United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park NC 27711
EPA-450'4-81-031 c
September 1981
Air
The Sensitivity of Complex
Photochemical Model Estimates
To Detail In Input Information
Appendix B - Specification
And Assessment Of Airshed
Model Input Requirements
-------
This report was furnished to the U.S. Environmental Protection
Agency by Systems Applications, Incorporated in fulfillment of
Contract 68-02-2870. The contents of this report are reproduced
as received from Systems Applications, Incorporated. The opinions,
findings and conclusions expressed are those of the author and not
necessarily those of the Environmental Protection Agency. Mention
of company or product names is not to be considered as an endorsement
by the Environmental Protection Agency.
-------
EPA-450/4-81-031C
The Sensitivity of Complex
Photochemical Model Estimates To Detail
In Input Information
Appendix B - Specification And Assessment
Of Airshed Model Input Requirements
EPA Project Officer: Edwin L Meyer. Jr.
Prepared for
U.S. Environmental Protection Agency
Office of Air, Noise and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park. North Carolina 27711
September 1981
-------
CONTENTS
LIST OF TABLES 1v
1. A REVIEW OF AIRSHED MODEL INPUTS B-l
2. ESTABLISHMENT OF CHARACTERISTIC LEVELS OF INPUT DATA
DETAIL TO BE EXPECTED FOR A RANGE OF MODEL APPLICATIONS B-6
3. ESTIMATION OF DATA ACQUISITION COSTS ASSOCIATED WITH
RAISING THE LEVEL OF DETAIL OF INPUT DATA B-12
4. ANALYSIS OF AIR QUALITY MODEL SENSITIVITY TO
VARIATIONS IN INPUTS - B-19
a. Studies Focusing on Air Quality Inputs B-25
b. Studies Focusing on Meteorological Inputs B-26
c. Studies Focusing on Chemistry Inputs B-29
d. Studies Focusing on Emissions Inputs B-30
e. Studies Focusing on Grid Specification B-31
5. ISSUES RELATED TO THE PREPARATION OF EMISSION INVENTORIES.... B-32
a. Mobile Source Emission Inventories B-37
b. Stationary Source Emission Inventories B-38
6. CONCLUSIONS B-39
REFERENCES R-l
111
-------
TABLES
B-l Input Parameters for the SAI Urban Airshed Model B-2
B-2 Summary of Data-Dependent Input Requirements of the
SAI Urban Airshed Model (EPA-5 Version) B-4
B-3 Summary of Routine Air Quality Monitoring Activities
in 14 Major Cities in the United States B-9
B-4 Levels of Detail in Data Used as Input to Grid-Based Air
Quality Simulation Models 8-13
B-5 Cost Categories for Air Quality Monitoring Systems B-16
B-6 Estimated Annual Cost to Augment An Existing Aerometric
Monitoring Network with Various Instruments B-17
B-7 Summary of Sensitivity Study Results Obtained with
Grid-Based Photochemical Airshed Models B-20
B-8 Control Measures and Emission Inventory Data Needs B-33
iV
-------
Appendix B
SPECIFICATION AND ASSESSMENT OF AIRSHED MODEL INPUT REQUIREMENTS
Systems Applications, Inc. (SAI) has been engaged in a research study
sponsored by the Environmental Protection Agency (EPA) to evaluate the use
of non-data-intensive methods for assessing the effectiveness of state
implementation plans (SIPs) for controlling photochemical oxidants. The
need for this work stems largely from the revisions to the SIPs that are
required by 1982. Because the Immediacy of such a deadline places serious
practical constraints, both upon those formulating the revisions and those
asessing them, it appears worthwhile to explore the use of complex air
quality simulation models (AQSMs) as a means of assessing the adequacy and
accuracy of simpler oxidant prediction methods.
This appendix reviews the input requirements of the SAI Urban Airshed
Model--a three-dimensional, time-dependent photochemical dispersion
model. Levels of detail characteristic of model input data are discussed
and estimates of data acquisition costs are made. (These estimates are
subject to change caused by regional differences in construction and
maintenance costs, labor costs, inflation, etc.) Finally, the results of
recent photochemical grid model sensitivity studies are presented to
provide a perspective on the sensitivity results discussed in the main
body of this report.
1. A REVIEW OF AIRSHED MODEL INPUTS
The objective of this review is to define comprehensively the many
data inputs required by a complex photochemical grid model and to inter-
pret the extensive model output information. Although several photochemi-
cal dispersion models now exist, the model recently refined by SAI under
EPA Contract 68-02-2429 (the so-called EPA-5 model) was selected primarily
because it has the most extensive input requirements of any operational
photochemical model developed to date.
Airshed model inputs can be broadly categorized as either data
related or nondata related. The list in table B-l are non-data-related
model inputs. Many of the inputs listed in table B-l can be prepared
B-l
-------
TABLE B-l. INPUT PARAMETERS FOR THE SAI URBAN AIRSHED MODEL*
Control Parameters
Region description:
UTM coordinates of
grid origin
Cell size; horizontal
and vertical
Grid size
Number of vertical layers
Simulation controls:
Run identification
Beginning and ending times
Time step size
Minimum time step
Convergence criteria
Averaging interval
Instantaneous output
interval
Print options
Chemistry Parameters
For each species:
Name
Reactive or unreactive
Steady-state initial conditions
Steady-state boundary conditions
Resistance to deposition
Upper and lower bounds on
numerical integration and
steady-state calculations
For each reaction:
Rate constant
Photolysis rate
Temperature dependence
Activation energy
Reference temperature
For each coefficient:
Name
Value
* Ames, J., et al. (1978) discuss the airshed model inputs in considerable
detail.
B-2
-------
without recourse to air quality measurements, emission information, and so
forth. Specification of certain input parameters, however, does require
some knowledge of the unique conditions surrounding a given model applica-
tion. For example, if the model is to be applied to a city containing
several large elevated point sources, then some estimate of the diurnal
distribution of plume rises must be made so that the top of the modeling
region is high enough to contain point sources emissions. Clearly, in
estimating the distribution of plume rises, one must have some information
about the meteorology of the region and the physical emission characteris-
tics of the sources.
Data-dependent input requirements of the SAI Airshed Model are
summarized in table B-2, which indicates the spatial and temporal resolu-
tion of each input. The resolution of each input corresponds to the
present configuration of the model. The indicated resolution is, in some
cases, greater than that required to operate the model and to obtain
acceptable simulation results. Moreover, the resolution identified in
table B-2 does not necessarily represent the maximum level of input
information detail that could potentially be used in a photochemical
simulation. A few examples of this point are discussed next.
Atmospheric stability is characterized as the Airshed Model by three
scalars: the temperature gradient below the base of the inversion (the
so-called "diffusion break"), the gradient through the stable layer, and
the exposure class. (The exposure classification is similar to, though
not exactly the same as, the Pasquill-Gifford stability categories.)
These inputs are used in the model diffusivity and plume rise algo-
rithms. Focusing on the first two scalars, one can see that for some
applications, the vertical gradients in ambient temperature vary from
place to place. The Los Angeles air basin is an example. Surface
temperatures near the coast are moderated by moist marine air, whereas
near the eastern end of the basin hot, dry, desert-like conditions
prevail. Moreover, the rate of adiabatic heating caused by air mass
subsidence varies across the basin, in part because of the higher water
content of the marine air. Thus, the temperature gradients in the mixed
and stable layers are expected to exhibit spatial as well as temporal
variability. Owing to the paucity of upper air temperature soundings,
however, the temperature structure is currently treated in the model as
scalar quantities, varying only in time.
Radiation intensity is another meteorological variable that is
treated as a scalar. Despite the findings of photochemical model sensi-
tivity studies, which consistently demonstrate the important role of solar
radiation in oxidant formation, radiation is treated as a scalar value
rather than a three-dimensional, time-varying field. For model applica-
tions in which large portions of the region experience partial obscuration
B-3
-------
TABLE B-2. SUMMARY OF DATA-DEPENDENT INPUT REQUIREMENTS OF THE
SAI URBAN AIRSHED MODEL (EPA-5 VERSION)
Spatial and Temporal
description
Meteorology
Horizontal (u-«)
«nndi (n/tec)
Reference height of
Surface wind eenitor-
Ing sutions («)
break (m)
»J>
Top »f Modeling
rvgton (n)
(round-level tenpera-
tures CO
Atmospheric pressure
(•*>
Te«c>tr*tur« gradient
belch diffusion break
CC/ii)
Tcnvertlure gradient
above diffusion break
CC/»)
Miter concentration
1n the aowiiphere
(PP»)
Exposure (tubilltjr
class)
ladiation intensity
factor (per nin)
Air ojuality
initial conditions
(pphm)
•oundary conditions
(pph»)
Concentritieni above
tap of Modeling
region (ppha)
Surface concentra-
tions at several
locations •Itnin
Mdeling rtglon («*•)
Surface characteristics
Surface roughness
(o)
Veaitatlon factor
The vertical comanrct. «. Is coxouted
by the airsKM lofltl. rentferinq thr
resultant wind field *ass consistent
Used in the diffutivUy algorithtr
elevation at uhlch the stability struc-
ture of the atmosphere changes narked)/
(e.g., an inversion or thenval internal
boundary layer)
Mot absolutely essential to awoel
operation
Used In plume rise calculations
Used in plune rise calculations
Used in kinetic andule
Used in diffutivlty algorithm
Used in kinetic nodule
tequired for NO. H02. 03, hMO;. H;D;.
olefins, paraffins, aldehydes. aroMt-
Ics. PAN. SO;. SOj. and CO
Required for same tpecles as above
Required for sine species as above
Required for verification and evalu-
ation of nodel performance (sane
species at above)
Used In diffuvivlty. surface sink, and
•icroscale algorithws
Used in surface sink algorithm
B-4
-------
TABLE B-2 (Concluded)
Description
(anssions
limped ground-level
•*1ssions Irar tri«-
fU. area sources.
