EPA-600/4-79-004
January 1979
REGIONAL AIR POLLUTION STUDY
Emission Inventory Summarization
by
Fred E. Littman
Rockwell International
Environmental Monitoring & Services Center
Environmental & Energy Systems Division
11640 Administration Drive
Creve Coeur, MO 63141
Contract No. 68-02-2093
Task Order 108J
Project Officer
Chuck Masser
Monitoring and Data Analysis Division
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
ENVIRONMENTAL SCIENCES RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
RESEARCH TRIANGLE PARK, NORTH CAROLINA 27711
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DISCLAIMER
This report has been reviewed by the Environmental Sciences Research
Laboratory, U. S. Environmental Protection Agency, and approved for pub-
lication. Approval does not signify that the contents necessarily reflect
the views and policies of the U. S. Environmental Protection Agency, nor
does mention of trade names or commercial products constitute endorsement
or recommendation for use.
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ABSTRACT
As part of the Regional Air Pollution Study (RAPS), data for an air
pollution emission inventory are summarized for point and area sources in
the St. Louis Air Quality Control Region. Data for point sources were
collected for criteria and non-criteria pollutants, hydrocarbons, sulfur
trioxide, particle size distribution, and heat. For area sources, data were
collected on criteria pollutants, hydrocarbons and heat.
All the data have been entered into the RAPS Data Bank. Hourly values
are available for all point sources; locations are identified by UTM coordi-
nates (zone 15) to within +10 m. Area sources are assigned to a network of
1989 grid squares of variable size. The emission inventory is applicable
for the years 1975 and 1976 and complements the RAPS aerometric data.
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CONTENTS
Abstract n11
Figures vii
Tables vlii
1.0 Introduction 1
1.1 The Regional Air Pollution Study (RAPS) 1
1.2 The RAPS Emission Inventory 2
2.0 Point Source Emission Inventory 8
2.1 Definition 8
2.2 Sensitivity Analysis 9
2.3 Data Acquisition 10
2.3.1 Major Sources 10
2.3.2 Minor Sources 13
2.3.3 Source Measurements 13
2.4 Data Handling and Coding Procedures 19
2.4.1 Data Input 19
2.4.2 Editing of Data 20
2.4.3 RAPS Emission Inventory Calculations 22
2.5 Sample Outputs 22
3.0 Special Point Source Inventories 30
3.1 Hydrocarbon Emission Methodology and Organic
Emission Inventory 30
3.1.1 Point Source Total Hydrocarbon Inventory 30
3.1.2 Point and Area Source Organic Emission Inventory .... 32
3.2 Point Source Non-Criteria Pollutant Emission Inventory 35
3.3 Sulfur Compounds and Particle Size Distribution 36
3.3.1 Sulfur Compounds (SOg) 36
3.3.2 Particulate Size Distribution 42
3.4 Point and Area Heat Emission Inventory 49
4.0 Area Source Emission Inventories 59
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CONTENTS
4.1 The RAPS Grid System 59
4.2 Residential and Commercial Emission Inventory 59
4.2.1 Space Heating 61
4.2.2 Evaporative Emissions 61
4.2.3 Structural Fires and Solid Waste Disposal 63
4.3 Highway Vehicle Emission Inventory 64
4.3.1 Line Source Definition 64
4.3.2 Highway Vehicle Line and Area Source Emissions 64
4.4 River Towboat Emissions 67
4.5 Emissions from Airport Operations 72
4.6 Emissions from Rail Operations 76
4.7 Off-Highway Mobile Sources 78
4.8 Fugitive Dust Emission Inventory 81
5.0 Summary 87
References 88
VI
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FIGURES
Number Page
1 Metropolitan Saint Louis Interstate Air
Quality Control Region 3
2 The Regional Air Monitoring Stations Network 4
3 S02 Emissions for the Saint Louis Air
Quality Control Region 6
4 RAPS Major Point Sources 12
5 Sample RAPS Coding Form 21
6 Hourly Point Source Listing 24
7 Daily Point Source Listing 25
8 Annual Point Source Listing 26
9 Modeler's Tape 27
10 Percentage Conversion of S02 to S03 43
11 Andersen Stack Sampler 48
12 Particle Size Distribution - Coal Fired Boiler 50
13 Example of RAPS Grid System Overlay 60
14 Daily Gas Flow vs. Average Daily Temperature 62
15 Traffic Map 66
16 St. Louis Area River Vessel Traffic 70
17 St. Louis AQCR Rail Network 79
18 Temporal Apportioning Factors 84
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TABLES
Number Page
1 Total Emissions for the St. Louis AQCR (Tons per Year) 8
2 Summary of Hourly Source Data 8
3 Summary of Annual and Pattern Data 9
4 Values of © for Selected Pairs (a, 1-C) 9
5 Maximum Allowable Error for Point Sources of Various Sizes .... 10
6 Comparison of S02 Emissions Based on Calculated and
Measured Flow Rates 16
7 Comparison of AP-42 and Experimental Emission Factors 18
8 Modeler's Tape Format 28
8 Modeler's Tape Format (Continued) 29
9 Methane Emission Sources 31
10 RAPS Hydrocarbon Classification 33
11 Summary of Total Organic Emissions Inventory by Class 34
12 Sources of Non-Criteria Pollutants 37
13 Pollutants in Coal 38
14 Pollutants in Residual Oil 39
15 Pollutants in Iron Production 40
16 Emissions for Selected Compounds for AQCR 70 36
17 Annual Trace Emissions Listing 41
18 Sulfur Oxide Analyses and Ratios 44
19 Particulate Inventory: Size Distribution 46
20 Particulate Inventory: Efficiency Distribution 47
21 Particle Size Distributions 49
22 Point Source Fuel Consumption 52
23 Area Source Fuel Consumption 53
24 Examples of Point Source Heat Emission Factors (HEM) 55
25 Summary of Annual Heat Emissions in AQCR 70, 1012 BTU 58
26 Summary of Stationary Residential and Commercial Emissions .... 65
viii
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TABLES
Number Page
27 Example of Hourly Line Source Emissions (grams) 68
28 Example of Hourly Area Source Emissions (kg/hour) 69
29 Example of River Vessel Emissions per Grid (0.1 kg/day) 73
30 Aircraft Operating Modes 72
31 Service Operating Modes 74
32 Example of Hourly Airport Emissions 75
33 Example of Annual Airport Grid Emissions (grams) 77
34 Categorization of Locomotive Types in St. Louis Rail
Activity Inventory 76
35 Examples of Annual Emissions from Rail Operations (kg/year) ... 80
36 Sample of Emissions from Off-Highway Mobile Sources 82
37 Summary of Annual Emissions of Fugitive Dust 86
IX
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1.0 INTRODUCTION
1.1 THE REGIONAL AIR POLLUTION STUDY (RAPS)
The Regional Air Pollution Study (RAPS) was conceived early in 1970 to
provide a rational, scientific basis for the management of air quality, as
mandated by the Clean Air Act (as amended). The basic premise of the Act
is that desired air quality standards can be obtained by setting appropriate
emission standards. The development of Implementation Plans, called for by
the Act, assumes that existing knowledge was at least minimally adequate for
planning.
The basic tool for the development of air quality management is the
simulation model, a mathematical description of the complex relationship
between emissions, atmospheric dispersion and transformation, and ambient
concentration. The development of any model presupposes: 1) a detailed
understanding of the physical, chemical and meteorological process involved,
and 2) availability of adequate emission data, meteorological information and
measurements of ambient concentrations of the pollutants under investigation.
At the beginning of the RAPS study, a number of simulation models had
been developed, but fewif anyhad been verified in the field. The primary
reason for this was the absence of an adequate data base, which would contain
accurate, high resolution data covering a sufficiently large area. Ambient
data were available with adequate time resolutionone hour or less--but the
stations providing such data were typically too few and improperly spaced to
cover a given area. Meteorological data were usually available only at very
few points in a given area, such as an airport, where they were gathered for
other purposes. Micrometeorological data related directly to ambient measure-
ments were generally unavailable.
Emission inventories have been in existence for some years, and owing to
the efforts of the National Air Data Branch of OAQPS, were being collected in
a uniform, machine readable format known as National Emission Data System
1
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(NEDS). However, the NEDS inventory contained essentially only annual data,
which cannot readily and reliably be converted to hourly values over the two
year RAPS program period.
Clearly, what was needed as a first step in the development of a rational
approach to the management of air pollution was an extensive, detailed data
base containing all these elements: emission, meteorological, and ambient
data, with a resolution in time and space and an accuracy adequate to provide
an input to simulation models. This became the first task of RAPS. (1)
The St. Louis Interstate Air Quality Control Region (AQCR 70) was chosen
as the site for RAPS (Figure 1). The selection was based on the need to find
a large city within the continental United States, which was away from oceans
and mountains and which typified the coal-burning industrial nature of many
urban areas, yet which lay in an extended region of rural country. Of the 33
Standard Metropolitan Statistical Areas larger than 400,000 population,
St. Louis emerged as the clear choice based on the following criteria:
' Surrounding Area
' Heterogeneous Emissions
' Area Size
' Pollution Control Program
' Historical Information
* Climate
1.2 THE RAPS EMISSION INVENTORY
The emission inventory is an integral part of RAPS. With the network of
Regional Air Monitoring Stations (RAMS) gathering minute-by-m'nute ambient and
micrometeorological data (Figure 2), an emission inventory of equal quality
was needed. Practical considerations make time intervals of less than one
hour unrealistic. It was desirable to have the emission data based on
measured values rather than calculated ones. Initially the principal pollutant
of interest to the modelers was S0?, which is intimately associated with
stationary (combustion) sources.
Thus, the initial thrust was to develop a detailed point source inventory
for S0?, based on hourly, measured values. It became quickly apparent that
the distribution of sources in the St. Louis AQCR made this technically feas-
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100 km
.
v 1 GREENE
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l._-./ Ul V
T/ / 3 \ JERSEY i
I/ i iwrni w / i |
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CRAWFORD WASHINGTON
: -- . -- ^J FRANCOIS
^
10 20 30 40 50
SCALE km
FIGURE 1. METROPOLITAN SAINT LOUIS INTERSTATE AIR QUALITY CONTROL REGION
(SHADED AREAS ARE INCLUDED IN THE AQCR)
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Circles denote radius
in km from Jefferson
Arch Memorial in down-
town St. Louis.
FIGURE 2. THE REGIONAL AIR MONITORING STATIONS NETWORK
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ible, since a relatively small number of large sources (emitting more than
1000 tons of S0? annually) accounted for well over 90 percent of all emissions
(Figure 3).
