United States
Environmental Protection
Agency
Office of Mobile Sources
Emission Control Technology Division
2565 Plymouth Road
Ann Arbor, Michigan 48105
EPA 460/3-85-008
September 1985
Air
&EPA
An Analysis of Chemistry Mechanisms
and Photochemical Dispersion Models
for Use in Simulating
Methanol Photochemistry
-------
EPA 460/3-85-008
An Analysis of Chemistry Mechanisms and
Photochemical Dispersion Models for Use in
Simulating Methanol Photochemistry
by
Howard Balentine
Craig Beskid
Larry Edwards
Rob Klausmeier
Steve Langevin
Radian Corporation
8501 Mo-Pac Blvd.
P.O. Box 9948
Austin, Texas 78766
and
Mark Eltgroth
MEF, Environmental, Inc.
2013 Wells Branch Parkway
Suite 201
Austin, Texas 78728
Contract No. 68-02-3889
Work Assignment 11
EPA Project Officer: Jane Armstrong
Technical Representative: Penny M. Carey
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Mobile Sources
Emission Control Technology Division
2565 Plymouth Road
Ann Arbor, Michigan 48105
September 1985
-------
This report was furnished to the Environmental
Protection Agency by Radian Corporation, 8501 Mo-Pac
Blvd., P.O. Box 9948, Austin, Texas, in fulfillment
of Work Assignment 11 of Contract No. 68-02-3889.
The contents of this report are reproduced herein as
received from Radian Corporation. The opinions,
findings, and conclusions expressed are those of the
authors and not necessarily those of the
Environmental Protection Agency. Mention of company
product names is not to be considered as an
endorsement by the Environmental Protection Agency.
Publication No. 460/3-85-008
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TABLE OF CONTENTS
Page
1.0 INTRODUCTION 1
1.1 Background 1
1.2 Objective 2
1.3 Report Description... 3
2.0 ANALYSIS OF AVAILABLE CHEMISTRY MECHANISMS FOR ASSESSING THE
USE OF METHANOL AS A MOBILE-SOURCE COMBUSTION FUEL 4
2.1 Methanol-Fueled Vehicle Emissions 5
2.1.1 Alteration of Emissions Inventory 5
2.1.2 Evaporative Emissions with Methanol 5
2.1.3 Exhaust Emissions Rates for Regulated
Pollutants 5
2.1.4 Exhaust Emission Rates for Unregulated
Pollutants 10
2.2 Overall Requirements of Photochemical Mechanisms to
Simulate Methanol Photochemistry 12
2.3 Review of Previous Methanol Studies 13
2.3.1 Chemical Reaction Mechanisms Used 13
2.3.2 Methanol Photochemistry 14
2.3.3 Formaldehyde Photochemistry 14
2.3.4 Methyl Nitrite Photochemistry 16
2.3.5 Post-Study Validation Experiment 18
2.4 Discussion of SAI and CIT Results 19
2.4.1 Base Case Results 19
2.4.2 Formaldehyde Emission Scenario Results 23
2.4.3 Other Results 24
2.4.4 Summary of the Results of SAI and CIT Studies... 25
2.5 Consideration of Alternate Chemistries 26
2.6 References for Section 2.0 30
3.0 REVIEW OF PHOTOCHEMICAL MODELS 32
3.1 Background 32
3.2 Models to be Reviewed 34
3.3 Eulerian Models 36
3.3.1 SAI Urban Airshed Model 37
3.3.2 CIT Airshed Model 39
3.3.3 MARC-1 Model 40
3.4 Trajectory Models 42
3.4.1 EKMA/OZIPM-2 Model 43
3.4.2 SAI Trajectory Model 46
3.4.3 RPM-II Model 46
3.4.4 STRATOS Model 48
3.4.5 GCKM/TRAJ Model 51
3.5 Summary and Recommendations 53
3.5.1 Recommendation of Model for Los Angeles 53
3.5.2 Recommendation of Model for Other Areas 56
3.6 References for Section 3.0 59
ii
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TABLE OF CONTENTS (Continued)
4.0 MECHANISMS REVIEWED 64
4.1 Selection of Chemical Mechanisms 64
4.2 Description of Mechanisms 65
4.2.1 Carbon Bond III 66
4.2.2 CIT 67
4.2.3 Demerjian 67
4.3 Implementation of Chemical Mechanisms in GCKM 68
4.3.1 GCKM Treatment of Chemistry 68
4.3.2 CBM-III 69
4.3.3 CIT 70
4.3.4 Demerjian 72
4.3.5 Addition of Methanol Chemistry to the Reaction
Mechani sms 75
4.4 References for Section 4.0 76
5.0 MODELING SENSITIVITY ANALYSIS 78
5.1 Brief Description of Model Used 78
5.2 Input Data Preparation 79
5.2.1 Atmospheric Conditions 80
5.2.2 Initial, Air Parcel and Aloft Precursor
Concentrations 80
5.2.3 Non-Methane Organic Compound Emissions 82
5.3 Sensitivity Study Results 82
5.3.1 Base Case Scenario 89
5.3.2 Fifty Percent Methanol Conversion Scenario 91
5.3.3 One Hundred Percent Methanol Conversion
Scenario 93
5.3.4 Ozone Background/Aloft Concentration Scenarios.. 95
5.3.5 Formaldehyde/Methyl Nitrite Emission Fraction
Scenarios 97
5.4 References for Section 5.0 101
6.0 SUMMARY AND RECOMMENDATIONS 102
6.1 Methanol Chemistry and Emissions Summary 102
6.2 Recommended Dispersion Models 104
6.3 Recommended Mechanisms 104
6.4 Recommended Additional Analyses 106
6.5 References for Section 6.0 107
APPENDIX A - Chemical Mechanisms Used in Sensitivity Analysis 108
APPENDIX B - Description of Species and Emission Fraction Calculations
and Sample Calculations 118
iii
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TABLE OF CONTENTS (Continued)
Page
APPENDIX C - Plots of Various Species for Each Model Sensitivity Run.. 125
APPENDIX D - Concentrations of Various Species for Each Model
Sensitivity Ron 139
APPENDIX E - Discussion of the CIT 50 Percent Methanol Scenario
Substitution 152
iv
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LIST OF TABLES
Table Page
2-1 Summary of Exhaust Emissions Data on Vehicles Burning
Methanol 6
2-2 Selected Results of the SAI Study 20
2-3 Selected Results of the CIT Study 22
3-1 Summary Review of Models 54
4-1 Comparison of Ozone Concentrations (ppm) Using CBM-III
Mechanism 71
4-2 Comparison of Ozone Concentration (ppm) Calculated by CIT
Mechanism with OZIPM-2 73
4-3 Benchmark Run Comparison of Ozone Concentration (ppm) Using
Demer j ian Mechanism. 74
5-1 Hourly Temperature and Mixing Height Schedules Used for
all Model Runs 81
5-2 Model Input Ozone Precursor Concentrations 83
5-3 CBM-III Reaction Mechanism Initial NMOC Species Concen-
trations in Air Parcel (ppm) 84
5-4 CIT Reaction Mechanism Initial NMOC Species Concentrations
in Air Parcel (ppm) 85
5-5 Demerjian Reaction Mechanism Initial NMOC Species Concen-
trations in Air Parcel (ppm) 86
5-6 Los Angeles 1987 Projected Mobile and Other Source
Emissions 87
5-7 Summary of Peak Ozone Concentration for Each Scneario as
Predicted by Each Mechanism 88
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LIST OF FIGURES
Page
Predicted Ozone Concentrations for Base Case Scenario
for Each Mechanism 90
5-2 Predicted Ozone Concentrations for 50% Methanol Conversion
Scenario for Each Mechanism 92
5-3 Predicted Ozone Concentrations for 100% Methanol Conversion
Scenario for Each Mechanism 94
5-4 Predicted Ozone Concentrations for Methanol Substitution
Scenarios for Each Mechanism 96
5-5 Predicted Ozone Concentration for Initial Ozone Concentra-
tion Scenarios for Each Mechanism 98
5-6 Predicted Ozone Concentrations for Formaldehyde/Methyl
Nitrite Emission Scenarios for Each Mechanism 100
vi
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1.0 INTRODUCTION
The Environmental Protection Agency (EPA) has contracted with Radian
Corporation to perform analyses related to the use of methanol as an alterna-
tive fuel. Under this contract, Radian reviewed photochemical reaction mecha-
nisms and atmospheric dispersion models available for nse in assessing conver-
sion of mobile sources to methanol fuel. This information is necessary for
future analysis of the potential improvements in air quality from using metha-
nol as an alternative fuel. This report summarizes the results of the analy-
ses performed by Radian under this contract.
1.1 Background
Methanol has been proposed as an alternative fuel for motor ve-
hicles. Because it is a chemically simpler fuel and burns much cleaner than
gasoline, methanol has the potential to substantially reduce the emissions
from automobiles. However, the impact of methanol-fneled vehicles on air
quality needs to be investigated. Specifically, the potential impact of the
methanol combustion products on ozone levels must be ascertained. The effect
of methanol and methanol-associated combustion products on ozone formation
will be a significant factor in the decision to encourage or discourage the
use of methanol as an alternative motor vehicle fuel.
Two preliminary studies have analyzed the impacts that methanol fuel
conversion may have on ozone formation in Los Angeles. In each study, emis-
sions of ozone precursor pollutant from methanol-fueled vehicles were analyzed
by using detailed photochemical dispersion models. Both studies reported that
the peak ozone level would almost certainly be reduced by at least 10-20% if
100% of the on-road motor vehicles were operated on methanol. One of these
studies considered only conversion of spark-fired vehicles, while the other
included all vehicles.
-------
One of these studies was performed by Written and Hogo of Systems
Applications, Inc. (SAI) of San Rafael, California, for ARCO Petroleum Pro-
ducts Company, DnPont, and Standard Oil of Ohio (SOHIO). The study is enti-
tled "Impact of Hethanol on Smog: A Preliminary Estimate" (SAI, 1983). The
second study was performed by the Jet Propulsion Laboratory (JPL) of the Cali-
fornia Institute of Technology (CIT). The study was performed for the Cali-
fornia Energy Commission and is entitled "California Methanol Assessment:
Volume II: Technical Report: Chapter 6: Air Quality Impact of Hethanol Use
in Vehicles" (CIT, 1983).
These studies are referred to hereafter as the SAI (1983) and the
CIT (1983) reports.
1.2 Objective
Because of the preliminary nature of these studies, EPA contracted
with Radian to provide background information in preparation for more exten-
sive studies. Two specific tasks were assigned to Radian.
Task 1: Review and analyze literature data concerning the
effects of methanol and methanol combustion products
on atmospheric chemistry. Next, select and modify
for methanol chemistry those chemistry reaction mech-
anisms most appropriate for use in simulation of
methanol photochemistry. After selection of the
mechanisms, determine through a modeling sensitivity
analysis the most appropriate mechanism or mechanisms
to be used to simulate methanol photochemistry.
Task 2: Review existing, available dispersion models that
could potentially be used to perform simulations of
various methanol emission scenarios. Using the mech-
anism identified in Task 1, select the most appro-
priate model for use in simulating ozone formation.
-------
No attempt to actually install the selected mechanism in the se-
lected model is to be made. Also, any attempt to determine the impacts of
methanol fuel conversions is beyond the scope of work of the study.
1.3 Report Description
The remainder of this report discusses in detail the results of the
two tasks performed by Radian.
Section 2.0 discusses in detail the photochemistry associated with
methanol and methanol combustion products. Included in the section is a dis-
cussion of emissions from methanol-fueled vehicles, the specific chemical re-
actions associated with methanol, and a brief review of potential mechanisms.
Also included in this section is a comparison of the results obtained from the
SAI and CIT modeling studies.
Section 3.0 reviews in detail the eight photochemical dispersion
models that could be used to simulate methanol photochemistry in the atmos-
phere. The models range from relatively simplistic trajectory (Lagrangian)
models to exceedingly complex numerical grid (Eulerian) models.
Section 4.0 discusses the three chemistry reaction sets selected for
use in the modeling sensitivity runs and the modification of the mechanisms to
handle methanol photochemistry. The implementation on the dispersion model
used in the sensitivity runs is also discussed.
Section 5.0 describes the sensitivity modeling analyses performed
using the three selected mechanisms.
Finally, Section 6.0 summarizes the recommendations made as to the
most appropriate model and mechanisms to be used to simulate methanol photo-
chemistry.
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2.0 ANALYSIS OF AVAILABLE CHEMISTRY MECHANISMS FOR ASSESSING
TEE USE OF METHANOL AS A MOBILE-SOURCE COMBUSTION FUEL
At the heart of any photochemistry model is the chemistry mechanism,
i.e., the set of reactions that is used to simulate actual photochemistry.
The purpose of this section is to (1) review the emissions that occur from
methanol-fneled vehicles, (2) review the generally recommended reactions which
must be added to a photochemical smog model to account for the presence of
methanol and its by-products (formaldehyde and perhaps methyl nitrite), and to
(3) review the available photochemical mechanisms. A detailed discussion of
each mechanism selected is given in Section 4.0.
To assess the possible impact of large-scale conversion to methanol
in the Los Angeles Basin, two major studies have been carried out (SAI, 1983;
CIT, 1983). Each study was done by a group with extensive background work in
the area of photochemical modeling and with experience applicable to the Los
Angeles region. Both studies reported that the peak ozone would almost cer-
tainly be reduced by at least 10-20% if 100% of the spark-fired motor vehicles
(CIT) or all vehicles (SAI) were operated on methanol. Even greater reduc-
tions are potentially possible since each group felt that many of the assump-
tions used in their respective studies were conservative (i.e., tended to
nnderpredict the possible benefits).
This section comments on the approaches, assumptions, models and
range of validity of the results for the above two studies. For particularly
critical points, sensitivity studies of the different chemistries will be rec-
ommended to better define their precision and accuracy. The results of these
sensitivity studies are. reported in Section 5.0. Suggestions for additional
information or research will be made where warranted.
2.1 Methanol—Fueled Vehicle Emissions
When modeling the potential impact of gasoline replacement by metha-
nol. changes in the emissions inventory of the mobile source fleet can be sig-
nificant. This section discusses four aspects of emissions from methanol-
fueled vehicles.
4
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2.1.1 Alteration of Emissions Inventory
One of the potential advantages of using methanol as a fuel is that
it burns relatively clean and evaporative emissions are simple and compara-
tively nnreactive. Compared to gasoline, burning methanol generally results
in fewer combustion products. The main combustion products are unburned meth-
anol, formaldehyde (ECHO, a partial combustion product), CO, NOZ, and smaller
amounts of post-combustion combination products, perhaps the only one of im-
portance being methyl nitrite (MeNO,). Because methanol burns cooler than
gasoline, it is expected that less NOZ will be produced.
2.1.2 Evaporative Emissions with Methanol
Evaporative emissions from vehicles burning pure methanol are rela-
tively unreactive (i.e., they are composed of pure methanol). Evaporative
emissions from alkane-methanol mixtures appear to be dominated by alkanes.
For example, one report (CIT) uses the assumption that the evaporative gases
from a 5.5% isopentane in methanol solution are 78% alkanes (by weight), or
similar to the alkane content of gasoline vapors. This report shows that the
methanol effectively replaces olefin and aromatic emissions with the alkanes
remaining approximately the same. Thus, for alkane-methanol blends, while the
vapors are "simpler," the percentage of the alkanes is deceptively high. Also
on a carbon percent basis, where the alkanes (e.g.. butane) are 85% carbon by
weight and the methanol is only 38% carbon by weight, the ratio of alkanes in
the vapors may be very high.
2.1.3 Exhaust Emission Rates for Regulated Pollutants
Table 2-1 summarizes exhaust emissions data from burning methanol in
spark ignition engines using the Federal Test Program test cycle. All of the
vehicles shown in Table 2-1 were carbureted except for the Rabbit, which was
fuel injected. Table 2-1 is broken down into as many exhaust components as
were published in the research literature. In some cases, the different com-
ponents are not mutually exclusive. As shown, emission rates of hydrocarbon
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TABLE 2-1. SUMMARY OF EXHAUST EMISSIONS DATA ON VEHICLES BURNING METHANOL
Reference: Lowry & Devoto, 1975
Vehicle/ECS
Operation
Pollutant: g/hph
HC
CO
N0x
1947 CER engine, one-cylinder No Controls
0 - 1.0
1000 rpm
Methanol
Fuel
1
12
Indolene
Fuel
2
10
1800 rpm
Methanol
Fuel
0.8
4.5
Indolene
Fuel
1.8
8
0 - 1.1
1000 rpm
Methanol
Fuel
1.6
4
Indolene
Fuel
2.5
2
1800 rpm
Methanol
Fuel
1
1
Indolene
Fuel
2.3
1.3
1
Reference: Hilden & Parks, 1976
Vehicle/ECS
Single-Cylinder engine, 0.60L ASTM-CFR
Fuel
Pollutant: mg/mi
N0x
Aldehyde
(F=formaldehyde)
Methanol (UBF)
0 = 1.0
Methanol
4 yg/J
0.1 yg/J
0.5 Ug/J
Indolene
0.03 yg/J
0.5 yg/J
90% Methanol
10% H20
2.5 Ug/J
0.1 yg/J
6.3 yg/J
0 - 0.8
Methanol
2.5 yg/J
0.2 yg/J
0.5 yg/J
Indolene
0.05 yg/J
0.5 yg/J
90% Methanol
10% H20
1 yg/J
5.5 yg/J
Note: UBF = Unburned Fuel
Gas = Gasoline
... continued
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TABLE 2-1. (Continued)
Kofercnce: CAR1I, 1982
Velilclc/KCS
Fuel
Test
Pollucanc: mg/tni
IIC
CO
NOx
Aldehyde
(F" formaldehyde)
Methanol (UBF)
Mechane
81 Eiicort
JNn AIR I
Bn.iellne |or EUR I No CAT
94.52 Meth.-inol, 5.52 Isopentane
81 test
240
4,770
410
(F) 8.4
30
60
82 test
200
3,020
170
(F) 17
270
45
81 test
510
23,500
740
(F) 82.6
830
130
82 test
1,211
28.300
490
(F) 70
2,550
35
81 Escort
Uasollnc
No CAT
94.52 Methanol,
5.52 Isopent.ine
230
6,630
240
(F) 13
280
64
1,890
51,000
1,010
(F)100
3,840
73
B.iaellnel No CAT
97. 5Z Ethanol,
2.52 CnsoMnc
81 tests
210
1,280
530
ll
(F) 23
(UBF) 200
46
1,150
10,540
2,490
170
(F) 50
1,310
(UBF)
80
Bnsel Ine
81 K.
No CAT
lib It
B.-isfl liu-l N., CAT
94.52 Mi-thanol^ 5.5ii 1 s.^rntan..
81 tests
150
1,330
200
(F) 37
280
14
840
12, 620
1,060
(F) 75
1.250
200
210
(F) 19
350
12
8KI)
7,320
2.J10
( F) 99
1 , 501)
60
Reference: Brinkman, 1981
Vehicle/ECS
Fuel
Pollutant: rag/mi
CO
NOx
Aldehyde
(F= formaldehyde)
Methanol (UBF)
Single-Cylinder Engine
0 = 1.0
CR = 7.5
CR = 12
CR = 18
Ethanol
0
3 Jg/J
(UBF)
1.5
-------
TABLE 2-1. Continued
Reference: Smith, Urban, & Balnea, 1982
Vehicle /ECS
Fuel
Pollutant: mg/mi
HC
CO
NOx
Aldehyde
(F-formaldehyde)
Methanol (UBF)
Parciculates
Methane
NHj
CN
Organic Amines
ppm
MeONO (Hrs)
81 Escort/ECR/PMP/OXD/3-way/Fuel Metering
Hoble Metal
Neat
Methanol
420
6,030
400
(F) 34
424
6.5
48
9.7
NO
0.016
0.5(12hr)
0.3(24hr)
n IfiRhrl
Promoted
Base Metal
Heat
Methanol
310
1,510
350
(F) 3
103
3
35
4.8
0.16
NO
No
Catalvst
Neat
Methanol
10,280 -
40,770
610
(F) 356
11,494
11
66
3
0.16
0.65
(table Metal
Gasoline
370
4,490
550
(F)<1.6
ND
9.7
81 Rabblt/3-wav Catalyst/
Closed Loop/MFI
table Metal
Neat
Methanol
390
880
680
(F) 9.7
440
4.8
ND (ohr)
Promoted
Base Metal
Neat
Methanol
480
2,740
1,510
(F) 32
542
6.5
11
table Metal
Gasoline
110
1,080
160
ND
ND
12
14
Reference: Energy Conservation Directorate, 1982
Vehicle /ECS
Fuel
Pollutant: mg/mi
HC
CO
NOx
Aldehyde
(F= formaldehyde)
Methanol (UBF)
Methane
78 Pinto, 2.3L Closed
loop, enlarged jets
Methanol
160
2,610
680
32
Indolene
300
2,510
980
11
2.3L Pinto Turbo
Base Spark
Timine
Methanol
1,710
38,100
850
5% Spark
Ad v;i nc e
Methanol
3,280
37,400
1,150
1,640
Indolene
1,750
26,400
3,940
1,750
. , . continued
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TABLE 2-1. Continued
Reference: Smith & Urh;m, 1982
Vehicle/ECS
Pollutant: mg/mi
HC
CO
N0x
Aldehyde
(F-formaldehyde)
Methanol (UBF)
Particulates
Methane
MeONO
Escort
Noble Met
(14.2 ppm)
420
(203 ppm)
6,048
(9.4 ppm)
403
(F) 34
408
5.5
48
<0.3 ppm
0.5 ppm
Rabbit
Noble Met
(15.8 ppm)
387
(41.4 ppm)
887
(16 ppm)
677
(F) 10.3
439
4.8
4.8
<0.3 ppm
Rabbit
P.B.M.
(31.7 ppm)
871
(133.6 ppm)
3,613
(30.8 ppm)
1,758
(F) 48
924
4.1 ppm
Escort
P.B.M.
306
1,516
355
(F) 3
152
2
35
Escort
No Cat
10,306
40,871
613
(F) 356
11,494
66
(new C.it)
P.B.M.
48-t
2,742
1,516
(F) 32
542
6.5
11
(new <:;irt>)
Noble Met
.168
1.2W
403
(F) 18
424
5
Reference: Edwards &
Vehicle/ECS
Fuel
Pollutant: mg/ml
HC
CO
NOX
Aldehyde
(F=formaldehyde)
Baisley, 1981
78 Pinto
3-way catalyst
Neat
Methanol
110 - 220
2040 - 3690
610 - 760
20 - 44
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(HC) and carbon monoxide (CO) when burning methanol are similar to the rates
of similar engines burning gasoline or Indolene (EPA's gasoline test fuel).
Methanol generally is included as a part of HC emissions, although the overall
emission mass, as HC. does not include the oxygen in the methanol. Methanol
emissions usually account for a majority of HC emissions; in some cases, the
methanol mass exceeds the total HC emissions (because of the oxygen).
CO emissions are sometimes greater when burning methanol than when
burning gasoline; in other cases, they are greater with gasoline. Overall,
for catalyst equipped vehicles, CO emission rates were relatively low with
either fuel.
The reductions in NOZ emissions associated with burning methanol ap-
pear to be significant for pre-1980 model year vehicles, but the reduction ap-
pears to be low to nil for 1981 and newer vehicles (correctly operating). It
is possible that the NOZ reductions associated with the three-way catalysts in
1980 and newer vehicles tend to mitigate the relative impact of burning metha-
nol. In other words, the lower NOZ emission rate associated with the use of
methanol is more significant when compared to the high rate of older, uncon-
trolled vehicles than when compared to the already low rate of newer, control-
led vehicles. Unfortunately, the data base is very limited, particularly the
data base comparing emissions from vehicles burning methanol with emissions
from vehicles burning gasoline.
2.1.4 Exhaust Emission Rates for Unregulated Pollutants
The review of the literature indicated that the primary combustion
product of concern was formaldehyde. Formaldehyde emissions varied between
less than 1% and 40% of the total HC value (on a mass basis) with an average
value of 9.3%. The 40% value was with a very rich air/fuel mixture. Aldehyde
emissions (including formaldehyde) are at their lowest level at an equiva-
lence, or air fuel ratio of 1:1 (Edwards, 1981). This result indicates that
closed fuel control systems, which are expected to be used on most future
light duty vehicles, will minimize formaldehyde emissions. Such closed loop
1.0
-------
systems tend to maintain an air-fuel ratio near 1:1, which corresponds to
closed loop operation.
Published formaldehyde emission levels from catalyst-equipped ve-
hicles burning methanol varied between 3 milligrams per mile and 48 milligrams
per mile. For correctly operating catalyst-equipped vehicles burning gaso-
line, formaldehyde emissions varied between non-detectable amounts to 11 mil-
ligrams per mile. For vehicles without catalysts, or with disabled emission
control systems that would reduce the catalytic converter efficiency, formal-
dehyde emissions were much greater for methanol fueled vehicles. In this
case, formaldehyde emissions varied from 50 milligrams per mile to 356 milli-
grams per mile. Obviously the catalytic converter effectively reduces formal-
dehyde emissions. This could indicate that formaldehyde emissions could be
excessive from tampered vehicles burning methanol. There were no comparative
data on formaldehyde emissions from gasoline fueled vehicles with disabled or
missing catalysts. However, formaldehyde emissions from a gasoline-fueled
vehicle equipped with a three-way catalyst but with the feedback system dis-
abled have been reported to be as high as 40 mg/mile (Wnebben, e_t al. 1982).
Other pollutants of concern include ammonia (NHS) and methyl nitrite
(CH,ONO or MeNOj). Smith, e_t al (1982) cited ammonia emissions of between 4.8
and 10.7 milligrams per mile for three-way catalyst-equipped vehicles burning
methanol. Reference 14 included the results of sampling for methyl nitrite.
