Portable Emission Measurement System Research Articles

Evaluation of the point sampling method and inter-comparison of remote emission sensing systems for screening real-world car emissions

Emissions from internal combustion vehicles are currently not properly monitored throughout their life cycle. Remote emission sensing (RES) is a technology that can measure emissions under real driving conditions without contact. Current light extinction based RES systems are capable of providing emission factors for various gases, but lack accuracy for particulate matter (PM). Point Sampling (PS) is an extraction-based RES technique that can measure gases as well as various particle metrics such as black carbon or particle number. In this work, we evaluated the performance of a recently developed PS system and the state-of-the-art light extinction based remote sensing devices EDAR (HEAT) and ORSD (OPUS RSE) during co-location measurements. Validation measurements with portable emission measurement systems and emissions screening of several thousand cars in three European cities provide detailed insights into system's performance. Meteorological evaluations showed that the PS capture rate is strongly influenced by wind, but no other weather influences were found. Both light extinction based systems are unable to measure during rain. We found that all three systems tested were capable of screening NOx emissions from pre-Euro 6 diesel cars. Measurement results show the ability of the PS system to quantify high and low PM emitters equally well. The open-path RES systems (EDAR, ORSD) are capable of estimating PM emissions from pre-Euro 5 diesel cars. However, deficiencies of open-path RES systems are evident in the quantification of PM emissions from newer engine technologies (diesel Euro 5 and beyond) and from petrol cars. The PS system has a 2 to 5 times lower capture rate than open-path RES systems, but the PS measurement results are more accurate (more than 5 times for PM and more than 1.35 times for NOx). The good accuracy of individual measurements makes PS a powerful tool for reliable high emitter identification.

Signal synchronization methods when measuring tailpipe emissions with PEMS

This study describes the effects of using different signal synchronization methods on the tailpipe emission factors obtained by on-road tests using PEMS (Portable Emission Measurement Systems). The event-based, static correlation, and dynamics correlation methods were applied to data obtained using different PEMS. The first database included 36 cargo vehicles following the same driving cycle in various cities in Colombia. The second database involved 16 passenger transport vehicles monitored under real operating conditions in Mexico. When comparing results with the event-based synchronization method as the reference, significant differences in emission factors (>25 %) were observed. Those differences depend directly on the physical setup of the PEMS. The dynamic correlation method exhibited the best performance in all cases. When using this method, we recommend to synchronize the CO2 mass flow and fuel consumption variables first and then adjust the obtained synchronization to other species to account for differences in species diffusivities.

Comparison of the Real-Driving Emissions (RDE) of a Gasoline Direct Injection (GDI) Vehicle at Different Routes in Europe

On-road real-driving emissions (RDE) tests with portable emissions measurement systems (PEMS) are part of the vehicle emissions regulations in the European Union (EU). For a given vehicle, the final emission results depend on the influence of the ambient conditions and the trip characteristics (including the driver’s behaviour) on the vehicle performance and the instrument measurement uncertainty. However, there are not many studies that have examined the emissions variability of a single vehicle following different routes. In this study, a 1.2 L gasoline direct injection (GDI) Euro 5b passenger car without a particulate filter and a PEMS was circulated in seven European laboratories. At their premises, the laboratories performed two to five repetitions of on-road trips compliant with the EU RDE regulation. The ambient temperature ranged between 7 °C and 23 °C. The average emission levels of the vehicle were 135 g/km for CO2, 77 mg/km for CO, 55 mg/km for NOx, and 9.2 × 1011 #/km for particle number. The coefficient of variance in the emissions following the same route was 2.9% for CO2, 23.8% for CO, 23.0% for NOx, and 5.8% for particle number. The coefficient of variance in the emissions following different routes in Europe was 6.9% for CO2, 9.1% for CO, 0.0% for NOx, and 9.1% for particle number. The previous values include the specific vehicle emissions variability under the narrow test conditions of this study, but only partly the PEMS measurement uncertainty because the same instrument was used in all the trips. The results of this study can be used by laboratories conducting RDE tests to assess their uncertainty budget when testing or comparing vehicles of similar technology.

