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OCR for page 100
Management of
Carbon Monoxide Air Quality
The Clean Air Act's mandate to "protect and enhance the quality of the
Nations air resources so as to promote the public health and welfare" and
subsequent scientific findings by the U.S. Environmental Protection
Agency (EPA) served as the basis for the National Ambient Air Quality
Standards (NAAQS) for carbon monoxide (CO). Chapter 1 discussed the
CO standards, trends in ambient CO, and the studies that were influential
in developing the health-based standards. Achieving and maintaining the
NAAQS requires monitoring ambient CO, developing emissions invento-
ries, implementing emissions regulations and related controls, and tracking
progress. This chapter discusses the primary air quality management ele-
ments needed to achieve those objectives the basic emissions control
strategies used to reduce emissions and the monitoring and modeling tools
used to characterize and assess the magnitude of the problem.
EMISSIONS CONTROL PROGRAMS
CO emissions control strategies have focused on controlling light-duty
vehicle (LDV) emissions. The decline in concentrations noted in the earli-
est stages of CO management in the 1 970s corresponded to the implementa-
tion of a major enhancement in control of motor-vehicle emissions. There
100
OCR for page 101
Management of CO Air Quality 101
are four approaches for reducing vehicle emissions: ( 1 ) new-vehicle certifi-
cation programs, (2) fleet-turnover incentives, (3) in-use vehicle control
strategies, and (4) transportation control measures (TCMs) (Guensler 1 99S,
2000~. This section discusses vehicle emissions control strategies in more
detail. As LDV emissions decrease, nonroad, area, and smaller stationary
sources may become critical for controlling CO in some locations. This
section concludes with a brief discussion on the regulation of these other
sources of CO pollution.
Federal New-Vehicle Emissions Standards
Lowering emissions certification standards on new vehicles has been
the largest source of reductions in CO emissions from LDVs. For example,
the Alaska Department of Environmental Conservation in its most recent
SIP for Fairbanks attributed over 70°/O of total emissions reductions over
the ~ 995-2001 time period to more stringent federal new-vehicle emissions
standards (ADEC 20011. Table 3-1 shows emissions standards for passen-
ger cars and light-duty trucks.2 CO emissions standards have dropped by
over an order of magnitude since their inception emissions from new
passenger cars have fallen from 84 grams per mile (g/mi) before emissions
controls were instituted to below the current 3 .4 g/mi, which began in ~ 981 .
New vehicle technologies offering much better environmental performance
Vehicles are certified using the federal test procedure (FTP) and the supple-
mental federal test procedure (SFTP), which specify the preconditioning a vehicle
is to undergo before testing, the laboratory conditions the test is to occur in, and a
specified driving cycle to be used. Testing is done at temperatures between 68°F
and 86°F. Manufacturers are allowed to certify compliance to the 50,000- or
100,000-mile (ml) standards (11 years or 120,000 mi for heavier trucks weighing
more than 5,750 lb) using low-mileage cars and an agreed-upon deterioration as-
sumption. However, vehicles may be recalled if emissions control systems are
found to be faulty. Tier 2 emissions standards, which will begin with model year
2004, are 120,000-mi standards.
2Light-duty trucks have been categorized for emissions certification purposes
as light light-duty trucks having a gross vehicle weight rating (GVWR) <3,750 lb
(LDT1) or from 3,750 to 5,750 lb (LDT2), and heavy light-duty trucks having a
GVWR from 5,751 to 8,500 lb (LDT3 and LDT4~. Trucks with a GVWR greater
than 8,500 lb are categorized as heavy-duty vehicles.
OCR for page 102
102 Managing CO in Meteorological and Topographical Problem Areas
TABLE 3-1 Federal Passenger-Car and Light-Truck Exhaust Emissions
Standards (g/mija
Passenger Cars Light Trucksb
ModelYear HC CO NOx HCb CO NOx
PrecontrolC 10.6 84.0 4.1
1968-1971 4.1 34.0 8.0 102.0 3.6
1972-1974C 3.0 28.0 3.1 8.0 102.0 3.6
1975-1976 1.5 15.0 3.1 2.0 20.0 3.1
1977-1978 1.5 15.0 2.0 2.0 20.0 3.1
1979 1.5 15.0 3.1 1.7 18.0 2.3
1980 0.41 7.0 2.0 1.7 18.0 2.3
_ .. . .
Tier O
1981-1983 0.41 3.4 1.0 1.7 18.0 2.3
1984-1986 0.41 3.4 1.0 0.8 10.0 2.3
1987-1993 0.41 3.4 1.0 0.8 10.0 2.3
1988-1993 0.41 3.4 1.0 0.8 10.0 1.2
Tier 1 (1994-)
. .
1994 (100~000-mi 0.25 3.4 0.4 0.25 3.4 1.2
standards in (0~31) (4.2) (0.6)
parentheses)
1995 (100,000-mi 0.25 3.4 0.4 0.25 3.4 0.4
standards in (0~31) (4.2) (0.6)
parentheses)
NLEVC (100,000-mi standards)
1999 0.09 4.2 0.3 0.09 4.2 0.3
aAll standards are for 50,000 mi unless otherwise noted.
bStandards before 1988 are for all light-duty trucks. Beginning in 1988, light-
duty trucks were separated into two weight classes (1988-1993) and then four
weight classes (1994-present). The standards after 1988 are for LDT1, which
have a 3,750 lb or less gross vehicle weight (GVW).
