Tailpipe emissions and fuel consumption of a heavy-duty diesel vehicle using palm oil biodiesel blended fuels.

Biodiesel application, such as using waste cooking oil biodiesel in Shanghai, China, is a sustainable solution that addresses the challenges posed by escalating air pollution, energy security, and climate change. Future efforts may involve blending biodiesel from alternative sources like crude palm oil with diesel in China. This study tested a China V heavy-duty diesel vehicle equipped with a selective catalytic reduction (SCR) system using various palm oil biodiesel blended fuels (B0, B5, B10, and B20). The findings indicated that using biodiesel blends led to decreased carbon monoxide (CO), hydrocarbon (HC), and particle number (PN) emissions compared to B0, while nitrogen oxide (NOX) emissions remained similar. Higher biodiesel content significantly reduced petroleum diesel consumption, but no statistically significant differences were found in total carbon dioxide (CO2) emissions and fuel consumption. Various factors such as vehicle speed, payload, and cold starts influence tailpipe emissions and fuel consumption. Specifically, high-speed phases notably reduced CO, HC, and PN emissions with the use of biodiesel blends. Lower payloads were linked to decreased CO2 emissions and fuel consumption but increased NOX emissions. Cold starts increased HC and NOX emissions, especially with higher biodiesel blending ratios. These results can provide valuable empirical insights into palm oil biodiesel emissions.

Kinetics and H2O Influence on NOx Trapping and Selective Catalytic Reduction over Ce/Pd Doping Catalyst.

In this paper, the removal effects and activation energy of Ce and Pd doping on pollutants (CO, C3H6, and NO) were comparatively analyzed by using characterization methods and constructed kinetic equations. Furthermore, the problems of the water influence mechanism on the NSR process were also discussed. The results show the following: (1) Pd doping effectively improves the removal of CO (80%) and C3H6 (71%) in the low-temperature section of the catalyst (150-250 °C) compared to Ce doping, while Ce doping exhibits excellent low-temperature conversion of NO. (2) The reaction activation energy of the LaKMnPdO3 catalyst was 9784 kJ/mol, which was significantly lower than that of the LaKMnCeO3 catalyst. (3) The presence of H2O has an important enhancement effect in the storage performance of the LaKMnPdO3 catalyst for NOx but decreases the catalytic reduction of NO. It provides a solution for the effective treatment of the increasing problems of particulate matter and ozone pollution.

High emission of black carbon in heavy-duty diesel vehicles: Insights from microscopic operating conditions.

The in-depth investigation of the high black carbon (BC) emission scenarios of heavy-duty diesel vehicles (HDDVs) is a crucial step toward developing effective control strategies. Chassis dynamometer tests were conducted for three in-use HDDVs, namely, vehicle #1, #2, and #3, focusing on the instantaneous BC characterizations during multiple driving conditions, i.e., speed phases and acceleration intervals. BC emission was found to increase with positive acceleration, and high acceleration could result in instantaneous BC spikes. The total BC emissions during velocity-acceleration interval 15-60 km h-1 and 0.1-0.9 m s-2 contributed to 43.4 ± 10.2 % of the whole-cycle emissions, while the proportions of time spent in the velocity-acceleration interval to the whole cycle were 23.1 ± 7.6 %. The cold-start microscopic operating condition was assessed by the cold-start extra emissions (CSEEs). Under various pre-defined cold-start durations, the proportions of CSEEs in the total cycle emissions were 9.4-21.0 %, 0-9.1 %, and 6.8-39.4 % for vehicles #1, #2, and #3, respectively. The CSEEs exhibited an initial rise, followed by a plateau as the assumed cold-start durations extended. A uniform cold-start duration of 600 s was established based on the criterion that the relative standard deviation (RSD) of CSEEs during the plateau period was <10 %. We proposed that the updated cold-start duration can enhance the accuracy and consistency of cold-start corrections in emission inventory models.

Exploring high-emission driving behaviors of heavy-duty diesel vehicles based on engine principles under different road grade levels.