airports. etc. (»»/hr)
Elevated stationary
POlnt SOurct emis-
sions (g»/hr)
Elevated
source awnsions
location end height
ef elevitee point
tource «ni*S10nS
<*)
locitien of drcrtft
flight »re«i (»)
Meit flui fror. tie-
vtted point sources
Spit 1*1 tnd Tempor»l
Kcsolutlon
tewrks
Acquired for HO, K)?. 0). MNQ;
olffini. piriffins. «d«h/oei, iro
Mticv. r*N. S02. SOJ. tnd CO
fmssions from t*11 turn for the
• bo*» ipeoes are
frot. •1rtr«ft takeoffl «n(J
landings for the above species are
required (as appropriate)
•eouired for confutation of effective
stack heights
depends en nagnftude Of
aircraft missions
Usefl 1n plmit rite a Igor it tr
rttes 1rat tutos
•tcrotcile
yartmtter («/iec )
Icquired for NO, NO.- use( 1n the
treitwert of lubgrte-tcale <*ino-
»cale)
by
•here 1 • vehicle tjrpe. ?>4 • i»««p*r
•thtclei in category i. and v.
ef vehicles 1n category i
B-5
-------
(as a result of clouds, haze, and so forth), spatial variation in solar
radiation may be important. Although current routine field measurements
do not allow the preparation of three-dimensional radiation fields, the
model's formulation does attempt to account, at least in principle, for
the vertical attenuation of solar radiation caused by aerosol scattering.
The prescription of initial and boundary conditions is another area
in which the model could accept more sophisticated input information if it
were available. Currently, total hydrocarbon concentration measurements
(or alternatively, nonmethane hydrocarbons) are apportioned among five
reactivity classes--olefins, paraffins, carbonyls, aromatics, and ethy-
lene. The relative fraction of each class is assumed to be spatially and
temporally invariant. For a homogeneous hydrocarbon source distribution,
this approach is reasonable, as long as the estimate of each species
fraction is accurate. In the absence of any hydrocarbon speciation
measurements, and particularly for a varied source distribution (e.g.,
refineries, automobiles, dry cleaners) such an apportionment scheme can
potentially degrade model performance. Specifically, it might increase or
decrease the oxidant maxima and alter the phasing of the formation of
secondary pollutants.
A final example of refined inputs that could be accepted by the model
if they were available, concerns the treatment of ground-level emis-
sions. Currently, the emission data file (EOF) lumps (for each ground-
level grid cell) all surface emissions into one emission rate, regardless
of whether the emissions are from airports, autos, refineries, rendering
plants, and so forth. If the goal of the modeling effort is to assess the
level of model accuracy and precision, this lumping procedure is adequate
only as long as the aggregate emission value is correct. But, if the
modeling objective is to assess the effectiveness of a specific control
strategy (say, a 60 percent reduction in refinery hydrocarbon emissions),
then it becomes necessary to modify the EOF to reflect such a scenario.
If gridded emission inventories are available that delineate each source
type, the model can readily accept the increased level of detail.
2. ESTABLISHMENT OF CHARACTERISTIC LEVELS OF INPUT DATA DETAIL TO BE
EXPECTED FOR A RANGE OF MODEL APPLICATIONS
Efforts performed under this task were twofold. First, a brief
review of the status of data acquisition activities was carried out in the
following 14 cities:
> Albuquerque, New Mexico
> Chicago, Illinois
B-6
-------
> Denver, Colorado
> Houston, Texas
> Las Vegas, Nevada
> Los Angeles, California
> New York, New York
> Philadelphia, Pennsylvania
> Phoenix, Arizona
> Portland, Oregon
> Sacramento, California
> San Francisco, California
> St. Louis, Missouri
> Washington, D.C.
The purpose of this review was to develop a general understanding of
the spectrum of urban-scale air monitoring activities throughout the
United States. The cities that were selected shared several attributes.
They were:
> Moderate to large in size.
> Representative of major geographical areas in the contig-
uous 48 states.
> Reflective of a variety of emission source activities,
including emphasis on transportation (Los Angeles),
petrochemical (Houston), light density residential
(Sacramento), heavy density residential (New York), heavy
industry (St. Louis), and other activities.
> Subject to exceedances of the one-hour federal oxidant
standard, in some cases by a factor of 2 or 3.
Furthermore, some of the cities that were selected have been the subject
of previous or ongoing photochemical modeling studies, thereby making it
possible to develop a more complete picture of the available data base.
B-7
-------
Investigation of the data bases of these cities was made through
telephone discussions and correspondence with many people, principally
officials from the EPA offices, state agencies, local air pollution
control agencies, and other individuals who had modeling experience in
certain of the cities. The results of this brief review are presented in
three parts in table B-3: meteorological, air quality, and emission
inventory data. Blanks in the table indicate that the information was
either unavailable or not readily accessible.
As expected, a wide range of number and type of measurements was
encountered. All cities have at least a few surface wind stations, but
the range in the amount of available upper level wind and atmospheric
stability data is very broad. Contrast, for example, St. Louis with
Phoenix or Houston. The amount of air quality monitors also varies and,
to a degree, reflects the predominant air quality concern in each city.
Note that oxidant monitoring in St. Louis and Los Angeles is extensive,
whereas in Las Vegas concern seems to focus more on carbon monoxide.
Insofar as this brief review could determine, none of the cities investi-
gated routinely carry out hydrocarbon speciation experiments or airborne
air quality measurements, though these measurements were sometimes
reported during occasional special field studies.
In table B-3, the area of greatest uncertainty is the chartactenza-
tion of the emission inventories. Often, the individuals responsible for
supervising the collection of air quality and meteorological data were not
involved in preparing emission inventories. Because the scope of this
review ruled out a detailed characterization for each city, it was
occasionally necessary to rely on the general understanding of certain
people of the emissions data base rather than speaking directly with the
person or persons who prepared each one. From table B-3(c), it is clear
that great variation exists from city to city in terms of the thoroughness
and complexity of the emission inventories. As an example, the St. Louis
mobile source inventory accounts for spatial variations in the diurnal
distribution of the percentage of cold automotive starts, whereas traffic
emissions in New York are determined borough by borough, based on the
vehicle miles traveled (VMT).
* One of the prominent difficulties encountered in the review was
uncertainty on the part of certain officials as to the current status of
the monitoring networks. Often, systems were being dismantled, brought
on line, or used only during special studies. In addition, some
agencies (or local air pollution control districts) were, at times,
unaware of the scope of monitoring activities carried out by other
groups, such as the National Weather Service, airports, the military,
and educational institutions.
B-8
-------
TABLE B-3. SUMMARY OF ROUTINE AIR QUALITY MONITORING ACTIVITIES IN
14 MAJOR CITIES IN THE UNITED STATES
(a) Number of Stations Performing Routine Air Quality Sampling
C1t»
Albwauerqu*. NM
CMc«go. 1L
Denver. CO
Neutun. IX
Lai Vcgat. NV
Lot Angelei. CA
Nw fork. NT
Mi1litfe1pM«, P*
•fcoenli. AT.
Portland. W
fecrawnto. CA
SJM Franeluo. CA
St. Loull. MO
Mining ton. B.C.
Oritiint
4
4
f
3
3
38
7
8
S
3
•
»
K
10
mt
3
3
3
2
27
7
S
2
3
•
U
ts
10
cc
s
9
3
24
23
8
S
U
•
K
K
10
3
1
3
0
17
e
21
11
10
0
2
3
4
11
3
3
4
U
K
10
Upper A1r
Ptrtlcu- „. Htiii/re-
lites *°} Bents
13 0 0
0 0 K
3
300
8 II
8
1 0 0
17 17 0
n 10 s
10
Hydro
urboi
tt»t1(
0
0
S
S
S •
• •
ttudits.
HeU: A I»r« entry IndlCltci ttet I pcrtlcwUr mtnurmrn\ U net Ukrn; • »Unk tn4«cttct
imctrulnt/ (i tfi «h«thcr or te rfiit eittnt tht •ctvurwnt U tik»n.
B-9
-------
TABLE B-3 (Continued)
(b) Number and Type of Daily Meteorological Measurements
Surface
M1nd
City »e1ociti
Albuquerque. W
Chicago. II
Denver. CO
Houston. T>
Las v*g«s. MV
Lot Angrlei, f>
ft*. York, NY
Philadelphia, PA
Phoenix, A!
Portland, OR
Sacramento. C*
Sen Francisco. CA
St. IMU. MO
MasMngton, D.C.
7
10
25
3
8
44
10
2
6
9
12
17
25
25
Upper Level
Surface AtBDS»nrr1c Hind
Te*p«rature Stability Velocity
7 •¥, IW,
10 »2 **2
2 U, U,
3 0 P,
1 AC?. AS, 0
9 We. AS, PXfl
10 ^
2 Pl
600
9 W, W,. P,
4 AS ^*
17 tU, U,
25 IDB 'H
25 W, M,
Solar
Insolation
1
3
1
1
1
2
3
1
1
1
1
17
6
2
Hu»1di
1
3
1
1
1
B
3
1
1
1
2
17
20
2
AC • acoustic Mwnaer.
AS • aircraft spiral.
RD • radiosonde.
to • raoinsonde.
• • plbal.
Motes: Subscripts refer to the nimber of Measurements taken each day. A zero entry Indicates
that • particular Measurement 1s not taken; a blank Indicates uncertainty as to whether
or to what extent the Measurement 1s taken. In some cases, the meteorological measure-
ments presented here were drawn from special studies conducted during the sumer smog
Mason; In other cases only routinely collected data are presented.
