Effects of improved spatial and temporal modeling of on-road vehicle emissions

Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NOx) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDV activity by time of day, day of week, and road type. More HDDV activity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5-15% higher NOx and PM emission rates during the weekdays and 15-40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NOx and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled.     

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 However, lack of traffic data and outdated emission software make this task difficult.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.     

Comparison of the MOVES2010a,MOBILE6.2, and EMFAC2007 mobile source emission models with on-road traffic tunnel and remote sensing measurements

The Desert Research Institute conducted an on-road mobile source emission study at a traffic tunnel in Van Nuys, California, in August 2010 to measure fleet-averaged, fuel-based emission factors. The study also included remote sensing device (RSD) measurements by the University of Denver of 13,000 vehicles near the tunnel. The tunnel and RSD fleet-averaged emission factors were compared in blind fashion with the corresponding modeled factors calculated by ENVIRON International Corporation using U.S. Environmental Protection Agency's (EPA's) MOVES2010a (Motor Vehicle Emissions Simulator) and MOBILE6.2 mobile source emission models, and California Air Resources Board's (CARB's) EMFAC2007 (EMission FACtors) emission model. With some exceptions, the fleet-averaged tunnel, RSD, and modeled carbon monoxide (CO) and oxide of nitrogen (NO_x) emission factors were in reasonable agreement (±25%). The nonmethane hydrocarbon (NMHC) emission factors (specifically the running evaporative emissions) predicted by MOVES were insensitive to ambient temperature as compared with the tunnel measurements and the MOBILE- and EMFAC-predicted emission factors, resulting in underestimation of the measured NMHC/NO_x ratios at higher ambient temperatures. Although predicted NMHC/NO_x ratios are in good agreement with the measured ratios during cooler sampling periods, the measured NMHC/NO_x ratios are 3.1, 1.7, and 1.4 times higher than those predicted by the MOVES, MOBILE, and EMFAC models, respectively, during high-temperature periods. Although the MOVES NO_x emission factors were generally higher than the measured factors, most differences were not significant considering the variations in the modeled factors using alternative vehicle operating cycles to represent the driving conditions in the tunnel. The three models predicted large differences in NO_x and particle emissions and in the relative contributions of diesel and gasoline vehicles to total NO_x and particulate carbon (TC) emissions in the tunnel.

Implications: Although advances have been made to mobile source emission models over the past two decades, the evidence that mobile source emissions of carbon monoxide and hydrocarbons in urban areas were underestimated by as much as a factor of 2–3 in past inventories underscores the need for on-going verification of emission inventories. Results suggest that there is an overall increase in motor vehicle NMHC emissions on hot days that is not fully accounted for by the emission models. Hot temperatures and concomitant higher ratios of NMHC emissions relative to NO_x both contribute to more rapid and efficient formation of ozone. Also, the ability of EPA's MOVES model to simulate varying vehicle operating modes places increased importance on the choice of operating modes to evaluate project-level emissions.     

Particulate matter speciation profiles for light-duty gasoline vehicles in the United States

Representative profiles for particulate matter particles less than or equal to 2.5 µm (PM_2.5) are developed from the Kansas City Light-Duty Vehicle Emissions Study for use in the U.S. Environmental Protection Agency (EPA) vehicle emission model, the Motor Vehicle Emission Simulator (MOVES), and for inclusion in the EPA SPECIATE database for speciation profiles. The profiles are compatible with the inputs of current photochemical air quality models, including the Community Multiscale Air Quality Aerosol Module Version 6 (AE6). The composition of light-duty gasoline PM_2.5 emissions differs significantly between cold start and hot stabilized running emissions, and between older and newer vehicles, reflecting both impacts of aging/deterioration and changes in vehicle technology. Fleet-average PM_2.5 profiles are estimated for cold start and hot stabilized running emission processes. Fleet-average profiles are calculated to include emissions from deteriorated high-emitting vehicles that are expected to continue to contribute disproportionately to the fleet-wide PM_2.5 emissions into the future. The profiles are calculated using a weighted average of the PM_2.5 composition according to the contribution of PM_2.5 emissions from each class of vehicles in the on-road gasoline fleet in the Kansas City Metropolitan Statistical Area. The paper introduces methods to exclude insignificant measurements, correct for organic carbon positive artifact, and control for contamination from the testing infrastructure in developing speciation profiles. The uncertainty of the PM_2.5 species fraction in each profile is quantified using sampling survey analysis methods. The primary use of the profiles is to develop PM_2.5 emissions inventories for the United States, but the profiles may also be used in source apportionment, atmospheric modeling, and exposure assessment, and as a basis for light-duty gasoline emission profiles for countries with limited data.

