development and excessive emissions of vehicle exhaust. Herein, we report a preparation of conjugated microporous polymer membranes (CMPM) with a hierarchical porous structure by electrospun polyvinylpyrrolidone (PVP) nanofibers as a template for effective removal of PM from airborne and vehicle exhaust. CMP membranes have hierarchical holes, where the macropores are from electrospun nanofiber membranes and the mesopores are from polymer synthesis. Taking advantage of its inherent physicochemical and thermal stability and hierarchical hole characteristics, the CMPM-based filter can work continuously for up to 36 h and still maintains a high removal efficiency (>99.56%), and also has a high filtration efficiency in the treatment of vehicle exhausts, with 95.18% for PM0.3, 98% for PM0.5 and >99% for PM2.5-10.0. The superior mechanical properties of CMPM allow the filter to be cleaned and reused. After three cycles, the filtration effectiveness of CMPM is still 94.83% for respirable particulate matter. Under high humidity (RH ≥ 95%) conditions, the CMPM-based filter showed higher than 95.37% filtration of PM0.3-10, and the oil adsorption rate could be maintained at 284% at high speed, proving the great potential of CMPM to clean air in complex situations.

Law Enforcement Against Environmental Pollution by Vehicle Exhaust Emissions

Enforcement of environmental laws relating to emissions from motor vehicle exhaust It is clear from the laws and regulations that the Ministry of Transportation is authorized to act as the implementing agency for periodic motor vehicle inspections and emission testing. The use of motorized vehicles contributes to air pollution which hinders the management and protection of the environment. There are many types of environmental pollution, but in particular air pollution has far-reaching detrimental effects and has a significant impact on environmental quality. By using normative legal research methods and literature studies, namely research that looks at document studies, there are several regulatory matters, including Article 206 of Government Regulation Number 22 of 2021 concerning the Implementation of Environmental Protection and Presidential Regulation (Perpres) No. 55 of 2019 concerning the Acceleration of the Battery Electric Vehicle Program for Road Transportation. One of the causes of environmental pollution, especially in the air, is the use of vehicles that use oil (fuel). Electric vehicles are currently the solution to developing environmentally friendly technologies. The government's efforts are carried out by completing emission tests and developing electric vehicles that affect both the environment and vehicle health.Keywords: Law Enforcement, Environment, Emissions, Electric Vehicles

Performance Test of 1500 cc Fuel Motorcycle Using Pertalite – Bioethanol Mixture Fuel on Exhaust Gas

The increasing world oil prices followed by the increasing demand for fossil fuels as well as the issue of the energy crisis and global warming which demands a better level of environmental quality have encouraged the government to take good policies in overcoming them. Currently the world has been thinking about alternative energy that can be used for diesel or otto motors. One of the alternative fuels developed is alcohol fuel. Alcohol is a type of hydrocarbon in which one of the hydrogen atoms is replaced by the hydroxyl radical OH. Many agricultural products in Indo-nesia have the potential to be developed as bioethanol. In this study, the effect of adding bioethanol on pertalite fuel will be studied. This is because pertalite is a fuel that is in accordance with the demands of automotive needs and developments. This study aims to determine the comparison of power, torque, and air-fuel ratio as well as to determine the comparison of the content of exhaust gas compounds tested on the Toyota Vios Limo 1500CC car using pertalite (E0) fuel, 10% pertalite-bioethanol mixture (E10). and 15% (E15), 20% (E20). The test results obtained that the calorific value of Pertalite fuel is 49411.98 kJ/kg, 10% gasohol has a value of 46764.82 kJ/kg, 15% gasohol has a value of 44262.82 kJ/kg, and then 20% gasohol has a val-ue of 45735.42 kJ/kg. The test results obtained an average torque and power per-formance of 10% gasohol fuel of 109.09 Nm and 71.73 HP. The highest air-fuel ratio in fuel but the table shows the highest air-fuel ratio in 15% gasohol fuel is 15.46 and then the lowest air-fuel ratio in 10% gasohol fuel is 11.58. Exhaust emis-sion levels for each compound tested for each fuel are still below the standard vehicle exhaust emission thresholds set by Toyota and the environmental agency.