Arrangements were made with the companies involved to obtain the necessary
data for 1975 and 1976. The data collected were not usually mass emissions of
S0?, but rather fuel consumption or production data, from which S0? emissions
were calculated. There were several reasons for this arrangement:
1) Fuel consumption data are usually quite accurate and readily obtain-
able.
2) The amount of sulfur in fuel is a direct measure of S0? produced.
3) The emission factors for S02 were quite reliable.
4) Other emissions can be calculated from the same base data.
As time progressed, inventories for other pollutants were added. At
this point, the following inventory data are available for the St. Louis area:
A. Point Sources
1) Criteria Pollutants - TSP, S09, NO , THC, CO
L- A
2) Hydrocarbon breakdown
3) Non-Criteria Pollutants
4) Heat
5) Sulfur trioxide
6) Particle Size Distribution
B. Area Sources
1) Criteria Pollutants - TSP, S09, NO , THC, CO
C- A
2) Hydrocarbon Breakdown
3) Heat
As a first step, a parametric study was conducted to determine the most
desirable characteristics of an emission inventory gathered for research
(rather than enforcement) purposes (2). The study recommended that measured,
rather than calculated data be used to the largest extent possible; determined
the potential uses and users of the inventory and their expected requirements;
proposed a methodology for developing an inventory of the desired quality;
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50 r< 100,000 TON/YEARS
LLI
o
DC
40
30
20
10
TOTAL A OCR
ALL POINT SOURCES
P.S. > 100 T/YR
P.S. > 1000 T/YR
P.S. > 5000 T/YR
P.S. > 10,000 T/YR
P.S. > 100,000 T/YR
TOTAL AREA SOURCES
NUMBER
SOURCES
358
184
67
26
13
4
TONS/YEAR
1 ,233,805
1 ,220,897
1,182,909
1,144,906
1,060,480
990,500
608,000
12,908
PERCENT OF
POINT SOURCES
100.0
96.9
93.8
86.8
81.1
49.8
PERCENT
OF TOTAL
100.0
98.9
95.9
92.8
85.9
80.3
49.3
1.1
0 50 100
SOURCE: NEDS Inventory (1973).
150 200 250
NUMBER OF POINT SOURCES
300
350
400
FIGURE 3. S02 EMISSIONS FOR THE SAINT LOUIS AIR QUALITY CONTROL REGION
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suggested a data handling system; and reviewed existing emission data for
St. Louis and emission models relevant to the objectives of RAPS. Cumulative
plots of the number of sources vs. percent of total emissions for individual
components were particularly useful in visualizing the scope of the problem
at St. Louis, as was the critical review of existing emission models. A para-
metric examination of the effect of source location error also proved useful
in designing the inventory.
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2.0 POINT SOURCE EMISSION INVENTORY
2.1 DEFINITION
For the Regional Air Pollution Study, point sources were defined initially
as sources emitting on the order of 0.01 percent of the total emissions of a
pollutant for the whole AQCR. As a result, point sources were considered to be
in excess of 100 tons per year for S02, 25 tons per year for particulates, 10
tons per year for CO, 30 tons per year for NO., and 10 tons per year for hydro-
carbons. In all, data were obtained from 574 sources.
TABLE 1. TOTAL EMISSIONS FOR THE ST. LOUIS AQCR (TONS PER YEAR)
Point Sources
(% of Total)
Area Sources
(% of Total)
Total
Particulates
45,224
(3%)
1,299,782
(97%)
1,345,006
so2
1,007,530
(97%)
30,813
(3%)
1,038,334
NOY
A
322,730
(72%)
125,567
(28%)
448,297
HC
47,610
(23%)
157,204
(77%)
204,814
CO
164,331
(11%)
1,325,556
(89%)
1,489,887
For the initial (S02) inventory, point sources were further classified into
major (> 1000 tons per year) and minor (between 100 and 1000 tons per year)
sources. Actual hourly emission data were collected for the major sources
only. Since the preliminary survey suggested that the major sources emitted
well over 90 percent S0?, that seemed quite adequate. The actual breakdown of
hourly point sources is shown in Table 2.
TABLE 2. SUMMARY OF HOURLY SOURCE DATA
Companies Locations
PART.
14 22 113
Sources of Pollutants
so2
146
NOX
113
HC
113
CO
82
HEAT
113
8
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Annual data and production patterns were gathered from sources emitting
between 10 and 1000 tons of any one pollutant. The breakdown is shown in
Table 3.
TABLE 3. SUMMARY OF ANNUAL AND PATTERN DATA
Companies
85
Locations
92
PART.
204
Sources
so2
128
of Pol
NO,
121
lutants
HC CO
285 123
HEAT
132
2.2 SENSITIVITY ANALYSIS
A sensitivity analysis was performed, using the method described by Ditto
(3). This type of analysis helps evaluate the maximum permissible error of
any part of the inventory, given a maximum permissible error for the whole
system, thus keeping the inventory at an equivalent level of accuracy. It also
provides confidence levels for the emission inventory. The analysis indicated
that a,, the error associated with subclass k, is related to §, the error
0
associated with the total amount of pollutant emitted, in the ratio of -f^-
V
where Q is the total amount emitted, while Q, is the amount emitted by subclass
k:
Qi
The values for & for different confidence levels and acceptance intervals
is shown in Table 4.
TABLE 4. VALUES OF 0 FOR SELECTED PAIRS (a. 1-C)
d)
+-> 03
Q- >
O) i-
O <1>
O 4->
Confidence Level (1-C)
^-\1-C
a ^"""^^
5%
10%
20%
90%
1 . 58%
3.16%
6.32%
95%
1.12%
2.24%
4.47%
99%
0.5%
1.0%
2.0%
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For a confidence level of 95 percent and an acceptance interval of 10
percent, $ becomes 2.24 and the maximum allowable error a. for various pollut-
ants is shown in Table 5.
TABLE 5. MAXIMUM ALLOHABLE ERROR FOR POINT SOURCES OF VARIOUS SIZES
Acceptance Interval 10%, Confidence Level 95%, 0 = 2.24%
Pollutant Total Emissions
Tons/Year
Particulates 45,224
S02 1,007,530
NOX 322,730
HC 47,610
CO 164,331
Allowable Error a, for Sources of
100 Tons/Yr
48%
225%
127%
49%
91%
1000 Tons/Yr
15%
71%
40%
15%
29%
10,000 Tons/Yr
23%
--
These data indicate that the required accuracy for smaller sources (such
as < 100 tons per year) can easily be achieved using annual data.
2.3 DATA^ ACQUISITION
2.3.1 Major Sources
Classically, data for emission inventories are acquired by the use of
questionnaires which are either mailed out or prepared by the interviewer or
inspector on the one-time basis.
The requirements of the RAPS inventory for hourly measured data for a
period of two years far exceeded the normal reporting routine and required
special arrangements with the management of the various facilities. Thus,
personal contact with the appropriate corporate office by mail, phone and,
ultimately, in person was considered essential to obtain the necessary cooper-
ation. The request was made for access to data which would provide a basis for
calculating hourly emissions. (4)
10
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Such data could be
stack concentration measurements
fuel consumption records
' process data
steam production records
These data, coupled with the necessary secondary information, such as stack
gas volume, concentration of sulfur in fuel or in process materials, etc.,
permitted the calculation of the weight estimates of pollutant (e.g., SCL)
emitted per hour. There are two levels at which the initial information has
to be gathered:
1) Management level
2) Operation level
At the management level, an "agreement in principle" was required; usually
operational personnel were present at these meetings. After an agreement was
reached, the details of the data acquisition were worked out with operational
personnel.
The following information was then secured from information gathered at
the operating level:
1) Source Description: address, location (by UTM coordinates to +_ 0.01
km), type of operation as indicated by the Standard Industrial Class-
ification (SIC) and Source Classification Code (SCC).
2) Data: pollutant concentration in stack (rarely available), quantity
and type of fuel burned, amount of steam or power produced, fuel
analysis, process information (weight and analysis), etc. All of
these data should be on an hourly basis; if they were not, the time
interval was noted, as well as time related variability.
3) Collection of Data: data collection was arranged so as to minimize
the effort required by the affected companies. As a rule data were
mailed in self-addressed envelopes once a month.
The locations of major sources of pollutants in the St. Louis AQCR are
indicated in Figure 4.
11
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12
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2.3.2 Minor Sources
For point sources emitting less than 1000 tons/year, as well as those
major sources where hourly data were not available, hourly emissions were
obtained from operating patterns. For these sources, the following information
was obtained.
1) Source description.
2) Work schedule.
3) Maximum process loads.
4) Monthly or shift fuel consumption.
5) Fuel analysis data.
The emission values were obtained and recorded as annual data, together
with the operating patterns. The pattern is capable of indicating the actual
operating hours, operating days (in Julian form) and weekly patterns by days.
For example, the entry
D:2-48, 50-184, 186-244, 246-365, W:l-5, H:8-17
denotes the operation of a plant which normally operates Monday through
Friday (W:l-5), from 8 AM to 5 PM (H:8-17), but is closed down for New Year's
(D:l), Washington's Birthday (D:49), Independence Day (D:185), and Labor Day
(D:245). If an hourly output for a specific hour and day is requested, the
computer will first make sure the plant was operating that day, then divide the
annual number by the actual number of hours of operation for a 5 day week, 9
hours a day operation, less the number of days when the plant was shut down.
2.3.3 Source Measurements
The RAPS emission inventory should ideally contain direct statements of
weight of pollutants emitted from each major source as a function of location
for every hour. The most direct way to acquire this information would appear
to be monitoring of stack emissions.
In actuality, emissions (in terms of weight of pollutant) cannot be
directly measured. Stack gas analyzers provide a measure of the concentration
of the pollutant, thus requiring another measurementstack gas volumebefore
the weight of the emitted pollutant can be determined. Stack gas volume, in
13
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turn, is not measured directly, but rather is determined by measuring the gas
velocity by traversing the cross-section of the stack. From the average
velocity and the known dimension of the stack, the volume of the stack gases
can be calculated. In addition, the molecular weight of the sampled gas has
to be determined to obtain the mass flow rate. Thus, the seemingly direct and
straight-forward approach to the determination of pollutant emissions by stack
analysis actually consists of a number of measurements, manipulations and cal-
culations, each of which contributes to the accuracy of the final figure.
Stack sampling is time consuming and expensive; for this reason a number of
representative sources were sampled and their stack effluents analyzed. (5)
An attempt was made to encompass a wide variety of the larger point sources:
large and medium sized power plants burning coal, fuel oil and gas; industrial
boilers of different types and sizes; and industrial operations, such as cata-
lyst recovery units in a petroleum refinery, and cement kilns, known or sus-
pected of being major sources of pollution.
