Portable Emission Measurement System Research Articles

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

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

The Influence of the Type and Condition of Road Surfaces on the Exhaust Emissions and Fuel Consumption in the Transport of Timber

Owing to society’s growing ecological awareness, researchers and car manufacturers have increasingly been focusing on the adverse impact of transport on the environment. Many scientific publications have been published addressing the influence of a variety of factors on the exhaust emissions generated by vehicles and machinery. In this paper, the authors present an analysis of the exhaust emissions of components such as CO, THC, and NOx in relation to the type and condition of the road surface. The analysis was performed on a heavy-duty truck designed for carriage of timber. The investigations were carried out with the use of the PEMS equipment (portable emission measurement system) on bitumen-paved roads and unpaved forest access roads. The portable measurement system allowed for an accurate determination of the influence of the road conditions on the operating parameters of the vehicle powertrain and its exhaust emissions. Additionally, the authors present the influence of the type of road surface on the vehicle fuel consumption calculated based on the carbon balance method.

Comparison of exhaust emission results obtained from Portable Emissions Measurement System (PEMS) and a laboratory system

Exhaust emissions testing of vehicles under real driving conditions (real driving emissions, RDE) using portable exhaust emissions measurements systems (PEMS) was introduced a few years ago by the European Commission as a mandatory test during type approval and later also for in-service conformity. This paper compares results from mobile systems for measuring exhaust gas emissions (PEMS) with a stationary laboratory (BOSMAL’s Exhaust Emissions Testing Laboratory). The tests were carried out using a passenger car equipped with a spark ignition engine, which was tested on chassis dynamometer over the WLTC cycle. The results showed that the differences between PEMS analysers and stationary analysers range from a few percent to a dozen or so percent, depending on the component and the measurement method.

Measurement of engine performance and maps-based emission prediction of agricultural tractors under actual operating conditions

This paper develops a comprehensive data collection system (CDCS) based on radio frequency identification (RFID) technology, on-board diagnostic system (OBD), and portable emission measurement system (PEMS) to study the performance of tractors in the real world. The actual operating modes, fuel consumption and emission levels of the tractor engine were studied and the braking thermal efficiency (BTE) under actual operating conditions was reported for the first time, with a maximum value close to 45%. The engine maps were developed and used to predict tractor emissions and fuel consumption, and the results showed that it can accurately predict tractor fuel consumption and carbon dioxide (CO2) emissions, with mean absolute percentage error (MAPE) and coefficient of determination (R2) values reaching over 90%. This article provides a reliable testing and prediction method for the actual performance of tractor engines, and suggests that real performance indicators should be given better attention.

The impact of the variation in driving conditions on the NOx emissions characteristics in PEMS test for heavy-duty vehicle

The real driving emission test using a portable emission measurement system is the advanced solution to assess NOx emissions from heavy-duty vehicles in recent years. To reflect the NOx emissions of heavy-duty vehicles more realistically, the restrictions on driving conditions will likely be relaxed in the next emission standard for heavy-duty vehicles. In this study, several tests with different payloads were conducted on a China VI heavy-duty vehicle under two different driving conditions. The test results show that some NOx emissions “spikes” may occur in any driving type during the tests of heavy-duty vehicles, and they are mainly related to the NOx conversion efficiency of the SCR system. In general, there is no obvious relevance between the NOx emission rate and the driving type, but the increase of the vehicle speed is beneficial to maintain the temperature of the SCR system in the high-efficiency zone, thus reducing the NOx emission rate. The variation of driving conditions will cause a change in the valid windows of the test, which will lead to a change in the evaluation results of NOx emission compliance. Adding motorway driving to the test route significantly increases the power threshold of the valid windows and reduces specific NOx emissions of the valid windows, with a minimum reduction of more than 30% and a maximum reduction of nearly 50% for “SENOx-90” in this study. The variation in driving conditions can severely affect the true NOx emissions assessment, especially for low-payload tests. In this study, the difference in NOx emissions assessed due to variation in driving conditions reached 1/3 of the total NOx emissions and the difference will gradually decrease as the vehicle load increases.

Evaluating the real-world emissions of diesel passenger Car in Indian heterogeneous traffic.

