EPA-450/3-78-008
January 1978
EVALUATION
OF EMISSION INVENTORY
METHODOLOGIES
f
FOR THE RAPS PROGRAM
.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
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EPA-450/3-78-008
EVALUATION
OF EMISSION INVENTORY
METHODOLOGIES
FOR THE RAPS PROGRAM
by
Ronald E. Ruff and Patricia B. Simmon
SRI International
333 Ravenswood Avenue
Menlo Park, California 94025
Contract No. 68-02-2047
EPA Project Officer: Charles Masser
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
January 1978
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This report is issued by the Environmental Protection Agency to report technical
data of interest to a limited number of readers. Copies are available free of charge
to Federal employees, current contractors and grantees, and nonprofit organizations -
in limited quantities from the Library Services Office (MD-35), U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711; or, for a fee, from
the National Technical Information Service, 5285 Port Royal Road, Springfield,
Virginia 22161.
This report was furnished to the Environmental Protection Agency by SRI International,
333 Ravenswood Avenue, Menlo Park, California 94025. The contents of this report
are reproduced herein as received from SRI International. The opinions, findings, and
conclusions expressed are those of the author and not necessarily those of the Environ-
mental Protection Agency. Mention of company or product names is not to be con-
sidered as an endorsement by the Environmental Protection Agency.
Publication No. EPA-450/3-78-008
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TECHNICAL REPORT DATA
(Please read Instructions on tin- reverse before completing)
1. REPORT NO.
EPA-450/3-78-008
2.
4. TITLE AND SUBTITLE
Evaluation of Emission Inventory Methodologies
for the RAPS Program
3. RECIPIfcNT'S ACCESSION NO.
6. REPORT DATE
January 1978
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Ronald E. Ruff and
Patricia B. Simmon
8. PERFORMING ORGANIZATION REPORT NO
SRI-4331
9. PERFORMING ORGANIZATION NAME AND ADDRESS
10. PROGRAM ELEMENT NO.
SRI International
333 Ravenswood Ave.
Menlo Park, California 94025
11. CONTRACT/GRANT NO.
68-02-2047
12. SPONSORING AGENCY NAME AND ADDRESS
EPA, Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
200/04
15. SUPPLEMENTARY NOTES
16. ABSTRACT
The general objective of the work described here is the evaluation and
quantification of the methodology being developed and used for the Regional Air
Pollution Study (RAPS) emissions inventory. Improved emission methodologies are
one of the RAPS objectives. However, they are essential to the realization of one
of the other principal objectivesnamely, the evaluation of mathematical air
quality simulation models. The output of any such simulation model is only as good
as the input emissions data supplied to it. The thrust of this work is to evaluate
the individual emissions models and relate them to their application to air quality
models.
7.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS |c. COSATI Field/Croup
Emission Methodologies
Regional Air Pollution Study
Air Pollutants
Emissions
8. DISTRIBUTION STATEMENT
Unlimited
19. SECURITY CLASS (This Report)
21. NO. OF PAGES
146
20. SECURITY CLASS (This page)
Unclassified
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION is OBSOLETE
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CONTENTS
LIST OF ILLUSTRATIONS ix
LIST OF TABLES xi
ACKNOWLEDGMENT xiii
I SUMMARY AND CONCLUSIONS 1
A. Introduction 1
B. Conclusions 2
II TECHNICAL BACKGROUND 3
A. Overview of RAPS Emission Inventory 3
B. Identification of Important Parameters 3
C. Quantification of Inventory Errors 4
1. Previous Studies 4
2. Discussion 5
III EMISSION INVENTORY EVALUATION PROCEDURE 7
A. General Sensitivity Analyses Procedure 7
B. Application to the RAPS Inventory 9
IV EVALUATION CRITERIA FOR THE RAPS EMISSION METHODOLOGIES. . . 11
A. Overview 11
B. Evaluation Criteria 14
V EVALUATION OF STATIONARY POINT SOURCE EMISSIONS 17
A. Sulfur Dioxide 19
1. Background 19
2. Evaluation 20
3. Large Point Sources 20
4. Small Point Sources 22
B. Nitrogen Oxides 22
C. Carbon Monoxide 23
D. Hydrocarbons 23
E. - Particulates 24
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VI EVALUATION OF STATIONARY AREA SOURCES EMISSIONS 27
A. Estimation of Spatial Resolution 29
1. Residential Sources 29
2. Commercial Sources 30
3. General Comments 30
B. Estimation of Temporal Resolution 30
1. Fuel Usage 30
2. Evaporative Hydrocarbon Losses 31
3. Solid Waste Disposal-Structural Fires 33
C. Discussion of Area Sources by Pollutant 33
1. Sulfur Dioxide 33
2. Nitrogen Oxides 34
. 3. Carbon Monoxide 34
4. Hydrocarbons 34
5. Particulates 35
D. Summary of Quality of Stationary Area Source
Methodology 35
VII EVALUATION OF HIGHWAY SOURCE EMISSIONS 37
A. Evaluation of Highway Source Inventory 37
B. Highway Emission Models 38
C. FTP Emission Model 39
D. Modal Emission Model 42
E. Emission Model Comparison 43
1. Model Input Data 43
2. Method of Determination of Composite
Emission Models 49
3. Results 49
F. FTP Model Sensitivity Analysis 59
1. Sensitivity to Error in a Single Input
Parameter 60
2. Sensitivity to Error in Two Input
Parameters 64
3. Conclusions 72
G. Modal Model Sensitivity Analysis 75
1. Sensitivity to Error in a Single Input
Parameter 75
2. Sensitivity to Error in Two Input
Parameters 77
3. Conclusions 81
vi
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VIII EVALUATION OF OTHER TYPES OF SOURCES OF EMISSIONS 83
A. Fugitive Dust 83
B. Aircraft 84
C. Off-Highway Mobile Sources 85
1. Lawn and Garden Equipment 86
2. Construction Equipment 86
3. Industrial Equipment 86
4. Farm Equipment 87
5. Outboard Motorboats 87
6. Summary 88
D. River Vessels 88
F. Separation of Hydrocarbon Emissions into Classes. ... 89
IX COST BENEFIT ANALYSIS 91
X EFFECTS OF EMISSION PARAMETER INACCURACIES ON AIR
QUALITY MODEL PREDICTIONS 95
A. Quantification of Errors in the Meteorological
Input Parameters 95
B. Case Study for a Single Source 98
1. Problem Formulation 98
2. Results 100
XI EFFECTS OF RAPS EMISSION INVENTORY ERRORS 113
A. Point Source Dominated Inventory (802) 113
1. Discussion 113
2. Summary 121
B. Area Source Dominated Inventory (CO) 123
1. Discussion 123
2. Sensitivity 123
3. Adequacy of the CO Inventory 124
C. Other Primary Pollutants 125
1. Total Hydrocarbons 125
2. Nitrogen Oxides 125
3. Particulates 125
D. Photochemical Model Implications 126
E. Other Inventory Factors 127
1. Source Heights 127
2. Area Grid Size 128
REFERENCES 129
vii
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ILLUSTRATIONS
L Assumed Distribution of Error 14
2 RAPS Grid System 28
3 Weekend and Weekday Diurnal Patterns for Motor Vehicles ... 32
4 Plot of CO Emissions Computed with FTP Methodology
and with Modal Model for Cases with Emissions Less
Than 900 g/veh-mi 50
5 Plot of HC Emissions Computed with FTP Methodology
and with Modal Model for Cases with Emissions Less
Than 60 g/veh-mi 51
6 Plot of NOX Emissions Computed with FTP Methodology
and with Modal Model for Cases with Emissions Less
Than 15 g/veh-mi 52
7 Plot of CO Emissions Computed with FTP Methodology
and with Modal Model for Cases with Emissions Less
Than 300 g/veh-mi 53
8 Downwind Concentrations from Union Electric Company
Souix Plant at Various Stabilities 101
9 Sensitivity of Concentrations to Flow Parameter
Variation (Souix Plant) 105
10 Sensitivity of Concentrations to Diffusion Flow
Coefficient Variations (Souix Plant) 108
ix
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TABLES
1 Estimates of RAPS Source Emissions 12
2 PEDCO Average Precision Data 14
3 Values of 9 for Selected Inventory Errors and
Confidence Levels 18
4 Maximum Allowable Error o^ for Point Sources of
Various Size 19
5 Emission Model Input Data 44
6 Emission Model Input Data Used for Model
Comparison Runs 45
7 Roadway Descriptor Classes 46
8 Fraction of Annual Travel by Vehicle Age 48
9 Model Comparison Statistics for CO 54
10 Model Comparison Statistics for HC 55
11 Model Comparison Statistics for NOV 56
X
12 Results of Tests for Significance of Differences
Between Correlation Coefficients of Various Subsets
of the Data Sample 58
13 Values of Input Parameters Assumed in FTP Model
Single Parameter Sensitivity Analyses 61
14 Average Fractional Error in FTP Emission Factors
Resulting from Error in a Single Input Parameter 62
15 Values of Input Parameters Assumed in FTP Model
Two Parameter Sensitivity Analysis 66
16 Average Fractional Error in FTP Emission Factors
Resulting from Error in Both Speed and Volume 67
17 Average Fractional Error in FTP Emission Factors
Resulting from Error in Both Cold Starts and Volume 69
18 Average Fractional Error in FTP Emission Factors
Resulting from Error in Both Speed and Percent Cold Starts. . 71
19 Input Parameter Error Values that Cause FTP Emission
Factor Error to Exceed 20% 73
20 Values of Input Parameters Assumed in Modal Model
Sensitivity Analysis 77
21 Average Fractional Error in Modal Emission Factors
Resulting from Error in a Single Input Parameter 78
xi
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22 Average Fractional Error in Modal Emission Factors
Resulting from Error in Both Volume and Cold Start 79
23 Input Parameter Error Values that Cause Modal
Emission Factor Error to Exceed 20% 82
24 MRI Estimates of Fugitive Dust Inventory Accuracy 84
25 Inventory Summary: Totals, Accuracy, and Cost 92
26 Characteristics of Meteorological Measurements at
25 Site Locations in RAPS Areas 99
27 Flow Parameters for Three Cases 104
28 Typical Errors in Significant Downwind Concentrations .... Ill
29 Ground-Level SC^CDM Reference Case 115
30 Sensitivity of S02 Estimates to Wind Direction Errors .... 116
31 Erroneous Wind Speed Distribution 116
32 Sensitivity of S02 Estimates to Wind Speed Errors 117
33 Erroneous Atmospheric Stability Distribution 118
34 Sensitivity of SC^ Estimates to Typical Atmospheric
Stability Estimation Errors 118
35 Sensitivity of S02 Estimates to Worst Cast Atmospheric
Stability Estimation Errors 119
36 Sensitivity of S02 Estimates to Typical Random Errors
in Emission Rate 120
37 Sensitivity of S02 Estimates to Worst Case Random Errors
in Emission Rate 121
38 Sensitivity of S02 Estimates to Typical Random Errors
in Stack Exit Gas Parameters 122
39 Summary 1: Significant Ground-Level Concentration
Average Error Caused by Typical Input Parameter Error .... 122
40 Summary 2: Significant Ground-Level Concentration
Average Error Caused by Worst-Case Input Parameter Error. . . 122
41 Area Source Sensitivity Comparison 124
xii
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ACKNOWLEDGMENT
The authors wish to express their gratitude to the people who pro-
vided special assistance during the preparation of this report. The
guidance of the EPA Project Officer, Mr. Charles Masser was vital in
ensuring that our effort was focused toward EPA objectives. The cooper-
ation of Dr. Fred E. Littman and Mr. John Piere of Rockwell International
was also invaluable, particularly in the timely transfer of information
on their efforts.
Our colleagues at SRI International were also instrumental in this
effort. Mr. Ronald T. H. Colis reviewed the manuscript in detail. Dr.
Chandrakant M. Bhumralkar, Dr. J. Raul Martinez, and Mr. Hisao Shigeishi
contributed extensively to sections of this report.
xiii
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I SUMMARY AND CONCLUSIONS
A. Introduction
The overall objective of the work described in this report is the
evaluation and quantification of the methodology being developed and
used for the Regional Air Pollution Study (RAPS) emissions inventory.
Improved emission methodologies are one of the RAPS objectives. How-
ever, they are essential to the realization of one of the other RAPS
principal objectives: the evaluation of mathematical air quality simu-
lation models. The output of any such simulation model is only as good
as the input emissions data supplied to it. The thrust of this work is
to evaluate the individual emissions models and relate them to their
application to air quality models. This project encompassed four major
elements:
An in-depth review of emission models and procedures
used in the total context of the RAPS emissions
inventory, to ensure that together they provide a
well-balanced approach to the overall objective.
An evaluation of the effectiveness of the emission
models to ensure an accuracy commensurate with the
needs of the RAPS program.
The identification of possible modifications to the
emission models and procedures.
Design of an experimental verification program that
could be conducted in St. Louis. This program would
validate the various emission models to fulfill an
objective of RAPS as well as for more general use.
During the development of validation procedures for the emissions
modules, benefit-cost ratios were determined. The recommended valida-
tion program was developed under this contract and is reported
separately (Shelar et al., 1976).
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B. Conclusions
Sections V through VIII contain an evaluation of the various emis-
sion inventory methodologies. With the exception of the methodologies
for sulfur dioxide, enough data are not generally available to support
objective conclusions. A number of recommendations are made concerning
possible improvements or validation of various emission estimates. Areas
that need the most further effort include:
Highway source inventoryvalidation is needed for both
model input data and the emission methodology (see
Section VII).
Point source inventoryadditional stack tests are
warranted for nitrogen oxides (see Section V.B).
Fugitive dust further work is needed to validate
existing results and to separate particles into size
categories (see Section VIII.A).
Sections X and XI estimate the effect of emission inventory
errors on air quality model performance. Analysis shows that the sulfur
dioxide inventory is the most accurate and most suitable for air quality
modeling purposes. Further validation work is needed to support any
firm conclusions about hydrocarbons and carbon monoxide. More research
on fugitive dust is required to support particulate modeling efforts.
Although there are limitations in the accuracy of the RAPS inventory,
the inventory is a definite asset. More significantly, it is the most
comprehensive inventory available to support air quality modeling objec-
tives. Significant improvements could be realized with nominal future
expenditures. The data system is flexible enough to allow refinements
in input data such as emission factors, fuel consumption, and land use.
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II TECHNICAL BACKGROUND
A. Overview oi RAPS Emission Inventory
The RAPS emission inventory is the most comprehensive in existence.
It was assembled primarily to support the development and verification
of air quality models. Generally, the previous inventories tell short
of providing the requisite accuracy and resolution (spatial and temporal),
For the RAPS inventory, the air quality modelers had an input to the
requirements prior to the collection activity (Littman et al., 1974).
In response to air quality modeling requirements, EPA adopted a
program to acquire the most responsive data and develop the best avail-
able methodology within the budget constraints of the program. This
report assesses the adequacy of the resulting product.
B. Identification of Important Parameters
The inventory is comprised of three general source types: point,
line, and area. For point sources, emission parameters consist of:
Emission rate
Location (UTM coordinates)
Stack parameters
- Height
Diameter
Gas exit velocity
Gas exit temperature.
Line source parameters involve only emission rate and coordinates of
the endpoints of each line segment (link). Area sources are represented
by a grid, one kilometer square, or an even multiple thereof. In
addition to emission rate, area source parameters are the grid size and
the coordinates given for the lower left (southwest) corner. Nominal
emission release heights are also needed for both line and area sources.
-------
Emissions, in this evaluation study, are considered for the primary
pollutants: sulfur dioxide (SCO , carbon monoxide (CO), total hydro-
carbons (THC), nitrogen oxides (NO ), and particulates. Some discussion
X
addresses the breakdown of hydrocarbons and particulates (size
distributions).
The temporal resolution of the emission data base is one hour. The
RAPS data handling system accesses the raw input data (fuel consumption,
automobile traffic data, and so forth), applies the appropriate emission
factors, and computes the emissions. Improvements in input data and
emission factors (models) can readily be incorporated into the data
handling software. In some cases, for large point sources, raw process
data was compiled hourly from January 1975 through March 1977. In other
cases, hourly rates were estimated on the basis of available data.
C. Quantification of Inventory Errors
1. Previous Studies
The effects of emission inventory errors on the prediction of
ambient air pollution concentrations have been evaluated in previous
studies. One of these studies (Koch et al., 1971), had a significant
bearing on the RAPS emission inventory methodology. Some of the more
relevant Koch study findings were:
Averaging area source emission rates over areas larger
than 1 square mile can lead to significant errors in
estimated pollutant concentrations.
Treating area sources as emitting from the same height
does not significantly affect estimated pollutant con-
centrations.
The Koch study also addressed the sensitivity of certain meteorological
parameters.
In another study (Hilst, 1970), a steady-state Gaussian model
was demonstrated to be relatively insensitive to random errors in the
specification of source strength. Hilst concluded that, among multiple
sources, random errors tend effectively to cancel. Of course, it was
recognized that systematic emission errors lead to systematic errors
in predicted concentrations.
4
-------
The SRI study (Littman et al., 1974) addressed the accuracy
requirements for source coordinates. Their sensitivity analysis showed
that large sources should be located within 10 meters of their true
coordinate.
2. Discussion
While the studies just mentioned (Littman, Koch, and Hilst)
provide valuable insight toward the specification of inventory para-
meters, they did not address the specific problem associated with RAPS.
Namely, is the RAPS inventory accurate enough such that it does not
significantly hinder efforts in the model development and evaluation
process? To answer this question, we must examine the other constraints
that limit the accuracy of prediction: errors in the meteorological
input data, and errors in the model formulation itself. Since model
error is subject to improvement over the years, our approach has been
to quantify errors induced by emission inventory inaccuracies in relation
to those induced by inaccuracies in the meteorological input parameters.
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Ill EMISSION INVENTORY EVALUATION PROCEDURE
A. General Sensitivity Analyses Procedure
Common to the emission inventory evaluation process is the appli-
cation of a sensitivity analysis procedure to the various components of
the RAPS emissions and air quality models. Sensitivity is formally
defined as the partial derivative of the output of a model to the input
parameter (s) in question. In the case of complex models, it is more
appropriate to consider incremental changes in output resulting from
incremental changes in input because the determination of the analytical
expressions for partial derivatives becomes too cumbersome.
One of the primary objectives of this work is to determine the
sensitivity of the air quality model output as a function of various
emission inputs in relation to meteorological inputs. Quantifying the
potential sources of error (output) in this manner gives the air quality
modeler a better understanding of the limitations on model performance
and identifies those types of input data that most need improvement.
The air quality model output, x> can ^e represented as some function
of emissions inputs, Q, and meteorological inputs G as follows:
,s' [G]t,s +
where x, x' > Q, and G are vectors in time (t) and space (s). The
,1. GITi
term represents an error in the formulation, which is only a mathematical
approximation to the physics and chemistry of the atmosphere. For our
purposes, it is assumed that the X' term represents a random error not
In the notation used here, the prime will be used to designate errors
caused by the model formulation; unprimed error terms will designate
the total error.
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related to the Q and G arrays. The sensitivity of any element in the X
array to any element in either the Q or G arrays is given by
oX
8
) ° )
or (2)
oXi _ of}[Q]t>s, [G]t>s}
FcT = acT
J J
However, Q's and G's are also only mathematical approximations or
measurements subject to error. These are expressed as
and (3)
The variations in the Q and G terms are numbers that can be used
xerr err
to quantify air quality model errors by substitution for incremental
changes in Q. and G. in the discretized approximation of Equation (2).
The G terms can be estimated from reports of previous meteoro-
err
logical studies. Parameters such as wind and diffusion coefficients
are approximated in some manner before they are used in an air quality
model. They are derived from measured data of questionable adequacy.
In the RAPS study, meteorological data available for model input are
quite comprehensive. However, significant errors will still arise.
The Q terms were estimated after conducting a separate sensi-
err
tivity analysis for the relevant emission model. The emission variable
can be expressed as:
(4)
8
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where g represents the emission model, and P represents the input para-
meters; Q' represents the model formulation error.
The sensitivity analysis for the emissions model is analogous to
that for the air quality model. Errors in the input parameters, P ,
are used to quantify emissions errors.
B. Application to the RAPS Inventory
In Sections V through IX, Q is estimated for all significant source
types. For some large point sources, source test statistics are used
to quantify Q . For other sources, we subjectively evaluate the term
on the basis of assumed accuracies.
Estimation of the G term is described in Section XI. Then, the
err '
relative effects of G and Q , as they impact model pollutant con-
err err
centration predictions, are presented.
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IV EVALUATION CRITERIA FOR THE RAPS EMISSION METHODOLOGIES
A. Overview
Adequate validation data are the key to the successful evaluation of
any emission model methodology. In the presence of such data, statistical
tests can quantify the model performance. Ideally, one would like to have
such data for each major source or, at the very least, each major source
type. Furthermore, ideally, these test data should be comprehensive
enough to constitute a statistically valid sample.
Unfortunately, validation data rarely exist in sufficient quantity.
Therefore, a truly objective methodology assessment is difficult. Never-
theless, a "semiobjective" evaluation is possible for the RAPS inventory.
The level of objectivity varies according to the source type and size.
In this report, various source types are evaluated relative to each
of the major pollutants--SO-, CO, NO , hydrocarbons (HC), and particulates.