B-10
-------
TABLE B-3 (Concluded)
(c) Description of Methods Used for Emission Inventories
Point and Area Sources Emission Inventory
dtjr
Alb***,.. *H
Chicago. 11
Denver, CO
Houston. Tl
Us Vegas. HV
Les Angeles. CA
fa. York, BY
Philadelphia. PA
Pnoenii. A7
Portland. OR
Sftcraecnto. CA
ton Francisco. CA
St. Louis. PC
"•*"""•"• § '
Formit
llnk-node:
VKI
(ridded
(ridded
link-node:
VKT
(ridded
(ridded
VKT
(ridded
(ridded
(ridded
(ridded
(ridded
Variable
lite grid
•ridded
Seeeiet
R, H. C
X, H. C
«. H. 5,
P. C
N. H, C
N. H. S. C
H. C, N
*'. H. C
S. P. C
M. H. S.
P. C
H. N. S.
*. N, S.
P. t
«, N. S.
P. C
(rid Site
•/A
50 i JO:
30 > 30:
1 «i
30 i 40:
1 tar
100 i SO:
2 m\
krough b>
borough
46 i 48.
2 »'
1 Hi
20 i 30:
2 tor.
2b i 2S:
2 tor
120 * (0:
1 tor
ISO * 200:
1-10 br.
4«1
Not/Cold
Start
Arw-.lde
texpora 1
reiolutton
Aret-Mlde
tenpora 1
distribu-
tion
Are»-«ride
tetieoral
distribu-
tion
Area-Hide
tenportl
dlstribu-
tior.
Area*«idt
temporal
resolu-
tion
Area-«1de
tetporal
distribu-
tion
Not/cold
distribu-
tions
appl led
to each
grid cell
Format
•EDS
(ridded
(ridded
•y counties
(ridded
Cridded
NEDS
NEDS
ly dis-
trict
(ridded
(ridded
(ridded
(ridded
!0*eiet
•. H. S.
P. C
N. X. C
N. H. S. P
«, S. H. f
N
N. H. S. C
S. P
N. H. S.
P. C
N. H. S.
0, C
N. N
*. H, S.
P. C
N. N. S,
P, c
N. H, S,
P, C:
hydrocarbon
speclation
M. N, S.
Seatiil
Are»-*1d«
1 mi
1 fii
County -
vi dt
1 tor
2 eii
Area -vide
Are* -vide
De&ends or
site o<
districts
2 tor
1 tor
1-10 tor
« •!
T«^>o'*l
Atinuil
averagr
B or ?4
hour, plus
set sent 1
Hourlj
Annuil
trrrtgt
Annul 1
aver* gt
An null
Hourly
Hourly
II • nitrogen eiidcs.
K • hydrocarbons.
S • sulfur oildts.
P • »*rt*culat*i.
C • carbon •onotidt.
B-ll
-------
Delineation of characteristic levels of detail in the data available
for photochemical modeling was attempted, once the foregoing assessment of
present urban-scale data bases was complete. With this review as a guide,
and realizing that certain measurements are clearly beyond the scope of
routine monitoring practices, table B-4 was formulated, yielding three
general "levels of detail" of data input to a complex model. Associated
with each of nine types of input information are statements reflecting the
type and amount of data one might expect for each level of detail. The
"maximum practical level" corresponds to the most exteiu.ve data base that
is currently available or that might be made available given present
funding constraints and the state of the art in photocheical modeling. In
many respects, the St. Louis and South Coast Air Basin data bases are
examples of this category.
At the other end of the spectrum is the "minimum acceptable level."
Although a data base so characterized might be adequate for modeling
purposes, certain assumptions must necessarily be invoked in preparing
model inputs. For example, Phoenix has no upper air temperature sound-
ings. To estimate mixing depths over Phoenix, one might assume that the
atmospheric structure at Tucson (where upper air soundings are available)
is reflective of conditions over Phoemx. Although mixing depth estimates
might be generated in this fashion, the extent to which they degrade model
performance and thus confound model performance evaluation efforts is
unknown. Between these two levels of detail lies a third category
entitled "commonly used level." Most of the data bases presented in table
B-3 fall within this category. This does not suggest, however, that these
data bases are well suited to model performance evaluation and applica-
tion. Some of the measurements that are either lacking or in short supply
in the intermediate category are ones to which model performance is quite
sensitive.
3. ESTIMATION OF DATA ACQUISITION COSTS ASSOCIATED WITH RAISING THE
LEVEL OF DETAIL OF INPUT DATA
The objective of this analysis was to derive preliminary estimates,
where possible, of the costs entailed in improving the quantity, quality,
or both of various components of an AQSM data base over that currently
being collected. If, in fact, one can identify the improvement in model
performance achieved through data from an additional wind station or
pyranometer, for example, then this could serve as the basis for quantify-
ing the degree of improvement in model performance ascribable to a
specific expenditure.
Relatively little in the recent literature serves as a guide in this
endeavor. One might expect that the lack of guidance is in part a result
B-12
-------
TABLE B-4. LEVELS OF DETAIL IN DATA USED AS INPUT TO GRID-BASED
AIR QUALITY SIMULATION MODELS
Input
Atwspherlc itibMUy
Ma«1mui» "ractlcil Level
Continuous nonltaring of nit-
Ing depths with acoustic soun-
der it one or nore locations
Several (i-8) verticil tem-
perature soundings through.
out the day it various loca-
tions within the Modeling
region
Numerous turf«ce temperiturt
awasure«i>ents recorded hourly
•t virlous locations through-
out the modeling region
One or nore Instrumented
Uwers providing continuous
e*asurenents of the ntied
thernal structure
Ce*nonlr Used level
* It* (3-5) temperature sound-
Ings it different times of the
diy it one or two locttlons
Several turfict temperature
Measurements recorded It var-
1ous locations throughout the
Modeling region
Hlnlmup Acceptable Level*
Twice dally temperature
soundings It in llrport
«1th1n or neirbjr the region
being Modeled
A fen (1-3) Surface temperi-
ture Measurements with which
to estimate tempo'il
virlitlon
Limited spatial resolution
or none It ill
• 1nd fields
Numerous fround-based monitor-
Ing stations reporting hourly
average values
Freouent upper i1r soundings
•t several locations through-
out the Modeling region
Continuous upper level measure-
nents on one or i few e leva let)
towers
Wind, Inversion, temperature.
ind terrain data used is input
to the 3-D numeric*! Model
yielding the Mass conserving
3-D wind field
Interpolations fro* ground-
based monitoring network and
limited (3-5) number of upper
level soundings at one or two
locations
Resultant wind field rendered
mass consistent by divergence-
free algontnr
Interpolations fro* 11»H«J
(3-S stations) routine
turftce wind data; thwet-
Icall; derived vertical pro-
file assumed
Solar radiation
Severil (3-5) UV pyrinoneters
located 1n the region, contin-
uously recording UV ridlitlon
levels
Vertical attenuation of radi-
ation it i few locitlons
several times dally determined
by aircraft observations
Spatial (3-D) Insolation fields
determined by Interpolation of
neisurtnents
A single, ground-based net
radiometer; Insolation assumed
constant over the region
Vertical attenuation estimated
empirically as a function of
aerosol miss
No radiation measurapients
available; estimated theo-
retical values based en uie
tolar tenlth ancle
Attenuation not accounted for
loundarv ind Inltla i
conditions
Hourly species concentrations
titripolited ind Interpolated
throughout the region using
data fro* the eitenslve
ground-based monitoring net-
work ; airborne data also
aviltable; hydrocarbon nli
Obtained from qis chromato-
graphlc analyses it several
times during the day
Sulfate concentrations avail-
able on in hourly basis It
several locitlons
Hourly concentrations eitripo-
lited ind Interpolated using
data from several ground-based
stations; hydrocarbon nli
obtained fro* gas chrom*to-
graphic analysis It One or two
Stations one or a fe* times
during the day
Sulfate concentrations based
en i dllly average and diurnal
eione curve
Hourly concentrations utra-
polated and Interpolated fro»
I Minimal routine mon1tor1n«
network; either hydrocarbon
Mia assumed or average value
Obtlined from I compilation
of available data taken in •
tlMllar irea
No data en concentration
variations aloft
Sulfate measurements Inferred
fro» values obtained 1n
Similar ar»«i
B-13
-------
TABLE B-4 (Concluded)
Input
Stationary source emissions
Practical Level
Separate grldded Inventories
for point and irei stationary
Sources; Characterization of
organic composition. ind NO/
N02 and SOj/SOi (mission
rates for major sources;
diurnal and seasonal varla-
tlons In nominal mission
rates for each major source
type
Level
Limped, grldded Inventory for
Stationary sources; «0 • pectes
fractlonatlon; seatoiill tr1
dlurnil variation In regional
emissions for each pollutmt
Minimum »ecept«plt it»fl*
Lumped tutlontry tewrct
(mission Inventory for Uie
region ti • wtiole. Halted
1nformit1on on the pcrtenUge
of eich source t/pe; ne te»-
poril «irt«t1on
Mydrocirbon spedes distri-
bution
Mli obtained from ;ts chnjmj*
too'ighlc *nilys1s ef samples
collected throughout the
renton, pirtlculirly neir
liroe sources
Cold start factors applied
grid by grid when calculi-
ting mobile source emissions
M1i obtained fro* standard
emission! factors (AP-42) to-
gether with i dttifled source
Inventory, supplemented n1th
one or t«c 1i, and
traffic data for tntrazonal
trios
AP.I2 emission factors,
aisjmet vehicle nil, and
Intrazonal VKT| estimated peak
and off-peak speeds, fewer
traffic counts available for
verification, VKT available
for fever najor arterlals
•ridded VMT, emission
factors estimated fro* It
state nil, and averaoe |fOC)
drlvlno profile; assumed
regional speed distribution
Vehicular cold start dlstH-
butt or
Spatial and temporal distri-
bution! of cold starts
Inferred from actual traffic
and demographic data
Cold starts temporally resolved
using traffic distribution; no
spatial resolution or spatial
resolution only from estimates
of drlvlna patterns
Cold starts as a flied t«r.
centaoe of all driving--
traffic data are not detailed
enough for spatial resolution
of cold starts; cold starts
estimated from demographic
data
Data for nodel performances
evaluation
Hourly averaoed spedes con.
centratlons for NO, NO;, Oi,
SO?, NWC, sulfate, CO, and
partlculates from an extensive
•round-based nenltorlno
network
Hourly averaged concentrations
of NO, N02, Oj. SOj. N«WC. CO.
and partlculates from several
ground-based stations
Dally averaoed sulfate i«easure-
rents available from a limited
(3-5) number of stations
Hourly averaoed concentrations
Of NO,. 0). THC. Kb, and CO
fro* a minimal routine vnl-
tori no network
• Using aata at this level of detail necessitates numerous assumptions.