The formation of methyl nitrite depends on the NO concentration and accord-
ingly on the dilution of the exhaust gases. Methyl nitrite is not produced in
the combustion chamber. Consequently, methyl nitrite emissions are measured
by simulating post-combustion reactions in a dilution chamber after the tail-
pipe. The results shown by Southwest Research Institute (1982, Table 10)
indicate that methyl nitrite varies from less than 0.3 ppm to 4.1 ppm. As a
comparison, hydrocarbon emissions (including methanol) varied between 14 and
32 ppm. At this time, the data are not adequate to accurately project methyl
nitrite emission levels from methanol-fueled vehicles. The above data only
provide a very rough ballpark estimate of how much methyl nitrite will be
produced.
11
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2.2 Overall Requirements of Photochemical Mechanisms To Simulate
Methanol Photochemistry
Photochemical reaction sets have been developed to simulate polluted
urban air masses. Since methanol is not normally present to a significant ex-
tent in urban smogs, models do not incorporate it explicitly. However, with
the introduction of methanol as a major fuel, substantial quantities of metha-
nol and methanol combustion products will be emitted to the urban atmospheres.
Although most mechanisms are capable of treating methanol chemistry through
the use of surrogate species, additions should be made to the existing models
to account for these new species.
First, any photochemical reaction set must be altered to accommodate
the principal new species, methanol. As will be explained below, this addition
is straightforward and well agreed upon. Accomodation of formaldehyde, the
primary oxidation product of methanol other than CO,, is a little more subtle.
Most models already have some provision for dealing with formaldehyde since it
is found in all urban smogs. Because it is a very important photochemical
radical generator, the model must respond appropriately to unusually large in-
creases in formaldehyde levels.
Methyl nitrite, a secondary product of methanol combustion, is emit-
ted in only small amounts. However, methyl nitrite may be important if the
model is sensitive to the number of radicals generated, since methyl nitrite
is photochemically very reactive and forms radicals. Thus, models must add
methanol and methyl nitrite chemistry and be able to accomodate the unusually
large increase in formaldehyde.
The number, and possibly the mixture, of hydrocarbons, or more spe-
cifically, the non-methane organic compounds (NMOC), may change with substan-
tial use of methanol fuel. The chemistry mechanism should therefore be vali-
dated over the expected ranges of NMOC concentrations and types of mixtures.
Also, with lower NOZ emissions expected, the mechanism should be validated in
the lower NOZ ranges. Since models are sensitive to NMOC/NOZ ratios, caution
12
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should be exercised when comparing results for different NMOC/NOZ ranges.
However, if both NMOC and NOZ are expected to be reduced or unchanged, the
NMOC/NOX ratio may not vary drastically.
2.3 Review of Previous Methanol Studies
Both SAI and CIT have been working for a number of years in photo-
chemical modeling. Their photochemical mechanisms were developed indepen-
dently. Each mechanism has been validated against smog chamber runs similar
to Los Angeles basin conditions and against actual Los Angeles area smog data.
Comments on each report and their respective approaches to the methanol fuel
chemistry modifications will be presented first, after which the specific re-
sults of the two studies will be compared.
2.3.1 Chemical Reaction Mechanisms Used
The SAI researchers used the photochemistry of the CBM-III model
(Carbon Bond Mechanism—third version). While much of the validation for this
mechanism has been done on CBM-II, most of it should be applicable since the
SAI researchers state that the changes were minor and involved only the aroma-
tic chemistry.
The CIT group has been working on photochemical smog mechanisms for
several years, with specific attention to the Los Angeles basin situation.
Their mechanism, called CIT in this report, has been developed essentially in-
dependently from other mechanisms and represents an equivalent but different
mechanism from the SAI approach. It has been well-documented in the Los An-
geles basin. While the. internal dynamics of the two mechanisms are somewhat
different, they are both based upon independent study reported in the litera-
ture. The predictions of the concentrations of the major species are similar
in both mechanisms.
13
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2.3.2 Methanol Photochemistry
The essential atmospheric chemistry of methanol is simple. Nearly
all researchers agree (Dodge. 1984) that there is only one significant reac-
tion and that is with the hydrozyl radical, OH:
CH,OH + OH —*• H0a + HCHO (2-1)
Even the rate of this reaction is well agreed upon, and the inclusion of this
reaction in any chemical reaction mechanism should be straightforward. Com-
pared to most HC species, the methanol molecule is rather slow to react (its
rate constant is about half that of butane). When it does react, as seen in
equation 2-1, it generates radicals and formaldehyde. Therefore, it cannot be
considered (or lumped) as a general hydrocarbon and must be treated as a sepa-
rate species. Since all mechanisms contain OH and HO, radicals, they should
easily be able to assimilate the newly introduced methanol reaction.
The CIT and SAI groups added essentially the same methanol chemis-
try, although CIT chose to represent it as a two-step reaction with CH,OH (the
hydrozy-methyl radical) as an intermediate. Since this radical reacts primar-
ily with oxygen to form formaldehyde and the HO, radical (the same products as
in equation 2-1 above), both approaches appear to be very straightforward and
equivalent if not identical. The formaldehyde must be dealt with a little
more carefully.
2.3.3 Formaldehyde Photochemistry
As seen in equation 2-1, formaldehyde is one of the products of at-
mospheric methanol reduction. It is also the second-most abundant product of
methanol combustion. Therefore, considerable amounts of formaldehyde may be
released to the atmosphere from methanol usage. Since formaldehyde is photo-
chemically active to generate radicals, it is an important source of radicals
in most mechanisms. Essentially all photochemically reactive models somehow
take account of formaldehyde, but many assume that it will be present only in
small quantities.
14
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In CBM-III, all carbonyl groups (C=0) are lumped together and called
CARB. They are given a rate constant which is a weighted average of typical
mixtures of carbonyl groups. The weighting is dominated by formaldehyde.
Since formaldehyde has assumed special significance in the current applica-
tion, SAI has chosen to introduce formaldehyde explicitly into the mechanism.
To do this, SAI introduced three additional, specific formaldehyde reactions
into the mechanism, two of them photolytic reactions and the other, formalde-
hyde reaction with hydroxyl radical. They are essentially the same reactions
that already exist in the mechanism for CARB, but their rate constants and
products are specific for formaldehyde, rather than generalized, lumped car-
bonyl rate constants and products.
This procedure is the obvious and logical thing to do. The SAI team
treats all other emissions as usual and classifies their emissions in terms of
the normal CBM-III. That is, all formaldehyde emissions other than from on-
road vehicles continue to be treated as before as part of the carbonyl bond
catagory. Thus, the new reactions apply only to formaldehyde generated from
methanol combustion.
The visible light that is photoreactive with NO, and the ultraviolet
light that is photoreactive with formaldehyde scatter differently as they pass
through the atmosphere. Therefore, the effective solar flux of the two dif-
ferent wavelengths is not simply proportional, but varies in a more complex
manner. The resulting differences in the N0a and formaldehyde photochemical
rate constants are not expected to be large, except perhaps at low sun angles
where the scattering differences are largest.
In the SAI study, the photochemistry of the formaldehyde was not
simply ratioed from the photolysis rate of NO, as is done for other photo-
chemical rates. Rather, the algorithm of Demerjian and Schere (1979) was
used. This algorithm takes into account both soler zenith angle and the
spectral dependency of the formaldehyde photochemical reaction. The CIT
mechanism also attempts to calculate solar flux difference due to spectral
dependency. However, the spectral dependence algorithm used by CIT is much
15
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more complex than that used by SAI. In the first hour or two of daylight,
when the sun angle is low, the CIT model would result in a lower, and perhaps
better, formaldehyde photolytic rate constant. Since formaldehyde builds up
during the nighttime, the rate of production of radicals in the early morning
is important when determining the dynamics of the chemistry of the air mass.
The CIT model may therefore be expected to be more accurate in simulating the
actual early morning chemistry and photochemistry of formaldehyde.
The CIT mechanism does not lump all carbonyl group reactions to-
gether, but treats formaldehyde reactions independently from lumped higher
aldehyde chemistries. Thus, no additional formaldehyde chemistry is needed.
Since the CIT mechanism uses rate constants for the higher aldehyde reactions
specifically chosen for these species, it may more closely represent the ac-
tual aldehyde chemistry than the modified CBM-III reaction set.
2.3.4 Methyl Nitrite Photochemistry
The CIT group did not address the effects of methyl nitrite chemis-
try. The reactions investigated by SAI are:
CH,ONO 7-*- CH,0 + NO (2-2)
CH,0 + 0, —- HCHO + H0a (2-3)
The reaction rates used by SAI are: 0.3 times the N0a photolysis rate for the
forward reaction 2-2, 4.4 x 10 ppm min for the back reaction, and 1.88
min for reaction 2-3.
The formation of methyl nitrite in the atmosphere is poorly under-
stood. It is questionable whether methyl nitrite would be present in quanti-
ties significant enough to make the above reactions important. However,
methyl nitrite is highly photolytic resulting in the formation of HO, radi-
cals. Because these radicals have a significant effect on ozone chemistry,
SAI included reactions 2-2 and 2-3 in their study. Methyl nitrite was treated
as an emission species from methanol fueled vehicles.
16
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The SAI report showed that to assume 1% of total methanol related
emissions to be methyl nitrite led to a small (3%) increase in peak ozone. If
higher levels of methyl nitrite are assumed (e.g.. 5%), the SAI report pre-
dicts the peak ozone is increased 10% over of that predicted with no methyl
nitrite. This indicates that the expected emissions or resultant ambient
levels of methyl nitrite should be carefully researched to verify that the
lower emission level assumptions are accurate.
The only fate of methyl nitrite in the mechanisms reviewed is photo-
dissociation into HO, radicals. That is, methyl nitrite can only be removed
during daylight hours while there is sunlight to photodissociate the mole-
cules. Consequently, substantial quantities of methyl nitrite would be
expected to build up at night and result in another possible large source of
radicals at first light. Formaldehyde also could have a similar fate. These
methyl nitrite reaction products could therefore significantly influence ozone
formation on the second day and subsequent days of an ozone episode.
The VOC (volatile organic compound) emissions schedule (SAI, 1983) .
gives some idea of the impact of the early morning potential radical popula-
tion due to formaldehyde and methyl nitrite. VOC emissions during the night-
time (i.e., between 2000 and 0600 the following morning) sum to about 17 times
the comparable emissions of the same species expected in the first daylight
hour between 0600 and 0700. If it is assumed (to a first approximation) that
the on-road/off-road (mobile and stationary) ratio of VOC emissions is main-
tained during the nighttime, the formaldehyde and methyl nitrite emissions
during the night would be approximately 17 times the amount of the same spe-
cies emitted in the first daylight hour.
Therefore, if all the nighttime formaldehyde and methyl nitrite
emissions were present at dawn, the large and sudden generation of radicals
would be expected to significantly affect the ozone production dynamics for
the day. Radical populations and daily ozone dynamics (other than peak ozone)
were not discussed in either report. Analysis of these populations and reac-
tion dynamics could be expected to improve understanding of the role played by
methyl nitrite in ozone chemistry.
17
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Ozone formation is very dependent upon the number of radicals gener-
ated, and aldehydes are a principal source of radicals. For many models, once
a certain level of radicals is reached that allows the mechanism "to get
going," ozone formation becomes very much less sensitive to additional radical
inputs. An early morning surge of radicals could have a major effect on the
evolution of the air mass. Predicting just peak ozone for a day. which may
not be a very sensitive indicator of the overall pollution, does not tell the
whole story. For example, an early high radical population should consider-
ably speed up the attainment of the ozone peak and result in larger overall
(integated) exposure, but still not change the peak ozone very much.
2.3.5 Post-Study Validation Experiment
Since the publication of the two methanol studies discussed above, a
smog chamber study has been completed involving methanol as one of the test
gases (Whitten and Pullman, 1984). The principal result is the demonstrated
importance of the non-methane organic compound-to-nitrogen oxides (NMDC/NOZ)
ratio in controlling the formation of ozone. At low NMOC/NOZ ratios (3:1, ppm
carbon:ppm NOZ), where there was little hydrocarbon, and using ozone genera-
tion as the indicator, methanol functioned almost as an inert species when it
replaced hydrocarbons. At a 9:1 ratio, methanol was about half as reactive as
the hydrocarbons it replaced. At high NMOC/NOZ ratios of 27:1, where the
hydrocarbons are abundant, there was essentially no difference when some of
the hydrocarbons were replaced by methanol. The authors state that the cur-
rent NMOC/NOZ ratios in the Los Angeles basin are usually near the 9:1 ratio.
In the same study, upon further validation with the same smog cham-
ber, the SAI mechanism was found to overpredict ozone at low NMOC/NOZ ratios.
The problem was rectified by dividing the aromatic components into two clas-
ses. The authors further conclude that the new mechanism, Carbon Bond - IV
(CBM-IV), should predict greater ozone reduction with methanol replacement,
especially at low NMOC/NOZ ratios.
18
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The CIT mechanism has not been subjected to any similar validation
study involving methanol in smog chambers.
2.4 Discussion of SAI and CIT Results
Both studies used the same approach to make predictions about how
the use of methanol as a primary fuel in automobiles would impact peak ozone
levels in the Los Angeles Basin. They both went back to a well-documented.
high-ozone episode and demonstrated that their model could adequately describe
(i.e., could predict to within ten percent) the incident. They then took the
same meteorological conditions and applied them to a day in the future where
certain assumptions had been made about methanol usage and other emission in-
ventories had been estimated. Because of all the assumptions necessary about
background levels, meteorology and emission inventories, the results should be
regarded only as approximate (accurate perhaps to a factor of two), and the
details are not intended to be scrutinized for minor differences. The results
are most useful for making comparisons with similar conditions (i.e., nearly
identical previous days or the same day with slightly different input assump-
tions) .
2.4.1 Base Case Results
Both studies chose their historical reference event to be an episode
of high ozone which occurred June 26-28, 1974 near Uplands. The SAI team
chose to model June 27, where an ozone maximum of 0.49 ppm was observed at
Fontana at 1600 PST. SAI demonstrated the approximate equivalence of a tra-
jectory and airshed model to predict the high ozone. They then used the same
meteorology along with emission inventories projected for 1987 and modeled
several different scenarios of methanol usage. The methanol emissions were
spread proportionally over both on-road and off-road sources. Because use of
methanol as a fuel may reduce the NMOC emissions and the NOZ emissions (metha-
nol burns cooler and produces less NOZ), different amounts of NMOC and NO
emissions were also studied. Some important results are assembled in Table
2-2.
19
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TABLE 2-2. SELECTED RESULTS OF THE SAI STUDY
Methanol
Usage
(%)*
0
50
100
100
100
100
0
0
100
Other Changes From
Base Case
Base Case
None
None
No On-Road Sources*
50% NOZ Reduction
50% NMOC Reduction
50% NOZ Reduction
50% Aloft NMOC
50% Aloft NMOC
Predicted
Ozone
(ppm)
0.27
0.24
0.21
0.19
0.26
0.19
0.31
0.21
0.15
Change From
Base Case
(%)
0
12
23
32
5
30
-14
23
44
Change From
100% Case
(%)
-29
-14
0
10
-24
10
-48
0
29
* Methanol Usage refers to methanol replacing petroleum fuels in all on-road
sources (spark-fired and diesel engines). Ten percent of the emissions (as
carbon) have been assumed to be formaldehyde. Substitution is on a per car-
bon basis.
+ This calculation represents a theoretical lower bound of the ozone reduction
assuming all on-road emissions were eliminated.
Source: (SAI, 1983)
20
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It is important to note that the SAI model predicts that if the NOZ
emissions are reduced the peak ozone will actually increase. Therefore, for
the scenario where lower NOX emissions accompany methanol usage, the SAI model
predicts less reduction in peak ozone. The last two rows demonstrate that the
peak afternoon ozone concentration is very sensitive to the background NMOC
aloft concentration. The effect of methyl nitrite (1% of emissions as methyl
nitrite) was small, about 3% increase in peak ozone. Again it should be
pointed out that SAI felt that most of their assumptions were conservative so
as to not overestimate the benefits of methanol conversion. For example, SAI
assumed 10% of all emissions associated with methanol were formaldehyde and
felt that they were probably overestimating the formaldehyde somewhat.
The SAI study did not report other components of the polluted air
mass that might be affected by methanol conversion. For example, they did not
give the concentrations of the major nitrogen sinks—PAN (peroxyacyl nitrate)
and nitric acid. They reported only peak ozone and did not discuss the over-
all integrated or downwind ozone exposure. The SAI group did conclude that
perhaps the most important dynamic described by the modeling is that the
rather unreactive methanol replaces .some of the relatively reactive hydro-
carbons in the emissions. The removal of the reactive hydrocarbons is the
primary cause of lower peak ozone when modeling methanol fuel use scenarios.
The CIT study used the adjacent day for their base case, June 28.
1974; on this day, the peak ozone observed was 0.38 ppm in Azuza at 1500 PDT.
The CIT model predicted 0.37 ppm, but this day was one of the days used to
"tune" the model, so the "prediction" should be quite accurate. The CIT group
reported a more elaborate study of possible methanol use scenarios to estimate
the emission levels of methanol and hydrocarbons. They focused on the year
2000, and in comparing several different studies of future vehicular activity,
it was shown that there are significant differences in the ranges of predicted
emissions. Selected results of their modeling are collected in Table 2-3.
21
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TABLE 2-3. SELECTED RESULTS OF THE CIT STUDY
Hethanol
Usage
(%)*
0
50
100
100
100
100
Other Changes From
Base Case
Base Case
None
None
50% NOZ Reduction
50% NOZ Reduction and
50% HC Reduction
All vehicular emissions
Set to Zero
Predicted
Ozone
(ppm)
0.33
0.30
0.29
0.28
0.27
0.25
Percentage Change
from Base Case
(%)
0
9
14
17
19
25
* Methanol Usage refers to methanol replacing gasoline in spark-fired engines;
21.2 weight percent of the exhaust hydrocarbon emissions were taken to be
formaldehyde. Substitution is on a mass basis.
Source: (CIT, 1983)
22
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2.4.2 Formaldehyde Emission Scenario Results
Because of the different target years and other assumptions, it is
difficult to compare the absolute amounts of reductions and emissions. How-
ever, it is constructive to attempt to compare the percentage reduction used
on each model for similar conditions. SAI assumed gasoline-to-methanol conver-
sion of all on-road vehicles, whereas CIT assumed conversion of only spark-
fired engines. (In Los Angeles, according to the CIT report, about 82% of all
on-road emissions are produced by spark-fired engines.)
One of the scenarios considered by SAI assumed 100% methanol conver-
sion of all on-road vehicles with no other hydrocarbon or NOZ emission reduc-
tions. For this scenario, SAI assumed that 10% (on a carbon basis) of the on-
road methanol exhaust and evaporative emissions were formaldehyde. In addi-
tion, 4% of all other methanol emissions were assumed to be formaldehyde.
These same assumptions, when applied to the CIT emissions inventory correspond
to an assumption that approximately 12% of the gasoline exhaust emissions is
replaced by formaldehyde (on a carbon basis).
Thus, the SAI scenario that assumed 20% of emissions as formaldehyde
corresponds, approximately, to an assumption of 24% of emissions as formalde-
hyde using the CIT inventory. Therefore, the CIT scenario that assumed 21.2%
of all gasoline exhaust emissions were formaldehyde does not exactly corres-
pond to the SAI 20% emissions scenario.
These calculations consider weight percent versus carbon percent,
the reports' assumptions about the relative amounts of off-road methanol emis-
sions, and the reports' ratios of spark-fired to diesel engines. Even more
subtle differences render any comparison approximate at best. These differ-
ences are due to blending assumptions, hydrocarbon species distributions and
emissions along slightly different trajectories. Thus, given the rather broad
limits and ranges of assumptions used by SAI and CIT, the 20% of methanol for-
maldehyde emission rate of SAI is roughly equivalent to the 21.2% of methanol
formaldehyde emission rate of CIT. However, there is still considerable room
for interpretation.
23
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The SAI team assumed 10% formaldehyde emissions to be most reason-
able, but they also calculated the peak ozone reduction assuming formaldehyde
at 20% of the methanol-related emissions. This second case, given the dis-
cussion in the previous paragraph, approximately corresponds to the CIT as-
sumption of 21.2% formaldehyde in emissions. In this case, SAI obtained only
a 13% peak ozone reduction in place of the 23% reduction for 10% formaldehyde
emission ratio; see Table 2-2. Their 13% result is very close to the 14% re-
duction reported by CIT for similar formaldehyde assumptions. To facilitate a
very rough comparison between Tables 2-2 and 2-3, about 9% (or 0.024 ppm)
should be added to the SAI ozone results to correspond with the higher (20%)
formaldehyde emission assumption of CIT.
Both studies also calculated the theoretical limit where all on-road
vehicular (or for CIT, all spark-fired vehicles) emissions are set to zero.
At this extreme, the formaldehyde assumption is moot. The CIT result was a
25% reduction; SAI calculated a 32% ozone reduction.
2.4.3 Other Results
The CIT group also reported for the 100% methanol replacement of
gasoline:
• a 21.5% reduction in PAN, and
• a 50% increase in ambient formaldehyde.
The PAN reduction was expected since the methanol-formaldehyde chemistry can-
not lead directly to any PAN (no acetyl groups exist). The PAN reduction is
certainly a beneficial result of methanol use since PAN is a major eye irri-
tant. The SAI team judged the increased formaldehyde concentration to be of
no acute consequence (except perhaps in closed areas such as garages), but the
long-term effects of low exposure to formaldehyde are not well known.
In most mechanisms where light is present and NOZ is increased, the
NOZ consumes ozone and the ozone concentration decreases. This rule is gen-
erally true in a typical polluted atmosphere. An exception to the above rule
24
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occurs at low NOZ or high hydrocarbon concentrations. The SAI results illus-
trate this response in their baseline case. Using a Level II EKMA trajectory
analysis, ozone concentration increased from 0.22 to 0.26 ppm when NOZ was re-
duced 22% (Whitten and Pullman, 1984, Table 1). But when CIT reduced NOZ 50%,
Case A to Case B with no other changes, ozone declined from 0.285 to 0.275
ppm. This is certainly not in a low-NOz regime. Such behavior is a result of
the many complex interactions within the model as a whole, and, while the re-
sult is not unreasonable, it does seem unusual.
Smog chamber results generally would favor the SAI results for a
high NOZ case (Jeffries, e_t al. 1981). It should be noted, however, that in
less polluted environments, where NOZ concentrations are lower, further NOZ
reduction does result in reduction in peak ozone. This may make the strategy
of methanol conversion more effective in environments less polluted, i.e..
with lower ambient NOZ, than the Los Angeles Basin.
It should also be emphasized that the SAI study showed that the peak
afternoon ozone concentration is sensitive to the background levels of NMOC,
especially NMOC aloft above the mixing height. Gipson (1984), however, found
limited sensitivity of peak predicted ozone to NMOC aloft concentrations.
Gipson did find that the VOC control requirements were very sensitive to NMOC
aloft concentrations. These studies show the benefit of long-term reductions
in area-wide VOC emissions that contribute to reductions in NMOC background
concentrations. Although the background NMOC level was not an issue in the
present studies, future work, which attempts to produce quantitative results,
should devote considerable effort to assessing appropriate background NMOC
concentrations.
2.4.4 S""""ary of the Results of SAI and CIT Studies
Given the differences in the photochemical reaction sets developed
and used by the two groups, it is perhaps somewhat fortuitous that the results
should agree so closely when the same assumption about formaldehyde is made.
25
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The key point to emerge in the resnlts of the two studies reviewed
is that the largest source of variation results from assumptions about input
parameters into the models. Examples include assumptions about:
• the percentage of formaldehyde and methyl nitrite in
the organic emissions,
• the effect of methanol on NOZ emissions,
• the concentration of background hydrocarbons,
• estimates about the amount of vehicular activity or
the extent of methanol-for-gasoline replacement, and
• meteorology, emissions inventory, etc.
Thus, there is little in the results of the two studies to differ-
entiate the two mechanisms. A consistent result is that, assuming about 20%
of the organic emissions are formaldehyde, the use of methanol fuel would lead
to approximately a 10-15% reduction in peak ozone for a high ozone episode in
Los Angeles. Reduction of formaldehyde emissions below these levels would
likely increase the percentage of ozone reduction. Other aspects of air qual-
ity would also be improved (e.g.. lower PAN).
The dynamics of the air mass should be studied to more accurately
assess the overall ozone exposure profile. The choice of chemistries may be
less important than assumptions about formaldehyde emissions, ambient hydro-
carbon concentrations and meteorology. As called for in both studies, smog
chamber studies on methanol and formaldehyde are needed in order to validate
the chemistries. Also, more studies are also needed to better establish the
emissions (i.e., methanol, formaldehyde, NOZ and methyl nitrite) from methanol
burning engines.
2.5 Consideration of Alternate Chemistries
When considering what mechanism to modify and use to predict the ef-
fect of widespread methanol usage on peak ozone concentrations, both methanol
26
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chemistry and the existing chemical mechanism must be looked at critically.
Hethanol chemistry is really quite simple. Methanol and methyl nitrite reac-
tions may be added directly to existing mechanisms. These modifications are
not expected to interfere with the existing chemistry reaction mechanism.
Another concern is formaldehyde. Formaldehyde is already in all ex-
isting mechanisms either explicitly or implicitly (i.e., as a lumped carbonyl
bond or group). One must therefore simply make sure that the mechanism has
the capability or capacity to deal with the higher formaldehyde levels ex-
pected to result from conversion to methanol. Most frequently, the mechanism
will not have been validated in this increased formaldehyde (or aldehyde)
range. The user should be alert for any possible "inconsistencies" that de-
velop which may be attributable to the unusually high aldehyde concentrations.