An Estimate of the NOX Emissions of Euro 6 Diesel Passenger Cars with Manipulated Emission Control Systems

The motivation for conducting this research stems from the increasingly applied manipulations of emission control systems (ECSs), especially those in diesel passenger cars (PCs). The study aimed to investigate the influence of manipulations of exhaust gas recirculation (EGR) valves and a diesel exhaust fluid (DEF)-dosing system on the nitrogen oxide (NOX) emissions of a Euro 6 diesel vehicle and, through the quantification of vehicles with manipulated ECSs, estimate the emissions of Euro 6 diesel PCs. Portable emissions measurement system (PEMS) measurements were performed on a Euro 6 diesel vehicle at a constant speed and on real driving emission (RDE) routes. The speed-dependent functions of the NOX hot emission factor (EF) were calculated for seven different scenarios. The results showed that the NOX EFs for the worst-case scenarios were more than two orders of magnitude higher than those where all ECSs were active. Applying the calculated EFs and the survey answers on the percentage of manipulated PCs to the Croatian Euro 6 diesel PC fleet, the results showed that the emission levels were up to 46.3% higher than the emissions calculated by the official computer program COPERT v5.6.5, with a tendency towards significantly higher values. The main conclusion is that vehicle manufacturers, policymakers, and the general public need to be informed about the enormous damage that in-use vehicles with manipulated ECSs cause to the environment and human health, in order to prevent such actions.

Evaluation of exhaust emissions of agricultural tractors using portable emissions measurement system in Korean paddy field

Recently, diesel engine emissions have been designated as a first-class carcinogen by the World Health Organization (WHO). As such, problems with diesel engine emissions continue to increase around the world. This study aimed to analyze the emissions (CO, NOx, PM) of agricultural tractors during farming operations in order to build a reliable national inventory of air pollutant emissions. Emission data were collected using a portable emission measurement system during actual agricultural operation. The load factor (LF) of the engine was calculated using the collected engine information, the emission factor was analyzed using the LF and the measured emission. The LF was significantly different from the current standard value of 0.48, which is used in Korea to calculate exhaust emissions. The deviation ratio of the emission factor was 0.039 ~ 56.59 compared to Tier-4 emission regulation standards. Under many conditions, the calculated emission factor was higher than the emission limit. Thus, this study provides useful information for emission inventory construction through emission calculation under actual conditions and suggests the need to realize the currently applied emission factor.

Evaluation of CO2 and NOx emissions from container diesel trucks using a portable emissions measurement system

Vehicle emission has been regarded to the major source of air pollution in urban area. Especially, the emissions originating from container diesel trucks (CDTs) constitute a significant portion of road transportation emissions in port city. However, the instantaneous measurement of CDTs emission in real road situation remains limited. In this study, we employed a portable emission measurement system (PEMS) to measure the real-world emissions of CDTs that meet the China IV and China V standards on highway and port roads. Based on the data, the impacts of vehicle speed, acceleration, and vehicle specific power (VSP) on CO2 and NOx emission rates are investigated. Additionally, random forests is employed to explore the interplay of driving, engine, and external factors on vehicle emission rates, as well as to predict these rates. Finally, the emission factors under diverse driving conditions are examined. The results indicate that elevated CO2 and NOx emission rates are primarily observed during instances of low and medium-speed acceleration on highway. On port road, the distribution of high CO2 and NOx emission rates are relatively scattered. The relationship between the emission rates of CO2 and NOx and VSP follows a third-order polynomial function. Engine power, engine speed, and VSP exhibit a strong correlation with CO2 and NOx emission rates. Remarkably, the CO2 and NOx emission factors on highway for China V CDT have decreased by 58.04% and 55.13%, respectively, compared to China IV CDT.