To reveal the outstanding high-emission problems that occur when heavy-duty diesel vehicles (HDDV) pass uphill and downhill, this study proposes a method to depict the nitrogen oxides (NOx) and carbon dioxide (CO2) high-emission driving behaviors caused by slopes from the perspective of engine principles. By calculating emission and grade data of HDDV based on on-board diagnostic (OBD) data and digital elevation model (DEM) data, the 262 short trips including uphill, flat-road and downhill are firstly obtained through the rule-based short trip segmentation method, and the significant correlation between the road grade and emissions of the short trips is verified by Kendall's Tau and K-means clustering. Secondly, by comparing the distribution changes of three speed categories (acceleration state, constant speed state and deceleration state), the differences in HDDV operating states under different grade levels are discussed. Finally, the machine learning models (Random Forest, XGBoost and Elastic Net), are used to develop the NOx and CO2 emission estimation model, identifying high-emission driving behaviors, particularly during uphill driving, which showed the highest proportion of high-emission. Explained by the feature importance and SHapley Additive exPlanations (SHAP) model that large accelerator pedal opening, frequent aggressive acceleration, and high engine load have positive effects both on NOx and CO2 emissions. The difference is in the air-fuel ratio that the engine in the rich or slightly lean burning state will increase CO2 emissions and the lean burning state will increase NOx emissions. In addition, due to the uncertainty of the actual uphill, drivers often undergo a rapid "deceleration-uniform-acceleration" process, which significantly contributes to high NOx and CO2 emissions from the engine perspective. The findings provide insights for designing driving strategies in slope scenarios and offer a novel perspective on depicting driving behaviors.

Characterization of on-road nitrogen oxides and black carbon emissions from high emitters of heavy-duty diesel vehicles in China.

Heavy-duty diesel vehicles (HDDVs) significantly contribute to atmospheric nitrogen oxides (NOX) and black carbon (BC), with high emitters within the HDDV fleet impacting the total emissions. However, emission patterns and contributions of high emitters are rarely explored from a fleet-perspective. We investigated NOX and BC emission factors (EFs) from 1925 HDDVs in Shenzhen by the plume-chasing method, and found that the fleet-average EFs decreased with stricter emission standards. Unexpectedly, the average NOX EF for the China IV fleet was comparable with that for the China III fleet due to possible ineffective aftertreatment in high-emitter sectors of China IV HDDVs. Decreasing trend in average NOX EF since 2017 reflected the effective emission controls by the implementation of China V standard. Besides, semi-trailer tractors exhibited a higher incidence of NOX over-emissions, whereas BC high emitters were more pronounced in box trucks. Total NOX and BC emissions from HDDVs in Shenzhen were revisited, reaching 54.0 and 1.1 Gg·yr-1, with updated NOX EF correcting a 26.2 % underestimation in national guidelines. Notably, eliminating high emitters yields greater emission reduction benefits than merely retiring old HDDVs, with BC reduction outpacing NOX. This study provides new insights into the implementation of targeted emission reduction measures for HDDVs.

Accuracy Improvement of Automatic Smoky Diesel Vehicle Detection Using YOLO Model, Matching, and Refinement.

The detection of smoky diesel vehicles is a key step in reducing air pollution from transportation. We propose a new method for identifying smoky vehicles that proceeds in three stages: (1) the detection of vehicle shapes, license plates, and smoke regions; (2) the implementation of the two matching techniques based on the smoke region-vehicle shape and smoke region-license plate relationships; and (3) the refinement of the smoke region detected. The first stage involves the evaluation of various You Only Look Once (YOLO) models to identify the best-fit model for object detection. YOLOv5s was the most effective, particularly for the smoke region prediction, achieving a precision of 91.4% and a mean average precision at 0.5 (mAP@0.5) of 91%. It also had the highest mean mAP@0.5 of 93.9% across all three classes. The application of the two matching techniques significantly reduced the rate of false negatives and enhanced the rate of true positives for the smoky diesel vehicles through the detection of their license plates. Moreover, a refinement process based on image processing theory was implemented, effectively eliminating incorrect smoke region predictions caused by vehicle shadows. As a result, our method achieved a detection rate of 97.45% and a precision of 93.50%, which are higher than that of the two existing popular methods, and produced an acceptable false alarm rate of 5.44%. Particularly, the proposed method substantially reduced the processing time to as low as 85 ms per image, compared to 140.3 and 182.6 ms per image in the two reference studies. In conclusion, the proposed method showed remarkable improvements in the accuracy, robustness, and feasibility of smoky diesel vehicle detection. Therefore, it offers potential to be applied in real-world situations.

