Transfer Methods for Vehicle Carbon Emission Models Based on the Parallel Transportation System

Size-resolved particulate matter emissions from heavy-duty diesel vehicles (HDDVs) and light-duty gasoline vehicles (LDGVs) operated under realistic driving cycles were analyzed for elemental carbon (EC), organic carbon (OC), hopanes, steranes, and polycyclic aromatic hydrocarbons. Measured hopane and sterane size distributions did not match the total carbon size distribution in most cases, suggesting that lubricating oil was not the dominant source of particulate carbon in the vehicle exhaust. A regression analysis using 17alpha(H)-21beta(H)-29-norhopane as a tracer for lubricating oil and benzo[ghi/perylene as a tracer for gasoline showed that gasoline fuel and lubricating oil both make significant contributions to particulate EC and OC emissions from LDGVs. A similar regression analysis performed using 17alpha(H)-21beta(H)-29-norhopane as a tracer for lubricating oil and flouranthene as a tracerfor diesel fuel was able to explain the size distribution of particulate EC and OC emissions from HDDVs. The analysis showed that EC emitted from all HDDVs operated under relatively high load conditions was dominated by diesel fuel contributions with little EC attributed to lubricating oil. Particulate OC emitted from HDDVs was more evenly apportioned between fuel and oil contributions. EC emitted from LDGVs operated underfuel-rich conditions was dominated by gasoline fuel contributions. OC emitted from visibly smoking LDGVs was mostly associated with lubricating oil, but OC emitted from all other categories of LDGVs was dominated by gasoline fuel. The current study clearly illustrates that fuel and lubricating oil make separate and distinct contributions to particulate matter emissions from motor vehicles. These particles should be tracked separately during ambient source apportionment studies since the atmospheric evolution and ultimate health effects of these particles may be different. The source profiles for fuel and lubricating oil contributions to EC and OC emissions derived in this study provide a foundation for future source apportionment calculations.

The US Environmental Protection Agency (EPA) estimates on-road vehicles emissions using the Motor Vehicle Emission Simulator (MOVES). We developed updated ammonia emission rates for MOVES based on road-side exhaust emission measurements of light-duty gasoline and heavy-duty diesel vehicles. The resulting nationwide on-road vehicle ammonia emissions are 1.8, 2.1, 1.8, and 1.6 times higher than the MOVES3 estimates for calendar years 2010, 2017, 2024, and 2035, respectively, primarily due to an increase in light-duty gasoline vehicle NH3 emission rates. We conducted an air quality simulation using the Community Multi-Scale Air Quality (CMAQv5.3.2) model to evaluate the sensitivity of modeled ammonia and fine particulate matter (PM2.5) concentrations in calendar year 2017 using the updated on-road vehicle ammonia emissions. The average monthly urban ammonia ambient concentrations increased by up to 2.3 ppbv in January and 3.0 ppbv in July. The updated on-road NH3 emission rates resulted in better agreement of modeled ammonia concentrations with 2017 annual average ambient ammonia measurements, reducing model bias by 5.8 % in the Northeast region. Modeled average winter PM2.5 concentrations increased in urban areas, including enhancements of up to 0.5 μg/m3 in the northeast United States. The updated ammonia emission rates have been incorporated in MOVES4 and will be used in future versions of the NEI and EPA's modeling platforms.

Measurements of black carbon (BC) with a high-sensitivity laser-induced incandescence (HS-LII) instrument and a single particle soot photometer (SP2) were conducted upwind, downwind, and while driving on a highway dominated by gasoline vehicles. The results are used with concurrent CO(2) measurements to derive fuel-based BC emission factors for real-world average fleet and heavy-duty diesel vehicles separately. The derived emission factors from both instruments are compared, and a low SP2 bias (relative to the HS-LII) is found to be caused by a BC mass mode diameter less than 75 nm, that is most prominent with the gasoline fleet but is not present in the heavy-duty diesel vehicle exhaust on the highway. Results from both the LII and the SP2 demonstrate that the BC emission factors from gasoline vehicles are at least a factor of 2 higher than previous North American measurements, and a factor of 9 higher than currently used emission inventories in Canada, derived with the MOBILE 6.2C model. Conversely, the measured BC emission factor for heavy-duty diesel vehicles is in reasonable agreement with previous measurements. The results suggest that greater attention must be paid to black carbon from gasoline engines to obtain a full understanding of the impact of black carbon on air quality and climate and to devise appropriate mitigation strategies.