B-14
-------
of broad geographical variations in labor rates, operating costs, and so
forth, and the ever-increasing cost of capital equipment, parts, and
supplies. An earlier study for the EPA (Miedema et al., 1973) addressed
the cost of implementing air quality monitoring networks of various sizes
in metropolitan areas where little if any monitoring previously existed.
This study, building upon earlier work by Hickey, Rowe, and Skinner
(1971), estimated monitoring costs for each state in the United States for
two scenarios:
> The required number of monitors based on federal regula-
tions
> The required number of monitors based on state regula-
tions.
The analysis carried out by Miedema et al. considered 31 cost elements
when formulating overall costs for a particular network. These cost
elements are listed in table B-5; as is immediately apparent, many cost
categories exhibit a wide range of variation, and most costs increase in
time. Accordingly, it is difficult to estimate many costs. For example,
if one wind monitoring station was purchased to augment the already dense
surface network in Los Angeles, the incremental costs required to train
personnel, accommodate the new data in the existing data reduction and
analysis system, provide for calibration and inspection equipment, and so
forth would probably be low compared with the analogous costs in Houston,
Texas. Along the same line, hourly labor costs in Albuquerque, New
Mexico, are less than those in San Francisco, California.
Notwithstanding the difficulties in formulating cost estimates, an
attempt was made to identify typical costs of routinely acquiring addi-
tional air quality and meteorological data. The results of this study are
presented in table B-6. In this analysis, fixed hardware costs were
amortized over a five-year period of time, but interest costs were
neglected. In some cases (surface winds, for example), the variable was
measured continuously; in other cases, the measurements were routine but
not hourly. Twice daily radiosonde soundings are an example. The
frequency of occurrence of each parameter is consistent with the maximum
level of detail, outlined in table B-4.
Various sources were consulted in developing the cost figures given
in table 6, including published reports, equipment manufacturers, managers
of air quality monitoring networks, and researchers working on special
studies. The costs for the surface air monitoring stations employing a
variety of instruments should be clearly viewed as lower bounds because
these figures are estimates made five years ago (Miedema et al., 1973).
In discussing the cost estimates with the authors of the Miedema et al.
B-15
-------
TABLE B-5. COST CATEGORIES FOR AIR QUALITY MONITORING SYSTEMS
(a) Fixed Costs
Hardware
Remote electronics
Central electronics
Test and maintenance equipment
Other digital equipment
Initial spares
Site installation, physical
Nonhardware
Specification preparation and program
management
System engineering
Software
Documentation
Training
Site installation, APCD labor
Site installation, vendor labor
(b) Variable Costs
Nonpersonnel
Personnel
Recurring spares
Utilities, site
Utilities, communications
Transportation, local
Transportation, other
Computer rental
Supplier
Facilities
Personnel, field technician
Personnel, sensor maintenance, corrective
Personnel, electrical maintenance, correction
Personnel, data analyst, routine
Personnel, data analyst, special
Personnel, laboratory technician
Personnel, chemist
Personnel, engineer
Personnel, clerical
Personnel, administrative
Source: Hickey, Rowe, and Skinner (1971).
B-16
-------
TABLE B-6. ESTIMATED ANNUAL COST TO AUGMENT
AN EXISTING AEROMETR1C MONITORING
NETWORK WITH VARIOUS INSTRUMENTS
Parameter Measured
Surface wind velocity
(continuously)
Upper level winds,
temperature, relative
humidity (twice daily)
Upper level winds
(twice daily)
Mixing depth
(continuously)
Mixing depth (twice
daily soundings)
Upper level winds
and temperature
structure (continuous)
Solar radiation
(continuous)
S02, particulates,
wind speed, wind
direction
S02, particulates,
wind speed and
direction, CO, 03,
and N02
SO?, particulates,
wind speed and dir-
ection. CO, 03, N02,
total hydrocarbons,
temperature, relative
humidity (continuously)
Instrument
Remote recording cup
anemometer and vane
Rawinsonde
Pibal
Monostatic acoustic sounder
Light aircraft with digital
recording temperature
sensor
Instrumented tower
Pyranometer
Surface air monitoring
station
Surface air monitoring
station
Surface air monitoring
station
Estimated
Annual
Cost
$ 7,500
71,000
7,000
13.500
40,600
45,000
3,800
44,000
66,700
100,000
B-17
-------
TABLE B-6 (Concluded)
Parameter Measured
Vertical S02 pollutant
burden (four sampling
days each week during
three-month smog season)
Hydrocarbon speciation
(twice daily, three
times a week during
three-month smog season)
S02, NOX, 03, particu-
lates, relative humid-
ity, bscat, turbulence,
(4-5 hours daily during
special field program)
Instrument
Estimated
Annual
Cost
Correlation spectrometer $ 81,000
Gas chromatograph
22,700
Airborne air quality
monitors
5,000*
Estimated cost per day.
B-18
-------
study, we found that no attempt has been made to revise the estimates to a
more current time frame.
In some instances, it is possible to estimate the cost of additional
monitors by examining current costs of data acquisition, analysis,
management, and so on. The California Air Resources Board (CARB), for
example, has found over the years, in comparing the overall cost of its
monitoring network with the total amount of data collected, that a typical
per-unit cost of data acquisition is about $1 per number. Thus, the cost
of one additional hourly surface temperature measurement in an existing
network might be on the order of $8,000 to $10,000 per year.
4. ANALYSIS OF AIR QUALITY MODEL SENSITIVITY TO VARIATIONS IN INPUTS
At the outset of the study (December 1977), a review of previous air
quality simulation model sensitivity studies was performed. In the
following subsection, the results of this review are presented. Because
the photochemical grid models studied (1) represent different model
structures (though they are based on the same general concept), (2)
represent different levels of model refinement, and (3) were applied to
different urban areas (e.g., Denver, San Francisco, Los Angeles), the
sensitivity results are not directly comparable in a quantitative sense.
Rather, they are indicative of trends in model performance likely to be
observed when certain inputs are varied.
Within the last five years, a limited number of sensitivity studies
have been performed with grid-based photochemical models. From a review
of the literature, we found that only two models--the Lawrence Livermore
National Laboratory LIRAQ model and the SAI Airshed Model—have undergone
extensive sensitivity analyses and have had the results of these studies
published in the open literature.
Table B-7 briefly summarizes recent grid model sensitivity studies.
Although it is likely that other sensitivity runs have been made,* the
ones identified in table B-7 are the only major sensitivity results that
have been identified by this review. It is apparent from the table that
several studies have investigated the impact on model predictions caused
by variations in several model variables. The eight studies are aggre-
gated according to four categories--air quality, meteorology, chemistry,
and emissions—which are discussed next.
Indeed, in carrying out a photochemical model simulation, iterative
adjustments made to initial conditions, boundary conditions, etc.,
constitute a form of sensitivity analysis, but these results are seldom
reported formally.
B-19
-------
TABLE B-7. SUMMARY OF SENSITIVITY STUDY RESULTS OBTAINED WITH
GRID-BASED PHOTOCHEMICAL AIRSHED MODELS
Study Group
MacCracken. M. C., and
G. 0. Sauter (197S)
Model Version
and Attributes
Sensit ivity Analysis
Variations
Influence on Model Predictions
Remarks
DO
ro
o
Deroerjian, K. L. (1976)
"EPA 3" VERSION
Liu. M. K.. et al.
(1976)
LIRAQ photochemical model
Two-dimensional time-
dependent grid model
Lumped kinetic mech-
anism similar to
Hecht-Seinfeld-Dodge
mechanism
Mass conserving wind
field
SA1 photochemical model:
SAI photochemical model:
"EPA 3" version
?5 x 25 x 6 grid
15-step Hecht-Seinfeld-
Oodge kinetics
Price numerical method
Empirical diffusion
algorithm
Two-dimensional wind
field
Relative humidity was
reduced from 40t by ?0*
Nominal temperature was
increased from ?85"K to
31)4'K
Light intensity was reduced
by b(«
Iiqht intensit y was
increased by ,i factor
of 2
Initial hydroi arhons ,irt>
increased hy j fdc tnr of ?