^_ X
For the latter two pollutants, reactive versus nonreactive HC and particu-
late size distributions are briefly considered also. Approximate magni-
&
tudes by category for point and area source emissions are presented in
Table 1. These estimates are used as a guide in the remainder of the
report.
Emission factors are the basis for converting a source activity level
to pollutant-specific emission rates. The activity level is described in
parameters that are readily measured (or estimated) for the type of source.
As an example, for power plants, fuel consumption rate is commonly used to
describe an activity level. To calculate the emission rate for sulfur di-
oxide, one can multiply the activity rate by the fuel sulfur content and
the emission factor.
*
Courtesy of J. Piere, Rockwell International,
11
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Table 1
ESTIMATES OF RAPS SOURCE EMISSIONS (TONS/YEAR)--1975
(Courtesy of Rockwell International)
Area Sources
River Vessels
Fugitive Dust
Unpaved roads
Agricultural tilling
Wind erosion
Construction
Aggregate storage
Unpaved airstrips
Paved roads
Highways
Area sources
Line sources
Railroads
Stationary Residential and Commercial
Residential fuel oil
Residential natural gas
Residential LPG
Residential coal
Commercial fuel oil
Commercial natural gas
Commercial LPG
Commercial coal
Evaporation from HC
Evaporation from HC-automatic tanks
Evaporation from HC-dry cleaning
Structural fires
Solid waste disposal
Off-Highway Mobile
Motorcycles
Lawn and garden equipment
Farm equipment
Construction equipment
Industrial equipment
Outboard motors
Stationary Industrial Sources
Airports
Total Area Sources
Point Sources
Fuel Combustion
Industrial Process
Solid Waste Disposal
Total Point Sources
Grand Total (all sources)
Particulate
(TSP)
196
458,605
60,244
524,583
138,156
937
470
64,464
704
6,278
878
547
432
386
2,079
895
218
0
1,608
0
0
0
306
127
3
32
390
964
734
0
133
65
1,264,434
11,165
31,884
319
43,368
1,307,802
GO
b°x
458
0
0
0
0
0
0
0
302
2,347
2,001
1,573
25
1
9,873
6,676
12
0
5,457
0
0
0
18
14
1
8
277
983
679
45
58
48
30,856
805,262
96,293
227
901,782
932,638
NO
NOX
3,353
0
0
0
0
0
0
0
5,856
67,185
11,960
655
3,453
1,623
312
3,000
2,612
0
529
0
0
0
115
24
2
124
3,287
12,652
8,765
46
149
555
126,257
275,687
13,404
182
289,273
415,530
HC
688
0
0
0
0
0
0
0
12,376
99,990
4,229
164
345
163
2,079
149
174
0
115
3,807
13,650
645
574
102
220
1,530
1,951
1,893
3,056
7,799
106
1,467
157,272
2,422
45,216
136
47,774
205,046
CO
1,319
0
0
0
0
0
0
0
120,081
1,033,770
4,360
273
863
406
9,355
199
435
0
414
0
0
Q
1,625
206
421
11,927
23,148
19,508
66,085
23,474
20
2,969
1,320,858
7,931
130,238
3,180
141,349
1,462,207
12
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For point sources, emission factors have been classified into source
classification codes (SCC). Each SCC represents a particular source type
and, consequently, has its own emission factor. These factors are based
on pre-RAPS data and have been published in AP-42,* Supplement 5. As part
of the RAPS program, new source test data were acquired for many of the
point sources in the St. Louis area. For these tested sources, new emis-
sion factors were established when the test results did not verify the
old (Supplement 5) factors. Consequently, the RAPS point source inventory
relies on a mixture of emission factors based on (1) stack test results
for the source in question, or, when not available, (2) AP-42, Supple-
ment 5.
Extensive evaluation of the AP-42 emission factors was undertaken
by PEDCO Environmental (Gibbs et al., 1974). Results of this EPA-
commissioned study form one of the foundations for the analysis presented
in this report. More recently, Littman et al. (1977), at Rockwell Inter-
national, performed an evaluation for a few of the large point sources in
the RAPS inventory. Verification studies for the mobile and area source
inventories, however, do not currently exist.
Some summary statistics from the PEDCO study are presented in Table 2.
Average precision is a measure of the variation among stack test results
according to pollutant. For S09, the statistical interpretation is that
about 687o of the results fall within 17.770 of the mean or average: for CO,
about 687=, would fall within 32.170 of the mean. Therefore, the average
precision can be thought of as being a measure of the repeatability of
stack measurements. This repeatability is inversely proportional to the
precision value. This concept is further illustrated in later sections.
*
EPA Publication AP-42 with Supplements, "Compilation of Air Pollutant
Emission Factors."
Ratio of the standard deviation to the mean of the test results--averaged
over SCC codes by pollutant.
13
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Table 2
PEDCO AVERAGE PRECISION DATA
Pollutant
Particulate
S00
z
NO
X
HC
CO
Average Precision of Test Data
0.196
0.177
0.134
0.203
0.321
B. Evaluation Criteria
The analysis errors in the inventory will be assumed to follow a
Gaussian distribution as shown in Figure 1. The symbols in the figure
indicate the error parameters of interest. The distribution of errors
about the true emission rate, T, is subject to bias (b) and variability
Precision, a , is a measure of the variability of the distribution.
FIGURE 1 ASSUMED DISTRIBUTION OF ERROR
14
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For the ideal emission model, b and a are both zero. Where possible
in the analyses to follow, limits are estimated for these parameters. The
effect of such errors on the performance of air quality models is eval-
uated later in Sections X and XI.
15
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V EVALUATION OF STATIONARY POINT SOURCE EMISSIONS
The methodology for stationary point sources is based on the appli-
cation and verirication of standard EPA emission factors. As described
by Littman (1974), the RAPS inventory is distinguished from previous
inventories by three factors:
Accuracy
Space resolution
Time resolution.
As a quantitative measure of overall accuracy and probable error, consider
the Weighted Sensitivity Analysis Program (Ditto et al., 1973). Although
this program does not supply any estimates on the absolute accuracy, it
does help evaluate the maximum permissible error of any part of the in-
ventory, if provided with a maximum permissible error for the whole sys-
tem. In its evaluation, the program keeps the inventory at an equivalent
level of accuracy and points out areas where accuracy has to be improved
to provide a desired overall accuracy. In addition, it also provides an
approach to establish confidence levels for the emission inventory.
The first step of the method is based on the following linear model :
-
k=l
where
Q = Total amount of pollutant emitted
100 0 = Percentage error associated with Q
Q, = Amount of pollutant emitted by subclass k
100 crk,= Percentage error associated with Q .
This linear model is postulated as an appropriate model to analyze the
propagation of error through the emission inventory. If each subclass
17
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contributes to the error an amount proportional to its relative physical
contribution, it can be shown for each k that
(6)
The analysis demonstrates that to obtain a predetermined level of pre-
cision for a source class, not all subclasses need to be measured with
the same precision; the greater the ratio of Q:Q , the greater the al-
K.
lowable value of a, . Conversely, a, approaches the value of 9 as the
ratio' approaches unity. Equation (6) allows calculation of allowable
inventory error per category, afc, after specification of allowable total
error, 9. Confidence in the values for 9 and a, is quantified by a sec-
ond step as described in the following paragraph.
By using Chebyshev's inequality, the second step allows one to estab-
lish probabilistically the confidence level for the inventory; i.e., what
is the probability that the actual overall error in emissions will not
exceed 9. Ditto et al. (1973) provide a complete treatment of the theory.
Using results from this reference, Table 3 shows the interrelationships
among overall emission inventory error, confidence level, and 9. As an
interpretation, Table 3 indicates that, for the emission inventory to be
accurate within 10% of the true value at a 95% confidence level, 9 must
be 2.24% (or less).
Table 3
VALUES OF 9 FOR SELECTED INVENTORY ERRORS
AND CONFIDENCE LEVELS
Inventory Error
5
10
20
Confidence Level
(%)
90
1.58
3.16
6.32
95
1.12
2.24
4.47
99
0.5
1.0
2.0
18
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A 107<> overall inventory accuracy specification was deemed reasonable
by Rockwell International. (Ideally, the specification should reflect
requirements of the air quality modeling community.) They used values
from the 1973 NEDS inventory to calculate the allowable error for point
source classes of various sizes, which are given in Table 4.
Table 4
MAXIMUM ALLOWABLE ERROR ak FOR POINT SOURCES OF VARIOUS SIZE^
(Acceptance Interval 10%, Confidence Level 95%, 6 = 2.24%)
Pollutant
so2
CO
NO
X
HC
Particulate
Total
Point Source
Emissions, Q
(tons/yr) ^
1,187,296
1,684,794
310,993
78,474
323,952
Allowable Error a, for Point Source of
(100 ton/yr)
244%
290%
125%
63%
127%
(1,000 ton/yr)
77%
92%
40%
20%
40%
(10,000 ton/yr)
24%
29%
--
--
Reprinted from Littman et al. (1974).
t,
Source: NEDS December 1973, except for CO, which is given in NEDS at
2,836,270 before correcting to the value shown in this table.
The allowable error results served as the foundation for the point
source data collection strategy. Sulfur dioxide (S0?) was the pollutant
emphasized initially in the inventory because it occupied the position
of highest priority within the RAPS. Therefore, S02 emissions will be
discussed first.
A. Sulfur Dioxide
1. Background
The SO- point source inventory and its verification are de-
scribed in detail by Littman, Griscom, and Klein (1977). For sources
emitting at least 1000 tons of SO per year, hourly fuel consumption
19
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data were acquired. For smaller point sources (less than 1000 ton/year),
hourly data were derived from annual data plus a detailed operating pat-
tern. This approach is consistent with the results of the weighted sensi-
tivity analysis.
2. Evaluation
Calculation of the SO- emission rate from the process data is
based on the following equation:
Q = W EF
Q = W EF = W K S (7)
where
Q = The emission rate
W = The fuel consumption rate
EF = The emission factor (normally EF = K S)
S = The percentage of sulfur in the fuel
K = An emission constant.
For air quality modeling purposes, it is assumed that a temporal resolu-
tion of one hour is sufficient. For sources emitting at least 1000 ton/
year, W is acquired on an hourly basis. These large sources are con-
sidered first.
3. Large Point Sources
As a first approximation, the error is related to the product
of S and K. S is measured directly, but not on an hourly basis. More
typically it is measured weekly or monthly. Emission factors have been
experimentally verified. Based on the PEDCO report (Gibbs et al., 1974),
the precision of measuring the sulfur content is generally less than 0.1,
depending on the type of fuel. The daily variability of the sulfur con-
tent can be at least that great.
Assessment of the precision of emission factors can only be
semiquantitative. While the PEDCO report does present precision and
20
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accuracy (bias) calculations for a wide variety of source types, these
calculations are based on source test data that are subject to inaccura-
cies. In recognition of this, EPA commissioned Rockwell International
to conduct emission factor verification studies in the St. Louis area.
These have been reported by Littman, Griscom, and Klein (1977).
The Rockwell International report concluded that some previous
stack tests were suspect, particularly those that relied on stack gas
velocity measurements such as used in EPA Method 2. Upon reviewing these
measurements, Rockwell found that stack flow values were high by varying
but substantial amounts. They further verified these conclusions using
fuel consumption rates.
Based on their most recent mass flow data, Rockwell concluded
that AP-42 emission factors for SCK were reasonably accurate. In a pri-
vate communication, Littman estimated the general precision of the RAPS
S0? emission factors to be within 0.15 with no significant bias. (This
roughly corresponds to a "typical error" of 157o.) The precision estimate
is supported by the PEDCO analysis (see Table 2).
The overall precision of the emissions model is the square root
of the sum of the square for the individual precisions based on: (1)
emission factors^" and (2) the daily variability of the sulfur content
Mathematically,
a = P V(0.15)2 4- (0.15)2 = 0.225 (8)
where a is defined in Figure 1. For purposes of this evaluation, a will
e e
be approximated at 0.2 for future calculations. Further it is assumed
that the bias, b, is negligible for these major SO sources. The Rockwell
studies tend to verify this assumption.
*
Dr. F. E. Littman, Rockwell International, private communication, 1976,
The precision of the sulfur content is inherent in the emission factor
precision.
21
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4. Small Point Sources
Statements made in the previous subsection (3) concerning the
accuracy of emission factors and sulfur measurements still apply here.
However, for small point sources, hourly process data are not collected.
Instead, annual data are acquired and modified by a detailed operating
pattern. Sufficient data are not on hand to validate the accuracy of
these operating patterns. For the purposes of future analyses, vie will
assume a precision of 0.4 and negligible bias.
B. Nitrogen Oxides
In the PEDCO report, the precisions of NO measurements (by SCO code)
X
are, on the average, comparable to those for SO^. However, a substantial
bias appears because emission factors for combustion sources are too high.
The Rockwell verification study corroborates the PEDCO results. Their
experimentally obtained factors ranged from a low of 7. TL to 12% of the
applicable AP-42 factors.
The RAPS data handling system allows for the input of plant specific
emission factors. Standard AP-42 emission factors are being used at un-
tested sites. It is reasonable to assume that these sites will also be
represented by emission factors that are too high. This will result in
the inventory being biased accordingly.
The amount of inventory bias is a function of the number of sources
verified, as described above. If we assume one-half of the emission fac-
tors are corrected, then it would appear that the other half is biased
by 7.7 to 727», or, on the average, 4070. The total inventory then will
be biased by one half that amount--20%.
If we assume precisions comparable to those for S02, a very tentative
estimate of NO emission inventory errors follows:
Large point sources:
b = +0.2 T.
a = 0.2
e
22
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Small point sources:
b = +0.2 T
a = 0.4
e
The impact of such errors on air quality predictions is discussed in Sec-
tions X and XI. The relative importance of these point source data is
evident in Table 1, which shows the dominant effect of point sources ac-
counting for almost 70% of NO emissions.
X
C. Carbon Monoxide
As shown in Table 1, point source CO emissions account for only a
few percent of the total inventory. As with NO , CO emissions cannot be
X
verified through material balance calculations. The Rockwell tests indi-
cate experimental emission factors much lower than those given in AP-42.
The PEDCO report does not contain sufficient data to enable any general
conclusions to be made. Therefore, we will assume that the precision
and bias are comparable to those for NO .
D. Hydrocarbons
Point source emissions make up about 25% of the total HC inventory.
For HC, point sources consist of those emissions released through a stack
or vent, resulting from either fuel combustion or evaporation.
Rockwell has been delegated the responsibility of measuring HC com-
ponents, both reactive and nonreactive. Primarily, the efforts were in
separating out methane and nonmethane components with a particular emphasis
on accurately measuring the nonmethane components. Littman, Griscom, and
Seeger (1977) describe a chromatographic technique that is linear with
3
respect to carbon number and HC concentration up to 3.5 X 10 per carbon
number. Griscom (1977) also estimated HC emissions by category. (The
categorizing of HC components in the inventory is an important input in
photochemical air quality simulation models.) This topic is discussed
more thoroughly in Sections VIII.F and XI.C. Our evaluation here is
limited to total hydrocarbons (THC).
23
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The Rockwell approach consists of modifying emission factors for
tested sources only. Other sources are estimated using AP-42 factors.
The process data are collected hourly for major combustion sources. For
evaporative sources, which account for about 4070 of the point source
inventory, data are presented annually. Therefore, the hour-to-hour
variability will be another source of inaccuracy.
All the above factors must be taken into account in estimating pre-
cision and bias. For combustible sources, these parameters should be
about the same as for NO and CO. For evaporative sources, for which
annual fuel loss data are used, the long-term bias should be negligible,
but the precision should be high because no corrections are made for loss
as a function of time. Our estimate of the composite of the above cases
is a bias of +0.1 T and a precision of 0.4.
E. Particulates
Based on the figures in Table 1, ground level TSP measurements should
be dominated by fugitive dust sources. However, the dominance is primarily
caused by the mass of large inert particles (diameters above 2 pm). Other
source types may be dominant emitters of the smaller (resperable) particles
or chemically active particles. Hence, accurate particulate data from.
nonfugitive dust sources are essential.
Previous inventories focused on TSP, but the RAPS inventory separates
particulate data by particle size categories. In general,
where
E. = W EF (1 - C.) F. P (9)
E = Emission rate for particles in category i
W = Mass flow rate
EF = Emission factor
F. = Fraction of particles in category i
C = Efficiency of control system for particles in
category i
P = Percentage of production subject to control.
24
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Rockwell was commissioned to assemble and verify the point source particu-
late inventory (both by TSP and size category). The work is described by
Littman, Griscom, and Wang (1977). Some emission factors, by particle
size, were derived from the Rockwell stack test data. Others were based
on other factors experimentally derived by Midwest Research Institute
(Weast et al., 1974).
The frequency categorization and control device efficiency are two
additional sources of inaccuracy in determining total emissions. Suf-
ficient data do not exist to allow us to make a reasonable estimate on
the bias and precision of these parameters and, hence, the particulate
point source inventory as a whole.
25
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VI EVALUATION OF STATIONARY AREA SOURCE EMISSIONS
Unlike the point source inventory, where emission estimates can be
evaluated by testing a relatively small number of sources, stationary
area sources can be numbered in the hundreds of thousands. Broadly speak-
ing, there are three basic categories of stationary area sources: resi-
dential, commercial, and industrial. The emissions from any one source
under varying conditions can be estimated within an assumed accuracy.
However, the complete characterization of every small source is a monu-
mental task well beyond the scope (and budget) of the RAPS survey. There-
fore, the general approach has been to derive fuel consumption, land use,
and other algorithms to estimate hourly emission rates per unit area.
The minimum size for an area source is a 1 km by 1 km square grid
with larger grid sizes being even multiples. Figure 2 illustrates the
RAPS grid system. In general, areas of heavy emissions and large spatial
variability are represented by the smaller grids and the more rural areas
(with low emission density) are represented by larger grids. The RAPS
grid system is consistent with requirements of most air quality models.
With the exception of evaporative hydrocarbon emissions, stationary
area source emissions are predominantly the result of demand fuel usage.
For commercial and residential sources, the emissions methodology was
developed by Environmental Science and Engineering, Inc. (Holden, 1975);
the industrial sources were estimated by Rockwell International (Littman
and Isam, 1977). These industrial sources are insignificant and, for our
purposes, can be neglected. Therefore, the focus of the section is the
commercial and residential area sources.
In the methodology, spatial allocations to the RAPS grid system was
based on census data* for residential estimates and land use data^ for
*
Bureau of Census, Population Estimates and Projections for 1972-73.
t
The East-West Gateway Coordinating Council, 1971-72 Existing Land Use
Update and Analysis.
27
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City of St. Louis
Clinton Co. Bond Co.
oo
St. Louis Co.
St. Charles Co.
Monroe Co.
4
Randolph Co.
St. Clair Co.
Jefferson Co.
-------
commercial estimates. Temporal distributions were derived from demand
fuel records. The 1973 NEDS inventory was the source of annual emissions
estimates.
The adequacy of any of the techniques is largely a function of the
pollutant and the currency of the basic data. The basic data are three
to five years old and should be updated. In the following two subsections,
spatial and temporal distribution methodologies are discussed. Then each
of the critical pollutants is evaluated separately.
A. Estimation of Spatial Resolution
Environmental Science and Engineering Inc. (ESE) distinguished be-
tween residential and commercial land use. Quantification of residential
areas was based on the U.S. Bureau of Census Fourth Count Computer Summary
Tables, which contain data on: size and nativity of families, education,
employment status, age of home, and fuel usage for space-heating, water
heating, and cooking for census tracts, county subdivisions, and counties.
The determination of the spatial distribution of residential fuel usage
was estimated from the data on these tapes for the tracts in the St. Louis
Standard Metropolitan Statistical Area (SMSA).
1. Residential Sources
ESE developed a methodology to equate the Census data to the
RAPS grid system (Figure 2). In essence, their procedure involved an
overlay of the RAPS grid system on tract maps. Each grid square was as-
signed a visually estimated land area percentage of the total tract. Al-
lowances were made for special terrain and land use effects (along the
banks of the Mississippi River; near Forest Park; and so forth). ESE
then checked results to ensure that 10070 of each tract had been apportioned
among the grid system.
Specifically, the census data provided information on the num-
ber of housing units using: (1) natural gas, (2) bottled (LP) gas, (3)
electricity, (4) fuel oil, (5) coal or coke, (6) wood, (7) other, and
(8) none. It became possible for ESE to determine annual tract fuel
usages by the following formula:
29
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Number of homes in tract heated
by fuel type i annual county residential
Number of homes in county heated area source use of fuel type i
by fuel type i
The annual county residential area source fuel usage of each fuel type
was available from the EPA NEDS Stationary Source Fuel Summary Reports
for the respective counties.
2. Commercial Sources
Land use for commercial sources was resolved in a similar manner
as that for residential sources, only the basic reference was different.
In this case, the East-West Gateway Coordinating Council report (previously
cited) became the basic reference. However, this report did not include
the type of fuel used. Therefore, this methodology assumes that the rela-
tive fuel type distribution is the same as that for residential space
heating in the same area.
3. General Comments
The ESE technique of spatially distributing area source emissions
is comprehensive and technically sound. For RAPS purposes, the basic data
sources should be updated and verified. Although St. Louis is not a rap-
idly growing area, it is growing and the spatial distribution of its popu-
lation and commercialization is changing.
B. Estimation of Temporal Resolution
For the sake of discussion, this area source inventory can be divided
into three categories: demand fuel usage, evaporative hydrocarbon emis-
sions, and solid waste disposal-structural fire activities. These topics
are discussed in the following paragraphs.