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.

Electrification-Enhanced Low-Temperature NOx Storage-Reduction on Pt and K Co-Supported Antimony-Doped Tin Oxides.

NOx storage-reduction (NSR), a promising approach for removing NOx pollutants from diesel vehicles, remains elusive to cope with the increasingly lower exhaust temperatures (especially below 250 °C). Here, we develop a conceptual electrified NSR strategy, where electricity with a low input power (0.5-4 W) is applied to conductive Pt and K co-supported antimony-doped tin oxides (Pt-K/ATO), with C3H6 as a reductant. The ignition temperature for 10% NOx conversion is nearly 100 °C lower than that of the traditional thermal counterpart. Furthermore, reducing the power in the fuel-lean period relative to that in the fuel-rich period increases the maximum energy efficiency by 23%. Electrically driven release of lattice oxygen is revealed to play vital roles in multiple steps in NSR, including NO adsorption, desorption, and reduction, for improved NSR activity. This work provides an electrification strategy for developing high-activity NSR catalysis utilizing electricity onboard hybrid vehicles.

Physical properties and structural characteristics of particulate matter emitted from a diesel engine fueled with biodiesel blends.

This research explores the influence of renewable fuels, including three kinds of biodiesel along with ethanol on the physical properties and structural characteristics of particulate matter (PM) emitted from a diesel engine in comparison with pure diesel. After adding 10 vol% of grape seed biodiesel, coffee biodiesel and eucalyptus oil into diesel, three biodiesel blended fuels (10% grape seed biodiesel (DGs10), 10% spent coffee ground biodiesel (DC10) and eucalyptus oil biodiesel (DEu10)) were produced and tested in this study. Besides, one ethanol blend containing 9 vol% of ethanol and 1 vol% of biodiesel (blend stabilizer) was also tested to do the comparison. In the present study, scanning transmission electron microscope (STEM) and scanning electron microscope (SEM) were employed for analyzing the microstructure, nanostructure and electron diffraction pattern of PM. Raman spectrometer (RS) was also used for the analysis of structural characterization of PM. In addition, several experimental instruments like microbalance, measuring cup, viscometer, oxygen bomb calorimeter and Gas Chromatography-Mass Spectrometer (GC-MS) were employed to detect the fuel properties, including density, heating value, viscosity, composition and cetane number. A conclusion can be drawn that both biodiesel blends and ethanol blend have a changing effect on the PM properties compared to pure diesel, where biodiesel blends have a slightly weaker influence than ethanol blend. Regarding the biodiesel blends, DGs10 has more impact than DC10 and DEu10 in changes of PM properties, particularly in the reduction of PM mass, making it a good candidate for renewable fuel for diesel engines.

NOx emission deterioration in modern heavy-duty diesel vehicles based on long-term real driving measurements.