The primary effect of aldehydes (and methyl nitrite) is that they
are a source of radicals when the sun is shining. The number of radicals
available to the mechanism greatly influences how that mechanism operates.
Most reactive chemistry sets are very sensitive to concentrations of radicals.
Therefore, the key factors in choosing a mechanism are how it will handle the
increased formaldehyde concentration and how correctly sensitive and respon-
sive it is to the population of radicals.
One of the first mechanisms to achieve widespread use was the Dodge
mechanism (Dodge, 1977). This mechanism is relatively old and is used in the
EKMA/OZIPP photochemical model developed for EPA. Many reaction sets have
been proposed over the last several years to supplant the Dodge mechanism.
None of them is clearly superior. The CBM-III is probably the most used and
familiar mechanism. (CBM-II, probably the most validated mechanism, has now
been snperceded by a slightly improved version, CBM-III.) After CBM-III, the
Demerjian mechanism has perhaps been most popular and does well with urban air
masses (Demerjian and Schere, 1979). Finally, the CIT mechanism is a readily
available newer mechanism consisting of 52 reactions.
27
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The Demerjian mechanism was developed with a good deal of "artistry"
and was tuned to urban plumes. It has, of coarse, been validated against smog
chamber data. It achieves good results with some rate constants that were
chosen to make the model work rather than chosen nsing the best or latest
available laboratory data. For example, the Demerjian ozone-olefin chemistry
generates significantly more radicals than current theories would suggest, but
they improve the model's results (Jefferies, et al. 1981). The mechanism and
its approach have been remarkably successful. However, because of the way it
was constructed, one is uncertain if modification might upset some delicate
balance.
The original Demerjian mechanism contained 37 reactions. A new ver-
sion of the Demerjian mechanism is now available involving 63 reactions. If
this mechanism performs as well as its simpler progenitor, it also should be a
good candidate for use with methanol. It contains the two formaldehyde reac-
tions explicitly (as well as higher aldehyde chemistry) and should be very
easily adapted. It would need only the methanol reaction and, if desired, the
methyl nitrite reaction.
Jeffries. Sexton and Salmi, in a comparative study of four chemical
mechanisms against smog chamber and urban plume data from St. Louis, concluded
that none of the mechanisms could satisfactorily predict the ozone actually
measured in St. Louis for a ten day period (Jeffries. e_t al. 1981). All of
the mechanisms considered (CBM-II, original Demerjian, CIT and Dodge) consis-
tently underpredicted ozone. For example, CBM-II underpredicted ozone by
greater than 25% on nine of the ten days studied. Both CIT and CBM-II mecha-
nisms were believed to underpredict because of low radical input and because
they are both somewhat oversensitive to radical availability. Both models
also "greatly overrespond to HC compositional changes" (Jeffries, et al.
1981). The authors noted that on some days ozone reduction was approximately
linear with reduced hydrocarbons. In essentially all cases, however, the
ozone aloft was the most important factor in determining ground level ozone.
28
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The reason for the poor performance of the mechanisms could not be
specified in most cases, but uncertainties in meteorology, emissions, chemis-
try, trajectories, etc. all contributed to the disappointing results of the
mechanisms. The contributions of the various chemistries to the poor predic-
tions were impossible to quantify. Based on the comparative study, it appears
that the best use of reactive photochemistry mechanisms at this time is to run
the same mechanisms under similar conditions while varying only one parameter
and observe the altered results. This type of sensitivity study is performed
to a limited degree in Section 5.0.
The reader should therefore be cautions of considering any of the
results in an absolute fashion; that is, one should not place great confidence
in any single result, since any correlation with the real world may be some-
what fortuitous. However, when comparing results of nearly identical situa-
tions, where only one or two parameters have been changed, the relative re-
sults may be reasonably accurate, and the direction of change in some result
(e.g., ozone concentration or PAN formation) should be correctly predicted.
If several mechanisms predict the same type and magnitude of changes for
similar situations, more credibility is given to the predicted responses.
Atkinson, Lloyd and Winges have published another photochemical
mechanism which seems to be the most popular of the newer reaction sets
(Atkinson, e_t al. 1982). If validation studies confirm its accuracy, it will
be another candidate for the methanol study. However, it is a very complex
mechanism and due to fractional and negative coefficients and a large number
of species, it can not be directly substituted into most models. Formaldehyde
is treated explicitly as is xylene.
Many of the other mechanisms have been developed for some special
purposes (e.g., heterogeneous reactions, emission plume simulations) and have
not been broadly validated. None of these other mechanisms would be expected
to out perform the CBM-III, CIT, Demerjian, or Atkinson mechanisms.
29
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2.6 References for Section 2.0
Atkinson, R., A. C. Lloyd and L. Winges (1984): An Updated Chemical Mechanism
for Hydrocarbon/N0z/S0a Photoozidations Suitable for Inclusion in Atmospheric
Simulation Models, Atmospheric Environment 16. 1341-1355 (1982).
Brinkman, D., (1981): Fuels and Lubricants Department, General Motors Re-
search Labs, Ethanol Fuel—A Single-Cylinder Engine Study of Efficiency and
Exhaust Emissions, Alternate Fuels SP-480, International Congress and Exposi-
tion, Cobo Hall, Detroit, Michigan, February 23-27, 1981.
California Institute of Technology for Jet Propulsion Laboratory (1983): Cal-
ifornia Methanol Assessment: Volume II: Technical Report: Chapter 6: Air-
Quality Impact of Methanol Use in Vehicles, Report for the California Energy
Commission.
Demerjian, K. L. and K. L. Schere (1979): Applications of a Photochemical Box
Model for Ozone Air Quality in Houston, Texas, In Proceedings: Ozone/Ozidants
Interactions with the Total Environment II. October 14-17, Houston, Texas
pp 414-421.
Dodge, M. C., (1977): Effect of Selected Parameters on Predictions of a
Photochemical Model EPA-600/3-77-048, U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina, 1977.
Dodge. M., (1984): Letter to Phillip Lorang (EPA, Ann Arbor) expressing EPA's
concurrence with the additions to the photochemical reaction sets to account
for methanol, May 9. 1984.
Edwards, C. F. and Baisley, W. H., (1981): Mechanical Engineering Department,
University of Santa Clara, Santa Clara, California, SAE Technical Paper
Series—Emission Characteristics of Methanol Fueled Vehicles Using Feedback
Carburetion and Three Way Catalysts, 811221, Fuels and Lubriants Meeting,
Tnlsa, Oklahoma, October 19-22, 1981.
Jeffries, H.E., K.G. Sexton, and C.N. Salmi (1981): The Effects of Chemistry
and Meteorology on Ozone Control Calculations Using Simple Trajectory Models
and the EKMA Procedure. Report to U.S. EPA by the University of North Carolina
under Contract No. 68-02-3523. EPA report No. EPA-450/4-81-034.
Lowry, 0. and Devoto, R. S. (1976): Georgia Institute of Technology. Atlanta,
Georgia, Exhaust Emissions from a Single-Cylinder Engine Fueled with Gasoline,
Methanol, and Ethanol. Combustion Science and Technology 1976, Vol. 12, pp
177-182.
SAI, (Whitten, G. 1 and H. Hogo), (1983): Impact of Methanol on Smog: A Pre-
liminary Estimate, Final Report, Publication No. 83044, Systems Applications,
Inc. Study performed for ARCO Petroleum Products Company, DuPont, and SOHIO.
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Smith, R. and Urban, Charles M., Southwest Research Institute, San Antonio,
Texas; Baines, Thomas H., Environmental Protection Agency, Ann Arbor,
Michigan, (1982): SAE Technical Paper Series—Unregulated Exhaust Emissions
from Hethanol-Fueled Cars 820967, West Coast International Meeting, San
Francisco, California, August 16-19, 1982.
Southwest Research Institute (1982): Characterization of Exhaust Emissions
from Methanol- and Gasoline-Fueled Automobiles, prepared for Environmental
Protection Agency, Ann Arbor, Michigan, PB83-116830, U.S. Department of
Commerce, National Technical Information Service, August 1982.
State of California. Air Resources Board (1982): Haagen-Smit Laboratory, 9528
Telstar Avenue, El Monte, California 91731, Alcohol Fueled Fleet Test Program
Project 3T8001, Fleets No. 2 and No. 3 Fourth Interim Report by the Mobile
Source Control Division, August 1982.
U.S. Department of Energy, (1982): Assessment of Methane-Related Fuels for
Automotive Fleet Vehicles, Assistant Secretary for Conservation and Renewable
Energy, Office of Vehicle and Engine ROD Under Contract No. DE-AC01-80CS50179,
February 1982.
Whitten, 6. Z. and J. B. Pullman (1984): Methanol Fuel Substitution Can
Reduce Urban Ozone Pollution, A Paper (B-9) given at the VI International
Symposium on Alcohol Fuels Technology, May 21-25. 1984, Ottawa, Canada, Volume
II. pp 2-61 - 2-67.
Wuebben. P., Wood. J., and Porter, N., (1982): Hydrocarbon and Aldehyde Ex-
haust Emission Species from Three-Way Catalyst Vehicles with Feedback System
Disablements, draft report. South Coast Air Quality Management District, EPA
Grant No. A00904813, August, 1982.
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3.0 REVIEW OF PHOTOCHEMICAL MODELS
Section 2.0 investigated the applicability of various chemical reac-
tion mechanisms for determining the air chemistry effects of switching spark-
fired motor vehicles to methanol fuel. This section reviews readily available
atmospheric transport and dispersion models. The models reviewed are those
which could be used in conjunction with a photochemical reaction mechanism to
quantitatively predict the atmospheric effects from such a fuel conversion.
In this review two recommendations will be made. One recommendation
will be for the model most suitable for use in the Los Angeles Basin. The
second recommendation will be for a model for use in arbitrary locations
around the country. A recommendation is being made specifically for Los
Angeles because of the special photochemical modeling problems that exist in
the Los Angeles Basin and the unique emission characteristics and modeling
history of Los Angeles.
3.1 Background
The model to be used for simulating atmospheric effects should, at a
minimum, consider the following processes:
• Transport
• Dispersion
• Chemical Transformation
• Emissions
Besides these basic requirements, other processes can also be considered (such
as deposition and vertical winds) to make the simulation more realistic. How-
ever, the more realistic the model is. the more stringent the data input re-
quirements are. Thus, the selection of a model for a specific case should be
determined by the type and amount of data which will be available for input.
Lack of data forces approximations to be made for some of the input. A model
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which requires less input has already made internal assumptions for the pro-
cesses not specifically modeled.
In this report eight models will be reviewed with the following cri-
teria in mind:
• Physical basis (realism)
• Chemistry capability
• Emissions handling
• Input requirements
• Validation
• Documentation
Physical basis includes how the model considers transport and dif-
fusion as well as other processes which add realism, and thus a better chance
of reliable simulations.
It is possible that any model selected for use in future modeling
studies may be required to simulate atmospheric processes during nighttime
conditions. The ability of the models to simulate nighttime transport will be
reviewed. It should be noted, however, that it is extremely difficult to con-
struct accurate wind fields or air parcel trajectories for nighttime situa-
tions (and daytime situations in many instances).
Since there may be more than one chemical mechanism to be used, and
chemical mechanisms change as knowledge increases, the models will be screened
for their ability to modify the chemical mechanisms already contained within
them.
The effect of altered emission profiles on ambient air quality is
the main concern of the methanol study. Because of this, the way that emis-
sions are handled by type, source characteristics, and temporal variation will
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also be reviewed. As already stated, the amount of data available for input
should be a factor in determining the model to be used. If large data bases
are required to formulate the input, and these data are not readily available,
the model's usefulness is decreased.
Validation gives a measure of how well a model works. However, val-
idation studies are only useful for predicting how the model will behave in
similar situations. Thus, the variation and number of validations carried out
is important. The final criteria, documentation, is probably the most import-
ant if the model is to be used by personnel other than the model developers.
Without readily available documentation describing the details of the required
input and limitations of a model, the general usefulness of a model is greatly
reduced.
3.2 Models To Be Reviewed
In this study, two general model types will be reviewed: Eulerian
and Lagrangian (or trajectory). The most widely used model type, Gaussian, is
not included in the review because it cannot consider any but the most simple
chemical reaction mechanisms. An Eulerian model considers a three dimensional
grid set over a specific region. It is the most realistic in that it can con-
sider physical processes in extreme detail. Because of its realism, an Eule-
rian model normally requires the largest amount of input. Eulerian models
also normally require the largest amount of computer resources. The Enlerian
models to be reviewed are:
• SAI Urban Airshed
• CIT Airshed
• MARC-1
Enlerian models can be used to predict concentrations over a large area as a
function of time.
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Trajectory models follow a single air parcel, which may be sub-
divided horizontally or vertically, as the parcel moves over a region. If
the parcel passes over a source, it is affected. Other missed sources are
assumed non-existent. Many assumptions are made as to horizontal and verti-
cal dispersion and mixing with surrounding air parcels. All material within
an air parcel or its subdivisions is assumed well mixed. It should be pointed
out that this last assumption is also made for each grid box in an Eulerian
model. A trajectory model usually has much simpler input requirements than an
Eulerian model and requires much fewer computer resources.
Trajectory models predict the time history of concentrations for a
single air parcel. Only concentrations at specific points along the parcel
trajectory can be determined, and then only for the time at which the parcel
passes over the point. It is possible to compute a trajectory so that the air
parcel arrives at a site at the time of maximum observed ozone. However, to
model an entire urban area, multiple trajectory model runs must be made, each
run with a different trajectory. Also, for validation, each measuring station
must generally be modeled separately since each station is usually affected by
separate air parcels with different histories.
The trajectory models to be reviewed are:
• EKMA/OZIPM-2
• SAI Trajectory
• RPM-II
• GCKM/TRAJ
• STRATOS
The OZIPM-2 model is not a true trajectory model. Rather, it is
more aptly classified as a "box" model, or a model where everything is assumed
to occur within a stationary "box" or volume. For convenience, however, the
OZIPM-2 model is discussed with the trajectory models.
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3.3 Eulerian Models
Enlerian models are the most realistic models. These models can
consider the effects of terrain and interactions of chemical constituents from
one part of the grid on other parts. Enlerian models work at the differential
level. That is, they can be made to look at as many processes as desired at
any level of detail. However, the greater the detail the greater the input
and computer resources required. Eulerian models can be made simpler by re-
ducing the number of grid boxes, but the realism suffers. A box model con-
sisting of one fixed box is, in principle, an Eulerian model but an extremely
simple one.
The ability of a dispersion model to simulate nighttime transport is
limited by the model's ability to accurately simulate nighttime wind flow.
Because Eulerian models use detailed wind preprocessors to generate wind
fields, Enlerian models are better able than trajectory models to simulate
nighttime transport. The three Eulerian models reviewed in this section can
be used to simulate nighttime transport given sufficient input data. The
chemistry mechanism used is also a limiting factor when used in a model to
simulate nighttime photochemical processes. This point is discussed in more
detail in Section 4.0.
The Enlerian models to be reviewed here are existing models which
have been used in validation or planning studies. All three models require
extensive input data that does not exist for most locations. Los Angeles and
a few other cities are exceptions in that extensive data bases exist for
modeling photochemical processes in the Los Angeles Basin. Therefore, the
review of Enlerian models is primarily intended to identify the most suitable
model for use in Los Angeles and other cities with similar data coverage and
modeling histories.
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3.3.1 SAI Urban Airshed Model
The Systems Applications, Inc. Urban Airshed Model (Ames et al.
1978) is a multi-layer grid model with varying cell heights. The chemistry
used in the model is a version of the Carbon-Bond Mechanism II (CBM-II) in-
corporating certain steady-state approximations. The mechanism is "hard-
wired," or is built into the model. Documentation exists in two volumes:
Ames et al. (1978) and Ames e_t al. (1979). The EPA has recently proposed
inclusion of the Urban Airshed Model in the list of EPA Guideline models that
are recommended for regulatory use (EPA, 1984).
Several versions of the Urban Airshed Model exist. However, only
one version has been publicly released by EPA with accompanying documenta-
tion. Many potential difficulties associated with the publicly available
version have been eliminated in more recent versions of the model. The EPA in
the near future will release a revised version of the Urban Airshed Model
including revised documentation. Even with the new version of the model, a
prospective user will likely have to consult with EPA or SAI in order to en-
sure proper use of the model for a given application.
The model allows the use of temporally varying emissions from area
and point sources. The allowed emitted species are those assumed reactive in
CBM-II.
The input required by the Urban Airshed Model includes source and
emission inventories, air quality boundary and initial conditions, and meteor-
ological data (wind field, vertical temperature soundings, and NO, photolysis
rates, relative humidity, and pressure). The emissions and meteorological
conditions are to be given in an hourly schedule. The initial concentrations
and meteorological conditions are required for each grid box.
This model has been compared with data taken in studies at Los An-
geles (Whitten and Hogo, 1981; Reynolds e_t al., 1979), Denver (Reynolds e_t a_l,
1978). Sacramento (Anderson e_t al., 1977), and Tulsa (Hogo e_t al. 1981). The
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EPA has summarized the results of these comparisoa studies (Layland and Cole,
1983). In general, the accuracy of the model can vary greatly depending upon
the day simulated and the quality and amount of input data. Consistent, accu-
rate results in predicting the peak ozone concentrations in place or time
could not be achieved. Caution must be used when analyzing the results of
these validations, however. Different versions of the models were used for
some of the cities, and so exact comparison among the studies is not possible.
The use of any new mechanism would require changing the computer
code. The degree to which the CBM-II mechanism is integral, or "hard-wired,"
in the code can only be assessed by a thorough study of the code. This exis-
tence of the integral CBM-II chemistry is one significant potential problem
associated with the use of the SAI model.
Radian has reviewed the SAI model (Radian, 1981) for the Texas Air
Control Board (TACB) and found several deficiencies with the model. The most
serious was with the wind field. The SAI model expects to be given wind
speeds and directions for each grid cell. A preprocessor is available (WIND)
to perform this function. However, the winds developed by this preprocessor
are average winds and are not necessarily consistent with the measured wind
values. The winds developed in the final wind field often do not agree with
the input wind data for specific cells. A certain amount of averaging which
takes place in the preprocessor allows resultant wind data to potentially
differ from the input data.
SAI has other wind preprocessors which it uses for specific loca-
tions. The necessity to have site-specific wind preprocessors greatly reduces
the usefulness of the model in arbitrary locations. New wind preprocessor
programs may have to be written if the model is used to model a new location.
During Radian's prior review of the Urban Airshed Model, several
minor inconsistencies were noted between the user's manual and the model.
Examples of these problems are that the units conversion option did not per-
form correctly, the user's guide has numerous discrepancies and oversights,
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and the coding is for an older version of FORTRAN and is not transportable to
all other computer systems.
3.3.2 CIT Airshed Model
The CIT Airshed model is an Enlerian model developed at the Califor-
nia Institute of Technology. Only two references (HcRae, et al. 1982, and
McRae and Seinfeld. 1983) describe the CIT model in the open literature. No
documentation is available of the details of input required. Telephone cover-
sations with Dr. Seinfeld at CIT are the basis of some of the information
presented here. The model is publicly available.
The model is best described as a research tool rather than as a
model ready for widespread use by users with varying degrees of experience
in modeling. This is the case for most large Eulerian models, and is not
specific to the CIT model. However, of the three Eulerian models reviewed
here, the CIT model is the most research-oriented of the three.
The chemistry mechanism in the model, the CIT mechanism, is pre-
sented in the two papers already cited. This mechanism is integral to the
model. For the mechanism to be changed in the model, code changes are re-
quired. Some steady-state assumptions are made. This is not bad in itself,
but usually forces some restriction on the order and relationships between the
reactions.
The model considers both area and point sources. The point source
emissions are allocated into the appropriate vertical cell after a plume rise
calculation. Line sources are allocated over areas. Species dependent depo-
sition rates are calculated for loss terms. Averaging is performed in the
model to fill any missing data.
The input requirements are similar to those of all other Enlerian
models. More detail as to input data requirements is not available without a
user's guide. As already stated, adequate documentation is not available for
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the model. Without documentation, it would be difficult for anyone but CIT
personnel to use the model.
Validation studies of the model have been carried out in the Los
Angeles area (McRae and Seinfeld, 1983) with good results. The model has al-
ready been modified for use in evaluating of methanol fuel conversion in the
Los Angeles area (CIT, 1983).
3.3.3 MARC - 1 Model
The MARC-1 model is a highly modular, three dimensional Eulerian
grid model developed at Radian. The original version is described in Elt-
groth (1982). Because of its modular nature, modifications to the MARC-1
FORTRAN code can be easily and quickly incorporated into the model.
The MARC-1 model is not in the public domain. It has been used by
Radian personnel during the course of Radian Corporation's consulting service
contracts. Modeling performed using MARC-1 would have to be arranged through
agreement with Radian.
The grid cell dimensions in MARC-1 are allowed to vary over a wide
range of length scales ranging from meters to kilometers. Diffusion rates are
determined by the length scales specific to the problem being simulated.
Thus, the MARC-1 model can be used in modeling dispersion from sources on a
scale of meters up to the scale of large urban areas.
The chemical mechanism to be used in the simulation is treated as
just another input with only minor restrictions on the actual mechanism used.
The reactions are allowed to vary with temperature, ultraviolet light, total
solar radiation, water vapor concentration, and aerosol surface area. Multi-
ple dependencies involving several of the above factors can be handled in the
existing model without modification. This chemistry handling is done by por-
tions of the General Chemical Kinetics Module (GCKM), which is also existent
in two other models to be reviewed here (GCKM/TRAJ and STRATOS). With the
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GCKM module, no a. priori assumptions are made on the details of the mechanism.
The ability of MARC-1 to readily accept almost any chemical mechanism without
modification is a major strength of the MARC-1 model.
Emissions are allowed from area and point sources. The emissions
are temporally variable with any frequency. Line sources are not explicitly
allowed, but must be allocated over an area. Point and area source input data
format is relatively simple and straightforward.
The input requirements are similar to those of all other Eulerian
models. However, the input processors were designed using the assumption that
full data coverage is not available. All missing data are estimated using in-
verse square averaging techniques on the data that is supplied. Because of
this capability, MARC-1 can be used with as much or as little data as are
available. Of course, the better the data coverage the better the results can
be expected to be.
The MARC-1 model is the merger of three other individually validated
models: IMPACT (Fabrick e_t §1, 1977), PHOENIX (Eltgroth and Hobbs, 1979),
and GCKM (Eltgroth, 1981). Each of the three component models in MARC-1 have
been through validation studies separately. The major weakness of the MARC-1
model, however, is that it has not been validated as a unit on the scales
(urban) that would be of interest for determining methanol impacts.
IMPACT has been tested in the dispersion of inert materials from
point sources (Ranzieri ,e_t al. 1979; Sklarew e_t al. 1980. Fabrick and Haas,
1980; Taylor e_t al., 1981; and Fabrick e_t al, 1982). In these trials, IMPACT
proved itself very capable of simulating the real world. It performed best
when it had the best data coverage.
PHOENIX, in validation studies at eight coal-fired power plants, has
been shown to be able to predict gas and aerosol transformations and interac-
tions in plumes mixing with ambient air (Eltgroth and Hobbs, 1979; Hobbs et
al., 1982a; Hobbs et al.. 1982b).
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The GCKM module used in the MARC-1 model is a very flexible module
used to simulate chemical reactions. It uses most arbitrary chemical mecha-
nisms. GCKM accepts a user-defined chemical reaction mechanism and calculates
the time history of chemical concentrations. Reaction rates are allowed to
vary with meteorological and pollutant variables. GCKM has been used to suc-
cessfully simulate smog chamber experiments. The GCKM simulations compare
very favorably with simulations performed using models with integral reaction
mechanisms (Eltgroth, 1981; Eltgroth and Sedenberg, 1983 a and b).
Modifications to GCKM sometimes need to be made to run specific
mechanisms. These modifications are generally simple and only involve al-
lowing an additional dependency, or combination of -dependencies, to be used
to compute rate constants.
Most of the documentation for MARC-1 is present in Eltgroth (1982).
Other recent changes to the model allowing the use of small-scale simulations
and deposition are found in Radian internal reports available from Radian.
The lack of overall validation in similar projects (urban source
ozone impacts) and the fact that MARC-1 not in the public domain seriously
detract from its desirability.
3.4 Trajectory Models
Trajectory (or Lagrangian) models are an alternative to Eulerian
models. Data requirements for a trajectory model are normally much less than
those for an Eulerian model. The amount of computer time and memory required
by a trajectory model is also less than that of an Eulerian model. The draw-
back of a trajectory model is that each run simulates only a portion of the
region of interest. A trajectory model also gives the concentration time his-
tory of an air parcel rather than of a fixed site. The concentrations in the
air parcel only correspond to the concentrations of a fixed site when the air
parcel moves over the site.
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Trajectory models normally ignore terrain. Whether or not an emis-
sion source affects the air parcel and is involved in the simulation depends
totally upon the trajectory. The influence of a given source on the air par-
cel can be calculated by the user or by using objective techniques in the
model.
A major drawtrack of trajectory models is their inability to simu-
late the effects of wind shear on the modeled air parcel. Wind shear effects
are very important at night and under many stations during the day. Because
the occurrence of wind shear effects both transport and dispersion, the in-
ability to simulate wind shear is a major problem associated with the use of
trajectory models.
The ability of trajectory models to simulate nighttime trajectories
is limited and will be discussed. Usually, trajectory models are limited in
their ability to simulate nighttime transport by the ability (or lack of it)
of the model to accurately simulate nighttime trajectories. Some trajectory
models cannot be used for simulating nighttime transport without the use of
supplemental wind processors to compute trajectories.
The trajectory models to be reviewed are existing models which have
been used in ozone assessment simulations.