Investigating the impact of high-altitude on vehicle carbon emissions: A comprehensive on-road driving study

This study addresses the literature gap concerning accurately identifying vehicle carbon emission characteristics in high-altitude areas. Utilizing a portable emission measurement system (PEMS) for real-world testing, we quantified the influence of altitude on carbon emissions from light-duty gasoline (LDGV) and diesel vehicles (LDDV). The Random Forest (RF) algorithm was employed to analyze the complex nonlinear relationships between altitude, meteorological conditions, driving patterns, and carbon dioxide (CO2) emissions, enabling predictions across different altitudes. The results showed that CO2 emissions progressively increase with elevation. Furthermore, as altitude increases, combustion efficiency declines, and the overall impact of driving conditions on emission rates diminishes. Altitude and meteorological factors significantly contributed to CO2 emissions, whereas driving conditions and road grades contributed less. Compared with the COPERT model, the RF model demonstrates strong accuracy in predicting carbon emissions at different altitudes. Specifically, the CO2 emission rate nearly triples as altitude increases from 2.0 km to 4.5 km. This research bridges a critical gap in the understanding carbon emissions from high-altitude vehicles, offering insights into policy development for emission reduction strategies in such regions. Future studies should integrate diverse testing methodologies and comprehensive surveys to validate and extend the findings.

Study on the Emission Characteristics of Typical City Buses under Actual Road Conditions

Five typical in-use city buses in Tangshan with different emission standards, fuel power types, and emission control technology routes were selected as the research objects. PEMSs (Portable Emissions Measurement Systems) and OBD (On-Board Diagnostic) remote monitoring technologies were used to conduct research on actual road conditions and emission characteristics. The research aimed to analyze the driving characteristics of different power types of bus engines and after-treatment systems, as well as their impact on emissions. The results indicated that the actual road driving is mainly in the urban and suburban areas of the local region. Additionally, the VSP (Vehicle Specific Power) was found to be distributed in the low- and medium-speed range of mode Bin0~Bin24. The emission rates of each pollutant showed an increasing trend with the increase in VSP. THC and NOx emissions of CNG and LNG buses are significantly higher than those of gas–electric hybrid buses. However, the PM emissions are lower. Therefore, it is necessary to strengthen the NOx emission control of CNG and LNG buses. With the improvement in emission standards and the use of hybrid technology, CO2 and NOx emissions of China IV buses can be reduced by 13% and 53.5% compared with China III buses of the same tonnage. The CO2 and NOx emissions of China V buses using natural gas combustion and an electric mixture are 20% and 18.8% lower than those of China V buses of the same tonnage using only natural gas. In the actual operation of the diesel–electric hybrid bus equipped with the SCR (Selective Catalytic Reduction) system, the working time of the engine is about 35.5 ± 5%, and the working time of the SCR system only accounts for 65.5 ± 12%. The low working conditions of the SCR system and the low temperature of SCR system are the reasons for the high NOx concentration at the rear end of the SCR system when the hybrid electric bus is running at low speeds.

Solid Particle Number (SPN) Portable Emission Measurement Systems (PEMS) for Heavy-Duty Applications

A heavy-duty engine is homologated in a test cell. However, starting with Euro VI regulation, the in-service conformity is controlled with the engine installed in the vehicle using portable emission measurement systems (PEMS). In Europe, the application of solid particle number (SPN) PEMS started in 2021 for compression ignition (diesel) vehicles and in 2023 for positive ignition vehicles, thus including those operating with compressed natural gas (CNG). Even though today only particles with sizes > 23 nm are regulated, the Euro 7 proposal includes particles > 10 nm. There are not many studies on the accuracy of the SPN PEMS, especially for heavy-duty applications. In this study, PEMS measuring > 23 and >10 nm from two instrument manufacturers were compared with laboratory-grade instruments. The particle detector of one PEMS was a condensation particle counter (CPC), and of the other a the diffusion charger (DC). The results showed the robustness and good accuracy (40% or 1 × 1011 #/kWh) of the PEMS for ambient temperatures from −7 °C to 35 °C, active regeneration events, different fuels (Diesel B7, HVO, and CNG), different test cycles, cold start or hot engine operations, and high exhaust gas humidity content. Nevertheless, for the DC-based PEMS, sensitivity to pre-charged urea particles was identified, and for the CPC-based PEMS, sensitivity to pressure changes with one vehicle was nnoticed. Nevertheless, the results of this study confirm that the PEMS are accurate enough to measure even the stricter Euro 7 limits.