A 30 portable emission measurement system (PEMS)test was conducted in this study to examine the effect of driving modes (aggressive and normal) and road type (urban and rural) on tailpipe emissions. Driving modes were assessed using relative positive acceleration and velocity × positive acceleration factors. The findings revealed that aggressive and normal driving modes differed significantly on urban and rural roads, as evident from paired sample t-test (p < 0.05). Furthermore, aggressive driving exhibited more prominent speed and acceleration on rural roads, while normal driving modes showed consistent acceleration or speed patterns regardless of road conditions as observed from kernel density estimation and box plot analysis. Emission rates (CO, CO2, HC, and NOx) significantly varied between aggressive and normal driving modes on urban and rural roads, as indicated by paired sample t-test analysis (p < 0.05). Aggressive driving increased CO2, CO, and HC emission rates for acceleration and deceleration modes by 18% to 40% compared to normal driving. Aggressive driving modes increased the emission factors (CO, HC, and CO2) by 5% to 25% compared to the normal driving mode on both urban and rural roads. Moreover, the NOx emission factors were also found significant during normal driving conditions on urban roads. This study provides real-world emission factors of diesel cars considering the impact of route, vehicle familiarity, and driving behavior induced by varying traffic conditions, which will contribute to improve the current emissions inventory on both a local and global level.

Real-driving CO2, NOx and fuel consumption estimation using machine learning approaches

Real driving emissions (RDE) testing are gaining attention for monitoring and regulatory purposes because of providing more realistic emission and fuel consumption measurements compared to laboratory tests. This study aims to develop machine learning (ML) based emission and fuel consumption estimation models using real-driving measurement data. A light-duty diesel vehicle equipped with a portable emissions measurement system (PEMS) was driven in an urban test route by 30 participant drivers of disparate backgrounds to obtain a wide variety of data in terms of driving behaviour and traffic conditions. The Pearson correlation coefficient was used to select the input variables among 36 driving behaviours and 6 engine parameters. The CO2, NOx and fuel consumption prediction models were developed using linear regression (LR), support vector machine (SVM) and Gaussian process regression (GPR). The results showed that all three models could predict CO2 with an absolute relative error (ARE) of less than 9%. The GPR model showed the best performance in CO2 prediction with an R2 of 0.74 and ARE of 3.30%. LR model showed the best prediction accuracy for NOx with an R2 of 0.80 and ARE of 8.91%. All three models worked well for fuel consumption prediction, however, GPR showed the best accuracy with an R2 of 0.81 and ARE of 3.52%. This method lays a foundation for developing route/region specific emission and fuel consumption models that will help to monitor and reduce the environmental impact and the amount of burned fuel. Moreover, developing models from different driver classes will provide valuable insights into emission-optimal driving behaviour which could be used to train new drivers.

Green light optimized speed advisory achieves fuel savings and CO2 emission reduction by profoundly impacting driving behavior

Green Light Optimized Speed Advisory (GLOSA) is an emerging smart transportation technology that guides drivers through signalized intersections during green light phases, showing promise in mobile source pollution control. However, there is still a lack of comprehensive understanding regarding its impact on driving behavior, fuel consumption, and CO2 emissions. In this study, we used heavy-duty diesel truck (HDDT) as an example and conducted a comprehensive path study from the changes in driving behavior to fuel consumption and CO2 emission reduction after the application of GLOSA, using a portable emission measurement system for real-world driving tests at two research scales: signal-controlled road section and signalized intersections. The study was innovatively conducted in three scenarios: Without GLOSA, With GLOSA, and GW GLOSA, which considers the simultaneous application of Green Wave (GW) traffic control system. Results show that applying GLOSA, the passing rate of vehicles at signalized intersections significantly improves, the driving cycle curve tends to be smoother, the cruise time ratio increases to 67.60%, and the travel time is reduced by 5%–13%. HDDT's average speed on the road segments (Seg) decreases by 25.15%, and the aggressive acceleration and deceleration behavior near signalized intersections are significantly reduced. The trip fuel consumption and CO2 emissions of HDDT decrease by 16.63%–23.51% and 16.06%–24.12%, respectively. These fuel savings and CO2 emission reductions mainly occur in the Seg and Signalized intersection-after sections. After the simultaneous application of GLOSA and GW, the trip average speed increases by 60.90%, and traffic efficiency is greatly improved. Although the fuel consumption and CO2 emissions are slightly higher compared to GLOSA alone due to the higher driving speed, GW GLOSA still offers relatively considerable fuel and environmental benefits. This study deepens people's understanding of GLOSA and GW and provides support for the formulation of transportation efficiency improvement and environmental protection strategies.

