Black Carbon and Polycyclic Aromatic Hydrocarbon Emissions from Vehicles in the United States–Mexico Border Region: Pilot Study

The investigators developed a system to measure black carbon (BC) and particle-bound polycyclic aromatic hydrocarbon (PAH) emission factors during roadside sampling in four cities along the United States–Mexico border, Calexico/Mexicali and El Paso/Juárez. The measurement system included a photoacoustic analyzer for BC, a photoelectric aerosol sensor for particle-bound PAHs, and a carbon dioxide (CO₂) analyzer. When a vehicle with measurable emissions passed the system probe, corresponding BC, PAH, and CO₂ peaks were evident, and a fuel-based emission factor was estimated. A picture of each vehicle was also recorded with a digital camera. The advantage of this system, compared with other roadside methods, is the direct measurement of particulate matter components and limited interference from roadside dust. The study revealed some interesting trends: Mexican buses and all medium-duty trucks were more frequently identified as high emitters of BC and PAH than heavy-duty trucks or passenger vehicles. In addition, because of the high daily mileage of buses, they are good candidates for additional study. Mexican trucks and buses had higher average emission factors compared with U.S. trucks and buses, but the differences were not statistically significant. Few passenger vehicles had measurable BC and PAH emissions, although the highest emission factor came from an older model passenger vehicle licensed in Baja California.     

Determining gaseous emission factors and driver's particle exposures during traffic congestion by vehicle-following measurement techniques

Vehicle gaseous emissions (NO, CO, CO2, and hydrocarbon [HC]) and driver's particle exposures (particulate matter < 1 microm [PM1], < 2.5 microm [PM2.5], and < 10 microm [PM10]) were measured using a mobile laboratory to follow a wide variety of vehicles during very heavy traffic congestion in Macao, Special Administrative Region, People's Republic of China, an urban area having one of the highest population densities in the world. The measurements were taken with high time resolution so that fluctuations in the emissions can be seen readily during vehicle acceleration, cruising, deceleration, and idling. The tests were conducted in close proximity to the vehicles, with the inlet of a five-gas analyzer mounted on the front bumper of the mobile laboratory, and the distance between the vehicles was usually within several meters. To measure the driver's particle exposures, the inlets of the particle analyzers were mounted at the height of the driver's breathing position in the mobile laboratory, with the driver's window open. A total of 178 and 113 vehicles were followed individually to determine the gaseous emission factor and the driver's particle exposures, respectively, for motorcycle, passenger car, taxi, truck, and bus. The gaseous emission factors were used to model the roadside air quality, and good correlations between the modeled and monitored CO, NO2, and nitrogen oxide (NO(x)) verified the reliability of the experiments. Compared with petrol passenger cars and petrol trucks, diesel taxies and diesel trucks emitted less CO but more NO(x). The impact of urban canyons is shown to cause a significant increase in the PM1 peak. The background concentrations contributed a significant amount of the driver's particle exposures.     

Emission factors for high-emitting vehicles based on on-road measurements of individual vehicle exhaust with a mobile measurement platform

Fuel-based emission factors for 143 light-duty gasoline vehicles (LDGVs) and 93 heavy-duty diesel trucks (HDDTs) were measured in Wilmington, CA using a zero-emission mobile measurement platform (MMP). The frequency distributions of emission factors of carbon monoxide (CO), nitrogen oxides (NO(x)), and particle mass with aerodynamic diameter below 2.5 microm (PM2.5) varied widely, whereas the average of the individual vehicle emission factors were comparable to those reported in previous tunnel and remote sensing studies as well as the predictions by Emission Factors (EMFAC) 2007 mobile source emission model for Los Angeles County. Variation in emissions due to different driving modes (idle, low- and high-speed acceleration, low- and high-speed cruise) was found to be relatively small in comparison to intervehicle variability and did not appear to interfere with the identification of high emitters, defined as the vehicles whose emissions were more than 5 times the fleet-average values. Using this definition, approximately 5% of the LDGVs and HDDTs measured were high emitters. Among the 143 LDGVs, the average emission factors of NO(x), black carbon (BC), PM2.5, and ultrafine particle (UFP) would be reduced by 34%, 39%, 44%, and 31%, respectively, by removing the highest 5% of emitting vehicles, whereas CO emission factor would be reduced by 50%. The emission distributions of the 93 HDDTs measured were even more skewed: approximately half of the NO(x) and CO fleet-average emission factors and more than 60% of PM2.5, UFP, and BC fleet-average emission factors would be reduced by eliminating the highest-emitting 5% HDDTs. Furthermore, high emissions of BC, PM2.5, and NO(x) tended to cluster among the same vehicles.     