Real-Time LNG Buses Emissions Prediction Based on a Temporal Fusion Trans-Formers Model

Emissions from transportation are one of the key factors preventing the achievement of carbon peaking and carbon neutrality by 2050, with particular attention to emissions from buses. Specifically, few research has been conducted on the exhaust emissions characteristics of liquified natural gas (LNG) buses under different driving scenarios. This study proposed a framework for predicting exhaust emissions of LNG buses based on the portable emission measurement system and GPS collaborative perception data. Firstly, the emission distribution characteristics of CO2, CO, HC, and NOx from LNG buses in real-world driving were analyzed by visualization methods. Then, the real-time exhaust emissions of LNG buses were predicted based on the temporal fusion transformers model for both urban and suburban sections of Zhenjiang City, and the model validity was verified. The current and past 10 s driving states were used for predicting the emission rate of LNG buses. The results showed that the proposed model outperforms other advanced algorithms in real-time exhaust emissions prediction of LNG buses, with an average R2 value higher than 0.94 and an average MAPE reduction of 14.19%. The error assessment revealed that the emission values and average emission rates are higher when driving in the urban section compared to the suburban section. Among the influencing factors, traffic conditions have the most significant impacts on the exhaust emissions of LNG buses, followed by road conditions and driving states, with relative feature importance of 48.9, 34.8, and 16.3%, respectively. Additionally, the current and past 10 s driving states also significantly influenced real-time predictions. This study provides an essential theoretical reference for reducing exhaust emissions for city buses.

Theoretical basis for conducting road tests for emissions of harmful substances in real driving conditions with vehicles of category N3

The article discusses the theoretical basis for testing vehicles of categories N3 in real driving conditions using a portable (portable) system for measuring emissions of harmful substances from exhaust gases. Bench and road tests were analyzed. Based on a study carried out in accordance with regulatory documents to confirm the type of vehicle and for vehicles in operation, a procedure for selecting engines and an acceptance card, requirements and test conditions are proposed. Keywords internal combustion engine, vehicle, exhaust gases, portable emission measurement system, test

Developing Fuel Efficiency and CO2 Emission Maps of a Vehicle Engine Based on the On-Board Diagnostic (OBD) Approach

A vehicle interacts with the road, other vehicles, and traffic control devices in real traffic conditions. The level of traffic influences driving patterns and, consequently, this can affect the vehicle´s fuel efficiency and emissions. This study aims to develop engine maps of fuel consumption and CO2 emissions for a light vehicle operating under real traffic conditions. A representative passenger vehicle of the Ecuadorian vehicle fleet, powered by gasoline, was selected for the experimental campaign that was developed on a test route designed according to real driving emission (RDE) regulation. An on-board diagnostic (OBD) device was used for recording in real-time engine and vehicle operating parameters. Moreover, CO2 emissions were estimated using the fuel rate registered from the OBD system of the vehicle This study proposed a novel methodology for developing two-dimensional contour engine maps based on OBD data. The result showed that the vehicle engine operated in real traffic conditions with a brake thermal efficiency (BTE) of 27%, a brake-specific fuel consumption (BSFC) of 275 g/kWh, and a carbon dioxide (CO2) energy-emission factor of 716 g/kWh. In terms of distance, the CO2 emission factor for the tested vehicle was approximately 190 g/km. Overall, this study demonstrates that the OBD approach is a potential method to be used to assess the fuel consumption and emissions of a vehicle operating under real-world traffic conditions, especially in Latin American countries, where portable emission measurement systems (PEMS) are not readily available.

Experimental investigations on emission characteristics of heavy-duty hybrid electric vehicles

The emission characteristics under different operating modes (engine mode and hybrid mode) and different test cycles (C-WTVC and CHTC) of a heavy-duty hybrid electric dump truck was investigated on the chassis dynamometer. The emission performance was recorded using Portable Emissions Measurement System (PEMS) and analyzed combined with the characteristic parameters of the test conditions. It is found that the NOx emission under hybrid mode is higher than that under engine mode, while the CO emission under hybrid mode is lower than engine mode. Under engine mode, the NOx emission of CHTC is higher than that of C-WTVC. However, under hybrid mode, the NOx emission of CHTC is lower than C-WTVC. Analysis of CO emission characteristics shows that under engine mode, CO emission is concentrated at low speed and small load condition, while under hybrid mode, CO emission is concentrated at high speed and large load condition.