The following sources were sampled in 1975 and 1976:
Illinois Power's Wood River Power Plant, Wood River, Illinois
Boiler No. 1, operated on gas
Boiler No. 2, operated on fuel oil
Boiler No. 4, operated on coal
Highland Power Plant, Highland, Illinois
Boiler No. 3, operated on coal
Stag Brewery, Belleville, Illinois
Boiler No. 1, operated on coal
General Motors Assembly Plant, St. Louis, Missouri
Boiler No. 2, operated on coal
Amoco Refinery, Hartford, Illinois
Boiler No. 6, operated on oil and gas
Catalyst Regeneration Unit
Chrysler Motors Assembly Plant, Fenton, Missouri
Boiler No. 1, operated on gas
14
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Owens-Illinois Glass Company, Alton, Illinois
"A" Glass Furnace, operated on gas
Alpha Portland Cement Company, Affton, Missouri
Cement Kiln, wet process, coal-fired
U. S. S. Agri-Chem, Crystal City, Missouri
Nitric Acid Production Unit
General Motors Assembly Plant (Re-test), St. Louis, Missouri
Boiler No. 2, operated on coal
In general, the test methods specified in the Appendix of Part 60, CFR
Title 40, "Standards of Performance for New Stationary Sources" were used.
The methods include:
Method 1 - Sample and Velocity Traverses
2 - Determination of Stack Gas Velocity
3 - Gas Analysis of C0p> Excess Air and Dry Molecular Weight
4 - Determination of Moisture in Stack Gases
5 - Determination of Particulate Emissions
6 - Determination of SO,, Emissions
7 - Determination of Nitrogen Oxide Emissions
8 - Determination of Sulfuric Acid Mist Emissions
Serious problems were encountered with stack gas velocity measurements
using Method 2. Using mass balance methods as a check, it became apparent
that the values obtained with an S-type Pitot tube, used in accordance with
Method 2, were high by amounts ranging from 8 to 78 percent. Reproducibility
was adequate, and repeated calibration of the Pitot tube indicated that
correct readings were obtained. A careful check of the literature indicated
that high readings had been observed by other investigators. Burton (6)
indicated that values of 104 to 150% of the rated value can be obtained.
Grove (7) presented data indicating that, (a) significant errors are always
positive, and (b) they can be very large. The most common source of errors
is due to cyclonic flow, unfortunately a fairly common occurrence in power
plant stacks, where "double entry" stacks (two boilers feeding one stack) are
frequently used.
15
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A comparison of measured and calculated flows is shown in Table 6. The
flow rate was calculated from known fuel consumption, fuel composition and
excess air data. The values obtained using flow rates based on mass balance
show a much better agreement with values obtained from emission factors, than
those based on Pi tot measurements.
TABLE 6. COMPARISON OF S02 EMISSIONS BASED ON CALCULATED AND
MEASURED FLOW RATES
Location
Wood River #1 (oil )
Wood River #4
Highland Power
Stag Brewery
General Motors
Amoco (boiler)
(catalytic cracker)
Weight of S02 Produced, Based On
AP-42 Emission Factor
153 Ibs/hr
5245
414
75
479
309
708
Mass Balance
178 Ibs/hr
5104
433
82
472
-
-
Measured Gas Flow
217 Ibs/hr
7035
658
125
546
320
354
For this reason, calculated flow rates were used whenever there was an
indication of non-linear flow in the stack, as indicated by the fact that
turning the Pitot tube 90° on axis did not give a zero reading on the manometer.
An alternate method was used for determining sulfuric acid mist. The
current standard method for S03 in stack gases is EPA Method 8 (CFR 40, 60.85,
Appendix-Test Methods). In this method, the sample of stack gases is drawn
through a series of impingers. The first impinger contains 100 ml of 80%
iso-propanol; the second and third 100 ml of 3% hydrogen peroxide. There is a
filter between the first and second impinger to retain entrained particulates.
The contents of the impingers are analyzed for sulfate using the barium
perchlorate-thorin method.
Recent work cast doubts on both accuracy and reproducibility of Method 8.
The method assumes that only SO- Csulfuric acid mist) will be retained in the
first impinger and filter (both of which are analyzed together). However,
Hillenbrand (8) found that substantial amounts of S02 are retained in the first
impinger, some of which is subsequently oxidized to S03, thus contributing to
16
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high results.* For this reason a different technique was used, which was first
described by Gokstfyr and Ross (9) and subsequently verified by Lisle and
Sensebaugh (10). The method is generally referred to as the Shell method. It
is based on the condensation of sulfuric acid mist at temperatures below its
dew point (but above the dew point of the water) in a condenser backed up by a
fritted glass filter. The condensate is washed out and titrated.
Data presented in references 9 and 10 indicate that adsorption of SO- is
essentially complete, repeatability is excellent, S0? in concentrations up to
2000 ppm does not interfere and a precision of +_ 0.3 ppm of S0» can be readily
attained.
The method was then evaluated in the Air Monitoring Center laboratories.
The results of the evaluation indicate an average 100.0 +_ 6.5% recovery with
no significant interference from any of the variables tested (11).
Using the most reliable available results, experimental emission factors
were calculated for S0?, NO,,, CO, HC, and particulates for the sources tested.
These emission factors are compared in Table 7 with the standard emission
factors from AP-42.
Even though relatively few source tests have been run, certain conclusions
can be drawn from the results obtained:
1. Determinations of stack gas volumes according to EPA Method 2 is
uncertain. Incorrect results are obtained in a high number of cases,
since the basic assumption of laminar flow, parallel to the walls of
stack, frequently does not occur.
2. Engineering calculations of mass flow, based on ultimate analysis of
the fuel and determinations of the excess air in the stack gases, give
reasonably accurate results. This is confirmed through sulfur balance
calculations. For example, the average experimental emission factors
for SOp for coal burning installations come out to 38.75S^ compared with
385^ suggested in AP-42.
3. The emission factors in Table 7 are applicable only to the specific
installations for which they were obtained. However, definite patterns
* A recent revision of Method 8 (F. R. 18 August 1977) eliminates this problem.
17
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appear to exist, which seem to have more general validity:
a) Emission factors for NO for large combustion sources (utilities)
A
appear to be too high by a variable, but substantial, margin.
The experimentally obtained factors range from a low of 7.7% to
72% of the applicable AP-42 factors. For smaller sources good
agreements were obtained.
b) Experimental emission factors for particulates similarly vary from
8 to 58% of the applicable AP-42 factors for installations which
do not have precipitators. In the presences of the latter, their
assumed efficiency becomes the determining factor.
c) Hydrocarbon and CO emissions, which are rather insignificant for
combustion sources, have also been found to be less than those
suggested by the AP-42 factors.
2.4 DATA HANDLING AND CODING PROCEDURES
The RAPS emission inventory is ultimately stored in the memory banks of a
Univac 1110 computer at the National Environmental Research Center at Research
Triangle Park, N. C. A "System 2000" data management system is used. The
necessary input, editing, and basic output programs were developed. A complete
User's Manual has been prepared, which describes the data handling system.
2.4.1 Data Input
The input program was designed to be similar to the National Emission Data
System (NEDS) format. In the RAPS data handling system no actual emission data
are recorded and stored (with a few exceptions). Instead, the files contain
fuel consumption or process data, which are converted to mass emission by appro-
priate manipulation as part of the output program. The advantage of this
system is its flexibility, since new or additional emission factors can be
added without disturbing the data base.
The data were received, usually once a month, in a variety of forms. An
instruction sheet was made up for each source, which details the entry method.
A uniform coding form was then made out, which served as a basis for the key-
punchings of computer cards.
19
-------
Five different types of input cards were used: Type I was used to record
hourly fuel consumption process information data. Type II was for recording
stack gas pollutant concentration and stack gas volumes; hourly values of
concentration and flow are on alternate fields. Type III was used to record
the annual data and corresponding patterns of minor sources. Type IV was used
for non-criteria pollutants (annual data). Finally, Type V was used for the
recording of special emission factors for non-criteria pollutants, heat emis-
sion, etc. A typical coding form is shown in Figure 5.
2.4.2 Editing of Data
The data were edited at several levels. The coding sheets were edited
visually for numerical errors, the punched cards were verified for keypunch
errors. They were then checked by editing programs, which checked for syntax
and proper limits of the entered values.
Every card was checked for certain "housekeeping" information, such as
proper card number (1-8), source description (point, area, line) and action
code (add or change).
Card I was checked for proper ID codes for State (14 or 26), County, Plant,
and City (>0).
Card II was checked for Stack No. (>0), UTM zone (15 or 16) UTM coordi-
nate for AQCR 70 (vertical between 4180 and 4330, horizontal
between 640 and 770 for zone 15, 240 and 320 for zone 16) and area
ID (between 1-1843, 2001-2465).
Card IV was examined for proper Method code (0-8), Pollutant name (both
criteria and non-criteria, including heat emissions) appropri-
ate Unit and Time Intervals and existing SCC number.
Card V was checked only if it is Type 3, when the program checks for
correct syntax* of operating patterns.
Card VI was checked only for card number, source description and action
code.
*"Syntax" refers to a finite system of rules determining a rational structure
of operating patterns.
20
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2.4.3 RAPS Emission Inventory Calculations
The data entered into the memory bank of the Univac 1100 at EPA-NERC were
not emission values, but rather consumption or process figures from which mass
emissions of pollutants can be calculated as part of the computer output
routine. Only in a few exceptional cases were emission concentrations recorded.
Data for major sources (defined in the case of S0?, as producing more than
1000 tons S0? per year) were calculated on an hourly basis; the inputs were
usually based on hourly values, though in some cases only shift, daily, weekly
or even monthly data were available. In all these cases an average hourly
value is calculated by the computer.
Annual data were recorded for minor sources (producing between 1000 and
10 tons S02 per year). The operating pattern was also recorded. An hourly
average value was calculated by the computer based on the actual operating
hours for each facility.
Base data were recorded in four different ways:
3 36
1. Fuel consumption (in tons, 10 gal, 10 or 10 cu/ft)
2
2. Steam production (10 Ibs steam per hour)
3. Power production (kw or mw per hour)
4. Process rate (Ib, ton, gal or bbl per hour)
In all cases the sulfur content of the fuel or material and the appro-
priate emission factor has to be known and recorded, either explicitly or by
reference to AP-42, "A Compilation of Air Pollution Emission Factors".
