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Scope Study for Expanding the Great Lakes Toxic Emission Regional Inventory to include Estimated Emissions from Mobile Sources
Chapter 4 Estimating Toxic Air Emissions from Highway Vehicles
Toxic air pollutants can be emitted from two motor vehicle systems by two emission producing processes: combustion products from the exhaust system and evaporation from the fuel storage and delivery system. Figure 4-1 shows these different emissions.
Exhaust emissions vary significantly with vehicle operating mode. There are three operating modes: cold start, hot start, and hot stabilized. The start modes refer to the first few minutes of operation after a engine has been started. A cold start and a hot start are differentiated by the duration between shutting off and restarting the engine. The hot stabilized mode includes all operation time except for the start mode period. The fuel-air mixture and the emission control equipment are two primary factors which cause the differences in emission amounts among operating modes. During cold start mode, the catalytic emission control systems do not provide full control until the appropriate operating temperature is reached. Moreover, a richer fuel-air mixture must be provided to start a "cold" engine. Therefore, VOC and PM emissions are higher in the cold start mode than in the hot start mode and reach the lowest amounts in the hot stabilized mode.
Evaporative emissions are composed primarily of volatile organic compounds and these emissions are highly dependent on temperature. The following are the six categories of evaporative emissions:
Based on the information presented above, there are three potential approaches for deriving toxic emissions for highway vehicles:
These emission estimation approaches are discussed in the following sections.
4-2. Toxic Emission Factors Based on Activity Level
Building a toxic emission inventory can be accomplished through the use of toxic emission factors combined with activity levels, such as miles traveled for a specific vehicle type with a specific control device, using a specific fuel and operating on a specific type of road. This is the same method used in toxic emission estimations for area and point sources. The EPA Factor Information Retrieval System (FIRE) Version 5.1a (1996) has compiled 61 such emission factors for highway vehicles. However, all these emission factors were compiled only for light duty gasoline vehicles and only for 10 pollutants. It may take years to develop the emission factors for all vehicle types and for all pollutants of concern.
4-3. Toxic Emission Factors Based on Fuel Usage
A second potential approach for deriving toxic emissions from mobile sources is to use toxic emission factors based on fuel usage. Establishing a motor vehicle emission inventory can also be accomplished through fuel-based emission factors, i.e., grams of pollutants emitted per unit of fuel burned, combined with fuel sales data. Singer and Harley (1996) demonstrated this approach in estimating CO emissions for the city of Los Angeles. They computed average CO emission factors by model year and by vehicle type (cars and trucks). They also computed a weighted average by using vehicle age distribution and fuel economy. The results indicate that this approach has several advantages:
Although this approach offers advantages, there are no such fuel-based emission factors available for toxic air pollutants and criteria pollutants other than CO. More effort is needed to compute the fuel-based emission factors rather than developing activity-based emission factors.
4-4. Combine Total Organic Gases and Particulate Matter Emissions with Speciation Profiles
A third potential approach for deriving toxic emissions from mobile sources is to estimate TOG and PM emissions with activity-based emission factors and speciate the TOG and PM emissions by using speciation profiles. TOG includes all hydrocarbons, aldehydes, alcohols, and other oxygenated compounds.
The TOG and PM profiles, as well as the amount of TOG and PM emissions, may vary with vehicle category, fuel type, emission control technology (noncatalyst, oxidation catalyst, three-way catalyst, three-way plus oxidation catalyst), level of vehicle maintenance, and operating mode. In addition, evaporative emission speciation profiles differ from exhaust emission profiles because they consist of more volatile fuel components with no combustion products. Discussions in the following sub-sections focus on estimating TOG and PM emissions and the availability of speciation profiles.
4-4-1. Estimating Total Organic Gases and Particulate Matter Emissions
The estimation process for TOG and PM emissions can be represented by the simple equation shown below:
There are three common emission factor models: MOBILE5 (a or c), EMFAC7F, and PART5. These models take into account the effects of numerous vehicle parameters on the amounts of pollutants emitted.
4-4-1-1. Introduction to Emission Factor Models
MOBILE5a is the most recent version of the EPA MOBILE models. This model follows the calculation procedures in the compilation of Air Pollutant Emission Factors - Volume II: Highway Mobile Sources (AP-42, 4th edition, September 1985; Supplement A to AP-42 Vol. II, January 1991). It provides emissions factors of hydrocarbons for eight vehicle types in two regions (low and high altitude) of the country. MOBILE5a is used in the preparation of emission inventories required by the CAA for non-California areas. Ontario uses MOBILE5c to estimate TOG emissions. MOBILE5c is a modified version of MOBILE5a reflecting the base emission rate and deterioration rate of Canadian vehicles.