Improved Version of Anion-exchange Resin-based Desulfurization Process for Alkali Metal Sulfates

Abstract

Vehicle gaseous emissions (NO, CO, CO₂, and hydrocarbon [HC]) and driver’s particle exposures (particulate matter <1 μm [PM₁], <2.5 μm [PM_2.5], and<10 μm [PM₁₀]) were measured using a mobile laboratory to follow a wide variety of vehicles during very heavy traffic congestion in Macao, Special Administrative Region, People’s Republic of China, an urban area having one of the highest population densities in the world. The measurements were taken with high time resolution so that fluctuations in the emissions can be seen readily during vehicle acceleration, cruising, deceleration, and idling. The tests were conducted in close proximity to the vehicles, with the inlet of a five-gas analyzer mounted on the front bumper of the mobile laboratory, and the distance between the vehicles was usually within several meters. To measure the driver’s particle exposures, the inlets of the particle analyzers were mounted at the height of the driver’s breathing position in the mobile laboratory, with the driver’s window open. A total of 178 and 113 vehicles were followed individually to determine the gaseous emission factor and the driver’s particle exposures, respectively, for motorcycle, passenger car, taxi, truck, and bus. The gaseous emission factors were used to model the roadside air quality, and good correlations between the modeled and monitored CO, NO₂, and nitrogen oxide (NO_x) verified the reliability of the experiments. Compared with petrol passenger cars and petrol trucks, diesel taxies and diesel trucks emitted less CO but more NO_x. The impact of urban canyons is shown to cause a significant increase in the PM₁ peak. The background concentrations contributed a significant amount of the driver’s particle exposures.     

Evaluating speed differences between passenger vehicles and heavy trucks for transportation-related emissions modeling

Heavy-duty trucks make up only 3% of the on-road vehicle fleet, yet they account for > 7% of vehicle miles traveled in the United States. They also contribute a significant proportion of regulated ambient emissions. Heavy vehicles emit emissions at different rates than passenger vehicles. They may also behave differently on-road, yet may be treated similarly to passenger vehicles in emissions modeling. Input variables to the MOBILE software, such as average vehicle speed, are typically specified the same for heavy trucks as for passenger vehicles. Although not frequently considered in modeling emissions, speed differences between passenger vehicles and heavy trucks may influence emissions, because emission rates are correlated to average speed. Differences were evaluated by collecting average and spot speeds for heavy trucks and passenger vehicles on arterials and spot speeds on freeways in Des Moines, IA, and Minneapolis/St. Paul, MN. Speeds were compared by study site. Space mean speeds for heavy trucks were lower than passenger vehicle speeds for all of the arterials with differences ranging from 0.8 to 19 mph. Spot speeds for heavy trucks were also lower at all of the arterial and freeway locations with differences ranging from 0.8 to 6.1 mph. The impact that differences in on-road speeds had on emissions was also evaluated using MOBILE version 6.2. Misspecification of average truck speed is the most significant at lower and higher speed ranges.     