2.5 SAMPLE OUTPUTS
Emission inventory data can be retrieved for any hour, day, week, month
or year for the following categories:
AQCR 70 Stack
State Point
County SCC
Plant
22
-------
Figures 6-9 illustrate some of the outputs available for point sources.
They range from hourly outputs for a single point source, to daily and yearly
data for a source (Figures 7 and 8).
Results are also available on modelers tapes (Figure 9). The format
of the tapes is indicated in Table 8.
23
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2482690.62
FIGURE 9. MODELER'S TAPE
27
-------
TABLE 8. MODELER'S TAPE FORMAT
First Block
Col umn
1
2-3
4
5-8
9
10-13
14
15-16
17
18-21
22
23-26
27-33
34-40
41-44
45-58
49-52
53-80
Record - (stack parameter card)
Field Size
1
2
1
4
1
4
1
2
1
4
1
4
7
7
4
4
4
28
Contents/Notes
FILLER - Blank
STATE - Alphanumeric *
14 = Illinois 26 = Missouri
FILLER - Blank
COUNTY - Alphanumeric *
FILLER - Blank
PLANT - Alphanumeric
FILLER - Blank
STACK - Alphanumeric
FILLER - Blank
AREA ID - Km2 **
FILLER - Blank
SIC - Alphanumeric *
UTMH - Km
UTMV - Km
HEIGHT - ft. or m t
DIAMETER - ft. or m t
TEMPERATURE - °F or °C t
FILLER - Blank
(continued)
* SAROAD Station Coding Manual, APTD-0907
** Ref. 20
t Metric option
28
-------
TABLE 8 (continued)
Subsequent Records
Column
1
2-13
14
15-26
27
28-39
40
41- 52
53
54-65
66
67-78
per Block -
Field Size
1
12
1
12
1
12
1
12
1
12
1
12
(data cards)
Contents/Notes
FILLER - Blank
PARTICULATE - Ib or kg t
FILLER - Blank
SOX - Ib or kg t
FILLER - Blank
NOX - Ib or kg t
FILLER - Blank
HC - Ib or kg t
FILLER - Blank
CO - Ib or kg t
FILLER - Blank
FLOW RATE - cfm or m3 t
t Metric option
29
-------
3.0 SPECIAL POINT SOURCE INVENTORIES
3.1 HYDROCARBON EMISSION METHODOLOGY AND ORGANIC EMISSION INVENTORY
3.1.1 Point Source Total Hydrocarbon Inventory
The initial hydrocarbon point source definition was 10 tons per year as
a lower limit (12). Contrasted with the SO,, inventory, 97 percent of which
are of point source origin, only about 23 percent of the hydrocarbons are of
industrial (point source) origin in the St. Louis AQCR. In order to improve
the accuracy an additional effort was made to include all industries emitting
more than one ton of hydrocarbons per year. Total emissions were about 47,610
tons, with 37 locations emitting more than 100 tons per year. Service stations
and dry cleaners are included in the area source inventory, even though
emissions from individual sources can be on the order of 5 tons per year.
Emission patterns for hydrocarbon sources vary widely due to the variety
of types of hydrocarbon sources. Data which were received continuously as
part of the RAPS point Source Inventory were generally on an hourly basis.
Hydrocarbon data from sources which produce or use coatings and solvents were
accompanied with hourly use patterns based on working hours during a year.
Evaporative emissions from petroleum storage were assumed to be generated on a
continuous basis and were, therefore, spread equally throughout the year. With
the exception of hydrocarbons from hourly combustion data, hydrocarbon
emission data were collected as annual data. Data were tabulated for total
hydrocarbons and non-methane hydrocarbons.
A sensitivity analysis similar to the one performed for other criteria
pollutants, indicated that for an acceptance interval of 10 percent at a 95
percent confidence level, the allowable error for the subclass of 100 tons per
year is 49 percent, for 10 ton sources 153 percent, and for 1 ton sources 485
percent.
30
-------
Some source tests were carried out to help determine the methane/
non-methane breakdown. The results, shown in Table 9 indicate that the only
substantial man-made source of methane is fuel combustion.
TABLE 9. METHANE EMISSION SOURCES
METHANE AS PERCENT OF
TOTAL HYDROCARBON
Source
Petroleum Refining - Storage
- Catalytic Cracker
Fuel Combustion - general
- utility boiler, oil
- industrial boiler, oil
- industrial boiler, coal
Surface Coating* - Heat Curing
- Air Dry
Degreasing
Industrial Manufacturing
Coking Plants
sec
4-03-002-01
3-06-002-01
1-XX-XXX-XX
1-01-005-01
1-02-004-01
1-02-002-09
4-02-XXX-XX
4-02-XXX-XX
4-01-002-XX
3-01-XXX-XX
3-03-003-XX
Hillenbrand
Ref (8)
78
2
0
0
0
RAPS Test
Ref (12)
nil
20
74
71
43
30
10
0
*NOTE: SCC numbers do not differentiate between heat curing and air drying.
For these source tests, samples were collected in 5 mil Teflon bags and
analyzed within 6 hours of the collection time; errors due to diffusion are
estimated to be less than 3 percent. An analytical technique was developed,
based on a Gow Mac Series 750 gas chromatograph, which was linear with respect
to carbon number and hydrocarbon concentration up to a level of 3.5 x 10 ppm
as carbon. Since the content of this inventory is included in the Point and
Area Source Organic Emission Inventory (Section 3.1.2), the data it contained
were not included in the RAPS memory bank.
31
-------
3.1.2 Point and Area Source Organic Emission Inventory
Subsequently the hydrocarbon inventory, (hereafter refered to as the
"volatile organics" inventory is keeping with EPA's nomenclature), was refined
and expanded to include area as well as point sources. Area sources contribute
156,204 tons per year, or about 77 percent of the total. The bulk of these
emissions originate from mobile sources: 113,240 tons per year.
In addition to the development of the methane/non-methane breakdown, the
organic inventory was further subdivided into five classifications (13). The
primary purpose of the breakdown of organic emissions into categories was to
provide emissions data for the evaluation of photochemical reaction models.
Chemical kinetic mechanisms present in air quality simulation models
require some form of organic classification in order to treat the reaction
processes and rates associated with their structural make-up. One such class-
ification scheme, required in using a lumped chemical kinetic reaction
mechanism approach, distributes organics in the atmosphere structurally into
paraffin, olefin, aromatic, aldehyde, and non-reactive classes. This classif-
ication has been determined for organics emissions in the St. Louis AQCR.
Sufficient reference data have been included to derive alternative schemes as
required.
In order to make a breakdown of organic emissions into appropriate
categories, their compositional analysis has to be known. Trijonis and Arledge
(14) have recently compiled analytical data for organic emissions for the
Los Angeles area. Although there are some differences between Los Angeles and
St. Louis emissions, the report has been useful in classifying the RAPS data.
Allowances were made for differences in composition in petroleum products
between the two areas, and additional data were gathered for activities not
present in Los Angeles, such as coking operations and the combustion of coal.
Organic emissions, divided into the five categories mentioned above, are
available for all point sources based on their Standard Classification Code
(SCC), and for all area sources. A typical classification is shown in
Table 10. The results, summarized in Table 11, are stored in the RAPS
emission inventory computer bank at Research Triangle Park.
32
-------
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3.2 POINT SOURCE NON-CRITERIA POLLUTANT EMISSION INVENTORY
The Clean Air Act of 1970, as amended, establishes the achievement of
clean air as a national goal. In pursuance of this goal, Air Quality Criteria
were developed and Air Quality Standards established for five pollutants:
sulfur dioxide, carbon monoxide, particulate materials, hydrocarbons and
oxidants. These pollutants are frequently termed "criteria pollutants".
It is well known, however, that a large number of other substances occur
in polluted air, some of which have toxic or carcenogenic properties, such as
mercury, asbestos or beryllium. The Clean Air Act requires the Administrator
to consider other pollutants and to determine whether they are hazardous.
Such a determination is conditioned on the magnitude of the health and welfare
effect; it, in turn, is a function of the occurrence of the pollutant as well
as its intrinsic toxicity.
Thus, one input into these considerations is an assessment of the sources
of such pollutants, as well as the pollution burden they create. For this
reason, a series of studies had been performed for the Environmental Protection
Agency, which were issued under the general heading of "National Inventory of
Sources and Emissions" (15). In this series, some 21 compounds were examined,
and emission factors were developed. Though no high degree of accuracy is
claimed for these factors, they can serve as a useful basis for first approxi-
mations of the emissions in a given area.
The following pollutants are included:
Arsenic Mercury
Asbestos Molybdenum
Barium Nickel
Beryllium Phosphorus
Boron Selenium
Cadmium Silver
Chromium Titanium
Copper Vanadium
Lead Zinc
Magnesium Benz-a-pyrene
Manganese
35
-------
The emission factors found in these reports were assigned to the sources
extant in the St. Louis AQCR, using their SCC designation (16). The emission
factors were modified to correspond to the production or consumption units
used in the RAPS data base and grouped into 19 sets. A partial listing of
these sets and corresponding SCC numbers is shown in Table 12.
This file is keyed to SCC numbers. When requested by the output program,
the computer will calculate the emissions for any pollutant for a given source
by multiplying its annual operating rate by the appropriate factor. It can
also provide the total amount of any one pollutant by grid ID, county, state
and AQCR. Tables 13, 14, and 15 show emissions for three sources of pollu-
tants: coal, residual oil and iron production.
Emissions for five compounds were retrieved from the data handling system
and are shown in Table 16.
TABLE 16. EMISSIONS FOR SELECTED COMPOUNDS FOR AQCR 70
Compound Emissions
(Ibs/year)
Arsenic 177,985
Cadmium 5,532
Lead 1,085,027
Mercury 19,376
Benz(a)pyrene 4,437
A computer printout giving the emission of five pollutants from a single
source is shown in Table 17.
3.3 SULFUR COMPOUNDS AND PARTICLE SIZE DISTRIBUTION
3.3.1 Sulfur Compounds (S03)
In combustion operations sulfur trioxide originates from the oxidation of
sulfur dioxide in the presence of excess air. Reaction rates are negligible
below 200°C, reach a maximum around 400°C and taper off to zero at 1000°C.