Vehicle Activity Dataset: A Multimodal Dataset to Understand Vehicle Emissions with Road Scenes for Eco-Routing

In the field of smart mobility, Artificial Intelligence (AI) approaches are influential and can make a highly beneficial contribution. Our project aims to develop a real-time ecological map of road traffic. This map will allow electric vehicles (EVs) and thermal vehicles (TVs) to display the cost of energy consumption and CO2 emissions on different road sections. In urban environments, road traffic emissions are a significant contributor to environmental pollution, with vehicle emissions being a major component. Addressing these impacts requires a thorough understanding of the operational behavior of vehicles on different road infrastructures within the region. This paper presents a novel, comprehensive dataset, the Vehicle Activity Dataset (VAD), designed to assess the emissions and fuel consumption characteristics of vehicles about their actual operating environment. Constructed from a large number of real-world driving scenarios, VAD incorporates emission data collected by an industrial Portable Emission Measurement System (PEMS), road scenes captured by an RGB camera, and the detection of different object classes within these images. The primary objective of VAD is to provide a comprehensive understanding of the relationship between vehicle emissions and the diverse range of objects present on the road. Experimental results in real road traffic environments through different studies demonstrate the robustness of the developed dataset.

Instantaneous CO2 emission modelling for a Euro 6 start-stop vehicle based on portable emission measurement system data and artificial intelligence methods

One of the increasingly common methods to counteract the increased fuel consumption of vehicles is start-stop technology. This paper introduces a methodology which presents the process of measuring and creating a computational model of CO2 emissions using artificial intelligence techniques for a vehicle equipped with start-stop technology. The method requires only measurement data of velocity, acceleration of vehicle, and gradient of road to predict the emission of CO2. In this paper, three methods of machine learning techniques were analyzed, while the best prediction results are shown by the gradient boosting method. For the developed models, the results were validated using the coefficient of determination, the mean squared error, and based on visual evaluation of residual and instantaneous emission plots and CO2 emission maps. The developed models present a novel methodology and can be used for microscale environmental analysis.

Data-driven method for estimating emission factors of multiple pollutants from excavators based on portable emission measurement system and online driving characteristic identification

This study aims to explore the factors that influence the emission characteristics of multiple pollutants from non-road mobile machinery (NRMM) under real-world conditions and to establish a data-driven method for calculating accurate emission factors. This research focused on NRMM excavators meeting the third-stage emission standards and identified the actual work characteristics of 108 excavators in different scenarios based on a self-developed testing system for 368,000 h. Additionally, a portable emission testing system (PEMS) was used to study the instantaneous emission characteristics under different driving styles and modes for 10 EC210 excavators with the largest engineering construction inventory. The results showed that the average time proportions of idling, working, and moving modes for excavators were 21 %, 66 %, and 13 %, respectively. The results also revealed that the instantaneous emission rates of multiple pollutants varied significantly under different driving styles and modes. Driving style affected the hydraulic pump power change rate through hydraulic pilot pressure, and engine load surge caused turbocharger response delay and in-cylinder combustion deterioration, which affected pollutant emissions. Driving mode affected the emission characteristics of idling, high-speed idling, moving, and working modes of excavators through the external characteristics corresponding to the engine speed gear set. The data-driven method for calculating emission factors differed from the traditional method for most indicators to varying degrees. In terms of fuel-based emission factors (EFfs), except for the EFfNOx indicator, which was 7.859 % higher than the traditional method, the other three indicators were significantly lower than the traditional method. In terms of power-based emission factors (EFps), except for EFpPM and EFpPN, the other two indicators were much higher than the traditional method. EFpCO and EFpNOx were 7.93 % and 20.332 % higher than the traditional method, respectively. It is recommended to use the data-driven method based on the actual driving data distribution to provide scientific support for accurately establishing the emission inventory.