1. Fuel Usage
Data supplied by Laclede Gas Company formed the basis for the
ESE temporal distribution algorithm. The data itself concerned natural
gas usage, but ESE generalized the analysis for other space heating fuels.
30
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The Laclede data included over a year of hourly gas flow and a number of
meteorological parameters (wind speed and direction, temperature, and
solar radiation).
Based on the LaClede statistics, ESE empirically derived flow
equations for natural gas usage as a function of temperature and wind
speed for times of space heating demand. In this analysis, space heating
was assumed to be in demand when temperatures were below 68°F (20°C).
When temperatures exceed 68°F (20°C), the ESE algorithm projects a base-
line value independent of the meteorological parameters. Estimates were
also corrected for time of day. The natural gas algorithm for space
heating is also applied to other fuels. Therefore, strictly for space
heating, emissions for these fuels are estimated as zero when temperatures
exceed 68°F (20°C).
The fuel demand algorithms are an effective way of temporally
distributing emissions. They are based on a large enough data base to
be realistic. There are bound, however, to be some inaccuracies. For
instance, the discontinuity of the equations at 68°F (20°C) is too abrupt
to be real. Nevertheless, the results should provide good estimates.
2. Evaporative Hydrocarbon Losses
Hydrocarbon emissions result from evaporation of dry cleaning
fluids, solvents from paint, and gasoline at service stations. The tem-
poral distribution of dry cleaning emissions was assumed uniform over
normal working hours. Paint emissions were similarly assumed to be uni-
form.
Gasoline emissions occur in two ways: filling losses from
underground storage tanks, and filling losses and spillage from the fill-
ing of vehicle tanks. The former distribution was assumed uniform over
the normal working day. The latter was assumed to be related to auto-
mobile traffic patterns as estimated by Ludwig and Dabberdt (1972). In
Figure 3, these diurnal traffic patterns are illustrated for weekend and
weekday. Both types of gasoline emissions are adjusted as a function of
month (based on marketing information).
31
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006
004
(T
UJ
z
o
<
tr
002
I 1 1 1 1 ( T
WEEKEND MEASURED. BROADWAY AND LOCUST
I-.-\
0.10
< 008
O
V
<
o
UJ
uj
$
-------
In Table 1, it can be seen that the most critical estimate is
that for evaporative losses from automobile gas filling and spillage.
Appropriately, ESE derived a comprehensive estimate for this case. Use
of the diurnal traffic patterns is, perhaps, the best criteria. However,
a composite pattern from a number of locations should be used. In the
analysis, data from one downtown location was used, which may or may not
be representative.
3. Solid Waste DisposalStructural Fires
Emissions from solid waste disposal through open burning and
incineration were taken from an inventory prepared by St. Louis County.
Temporal allocation was based on an 8:00 a.m. to 5:00 p.m. workday.
Structural fires, on the other hand, occur randomly and, temporally, the
resulting emissions are assumed to be uniformly distributed over the year.
Generally, emissions in this category are insignificant on an annual
basis.
C. Discussion of Area Sources by Pollutant
In the following paragraphs, each of the criteria pollutants is dis-
cussed separately.
1. Sulfur Dioxide
Space heating requirements almost totally account for area
source SO^ emissions. Fuel oil and coal are the primary fuels here. The
consumption of these fuels can be estimated accurately for a year. The
unknowns are the fuel sulfur content, the temporal distribution, and the
spatial distribution. All of these parameters can be estimated or modeled
(as discussed previously).
There are probably compensating factors (offsetting errors) that
should be taken into consideration. For instance, an underestimation of
a spatial allocation in one grid may be offset somewhat by an overestima-
tion in an adjacent grid, or fuel with a below average sulfur content from
one area may be offset by an above average sulfur content in another area--
and so forth.
33
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In general, with some checking of the yearly fuel consumption
data and sampling of fuels (for sulfur content) the methodology should
produce negligible annual bias. The emission factors should be as ac-
curate as those for point sources. Nevertheless, the accuracy of the
emission estimate from any particular grid at a specified time is prob-
ably subject to a high degree of variability. For the sake of future dis-
cussion, we will estimate that the precision is approximately 0.75 and the
bias is negligible.
2. Nitrogen Oxides
The problems in estimating NO are much the same for area sources
X
as they were for point sources once the effects of spatial and temporal
distribution are factored out. Spatial allocations are much the same as
they were for SO . Temporal allocations are also much the same. The
A
Laclede Natural Gas data are, if anything, more accurate here since NO
X
emissions from natural gas are prominant.
For the purposes of our evaluation, we will again estimate the
precision at 0.75. This time we should acknowledge that a bias probably
exists. The PEDCO report, in analyzing the precision of area sources,
relies heavily on precisions computed for point source emission factors.
Using this rationale, the bias could average on the order of +0.4 T.
3. Carbon Monoxide
For lack of better data, the accuracy of CO emission estimates
should be comparable to those for NO
X
4. Hydrocarbons
Evaporative sources dominate the inventory. Therefore, the bias
should be negligible for these sources since they are based on fuel bal-
ance estimates. The precision should be comparable to that for other pol-
lutants.
34
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5. Particulates
Except for fugitive dust, particulate emissions from area sources
are rather small when compared to other sources. Emissions predominantly
are a function of space-heating requirements. Precision and bias estimates
should be comparable to those for NO and CO.
X
D. Summary of Quality of Stationary Area Source Methodology
The general methodology proposed by ESE should adequately fulfill
the accuracy requirements of the RAPS inventory. Improvements can be
realized in a number of areas such as:
Improvement of verified emission factors for point sources
could be applied to area sources using the same processes.
A random analysis of fuels should be conducted to verify
ash and sulfur content.
Verification of fuel use data-records from major fuel sup-
pliers should be checked.
In general, the temporal and spatial allocation of the emissions should
be sufficient. However, the diurnal pattern assumed for evaporative emis-
sions for hydrocarbons should be updated. This would almost automatically
result from a similar analysis needed in the mobile source inventory.
35
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VII EVALUATION OF HIGHWAY SOURCE EMISSIONS
A. Evaluation of Highway Source Inventory
As shown in Table 1, emissions from conventional highway sources
dominate the inventory for two pollutants (CO and HC) and significantly
effect the third (NO ). Yet, no St. Louis field test data (comparable
X
to stack test data) exist. Hence, any evaluation will lack objectivity.
Lack of these field test data is the most serious deficiency in the entire
RAPS inventory. To fulfill this need, a field test plan was developed as
part of this contract and is reported separately (Shelar et al., 1976).
In the absence of data to corroborate estimated emissions, we must
rely on a subjective evaluation of the quality of the input data. Even
in this respect, accuracy of most input parameters is not known. For
instance, the 58 second-by-second speed and time profiles are basic input
data to the RAPS highway (modal) model. Though these are based on actual
measurements, the sample size is relatively small. Therefore, the repre-
sentivity of these speed and time profiles should be determined through
further tests.
The discussion in this section is minimal on explicit treatment of
traffic volume. The number and type of vehicles on the road at any given
time are crucial. Since emissions are estimated on an hourly basis, the
diurnal traffic variations must be defined throughout the RAPS area. For
vehicle mix, local vehicle registration data are used. The resulting mix
(percent) is assumed as uniform in time and space. In reality, vehicle
mix is related to economic and social issues which vary from one part of
the area to another. On the average, this variability might be negligible.
However, this should be confirmed by random field tests.
The number of vehicles on the road at any specified hour (of the year)
is based on 1975 ADT estimates adjusted by a temporal distribution algor-
ithm. The traffic volume is adjusted for:
37
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Month of the year through evaluation of gasoline sales
records
Day of the week, weekend versus weekday as shown in Fig-
ure 3.
Hour of day, as shown in Figure 3.
The latter two factors are based on site specific data that are several
years old. Such data should be sampled in various parts of the city on
different highway types. The resulting analysis should then be used to
determine whether one diurnal cycle is sufficient.
Potential inaccuracies in all model input parameters must be deter-
mined through field test data. Model performance should also be deter-
mined through field tests. Only then can an accuracy assessment be made
on the highway source emission inventory. As the inventory exists now,
it is probably the best of its type in existence. However, an estimation
of bias and precision, as per Figure 1, is not too meaningful at this
time. Nevertheless, to carry the analysis further will estimate bounds
on the precision (a ) as 1.0.
About 807o of the highway emissions are represented by line sources
(links). The remainder are allocated to grid squares as area sources as
shown in Figure 2. This allocation of highway emissions to grids and
lines is consistent with requirements of air quality models (line and
area source types). As part of the quality assurance program, the ac-
curacy of road coordinates were verified through random sampling.
B. Highway Emission Models
The previous discussion has focused on the need to verify highway
model input parameters. The sensitivity of two models is treated in the
next subsection. Although the modal model is used for the RAPS highway
inventory, we were commissioned to compare its sensitivity and estimates
to those generated by an alternative modelbased on the Federal Test
Procedure (FTP).
Emission models play an important role in the determination of mobile
source emission inventories. First, the choice of the emission model to
be used must be made, considering both the accuracy of the model and the
38
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difficulty and cost of obtaining the input data required by the model.
Second, once a model has been selected, its sensitivity to errors in the
input data must be assessed to determine the effects of such errors on
emission estimates. In fact, the model sensitivity to input may be a
factor in the model selection process.
For the RAPS evaluation, two emission models were considered: (1)
the methodology presented in AP-42, Supplement 5, and (2) the modal emis-
sion model (Kunzelman, et al., 1974). The following subsections briefly
describe these models. A comparison was made of the emissions produced
by the two models over a wide range of model input. The results of this
comparison are discussed in Section VII.E. The results of an analysis
of the sensitivity of each model to errors in the input data are described
in Sections VII.F and VII.G.
C. FTP Emission Model
The emission factor methodology described in AP-42, Supplement 5
(referred to here as the FTP emission model), enables prediction of emis-
sion factors for CO, HC, and NO . These basic emission factors for light-
X
duty vehicles (LDV) and light-duty trucks (LDT) were derived from measure-
ments made on a variety of vehicles operating over the Federal Test
Procedure (FTP) driving sequence. The conditions present for the FTP
driving cycle tests are: (1) the cycle has an average route speed of
19.6 mph, and (2) the ambient air temperature is approximately 75°F
(24°C). The emission factors presented in Supplement 5 represent a com-
bination of 20% of the vehicles operating in a cold condition, 2770 in a
hot start-up condition, and 537o in a hot stabilized condition. The emis-
sion factors vary for each model year and the age of the model year at
the time of interest.
To compute emission factors for speeds, temperatures, and percentages
of cold and hot starting vehicles other than those given above, correction
factors are applied to the basic emission factors using the following
equation:
39
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n
, c. m. v. z. r. (10)
npstwx / j ipn in ips ipt iptwx
i=n-12
where
e = Composite emission factor for LDV and LDT for
npstwx T , ,,
calendar year n, pollutant p, average speed s,
ambient temperature t, percentage cold opera-
tion w, and percentage hot start operation x
(g/mi)
c. = FTP mean emission factor for the ith model year
during calendar year n for pollutant p (g/mi)
m. = Fraction of annual travel by the ith model year
in , . , , J J
during calendar year n
v. = Speed correction factor for the ith model year
for pollutant p and average speed s
z. = Temperature correction factor for the ith model
P year for pollutant p and ambient temperature t
r = Hot/cold vehicle operation correction factor
P for the ith model year for pollutant p, ambient
temperature t, percentage cold operation w, and
percentage hot start operation x.
Speed correction factors apply to speeds between 5 and 45 mph, with the
end point value used for a speed outside the range. Temperature dependent
correction factors apply to a temperature range of 20 to 80°F (-6.7 to
26.7°C), with the end-point value used for a temperature outside the range.
The above equation applies to both LDV and LDT, although the values of the
parameters differ for the two types of vehicles.
Heavy-duty gasoline truck (HDG) and heavy-duty diesel truck (HDD)
emission factors are based on the San Antonio Road Route test and assume
100% warmed-up vehicle operation at an average route speed of approximately
18 mph. To adjust these emission factors for other average route speeds,
a speed correction factor is applied according to the following equation:
n
e = 7 c. m. v. (11)
nps £^j ipn in ips
i=n-12
40
-------
where
e = Composite emission factor for HDG and HDD for
" calendar year n, pollutant p, and average speed
s (g/mi)
c. = Test procedure emission factor for pollutant p
P for the ith model year in calendar year n (g/mi)
m. = Fraction of annual travel by the ith model year
during calendar year n
v. = Speed correction factor for the ith model year
for pollutant p and average speed s.
Equation 11 applies to both HDG and HDD, although the values of the param-
eters differ for the two types of vehicles.
In addition to exhaust emission factors, the FTP methodology provides
for computation of evaporative and crankcase hydrocarbon emission factors
for LDV, LOT, and HDG. Composite crankcase and evaporative HC emission
factors are determined using the equation:
n
f = > h.m. (12)
n ^^ i in
i=n-12
where
f = Combined evaporative and crankcase HC emission fac-
tor for calendar year n (g/mi)
h. = Combined evaporative and crankcase HC emission fac-
tor for the ith model year (g/mi)
m = Fraction of annual travel by the ith model year dur-
ing calendar year n.
Of course, each of the above parameters differ for the three vehicle types,
Once exhaust and, for hydrocarbons, evaporative and crankcase emis-
sion factors have been computed for all types of vehicles, the emission
factor for each vehicle type is weighted according to the percentage of
the total vehicle population of that vehicle type. The weighted factors
are summed to obtain a composite emission factor.
41
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D. Modal Emission Model
The automobile exhaust emission modal analysis model was developed
to reproduce light-duty vehicle exhaust emissions over any specified
driving sequence containing accelerations, decelerations, steady-speed
operation, and idling for CO, HC, NO , and C09. A functional form for
X ^
the emission rate function for steady-speed cases and for acceleration
and deceleration cases was determined from test data recorded over the
Surveillance Driving Sequence. The steady speed emission rate function
is given by:
es(v) = Sl + S2v + S3v2 (13)
where
v = Speed
S ,S ,S = Constants.
For nonzero accelerations and decelerations, the emission rate function
is given by:
222
e.(v,a) = b1 + b2v + b,,a + b.av + b^v + b,a + b._v a
+ b0a2v + b.a2v2 (14)
o y
where
a = Acceleration (or deceleration)
b1 to bq = Constants.
The instantaneous emission rate function for a given vehicle and pol-
lutant is given by:
e(v,a) = h(a)e(v) + [1 = h(a)]e, (v,a) (15)
S A
where h(a) is a weighting function dependent upon acceleration and bounded
by the values of 0 and 1, which allow for a smooth, continuous transition
from steady speed to acceleration and deceleration emission rate functions,
42
-------
The emission rate function for a group of vehicles has been deter-
mined by averaging the coefficients that make up the emission rate func-
tions of each vehicle in the group. The average vehicle response is
obtained by integrating its rate function over the second-by-second speed-
time curve specified by the driving sequence. The emission response of
the group during any driving sequence is determined by multiplying the
average vehicle response by the number of vehicles in the group. These
emissions are then added to the emissions computed for the other groups
that form the total vehicle population on the highway.
Temperature and percent cold-start vehicle correction factors origi-
nally used in the modal model were those given in AP-42, Supplement 5
for model years prior to and including 1971. The coefficients, S , S ,
S-, and b1 to bg, however, have been updated and, as now used, relate to
vehicles of model years prior to and including 1975. Light-duty vehicle
modal emissions may be projected to years beyond 1975 by applying the
ratio of future year emissions to those of the 1975 FTP emission factors
for LDV for the appropriate pollutant.
E. Emission Model Comparison
Emission predictions resulting from the FTP and modal models were
compared to determine whether the collection of the detailed input data
required by the modal model is warranted or whether the FTP methodology,
with its somewhat simpler input data, predicts emissions with sufficient
accuracy for RAPS purposes.
1. Model Input Data
The input data required by the two models are listed in Ta-
ble 5. To include all possibilities likely to be encountered in the RAPS
highway network, the CO, HC, and NO emission factors were computed with
X
each model for a wide range of input conditions. We chose the following
input conditions: two calendar years, three temperatures, three per-
centages of cold-start vehicles (with associated percentages of hot-
starting vehicles), 25 speed and time profiles (for the FTP model, cor-
responding average route speeds, computed by taking a simple average of
43
-------
Table 5
EMISSION MODEL INPUT DATA
FTP Model Required Input
Modal Model Required Input
Pollutant type
Calendar year
Ambient temperature
Percentages cold-starting and
hot-starting vehicles
Average route speed
Fraction of total vehicles of
each vehicle type
Fraction of annual travel
driven by each model year of
each vehicle type during the
calendar year of interest
Traffic volume
Pollutant type
Calendar year
Ambient temperature
Percentage cold-starting
vehicles
Second-by-second speed and
time profile over the route
Fraction of annual travel
driven by LDV of each model
year during 1971
Traffic volume
the second-by-second speeds). Values of each variable were selected so
that the range of values for each variable allowed by the emission models
was represented. Table 6 lists the values of the variables used in emis-
sion model computations. Emissions were computed for all combinations of
these values, with the exception of those that are not physically realistic,
The exceptions will be discussed later. The total number of possible cases
for the various combinations of input variable values is 1350.
The speed and time profiles were based on data obtained by the
Department of Transportation (DOT). Of the 25 speed and time profiles
used in the modal computations, 24 are the results of a study made by
Washington University for EPA in which an attempt was made to isolate
"representative" profiles for the various roadway types in the St. Louis
area. In this study, three roadway type descriptors were defined: road-
way function, average daily traffic (ADT), and volume to capacity (V/C)
ratio^ For each descriptor7~~;classes were definecPas shown' in Table 7.
There are 48 different combinations of classes for the three descriptors
44
-------
Table 6
EMISSION MODEL INPUT DATA USED FOR MODEL COMPARISON RUNS
Variable
Values of Variable Used
FTP Model
Modal Model
Calendar year
Temperature °F (°C)
Percentage cold
starting and hot
starting vehicles*
Pollutant
Vehicle speed
Fraction of total
vehicles of each
vehicle type
Fraction of annual
travel driven by
each model year
of each vehicle
type
1975, 1977
20,50,80 (-6.7,10,26.7)
0/0,50/10,100/0
CO, HC, NO
X
Average route speed
corresponding to each
of 25 speed/time pro-
files
National average
vehicle type mix
National average
model year mixes
1975, 1977
20,50,80 (-6.7,10,26.7)
0,50,100
CO, HC, NO
x
j.
25 speed/time profiles'
1971 national average1
Correction factors involving hot-starting vehicles apply only to the
FTP model.
See text for description of speed/time profiles used.
Taken fromAP-42, Supplement 5. See text and Table 8.
(and thus 48 different roadway types), but only 29 of the types actually
exist in the St. Louis Area.
In the study, speed/time profiles for both peak and off-peak
hours were chosen that were considered "representative" of each of the 29
roadway types. The 58 second-by-second profiles chosen are of various
durations in time alrHi~~~c~ove"rr different lengths of roadway. Examination of
the 58 profiles revealed that for the purpose of emission model comparison
it is possible to eliminate ADT as a descriptor. In general, for each
combination of functional class of roadway and V/C ratio class, roadways
45
-------
Table 7
ROADWAY DESCRIPTOR CLASSES
Descriptor
Descriptor Classes
Roadway function
1 = freeway
2 = principal arterial
3 = minor arterial
Average daily traffic (ADT)
Freeway
1 = <40,000 vehicles
2 = 40,000-60,000
3 = 60,000-80,000
4 = >80,000
Principal arterial
1 = < 10,000
2 = 10,000-20,000
3 = 20,000-30,000
4 = >30,000
Minor arterial
1 = <5,000
2 = 5,000-10,000
3 = 10,000-15,000
4 = > 15,000
Volume to capacity (V/C) ratio
1
2
3
4
<0.3
0.3-0.6
0.6-0.9
>0.9
exist for only two or three of the ADT classes. The variation among each
of these sets of two or three profiles was found to be minimal, so one
profile was chosen to represent each combination of roadway function class
and V/C ratio class. Since there are 3 roadway function classes and 4
V/C ratio classes, 12 peak hour and 12 off-peak hour speed and time pro-
files were selected for use in the model comparison.
As shown in Table 7, the roadway function classes are freeway,
principal arterial, and minor arterial. Among the original 58 speed and
time profiles there were no freeway cases that were congested, even the
peak hour case having V/C ratio class 4 and ADT class 4 (see Table 7).
In fact, the lowest average route speed for freeways was 50.0 mph with
a standard deviation of speed over the profile of 1.42 mph. Since the
46
-------
DOT study in the St. Louis area did not contain a congested freeway pro-
file during peak travel hours, it was felt one should be added to the data
set used for emission model comparison. Therefore, a speed and time pro-
file was fabricated from a travel distance and travel time study conducted
on a section of Interstate 80 near Berkeley, California. This was a very
congested case, with an average route speed of 4.6 mph.
A further note is in order about the speed and time profiles
for the 29 roadway types. For most cases, comparison of off-peak hour
profiles and peak hour profiles, with identical roadway descriptors, shows
only slight differences in average route speed or the standard deviation
of average route speed over the profile. There are a few notable excep-
tions. For many of the roadway types in the St. Louis area, variation
between peak and off-peak hour profiles would be expected. This fact,
as well as the absence of a congested freeway case, was discussed with
the investigators who performed the speed and time profile study. The
explanation given for these factors is that the data sample from which
the 58 "representative" speed and time profiles were chosen is limited.
