Sensitivity analysis and evaluation of microFacCO: a microscale motor vehicle emission factor model for CO emissions

This paper presents a sensitivity analysis of a microscale emission factor model (MicroFacCO) for predicting real-time site-specific motor vehicle CO emissions to input variables, as well as a limited field study evaluation of the model. The sensitivity analysis has shown that MicroFacCO emission estimates are very sensitive to vehicle fleet composition, speed, and ambient temperature. For the present U.S. traffic fleet, the CO emission rate (g/mi) is increased by more than 500% at 5 mph in comparison with a speed greater than 40 mph and by approximately 67% at ambient temperatures of 45 degrees F and > or = 95 degrees F in comparison with an ambient temperature of 75 degrees F. The input variable "emission failure standard rate" is more sensitive to estimating emission rates in the 1990s than in the 2000s. The estimation of emission rates is not very sensitive to relative humidity. MicroFacCO can also be applied to examine the contribution of emission rates per vehicle class and model year. The model evaluation is presented for tunnel studies at five locations. In general, this evaluation study found good agreement between the measured and the modeled emissions. These analyses and evaluations have identified the need for additional studies to update the high-speed (>35 mph) air conditioning (A/C) correction factor and to add effects due to road grades. MicroFacCO emission estimates are very sensitive to the emission standard failure rate. Therefore, the model performance can be greatly improved by using a local emission standard failure rate.     

Sensitivity analysis and evaluation of MicroFacPM: a microscale motor vehicle emission factor model for particulate matter emissions

A microscale emission factor model (MicroFacPM) for predicting real-time site-specific motor vehicle particulate matter emissions was presented in the companion paper titled "Development of a Microscale Emission Factor Model for Particulate Matter (MicroFacPM) for Predicting Real-Time Motor Vehicle Emissions". The emission rates discussed are in mass per unit distance with the model providing estimates of fine particulate matter (PM2.5) and coarse particulate matter. This paper complements the companion paper by presenting a sensitivity analysis of the model to input variables and evaluation model outputs using data from limited field studies. The sensitivity analysis has shown that MicroFacPM emission estimates are very sensitive to vehicle fleet composition, speed, and the percentage of high-emitting vehicles. The vehicle fleet composition can affect fleet emission rates from 8 mg/mi to 1215 mg/mi; an increase of 5% in the smoking (high-emitting) current average U.S. light-duty vehicle fleet (compared with 0%) increased PM2.5 emission rates by -272% for 2000; and for the current U.S. fleet, PM2.5 emission rates are reduced by a factor of -0.64 for speeds >50 miles per hour (mph) relative to a speed of 10 mph. MicroFacPM can also be applied to examine the contribution of emission rates per vehicle class, model year, and sources of PM. The model evaluation is presented for the Tuscarora Mountain Tunnel, Pennsylvania Turnpike, PA, and some limited evaluations at two locations: Sepulveda Tunnel, Los Angeles, CA, and Van Nuys Tunnel, Van Nuys, CA. In general, the performance of MicroFacPM has shown very encouraging results.     

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.     

Multispecies remote sensing measurements of vehicle emissions on Sherman Way in Van Nuys,California

As part of the 2010 Van Nuys tunnel study, researchers from the University of Denver measured on-road fuel-specific light-duty vehicle emissions from nearly 13,000 vehicles on Sherman Way (0.4 miles west of the tunnel) in Van Nuys, California, with its multispecies Fuel Efficiency Automobile Test (FEAT) remote sensor a week ahead of the tunnel measurements. The remote sensing mean gram per kilogram carbon monoxide (CO), hydrocarbon (HC), and oxide of nitrogen (NO_x) measurements are 8.9% lower, 41% higher, and 24% higher than the tunnel measurements, respectively. The remote sensing CO/NO_x and HC/NO_x mass ratios are 28% lower and 20% higher than the comparable tunnel ratios. Comparisons with the historical tunnel measurements show large reductions in CO, HC, and NO_x over the past 23 yr, but little change in the HC/NO_x mass ratio since 1995. The fleet CO and HC emissions are increasingly dominated by a few gross emitters, with more than a third of the total emissions being contributed by less than 1% of the fleet. An example of this is a 1995 vehicle measured three times with an average HC emission of 419 g/kg fuel (two-stroke snowmobiles average 475 g/kg fuel), responsible for 4% of the total HC emissions. The 2008 economic downturn dramatically reduced the number of new vehicles entering the fleet, leading to an age increase (>1 model year) of the Sherman Way fleet that has increased the fleet's ammonia (NH₃) emissions. The mean NH₃ levels appear little changed from previous measurements collected in the Van Nuys tunnel in 1993. Comparisons between weekday and weekend data show few fleet differences, although the fraction of light-duty diesel vehicles decreased from the weekday (1.7%) to Saturday (1.2%) and Sunday (0.6%).