Integrated MOVES model and machine learning method for prediction of CO2 and NO from light-duty gasoline vehicle

With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management. Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R2 values. On average, the improvement for CO2 was 0.132, and for NO, it was 0.261. This work will provide references for MOVES improvements in practical scenarios and better predict pollutant emissions for LDGVs using limited resources of field tests in cities outside the US.

Real world emission characteristics of Chinese fleet and the current situation of underestimated ship emissions

Ships are considered important contributors to air pollution and have seriously health impact globally. Although many researchers have carried out studies on emission factors (EFs) in recent years, studies covering various types of ships are rare and the chemical characteristics of particulate matter (PM) emitted by them are unclear. Due to the diversity of different national fleets, the current emission inventory in China established by citing foreign EFs will lead to inaccurate calculation results and generate large uncertainty. In this study, the EFs for ocean-going, harbour, offshore and inland vessels were measured by the portable emission measurement system (PEMS). The specific compositions of PM were analyzed, including carbonaceous components, water-soluble ions (WSI), elements, and polycyclic aromatic hydrocarbons (PAHs). Combined with localized EFs, the ship emission inventory of Qingdao Port was established based on Automatic Identification System (AIS) data. The results show nitrogen oxide (NOx) is the primary pollutant discharged by all ships, followed by sulfur dioxide (SO2). The emissions of SO2 and PM from inland and offshore vessels fueled with high sulfur fuel (HSF) are significantly higher than other ships. Carbonaceous components accounted for 43.4%–53.9% of the total mass of PM, WSI accounted for 9.8%–14.3%, and elements accounted for 16.5%–28.6%. Although the use of low sulfur fuel (LSF) has reduced the emission of V and Ni from ships, it could still be treated as tracer elements of ship emissions. The annual emissions of SO2, NOX, PM2.5, THC and CO from ships in Qingdao port in 2020 were 9728.5, 38503.9, 1424.9, 1331.5 and 2440.4 tons, respectively. The ship emission inventory established based on EPA-recommended EFs resulted in underestimating approximately 16.8% of ship pollutants, especially for SO2 and PM. Ocean-going ships and offshore vessels are major emitters in the study waters. This study is hoped to provide a valuable reference for the further regulation and policy formulation of ship emissions in China.

Analysis of exhaust emission processes during the Real Driving Emissions test

The article deals with the study of exhaust emissions from a combustion engine in the Real Driving Emission (RDE) test. These tests are a simulation of real conditions of use of motor vehicles. Nowadays, RDE tests are mandatory for Light Duty Vehicle (LDV) and Heavy Duty Vehicle (HDV) vehicles and in the future, restrictive standard. Euro 7, which combines stricter limits with a comprehensive RDE test cycle, is becoming a challenge for current vehicle engineering. The paper presents the results of pollutant emission tests from a passenger car (PC). In the tests of LDV in the RDE test, the results of which are analyzed in the article, the Portable Emissions Measurement System (PEMS) mobile exhaust emission testing system was used. The processes describing the operating states of the vehicle and the combustion engine, as well as the processes of exhaust emission intensity and the intensity of the number of particulate (PN), were examined. The correlation between the considered processes was investigated. The emission of carbon monoxide, hydrocarbons, nitrogen oxides, particulate and carbon dioxide as well as the road PN were examined. The zero-dimensional statistical characteristics of the examined processes were also determined. The probability density and power spectral density of the processes were established. A great diversity was found in the properties of the process distributions, as well as in the dynamic properties of the processes. In the summary of the analysis of the results of the car speed process, the operating states of the combustion engine and the processes of exhaust emission intensity and the process of the intensity of PN in the RDE test, conclusions were formulated regarding, among others, course of the intensity of these compounds, correlation of the processes of pollution emission intensity and the intensity of the PN with the process of car speed, distribution of processes.