Implications: PM_2.5 speciation profiles were developed from a large sample of light-duty gasoline vehicles tested in the Kansas City area. Separate PM_2.5 profiles represent cold start and hot stabilized running emission processes to distinguish important differences in chemical composition. Statistical analysis was used to construct profiles that represent PM_2.5 emissions from the U.S. vehicle fleet based on vehicles tested from the 2005 calendar year Kansas City metropolitan area. The profiles have been incorporated into the EPA MOVES emissions model, as well as the EPA SPECIATE database, to improve emission inventories and provide the PM_2.5 chemical characterization needed by CMAQv5.0 for atmospheric chemistry modeling.     

Trends in on-road vehicle emissions and ambient air quality in Atlanta,Georgia, USA,from the late 1990s through 2009

On-road vehicle emissions of carbon monoxide (CO), nitrogen oxides (NO_x), and volatile organic compounds (VOCs) during 1995–2009 in the Atlanta Metropolitan Statistical Area were estimated using the Motor Vehicle Emission Simulator (MOVES) model and data from the National Emissions Inventories and the State of Georgia. Statistically significant downward trends (computed using the nonparametric Theil-Sen method) in annual on-road CO, NO_x, and VOC emissions of 6.1%, 3.3%, and 6.0% per year, respectively, are noted during the 1995–2009 period despite an increase in total vehicle distance traveled. The CO and NO_x emission trends are correlated with statistically significant downward trends in ambient air concentrations of CO and NO_x in Atlanta ranging from 8.0% to 11.8% per year and from 5.8% to 8.7% per year, respectively, during similar time periods. Weather-adjusted summertime ozone concentrations in Atlanta exhibited a statistically significant declining trend of 2.3% per year during 2001–2009. Although this trend coexists with the declining trends in on-road NO_x, VOC, and CO emissions, identifying the cause of the downward trend in ozone is complicated by reductions in multiple precursors from different source sectors.

Implications:Large reductions in on-road vehicle emissions of CO and NO_x in Atlanta from the late 1990s to 2009, despite an increase in total vehicle distance traveled, contributed to a significant improvement in air quality through decreases in ambient air concentrations of CO and NO_x during this time period. Emissions reductions in motor vehicles and other source sectors resulted in these improvements and the observed declining trend in ozone concentrations over the past decade. Although these historical trends cannot be extrapolated to the future because pollutant concentration contributions due to on-road vehicle emissions will likely become an increasingly smaller fraction of the atmospheric total, they provide an indication of the benefits of past control measures.     

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.     

Radical precursors and related species from traffic as observed and modeled at an urban highway junction

Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals and are believed to favor ozone formation significantly. Traffic emission data for both compounds are scarce and mostly outdated. A better knowledge of today's HCHO and HONO emissions related to traffic is needed to refine air quality models. Here the authors report results from continuous ambient air measurements taken at a highway junction in Houston, Texas, from July 15 to October 15, 2009. The observational data were compared with emission estimates from currently available mobile emission models (MOBILE6; MOVES [MOtor Vehicle Emission Simulator]). Observations indicated a molar carbon monoxide (CO) versus nitrogen oxides (NO_x) ratio of 6.01 ± 0.15 (r ² = 0.91), which is in agreement with other field studies. Both MOBILE6 and MOVES overestimate this emission ratio by 92% and 24%, respectively. For HCHO/CO, an overall slope of 3.14 ± 0.14 g HCHO/kg CO was observed. Whereas MOBILE6 largely underestimates this ratio by 77%, MOVES calculates somewhat higher HCHO/CO ratios (1.87) than MOBILE6, but is still significantly lower than the observed ratio. MOVES shows high HCHO/CO ratios during the early morning hours due to heavy-duty diesel off-network emissions. The differences of the modeled CO/NO_x and HCHO/CO ratios are largely due to higher NO_x and HCHO emissions in MOVES (30% and 57%, respectively, increased from MOBILE6 for 2009), as CO emissions were about the same in both models. The observed HONO/NO_x emission ratio is around 0.017 ± 0.0009 kg HONO/kg NO_x which is twice as high as in MOVES. The observed NO₂/NO_x emission ratio is around 0.16 ± 0.01 kg NO₂/kg NO_x, which is a bit more than 50% higher than in MOVES. MOVES overestimates the CO/CO₂ emission ratio by a factor of 3 compared with the observations, which is 0.0033 ± 0.0002 kg CO/kg CO₂. This as well as CO/NO_x overestimation is coming from light-duty gasoline vehicles.