The National Low Emissions Vehicle (NLEV) Program introduces California
low-emissions cars and light-duty trucks into the Northeast in 1999 and the rest
of the country in 2001.
Sources: Davis 1997; Chrysler Corporation 1998.
made these achievements possible. This is in contrast to in-use emissions
controls, such as vehicle emissions inspection and maintenance (~/M) pro-
OCR for page 103
Management of CO Air Quality 103
grams and oxygenated fuels programs, which do not force the adoption of
improved vehicle emissions control technologies and hence reduce vehicle
emissions by a much smaller fraction (NRC 1999, 2001~.
Recent New-Vehicle Emissions Standards
Federal passenger car CO standards have remained at 3.4 g/mi (50,000-
mi standard). However there are myriad regulations that have resulted in
reductions in vehicle CO emissions. For example, though Tier 1 standards
did not affect passenger-car CO emissions, they reduced CO standards for
light-duty bucks. Tailpipe emissions of CO end hydrocarbon (HC)respond
similarly to changes in air-fuel ratios, and CO is reduced by many of the
same vehicle emissions control technologies as HC. Thus, the more strin-
gent HC standards imposed since 1981 have resulted in concomitant reduc-
tions in CO.
In addition to these reduced HC standards that result in reduced CO
emissions, there are a number of other changes that directly affect CO
emissions. With the introduction of Tier 1 standards in 1994, the durability
requirements increased from 50,000 mi to 100,000 mi. The supplemental
federal test procedure (SFTP), which is discussed in a subsequent section,
controls CO during non-FTP conditions of high acceleration and high
speed. Cold-start standards, also discussed in a subsequent section, will
limit CO emissions during cold-temperature starts.
The recently finalized Tier 2 regulations will also impact CO emis-
signs. Control of tropospheric (ground-level) ozone (03), which is caused
principally by the interaction of nitrogen oxides (NOX), certain reactive
volatile organic compounds (VOCs), and sunlight on hot summer days, has
been a continuing need. On February 10, 2000, EPA promulgated a new
series of vehicle emissions regulations, known as Tier 2, intended to par-
tially address this problem by regulating passenger-car and light-duty truck
NOX emissions. Tier 2 requires each manufacturer to meet a sales-weighted
"corporate average NOX standard" of 0.07 g/mi. Lowering fuel sulfur con-
tent, which is discussed in the section on in-use emissions controls, is also
an integral part of the Tier 2 strategy.
Table 3-2 lists new emissions limits for NOX, non-methane organic
gases (NMOG), CO, formaldehyde (HCHO), and particulate matter (PM)
by "bin." Manufacturers certify their vehicles in these bins, ensuring that
these vehicles comply with all emissions levels associated with the bins.
OCR for page 104
104 Managing CO in Meteorological and Topographical Problem Areas
TABLE 3-2 Tier 2 and Interim Non-Tier 2 Full-Useful-Life Exhaust
Mass Emissions Standards (g/mi)
Bin Number NO NMOG CO HCHO PM
x
11ac 0 9 0.280 7.3 0.032 0.12
10a b d 0.6 0.156/0.230 4.2/6.4 0.018/0.027 0.08
ga,b.e 0.3 0.090/0.180 4.2 0.018 0.06
8 f 0.20 0.125/0.156 4.2 0.018 0.02
7 0.15 0.090 4.2 0.018 0.02
6 0.10 0.090 4.2 0.018 0.01
5 0.07 0.090 4.2 0.018 0.01
4 0.04 0.070 2.1 0.011 0.01
3 0.03 0.055 2.1 0.011 0.01
2 0.02 0.010 2.1 0.004 0.01
1 0.00 0.000 0.0 0.000 0.00
aThis bin and its corresponding intermediate life bin are deleted at end of 2006
model year (end of 2008 model year for HLDTs and MDPVs).
Higher NMOG, CO, and HCHO values apply for HLDTs and MDPVs only.
This bin is only for MDPVs.
Optional NMOG standard of 0.280 glm: applies for qualifying LDT4s and qual-
if~ing MDPVs only.
Optional NMOG standard of 0.130 g/mi applies for qualifying LDT2s only.
fHigher NMOG standard deleted at end of 2008 model year.
Source: 65 Fed. Reg. 28 (2000), p. 6855.
For example, a manufacturer might certify their sport utility vehicle (SW)
in bin 7, their passenger car in bin 5, and an economy car in bin 3. All
vehicles must meet the full useful life (which has been raised from 100,000
to 120,000 mi) certification limits for their respective bin. NOX emissions
standards for the three bins would then be sales-weighted and compared
with the average NOX standard of 0.07 g/mi.
Tier 2 regulations also allow manufacturers to trade and bank credits.
In years that a manufacturer's corporate average falls below 0.07 g/mi it
can generate credits which it can bank and use in years when its corporate
average exceeds 0.07 g/mi or it can sell these credits to manufacturers
whose corporate average is above 0.07 g/mi.