Idle emissions from heavy-duty diesel and natural gas vehicles at high altitude

Idle emissions of total hydrocarbon (THC), CO, NOx, and particulate matter (PM) were measured from 24 heavy-duty diesel-fueled (12 trucks and 12 buses) and 4 heavy-duty compressed natural gas (CNG)-fueled vehicles. The volatile organic fraction (VOF) of PM and aldehyde emissions were also measured for many of the diesel vehicles. Experiments were conducted at 1609 m above sea level using a full exhaust flow dilution tunnel method identical to that used for heavy-duty engine Federal Test Procedure (FTP) testing. Diesel trucks averaged 0.170 g/min THC, 1.183 g/min CO, 1.416 g/min NOx, and 0.030 g/min PM. Diesel buses averaged 0.137 g/min THC, 1.326 g/min CO, 2.015 g/min NOx, and 0.048 g/min PM. Results are compared to idle emission factors from the MOBILE5 and PART5 inventory models. The models significantly (45-75%) overestimate emissions of THC and CO in comparison with results measured from the fleet of vehicles examined in this study. Measured NOx emissions were significantly higher (30-100%) than model predictions. For the pre-1999 (pre-consent decree) truck engines examined in this study, idle NOx emissions increased with model year with a linear fit (r2 = 0.6). PART5 nationwide fleet average emissions are within 1 order of magnitude of emissions for the group of vehicles tested in this study. Aldehyde emissions for bus idling averaged 6 mg/min. The VOF averaged 19% of total PM for buses and 49% for trucks. CNG vehicle idle emissions averaged 1.435 g/min for THC, 1.119 g/min for CO, 0.267 g/min for NOx, and 0.003 g/min for PM. The g/min PM emissions are only a small fraction of g/min PM emissions during vehicle driving. However, idle emissions of NOx, CO, and THC are significant in comparison with driving emissions.     

Evaluating Speed Differences between Passenger Vehicles and Heavy Trucks for Transportation-Related Emissions Modeling

Abstract

Heavy-duty trucks make up only 3% of the on-road vehicle fleet, yet they account for >7% of vehicle miles traveled in the United States. They also contribute a significant proportion of regulated ambient emissions. Heavy vehicles emit emissions at different rates than passenger vehicles. They may also behave differently on‐road, yet may be treated similarly to passenger vehicles in emissions modeling. Input variables to the MOBILE software, such as average vehicle speed, are typically specified the same for heavy trucks as for passenger vehicles. Although not frequently considered in modeling emissions, speed differences between passenger vehicles and heavy trucks may influence emissions, because emission rates are correlated to average speed. Differences were evaluated by collecting average and spot speeds for heavy trucks and passenger vehicles on arterials and spot speeds on freeways in Des Moines, IA, and Minneapolis/St. Paul, MN. Speeds were compared by study site. Space mean speeds for heavy trucks were lower than passenger vehicle speeds for all of the arterials with differences ranging from 0.8 to 19 mph. Spot speeds for heavy trucks were also lower at all of the arterial and freeway locations with differences ranging from 0.8 to 6.1 mph. The impact that differences in on‐road speeds had on emissions was also evaluated using MOBILE version 6.2. Misspecification of average truck speed is the most significant at lower and higher speed ranges.     

Emission Factors for High-Emitting Vehicles Based on On-Road Measurements of Individual Vehicle Exhaust with a Mobile Measurement Platform

ABSTRACT

Fuel-based emission factors for 143 light-duty gasoline vehicles (LDGVs) and 93 heavy-duty diesel trucks (HDDTs) were measured in Wilmington, CA using a zero-emission mobile measurement platform (MMP). The frequency distributions of emission factors of carbon monoxide (CO), nitrogen oxides (NO_x), and particle mass with aerodynamic diameter below 2.5 μm (PM_2.5) varied widely, whereas the average of the individual vehicle emission factors were comparable to those reported in previous tunnel and remote sensing studies as well as the predictions by Emission Factors (EMFAC) 2007 mobile source emission model for Los Angeles County. Variation in emissions due to different driving modes (idle, low- and high-speed acceleration, low- and high-speed cruise) was found to be relatively small in comparison to intervehicle variability and did not appear to interfere with the identification of high emitters, defined as the vehicles whose emissions were more than 5 times the fleet-average values. Using this definition, approximately 5% of the LDGVs and HDDTs measured were high emitters. Among the 143 LDGVs, the average emission factors of NO_x, black carbon (BC), PM_2.5, and ultrafine particle (UFP) would be reduced by 34%, 39%, 44%, and 31%, respectively, by removing the highest 5% of emitting vehicles, whereas CO emission factor would be reduced by 50%. The emission distributions of the 93 HDDTs measured were even more skewed: approximately half of the NO_x and CO fleet-average emission factors and more than 60% of PM_2.5, UFP, and BC fleet-average emission factors would be reduced by eliminating the highest-emitting 5% HDDTs. Furthermore, high emissions of BC, PM_2.5, and NO_x tended to cluster among the same vehicles.