Identifying emissions hotspots and strategies to reduce real-world fuel use and emissions for passenger rail: A spatially resolved approach

The objectives of this work are to model spatially resolved passenger locomotive fuel use and emission rates, locate emissions hotspots, and identify strategies to reduce trip train fuel use and emissions. Train fuel use and emission rates, speed, acceleration, track grade, and track curvature were quantified based on over-the-rail measurements, using portable emission measurement systems, for diesel and biodiesel passenger rail service on the Amtrak-operated Piedmont route. Measurements included 66 one-way trips and 12 combinations of locomotives, consists, and fuels. A locomotive power demand (LPD) based emissions model was developed based on the physics of resistive forces opposing train motion, taking into account factors such as speed, acceleration, track grade, and curvature. The model was applied to locate spatially-resolved locomotive emissions hotspots on a passenger rail route, and also identify train speed trajectories with low trip fuel use and emissions. Results show that acceleration, grade, and drag are the major resistive forces affecting LPD. Hotspot track segments have 3 to 10 times higher emission rates than non-hotspot segments. Real-world trajectories are identified that reduce trip fuel use and emissions by 13 % to 49 % compared to the average. Strategies for reducing trip fuel use and emissions include dispatching energy-efficient and low-emitting locomotives, using a 20 % blend of biodiesel, and operating on low-LPD trajectories. Implementing these strategies will not only decrease trip fuel use and emissions but reduce the number and intensity of hotspots and, thus, lowering the potential for exposure to train-generated pollution near railroad tracks. This work provides insights on reducing railroad energy use and emissions, which would lead to a more sustainable and environmental-friendly rail transportation system.

Differences in compositions and effects of VOCs from vehicle emission detected using various methods

Vehicle exhaust and oil fuel evaporation emit volatile organic compounds (VOCs). The differences in VOC compositions and their effects determined using different methods have not been addressed sufficiently. In this study, VOC samples are obtained from single gasoline and diesel vehicle exhausts using a portable emission measurement system, from a tunnel in Yichang City, and from gasoline and diesel evaporation at gas stations. A total of 107 VOCs are analysed. The calculated VOC source profiles (based on VOC source profiles of single-vehicle type and vehicle fleet composition in the tunnel) and the tested source profiles (from a tunnel test) are compared. The results show that gasoline burning can reduce alkenes from a mass fraction of 53.1% (for evaporation) to 3.6% (for burning), as well as increase the mass fraction of alkenes from 1.3% (for diesel evaporation) to 34.0% (for diesel burning). The calculated VOC source profiles differed from the tested VOC source profiles, with a coefficient of divergence of 0.6. Ethane, ethylene, n-undecane, and n-dodecane are used to distinguish VOCs in gasoline and diesel exhausts. Cis-2-butene, 2-methylpentane, m/p-xylene, o-xylene, and n-decane can be used to separate gasoline from diesel. The xylene/ethylbenzene ratios accurately reveal the photochemical age. Gasoline burning increases health risks associated with VOCs compared with gasoline evaporation. Furthermore, it modifies the main contributor to ozone formation potential. This study is expected to facilitate refined VOC source apportionment and studies pertaining to speciated emission inventories.

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.

Comparison of the Energy Consumption and Exhaust Emissions between Hybrid and Conventional Vehicles, as Well as Electric Vehicles Fitted with a Range Extender

The introduction of new Euro exhaust emission standards and CO2 limits has forced carmakers to implement alternative hybrid and electric powertrains. We are observing a dynamic advancement of this sector. The authors’ primary motivation was to perform a series of measurements of the exhaust emissions and fuel mileages from vehicles fitted with hybrid, conventional and electric (range extender) powertrains. Three vehicles were used in the research project. The first one was a passenger car with a full hybrid powertrain. The vehicle was fitted with a 1.6 dm3 spark ignition engine. The second one was fitted with a 2.2 dm3 diesel engine. The third one was fitted with a 125 kW electric motor and a 28 kW combustion engine used as a range extender. The investigations were carried out according to the RDE (Real Driving Emission) methodology on a test route composed of urban, rural and highway portions. The test route was set in the Poznan agglomeration, and its distance was approx. 80 km. For the measurements, the authors used SEMTECH-DS from the PEMS (Portable Emissions Measurement System) equipment group. Based on the obtained results, the authors validated the test route in terms of the RDE compliance and determined the exhaust emissions and fuel mileages. The authors also analyzed the influence of the conditions of the measurements on the powertrain characteristics of each of the tested vehicles.

