Use of n-Fold Cross-Validation to Evaluate Three Methods to Calculate Heavy Truck Annual Average Daily Traffic and Vehicle Miles Traveled

Abstract

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.     

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.     

Use of subsampled traffic data to estimate roadway emissions, including conversion to MOBILE6 vehicle classifications

Air quality is degraded by many factors, among which the emissions from on-road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy-making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NOx), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NOx emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.     

Use of Subsampled Traffic Data to Estimate Roadway Emissions,Including Conversion to MOBILE6 Vehicle Classifications

Abstract

Air quality is degraded by many factors, among which the emissions from on‐road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy‐making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NO_x), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NO_x emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.     

Estimating Emissions from Air Concentration Measurements

A one-year-long experiment in which two different tracers were simultaneously released from two different locations was used to test various hybrid receptor modeling techniques to estimate the tracer emissions using the measured air concentrations and a meteorological model. Air concentrations were measured over an 8-hour averaging time at three sites 14 to 40 km downwind. When the model was used to estimate emissions at only one tracer source, 6 percent of the short-term (8-h) emission estimates were within a factor of 2 of the actual emissions. Temporal averaging of the 8-h data enhanced the precision of the estimate such that after 10 days 42 percent of the estimates were within a factor of 2 and after six months all of them (each source-receptor pair) were within a factor of 2. To test the ability of the model to separate two sources, both tracer sources were combined, and a multiple linear regression technique was used to determine the emissions from each source from a time series of air concentration measurements representing the sum of both tracers. In general, 50 percent of the short-term estimates were within a factor of 10, 25 percent were biased low, and in another 25 percent the regression technique failed. The bias and failures are attributed to low or no correlation between measured air concentrations and model calculated dispersion factors. In the regression method increased temporal averaging did not consistently improve the emission estimate since the ability of the model to distinguish emissions between sources was diminished with increased averaging time. However, including progressively longer time periods (more data) into the regression or spatially averaging the data over all the receptors was found to be the most effective method to improve the estimated emissions. At best about 75 percent of the estimated monthly emission data were within a factor of 10 of the measured values. This suggests that the usefulness of meteorological models and statistical methods to address questions of source attribution requires many data points to reduce the uncertainty in the emission estimates.     

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/Juarez. 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.     

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.     

PCDD/F emissions from heavy duty vehicle diesel engines

The currently available information on PCDD/F emissions from diesel vehicles is briefly surveyed. Considerable uncertainty is identified concerning the emissions from heavy duty diesel trucks which have been measured only twice so far. These measurements led to emission factors differing by a factor of 200; similar discrepancy was also revealed by measurements of ambient air in traffic tunnels. New PCDD/F emission measurement results are presented which have been carried out at the exhaust systems of a stationary engine and of a modern heavy duty vehicle engine at transient operation conditions simulated on a test bench. PCDD/F concentrations in the exhaust gases were found to be in the range of control blank samples. Based on the highest concentration observed in the truck engine exhaust (9.7 pg I-TEQ/dry standard m3) a worst case estimate of the annual PCDD/F emission freight from diesel fuel combustion in the European countries of about 30 g I-TEQ/year is calculated. This emission appears to be irrelevant compared to the overall emission rate of more than 6,000 g I-TEQ/year being inventoried recently. Finally the possibilities to link congener/homologue profiles of diesel emission to profiles found in food or human samples are discussed.     

Comparison of semipermeable membrane device (SPMD) and large-volume solid-phase extraction techniques to measure water concentrations of 4,4'-DDT, 4,4'-DDE, and 4,4'-DDD in Lake Chelan, Washington

Semipermeable membrane devices (SPMDs) spiked with the performance reference compound PCB29 were deployed 6.1 m above the sediments of Lake Chelan, Washington, for a period of 27 d, to estimate the dissolved concentrations of 4,4'-DDT, 4,4'-DDE, and 4,4'-DDD. Water concentrations were estimated using methods proposed in 2002 and newer equations published in 2006 to determine how the application of the newer equations affects historical SPMD data that used the older method. The estimated concentrations of DDD, DDE, and DDD calculated using the older method were 1.5-2.9 times higher than the newer method. SPMD estimates from both methods were also compared to dissolved and particulate DDT concentrations measured directly by processing large volumes of water through a large-volume solid-phase extraction device (Infiltrex 300). SPMD estimates of DDD+DDE+DDT (SigmaDDT) using the older and newer methods were lower than Infiltrex concentrations by factors of 1.1 and 2.3, respectively. All measurements of DDT were below the Washington State water quality standards for the protection of human health (0.59 ng l(-1)) and aquatic life (1.0 ng l(-1)).     