The California Air Resource Board (CARB) has developed and maintains its own emission factor model for use in the state of California. EMFAC7F is the latest version of the model. It produces composite emission factors for TOG and particulate matter.
The basic structure of MOBILE5a and EMFAC7F is similar. However, a significant difference between these two models is the way that each model allocates emissions associated with a vehicle trip. EMFAC7F produces separate emission factors for cold starts and hot starts in units of grams per trip, and for hot stabilized vehicle operation in units of grams per mile. On the other hand, MOBILE5a spreads the emissions from vehicle starts over the entire trip so it provides a single emission factor in units of grams per mile for both starts and stabilized vehicle operation. These differences affect the travel-related data requirements of the models.
The PART5 model was developed by the EPA Office of Mobile Sources. It calculates particle emission factors, including exhaust particulate, brakewear, tirewear, and reentrained road dust, for particle sizes of 1-10 mm. PART5 is consistent with MOBILE5a in format and fleet characterization data.
According to the survey results, the Great Lakes States and Ontario have used MOBILE5 (a or c) in the preparation of their emission inventories for VOC. New York has also used PART5 to estimate PM10 emissions. With reasonable default values, MOBILE5 (a or c) input parameters are available or could be available for each county in the Great Lakes States and for each defined UTM grid in Ontario. Thus, Section 4-4-1-2 and Section 4-4-1-3 discuss MOBILE5a and the travel activity corresponding to MOBILE5a emission factors in detail, respectively.
4-4-1-2. The MOBILE5a Model
MOBILE5a model has five core components:
The basic emission rates are idealized rates based on standardized vehicle. The other four components adjust the idealized rates in the model so that the estimated emission rates are more representative of emissions from a vehicle operating under the actual conditions. Equation 4-2 illustrates the relationship between these components.
The running loss emissions depend on several variables: the average speed of travel, the ambient temperature, the volatility (RVP) of the fuel, and the length of the trip. Please note that the trip length refers to the duration of the trip (how long the vehicle has been traveling), not to the distance traveled in the trip (how far the vehicle has been driven). Test data show that for any given average speed, ambient temperature, and fuel volatility, running loss emissions are zero to negligible at first, but increase significantly as the duration of the trip is extended and the fuel tank, fuel lines, and engine become heated. MOBILE5a models running losses as a direct function of the input temperature, fuel volatility, and average speed. The user has option of either selecting an internal weighting of trip lengths or specifying trip length distributions. The default trip length distribution in MOBILE5a is shown below:
Although diurnal, hot soak, and running loss evaporative emissions are related to vehicle travel, only running loss emissions are directly impacted by travel-related parameters input to MOBILE5a. The remaining discussion of MOBILE5a will focus on exhaust emissions.
a). Basic Emission Rates (BERs)
Basic emission rates are expressed in the form of linear equations in MOBILE5a for each vehicle type/pollutant/model year group. These linear equations consist of a zero-mile level, or y-intercept, and one or two deterioration rates, or slopes (increase in emissions per 10,000 miles accumulated mileage). The BERs are developed by operating a small number of vehicles under tightly controlled standard test conditions. No need exists for modification of BERs in MOBILE5a by the Great Lakes States. However, in order to develop emission factors for the Ontario Province, the use of MOBILE5c is necessary. In MOBILE5c, Environment Canada revised the BERs, deterioration rates, and technology penetration rates for the Canadian fleet.
EPA has assumed the default values for some operating mode mix inputs as the coefficients in Equation 4-3.
b). Fleet Characteristics
MOBILE5a produces emission factors for the all eight categories of vehicles:
The emission factor for each individual category represents all the vehicle makers and models in the category. For example, the emission factor for LDGV represents all makers of gasoline powered passenger cars including Ford, Chevrolet, Chrysler, Toyota, etc., of all ages, that are assumed to be operating in the analysis year. The average emission rate for each of the eight vehicle categories is significantly affected by the age distribution and the rate of mileage accumulation within the vehicle category. MOBILE5a assumes that 25 model years of vehicles comprise the fleet. The BERs characterize emissions for each specific model year in the vehicle fleet. The VMT fraction for each model year can be calculated as follows:
MOBILE5a provides the user the option of using either a national average VMTm or data that characterize local conditions.