IMPLICATIONS This study presents the characterization of on-road vehicle emissions in Wilmington, CA, by sampling individual vehicle plumes. Approximately 5% of the vehicles were high emitters, whose emissions were more than 5 times the fleet-average values. These high emitters were responsible for 30% and more than 50% of the average emission factors of LDGVs and HDDVs, respectively. It is likely that as the overall fleet becomes cleaner due to more stringent regulations, a small fraction of the fleet may contribute a growing and disproportionate share of the overall emissions. Therefore, long-term changes in on-road emissions need to be monitored.     

A Fuel-Based Motor Vehicle Emission Inventory

Abstract

A fuel-based methodology for calculating motor vehicle emission inventories is presented. In the fuel-based method, emission factors are normalized to fuel consumption and expressed as grams of pollutant emitted per gallon of gasoline burned. Fleet-average emission factors are calculated from the measured on-road emissions of a large, random sample of vehicles. Gasoline use is known at the state level from sales tax data, and may be disaggregated to individual air basins. A fuel-based motor vehicle CO inventory was calculated for the South Coast Air Basin in California for summer 1991. Emission factors were calculated from remote sensing measurements of more than 70,000 in-use vehicles. Stabilized exhaust emissions of CO were estimated to be 4400 tons/day for cars and 1500 tons/day for light-duty and medium- duty trucks, with an estimated uncertainty of ±20% for cars and ±30% for trucks. Total motor vehicle CO emissions, including incremental start emissions and emissions from heavy-duty vehicles were estimated to be 7900 tons/day. Fuelbased inventory estimates were greater than those of California's MVEI 7F model by factors of 2.2 for cars and 2.6 for trucks. A draft version of California's MVEI 7G model, which includes increased contributions from high-emitting vehicles and off-cycle emissions, predicted CO emissions which closely matched the fuel-based inventory. An analysis of CO mass emissions as a function of vehicle age revealed that cars and trucks which were ten or more years old were responsible for 58% of stabilized exhaust CO emissions from all cars and trucks.     

Deriving fuel-based emission factor thresholds to interpret heavy-duty vehicle roadside plume measurements

Remote sensing devices have been used for decades to measure gaseous emissions from individual vehicles at the roadside. Systems have also been developed that entrain diluted exhaust and can also measure particulate matter (PM) emissions. In 2015, the California Air Resources Board (CARB) reported that 8% of in-field diesel particulate filters (DPF) on heavy-duty (HD) vehicles were malfunctioning and emitted about 70% of total diesel PM emissions from the DPF-equipped fleet. A new high-emitter problem in the heavy-duty vehicle fleet had emerged. Roadside exhaust plume measurements reflect a snapshot of real-world operation, typically lasting several seconds. In order to relate roadside plume measurements to laboratory emission tests, we analyzed carbon dioxide (CO₂), oxides of nitrogen (NO_X), and PM emissions collected from four HD vehicles during several driving cycles on a chassis dynamometer. We examined the fuel-based emission factors corresponding to possible exceedances of emission standards as a function of vehicle power. Our analysis suggests that a typical HD vehicle will exceed the model year (MY) 2010 emission standards (of 0.2 g NO_X/bhp-hr and 0.01 g PM/bhp-hr) by three times when fuel-based emission factors are 9.3 g NO_X/kg fuel and 0.11 g PM/kg using the roadside plume measurement approach. Reported limits correspond to 99% confidence levels, which were calculated using the detection uncertainty of emissions analyzers, accuracy of vehicle power calculations, and actual emissions variability of fixed operational parameters. The PM threshold was determined for acceleration events between 0.47 and 1.4 mph/sec only, and the NO_X threshold was derived from measurements where after-treatment temperature was above 200°C. Anticipating a growing interest in real-world driving emissions, widespread implementation of roadside exhaust plume measurements as a compliment to in-use vehicle programs may benefit from expanding this analysis to a larger sample of in-use HD vehicles.