The entire data sample contained no congested freeway cases, and the cases
chosen as representative are the most representative of the data that was
available.
As mentioned above, emissions were computed using both the FTP
and modal models for all combinations of 2 calendar years, 3 temperatures,
3 percentages of cold-starting vehicles, 3 pollutants, and 25 average
route speeds or speed and and time profiles, with the exception of those
cases that were not physically realistic.
The cases considered unrealistic are the following: (1) the
162 cases having 1007o cold-start vehicles on freeways; (2) the 144 cases
having 10070 cold-start vehicles on principal arterials; and (3) the 144
cases having 070 cold-start vehicles on minor arterials. Thus, of the
1350 possible combinations, 900 cases (300 for each pollutant) were con-
sidered realistic and were used in the model comparison computations.
Other input data required by the FTP model include the fractions
of total vehicles of each vehicle type and the fractions of annual travel
47
-------
driven by each model year of each vehicle type. The vehicle type mix
used in model calculations is the national average vehicle type mix:
80.4% LDV, 11.8% LOT, 4.6% HDG, and 3.2% HDD. The national average model
year mixes used in FTP emission computations for 1971, 1975, and 1977,
are listed in Table 8.
Table 8
FRACTION OF ANNUAL TRAVEL BY VEHICLE
Vehicle Age
(years)
1
2
3
4
5
6
7
8
9
10
11
12
13+
Fraction of Annual Travel
Calendar Year
1971
LDV
.116
.135
.125
.122
.106
.086
.083
.072
.051
.037
.023
.012
.033
LOT
.094
.138
.127
.131
.098
.083
.076
.057
.044
.032
.023
.016
.081
HGT
.062
.111
.117
.122
.093
.080
.066
.057
.047
.040
.031
.021
.153
Calendar Years
1975 and 1977
LDV
.112
.143
.130
.121
.108
.094
.079
.063
.047
.032
.019
.013
.039
LOT
.094
.141
.132
.123
.098
.083
.076
.057
.044
.032
.023
.016
.081
HGT
.062
.124
.117
.110
.093
.080
.066
.057
.047
.040
.031
.021
.153
HDT
.096
.169
.168
.164
.110
.080
.067
.048
.034
.018
.011
.007
.029
Taken from AP-42, Supplement 5.
The modal model also requires input of the fraction of annual
travel driven by each LDV model year. Since the coefficients used in
computations with the modal model are derived from calendar year 1971
data, the national average model year mix for 1971, listed in Table 8,
was used as model input. To project modal emissions to future years (in
this study, years 1975 and 1977), the future year FTP emission factor for
the case being modeled is computed using the vehicle model year mix for
that year. The 1971 FTP emission factor is also computed, using the 1971
vehicle model year mix. Then, the ratio of the FTP future year to FTP
48
-------
1971 emissions is applied to the 1971 modal LDV emissions to project the
modal LDV emissions to a future year.
2. Method for Determination of Composite Emission
Factors
The emission factor methodology in AP-42, Supplement 5, allows
computation of CO, HC, and NO exhaust along with HC evaporative and
X
crankcase emission factors for LDV, LOT, HDG, and HDD. Each of these
emissions is weighted according to the fraction of the total vehicle
population belonging to that vehicle type; then the weighted factors are
summed to produced a composite emission factor for the highway.
The modal emission model allows for computation only of LDV
exhaust emission factors. To compare the output of the two models, LDV
modal emissions were converted to a composite emission factor. This was
done using the following method: (1) the ratio of the composite FTP ex-
haust emission factor to the FTP LDV exhaust emission factor was computed
for each case; (2) the modal LDV exhaust emission factor was multiplied
by this ratio; and (3) for HC, the FTP evaporative and crankcase composite
emission factor was added to the result of (2) above. The emission factor
computed using this procedure is considered to be a composite modal emis-
sion factor. The basic assumptions of the procedure are: (1) the func-
tional form of LDT, HDG, and HDD emissions over the driving sequence is
the same as the functional form of LDV emissions over the sequence; (2)
the ratio of composite exhaust emissions to LDV exhaust emission is the
same for both the FTP and modal models; and (3) for HC, the functional
forms of exhaust emissions and of evaporative and crankcase emissions are
not the same.
3. Results
CO, HC, and NO emission factors were computed with the two
X
emission models- for each of the 300 cases. For each pollutant, the 300
modal emission values were plotted against the corresponding FTP emission
values. The range of emission values was such that when all 200 points
were plotted on the same figure, individual points could not be
49
-------
distinguished in areas where points were clustered. Therefore, to gain
sufficient resolution so that individual points could be distinguished,
the plots presented in Figures 4 to 6 include only those points within
a limited range. The range was chosen so the region where the majority
of points was clustered could be shown in detail. Figure 7 is an en-
largement of a portion of Figure 4. The regression lines on Figures 4
to 7 are a best fit to each of the entire 300 point data sets.
Various statistical quantities were computed from the emission
factors for each pollutant. The quantities computed include; (1) the
correlation coefficient and linear regression constants (FTP emission =
A X Modal emission + B) for the modal versus FTP emission factors; and
900
800
700
2 600
l
1
o 500
o
(7>
(O
LJ
400
300
200
100
I
CO
\
I
0 100 200 300 400 500 600 700 800 900
MODAL EMISSIONS (G /VEH-MI)
FIGURE 4 PLOT OF CO EMISSIONS COMPUTED WITH FTP METHODOLOGY AND WITH
MODAL MODEL FOR CASES WITH EMISSIONS LESS THAN 900 g/veh-mi
50
-------
60
50
2
I
i
o
V)
o
tn
-------
12
10
UJ
8
VI
o
LJ
a
c
-J i I , J ,
NO
M **4kl>>. I
« »«^ . i* tt'v.1.
...**.i/!.* "* '.^C*i """"*
i *ri .->-.» V "A*tIJ.'-
%
i
i
i
6 8 10
MODAL EMISSIONS (G /VEH-MI)
12
FIGURE 6 PLOT OF NOX EMISSIONS COMPUTED WITH FTP METHODOLOGY AND WITH
MODAL MODEL FOR CASES WITH EMISSIONS LESS THAN 15 g/veh-mi
The correlation between the modal and FTP emission models for
CO and HC is very high, as shown in Tables 9 and 10. For both pollutants
the correlation coefficients for all cases and for each subset of cases
are greater than 0.9. Also, the fits of the regression lines are good,
as seen in Figures 4 and 5. The correlations of the subsets were com-
puted and compared to determine which cases correlated the best and which
cases had the poorest correlation. A test was applied to the correlation
coefficients of the subsets which were being compared to determine whether
the differences between them are statistically significant. A test sta-
tistic, z, was computed according to the following equations:
52
-------
300
250
2 200
I
r
UJ
o
o
V)
CO
5
150
100
50
~r
CO
" %' . X*
. .; '' .
i. «.«»
. ,'. . ,
.'7?, '* '
I
I
I
50 100 150 200
MODAL EMISSIONS (G /VEH-MI)
250
300
FIGURE 7 PLOT OF CO EMISSIONS COMPUTED WITH FTP METHODOLOGY AND WITH
MODAL MODEL FOR CASES WITH EMISSIONS LESS THAN 300 g/veh-mi
where
Z. = 1/2 In
= 1/2 In
1 - r.
1 + r.
1 - r
2 J
Zl -
Z =
(16)
(17)
(18)
N2-3
rl> r2
N1»N2
= The two correlation coefficients being compared
= The sizes of the samples from which the correla-
tion coefficients were computed.
53
-------
Table 9
MODEL COMPARISON STATISTICS FOR CO
Cases
All cases
1975
1977
Freeway
Principal arterial
Minor arterial
Peak hour
Off-peak hoyr
Congested
Noncongested
20° temperature
50° temperature
80° temperature
0% cold starts
50% cold starts
100% cold starts
Number
of Cases
in Sample
300
150
150
108
96
96
156
144
84
216
100
100
100
102
150
48
Correlation
Coefficient
.9714
.9793
.9762
.9874
.9697
.9412
.9748
.9549
.9722
.9354
.9736
.9758
.9847
.9823
.9754
.9390
Linear Regression
Constants
A
.793
.730
.908
.849
.785
.665
.802
.773
.815
.779
.772
.933
.731
.753
.806
.648
B
4.39
7.51
-1.70
0.12
1.70
21.55
1.91
7.42
-3.22
6.66
4.08
2.76
2.05
0.40
4.98
29.51
Modal
Average
94.0
105.3
82.7
75.7
101.7
106.9
112.7
73.7
174.6
62.7
140.9
78.4
62.8
48.7
112.6
132.2
Standard
Deviation
100.6
111.9
86.3
119.8
90.1
82.3
121.6
65.3
145.8
47.8
139.3
69.2
52.3
42.3
117.5
96.9
FTP
Average
78.9
84.4
73.4
64.4
81.5
92.6
92.3
64.4
139.1
55.5
112.8
75.9
48.0
37.0
95.8
115.2
Standard
Deviation
82.1
83.4
80.3
103.0
72.9
58.1
100.0
52.9
122.3
39.8
110.5
66.2
38.9
32.4
97.1
66.9
U1
-------
Table 10
MODEL COMPARISON STATISTICS FOR HC
Cases
All cases
1975
1977
Freeway
Principal arterial
Minor arterial
Peak hour
Off-peak hour
Congested
Noncongested
20° temperature
50° temperature
80° temperature
0% cold starts
50% cold starts
100% cold starts
Number
of Cases
in Sample
300
150
150
108
96
96
156
144
84
216
100
100
100
102
150
48
Correlation
Coefficient
.9635
.9694
.9582
.9894
.9621
.9253
.9781
.9174
.9803
.9313
.9643
.9690
.9795
.9801
.9784
.9266
Linear Regression
Constants
A
.842
.809
.896
.851
.830
.818
.842
.869
.855
1.04
.825
.880
.703
.757
.824
.810
B
1.55
1.80
1.17
1.14
1.30
2.51
1.38
1.51
..694
.419
2.12
1.71
1.71
1.32
1.91
3.07
Modal
Average
9.10
9.89
8.32
8.21
9.73
9.47
10.28
7.82
14.40
7.04
11.91
8.13
7.27
7.07
9.95
10.77
Standard
Deviation
5.86
6.37
5.19
7.03
5.09
4.95
7.02
3.88
7.94
2.84
7.76
4.22
3.58
3.66
6.66
5.83
FTP
Average
9.21
9.80
8.62
8.13
9.37
10.26
10.04
8.30
13.00
7.73
11.94
8.87
6.81
6.68
10.10
11.79
Standard
Deviation
5.12
5.31
4.85
6.05
4.39
4.37
6.04
3.67
6.92
3.17
6.64
3.83
2.57
2.83
5.60
5.09
Ul
Ul
-------
Table 11
MODEL COMPARISON STATISTICS FOR NO
Cases
All cases
1975
1977
Freeway
Principal arterial
Minor arterial
Peak hour
Off-peak hour
Congested
Noncongested
20° temperature
50° temperature
80° temperature
0% cold starts
50% cold starts
100% cold starts
Number
of Cases
in Sample
300
150
150
108
96
96
156
144
84
216
100
100
100
102
150
48
Correlation
Coefficient
.3669
.4154
.3108
.5838
.0071
.1547
.3845
.3618
.0640
.6853
.4039
.2251
-.0240
.3451
.1290
-.1420
Linear Regression
Constants
A
.140
.163
.116
.220
.003
.070
.181
.113
.020
.248
.136
.067
-.007
.152
.047
-.058
B
5.00
4.86
5.15
4.78
5.60
5.27
4.71
5.22
5.28
4.56
5.43
5.45
5.42
5.07
5.52
5.71
Modal
Average
6.13
6.26
5.99
6.89
6.47
4.92
6.06
6.20
6.50
5.98
6.67
6.13
5.58
7.05
5.98
4.62
Standard
Deviation
1.65
1.67
1.62
1.47
1.68
1.00
1.40
1.87
1.82
1.55
1.85
1.56
1.30
1.68
1.39
0.87
FTP
Average
5.86
5.88
5.84
6.29
5.62
5.62
5.81
5.91
5.41
6.04
6.34
5.86
5.39
6.14
5.80
5.44
Standard
Deviation
0.63
0.65
0.60
0.55
0.59
0.46
0.66
0.58
0.56
0.56
0.62
0.46
0.36
0.74
0.51
0.36
Cn
-------
The test statistic is then compared with the confidence coefficients cor-
responding to the 95 and 997o confidence levels to determine whether the
differences in the correlation coefficients are significant at the 95 or
997, confidence level. Table 12 lists the results of the significance
tests.
The significance tests show that the correlation between emis-
sion model results are equally good for emissions from the 1975 vehicle
mix and the 1977 vehicle mix. The freeway cases are better correlated
than the principal or minor arterial cases, and the principal arterial
cases are better correlated than the minor arterial cases. However, peak-
hour cases show better correlation than do nonpeak hour cases, and con-
gested cases are better correlated than noncongested cases. The congested
and noncongested results seem inconsistent with the correlations found for
the three highway-type subset comparisons. Since all except one of the
freeway cases are noncongested and over half of the principal arterial
cases are congested and those cases comprise 717o of the congested cases,
it would appear that the principal arterial cases should have better cor-
relation than the freeway cases. This apparent inconsistency has not been
resolved. Since the peak-hour cases are better correlated than the off-
peak hour cases and most of the congested cases are for the peak hour,
the peak and off-peak hour and the congested and noncongested results
appear consistent.
The cases having an ambient air temperature of 80°F (26.7°C)
are better correlated than those having a temperature of 20°F (-6.7°C).
The poorer correlation of the 20°F cases is probably a result of error
introduced by the temperature correction factors of the emission models.
The temperature at which the basic emission factor measurements were made
was near 75°F (25°C). Thus, for the 80°F cases, the effect of the tem-
perature correction factors was minimal since the factors for 80° are
near unity; for the 20° cases, however, the factors are large.
The correlation of cases having 070 cold-start vehicles is sig-
nificantly better than the correlation of cases having 10070 cold-start
vehicles. Also, cases with 507o cold-start vehicles show better correla-
tion than cases with 1007» cold-start vehicles. The explanation for this
57
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Table 12
RESULTS OF TESTS FOR SIGNIFICANCE OF DIFFERENCES BETWEEN CORRELATION COEFFICIENTS
OF VARIOUS SUBSETS OF THE DATA SAMPLE
Pollutant
HC and CO
HC and CO
HC and CO
HC and CO
HC and CO
HC and CO
HC and CO
HC and CO
HC and CO
HC and CO
HC and CO
HC
CO
Cases for Which Correlation Coefficients
are Being Compared
1975 1977
Freeway Principal arterial
Freeway Minor arterial
Principal arterial Minor arterial
Congested Noncongested
20° 50°
20° 80°
50° 80°
0% cold starts 50% cold starts
0% cold starts 100% cold starts
50% cold starts 100% cold starts
Peak hour Off-peak hour
Peak hour Off-peak hour
Significance
Not significant
Significant at a 99%
confidence level
Significant at a 99%
confidence level
Significant at a 95%
confidence level
Significant at a 99%
confidence level
Not significant
Significant at a 95%
confidence level
Not significant
Not significant
Significant at a 99%
confidence level
Significant at a 99%
confidence level
Significant at a 99%
confidence level
Significant at a 95%
confidence level
Cases Showing
Highest Correlation
Freeway
Freeway
Principal arterial
Congested
80°
0%
50%
Peak hour
Peak hour
-------
is similar to that for temperature: error is probably introduced by the
correction factors--in this case, the cold-start correction factors. The
basic emission factors used in the FTP model assume 207<> cold-start vehicles;
the modal model coefficients, however, are based on 0% cold-start vehicles.
Thus, for percentages of cold-start vehicles greater than the assumed base
levels, the influence of the correction factors is greater and correlation
between the models is reduced.
It is probable that the poorer correlation of the 10070 cold-
start cases is a major influence on the correlation of the minor-arterial
cases, compared with the other highway type subsets. One-half of the minor
arterial cases have 10070 cold-start vehicles; the freeway and principal
arterial cases include only 0 and 507o cold-start cases, respectively.
Since the 1007<> cold-start cases show poorer correlation than do the 0 and
507o cases, the correlation of the minor arterial cases, compared with
freeway and principal arterial cases, would be reduced.
Examination of Table 11 reveals that correlation between the
emission models for NO is poor. Figure 6 shows that the variation among
X
modal NO emission values for the test cases is considerably greater than
X
the variation among FTP emission values for the same test cases. Table 11
gives the standard deviation of modal emissions for the 300 test cases as
1.65 g/veh-mi and the FTP standard deviation as 0.63 g/veh-mi. This large
difference is due to the data bases from which the emission models
were derived. The emission values from which the modal coefficients, de-
scribed in Section VII.D, were computed were not corrected for humidity;
the FTP emission factors were corrected. It is believed that for NO ,
x
the FTP methodology is a more accurate means of computing emissions.
F- FTP Model Sensitivity Analysis
The input data required by the FTP methodology are listed in Table 5.
An analysis was performed to assess the error that would be introduced into
the emission factors if there were an error in estimating the input data.
First, the analysis was centered on determination of relative error result-
ing from error in a single input variable. Then, based on the results of
59
-------
this analysis, relative error resulting from error in two input param-
eters was determined.
1. Sensitivity to Error in a Single Input Parameter
For each pollutant, the sensitivity to error in each of the
following four input parameters was determined: average route speed,
ambient temperature, percentage of cold-start vehicles, and traffic vol-
ume. It was assumed that the other input parameters, such as calendar
year, vehicle -type mix, and model year mix, could be accurately assessed.
The national average vehicle type mix and model year mix were used in the
calculations.
Speed Sensitivity AnalysisIn the speed sensitivity analysis,
relative error caused by erroneous speed was computed for each of three
roadway types: freeways, principal arterials, and minor arterials. Sev-
eral assumptions were made to distinguish the roadway types. It was as-
sumed that average route speeds on freeways vary from 5 to 45 mph; speeds
on principal arterials vary from 5 to 40 mph, and speeds on minor arterials
vary from 5 to 35 mph. The following were assumed for all roadway types:
(1) a speed of 5 mph can be as much as +10 mph in error; (2) a speed of
10 mph can be as much as +10 or -5 mph in error; (3) speeds from 15 to 35
mph can be as much as +10 or -10 mph in error. In addition, it was as-
sumed that a speed of 40 mph on a principal arterial or a freeway can be
as much as +5 or -10 mph in error, and a speed of 45 mph on a freeway can
be as much as -10 mph in error.
Emission factors were computed for all combinations of 2 calen-
dar years, 3 temperatures, 3 percentages of cold-start vehicles, and 9
average route speeds. Table 13 lists the values of these parameters used
in the analysis. For each roadway type, the average relative error in
estimating emission factors caused by errors of +5, -5, ±5, +10, -10,
and ±10 mph in speed was computed for each pollutant, considering the
assumptions listed above. The results of these computations are shown
in Table 14. As could be expected, the larger the magnitude of the error
in speed, the greater the relative error in the emission factor. For HC
60
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Table 13
VALUES OF INPUT PARAMETERS ASSUMED IN FTP MODEL SINGLE
PARAMETER SENSITIVITY ANALYSIS
Parameter
Speed
Calendar year
Temperature
Cold starts
Units
Values Assumed
Temperature
Calendar year
Cold starts
Speed
Speed Sensitivity Analysis
mph 5,10,15,20,25,30,35,40,45
1975, 1977
°F 20,50,80
% 0,50,100
Temperature Sensitivity Analysis
°F 20,21,22,38,39,40,41,42,58,59,60,
61,62,78,79,80
1975, 1977
% 0,50,100
mph 5,15,25,35,45
Cold-Start Sensitivity Analysis
Cold starts
Calendar year
Temperature
Speed
°F
mph
0,5,10,15,20,25,30,35,40,45,50,
55,60,65,70,75,80,85,90,95,100
1975, 1977
20,50,80
5,15,25,35,45
and CO, underestimated speeds produce larger errors then do overestimated
speeds of the same magnitude. This is because of the exponential form of
the speed correction factor functions. For NO , the slight difference in
X
relative error between cases with underestimated and overestimated speeds
of the same magnitude is not significant; it results from the choice of
cases used to compute the average relative error. From the HC and CO re-
sults, it appears that, for these two emission types, it is better to over-
estimate average route speed than it is to underestimate it.
The fractional error caused by a particular amount of error
in speed is least for freeways and greatest for minor arterials. The
61
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Table 14
AVERAGE FRACTIONAL ERROR IN FTP EMISSION FACTORS RESULTING
FROM ERROR IN A SINGLE INPUT PARAMETER
Pollutant
HC
CO'
NO
X
HC
CO
NO
X
HC
CO
NO
X
Facility Type
Freeway
Freeway
Freeway
Principal arterial
Principal arterial
Principal arterial
Minor arterial
Minor arterial
Minor arterial
Error in Average Route Speed
(mph)
+10
.24
.37
.08
.24
.37
.08
.24
.37
.08
-10
.35
.69
.08
.40
.77
.09
.45
.85
.09
±10*
.29
.53
.08
.31
.55
.08
.32
.57
.09
+5
.14
.22
.05
.14
.22
.05
.15
.24
.05
-5
.18
.31
.05
.20
.34
.05
.22
.38
.06
±5*
.16
.26
.05
.17
.28
.05
.18
.30
.05
Pollutant
HC
CO
NO
X
Error in Ambient Temperature
(°F)
+2
.02
.03
.00
-2
.02
.03
.01
±2*
.02
.03
.01
+1
.01
.01
.00
-1
.01
.02
.00
±1*
.01
.01
.00
Pollutant
HC
CO
NO
X
Error in Percentage Cold-Starting Vehicles
±5Qt
.37
.77
.06
+20
.16
.37
.02
-20
.13
.24
.02
±20*
.15
.30
.02
+10
.08
.18
.01
-10
.07
.12
.01
±10*
.07
.15
.01
The average fractional error in emission factors resulting
from input parameter error of ±X may or,may not be the simple
average of the fraction error caused by input parameter error
of +X and -X, since in some cases different sample sizes were
used in computing the +X and -X errors.