The technologies relevant to the Tier 2 standards will also have benefits
for CO reduction. Since the mid-1980s, modern computer-controlled en-
OCR for page 105
Management of CO Air Quality 105
gines have used electronic fuel injectors rather than carburetors to deliver
fuel to cylinders in LDVs and most light-duty trucks. The engine computer
system reads the signal from an O2 sensor in the exhaust system and contin-
uously adjusts the air-fuel ratio. The continuous feedback adjustment of
the air-fuel ratio is known as closed-Ioop control. The feedback provides
enough air to burn the fuel while maintaining the optimal catalytic-con-
verter efficiency (referred to as the stoichiometric ratio) for control of CO,
HC, and NOX. Figure 3-1 shows the air-fuel ratio effects on catalyst con-
version efficiency.
During hard acceleration and high-speed operations, however, engine
computers often use fuel-enrichment strategies to enhance engine perfor-
mance for short time periods and to protect sensitive engine components
from high-temperature damage. Likewise, fuel-enrichment strategies are
often used during cold starts. Cold temperature CO standards and the
SFTP, which are discussed in the following sections, are intended to further
control CO for these conditions. Thus, in modern engines, CO as well as
HC emissions are most prominent during enrichment associated with heavy
loads, hard accelerations, and cold starts. Enrichment factors are much
larger for CO compared with HC (see Figure 3-2) (M. Barth, University of
California, Riverside, personal communication, October 30, 2002; Scora et
al. 2000~. Conditions that produce a 10- to 1 00-time increase in CO emis-
sions produce a 1- to 10-time increase in HC emissions.
The primary methods for meeting the Tier 2 standards—ensuring
stoichiometric engine operation over a broader range of operation and
promoting faster catalyst warm-u~will have benefits for CO reductions.
As shown in Figure 3-3, the prototype Tier 2 vehicle maintains a stoichi-
ometric air-fuel ratio more effectively than a 1996 vehicle certified to Cali-
fo~nia's low emissions vehicle (LEV) standard.3 Although the current
3The CAAA90 authorized California, which has the nation's worst air pollution
problems, to impose stricter vehicle emissions standards than those for the rest of
the nation. California's low emissions vehicles (LEV) regulations require manufac-
turers to meet fleet-weighted average emissions lower than those mandated by the
federal Tier I regulations beginning with the 1994 model year. The California LEV
program includes five progressively more stringent categories: transitional low
emissions vehicles (TLEVs), LEV, ultra-low emissions vehicles (ULEVs), super
ultra-low emissions vehicles (SULEVs), and zero emissions vehicles (ZEVs).
OCR for page 106
106 Managing CO in Meteorological and Topographical Problem Areas
100
he
~ 80
z
z
o
Cl)
o
60
40
20
WINDOW ~
NOX ~
RICH A/F
MIXTURE
\
\
BEST OPERATING
AREA FOR 3-WAY
CATALYST
LEAN AJF
MIXTURE
O _
13:1 14:1 14.7:1 15:1 16:1
AIR-FUEL MIXTURE RATIO
\
1 J
FIGURE 3-1 Catalyst conversion efficiency as a fimction of airmail ratio.
Source: Adapted from Canale et al. 1978. Reprinted with permission; copyright
1978, Society of Automotive Engineers.
3.4 g/mi federal new-vehicle standard for passenger cars dates to 1981, the
use of advanced-technology three-way catalytic converters and continued
improvements in stoichiometric ratio controls have had and will continue
to have a collateral CO benefit.
However, it will take years for Tier 2 regulations to be implemented
(2007 for LDVs and 2009 for heavier light-duty trucks), and even longer
for fleet turnover to occur and for the full benefits of the new technologies
to be realized. An increase in vehicle durability has accompanied techno-
logical improvements. According to Davis (2001) the rational average age
of in-use passenger cars has increased from a mean of 5.6 years in 1970 to
8.9 years in 1999. The median lifetime of a 1990 model year passenger car
is 4.6 years longer (16.1 years) than that of a 1970 model year car. This
increase in vehicle durability will slow the penetration of vehicles with
newer emissions control technologies into the fleet. In the meantime, the
ongoing improvements resulting from HC standards under Tier 1 and
NLEV, the cold-start CO standards, the increased durability required under
Tier 1, and the introductions of the SFTP will continue to encourage the
downward trend in CO emissions from light-duty vehicles in advance of
Tier 2.
OCR for page 107
Management of CO Air Quality 107
Engine Start
Hot Stabilized
Exhaust
Engine On
Acceleration
Enrichment
Grade
F nri~hmPnt
Space and Time (seconds)
Engine Off
FIGURE 3-2 Hypothetical carbon monoxide emissions rates for typical vehicle
operation.
In addition, some emissions control strategies for controlling cold-start
emissions are particular to HCs and do not improve CO emissions. Some
of the lowest-emitting vehicles in California (called super ultra-Iow emis-
sions vehicles tSULEV]) use a carbon canister to store uncombusted HC
emissions during cold starts. The HC emissions are then recirculated
through the catalyst after light-off. Such a control strategy does not reduce
CO emissions.