Fuel consumption, emissions estimation, and emissions cost estimates using global positioning data

The methodology laid out in this paper shows that typical operational data from vehicle fleets monitored by a global positioning system (GPS) can be used to estimate heavy-duty diesel vehicle (HDDV) emissions, thereby enabling waste managers and governing bodies to internalize the responsibility for socioenvironmental costs traditionally absorbed by external parties. Although municipal solid waste (MSW) collection vehicles are the subjects of this particular study, the methodology presented here can be applied to any fleet of vehicles monitored by GPS. This study indicates that MSW collection trucks may be considerably less fuel efficient in the field than published values for HDDV fuel efficiency suggest. The average fuel efficiency of one MSW collection truck was estimated as 0.90 +/- 0.44 km/L (2.12 +/- 1.03 mi/gal). This same truck would generate approximately 42 metric tons of CO2 equivalents/yr, which is comparable to the greenhouse gas emissions of a large sport utility vehicle driving six times the distance, in town, for a year. In terms of the impacts such emissions have, projections for the monetary cost of emissions are available but highly variable. They suggest that the external monetary costs of emissions range between 6 and 39% of the annual fuel costs for the studied MSW collection truck. The results of this study indicate a need for further research into valuation of the hidden, external costs of emissions, borne by local and global socioecological communities. The possible implications of this result include poorly advised fleet procurement decisions and underestimation of MSW collection fleet emissions.     

Near-road air pollution impacts of goods movement in communities adjacent to the Ports of Los Angeles and Long Beach

A mobile platform was outfitted with real-time instruments to spatially characterize pollution concentrations in communities adjacent to the Ports of Los Angeles and Long Beach, communities heavily impacted by emissions related to dieselized goods movement, with the highest localized air pollution impacts due to heavy-duty diesel trucks (HDDT). Measurements were conducted in the winter and summer of 2007 on fixed routes driven both morning and afternoon. Diesel-related pollutant concentrations such as black carbon, nitric oxide, ultrafine particles, and particle-bound polycyclic aromatic hydrocarbons were frequently elevated two to five times within 150 m downwind of freeways (compared to more than 150 m) and up to two times within 150 m downwind of arterial roads with significant amounts of diesel traffic. While wind direction was the dominant factor associated with downwind impacts, steady and consistent wind direction was not required to produce; high impacts were observed when a given area was downwind of a major roadway for any significant fraction of time. This suggests elevated pollution impacts downwind of freeways and of busy arterials are continuously occurring on one side of the road or the other, depending on wind direction. The diesel truck traffic in the area studied was high, with more than 2000 trucks per peak hour on the freeway and two- to six-hundred trucks per hour on the arterial roads studied. These results suggest that similarly-frequent impacts occur throughout urban areas in rough proportion to diesel truck traffic fractions. Thus, persons living or working near and downwind of busy roadways can have several-fold higher exposures to diesel vehicle-related pollution than would be predicted by ambient measurements in non-impacted locations.     

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.     

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.     

Estimation of vehicular emissions by capturing traffic variations

Increase in traffic volumes and changes in travel-related characteristics increase vehicular emissions significantly. It is difficult, however, to accurately estimate emissions with current practice because of the reliance on travel forecasting models that are based on steady state hourly averages and, thus, are incapable of capturing the effects of traffic variations in the transportation network. This paper proposes an intermediate model component that can provide better estimates of link speeds by considering a set of Emission Specific Characteristics (ESC) for each link. The intermediate model is developed using multiple linear regression; it is then calibrated, validated, and evaluated using a microscopic traffic simulation model. The improved link speed data can then be used to provide better estimates of emissions. The evaluation results show that the proposed emission estimation method performs better than current practice and is capable of estimating time-dependent emissions if traffic sensor data are available as model input.     

Estimation of road traffic emission factors from a long term tracer study

Road traffic emissions, one of the largest source categories in megacity inventories, are highly uncertain. It is essential to develop methodologies to reduce these uncertainties to manage air quality more effectively. In this paper, we propose a methodology to estimate road traffic emission factors (EFs) from a tracer experiment and from roadside pollutants measurements. We emitted continuously during about 300 non-consecutive hours a passive tracer from a finite line source placed on one site of an urban street. At the same time, we measured continuously the resulting tracer concentrations at the other side of the street with a portable on-line gas chromatograph. We used n-propane contained in commercial liquid petroleum gas (LPG) as a passive tracer. Propane offers several advantages to traditional tracers (SF6, N2O, CFCs): low price, easily available, non-reactive, negligible global warming potential, and easy to detect with commercial on-line gas chromatographs.The tracer experiment was carried out from January to March 2007 in a busy street of Ho Chi Minh City (Vietnam). Traffic volume, weather information and pollutant concentrations were also measured at the measurement site. We used the results of the tracer experiment to calculate the dilution factors and afterwards we used these dilution factors, the traffic counts and the pollutant concentrations to estimate the EFs. The proposed method assumes that the finite emission line represents the emission produced by traffic in the full area of the street and therefore there is an error associated to this assumption. We use the Computational Fluids Dynamics (CFD) model MISKAM to calculate this error and to correct the HCMC EFs. EFs for 15 volatile organic compounds (VOCs) and NO are reported here. A comparison with available studies reveals that most of the EFs estimated here are within the range of EFs reported in other studies.     