Errors of +50 and -50 were grouped together because the sample
size was small.
62
-------
differences arise from the assumptions made about what average route
speeds occur on the different roadway types. The differences in frac-
tional error of emission factors among the roadway types suggest that
the error is smaller when high speeds are misestimated than when the low
speeds are misestimated; the exponential nature of the speed correction
function causes less sensitivity to error at the higher speeds. The maxi-
mum error computed for HC is 457>, for CO is 85%, and for NO is 9%.
X
Temperature Sensitivity AnalysisTo perform an analysis of the
sensitivity of FTP emission factors to temperature, the emission factors
for all combinations of the 2 calendar years, 3 percentages of cold-
starting vehicles, 5 average route speeds, and 16 temperatures listed in
Table 13 were computed. For each pollutant, the sensitivity of the emis-
sion factors to errors of +2, -2, ±2, +1, -1, and ±1 degrees Farenheit was
assessed; the results appear in Table 14. The largest emission factor
error results from the cases with the largest error in temperature. Tem-
perature can be measured quite accurately, so it is doubtful that it will
be in error by more than 2°F (1°C). Since the largest relative error
computed for any pollutant is less than three percent, even for 2°F tem-
perature error, the sensitivity of the FTP methodology to erroneous tem-
perature is virtually negligible.
Cold-Start Vehicle Sensitivity AnalysisTable 13 lists the
values for the 2 calendar years, 3 temperatures, 5 average route speeds,
and 21 percentages of cold-start vehicles used in the analysis of FTP
emission factor sensitivity to error in estimating the percentage of
cold-start vehicles. The average relative errors in emission factors
resulting from cold-start errors of ±50, +20, -20, ±20, +10, -10, and ±107o
were computed and are given in Table 14. As expected, the larger the mag-
nitude of the error in percent cold-start vehicles, the larger the error
in the emission factor. For HC and CO, overestimating the percent of
cold-start vehicles produces larger error than underestimating it by the
same magnitude. This is due to the functional form of the cold-start
correction factor. On the average, for CO, 777o error in emission factor
is introduced by underestimating or overestimating the percent cold-start
63
-------
vehicles by 5070 or more. Less than 67, error in NO emission factors re-
X
suits from even a 507, error in percent of cold-start vehicles. The HC
emission factor error is as large as 37% when the cold-start factor is
in error by 507, .
Traffic Volume Sensitivity AnalysisSince pollutant emissions
are directly proportional to the volume of vehicles on a roadway, an error
in volume of a particular magnitude produces a corresponding error in
emission factor. This will hold true as long as the roadway is uncongested.
However, if the'volume is in error and the average route speed is therefore
estimated erroneously, the resultant error in emissions would not be di-
rectly proportional to the volume error. An analysis of error introduced
by congested conditions is considered beyond the scope of this study.
2. Sensitivity to Error in Two Input Parameters
The results of the single parameter sensitivity tests show that
FTP emission factors are relatively insensitive to the error in temperature
measurements likely to be encountered in RAPS. Therefore, the sensitivity
of emission factors to temperature error in conjunction with error in
another input parameter was not considered. The two parameter sensitivity
analysis was conducted for: error in speed and volume; percent cold-
start vehicles and volume; and speed and percent cold-start vehicles.
Rather than compute emission factors for various combinations
of volume error and speed or cold-start error to obtain average relative
error caused by error in volume and speed or cold starts, an equation was
derived to compute the relative error. This is possible because of the
direct proportionality of volume error to emission factor error. Let v
be the correct volume and v ' be the amount by which the volume is in error,
positive or negative. Let E be the correct emission factor and E be the
positive or negative amount the emission factor is in error because of
error in speed or percent cold-start vehicles. Then the relative error
is given by
vE - (v + v ') (E + E ')
error = - --*
64
-------
Reorganizing and reducing,
error = I ?- + %- I 1 + ^- II (20)
But the quantity v /v is the fractional amount the volume is in error.
Therefore, for any fraction of volume error, the total relative error
caused by error in both volume and a second input parameter is a simple
function of the fractional error due to error in the second input
parameter. Equations with the same form as Eqs. (19) and (20) were used
in the speed and volume and cold-start and volume sensitivity analyses.
Speed and Volume Sensitivity Analysis--Table 15 lists the values
of speed, calendar year, temperature, and percent cold-start vehicles used
in the speed and volume sensitivity analysis. This analysis was performed
using speeds ranging from 5 to 45 mph. Computations were made for volume
error of +20, -20, +10, -10, +5, -5, ±20, ±10, and +5% and for speed error
of +5, -5, ±5, +10, -10, and ±10 mph. The relative error resulting from
all combinations of volume and speed error classes for each pollutant are
given in Table 16. The largest error results when the effect of the errors
in both volume and speed produces emission factor errors of the same sign.
For HC and CO, the largest error occurs when volume is overesti-
mated and speed is underestimated, or when volume is underestimated and
speed is overestimated. In these cases, when there is an error in either
the speed or volume parameter, the emission factor error will become larger
as the magnitude of the error in the other parameter is increased. How-
ever, when volume and speed are both either underestimated or overesti-
mated, the emission factor error introduced by error in one of the input
parameters has the opposite sign of the emission factor error caused by
error in the other input parameter. The net effect is a varying degree
of error cancellation, depending on the magnitude of the error in each in-
put parameter. For a given amount of error in volume, the larger the mag-
nitude of the error in speed, the larger the emission factor error. But
for a given amount of error in speed, as the magnitude of the volume error
decreases the emission factor error may either (1) increase, or (2)
65
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Table 15
VALUES OF INPUT PARAMETERS ASSUMED IN FTP MODEL TWO
PARAMETER SENSITIVITY ANALYSES
Parameter
Units
Values Assumed
Speed and Volume Sensitivity Analysis
Speed mph 5,10,15,20,25,30,35,40,45
Calendar year -- 1975, 1977
Temperature °F 20,50,80
Cold starts % 0,50,100
Cold-Start and Volume Sensitivity Analysis
Cold starts
Calendar year
Temperature
Speed
°F
mph
0,5,10,20,25,30,35,40,45,50,55,60,
65,70,75,80,85,90,95,100
1975, 1977
20,50,80
5,15,25,35,45
Speed and Cold-Start Sensitivity Analysis
Speed
Cold starts
Calendar year
Temperature
mph 5,10,15,20,25,30,35,40,45,
% 0,5,10,15,20,25,30,35,40,45,50,55,
60,65,70,75,80,85,90,95,100
1975, 1977
°F 20,50,80
decrease and then increase. Thus, in some cases, for a given amount of
speed error, the total emission factor error is smaller for a large vol-
ume error than it is for a small volume error. This occurs because the
emission factor errors related to speed and a larger volume error more
nearly cancel than do the errors related to speed and a smaller volume
error.
For NO when speed and volume are both overestimated or under-
x r
estimated, they produce emission factor errors of the same size. However,
the emission factor error resulting from underestimated volume and over-
estimated speed, or from overestimated volume and underestimated speed
66
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Table 16
AVERAGE FRACTIONAL ERROR IN FTP EMISSION FACTORS RESULTING
FROM ERROR IN BOTH SPEED AND VOLUME
Pollutant
HC
CO
NO
X
Error
in Speed
(mph)
+5
-5
±5
+10
-10
±10
+5
-5
±5
+10
-10
±10
+5
-5
±5
+10
-10
±10
Error in Volume
(%)
+20
.11
.42
.26
.12
.62
.37
.11
.57
.34
.24
1.03
.63
.21
.20
.20
.23
.18
.21
-20
.31
.13
.22
.39
.15
.27
.37
.13
.25
.49
.36
.42
.20
.20
.20
.18
.22
.20
+10
.08
.30
.19
.16
.49
.32
.14
.44
.29
.30
.86
.58
.11
.10
.10
.15
.08
.11
-10
.22
.10
.16
.31
.22
.27
.30
.18
.24
.43
.52
.48
.09
.10
.10
.08
.14
.11
+5
.10
.24
.17
.20
.42
.31
.18
.38
.28
.33
.78
.55
.08
.05
.06
.11
.07
.09
-5
.18
.12
.15
.27
.29
.28
.26
.25
.25
.40
.61
.50
.04
.07
.06
.06
.11
.08
±20*
.21
.27
.24
.26
.39
.32
.24
.35
.30
.37
.69
.53
.20
.20
.20
.21
.20
.20
±10*
.15
.20
.17
.24
.36
.30
.22
.31
.27
.37
.69
.53
.10
.10
.10
.11
.11
.11
±5*
.19
.18
.16
.24
.35
.29
.22
.31
.26
.37
.69
.53
.06
.06
.06
.08
.09
.09
The average fractional error in emission factors resulting from input
parameter error of ±X may or may not be the simple average of the
fractional error caused by input parameter error of +X and -X, since
in some cases different sample sizes were used in computing the +X
and -X errors.
have opposite signs and have a cancelling effect. For a given speed error,
larger volume error produced larger emission factor error. This is also
true for cases in which emission factor errors resulting from speed error
and volume error both have the same sign. But when these errors have op-
posite signs for a given amount of volume error, the resultant emission
factor error from'a smaller amount of speed error may be larger than the
emission factor error from a larger amount of speed error.
The magnitude of the emission factor error resulting from error
in both volume and speed varies among the pollutants. The maximum error
67
-------
for all three pollutants occurs when speed is underestimated by 10 mph
and volume is overestimated by 207o. The maximum errors are: HC, 6270;
CO, 103%; and NO , 23%.
X
Cold-Start and Volume Sensitivity AnalysisTable 15 lists the
values of percent of cold-start vehicles, calendar year, temperature, and
speed used in the cold-start and volume sensitivity analysis. Volume
errors were computed for +20, -20, +10, -10, +5, -5, ±20, ±10, and ±5%;
cold start errors were computed for +10, -10, ±10, +20, -20, ±20, and
±5070. The average relative errors in emission factors for each pollu-
tant resulting from all combinations of the above classes of volume and
cold-start errors are given in Table 17.
For HC and CO, the largest emission factor error results when
cold starts and volume are both either underestimated or overestimated.
For these cases, for a given amount of error in one input parameter (cold
starts or volume), the emission factor error will become larger as the
magnitude of the error in one input parameter (volume or cold starts) is
increased. When cold starts are underestimated and volume is overesti-
mated or when cold starts are overestimated and volume is underestimated,
the emission factor errors resulting from error in volume and from error
in cold starts partially cancel. For these cases, for a given amount of
volume error, the emission factor error will become larger as the magni-
tude of the error in cold starts becomes larger. However, for a given
amount of error in cold starts, the emission factor error will (1) de-
crease, (2) decrease and then increase, or (3) increase as the volume
error magnitude decreases. The reason such effects are observed was dis-
cussed previously in this report.
For NO , emission factor errors are additive and therefore larg-
x'
est when cold start is overestimated and volume is underestimated, or when
cold start is underestimated and volume is overestimated. For these cases,
for a given error in one of the input parameters being tested, the emis-
sion factor error decreases as the magnitude of the error in the other
input parameter decreases. When error in cold start and volume are both
either underestimated or overestimated, the emission factor error resulting
68
-------
Table 17
AVERAGE FRACTIONAL ERROR IN FTP EMISSION FACTORS RESULTING
FROM ERROR IN BOTH COLD STARTS AND VOLUME
Pollutant
HC
CO
NO
X
Error in
Cold Start
(7»)
+10
-10
±10*
+20
-20
±20*
±50^
+10
-10
±10*
+20
-20
±20*
±50t
+10
-10
±10*
+20
-20
+20*
±50t
Error in Volume
(°i \
{ ")
+20
.30
.12
.21
.39
.09
.24
.48
.42
.08
.25
.64
.11
.37
.94
.19
.21
.20
.17
.23
.20
.20
-20
.14
,25
.20
.11
.31
.21
.31
.12
.30
.21
.14
.39
.27
.60
.21
.19
.20
.22
.18
.20
.20
+10
.19
.05
.12
.28
.07
.18
.42
.30
.05
.17
.50
.16
.33
.86
.09
.11
.10
.08
.13
.10
.10
-10
.05
.16
.11
.08
.22
.15
.32
.08
.21
.15
.23
.32
.27
.69
.11
.09
.10
.12
.08
.10
.11
+5
.14
.03
.08
.22
.09
.15
.39
.24
.08
.16
.43
.20
.32
.81
.04
.06
.05
.03
.08
.05
.07
-5
.04
.11
.08
.11
.17
.14
.35
.12
.16
.14
.30
.28
.29
.73
.06
.04
,05
.07
.03
.05
.07
±20*
.22
.19
.20
.25
.20
.22
.39
.27
.19
.23
.39
.25
.32
.77
.20
.20
.20
.20
.21
.20
.20
±10*
.12
.10
.11
.18
.14
.16
.37
.19
.13
.16
.37
.24
.30
.77
.10
.10
.10
.10
.10
.10
.11
±5*
.09
.07
.08
.16
.13
.15
.37
.18
.12
.15
.37
.24
.30
.77
.05
.05
.05
.05
.05
.05
.07
The average fractional error in emission factors resulting from input
parameter error of ±X may or may not be the simple average of the
fractional error caused by input parameter error of +X and -X, since
in some cases different sample sizes were used in computing the +X
and -X errors.
Errors of +50 and -50 were grouped together because the sample size
was small.
from error in one input parameter has the opposite sign to the emission
factor resulting from error in the other input parameter. For a given
value of error in cold start, the emission factor error decreases as the
magnitude of error in volume decreases. But for a given amount of error
in volume, the emission error increases as the magnitude of cold-start
error decreases.
69
-------
The number of cases with cold starts underestimated or overesti-
mated by 50% were few enough that it was felt that these cases should be
combined to be statistically significant. The behavior of the emission
factor error as the error in volume changes is evident from Table 17.
The magnitude of emission factor error resulting from error in
both cold start and volume is greatest for CO and least for NO . The
X
maximum HC and CO emission factor error occurs when cold starts are ±50%
in error and volume is overestimated by 20%. The maximum value for HC
is 48% and for CO is 94%. For NO the maximum error of 23% occurs when
X
cold starts are underestimated by 207o and volume is overestimated by 20%.
Speed and Cold-Start Sensitivity AnalysisTable 15 lists the
values of speed, percent cold-start vehicles, calendar year, and tempera-
ture used in the speed and cold-start sensitivity analysis. Computations
were made for speed error of +5, -5, ±5, +10, -10, and ±10 mph and for
cold-start error of +10, -10, ±10, +20, -20, ±20, and ±50%. The average
fractional error in emission factor resulting from all combinations of
the above error classes are given for each pollutant in Table 18.
The largest emission factor errors for HC and CO occur when
speed is overestimated and cold start is underestimated or when speed
is underestimated and cold start is overestimated. For these cases, for
an error in one input parameter (speed or cold start), emission factor
increases with increasing error magnitude in the other input parameter.
When speed error and cold-start error are both underestimated or overesti-
mated, for a given value of cold-start error, emission factor error in-
creases as the magnitude of speed error is increased. But for a given
amount of speed error, emission factor error may increase or decrease as
cold-start error magnitude increases.
For NO , emission factor error is largest when speed is over-
X
estimated and cold start is underestimated, or when speed is underesti-
mated and cold start is overestimated. For a given amount of error in
speed, emission factor error changes little as cold-start error magnitude
increases. Since the sample sizes are not large, the differences in emis-
sion factor errors are considered negligible. For a given value of
70
-------
Table 18
AVERAGE FRACTIONAL ERROR IN FTP EMISSION FACTORS RESULTING
FROM ERROR IN BOTH SPEED AND PERCENT COLD STARTS
Pollutant
HC
CO
NO
X
Error
in Speed
(mph)
+5
-5
±5*
+10
-10
±10*
+5
-5
±5*
+10
-10
±10*
+5
-5
±5*
+10
-10
±10*
Error in Cold Starts
ft \
(/°)
+10
.10
.28
.19
.18
.47
.32
.15
.57
.36
.27
1.02
.65
.04
.06
.05
.08
.09
.08
-10
.19
.12
.16
.29
.28
.28
.31
.19
.25
.44
.51
.47
.06
.04
.05
.09
.08
.08
±10*
.15
.20
.17
.23
.37
.30
.23
.38
.30
.36
.76
.56
.05
.05
.05
.08
.08
.08
+20
.11
.38
.24
.16
.58
.37
.21
.82
.52
.25
1.35
.80
.04
.06
.05
.07
.09
.08
-20
.25
.11
.18
.33
.21
.27
.40
.17
.28
.51
.36
.44
.06
.04
.05
.10
.07
.09
±20*
.18
.24
.21
.25
.40
.32
.30
.49
.40
.38
.86
.62
.05
.05
.05
.08
.08
.08
±50t
.33
.46
.40
.34
.59
.47
.64
1.07
.86
.57
1.47
1.02
.08
.08
.08
.10
.10
.10
V
The average fractional error in emission factors resulting
from input parameter error of ±X may or may not be the simple
average of the fractional error caused by input parameter
error of +X and -X, since in some cases different sample sizes
were used in computing the +X and -X errors.
Errors of +50 and -50 were grouped together because the sample
size was small.
cold-start error, emission factor error increases as the magnitude of speed
error is increased. These two trends also apply to cases where speed error
and cold-start error are both either underestimated or overestimated.
The error in CO emission factors resulting from error in speed
and percent of cold-start vehicles is the largest error of the three pol-
lutants. The largest errors in both HC and CO emission factors occur
when cold start is 5070 in error and the speed is underestimated by 10 mph;
71
-------
the maximum values are 59% for HC and 14770 for CO. NO error is smaller,
x '
not exceeding 10%.
3. Conclusions
It has been shown that relatively large error in FTP emission
factors are produced when model input parameters are measured or estimated
erroneously. To illustrate more clearly the effect of input error, an
arbitrary threshold value of 20% was chosen, and a table was made to show
which input parameter error classes or combination of parameter error
classes would produce emission factor error exceeding the threshold value.
Table 19 shows which error classes exceed the threshold value for each
pollutant.
Cold-start error of 20% or greater produces emission factor
error greater than 20% for CO; for HC, the threshold value is not ex-
ceeded unless cold-start error is about 50%. NO emission factor error
X
is less than 2070 even when cold-start error is as large as 50%.
Error in emission factors caused by temperature error of a mag-
nitude likely to be encountered in RAPS data has been determined to be
negligible; this was discussed in the preceding section.
For HC and CO, most of the speed-error classes produced errors
exceeding the emission threshold value. However, NO emission factors
X
were in error by less than 207o for all of the speed-error values.
Error in both volume and speed cause the HC emission factor
error to exceed the threshold value for several combinations of values
of speed error and volume error, as seen in Table 19. CO emission factors
are slightly more sensitive than the HC factors. The only error classes
causing NO emission factor error to exceed the threshold value are those
X
with 20% volume error.
The results of the volume and cold-start analysis appear in
Table 19. They are very similar to those described above for the volume
and speed analysis.
The HC emission factor error resulting from error in both speed
and cold start exceeds the threshold value for many of the combinations
72
-------
Table 19
INPUT PARAMETER ERROR VALUES THAT CAUSE FTP EMISSION
FACTOR ERROR TO EXCEED 20%
Pollutant
HC
CO
NO
X
Error in Cold Start
(%)
+10
-10
+20
X
-20
X
±50
X
X
Pollutant
HC
CO
NO
X
Error in Minor Arterial Speed
(mph)
+5
X
-5
X
X
+10
X
X
-10
X
X
Pollutant
HC
CO
NO
X
Error
in Speed
(mph)
+5
-5
+10
-10
+5
-5
+10
-10
+5
-5
+10
-10
Error in Volume
(/o)
+5
X
X
X
X
X
-5
X
X
X
X
X
X
+10
X
X
X
X
X
-10
X
X
X
X
X
X
+20
X
X
X
X
X
X
X
-20
X
X
X
X
X
X
73
-------
Table 19 (Concluded)
Pollutant
HC
CO
NO
X
Error in
Cold Start
(°l \
\i°)
+10
-10
+20
-20
±50
+10
-10
+20
-20
±50
+10
-10
+20
-20
+50
Error in Volume
("l\
\i°)
+5
X
X
X
X
X
-5
X
X
X
X
+10
X
X
X
X
X
-10
X
X
X
X
X
X
+20
X
X
X
X
X
X
X
X
-20
X
X
X
X
X
X
X
X
Pollutant
HC
CO
NO
X
Error
in Speed
(mph)
+5
-5
+10
-10
+5
-5
+10
-10
+5
-5
+10
-10
Error in Cold Start
(°i \
\l°)
+10
X
X
X
X
X
-10
X
X
X
X
X
+20
X
X
X
X
X
X
-20
X
X
X
X
X
X
±50
X
X
X
X
X
X
X
X
74
-------
of speed error and cold-start error. CO emission factors are in error by
more than 20% for almost all combinations of speed error and cold-start
error tested. However, NO error was under the threshold value for all
X
error classes tested.