Implications: Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals that ultimately contribute to ozone formation. There still exist uncertainties in emission sources of HONO and HCHO and thus regional air quality modeling still tend to underestimate concentrations of free radicals in the atmosphere. This paper demonstrates that the latest U.S. Environmental Protection Agency (EPA) traffic emission model MOVES still shows significant deviations from observed emission ratios, in particular underestimation of HCHO/CO and HONO/NO_x ratios. Improving the performance of MOVES may improve regional air quality modeling.     

Modeling cold soak evaporative vapor emissions from gasoline-powered automobiles using a newly developed method

ABSTRACT

Volatile organic compounds (VOCs) evaporate and vent from a vehicle’s fuel tank to its evaporative control system when the vehicle is both driven and parked. VOCs making it past the control system are emissions. Driving and parking activity, fuel volatility, and temperature strongly affect vapor generation and the effectiveness of control technologies, and the wide variability in these factors and the sensitivity of emissions to these factors make it difficult to estimate evaporative emissions at the macro level. Established modeling methods, such as COPERT and MOVES, estimate evaporative emissions by assuming a constant in-use canister condition and consequently contain critical uncertainty when real conditions deviate from that standard condition. In this study, we have developed a new method to model canister capacity as a representative variable, and estimated emissions for all parking events based on semi-empirical functions derived from real-world activity data and laboratory measurements. As compared to chamber measurements collected during this study, the bias of the MOVES diurnal tank venting simulation ranges from ?100% to 129%, while the bias for our method’s simulation is 1.4% to 8.5%. Our modeling method is compared to the COPERT and MOVES models by estimating evaporative emissions from a Euro-3/4/5 and a Tier 2 vehicle in conditions representative for Chicago, IL, and Guangzhou, China. Estimates using the COPERT and MOVES methods differ from our method by ?56% to 120% and ?100% to 25%, respectively. The study highlights the importance for continued modeling improvement of the anthropogenic evaporative emission inventory and for tightened regulatory standards.

Implications: The COPERT and MOVES methodologies contain large uncertainties for estimating evaporative emissions, while our modeling method is developed based on chamber measurements to estimate evaporative emissions and can properly address those uncertainties. Modeling results suggested an urgent need to complete evaporative emissions inventories and also indicated that tightening evaporative emission standards is urgently needed, especially for warm areas.     

Validation of temporal and spatial consistency of facility- and speed-specific vehicle-specific power distributions for emission estimation: A case study in Beijing,China

Vehicle-specific power (VSP) has been found to be highly correlated with vehicle emissions. It is used in many studies on emission modeling such as the MOVES (Motor Vehicle Emissions Simulator) model. The existing studies develop specific VSP distributions (or OpMode distribution in MOVES) for different road types and various average speeds to represent the vehicle operating modes on road. However, it is still not clear if the facility- and speed-specific VSP distributions are consistent temporally and spatially. For instance, is it necessary to update periodically the database of the VSP distributions in the emission model? Are the VSP distributions developed in the city central business district (CBD) area applicable to its suburb area? In this context, this study examined the temporal and spatial consistency of the facility- and speed-specific VSP distributions in Beijing. The VSP distributions in different years and in different areas are developed, based on real-world vehicle activity data. The root mean square error (RMSE) is employed to quantify the difference between the VSP distributions. The maximum differences of the VSP distributions between different years and between different areas are approximately 20% of that between different road types. The analysis of the carbon dioxide (CO₂) emission factor indicates that the temporal and spatial differences of the VSP distributions have no significant impact on vehicle emission estimation, with relative error of less than 3%.