3.4.1 REMA/OZIPM-2 Model
The Empirical Kinetic Modeling Approach (EKMA) (EPA, 1977; and Gip-
son et al, 1981) is probably the most widely used methodology for simulating
ozone impacts due to emissions. EKMA analyses are performed using the Ozone
Isopleth Plotting Package (OZIPP) model (Whitten and Hogo, 1978a). The latest
version of the EKMA/OZIPP model. Ozone Isopleth Plotting with Optional Mecha-
nisms/Version 2 (OZIPM-2) (Gipson, 1984), is the model reviewed here. The
OZIPP and OZIPM-2 models are both publicly released models.
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The chemistry in OZIPM-2 is more flexible than that in the original
OZIPP model. However, the model is still designed around the CBM-III chemical
reaction scheme. For example, specific species names must be used for certain
species and certain photolytic reaction rates are integral to the code. Thus,
it is still difficult to alter or expand the mechanism for an arbitrary reac-
tion mechanism. The addition of the required methanol reactions to CBM-III is
not difficult.
In OZIPM-2, the air parcel is assumed to be of constant horizontal
dimensions with a time-varying depth. The emissions can be entered either as
hourly values of fractions of initial concentrations or as mass emission
rates. The species splits (how much in each bond category) is an input to the
model. Species splits, or carbon fractions, are assumed to be constant for a
given simulation. The specification of emissions as concentrations eliminates
the need of the model to consider how the horizontal dimensions of the air
parcel change with time. However, since volume changes affect the concentra-
tions in the air parcel, the parcel's horizontal and vertical dimension fluc-
tuations must be considered when determining the initial concentration frac-
tions which represent emissions.
The standard EKMA simulation (whether using OZIPP or OZIPM-2) as-
sumes a straight-line trajectory. This type of simulation is termed Level III.
As an option, the user may calculate a trajectory and have the trajectory's
path determine which emission will affect the moving air parcel. The user
then assigns these emissions to the air parcel as if the parcel trajectory
were a straight line crossing each of the identified sources. This is termed
a Level II analysis.
The OZIPM-2 model, when used in a Level III analysis, cannot simu-
late nighttime transport. If used in a Level II analysis, OZIPM-2 can be used
for nighttime simulations if an appropriate trajectory preprocessor with ade-
quate input data is used.
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No single preprocessor has been designated for determining the tra-
jectory path in Level II analyses with OZIPH-2. It is up to the user to per-
form this calculation. The South Coast Air Quality Management District
(SCAQMD) is currently performing a Level II trajectory analysis using OZIPM
and trajectories computed using the CIT Airshed model wind preprocessor (Lin,
1984).
Level II analysis with the use of CBM-III has been validated in
studies performed by the South Coast Air Quality Management District (Liu,
1984). Multiple trajectories were calculated assuming a range of errors
possible in the wind measurements. The envelope described by the multiple
trajectories determined which emission sources would be included in the sim-
ulation.
The input required to run EKMA/OZIPM-2 are:
• Initial conditions
• Reaction mechanism
• Location (for light intensity calculation)
• Mixing heights
• Ozone and precursor transported values
• Emissions as initial concentration
fractions or mass emission rates
This input data requirement is much less intensive than that for an Eulerian
model and thus makes the model available for use in many more cases than an
Enlerian model. In fact, other than wind and perhaps stability data, the in-
put required for OZIPM-2 may be all that is available for a given typical
modeling problem. Thus, for many situations, OZIPM-2 is more readily used
than is an Enlerian model.
Because OZIPP and OZIPM-2 are so widely used, they have been in-
volved in many model inter-comparisons and validations. In comparisons with
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field data it was indicated that OZIPP was not always reliable for predicting
absolute concentrations of ozone (Jeffries e_t a_l, 1981, and Gipson, 1982). In
fact, Gipson (1982) found that putting more effort into eliminating uncer-
tainty in the input data (using Level II instead of Level III analysis) in-
stead of using simplifying assumptions worsened the agreement between modeled
ozone concentration and observed ozone data. While the limited cases analyzed
by Gipson may not be representative for all modeling situations, they indicate
that increased model sophistication may not be warranted in all circumstances.
The documentation for OZIPM-2 is plentiful (EPA, 1977; Gipson ejt al.
1981, and Gipson, 1984. The documentation includes discussions of the method-
ology for calculating default values for missing input data.
3.4.2 SAI Trajectory Model
The SAI Trajectory model (Reynolds e_t al., 1979) is a subset of the
SAI Urban Airshed model. The major differences between the two models, in
terms of input, computations, and predictions along a specific trajectory, are
the elimination of horizontal dispersion, convergent and divergent winds, and
the use of surface winds only for trajectory calculation. Potential users
would have to obtain the SAI Trajectory model from SAI.
Because of the similarities, the general findings in the review of
the Urban Airshed model also hold for the Trajectory model. In comparisons
between these two SAI models, it was found (Whitten and Hogo. 1981) that the
Trajectory model is often more in agreement with the observations than the
more complex Urban Airshed model.
3.4.3 RPM-II Model
The SAI Reactive Plume Model. Version II. (RPM-II) (Stewart e_t a_l.
1980), assumes the air parcel consists of five cells, each of which is uni-
formly mixed in the vertical and arranged across the plume or trajectory.
Each cell extends from the surface to the mixing height. The background
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concentrations surrounding the trajectory can be calculated by the model.
Mixing with the air above and to the sides of the trajectory as well as be-
tween cells is allowed. The RPM-II model is publicly available.
RPM-II uses the CBM-II chemical reaction mechanism. This mechanism
is integral to the model. Assumptions specific to the CBM-II chemistry and
involving steady-state solutions are found throughout the model. Thus, modi-
fication to include methanol and updated reactions is difficult.
Point sources are the only emission sources allowed to influence the
air parcel. Area and line sources are not considered.
The input data required are:
• Wind speed
• Plume width and depth or stability class
• Ambient and initial plume concentrations
• Point source emission rates and locations
• NO, photolysis rates
The trajectory in the model is assumed to be a straight line. To perform a
Level II modeling analysis using RPM-II, trajectory preprocessing would have
to be performed to determine which point sources are intersected by the tra-
jectory and at what downwind locations. Because of the straight-line trajec-
tory assumption inherent in RPM-II, the model could not be used to simulate
nocturnal trajectories without use of a trajectory preprocessor.
Comparisons of RPM-II with field data have been carried out (Yocke
e_t al. 1980; and Stewart and Liu, 1981) with good results. However, the data
sets used for comparison were incomplete (no hydrocarbon data) and the missing
data were tuned for best fit. According to a sensitivity test (Tesche and
Roth, 1976), the parameters to which RPM-II is most sensitive are, in order:
47
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• Background hydrocarbon concentrations
• N0a photolysis rates
• Background NOZ concentrations
• Background ozone concentrations
This sensitivity makes it difficult to assign a reliability to the field com-
parisons.
Documentation is available (Stewart e_t al. 1980). The documentation
is clear and accurate.
The model's calculation of the concentrations in the air surrounding
the trajectory lends an extra amount of realism to the model. However, this
option is only useful in clean backgrounds with a point source plume, not in
urban areas, and therefore it would not be useful for mobile source applica-
tions.
3.4.4 STRATOS Model
STRATOS is a highly modified refined version of ELSTAR (Lnrman,
1979), a model developed by Environmental Research and Technology (ERT). The
STRATOS model was developed by Radian Corporation. It is publicly available.
The STRATOS model is a multi-layer air parcel model which computes a
trajectory path, allocates emissions as a function of time to the air parcel,
and computes the concentrations in each layer of the parcel as a function of
time (Balentine ejt al. 1984). It is designed to perform detailed Level II
modeling analyses.
The chemical kinetic mechanism solver of STRATOS is the same GCKM
module as that in MARC-1 (Section 3.3.3) and GCKM/TRAJ (Section 3.4.5).
STRATOS has essentially no restrictions on which chemical mechanism may be
used.
48
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STRATOS considers both point and area sources. The sources are de-
fined on a grid. If the trajectory comes in contact with a grid cell contain-
ing a source, that source is assumed to emit into the parcel. Plume rise cal-
culations are done for point sources to place the emissions into the proper
layer. In this respect STRATOS is very much like an Eulerian model.
The amount of input required is quite extensive and includes:
• Air parcel and grid sizes
• Layer dimensions
• Wind data
• Location
• Initial concentrations
• UV and water vapor data
• Temperature soundings
• Surface roughness
• Horizontal diffusion
• Deposition rates
• Rural emissions
• Point and area sources
• Reaction mechanism
Because of the extensive data requirements of STRATOS, creation
of input data files can.be difficult and time-consuming. In this respect,
STRATOS is similar to the previously reviewed Enlerian models.
STRATOS has been partially validated for the Philadelphia area
(Radian, 1984). The results showed good agreement between the model and the
data. However, much of the required input was missing and "best guesses" had
to be provided. In sensitivity testing, peak predicted ozone concentrations
49
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were found to be very dependent on the trajectory selected. Since the trajec-
tory determines the emissions, this is no great surprise. This sensitivity of
model results to the uncertainty in the trajectory is common for all trajec-
tory models.
The documentation for STRATOS (Balentine crt al. 1984) is complete
with examples. The documentation has to be followed to the letter because
many of the inputs follow different rules as to the number of inputs required
or maximum or minimum numbers allowed. This can make preparing the model
input difficult.
STRATOS is actually three programs. The first computes the trajec-
tory path, the second allocates emissions, and the third computes the concen-
trations in each layer. This is the same breakdown as that contained in the
GCKM/TRAJ model discussed in the next section. However, STRATOS assumes that
the air parcel contains five vertical layers while the simpler GCKM/TRAJ model
assumes an undivided air parcel. Also, the STRATOS model allocates emissions
to the air parcel in a much more complex manner than that used in GCKM/TRAJ.
Both models use portions of the first program, called METMOD, for trajectory
calculation.
METMOD uses wind speed and direction values as functions of height,
time, and location to give a composite trajectory path. Wind data for up to
five vertical layers can be input into METMOD. The winds are weighted in the
vertical for determining the air parcel path. These weights are supplied by
the user and give the capability of weighting winds at some altitudes more
than others. METMOD makes STRATOS and GCKM/TRAJ somewhat unique for trajec-
tory models in that a very sophisticated, multi-level trajectory module is
used to compute for the air parcel trajectory without using separate trajec-
tory preprocessors. The METMOD preprocessor can construct realistic noctur-
nal trajectories if enough meteorological data are available.
50
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3.4.5 GCKM/TRAJ Model
The Radian-developed General Chemical Kinetics Model with Trajectory
Option (GCKM/TRAJ) (Balentine et al. 1984) is a single air parcel trajectory
model capable of performing Level II modeling analyses. An option allows the
user to consider horizontal air parcel dispersion as a function of stability
class and wind speed. The user defines the depth of the air parcel with time.
The model is composed of three modules. The first module calculates
the trajectory path, the second allocates emissions to the trajectory, and the
third calculates the chemical concentrations in the air parcel as a function
of time and distance.
Radian has not released the current version of the GCKM/TRAJ model
to the public domain. An early version of the model without the trajectory
and emissions preprocessors is in the public domain. Radian is presently sup-
plying an executable version of GCKM/TRAJ to the Louisiana Department of En-
vironmental Quality and EPA Region VI.
Trajectories in GCKM/TRAJ are computed using the METHOD wind data
preprocessor of the STRATOS model. METHOD is discussed in more detail in Sec-
tion 3.4.4. A significant feature of GCKM/TRAJ is that trajectories needed
for performing Level II analyses can be computed without recourse to other
trajectory preprocessors as is needed with OZIPM-2 or RPM-II.
I
Validation studies have not been competed for GCKM/TRAJ. A valida-
tion study is currently underway to validate GCKM/TRAJ for use in southern
Louisiana (Baton Rouge).. As discussed in Section 3.3.3, the GCKM chemical
kinetics solving module contained in GCKM/TRAJ has been validated using smog
chamber studies (Eltgroth, 1981). In addition, the METHOD trajectory model
was used to compute the trajectories used in the successful partial validation
of STRATOS for Philadelphia (Radian, 1984).
51
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The model allows emissions from point and area sources. Using input
from the METHOD trajectory preprocessor, a second preprocessor called EMIT
computes which sources will interfere with the air parcel. For area sources,
EMIT allocates some fraction of the area source emissions to the parcel, de-
pendent on the amount of area source actually traversed by the parcel. Mobile
and stationary area sources are treated separately. Mobile source emission
factors dependent on the time of day can be entered to allow mobile source
emissions to vary during the day.
The chemistry portion of the model (GCKM) is the same as that used
in the MARC-1 model described in Section 2.3. GCKM/TRAJ will therefore accept
a user-defined reaction mechanism with reaction rate dependencies on:
• Temperature
• Ultraviolet light
• Total solar radiation
• Water vapor concentration
The input required to run GCKM/TRAJ is:
• Surface wind data
• Wind soundings
• Vertical wind weighting scheme to determine trajectory
• Parcel size
• Stability class
• Initial and boundary concentrations
• UV, TSR, water vapor and temperature schedules
• Reaction mechanism
The documentation for GCKM/TRAJ consists of an up-to-date, rela-
tively thorough User's Manual.
52
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3.5 S^"in>|ary and Rccoimn6iidations
A summary of the review of the models is shown in Table 3-1. The
models are evaluated on the six criteria:
• Physical basis (realism)
• Chemistry capability
• Emissions handling
• Input requirements
• Validation
• Documentation
This table only recaps the reviews found in Subsections 3.3 and 3.4.
3.5rl Recommendation of Model for Los Angeles
The Los Angeles area presents a very complex problem for studying
the effects of emission changes. Los Angeles has been extensively studied
(SAI, 1983; and CIT, 1983). Emission and meteorological data bases are avail-
able.
Because of the complexity of the situation and the availability of
data, an Eulerian model seems to be advisable. Of the three Eulerian models
reviewed, two have been used to simulate the Los Angeles area: Urban Airshed
Model and CIT Airshed Model. Of the two models, the Urban Airshed Model is
the preferred model because of the existence of documentation and its proposed
specification by the EPA as a guideline model.
Even with the existing documentation, it would be difficult to use
the Urban Airshed model without direct aid from SAI on preparing wind field
and area source input data. Preprocessors to perform these calculations exist
53
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TABLE 3-1. SUMMARY REVIEW OF MODELS
Model
Physical
Basis
Chemistry
Emissions
Input
Validation
Readily Available
Documentation
SAI
Drban
Airshed
CalTech
Airshed
MARC 1
EKMA/
OZIPM-2
SAI
Traj.
RPM-II
GCKM/
TRAJ
STRATOS
Eulerian
CBM-II
Point
Area
(1)
Eulerian
Eulerian
Trajectory
Trajectory
Trajectory
Trajectory
Trajectory
CalTech
User3
CBM-III
CBM-II
CBM-II
User3
User3
Point
Area
Point
Area
Not
Specific
Point
Area
Line
Point
Point
Area4
Point
Area
(1)
(1)
(2)
(1)
(2)
(1)
(2)
Tes, L.A.
and various
urban areas
Yes, L.A.
Yes
Yes, L.A.
No
No
Yes, partial
validation
for Phila-
delphia
Yes. but
incomplete;
revised docu-
mentation in
preparation
No
Yes
Yes5
Yes. but
incomplete
Yes
Yes
Yes
1 - Eulerian type input: terrain. 3-D wind field, 3-D concentrations, emissions, met state
parameters.
2 - Trajectory type input: wind speed, initial concentrations, emissions, met state parameters.
3 - User specifies reaction mechanism to be used.
4 - Area sources composed of stationary sources and mobile sources are handled separately.
5 - A user's manual for EKMA/OZIPM-2 using the CBM-III mechanism is being written by 6. Gipson
at EPA/OAQPS.
-------
for Los Angeles since the model has already been used for such a simulation.
These preprocessors and information on how to operate them could presumably be
obtained through SAI.
A drawback of the SAI Urban Airshed model is the fact that the CBM-
II chemistry is integral to the model. Thus, in order to perform simulations
using other mechanisms, the user would have to make potentially extensive
modifications to the model.
The second model to be recommended for use in Los Angeles is OZIPM-2
to perform a Level II EKMA trajectory analysis. This trajectory model has
been shown to operate well in the area (Liu, 1984). OZIPM-2 has two draw-
backs. First, the user will be required to determine the trajectory path and
which emissions affect the trajectory. The SCAQMD is currently using the CIT
Airshed model wind preprocessor to generated trajectories for use with OZIPM-
2. Second, the OZIPM model is designed around the CBM-III chemical mechanism.
Modifications to OZIPM would be needed to use alternative mechanisms.
The third model to be recommended for the Los Angeles area is
MARC-1. The reason for this selection is that the model is a flexible model
and is well-documented. MARC-1 is not restricted to use of a single chemistry
mechanism. In addition, MARC-1 contains most of its own preprocessors for
wind field calculation and source allocation. However, major effort would be
required in preparing the input files.
The MARC-1 model would be second in the recommended list except for
the serious drawback that it has yet to be validated as an entire model. The
submodels from which MARC-1 was created, however, have been successfully vali-
dated separately. If the model is validated in the future in the Los Angeles
region, its recommendation would increase to second.
Because of the lack of documentation, users of the CIT Airshed model
would have to rely too heavily on its developers for assistance. In addition,
the CIT model contains the CIT chemistry mechanisms integral to the code. As
55
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'discussed in Section 6.3, the CIT mechanism is not a recommended mechanism for
use in future studies of methanol photochemistry until the difficulties iden-
tified in Section 5.0 are resolved.
The recommended models for use in the Los Angeles area are, then, in
order:
1. SAI Urban Airshed
2. Level II EKMA using OZIPM-2
3. MARC-1
3.5.2 Recommendation of Model for Other Areas
Four models are recommended for use in areas outside of Los Angeles.
The model to use in a specific case is determined from the data coverage. The
models recommended are:
Urban - High coverage of emissions, upper air, meteorological
Airshed and air quality data.
Model
STRATOS - Moderate coverage of upper air, meteorological and air
quality data.
GCKM/TRAJ - Areas where Level II analyses are needed but only minimum
data are available.
OZIPM-2 - All other minimum data coverage areas.
Selection of a model for an arbitrary location must consider the
amount of input data that may be available. At a minimum, basic meteorologi-
cal wind data (upper air and surface) and emission data (location and emission
rates) will be assumed to be available. The emission data may not be spe-
ciated as to hydrocarbon classes. No matter which model is selected, some
estimate of speciation will be required to allow use of the chemical reaction
mechanisms being considered.
56
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The consideration of data coverage leads to two model recommenda-
tions. The first recommended model would be for use in simulation of a region
which is well-characterized as to emissions, meteorology, and air quality
(surface and aloft). In this case, the Eulerian Urban Airshed Model would be
recommended.
Where data coverage is sparse, the use of an Eulerian model would
likely not be warranted since a number of simplifying assumptions would have
to be made in order to run the model. These assumptions may lead to solu-
tions, at a greatly increased cost, no better than the trajectory models which
have many assumptions built in. Thus, a trajectory model would be recommended
for this case. Three models are recommended, depending upon the relative
degree of data coverage and the type of analysis to be performed.
Discussion of Recommendation for Data Rich Area
The Urban Airshed Model is the recommended model for use in an arbi-
trary location with excellent data coverage. There are two primary reasons
for this selection. First, the model is proposed as a guideline model by
EPA. Second, it has been extensively validated. However, as noted earlier,
different versions of the model were validated. Therefore, the validation
results may not be exactly transferable to the revised version likely to be
used in future analyses.
There are two potential difficulties involved with the Urban Airshed
Model. First, the model is presently restricted to use of the CBM-III mecha-
nism. Second, input data preprocessors may have to be developed if the model
is used in locations where it has not previously been used.
The MARC-1 model does not have the two above limitations. However,
due to the lack of validation of MARC-1, the model is not recommended as the
primary model for use in data rich areas. If at some point, MARC-1 is vali-
dated for use in a given location, the MARC-1 model should be considered for
use in future modeling for that location.
57
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The CIT Airshed model is also not recommended for two reasons.
First, the CIT Airshed model, like the SAI Urban Airshed model, uses a chemis-
try mechanism (CIT) integral to the model. Second, the CIT model is not well
enough documented for non-developers to be expected to operate the model.
Discussion of Recommendation for Data Poor Area
One of the major concerns of a trajectory model selection is the mo-
del's ability to use chemical reaction mechanisms which may be altered as know-
ledge increases. Two trajectory models have only very limited restrictions on
the chemistry used since the user supplies the reaction mechanism as an input
to the model. These are GCKM/TRAJ and STRATOS. In fact, the GCKM/TRAJ model
is the model that was used, without modification, to perform all the sensitiv-
ity analyses discussed in Section 5.0. These sensitivity analyses involved
using methanol-modified versions of the CB-III, Demerjian, and CIT mechanisms
(see Section 4.0).
The SAI Trajectory model has the same problem with the wind field
calculation as the SAI Urban Airshed model. Due to its integral mechanism and
the wind field problem, it too is eliminated from recommendation. This leaves
only the two trajectory models GCKM/TRAJ and STRATOS, for use in performing
Level II analyses.
The selection of which of these two models to recommend for use in
performing Level II analyses is once again dependent on data coverage. If
chemical concentration data are available for heights up to the mixing depth,
STRATOS is recommended. If only surface data, or limited upper air concentra-
tion data are available, GCKM/TRAJ is recommended.
For performing a Level III analysis, the EKMA/OZIPM-2 model should
be used. OZIPM-2 is in wide use and has been extensively documented and vali-
dated for use in performing Level III analyses. Because of its widespread
application for regulatory purposes. OZIPM-2 is recommended for data poor
analyses where Level III analyses are sufficient. A drawback of OZIPM-2 is
58
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the requirement to use the Carbon Bond (or related) series of chemical mecha-
nisms in the model. If a chemical mechanism other than CBM-III is used, the
GCKM/TRAJ model can be used to perform the Level III analysis.
RPM-II would require alteration to consider a chemical reaction
mechanism other than the one already residing in the model (CBM-II). The ex-
tent of alteration required would not be known until a thorough review of the
actual code was performed. In addition, RPM-II is designed for use in model-
ing point source emissions only. Therefore the RPM-II model is not being
recommended.
3.6 References for Section 3.0
Ames, J., S.R. Hayes. T.C. Myers, and D.C. Whitney (1979): Systems Manual for
the SAI Airshed Model. Draft report to U.S. Dept. of Transportation by Systems
Applications, Inc. under Contract No. DOT-FH-11-8529.
Ames, J.. T.C. Myers, L.E. Reid, D.C. Whitney, S.H. Golding, S.R. Hayes, and
S.D. Reynolds (1978): The User's Manual for the SAI Airshed Model. Draft re-
port to U.S. EPA by Systems Applications, Inc. under Contract No. 68-02-2429.
Anderson, G.E., et al (1977): Air Quality in the Denver Metropolitan Region:
1974-2000. EPA-908/1-77-002 Systems Applications, Inc.
Balentine, H.W., Eltgroth, M.W., Smith, C.. and Langevin, S.A. (1984): User's
Manual for the General Chemical Kinetic Model with Trajectory Option (GCKM/
TRAJ), DCN 84-244-005-04, Radian Corporation of Austin, Texas, September,
1984.
Balentine. H.W., and S. Langevin (1984): Radian Contract 244-005 with the
Louisiana Department of Environmental Quality to perform modeling for the 1984
Ozone SIP revision for Baton Rouge, Louisiana.
Balentine, H.W., M.W. Eltgroth, P.J.Haas, W.I. Sedenberg, A.J. Fabrick, S.M.
Znbrick, and R.J. Evans (1984): User's Guide to the STRATOS Model. Report to
U.S. EPA by Radian Corporation under contract 68-02-3520. Radian report No.
DCN 82-241-012-20.
CIT (1983): California Methanol Assessment: Volume II: Technical Report:
Chapter 6: Air-Quality Impact of Methanol Use in Vehicles, Report for the
California Energy Commission.
Eltgroth, M.W. (1981): A General Chemical Kinetic Simulation Computer Program
for Use with Multiple Reaction Mechanisms. Radian Corporation report.
59
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Eltgroth, M.W. (1982): User's Manual for the Multiple Atmospheric General
Impact Calculation Model (MAGIC). Report to Hydro-Quebec, Montreal, Quebec
by Radian Corporation. Radian report No. DCN 82-241-378-22.
Eltgroth, M.W., and P.V. Hobbs (1979): Evolution of Particles in the Plumes of
Coal-Fired Power Plants-II. A Numerical Model and Comparisons with Field Mea-
surements. Atmospheric Environment. 13, pp. 953-975.
Eltgroth, M.W., and W.J. Sedenberg (1983a): Modeling Assessment of the Impact
of Urban NOZ Emissions on Downwind Concentrations of Ozone, Technical Memoran-
dum: Modification of a Lagrangian Trajectory Model. Phase 2, Task 1. Radian
Corporation report DCN 83-240-012-16.
Eltgroth, M.W.. and W.J. Sedenberg (1983b): Modeling Assessment of the Impact
of Urban NOX Emissions on Downwind Concentrations of Ozone, Technical Memoran-
dum: Modification of a Lagrangian Trajectory Model. Phase 2, Task 3, Radian
Corporation report DCN 83-240-012-18.
Eltgroth. M.W., and C. Smith (1984): The General Chemical Kinetics Model
(GCKM) User's Manual. Radian Corporation report DCN 84-244-005-03.
EPA (1977): Uses, Limitations and Technical Basis of Procedures for Quantify-
ing Relationships Between Photochemical Oxidents and Precursors. EPA report
EPA-450/2-77-021A, U.S. EPA, Research Triangle Park. NC.
EPA (1984): Guideline on Air Quality Models (Revised)-Draft, U.S. EPA,
Research Triangle Park, NC.
Fabrick, A.J.. and P.J. Haas (1980): User Guide to IMPACT: An Integrated Model
for Plumes and Atmospheric Chemistry in Complex Terrain. Radian Corporation
report DCN 80-241-403-01.