HCB,PCB, PCDD and PCDF emissions from ships

Since current estimates of hexachlorobenzene (HCB), polychlorinated biphenyls (PCB), dioxins (PCDD) and furans (PCDF) from ships are based on a relatively limited and old data set, an update of these emission factors has been outlined as a target towards improved Swedish emission inventories. Consequently, a comprehensive study was undertaken focusing on these emissions from three different ships during December 2003 to March 2004. Analyses were performed on 12 exhaust samples, three fuel oil samples and three lubricating oil samples from a representative selection of diesel engine models, fuel types and during different “real-world” operating conditions.The determined emissions corresponded reasonably well with previous measurements. The data suggest however that previous PCDD/PCDF emission factors are somewhat higher than those measured here. As expected the greatest emissions were observed during main engine start-up periods and for engines using heavier fuel oils. Total emissions for 2002, using revised emission factors, have been calculated based on Swedish sold marine fuels and also for geographical areas of national importance. In terms of their toxic equivalence (WHO-TEQ), the PCDD/PCDF emissions from ships using Swedish fuels are small (0.37–0.85 g TEQ) in comparison to recent estimates for the national total (ca. 45 g TEQ). Emissions from other land-based diesel engines (road vehicles, off-road machinery, military vehicles and locomotives) are estimated to contribute a further 0.18–0.42 g TEQ. Similarly, HCB and PCB emissions from these sources are small compared to 1995 national emission inventories.     

Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 1--Modeling fuel consumption and emissions based on speed and vehicle-specific power

To improve the accuracy and applicability of vehicular emission models, this study proposes a speed and vehicle-specific power (VSP) modeling method to estimate vehicular emissions and fuel consumption using data gathered by a portable emissions monitoring system (PEMS). The PEMS data were categorized into discrete speed-VSP bins on the basis of the characteristics of vehicle driving conditions and emissions in Chinese cities. Speed-VSP modal average rates of emissions (or fuel consumption) and the time spent in the corresponding speed-VSP bins were then used to calculate the total trip emissions (or fuel consumption) and emission factors (or fuel economy) under specific average link speeds. The model approach was validated by comparing it against measured data with prediction errors within 20% for trip emissions and link-speed-based emission factors. This analysis is based on the data of light-duty gasoline vehicles in China; however, this research approach could be generalized to other vehicle fleets in other countries. This modeling method could also be coupled with traffic demand models to establish high-resolution emissions inventories and evaluate the impacts of traffic-related emission control measures.     

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.     

Optimized combinations of abatement strategies for urban mobile sources

The maximum incremental reactivity (MIR) scale was chosen as a practical index for quantifying ozone-forming impacts. The integer linear and nonlinear programming techniques were employed as the optimization method to maximize MIR and volatile organic compound (VOC) reductions, and minimize ozone's marginal cost with varied control costs. Mobile vehicles were divided into nine categories according to the demands of decision makers and the distinctive features of local circumstance in metro-Taipei. The emission factor (EF) and vehicle kilometers traveled (VKT) of each kind of vehicle were estimated by MOBILE5B model via native parameters and questionnaires. Compressed natural gas (CNG) and inspection and maintenance (I/M) were the alternative control programs for buses and touring buses; liquefied petroleum gas (LPG), I/M, methanol, electrical vehicle (EV) were for taxis and low duty gasoline vehicles. EV, methanol, and I/M were the possible control methods for two-stroke and four-stroke engine motorcycles; I/M programs for low-duty diesel trucks, heavy-duty diesel trucks, and low-duty gasoline trucks. The results include the emission ratios of specific vehicle to all vehicles, the best combination of abated measures based on different objectives, and the marginal cost for ozone and VOC with varied control costs.     

Spatial PM2.5 mobile source impacts using a calibrated indicator method

Motor vehicles are major sources of fine particulate matter (PM_2.5), and the PM_2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM_2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NO_x) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NO_x estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM_2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects.

Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM_2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM_2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.