In addition, the composite (all vehicle, or fleetwide) emission factors are dependent on the vehicle miles traveled mix (VMT mix) which specifies the fraction of total highway VMT that is accumulated by each of the eight vehicle categories. MOBILE5a uses national average values for VMT mix as defaults. The user also can employ data more representative of local conditions.
c). Correction Factors
In MOBILE5a, emission factors are calculated by multiplying the BERs by a series of correction factors including temperature, operating mode, and speed. The calculation can be simplified by the following equation.
Temperature Correcting Factor
Since the BERs are developed based on standard testing conditions under which temperature is within a range of 68 to 86oF, MOBILE5a applies correction factors to the BERs for temperatures outside this range. The user should obtain temperature data and develop appropriate temperature profiles for a given mobile source analysis.
Operating Mode Correction Factor
As discussed previously, the operating mode affects exhaust emissions. They are higher in start modes and are reduced significantly once the catalytic converter and engine warm up. MOBILE5a applies operating mode mix corrections to light duty vehicles. It adjusts the BERs with operating modes in two ways: bag correction and bag weight adjustments. The operating modes are usually referred to as bags because the vehicle exhaust is collected in three separate teflon bags for each operating mode in emission factor measurements (bag 1 for the cold start mode, bag 2 for the hot stabilized mode, and bag 3 for the hot start mode). The model automatically makes separate bag corrections to alter the BERs so that they are more representative of the vehicle fleet being modeled. The bag correction factors for the catalyst vehicles are based on vehicle mileage characteristics of the fleet. However, the bag weight adjustments are made by the user through changing the weight of each bag (operating mode mix fractions) to alter the BERs. Two technology types (catalyst and non-catalyst) are also considered in developing the bag weights because technology type affects the time required for a vehicle to warm up and change operating modes. MOBILE5a assumes that the percent of miles traveled in the stabilized mode is the same for both technology types and sets default operating mode weighting factors as follows:
Speed Correction Factor
Both exhaust and running loss evaporative emissions vary significantly with the average speed. The "speed" in MOBILE5a is simply the total trip distance divided by the total trip time, including all stopped delay times. The basic emission rates in MOBILE5a are an approximation of the average rates of emission over a trip with an average speed of 19.6 miles per hour (mph). There are three speed correction regimes in MOBILE5a: low-speed (under 19.6 mph), mid-range (19.6 to 48 mph), and high- speed (48 to 65 mph). An example of the general relationship between speed and VOC emission factor is shown in Figure 4-2.
d). Fuel Characteristics
Evaporative emissions and exhaust emissions (to a lesser extent) vary with fuel volatility. The BERs are developed based on gasoline with volatility of 9.0 psi as measured by Reid vapor pressure (RVP). MOBILE5a adjusts the emission factors by using RVP correction factors for the fuel with volatilities other than 9.0 psi RVP.
The use of oxygenates (alcohols or ethers) in gasoline and reformulated gasoline can reduce TOG emissions. MOBILE5a is also capable of modeling the impacts of oxygenated fuel and reformulated gasoline on emission factors.
e). Emission Control Programs
Many areas in the United States have implemented inspection and maintenance (I/M) or anti-tampering programs (examples of emission control tampering are misfueling, removal or disablement of catalytic converters) to further reduce mobile source emissions. The BERs are developed from vehicles not affected by these emission control programs. MOBILE5a has the capability of modeling the impacts of the control programs. The procedures used in the model to estimate the emission reduction from control programs are complex, especially for exhaust emissions. The calculations are based on vehicle technology parameters and design and effectiveness of the vehicle emission control programs, but are not travel-related parameters.
f). MOBILE 5a Inputs
The appropriate MOBILE5a inputs should be determined by following EPAs recommendations and suggestions in Procedures for Emission Inventory Preparation: Volume IV: Mobile Sources (EPA, 1992) and Users Guide to MOBILE5 (EPA, 1994). Table 4-1 summarizes the guidance and sources of the input parameters.
