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OCR for page 33
2
Current and Possible Future Uses of
MOBILE in Air-Quality Management
AN EMISSIONS-FACTOR MODEL is fundamental for assessing the nature and
magnitude of on-road motor vehicle emissions and their impacts on ambi-
ent air quality. In the United States, excluding California, the MOBILE
model has been the only model used in policy and regulatory settings to
simulate actual emissions from automobiles over widely varying scales of
resolution. (California uses the Motor Vehicle Emissions Inventory model-
ing suite for the assessment of vehicle emissions and their controls, as
discussed in Chapter 5.) MOBILE is used in the development of national,
regional, and urban emissions inventories; the simulation of regional air
chemistry and microscale dispersion of pollutants; the assessment of the
effectiveness of control strategies; the documentation of emissions reduc-
tions in State Implementation Plans (SIPs); the assessment of air-quality
impacts of transportation projects, including the demonstration of confor-
mity of transportation and air-quality plans; and the assessment of air-
quality impacts of transportation-control measures and projects.
Traditionally, the management activities of air-quality regulatory agen-
cies at the local, state, and federal level have used MOBILE to estimate
vehicle emissions. Increasingly, transportation agencies, principally state
departments of transportation and local metropolitan planning organiza-
tions ~IPOs), have become more reliant on MOBILE in fulfilling their new
obligations under the Clean Air Act Amendments of 1990 (CAAA90) and
the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA).
33
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34 MODE[/NG MOB/LE-SOURCE EM/SS/ONS
These acts expand requirements for state transportation departments and
MPOs to assess the air-quality effects of transportation plans and projects.
Also, the automotive and oil industries, consultants, and academic organi-
zations use MOBILE in a variety of ways related to air-quality regulation
and, more broadly, to develop a better understanding of the dynamics of
atmospheric pollutants.
FUTURE MOBILE-SOURCE EMISSIONS-MODELING ISSUES
Originally, MOBILE was developed to estimate overall emissions levels,
trends over time, and the effectiveness of mobile-source emissions-control
strategies. The model has undergone significant evolution since then,
which is summarized in Chapter 3. Current uses of the model include de-
veloping emissions inventories and reductions in SIPs, demonstrating con-
formity of transportation and air-quality plans, and providing emissions
estimates for dispersion and photochemical air-quality modeling. Thus,
the original role of MOBILE has been expanded in ways that now require
higher standards of accuracy that incorporate a greater degree of complex-
ity. A good example of this evolution is demonstrated by MOBILE's cur-
rent use in ozone attainment modeling, which requires precise spatial and
temporal estimation of speciated precursor emissions to predict ambient
ozone levels for a particular day and region. This application is more diffi-
cult and demands greater precision than MOBILEl's use for modeling of
exhaust emissions as a function of age or mileage.
The need for vehicle emissions models will continue in the future and
the demand for more accuracy and versatility will increase. SIP require-
ments to meet proposed new fine particulate matter (PM-2.5) and 8-fur
ozone standards, regional visibility rules, and increased interest in air
toxics from mobile sources will magnify concerns about MOBILE's applica-
bility and accuracy. Although MOBILES, the upcoming version of MO-
BILE, will address some of these concerns, it will likely fall short of the
regulatory burden placed on its use.
For instance, MOBlLE6 assumes lower rates of deterioration of vehicle
emissions-control systems than earlier versions because of indications that
newer technology is more durable. Yet, as discussed in Chapter 3, the ex-
tent to which the data supporting this position is fully representative is
questionable. A desire for more accurate microscale modeling of localized
transportation-control measures and more accurate photochemical model-
ing will add to the growing need for instantaneous or modal emissions
modeling and better spatial disaggregation of emissions. Disaggregation
of certain model components, such as the separation of start emissions
from running emissions or the development of facility-specific speed cor-
OCR for page 35
CURRENT AND POSSIBLE FUTURE USES OF M OBILE 35
rection factors, will provide better resolution, but the activity data re-
quired to implement these changes may not be available in all regions.
The demand for a more accurate MOBILE model also raises concurrent
demands for more-accurate transportation-activity factors such as average
vehicle speeds, congestion levels, roadway classification, or miles traveled.
Finally, it is conceivable that future users could require a real-time version
of a mobile emission model to manage traffic flow in order to minimize
emissions in critical air-quality areas. No single model is available to ac-
curately address all of these possible uses.
MODELING AIR QUALITY: AN INTERDISCIPLINARY ENDEAVOR
Efforts to evaluate the air-quality impact of on-road motor vehicles are
inherently interdisciplinary, and require the interaction of three different
models and related areas of expertise: travel-demand models, emissions
models, and air-quality models. Travel-demand models determine the
amount of transportation activity occurring in a region based on an under-
standing of the daily activities of individuals and employers as well as the
resources and transportation infrastructure available to households and
individuals when making their activity and travel decisions (Harvey and
De akin 1993~. This includes measures such as number of trips, time of
day, length of trip, mode of transportation, route or location of trips, aver-
age speed of travel, and age of vehicle. The number of transit trips, auto-
mobile occupancy, and vehicle miles of travel (VMT) are common perfor-
mance measures used to measure transportation activity.
The second component corresponds to mobile-source emissions rates.
MOBILE estimates emissions rates based on vehicle type, average speed,
ambient temperature, and other factors. The product of the transportation
activity and the emissions rates from MOBILE results in emissions esti-
mates for each modeled pollutant (carbon monoxide (CO), volatile organic
compounds (VOCs), and nitrogen oxides (NOW. It is critical that esti-
mates of transportation activity and emissions rates be in balance with
respect to fidelity, accuracy, and precision to ensure the reasonableness of
the emissions estimates. It is impossible to have the same for both (in
some cases, the vehicle activity estimates will be more precise, or more
accurate, or more refined, and in others the emissions rates will be so).