~ summary, the impact of Tier 2 requirements is complex. The CO
limits for the higher-emissions vehicles, bins 5-8 (bins 9-1 1 disappear after
2009), is 4.2 g/mi based on a full useful life of 120,000 mi. On the surface,
this limit is essentially the same as the Tier ~ and NLEV limits. However,
Tier 2 standards are 120,000-mi standards, which should improve vehicle
in-use performance. These bins will apply to passenger cars as well as all
categories of light-duty bucks (LDT1-LDT4~. This wiTIrequirethatLDT2-
LDT4, which under Tier 1 standards have 100,000-mi CO standards from
5.5 g/mi to 7.3 g/mi, meet the current CO standards for passenger cars (4.2
g/mi). At a national level, the result of these bins will be a reduction in
CO. If bins 6-8 are used for any vehicle, then bins 14 must be used to
average NOX below bin 5. Using the example of a manufacturer certifying
their SUV in bin 7 (CO standard at 4.2 g/mi), their passenger car in bin 5
(CO standard at 4.2 g/mi), and their economy car in bin 3 (CO standard at
OCR for page 108
108 Managing CO in Meteorological and Topographical Problem Areas
1996 California Low Emissions Vehicle
15.0
14.9
14.8
14.7-
o
14.6
~ 14.5
2 14.4
14.3
14.2
14.1
14.0
15.0 ~
14.9 ~
14.8 ~
O 14.7-
._
14.6-
14.5-
14.4-
14.3 ~
14.2 ~
14.1 ~
14.0 ~
.
·.
~ ~~ .
· ~
. ~ . . .
· ~ · ~ ~ %. ~ ~ ~1
; -. . ~ - .~\ e. ~$.~,S^'
, _,' - An_ ~ ~ _ ~ ~ a, ~ ~
· ~ AIMS ~
.
.
A,
;. - · ·
:
· ~
~ i.
0 100 200 300 400 500
Time (s)
2003 Tier 2
· ~
·;
0 100 200 300 400 500
Time (s)
FIGURE 3-3 Example of the improved control of air-fuel ratio resulting from
new Tier 2 vehicle technologies. Source: Dana 2002. Reprinted with permis-
sion from the author.
OCR for page 109
Management of CO Air Quality 109
2.1 g/mi), the resulting fleet sales-weighted CO will be Tower than 4.2 g/mi.
However, this is a national fleet average. Local vehicle fleets may differ
from the national average.
Cold-Temperature CO Standards
As described in Chapter 1, CO is predominantly a winter problem that
occurs in regions known for extreme winter conditions (e.g., Fairbanks,
Alaska). During cold starts the engine computer signals the fuel injectors
to add excess fuel to the intake air to ensure that enough fuel evaporates to
yield a flammable mixture in the engine cylinders. A typical engine-com-
puter strategy injects several times the stoichiometric amount offue] during
the first few engine revolutions, using a fixed fueling schedule to reach
idling conditions. Excess fuel continues to be injected until the engine and
O2 sensor are warmed up and the exhaust-catalyst inlet temperature reaches
about 250-300°C, sufficient for the catalyst to oxidize CO to CO2. This
open-Ioop operation, before catalyst light-off (the time it takes the catalyst
to reach peak efficiency after start), can continue for several minutes at Tow
ambient temperatures. Cold-start enrichment is responsible for a signifi-
cant fraction of CO, air tonics, and unburned HCs from properly operating
vehicles. Once the engine and emissions control systems are warmed up,
combustion becomes stoichiometric, and CO is converted to CO2 in the
catalyst, keeping CO emissions very low under typical operating condi-
tions. Warm up times under mild ambient conditions, at around 70-80°F,
can be around 1 min for modern catalysts and even as short as a few sec-
onds for modern close-coupled catalysts (catalysts close to the engine).
When ambient temperatures are -20°F or Tower, however, catalyst and
engine warm-up times can exceed 5 min (Sierra Research 1999~. In the
case of Fairbanks, Alaska, this means that idling and cold-start emissions
from LDVs are particularly high and make up a significant proportion of
overall CO emissions. ADEC (2001) and NRC (2002) provide more dis-
cussion of the role that cold-start emissions play in Fairbanks.
Since 1994 new cars and the lightest category of light-duty trucks
(LDT1) have been required to meet a CO limit of 10 g/mi on the federal
test procedure (FTP) cycle conducted at 20°F. For heavier light-duty trucks
(trucks between 3,751 and 8,500 Ib gross vehicle weight tGVW]), the stan-
dard is 12.5 g/mi. The cold-temperature CO emissions standard has been
unchanged since it was promulgated in ~ 994, though certification data from
OCR for page 110
110 Managing CO in Meteorological and Topographical Problem Areas
EPA's certification database show that there have been continued improve-
ment in cold-start emissions (Figure 3-4~. Reducing the 10 g/mi limit for
the 20°F cold-start test or reducing the test temperature might provide addi-
tional CO emissions reductions for cold northern regions, such as Fair-
banks. Indeed, CAAA90 mandated that if six or more areas were desig-
nated as nonattainment as of July 1, ~ 997, EPA must require cars to meet
a Phase I] cold-start emissions limit of 3.4 g/mi. In their presentations to
the committee, representatives of the State of Alaska and the Fairbanks
North Star Borough discussed how the adoption of the Phase II cold-start
standard would aid in Fairbanks's effort toward long-term attainment ofthe
CO NAAQS (Hargesheimer 2001; King 2001; Verrelli 2001~. However,
EPA has yet to formally dete~ine the number of CO nonattainment areas
that existed as of the deadline.