Characterizing CO2 and NOx emission of vehicles crossing toll stations in highway

Electronic toll collection (ETC) has been designed and implemented to improve traffic efficiency and reduce traffic-related air pollution. However, the real-world impact of ETC on these factors is currently under-qualified. With this consideration, we attempted to reveal the effect of ETC on traffic efficiency and traffic pollution using portable emission measurement systems. Firstly, in contrast to the manual toll collection (MTC), ETC offer lower emissions and time savings, and there exist a quadratic relationship between emission rates and vehicle specific power. Secondly, vehicles in acceleration state emit more pollutants than other situations such as deceleration, steady cruising and idling in both ETC and MTC lanes. Thirdly, ETC lanes show 34.25% and 13.63% lower CO2 and NOx emissions, respectively, along with a 46.55% timesaving compared to MTC lanes. Finally, the study identifies engine load percent as the primary factor impacting emissions, accounting for 60–88% of the variance.

Real-world tailpipe emissions from autorickshaws (3-wheelers) under heterogeneous traffic conditions.

The current study aimed to measure real-world emissions of three-wheeled autorickshaws powered by CNG and parameters (such as speed, acceleration, air-fuel (A/F) ratio, and rpm) influencing 3-wheeler emission rates. Test vehicles manufactured under Bharat Standards BS-III and BS-IV were monitored for exhaust emissions in Delhi city using a portable exhaust emission measurement system (AVL Ditest Gas 1000). The average emission rates of CO, HC, and NO gases for on-road autorickshaws were found to be 0.015 ± 0.017, 0.003 ± 0.0017, and 0.007 ± 0.005 g/s, respectively. Further, the highest emission factor values of 3.98 g/km and 3.93 g/km were estimated for CO and HC+NO gases, respectively. These values were found to be 1.4-3.2 times higher than the respective BS emission norms (BS III-CO =1.25 g/km, HC+NO = 1.25 g/km; BS-IV-CO = 0.94 g/km and HC+NO = 0.94 g/km). In this study, it was observed that the driving pattern and emissions were affected by traffic characteristics, driver behavior (constant acceleration and deceleration), and vehicle characteristics. The air-fuel ratio (A/F) was found to correlate highly with emission rates, followed by acceleration/deceleration and speed. Further analysis found that more than 70% of the aggregated emissions were due to acceleration and deceleration, which contributed to nearly 70% of the travel time. This was followed by the breakdown of speed and emissions into different bins, which found that 20-30 kmph has a higher emission rate and 40-50 kmph bin has a lower emission rate.

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

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

The Effects of Varying Altitudes on the Rates of Emissions from Diesel and Gasoline Vehicles Using a Portable Emission Measurement System

The high altitude in mountainous regions results in lower atmospheric pressure, oxygen concentration and temperature, leading to lower combustion efficiency in motor vehicles. Therefore, there may be differences in carbon dioxide (CO2), carbon monoxide (CO), and nitrogen oxides (NOx) emissions characteristics at different altitudes. In this study, a portable emission measurement system was used to investigate the effects of varying elevations on the emission factors of CO2, CO, and NOx on diesel and gasoline-powered vehicles at altitudes ranging from 2270 to 4540 m in the Qinghai–Tibet Plateau of China. Additionally, the influencing factors of CO2, CO, and NOx emissions were studied. Results showed that the CO2, CO, and NOx emission factors for diesel vehicles varied in the range of 161.83–195.54, 0.59–0.77, and 4.61–6.58 g/km; the population means with 90% confidence intervals were 178.54, 0.68, and 5.60 g/km, respectively. For gasoline vehicles, the CO2, CO, and NOx emission factors varied in the range of 161.66–181.98, 0.95–1.06, and 0.12–0.25 g/km; the population means with 90% confidence intervals were 171.82, 1.01, and 0.19 g/km, respectively. Overall, the emission factors of diesel vehicles were higher than those of gasoline vehicles, and the emissions increased with increasing altitude. Atmospheric pressure was identified as the primary environmental factor affecting CO2, CO, and NOx emissions. As the speed of motor vehicles increased, the emission of CO2 also increased, while there was a quadratic relationship with acceleration. This study provides a reference and guidance for vehicle pollution control in high-altitude regions.