However, transportation and air quality planners should understand the
fidelity, accuracy, and precision for each component and take these into
account in policy analysis (such as through uncertainty analysis and devel-
opment of confidence bounds).
The third component of the modeling trilogy is the regional and micro-
scale modeling of air quality. These models translate emissions invento-
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36 MODE[JNG MOB!LE-SOURCE EMISSIONS
ries into ambient pollutant concentrations that vary through space and
time. Translating emissions to ambient concentrations can be done di-
rectly, for example, by using microscale carbon monoxide modeling. This
method estimates concentrations in "hot spot" areas (critical intersections
and sites with violations or possible violations of the NAAQS) by simulat-
ing the dispersion of the pollutant, using a variety of dispersion parame-
ters, such as wind speed and direction. This is also done through urban-
scale and regional-scale air-quality models that calculate ozone concentra-
tions by simulating both atmospheric chemistry and meteorology. Again,
attention to fidelity, accuracy, and precision is needed in each of these
three types of models to ensure balance in their integration.
Travel-Demand Modeling
There are five traditional components of the sequential travel-demand
model, namely demographic forecasting and the four-step travel-demand
modeling process (Figure 2-1~.
Demographic data The location of households and employment
(categorized as basic, retail, and service) in small traffic survey zones with-
in the urban region. This includes the forecasts of regional economic
growth, land use patterns, and future demographic trends.
.
Trip generation The estimation of the number of trips by zone by
time of day and type (both trips originating in a zone, termed trip produc-
tion, and trips terminating in a zone, termed trip attraction).
Trip distribution The pairing of trip productions with trip attrac-
tions resulting in a full spatial pattern of travel by purpose and time of
day.
.
Mode choice The determination of mode of travel, specifically
walk, bicycle' drive alone, high occupancy vehicle (HOV), bus, rail, or truck
travel.
Route assignment or choice Trips are assigned to paths in the
transportation infrastructure by minimizing travel times or travel times
and costs, and incorporating average speed and other impedance feed-
backs.
The travel-demand models answer: "Will I travel," "How often and
when," "Where," "By which mode," and "By which route"? They provide
the MOBILE model with information on average vehicle speeds for each
roadway segment that may be aggregated by roadway type or facility (e.g.,
freeways, arterials, collectors, and freeway ramps). The following points
describe some important issues that relate to the use of travel-demand
modeling in modeling air quality.
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CURRENT AND POSSIBLE FUTURE USES OF MOBILE 37
,| DEMOGRAPHIC |4
l DATA l
TRIP
GENERATION
BY TIME-OF-DAY
ROA DWAY ~l TRIP
NETWORK 71 DISTRIBUTION
TRANSIT
NETWORK
r
HOV NETWORK
MODE
CHOICE
1
~ ~ 1'
ROA DW, KY ~ HOV T - NSIT
ASSIGNMENT ~1 ASSIGNMENT ASSIGNMENT
1
FIGURE 2-1 Sequential travel-demand forecasting process used in Dallas,
Texas. Source: NCTCOG 1999.
Seasonal variations Most travel models are calibrated for typical
weekdays when primary and secondary schools are in session. Therefore,
travel is modeled for a typical weekday in February through May and Sep-
tember through November. However, air-quaTity assessments typically do
not follow such convenient transportation schedules, because ozone is a
frequent summer problem and carbon monoxide is often a winter one.
Therefore, the travel-activity information may be modified to mode! the
emissions type and season of interest.
Adjustments for weekend/weekday Travel models typically sim-
ulate weekday traffic, with adjustments for weekend emissions inventories
often being required for air-quality assessments. Travel survey data is
being collected to assist estimating weekend travel.
Duration within day—Most travel forecasts are for a typical 24-hr
period. These can be adjusted to simulate peak-hour and peak-period con-
ditions with time-of-day factors. Air-quality models typically-need emis-
sions for each hour of the day. This task is often performed using travel
OCR for page 38
38 MODELING MOB`LE-SOURCE EMISSIONS
start times by hour and trip type from travel diaries. However, these ad-
justments are highly uncertain and rarely validated.
Travel by grid Most travel forecasts are conducted for specific
transportation facilities or segments. However, air-quality models often
need information by "grid," or aggregations of transportation facilities.
Geographical information system software is an efficient tool for aggregat-
ing travel into grids.
Multidimensional Synergistic Impacts from
Adjustments to Travel Activity Results
As needs for precise estimates of emissions and air quality grow, it is
common to use travel activity inputs based on typical weekday travel that
may have been adjusted for season, weekend travel, time of day, and grid.
Demands for even greater precision might require travel activity estimates
for specific vehicle age categories, meteorological conditions, or vehicle
types. The committee feels that the level of detail associated with current
travel-demand models is insufficient to make these simultaneous adjust-
ments without introducing substantial additional uncertainty. A good ex-
ample of this problem is the need in air quality modeling to estimate ag-
gregate heavy-duty vehicle (HDV) activity and adjust these estimates for
time of week and time of day. Because HDVs produce a disproportionate
amount of NOx emissions, they greatly impact the ability to model ozone
accurately. Yet the multiple adjustments of travel activity results needed
to produce estimates of HDV activity by time of day introduces an un-
known level of uncertainty to emissions and air quality simulations.