Implications: Regulatory agencies, civil society, and the public at large have a growing interest in vehicle emission compliance in the real world. Leveraging roadside plume measurements to identify vehicles with malfunctioning emission control systems is emerging as a viable new and useful method to assess in-use performance. This work proposes fuel-based emission factor thresholds for PM and NOx that signify exceedances of emission standards on a work-specific basis by analyzing real-time emissions in the laboratory. These thresholds could be used to prescreen vehicles before roadside enforcement inspection or other inquiry, enhance and further develop emission inventories, and potentially develop new requirements for heavy-duty inspection and maintenance (I/M) programs, including but not limited to identifying vehicles for further testing.     

On-road measurement of fine particle and nitrogen oxide emissions from light- and heavy-duty motor vehicles

An updated assessment of fine particle emissions from light- and heavy-duty vehicles is needed due to recent changes to the composition of gasoline and diesel fuel, more stringent emission standards applying to new vehicles sold in the 1990s, and the adoption of a new ambient air quality standard for fine particulate matter (PM_2.5) in the United States. This paper reports the measurement of emissions from vehicles in a northern California roadway tunnel during summer 1997. Separate measurements were made of uphill traffic in two tunnel bores: one bore carried both light-duty vehicles and heavy-duty diesel trucks, and the second bore was reserved for light-duty vehicles. Ninety-eight percent of the light-duty vehicles were gasoline-powered. In the tunnel, heavy-duty diesel trucks emitted 24, 37, and 21 times more fine particle, black carbon, and sulfate mass per unit mass of fuel burned than light-duty vehicles. Heavy-duty diesel trucks also emitted 15–20 times the number of particles per unit mass of fuel burned compared to light-duty vehicles. Fine particle emissions from both vehicle classes were composed mostly of carbon; diesel-derived particulate matter contained more black carbon (51±11% of PM_2.5 mass) than did light-duty fine particle emissions (33±4%). Sulfate comprised only 2% of total fine particle emissions for both vehicle classes. Sulfate emissions measured in this study for heavy-duty diesel trucks are significantly lower than values reported in earlier studies conducted before the introduction of low-sulfur diesel fuel. This study suggests that heavy-duty diesel vehicles in California are responsible for nearly half of oxides of nitrogen emissions and greater than three-quarters of exhaust fine particle emissions from on-road motor vehicles.     

An empirical method for predicting exhaust emissions of regulated pollutants from future vehicle technologies

A methodology is presented for estimating emissions of passenger cars and light commercial vehicles complying with future European Union emission standards, which introduces appropriate reductions over the emission factors of existing vehicle technologies. For three-way catalyst gasoline vehicles, future real-world emissions are assumed to decrease by the same ratio as emission standards. Additionally, distinction is made between emissions during the thermally stabilised emission control system operation and emissions during the cold-start phase, where reductions are mainly due to the decreasing light-off time of future catalyst technologies. In case of diesel vehicles, some of the emission standards, such as 1993 CO, did not represent the actual emission level of vehicles at the time. Therefore, reductions brought over the 1993 emission factor are based both on relevant emission standards reductions and on technological considerations. In a second step, the derived emission factors are corrected to account for vehicle age and fuel quality effects. Vehicle age is introduced in the calculation via emission degradation functions of the total vehicle-accumulated mileage. The impact of improved fuels on the emissions of existing and future vehicle technologies is also modelled by applying correction factors depending on fuel specifications. A number of examples are given by applying the methodology on forecast activity data for different European countries to illustrate the expected effects of future vehicle technologies and fuels.     