4-4-1-3. Travel Activity
Output from MOBILE5a together with travel activity expressed in terms of Vehicle Miles Traveled (VMT) yields an estimate of emissions for TOG. VMT can be estimated from direct observations or from travel demand network models. Both VMT methods are related to the Highway Performance Monitoring System (HPMS) developed by the U.S. Department of Transportation (DOT) Federal Highway Administration. Both EPA and DOT have endorsed the HPMS as the most appropriate source of VMT estimates. HPMS estimates VMT based on direct observation of travel. On the other hand, travel demand network models estimate VMT values based on many assumptions and estimates, not on direct observation of travel in that year. With their time-consuming and resource-intensive updating procedures, travel demand network models are not a practical alternative to HPMS as an annual VMT estimation method. However, EPA encourages states to use travel demand network models to temporally (i.e., time of day) and spatially allocate VMT, and EPA also allows the use of those models to estimate speed and other input parameters for the MOBILE5a model.
Some states have more comprehensive and more accurate monitoring systems than HPMS (IL EPA, 1993; MPCA, 1995). These state-operated systems estimate VMT on all roads in the respective state including private roads and also use all available reliable counts and other data, whereas HPMS does not include private road data and uses rather small sample sizes. Moreover, these state-operated systems make VMT estimates from large samples, and these estimates are internally more consistent and statistically more robust than HPMS. For the states that have their own monitoring systems, it is recommended these states use VMT estimates from their own monitoring systems.
In the following sections, the discussions will be focused on the HPMS approaches to VMT estimates.
a). Overview of Highway Performance Monitoring System (HPMS)
Under the HPMS, traffic counts are taken on unique sample panels for each Federal Aid Urbanized Area (FAUA) within the state, then are adjusted for day-of-week and season and expanded to include the areas entire roadway network. The HPMS consists of all public highways or roads within a state. The reporting strata for the HPMS include area types and functional classes shown in Figure 4-3. A third level of stratification is based on 13 volume groups to reduce sample size, insure the inclusion of high volume sections in the sample, and increase the precision of VMT at a lower sample rate. The HPMS provides a statistically valid, reliable, and consistent VMT data base for analyses within states and between states.
Figure 4-3. Area types and functional classes in the reporting strata of the HPMS
b). Estimating VMT in Large Urban Areas
If the boundaries of an inventoried area (such as a county) are coincident with the boundaries of the FAUA, then the county VMT should be the HPMS VMT for each functional system.
If the boundaries of an inventoried area are entirely within the boundaries of the FAUA, then the FAUA VMT by HPMS functional system should be allocated to the county on the basis of the number of roadway miles according to Equation 4-7.
If an emission inventory area is entirely outside the boundaries of all FAUAs, states may use any reasonable method to estimate VMT on the separate functional systems within the area. However, the recommended method in a completely rural area is based on the expansion factor used for the universe of rural samples within the state. Within HPMS, all rural areas within the state are grouped into one sampling universe. This recommended method is described by Equation 4-8.
If some of the inventoried area is inside an FAUA and some of the inventoried area lies outside of any FAUA, then VMT in that portion of the inventoried are within the FAUA should be estimated by equation 4-7, while VMT in the portion outside of the FAUA should be estimated according to Equation 4-8.
As mentioned previously, HPMS sample panels do not include local functional systems. HPMS can be supplemented by state-provided estimates of VMT in the local functional systems. These estimates are based on a method chosen by the state in light of its own circumstances and generally are not based on current ground counts at statistically representative sites. Increasing the accuracy of these estimates is important for the emission inventory.
c). Estimating VMT in Rural and Small Urban Areas
The general method of estimating VMT in rural and small urban areas is to apportion the statewide VMT to the county or other area for which mobile source emission estimates are required. Statewide VMT data are available directly from state transportation agencies or from Table VM-2 in "Highway Statistics" which is published annually by the Federal Highway Administration. These data are prepared by all state transportation agencies and based upon and consistent with HPMS. Several options can be used to apportion VMT, such as roadway miles, motor vehicle registrations, population, and fuel sales. The selection of options is dependent on the availability of the required data. However, the recommended method for estimating VMT is by roadway miles. Equation 4-9 describes the estimate method.
The apportioning factor can be derived as the ratio of a parameter in the area of interest to the parameter in the state. The parameters include roadway miles of functional class f, total motor vehicles of all types registered, total population, and total quantity (gallons) of gasoline and diesel fuel sold. Information on total quality (gallons) of gasoline and diesel fuel sold can be obtained from a state revenue agency or from Table MF-25 and MF-26 in "Highway Statistics".