Emissions Modeling
A primary use of MOBILE is for developing on-road mobile-source emis-
sions inventories for use in air-quality planning. Emissions rates devel-
oped in MOBILE are combined with average vehicle speeds and travel ac-
tivity estimates to develop these inventories. The emissions rates gener-
ated by MOBILE require a multitude of input assumptions. For most in-
put assumptions, MOBILE provides national default values or users can
input TocaDy specific values. Regions are free to choose when to use na-
tional defaults or local data. This decision is sometimes made on the basis
of whether the national default or local data wiD positively affect their at-
tainment demonstration or conformity analysis. MOBILE is particularly
sensitive to input assumptions for vehicle age, VMT by vehicle class, aver-
age vehicle speeds, and temperature all of which can vary widely from
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CURRENT AND POSSIBLE FUTURE USES OF MOBILE 39
region to region. Below is a brief discussion of parameters that are impor-
tant in the application of MOBILE to an individual region. Chapter 3 of
the report discusses the technical components of MOBILE in more detail.
Vehicle Registration
MOBILE uses vehicle registration data to determine the percentage of
the vehicle fleet for each combination of vehicle type with vehicle age.
MOBILE combines this information with average mileage accumulation
rates to determine the fraction of overall travel in a region associated with
each vehicle type disaggregated by age and average fleet emissions rates.
The MOBILE documentation published by the U.S. Environmental Protec-
tion Agency (EPA) strongly encourages users of MOBILE to develop locally
specific vehicle registration distributions because the default values reflect
national averages for 1990.
Emissions inventory estimates are affected by assumptions about vehi-
cle registration distributions. Within an urban area, the vehicle fleet com-
position can vary significantly across subregions, for example, in relation
to development patterns and the economic status of the population. Areas
with newer development and higher average income levels tend to have
newer vehicle fleets, resulting in lower emissions rates than in older areas.
The choice of a particular vehicle registration distribution can affect on-
road emissions inventories by approximately 5 to 10% (Pollack et al. 1991~.
As a result, estimates of on-road mobile-source emissions require accurate
vehicle registration distributions at an appropriate level of detail for a par-
ticular application.
Vehicle Miles of VMT Travel Mix
A VMT mix identifies the percentage of VMT that is accumulated by
each of the eight vehicle classifications used by the MOBILE model. MO-
BILE uses this VMT mix to generate composite vehicle-emissions factors.
MOBILE calculates a typical urban area VMT mix based on national data
for several variables, including registration distributions, annual mileage
accumulation rates, percentage of diesel sales, and number of vehicles.
EPA recommends that users develop locally specific estimates of VMT mix
for SIP emissions inventories.
Policy decisions regarding mobile-source controls are affected by
assumptions incorporated into the VMT mix data. National default values
based on averages might not accurately represent the VMT mix in any
given region. For example, if a particular region has a high percentage of
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40 M ODE[/NG M OB/1E-SOURCE EMISSIONS
heavy-duty vehicles, the default VMT mix would lead to an underestima-
tion of NOx emissions.
Average Speed
MOBILE uses regional average vehicle speeds estimated by travel de-
mand models to develop emissions rates. The model develops base emis-
sions rates for various vehicle classes using standard driving cycles such
as the Federal Test Procedure (FTP). These base emissions rates are then
adjusted to a particular location's average speed using speed correction
factors. Speed correction factors are intended to reflect the differences
between emissions rates under test conditions and emissions rates under
regional driving conditions. More detailed descriptions of the use of these
test cycles, speed correction factors, and facility correction factors are con-
tained in Chapter 3.
It is important to point out that traditional travel-demand models do
not estimate average speeds directly, but rather produce average speeds
from estimates of traffic volumes. This is important because these speed/
volume relationships are not very accurate and are sometimes adjusted
during calibration so that modeled traffic volumes match observed vol-
umes.
Temperature
MOBILE requires locally specific temperature data, in part because no
national defaults would be appropriate for temperature. Thus, users must
develop average temperature data to develop on-road mobile-source emis-
sions inventories. Analysis of the MOBILE model shows that at higher
temperatures, a one-degree change in temperature results in a 1% change
in emissions factors (Pollack et al 1991), though this premise has not been
validated. As a result, temperature variations within a region may signifi-
cantly impact emissions inventory estimates.
Air-Quality Modeling
Air-quality models have become the central tool for analyzing how fu-
ture emissions changes, including changes due to new control strategies,
will affect air-quality (NRC 1991~. Ozone, for example, is not produced
directly from emissions sources, but rather through complex chemical re-
actions involving VOCs, NOx, and sunlight (solar radiation). High ozone
episodes occur during periods of stable atmospheric conditions that are
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CURRENT AND POSSIBLE FUTURE USES OF M OBILE 4 ~
accompanied by high temperature and low winds. Regional photochemical
models attempt to predict the formation of ozone for multi-day events us-
ing meteorological data, emissions inventory data, and complex air-chem-
istry equations. In contrast, CO problems are much more localized in na-
ture and require different air-quality models that represents site-specific
dispersion of this pollutant. Vehicles emit CO directly, and its local con-
centration depends upon the rate that it is emitted and dispersed in the
atmosphere. A major problem for air-quality managers is identifying con-
trols that will reduce CO, ozone, and PM. On-road mobile-source emis-
sions are important contributors to each of these pollutants. For most of
the nation, the estimation of on-road emissions data needed for air-quality
modeling studies are derived from MOBILE using results from travel-de-
mand modeling.
In a typical application (e.g., analyzing ozone-controT strategies), an air-
quality model will be applied to simulate photochemical pollutant concen-
trations during a 3- to 5-day episode, using emissions and meteorology
data specific to the period of application. Typical model resolution, and
hence the scale of emissions inputs, is approximately 5 kilometers (km).