Effect of the Volumetric Flow Rate Measurement Methodology of Positive Pressure Ventilators on the Parameters of the Drive Unit

The nature and conditions of the execution of tests (open or duct flow) in terms of evaluating the flow rate generated by positive pressure ventilators (PPV) may affect the parameters of the drive unit recorded during testing. In this article, popular PPVs (conventional type—W1 and turbo type—W2) of about 4.2 kW were tested under open flow (Method A) and duct flow (Method B) conditions. During the tests, engine load values were recorded: torque, speed, horsepower and, using portable emissions measurement systems (PEMS), exhaust gas emissions: carbon monoxide (CO), carbon dioxide (CO2), hydrocarbons (HC), nitrogen oxides (NOx) and fuel consumption. Depending on the method used to measure ventilator flow rates, drive units can have different drive power requirements (from 3.2% to 4.5%). Changes in drive unit operating conditions induced by the flow measurement method are observed in the results of fuel consumption (from 0.65% to 9.8%) and emissions of harmful exhaust compounds: CO2 up to 2.4%, CO up to 67%, HC up to 93.2% and NOx up to 37%. The drive units of turbo type fans (W2) are more susceptible to the influence of the test methods in terms of flow assessment, where they have higher emissions of harmful exhaust gases when tested by Method A. Flow measurement methods affect the oscillation of propulsion power, which contributes to disturbances in the control of the fuel–air mixture composition. The purpose of this article is to analyse the impact of testing methods for measuring the flow rate of positive pressure ventilators on the performance of the drive unit.

Assessing light flex-fuel vehicle emissions with ethanol/gasoline blends along an urban corridor: A case of Fortaleza/Brazil

Brazil is the 9th largest producer of vehicles in the world, with 62.7% of the global fleet of Flex-Fuel Vehicles (FFVs). These vehicles used in Brazil operate with E27 (anhydrous ethanol used for gasohol blending) or E100 hydrous ethanol or any blend between these two. In recent years, Fortaleza city/Brazil has implemented Bus Rapid Transit (BRT) systems in two of the city’s most important avenues. Fortaleza BRT has two-way busways on the central verge of the roadway separated by two lanes from mixed traffic used by passenger cars, light commercial trucks, motorcycles, and heavy-duty vehicles. These systems have been regarded as a potential solution to improve some traffic factors, for only Buses, such as the average driving speed, thus benefiting both transportation and the environment. Thus, this research proposes a comprehensive impact evaluation of an FFV when traveling on BRT corridors, specifically on lanes from mixed traffic, and evaluate the emission factors, specifically on lanes from mixed traffic, composed of a wide variety of vehicle types. All tests in this study were performed at lanes from mixed traffic. Carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbons (HCt) emissions were experimentally measured using a Portable Emission Measurement System (PEMS) under real-world traffic conditions in Fortaleza. FFV tailpipe emissions from E27, E85, and E100 fuels were highly sensitive to the power demand and traffic flow. Additionally, the VSP (Vehicle Specific Power) methodology was applied to characterize the driving modes. Overall, results showed the following: CO2 was, by far, the most highly emitted gas; the highest CO release occurred with E85 blend; NOx emission factors were similar for both E27 and E85, but much greater for E100; and HCt did not show a pattern, presenting both the highest and lowest emission levels with E100.