Supplemental Federal Test Procedure
An additional source of CO reductions is the SFTP. The technical
community has long known about the absence of high speeds and accelera-
tions from the FTP. The SFTP introduces speeds as high as 80 MPH and
maximum accelerations of 8.4 MPH/s into the certification test (61 Fed.
Reg. 54852 t199611. The FTP tests at a maximum speed of 57 MPH and
a maximum acceleration of 3.3 MPH/s. Certification to this new cycle will
be phased in during the 2000 and 2004 model years. This test procedure
should ensure that vehicle emissions control systems will provide improved
emissions control over a wider range of vehicle speeds and loads. Much of
the improved emissions control will come from reduced use of fuel-rich
mixtures at higher Toads. EPA estimates a CO emissions reduction of 1 1%
from the LDV fleet in 2020 as a result ofthe SFTP (EPA 1996~. However,
it should be noted that for some locations with severe winter Hiving condi-
tions, such as Fairbanks, the high speed/high acceleration driving condi-
lions within the SFTP are not considered representative. Thus, the benefits
from certifying vehicles to the SFTP may be smaller there.
Mobile-Source Compliance Programs
Mobile-source compliance programs are intended to ensure that vehi-
cles meet emissions standards throughout their useful life. There are three
OCR for page 138
138 Managing CO in Meteorological and Topographical Problem Areas
Stationary Sources
Stationary sources, especially power plants and large industries, may
also have a large impact on local CO concentrations. As previously men-
tioned, stationary-source emissions factors can be determined from AP42
(EPA 1995~. Stationary-source operations are usually more consistent then
mobile-source operations, thus stationary emissions are easier to quantify.
Activity, such as fuel usage (often in the form of BTUs generated or
amount of fuel consumed per year), is multiplied by an emissions factor to
estimate the total mass of CO emitted per year. However, CO exceedances
in Birmingham, Alabama, demonstrate that an unregulated point source that
experiences process upsets can become a large emissions source sufficient
in itselfto create CO exceedances. Utilizing emissions factors from AP42
would underestimate the contribution from sources such as the one in Bir-
mingham.
In addition, estimating emissions from area sources, such as residential
heating, is likely to be highly uncertain. During this study, the committee
noted that the emissions inventory for Missoula, Montana, attributed ~ 8°/0
of CO emissions to wood burning, whereas the inventory for Fairbanks,
Alaska, attributed only 3°/0 of CO emissions to that source. The disparity
existed despite Missoula's fairly substantial effort to control emissions
from wood stoves. The committee also questioned whether the increasing
popularity of fuel-oi! stoves has resulted in the underestimation of this
source in inventories. It is clear that emissions inventories for stationary
sources need improvement.
Air Quality Models
Air quality modeling is an essential element of air quality management.
Models can be used to evaluate plans for attainment of an NAAQS (also
referred to as an attainment demonstration), to evaluate the effects of new
construction projects, and to conduct further research into what causes
pollution episodes and how they can be predicted. A number of modeling
techniques requiring various levels of scientific expertise, input data, and
computing resources are available for these purposes. The simplest mod-
els, rollback models, assume a direct correlation between emissions and
ambient pollutant concentrations; the most complicatedmodels, grid-based
air quality models, resolve temporal and spatial variations in pollutant
concentrations and the effects of meteorology, emissions, chemistry, and
OCR for page 139
Management of CO Air Quality 139
topography. Models are also characterized by the size of the problem they
address: microscale models simulate pollution from a point source or
intersection; mesoscale models simulate metropolitan or multistate pollu-
tion; and large-scare models simulate continental or global pollution.
In attainment demonstrations presented in SIPs, states are required by
EPA to model how emissions reductions will lead to the desired air quality
improvements. Three types of models have been used to demonstrate at-
tainment ofthe CO NAAQS: rollback (also knows as statistical rollback),
Gaussian dispersion, and numerical predictive models.
Rollback Models
The simplest of the three models used for attainment demonstrations is
the statistical rollback mode] in which the needed reduction in emissions
is assumed to be proportional to the required reduction in ambient CO
concentrations (ADEC 2001~.
CObaSeYear CONAAQS
/0 reduct~orl=
C°baseyear Background
where
CObase year = the second highest 8-hour average in the base year;
CONA 4QS = the NAAQS of 9 ppm (or sometimes 9.4 ppm); and
Cobackground = an average regional background CO in the absence
~ . .
01 emissions.
Although easy to implement, rollback models do not explicitly consider the
role of meteorology or the spatial heterogeneity of CO emissions and con-
centrations. EPA has allowed states to use rollback models rather than the
more resource-intensive dispersion and urban-airshed models described
below, to demonstrate attainment in smaller cities. An improvement on the
simple rollback model is the probabilistic rollback model used in CO mod-
eling for the Puget Sound area of the State of Washington (Ioy et al. 1995~.