The modeling results are evaluated against observed ambient measure-
ments to assess the validity for use in control-strategy assessment. Errors
in the emissions and other model inputs are evident from disagreements
between observations and model simulations, not only for ozone, but for
the precursors as well. Because of nonlinearities in the formation of ozone,
it is important that the observations and the simulated values agree rea-
sonably weU. Otherwise, the model response to emissions changes will be
suspect. With this in mind, EPA has developed model performance guide-
lines (EPA l999b).
At present, it appears that uncertainties in the emissions, and mobile-
source emissions in particular (NRC 1991; Harley et al. 1993a, b), are ma-
jor contributors to poor model performance. As described in more detail in
Chapter 4, a variety of studies have concluded that mobile-source VOC
emissions are significantly underestimated in the models. The perform-
ance of air-quality models improves significantly when estimates of
mobile-source VOC emissions are increased. However, there are many
other factors that contribute to the poor performance of air quality models,
including errors in the atmospheric chemical mechanisms and meteorolog-
ical inputs within the air quality models as well as uncertainties in the
travel activity inputs discussed earlier. After a model achieves acceptable
performance, it is used to test control strategies, in particular to identify
the set of controls that are likely to lead to attainment. Historically, at-
tainment has meant that predicted ozone is less than 0.12 parts per mil-
lion (ppm) in each subarea of the region. Thus, the target is an absolute
number, and modeling results are used in an absolute sense.
The use of MOBILE to develop emissions inventories for ozone modeling
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42 M ODEL/NG M OBlLE-SOURCE EMISSIONS
(and PM modeling in the future) highlights the evolution of the demands
placed upon it. Such applications require estimates at relatively fine spa-
tial and temporal resolutions, not simply across a fleet of vehicles in a
broad urban area. Topographical features, such as hills, might play a ma-
jor role in emissions and atmospheric processes. Further, the varying com-
position of the fleet can become important if one area of a city is more like-
ly to contain high-emissions vehicles than another. MOBILE was not orig-
inally designed to support applications that require a high resolution and
accuracy of emissions inventories. It is not apparent to the committee that
an emissions-modeling tool designed specifically for use in air-quality mod-
eling would have been designed in the same way as MOBILE.
The importance of providing finer spatial resolution is highlighted by a
recent study ~ackshminarayanan, 1999~. That study used a mobile
source inventory for the Atlanta, GA, area, as developed by MOBILE5a, to
simulate ozone, nitrogen dioxide and an air toxic (formaldehyde) concen-
trations in the region. Next, results from MEASURE (Guensler et al.,
1998; see Chapter 5 for details) were used to spatially and temporally real-
locate those emissions, thus keeping the same basin-wide mass emissions
of each species, but changing the details of the time and location of emis-
sions. In this process, the study was also able to develop the emissions at
finer grid resolutions than the 4 x 4 km MOBILE inventory. The photo-
chemical model was then re-applied using grid resolutions of 1 x 1 km, 2 x
2 km and 4 x 4 km. Peak levels of nitrogen dioxide and formaldehyde
were found to be very sensitive to grid size, varying by up to a factor of five
or more. Ozone levels were less sensitive. Thus, this study concluded that
accurate exposure assessment of primary pollutants, such as air taxies and
particulate matter, are likely to require spatially and temporally detailed
. . . ~ .
emissions Information.
In part because of the difficulties in estimating emissions inventories,
guidelines for future ozone air-quality modeling might be used in a more
relative sense. Thus, rather than ensuring that all concentrations simu-
lated by the model are at or below 0.12 ppm (or 0.08 ppm for the new 8-fur
standard), a relative reduction from a base scenario could be used to as-
sess the adequacy of emissions controls (EPA l999b). For example, if the
base calculation led to a maximum ozone concentration of 0.156 ppm, and
the control case had a peak of 0.132 ppm, this would represent a reduction
of 15%. That 15% relative reduction would then be used to test for attain-
ment. If the design value was 15% over the limit, then the modeling test
would be passed.1 The use of the model in such a relative sense is believed
to be more accommodating of uncertainties, such as those in the emissions
1For a more detailed explanation of this type of attainment demonstration, see
EPA (1999b).
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CURRENT AND POSSIBLE FUTURE USES OF MOBILE 43
themselves. Thus, as MOBILE has been evolving to provide absolute emis-
sions levels (as opposed to relative levels), the regulatory application of
air-quality models may be moving towards relative uses, in part, because
of problems in validating these models.
Ozone modeling is not the only purpose for a model such as MOBILE.
Air toxic agents are a societal concern, and automotive emissions contain
significant quantities of hazardous air pollutants (HAPs) or air tonics,
such as benzene, formaldehyde, and 1,3 butadiene. Unlike ozone, elevated
levels from these primary emissions are found near roadways. Concentra-
tions in nearby areas might be much reduced by dispersion. A recent Cali-
fornia Air Resources Board (CARB) study concluded that concentrations of
pollutants inside vehicles can be two-to-seven times greater than concen-
trations at air-monitoring stations (CARB 1998~. This has important im-
plications in exposure assessments, because it places greater emphasis on
knowing the detailed spatial location of emissions in relationship to poten-
tially exposed populations. Unlike ozone modeling, which might require
emissions with a spatial resolution of 4 km or so, HAP exposure assess-
ment might require resolution at a scale of tens or hundreds of meters.
MOBTOX (or a version of MOBILE that estimates air toxic agents), in
principle, could be applied at this level, but it lacks many features that
might be important at such fine spatial scales (e.g., the influence of topo-
graphical features and specific traffic-control measures) that get averaged
out over larger areas. The use of MOBILE to model the concentrations of
HAP s near roadways raises issues similar to those of using MOBILE to
model micro-area carbon monoxide.