Fabrick, A.J.. P.J. Haas, and W.J. Sedenberg (1982): Comparison of Dispersion
Models Used for Complex Terrain Simulation. Presented at the 3rd Joint Confer-
ence on Applications of Air Pollution Meteorology, San Antonio, Texas (Jan-
nary) . Sponsored by the American Meteorology Association.
Fabrick, A.J., R.C. Sklarew, and J. Wilson (1977): Point Source Model Evalua-
tion and Development Study. Contract A5-058-87, California Air Resources
Board, Science Applications. Inc.. West Lake Village. CA.
Gipson. G.A., (1984): Personal communication with Howard Balentine, Radian
Corporation, August, 1984.
Gipson, G.A., (1984): User's Manual for OZIPM-2: Ozone Isopleth Plotting with
Optional Mechanism/Version 2. EPA report EPA-450/84-024, U.S. EPA, Research
Triangle Park. NC.
Gipson. G.L. (1982): An Evaluation of the Empirical Kinetic Modeling Approach
Using the St. Louis RAPS Data Base. U.S. EPA report No. EPA-450/4-82-009.
60
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Gipson, G.L., W.P. Freas. R.F. Kelly, and E.L. Meyer (1981): Guideline for Use
of City-specific EKMA in Preparing Ozone SIP's, EPA report EPA-450/4-80-027,
U.S. EPA, Research Triangle Park, NC.
Hobbs, P.V., M.W. Eltgroth, and D.A. Hegg (1982a): Visibility Studies at the
Four Corners Power Plant. Final Report to the Arizona Public Service Company
for Contract EC79-2929-001.80 by the University of Washington. Seattle, WA.
Hobbs, P.V., D.A. Hegg, and M.W. Eltgroth (1982b): Plume Chemistry Study in
the Vicinity of the Arizona Public Service Choila Power Plant. Final Report to
the Arizona Public Service Company for Contract EC80-3438-011.80 by the Uni-
versity of Washington, Seattle, WA.
Hogo, H., 6.Z. Whitten, and S.D. Reynolds (1981): Application of the Empirical
Kinetic Modeling Approach (EKMA) to the Tulsa Area. Report to U.S. EPA by Sys-
tems Applications, Inc. under Contract No. 68-02-3376.
Jeffries, H.E., K.6. Sexton, and C.N. Salmi (1981): The Effects of Chemistry
and Meteorology on Ozone Control Calculations Using Simple Trajectory Models
and the EKMA Procedure. Report to U.S. EPA by the University of North Carolina
under Contract No. 68-02-3523. EPA report No. EPA-450/4-81-034.
Layland, D.E., and H.S. Cole (1983): A Review, of Recent Applications of the
SAI Urban Airshed Model, EPA report EPA-450/4-84-004, U.S. EPA, Research
Triangle Park, NC.
Lin, C. (1984): Private communication with Dr. Mark Eltgroth, MEF Environ-
mental, Incorporated.
Lurman, F. (1979) User's Guide to the ELSTAR Photochemical Air Quality Simula-
tion Model. Environmental Research and Technology, Inc. NTIS PB80-109184.
McRae. G.J., W.R. Goodin, and J.H. Seinfeld (1982): Development of a Second-
generation Mathematical Model for Urban Air Pollution - I. Model Formulation.
Atmospheric Environment. 16, pp. 679-696.
McRae, G.T., and J.H. Seinfeld (1983): Development of a Second-generation
Mathematical Model for Urban Air Pollution - II. Evaluation of Model Perfor-
mance. Atmospheric Environment. 17, pp. 501-522.
Radian Corporation (1981): Photchemical Dispersion Model Sensitivity Study -
Houston Area, Task 2 - Interim Report. Report to Texas Air Control Board by
Radian Corporation. Radian report No. DCN 81-241-259-02.
Radian Corporation (1984): Modeling Assessment of the Impact of Urban NOZ
Emissions on Downwind Concentrations of Ozone. Technical Memorandum: Modifi-
cation of a Lagrangian Trajectory Model. Report to U.S. EPA from Radian Cor-
poration under contract No. 68-02-3520. Radian report No. DCN 84-241-012-19.
61
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Ranzieri, A.J., P.D. Allen, and J.W. Tilden (1979): Evaluation of a Regional
Photochemical Model for the Sacramento Area. State of California Air Resources
Board, Research Division.
Reynolds, S.D., e_t a_l (1979): Photochemical Modeling of Transportation Control
Strategies. Volume I. Model Development, Performance Evaluation, and Strategy
Assessment. Draft Final report for U.S. Dept. of Transportation by Systems Ap-
plications. Inc. Report No. EF79-37.
Reynolds, S.D., T.W. Tesche, and L.E. Reid (1978): An Introduction to the SAI
Airshed Model and Its Usage. Draft report to U.S. EPA by Systems Applications,
Inc. Report No. EF78-53R3.
Schere, K. L., (1984): Personal communication with Dr. Mark Eltgroth, MEF
Environmental, Incorporated.
Seinfeld, J.H. (1984): Private communication with Dr. Mark Eltgroth, MEF
Environmental, Incorporated.
Sklarew, R.C., A. Joncich, K.T. Tran, and M.J. Oliver (1980): IMPACT Model
Validation for Burrard Thermal Gas Tracer Study, 24 to 30 September. 1979.
Form and Substance, Inc., Westlake Village, CA. Report to British Columbia
Hydro and Power Authority.
Stewart. D.A., and M. Liu (1981): Development and Application of a Reactive
Plume Model. Atmospheric Environment. 15. pp. 2377—2393.
Stewart. D.A., M.A. Tocke, and M. Liu (1980): User's Guide to the Reactive
Plume Model. RPM - II. Systems Applications, Inc. report No. 74-EF80-75.
Taylor, G.H., A.J. Schandt. and K.T Tran (1981): IMPACT Model Validation
Program for the Proposed Lucerne Valley Generating Station, North American
Weather Consultants report No. SBAQ-81-2, Salt Lake City, DT. Report to
Southern California Edison Company.
Tesche, T.W.. and P.M. Roth (1976): Appendix B, RPM - II Sensitivity Study.
Reprinted from I.A. Ogren et al (1976), "Determination of the Feasibility of
Ozone Formation in Power Plant Plumes." EPRI report EPRI EA-307 by Meteor-
ology Research, Inc. and Systems Applications, Inc.
Whitten, G.Z., and H. Hogo (1978a): User's Manual for Kinetics Model and
Ozone Isopleth Plotting Package. EPA report EPA-600/8-78-014a. U.S. EPA, Re-
search Triangle Park, NC.
Whitten, G.Z., and H. Hogo (1981): Comparative Applications of the EKMA in the
Los Angeles Area. Report to U.S. EPA by Systems Applications. Inc. under Con-
tract No. 68-02-2870.
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Whitten, G.Z., and H. Hogo (1983): Impact of Methanol on Smog: A Preliminary
Estimate. Final Report Publication NO. 83044 for ARCO Petroleum Products by
Systems Applications, Inc.
Tocke, M.A., D.A. Stewart, J. Johnsen, and R.J. Frost (1980): Evaluation of
Short-Term N0a Plume Models for Point Sources, Volume 1. Systems Applications,
Inc. report No. 103-EF80-91R.
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4.0 MECHANISMS REVIEWED
4.1 Selection of Chemical Mechanisms
Five chemical reaction mechanisms which simulate ozone formation
were considered for evaluation in the sensitivity analysis. These included:
• Dodge (Dodge, 1977)
• Carbon Bond III (Killus and Whitten, 1982)
• California Institute of Technology (CIT) (McRae,
et al. 1982)
• Demerjian (Schere and Demerjian, 1977)
• Atkinson. Lloyd. Winges (Atkinson, et al, 1982)
Of these. Carbon Bond III, Demerjian, and CIT were selected for evaluation.
The Dodge mechanism was excluded from further analysis for three
reasons. First, the Dodge mechanism is not considered to be an up-to-date
mechanism. Second, it cannot readily handle the changes in reactivity (spe-
cies composition) which were necessary for the sensitivity analyses. Finally,
model runs identical to those runs for the other mechanisms in the sensitivity
analyses would not be possible because of the reactivity limitations in the
Dodge mechanism.
The Dodge mechanism, which is contained in EPA's EKMA/OZIPP model,
assumes a VOC mix of 25% n-bntane and 75% propylene plus an additional 5% as
aldehydes. This composition is a surrogate composition derived only to fit
the Bureau of Mines smog chamber results from which the mechanism was devel-
oped. The actual chemical species in an emissions inventory or polluted air
mass cannot be explicitly assigned to these surrogate VOC classes. Therefore,
changes in vehicle exhaust composition cannot readily be handled without in-
validating the mechanism.
64
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The Atkinson, Lloyd, Winges mechanism was excluded primarily because
it is generally unavailable to the public and it is complex. This mechanism
is contained within the Environmental Research and Technology (ERT) ELSTAR
model. The mechanism contains several photolytic reactions which are not ex-
plicitly contained in the other mechanisms considered. It also utilizes a
lesser degree of lumping of VOC species, therefore resulting in a large number
of surrogate VOC classes for which emissions data were not available. Because
of these difficulties, it was decided that more detailed sensitivity analyses
of the remaining mechanisms was a more efficient use of resources than the ef-
fort required to obtain, implement, and test the Atkinson, Lloyd, Winges mech-
anism.
All three of the mechanisms reviewed in detail in this section are
limited to a certain degree in their ability to simulate nighttime atmospheric
chemical reactions. Many times, equilibrium assumptions are made regarding
certain photochemical reactions. These assumptions, while valid during day-
time periods, are not valid at night when no photochemical activity occurs.
Because of the potential problem associated with use of chemical mechanisms
for nighttime simulation, detailed analysis of each mechanism should be per-
formed prior to use in nocturnal simulations.
One such analysis has been performed for the CBM-III mechanism. Dr.
Jeffries of the University of North Carolina has supplied Radian with a modi-
fied CBM-III mechanism for use in the STRATOS model validation program (Elt-
groth and Sedenberg, 1983b). For the CBM-III mechanism, the modification con-
sisted of replacing the NO, + NO, reaction with three reactions to allow noc-
turnal non-photolytic removal of N0a.
4.2 Description of Mechanisms
The three mechanisms selected for evaluation, CBM-III, CIT, and Dem-
erjian, are briefly described below. More detailed discussions of the mecha-
nisms are contained in the references cited above and elsewhere (Jeffries et
al. 1981; McRae et. al. 1984). A listing of each mechanism, with methanol
65
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chemistry added, is included in Appendix A. These listings of each mechanism
are in the format for input into the Radian GCKM photochemical model.
4.2.1 Carbon Bond III
The Carbon Bond-Ill (CBM-III) mechanism is a lumped structure mecha-
nism developed by SAI, Inc. A recent version of the EPA EKMA/OZIPP model,
EKMA/OZIPM-2, uses the CBM-III mechanism. This EPA version of the CBM-III
mechanism, which consists of 76 reactions, was used in the present study.
VOC species in the atmosphere, or in an emissions inventory, are as-
signed to each bond class in the mechanism according to their molecular struc-
ture. The bond classes in CBM-III are:
Paraffin (PAR) - single-bonded carbon atoms such as those
in alkane compounds
Olefin (OLE) - terminally double-bonded carbon atoms such
as those in the alkene compounds
Ethylene (ETH) - slowly reactive double-bonded carbon atoms,
primarily consisting of ethylene
Aromatic (ARO) - reactive aromatic rings
Carbonyl (CARB) - carbonyl compounds such as those in the
aldehydes and ketones and olefin compounds
with internal double bonds
Dicarbonyl (DCRB) - highly photolytic, dicarbonyl compounds;
these are primarily intermediate species
which are not usually treated as inputs
to the mechanism
Because CBM-III is based on bond structure, a single hydrocarbon
species may be assigned to more than one of the above classes. For example,
butene contains one double bond and two single bonds. Therefore, one mole of
butene contains one mole of OLE bond and two moles of PAR bond.
66
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4.2.2 err
The CIT mechanism, contained in the CIT Airshed Model, consists of
53 chemical reactions. The version used for this study was obtained from a
recent study prepared for the EPA (McRae, e_t al. 1983). It is a lumped mole-
cule mechanism consisting of the following surrogate VOC classes:
V
Alkane (ALE) - all alkanes
Olefin (OLE) - all alkenes except ethylene
Ethylene (ETH) - exclusively ethylene
Aromatic (ARO) - all compounds with reactive aromatic rings
Formaldehyde (ECHO) - exclusively formaldehyde
Higher Aldehydes (ECHO) - aldehydes other than formaldehyde
In the CIT mechanism, each hydrocarbon compound is assigned to one of the
molecular classes according to the definitions above. Thus, for all com-
pounds, other than ethylene and formaldehyde which are treated explicitly,
the mechanism does not recognize any functional differences among different
compounds in the same class. For example, xylene, ethylbenzene, and toluene
are all placed in the aromatic class, and the carbon groups atached to the
aromatic ring are not treated separately.
4.2.3 Demerjian
The Demerjian mechanism is contained in the Photochemical Box Model
developed for EPA. A User's guide for this model is availabale (Schere and
Demerjian, 1984). The most up-to-date version of the Demerjian mechanism was
used for this study. In its present state, the mechanism consists of 61 re-
actions compared to 37 in the original version.
This mechanism is a lumped molecular mechanism consisting of the
following VOC classes:
67
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Paraffin (PAR) - all paraffinic (alkane) compounds
Olefin (OLE) - all alkenes except ethylene
Ethylene (ETH) - exclusively ethylene
Aromatic (ARO) - all compounds with reactive aromatic rings
except toluene
Toluene (TOL) - exclusively toluene
Formaldehyde (ECHO) - exclusively formaldehyde
Higher Aldehydes (ALD) - aldehydes other than formaldehyde
The comments included in the description of the CIT mechanism also apply to
the Demerjian mechanism, with the exception that Demerjian treats toluene
separately from other aromatics.
4.3 Implementation of Chemical Mechanisms in GCKM
Each of the three mechanisms was obtained from the sources pre-
viously identified and adapted for input to Radian's General Chemical Kinetics
Model (GCKM). Methanol chemistry was then added to each mechanism. Correct
implementation of the CBM-III and Demerjian mechanisms was verified by execut-
ing the GCKM model with a test case obtained with each mechanism. The impor-
tant details of this procedure are described below.
4.3.1 GCKM Treatment of Chemistry
A discussion of GCKM's treatment of chemical mechanisms is included
in Appendix A. An important feature of GCKM is its ability to calculate the
NOa photolysis rate for clear sky conditions as a function of day of year,
time of day, and geographic location, if these parameters are input to the
model. Schedules of ultraviolet radiation (UV), total solar radiation (TSR),
temperature (T), and water vapor concentration (W) can also be input to the
model. Reaction rates for any reaction in a mechanism can then be calculated
from these inputs with one of the two following equations:
68
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K = kx (TJV. NO, photolysis rate, or TSR)k* (4-1)
K = kx exp (k,/T) (4-2)
The values of kx and k, and the dependence (UV. NO, rate, TSR, or T)
must be included as part of each reaction in the mechanism input to the model.
If a water dependency (W) is specified, the model multiplies the reaction rate
by the water vapor concentration.
4.3.2 CBM-III
The CBM-III mechanism included in the EKMA/OZIPM-2 model (Gipson,
1984) was input to GCKM. The mechanism contains seven photolytic reactions
and several reactions whose rates have a temperature dependency identical to
that in equation 4-2. As discussed in Section 4.3.1, GCKM includes an algo-
rithm for this type of temperature dependency. The rates for the photolytic
reactions are calculated internally by EKMA/OZIPM-2. The EPA Guideline for
Using the Carbon Bond Mechanism in City—Specific EKMA (Gipson, 1984) contains
a benchmark OZIPM-2 run using CBM-III. The reaction rates (k) for each photo-
lytic reaction for each hour of the day from 8:00 a.m. to 6:00 p.m. are in-
cluded.
For each photolytic reaction, a linear regression analysis was per-
formed using InK and In (NO, photolysis rate) in the following relationship:
InK = lnkx + k, In (NO, photolysis rate) (4-3)
Equation 4-3 is a transformation of equation 4-1 obtained by taking the nat-
ural logrithm of both sides of the equation.
A correlation coefficient of 1.00 was obtained for the regression
analysis for each reaction. This result indicates that equation 4-1 in Sec-
tion 4.3.1 can be used to calculate the rate (K) of each photolytic reaction
from the NO, photolysis rate. Therefore k± and k,, computed in the regression
69
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analysis, were used in GCKM to compute reaction rates for each photolytic re-
action. The maximum difference between a GCKM computed rate constant and the
EPA benchmark case rate constant for any one hour was 6.9 percent. For most
hours, the difference was less than 2.0 percent.
The GCKM model was then executed with the benchmark inputs for EKMA/
OZIPM-2. The ozone concentrations calculated by GCKM are compared to those
calculated by EKMA/OZIPM-2 in Table 4-1. Agreement between the two models is
within 11% for all hours. The difference in computed maximum ozone concentra-
tion is due to the slight differences in the photolytic rate constants between
the EPA and GCKM model runs.
4.3.3 CIT
The CIT mechanism used for this study was obtained from a recent re-
port prepared for EPA (McRae ejt al. 1983). This version was determined to be
the most up-to-date version available (Sienfeld, 1984). The mechanism con-
tains seven photolytic reactions in addition to NO, photolysis. The photolyic
reactions are calculated as a constant factor of the NO, photolysis rate,
which can be readily accommodated by GCKM.
Two types of temperature dependencies are found in the CIT mecha-
nism. Most are of the form of equation 4-2 in Section 4.3.1. The remainder
are of the form:
K - A x TB x exp(-C/T) (4-4)
For these reactions, a .constant temperature of 289°K was assumed for the first
appearance of "T" in equation 4-4, simplifying the equation to be identical to
equation 4-2. Because of the relatively small diurnal fluctuations in ambient
temperature and the small values of "B" in the CIT mechanism, this simplifica-
tion does not introduce a significant error.
70
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TABLE 4-1. COMPARISON OF OZONE CONCENTRATIONS (PPM)
USING CBM-III MECHANISM
Hour
0800
0900
1000
1100
1200
1300
1400
1500
1600
1700
1800
GCKM
0.00
0.012
0.045
0.11
0.19
0.27
0.30
0.30
0.31
0.31
0.32
OZIPM-2
0.00
0.012
0.047
0.11
0.20
0.28
0.31
0.33
0.34
0.35
0.36
71
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A test case for the CIT mechanism could not be obtained from the de-
velopers. Therefore, as an alternative, the benchmark case nsed for CBM-III
was also used to test the CIT mechanism. This was done by converting carbon
bond rates to molecular class rates for all input NMOC concentrations. The
CBM-III rather than the Dimerjian mechanism test case was used because the
Demerjian test case assumed constant photolysis rates while the CBM-III case
nsed typical time-varying photolysis rates.
Because this procedure involves some assumptions, the conversion is
only an approximation. Also, because the mechanisms are different, identical
inputs will not necessarily result in the prediction of identical ozone con-
centrations. Likewise, good agreement between the two mechanisms does not
necessarily mean the mechanism is encoded properly. Nevertheless, the results
of the comparison are presented in Table 4-2. A peak ozone concentration of
0.35 is predicted by CIT compared to 0.36 by EKMA/OZIPM-2. This represents an
agreement within 3%. However, there are significant differences in predicted
ozone concentration early in the simulation.
4.3.4 Demerjian
A copy of the Demerjian mechanism and a test run were obtained from
Schere (Schere. 1984). The mechanism contains eleven photolytic reactions,
whose rates are internally calculated by the Photochemical Box Model. The
same linear regression procedure described for the CBM-III mechanism was used
to calculate photolytic reaction rates from the NO* photolysis rates. Temper-
ature-dependent reaction rates were either of the form in equation 4-2 of
Section 4.3.1 or complex three-body calculations. For the three-body temper-
ature dependencies, the. reaction rate at a temperature of 298°K was used, thus
introducing a slight deviation from the developer's version of the mechanism.
Table 4-3 a compares the ozone values calculated by GCKM and those
calculated by the Photochemical Box Model. Although the time intervals are
not identical for the two cases, agreement is within 2% when concentrations
exceed 0.1 ppm. The test case used, however, assumed constant photolytic rate
72
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TABLE 4-2. COMPARISON OF OZONE CONCENTRATION (PPM)
CALCULATED BY CIT MECHANISM WITH OZIPM-2
Hour CIT (GCKM) CBM-III (OZIPM-2)
0800 0.00 0.00
0900 0.079 0.012
1000 0.033 0.047
1100 0.079 0.11
1200 0.13 0.20
1300 0.18 0.28
1400 0.23 0.31
1500 0.28 0.33
1600 0.32 0.34
1700 0.34 0.35
1800 0.35 0.36
73
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TABLE 4-3. BENCHMARK RUN COMPARISON OF OZONE CONCENTRATION
(PPM) USING DEMEBJIAN MECHANISM
GCKM
Time (min)
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
Ozone
0.013
0.023
0.039
0.063
0.097
0.14
0.18
0.23
0.27
0.31
0.35
0.40
0.44
0.49
0.54
0.58
0.62
0.65
0.65
Photochemical
Time (min)
22.7
42.7
62.7
82.7
102.7
122.7
142.7
162.7
182.7
202.7
222.7
242.7
262.7
282.7
302.7
322.7
342.7
362.7
377.7
Box Model
Ozone
0.014
0.025
0.041
0.066
0.10
0.14
0.18
0.23
0.27
0.31
0.35
0.40
0.44
0.48
0.53.
0.58
0.62
0.65
0.65
74
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constants for all photolytic reactions. A test case with time-varying photo-
lytic rate constants was not obtained.
4.3.5 Addition of Methanol Chemistry to the Reaction Mechanisms
After each mechanism was tested and determined to be satisfactorily
encoded for input to GCKM, additional reactions to simulate methanol chemistry
were added. As discussed in Section 2.0, methanol chemistry can be repre-
sented by the single reaction:
CH,OH + OH -*- HO, + HCHO (4-5)
-i _i
The reaction has a well agreed upon rate of about 1500 ppm min (Dodge,
1984; SAI, 1983; CIT, 1983). Reaction 4-5 was added to each of the three
mechanisms.
In the CIT and Demerjian mechanisms, HCHO chemistry is treated ex-
plicitly. Therefore, no other changes were required. In CBM-III, HCHO is
included in the CARB group. Because the HCHO fraction becomes significant, if
not dominant, with a methanol-fueled vehicle fleet, it is desirable to treat
HCHO explicitly in the mechanism. Therefore, the following reactions were
also added to the CBM-III mechanism:
HCHO + OH —*- H0a + CO (4-6)
HCHO hV 2HOa + CO (4-7)
HCHO *L£ H, + CO (4-8)
The rates used by SAI for these reactions in their methanol photochemistry
study were obtained from Whitten (1984). For reaction 4-6, the rate is 15,000
ppm min . For reaction 4-7, the rate is 0.00271 x (NO photolysis rate).
And for reaction 4-8, the rate is 0.00376 x (NO photolysis rate). Although
these same reactions are contained in the CIT and Demerjian mechanisms, the
75
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rate constants are somewhat different in each mechanism. At higher formalde-
hyde concentrations, the differences in rate constants (up to a factor of two)
for these reactions may have a significant effect on the peak ozone predicted
by each mechanism. For example, the CIT mechanism uses a rate of 0.00113 z
(NO, photolysis rate) for reaction 4-7 (HcRae et al., 1983). which is less
than half the above rate used by SAI.
In the SAI methanol photochemistry study, methyl nitrite (MeNO,)
was identified as a species which may be introduced into the atmosphere in
significant quantities with the conversion to methanol fueled vehicles (SAI.
1983). Because methyl nitrite is highly photolytic, the SAI researchers chose
to include its chemistry in their mechanism by adding the following reactions:
MeNO, ^± CH,0 + NO (4-9)
CH,0 -*• HCHO + H0a (4-10)
The rate for the forward reaction 4-9 is 0.3 z (NO, photolysis rate), and the
4 -1 -1
back reaction has a rate of 4.4 z 10 ppm min . Reaction 4-10 has a rate
_i
of 1.88 min . These reactions were added to all three mechanisms in the
present study. The reaction rates are those used by SAI (Whitten, 1984) .
4.4 References for Section 4.0
Atkinson, R., A. C. Lloyd, L. Winges, (1982): An Updated Chemical Mechanism
for Hydrocarbon/N0x/S0, Photo-ozidations Suitable for Inclusion in Atmospheric
Simulation Models, Atmospheric Environment. Vol. 16, 6, pp. 1341-1355.
CIT (1983): California Methanol Assessment: Volume II: Technical Report:
Chapter 6: Air Quality Impact of Methanol Use in Vehicles, Report for the
California Energy Commission.
Dodge. M. C., (1977): Effect of Selected Parameters on Predictions of a
Photochemical Model, EPA-600/3-77-048, U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina.
Dodge, M.C., (1984): Letter to Phillip Lorang (EPA, Ann Arbor) ezpressing
EPA's concurrence with the additions to the photochemical reaction sets to
account for Methanol, May 9, 1984.
76
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Eltgroth, M.W., and W.J. Sedenberg (1983b): Modeling Assessment of the Impact
of Urban NOX Emissions on Downwind Concentrations of Ozone, Technical Memoran-
dum: Modification of a Lagrangian Trajectory Model. Phase 2, Task 3, Radian
Corporation report DCN 83-240-012-18.
Gipson, J. L., (1984): "Guideline for Using the Carbon-Bond Mechanism in
City-Specific EKMA," EPA-450/4-84-005, U.S EPA Office of Air Quality Planning
and Standards, Research Triangle Park, North Carolina.
Jeffries, H. E., K. 6. Sazton, and C. N. Salmi, (1981): The Effects of
Chemistry and Meteorology on Ozone Control Calculations Using Simple
Trajectory Models and the EKMA Procedure, Contract No. 08-02-3523,
EPA-450/4-81-034, U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina.