Implications: On-road remote sensing emission measurements of light-duty vehicles on Sherman Way in Van Nuys, California, show large historical emission reductions for CO and HC emissions despite an older fleet arising from the 2008 economic downturn. Fleet CO and HC emissions are increasingly dominated by a few gross emitters, with a single 1995 vehicle measured being responsible for 4% of the entire fleet's HC emissions. Finding and repairing and/or scrapping as little as 2% of the fleet would reduce on-road tailpipe emissions by as much as 50%. Ammonia emissions have locally increased with the increasing fleet age.     

Changes in air quality at near-roadway schools after a major freeway expansion in Las Vegas,Nevada

Near-roadway ambient black carbon (BC) and carbon monoxide (CO) concentrations were measured at two schools adjacent to a freeway and at an urban background school 2 km from the freeway to determine the change in concentrations attributable to vehicle emissions after the three-lane expansion of U.S. Highway 95 (US 95) in Las Vegas, Nevada. Between summer 2007 and summer 2008, average weekday small-vehicle volume increased by 40% ± 2% (standard error). Average weekday large-vehicle volume decreased by 17% ± 5%, due to a downturn in the economy and an associated decline in goods movement. Average vehicle speed increased from 58 to 69 mph, a 16% ± 1% increase. The authors compared BC and CO concentrations in summer 2007 with those in summer 2008 to understand what effect the expansion of the freeway may have had on ambient concentrations: BC and CO were measured 17 m north of the freeway sound wall, CO was measured 20 m south of the sound wall, and BC was measured at an urban background site 2 km south of the freeway. Between summer 2007 and summer 2008, median BC decreased at the near-road site by 40% ± 2% and also decreased at the urban background site by 24% ± 4%, suggesting that much of the change was due to decreases in emissions throughout Las Vegas, rather than only on US 95. CO concentrations decreased by 14% ± 2% and 10% ± 3% at the two near-road sites. The decrease in BC concentrations after the expansion is likely due to the decrease in medium- and heavy-duty-vehicle traffic resulting from the economic recession. The decrease in CO concentrations may be a result of improved traffic flow, despite the increase in light-duty-vehicle traffic.

ImplicationsMonitoring of BC and CO at near-road locations in Las Vegas demonstrated the impacts of changes in traffic volume and vehicle speed on near-road concentrations. However, urban-scale declines in concentrations were larger than near-road changes due to the impacts of the economic recession that occurred contemporaneously with the freeway expansion.     

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.     

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.     

Cold-start emissions of modern passenger cars at different low ambient temperatures and their evolution over vehicle legislation categories

The emissions of modern gasoline and diesel passenger cars are reduced by catalysts except in cold-starting. Since catalysts require a certain temperature (typically above 300 °C) to work to full efficiency, emissions are significantly higher during the warm-up phase of the car. The duration of this period and the emissions produced depend on the ambient temperature as well as on the initial temperature of the car's propulsion systems.The additional emissions during a warm-up phase, known as “cold-start extra emissions” (CSEEs) for emission inventory modelling, are mostly assessed by emission measurements at an ambient temperature of 23 °C. However, in many European countries average ambient temperatures are below 23 °C. This necessitates emission measurements at lower temperatures in order to model and assess cold-start emissions for real-world temperature conditions.This paper investigates the influence of regulated pollutants and CO₂ emissions of recent gasoline and diesel car models (Euro-4 legislation) at different ambient temperatures, 23, ?7 and ?20 °C. We present a survey and model of the evolution of cold-start emissions as a function of different car generations (pre-Euro-1 to Euro-4 legislations). In addition the contribution of CSEEs to total fleet running emissions is shown to highlight their increasing importance.For gasoline cars, it turns out that in average real-world driving the majority of the CO (carbon monoxide) and HC (hydrocarbon) total emissions are due to cold-start extra emissions. Moreover, the cold-start emissions increase considerably at lower ambient temperatures. In contrast, cold-start emissions of diesel cars are significantly lower than those of gasoline cars. Furthermore, the transition from Euro-3 to Euro-4 gasoline vehicles shows a trend for a smaller decline for cold-start extra emissions than for legislative limits. Particle and NO_x emission of cold-starts are less significant.     