Initial NO^ concentrations
were increased hy a factor
of 2
Boundary conditions were
reduced by SUt
Initial and boundary con-
ditions were reduced by
bOT
Hind directions were random-
ly perturbed by
0 or t22.5*
Wind speeds were randomly
perturbed by 0 or tl mph
Wind station measure-
ments were:
Peak ozone increased by 3% and peak
N0? decreased by 4X
Peak ozone decreased by 2X and peak
NOj increased by SI
Peak ozone decreased by 70% and
Nl)^ peak magnitude remained
unchanged but was delayed 4
hours
Peak ozone increased by 100* and
NO;, pe.ik magnitude slightly
increased and preceded base case
peak by 1-3/4 hours
NiK peak increased hy fit arid was
ife 1 ayert jpprox imdte! y I hour;
ozone peak was not reported, but
the increase in ozone concentrations
was delayed by up to 3 hours
NO-, peak increased by 10* and was
delayed slightly; 0-j remained
unchanged
"Minor" differences occurred in
ozone prediction in the eastern
and northern portions of the L.A.
basin; "significant" differences
were observed in the western
and central portions of the basin
Predicted ozone in the northern
and eastern edges of basin were
reduced 20 to 30*
A b.9* average deviation for manu-
ally prepared and 4.9t
for automatically prepared
wind fields (based on CO
predictions)
A 4.9* average deviation for man-
ually prepared and ?.6< for auto-
matically prepared wind fields
(based on CO predictions)
Maximum absolute deviation from
the base case results for CO
were
LIRAQ sensitivity runs focused
on the kinetic module; accord-
ingly, sensitivity results are
more reflective of smog chamber
simulations than they are of airshed
simulations
In the automatic.wind field studies,
perturbations were made to the
monitoring station measurements and
then automatic procedures were
employed to derive gridded wind
fields. In the manual wind field
cases, perturbations were made to
the gridded wind fields after they
had been prepared manually
The response of the model to
variations in wind speed varies
with each chemical species and
is time dependent
-------
TABLE 3-7 (Continued)
Study Group
Model Version
and Attributes
Sensitivity Analysis
V.ir idt ions
Two-dimensional
initial conditions
Increased SOI
Increased ?5X
Decreased ?0.?t
Decreased SOX
Horizontal diffusion was
decreased to II and in-
creased to 500 m?/sec
CD
I
ro
Vertical diffuswity was
decreased to 0.5 m'Vsec
and increased to 50 m?/sec
Mixing depths were in-
creased and decreased by
35X
Radiation intensity was
increased and decreased
by 30*
Inf luence on Model Predictions
19.bt
U.8t
51./t
for KH = 0, the maximum abso-
lute deviation for CO ranged
between 0.52 and O.OZt from
0600 to 1600 hours
For KH =• 500 mz/sec, the
maximum absolute deviation for
CO ranged between 4.4 and 12.91
from 0600 to 1600 hours
The effect of varying vertical
diffusivity by an order of mag-
nitude was about the same as
that of varying the wind speed
by 25 to 501
Maximum absolute percentage
deviations for the increase and
decrease, respectively, were:
For CO, 8t and 12t
For NO, lit and 18.51
for N0?, B.5t and 15.5X
For 03, 11.5J and 23t
Remark s
Maximum absolute percentage
deviations for the increase
and decrease, respectively,
were:
The base case value was 5 m?/sec
The buildup of the mixing depth
variation effect is time-
dependent
Decreasing the mixing depth has
a greater effect on the ground-
level concentrations than in-
creasing it; this result is more
pronounced for reactive pollu-
tants
The effect of changing the mix-
ing depth is not uniform over
the modeling region; it varies
from place to place
The effect on ground-level con-
centrations of changing the
mining depth is roughly the same
as that of changing the wind
speed, as would be expected from
a dimensional analysis
The effects of varying the
radiation intensity are time-
dependent
-------
TABLE B-7 (Continued)
Study Group
Model Version
and Attributes
Sensitivity Analysis
Variations
Emissions rate (ground
based) Mas increased and
decreased by 15*
CO
I
ro
ro
Reynolds. S. 0.. et al.
SAI photochemical model:
"EPA 3" version [see Liu
et al. (1976)]
Uniform wind velocities with
height were compared with
vertical variation in hori-
zontal winds given by a
power law formulation
Anderson. G. E.. et al.
(1977)
SAI photochemical model:
"Denver" version
Wind speeds were reduced
by 3 it
Influence on Model Predictions
For NO, 17* and 40*
For N02. 74* and 55*
For 03, 9* and 11*
The effects of increasing
and decreasing emissions
rates are almost identical;
peak basin-wide average per-
centage changes in CO and
NO^ were about the same
(6-8*)
[he maximum average percentage
deviations were: 28.St for NO, IS*
for N0?. 24* for CO, and 14* for 03
The maximum average deviations in
pphm were: -0.35 for NO, -1.1 for
N0?, -4 for CO, and -2 for Oj
The maximum deviations in pphm
were: 7.5 for NO, 15 for NO;,, 30
for CO, and 26 for 03
Maximum predicted ozone increased
by 44; maximum area for which [Oj] >
0.08 ppm increased by 12*
Remarks
The effect of changing light
intensity is as significant
as that of changing wind speed
The study results are summar-
ized by the following ranking
of the relative importance
of the input parameters (A -
most important and D - least
important):
Parameter or
Variable CO NO 03 N02
Hind speed
A A A
Horizontal 0 D 0 0
diffusivity
Vertical C C C C
diffusivity
Mixing depth B 8 B B
D A A B
Radiation
intensity
Emissions
rate
B
B B
The effects of including wind shear
were similar to those of increasing
surface wind velocities by roughly
25t because velocities within the
mixed layer are increased between 20
and 701 of the surface values as a
result of shear
-------
TABLE B-7 (Continued)
Study Group
Model Version
and Attributes
03
ro
to
Killus. J. P. (1977)
(private communication)
Anderson, G. E. (1977)
(private communication)
Attaway. L. 0., et al.
31-step carbon bond
chemistry
3-D Mind field
Lower microscale layer
Lamb and Liu diffusivity
algorithms
30 x 30 x 7 grid
SHASTA numerical method
Suffice removal
Three-dimensional initial
conditions
DeHandel et a\. (1979)
SA1 photochemical model:
"Denver" version [see
Anderson et at. (1977)
for mode) attributes]
SAl photochemical model:
"Denver* version [see
Anderson et 0.08 ppm increased by 301
No difference occurred in the time,
location, or magnitude of maximum
ozone concentration; differences
among predicted ozone concentrations
in all runs were not more than 0-010
ppm in one or two grid cells at most
No difference occurred in the time,
location, or magnitude of maximum
ozone concentration; differences
among predicted ozone concentrations
in all runs were not more than 0.010
ppm in one or two grid cells at most
The coarser grid resolution led to
no noticeable change in the time
to peak" NO, N0?, and Oi concentra-
tions; the magnitude of peak con-
centrations was reduced for NO
(691), N0? (211). and 03 (131)
The maximum impact of increased
source emissions anywhere in the
model inq region was an increase
in hourly averaged NO and NO^ con-
centrations (I? and 5 pph, respec-
tively) and a decrease in 03
(-4 ppb)
The estimated maximum increment in
three-hour-average S0? (0900-1200)
concentrations was 70 ppb immediate-
ly downwind of the facility; concen-
tration differences dropped below
10 ppb at a distance of 24 miles
downwind of the source
Region-wide maximum ozone concentra-
tions were reduced by !0 percent
from a baseline peak of 0.20 ppm
A synergisn exists between wind
speed and mixinq depth
In each scenario, no more than 71
of the reqion-wide emissions were
redistributed; changes of this
size in the spatial distribution
of emissions has little effect an
secondary pollutants such as ozone
By the time ozone forms, its pre-
cursors have been distributed over
a much greater area than their source
regions; accordingly, the influence of
increased grid size on ozone predic-
tions should be less than that for
primary pollutants such as NO
The effect was decidedly local and
did not influence peak oiidant
concentrations
The 70 ppb value is an upper bound
because the mesoscale model over-
estimated ground-level concentra-
tions in the vicinity of buoyant
point sources
The time of occurrence of the ozone
peak remained unchanged
-------
TABLE B-7 (Concluded)
Study Group
ftxtel Version
anj Attributes
Sensitivity Analysis
»dr i il mns
Influ^nci- mi Huilel I'rpJ ir. t ions
Memarks
Compact kinetic mechanism
similar to Hecht-Seinfeld-
Dodge mechanism
Mass-conserving wind field
Sou ten, D. R.. et al. (1980) SAI photochemical model:
"IPA 5" version [see Reynolds
et al. (1979)]
Killus, J. P.. et al. (1980) SAI photochemical model:
•EPA-5- version [see Reynolds
et al. (1979)]
SAI photochemical node):
"EPA-S" [see Ames et al.
(1978)]
Multiple-day simulation for
Los Anqeles, CA
CD
ro
Reynolds, S. 0., et al.
(1979)
SAI photochemical model:
"EPA-S" version [sec
Reynolds et al. (1979)]
(missions were distributed
according to the demographic
distribution
Kea(tive hydro* arbon emis-
sions from bioqenic sources
(57 percent of the RHC
inventory) were eliminated
from the inventory
Three-dimensional initial
condition field from monitor-
inq data versus "clean air"
Keqion-Mide ma«imum o/one concentra-
tions nere reduced by ?S percent;
the predicted peak ozone level
occurred three hours after the base
case maximum
Area-wide n/one levels on the second
of a multiple-day simulation were
reduced by no more than 2 ppb
Background hydrocarbon 0.06
ppmC versus 0.18 ppmC
Wind fields for the airshed
model were prepared usinq the
following procedures:
1. An interpolation
algorithm
?. A tMu-dimensional wind
model
3. A three-dimensional Kind
model
Essentially zero effect on second
day of simulation; some effect on
first day, especially in western
portion of mode I inq region
in the morning
No orone predictions above 0.? ppm
on first Jay; no o/one above back-
qrnund on second day
No effect on ofone peaks; minor
effects at some stations
NO,
Procedure
I -0.7 6.8 -?.0 3.5
? 1.4 B.6 -l.S 4.0
3 0.1 7.6 -?.? 3.7
The delay in the oione peak nas
attributed to a redistribution of
point source NO, emissions from
industrial areas to residential areas
For the meteorology studied,
hioqenic hydrocarbon emissions
had no major bearing on peak
calculated ozone levels
Effects on the second day of twn-day
simulation are driven by emissions
Effects on the second day of two-day
simulation are driven by emissions
Background HC has limited effects
below certain point
Eiamination of the ozone results
reveals the following:
Use of interpolated *md fields
leads to the qreatest bias toward
underestimation at the highest con-
centration levels
Compared with the intercalated and
three-dimensional wind field simula-
tions, the tKo-dimensional wind field
simulation exhibits a greater
tendency toward overestimation for
most of the observed concentration
range
The three-dimensional wind field
simulation exhibits less bias (posi-
tive or negative) overall than do the
other two simulations
Accuracy.