In general, CO emission factors are most sensitive to error in
input parameters, and NO emission factors are the least sensitive to
X
errors in input parameters. HC emissions are somewhat less sensitive
than CO emissions, but are considerably more affected by input parameter
error than are NO emission factors.
G. Modal Model Sensitivity Analysis
The input parameters required by the modal model are listed in Ta-
ble 5. The modal model sensitivity analysis was centered on assessment
of emission factor error introduced by error in each of three input param-
eters: temperature, percent of cold-start vehicles, and traffic volume.
Based on the model sensitivity to error in a single input parameter, an
analysis of emission factor error introduced by error in two parameters
was performed.
It was assumed that calendar year, vehicle type mix, and model year
mix could be accurately assessed. Assessment of the effects of error in
the second-by-second speed and time profiles was considered beyond the
scope of this study.
1. Sensitivity to Error in a Single Input Parameter
Since modal temperature and cold-start correction factors are
not dependent on vehicle model year, the effect of error in either of
these parameters on the emission factor can be assessed independent of
the value of the uncorrected emission factor. This is also true for error
in traffic volume. The following equations illustrate the independence:
I aE - a'E | ._,_
error = I - aE - I ' (21)
75
-------
Reducing,
*3 I
error = 1 - | (22)
where
a = The correction factor for the correct temperature
or cold start or the correct volume
a = The correction factor for the erroneous temperature
or cold start or the erroneous volume
E = The uncorrected modal emission factor.
Equation (22) was used to assess the sensitivity of modal emission factors
to error in temperature, cold start, and volume.
Temperature-Sensitivity Analysis--All combinations of the values
of temperature and percent of cold-start vehicles listed in Table 20 were
used in the temperature-sensitivity analysis. For each pollutant, the
sensitivity of the emission factors to errors of +2, -2, ±2, +1, -1, and
±1 degree Farenheit was assessed; the results appear in Table 21. Since
temperature error is not likely to be greater than 2°F (1°C), and the
maximum emission factor error computed was 3%, it appears that modal model
sensitivity to error in temperature is not significant.
Cold-Start Sensitivity AnalysisTable 20 lists the percent of
cold-start vehicles and the temperatures used in the modal cold-start
sensitivity analysis. For each pollutant, the average errors in emission
factor resulting from cold-start error of +10, -10, ±10, +20, -20, ±20,
and ±50% were computed. In general, the greater the magnitude of the
cold-start error, the greater the error in emission factors. The maximum
error occurs when cold-start is 50% in error; the error value for HC is
49%, for CO it is 82%, and for NO it is 13%.
Traffic Volume Sensitivity AnalysisThe effect on emission
factors of error in traffic volume is the same for both the modal and
the FTP emission models. See the discussion for the FTP model in Sec-
tion VII.F.
76
-------
Table 20
VALUES OF INPUT PARAMETERS ASSUMED IN MODAL MODEL
SENSITIVITY ANALYSES
Parameter Units Values Assumed
Temperature Sensitivity Analysis
Temperature °F 20,21,22,28,29,30,31,32,38,39,40,41,42,48,
49,50,51,52,58,59,60,61,62,68,69,70,71,72,
78,79,80
Cold starts % 0,20,40,60,80,100
Cold-Start Sensitivity Analysis
Cold starts % 0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,
75,80,85,90,95,100
Temperature °F 20,30,40,50,60,70,80
Cold-Start and Volume Sensitivity Analysis
Cold starts % 0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,
75,80,85,90,95,100
Temperature °F 20,30,40,50,60,70,80
2. Sensitivity to Error in Two Input Parameters
As was the case in the FTP model temperature sensitivity analy-
sis, the modal model is relatively insensitive to error in temperature.
Therefore, emission factor sensitivity to temperature error in conjunction
with error in another input parameter was not considered. Therefore, the
modal model two parameter sensitivity analysis was performed only for
error in both cold start and volume. The following equation describes
the emission factor error:
I vaE - (v + v ') a 'E i
error = | > | . (23)
Reducing,
error = | 1 - ^ (l + ^ ) I (24)
77
-------
Table 21
AVERAGE FRACTIONAL ERROR IN MODAL EMISSION FACTORS RESULTING
FROM ERROR IN A SINGLE INPUT PARAMETER
Pollutant
HC
CO
NO
x
Error in Ambient Temperature
(°F)
+2
.03
.03
.01
-2
.02
.03
.01
±2*
.03
.03
.01
+1
.01
.02
.00
-1
.01
.01
.00
±1*
.01
.01
.00
Pollutant
HC
CO
NO
X
Error in Percent Cold-Start Vehicles
ft \
('<>)
+10
.11
.20
.03
-10
.08
.12
.03
±10*
.10
.16
.03
+20
.22
.39
.05
-20
.16
.24
.05
±20*
.19
.31
.05
±50t
.49
.82
.13
The average fractional error in emission factors resulting from
input parameter error of ±X may or may not be the simple average
of the fractional error caused by input parameter error of +X
and -X, since in some cases different sample sizes were used in
computing the +X and -X errors.
Errors of +50 and -50 were grouped together because the sample
size was small.
where
a = The correction factor for the correct percent of
cold starts
a = The correction factor for the erroneous percent of
cold starts
E = The temperature-corrected emission factor
v = The correct volume
v' = The amount by which the volume is in error.
Again, the emission factor error is independent of the value of the
temperature-corrected emission factor.
78
-------
The sensitivity of modal emission factors was computed for all
combinations of cold-start error of +10, -10, ±10, +20, -20, ±20, and ±50%
and for volume error of +20, -20, +10, -10, +5, -5, ±20, ±10, and ±5%.
The results of the analysis appear in Table 22.
Table 22
AVERAGE FRACTIONAL ERROR IN MODAL EMISSION FACTORS RESULTING
FROM ERROR IN BOTH VOLUME AND COLD-START
Pollutant
HC
CO
NO
X
Error in
Cold Start
(%)
+10
-10
±10*
+20
-20
±20*
±50 1
+10
-10
±10*
+20
-20
±20*
±50 1
+10
-10
±10*
+20
-20
±20*
±50t
Error in Volume
/at \
( L )
\'°/
+20
.33
.11
.22
.47
.07
.27
.59
.43
.08
.26
.67
.10
.38
.98
.17
.23
.20
.14
.26
.20
.22
+10
.22
.04
.13
.34
.08
.21
.54
.31
.04
.18
.53
.16
.35
.90
.07
.13
.10
.05
.16
.10
.15
+5
.17
.04
.10
.28
.12
.20
.51
.26
.07
.16
.46
.20
.33
.86
.02
.08
.05
.02
.11
.06
.14
-5
.06
.13
.09
.16
.20
.18
.46
.14
.16
.15
.32
.28
.30
.77
.07
.03
.05
.10
.02
.06
.12
-10
.05
.17
.11
.11
.25
.18
.44
.09
.21
.15
.25
.31
.28
.73
.12
.08
.10
.15
.05
.10
.13
-20
.12
.26
.19
.10
.33
.21
.39
.11
.30
.20
.15
.39
.27
.65
.22
.18
.20
.24
.16
.20
.19
±20*
.23
.19
.21
.28
.20
.24
.49
.27
.19
.23
.41
.24
.33
.82
.20
.21
.20
.19
.21
.20
.20
±10*
.14
.11
.12
.23
.16
.19
.49
.20
.12
.16
.39
.24
.31
.82
.10
.10
.10
.10
.11
.10
.14
±5*
.11
.08
.10
.22
.16
.19
.49
.20
.12
.16
.39
.24
.31
.82
.15
.05
.05
.06
.06
.06
.13
The average fractional error in emission factors resulting from input
parameter error of ±X may or may not be the simple average of the
fractional error caused by input parameter error of +X and -X, since
in some cases different sample sizes were used in computing the +X
and -X errors.
Errors of +50 and -50 were grouped together because the sample size
was small.
79
-------
For HC and CO, the largest emission factor error results when
cold start and volume are both either underestimated or overestimated.
For these cases, for a given value of error in one input parameter (cold
start or volume), the emission factor error increases as the magnitude
of the error in the other input parameter is increased. When cold starts
are underestimated and volume is overestimated or when cold starts are
overestimated and volume is underestimated, for a given value of error
in one input parameter, the emission factor may (1) increase, (2) decrease,
or (3) decrease and then increase as the magnitude of the error in the
other input parameter is increased. As in the FTP sensitivity analysis,
this is a result of a varying degree of cancellation of the error intro-
duced by error in the two input parameters.
For NO , the largest emission factor error results when volume
X
is overestimated and cold starts are underestimated, or when volume is
underestimated and cold starts are overestimated. For these cases, for
a given amount of error in one input parameter, emission factor error in-
creases as the magnitude of the error in the other input parameter is
increased.
When errors in cold starts and volume are both either underesti-
mated or overestimated, for a given amount of cold-start error, the emis-
sion factor error increases as the magnitude of volume error increases.
But for a given amount of volume error, the emission factor error may de-
crease or remain constant as the magnitude of cold-start error is in-
creased.
For HC and CO, the largest emission factor error occurs when
volume is overestimated by 20% and the percent of cold starts is in error
by 50%. The maximum HC error is 59%, and the maximum CO error is 98%.
The maximum NO emission factor error of 26% occurs when volume is over-
x
estimated by 20% and cold starts are underestimated by 20%.
The sample of cases having cold-start error of 50% was small,
so both underestimated and overestimated cases were combined. It is
probable that error resulting from a larger sample of cases having either
50% overestimated or 50% underestimated cases alone may be higher than
80
-------
the error computed when all 50% cases are combined. Therefore, worst-
case error would probably occur for cases with 50% overestimated or under-
estimated cold-start error.
3. Conclusions
As was seen in the preceding sections, emission factor error
introduced by error in some input parameters can be appreciable, partic-
ularly for HC and CO. There is considerable variation between pollutants
as to the magnitude of error produced by various input parameter errors
and as to what value or pair of values of input parameter errors produce
a given level of emission factor error. As in the FTP model analysis,
an arbitrary value of emission factor error (20%,) , was selected as a
threshold error value. Table 23 shows what values of input error will
produce emission factor error exceeding the threshold value.
For HC and CO, 10% cold-start error does not exceed the thresh-
old, but for the most part, 20 and 5070 cold-start errors do exceed it.
However, as much as 50% cold-start error does not produce more than 20%
NO emission factor error. The preceding sections showed that error in
X
temperature produces negligible emission factor error.
For HC, any of the chosen values of cold-start error in com-
bination with at least one of the volume error classes exceeds the thresh-
old, and vice versa. There are even fewer combinations of error classes
that do not produce more than 20% CO emission factor error. NO emission
A
factor error is less than 20% for most combinations of error classes, ex-
cept for those with 20% volume error.
81
-------
Table 23
INPUT PARAMETER ERROR VALUES THAT CAUSE MODAL EMISSION
FACTOR ERROR TO EXCEED 20%
Pollutant
HC
CO
NO
X
Error in Cold Start
(%)
+10
-10
+20
X
X
-20
X
±50
X
X
Pollutant
HC
CO
NO
X
Error in
Cold Start
(°/\
\'o}
+10
-10
+20
-20
±50
+10
-10
+20
-20
±50
+10
-10
+20
-20
±50
Error in Volume
(%)
+5
X
X
X
X
X
-5
X
X
X
X
+10
X
X
X
X
X
X
-10
X
X
X
X
X
X
+20
X
X
X
X
X
X
X
X
X
-20
X
X
X
X
X
X
X
X
82
-------
VIII EVALUATION OF OTHER TYPES OF SOURCES OF EMISSIONS
A. Fugitive Dust
Fugitive dust is perhaps the most difficult type of emission to quan-
tify. Midwest Research Institute (Cowherd and Guenther, 1976) compiled
the inventory for the following sources: (1) unpaved roads, (2) agri-
cultural land tilling, (3) wind erosion of agricultural land, (4) con-
struction sites, (5) aggregate storage piles, and (6) unpaved airstrips.
Rockwell International (Littman and Isam, 1977) has estimated fugitive
dust emissions from paved roads.
For each of the over 2000 area sources, data were compiled on annual
emissions of fugitive dust. Hourly apportioning factors were derived to
account for emission variations by hour of the day, day of the week, and
season of the year. Total particulate emissions (particles smaller than
30 urn) from categories (1) through (6) listed above, were about 1,183,000
tons/year. It was assumed that 2070 of the emissions involved particle
sizes smaller than 5 (J.m. Therefore, for these smaller particles, TSP
emissions are about 237,000 tons/year. (The fugitive dust estimated in
Table 1 include all particle sizes.)
As can be seen in Table 1, fugitive dust emissions dominate the par-
ticulate inventory. Dust from unpaved roads and wind erosion of agri-
cultural land is the major source of fugitive dust emissions.
Midwest Research Institute (MRI) estimated relative errors for each
of the source categories. These are represented in Table 24. These errors,
corresponding to a 9070 confidence level, were determined by a progressive
analysis of errors associated with each calculation step. One could con-
clude that estimates for paved roads should be somewhat better than those
for unpaved roads. Traffic volumes and vehicle speed should be much better
known for paved roads. In view of all the uncertainties, the estimates in
Table 4, appear somewhat optimistic, particularly for the "hourly adjust-
ment factors."
83
-------
Table 24
MRI ESTIMATES OF FUGITIVE DUST INVENTORY ACCURACY
(Estimated Relative Error)
Source Category
Unpaved roads
Agricultural tilling
Wind erosion
Construction
Aggregate storage
Unpaved airstrips
Source
Extent
±5%
±15%
±30%
±35%
±25%
±15%
Corrected
Emission
Factor
±20%
±30%
±20%
±30%
±30%
±25%
Hourly
Adjustment
Factor
±15%
±20%
±15%
±20%
±20%
±20%
In general, the fugitive dust inventory is probably somewhat less ac-
curate than those inventories that depend on fuel consumption data. Fur-
thermore, information on the size distribution from fugitive dust sources
is virtually nonexistent. Some field data should be collected to (1) give
breakdowns by particle size and (2) verify assumptions inherent in the MRI
methodology.
B. Aircraft
The aircraft emission inventory was developed by GCA Corporation
(Patterson et al., 1974). Even in the Lambert Field area, aircraft (and
support vehicles) account for only modest emissions of the criteria pol-
lutants. The maximum emissions found in any grid within the Lambert Field
vicinity is 793 tons per year of carbon monoxide (grid 523). Emissions
for the other pollutants were less. For the area as a whole, aircraft
emissions account for a very small portion of the emissions burden (less
than 1%).
Sources of emissions include airctaft operation, engine maintenance
testing, ground support vehicles, and fuel storage and handling. Three
types of airports were considered: municipal, military, and civilian.
For each airport, the number, type, and operating patterns of the aircraft
84
-------
were considered. Ground support vehicle and fuel handling emissions are
related to the volume and type of aircraft activity at each airport. GCA
applied aircraft emission rate data which was compiled by Cornell Aero-
nautical Laboratory. These data are summarized in AP-42. Various modes
of aircraft operation were considered: idle, taxi, take-off, landing,
climb-out, and approach.
The number and type of aircraft operating out of each airport is
fairly well known. For Lambert Field, scheduled airline data are known
accurately. GCA gathered data from the Official Airline Guide, the Fed-
eral Aviation Authority, and the airport managers office. Similar data
are available from other airports. The number and types of sources gen-
erally are quite well known. The validity of the emission factors would
appear to be the most questionable item.
In general, the aircraft sources are specified with better accuracy
than other mobile sources, with the possible exception of railroads. For
our purposes, we will assume negligible bias and a precision of unity.
C. Off-Highway Mobile Sources
The off-highway mobile source emission inventory was compiled by
Rockwell International (Littman and Isam, 1977), using in part methodolo-
gies developed by Hare and Springer (1974). Six categories have been
calculated for the 1989 RAPS grid squares. These categories included:
motorcycles, lawn and garden equipment, industrial equipment, construction
equipment, farm equipment, and outboard motorboats. From Table 1, it can
be seen that all categories, except motorcycles, account for modest quan-
tities of pollutants (CO, NO and HC) normally attributed to mobile sources
X
Temporal apportionments were made on the basis of seasonal activity
and normal daily operating estimates. Emissions were distributed uni-
formly over the operating period. For instance, industrial equipment was
assumed to operate year round from 8:00 a.m. to 6:00 p.m.; lawn and gar-
den equipment was assumed to operate from April through September, 9:00
a.m. to 7:00 p.m.
85
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The primary problem is lack of accurate background data on which to
base emission calculations. Although emission factors are known to be
within typical precisions (0.1 to 0.3), the number, type, and operating
patterns of most off-highway equipment are not known well. Each type,
except motorcycles, are considered separately below.
1. Lawn and Garden Equipment
The lawn and garden equipment category includes riding mowers,
walking mowers, garden tractors, and motor tillers. There are registra-
tion data to quantify numbers of these devices. Therefore, Rockwell
acknowledges that emission figures at the grid level are meant solely to
give an "order-of-magnitude" calculation.
The emission estimates were based on U.S. Census data on the
distribution and types of houses, emission factors, an assumed linear
correspondence between one-unit housing structures and small utility
engines, and a seasonal utilization factor. While this approach does
serve to quantify emissions, the bias and precision could be quite high.
In absence of any verification data, we will assume a bias of 0.5 and
precision of 2.0.
2. Construction Equipment
The major sources of data on construction equipment are national
figures on units shipped per year, annual usage, total horsepower in use,
load factors, and duty cycles. Rockwell apportioned national data to the
RAPS area based on a ratio of national-to-state construction volumes and
state-to-county populations.*
Composite nationwide emission factors for an assumed distribu-
tion for each of ten construction categories were compiled for the nation
as a whole and then apportioned to the RAPS area in the manner based on a
ratio of national-to-state construction volumes. Grid emissions were
Illinois and Missouri construction volumes were not available by the
county.
86
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based on a ratio of grid construction acreage to county construction
acreage. The accuracy of the construction inventory should be comparable
to that for lawn and garden equipment.
3. Industrial Equipment
The industrial equipment category includes fork lifts, utility
carts, small tractors and wheel loaders, quarrying machinery, portable
generators, and so forth. Rockwell categorized these devices into small-
utility and heavy-duty engines. The methodology for industrial equipment
is similar to that used for construction equipment. National emissions
data serve as the starting point. County emission estimates were derived
from a ratio of national industrial activity to county industrial activity,
Grid apportionments were based on locations of industrial plants on a grid
basis. Again, accuracies are probably comparable to the other off-highway
mobile source inventories.
4. Farm Equipment
Farm equipment includes farm tractors, garden tractors, self-
propelled combines, forest harvesters, balers, irrigation pumps, and aux-
iliary engines. Background data on equipment population was available
through 1969 Census of Agriculture estimates. Based on EPA usage esti-
mates and emission factors, county emissions were compiled.
Emissions were then appropriated by grid based on farm acreage
data prepared by MRI in compiling the fugitive dust inventory. In gen-
eral the farm equipment inventory is more accurate than the construction
and industrial equipment inventory.
5. Outboard Motorboats
From Table 1 it can be seen that emissions are modest for two
pollutants (HC and CO) and negligible for others. Emission factors are
low for typical outboard operation because a certain portion of the ex-
hausts are removed in the water (exhaust outlets are normally below water
level). Boat registration data was the basis for calculations. Annual
87
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usage and EPA emission factors were applied to compile the regional in-
ventory. Spatial apportionments were made on the basis of navigable water
ratios (county-to-state, grid-to-county). The accuracy of this inventory
is about the same as that for industrial equipment.
6. Summary
As stated in the Rockwell report, this inventory provides "order-
of-magnitude" estimate only. Also, the applied background data in many
cases is several years old. Therefore, the overall accuracy is much a
question mark. For our purposes, we will assume an overall bias of
±0.5 T and precision of 2.
D. River Vessels
Emissions from river vessels are dominated by towboat traffic. Esti-
mates of emissions from towboats have been prepared by the Department of
Transportation (Sturm, 1976). As shown in Table 1, emissions from river
vessels are almost insignificant, accounting for less than one percent of
the total. The DOT report accurately quantifies the yearly estimates,
based on current emission factors. However, the report concludes that
hourly emissions are not worth the effort to accurately quantify, given
their low contribution to the total. Therefore, in this report, we will
assume a bias of T and coefficient of precision of 2.
E. Railroads
Railroad emissions are more substantial than river vessel emissions,
but do not contribute signficantly to the total. They are large enough
to be observable locally in the vicinity of railroad tracks during active
periods. Railroad emissions were compiled by Walden Research (Wiltsee
et al., 1977). Walden developed separate methodologies to compile emis-
sions from two types of rail activity: line-haul-operations and switch
yard activity. The study inventoried emissions only from diesel locomo-
tive operations since other type emissions were deemed insignificant.
88
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Features of the Walden methodology include:
Department of Transportation (DOT) compilations of:
- Routing, runtime, and locomotive information for each
train in the study area
- Total active and idle hours and locomotive information
for each rail yard in the AQCR
- Interyard transfer routing and runtime.
A system of links simulating the rail network.
Classification of locomotives into the five engine cate-
gories specified in AP-42.
Derivation of active and idle load factors for switch and
road locomotives.
Characterization of a "typical" transfer engine used in
the St. Louis AQCR.