Investigating the real-world emission characteristics of light-duty gasoline vehicles and their relationship to local socioeconomic conditions in three communities in Los Angeles,California

This paper discusses results from a vehicular emissions research study of over 350 vehicles conducted in three communities in Los Angeles, CA, in 2010 using vehicle chase measurements. The study explores the real-world emission behavior of light-duty gasoline vehicles, characterizes real-world super-emitters in the different regions, and investigates the relationship of on-road vehicle emissions with the socioeconomic status (SES) of the region. The study found that in comparison to a 2007 earlier study in a neighboring community, vehicle emissions for all measured pollutants had experienced a significant reduction over the years, with oxides of nitrogen (NO_X) and black carbon (BC) emissions showing the largest reductions. Mean emission factors of the sampled vehicles in low-SES communities were roughly 2–3 times higher for NO_X, BC, carbon monoxide, and ultrafine particles, and 4–11 times greater for fine particulate matter (PM_2.5) than for vehicles in the high-SES neighborhood. Further analysis indicated that the emission factors of vehicles within a technology group were also higher in low-SES communities compared to similar vehicles in the high-SES community, suggesting that vehicle age alone did not explain the higher vehicular emission in low-SES communities.

Evaluation of the emission factor distribution found that emissions from 12% of the sampled vehicles were greater than five times the mean from all of the sampled fleet, and these vehicles were consequently categorized as “real-world super-emitters.” Low-SES communities had approximately twice as many super-emitters for most of the pollutants as compared to the high-SES community. Vehicle emissions calculated using model-year-specific average fuel consumption assumptions suggested that approximately 5% of the sampled vehicles accounted for nearly half of the total CO, PM_2.5, and UFP emissions, and 15% of the vehicles were responsible for more than half of the total NO_X and BC emissions from the vehicles sampled during the study.

Implications: This study evaluated the real-world emission behavior and super-emitter distribution of light-duty gasoline vehicles in California, and investigated the relationship of on-road vehicle emissions with local socioeconomic conditions. The study observed a significant reduction in vehicle emissions for all measured pollutants when compared to an earlier study in Wilmington, CA, and found a higher prevalence of high-emitting vehicles in low-socioeconomic-status communities. As overall fleet emissions decrease from stringent vehicle emission regulations, a small fraction of the fleet may contribute to a disproportionate share of the overall on-road vehicle emissions. Therefore, this work will have important implications for improving air quality and public health, especially in low-SES communities.     

Temperature effects on particulate emissions from DPF-equipped diesel trucks operating on conventional and biodiesel fuels

Emissions tests were conducted on two medium heavy-duty diesel trucks equipped with a particulate filter (DPF), with one vehicle using a NOx absorber and the other a selective catalytic reduction (SCR) system for control of nitrogen oxides (NOx). Both vehicles were tested with two different fuels (ultra-low-sulfur diesel [ULSD] and biodiesel [B20]) and ambient temperatures (70ºF and 20ºF), while the truck with the NOx absorber was also operated at two loads (a heavy weight and a light weight). The test procedure included three driving cycles, a cold start with low transients (CSLT), the federal heavy-duty urban dynamometer driving schedule (UDDS), and a warm start with low transients (WSLT). Particulate matter (PM) emissions were measured second-by-second using an Aethalometer for black carbon (BC) concentrations and an engine exhaust particle sizer (EEPS) for particle count measurements between 5.6 and 560 nm. The DPF/NOx absorber vehicle experienced increased BC and particle number concentrations during cold starts under cold ambient conditions, with concentrations two to three times higher than under warm starts at higher ambient temperatures. The average particle count for the UDDS showed an opposite trend, with an approximately 27% decrease when ambient temperatures decreased from 70ºF to 20ºF. This vehicle experienced decreased emissions when going from ULSD to B20. The DPF/SCR vehicle tested had much lower emissions, with many of the BC and particle number measurements below detectable limits. However, both vehicles did experience elevated emissions caused by DPF regeneration. All regeneration events occurred during the UDDS cycle. Slight increases in emissions were measured during the WSLT cycles after the regeneration. However, the day after a regeneration occurred, both vehicles showed significant increases in particle number and BC for the CSLT drive cycle, with increases from 93 to 1380% for PM number emissions compared with tests following a day with no regeneration.