* Precision.
Grid restitution was related
from ?«? miles tn 4 x 4
miles
taierally, d reduction in the max-
imum ii/iine concentration occurs at
the monitorinq stations together with
d "broadening" of the diurnal ozone
prof i le
-------
a. Studies Focusing on Air Quality Inputs
Sensitivity analyses in which air quality inputs have been varied
were reported by MacCracken and Sauter (1975) and Demerjian (1976).
Collectively, these studies examined perturbations in model predictions
from base case simulations caused by the following changes:
> Initial hydrocarbon concentrations increased by a factor
of 2.
> Initial NC^ concentrations increased by a factor of 2.
> Boundary conditions reduced by 50 percent.
> Initial and boundary conditions reduced by 50 percent.
The measures of model performance that were used in these studies included
the percentage change in the magnitude of the peak 03 and N0£ concentra-
tions and the time delay in reaching peak concentrations. In each case,
the overall impacts on the spatial maximum 0^ and N0£ concentrations (in
percentage variation from the base case) were far less than the changes
made in initial or boundary conditions.
These early studies represent an initial step in analyzing the impact
of variations in air quality inputs (i.e., initial and boundary condi-
tions) on grid model predictions. Although they provide insight into the
expected order of magnitude of changes in model predictions (at least over
the range for which the inputs were varied), other issues need to be
investigated:
> What is the impact on predictions caused by variations in
the assumed initial and boundary condition hydrocarbon
species compositions?
> What is the impact on predictions caused by variations in
boundary,conditions over a much wider range of concentra-
tions than have previously been explored? In some
simulations, uncertainties in boundary conditions upwind
of the urban area, and pollutant concentrations in layers
aloft, have been much greater than the range of values
explored in sensitivity studies to date.
B-25
-------
> What is the impact on predictions caused by various
procedures for creating initial and boundary condition
fields?
> What is the impact on model ozone predictions caused by
computer simulations of multiple-day periods?
To this point, the discussion of sensitivity analyses has focused on
airshed model simulations of one day or less. As we point out in the main
body of this report, a reliance on single-day simulations as a means of
revealing model sensitivities tends to overstate the importance of air
quality data (used to specify initial and boundary conditions, and to
understate the importance of other data, i.e., meteorological and emission
inputs). Recently, Killus et al. (1980) reported results of a multiple-
day simulation for Los Angeles. Using this simulation as a basis for
comparison, Souten et al. (1980) conducted a sensitivity simulation to
examine the influence of a 57 percent reduction in reactive hydrocarbon
initial conditions on predicted ozone maxima on the second day of a smog
episode. As indicated in table B-7, model ozone calculations were
perturbed by no more than 2 ppb on the second day. These preliminary
findings suggest that the need for detailed air quality monitoring data
may be reduced if it is possible to develop satisfactory multiple-day
simulations for a particular urban area. Of course, as the need for air
quality data is reduced by use of multiple-day simulations, the need for
improved meteorology becomes more pronounced.
b. Studies Focusing on Meteorological Inputs
Sensitivity studies involving meteorological inputs have investigated
variations in wind fields, mixing depths, and diffusion rates. For
conservative pollutants, it was found that the airshed model predictions
are noticeably more sensitive to reductions in wind speed than to
increases (Liu et al., 1976). Furthermore, in another study (Anderson et
al., 1977), wind speed reductions appeared to have a far smaller effect on
secondary pollutant (ozone) concentrations than on primary concentra-
tions. Finally, the effect of including wind shear (vertical variation in
wind speed with height) in place of uniform winds was found to be compar-
able to a 25 percent increase in surface wind speeds (Reynolds et al.,
1976).
Linear interpolation of ozone concentrations observed at street-side
monitors may grossly underpredict the magnitude of an area-wide ozone
levels. Other interpolation schemes, for example, based on mass
balances or Poisson fitting routines, may provide more realistic
estimates (in some cases).
B-26
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The study by Liu et al. (1976) indicated that model performance may
be degraded more by a reduction in the magnitude of an input variable than
by an increase in the magnitude of an input. This trend was found to be
the case for mixing depths in their study. Moreover, a concurrent
reduction in wind speed and direction revealed that a synergism exists
between wind speeds and mixing depths (Anderson et al., 1977). Sensi-
tivity analyses in which horizontal turbulent diffusivity was varied from
zero to probably an extreme value for the urban atmosphere during smog
episodes (~500 m^/sec) showed only a minor effect on conservative pol-
lutant concentrations. The effect on reactive species would probably be
even smaller. However, the effect on ozone levels of varying the vertical
diffusivity an order of magnitude (below and above a base case value) was
comparable to varying wind speeds by 25 to 50 percent. Also, a decrease
in the vertical diffusivity had a more pronounced impact on ozone predic-
tions than an increase in diffusivity.
In short, the sensitivity studies carried out to date indicate that
photochemical model predictions are more sensitive to overall reductions
in the magnitude of parameters associated with contaminant dilution—wind
speed, mixing depth, and diffusivity--than to corresponding increases in
the parameters.
This review found only two studies that addressed the impact on model
predictions caused by alternative procedures for preparing meteorological
inputs, specifically wind fields (Liu et al., 1976; Reynolds et al.,
1979). Liu et al. (1976) investigated two procedures:
> Manual preparation of the wind field by smoothing and
interpolating measurement data.
> Automatic preparation of the wind field by numerical
weighting and smoothing routines.
The studies by Liu et al. involved (1) randomly varying wind speed
measurements by 0 or ±1 mph, and (2) randomly varying wind direction
measurements by 0 or 122.5*. Wind measurements so perturbed were used in
the manual and automatic wind field preparation processes. Neither type
of perturbation .had much influence on grid average concentration devia-
tions (about the base case). However, the maximum local deviations (about
the base case) were larger, particularly for the case of variable wind
direction.
Reynolds et al. (1979) examined the influence on airshed model ozone
predictions caused by the use of alternative wind field generation
procedures. Three approaches to the prescription of wind field were
investigated:
B-27
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> Use of an interpolation scheme, together with an objective
procedure for minimizing wind field divergence aloft.
> Use of a two-dimensional, diagnostic wind model (Liu et
al., 1974).
> Use of a three-dimensional, mass consistent, diagnostic
wind model (Yocke and Liu, 1978).
Upon examination of the ozone results, Reynolds noted the following:
> Use of interpolated wind fields leads to the greatest bias
toward underestimation at the highest concentration
levels.
> Compared with the interpolated and three-dimensional wind
field simulations, the two-dimensional wind field simula-
tion exhibits a greater tendency toward overestimation for
most of the observed concentration range.
> The three-dimensional wind field simulation exhibits less
bias (positive or negative) overall than do the other two
simulations.
Estimates of model accuracy and precision were derived through
computation of the first and second moments of the distribution of
residuals (differences between hourly model calculations and observa-
tions). The three wind field sensitivity runs produced these results for
ozone and N02:
Ozone (pphm) NO? (pphm)
Simulation
Interpolated wind field
inputs
Two-dimensional model
wind field inputs
Three-dimensional model
Accuracy
-0.7
1.4
0.3
Precision
6.8
8.6
7.6
Accuracy
-2.0
-1.5
-2.2
Precision
3.5
4.0
3.7
wind field inputs
B-28
-------
Reynolds et al. concluded that, on the basis of computed measures of
accuracy, precision, and bias, and of precision at upper percentile ozone
concentration levels, the three-dimensional wind model appears to offer
the best simulation results. However, there were several instances where
this procedure for supplying wind inputs led to poorer model performance,
such as at a particular monitoring station or over a particular range of
observed concentrations.
With the exception of the two studies just discussed, all of the
sensitivity studies to date have been designed so that the perturbation to
diffusivities or wind fields is uniform across the modeling grid. The
same is true for studies involving mixing depths. With the results of
past sensitivity studies as a foundation, certain additional analyses
might be performed to investigate
> The impact on model predictions of using a fully three-
dimensional wind field rather than a uniform field (x,y
variations only) or a uniform fielrf "extended aloft",
based on theoretical arguments.
> The impact on model predictions caused by horizontal
variability in the vertical diffusivity fields.
In the first case, the extent to which model predictions are influenced by
the procedure for preparing wind fields will undoubtedly be governed by
the meteorological complexity of the urban area whose data base is used in
the sensitivity analysis. Model predictions might be much more sensitive
to wind field preparation procedures used in a Los Angeles application,
for example, than in Tulsa, Oklahoma. Similarly, the horizontal varia-
bility in vertical diffusivity is greater over an urban area exhibiting
irregular or complex topography than over smooth terrain.
c. Studies Focusing on Chemistry Inputs
Sensitivity studies on the kinetic mechanisms of photochemical models
have centered on variations in ambient temperature, relative humidity, and
solar radiation. The first two parameters have been shown to be rela-
tively uninfluential in affecting model predictions, at least for the
ranges in each variable that were explored (MacCracken and Sauter,
1975). In contrast, variations in solar radiation, which affect the
photolysis rates of NC^, aldehydes, HNC^, and HgC^ have been shown to be
quite significant. For example, MacCracken and Sauter (1975) found that a
50 percent reduction in light intensity reduced the peak ozone concentra-
tion by 70 percent.
B-29
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Other possible sensitivity studies involving AQSM kinetic mechanisms
could be entertained that might comprise an examination of the effects of
> Attenuating the intensity of solar radiation with height
instead of assuming uniform values throughout the depth of
the modeling region.