Implications: The temporal and spatial differences have no significant impact on the development of the facility- and speed-specific VSP distributions for the vehicle emission estimation. The database of the specific VSP distributions in the VSP-based emission models can maintain in terms of time. Thus, it is unnecessary to update the database regularly, and it is reliable to use the history vehicle activity data to forecast the emissions in the future. In one city, the areas with less data can still develop accurate VSP distributions based on better data from other areas.     

Assessing the impact of converting roundabouts to traffic signals on vehicle emissions along an urban arterial corridor in Qatar

The type of control at intersections has a major effect on the operation of any urban corridor. Different predefined procedures are available to calculate some of the main operational characteristics, such as capacity, delay, and level of service, in order to select the best type of control. However, there are other important factors that affect major arterials operational characteristics, factors that are not fully addressed, such as the impact of emissions. In this study, a microscopic simulation approach using VISSIM and MOVES was developed to assess the environmental effect of converting four three-lane roundabouts to signalized intersections along a heavily congested urban corridor in Qatar. A decision was made to switch all roundabouts to traffic signals for better operations. Preliminary results indicated that the signal control outperformed the roundabout in the range of 37% to 43% reduction in emissions. A more detailed analysis revealed that roundabout corridor operations’ effects on emission rates are divergent from those of signalized corridors, particularly upstream and downstream of the intersections. Immediate roundabout upstream approaches are driver behavior dependent, characterized by substantial coasting at lower speeds and subsequent re-accelerating with less idling, described as acceleration events, which resulted in high emission rates, while signalized corridors are signal timing dependent, characterized by ample idling with less coasting and re-acceleration, resulting in reduced emission rates. The results also revealed that there was no significant difference between emission rates in the vicinity of the two types of control. Both recorded nearly the same emission rate.

Implications: A microscopic simulation approach using VISSIM and MOVES was developed to assess the environmental effect of converting four three-lane roundabouts to signalized intersections along a heavily congested urban corridor in Doha, Qatar. Intersection geometries along with the control type have significant impact on emission rates and play a major role in assessing environmental impacts. US EPA MOVES was calibrated to Qatar conditions which can be used to estimate emission factors and quantify vehicular emissions along other corridors in the country. The results can also be beneficial for other countries within the region.     

Vehicle emission implications of drivers’ smart advisory system for traffic operations in work zones

Wireless communication systems have been broadly applied in various complicated traffic operations to improve mobility and safety on roads, which may raise a concern about the implication of the new technology on vehicle emissions. This paper explores how the wireless communication systems improve drivers’ driving behaviors and its contributions to the emission reduction, in terms of Operating Mode (OpMode) IDs distribution used in emission estimation. A simulated work zone with completed traffic operation was selected as a test bed. Sixty subjects were recruited for the tests, whose demographic distribution was based on the Census data in Houston, Texas. A scene of a pedestrian’s crossing in the work zone was designed for the driving test. Meanwhile, a wireless communication system called Drivers Smart Advisory System (DSAS) was proposed and introduced in the driving simulation, which provided drivers with warning messages in the work zone. Two scenarios were designed for a leading vehicle as well as for a following vehicle driving through the work zone, which included a base test without any wireless communication systems, and a driving test with the trigger of the DSAS. Subjects’ driving behaviors in the simulation were recorded to evaluate safety and estimate the vehicle emission using the Environmental Protection Agency (EPA) released emission model MOVES. The correlation between drivers’ driving behavior and the distribution of the OpMode ID during each scenario was investigated. Results show that the DSAS was able to induce drivers to accelerate smoothly, keep longer headway distance and stop earlier for a hazardous situation in the work zone, which driving behaviors result in statistically significant reduction in vehicle emissions for almost all studied air pollutants (p-values range from 4.10E-51 to 2.18E-03). The emission reduction was achieved by the switching the distribution of the OpMode IDs from higher emission zones to lower emission zones.