If a state transportation agency is capable of reporting VMT in rural and small urban areas directly, the indirect method for apportioning statewide VMT should not be used.
d). Seasonal and Daily Adjustment
In Section 3-2-3-2, it has been decided to use at least 6 typical days per year to estimate the annual emissions. HPMS provides annual average daily traffic (AADT) VMT. These AADT VMT should be adjusted for seasonal effects and daily effects. The adjustment factors should be developed by each state and province based on direct observation or travel demand network models.
4-4-2. Procedures for Estimating Toxic Air Emissions by Speciating TOG and PM Emissions
There are two methods to estimate the fleet-average toxic emissions: use fleet composite speciation profiles or use individual speciation profiles which only can be applied to specific vehicles. The first method (fleet composite speciation profiles) requires much less effort while the second method (individual speciation profiles) is more time consuming but provides more accurate results. The choice of methods depends on the availability of speciation profiles, the accuracy requirement of the emission inventory, and the availability of staff and dollar resources. In some cases, emission estimations may be made by using a combination of data from these two methods. This section discusses procedures for estimating toxic emissions for each of these methods.
4-4-2-1. Using Composite Speciation Profiles
Step I. Estimating Emissions for Each Typical Day at Each County
Step II. Estimating Annual Emissions for Each County
These procedures for estimating toxic emissions at the county level are explained in Equation (4-10).
The appropriate fleet composite speciation profiles can be developed from onroad measurements or statistical analyses of ambient measurements.
4-4-2-2. Using Individual Speciation Profiles
Step I. Estimating Emissions for Each Typical Day at Each County
Step II. Estimating Annual Emissions for Each County
The following equations illustrate the procedures to estimate annual emissions by using individual speciation profiles (for exhaust and running loss emissions).
4-4-2-3. Concerns about Speciation Profiles
The EPA Emission Factor and Methodologies Section has pointed out that EPA doesnt recommend using SPECIATE to estimate air toxic emissions except mobile source TOG profiles (Pope 1995a). In 1992, EPA updated the speciation profiles and provided the most current and complete estimate of toxic emission components of TOG emissions for gasoline-powered vehicles. However, the EPA TOG speciation profiles (EPA, 1993a) do not provide information for all the pollutants (e.g., polycyclic organic matter) that will be inventoried in the Great Lakes region. Also, there is only one TOG speciation profile available for diesel vehicles and this profile only indicates weight percentages in TOG emissions for four pollutants of interest. Therefore, additional information sources are shown below.
The California Air Resources Board (CARB, 1991), Auto/Oil Air Quality Improvement Research Program (Sawyer, 1993), and System Applications International (Ligocki, 1995) have also developed TOG speciation profiles for gasoline and diesel vehicles. The Desert Research Institute (Fujita, 1995a) developed a composite VOC speciation profile for diesel truck exhaust based on ambient air measurements made in Arizona and a summer and a winter VOC profile for gasoline automotive exhaust emissions based on both ambient air measurements and a chemical mass balance receptor model. The Desert Research Institute (Fujita, 1995b) also derived 78 VOC (or NMHC) speciation profiles, including 44 for motor vehicle exhaust, 30 for gasoline vapor, 4 for diesel vapor, from measurements conducted by their institute and by other people in recent years.
In addition, EPA has reviewed emission mass fractions for four TOG toxics (benzene, 1,3-butadiene, formaldehyde, and acetaldehyde) for gasoline vehicles by catalyst technology and fuel oxygenated type and content, and for diesel vehicles in the "Motor Vehicle-Related Air Toxics Study" (EPA, 1993).
In general, fewer speciation data exist for diesel exhaust than for gasoline exhaust. Although heavy-duty diesel vehicles contribute a large portion of diesel emissions, most diesel speciation profiles are for light-duty diesel vehicles. Additional study is needed to develop more speciation data for diesel vehicles. Table 4-2 shows the existing EPA and CARB TOG speciation profiles for highway vehicles.
The PM profiles in SPECIATE version 1.5 (EPA, 1993a) are not of high enough quality to be used to estimate toxic air emissions for mobile sources (Pope, 1995b). Gasoline particulate matter and diesel particulate matter are considered as individual toxic air pollutants by EPA (EPA, 1993). Diesel particulate matter is under consideration as a toxic air pollutant by the CARB also (CARB, 1994). The PART5 model estimates emissions of particulate matter, including lead, sulfate and soluble organic particulate. Efforts are needed to link these estimates to individual air toxics, especially metals and compounds other than lead. Table 4-3 presents the existing EPA and CARB PM speciation profiles for highway vehicles. Because leaded gasoline has been prohibited for use in highway vehicles since January 1, 1996, the speciation profiles for leaded gasoline are not included in the table.