Gaussian Dispersion Models
A second type of model that has been used for CO-attainment demon-
strations is a Gaussian dispersion model, which is typically used to simulate
CO concentrations for microscale analysis in the vicinity of intersections
OCR for page 140
140 Managing CO in Meteorological and Topographical Problem Areas
or along major traffic corridors (EPA 1992~. One of the first effective
Gaussian dispersion models for mobile sources was CALrNE3, which is
still in use. Inputs for this model include meteorological data, such as wind
speed and atmospheric inversion strength in the vicinity of the pollutant
source, and temporally resolved emissions. Emission factors developed
from other emissions models (MOBILE and EMFAC), along with traffic
volumes, roadway geometries, and intersection information, are used to
determine the emissions along a roadway. Dispersion modeling then in-
cludes transport and mixing to calculate local concentrations. The model
is Gaussian in nature, meaning it assumes that a plume of pollutant gas
released from a point source can be described by a widening Gaussian
function (a bell-shaped curve) as it travels downwind (Wayson 1999~. The
model also makes the assumption that roadway segments can be cut into
small sections with a point source approximation applied to each and their
plume concentration contributions summed at a receptor site. This concept
allows roadway curves or winds nearly parallel to the roadway to be mod-
eled effectively.
The shortcoming of CALINE3 is that it is only useful for vehicles that
are moving at a constant rate of speed. At locations of high CO emissions
(such as intersections), increased emissions due to vehicle delay and idling
must be accounted for. To do that, two models are in use today:
CAL3QHC and CALLNE4. Both use the same general approach to estimate
dispersion as CALINE3 does. CAL3QHC is used in 49 states, and
CALLNE4 is used in California.
Gaussian dispersion models are typically used for local area (micro-
scaTe) analysis and are used extensively in CO-related evaluations, includ-
ing project-level conformity determinations. Modeling is done for the
worst hour to compare with the 1-hour average CO NAAQS. Worst-case
conditions (a windspeed of 1 MPH and a stable atmosphere) are often used.
A persistence factor, whichis a multiplier ofthe peak 1-hour concentration
that is based on changes in wind patterns and traffic, is used to estimate an
8-houraverageconcentrationforcomparisonwiththeS-hourNAAQS. The
model results often determine whether a project can go forward.
The American Meteorological Society (AMS) policy statement on
dispersion modeling (Henna 1978) concluded that these models are accu-
rate within a factor of 2 for reasonably steady horizontally homogeneous
conditions; however, they will be less accurate, for example, when obstacle
wakes flows (e.g., from buildings or vehicles) and extremely stable thermo-
dynamic lapse rates occur. Dispersion accuracy will also be Tower, as listed
OCR for page 141
Management of CO Air Quality 141
in the AMS statement, for "dispersion over forests, cities, water and rough
terrain."
Grid-based Air Quality Models
The most complicated models used for attainment demonstrations
simulate how a pollutant concentration varies with time and space over an
entire urban area. These numerical predictive models, generally intended
for regional analysis, can simulate emissions from multiple sources end the
dispersion, advection, and photochemical reactions of gaseous pollutants
in the atmosphere. These models are integrated separately from meteoro-
Togical models. Grid-based models, such as Models-3 and the urban air-
shed mode] (UAM), have been used for many years to simulate 03, which
is a region-wide or mesoscale pollutant. The UAM has been adapted to
simulate CO in Denver (Colorado Department of Public Health and Envi-
ronment 2000~. Because ofthe local nature of high-CO episodes, extensive
modeling ofthe entire urban airshed may be unnecessary for CO-attainment
demonstrations. Airshed modeling is resource-intensive, requiring detailed
knowledge of an area's meteorology (usually based on the output of a
mesoscaTe weather model constrained by observations), spatially and
temporallyresolved emissions inventories, and measurements ofthe pollut-
ant at several locations to allow model evaluation. Highly trained person-
net are needed to conduct the simulations.
More complicated models are not always appropriate for attainment
demonstrations, but they can be valuable in improving the understanding
of the interactions among atmospheric processes. Even better research
tools than the numerical predictive models described above (such as
Models-3 and the UAM) are process numerical models, which allow pro-
cesses specific to air quality modeling and meteorology to be coupled
within a single computational framework. Process numerical models typi-
cally are formulated by adding pollutant emissions, chemistry, and trans-
port into an existing meteorological model rather than simply using the
meteorological data as a mode! input. The relatively nonreactive behavior
of CO makes it an ideal chemical species for simulation in a weather
model. Predictions of CO, for example, can be straightforward in the Na-
tional Weather Service Eta model, Is which has a horizontal grid framework
Resee NWS 2002 for information on the NWS Eta model.
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142 Managing CO in Meteorological and Topographical Problem Areas
of 12 x 12 km over the contiguous United States. However, this resolution
is insufficient for most CO problem areas. Initial work to simultaneously
simulate atmospheric flow and diffusion of CO at high spatial and temporal
resolution is described by Fullerton (20021.
Box Models
Box models are another tool available for microscale analysis of air
pollution. The "box" is some volume of air into which emissions are in-
jected. Box models may divide a region into cells of equal volume and use
mass balances to treat the transfer of CO among cells. In their simplest
application, they can consist of a single box. The cells may also be sepa-
rated in the vertical direction. Air within each cell is assumed to be well
mixed. Simplifications ofthis concept lead to the common single-cell box
model.