Expressways in mountainous plateau areas exhibit complex driving conditions and harsh climatic characteristics that continuously impact vehicle carbon emissions throughout their entire lifecycle and determine the carbon emission levels of expressways during the operational period. To study the carbon emission characteristics of expressways in the western Sichuan Plateau mountainous area, a coupling simulation analysis method combining CarSim/TruckSim simulation software and the Motor Vehicle Emissions Simulator (MOVES) was employed to analyze the spatial distribution characteristics of vehicle-specific power (VSP). This analysis was based on the alignment data and operational environment of the Wenchuan‒Barkam Expressway in the mountainous area of the western Sichuan Plateau. A conversion formula was established to calculate the cumulative VSP per second and total carbon emissions of each unit. Statistical analysis was conducted on the distribution of carbon emissions equivalent per kilometer across the different alignment units. Distribution ranges of carbon emissions equivalent per kilometer for light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) at each alignment unit were 60‒240 g and 100‒1600 g, respectively. The grey relational analysis method was used to quantify the relationship between the carbon emissions equivalent per kilometer of alignment units, circular curves radius, and average grade. Based on the vertical variations in climatic changes with altitude in the mountainous plateau area, a comparative analysis was performed on the trends in the effects of altitude, season, and vehicle starting frequency on carbon emissions. Frequent vehicle starts significantly impacted carbon emissions, and this impact was significantly higher in winter than in summer. Carbon emissions equivalent of the LDV and HDV starting twice on average in summer were approximately 1.09- and 1.04-times higher, respectively, than that when vehicles were started 0.5 times; whereas, in winter, they were 1.17- and 1.07-times higher, respectively.

On-road emission rate measurements of carbon monoxide (CO), nitrogen oxides (NO/sub x/), and gaseous hydrocarbons (HC) from light-duty gasoline (spark-ignition) vehicles and from heavy-duty diesel vehicles operating at constant speed highway conditions are described. The measurements were made at the Allegheny Mountain Tunnel on the Pennsylvania Turnpike during July of 1981. Over 98,000 highway vehicle km were monitored during the study. The on-road results are compared with predictions from the EPA computer model MOBILE 2 and to other vehicle emissions studies. An effective vehicle speed was determined to account for the actual power requirements of vehicles going through the tunnel. The effective speed was then used in MOBILE 2 to predict the on-road results. CO and HC emission rates calculated by MOBILE 2 for low altitude compare within the standard deviations of the on-road measurements at the Allegheny elevation of 707 m, while predicted diesel NO/sub x/ is 2.3sigma above the on-road result and gasoline NO/sub x/ is 100% above the on-road upper limit. If the Allegheny elevation is sufficient to affect on-road emission rates, then MOBILE 2 also overpredicts CO emission rates by as much as 33%.

Refined 2013-based vehicle emission inventory and its spatial and temporal characteristics in Zhengzhou, China

Characteristics of NOX and NH3 emissions from in-use heavy-duty diesel vehicles with various aftertreatment technologies in China

Vehicle emissions are a major source of carbonyls, which play an important role in atmospheric chemistry and urban air quality. Yet, little data are available for speciated carbonyls emitted by vehicles and especially by heavy-duty diesel vehicles. On-road vehicle emissions of carbonyls have been measured in May 1999 at the Tuscarora Mountain Tunnel, PA. Ten saturated aliphatic aldehydes, 4 saturated aliphatic ketones, 4 unsaturated aliphatic carbonyls, 4 aliphatic dicarbonyls, and 9 aromatic carbonyls have been identified and their concentrations measured. For light-duty (LD) vehicles, total carbonyl emissions were ca. 6.4 mg/km, and the 10 largest emission factors were, in decreasing order, those of formaldehyde (2.58 +/- 1.05 mg/km, ca. 40% of total carbonyls), acetone, acetaldehyde, heptanal, crotonaldehyde, 2-butanone, propanal, acrolein, methacrolein, and benzaldehyde. For weight class 7-8 heavy-duty diesel vehicles (7-8 HD), total carbonyl emissions were ca. 26.1 mg/km, and the 10 largest emission factors were, in decreasing order, those of formaldehyde (6.73 +/- 2.05 mg/km, ca. 26% of total carbonyls), acetaldehyde, acetone, crotonaldehyde, m-tolualdehyde, 2-pentanone, benzaldehyde, a C5 saturated aliphatic aldehyde isomer, 2,5-dimethylbenzaldehyde, and 2-butanone. Aromatic carbonyls, unsaturated aliphatic aldehydes, and aliphatic dicarbonyls represented larger fractions of the total carbonyl emissions for 7-8 HD vehicles than for LD vehicles. For HD vehicles, formaldehyde and acetaldehyde emission factors measured in this study are ca. 4-5 times lower than those measured in previous work. For LD vehicles, emission factors measured in this study are generally lower than those measured in earlier work and are about the same, within reported uncertainties, as those measured in 1992 in the same highway tunnel.