Users of Modeling Components
Both the public and private sectors use the transportation, emissions-
factor, and air-quality modeling components of air-quaTity planning and
regulatory processes. The broad community of model users includes gov-
ernment agencies, private consulting firms, public interest groups, and
other researchers. The primary purpose for this modeling effort is to fulfill
specific transportation and environmental legislative and regulatory re-
quirements.
Governmental users include agencies at the federal, state, regional, and
local levels that use these models to conduct transportation and environ-
mental analysis. Private consulting firms often contract with government
agencies and industry to conduct specific planning and environmental
studies using these modeling tools. Public interest groups, such as envi-
ronmental organizations, are often stakeholders in transportation and en-
vironmental planning studies that rely on these technical-modeling tools.
Universities train future users of these models, and they perform research
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50 MODELING MOBILE-SOURCE EMISSIONS
scales under average conditions. In regional applications, however, signifi-
cant error can be introduced using MOBILE's generic national defaults.
Policy Implications ant] Future Direction
Output from MOBILE for use in national and regional air-quality strat-
egies has profound technical and economic effects on the direction of the
nation's air-quality management activities. Significant inaccuracies can
result in misdirected control strategies that prolong public exposure to
health hazards or waste large sums of money. It is not certain what major
inaccuracies will exist in the new MOBILES, although clearly the major
changes from MOBILE5b indicate that there have been such inaccuracies
in the past.
Evaluation of Control Strategies, Emissions Inventory,
and Rate of Progress
Primary Users ant! Purpose
State and local governments, MPOs, consultants, research institutions,
and others apply MOBILE to develop regional emissions inventories, eval-
uate alternative mobile-source emissions-control strategies, and track
trends in control-strategy implementation. In particular, MOBILE is used
in ozone nonattainment to demonstrate how a region will comply with the
Clean Air Act Amendment of 1990 requirement to reduce VOC emissions
by 15% *om 1990 to 1996 and 3% annually until attainment.
Issues and Limitations
Some applications, particularly region-wide ones, warrant an aggregate
approach to estimating vehicle emissions. In such applications, it may be
adequate to develop representative emissions factors from region-wide pa-
rameters, such as average vehicle speeds by facility class (see Chapter 3
for a description of facility correct factors) and registration distribution,
that can be combined with VMT to estimate regional emissions. MOBILE
is well-suited to these types of applications. However, there are some con-
cerns about the inability to see all the underlying assumptions in the
model, some of which might have to be altered to fit local observations.
The committee feels that such aggregations still introduce inaccuracies in
the end result because the assumed average may not truly represent the
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CURRENT AND POSSIBLE FUTURE USES OF M OBILE 5 ~
real average in a skewed distribution. Some regional-control strategies
cannot be modeled within the current MOBILE structure, including land
use changes, vehicle scrappage, and clean-fueled fleet incentive programs.
The accuracy of projections used in SIP development depend to a great
extent on the accuracy of assumptions about such factors as fleet turnover,
effectiveness of control strategies, and future deterioration rates of vehi-
cles. Generally, there is concern that national default parameters coded in
MOBILES are outdated, and that their use in SIP development is not ap-
propriate. Additionally, overall concern is high that the vehicle-operating
patterns in the model do not correspond to present and future on-road con-
ditions. EPA is addressing some of these concerns through the addition of
an "off cycle" factor that accounts for higher average speeds and air condi-
tioning use. Improving these aspects of MOBILE will also require im-
provement in vehicle-activity.
Policy Implications and Future Directions
MOBILE users have many questions about its accuracy. For example,
MOBILE's ability to correctly evaluate the impacts of air quality improve-
ment initiatives, such as vehicle emission inspection and maintenance pro-
grams (Harrington et al. 1998) and the use of oxygenates in winter (NRC
1996; NSTC 1997) has been questioned. The development of SIPs requires
accuracy in emissions inventories and crediting of emissions reductions
from controls, both of which are particularly sensitive to errors. Little has
been done to address this issue, and it will undoubtedly become more sig-
nificant in the absence of a significant model revision based on better data
and science. To date, MOBILE revisions have been infrequent, with the
last major update (MOBILES) released in 1993. It is unclear at this point
whether the upcoming version of the model, MOBILE6, will increase or
decrease regional emissions predictions compared to MOBILES. It is like-
ly that at least VOC emissions will increase. This uncertainty as well as
delays in the release of MOBILE6 greatly complicate SIP development.
A long-range plan is needed to determine the appropriate update fre-
quency schedule and whether there should be updates issued more contin-
uously. There is significant concern among the committee about the seven-
year gap between MOBILES and MOBILE6. This delay results in the use
of a version of MOBILE containing information known to be obsolete and
incorrect. Users need updates that incorporate the latest findings on the
factors that affect emissions and the effectiveness of control strategies so
that SIPs can be based on the most accurate information. One possibility
may be to allow users access to new information and allow them the flexi-
bility of incorporating such information into SIPs and other planning pro-
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52 MODELING MOB`LE-SOURCE EMISSIONS
cesses. However, there are also problems that might com
with more fre-
quent or continuous updates, such as inconsistencies between models used
in SIP budgets and subsequent conformity determinations.
The need for more accurate emissions inventories and assessment of
controls requires expanding the capabilities of MOBILE or developing new
models. Users desire more disaggregation of model inputs so that local
conditions can be better represented. There is a need to assess impacts of
some alternative strategies that are not presently incorporated in
MOBILE, such as alternative-fueled vehicles. Road grade is an important
local factor that will not be incorporated in the current or updated versions
of MOBILE. Users will also expect that some form of instantaneous mod-
eling will be provided so that both individual projects and larger-area
transportation systems can be assessed in a more accurate manner.