Rapid conversion takes place only in the presence of a catalyst. The reaction
has been investigated fairly extensively; most investigators agree that
36
-------
TABLE 12. SOURCES OF NON-CRITERIA POLLUTANTS
SCC CODE
3-04-002-02
3-04-003-01
3-04-003-03
3-04-003-30
3-04-004-03
3-05-006-03
3-05-006-99
3-05-007-01
3-05-007-02
3-05-013-01
3-05-014-01
3-06-002-01
3-90-002-01
3-90-004-01
3-90-004-99
3-90-005-01
3-90-005-05
3-90-005-99
4-02-001-01
4-02-999-99
5-01-001-01
5-01-001-02
5-02-001-02
5-03-001-01
5-03-001-02
5-03-001-05
SET
15
16
16
16
17
18
18
18
18
19
20
21
01
02
02
03
03
03
22
23
24
24
24
24
24
24
BRASS/BRONZE MELT
GRAY IRON
GRAY IRON
GRAY IRON
LEAD SMELT SEC
CEMENT: KILN: OIL-FIRED
CEMENT: OTHER/NOT CLASSIFIED
CEMENT MFG. WET
CEMENT MFG. WET
FRIT MFG.
GLASS MFG.
GEN. FLUID CRACKER
BITUMINOUS COAL (CEMENT KILN/DRYER)
RESIDUAL OIL (ASPHALT DRYER)
RESIDUAL OIL (OTHER/NOT CLASSIFIED)
DISTILLATE OIL (ASPHALT DRYER)
DISTILLATE OIL (METAL MELTING)
DISTILLATE OIL (OTHER/NOT CLASSI
FIED)
PAINT
OTHER/NOT CLASSIFIED
INCINERATOR (MUNICIPAL)
INCINERATOR (MUNICIPAL)
GENERAL INCINERATOR
INCINERATOR
INCINERATOR
INCINERATOR
37
-------
TABLE 13. POLLUTANTS IN COAL
SET
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
POLLUTANT
ARSENIC
ASBESTOS
BARIUM
BERYLLIUM
BORON
CADMIUM
CHROMIUM
COPPER
LEAD
MAGNESIUM
MANGANESE
MERCURY
MOLYBDENUM
NICKEL
PHOSPHORUS
SELENIUM
SILVER
TITANIUM
VANADIUM
ZINC
BAP
(Pounds)
QUANT.
.0029
.015
.00058
.018
.0039
.004
.0022
.105
.0077
.001
.0015
.0026
.051
.0025
.001
.018
.0069
.017
.000007
UNITS: PER
TON COAL BURNED
38
-------
TABLE 14. POLLUTANTS IN RESIDUAL OIL
SET
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
POLLUTANT
ARSENIC
ASBESTOS
BARIUM
BERYLLIUM
BORON
CADMIUM
CHROMIUM
COPPER
LEAD
MAGNESIUM
MANGANESE
MERCURY
MOLYBDENUM
NICKEL
PHOSPHORUS
SELENIUM
SILVER
TITANIUM
VANADIUM
ZINC
BAP
(Pounds)
QUANT.
.0007
.010
.0038
.9524
.012
.0004
.008
.4048
.072
.0050
.004
.0044
1.2143
.0333
.000033
UNITS: PER
1000 GAL OIL BURNED
1
f
39
-------
TABLE 15. POLLUTANTS IN IRON PRODUCTION
SET
10
10
10
10
10
10
10
10
10
POLLUTANT
ARSENIC
ASBESTOS
BARIUM
BERYLLIUM
BORON
CADMIUM
CHROMIUM
COPPER
LEAD
MAGNESIUM
MANGANESE
MERCURY
MOLYBDENUM
NICKEL
PHOSPHORUS
SELENIUM
SILVER
TITANIUM
VANADIUM
ZINC
BAP
(Pounds)
QUANT.
.015
.022
.019
.0225
.0015
.052
.0001
.0014
.020
UNITS: PER
TON PIG
IRON
40
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41
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concentrations in flue gases range from 0.5 to 5%; absolute values tend to be
below 50 ppm SO,. In the presence of moisture SO., hydrates to H~SO. and is
normally collected in this form as aerosol droplets.
Sulfur trioxide concentrations were studied experimentally in the RAPS
area (17), using the "Shell" method discussed in Section 2.3.3. The SO,
concentrations ranged from 2.7 to 44.3 ppm, well within the range indicated by
other investigators. As indicated in Figure 10, there appears to be a marked
dependence on excess oxygen. The percentage of SO, increased with increasing
oxygen up to about 9%, then dropped rapidly. This may be due to the cooling
effect of large amounts of excess air. There did not seem to be any correla-
tion with the sulfur content of the fuel nor did there appear to be any marked
effect of boiler capacity on the concentration of SO, produced. Data are
presented in Table 18.
The RMS average SO., emission appears to be about 1.85% of the SOp
emission. This factor can be used to calculate SO, emissions based on the
corresponding SO^ emissions. Using the current figures for S0?, this amounts
to an annual emission of 18,639 tons of SO., per year for the St. Louis AQCR.
In addition, a small amount is discharged from the five sulfuric acid plants
in the area.
3.3.2 Particulate Size Distribution
Emissions of particulate materials constitute a more complex problem than
gaseous emissions, since the properties of particles are determined not only
by their composition, but also by their size and shape. In fact, the most
important properties of particles, their effect on visibility, their life-time
as suspended materials, and to a large extent, their effect on health, are all
determined by particle size. On all of these counts small particles, five
microns or less in diameter, are responsible for most of the observed effects.
The common methods of collection and reporting of particulate emissions
do not distinguish particle size. Total particulate matter is reported on a
weight basis, which biases the results in favor of large particles. Since
large particles are only of local importancethey settle out rapidlyand
are generally not involved in health effects since they are readily retained
42
-------
% so
3
3
2 -
10
12 °7o 0-
FIGURE 10. PERCENTAGE CONVERSION OF S02 TO S03
43
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by the body's screening mechanisms, there are good reasons why participate
emission data should be reported in such a way as to provide maximum informa-
tion on small particles.
There is no universally accepted definition of "fine particles", but
most authors agree on a range of 3 to 5 microns as the upper limit. Particles
smaller than approximately 5 microns have settling velocities in still air
of the order of 0.01 cm/sec and tend to stay aloft almost indefinitely.
The most up-to-date study of fine particulate emissions is contained in
EPA Technical Report entitled "Fine Particulate Emission Inventory and Control
Survey" (18). The methodology contained in that report was applied to the
St. Louis AQCR. In addition, samples were taken at representative emission
sources using an Andersen cascade impactor.
The methodology mentioned above provides a typical size breakdown for
particulate emissions from a large variety of sources. Applicable values
were attached to appropriate SCC codes and placed into a SIZE file (Table 19).
A similar file was established for the fractional efficiency of control
equipment (Table 20). By applying the appropriate factors to the total
particulate emission values contained in the RAPS inventory, emissions of
particulates of a given particle size can be calculated. This methodology was
incorporated into the RAPS data handling system. Thus, a size breakdown for
particulate emissions can be obtained for any point source and all categories
listed in Section 2.5.
In addition, experimental particle size distributions were determined (17)
using an Andersen Mark I cascade impactor shown schematically in Figure 11.
A summary of the results of the testing is given in Table 21. Particle size
is given as aerodynamic size for spherical particles with unit density.
45
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AIR FLOW
FIGURE 11. ANDERSEN STACK SAMPLER
48
-------
TABLE 21. PARTICLE SIZE DISTRIBUTIONS
Source
111. Power - Wood River
Highland Electric
Stag Brewery
General Motors
Amoco
Owens Illinois
Alpha Cement
SCC Code
1-01-002-02
1-01-002-08
1-02-002-05
1-02-002-09
3-06-001-02
3-05-014-01
3-05-007-05
% vs.
>7y
22.
26.
37.
14.
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5.
29.
3-7y
5
6
4
3
9
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0
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18.
16.
24.
8.
8.
38.
8
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0
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0
4
Particle Size microns
l-3y
18.
10.
7.
18.
22.
41.
14.
5
0
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5
0
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0.5-ly <0.
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9.
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20.
33.
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11.
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9
8
7
6
4
0
0
Typically, a bimodal distribution was encountered, generally with a peak
around 5 microns, and another one for particles less than one micron. A
typical distribution is shown in Figure 12.
3.4 POINT AND AREA HEAT EMISSION INVENTORY
This is a rather specialized inventory, since heat emissions in themselves
are considered neither a "threat to public health" or "public welfare"--the
criteria for the designation of pollutant. However, the cumulative heat
emissions from large urban areas do create a "heat island" of warm air, thus
introducing a meso-meteorological disturbance. Since meteorological conditions
are of overriding importance to air pollution, an understanding of the magni-
tude and behavior of heat emissions is of considerable importance to air
pollution meteorology.
Heat emissions to the atmosphere originate, directly or indirectly, from
the combustion of fossil fuels (there are no nuclear plants in the St. Louis
AQCR (19). With the exception of a small amount of energy radiated into
space as light, and the energy carried out of the AQCR by cooling water
(primarily the Mississippi River), all of the energy released by the combus-
tion of fuels is sooner or later released to the atmosphere as heat, either
at the point of production (the power stations) or where it is consumed.
49
-------
c
-------
In the production of power, only a fraction of the energy released when
the fuel is burned is emitted as heat emissions to the atmosphere. The
remaining fraction of energy is converted to heat at the point of ultimate
use. This is true whether the fuel is natural gas, electricity, gasoline or
other sources.
Heat emission from point sources is only a small, albeit an important
fraction of the total heat emissions. It is estimated that point source
heat emissions account for about 11 percent of the total emissions in the
AQCR. Point source emissions are, however, in the form of concentrated plumes,
while other heat emissions are diffused. Thus, the meteorological dispersion
behavior of these sources is likely to be quite different.
A first approximation of the amount of heat generated by the combustion
of fuels in the St. Louis Air Quality Control Region (AQCR) can be obtained
from fuel combustion figures of the 12 counties making up the AQCR. The
breakdown is shown in Tables 22 and 23.
The data presented in these tables indicate that coal supplies 70 percent
of the energy requirement of stationary point sources, but only 2.6 percent of
the area sources. Natural gas supplies 20.5 percent of the requirement of
point sources, but 69 percent of the area sources. The total amount of heat
generated by conversion of fuel to energy by both stationary and mobile sources
1?
is of the order of 869 x 10 Btu per year.
The amount of heat actually released to the atmosphere is, however,
considerably less. There are several reasons for this. In a utility boiler,
about 85 percent of the energy created by burning fuels is used in the pro-
duction of steam (large utilities do a little better). Thus, about 15 percent
of the heat content of the fuel escapes in the stack gases, which constitute
12
the "point" heat emission sources. This amounts to about 61 x 10 Btu. Less
than half of the remaining 85 percent is converted to electricity, which even-
tually is released as an "area" source of heat. The other halfactually,
about 55 percentis carried off by cooling water, which in this area is the
Mississippi River. Since this raises the river water temperature only mini-
mally, virtually none of this heat is given off to the atmosphere. Addition-
ally, about 15 percent of the power produced is used outside the AQCR. The
51
-------
TABLE 22. POINT SOURCE FUEL CONSUMPTION
County
(Illinois)
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
(Missouri)
Franklin
Jefferson
St. Charles
St. Louis
(City)
St. Louis
(County)
Total point
consumption
Aver. Btu x
SCC unit
Total heat
Btu x 1012
Coal
(Bitum.)