Implications: Transportation section is a significant source of greenhouse gas emissions. Many studies demonstrate that the wireless communication system dedicated for safety and mobility issues may contribute to the induction in vehicle emissions through changing driving behaviors. An insight into the correlation between the driving behaviors and the distribution of Operating Mode (OpMode) IDs is essential to enhance the emission reduction. The result of this study shows that with a Drivers Smart Advisory System (DSAS) drivers accelerated smoothly and stopped earlier for a hazardous situation, which induce the switch of the OpMode IDs from high emission zones to lower emission zones.     

Development of operating mode distributions for different types of roadways under different congestion levels for vehicle emission assessment using MOVES

The Motor Vehicle Emission Simulator (MOVES) quantifies emissions as a function of vehicle modal activities. Hence, the vehicle operating mode distribution is the most vital input for running MOVES at the project level. The preparation of operating mode distributions requires significant efforts with respect to data collection and processing. This study is to develop operating mode distributions for both freeway and arterial facilities under different traffic conditions. For this purpose, in this study, we (1) collected/processed geographic information system (GIS) data, (2) developed a model of CO₂ emissions and congestion from observations, (3) implemented the model to evaluate potential emission changes from a hypothetical roadway accident scenario. This study presents a framework by which practitioners can assess emission levels in the development of different strategies for traffic management and congestion mitigation.

Implications: This paper prepared the primary input, that is, the operating mode ID distribution, required for running MOVES and developed models for estimating emissions for different types of roadways under different congestion levels. The results of this study will provide transportation planners or environmental analysts with the methods for qualitatively assessing the air quality impacts of different transportation operation and demand management strategies.     

Improved spatial allocation methodology for on-road mobile emissions

Emissions from automobiles and trucks operating on public roads represent a major portion of the air pollutants included in emission inventories. When emission data are prepared for air quality modeling studies, such as those supporting development of a State Implementation Plan, an emission processor matches the spatial and temporal resolution of the emissions to the requirements of the modeling study. However, the spatial location of vehicular emissions is not known and must be estimated. This paper presents a methodology for determining the spatial distribution of the roads belonging to a road class using geospatial data functions, such as those commonly provided by a geographic information system. Vehicle-miles traveled (VMT) are then allocated to medium-resolution (12 x 12-km) and fine-resolution (4 x 4-km) modeling grids using both this methodology and the existing top-down methodology, which uses population density. The results show a significant difference in the spatial distribution of VMT between these two methodologies. Based upon these results, we recommend using the road class-specific methodology in lieu of the population methodology for spatially allocating vehicular emissions for medium- and finer-resolution modeling grids.     

Real-world vehicle emissions and VOCs profile in the Taipei tunnel located at Taiwan Taipei area

An in situ field experiment was conducted in a highway road tunnel in the Taipei City to determine the motor vehicle emission factors (EF) of different kinds of air pollution species. These are carbon monoxide (CO), oxides of nitrogen (NO_x), non-methane hydrocarbons (NMHC) and VOCs species. About 56 species of VOCs were sampled by canister sampler and followed by the GC-MS analyzing. Furthermore, the tunnel-drafting rate was determined by SF₆ tracer method.The EF for the highway vehicles determined from this experiment are 3.64, 0.90, 0.44 and 0.24 gm km⁻¹ veh⁻¹ for CO, NO_x, NMHC and the totally measured VOCs, respectively. A comparison of the EFs from the road tunnel experiment to the estimates by the USEPA MOBILE5b (M5b) and the modified Taiwan EPA MOBILE-TAIWAN2.0 (MT2.0) provides a first-hand evaluation of the model characteristics. M5b and MT2.0 both tend to underpredict CO by 10% and 20%, respectively. While M5b overpredicts NO_x and NMHC by 40% and 20%, respectively; MT2.0 has fairly good predictions for these two species. From the GC-MS analysis of the canister samples, it was found that the most abundant species from the traffic-emitted VOCs in Taipei road tunnel are toluene, ethene and 1,2,4-trimethyl-benzene (1,2,4-TMB) by the weight basis. However, ethene, acetylene and toluene are the most abundant in VOCs based on volume. The VOCs’ weight composition in terms of the carbon bond classification is 28% by the paraffins, 33% by the olefins and 39% by the aromatics, respectively. In order to evaluate the ozone formation potential from the typical road emission in Taipei area, the maximum increment reactivity is calculated. It was found that about 1015 mg of O₃ is induced by per vehicle per kilometer traveled emission. Among them, ethene, 1,2,4-TMB and propene from the road vehicle's emission contribute most to the ozone-formation reactivity.     