Killus, T. P. and 6. Z. Whitten, (1982): A New Carbon-Bond Mechanism for Air
Quality Simulation Modeling, Contract No. 68-02-3281, Environmental Sciences
Research Laboratory, U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina.
McRae, G. J., J. A. Leone, and T. H. Seinfeld, (1983): Evaluation of Chemical
Reaction Mechanisms for Photochemical Smog. Part I. Mechanism Descriptions
and Documentation. Environmental Sciences Research Laboratory, U.S
Environmental Protection Agency, Research Triangle Park, North Carolina.
SAI, (Whitten, G. Z and H. Hogo), (1983): Impact of Methanol on Smog: A Pre-
liminary Estimate, Final Report, Publication No. 83044, Systems Applications,
Inc. Study performed for ARCO Petroleum Products Company, DuPont. and SOHIO.
Schere, K. L., (1984): Personal communications with S. A. Langevin of Radian
Corporation.
Schere, K. L. and K. L. Demerjian, (1977): A Photochemical Box Model for
Urban Air Quality Simulation, Proceedings. 4th Joint Conference on Sensing of
Environmental Pollutants. American Chemical Society.
Schere, K. L. and K. L. Demerjian, (1984): "User's Guide for the Photochemi-
cal Box Model (PBM)," EPA-600/8-84-0229, Environmental Sciences Research Lab-
oratory, Office of Research and Development, Research Triangle Park, North
Carolina.
Sienfeld, J. H., (1984): Personal communication with Steve Langevin, Radian
Corporation. August, 1984.
Whitten, G., (1984): Personal communication with Steve Langevin, Radian
Corporation, August, 1984.
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5.0 MODELING SENSITIVITY ANALYSIS
According to previous studies (SAI, 1983; CIT, 1983), it appears
that methanol used as a fuel produces less ozone than petroleum-based fuels.
However, before a complete assessment can be made of the change in ozone for-
mation that would result from a significant conversion to methanol fuels, the
sensitivity of each chemical mechanism to various input parameters must be
established.
This section describes the results of 24 computer modeling sensitiv-
ity runs performed using Radian Corporation's General Chemical Kinetic Model
(GCKM) and three atmospheric chemistry reaction mechanisms; the Carbon Bond
III mechanism (CBM-III), the California Institute of Technology mechanism
(CIT), and the Demerjian mechanism (Demerjian). The mechanisms have been mod-
ified as described in Section 4.0 to include identical methanol chemical reac-
tions. Each mechanism does, however, handle non-methane organic compounds
(NMOC) differently. The purpose of this modeling study is to assess the sen-
sitivity of photochemical ozone formation to methanol emission, for the
methanol-adapted CBM-III, CIT, and Demerjian mechanisms.
To present the results of the sensitivity analysis, this section is
organized in the following manner. First, a brief discussion of the model
used and the preparation of the input data is presented. Next, the base case
modeling results for each mechanism are discussed. Third, the model sensitiv-
ity to the ratio of gasoline powered vehicles to methanol powered vehicles in
the motor vehicle fleet is discussed. The HCHO/MeNO, ratio of methanol emis-
sions is discussed fourth. Finally, a discussion of the relationship of sur-
face and aloft ozone concentrations to ozone formation is presented.
5.1 Brief Description of Model Used
The Radian GCKM/TRAJ dispersion model was used to perform the model-
ing sensitivity study discussed in this section. This model is the same model
discussed in Section 3.4.5. However, the trajectory option of GCKM/TRAJ was
78
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not used and so the model will be called simply GCKM for clarity in the re-
mainder of this section.
The GCKM model can be operated in one of three modes. The mode dis-
cussed in Section 3.4.5 is the Atmospheric mode in which horizontal diffusion
of the air parcel is simulated, air parcel trajectories are computed using
METHOD, and emissions occur into the parcel explicitly. A second mode of GCKM
allows simulation of smog chamber results. The third mode, used in this
analysis, is called the EKMA mode and closely duplicates the EPA OZIPH-2
model.
In the EKMA mode of GCKM, horizontal diffusion of the air parcel is
not simulated. Emissions are accounted for as emission fractions, or the
addition of precursor pollutant concentrations to the air parcel as a fraction
of the initial parcel concentration. Straight line trajectories are assumed,
and so trajectories are not computed.
A single well mixed column of air is assumed to extend from the
ground up to the top of the mixed layer. The mixed layer is allowed to expand
vertically during the day as the mixing height rises. Concentration gradients
at the edge of the well-mixed air column are assumed to be negligible so that
horizontal diffusion can be ignored.
A more complete description of the GCKM/TRAJ model is given in the
model User's Guide (Balentine, e_t al., 1984).
5.2 Input Data Preparation
The first step in this sensitivity analysis was to establish a rea-
sonable data base for use in modeling. This data base defined the atmospheric
conditions, initial, surface, and aloft species concentrations, and most im-
portantly, the quantity of reactive emissions. This section will discuss the
basis for the choices of each of these model inputs.
79
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5.2.1 Atmospheric Conditions
The model inputs for all GCKM simulations are based on the atmos-
pheric conditions of June 27, 1974, for Los Angeles, California. There are
two reasons for selecting this date. First, monitoring data have shown that
ozone concentrations were elevated on June 27, 1974, (EPA. 1977; SAI, 1983).
Second, previous ozone modeling studies have predicted elevated ozone concen-
trations when June 27, 1974 atmospheric conditions were modeled (SAI, 1983,
EPA, 1977). Therefore, for consistency and comparability of modeling results,
all GCKM simulations were performed for the Los Angeles, California, area with
June 27, 1974, atmospheric conditions. Since ozone formation is a function of
sunlight, all GCKM model runs simulated the 10-hour period between 0800 hours
and 1800 hours.
Atmospheric conditions such as hourly temperature and mixing height
variation also affect ozone formation. Table 5-1 lists the temperature and
mixing height GCKM input values for all model simulations.
5.2.2 Initial. Air Parcel and Aloft Precursor Concentrations
The selection of reasonable initial concentrations of ozone pre-
cursors in the air parcel and above the mixing height is very important in an
ozone formation sensitivity analysis. For this analysis, 0.5 ppm NMOC, 0.064
ppm NOX, and 0.06 ppm 0, were selected as the GCKM inputs for the initial air
parcel concentrations. These values are typical concentrations for Los
Angeles (EPA, 1981). The initial parcel concentration was assumed to consist
of a background surface concentration and a concenration due to emissions from
manmade sources.
The NMOC initial concentration was speciated for each reaction mech-
anism and held constant at 0.5 ppmC throughout the analysis. An example of
the calculation method used to allocate the initial concentration among each
species class is shown in Appendix B. The NOX concentration was split as 75
percent NO and 25 percent N02 (on a molar basis). This NOZ ratio was constant
for each mechanism.
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TABLE 5-1. HOURLY TEMPERATURE AND MIXING HEIGHT SCHEDULES
USED FOR ALL MODEL RUNS
Time
(hrs)
0800
0900
1000
1100
1200
1300
1400
1500
1600
1700
1800
Temperature
(°C)
23.9
26.8
29.9
32.1
33.7
36.0
37.5
38.6
36.6
29.3
26.7
Mixing Height
(m)
70.1
116.2
156.3
300.7
356.9
447.6
528.4
539.1
523.9
520.0
520.0
(Source: SAI, 1983)
Note: Values representative of Los Angeles for June 27, 1974.
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Aloft concentrations for NMOC, NOZ, and Oa were selected from typi-
cal values for the Los Angeles area reported by Systems Applications, Inc.
(SAI, 1983). Both NMOC and NOX aloft concentrations were speciated by the
same methods described for the initial concentrations. The typical Los
Angeles area aloft Os concentration selected for input to GCKM was 0.08 ppm.
Table 5-2 lists the initial surface and aloft concentrations, by
species, for each mechanism. Tables 5-2 through 5-5 present the NMOC initial
species concentrations by mechanism for each case modeled.
5.2.3 Non-Methane Organic Compound Emissions
The projected Los Angeles, California, 1987 CBM-III emission inven-
tory presented by SAI was chosen as the emissions basis for all GCKM simula-
tions. Table 5-6 shows the NMOC and NOZ emissions from this inventory. Total
NMOC emitted was assumed to be constant for all scenarios and for each mecha-
nism. Hourly emissions were calculated by dividing the daily emission totals
by 24 and assuming a constant rate for the ten-hour simulation. These hourly
emissions were then input to GCKM as fractions of the air parcel initial con-
centrations using the same methodology used for the OZIPM-2 model. Because
the parcel initial concentration was held constant for each scenario and the
initial concentration was assumed to be caused by emissions, constant emission
fractions were used for each scenario for each mechanism. The calculation of
the emission fraction used for each run is given in Appendix B.
5.3 Sensitivity Study Results
Twenty-four model sensitivity runs were performed using the CBM-III,
CIT, and Demerjian mechanisms. The runs consisted of a simulation for each
mechanism for each of the eight modeling scenarios. Brief descriptions of
these scenarios and the resulting peak ozone concentrations, predicted for the
scenarios, are given in Table 5-7. Peak ozone concentration differences be-
tween scenarios, for a given mechanism, were generally small. The exception
is for the CIT mechanism which predicted very significant differences in peak
ozone concentrations between scenarios.
82
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TABLE 5-2. MODEL INPUT OZONE PRECURSOR CONCENTRATIONS
Species
CBH-III Mechanism
PAR
ETH
OLE
ARO
CARD
CIT Mechanism
OLE
ALZ
ARO
ETH
RCHO
ECHO
Demerjian Mechanism
Initial
d
d
d
d
d
d
d
d
d
d
d
Aloft
All
Mechanisms
NOX
NO
NOa
Oi
CO
HiOi
0.060
0.045*
0.015
0.06b
2.0
1 .OE-6
0.003
0.002a
0.001
0.08°
0
0
0.0916
0.0021
0.0003
0.0011
0.0289
0.0005
0.019
0.013
0.0022
0.0077
0.011
OLE
PAR
ETH
ALD
ARO
TOL
ECHO
d
d
d
d
d
d
d
0.0005
0.019
0.0022
0.0077
0.0008
0.0005
0.011
&Based on assumption of total NOX split of 75% NO, 25% NO, per EPA, 1981.
^Decreased to 0.03 ppm for 0} aloft sensitivity case.
cDecreased to 0.04 ppm for 0, aloft sensitivity case.
&Based on emissions per SAI, 1983. Varies for each sensitivity case. See
Tables 5-4 through 5-6.
83
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00
TABLE 5-3. CBM-III REACTION MECHANISM INITIAL NMOC SPECIES
CONCENTRATIONS IN AIR PARCEL (PPM)
Species
PAR
ETH
OLE
ARO
CARB
CB,OH
HCHO
MeNO,
Base Case
0.3625
0.0160
0.0130
0.0105
0.0180
0
0
0
50% Methanol
Vehicles Case
0.2848
0.0114
0.0084
0.0077
0.0144
0.1025
0.0115
0.0011
10% HCHO
1% MeNO,
0.2071
0.0072
0.0041
0.0049
0.0111
0.2050
0.0231
0.0023
100% Methanol
10% HCHO
5% MeNO,
0.2071
0.0072
0.0041
0.0049
0.0110
0.1958
0.0231
0.0115
Vehicles Cases
20% HCHO
5% MeNO,
0.2071
0.0072
0.0041
0.0049
0.0110
0.1728
0.0461
0.0115
20% HCHO
1% MeNO,
0.2071
0.0072
0.0041
0.0049
0.0110
0.1820
0.0461
0.0023
Note: Species concentrations are based on 0.5 ppmC selected as a typical 6-9 a.m. NMOC
concentration for Los Angeles. California (SAI, 1983). Percents are based on moles
of NMOC.
-------
CO
Ul
TABLE 5-4. CIT REACTION MECHANISM INITIAL NMOC SPECIES
CONCENTRATIONS IN AIR PARCEL (PPM)
Species
ALE
ETH
OLE
ARO
ECHO
CB,OB
ECHO
MeNO,
Base Case
0.0620
0.0133
0.0190
0.0104
0.0038
0
0.0055
0
50% Methanol
Vehicles Case
0.0508
0.0098
0.0121
0.0076
0.0032
0.1025
0.0162
0.0011
10% HCHO
1% MeNOa
0.0384
0.0065
0.0062
0.0050
0.0025
0.2050
0.0269
0.0023
100% Methanol
10% HCHO
5% MeNO,
0.0384
0.0065
0.0062
0.0050
0.0025
0.1958
0.0269
0.0115
Vehicles Cases
20% HCHO
5% MeNO,
0.0384
0.0065
0.0062
0.0050
0.0025
0.1728
0.0499
0.0115
20% HCHO
1% MeNO,
0.0384
0.0065
0.0062
0.0050
0.0025
0.1820
0.0499
0.0023
Note: Species concentrations are based on 0.5 ppmC selected as a typical 6-9 a.m. NMOC
concentration for Los Angeles, California (SAI, 1983). Percents are based on moles
of NMOC.
-------
TABLE 5-5. DEMERJIAN REACTION MECHANISM INITIAL NMOC SPECIES
CONCENTRATIONS IN AIR PARCEL (PPM)
Species
PAR
ETH
OLE
ARO
TOL
ALD
CH.OH
ECHO
MeNOa
Base Case
0.0620
0.0133
0.0190
0.0062
0.0042
0.0038
0
0.0055
0
50% Methanol
Vehicles Case
0.0508
0.0098
0.0121
0.0046
0.0030
0.0032
0.1025
0.0162
0.0110
10% HCHO
1% MeNO,
0.0384
0.0065
0.0062
0.0030
0.0020
0.0025
0.2050
0.0269
0.0023
100% Methanol
10% HCHO
5% MeNO,
0.0384
0.0065
0.0062
0.0030
0.0030
0.0025
0.1958
0.0269
0.0115
Vehicles Cases
20% HCHO
5% MeNO,
0.0384
0.0065
0.0062
0.0030
0.0030
0.0025
0.1728
0.0499
0.0115
20% HCHO
1% MeNO,
0.0384
0.0065
0.0062
0.0030
0.0030
0.0025
0.1820
0.0499
0.0023
Note: Species concentrations are based on 0.5 ppmC selected as a typical 6-9 a.m. NMOC
concentration for Los Angeles, California (SAI, 1983). Percents are based on moles
of NMOC.
-------
TABLE 5-6. LOS ANGELES 1987 PROJECTED MOBILE AND OTHER SOURCE EMISSIONS
Emissions (g-moles/dav)
Motor Vehicles All Other Sources Totals
NMOC 90,600 106,000 196.000
(as carbon)
NOX 39,700 22,100 61,800
(Source: SAI, 1983)
Note: All emissions are rounded to three significant figures.
87
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TABLE 5-7. SUMMARY OP PEAK OZONE CONCENTRATION FOR EACH
SCENARIO AS PREDICTED BT EACH MECHANISM
Scenario Title
Scenario Description
Emissions from
Methanol-Fueled Initial
Vehicles (% of Total) Concentrations (ppm)
CHiOH HCHO MeNOa Oi Surface Oi Aloft
Predicted Peak Ozone
Concentration (ppm)
CBM-III CIT Demerjian
00
00
Base Case
50% Methanol Vehicles
Case.
100% Methanol Vehicles
Case
Ozone Aloft Sensitivity
Case. 100% Methanol
Vehicles
Surface Ozone Sensitiv-
ity Case, 100%
Methanol Vehicles
Formaldehyde/Methyl
Nitrite Case (10%/5%)
Formaldehyde/Methyl
Nitrite Case (20%/1%)
Formaldehyde/Methyl
Nitrite Case (20%/5%)
000
89 10 1
89
89
85
79
75
10
89 10
10
10
20
20
0.06
0.06
0.06
0.06
0.03
0.06
0.06
0.06
0.08
0.08
0.08
0.04
0.04
0.08
0.08
0.08
0.26 0.20 0.32
0.25 0.28 0.30
0.24 0.17 0.29
0.22 0.14 0.28
0.22 0.13 0.27
0.25 0.18 0.30
0.25 0.19 0.30
0.25 0.20 0.30
-------
Plots of the concentrations of primary species (NO, NO,. 0,, PAN,
and NMOC) versus time for each sensitivity run are given in Appendix C. De-
tailed discussions of each scenario and the modeling results are given in the
remainder of this section. Listings of the predicted species concentrations
for each time step for all sensitivity runs are given in Appendix D.
5.3.1 Base Case Scenario
The base case model run was made for each of the three mechanisms.
The base case was for June 27, 1974, with 1987 emission rates. All vehicles
were assumed to be burning gasoline. No methanol chemistry was included in
the mechanisms. The purpose of the base case was to establish a starting
point from which input parameters could be varied and comparisons made.
The model inputs for the base case have been presented in Section
5.2. Inputs were identical for the three mechanisms with the exception of the
distribution of the NMOC species. These differences reflect the different
lumping procedures used by each mechanism rather than a difference in the NMOC
composition in the atmosphere. Total NMOC (as carbon) was held constant at
0.5 ppmC.
Figure 5-1 presents the base case ozone concentrations as a function
of time for each of the three mechanisms. Peak ozone was reached at 600 min-
utes (6:00 PM LDT) with all three mechanisms. The Demerjian mechanism pre-
dicted a peak ozone concentration of 0.32 ppm, CBM-III predicted a peak of
0.26, and CIT predicted a peak of 0.20 ppm. Because the base case did not
correspond to an observed ozone maximum (due primarily to use of projected
1987 emission rates), the modeled values can only be compared with each other.
The mean ozone concentration predicted by the three mechanisms was 0.26 ppm,
which is equal to the peak predicted by CBM-III. The Demerjian and CIT peaks
were 23% above and 23% below the mean, respectively.
89
-------
0.5
C
0
N
C
E
N
T
R
A
T
I
0
N
0.4
0.3
0.2
P
P
M
0.
0
0 60 120 180 240 300 360 420 480 540 600
TIME CMINUTES)
- CBIII CHEMISTRY CIT CHEMISTRY DEMERJIAN CHEMISTRY
Figure 5-1. Predicted Ozone Concentrations for Base
Case Scenario for Each Mechanism.
-------
5.3.2 Fifty Percent Methanol Conversion Scenario
Each mechanism was run assuming that fifty percent of the vehicles
in the Los Angeles area inventory were converted to methanol fuel. The emis-
sions from the methanol-fueled vehicles were assumed to be 89% methanol
(CH,OH), 10% formaldehyde (ECHO), and 1% methyl nitrite (MeNOa). The re-
maining NMOC was speciated according to the appropriate chemical reaction
mechanism. All percentages are based on moles of NMOC. All other inputs,
including initial NO,, 0,, NMOC concentrations and emission fractions, were
left unchanged from the base case. The initial NMOC species concentration
resulting from this scenario have been presented in Tables 5-4 through 5-6 of
Section 5.1.
Figure 5-2 presents the ozone concentrations as a function of time
for each mechanism for the 50% conversion scenario. Compared with the base
case, the Demerjian and CBM-III mechanisms predicted a slight decrease in peak
ozone concentration (0.02 ppm and 0.01 ppm, respectively) with the 50% conver-
sion to methanol vehicles. The CIT mechanism predicted an increase from 0.20
ppm to 0.28 ppm. This increase was due to a rapid rise in ozone concentration
from 60 to 120 minutes into the simulation (9:00 AM to 10:00 AM). The in-
crease in ozone coincided with a large increase in PAN, a large decrease in NO
and NO,, and a large increase in radical population (OH and HO,) compared to
the base case.
The behavior predicted by the CIT mechanism for the 50% conversion
scenario is not consistent with the trends predicted by the other scenarios or
by the CBM-III and Demerjian mechanisms. Both the ozone and PAN concentra-
tions rise dramatically at 90 minutes into the simulation and peak at 120
minutes. The PAN concentration at 120 minutes is approximately two times
higher than any other CIT run. A plot of the 50% case including ozone, PAN,
NO, NO,, and NMOC is given in Appendix C. Modeled 30-minnte concentrations
are given in Appendix D. Additional discussion of the CIT 50% conversion
scenario is given in Appendix E.
91
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0.5
10
NJ
C
0
N
C
E
N
T
R
A
T
I
0
N
P
P
M
0.4
0.3
0.2
0. I
0
0 60 120 180 240 300 360 420 480 540 600
TIME CMINUTES)
- CBIII CHEMISTRY CIT CHEMISTRY DEMERJIAN CHEMISTRY
Figure 5-2. Predicted Ozone Concentrations for 50%
Methanol Conversion Scenario for Each
Mechanism.
-------
The results of the 50% conversion scenario modeling seem to indicate
that the mechanism is overly sensitive to changes in the NMOC species distri-
bution. It should be noted that because we were unable to obtain an actual
test case from the developers to verify the CIT mechanism, some uncertainty
exists as to its correct implementation in this study. However, other re-
searchers have also obtained poor results with the earlier versions of the CIT
mechanism (Jeffries, e_t al. 1981).
The results of the 50% methanol conversion case and the problems
with obtaining and implementing the CIT mechanism tend to make the mechanism
less desirable for future studies compared to CBM-III and Demerjian. Addi-
tional analysis is needed to determine the source of the unusual results for
the CIT mechanism for the 50% conversion scenario.
5.3.3 One Hundred Percent Methanol Conversion Scenario
Each mechanism was run assuming that 100% of the vehicles in the
projected 1987 Los Angeles area inventory were converted to methanol fuel.
The emissions from the methanol fueled vehicles were assumed to be 89% CH,OH,
10% ECHO, and 1% MeNO,. Non-vehicle NMOC emissions were speciated according
to the appropriate chemical reaction mechanism for each GCKM run. All other
inputs were the same as the base case.
Figure 5-3 presents the ozone concentrations as a function of time
for each mechanism for the 100% conversion scenario. Compared with the base
case, the CBM-III. CIT and Demerjian mechanisms predicted a decrease in peak
ozone concentrations (0.02 ppm, 0.03 ppm and 0.03 ppm, respectively) with the
100% conversion to methanol vehicles.
Each mechanism appeared to be equally sensitive to methanol emis-
sions when compared to the base case. The CBM-III and Demerjian mechanism
each predictd a further decrease of 0.01 ppm in peak ozone when methanol con-
version increases from 50% to 100%. The CIT mechanism results did not show a
93
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0.5
VO
C
0
N
C
E
N
T
R
A
T
I
0
N
P
P
M
0.4
0.3
0.2
0. 1
0
0 60 120 !80 240 300 360 420 480 540 600
TIME CMINUTES)
- CBIII CHEMISTRY CIT CHEMISTRY DEMERJIAN CHEMIsfRY
Figure 5-3. Predicted Ozone Concentrations for
100% Methanol Conversion Scenario for
Each Mechanism.
-------
continuation of the same behavior exhibited for the 50% conversion scenario.
Thus, the CIT results for the 50% methanol conversion are not only inconsis-
tent with the other two mechanisms, bat also inconsistent with the CIT base
case and 100% conversion results. This is clearly illustrated in Figure 5-4
which presents plots of the hourly ozone concentration predicted for each
scenario for each of the three mechanisms. In CBM-III and Demerjian, the
ozone concentration for the 50% case remains between the concentration for the
base case and 100% case throughout the simulation. In CIT, the ozone concen-
tration for the 50% case increases rapidly early in the simulation and remains
elevated above the predicted ozone concentration for both the base case and
the 100% case.
5.3.4 Ozone Background/Aloft Concentration Scenarios
Three ozone background/aloft concentration scenarios were examined
for each chemical reaction mechanism for the 100% methanol conversion case. A
surface ozone concentration of 0.06 ppm and an aloft ozone concentration of
0.08 ppm were used as the initial case. In each of the two remaining cases,
the existing ozone concentration at the surface or aloft was varied. All
other model inputs were the same as the base case for each reaction mechanism.
The ozone sensitivity cases examined were:
• Surface ozone concentration = 0.06 ppm
Aloft ozone concentration - 0.08 ppm
• Surface ozone concentration = 0.06 ppm
Aloft ozone concentration - 0.04 ppm
• Surface ozone concentration = 0.03 ppm
Aloft ozone concentration = 0.04 ppm
The purpose of these cases was to examine the effect of existing
surface and aloft ozone concentrations on the peak ozone values predicted
by GCKM using the CBM-III, the CIT, or the Demerjian chemical reaction mecha-
nisms.
95
-------
A. Carbon Bond III Chemistry
e.s
e.4
c
o
g ..3
N
T
R
A
T
I
0
N
8.2
.I
120 188
•8X METHANOL
24e 388 308
TINE CMINUTES)
- S8X METHANOL
428 488 548 088
•188X METHANOL
B. CIT Chemistry
8.5
0
Z
0
Ee.4
8.3
8.2
08
128 188
•8» HETHANOL
248 388 308
TIME (MINUTES)
S8X METHANOL
428 488 548 O88
I88X HETHANOL
8.5
C. Demerjian Chemistry
8.4
8.3
8.2
8.1
8
8 08 128 IBB 248 388 388 428 488 548 01
TINE CMINUTES5
-8X METHANOL S8» METHANOL 188X METHANOL
Figure 5-4, Predicted Ozone Concentrations for Methanol
Substitution Scenarios for Each Mechanism-
96
-------
Plots of the predicted ozone concentrations for each of the three
ozone sensitivity cases are presented in Figure 5-5 for the CBM-III. CIT, and
Demerjian mechanisms. These plots show that ozone formation by the CBM-III,
CIT, and Demerjian reaction mechanisms was decreased by a decrease in either
surface or aloft ozone concentrations. However, for the CBM-III and CIT mech-
anisms, the decrease in ozone formation was more related to a decrease in
aloft ozone concentrations than surface ozone concentrations. For example, as
aloft ozone concentration decreased from 0.08 ppm to 0.04 ppm, the CBM-III pre-
dicted ozone concentration decreased 0.02 ppm (from 0.24 ppm to 0.22 ppm).