NOx emissions from diesel vehicles generally deteriorate with increased durability mileage owing to the wear and deterioration of engines and after-treatment systems. Three China-VI heavy-duty diesel vehicles (HDDVs) were selected for four-phase long-term real driving emission (RDE) tests using the portable emission measurement system (PEMS). After 200,000 km of on-road driving, the maximum NOx emission factor of the test vehicles (387.06 mg/kWh) was found to be significantly lower than the NOx limit of 690 mg/kWh. Under all driving conditions, the NOx conversion efficiency of selected catalytic reduction (SCR) decreased almost linearly as the durability mileage increased. Importantly, the deterioration rate of the NOx conversion efficiency in low-temperature intervals was discernibly higher than that in high-temperature intervals. The NOx conversion efficiency at 200 °C dropped by 16.67-19.82% with higher durability mileage; however, the highest values at 275-400 °C only decreased by 4.11%. Interestingly, the SCR catalyst at 250 °C showed strong NOx conversion efficiency and durability (maximum decline of 2.11%). Overall, the poor de-NOx performance of SCR catalysts at low temperatures significantly challenges the long-term effective control of NOx emissions from HDDVs. Thus, improving the NOx conversion efficiency and durability at low-temperature intervals is the top priority for SCR catalyst optimization; NOx emissions from HDDVs at low velocities and loads should also be monitored by environmental authorities. The linear fitting coefficient for the NOx emission factors of the four-phase RDE tests was 0.90-0.92, indicating that NOx emissions deteriorated linearly with an increase in mileage. Based on the linear fitting results, the NOx emission control of the test vehicles during 700,000 km of on-road driving was highly likely to be qualified. These results can be used by environmental authorities to supervise the NOx emission conformity of in-use HDDVs after validation using other types of vehicles.

Comprehensive treatment strategy for diesel truck exhaust.

At present, diesel vehicles still play an irreplaceable role in the traditional energy field in China. Diesel vehicle exhaust contains hydrocarbons, carbon monoxide, nitrogen oxides, and particulate matter, which can lead to haze weather, photochemical smog, and the greenhouse effect; endanger human health; and damage the ecological environment. In 2020, the number of motor vehicles in China reached 372 million, and the number of automobiles reached 281 million, of which 20.92 million are diesel vehicles, accounting for only 5.6% of the number of motor vehicles and 7.4% of the number of automobiles. Nevertheless, diesel vehicles emitted 88.8% of nitrogen oxides and 99% of particulate matter in total vehicle emissions. Diesel vehicles, especially diesel trucks, have become the top priority of motor vehicle pollution control. However, there are few reviews on the comprehensive treatment of diesel vehicle exhaust. This review provides an overview of exhaust gas composition, hazards, and treatment techniques. Phytoremediation, three-way catalytic conversion, rare earth catalytic degradation, and nanoscale TiO2 catalytic degradation are briefly described.

Assessing the effects of short-term traffic restriction policies on traffic-related air pollutants.

The implementation of short-term traffic restriction policies (TRPs) during major events positively influences the traffic emission reduction. However, few studies explore the impact of diesel vehicle emissions on air quality during short-term TRP. In particular, the intertwined influences of short-term TRP and Spring Festival remains unclear. Based on Beijing 2022 Olympic Games, this study analyzed the spatiotemporal changes in urban air quality and diesel vehicle emission during short-term TRP. The results showed that the TRPs and Spring Festival contributed equally to the improvement of air quality and reduction of diesel vehicle emissions. The "interruption-recovery" pattern of short-term TRPs is characterized by a longer duration and a slower decline/recovery rate. Additionally, the individual contribution rate of diesel vehicle emissions to urban air pollutants was 15-20 % higher than that of meteorological factors during short-term TRPs.

Green surfactant-modified TiO2 nanoparticles doped with La-Cr bimetal for NOx removal.

Cost-effective new environmental catalysts play a crucial role in purifying NOx from exhaust gas of coal mine diesel vehicle. A new, environmentally friendly catalyst with high catalytic activity and good redox properties was prepared by a microwave-assisted sol-gel method using TiO2 nanoparticles as a catalyst, which were doped with La and Cr, and adding the surfactant dimethyldiallylammonium chloride (DMDAAC) as an organic modifier. The morphological characteristics, crystalline structure, functional groups, and elemental types of the catalyst were characterized, and the properties of the catalyst, such as redox ability and catalytic activity, were examined with H2-temperature-programmed reduction experiments and activity tests. The results showed that the addition of surfactant suppressed the growth of particle size, increased the specific surface area, and improved the redox ability and catalytic activity of the catalyst. I hope to reduce the pollution of NOx to environment and achieve efficient cleaner production.