In general, the Walden methodology appears thorough in estimating annual
emissions and spatial apportioning. The main inaccuracy appears to be that
of time distinction. In general, this problem could be overcome by col-
lecting minimal data on diurnal variation. On an annual basis, the rail-
road inventory should be somewhat better than conventional highway sources.
For our purposes, we will assume that bias is negligible and coefficient
of precision is 0.75.
F, Separation of Hydrocarbon Emissions into Classes
Once total hydrocarbon emissions were determined for each source,
a classification scheme was applied to estimate the proportion of these
emissions contributed by the following classes: nonreactive, paraffins,
olefins, aromatics, and aldehydes (Griscom, 1977). Emission factors by
hydrocarbon type were established for each source category. These fac-
tors were based on an earlier study which consisted of compositional anal-
yses on organic emissions in Los Angeles (Trijonis and Arledge, 1975). As
Rockwell noted, there were three limitations in applying the Los Angeles
results to St. Louis:
(1) The classification scheme (e.g., olefins) required for
RAPS was somewhat different than that used in the Los
Angeles study.
89
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(2) The compositions of petroleum products and solvent usage
may be different between Los Angeles and St. Louis.
(3) Certain source types existing in St. Louis were not con-
sidered in the Los Angeles study.
In spite of the above limitations, Rockwell was able to make some
reasonable assumptions by augmenting the Los Angeles results with those
from a host of other studies. (See the Griscom report, 1977, cited above.)
For point sources, emission factors, by hydrocarbon type, were de-
veloped for each SCC. Area sources were generally described according to
the source types listed in Table 1. One exception is the highway sources,
which were separated into 1DV, HDG, and HDD classes. (Separate factors
for exhaust and evaporative losses were used for LDV and HDG.) The ac-
curacy of the emission factors, by hydrocarbon class, cannot reliably be
estimated since verification data are not available. In view of the
limitations stated above and assumptions made in both the Griscom and
Trijonis studies, it is clear that the accuracy in the emission estimates
by class is substantially less than that for total hydrocarbons.
90
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IX COST BENEFIT ANALYSIS
The Lack of field validation data precludes a completely objective
analysis of cost and benefits. However, based on the background and in-
formation presented in our previous discussion on bias and coefficient of
precision, we can make some relative estimates. These estimates in turn,
can be used to provide insight on (1) the effectiveness of monies already
spent and (2) where future improvements can best be applied.
The emissions inventory is summarized in terms of: emission totals,
estimated accuracy, and costs. The totals are derived from Table 1. The
estimated accuracy relates to combined coefficient of precision and bias
estimates previously discussed in the various emission evaluations. The
&
cost estimates were provided by EPA. The total inventory budget, ex-
clusive of the data handling system, was about $870,000. This was divided
as shown in Table 25.
The classification of the inventory by pollutant is shown in Table 25.
Exclusive of TSP, the point source inventory made up 49.8% of all the to-
tals; the highway inventory made up 37.970; and so forth. The accuracy
ranking shows that by far the point source inventory is the most accurate
with an average coefficient of precision-bias of 0.275. (This may be
taken generally to be the sum of the estimated coefficient and one-half
the bias.) The most outstanding figure corresponds to the accuracy of
the highway (mobile source) inventory. It is about the same as it is for
those inventory types (airports, railroads, and so forth) contributing
much less to the total inventory budget. This is the most outstanding
inconsistency in the accuracy of the inventory. (Accuracy should be pro-
portional to percent of the emission inventory budget.)
Masser, C. (private communication, July 1977)
91
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Table 25
INVENTORY SUMMARY: TOTALS, ACCURACY, AND COST
Inventory
Point Source
Highways
Stationary,
residential and
commercial
Stationary,
industrial
Off highway
mobile
Railroads
River vessels
Airports
Fugitive dust
Percent of Total Emissions
SO
X
96.7
0.3
2.5
_
0.2
0.2
--
--
--
NO
X
69.6
17.5
3.0
_
6.0
2.9
0.8
0.1
--
CO
9.7
78.9
0.9
9.9
0.3
0.1
0.2
--
HC
23.3
54.8
10.7
__
8.0
2.1
0.3
0.7
--
Particulate*
3.3
0.5
0.5
. _
0.2
0.1
--
--
95.4*
(Avg)4t
49.8
37.9
4.3
0.0
6.0
1.4
0.3
0.3
--
Accuracy Ranking
SO
X
0.20
1.0
0.75
« _
2.0
0.75
2.0
1.0
--
NO
X
0.30
1.0
0.95
_
2.0
0.75
2.0
1.0
--
CO
0.30
1.0
0.95
_
2.0
0.75
2.0
1.0
--
HC
0.30
1.0
0.95
.._
2.0
0.75
2.0
1.0
--
Particulate*
0.'30
1.0
0.95
__
2.0
0.75
2.0
1.0
--
(Avg)4+
0.275
1.0
0.90
- -
2.0
0.75
2.0
1.0
--
Order*
1
4
3
_ _
_ _
2
5
4
--
Percent
Cost
44.4
25.3
6.9
2.6
4.6
2.9
2.9
7.5
3.4
NJ
For information only; these figures are not used in calculating averages.
(Avg)4 denotes the average of the four gaseous pollutants (SO , NO , CO, and HC).
. XX
The inventories (e.g., point source, highways) are ordered from 1 to 5 from the most accurate to the least accurate.
-------
To an extent, the same type of inconsistency appears in the percent
cost category. For the smaller inventories, anywhere from 3.4 to 6.970
of the budget was expended. This is reasonable since it requires a mini-
mal amount (about $30,000) to quantify any of these smaller inventories.
Although their individual contributions might be negligible, this would
not be known until the study has been completed. The highway inventory,
however, constitutes almost 4070 of the emissions budget (excluding TSP) ,
but only about 257o of the cost budget. If we are to assume that the ac-
curacy of the point source inventory is adequate, than a similar accuracy
should be the goal of future highway inventories.
93
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X EFFECTS OF EMISSION PARAMETER INACCURACIES
ON AIR QUALITY MODEL PREDICTIONS
The objective of the analysis in this section is the characterization
of the effects of emission parameter errors when they are used as input
to air quality simulation models. In the analysis to follow, steady-state
Gaussian diffusion models are used. Of course, the final RAPS models will
be more sophisticated. However, the results generated herein are easily
extended to these more sophisticated models within the accuracy constraints
of the analytical techniques applied.
A. Quantification of Errors in the Meteorological Input
Parameters
One of the primary objectives of this work was to determine the sensi-
tivity of the air quality model output as a function of various emission
inputs in relation to the sensitivity as a function of the meteorological
input parameters. Quantifying potential sources of error in this relative
manner allows the air-quality modeler to gain a better understanding of
the limitations on model performance and identifies those types of input
data that need to be improved.
The air quality model output X can be represented as some function
of emissions input Q and meteorological input G. Thus,
(25)
where X, Q, and G are vectors in time (t) and space (s). Any element in
the X array is sensitive to variations of any element in both Q and G
arrays and to errors in the measurement of these elements. In this sec-
tion we examine the accuracy of measuring the meteorological elements
that are used to derive meteorological input parameters for the models.
95
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In the sensitivity analyses to follow, Gaussian, plume models are
used. Accordingly, our discussion here relates to input parameters for
Gaussian plume models. However, we do not imply here that the Gaussian
plume formulation will appear in the final versions of any of the RAPS
models. In fact, a RAPS modeling objective is to improve on the Gaussian
model. Nevertheless, we believe that the utility of the formulation in
our sensitivity analysis will produce representative values.
Most Gaussian plume models that are used to analyze pollutant con-
centrations in an .urban environment require the following meteorological
parameters as input:
Wind speed and direction
Diffusion parameters, both crosswind (cr ) and vertical (a )
Vertical mixing ceiling (height).
Major differences among model applications pertain to the degree of
detail of meteorological input data and the averaging time periods of the
data. For example, whereas some models hypothesize the existence of mean
meteorological conditions, others use the joint frequency distribution of
meteorological parameters, including wind direction, speed, and stability
class. At least one model (APRAC-1A) uses wind data from airport weather
stations and estimates mixing layer ceilings from the nearest radiosonde
data and maximum afternoon temperature.
The meteorological measurements needed to determine the meteorologi-
cal input parameters in air quality models may be listed as:
Wind speed and direction
Solar radiation
Cloud cover and types
Vertical temperature profiles/height of mixing layer.
As mentioned earlier, the wind measurements are used directly as
input parameters in the air quality models; other parameters are derived.
The accuracy and variations of wind consequently have a direct bearing on
the performance of air quality models.
96
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The airflow over a metropolitan area is a critical factor in air
quality since it is the medium that transports many effluents. Also,
transport occurs throughout the depth of mixed layer; therefore, wind
through the planetary boundary layer, rather than only the surface wind,
is a relevant parameter. The variations of wind over the RAPS area have
been verified by special field experiments. It has been found that:
Airflow over RAPS area is modified to heights well above 300
meters.
Wind speeds, varied by 20 to 30%, and wind direction by 20 to
30 degrees near the metropolitan area (Ackerman, 1974).
These findings have a significant bearing on the sensitivity analy-
sis of air quality models. If the accuracies of wind measuring instru-
ments and of pilot balloon observations are considered, the complexity of
performing the analysis becomes more evident. For example, it is very
difficult to predict the accuracy obtainable from a pilot balloon ascent
in which the most important source of error is the uncertainty in the
rate of ascent of the balloon. Even if two theodolites are used, no
great precision should be expected, and it has been estimated that even
the best observations at low altitudes do not give a true wind more
closely than ± 2 degrees in direction and ± 1 mps in speed (Middleton and
Spilhaus, 1953). These errors are comparable to those that occur in
surface-based instruments, especially the errors in measuring the direc-
tion.
The mixing ceilings are estimated from vertical temperature profile
measurements. The instruments used to measure temperatures at network
sites, because of their limited range, are inadequate for this purpose.
If radiosonde temperatures are used (as in the case of APRAC-1A), then
errors in measurements can play a significant role in the prediction of
concentrations. Errors caused by lag of a radiosonde thermometer at
300 m range from 2°C to 7°C as reported by Middleton and Spilhaus (1953,
Figure 185) .
The estimated diffusion parameters that depend on measurements of
solar radiation and observations of cloud cover can be affected not only
by instrumental errors, but also by human errors in estimating cloud
97
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cover. Other techniques of estimating these parameters depend on wind
or temperature measurements, and they are sensitive to those errors just
described above.
The meteorological network and instruments used in the RAPS study
are described by Myers and Reagan (1975). Besides air quality measure-
ments made at 25 sites, meteorological measurements were also made as
part of the data base for the modeling work. In addition, solar and sky
radiation, temperature gradients, and turbulence measurements were made
at some sites.
For the radiation measurements, comparisons were made between urban
and rural, and upwind and downwind. For the turbulence measurements,
values were obtained for different types of surface roughness and thermal
characteristics. The vertical temperature difference between 5- and 30-
meter levels was measured. Table 26 shows some of the pertinent character-
istics, including accuracy, of the instruments used for measuring meteoro-
logical elements. In addition to the measurements made at the network of
stations, special upper air observations were made by pilot balloon and
radiosonde techniques.
B. Case Study for a Single Point Source
Prior to considering the RAPS emission inventory as a whole, a single
source sensitivity analysis is presented. The purpose of this analysis
is to demonstrate how input data errors change the magnitude and spatial
distribution of the predicted air pollution concentrations. This insight
will facilitate the interpretation of the regional sensitivity analysis
to follow.
1. Problem Formulation
A case study was conducted for one of the Union Electric power
plants in St. Louis. The Sioux power plant was selected because its
stack flow maximum values are similar to those of a TVA plant for which
more complete characteristics are available. A steady-state Gaussian
plume model was used to calculate SO- concentrations downwind of the
98
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Table 26
CHARACTERISTICS OF METEOROLOGICAL MEASUREMENTS AT 25 SITE LOCATIONS IN RAPS AREAS
Meteorological
E1ement
Manufacturer
Range
Starting Threshold
(i,e., sensitivity)
Accuracy
Remarks
vO
vO
Wind speed
Wind direction
Temperature
Temperature
gradient
Dew point
Solar radiation
Turbulence
Pressure
MRI
MRI
MRI
MRI
EG&G
Eppley Lab
R. M. Young Co.
MRI
0 to 22 mps
0 to 540 deg
-20 to +50°C
-5 to +5°C
-40 to +50°C
0 to 4 cal
r\
cnr/min
0 to 22 mps
914 to 1067 mb
0.22 mps
0.3 mps
0.22 mps
<0.270
±0.17 mps or 1%
whichever is
greater
±2.5 deg
Not known
Not determined
1°C, normal
0.02%
Range in specific
instances is not
adequate because
winds in excess
of 25 mps occur
in St. Louis
Subject to exces-
sive drift and
malfunction
Meaningful data
for only 30 to
40% of the time
Operated only
during intensive
field investiga-
tions of RAPS
-------
Sioux plant for various perturbations in meteorological and emission
inputs.
is:
In the Gaussian formulation, the ground level concentration (X)
X =
rr a cr U
y z
exp -
(26)
where:
U = the mean wind speed, which will be allowed to vary between
2 and 8 m/s
cr ,a = the Pasquill (Slade, 1968) horizontal and vertical disper-
sion coefficients, a function of downwind distance and
stability class
y = the lateral distance to the plume centerline
Q = the source strength
H = the effective plume height
The plume rise is a function of the stack flow parameters (exit velocity
and temperature) and the prevailing atmospheric conditions. The plume
rise formulas chosen were proposed by Briggs (1971, 1972) and are cur-
rently being used in EPA point source models (Turner and Busse, 1973).
2.
Results
The maximum steady-state downwind concentrations for nine dif-
ferent atmospheric conditions are illustrated in Figure 8. The concentra-
tions were calculated for maximum plant capacity (Q = 1760 g/h) and a
receptor located in the lateral centerline of the plume (y = 0). Reduc-
tions in plant load to half capacity and the effects of the reductions on
the downwind concentrations were then calculated for three sets of exit
temperature (T ) and volumetric velocity (V ) conditions:
(1) Both Te and Vg were assumed to be unaffected by the load change
(i.e., their maximum values were used).
(2) The maximum value for Te was used; Ve was made directly propor-
tional to the load (one-half maximum in this case). This
technique is referred to as the "proposed RAPS" method.
100
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STABILITY INDEX 3
0.07
0.06
0.05
u
0.03
0.02
0.01
WIND SPEED
2 m/s
5 m/s
8 m/s
LINE TYPE
solid
small desh
med dash
(a)
, I .... I
O.I
0.2
0.5 I 2 5
DISTANCE kilometers
to
20
50
* Note: The Greek symbol, Xf used throughout the text was not an allowable output
character in the computer-generated plots for Figures 8, 9, and 10. Hence,
the phonetic term "CHI" was used to represent the concentration of S02
in ambient air.
FIGURE 8 DOWNWIND CONCENTRATIONS FROM UNION ELECTRIC COMPANY SOUIX
PLANT AT VARIOUS STABILITIES
101
-------
5TAB1UTY INDEX 4
0.020
0.015
a
a
£ 0.010
o
0.005
0
1 ' ' 1 ' ' ' ' 1 1 ' ' 1 ' ' ' ' ) I ; '
WIND SPEED UNE TYPE / \
- 2 m/s solid / \ _
5 m/s small dash ' x
8 m/s med dash / \
t i
i /
* /
/ /
t
~~ / /' *~
/ i
/ /
/
.. » i _
i '
* i
/ '
» »
* i
' i
' /
/
" <") / / .;
1 .. 1 .... 1 1 . /^-r'.-.'f f ^^T
.1 0.2 0.5 1 2 5 10 20 5
DISTANCE kilometers
FIGURE 8 (Continued)
102
-------
5TASIUTY INDEX » 5
0.025
0.020
0.015
x
o
0.010
0.005
WIND SPEED
2 m/s
5 m/s
8 m/s
LINE TYPE
solid
small dash
med dash
' (c)
O.I
0.2
0.5
I 2
DISTANCE -
5
kilometers
FIGURE 8 (Concluded)
(3) Both Te and Vg were assumed to follow a relationship of the
form A-L + B, where L is the load, and A and B are the pro-
portionality and offset conditions, respectively. The actual
numbers were taken from a relationship empirically derived
for a similar Tennessee Valley Plant in Kingston, Tennessee
(Ruff et al., 1976).
The actual numbers are summarized in Table 27. The Gaussian
model was run for a few different atmospheric conditions, using the three
sets of flow parameters shown. Figure 9 shows the downwind concentrations
that result from different flow parameter selection strategies.
103
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Table 27
FLOW PARAMETERS FOR THREE CASES
(Union Electric, Sioux Plant--5070 Capacity)
Case
Maximum
Proposed RAPS
Actual
(modeled from TVA data)
Ve(m3/s)
483
236
277
Te(°K)
435
435
416
Clearly, use of the maximum values provides unacceptable errors
*
of 100% and greater. The RAPS technique produces maximum errors on the
order of 15% for this case. This low figure is a result of offsetting
errors, in that T is overestimated while V is underestimated. If the
e e
errors from either parameter are considered separately, the concentration
error will increase by a factor of about two.
To place the errors just described in perspective with those of
meteorological origin, perturbations in the Gaussian diffusion coefficients
(a , a ) were considered. These coefficients are generally derived from
z y
primary meteorological measurements, such as vertical temperature or
velocity profiles, solar raidation and wind speed, or wind fluctuations.
While the RAPS data base is rich in such measurements, the current state
of the art permits generalizing such measurements into only six broad
categories of atmospheric stability. In our analysis, we allow a devia-
tion of one-quarter of the difference between category values as being
representative of a coefficient standard deviation (typical error). We
feel such an approach in estimating likely errors is a reasonable one
and allows for improvements in the technique of estimating such parameters.
This statement holds for both the current Gaussian models and the more
complex K-theory formulations.
Errors in the context here are comparable to a standard deviation of cal-
culated values divided by a reference (true) value expressed in percent.
104
-------
0.035
0.030
0.025
|o.020
X 0.015
o
0.010
0.005
STABILITY INDEX = 3
WIND SPEED 2.0 m/s
i i rTwi i
FLOW PARAVETER LINE TYPE
actual solid , ._
maximum small jdash
proposed med dash
(a)
O.I
0.2
1 , , , , !
I
0.5 I 2 5
DISTANCE kilometers
10
20
50
FIGURE 9 SENSITIVITY OF CONCENTRATIONS TO FLOW PARAMETER VARIATION
(Souix Plant)
105
-------
o
0.05
0.04
0.03
0.02
0.01
(b)
STABILITY INDEX = 3
WIND SPEED 5.0 m/s
FLOW PARAMETER
actual
maximurn
proposed
O.I 0.2
I
I
0.5 I 2 5
DISTANCE kilometers
FIGURE 9 (Continued)
10
20
50
106
-------
STABILITY INDEX = 3
WIND SPEED 4.0 m/s
O.W5
0.040
0.035
0.030
aO.025
- 0.020
o
0.015
0.010
0.005
maximum
proposed
|JNE TYPE
small dash
mad dash
(c)
O.I
0.2
0.5 I 2 5
DISTANCE kilometers
10
20
50
FIGURE 9 (Concluded)
The effect of allowing the Gaussian diffusion coefficients to
assume values that deviate from their categorical standards is shown in
Figure 10. Consider Figure 10(a): an interpretation is that the true
values could correspond to stability indexes of 2.75 or 3.25, which are
represented by the dashed lines. However, owing to the inherent limita-
tions in determining the stability class to that precision, those cases
would be placed in Class 3 (the solid line). The differences between
the dashed and the solid lines are indicative of the errors that result.
These differences are quite pronounced in this case.
It should be noted that the accuracy of the stability parameters
decreases with the distance from the source. Turner indicated that errors
107
-------
STABILITY INDEX 3
WIND SPEED - 5.0 m/s
E
a.
a
X
o
0.12
0.10
0.08
0.06
0.04
0.02
I I I
STABILITY LINE
standard solid
Edev srnal
oerv meo
(a)
O.I
0.2
TYPE
I dash
dash
0.5 I 2 5
DISTANCE kilometers
10
20
50
FIGURE 10 SENSITIVITY OF CONCENTRATIONS TO DIFFUSION FLOW COEFFICIENT
VARIATIONS (Souix Plant)
108
-------
STABILITY INDEX 4
WIND SPEED 5.0 m/s
0.035
0.030
0.025
|o.020
I 0.015
o
0.010
0.005
I I
STABILITY
standard
>5 gev
(b)
I
UNE TYPE
solid
small dash
meo dash
I
O.I 0.2
0.5 I 2
DISTANCE
5
kilometers
FIGURE 10 (Continued)
109
-------
STABILITY INDEX 5
WIND SPEED - 5.0 m/s
0.035
0.030
0.025
|o.020
£ 0.015
o
0.010
0.005
STABILITY
standard
0-25 gev
0-25 oev
r (C)
I T
UNE TYPE
solid.
smajl
med
I
O.I
0.2
0.5 I 2 5
DISTANCE kilometers
10
20
50
FIGURE 10 (Concluded)
may be severalfold, in some cases reducing to factors of two within a few
hundred meters of the source. Our estimates are conservative compared to
those of Turner (1970).
The accuracy of the emission factor, on the basis of recent
stack tests by Rockwell International appears to be quite good. Littman
*
estimated the standard error at five percent. The daily fluctuation
from the mean of the fuel sulfur content is assumed to be 10% or less.