Improved Spatial Allocation Methodology for On-Road Mobile Emissions

ABSTRACT

Emissions from automobiles and trucks operating on public roads represent a major portion of the air pollutants included in emission inventories. When emission data are prepared for air quality modeling studies, such as those supporting development of a State Implementation Plan, an emission processor matches the spatial and temporal resolution of the emissions to the requirements of the modeling study. However, the spatial location of vehicular emissions is not known and must be estimated. This paper presents a methodology for determining the spatial distribution of the roads belonging to a road class using geospatial data functions, such as those commonly provided by a geographic information system. Vehicle-miles traveled (VMT) are then allocated to medium-resolution (12 x 12-km) and fine-resolution (4 x 4-km) modeling grids using both this methodology and the existing top-down methodology, which uses population density. The results show a significant difference in the spatial distribution of VMT between these two methodologies. Based upon these results, we recommend using the road class-specific methodology in lieu of the population methodology for spatially allocating vehicular emissions for medium- and finer-resolution modeling grids.     

PM Emissions Emanating from Limited-Access Highways

ABSTRACT

Motor vehicle contributions to primary particulate matter (PM) emissions include exhaust, tire wear, brake and clutch wear, and resuspended road dust. Relatively few field studies have been conducted to quantify fleetaverage exhaust emissions for actual on-road conditions. Therefore, direct measurements of motor vehicle-related PM emissions are warranted. In this study, PM₁₀ and PM_2.5 mass concentrations were measured near two major highways in the St. Louis area over the period from February–April 1997. Samplers were deployed both upwind and downwind of the roadways to capture the transport and dispersion of PM with distance from the roadway. The observed microscale concentration fields were compared to estimates using the PART5 emission factor model together with the CALINE4 highway dispersion model. Traffic- induced PM mass concentrations observed downwind of the roadway were always less than PART5/CALINE4 predictions; average percent differences for observed traffic-induced mass concentrations compared to predicted values were ?34% for PM_2.5 and -70% for PM₁₀. In most cases, the observed PM concentration decay with increasing distance from the roadway was steeper than predicted by dispersion modeling. Motor vehicle-induced emission factors were reconstructed by fitting CALINE4 to the observed concentration data with the emission factor as the sole adjustable parameter. Reconstructed fleet-average motor vehicle emission factors for the urban interstate highway were 0.03–0.04 g/VMT for both PM_2.5 and PM₁₀, while the fleet-average emission factors for the rural interstate highway were 0.2 and 0.3 g/VMT for PM_2.5 and PM₁₀, respectively.     

The Effect of Reformulated Gasoline on Ambient Carbon Monoxide Concentrations in Southeastern Wisconsin

Abstract

Reformulated gasoline (RFG) contains oxygen additives such as methyl tertiary butyl ether or ethanol. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of carbon monoxide (CO) and volatile organic compounds (VOCs). Because VOCs react with sunlight to form ozone (O₃), the Clean Air Act requires severe O₃ nonattainment areas such as southeastern Wisconsin to use RFG. On July 17, 2001, the U.S. Environmental Protection Agency (EPA) granted Milwaukee, WI, and Chicago, IL, a waiver from the VOC reduction requirement of Phase II RFG. The VOC reduction requirement was lowered from 27.4% of the 1990 baseline fuel to 25.4%. The assumption was that ethanol-blended RFG would lower summertime CO concentrations sufficiently to offset the increased VOC emissions. The waiver is estimated to increase VOC emissions by ～0.8%, or 0.4 t of VOC on a hot summer weekday. This study evaluates whether RFG has been effective in lowering southeastern Wisconsin ambient CO concentrations. Three years of ambient CO data before RFG was introduced were compared with the first three years of ambient CO data after RFG was introduced. This paper also evaluates how the meteorology, vehicle inspection/maintenance program, vehicle miles traveled, and stationary source emissions influence CO concentrations. The winter decrease in ambient CO concentrations was found to be statistically significant, while the summer data showed no statistically significant change, indicating that RFG is most effective lowering ambient CO concentrations in cold weather.     