The same aloft ozone concentration decrease resulted in a 0.03 ppm (0.17 ppm
to 0.14 ppm) reduction in peak ozone concentration for the CIT mechanism.
Peak ozone based on the Demerjian mechanism decreased 0.01 ppm.
Surface ozone concentrations affected predicted peak ozone concentra-
tions very little. For a surface ozone concentration reduction from 0.06 ppm
to 0.03 ppm, the CBM-III peak ozone concentration remained 0.22 ppm. The CIT
and Demerjian mechanism peak ozone concentrations are reduced by only 0.01 ppm
(0.14 ppm to 0.13 ppm. and 0.28 ppm to 0.27 ppm, respectively). (See Table
5-7.)
Two conclusions can be drawn from this case. First, while the CBM-
III and Demerjian mechanisms are sensitive to decreases in aloft ozone concen-
trations, the CIT reaction mechanism is the most sensitive chemical reaction
mechanism to decreases in aloft ozone concentrations. Second, decreases in
surface ozone concentrations do not significantly affect peak ozone concen-
trations for the CBM-III, the CIT, and the Demerjian chemical reaction mecha-
nisms.
5.3.5 Formaldehyde/Methyl Nitrite Emission Fraction Scenarios
Four different HCHO/MeNOj emission ratios were examined for the 100%
methanol conversion case. All other model inputs for these runs did not vary
from the base case inputs. The CH,OH, ECHO. MeNO, distributions examined
were:
97
-------
A. Carbon Bond III Chemistry
B. CIT Chemistry
C. Demerjian Chemistry
0
z
0
N
E
C
0
N
C
C
N
T
R
A
T
I
0
N
J.4
a. i
p
p
n
.„-"
s^rr^"
a ea tea taa 24a soa sea 420 480 540
TIHC CMINUTES)
suw 03/M.orr 03 —.88/.0S ••••.00/.B4—.es/.04
0.5
0.4
1. I
8 80 12B 180 240 300 360 420 480 640
TIME (MINUTES)
SURF OS/ALOFT as .ae/.os -..•.08/.B4 .03/.04
a.s
a.3
a.2
a.i
a 88 IM 188 248 380 308 420 480 540 880
TIME CniNUTES)
SURF 03/ALOTT 03 .08/08 -..-.ee/.O^ .O3/.04
Figure 5-5. Predicted Ozone Concentration for Initial
Ozone Concentration Scenarios for Each
Mechanism.
98
-------
• 89% CHjOH, 10% ECHO. 1% MeNO,
• 85% CB.OB, 10% ECHO, 5% MeNO,
• 79% CH.OH, 20% ECHO, 1% MeNO,
• 75% CEiOH. 20% ECHO, 5% MeNO,
These BCHO/MeNO, emission rates were chosen based on information
found in the literature (SAI, 1983). Systems Applications, Inc. indicated
that the likely range of methanol conversion to formaldehyde is 0 to 20% of
total methanol emissions. SAI also indicated that MeNO, emissions are 1 to 5%
of the total methanol emissions. Again, as in all cases, total NMOC was held
constant at 0.5 ppmC.
Plots of the predicted ozone concentrations for each of the four
ECHO/MeNO, emission fraction sensitivity cases are presented in Figure 5-6 for
the CBM-III, CIT, and Demerjian mechanisms. As the ECEO emissions were in-
creased from 10% to 20% (MeNO, constant at 1%), peak ozone concentrations in-
creased slightly for all chemical mechanisms (0.01 ppm for CBM-III, 0.02 ppm
for CIT, and 0.01 for Demerjian). Also, the rate of hourly ozone production
increased slightly for each mechanism. As MeNO, emissions (ECHO constant at
10%) were increased from 1% to 5% of methanol emissions only a slight increase
in peak or hourly ozone concentration was observed (approximately 0.01 ppm for
all reaction mechanisms). As the ECBO emissions increased from 10% to 20%,
and the MeNO, emissions increased from 1% to 5% of methanol emissions, peak
ozone concentrations increased 0.01 ppm for CBM-III and Demerjian. The CIT
mechanism showed a 0.03 ppm increase in peak ozone concentrations.
In general, the CBM-III and Demerjian reaction mechanisms are only
slightly sensitive to BCEO emissions from 10% to 20% of methanol emissions and
MeNO, emissions from 1% to 5% of methanol emissions. Only the CIT mechanism
shows a moderate sensitivity to increases in ECBO or MeNO, concentrations (a
0.03 ppm ozone increase).
99
-------
A. Carbon Bond III Chemistry
B. CIT Chemistry
9.3
03
02
ee 120 i60 zie 3m 360 120 iee sie eee
TIME CMINUTES)
HCHO/MEN02 —iex/ix »i8x/sx a2ex/ix *2ex/sx
60 120 tee 210 300 368 120 168 518 690
TIME (MINUTES)
HCHO/MENO2 1BX/1X ->I8X/SX H2BX/IX '28X/5X
C. Demerjian Chemistry
a 5
02
a. i
68 128 188 218 309 369 128 168 518 608
TIME CMINUTES)
HCHO/MEN02 I8X/IX »!0X/SX O28X/IX
Figure 5-6. Predicted Ozone Concentrations for
Formaldehyde/Methyl Nitrite Emission
Scenarios for Each Mechanism.
100
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5.4 References for Section S.O
Balentine, H.W.. Eltgroth, M.W.. Smith, C., and Langevin, S.A. (1984): User's
Manual for the General Chemical Kinetic Model with Trajectory Option (GCKM/
TRAJ), DCN 84-244-005-04, Radian Corporation of Austin, Texas, September,
1984.
California Institute of Technology for Jet Propulsion Laboratory (1983): Cal-
ifornia Methanol Assessment: Volume II: Technical Report: Chapter 6: Air-
Quality Impact of Methanol Use in Vehicles, Report for the California Energy
Commission.
EPA (1977): Uses, Limitations and Technical Basis of Procedures for Quantify-
ing Relationships Between Photochemical Ozidents and Precursors. EPA report
EPA-450/2-77-021A, U.S. EPA, Research Triangle Park, NC.
Eltgroth, M.W. (1981): A General Chemical Kinetic Simulation Computer Program
for Use with Multiple Reaction Mechanisms. Radian Corporation report.
Gipson, G.L., W.P. Freas, R.F. Kelly, and E.L. Meyer (1981): Guidelines for
Use of City-Specific EKMA in Preparing Ozone SIP's, EPA report EPA-450/4-80-
027, U.S. EPA, Research Triangle Park, NC.
Hogo, H., G.Z. Whitten, and S.D. Reynolds (1981): Application of the Empiri-
cal Kinetic Modeling Approach (EKMA) to the Tulsa Area. Report to U.S. EPA
by Systems Applications, Inc. under Contract No. 68-02-3376.
Jeffries, H.E., K.G. Sexton, and C.N. Salmi (1981): The Effects of Chemistry
and Meteorology on Ozone Control Calculations Using Simple Trajectory Models
and the EKMA Procedure. Report to U.S. EPA by the University of North Caro-
lina under Contract No. 68-02-3523, EPA report No. EPA-450/4-81-034.
101
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6.0 SUMMARY AND RECOMMENDATIONS
This section briefly summarizes Radian's recommendations. These
recommendations cover the four major areas addressed respectively in Sections
2.0, 3.0, 4.0, and 5.0.
1. Methanol Chemistry and Emissions Summary
2. Recommended Dispersion Models
3. Recommended Mechanisms
4. Recommended Additional Analyses
6.1 Methanol Chemistry and Emissions Summary
One of the potential advantages of using methanol as a fuel is that
it burns relatively clean and evaporative emissions are simple and compara-
tively unreactive. Compared to gasoline, burning methanol generally results
in fewer combustion products. The main combustion products are unbnrned meth-
anol, formaldehyde (ECHO, a partial combustion product), CO, NOZ, and smaller
amounts of post-combustion combination products, perhaps the only one of im-
portance being methyl nitrite.
The essential atmospheric chemistry of methanol is simple. Nearly
all researchers agree (Dodge, 1984) that there is only one significant reac-
tion and that is with the hydrozyl radical, OH:
CH,OH + OH —•- H0» + HCHO (6-1)
Even the rate of this reaction is well agreed upon, and the inclusion of this
reaction in any chemical reaction mechanism should be straightforward. Com-
pared to most hydrocarbon species, the methanol is rather slow to react. The
rate constant of methanol is about half that of butane. When methanol does
react, as in equation 6-1, it generates radicals and formaldehyde. Therefore,
it should not be considered (or lumped) as a general hydrocarbon but rather is
102
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best treated as a separate species. Since all mechanisms contain OH and H0a
radicals, they should be able to assimilate the newly introduced methanol,
formaldehyde, and methyl nitrite reactions.
As seen in equation 6-1, formaldehyde (ECHO) is one of the products
of the atmospheric methanol reaction. Formaldehyde is photochemically active
to ultraviolet light and generates radicals when it dissociates. It is an im-
portant source of radicals in most mechanisms. Essentially all atmospheric
photochemical mechanisms take account of formaldehyde.
Methyl nitrite is not formed in the combustion chamber but in the
exhaust of the engine. Emissions of methyl nitrite are proportional to the NO
concentration in the exhaust, and hence the exhaust dilution.
The only fate of methyl nitrite in the mechanisms reviewed is photo-
dissociation into radicals. That is, methyl nitrite can only be removed dur-
ing daylight hours while there is sunlight to photodissociate the molecules.
Consequently, substantial quantities of methyl nitrite would be expected to
build up at night and result in another possible large source of radicals at
first light. Formaldehyde also could have a similar fate. These methyl ni-
trite reaction products could therefore potentially have a significant influ-
ence on ozone formation on the second day and subsequent days of an ozone epi-
sode. However, very little is known about methyl nitrite chemistry in the
atmosphere. Therefore, smog chamber studies should be conducted to evaluate
speculations regarding its fate.
The number, and possibly the mixture, of hydrocarbons emissions may
change with substantial use of methanol fuel. Any chemistry mechanisms used
to simulate methanol photochemistry should be validated over the expected
ranges of NMOC concentrations and types of mixtures. Also, with lower NOZ
emissions expected, the model should be validated in the lower NOZ ranges.
Since many models are sensitive to NHOC/NOX ratios, caution should be exer-
cised when comparing results if the NMOC/NOZ ratios are different. However,
if both NMOC and NOZ are expected to be reduced or unchanged, the NMOC/NOx
ratio may not vary drastically.
103
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6.2 Reco""nended Dispersion Models
No one model is recommended for use in all cases. The models re-
viewed have been recommended in an order reflecting data coverage. Some
models have been eliminated because of difficulties in having the model change
its chemical reaction mechanism or other basic problems.
For simulations in the Los Angeles area the recommended models, in
order of preference, are:
1. SAI Urban Airshed
2. Level II EKMA using OZIPH-2
3. MARC-1
Each of these models should be capable of being used for Los Angeles. The
first two models have been used in previous (and current) modeling studies for
Los Angeles. The MARC-1 model is included as an alternative Enlerian model.
For simulations outside of Los Angeles the recommended models are:
• Urban Airshed Model
• STRATOS
• OZIPM-2
• GCKH/TRAJ
The selection of the model to use is determined by the amount of data coverage
available and the type of analysis to be performed. The user should select
the model for which the appropriate amount of data is available.
6.3 Recommended Mechanisms
The CBM-III and Demerjian chemical kinetic mechanisms are recom-
mended for use in future assessments of the impact of methanol fuel conversion
on ozone photochemistry.
104
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Both the CBM-III and Demerjian mechanisms predicted similar ozone
concentrations for the limited set of scenarios modeled. No significant dif-
ference in each mechanism's simnlation of ozone photochemistry was noted.
The limited set of sensitivity runs performed for this study is not
sufficient to allow one mechanism to be recommended over the other. However,
two additional factors need to be considered when selecting a mechanism.
First, the size of the mechanism should be considered. The Demer-
jian mechanism has fewer reactions than does the CBM-III mechanism (61 .versus
76). Because of its smaller size, the Demerjian mechanism runs faster when
used in a dispersion model. This slight difference in speed can potentially
be significant if a large number of analyses are to be performed.
Second, the acceptance and present use of the mechanism needs to be
considered. The CBM-III is presently widely used. It is currently contained
in a revised version of the SAI trajectory model and in the EKMA/OZIPM-2
models. EPA has prepared a set of recommendations for use of the CBM-III in
performing city specific EKMA analyses (Gipson, 1984). Therefore, the CBM-III
mechanism presently is more readily usable for performing photochemical simu-
lations than is the Demerjian mechanism.
Additional model simulations must be performed to validate the mech-
anisms for methanol photochemistry. The most important simulations will in-
volve modeling smog chamber studies using methanol combustion products as
input species. Very few of these types of smog chamber runs presently exist.
No recommendation as to the future use of the CIT mechanism can be
made until the cause of the unusual results of the 50% methanol conversion
scenario are analyzed and poential problems with the implementation of the
mechanism solved. The developers of the CIT mechanism were not able to supply
a set of test cases to use to check out the mechanism. A test case is essen-
tial if a user is to ensure proper functioning of the mechanism. Such a test
case must be obtained prior to further analysis of.the CIT mechanism.
105
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6.4 Recommended Additional Analyses
A significant limitation of the mechanism sensitivity analyses was
the lack of a test case with which to test proper implementation of the CIT
mechanism. Without such a test case, and because of the unusual results ob-
tained for the 50% methanol conversion scenario, there are serious doubts
about the proper implementation of the CIT mechanism used in this study.
Additional analyses should be performed using the CIT mechanism to determine
the cause of the poor performance of the mechanism on the 50% methanol con-
version scenarios.
The following additional analyses should be performed for the CIT
mechanism.
• Obtain and test a benchmark run to ensure proper func-
tioning of the mechanism.
• Obtain a test case from the CIT mechanism developers
closely duplicating the 50% methanol conversion
scenario.
• Based upon the results of the first two test cases,
execute additional test cases to pinpoint the source
of any potential problems.
Model sensitivity analyses should be expanded beyond those few cases
performed for this study. These sensitivity runs should include the following
cases.
• Both mechanisms should be run over a two-day period
to look at the dynamics of ozone formation on the
second day. As discussed in Section 2.3.4, methyl
nitrite (and formaldehyde) will tend to accumulate
overnight. Both will tend to photodissociate after
sun rise leading to a potentially large radical popu-
lation during the morning of the second day. The
initial second day conditions modeled should be iden-
tical to those of the first day and the subsequent
differences monitored as a function of time. Species
of interest to monitor hour by hour include: ozone,
PAN, NO. NO,, HO, HO,, and the total radical popula-
tion.
106
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• Several runs should be made assuming different levels
of hydrocarbon concentrations, both initial and those
due to emissions. These runs would be used to check
the results of Jeffries, c_t aj. (1981) that ozone re-
duction is approximately linearly proportional to hy-
drocarbon reduction.
• Additional runs should be made examining the sensi-
tivity of each mechanism to background and aloft hy-
drocarbon and ozone concentrations.
• Different mechanisms make different assumptions con-
cerning computation of photolytic rate constants.
The sensitivity of methanol chemistry to various
photolytic rate constants should be investigated. Of
particular concern are the rate constants used for
formaldehyde and higher aldehydes since these species
play a major role in photochemistry and their concen-
trations in the atmosphere may be significantly al-
tered by methanol conversion.
6.5 References for Section 6.0
Dodge, M. (1984): Letter from Marcia Dodge (RTF, EPA) to Phillip Lorang (EPA,
Ann Arbor) expressing EPA's concurrence with the additions to the photochemi-
cal reaction sets to account for methanol. Hay 9, 1984.
Gipson, G.L., (1984): Guideline for Using the Carbon Bond Mechanism in City-
Specific EKMA, EPA Report EPA-45074-84-005, U.S. EPA. Research Triangle Park,
NC, February 1984.
Jeffries, H.E., E.G. Sexton, and C.N. Salmi (1981): The Effects of Chemistry
and Meteorology on Ozone Control Calculations Using Simple Trajectory Models
and the EKMA Procedure. Report to U.S. EPA by the University of North Carolina
under Contract No. 68-02-3523. EPA report No. EPA-450/4-81-034.
107
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APPENDIX A
CHEMICAL MECHANISMS USED
IN SENSITIVITY ANALYSIS
108
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APPENDIX A
This appendix contains a brief discussion of GCKM's treatment of chemical
mechanisms, followed by a listing of each mechanism as it was input to the
model.
To define the mechanism to be simulated, the GCKM expects to receive a
group of cards, one card for each reaction, terminated by a blank card. Each
card is of the form
r R + r R + r R = p P + p P + p P + p P + P P k.kF
11 a a 11 11 aa 11 44 it i a
where r = coefficient for the reactant R
p = coefficient for the product P
klfk, = reaction rates
F - indicates the parameters upon which the reaction rates depend
The reaction itself has the following restrictions on its format:
• R and P are species names (alphanumeric, first character
alphabetic, only first four characters recognized).
• r and p can be written in I or F format (each coefficient
must be less than 20 characters in length).
• Embedded blanks are ignored.
• Hissing coefficients are assumed to be equal to one.
• Each species and coefficient pair must be separated by a
"+" sign (except reactants and products separated by "=").
• The reaction must be separated from the reaction rate
portion of the input by at least one space.
• The reaction set must be terminated by a blank card.
• There can be three or less reactants and five or less
products.
109
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Note that it is possible to have only one reactant and no product or only one
product and no reactant. This allows the existence of permanent sinks or
sources independent of other species. This is an important capability when
modeling smog chamber data where extra reactions of this form can be used to
simulate wall effects.
The reaction rate parameters klf ka, and F specify the reaction rate and
the parameters that the reaction rate can depend on. The following depen-
dencies are allowed:
• ultraviolet (UV) radiation
• total solar radiation (TSR)
• temperature
• water vapor concentration
If there are no dependencies (i.e., constant reaction rate) the only
parameter required is k,. If the reaction rate depends on UV, TSR, or tem-
perature, then the rate parameters are given as
• ki. k,D (for DV)
• kx, kaS (for TSR)
• ki, k»T (for temperature)
Unless water vapor is explicitly present in the reaction, water vapor depen-
dence can be indicated by putting a "W" at the end:
• ki, k,UW
• ki, k,SW
• kx, k,TW
The water dependence is included so that the explicit handling of water vapor
in the reactions can be ignored. If water is explicitly present in the reac-
tions, the "W" option should not be used.
110
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The following formulas are used by the model to give the actual reaction
rate (k):
k
k - k (UV or TSR) *
a.
k = kt exp (k^/T)
The use of the "W" option instructs the program to multiply the reaction rate
by the water vapor concentration.
The following format requirements are assumed by interpreting programs
for the reaction rate parameters:
• The parameter group is separated from the reaction itself
by at least one space.
• kj. and kt are separated by a comma (if ka required).
• No embedded spaces are allowed in the parameter groups.
• kt and k, can be expressed in an integer, floating point
or exponential format (each parameter <_ 20 characters).
• If the "W" option is used, it must be the last character.
If a card's reaction or rate cannot be interpreted, the card image is printed
and execution is stopped.
Ill
-------
LISTING OF CBM-III MECHANISM
AS INPUT TO GCKM
112
-------
1:KO?=N3«0 1..1.U
2:0=05 4.4E6
3:NO»03*N02 3452.-145CT
4:N02»03=N03 178.6,-245CT
CBM-III
6:OH«0!*H02 2867.-1300T
7:HO?»03*ON 4J0.6.-1525T
8:OH«N02=HN03 16TCO
9:OH«CO*H02 44d
10:2NO=2N02 1.5E-4
11 :NO«NOi = 2l»02 28000
12:N02«NO!=2MN03 4.63E-19,1.C6E*TW
13:NO«H02=N02«OH 120CQ
H:2H02=M202 1500C
15:»«PAS= 1.E5
16:CH«PAR = HF02 7858,-56()T
17:0«OLE»ME02*AC03«» 8CJ5.-325T
18:0«OLE=CABB«PAR 8035,-3?5T
19:CH«OLE-RA02 6043,5401
20:03«OLE'CARB«CRIG 4.67.-19COT
21:03«OLE=CA«B«KCRG«« 4.67,-19001
22:0*ETH*I«E02«H02*CO 8791,-end
2I:0«ETH=CARB«PAR 8791,-SHOT
24:OH«ETH=Rb02 3330,J82T
25:0}«ETM=C»RB«tRIG 12.91.-2560T
2*:NO«*COX=NO?««E02 1.G4E4
27:NO«RB02-N02«2CARB«H02 12TCC
Z9:NO««£03=N02«CARB«HEOZ»X 3730
30:NO*HE02*N02*CARB«H02 74C"
31:NO«1£02'NRAT 900
32:03«RP02=2CARB«H02 5
33:03«RA02=2C»RB«H02 20
34:OH4C«RP»CR02*X 100
35:OH«CARB*H02«CO 9QCO
3*:CH«CARB=AC03«x 8200
37:CARH=CO«H2 .00533,1.J77U
38:CA<*B = 1.333333H02»CO«.6666667F>E02«.6<66667x .00359 ,1 .661U
39:NOZ«AC03 = PAN 7CiJO
40:PAK=AC03*N02 1.04E18.-135GCT
41:H07»AC03= 1.5E4
90HO
»2«CARB 1.2C4
44:N02«CRIG'NOi«CARB 830T
45:CARP«CR10= 2000
46:NO«MCRG=N02»CARB«PAR 1.2E4
47;N02«''CRG = NO
46 :CA&B«"URfi= 2^00
670
51:CRIG=2HO?«CO 9C
S2:»"CSG= 150
102 425
55:»CRG-CARP«2H02»CO 85
56:OM««°o=a»RO 4.493E4,-6nOT
57:OH«ARO=H02«OPEN 5.55F4,-4:CT
58:NO«4Ai)0 = N024PHEN4M02 *"OC
59:OPFN*NO*N02«OCRB«X«APRC 6"PO
61:APPC=2CARB»2CO IE*
62:FHEN«N03=PMO«HN03 500C
6?:PMO«N02=NPHN 4000
64;PHO«H02=PHEN 5E4
65:OP?N«03=OCRB«X«APRC 4C
67:.rC«H=H02«1.0AC03«1.0CO .0198, .986U
68:PMFN«OM=PHO 1E4
6':CS02
-------
LISTING OF CIT MECHANISM
AS INPUT TO GCKM
114
-------
CIT 1:N02=NO«0 1.f1.U
2:0=03 7.896E5.5.1E2T
3:NO«03=N02 3.13E3,-145CT
4:0-»N02=NO 1.34E4
5:0+NO=N02 5.6COE2,5.840F2T
6:0+W02=N03 3.590E3
7:C3+N02=N03 1 .740E2.-2.45CE3T
9:NO«N03=2N02 2.7QOE4
9:N02+N03=N205 7382.
10:N205=N02*N03 3 .44E16.-1.06E*T
11:N205=2HN03 1.50E-5U
12:NO»OH=HN02 1.70E*
13:HMOa=OH+NO 1.84E-1.1.U
1*:M02«H02=HN02 5.81E-2.1.006E3T
15:HN02«OH=N02 9.77E3
16:N02«H02=HN04 5.81E1.1.G06E3T
17:HN04=H02«N02 1.80E15.-995CT
18:»*0«M02=N02«OH«CON1 1.200E4
19:R02+NO=N02«RO»CON2 1.200E4
2'J:RC03 + NO=H02»»02*CON3 3.79E3
21:N02+OM=HN03 1.52E4
22:OH«CO=H02 4.40E2
23:03=0 7.13E-2.1.U
24:03«OH=H02 2.22E3t-1.0CE3T
25:P3«H02=OH 16.27,-SBOT
26:2H02=H202 ?.2BE3
27:M202=20H 2.S6E-3.1.U
2?<:HCHO = 2H02»CO + NEy1 3.75C-7.1.U
29:HCHO=CO 8.10E-3.1.U
30:OH+HCMO=M02»CO 1.389E4
31:BCHO=R02«H02«CO 3.20E-7.1.U
32:fH+RCHO=RC03 2.570E4
33:ETH*OH=R02 1.16CE4
34:ETH+0=R02«M02 1.220E3
35:OLE+OH=R02 5.?50E4
36:OLE+0=R02*RC03 9.93E3
37:CLE»03 = 0.270H«RCHO«0.6*H02 .C<56
38:OLE + 03=0.37>»0»HCHO + 0.61R02 .056
39:*LK*OH=R02 6.06E3
40:*LH+0=R02+OM 99.8
41:OH«ARO-R02*RCHO 2.21E4
42:RO=3.5PCHO+0.5HCHO+M02 2.0CE5
43:PO*NO=RONO 1.47E4
44:»OMO>RO«NO 0.22.1.U
45:PO*N02=RN03 7.35E3
46 :RO«N02 = RCHO»HN02 639.
47:R02«N02-RN04 5.50E3
48:PN04=N02+R02 1.80E1S,-°95OT
49:R02«N02 = «»CHO + HM03 5.5C
50:R02»R02=2RO 68.5,223.7
51:RC03+N02=PAM 2.07E3
5?:PAN=N02*RC03 4.77E16.-1.252E4T
53:NR=NR 1.00E6
54:CHCO+OH=H02+HCHO 150U.
55:rONO-CH30«NO 0.3.1.U
56:CH30+NO=MONO 4.40E4
57:CH50=HCHO«HO? 1.88
115
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LISTING OF DEMERJIAN MECHANISM
AS INPUT TO GCKM
116
-------
1:N02=NO+0 1..1.U
DEMERJIAN 2:0=03 4.73E6
3:03+NO=NO?«02 3250.,-1410.T
4:03«N02=N03«02 177..-2450T
5:03=010*02 0.013,2.441)
6:01D=0 4.23E10
7:01D=2HO 3.4E5W
8:NOVNO = 2H02 2.96E4
9:N03*N02=N205 1780.