Study of durability of diesel vehicle emissions performance based on real driving emission measurement.

Diesel vehicle emissions generally deteriorate with vehicle mileage due to the wear and deterioration of vehicle parts. Most of the experimental studies on vehicle emission durability were carried out based on the standard operation cycles of engine or vehicle, few research investigated vehicle emission deterioration characteristics under real driving conditions. In this research, the real driving emission (RDE) test method was used to investigate and evaluate the emission deterioration characteristics of two China-V diesel vehicles equipped with DOC and SCR systems. The experimental results show the emissions of CO and NOx from the N2 and N3 diesel vehicles increase with the vehicle mileage, showing the tendency of emission deterioration. The calculated deterioration factors of N2 and N3 diesel vehicle CO and NOx emissions are greater than the recommended values in China standard HJ 438-2008, which means experimental study on the vehicle emissions durability is necessary. The vehicle emissions deterioration depends on real driving conditions and the vehicle usage over vehicle lifetime.

Physicochemical and cell toxicity properties of particulate matter (PM) from a diesel vehicle fueled with diesel, spent coffee ground biodiesel, and ethanol.

The literature shows that information about the physical, chemical, and cell toxicity properties of particulate matter (PM) from diesel vehicles is not rich as the existence of a remarkable number of studies about the combustion, performance, and emissions of diesel vehicles using renewable liquid fuels, particularly biodiesels and alcohols. Also, the PM analyses from combustion of spent coffee ground biodiesel have not been comprehensively explored. Therefore, this research is presented. Pure diesel, 90% diesel + 10% biodiesel, and 90% diesel + 9% ethanol + 1% biodiesel, volume bases, were tested under a fast idle condition. STEM, SEM, EDS, Organic Carbon Analyzer, TGA/DSC, and Raman Spectrometer were employed for investigating the PM physical and chemical properties, and assays of cell viability, cellular reactive oxygen species, interleukin-6, and tumor necrosis factor-alpha were examined for investigating the PM cell toxicity properties. It is found that the application of both biodiesel and ethanol has the potential to change the PM properties, while the impact of ethanol is more than biodiesel on the changes. Regarding the important aspects, biodiesel can be effective for better human health (due to a decrease in cell death (-60.8%)) as well as good diesel particulate filter efficiency (due to lower activation energy (-7.6%) and frequency factor (-83.2%)). However, despite a higher impact of ethanol on the reductions in activation energy (-24.8%) and frequency factor (-99.0%), this fuel causes an increase in cell death (84.1%). Therefore, biodiesel can be an appropriate fuel to have a positive impact on human health, the environment, and emissions catalysts performance, simultaneously.

Study on pollutant emission characteristics of different types of diesel vehicles during actual road cold start.

The current regulations of heavy-duty vehicles in China do not include the emissions in the cold start stage into the overall emission evaluation. However, the speed of heavy-duty diesel vehicles in the cold start stage is often low and the proportion of idle-conditions is large, resulting in the difference between the actual test results and evaluation results of emissions. Therefore, in order to accurately evaluate the impact of emission during cold start on the overall emission, in this study, the OBS-ONE portable vehicle emission test equipment was used to test the emission of three representative heavy-duty diesel vehicles with different types under actual road driving conditions, and the cumulative averaging (CA) method was adopted to calculate and analyze the test emission data. Firstly, the cold start emission of different types of heavy-duty vehicles was evaluated. The results show that the contribution rate of pollutant emission in the cold start stage is high, in which NOx emission accounts for 40-90% of the whole trip. It was unreasonable for regulations to exclude data in the cold start stage. The cold start duration of vehicle A is nearly 300 s longer than that of vehicle C, however, the NOx and PN emission factors of vehicle A are nearly 10 times and 100 times smaller than that of vehicle C at the cold start stage respectively. The cold start duration, fuel consumption and the emission factors in cold start stage of different types of heavy-duty diesel vehicles do not have a unified law. Secondly, the emission characteristics and differences of different types of heavy-duty vehicles are studied at the instantaneous level, and the internal mechanism causing the emission differences is explored and revealed. In the cold stage, CO2 emission shows a good correlation with the fuel consumption. CO, NOx emissions show a good correlation with the fuel consumption when the engine and post-treatment temperature are low, and CO and NOx emissions decrease with the increase of engine and post-treatment temperature. PN emissions are mainly related to the engine working state. Finally, the influence of dynamic parameters v·a and RPA on pollutant emission was analyzed. The results show that driving force is an important factor affecting CO2 emission, and RPA has no obvious correlation with emission at cold start stage.