> Prescribing the individual photolysis rates for NOg,
aldehydes, HNC^, and h^C^, instead of assuming that the
photolysis rates of the last three species are propor-
tional to the nitrogen dioxide photolysis rate.
> Evaluation of alternative kinetic mechanism such as those
proposed by Falls and Seinfeld (1978), Durbin and Hecht
(1975), or Whitten and Hogo (1977).
Clearly other sensitivity studies focusing on chemistry inputs can be
envisioned, (e.g., to vary chemical reaction rate constants). However,
these are perhaps best reserved for the more complex photochemical smog
chamber simulations (Whitten and Hogo, 1977) in which explicit rather than
condensed mechanisms are used.
d. Studies Focusing on Emissions Inputs
Several basic sensitivity studies have been performed with source
emissions:
> Overall increases or decreases in emission rates.
> Relaxation of the spatial resolution of the emission
inventory to accommodate a coarser airshed grid.
> Examination of the impact of single point sources or
individual source categories on basin-wide oxidant or
sulfate levels.
> Localized reductions in emissions with proportional
increases elsewhere in the region to give overall emission
rates equal to those in the base case.
These first three sensitivity analyses are quite straightforward. As
indicated in table B-7, studies involving small overall emission increases
or reductions, aggregation of sources into a slightly larger grid, and
examination of the influence of minor sources on basin-wide air quality
have found that the impact on basin-wide model predictions is relatively
small.
B-30
-------
One sensitivity analysis performed by Anderson et al. (1977) focused
on the influence of spatial variations in emission rates. They found that
a reduction in emissions of 25 percent in any one of eight satellite
Denver suburbs did not influence the time, location, or magnitude of the
region-wide maximum ozone concentration. (In each scenario, no more than
7 percent of the region-wide emissions were redistributed.)
e. Studies Focusing on Grid Specification
DeMandel et al. (1979) report several interesting sensitivity studies
that use the LIRAQ model developed at Lawrence Livermore National Labora-
tory. One evaluation reduced the model's horizontal resolution from 5 km
to 10 km. In the single-day simulation, peak calculated ozone levels were
reduced 10 percent from 0.20 ppm to 0.18 ppm. This reduction was
explained on the basis of "spatial smoothing". The emission densities of
precursor species were reduced by spatial averaging over the larger grid
cell size. This resulted in lower concentrations of precursors and lower
reaction rates.
Reynolds et al. (1979) compared airshed model ozone predictions based
on grid resolutions of 2 miles (3.2 km) and 4 miles (6.4 km). Comparison
of the temporal ozone profiles at the monitoring stations indicated that,
for the most part, the profiles do not change appreciably when the 4x4
mile simulation is introduced. Four exceptions were the Reseda, Upland,
Azusa, and Pasadena stations. Examination of the profiles indicates that
reducing the grid resolution to 4 x 4 miles leads to:
> An increase in predicted concentrations of ozone at Reseda
by a few pphm and a broadening of the temporal profile.
> A reduction of the predicted peak ozone level at Upland by
roughly 5 pphm.
> A reduction in the predicted peak ozone level at Azusa by
about 6 pphm.
> A reduction in the predicted peak ozone level at Pasadena
by about 10 pphm.
Reynolds et al. concluded that a decrease in grid resolution may lead to a
slight reduction in peak predicted concentrations, at least at certain
monitoring stations. Furthermore, the 4x4 mile grid run yielded results
that were more "accurate" over the entire concentration range, though at
peak concentration levels it was less accurate than was the 2 x 2 mile
grid simulation.
B-31
-------
The studies just discussed represent an Important but preliminary
step in understanding the sensitivity of photochemical grid models to
variations in emissions. While an understanding of the model's sensitiv-
ity to overall changes in emissions is naturally of interest, other issues
need to be addressed. In the next subsection we consider certain analyses
that might be carried out to determine grid model response to various
changes in the components of an emission inventory.
5. ISSUES RELATED TO THE PREPARATION OF EMISSION INVENTORIES
Air quality models are generally used in two ways: model performance
evaluation and application. Model evaluation consists of tests of the
model using a data set or sets to determine the extent to which the model
replicates field measurements. One of the objectives of the evaluation
phase is to ascertain whether biases exist in the model performance that
might later be alleviated by a more suitable treatment of atmospheric
processes, alternative numerical methods, more accurate and detailed model
inputs, and so on. In evaluative studies, the disaggregation of various
sources in an emission inventory by source type is seldom necessary. What
is required is overall grid volume emission rates for each pollutant
species. Ideally, the temporal distribution of emission rates within each
cell is known or inferred from demographic, industrial, commercial, and
other types of data.
In contrast, in an applications study, a model is typically used with
an assumed set of "worst case" meteorological conditions in conjunction
with an emission inventory that reflects a proposed or anticipated change
in emissions from some baseline level. If the reduction (or increase) in
emissions is uniform, regardless of whether the concern is region-wide or
within a given subarea, the emission inventory used for model performance
evaluation may suffice. However, if the applications study focuses on the
effectiveness of a particular emission control tactic in maintaining or
reaching a particular air quality goal, then a more detailed emission
inventory may be essential. In the following paragraphs typical emission
control measures are identified together with the corresponding level of
detail required of an emission inventory so that a complex model could be
used to assess the effectiveness of the measure.
To provide a structure for this discussion, table B-8 presents
various emission control measures and strategies/ which were selected by
the San Francisco Association of Bay Area Governments (ABAG, 1977) from
A control measure is an individual emission reduction proposal; a
control strategy may entail two or more control measures.
B-32
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TABLE B-8. CONTROL MEASURES AND EMISSION INVENTORY DATA NEEDS
Control Measure
Stationary source measures
Restrictions on the type of
industrial solvents used
Closed organic storage
Limitation on the maximum SO
emissions of any source to a
prescribed level
Limitation on the maximum
sulfur content in fuel
Best available control tech-
nology on new or existing
sources
New source review with or
without offset
Reduction 1n motor gasoline
vapor pressure
Data Needs in the
Source Emission Inventory
Location, size, and operating
characteristics* of coating
facilities
Location, size, and storage
characteristics of facilities
handling organic chemicals and
fuels
Location, size, and operating
characteristics of all S02
emission sources larger than
a prescribed level
Location, size, and operating
characteristics of combustion
sources operating on high sulfur
fuel; emission rates given a
switch to low sulfur fuel
Location, size, and operating
characteristics of new or
existing sources
Location, size, and operating
characteristics of new source
as well as the existing source(s)
to which the offset is to be
applied
Location, size, and operating
characteristics of all facil-
ities handling significant quan-
tities of gasoline (see also
mobile source emissions measures)
Operating characteristics of a particular emission source may
Include such factors as nominal pollutant emission rate, emissions
composition, heat flux, elevation of point of emissions, flow rate,
diurnal and seasonal variations in emission rates, composition of
fuel, and so on.
B-33
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TABLE B-8 (Continued)
Control Measures
NOX control of off-highway
construction and agricultural
activities
N0j< limitations on new
boilers and furnaces
Data Needs in the
Source Emission Inventory
Temporal and spatial description
of construction and agricultural
activities (e.g., crop burning)
and characterization of emis-
sion rates
Location, size, and operating
characteristics of new boilers
and furnaces
Mobile source emission
measures
Exhaust emission controls
Evaporative emission controls
Gridded vehicular emission rates
embodying:
Spatially and temporally resolved
traffic flow characteristics,
such as traffic volume, overall
driving speed, cruise speed(s),
acceleration and deceleration
range, percentages of time
spent at cruise and at idle,
number of speed changes per mile,
number of cold starts, etc.
Vehicle mix (Including age dis-
tribution of vehicle population)
and model split (between motor
vehicles and busses, trains,
rapid transit, etc.)
Emissions factors based on ele-
vation, the "average vehicle in
the region," EPA heavy duty
vehicle emission estimates,
unique terrain features (grades),
etc.
Gridded estimates of the distribu-
tion of "hot soaks" (see reduction
in motor gasoline vapor pressure
measures)
B-34
-------
TABLE B-8 (Concluded)
Control Measures
Operation of a retrofit
program
Emission standards for
other mobile sources
Motor vehicle inspection
and maintenance programs
Data Needs in the
Source Emission Inventory
Identification of the age distribu-
tion of the regional vehicle popu-
lation and emission rates result-
ing from evaporative emissions and
catalytic exhaust emission retro-
fit dtvices
Emission rates embodying spatial
and temporal resolution for mobile
sources, including motorcycles,
agricultural equipment, construc-
tion equipment, vessels, locomo-
tives, aircraft* recreational
vehicles, and miscellaneous util-
ity engines (log splitters, tree
cutters, etc.)
Estimate of number of vehicles
inspected annually and percentage
emission reduction attributable
to vehicle maintenance, replace-
ment, etc.
Transportation control measures
Improvement in traffic flow
(e.g., ramp metering)
Reduction of peak-period
traffic volumes
Control over auto use and
access (e.g., parking limi-
tations, gas rationing, tolls)
Encouragement of alternative
travel modes (ride sharing,
bicycling, etc.)
Similar to those under exhaust
emissions controls; in addition,
estimates of modal shifts and
changes in VMT due to the control
measure
Temporal and spatial resolution
of trip origins and destinations
Similar to improvement of traffic
flow measures above
Similar to improvement of traf-
fic flow measures above
B-35
-------
proposals that might be adopted for controlling emissions from stationary,
mobile, and land use sources. Though not exhaustive, the measures do
reflect a range of possible control methods that might be investigated
using air quality models in future SIP analyses.
Considering stationary source control measures first, table B-8
reveals that, for adequate testing of many of the measures, disaggregation
of stationary sources by type and size of operation is necessary.