Implications:?The use of diesel particulate filters (DPFs) on trucks is becoming more common throughout the world. Understanding how DPFs affect air pollution emissions under varying operating conditions will be critical in implementing effective air quality standards. This study evaluated particulate matter (PM) and black carbon (BC) emissions with two DPF-equipped heavy-duty diesel trucks operating on conventional fuel and a biodiesel fuel blend at varying ambient temperatures, loads, and drive cycles.     

Idle Emissions from Heavy-Duty Diesel and Natural Gas Vehicles at High Altitude

ABSTRACT

Idle emissions of total hydrocarbon (THC), CO, NO_x, and particulate matter (PM) were measured from 24 heavy-duty diesel-fueled (12 trucks and 12 buses) and 4 heavy-duty compressed natural gas (CNG)-fueled vehicles. The volatile organic fraction (VOF) of PM and aldehyde emissions were also measured for many of the diesel vehicles. Experiments were conducted at 1609 m above sea level using a full exhaust flow dilution tunnel method identical to that used for heavy-duty engine Federal Test Procedure (FTP) testing. Diesel trucks averaged 0.170 g/min THC, 1.183 g/min CO, 1.416 g/min NO_x, and 0.030 g/min PM. Diesel buses averaged 0.137 g/min THC, 1.326 g/min CO, 2.015 g/min NO_x, and 0.048 g/min PM.

Results are compared to idle emission factors from the MOBILE5 and PART5 inventory models. The models significantly (45-75%) overestimate emissions of THC and CO in comparison with results measured from the fleet of vehicles examined in this study. Measured NO_x emissions were significantly higher (30-100%) than model predictions. For the pre-1999 (pre-consent decree) truck engines examined in this study, idle NO_x emissions increased with Health and Environment; June 30, 1999 (available from the authors).     

Real-world emissions of in-use off-road vehicles in Mexico

Off-road vehicles used in construction and agricultural activities can contribute substantially to emissions of gaseous pollutants and can be a major source of submicrometer carbonaceous particles in many parts of the world. However, there have been relatively few efforts in quantifying the emission factors (EFs) and for estimating the potential emission reduction benefits using emission control technologies for these vehicles. This study characterized the black carbon (BC) component of particulate matter and NOx, CO, and CO₂ EFs of selected diesel-powered off-road mobile sources in Mexico under real-world operating conditions using on-board portable emissions measurements systems (PEMS). The vehicles sampled included two backhoes, one tractor, a crane, an excavator, two front loaders, two bulldozers, an air compressor, and a power generator used in the construction and agricultural activities. For a selected number of these vehicles the emissions were further characterized with wall-flow diesel particle filters (DPFs) and partial-flow DPFs (p-DPFs) installed. Fuel-based EFs presented less variability than time-based emission rates, particularly for the BC. Average baseline EFs in working conditions for BC, NOx, and CO ranged from 0.04 to 5.7, from 12.6 to 81.8, and from 7.9 to 285.7 g/kg-fuel, respectively, and a high dependency by operation mode and by vehicle type was observed. Measurement-base frequency distributions of EFs by operation mode are proposed as an alternative method for characterizing the variability of off-road vehicles emissions under real-world conditions. Mass-based reductions for black carbon EFs were substantially large (above 99%) when DPFs were installed and the vehicles were idling, and the reductions were moderate (in the 20–60% range) for p-DPFs in working operating conditions. The observed high variability in measured EFs also indicates the need for detailed vehicle operation data for accurately estimating emissions from off-road vehicles in emissions inventories.

Implications: Measurements of off-road vehicles used in construction and agricultural activities in Mexico using on-board portable emissions measurements systems (PEMS) showed that these vehicles can be major sources of black carbon and NO_X. Emission factors varied significantly under real-world operating conditions, suggesting the need for detailed vehicle operation data for accurately estimating emissions inventories. Tests conducted in a selected number of sampled vehicles indicated that diesel particle filters (DPFs) are an effective technology for control of diesel particulate emissions and can provide potentially large emissions reduction in Mexico if widely implemented.     