The effect of reformulated gasoline on ambient carbon monoxide concentrations in southeastern Wisconsin

Reformulated gasoline (RFG) contains oxygen additives such as methyl tertiary butyl ether or ethanol. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of carbon monoxide (CO) and volatile organic compounds (VOCs). Because VOCs react with sunlight to form ozone (O3), the Clean Air Act requires severe O3 nonattainment areas such as southeastern Wisconsin to use RFG. On July 17, 2001, the U.S. Environmental Protection Agency (EPA) granted Milwaukee, WI, and Chicago, IL, a waiver from the VOC reduction requirement of Phase II RFG. The VOC reduction requirement was lowered from 27.4% of the 1990 baseline fuel to 25.4%. The assumption was that ethanol-blended RFG would lower summertime CO concentrations sufficiently to offset the increased VOC emissions. The waiver is estimated to increase VOC emissions by approximately 0.8%, or 0.4 t of VOC on a hot summer weekday. This study evaluates whether RFG has been effective in lowering southeastern Wisconsin ambient CO concentrations. Three years of ambient CO data before RFG was introduced were compared with the first three years of ambient CO data after RFG was introduced. This paper also evaluates how the meteorology, vehicle inspection/maintenance program, vehicle miles traveled, and stationary source emissions influence CO concentrations. The winter decrease in ambient CO concentrations was found to be statistically significant, while the summer data showed no statistically significant change, indicating that RFG is most effective lowering ambient CO concentrations in cold weather.     

Understanding the emission impacts of high-occupancy vehicle (HOV) to high-occupancy toll (HOT) lane conversions: Experience from Atlanta,Georgia

Converting a congested high-occupancy vehicle (HOV) lane into a high-occupancy toll (HOT) lane is a viable option for improving travel time reliability for carpools and buses that use the managed lane. However, the emission impacts of HOV-to-HOT conversions are not well understood. The lack of emission impact quantification for HOT conversions creates a policy challenge for agencies making transportation funding choices. The goal of this paper is to evaluate the case study of before-and-after changes in vehicle emissions for the Atlanta, Georgia, I-85 HOV/HOT lane conversion project, implemented in October 2011. The analyses employed the Motor Vehicle Emission Simulator (MOVES) for project-level analysis with monitored changes in vehicle activity data collected by Georgia Tech researchers for the Georgia Department of Transportation (GDOT). During the quarterly field data collection from 2010 to 2012, more than 1.5 million license plates were observed and matched to vehicle class and age information using the vehicle registration database. The study also utilized the 20-sec, lane-specific traffic operations data from the Georgia NaviGAtor intelligent transportation system, as well as a direct feed of HOT lane usage data from the State Road and Tollway Authority (SRTA) managed lane system. As such, the analyses in this paper simultaneously assessed the impacts associated with changes in traffic volumes, on-road operating conditions, and fleet composition before and after the conversion. Both greenhouse gases and criteria pollutants were examined.

Implications: A straight before-after analysis showed about 5% decrease in air pollutants and carbon dioxide (CO₂). However, when the before-after calendar year of analysis was held constant (to account for the effect of 1 yr of fleet turnover), mass emissions at the analysis site during peak hours increased by as much as 17%, with little change in CO₂. Further investigation revealed that a large percentage decrease in criteria pollutants in the straight before-after analysis was associated with a single calendar year change in MOVES. Hence, the Atlanta, Georgia, results suggest that an HOV-to-HOT conversion project may have increased mass emissions on the corridor. The results also showcase the importance of obtaining on-road data for emission impact assessment of HOV-to-HOT conversion projects.