Medium- and Heavy-Duty Vehicles (MHDVs) are essential to global logistics and transportation. Around 77% of these vehicles are diesel-powered due to its powertrain mechanical characteristics that make them reliable and energy efficient. However, the environmental implications of diesel combustion, particularly its direct contribution to air pollution and greenhouse gas (GHG) emissions, have increased the interest in alternative fuel vehicles (AFVs). These alternatives include Natural Gas (NG), Propane, Hybrid Electric Vehicles (HEVs), and Battery Electric Vehicles (BEVs), each presenting unique benefits and challenges in terms of required fueling infrastructure, energy efficiency, environmental impact, and total cost of operation (TCO). Maintenance costs are a significant portion of the TCO for MHDVs and vary across different fuel types, vehicle vocations, and operational conditions such as weather and road conditions in the area that these vehicles operate. Machine Learning (ML) algorithms can analyze historical maintenance data and operational parameters to identify patterns and predict vehicle cost per mile with 95% accuracy. This predictive capability enables fleet management companies to schedule maintenance more efficiently, reduce unplanned vehicle degradation, and manage the lifecycle costs of their fleet more effectively. Moreover, the transition to alternative fuels in HDVs underscores the necessity for tailored maintenance strategies that address the specific needs of these vehicles. Additionally, a user-friendly graphical interface was developed to make the predictive maintenance cost ML model accessible for MHDV fleet or vehicle owners with no need of programming or specific software knowledge.

Carbon-emission calculation method during operation period based on asphalt pavement performance

A vehicle emissions inventory is an account of emissions produced by all vehicles in an area. Many different factors can affect the emissions and the measurement of the emissions that are used to create an emissions inventory. These factors for heavy-duty diesel vehicles have been addressed and their relative affect on emissions was evaluated from test data and analytical analyses. These elements can affect the emissions by a factor of 15 depending on testing conditions.;One purpose that an emissions database serves is to provide a source for predicting future emissions for a specific vehicle or many vehicles in a general area. Using a database directly for prediction is ideal, but no comprehensive data set currently exists that covers all of the different vehicle and component combinations that exist in current use. Numerical models can be used to take the existing information about vehicle emissions and calculate the emissions that would be produced by all the vehicles in an inventory. The Transportable Heavy-Duty Vehicle Emissions Testing Laboratories at WU have collected emissions data from heavy-duty vehicles for approximately 8 years. This existing data were used in an analysis to develop a method that can produce emissions factors in grams per mile for all heavy-duty vehicles from a small database of measured emissions. The method developed categorizes the emissions according to the vehicle speed and acceleration. The WVU emissions data were combined with truck activity data derived by Battelle Memorial Institute to create emissions factors in grains per mile that reflects actual driving patterns. These factors were then compared to measured emissions from the THDVETL and errors were found to be as low as 5%.

Real-world black carbon emissions of gasoline vehicles at urban intersections

The rapid growth of vehicle sales and usage has highlighted the need for greenhouse gas (GHG) emission reduction in Macau, a special administrative region (SAR) of China. As the most primary vehicle type, light-duty vehicles (LDV, including light-duty gasoline vehicles (LDGVs) and light-duty diesel vehicles (LDDVs)) play a key role in promoting the GHG reduction and development of green transportation system in Macau. This study, on the basis of real-world tested and statistical data, firstly performed a streamlined life-cycle assessment (SLCA) on LDVs, to evaluate the potential GHG emissions and reduction through shifting to hybrid electric vehicles (HEVs) and electric vehicles (EVs). The results show that the mean GHG emissions from the LDGVs, LDDVs, and HEVs per 100 km were 25.16, 20.30, and 15.00 kg CO2 eq, respectively. Under the current electricity mix in Macau, EVs with the emissions of 12.39 kg CO2 eq/100 km can achieve a significant GHG emission reduction of LDVs in Macau. The total GHG emissions from LDVs increased from 124.99 to 247.82 thousand metric tons over the periods 2001–2014, with a 5.42% annual growth rate. A scenario analysis indicated that the development of HEVs and EVs—especially EVs—has the potential to control the GHG emissions from LDVs. Under the electricity mix of natural gas (NG) and solar energy (SE), the GHG emissions from EVs would drop by about 22 and 28%, respectively, by 2030. This study develops a useful approach to evaluate the potential GHG emissions and its reduction strategies in Macau. All the obtained results could be useful for decision makers, providing robust support for drawing up an appropriate plan for improving green transportation systems in Macau.

Particulate emission factors for mobile fossil fuel and biomass combustion sources

ABSTRACTIn urban highway networks, some factors may influence the amount of average kilometric vehicle carbon emission (AKVC). Especially in highway networks of a large metropolitan area, the factors could be more diversified. In the present paper, the particular factors in the statistics of the districts in Seoul City that affect the AKVCs were investigated. Individual and combined factors were considered. The individual factors in the present study included the number of intersections, the number of intersections per kilometre, the road portion to the total area, the number of registered vehicles, the number of commuters, the population, the population density, the total land area and the road length. The combined factors considered included the number of intersections per kilometre over the number of registered vehicles, the number of intersections per kilometre over the road portion to the total area, and the road portion to the total area over the number of registered vehicles. Using the stepwise regression, it was demonstrated that the number of intersections per kilometre is the only significant factor affecting the AKVC in the road network of the district. All other factors are regarded as insignificant in affecting the AKVC. Additionally, correlation analysis confirms the result. From the study, as ‘the number of intersections per kilometre’ is the most influencing factor on reduction of vehicle carbon emissions in the road network, it may be implied that the number of intersections should be controlled or reduced in the road network of a city.