Tons
4,144
2,681,251
5,952,699
158,077
250
5,673
18,760*
1,561,433
205,266
2,282,034
source fuel
12,869,587
106/
22
content
283
All Point Sources
Residual
Oil
103 gal
152,506
100
28,832
3,401
850
1,076
4,065
190,830
150
29
405
Distillate Diesel
Oil Fuel
103 gal 103 gal
742 60
277
55,786
686 37
4,712
156
3,525
450
786
191
638
67,949 97
140 140
9.5
x 1012 Btu
Natural
Gas
106 cu.ft.
221
57,425
159
5,606
41
1,538
184
5,032
13,177
83,383
1,000
83
* Listed as anthracite
52
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actual amount of heat released to the atmosphere in the St. Louis AQCR is
12
estimated at 534 x 10 Btu per year.
By contrast, essentially all of the heat released by area sources (space
heating, cars and trucks, as well as a minor amount of process heating) is
emitted as heat emissions to the atmosphere. The heat produced by the burning
of fuel used for space heating is given off continuously to the atmosphere.
One might expect that automobiles and electric motors convert a sizeable
fraction of the energy they use into work, rather than waste-heat. Actually,
only a time delay is involved. All kinetic energy stored, for example, in a
moving automobile is converted to heat by friction and given up to the
atmosphere, whether the friction is air resistance, rolling resistance or
braking. Thus, the full amount of the heat content of fuels used in area
sources plus a large fraction of the fuel used in point sources constitutes
diffuse heat emission.
The determination of point source heat emissions involved the development
of appropriate heat emission factors. Thus, in order to calculate the amount
of heat emitted by a source, we need to know the amount of fuel consumed (or
materials produced) per unit time, the heat value of the fuel, and the effic-
iency of the operation. These three factors were considered in deriving the
heat emission factors for each source. These factors were keyed into the
Standard Classification Code (SCC); thus, they will give values of the heat
emission in units of 10 Btu per SCC unit. SCC units are: tons (for coal,
-l /-
etc.), 10 gal. (for oil, etc.) and 10 cu. ft. (for gases). The factors
were designated by the code HEM and were entered into the RAPS file together
with the appropriate source code.
Heat emission factors (HEM) cover a wide range of values. Those based on
fuel consumption tend to average around .20, indicating a fuel conversion
efficiency of 80 percent; their range is from .109 for the most efficient,
modern power plants of the large utilities to .30 for older industrial
installations. In-process fuel consumption is less efficient, with values
generally of the order of .35, indicating 65 percent fuel utilization as, for
example in a glass furnace. Even higher factors characterize municipal
incinerators (.40). Finally, factors of 1.0 appear where fuel is wasted, such
as in refinery flares. Some examples of typical heat emission factors are
shown in Table 24.
54
-------
TABLE 24. EXAMPLES OF POINT SOURCE HEAT EMISSION FACTORS (HEM)
*
***
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
PLANT STKID
MUNICIPAL POWER PLANT BREESE 01
CARLYLE MUNICIPAL POWER 01
ILLINOIS POWER CO. WOOD RIVER 01
ILLINOIS POWER CO. STALLING 01
04
UNION ELECTRIC CO. VENICE 01
AMOCO OIL REFINERY 01
03
05
06
07
09
10
12
14
17
21
22
23
24
CLARK OIL CO. 01
02
03
04
05
06
na
sec
10100208
20100301
10100501
10100501
10100501
10100501
30600102
30600102
30600102
30600102
30600102
30600103
30600103
30600103
30600108
30600102
30600999
30600999
30600999
30600999
30600102
30600102
30600102
30600102
30600102
30600102
wfinmn?
PLUTNT
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HEM
HFM
EMFACT
.200
.200
.131
.200
.200
.139
.150
.150
.150
.150
.200
.250
.250
.250
.250
.250
1.000
.200
1.000
.200
.250
.250
.250
.250
.250
.250
?^n
55
-------
Some heat emission factors are based on throughput or material produced.
Such factors are no longer directly related to combustion efficiency and can,
therefore, have any numerical value.
Area heat emission sources are assigned to a grid system developed for
the St. Louis AQCR, which divides the study area into 1989 grid squares (20).
2
These squares vary in size from 1 to 100 km , depending on population densities.
Area heat emission fall into three broad categories:
1) Heat emissions from the burning of fuel in stationary installation
(e.g., space heating).
2) Heat emissions from the consumption of electric power, all of which
eventually show up as heat.
3) Heat emissions from burning of fuel in mobile sources (e.g., cars,
trucks, rail and airport operations).
Residential heating in the AQCR accounts for about 30 percent of the
12
area heat emissions, or about 140 x 10 Btu per year. As shown in Table 25,
1 ?
natural gas accounts for the bulk of it (87 x 10 Btu), followed by LPG,
oil, coal and wood. Hourly fuel consumption and, consequently, heat emissions
were obtained using a regression equation based on actual hourly fuel consump-
tion records for St. Louis. The equation takes into consideration ambient
temperature and wind speed.
12
Commercial heating was estimated to produce about 56.4 x 10 Btu per
year. It was distributed spatially according to estimated land use per grid
square and temporally using the same technique as residential heating.
12
Industrial area heat emissions, amounting to about 49 x 10 Btu per
year, were calculated by assigning an Area Heat Emission Factor (AHF) to each
industrial source. This factor depends on the manner in which process heat is
handled at each plant, that is, whether cooling water is used, how it is
recirculated, etc.
12
The consumption of electric power accounts for 68 x 10 Btu per year,
second only to the heat released by the burning of gas. About 35 percent of
the power consumption is residential, 10 percent commercial, 55 percent
industrial. Heat emissions from residential usage were assigned to grid
56
-------
squares based on the number of housing units and an average consumption of
8333 kwh per unit per year. Commercial power usage was based on land use
data. The remainder was assigned to industrial usage to those grid squares
containing industrial plants. Hourly values were calculated from seasonally
adjusted power consumption data.
Heat emissions from mobile sources account for a large percentage of
12
the total heat emissions. They were estimated at 150 x 10 Btu per year,
almost 30 percent of the total emissions. The basis for their distribution
is "Vehicle-Kilometer-Traveled" (VKT) per grid square. This number is trans-
formed into heat emissions per grid square using averaged fuel consumption
figures of 21 km per gallon for light-duty vehicles, 9.7 km per gallon for
heavy-duty vehicles.
Off-highway mobile sources, airports, railroads and vessels all con-
tribute minor amounts to the heat emission in the AQCR and were also included.
A summary of heat emissions is shown in Table 25. The heat emission software
provides an hour-by-hour output on a grid square basis, including a stack-by-
stack listing of point sources.
57
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4.0 AREA SOURCE EMISSION INVENTORIES
The term "area sources" encompasses emissions from a large number of
point sources, which are individually either too small to be of consequence
(for example, domestic heating units), or difficult to measure under actual
operating conditions, such as automobiles. While such sources may be individu-
ally negligible, their large number and accumulative total emissions make
them significant contributors to air pollution.
4.1 THE RAPS GRID SYSTEM
Customarily, in inventories to be used in simulation models, area sources
are distributed over a network of grids. For the purposes of RAPS, a grid
network was designed (20), based on the following design parameters:
1) The grid network is formed by squares, though not necessarily of
the same size.
2) The length of the side of each square needs to be an integer;
e.g., 1, 2, 3, 5 or 10 km.
3) The network is based on the Universal Transverse Mercator coordinate
system (Zone 15).
4) Each square should have approximately the same population, but cannot
2
be smaller than 1 km .
The eastern portions of the AQCR is in UTM Zone 16. To simplify
modeling requirements, UTM Zone 15 was extended eastward to include the whole
AQCR, and all grid square locations are based on Zone 15 coordinates. A grid
square is defined by its size and the location its SH corner on the UTM coordi-
nate system.
The system is shown in Figure 13.
4.2 RESIDENTIAL AND COMMERCIAL EMISSION INVENTORY
This inventory deals with several major categories of area emissions:
59
-------
60
-------
residential and commercial fuel use for space heating, evaporative hydro-
carbon losses from dry cleaning, surface coating and gasoline marketing,
solid waste disposal and structural fires. Emissions from each of these
categories were apportioned to the 1989 grid squares and given an appro-
priate temporal distribution (21).
4.2.1 Space Heating
The emissions from residential fuel use were based upon the emission
factors from AP-42 for each fuel, and local fuel use. The distribution within
the RAPS grid system was determined by allocating total county fuel use for
residential usage to each grid area in proportion to the number of housing
units using that fuel in the grid area. The temporal variation of fuel use
was established by statistically analyzing hourly gas usage obtained from
Laclede Gas Company (Figure 14). This analysis identified a base component
of usage which has a distinct relationship with the time of day, but is
largely independent of meteorological influence. The remaining component
was shown to be strongly affected by the ambient temperature and wind
velocity.
The emissions from commercial/institutional fuel usage were also based
upon emission factors in AP-42 and local fuel use. County commercial/
institutional land use area was located using local land use planning maps
and assigned to each grid square as a grid area percentage. Fuel use and
hence emissions were calculated in accordance with the commercial/institu-
tional land use area in each grid square. A corrected factor was used to
allow for uneven fuel distribution in the vicinity of main fuel distribution
(gas) lines. The same temporal distribution established for residential fuel
use was also used for commercial/institutional use.
4.2.2 Evaporative Emissions
The major components of residential and commercial-institutional area
sources of evaporated hydrocarbons were identified as surface coating
(primarily painting), gasoline handling, and dry cleaning.
According to the St. Louis County dry cleaning plant survey, 89 percent
of dry cleaning establishments use perchloroethylene solvent and 63 percent of
61
-------
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all clothes are cleaned with this material. Based on the St. Louis County
data, .53 pounds of hydrocarbons from this type of source are emitted per
capita per year. It was assumed that this per capita figure was applicable
for the entire AQCR. The emissions occur during normal working hours (8 AM
to 5 PM, Monday through Friday) and were temporally allocated to this time
period. The emissions were spatially distributed according to the commercial
land use in each grid square.
Paint consumption was estimated from regional sales figures at 2.3
gallons per capita for Illinois, 2.7 gallons for Missouri. An averaged
value of 1.14 pounds of volatile hydrocarbons per gallon of paint was used.