10:N205=NOJ+N02 3.11
11:N205=2HN03 1.92E-SW
12:HONO=MO«NO .205,1.043U
13:MO«CO=H02+C02 414.
14:H02*N02=MONO«02 4.40
15:H02*NO=HO«N02 5470.,240.T
16:H02+N02=H04N 1.52E3
17:H04N=H02*N02 3.26
18:HO+HONO=N02+M20 9750.
19:HO*N02=HN03 1.49E4
21:HO«NO=HONO 7.35E3
21:HO*HN03=N03 22.2,6501
22:H02 + 03=HO«202 20.7,-580.T
23:HO*03=H02«02 2360.,-940.T
24:H02»H02=H202+02 66.5,1200.T
25:H202=2MO .00143,1.598U
26:ETH»03=.4R02+HCHO*.1H02 2.5E-3
27:ETH«HO=FR02«HCHO 1200C.
28:OLE+0=»02»*LO*H02 51CO.
29:OLE+03=.75R02«.75ALO+.4H02 9.5E-2
30:OLE*HO=R02».75*LO+.25HCHO 550CO.
31:P*R+HO=R02 5000.
32:HCHO=CO .006.1.26U
33:HCHO=2H02«CO .0051.1.54U
34:HCHO*MO=H02«H20«CO 16QTO.
35:R02+NO=RO+N02 110CO.
36:RO=.75»LO+H02».25HCHO 1.55E8,-20ro.T
37:ALD=R02*H02+CO .0014.1.76U
38:*LO*HO = R102-»H20 24000.
39:FR02+NO=FRO«N02 11000.
40:FRO=HCHO«H02 2.27E5
41:R102+N02=PAN 8870.
42:PAN = R102«N02 3.6E18 ,-13330.T
43:RO«N02=RN03 100.
44:R02«03=RO+202 2.00
45:R102+NO=R02«N02 20700.
46:TOL+HO=RT02«*LD«OCB1 8700.
47:ARO+HO=RX02»»LO+DCB1 3*000.
48:RT02»NO=RTO*N02 11000.
49:RTO=OCE1«H02«CO 1.86E5
50:RX02*MO=RXO+N02 11000.
51:RXO=OCP2*H02+CO 1.86E5
52:DCP1=2CO+2H02 .C04.1.U
53:OCP1=.20HCHO»1.80CO .3Q4.1.U
54:OCB1«HO=H02+2CO 17000.
55:DCB2=R102»H02»2CO .OC88..918U
56:DCB2=.5JALD«.5CHCHO«.50FR02 .0088,.918U
57:DCB2+HO=R102«CO 25600.
58:RT02+03=RTO*202 2.00
59:RX02»03=RXO»202 2.CO
6C":P102*03=RO?«?02 2.00
61:OLE»03=COOH«ALD 1.50E-3
62:CHAO+HO=H02+HCHO 1500.
63:«ONO=CH30»NO 0.3.1.U
64:CHJO+NO="ONO 4.40E4
65:CH30=HCHO*H02 1.88
117
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APPENDIX B
DESCRIPTION OF SPECIES AND EMISSION FRACTION CALCULATIONS
AND SAMPLE CALCULATIONS
118
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B.I Conversion of Carbon Fractions from CBM-III to CIT and Demerjian
Fractions and Species Concentrations
The CBM-III carbon fractions for the initial parcel NNOC concentra-
tion and the aloft NMOC concentration were taken from the SAI study (SAI,
1983). These fractions are shown in Table B-l. The CBM-III fractions were
converted to carbon fractions for the CIT and Demerjian mechanisms using the
procedure shown in Table B-l. The procedure is not exact since the original
species profile used by SAI to calculate CBM-III fractions was not available.
The calculation procedure makes use of "typical" urban carbon frac-
tions for the CIT and CBM-III mechanisms (McRae e_t al.. 1983). These frac-
tions, shown in Table B-l, were derived from the same initial species profile.
The underlying assumption in our procedure is that the ratio of corresponding
species fractions for two mechanisms (OLE/OLE, ALK/PAR, etc.) will be approxi-
mately the same for all urban VOC samples.
Since the procedure is an approximation, the total carbon fraction
obtained by summing the fractions for each species does not equal 1.0. To
correct this, each computed species fraction was normalized by dividing by the
summed total carbon fraction.
This procedure does not necessarily yield the same CIT and Demerjian
carbon fractions that would have been calculated had the original species
profiles used by SAI been available. However, a single urban species profile
could be developed that would yield the given CBM-III and computed CIT carbon
fractions in Table B-l. Therefore, the procedure is reasonable for the pur-
poses of this study.
Species concentrations were calculated from the carbon fractions in
Table B-l and the assumed initial NMOC concentration (0.5 ppmC). This
procedure is shown in Table B-2 for the CIT and Demerjian mechanisms. The
only difference between the lumping procedures for these mechanisms is that
Demerjian splits the ARO fraction into 60% ARO and, 40% TOL (toluene). The
119
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TABLE B-l. CALCULATION OF CIT CARBON FRACTIONS FROM CBM-III CARBON FRACTIONS
NJ
O
Typical Typical Preliminary Normalized
CBM-III Carbon Urban CIT Urban CBM-III CIT Carbon CIT Carbon
Fraction for Carbon Carbon Fraction for Normalizing Fraction for
Species Los Angeles* Fraction Fraction** Los Angeles Factor Los Angeles
OLE
ALE (PAR)
ARO
ETH
ECHO (CARB)
RCHO (CARB)
TOTAL
0.051
0.725
0.126
0.062
0.036
0.036
1.00
x 0.051 v
x 0.30 v
X 0.11 v
x 0.021 T
x 0.0093 T
x 0.014 T
0.51°
0.011
0.32
0.072
0.021
0.025
0.025
0.45°
0.24 T 1.21
0.68 T 1.21
0.19 T 1.21
0.062 T 1.21
0.013 T 1.21
0.020 T 1.21
1.21
0.20
0.56
0.16
0.051
0.011
0.017
1.00
aSAI (1983). Also presented in Table B-3.
bComputed from data in Table 42 of McRae et_ a_l, 1983.
CA non-reactive fraction of ~0.50 is not included; therefore, the total is less than 1.0.
-------
TABLE B-2. CALCULATION OF CIT AND DEMERJIAN NMOC
SPECIES CONCENTRATIONS
Species
OLE
ALE
ARO
ETH
ECHO
RCHO
Carbon
Fraction8
0.20
0.56
0.16
0.051
0.011
0.017
Total NMOC
Cone. (ppmC)
x 0.50 T
x 0.50 v
x 0.50 T
x 0.50 T
x 0.50 T
x 0.50 T
Carbon
Nnmberb
5.24
4.56
7.56
2.00
1.00
2.24
Species
Cone. (ppmV)
0.019
0.062
0.0104°
0.0133
0.0055
0.0038
aComputed in last column of Table B-l.
bCIT Carbon Numbers from McRae et. §i (1983).
°For Demerjian mechanism. ARO species cone, split 60% ARO. 40% TOL (toluene).
121
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TABLE B-3. CBM-III CARBON FRACTIONS USED FOR LOS ANGELES AREA8
Species
OLE
PAR
ARO
ETH
CARS
Gasoline Powered
Vehicles
0.0753
0.6750
0.1475
0.0291
0.0731
City Center
Stationary
Sources
0.0300
0.7678
0.1083
0.0409
0.0530
Combined**
0.051
0.725
0.126
0.062
0.036
Aloft
0.004
0.694
0.051
0.032
0.219
aSAI (1983).
^Combined Carbon Fractions are weighted average of gasoline powered vehicle
and stationary source emission carbon fractions.
122
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same procedure was performed for the CBM-III mechanism using the corresponding
CBM-III carbon fractions given by SAI.
The procedure presented in Tables B-l and B-2 is for the base case
inputs to the CIT mechanism shown in Table 5-5 of the main body of the report.
For each test case, the NMOC emissions were assumed to occur from three
sources: 1) stationary sources; 2) gasoline powered vehicles; and 3) methanol
powered vehicles. The same calculation procedure was followed for the first
two components, using the respective CBM-III carbon fractions taken from the
SAI study (SAI, 1983) and shown in Table B-3. The NMOC due to methanol com-
bustion was split among ECHO, CH,OH, and MeNOj as described in the report.
Using the procedures described above, the initial concentrations for
each species were determined separately for the stationary sources, gasoline
powered vehicles, and methanol powered vehicles, for a given sensitivity case.
The initial concentrations for each species were then added together to pro-
duce the total initial concentration for each species for the given sensi-
tivity test case.
B.2 Emissions Fraction Calculation
1. Calculate moles of species for each hour and divide by area of air parcel
for each mechanism to calculate initial emission density
Moles (NMOC)/dav
_
1 Parcel Area z 24 hours/day
NMOC = 196000 moles/day
Parcel Area - 25 fan1
n 328 moles NMOC
Qj -
123
-------
2. Now calculate Qo where Qo is the initial emissions density needed to
produce the initial air parcel concentration in one hour.
QO = a C0H0
and
H0 = initial mixing height = (70.1m)
C0 = initial air parcel concentration = 0.5 ppmV NMOC
_ 1 mole NMOC 1 mole air 10" L
0 10« mole air - ppmV x 24.4 L x km»
41.000 moles NMOC
km* - ppmV
_ 41,000 moles NMOC .- . . ln „., . .
QO = — km»-hr-PPMV - * ( Ppm) X (0'0701 km)
_ 1.437 moles NMOC
km»-hr
3. Now calculate hourly emission fraction (Ej)
= 0.23
1437 °""es
1437 km»-hr
4. The above computed emissions fraction of 0.23 was assumed to be constant
for each mechanism for each scenario modeled. Implicit in this assumption
are the following assumptions.
a) The initial parcel concentrations are due to previous emissions.
b) The carbon (or species) fractions are the same for the emissions
and the initial concentrations .for each species.
c) By adjusting the parcel initial concentrations for each species
according to the emissions scenario, the subsequent emissions are
a constant fraction of these initial concentrations (for each
species).
d) The total mass of NMOC emissions does not change with scenario
modeled.
124
-------
APPENDIX C
PLOTS OF VARIOUS SPECIES FOR EACH MODEL SENSITIVITY RUN
125
-------
TABLE C-l. IDENTIFICATION PLOTS FOR MODEL SENSITIVITY RUNS
Run
Number Plot Title
1 CB III Chemistry Base Case
2 CIT Chemistry Base Case
3 Demerjian Chemistry Base Case
4 CB III Chemistry 50% Case
5 CIT Chemistry 50% Case
6 Demerjian Chemistry 50% Case
7 CB III Chemistry 100% Case
8 CIT Chemistry 100% Case
9 Demerjian Chemistry 100% Case
10 CB III Chemistry Surface 03=.06 Aloft 03=.04 All Methanol Vehicles
10A CB III Chemistry Surface 03=.03 Aloft 03=.04 All Methanol Vehicles
11 CIT Chemistry Surface 03=.06 Aloft 03=.04
11A CIT Chemistry Surface 03=.03 Aloft 03=.04
12 Demerjian Chemistry Surface 03=.06 Aloft 03=.04
12A Ozone Aloft 03=0.04 ppm Surface 03=0.03 Demerjian Chemistry
13 CB III Chemistry HCHO/MENO* = 10%/5% With All Methanol Vehicles
14 CIT Chemistry HCHO/MENOa = 10%/5% With All Methanol Vehicles
15 Demerjian Chemistry HCHO/MENOa = 10%/5% With All Methanol Vehicles
16 CB III Chemistry HCHO/MENO» = 20%/1% With All Methanol Vehicles
17 CIT Chemistry HCHO/MENOi = 20%/1% With All Methanol Vehicles
18 Demerjian Chemistry HCHO/MENOa = 20%/1% With All Methanol Vehicles
19 CB III Chemistry HCHO/MENOa = 20%/5% With All Methanol Vehicles
20 CIT Chemistry HCHO/MENOa = 20%/5% With All Methanol Vehicles
21 Demerjian Chemistry HCHO/MENOa = 20%/5% With All Methanol Vehicles
126
-------
CBIII CHEMISTRY BASE CASE
0.5
ee 120 tee 248 see see -420 4ee S4e eee
TIME CMINUTES3
-NOXI0 *—+ N02X10 a a 03 O—OPANXI0 * » NMOC
CIT CHEMISTRY BASE CASE
0.5
60 120 180 210 380 36e 120 488 540 600
TIME CMINUTES3
•NOxte 4 — + N02xl0 D DOS 0 OPANx)0
127
-------
DEMERJIAN CHEMISTRY BASE CASE
8.5
68 128 188 248 388 368 428 468 548 688
TIME CMINUTES3
-NOX18 + + N02XI8 a a 83 0 OPANX10 * » NMOC
CBIII CHEMISTRY S0X CASE
8.5
C
0
N 8.4
C
E
N
T
R 8.3
A
T
I
0
N 8.2
P
P 8. I
M
1 28 I 88
-NOX10
248 388 360
TIME CMINUTES3
N02X10 o—a 03 O OPANXI0
428 488 548 680
.NMOC
128
-------
CIT CHEMISTRY S05< CASE
0.5
60 120 160 240 300 360 420 460 540 600
TIME CMINUTES5
-NOX10 f tN02X10 D n03 o—OPANX10 a a NMOC
DEMERJIAN CHEMISTRY 50X CASE
0.5
120 180
-NOX10
240 300 360
TIME CMINUTES3
N02X10 a a 03 c OPANX10
420 480 540 600
NMOC
129
-------
CBIII CHEMISTRY IOOK CASE
0.5
60 120
-NOXI0 +-
180 2-40 300 360 420 480
TIME CMIMUTES3
•+N02XI0 o 003 « »PAtlX10 ——' MMOC
5-40 600
CIT CHEMISTRY 100X CASE
0.5
-NOX10
120 160 240 300 36B
TIME CMINUTES5
i- f N02XI 0 o—o 03 o o PANX1 0
420 460 540 600
NMOC
130
-------
DEMERJIAN CHEMISTRY 100X. CASE
8.5
8 68 120 160 240 300 360 420 460 540 600
TIME CMINUTES5
NOXI0 + +N02XI0 a o03 « >PANX10 *—» NMOC
CBIII CHEM 03 CASE; SURF 03=.06;ALOFT 03=.04; ALL METHANOL VEHICLES
0.5
0 60 120 180 240 300 360 420 480 540 600
TIME CMINUTES3
NOXI0 * +N02XI0 ° a 03 « &PANXI0 • ' MMOC
131
-------
CBIII CHEM 03 CASE;SURF 03=.03;ALOFT 03=.04;ALL METHANOL VEHICLES
8.5
0 68 120 180 240 300 363 420 480 540 600
TIME CMINUTES5
NOXI0 + *N02X10 o o03 0—OPANX10 * • NMOC
CIT CHEM 03 CASE;SURF 03=.06;ALOFT 03=.04;ALLMETHANOL VEHICLES
0.5
60 120 160 240 300 360 420 480 540 600
TIME CMINUTES5
-NOxlB 4 — * N02xl0 O 003 O OPANxl0 " A NMOC
132
-------
CIT CHEM 03 CASE;SURF 03=.03;ALOFT 03=.04 ALL METHANOL VEHICLES
0.5
60 120 180 240 300 360 ' 420 480 540 600
TIME CMINUTES5
•N0xl0 4 — + N02xl0 O 003 0 OPANxl0a aNMOC
DEMO CHEM 03 CASE;SURF 03=.06;ALOFT 03=.04;ALL METHANOL VEHICLES
0.5
0 60 120 180 240 300 360 420 480 540 600
TIME CMIMUTES3
MOX10 + +N02XI0 o D03 c »PAMXI0 • • MMOC
133
-------
DEMJ CHEM 03 CASE;SURF 03-.03;ALOFT 03-.04;ALL METHANOL VEHICLES
0.5
0.4
0.3 -
0.2
0. I
60 120 160 240 300 360 420 460 540 600
TIME CMINUTES3
-NOX10 + fN02XI0 a a 03 O——OPANX10 * • NMOC
CBIII CHEM HCHO/MEN02= 10K/SX. ALL METHANOL VEHICLES
0.5
60 120 180 240 300 360 420 480 540 600
TIME CMINUTES3
-NOXI0 + +N02X10 D oQ3 « OPANX10 ^
134
-------
CIT CHEM HCHO/MEN02=t05i/S5« WITH ALL METHANOL VEHICLES
B.5
0 613 120 180 248 300 360 420 460 540 600
TIME CMINUTES3
NOX10 * 1-N02X10 o o03 O OPANXI0 » » NMOC
DEMERJIAN CHEM HCHO/MEN02=I0X/554 WITH ALL METHANOL VEHICLES
0.5
60 120 180 240 30Q 360 420 480 540 600
TIME CMINUTES3
-NOX10 + fN02X10 o o 03 »-—»PANX!0 • » NMOC
135
-------
CBIII CHEM HCHO/MEN02=20X/1X WITH ALL METHANOL VEHICLES
0.5
60 120 180 240 300 360 420 480 540 600
TIME CMINUTES3
-NOX10 + +N02XI0 o o 03 o OPANXIO « * NMOC
CIT CHEM HCHO/MEN02-20X/1X WITH ALL METHANOL VEHICLES
0.5
60
120
-NOX10
180 240 300 360 420 480 540 600
TIME CMINUTES3
-fN02X 10 o o 03 o o PANXI 0 «——» NMOC
136
-------
DEMERJIAN CHEM HCHO/MEN02=20X/1 X WITH ALL METHANOL VEHICLES
0.5
60 120 180 240 300 360 420 480 540 600
TIME CMINUTES5
-NOXI0 * +N02X10 o oQ3 o »PANXI0 ' ' NMOC
CBIII CHEM HCHO/MEN02=20X/SX WITH ALL METHANOL VEHICLES
0.5
60 120
-NOXI 0
180 240 300 360 420 480 540 600
TIME CMINUTES3
+N02XI0 o—o 03 o——OPANX10 «—* NMOC
137
-------
CIT CHEM HCHO/MEN02-20X/S/'. WITH ALL METHANOL VEHICLES
8.S
60 120 188 248 388 368 428 -488 548 688
TIME CMINUTES3
-NOXI8 » »N02XI8 o 003 O—OPANXI8 » • NMOC
DEMERJIAN CHEM HCHO/MEN02=20X/5X WITH ALL METHANOL VEHICLES
8.5
68 128 188 240 300 360 420 480 540 600
TIME CMINUTES3
-NOXI0 + +N02X10 a °03 « »PANX10 • 'NMOC
138
-------
APPENDIX D
CONCENTRATIONS OF VARIOUS SPECIES FOR EACH MODEL SENSITIVITY RUN
139
-------
CBIII CHEMISTRY BASE CASF
C 1
0. .
30. .
60. .
90. .
120. .
150. .
180. .
?10. .
240. .
270. .
3DO. .
330. .
360. .
390. .
420. .
450. .
480. .
510. .
540. .
570. .
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t cherr
0. .
7 0 . .
60. .
90. .
120. .
150. .
1 J n
210. .
24% .
270. .
30C. .
J3H. .
360. .
390. .
420. .
45C. .
480. .
513. .
540. .
570. .
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450-001
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980-002
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333-002
227-002
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105-002
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588-003
503-003
460-003
436-003
418-003
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A 52-0 03
364-003
309-003
i s t ry ba
NC
45 "-CJ1
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£65-~C«2
512-032
612-002
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259-032
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169-902
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se case
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.165+: 00
.167+00:
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140
-------
DEv£f?JlAK CHEMISTRY BASE CASE
0.
3C.
60.
90.
120.
150.
180.
210.
240.
270.
300.
330.
360.
390.
423.
450.
430.
510.
540.
570.
600.
CBIII
C.
30.
w* W •
6C.
90.
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160.
21C.
240.
27C.
300.
330.
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450.
480.
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NO
,45r-001
.P44-002
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.663-002
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CHEMISTRY
NO
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.995-002
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.836-003
.663-003
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.462-003
.466-003
.456-CG3
.410-003
.348-CQ3
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.150-001 .
.374-CH1 .
.365-Gr1 .
.373-011 .
.364-OC1 .
.2S7-J01 .
.217-001 .
.194-0"! .
;165-OT1 .
.13C-OC1 .
.989-OP2 .
.783-GC2 .
.654-302 .
.598-022 .
.564-CT2 .
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.511-032 .
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.477-002 .
.432-Jr2 .
5 OX CASE
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.150-001 .
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.361-001 .
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.371-OC1 .
.306-001 .
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.179-001 .
.149-0 01 .
.124-001 .
.1C4-OC1 .
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.923-002 .
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.961-OT2 .
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.1n9-GT1 •
03
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511-C01
651-OC1
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931-CT1
1 C5*3Cn
117*000
139*"?Cr
1 6 3 * 0 H 0
186*00n
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227+OCH
239+PQC
258+COC
273*TOO
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301*HCT
30b*nor;
31 3 *5 CC
315+ngo
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6 o r - ? c 1
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cit chemistry 50
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142
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CPIII CHEMISTRY 1305: CASE
3.
30.
60.
90.
120.
150.
180.
210.
240.
270.
300.
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APPENDIX E
DISCUSSION OF THE CIT 50 PERCENT METHANOL
SCENARIO SUBSTITUTION
152
-------
APPENDIX E
The modeling results of the CIT 50% methanol conversion scenario are
highly unusual. The objective of this section is to discuss potential causes
for the suspect results of this modeling scenario with the CIT mechanism.
There are four major potential causes for the results of the CIT 50%
conversion scenario. These causes are
1) improper input data,
2) improper functioning of the model,
3) improper functioning of the mechanism due to improper
installation in the model, and
4) inability of the mechanism to accurately simulate the
specific case modeled.
Improper Input Data
It is highly unlikely that improper input data are the cause for the
poor results on the 50% conversion scenario. Tables E-l and E-2 present the
input data, as transcribed from the run printouts, for the CIT base case, 50%,
and 100% conversion scenarios. The data in Table E-l are the initial parcel
concentrations, in ppmV, for each of the three runs. The data in Table E-2
are the remaining data input into the model for each run. All the data in
Table E-2 were identical for each run including NO, photolysis rates, meteoro-
logical parameters, and emission fractions. Consequently, the only input dif-
ferences between the runs are those contained in Table E-l.
The last two columns in Table E-l present the difference in concen-
tration for each species between the 50% case and base case and between the
153
-------
TABLE E-l. MODELED PARCEL INITIAL CONCENTRATIONS (ppmV)
Species
NO,
NO
o,
ALE
ETH
OLE
ARO
ECHO
RCHO
CH,OH
MeNO,
CO
HaO,
Base Case
0.0150
0.0450
0.0600
0.0620
0.0133
0.0190
0.0104
0.0055
0.0038
0.0
0.0
0.0
l.OE-6
Scenario
50%
Methanol
Conversion
0.0150
0.0450
0.0600
0.0508
0.0098
0.0121
0.0076
0.0162
0.0032
0.1025
0.0011
0.0011
l.OE-6
100%
Methanol
Conversion
0.0150
0.045
0.0600
0.0384
0.0065
0.0062
0.0050
0.0269
0.0025
0.2050
0.0023
0.0023
l.OE-6
Concentration
Differences
50% Less
Base Case
0.0
0.0
0.0
-0.0112
-0.0035
-0.0069
-0.0028
0.0107
-0.0006
0.1025
0.0011
0.0011
0.0
100% Less
50% Case
0.0
0.0
0.0
-0.0124
-0.0033
-0.0059
-0.0026
0.0107
-0.0007
0.1025
0.0012
0.0012
0.0
154
-------
TABLE E-2. HOURLY INPUT PARAMETERS FOR THE CIT MECHANISM
SENSITIVITY RUNSa
Time
Since 8:00
(min)
0
60
120
180
240
300
360
420
480
540
600
NO, Photoylsis
Rate
0.2678
0.3792
0.4405
0.4797
0.5009
0.5077
0.4992
0.4761
0.4323
0.3610
0.2496
Temperature
23
26
29
32
33
36
37
38
36
29
29
Mixing
Height
(m)
70
116
156
300
356
447
528
539
523
520
520
Pjn-i $ si on Fractions
NO
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
NOi
0.72
0.72
0.72
0.72
0.72
0.72
0.72
0.72
0.72
0.72
0.72
NMOCb
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
&The same input data, except initial parcel concentrations, were used for all
the CIT mechanism sensitivity runs.
t>An hourly emission fraction of 0.23 was used for each species emitted. These
species were: ALK, ETH, OLE, ARO, ECHO, HCH, CH,OH, MeNO*.
155
-------
50% and 100% cases. There should be equal, linear changes in initial concen-
tration going from the base case to the 50% case and then to the 100% case.
As can be seen in Table E-l. the differences are equal except for rounding
errors. There is no discontinuity in the progression of initial conditions
that could explain the unusual results of the 50% conversion scenario.
Improper Functioning of the GCKM Model
It is possible that the GCKM model used to perform the modeling has
an error in the coding that results in.the unusual results of the 50% conver-
sion modeling scenario. The GCKM model was used for modeling all the other
scenarios and for other modeling projects performed at Radian without similar
unusual results occurring. Thus, while possible, the probability of a model
"bug" is probably lower than that of an improper incorporation of the CIT
mechanism in the model. Without further analysis, however, a model "bug"
cannot be ruled out.
Improper Installation of the Mechanism
The most likely reason for the unusual results is improper instal-
lation of the CIT mechanism in the GCKM model. As discussed in 4.0, Radian
was unable to obtain a test case from the developers of the CIT mechanism.
Without such a test case, it was not possible to ensure proper functioning of
the mechanism.
Inability of CIT Mechanism to Simulate the Scenario
It is possible, but unlikely, that the CIT mechanism has a "bug" in
it that manifests itself during the 50% conversion scenario. Again, without
further analysis and a test case from the developers, the proper functioning
of the CIT mechanism cannot be ensured.
156
------- |