Characterization of Particle Number Setups for Measuring Brake Particle Emissions and Comparison with Exhaust Setups

The stringency of vehicle exhaust emissions regulations resulted in a significant decrease in exhaust particulate matter (PM) emissions over the years. Non-exhaust particles (i.e., from brakes and tyres) account for almost half or more of road transport-induced ambient PM. Even with the internal combustion engine ban in 2035, electrified vehicles will still emit PM from brake and tyre wear. Consequently, non-exhaust PM emissions cannot decrease significantly without any regulatory measures. Because independent research carried out under different methods is not readily comparable, a Global Technical Regulation (GTR), which sets the procedures and boundaries of testing brake wear particle emissions, is currently under development. This overview describes the particle number (PN) measurement setup based on the well-known exhaust emissions PN methodology. We provide the technical requirements and the expected maximum losses. In addition, we estimate the effect of particle losses on the differences between different setups for typical size distributions observed during brake testing. Finally, we compare brake testing PN specifications to those of exhaust PN.

Emission load analysis of land transportation on urban roads in Jember regency, Indonesia (a case study of Sultan Agung Road)

Jember Regency is located in East Java with a population density of 2,536,729 people in 2020. The higher population in Jember will be proportional to the high transportation activity. This will cause problems regarding air pollution due to vehicle exhaust emissions in Jember Regency. The aims of this study are to estimate the annual emission load of air pollutants on a segment of Jember City's urban roads and to recommend emission reduction strategies. The selection of strategies is conducted by considering experts’ judgments through the Analytic Hierarchy Process (AHP). Sultan Agung Road in Jember City was the target of this study because it is a national road in the middle of a busy shopping area around it. The results of this study show that the emission burden generated from the land transportation sector on Sultan Agung Road in Jember Regency includes 177.486 tons of CO/year; 48.638 tons of HC/year; 9.481 tons of NO2/year; 2.534 tons of PM10/year; 1,245.15 tons of CO2 /year and 0.421 tons of SO2 /year. Three strategies recommended by the experts to reduce emissions are BRT (Bus Rapid Transit) Operational Development, The use of Solar Cells in Public Street Lighting, and Smart Driving.

Experimental research on the RUDN University campus on the issue of exhaust fumes

The object of the study is the campus/student housing of the Russian Peoples' Friendship University named after Patrice Lumumba. According to a number of monitoring centres, the Central Federal District of Russia ranks first among all districts in terms of vehicle exhaust emissions, with some 2,825 tonnes of vehicle exhaust emitted per year. Noise pollution is also an important contributor to environmental and human health impacts. Its impact extends to the surrounding area. The chapter describes the field research on the RUDN campus, as well as the results of the conducted research.

ЗАХВОРЮВАННЯ СИСТЕМИ ОРГАНІВ ДИХАННЯ ВІД ВПЛИВУ ХІМІЧНИХ ЗАБРУДНЮВАЧІВ ПОВІТРЯ НА ТЕРИТОРІЇ ВОЄННИХ ДІЙ

The problem of the respiratory diseases in the military and civilians caused by exposure to air pollutants on the territory of hostilities is new for Ukraine. Given the variety of sources of air pollution, the combined exposure to diverse hazards, this issue requires careful research and analysis of experience gained in war zones in different countries of the world. Aim: analysis of epidemiological and clinical data on air pollution in the territory of hostilities and its influence on respiratory system of the military and civilians. Materials and methods. Analytical review of scientific publications was carried out using abstract databases of scientific libraries: Pub Med, Medline and text databases of scientific publishing houses Pub Med, Central, BMJ group and other VIP-databases. Results. In addition to explosive gases, atmospheric air pollutants in the territory of hostilities include vehicle exhaust emission, sulfur mustard gas, organic chlorines, pollutants of burn pits: particulate matter, dioxin, n-hexane, benzene, etc., which form a complex combined effect on the respiratory system. The most common chronic respiratory diseases in combat zone are: bronchial asthma, constrictive bronchiolitis, persistent bronchoobstructive conditions, interstitial lung diseases (hypersensitivity pneumonitis, cryptogenic organizing pneumonia (so called deploymentrelated lung diseases). Data on functional, imaging, and pathomorphological biomarkers of chronic respiratory diseases are insufficient and inconsistent. Conclusions: A wide range of air pollutants in the war zone has a combined effect on the health of the military and civilians, leading to the development of inflammatory, allergic, fibrotic, immune-related respiratory diseases. Understanding the mechanism of development of such diseases, determining biomarkers of functional, imaging and pathomorphological signs can be a serious tool in the diagnosis of respiratory diseases in the military and civilian population in the war zone. Key words: respiratory diseases, the territory of military operations, exposure to chemical air pollutants