SIP Demonstration of Attainment
Primary Users and Purpose
States and, in some cases, metropolitan planning organizations (MPOs)
are required to develop a demonstration of attainment for SIPs in ozone
and CO nonattainment regions. These must be submitted to and approved
by EPA. Such an analysis demonstrates that the proposed emissions-re-
duction strategies will attain and maintain ambient ozone and CO concen-
trations below the NAAQS. For this, an urban or regional-scale air-quality
model is used with on-road mobile-source emissions estimated from MO-
BILE and data on other emissions sources (stationary, biogenic, area
sources, and non-road mobile sources).
Issues ant] Limitations
Regional air-quality models are complex and generally require detailed
temporal and spatial allocation of emissions. Applications of urban and
regional air-quality models usually simulate a 3- to 10-day ozone episode.
The model requires hourly "ridded emissions for NOx and VOCs, as well as
speciation of VOCs. Additionally, emissions must be disaggregated by
emissions mode (e.g., exhaust and evaporative), technology, and emitter
group. As discussed in Chapter 4, there is strong evidence that MOBILE
emissions, including emissions by mode, are inaccurate and thus inhibit
accurate air-quality modeling. Inconsistencies may also result from other
emissions sources, due to the lack of temporal and spatial detail needed to
model regional air quality. For example, locomotive, marine, and construc-
tion emissions might be difficult to accurately characterize because critical
data are proprietary.
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CURRENT AND POSSIBLE FUTURE USES OF MOBILE 53
Policy Implications and Future Directions
One important output from MOBILE is emissions-rate detail for inclu-
sion in SIP photochemical modeling. Ultimately, photochemical model ac-
curacy depends on the accuracy of the emissions estimates, as well as the
methods used to spatially and temporally allocate on-road emissions esti-
mates, and to speciate the VOC emissions. Errors in these steps can po-
tentially lead to inaccurate conclusions and selection of sub-optimal con-
trol strategies. Because of difficulties in accurately determining overall
emissions inventories, future guidelines for demonstrating attainment
may be based on a relative rather than an absolute reduction in maximum
ozone concentrations. This, however, will not eliminate the issues associ-
ated with spatially and temporally allocating emissions within a region.
In addition, a lower NAAQS standard for ozone will require better spatial
and temporal disaggregation over wider regions.
Transporlation Conformity and Evaluation of
Transportation Impacts in a Nonaltainment Area
Primary Users and Purpose
The MPO is responsible for performing an air-quality conformity analy-
sis for nonattainment areas. Conformity is a determination that emissions
from transportation plans, programs, and projects in a nonattainment
area do not exceed mobile source emissions budgets established in SIPs
(Federal Highways Administration 1992~. The conformity demonstration
is intended to show that transportation activities will not cause or contrib-
ute to any new violation of air-quality standards, increase the frequency or
severity of existing violations, or delay timely attainment of standards
(EPA 1993a). The conformity analysis is done for the system of projects
contained in a region's transportation improvement program (TIP) and
transportation plan (see glossary for definitions of transportation improve-
ment program and transportation plan). Regions in CO and PM-10
nonattainment must also conduct project-level conformity analysis ("hot
spot" analysis) for critical intersections and sites with violations or possi-
ble violations of the NAAQS.
Similar to emissions inventories developed for a SIP, the transportation
conformity analysis consists of determining emissions estimates as a func-
tion of vehicle activity and region-specific emissions factors. However, this
must be done for a specific system of projects or programs, and on a small-
er scale than for the SIP. In some areas, where planning organizations
have limited responsibilities, the state departments of transportation may
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54 MODE1/NG MOBI[E-SOURCE EMISSIONS
perform the technical analyses. The purpose of an air-quality conformity
analysis is to ensure that transportation projects identified in a region's
Tong-range transportation plan and short-range TIP are consistent with
local air-quality goals and objectives. The conformity analysis is needed to
ensure that calculated emissions levels for federally funded and regionally
significant projects do not cause emissions to exceed the targets specified
in the relevant SIP.
Issues and Limitations
A critical aspect of the conformity determination is that it is developed
for a system of individual projects. Because MOBILE uses top-down pro-
cedures to estimate emissions, it is not suited for conformity applications.
The need for microscale modeling (the modeling of specific corridors or in-
tersections) in the conformity analysis is not adequately supported by the
aggregate regional and national data and assumptions used in MOBILE.
The conformity analysis also must show consistency among the SIP, trans-
portation plan, and TIP. This task can be challenging because the trans-
portation plan horizon date is much further out than that of the SIP. The
SIP is based on the attainment dates set in the Clean Air Act and the
transportation plan horizon is 20 years. Federal conformity regulations
require analysis for the "out years", the years beyond the deadline of the
SIP but within the deadline of the long-range transportation plan. This
creates an inconsistency problem because the maximum growth a region
can accept depends on the level of vehicle technology and fuels that are
accounted for in the SIP.
The current use of MOBILE5b model in conformity analysis is limited
by the model's ability to appropriately model emissions estimates for the
out years beyond the SIP deadline. Specific out-year technological assump-
tions used when MOBILES was developed in 1993 may not accurately rep-
resent current assumptions. This could cause an unnecessary strain on
regions, as they are forced to meet transportation plan budget tests based
on outdated forecasts.