Evaporation of the hydrocarbons was allocated to each grid on a population
basis, with a temporal distribution corresponding to an 8 AM to 5 PM, five
day work week. No seasonal adjustments were made.
Evaporative losses of hydrocarbons at service stations occur from filling
losses of underground and automobile tanks and from spillage. County emission
figures were derived from state gasoline sale totals, assuming gravity drop
splash filling without control equipment. The losses were spatially distri-
buted according to the amount of commercial land use in each grid square.
Temporal distribution was based on the diurnal traffic cycle for St. Louis,
using separate values for weekdays, Saturdays and Sundays. A monthly cor-
rection factor was used to allow for the differences in summer and winter
gasoline sales.
4.2.3 Structural Fires and Solid Waste Disposal
Emissions from structural fires were difficult to quantify; however,
average data were used and total estimated county emissions were allocated to
grid areas based on the number of homes in the grid. Emissions were based
on an average of 7 tons of combustible material burned in each fire and
emission factors for open burnings for municipal solid waste. A uniform
temporal distribution was used.
Emissions from the disposal of solid wastes are largely restricted to
commercial-institutional enterprises and large incinerators because of the
restrictive air pollution regulations. Thus, the county emissions were
allocated to grid areas in proportion to the commercial-institutional land
63
-------
use in each grid square. Emissions were given a temporal distribution on an
8 AM - 5 PM, five day work week. A summary of emissions from this category
is shown in Table 26.
4.3 HIGHWAY VEHICLE EMISSION INVENTORY
4.3.1 Line Source Definition
This inventory was developed in two stages. The first stage was devel-
oped to document the criteria for determining the major freeway and arterial
roadways to be considered emission line sources in a metropolitan area (22).
In developing these criteria for the St. Louis Air Quality Control Region
(AQCR) the 1975 traffic data maps were used. The data collection effort
focused primarily on areas designated as urban or in the process of urban-
izing. Data pertaining to traffic volume, vehicle mix, and average vehicle
speed, were collected for the roadways in the St. Louis AQCR. Figure 15
shows the road network in St. Louis City and County.
All roadways were plotted as links with UTM coordinates and RAPS Gridding
System grid square numbers, with appropriate traffic data (such as average
daily total traffic) being assigned to each link. A complete data base for
freeways, principal arterials, and minor arterials was thus established,
along with an elaborate software package for retrieval of these data.
4.3.2 Highway Vehicle Line and Area Source Emissions
The second stage developed the detailed procedure for obtaining emissions
from mobile sources for each grid square for both line and area sources (23).
The East-West Gateway Coordinating Council under sponsorship of the U.S.
Department of Transportation performed an extensive study in the St. Louis
area, in which driving characteristics on several hundred miles of freeways
and major arterials were measured. Using this speed versus time data a com-
puter program was developed which differentiates 58 different driving
patterns (29 peak hour and 29 off-peak hour patterns) depending on the road
classification, average daily traffic, and volume-to-capacity ratio. Using
these driving patterns as input to the EPA developed modal emission program,
emissions for both peak and off-peak hours were calculated. The hourly
emissions are then totaled for total daily emissions. Factors considered
64
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66
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include vehicle age and mixture, gasoline and diesel engined traffic, hourly
distribution of traffic, and hot and cold starts. The effective date of the
modal emission model is July 1975. In addition to evaporative hydrocarbon
losses, the variation of emissions due to weekend vs. weekday traffic volumes
were calculated. Re-entrained dust from paved roads (assuming an emission
factor of 5 grams per vehicle mile traveled) was also calculated, and was
kept as a separate category (i.e., it was not combined with exhaust partic-
ulate emissions). In addition to line source emissions, the program computes
area source vehicle emission. Since it had been shown that traffic on feeder
routes was proportional to line source traffic, the sources were expressed
as a fraction of line source traffic. The fraction appropriate for St. Louis
was determined to be 18%, which differs significantly from an average
national value of 40%. Table 27 shows a sample printout of two line source
links, Table 28 emissions from mobile sources in a grid square.
Emissions data for criteria pollutants from highway vehicles are avail-
able for any hour and grid square. The same data are also available for each
link.
4.4 RIVER TOWBOAT EMISSIONS
The emissions were estimated for river towboats operating on the water-
ways within the RAPS region. This area includes the Mississippi River, from
Mile 100 below St. Louis to Mile 235 above St. Louis; and the Missouri River
from the confluence of the Mississippi River to near Mile 95. River miles
given here are based on Corps of Engineers river distance measured above the
mouth of the Ohio River. The Gateway Arch in St. Louis is located near
Mile 180. This area is shown in Figure 16.
The emissions calculated include: carbon monoxide (CO), oxides of
nitrogen (NOX), total hydrocarbon (THC), oxides of sulfur (SO,,), and Partic-
ulates (Part). The study methodology was limited to air pollutants emitted
from river towboat diesel engines. Emissions originating from ship
electrical-service generating units, cargo, loading and unloading activities,
and fueling and maintenance operations were not included (24).
The methodology consists of estimating river traffic and propulsion
engine characteristics from limited statistical information on river traffic
67
-------
TABLE 27. EXAMPLE OF HOURLY LINE SOURCE EMISSIONS (grams)
LINK 3081 OR Hi
FUNCTIONAL CLASS :
PAKf
69
46
21
1 2
10
'' 6
1.09
230
208
IV 3
1 82
200
220
208
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262
309
310
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280
331
332
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214
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ILLINOIS R.
CHAIN OF ROCKS
CANAL
ALTON, WOOD RIVER
above" St. Louis
below" St. Louts
FIGURE 16. ST. LOUIS AREA RIVER VESSEL TRAFFIC
(NUMBERS REFER TO VESSELS PER DAY)
70
-------
and from observations by Coast Guard and Army Corps of Engineers personnel
familiar with river vessel operations. The traffic volumes and engine types
were then used to calculate emissions, based on measurements of emission
factors for similar diesel engines used on Coast Guard vessels and rail
locomotives. No emission testing of towboats was undertaken during the
RAPS program.
Emission estimates were made from the averages of daily river traffic
volume and towboat engine characteristics. The volume of traffic was low
and the range of vessel characteristics large, so that an estimate of hourly
emission rates was not practical. The traffic data acquired displayed little
variation on a daily or monthly basis, therefore, no temporal pattern was
applied to the emissions. No error estimate or sensitivity analysis was
made, as a greater level of effort would be required to establish the dis-
tribution and accuracy of all the variables. The estimation is believed to
be sufficiently accurate to determine the relative contribution of towboats
to St. Louis air pollution and to serve as input data for the urban atmo-
spheric dispersion models. Air pollutants emitted by river vessels were
calculated as:
Emissions = Horsepower-hours x Emission Factors
Horsepower-hours per grid equal (No. of towboats per day) x (average HP x
average throttle position) x within grid travel distance/average speed).
From other information sources, it is known that ice on the Missouri
River and on the upper reaches of the Mississippi curtail vessel movements
during the winter months. Likewise, periods of high water reduce towboat
operations. The Mississippi River throughout the region is normally open to
river traffic all year long. Stoppages in towboat operations due to weather
are infrequent and thus no emission reduction is assumed for this area. The
Missouri River is normally closed to navigation from the beginning of
December to the first of March. (The actual dates are a function of the
weather conditions.) Therefore, vessel emissions are considered to be zero
for the Missouri River from 1 December to 1 March each year. The annual
emission results were:
71
-------
TOWBOAT EMISSIONS:
TONS/YEAR
Participate
198
so2
462
NO,
3297
HC
939
CO
2101
These emissions were distributed over only the grid squares containing river
vessel activity. The apportioning factor which was used to distribute the
emissions was linear length over which the vessels traversed within the grid.
An example of daily river vessel emissions per grid is shown in Table 29.
4.5 EMISSIONS FROM AIRPORT OPERATIONS
A methodology was developed to detail emissions resulting from airport
operations for civilian, municipal, and military airports in the St. Louis
AQCR (25). The sources involved include aircraft operations and engine
maintenance testing, ground support vehicles, and fuel storage and handling.
For these sources, the emission rate, emission location, and emission duration
need to be described.
To estimate hourly emissions from aircraft flight operations five para-
meters must be known:
- Temporal activity patterns
- Spatial activity patterns
- Percent volume distribution of aircraft types
- Time spend in the different operating modes
- Emission factors.
For flight operations, the following elements were considered:
TABLE 30. AIRCRAFT OPERATING MODES
Engine operating
Mode time included in mode
Taxi Transit times between ramp and apron, apron and
runway and time required for turning and align-
ment between taxiway and runway.
Idle Push back from gate; waiting for signal to begin
taxiing; waiting at taxiway intersections; runway
queuing; gate queuing.
Landing Touchdown to beginning of taxi in taxiway.
Takeoff After alignment with runway to lift-off.
Approach 3000 ft. altitude to touchdown.
Climb-out Lift-off to 3000 ft. altitude.
72
-------
TABLE 29. EXAMPLE OF RIVER VESSEL EMISSIONS PER GRID (kg x 10"1/day)
GRID
197
242
286
287
288
308
309
355
392
394
503
527
528
529
568
569
699
737
738
739
813
814
815
847
848
849
877
914
915
916
917
923
924
925
955
956
977
998
999
PART
30.0
148.1
2.3
2.5
7.0
7.7
8.6
145.6
14.0
99.3
31.2
231.0
176.8
119.8
6.8
15.2
153.4
9.4
6.5
138.5
58.3
30.7
8.5
29.3
35.2
8.0
160.7
4.7
8.0
47.1
23.5
7.8
36.1
11.0
41.2
3.2
28.6
45.2
3.1
sov
A
70.0
345.7
5.4
6.0
16.4
18.0
20.2
339.7
32.8
231.8
72.8
539.1
412.6
279.7
15.8
35.6
357.9
21.9
15.3
323.2
136.1
71.8
19.8
68.2
81.9
18.6
375.1
11.1
18.6
110.0
55.0
18.2
84.1
25.7
95.8
7.5
66.8
105.0
7.1
NOV
"A
510.8
2522.4
39.7
43.5
118.8
130.7
146.4
2478.6
237.4
1691.6
531.6
3933.2
3010.2
2040.7
114.7
257.2
2611.5
158.2
110.8
2358.2
993.0
523.8
170.3
536.9
623.8
155.0
2736.8
73.6
136.1
802.8
401.4
132.9
639.4
187.6
776.7
54.7
487.4
824.9
91.2
HC.