Vertical measurements of stable nitrogen and oxygen isotope composition of fine particulate nitrate aerosol in Guangzhou city: Source apportionment and oxidation pathway

Nowadays, the emission source and formation mechanism of fine particulate nitrate (pNO3−) in China are mired in controversy. In this study, the stable nitrogen isotope (δ15N-NO3−) and triple oxygen isotope (Δ17O-NO3−) were determined for the pNO3− samples collected at three heights under different atmospheric oxidation capacity (AOC) (Ox = O3 + NO2: 107 ± 29 μg m−3 at ground, 102 ± 28 μg m−3 at 118 m, 122 ± 23 μg m−3 at 488 m) conditions during the sampling period based on the Canton Tower, Guangzhou, China. The Bayesian mixing model showed that coal combustion was the largest contributor to pNO3− in this city, followed by biomass burning, vehicle exhaust, and soil emission. Interestingly, we found that vertical NOx and pNO3− concentrations displayed an opposite pattern owing to the different formation mechanisms among heights. The average contributions of oxidation pathways for (NO2 + OH, P1), (NO3 + DMS/HC, P2), and (N2O5 + H2O, P3) were 61 %, 12 %, and 27 % at the ground, respectively, and these values would vary greatly among heights. These results implied that both AOC and NOx loading played an important role in pNO3− production. The pNO3− displayed a positive correlation with NOx (r = 0.95) with an enhanced contribution of the P1 pathway under the relatively high AOC condition. However, pNO3− has a negative correlation with NOx (r = −0.99) with a rise of heterogeneous reaction (P2 and P3) under the relatively low AOC condition. Therefore, the current emission control strategy for air pollution in China needs to consider the AOC conditions among regions to effectively mitigate particulate air pollution.

Enrichment of Organic Acids in Fine Particles Over a Megacity in South China

AbstractOrganic acids are abundant aerosol components that impact the hygroscopicity and multiphase processes of particulate matter, yet their changing compositions and sources are poorly understood. In this study, filter‐based PM2.5 samples were collected concurrently at three sites (urban, rural, and coastal) during the summer and winter of 2018 in the megacity Guangzhou, south China, to determine organic acids including C9–C32 fatty acids, C4–C10 dicarboxylic acids, and aromatic acids. On average, the total concentrations of the speciated organic acids were higher in winter than in summer, and were the highest at the urban site. Compared to observations at the same sites in winter 2009, average concentrations of organic acids in winter 2018 increased by 35.0%, 66.0%, and −3.2% at the urban, rural and coastal sites, while their fractions in organic matter (OM) increased by approximately 261.5%, 266.7%, and 61.1%, and their fractions in PM2.5 increased by approximately 318.2%, 308.7%, and 221.1%, respectively. Source apportionment of organic acids revealed that cooking emission and vehicle exhaust were the major sources at the urban site, plant wax and vehicle exhaust were the two largest sources at the rural site with substantial contributions from biomass burning in winter, while vehicle exhaust and ship emission were the two major sources at the coastal site. Less contributions from secondary production were retrieved in winter 2018 than in winter 2009, and the enrichment of organic acids in PM2.5 or OM was attributed to their elevated primary emissions from vehicles and ships, cooking, and plant wax.

A dual attention-based fusion network for long- and short-term multivariate vehicle exhaust emission prediction