Though box models are not used in attainment demonstrations, they are
particularly useful to understand how various emissions scenarios and
meteorological conditions affect pollutant concentrations. For example, a
box model for CO in Anchorage, Alaska, has been used to quantify how
mechanical turbulence from roadway traffic might increase the mixing
height and reduce CO concentrations on severe-stagnation days compared
with concentrations observed in residential neighborhoods (Morris 2001~.
Appendix C describes a single-cell box model, with and without
recirculation. The committee's interim report on Fairbanks describes the
application of such a model to Fairbanks, Alaska (NRC 2002~.
Summary of Air Quality Models
There is no single air quality model that is the best for CO for all Toca-
tions. Typically the choice depends on the severity of the problem, the
available data, and the resources available for modeling. It its interim
report (NRC 2002), the committee recommended that Fairbanks, Alaska,
use a simple box-model approach for air quality planning purposes in the
near term. A box model simulates the effects of emissions end meteorology
in a well-mixed controlled volume. The committee felt that such an ap-
proach could provide greater insights into the effects of the timing of CO
emissions and of meteorological variables, in this particular situation, given
the limited vertical dispersion and available data. Box models could sup-
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Management of CO Air Quality 143
plement Fairbanks's current approach of using a simple rollback model,
which they used in their attainment demonstration (ADEC 20011.
In the long-term, the committee recommended that more work be done
to develop, apply, and evaluate more sophisticated, physically comprehen-
sive models that would simulate how CO concentrations vary with time and
space. Because CO is relatively conservative on time scales of hours,
knowledge ofthe temporal and spatial distribution of CO emissions and of
the observed CO concentration field provide an effective diagnosis of atmo-
spheric dispersion patterns. For chemical species that are eliminated by
reactions in the atmosphere, knowledge of the CO dispersion provides an
observational constraint on the concentration fields ofthe reactive species.
The committee concluded that more physically comprehensive models
should be used for planning, forecasting, and assessing human exposures
to high CO concentrations. It is important that mode] development and
testing be specific to the extreme conditions that occur in CO problem areas
such as Fairbanks. However, model development must occur in concert
with improved monitoring to enable model evaluation. The committee
believes that even in areas such as Fairbanks, which has experienced very
few exceedances since 1996, and none since 2001, the development of
comprehensive models is still worthwhile. The number of periods of ele-
vated CO levels experienced in Fairbanks indicates that the city is still
susceptible to exceedances. Furthermore, CO modeling can be used to
better understand and characterize CO hot spots as well as other criteria
pollutants and air tonics. The development of a better modeling approach
today will benefit all problem areas in the future.
Despite advances in air quality modeling capabilities over the last 30
years, many improvements are still possible and necessary. One problem
is that the vertical and horizontal resolution of models is too coarse to
capture the variability in pollutant concentrations, which is necessary to
identify local hot spots and is important for determining local concentra-
tions downwind of hot spots. In addition, the validity of mode] representa-
tion becomes questionable when unusual meteorological conditions occur,
and that could lead to errors in the prediction (Pielke 2002~. Models used
for regulatory purposes can suffer a loss of realism as a result of such short-
comings, leading to costly errors in planning. Models also need more real-
istic three-dimensional dynamics (advection, pressure gradient forcing'
turbulences and more realistic parameterizations of smaller-scale processes
(e.g., turbulence fi om buildings, radiative flux divergence changes in the
temperature profile associated with aerosols in the lower levels ofthe atmo-
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144 Managing CO in Meteorological and Topographical Problem Areas
sphere). The models also need higher spatial and temporal resolution.
Ensemble runs of the models should be performed to provide a more realis-
tic envelope of simulated dispersion patterns. However, the committee
recognizes that this adds cost and time to the evaluation. Not only can
these models be used for air quality applications, models with higher reso-
lution can also assist in homeland defense because they can help understand
the dispersion of accidental or deliberate releases of chemical, biological,
and radiological materials.
In 2003, a large dispersion research project will be undertaken to help
define important dispersion parameters, primarily for homeland security
purposes (DOE/DOD 2002~. The project will be a month-Ion" study con-
ducted by a combination of federal and state governmental agencies with
support from multiple universities. Research will include releases oftracer
gases with careful measurements of meteorological parameters to determine
dispersion trends for city-wide dispersion, dispersion in street canyons,
infiltration to buildings, and effects of topography.
Statistically Robust Methods to Assist in Tracking Progress
The air quality models described above assess the effectiveness of
emissions controls and the prospects for attaining the CO standard by repre-
senting critical processes within a physically based model of the system.
An alternative to those physical models is to take a statistical approach
assessing the relationship among human activities, CO emissions, meteorol-
ogy, and ambient air quality, as described below.