In addition to the information provided above, TOG and PM profiles may be derived from the EPA report series, entitled "Locating and Estimating Air Toxic Emissions," and from recent measurements and research results.
Although speciation data are not adequate to estimate toxic air pollutants emissions, combining TOG and PM emissions with speciation profiles is currently the most feasible approach for the Great Lakes States and Ontario in preparation of the regional toxic air emission inventory for highway vehicles.
CARB, Proposed Identification of Diesel Exhaust as a Toxic Air Contaminant, Draft Technical support document, State of California Air Resource Board, June, 1994.
CARB, Speciation Manual, Volume 1: Identification of Volatile Organic Compound Species Profiles, Volume 2: Identification of Particulate Matter Species Profiles, 2nd Edition, State of California Air Resource Board, August 1991.
EPA, Factor Information Retrieval System (FIRE) Version 5.1a, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, May 1996.
EPA, Motor Vehicle-Related Air Toxics Study, EPA 420-R-93-005, Office of Mobile Sources, Emission Planning and strategies Division, Ann Arbor, MI, April, 1993.
EPA, Procedures for Emission Inventory Preparation: Volume IV: Mobile Sources, U.S. Environmental Protection Agency, Office of Mobile Sources, Ann Arbor, MI and Office of Air Quality Planning and Standards, Research Triangle Park., NC, EPA-450/4-81-026d (Revised), 1992.
EPA, Users Guide to MOBILE5, U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Mobile Sources, Ann Arbor, MI, EPA-AA-AQAB-94-0, May 1994.
EPA, Volatile Organic Compounds (VOC)/Particulate Matter (PM) Speciation Data System (SPECIATE), Version 1.5, Emission Inventory Branch (MD-14), Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 1993a.
Fujita, E, Lu, Z., Robinson, N.F., Watson, J.G., VOC Source Apportionment for the Coastal Oxidant Assessment for Southeast Texas, Draft Final Report, Energy and Environment Engineering Center, Desert Research Institute, Reno, NV, August 1995b.
Fujita, E, Zielinska, B., Arizona Hazardous Air Pollution Research Program, Volume 2: Findings, Appendices, ENSR Document No. 0493-013-910, ENSR Consulting and Engineering, Camarillo, CA 93012, December 1995a.
IL EPA, 1990 Ozone Precursors Emissions Inventory for the Chicago Area Illinois Ozone State Implementation Plan, p153, Illinois. Environmental Protection Agency, Spring field, IL, November, 1993.
Ligocki, M.P., Gardner, L., Tunggal, H.H., Heiken, J.G., Atkinson, D.W., and Axelrad, D., "Cumulative exposures to air toxics: emission inventories for mobile and stationary sources," in Proceedings of the 1995 A&WMA 88th Annual Meeting, 95-RA110.01, Air & Waste Management Association, Pittsburgh, PA, 1995.
MPCA, Minnesota 1993 Periodic Carbon Monoxide State Implementation Plan Emissions Inventory for the Twin Cities Seven County Metropolitan Area and Wright County, p25, Minnesota Pollution Control Agency, St. Paul, MN, September, 1995.
Pope, A., Emission Factor and Methodologies Sections, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, Personal communication, October 13, 1995b.
Pope, A., Emission Factor and Methodologies Sections, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, Teleconference, November 7, 1995a.
Sawyer, R.F., "Trends in auto emissions and gasoline composition," Environmental Health Perspectives, 101(6): 10-12 (1993). Auto/Oil Air Quality Improvement Research Program database may be obtained by writing to Coordinating Research Council, Inc., 219 Perimeter Center Parkway, Atlanta, GA 30346.
Singer, B.C. and Harley, R.A., "A Fuel-Based Motor Vehicle Emission Inventory", Journal of the Air & Waste Management Association, 46: 581-593 (1996).
Table 4-1. Guidance and sources for MOBILE5 input parameters
Table 4-2. TOG speciation profiles for highway vehicles
Table 4-3. PM speciation profiles for highway vehicles