The increasing number of vehicles is one main cause of atmospheric environment pollution problems. Timely and accurate long- and short-term (LST) prediction of the on-road vehicle exhaust emission could contribute to atmospheric pollution prevention, public health protection, and government decision-making for environmental management. Vehicle exhaust emission has strong non-stationary and nonlinear characteristics due to the inherent randomness and imbalance nature of meteorological factors and traffic flow. Therefore accurate LST vehicle exhaust emission prediction encounters many challenges, such as the LST temporal dependencies and complicated nonlinear correlation on various emission gases, including carbon monoxide (CO), hydrocarbon (HC), and nitric oxide (NO), and external influence factors. To resolve these challenging issues, we propose a novel hybrid deep learning framework, namely Dual Attention-based Fusion Network (DAFNet), to effectively predict LST multivariate vehicle exhaust emission with the temporal convolutional network, convolutional neural network, long short term memory (LSTM)-skip based on recurrent neural network, dual attention mechanism, and autoregressive decomposition model. The proposed DAFNet consists of three major parts: 1) a nonlinear component to effectively capture the dynamic LST temporal dependency of multivariate gas by the temporal convolutional network, convolutional neural network, and LSTM-skip. Moreover, the above two networks employ an attention mechanism to model the internal relevance of the LST temporal patterns and multivariate gas, respectively. 2) a linear component to tackle the scale-insensitive problem of the neural network model by an autoregressive decomposition model. 3) the external components are taken to compensate the impact of external factors on vehicle exhaust emission by the multilayer perceptron model. Finally, the proposed DAFNet is evaluated on two real-world vehicle emission datasets in Zibo and Hefei, China. Experimental results demonstrate that the proposed DAFNet is a powerful tool to provide highly accurate prediction for LST multivariate vehicle exhaust emission in the field of vehicle environmental management.

Real-Time Measurement of NOx Emissions from Modern Diesel Vehicles Using On-Board Sensors

In this work, the performance of on-board vehicle exhaust emission sensors is investigated and compared to reference laboratory and on-road instrumentation for two modern diesel light-duty commercial vehicles, type-approved as Euro 6d-TEMP-EVAP-ISC and Euro 6d-ISC-FCM. The first step of the analysis was to perform emissions tests in the laboratory and compare the NOx concentrations registered by the vehicle sensors available at the engine-out and tailpipe positions with those recorded by reference laboratory instrumentation. In a second step, tests were also conducted on road, comparing the performance of on-board sensors with those of Portable Emission Measurement System (PEMS) analysers, which were taken as references. The uncertainty related to exhaust flow measurements was also addressed. In particular, emissions factors calculated using the flow rates measured either in the laboratory or on-road were compared to those obtained by computing exhaust flows with on-board recorded data available from the vehicle electronic control unit. Results showed maximum deviations on the order of 34% in laboratory tests and of 21% during on-road measurements. Finally, measurements were also carried out during a diesel particulate filter regeneration event, showing the good performance of the on-board sensors even when high NOx concentrations were present. These conditions can be similar to those experienced in the case of an after-treatment system malfunction or of a high-emitting event, and can thus be of interest for real-time malfunction identification and monitoring.

Towards Low Carbon: A Lightweight Design of Automotive Brake Hub

Carbon peaking and carbon neutrality have become important considerations in today’s manufacturing industry. Vehicle lightweight design can reduce carbon emissions and it is an important means to achieve carbon peak and carbon neutrality. In this study, the lightweight design method of automotive brake hub towards low carbon and the calculation method of low-carbon benefit are presented. A brake hub is the core of a drum brake, working together with a friction plate and brake shoe to complete the braking process. The requirements for the safety performance of brake hub are becoming increasingly more stringent in order to improve the stability and safety of the braking process. The brake hub ZD02-151122A manufactured by Anhui Axle Co., Ltd.(Suzhou, China), was used as the research object. The lightweight optimization of the brake hub was designed under the lightweight drive to reduce the shape variables and stress values of the brake hub and to reduce the mass. The proposed optimization scheme changed the chamfering to 45 × 45 and increased the number of bolt holes to eight. Compared with the original brake hub, the maximum strain, maximum stress value, stress concentration coefficient, and mass were reduced by 15.38%, 17.66%, 1.50%, and 17.40%, respectively, which achieved the specified optimization goal of improving mechanical properties and reducing mass. Towards low carbon, the reduction in carbon emissions from the optimized brake hub manufacturer and the vehicle during operation was calculated. For Anhui Axle Co., Ltd., the carbon emission can be reduced by 4.21 × 106 kg per year. Moreover, vehicle exhaust emissions can be reduced by 8.76 × 108 kg if all trucks produced by a medium-sized vehicle assembly company are driven on the road until being scrapped. This study serves as a reference for design optimization and low-carbon benefit analysis of other major automotive components.