The PEDCO analysis does not support this optimistic figure; however,
the Rockwell International stack tests are more current.
110
-------
This is analyzed and reported on a monthly basis. Therefore, the calcu-
lation of the emission parameter, Q is normally within 15% of the actual
value.
The approximate errors for the Union Electric Sioux plant are
summarized in Table 28. We emphasize that this is a study of only one
case for a few meteorological and emission rate conditions. Nevertheless,
it illustrates the effects of not altering the flow parameter estimates
with varying plant loads. We recommend the use of simple models of the
stack exit volumetric flow and temperature. Such models should be based
on data from the plants, when available. When the data are not available,
the "proposed RAPS" technique of scaling back the volumetric flow with
constant stack temperature should be used. By reducing errors in all
emission parameters to within 15%, modelers can focus on improvement on
the atmospheric portion of the models.
Table 28
TYPICAL ERRORS IN SIGNIFICANT DOWNWIND CONCENTRATIONS
Sensitivity Parameter
Emission rate
Flow parameters (maximum values
at half capacity)
Flow parameters (proposed RAPS
at half capacity)
Dispersion coefficients
Error
15
> 100
15
> 100
The PEDCO analysis does not support this optimistic figure; however,
the Rockwell International stack tests are more current.
Ill
-------
XI EFFECTS OF RAPS EMISSION INVENTORY ERRORS
A. Point Source Dominated Inventory (S02)
1 . Discussion
As discussed in Section III, the air quality model output, X,
can be represented as some function of emissions inputs, Q, and meteorologi-
cal input G as follows:
<27>
where X x' , Q, and G are vectors in time (t) and space (s). The X'
. err' ' v err
V?
term represents an error in the formulation that is only a mathematical
approximation to the physics and chemistry of the atmosphere. For our
purposes, it is assumed that the X' term represents a random error not
related to the Q and G arrays. The sensitivity of any element in the X
array to any element in either the Q or G arrays is given by
a x. a f [Q] [GI.
1 = I t,S, t.S
3 Q. a Q.
or
ax a f [Q] [G]
i. _ ! L i s < L »t» I /oo\
a G ~ a G. (28)
*
In the notation used here, the prime will be used to designate errors
due to the model formulation, unprimed error terms will designate the
total error.
113
-------
The variations in the Q and G terms are numbers that can be used to
quantify air quality model errors by substitution for incremental changes
in Q. and G. in the discretized approximation of Eq. (28).
Our sensitivity analysis to date has consisted of evaluating
individual Q. and G. parameters in relation to their effect on X.. The
parameters involved are summarized as follows:
X = ground level SO concentrations
Q, = SO emission estimate (g/s)
Q9 = Stack gas exit parameters (temperature K; volume flow
m3/s)
G, = Wind direction (degrees)
G~ = Wind speed (m/s)
G_ = Atmospheric stability (six classes derived from the
atmospheric lapse rate in C/100 m) .
Using our simulated RAPS inventory and the CDM (Busse and
Zimmerman, 1973) , the following reference set of meteorological param-
eters were chosen:
Wind direction sectors: NNW (70%); NW (30%)
Wind speed average: U3 (4.43 m/s)
Atmospheric stability: Class 4.
This set of parameters is typical of winter conditions in the St. Louis
area. The ground-level concentrations classified by area, small point,
and large point sources were calculated for six downwind locations cor-
responding to RAPS monitoring stations. Results of the reference run
are presented in Table 29.
First let us examine the sensitivity of ground-level concentra-
tion to errors in the wind direction input, G. If we assume an instrument
and interpolation error in degrees, we must relate this to a frequency
distribution as required by the CDM. It must also be noted that the CDM
requires uniform wind fields, although more current models might require
complex wind fields. Nevertheless, our analysis is valid and applicable
-------
Table 29
GROUND-LEVEL SO- CDM REFERENCE CASE
j£~
(in
Coordinates
Station
104
109
110
115
117
123
UTMX
747.31
755.80
747.21
757.11
760.56
777.32
UTMY
4277.30
4279.89
4272.21
4297.80
4272.82
4286.38
Area
Source
33
4
25
1
6
1
Small
Point
5
10
10
2
10
17
Large
Point
0
741
50
148
322
57
Total
38
755
86
151
338
75
in both cases since we are assuming that the true wind for our reference
case is uniform (spatially) throughout the region.
In our analysis, we assume that wind direction error will result
in an incorrect wind category 10% of the time. This is traceable to an
estimated wind direction error of 2.5 degrees. The assumed erroneous
distribution is:
NNW: 60%
NW: 30%
WNW: 10%
Table 30 summarizes the results of the computer analysis including esti-
mates of ground-level concentrations, individual errors, average error,
and average significant error. Individual errors are the percentage
difference between the Table 30 totals and those for the reference case
(Table 29). The average significant error is the mean error for stations
3
with concentrations over 100 |J>g/m .
A typical erroneous wind speed distribution is estimated in a
manner similar to that described for wind direction above and is sum-
marized in Table 31. Results for the run below are displayed in Table 32.
The third meteorological parameter, G-, considered was atmo-
spheric stability. This parameter is not measured directly and must be
115
-------
Table 30
SENSITIVITY OF SO ESTIMATES TO WIND DIRECTION ERRORS
(in
Station
104
109
110
115
117
123
Area
Source
31
5
24
1
6
1
Small
Point Source
6
10
8
1
9
15
Large
Point Source
7
643
43
128
279
64
Total
44
658
75
*
130
*
294
80
Error
16
13
13
14
13
7
Average error = 13; average significant error = 13.
*
Used to calculate average significant error (for totals greater
than 100 jig/m3.
Table 31
ERRONEOUS WIND SPEED DISTRIBUTION
Wind Speed Category Distribution
(7.)
(Mean Wind Speed in mps)
Class
Sector
NNW
NW
2
(2.46)
10
5
3
(4.47)
50
20
4
(6.93)
10
5
Total
70
30
116
-------
Table 32
SENSITIVITY OF SO ESTIMATES TO WIND SPEED ERRORS
Station
104
109
110
115
117
123
Area
Source
35
5
27
1
7
1
Small
Point Source
5
11
11
2
10
18
Large
Point Source
0
765
51
145
329
56
Total
40
*
780
88
*
148
*
345
74
7o Error
5
3
2
2
2
1
Average error = 3; Average significant error = 2.
*
Used to calculate average significant error.
derived from other measurements. For this sensitivity analysis, atmo-
spheric stability is assumed to be determined solely by the average
thermal lapse rate. We emphasize that all assumed errors are based on
conservative estimates of instrument interpolation and interpretation ac-
curacies. The assumed stability erroneous distribution is shown in
Table 33. Results for this run are displayed in Table 34.
So far, we have considered estimates of errors for meteorologi-
cal inputs used with COM. We have chosen those conditions under which
CDM is most accurate (steady-state conditions, uniform wind fields,
moderate wind speeds) . Our assumption is that variations in the meteoro-
logical variables occur over a short undefined period and manifest them-
selves in a sequence of discrete steady-state conditions. Our error esti-
mates have been on the conservative side. There are occasions when more
severe errors are encountered. As an example, atmospheric stability could
be completely misclassified. This condition is illustrated in Table 35,
which indicates the results of misclassifying stability to the next lower
level. Even if RAPS achieves its objectives, errors such as these will
117
-------
Table 33
ERRONEOUS ATMOSPHERIC STABILITY DISTRIBUTION
Atmospheric Stability Distribution
(*/}
{/o}
Sector
NNW
NW
Class 3
14
6
Class 4
56
24
Table 34
SENSITIVITY OF S02 ESTIMATES TO TYPICAL
ATMOSPHERIC STABILITY ESTIMATION ERRORS
Station
104
109
110
115
117
123
Area
Source
30
4
23
1
6
1
Small
Point Source
6
9
9
2
9
15
Large
Point Source
0
647
45
138
277
51
Total
36
*
660
77
*
141
*
292
67
% Error
5
13
10
7
14
11
Average error = 10; Average significant error = 11.
*
Used to calculate average significant error.
still occur, but they should be infrequent. We choose to consider the
results presented in Tables 30, 32, and 34 as being more typical.
The sensitivity analysis for the emission parameters, Q-^ and Q_,
depends upon a random error about the mean estimate. The error follows
a Gaussian distribution. A standard deviation of error is estimated. The
model (CDM) uses emission parameter values that vary from the control case
according to the equation
118
-------
Table 35
SENSITIVITY OF S02 ESTIMATES TO
WORST CASE ATMOSPHERIC STABILITY ESTIMATION ERRORS
Station
104
109
110
115
117
123
Area
Source
19
3
16
1
6
1
Small
Point Source
11
4
4
1
3
6
Large
Point Source
0
270
23
99
98
29
Total
30
277"
42
100*
*
107
36
% Error
21
66
51
51
68
52
Average error = 52; Average significant error = 62.
*
Used to calculate average significant error.
Qk+6k
k=l
k=l
(30)
where
Q' = The actual value of the parameter for source k
Q = The estimated emissions value for source k
K.
e, = The error which is randomly selected but follows
a normal distribution.
In the first sensitivity analysis for emission rate variations,
Q, standard deviations for the different types of sources were assumed
as follows:
Large point: 0.2Q
Small point: °-^Qk
Area: 0.75Qk.
119
-------
An interpretation for large point sources is that the standard deviation
for the error, e, , is 20% of the estimated value. Assuming a normal dis-
tribution, approximately 6870 of the sources will have errors within 2070
of the estimated value, 95% of the sources will have errors with 2 stan-
dard deviations (4070) , and so forth. A random number generator is called
for each source in the CDM run. The actual value, e, , for that source is
' k
a function of the random number, which is part of a normally distributed
set. The results of the sensitivity analysis for Q, are given in Table 36.
Table 36
SENSITIVITY OF S02 ESTIMATES TO TYPICAL
RANDOM ERRORS IN EMISSION RATE
Station
104
109
110
115
117
123
Area
Source
31
4
26
2
7
1
Small
Point Source
6
13
11
1
11
17
Large
Point Source
0
684
47
156
300
60
Total
37
*
701
83
*
159
*
317
79
% Error
3
7
3
5
6
5
Average error = 5; Average significant error = 6.
&
Used to calculate average significant error.
In the second sensitivity analysis for emission rates we as-
sumed a worst case standard deviation for random error as follows:
Large point: 0-^Qi,
Small point: 0.8Q
iC
Area: l«5Qk
The results of this sensitivity analysis are presented in Table 37.
120
-------
Table 37
SENSITIVITY OF S02 ESTIMATES TO WORST CASE
RANDOM ERRORS IN EMISSION RATE
Station
104
109
110
115
117
123
Area
Source
44
4
35
2
7
1
Small
Point Source
6
4
7
2
6
18
Large
Point Source
0
902
45
121
379
52
Total
49
911*
87
124*
392*
72
% Error
29
21
1
18
16
4
Average error = 15; average significant error = 19.
*
Used to calculate average significant error.
A similar sensitivity analysis was conducted for variations in
Q~, the stack flow parameters (exit temperature and volumetric flow). In
this analysis we assumed standard deviations for both parameters as fol-
lows :
Temperature: 0.1 T
Volume flow: 0.2 VF
where T and VF represent the estimated values for exit temperature and
volume flow, respectively.
In this analysis, variations in the Q~ parameters are assumed,
equal for small and large point sources and are not a factor for area
sources. Results of this sensitivity run are given in Table 38.
2. Summary
The results of our sensitivity analysis for SO,, are summarized
in Tables 39 and 40.
The actual numbers in the two summaries are representative, but
they are subject to wide variations, depending on the exact meteorology
121
-------
Table 38
SENSITIVITY OF SC>2 ESTIMATES TO TYPICAL
RANDOM ERRORS IN STACK EXIT GAS PARAMETERS
Station
104
109
110
115
117
123
Area
Source
33
4
25
1
6
1
Small
Point Source
4
10
10
1
9
17
Large
Point Source
0
714
47
124
312
52
Total
36
728*
82
127*
327*
70
% Error
5
4
5
16
3
7
Average error = 7; average significant error = 8.
*
Used to calculate average significant error.
Table 39
SUMMARY 1: SIGNIFICANT GROUND-LEVEL CONCENTRATION
AVERAGE ERROR CAUSED BY TYPICAL INPUT PARAMETER ERROR
Parameter
Q1
Q2
Gl
G?
G3
= Emission rate
= Stack exit parameters
= Wind direction
= Wind speed
= Atmospheric stability
Error
6
8
13
2
11
Table 40
SUMMARY 2: SIGNIFICANT GROUND-LEVEL CONCENTRATION
AVERAGE ERROR CAUSED BY WORST-CASE INPUT PARAMETER ERROR
Parameter
Q
G3
= Emission rate
= Atmospheric stability
Error
(01 \
(to)
19
62
122
-------
and relative orientation of sources. In any case, the analysis does re-
veal that the accuracy of the S0_ emission estimation methodology is con-
sistent with accuracies for meteorological inputs, even allowing for
reasonable improvements in quantifying the meteorological parameters.
Moreover, our test case assumed that the model represented the physics
of the atmosphere perfectly. Therefore, our approach was quite conserva-
tive.
Our tests also revealed, that, as expected, S0» ground-level
concentrations are primarily caused by large (1000 tons/year and above)
point sources. These are the sources that are being monitored most
closely; i.e., hourly process data are being collected. Also, a number
of stack measurements were collected to verify emission factors.
B. Area Source Dominated Inventory (CO)
1. Discussion
Much of the discussion for point sources (Section XI.A) applies
to area sources also. The sensitivity analysis for meteorological param-
eters generally applies. The percentage of error in area source CO esti-
mates caused by variations in meteorological parameters (G.. , G_ , and G_)
will be somewhat less than for a point source dominated inventory. This
lower percentage of error results from area sources being more uniformly
spread out over relatively large areas and, hence, less sensitive to hori-
zontal variations in the meteorological parameters. Also, area source
emissions are not as sensitive to plume rise variations resulting from
errors in stack characteristics, Q9. Therefore, air quality estimates
should be less sensitive to errors from area sources than to errors from
point sources.
2. Sensitivity
During the course of this study, the RAPS inventory was unavailable
for use as an input to a comprehensive sensitivity analysis. Neverthe-
less, we were able to make some estimates based on previous analyses for
S02, which also tr.eated area sources.
123
-------
To give some idea of the sensitivity of area dominated sources
to emission estimate inaccuracies, we have reduced previous results from
Tables 29, 36, and 37. This result appears in Table 41. As shown, even
for large errors (coefficients of precision of 0.75 T and 1.5 T), the aver-
age observed error in ground-level concentration is between 7 and 297o.
Table 41
AREA SOURCE SENSITIVITY COMPARISON
Station
104
109
110
115
117
123
Average
Ref
33
4
25
1
6
1
a = 0.75 T
e
Value
31
4
26
2
7
1
Error
6
0
4
17
7
CT = 1.5 T
e
Value
44
5
35
2
7
1
--
Error
(7o)
33
25
40
17
29
3. Adequacy of the CO Inventory
It is tempting to say that errors in the CO inventory should
not result in prediction errors in excess of 307o. Based on the number
of assumptions in estimating the error's coefficient of precision, this
upper bound of 307» is tenuous at best. Nevertheless, it is the best esti-
mate that can be made at this time.
It should also be noted that sensitivity is a function of the
air quality model used. For our evaluation, CDM was used. We have as-
sumed that the CDM results are representative of what other models might
yield. Also, our sensitivity analysis did not use the CO RAPS inventory,
which was not available. Therefore, the actual numbers (7 and 297o) could
change somewhat if the real inventory were used.
124
-------
C. Other Primary Pollutants
1. Total Hydrocarbons
As in the case for CO, the THC inventory is dominated by high-
way source emissions. However, THC point sources and stationary area
sources do contribute more than they do for CO. Nevertheless, the point
sources contribute 23.370 of the total THC emissions (compared with 9. TL
for CO; see Table 25).
Since we have estimated comparable accuracies for both CO and
THC (see Table 25), it would appear that that air quality prediction
errors caused by errors in the THC emissions inventory should be about
the same for both pollutants--? to 30%.
2. Nitrogen Oxides
The NO inventory is dominated by point source emissions, al-
X
though to a lesser extent than point source emissions dominated the S0_
inventory. As discussed in Section V.B, the NO estimates are not as
accurate as the SO- estimates. Furthermore, the point source data may
have considerable bias estimated as 20% (see subsection V.B) . Neglect-
ing this bias, we estimate that random errors in the NO emission inventory
X
could typically produce errors in predicted ambient NO concentrations com-
X
parable to the worst case SO- estimates, perhaps somewhat less than aver-
age 19% shown in Table 37.
3. Particulates
The TSP inventory consists primarily of fugitive dust sources.
This inventory relies on a totally different methodology than other sources,
The TSP inventory is also much more cumbersome to verify. Only limited
quantities of field test data exist. These are not adequate to assess
fully the bias and coefficient of precision of the emission estimates.
This is a current research area that may produce results that can be used
to assess the accuracy of the RAPS data. Because of these several factors,
we have not included an estimate on TSP emission inventory inaccuracies.
More work is needed on particle size distribution, and more effort must be
expended in collecting validation data.
125
-------
D. Photochemical Model Implications
Hydrocarbons (HC) and nitrogen oxides (NOX) are the two primary pol-
lutants of interest in the context of photochemical oxidants. Although
estimates of pollutant emissions contain inaccuracies, it is worthwhile
to have an estimate of the magnitude of the errors that might be encoun-
tered when computing secondary pollutants such as 0- and NO- . This prob-
lem can be treated within the framework of sensitivity analysis since
some kind of transfer function linking secondary and primary pollutants
must be postulated.
Let S denote the concentration of the secondary pollutant i.e., 0«
0-- and let Q and %o deno
2. HC X
the transfer function has the form
or N00-- and let Q and %o denote the emissions of HC and NO . Then,
2. HC X X
S = f(x, y, z, t, QRC, QNQ , G) (30)
where x, y, and z denote the spatial coordinates, t denotes the time co-
ordinates, and G denotes generically the weather conditions. The function
f may be as simple as the linear rollback equation, or as complicated as
a nonlinear chemical diffusion model.
Estimates of pollutant emissions are generally available as aggre-
gated tonnages over a large area, e.g., a metropolitan area. One can
postulate a range of error for the aggregated estimate, e.g., ±257o, and
attempt to estimate the effect of this error on S. In this case localized
effects are ignored and only the chemistry of HC and NO remains as the
X
major factor relating emissions and secondary pollutants.
For ozone, an upper bound on the magnitude of the change in concen-
may be obtained by a simple proportional model:
AS « AQ (31)
This applies only if AQ and AQjqQ are relatively small (less than 20%)
and their respective signs are the same. If the signs are different--
e.g. , if E is low and ENQ is high then Eq. (31) can yield very mis-
leading results for AO^ because of the nonlinearity of the chemical
interaction between HC and NO .
x
126
-------
Equation (31) also applies to N02 only in the case of A QNQ and AQ
have the same sign and roughly the same magnitude. If the size of the
error for N09 emissions differs greatly from the error of the HC emissions,
then Eq. (31) will yield a low value for the impact of the error on NO-
levels. For example, if A (}_ = -15% and A Qwr. = -2%, then Eq. (31) would
HC ux
yield an optimistic (low) value for ANO?.
In summary, errors in aggregated areawide emissions can be used to
provide pessimistic (large) upper bounds for 0~ uncertainties and optimis-
tic (small) lower bounds for N0~ inaccuracies. This can be done only when
certain conditions associated with the size and sign of the emissions esti-
mates are satisfied. Highly inaccurate error bounds will be obtained when
the errors in HC and NO emissions have different signs or their respec-
X
tive magnitudes are far apart, or both. For the RAPS inventory, we cannot
be sure whether the signs are typically different or the same. One compli-
cation is that HC emissions are dominated by highway sources and NO is
X
dominated by large point sources. Further field tests, as described ear-
lier, would alleviate this uncertainty.
E. Other Inventory Factors
Previous studies have addressed such issues as grid size (for area
source), resolution of point source coordinates, and source heights (see
Section II). The resolution of point source coordinates is generally
accepted to be about 10 m. The rationale was described in a previous SRI
report (Littman et al., 1974). In general, Rockwell International has
endeavored to use the 10-m criteria in their compilation.
1. Source Heights
Some discussion on source heights appears in the background
literature (see Section II). Earlier, in Section XI.A, we have shown the
effect of inaccuracies of source height estimates (including plume rise
calculations) for point sources. For the other dominant inventory (high-
way sources), the emission heights are well known.
127
-------
2. Area Grid Size
The smallest grid in the RAPS system is 1 km by 1 km. A previ-
ous sensitivity analysis, described in Section II, indicates that grids
o
larger than 1 mi can lead to significant errors in estimated pollutant
concentrations.
In reality, the required grid size is a function of: (1) the
spatial variability of emissions, and (2) the ability of the air quality
model to handle the resulting grid matrix.
For the problem of handling the grid matrix, with the advent
of more efficient computers and more advanced models, the 1-km grid will
in the future be larger than desired. At present, it is consistent with
the current state of the art.
For economy and efficiency, the 1-km grids are convenient now
because they are consistent and compatible with the census data. There-
fore, 1-km grids appear to be a reasonable compromise in satisfying RAPS
requirements. Should smaller grid sizes be required at some future date,
new algorithms could be used to refine the spatial resolution.
128
-------
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