An Analysis of Michigan and California CO Remote Sensing Measurements

Abstract

Remote sensing measurements of CO emissions from on-road vehicles were made in California in 1991 and in Michigan in 1992. It was determined that both fleets had a small linear increase in the high emitter frequency (vehicles emitting more than 4% CO) as a function of vehicle age for 1986 and newer model vehicles. Although high emitting vehicles were only a small minority of the fleet, they had a dominant impact on the mean CO and total CO emitted by the fleet. In Michigan, the highest emitting 5% of passenger cars generated 45% of the CO from cars. In California, the highest emitting 5% of passenger cars generated 38% of the CO from cars. There was a high correlation between the mean CO emitted by each model year of vehicle and the frequency of high emitting vehicles within the model year for both the Michigan and California fleets. The frequency of high emitters within any model year had no obvious relation to that model year’s certification standards. The high emitter frequencies for vehicles less than nine years old were very similar for the California and Michigan fleets. An increase in the high emitter frequency in the ten-year-old and older Michigan passenger car fleet (relative to the California passenger car fleet), suggests, but does not conclusively demonstrate, that the rate of high emitters in Michigan and California is reduced by the inspection and maintenance (I/M) programs.     

On-road remote sensing measurements and fuel-based motor vehicle emission inventory in Hangzhou,China

Motor vehicles are one of the largest sources of air pollutants worldwide. Despite their importance, motor vehicle emissions are inadequately understood and quantified, esp. in developing countries. In this study, the real-world emissions of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxide (NO) were measured using an on-road remote sensing system at five sites in Hangzhou, China in 2004 and 2005. Average emission factors of CO, HC and NO_x for petrol vehicles of different model year, technology class and vehicle type were calculated in grams of pollutant per unit of fuel use (g l⁻¹) from approximately 32,260 petrol vehicles. Because the availability of data used in traditional on-road mobile source estimation methodologies is limited in China, fuel-based approach was implemented to estimate motor vehicle emissions using fuel sales as a measure of vehicle activity, and exhaust emissions factors from remote sensing measurements. The fuel-based exhaust emission inventories were also compared with the results from the recent international vehicle emission (IVE) model. Results show that petrol vehicle fleet in Hangzhou has significantly high CO emissions, relatively high HC and low NO_x, with the average emission factors of 193.07±15.63, 9.51±2.40 and 5.53±0.48 g l⁻¹, respectively. For year 2005 petrol vehicles exhaust emissions contributed with 182,013±16,936, 9107±2255 and 5050±480 metric ton yr⁻¹ of CO, HC and NO_x, respectively. The inventories are 45.5% higher, 6.6% higher and 53.7% lower for CO, HC and NO_x, respectively, than the estimates using IVE travel-based model. In addition, a number of insights about the emission distributions and formation mechanisms have been obtained from an in-depth analysis of these results.     

Carbon monoxide modeling from transportation sources.

Street canyon module and gaussian line source module of a regional-scale dispersion model Indic Airviro were used to simulate ambient carbon monoxide (CO) concentrations due to traffic flow at two roadside monitoring locations in Singapore. The fleet average emission factors for each vehicle category was estimated from US EPA MOBILE 5 A guidelines as a function of speed, vehicle deterioration rates and model years. 1-h CO concentrations and worst case 8-h levels have been simulated and compared with measured readings. This study used model-simulated rooftop concentration levels from non-localized sources as background levels at the two sites. The resulting CO concentrations correlate well with actual measured levels and provide a unique approach to predict the impact of CO from transportation.     

Development of a microscale emission factor model for particulate matter for predicting real-time motor vehicle emissions

The U.S. Environmental Protection Agency's National Exposure Research Laboratory is pursuing a project to improve the methodology for modeling human exposure to motor vehicle emissions. The overall project goal is to develop improved methods for modeling the source through the air pathway to human exposure in significant exposure microenvironments. Current particulate matter (PM) emission models, particle emission factor model (used in the United States, except California) and motor vehicle emission factor model (used in California only), are suitable only for county-scale modeling and emission inventories. There is a need to develop a site-specific real-time emission factor model for PM emissions to support human exposure studies near roadways. A microscale emission factor model for predicting site-specific real-time motor vehicle PM (MicroFacPM) emissions for total suspended PM, PM less than 10 microm aerodynamic diameter, and PM less than 2.5 microm aerodynamic diameter has been developed. The algorithm used to calculate emission factors in MicroFacPM is disaggregated, and emission factors are calculated from a real-time fleet, rather than from a fleet-wide average estimated by a vehicle-miles-traveled weighting of the emission factors for different vehicle classes. MicroFacPM requires input information necessary to characterize the site-specific real-time fleet being modeled. Other variables required include average vehicle speed, time and day of the year, ambient temperature, and relative humidity.     