Integrated effects of SCR, velocity, and Air-fuel Ratio on gaseous pollutants and CO2 emissions from China V and VI heavy-duty diesel vehicles.

Vehicle exhaust, an important source of air pollution, is affected by many factors, including driving conditions, combustion efficiencies, and the usage of emission control devices. In this study, the Portable Emission Measurement System (PEMS) was used to test the emissions from China V and China VI heavy-duty diesel vehicles to evaluate the integrated effects of Selective Catalytic Reduction (SCR), velocity, and air-fuel ratio on carbon dioxide (CO2) and nitrogen oxide (NOx) emissions. Our results reveal that the average distance-based CO2 and CO emission factors at high velocities (50-90 km/h) are 25% and 61% lower than those at low velocities (less than 50 km/h). The use of SCR increases CO2 emissions in the range of 70-90 km/h (an average increase of 10.9%). In addition, SCR leads to a 55% NOx emission reduction at low velocities and 89% at high velocities, with an overall average reduction of 84%. We also find that SCR leads to a significant reduction in the correlation between NOx emissions and air-fuel ratio (0.76 vs 0.47 for China V truck; 0.72 vs 0.05 for China VI truck), but it does not cause a drastic reduction in the correlation coefficients between CO2 emissions and air-fuel ratio, which can be used to detect whether SCR is working effectively.

Nitrous acid (HONO) emission factors for diesel vehicles determined using a chassis dynamometer.

Vehicle exhaust gases are important sources of nitrous acid (HONO). In this study, HONO in diesel vehicle exhaust was measured by incoherent broadband cavity-enhanced absorption spectroscopy using a chassis dynamometer system. The mean HONO concentrations in exhaust gases emitted by passenger cars and light-duty trucks were high when the after treatment devices were not fully working during the warming up period. The HONO/NOx ratio is a good index of HONO formation. The HONO/NOx ratios were 9.7 × 10-3-18.1 × 10-3, and were higher than what we found in a previous study. The estimated HONO emission factors were 7.71-64.70 mg (kg fuel)-1, and were lower than were found in previous studies. The results indicated that the frequency particulate matter is removed from a diesel particle filter affects the HONO concentration in the emitted gases and the HONO emission factor.

Research on a China 6b heavy-duty diesel vehicle real-world engine out NOx emission deterioration and ambient correction using big data approach.

China VI standard proposed higher requirements for durability of heavy-duty diesel vehicles emissions. Previous research which took advantages of both on-board sensors and big data approach to get the NOx deterioration factor was rather scarce. This paper used big data approach to study the deterioration of engine out NOx emission based on 254,622 km operation data getting from the on-board sensors or ECUs (Electronic Control Unit). Meanwhile, a formula for on-board NOx correction for ambient humidity and temperature had been fitted. The analyses revealed that the engine out NOx deterioration factor (DF) of the maximum weight steady-state condition was about 1.005 after 254,622 km durability test; as for transient conditions, the DF was not more than 1.092 during 254,622 km durability test. For a same steady working condition, the engine out NOx mass flow (g/h) was negatively linearly correlated with absolute humidity (Ha) (R2 = 0.997). If Ha was lower than 12 g/kg, Ha almost had no effect on engine out NOx concentration (ppm). Otherwise, there was also a negatively linear relationship between them (R2 = 0.978). It is hoped that the methods and conclusions of this paper could provide some enlightenment for future NOx emission deterioration research.

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 (NOx), 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 NOx 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 NOx 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 NOx 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.