For example, prior to the regulations implementing the national low-
emissions vehicle (NLEV) standards and new regulations on heavy-duty
diesel vehicles (HDDVs) in 2004, a region experiencing rapid growth would
find it difficult to pass a transportation plan horizon-year budget test due
to vehicle activity outracing vehicle technology. Figures 2-7 and 2-8 dem-
onstrate this observation using conformity analysis results from a non-
attainment area (North Central Texas Council of Governments 19981.
Estimates that include the effects of the new NLEV and HDDV programs
in MOBILE, show that NOX and VOC emissions will be reduced by 43%
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CURRENT AND POSSIBLE FUTURE USES OF M OBILE 55
400
~300 -
o
-
a
'n200
._
Z100 -
o
AIR-QUALITY CONFORMITY
COMPARISON OF EMISSIONS LEVELS
NOx
· MOBILITY 2020/1998 TIP
- ~ MOBILITY 2020/1999 TIP with revised emissions
_ 1 1 1 1 1 1 1 1 1 1
1990 1999 2005 2010 2020
Analysis Year
FIGURE 2-7 Effect of NLEV program and new HDDV regulations on NO
emissions for Dallas metropolitan region. Source: NCTCOG 1998.
)x
and 32%, respectively, by the year 2020. Since this time, EPA has devel-
oped even more dramatic improvements through Tier 2 emissions stan-
dards and fuel sulfur reductions.
Policy Implication and Future Direction
Conformity analysis is often conducted annually for the TIP develop-
ment and every three years for the transportation plan. This analysis is
most critical to MPOs and state transportation departments because of its
potential impacts on constructing transportation projects. In many cases,
the conformity determination is made on a system of projects that show
relatively small differences in emissions, especially compared to the effects
of vehicle technology assumptions. MOBILE's aggregate approach to
emissions estimates makes it poorly suited to accurately characterize such
relatively small emissions impacts. Additionally, transportation projects
often have a complex impact on traffic characteristics, and hence emis-
sions, that are difficult to represent in the current linkage of travel-de-
mand models to MOBILE. It is important that MOBILE be improved so
that it is able to more accurately perform conformity or that EPA develops
tools especially for such analysis.
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56 MODELING MOBI[E-SOURCE EMISSIONS
AIR-QUALITY CONFORMITY
COMPARISON OF EMISSIONS LEVELS
400 - .
~5
In 3 0 0
o
._
~ 200 -
._
LU
100 -
o
VOC
MOBILITY 2020/1998 TIP
\
+ MOBILITY 2020/1999 TIP with revised emissions I
VOC Emission Budget = 165 tons per day
1990 1999 2005
Analysis Year
2010 2020
FIGURE 2-8 Effect of NLEV program and new HDDV regulations on VOC
emissions for Dallas metropolitan region. Source: NCTOG 1998.
Conformity analysis will require that either MOBILE or an alternate
conformity tool be quickly adaptable to vehicle technology advances and
future regulatory initiatives. Examples include accounting for the propor-
tion of the vehicle fleet to be zero-emission vehicles (ZEV), the Tier 2 emis-
sions standard, and limits on sulfur in gasoline. Although these modifica-
tions are part of the model used for assessing the Tier 2 proposal (the Tier
2 Model), and will be a part of MOBILES, they cannot be accounted for in
MOBILE5b, the model currently used by state and local agencies for SIP
development and conformity analysis. Due to the timeline inconsistency
between SIP and transportation plans, this is most relevant for a trans-
portation conformity analysis, because it is these new technologies and
initiatives that have the largest impact on out-year emissions.
Transportation Control Measure Effectiveness and CMAQ Eligibility
Primary Users and Purpose
States and MPOs use both travel-demand models and the MOBILE
model to aid in the selection of transportation-control measures (T CM) for
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CURRENT AND POSSIBLE FUTURE USES OF M OBILE 57
SIP credit and to aid in the evaluation and selection of appropriate pro-
jects for congestion mitigation and air quality (CMAQ) funding. TCMs are
projects specifically designed to assist in reducing overall emissions by re-
ducing travel demand (VMT and trips), encouraging the use of alternative
modes, and smoothing traffic flow. Traditional TCMs include high-occu-
pancy vehicle lanes (HOV), signal and intersection improvements, bicycle
and pedestrian improvements, light or commuter rail, advanced transpor-
tation management technology (e.g., freeway management of incidents and
accidents), and travel-demand management strategies (e.g., parking pric-
ing). TCMs are evaluated and selected for CMAQ funding often using
technical methodologies to estimate their effects on emissions. After eval-
uation and commitment by the MPO, these projects can be inventoried and
used in emissions-reduction strategies in SIPs.
Issues and Limitations
One critical issue with TCM and CMAQ projects is the inability to eval-
uate their impacts using the traditional travel-demand modeling process
outlined in Figure 2-1. Travel-demand models, the backbone of transporta-
tion planning, cannot assess small-scare project specifics such as those
commonly found with intersection and signal improvements. Therefore,
air-quality impacts of most TCM and CMAQ projects are evaluated "off-
model" or with post-processing techniques and do not benefit from the
many internal travel-model features affecting volumes and speeds region-
wide. As with the conformity analysis, this affects the accuracy of the
emissions-reduction estimates from MOBILE because MOBILE is used to
estimate the effects of a small change in travel parameters on a subset of
the overall vehicle fleet.
Because no formal blueprint outlines appropriate methodologies at the
national level, each region has developed its own approach to evaluating
TCM and CMAQ priorities, and these variations may cause important na-
tionwide inconsistencies. Consistency in evaluation of TCM and CMAQ
projects has been enhanced by post-processing software packages that es-
timate several useful measures and assess the likely effects of a particular
project. Such models are designed to link directly to the traditional four-
step travel-demand modeling process through trip tables, which are
passed back and fourth as necessary. Unfortunately, software packages to
estimate effects on travel activity or air quality do not exist for all TCM
and CMAQ categories, and regions must devise their own methods to eval-
uate some TCMs (e.g., intelligent transportation systems for freeway man-
agement).