Probability of an Exceedance
Reddy (2000) carried out an analysis of the probability of a future CO
exceedance in Denver that might be broadly applicable to other areas. The
analysis took into account the historical variability in CO concentrations as
a result of meteorology and unusual traffic events. The purpose of his
analysis was to determine the risk of a CO exceedance associated with
eliminating or altering the oxyfuels program during the first week in Febru-
ary for the future years 2002-2013. He used CO monitoring data from the
CAMP site (AIRS ID 08-031-002), which is the site in Denver that has
historically shown the greatest number of exceedances. He used daily peak
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Management of CO Air Quality 145
8-hour average CO concentrations for the first week in February for the 20-
year period of 1975-1994. Because these values depended on the emissions
during those years in addition to stochastic meteorology and occasional
unusual traffic, Reddy corrected past CO concentrations for each year to
what they would have been if the emissions for that year had been the same
as those projected for 2002.
The natural logarithms of the corrected peak 8-hour average CO con-
centrations were normally distributed; the 8-hour averages themselves were
not. By estimating future emissions inventories for the years 2002-2013,
based on projected fleet composition and VMT, and assuming that the
lognormal distribution would hold for future years, Reddy was able to
calculate the probability of an exceedance on a single day of the first week
in February (P~ ~ for the future years. He then used Equation 1 to compute
the probability of one or more exceedance days during an entire week
(P79
P76 = 1 - (1-P~ 97.
(1)
Reddy found a better than 5°/O chance that an exceedance might occur if
Denver immediately suspended the oxyfuels program for the period 2002-
2013. The study also found that Denver would likely not have an exceed-
ance if 1.5% oxygen (which is less than the oxygen content used in the
current oxyfuels program) was used in fuels for 2002 and 2003 before
suspending the use of oxyfuels for 2004 through 20 ~ 3.
Equation ~ assumes that exceedance events are independent over time
(thus the probabilities can be multiplied, as in the second term on the right
hand side of the equation). The assumption might not hold, for example,
exceedance events might be positively associated over time. Given this
possibility, Reddy' s method might overestimate P7 a. Alternatively, we can
modify Reddy's equation as follows:
Expected number of exceedances = N X Pi 4,
(2)
where N denotes the number of days in the time period being considered,
under the assumption that the probability of exceedance is uniform over the
time period. For Reddy's application, the time period considered is the first
week in February, thus N= 7. Under more general conditions, Equation 2
can be modified as follows:
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146 Managing CO in Meteorological and Topographical Problem Areas
Expected number of exceedances = I. ~ Pi ~
(3)
where Pi denotes the probability of exceedance on the i-th day. Equation
3 does not assume that the exceedance probability is uniform over time.
For example, one might use a different exceedance probability for week-
days versus weekends.
The same procedure that Reddy used, or the modified one discussed
above, could be applied to monitoring sites in other cities and for times
other than the first week in February (e.g., a whole winter season), provided
that there are enough historical data to establish the distribution of CO
concentrations and to estimate emissions inventories for past and future
years.
Meteorological De-trending
Ambient CO concentrations across the nation are going down. Un-
doubtedly many of these reductions are due to emissions controls. Part of
the kend, however, may also be meteorological. A warmer winter with less
stagnation can lead to Tower winter CO levels. As noted by Neff (2001),
Denver may be experiencing lower CO levels than would be expected from
emissions reductions alone because of warmer winters with greater vertical
mixing. How can the impact of meteorological trends on the observed
concentrations be removed in order to assess the impact of emissions con-
trols and to show true progress towards meeting air quality standards in the
future, when meteorological conditions may not tee so favorable? One must
"de-trend" the observations.
Meteorological de-trending is accomplished by identifying how meteo-
rological variables impact pollutant concentrations and removing the influ-
ence of those variables. One way would be to create a physically realistic
model that can simulate many years, developing emissions-to-air quality
relationships and showing how they respond to meteorological influences.
However, this approach would be cumbersome and would introduce signifi-
cant uncertainties. The influence of meteorology is more typically identi-
fied using an empirical approach. Many years worth of concentration data
are analyzed, along with the corresponding meteorological data, to develop
a statistically based model. That model is then used to remove meteorolog-
ical impacts (Kuebler et al. 2001; Porter et al. 2001).
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Management of CO Air Quality 147
Recent work by Flaum and colleagues used a multistep process to
resolve the trends in ozone (03) into four components: a long-term trend,
presumably due to emissions controls; a seasonal component; a component
driven by meteorological fluctuations; and a noise component (Flaum et al.
19961. Kuebler et al. (2001) used a similar approach, not only for 03, but
also for CO, NOX, and VOCs, and compared the meteorologically de-
trended concentrations of the primary pollutants with the trends in emis-
sions estimates. From that, a direct relationship between the emissions
levels and pollutant concentrations could be established.
The latter approach appears appropriate here given its prior use for CO,
though the explanatory variables may depend on location. For example, in
Fairbanks, a nonlinear response to temperature is expected because CO
concentrations appear to be highest at about -20°F to 20°F, not at much
lower or much higher temperatures. This approach is convenient for local
air quality management organizations because it requires relatively little
data (e.g., a long-term record of CO concentrations and meteorological
variables such as temperature and windspeed would suffice, though more
factors are useful) and nominal computational power.
The de-trending analyses also can provide extra information for air
quality planning. As noted above, de-trending can be used to help develop
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148 Managing CO in Meteorological and Topographical Problem Areas
probabilities of exceeding the NAAQS for CO at various emissions levels.
From that, the necessary level of emissions can be identified in a more
statistically robust fashion.
Representative terms from entire chapter:
pollutant concentrations