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.     

Characterization of On-Road Vehicle NO Emissions by a TILDAS Remote Sensor

ABSTRACT

A tunable infrared laser differential absorption spectrometer (TILDAS) was used to remotely sense the nitric oxide (NO) emissions from 1,473 on-road vehicles. The real-world measurement precision of this instrument in the limit of low NO concentration is 5 ppm of the vehicle exhaust, which corresponds to a 3o detection limit of 15 ppm. Our analysis of the distribution of negative concentration measurements produced during this experiment supports this claim, showing that the instrumental noise for this set of measurements was at most 8 ppm in the limit of low NO concentration. The high sensitivity of this instrument allowed us to measure the NO emissions of even the cleanest vehicles. The measured vehicle fleet NO emissions closely fit a gamma distribution with 10% of the fleet contributing about 50% of the total fleet emissions. Newer vehicles had lower NO emissions than older ones, but high NO emitters were found in every vehicle age cohort. On a vehicle-by-vehicle basis, NO emissions correlated very weakly with vehicle velocity, acceleration, power per unit mass, carbon monoxide (CO) emissions, and hydrocarbon (HC) emissions. High NO emitting vehicles could not be identified by remote sensing of CO or HC emissions and vice versa. When we compared the NO emissions for 117 vehicles measured more than one time, about half of the high NO emitters were found to be very consistent, while the other half varied significantly.     

Contributions to CO concentrations from biomass burning and traffic during haze episodes in Singapore

Prediction of ambient carbon monoxide (CO) due to haze in the presence of transportation sources at a busy expressway site in Singapore was made using street Canyon and Gaussian line source modules of a regional-scale Indic Airviro dispersion model for the haze episodes that occurred in the years 1994 and 1997. The fleet average emission factors for each vehicle category were estimated from US EPA MOBILE 5 A guidelines as a function of speed, vehicle deterioration rates and model years. One hour CO concentrations during the non-haze period for the year 1995 were first simulated and compared with measured readings to test the accuracy of the proposed approach. The calibrated model was then used to compute hourly CO values for the 1994 and 1997 haze episodes. The difference between the modeled CO values with and without haze provided CO contribution due to haze. An analysis of CO values estimated through modeling with experimental measurements made during haze periods confirmed this unique approach to establish concentration of CO due to haze in the presence of transportation sources.     

A Multipurpose Flare Stack for Control of Chemical Process Wastes

Abstract

To test the effectiveness of California’s vehicle inspection/ maintenance (I/M) program, exclusive of vehicle-owner intervention, a fleet of more than 1,100 vehicles that previously had failed California’s Smog Check test were sent to randomly selected Smog Check stations in the Los Angeles area for covert inspections and repairs. The two-speed idle test was used for repairs. For those vehicles that were repaired at the first inspection, their FTP emission reductions were 25%, 14%, and 11% for hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NO_x), respectively, although emissions testing for NO_x was not performed at the Smog Check stations. Idle HC and CO emissions increased for 35% and 43% of the vehicles, respectively, after repairs. This data set shows that most vehicles that fail the Smog Check inspection are only marginal emitters, with 61% and 44% of the total potential for HC and CO emission reductions, respectively, coming from only 10% of the vehicles that currently fail the inspection. When the vehicles were rank-ordered by idle emissions from dirtiest to cleanest, emission reduction costs for the highest-emitting 10% of the fleet averaged $l,100/ton and $250/ton for HC and CO, respectively, attributing all the costs to each pollutant exclusively. For the remaining vehicles, costs increased dramatically.     

Observations and Model Simulations of Carbon Monoxide Emission Factors from a California Highway

ABSTRACT

A series of twelve intensively monitored 1-hr CO dispersion studies were conducted near Davis, CA, in winter 1996. The experimental equipment included twelve CO sampling ports at elevations up to 50 m, three sonic anemometers, a tethersonde station, aircraft measurements of wind and temperature profile aloft, and a variety of conventional meteorological equipment. The study was designed to explore the role of vehicular exhaust buoyancy during worst-case meteorological conditions, such as low winds oriented in near-parallel alignment with the road during a surface-based nocturnal inversion. From the study, field estimates of the CO emission factor (EF) from a California vehicle fleet were computed using two different methods. The analysis suggests that the CT-EMFAC/ EMFAC (EMission FACtor) models currently used to conduct federal conformity modeling significantly overpredict CO emissions for high-speed, free-flowing traffic on California highways.