Clearly, if one were attempting to assess the impact of controls imposed
on dry cleaners, for example, on basin-wide oxidant levels, it would be
necessary to locate and define the emission strengths of these numerous
sources throughout the urban area. Such a level of detail typically does
not exist in most conventional emission inventories. Controls on refinery
operations might be easier to analyze given an aggregated emission
inventory because of the far fewer number of sources in an urban area and,
perhaps, because of a better estimation of overall refinery emission
rates. (Note, however, that the distribution of reactive hydrocarbon
emissions from refineries is probably poorly known because of numerous
fugitive sources and hydrocarbon species.)
Measures that attempt to reduce vehicular emissions are broadly
categorized in table B-8 under the headings "mobile source emission
measures" and "transportation control measures." Examples of control
measures in these categories include
> Stringent exhaust and evaporative emission controls
> Inspection, maintenance, and retrofit programs
> Ramp metering
> Parking limitations and regulations
> Gas rationing
> Increased gas and parking taxes
> "Smog charges"
> Fare reductions on public transit
> Bus and carpool lanes
> Auto-free zones.
B-36
-------
As with stationary sources, an analysis of these or other vehicular
emission control measures is complicated by the aggregation that takes
place in preparing conventional inventories of the emission rates from
various sources into a composite value. The processes by which this
confounding takes place is summarized next and then suggestions are
offered as to how the loss of detailed information on particular sources
might be avoided in the preparation of new inventories.
a. Mobile Source Emission Inventories
Three general procedures are used in compiling mobile source emission
inventories:
> Manual link-by-link summation
> Automated link-by-link summation
> Estimation based on gasoline sales.
The first method, a tedious one, requires estimation of emissions from
each section of freeway and arterial streets on the basis of traffic
counts (available on maps from local agencies), peak and off-peak speeds,
light versus heavy duty vehicle mix, and "minor" street traffic volumes.
Corridor inventories are generated through these analysis; regional
inventories are derived by apportioning the corridor emissions to a
regional grid and assuming that minor streets contribute some fraction of
the corridor emissions.
Automated link-by-link emission inventories are based on regional
transportation models. The transportation forecasting model is used to
simulate trip generation, travel on various roadway segments, peak and
off-peak speeds, total VMT, cold starts, hot soaks, and so forth. These
estimates, when combined with appropriate emission factors [such as those
contained in AP-42 (EPA, 1972) and the most recent supplements], are used
to generate emission rates that are then "loaded" onto a regional emission
grid. More flexible than the previous method, the automated approach
(which uses a simulation model for traffic characteristics) sacrifices
some accuracy by using the transportation model to calculate VMT rather
than using actual data.
Finally, gross attempts to construct a regional mobile source
inventory can be based on an inventory of regional gasoline sales.
Lacking temporal and spatial resolution, this procedure is the least
desirable of the three.
B-37
-------
Regardless of which of the three basic methods is employed, some
details of the vehicular operations (and their spatial and temporal
variabilities) that lead to emissions are lost when preparing a gridded
regional inventory. Fortunately, in some cases, information concerning
vehicle mix, temporal distributions, and so forth can be retrieved. For
example, if a modal shift was anticipated because of a particular control
strategy, one could go back through the calculations of a manually
prepared inventory and apply different light versus heavy duty vehicle mix
ratios. Less tedious, with an automated transportation forecasting model,
one could change model split factors and rerun the computer code, generat-
ing a new set of traffic estimates, which could then be used to revise the
mobile source emission inventory.
b. Stationary Source Emission Inventories
This component of the overall inventory consists of major point
sources (refineries, smelters, power plants, and so on) and "other"
sources. The first category generally does not represent a major problem
in constructing an inventory because the main sources are usually easily
identifiable. However, frequently the emission characterization of major
point sources is made on an annual or "nominal" basis and thus may depart
substantially from actual day-to-day emission rates.
Lumped into the "other" source categories are facilities such as
cleaners, gas stations, residential chimneys, coating and manufacturing
industries, and so forth. Aggregation of these sources into a regional
inventory is often considerable. For example, rather than identifying the
location and size of each dry cleaner in an urban area, because of time
and resource constraints, the inventory may be prepared by (1) determining
the total number of dry cleaners in the area (perhaps from the telephone
directory), (2) estimating an average perchloroethylene rate for a typical
dry cleaning shop (see EPA, 1972), and (3) apportioning the total emis-
sions on a regional grid according to a demographic distribution. While
this procedure may be satisfactory from the model verification point of
view, it is not acceptable if one is interested in examining the reduction
in basin-wide oxidant levels caused in part by controls on evaporative
emission sources that include dry cleaners.
Several conclusions can be drawn from the foregoing discussions and
the review of previous model sensitivity studies:
> Although the emission inputs required to operate a complex
model are relatively straightforward (i.e., gridded
emission fluxes of each pollutant), procedures for
compiling these inputs exhibit wide variability, ranging
B-38
-------
from sophisticated traffic forecasting models to simple
estimates based on regional fuel sales.
In general, emission inventories destined for use in
control strategy evaluation must exhibit a greater degree
of detail and disaggregation of the various source types
than an inventory used in model verification.
Existing emission inventories do not permit (without
additional modification) the evaluation of many possible
emission control measures and strategies; only rather
general analyses (such as overall emission reductions or
modifications of large, stationary sources) are readily
facilitated with current inventories.
In the modification of existing, or the preparation of
new, emission inventories, consideration should be given
to the range of emission control strategies that are most
promising for the region of interest; in so doing, the
particular source types amenable to control can be
inventoried separately, thereby establishing a basis for
future control strategy evaluation.
Owing to the wide range in methods used to estimate
stationary source emission rates and to develop traffic
volumes (and hence mobile source emission rates), it is
difficult to estimate the costs entailed in enhancing the
level of detail in emission inventories. Accurate
estimates of the costs required to improve an inventory
for a given city can be made only after an examination of
the distribution of source types and the procedures used
in forecasting traffic volumes.
6. CONCLUSIONS
This appendix presents a broad overview of the range in data input
requirements of present generation photochemical grid models. The SAI
Urban Airshed Model has been used as the prototype for this discussion.
Review of the monitoring and data acquisition activities at various urban
areas in the United States reveals a rather broad range in the quality and
quantity of the data collected. Only a very cursory attempt has been made
to estimate costs of data acquisition because of (1) the wide geographical
differences in the cost of such activities, and (2) the rapid rate at
which inflation is presently increasing the cost of these activities.
B-39
-------
Several model sensitivity studies have been performed in the last few
years. In the main, studies involving the Lawrence Livermore National
Laboratory's LIRAQ model and the Airshed Model have been the only ones
reported in the open literature. Most, but not all, studies have involved
uniform reduction or increase in the magnitude of a model input. Only
recently have sensitivity studies been performed that address the impact
on model calculation caused by the selection of alternative procedures for
preparing model inputs.
B-40
-------
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Anderson, G. E., et al. (1977), "Air Quality in the Denver Metropolitan
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Attaway, L. D., et al. (1976), "Maintenance Shutdown of Tail Gas Treating
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EPA (1972), "Compilation of Air Pollutant Emission Factors," AP-42, U. S.
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Falls, A. H.-, and J. H. 'Seinfeld (1978), "Continued Development of a
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R-l
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Miedema, A. K., et al. (1973), "Cost of Monitoring Air Quality in the
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Research Triangle Park, North Carolina.
Reynolds, S. D., et al. (1979), "Photochemical Modeling of Transportation
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Reynolds, S. D., et al. (1976), "Continued Development and Validation of a
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Souten, D. R., T. W.Tesche, and W. R. Oliver (1980), "Evaluation of the
Air Quality Impacts of Alternative Air Pollution Control Policies
Utilizing the Airshed Grid Modeling Approach for the South Coast Air
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California.
Whitten, 6. Z., and H. Hogo (1977), "Mathematical Modeling of Simulated
Photochemical Smog," EPA-600/3-77-011, Systems Applications, Incor-
porated, San Rafael, California.
R-2
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Yocke, M. A., and M. K. Liu (1978), "Modeling Wind Distributions over
Complex Terrain," EPA-68-03-2446, SAI No. EF78-78, Systems Applica-
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R-3
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rECHMCAL RtPORT DATA
(Picase r<••*•.' t'-istntcrin:i'! •: tue -c'tvnv i^j^re f.
1. REPORT NO.
EPA-450/4-81-Q31C
4. TITLE AND SUBTITLE
The Sensitivity of Complex Photochemical Model Estimate
to Detail in Input Information ~ Appendix B: Specifi-
cation and Assessment of Airshed Model Input Requiremen
6. PERFORMING ORGANIZATION CODE
s
7. AUTHOR(S)
T. W. Tesche
8. PERFORMING ORGANIZATION KEPORT NO.
SAI No. 332 EF81-4
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Systems Applications, Incorporated
950 Northgate Drive
San Rafael, California 94903
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
3. RECIPIENT'S ACCESSION NO.
5. rIEFORT DATE
10 PROGRAM ELEMENT NO
11. CONTRACT/GRAN f NO.
68-02-2870
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENC/ CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
Report identifies key inputs to a photochemical grid model (Urban Airshed Model).
A literature review summarizing sensitivity tests performed prior to 1981 is also
presented. Costs associated with obtaining various kinds of input data in past
studies are also estimated.
7.
KE Y WORDS »ND DOCUMENT ANALYSIS
DESCRIPTORS
Photochemical grid models
Urban Airshed Model
Sensitivity tests
Model inputs
Ozone
b. IDEN1 IFIEflS/OPEN ENDED TERMS
8. DISTRIBUTION STATEMENT
Unclassified
c. COSATi ! ielu.'Group
I 19. FCURITY CLASS / / his Report 1 ,"1. NO. OF PAGES
!_ ] 48
EPA Form 2220-1 (Rev. 4-77) PRt'.iOU;. &ci TION is OBsoi.trt
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