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58 MODE[/NG MOB`[E-SOURCE EMISSIONS
Policy Implications and Future Directions
Nonattainment regions must evaluate TOM and CMAQ project catego-
ries, and these areas would benefit from evaluation methods that quantify
changes in nontraditional transportation and in air quality. This will im-
prove consistency, accuracy, and efficiency, for which national benefits
could be inventoried. As with conformity analysis, the current MOBILE
model is poorly suited to estimate the emissions-reduction benefits of
TCMs. Users need more refined tools that provide a greater resolution of
the impact of TCMs on traffic flow and emissions.
National Environmental Policy Act and
Evaluation of Major Capital Investments (Transit and Highway)
Primary Users and Purpose
The National Environmental Policy Act (NE PA) requires documentation
of the environmental impacts caused by major capital investments that
use federal funds, such as the construction of major transit and highway
projects. NE PA requires that a project will not result in a violation of air
quality standards and that the project be included in a TIP. NE PA also
requires planners to provide a relative comparison of the air quality im-
pacts of alternatives including the no-build alternative. Many agencies
rely on MOBILE results to evaluate air-quality impacts and suggest alter-
native transportation investment options. The primary users of the MO-
BILE model in evaluating major capital transportation investments and
developing environmental impact studies are state departments of trans-
portation, federal resource agencies, state resource agencies, MPOs, local
governments, consultants (generally working for these government units),
and universities.
Issues ant! Limitations
MOBILE is designed to evaluate emissions impacts on a regional level
not at finer levels such as corridors. Because many of the internal defaults
in MOBILE are based on national and regionwide estimates, it cannot pro-
vide the resolution needed to assess impacts for individual corridor-specific
projects. One example to demonstrate this point is that the vehicle regis-
tration data used to estimate vehicle age at the county level is not the
same in each corridor in the county, especially considering the dramatic
variations in income levels within a county.
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CURRENT AND POSSIBLE FUTURE USES OF M OBILE 59
Policy Implications and Future Directions
Because MOBILE is the recognized national emissions factor model, its
results have broad impacts. For example, it may be used to estimate the
difference between a four-lane and a six-lane freeway, the effects of a new
rail line or a reversible HOV lane, or the impact of a range of TOM strate-
gies. As described above, MOBILE is clearly not the most appropriately
designed model to carry out such analysis. It is recommended that an-
other tool be developed to accurately quantify small-scale impacts. How-
ever, EPA must first approve a new model before it is used to evaluate
emissions impacts. Thus, not only does there need to be development of
improved methods for evaluating such projects, the EPA will also need
procedures for vetting, documenting, and evaluating new models and
methods for estimating corridor-level and other small-scale emissions im-
pacts.
SUMMARY OF POLICY IMPLICATIONS AND RECOMMENDATIONS
Modeling the air-quality impacts of on-road vehicles is an interdisciplin-
ary effort that encompasses the modeling of travel demand, emissions, and
air quality. These individual components must be systematically inte-
grated to develop analyses with adequate consistency, fidelity, accuracy,
and precision. Each of the three types of models has a different focus (i.e.,
transportation models generally focus on transportation segments, emis-
sions models on engine modes, and air-quality models on photochemical
reactions and dispersion). Some kinds of uncertainties are inherent within
each modeling domain, others occur at the interfaces when output from
one model is used as the basis for input into the next. EPA should take
steps to improve the linkages among the three models and improve the
methods that are used to process MOBILE outputs for use in regional air-
quality modeling.
MOBILE is a single piece of software with a minimum of six different
categories of uses. It is best suited for aggregate analysis and assessment
of national and regional regulatory strategies and the development of SIPs
for metropolitan areas. It is poorly-suited for analyses of a system of pro-
jects or corridor analyses characteristic of conformity applications, assess-
ment of TCMs, and environmental-impact assessments.
Inconsistencies among these differing categories of uses led the commit-
tee to conclude that likely no single model is appropriate for all applica-
tions. As described further in Chapter 6, the use of MOBILE should be
supplemented with the development and adoption of alternative models
specifically designed to better link traffic flow in local settings to emis-
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60 MODELING MOBILE-SOURCE EMISSIONS
signs. EPA should develop one or more new mobile-source emissions mod-
eling processes, perhaps using a Geographic Information System platform,
that incorporate localized driving cycles and other conditions that influ-
ence emissions. EPA should also consider the development of partnerships
for data collection to better characterize emissions rates under a variety of
conditions.
MOBILE has a critical role in estimating and managing the levels of
mobile-source emissions control. Future focus on new emissions stan-
dards, the growing concern about air toxic emissions, and the growing cost
of control strategies Will increase the demands for accuracy and detail
from MOBILE. A strategic and comprehensive long-range plan is needed
to better identify emerging needs for the modeling of automotive emis-
sions, define the levels of detail and accuracy needed to meet those needs,
and set priorities to improve MOBILE.
EPA should modify its policy of issuing MOBILE updates on a batch,
infrequent basis. Providing updates (known as information fact sheets) for
major factors as soon as they are known, such as the adoption of NLEV
standards or emissions-reduction credits for oxygenated fuels, would allow
users to account for the latest technologies and revisions in SIP develop-
ment as soon as possible. The committee recognizes the difficulties of
working with a moving target, but concludes that the benefits of up-to-
date modeling outweigh the disadvantages of more *equent changes.
Representative terms from entire chapter:
conformity analysis