Remote Sensing Data and a Potential Model of Vehicle Exhaust Emissions

In June 1991, General Motors Research and Development Center (GMR&D) participated in a remote sensing study conducted by the California Air Resources Board and the U. S. Environmental Protection Agency. During this study, the GMR&D remote sensor was used to measure the carbon monoxide (CO) and hydrocarbon (HC) emissions from approximately 15,000 vehicles. The vehicle type (passenger car, light-duty truck, or medium/heavy-duty truck), manufacturer, and model year were identified for each vehicle by acquiring registration data from the state of California. Analyses were performed separately for each vehicle type and for passenger cars by separate model years. The data indicate that the passenger cars with the highest 10% of CO emissions generated approximately 58% of the total CO from all cars. Similarly, the 10% highest HC-emitting cars generated 65% of the total HC from cars. It was found that for each model year of vehicle, the distribution of emission concentrations followed a logarithmic relationship. The logarithmic functions that describe these relationships can be used to estimate the fraction of vehicles that emitted at or above any given concentration of CO or HC. However, these logarithmic functions only describe measured distributions for vehicles emitting more than 1% CO and 0.015% HC.     

Individual Measurements of the Emission Factor of Aerosol Black Carbon in Automobile Plumes

As automobiles passed a measuring point, we recorded the concentrations of carbon dioxide and aerosol black carbon (BC) in their dispersing exhaust plumes. After subtraction of background levels, the ratio of the increments of these species allows us to calculate the emission factor of BC per unit mass of fuel from each individual vehicle. These factors spanned a range of greater than two orders of magnitude, representing the emission of from 4 × 10^?6 to 10^?3 grams of aerosol black carbon per gram of carbon consumed in the fuel. Their distribution showed that 20 percent of the vehicles accounted for 65 percent of the emissions. The real-time measurement methodology allows for a determination of the distribution of emission factors across the actual population of sources. These results are similar to the wide range of carbon monoxide emission factors reported recently.     

Comparison of vehicle activity and emission inventory between Beijing and Shanghai

Vehicle emission inventory is a critical element for air quality study. This study created systemic methods to establish a vehicle emission inventory in Chinese cities. The methods were used to obtain credible results of vehicle activity in Beijing and Shanghai. On the basis of the vehicle activity data, the International Vehicle Emission model is used to establish vehicle emission inventories. The emissions analysis indicates that 3 t of particulate matter (PM), 199 t of nitrogen oxides (NO(x)), 192 t of volatile organic compounds (VOCs), and 2403 t of carbon monoxide (CO) are emitted from on-road vehicles each day in Beijing, whereas 4 t of PM, 189 t of NO(x), 113 t of VOCs, and 1009 t of CO are emitted in Shanghai. Although common features were found in these two cities (many new passenger cars and a high taxi proportion in the fleet), the emission results are dissimilar because of the different local policy regarding vehicles. The method to quantify vehicle emission on an urban scale can be applied to other Chinese cities. Also, knowing how different policies can lead to diverse emissions is beneficial knowledge for other city governments.     

On-road emission characteristics of heavy-duty diesel vehicles in Shanghai

On-road vehicle tests of nine heavy-duty diesel trucks were conducted using SEMTECH-D, an emissions measuring instrument provided by Sensors, Inc. The total length of roads for the tests was 186 km. Data were obtained for 37,255 effective driving cycles, including 17,216 on arterial roads, 15,444 on residential roads, and 4595 on highways. The impacts of speed and acceleration on fuel consumption and emissions were analyzed. Results show that trucks spend an average of 16.5% of the time in idling mode, 25.5% in acceleration mode, 27.9% in deceleration mode, and only 30.0% at cruise speed. The average emission factors of CO, total hydrocarbons (THC), and NO_x for the selected vehicles are (4.96±2.90), (1.88±1.03) and (6.54±1.90) g km⁻¹, respectively. The vehicle emission rates vary significantly with factors like speed and acceleration. The test results reflect the actual traffic situation and the current emission status of diesel trucks in Shanghai. The measurements show that low-speed conditions with frequent acceleration and deceleration, particularly in congestion conditions, are the main factors that aggravate vehicle emissions and cause high emissions of CO and THC. Alleviating congestion would significantly improve vehicle fuel economy and reduce CO and THC emissions.