Quantifying traffic-related carbon emissions on elevated roads through on-road measurements

Vehicles generally move smoothly and with high speeds on elevated roads, thereby producing specific traffic-related carbon emissions in contrast to ground roads. Hence, a portable emission measurement system was adopted to determine traffic-related carbon emissions. The on-road measurement results revealed that the instantaneous emissions of CO2 and CO from elevated vehicles were 17.8% and 21.9% higher than those from ground vehicles, respectively. Based on it, the vehicle specific power was confirmed to exhibit a positive exponential relationship with instantaneous CO2 and CO emissions. In addition to carbon emissions, carbon concentrations on roads were simultaneously measured. The average CO2 and CO emissions on elevated roads in urban areas were 1.2% and 6.9% higher than those on ground roads, individually. Finally, a numerical simulation was performed, and the results verified that elevated roads could deteriorate the air quality on ground roads but improve the air quality above them. What ought to be paid attention to is that the elevated roads present varied traffic behaviour and cause particular carbon emissions, indicating that comprehensive consideration and further balance among the traffic-related carbon emissions are necessary when building elevated roads to alleviate the traffic congestion in urban areas.

Quantifying the Coupling Relationship of Road Grade Impacting Heavy-Duty Diesel Truck Operation Emissions in a Mountainous City in China

To reveal the complex impact of the road grade on heavy-duty diesel truck (HDDT) emissions, the dynamic coupling relationships among the road grade, vehicle operation, and emissions were quantified based on portable emission measurement system data from 24 HDDTs in Chongqing, China. The results showed that the average emission factors of nitrogen oxides (NO x) and carbon dioxide (CO2) of China IV HDDTs were the highest among all tested HDDTs, and NO x emissions often exceeded their corresponding standard limit values. Next, the coupling relationships of road grade, speed, and emissions were studied. Graded roads caused a 4%–44% decrease in the average vehicle speed, resulting in a coupling effect on emissions. For all pollutants and vehicle emission standards, the HDDT emissions on uphill roads were much higher, while those on downhill roads did not deteriorate significantly owing to the increased acceleration operation. Overall, ignoring the road grade led to estimation errors of between −99.14% and 291.30% for CO2 and between −99.21% and 247.73% for NO x. Speed was less correlated with emissions on downhill roads (≤−3%). For uphill roads (&gt;0%), the joint effect of road grade and speed caused a significant increase in CO2 and NO x emissions up to 21.26 and 17.29 times compared to idling emissions. A Sigmoid function successfully modeled the coupling relationship (average R2 = 0.95), and grouped S-shaped mapping curves were revealed for between −8% and 7% road grade and emissions. The model can estimate the influence of road grades on emissions at different speeds, thus providing support for emission reduction control.

Evaluating the Measurement Uncertainty of On-Road NOx Using a Portable Emission Measurement System (PEMS) Based on Real Testing Data in China

An evaluation of the measurement uncertainty of on-road NOx emissions using portable emission measurement system (PEMS) based on real local testing data collected in China was carried out as per the type B method defined in the EN 17507 standard. The aim of this work was to quantify the “absolute” measurement uncertainty of PEMSs, which excluded “PEMS relative to laboratory constant volume sampler (CVS)” uncertainty from the calculation of on-road NOx measurement uncertainty using PEMSs. PEMS instruments from three mainstream manufacturers were employed. The zero drift of the NOx analyzers was evaluated periodically during the real driving emissions (RDE) test, and it was noticed that there was neither a linear nor step model of zero drift, with no correlation with the boundary conditions or measurement principle. Additionally, from the 256 valid RDE tests, the zero drift always ranged from 3.8 ppm to −3.8 ppm, and more than 95% of the span drifts were within a range of 1.5%. Based on the laboratory testing of ten vehicles using the worldwide harmonized light-duty vehicle test cycle (WLTC), the type B uncertainty of PEMS NOx measurements corresponding to China-6a and China-6b limits was assessed. An uncertainty of 26.5% for China-6a was found (NOx limit = 60 mg/km over the WLTC), which is very close to the 22.5% from the EU evaluation results (NOx limit = 80 mg/km over the WLTC); the uncertainty with respect to China-6b was found to be 42.8% because the type-I limit was tuned down to 35 mg/km. This result indicates that, with the ever-tightening regulatory limits of vehicle NOx emissions, big challenges will be posed in terms of the reliability of PEMS measurements, which requires PEMS manufacturers to improve the performance of the instruments and policymakers to refine the test procedures and/or result calculation method to minimize the impacts.