100.2
495.0
8.0
8.8
24.1
26.5
29.7
486.4
48.2
331.9
104.3
771.9
590.7
400.5
23.3
52.3
512.5
32.1
22.5
462.8
194.8
102.7
54.4
136.9
143.4
46.2
537.1
20.0
26.7
157.5
78.7
26.0
146.5
36.7
215.6
10.7
95.6
209.2
49.5
CO
195.0
963.1
15.3
16.7
45.8
50.3
56.4
946.4
91.5
645.9
203.0
1501.8
1149.4
779.2
44.2
99.2
997.1
61.0
42.7
900.4
379.1
200.0
99.4
256.5
272.5
85.0
1045.0
31.0
52.0
306.5
153.2
50.7
278.5
71.6
399.7
20.9
186.1
392.3
86.4
73
-------
Ground Support emissions used at municipal airports result from the service
vehicles shown in Table 31.
_ TABLE 31. SERVICE VEHICLES USED AT A MUNICIPAL AIRPORT
Vehicle
1. Tractor
2. Belt loader
3. Container loader
4. Cabin service
5. Lavatory truck
6. Water truck
7. Food truck
8. Fuel truck
9. Tow tractor
10. Conditioner
11. Airstart
Transporting engine
Diesel power unit
12. Ground power unit
Transporting engine
Gasoline power unit
Diesel power unit
13. Transporter
At civilian and military airports, service vehicles are primarily fuel-
ing trucks.
The information is processed by a computer program. The program output
is listed directly in emissions of pollutant per grid square per hour. It
utilizes the data on aircraft operations mentioned above and modal emission
factors summarized in AP-42. The methodology also takes into consideration
the wind direction in order to determine active runway, types of aircraft
and services at each of the 15 general aviation airports, Lambert Municipal
airport and Scott Air Force Base. Table 32 shows a printout of hourly
74
-------
TABLE 32. EXAMPLE OF HOURLY AIRPORT EMISSIONS
RAPS ARF.A SOURCE REPORT
HOURLY AIRPORT EMISSIONS IN GRAMS
MR
3
4
5
A
7
0
9
10
11
12
1,5
14
15
16
17
18
19
20
21
22
23
24
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HOURLY ATRfORI EMISSIONS IN GRAMS
HR
1
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4
5
A
7
tt
9
10
11
12
13
14
15
16
17
1!)
19
20
21
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23
24
(3RIH
523
1/<>:,'/75
PAR r
i
i
i
t
i
i
i
t
i
i
i
.1
i
.41
.29
.26
.O/
.11
.09
. 3,5
.nr;
. 11
.61
,63
.A/
.61
. '4
.r,7
.19
.74
. 70
,4<)
.27
. I 3
.0!
.74
.31
SOX
1
1
"
1
1
1
1
)
o
1
t
1
.47
.31
.24
.09
. 1 1
. 10
.40
.01
.Aft
.00
.91
.01
.94
. 13
.01
.79
. JO
.03
,74
. 52
.37
. VO
.90
.37
NUX
6.
3.
'i
,
1 .
,
4.
10.
16.
20.
1H.
20.
19.
21 .
37.
](!.
:>i .
20.
1 7.
irj.
13.
10.
9.
3.
06
76'
62
ft'
43
93
1 1
01
n:,
30
; . >
3.'
:5
* /2
.73
.Ii9
, 25
.76
.49
. t 1
,11
.24
. 77
, '^2
.!i2
.03
.12
.01
.51
.51
.74
.45
.01
1
26
22
~>*>
5
9
9
29
(to
132
150
151
I!i2
14(1
] 5 7
14/
132
160
i;.n
132
119
104
71
66
29
(.:<»
.06
.61
.59
.76
, ' J2
. 1 3
.96
.ft 3
.91
.57
.53
.47
.72
. 79
. 74
, 56
.11
.90
.13
.23
.21
.17
.13
.75
75
-------
2
emissions for a 1 km grid square located near Lambert field. Table 33 shows
examples of annual emissions for a number of affected grid squares.
4.6 EMISSIONS FROM RAIL OPERATIONS
A methodology was developed for calculating and reporting fuel use
and air pollutant emissions from railroad locomotive activity (26). The pro-
cedure utilizes automated techniques to report rail activity on the RAPS
variable-sized grid system.
Separate methodologies were developed for the two major types of rail
activityroad or line-haul operation and activity within switch yards.
The methodology for road locomotives utilizes a line source concept and node
points within the study area. The methodology for switch yard operation
utilizes an area source concept. Both methodologies use as a basic unit
locomotive horsepower-hours and were programmed to provide an analysis of
fuel use and emissions for five criteria pollutants on a grid-by-grid basis
as well as for the entire study area.
Locomotive diesel engine fuel use and emissions vary considerably among
the various engine types, sizes, and modes of operation. Thus, it was
necessary to categorize each locomotive of the three principal manufacturers
into one of five engine categories based on horsepower rating and maker.
Load factors, representing the average portion of available horsepower
typically used in performing an activity, were derived from previous studies.
The types of locomotives in use in the St. Louis AQCR are shown in
Table 34.
TABLE 34. CATEGORIZATION OF LOCOMOTIVE TYPES IN
ST. LOUIS RAIL ACTIVITY INVENTORY
Maker Horsepower Range Engine Type
ALCO (Montreal Locomotive) 1,000 4-Stroke Switch
ALCO (Montreal Locomotive) 1,600-2,000 4-Stroke Road
General Electric 600 4-Stroke Switch
General Electric 2,500-3,300 4-Stroke Road
General Motors (EMD) 600-1,500 2-Stroke Supercharged Switch
General Motors (EMD) 1,600-2,300-2,500 2-Stroke Supercharged Road
General Motors (EMD) 2,400-2,750-3.600 2-Stroke Turbocharged Road
76
-------
TABLE 33. EXAMPLE OF ANNUAL AIRPORT GRID EMISSIONS (grams)
GRID
76
241
242
389
451
452
467
492
493
522
523
524
559
560
588
589
o 1 6
653
654
683
883
1.401
1402
1403
1424
1444
:i 620
1621
1633
1 637
1709
.!. P. :! 5
202 i.
2024
2102
2:103
2104
2125
2:161
2187
2286
2289
,», /;, Q .-}
«'.. A.. / /
o ';; Q -7
,.. A- / ..,*
22 9 7
2388
CO
20337
47454
20337
8416
4 8681
29800
11298
594381
158389
41128
718591
100966
19112
48319
3859
3859
3859
3859
3859
3859
1 0 6 8 3
3609
38211
4856
20245
1022
109880
36646
6034
65705
21090
29176
c;> 3 3 3
-VvJI 3
45194
33177
11551
2800
97921
841.6
44867
6754
24960
5(^542
8521
41445
HC
16?2
3992
1726
1 1 1 9
4294
S3 82
1 .1 1 4.
445858
36649
1064
6 7506 1
10508
939:1
5093
501
501
501
501
501
50.1.
904
1 8 1 5
2 9 0 7
302
! A '; "j
.1. Y .\:. ^ .'
23
82936
3282
642
3760
1786
2375
782
296
3812
2921
787
181
8 0 0 8
1 1 1 9
3782
574
1675
4909
2133
809
NOX
34
80
3 4
37985
4926
24590
19
69951
42546
3620
123442
10054
11030
19760
1236:-;
:i 2365
1 2 .> 6 5
1.2365
12365
12365
1 8
7
70
1 1
42
4
11/40
4531
1 0
8771
35
49
/
i. 0
/ o
48
25
6
'! jf *'
.1. t.) ,.;
37985
66
">
>'
r;:- i::-
. .' \.i
93
19
3464
SOX
20
48
20
27 74
5 1 7
1687
1. i
8213
3 3 7 6
326
10298
1185
1002
1 300
100 j
1.003
1.003
1003
1003
1003
1 1
3
39
4
20
:i 1 1 2
325
:::
632
21
30
6
-J
46
34
1 1
">
100
2 7 7' 4
46
..,
'"i !"'
-.''. -...'
61
8
383
PART
1773
2481
1.099
;::> /> ;;?
' > ' .'
4246
3 0 6
8 8 6 3
5347
588
1 1 1 2
6 8 1.
68 i
681
681
681.
6 8 1
4403
1470
3004
1 773
X 8 6 7'
77
-------
Application of this methodology in the St. Louis AQCR required an inven-
tory of railroad activity (Figure 17). This inventory was supplied by the
U.S. Department of Transportation, Transportation Systems Center, and included
information on the routing, run time, and locomotive(s) for each train in
operation on a typical day plus estimates of switch yard and transfer activity.
Fuel use and emission factors were those presented in the Environmental Pro-
tection Agency's document, Compilation of Air Pollutant Emission Factors
(AP-42). Emissions from only diesel locomotive operations were included in
the inventory. The quantity of emissions from evaporation and spillage of
fuel and volatile freights have thus not been evaluated.
Approximately 800 of the 1989 grids comprising the RAPS grid system con-
tained railroad activity. Annual fuel use by locomotives was estimated at
70,000 thousand gallons and total AQCR rail pollutant emissions are as
follows:
Pollutant Emissions (tons/yr)
Particulates 876
Sulfur Oxides 2,000
Carbon Monoxide 4,350
Hydrocarbons 4,220
Nitrogen Oxides 11,930
Locomotive emission factors (measured as pounds of pollutant emitted per
thousand gallons of fuel used) were derived based on the mix of engine types
in St. Louis. There was no attempt to develop any daily or seasonal temporal
activity pattern. Examples of emissions from rail operations apportioned to
the appropriate grid squares, are shown in Table 35.
4.7 OFF-HIGHWAY MOBILE SOURCES EMISSIONS
Emissions from off-highway motorcycles, motor boats, lawn and garden
equipment, construction, industrial and farm equipment were estimated by a
variety of techniques (27 & 28). Data for these categories are scarce even
on the national level. The methodologies involved a breakdown of national or
state data, sales figures, registrations, etc. to county and ultimately grid
square level on the most appropriate basis available. This involved population
78
-------
OL
O
O
oo
a:
n>
CJ
79
-------
TABLE 35. EXAMPLES OF ANNUAL EMISSIONS FROM RAIL OPERATIONS (kg/year)
GRID
PART
SOX
NOX
HC
2
4
8
9
12
1 4
15
19
20
21
'> '>
A» A;,
27
28
51
52
57
59
61
66
67
68
69
73
74
75
77
85
86
87
88
95
104
105
106
1 1 0
1 1 8
131
134
135
136
1 4 1
144
156
160
1108/7
2423*4
.!. ,A / 5 , b
1 204,0
1368,2
2204,7
2322,4
1968,3
9 1 6 , 1
596,2
4555,2
1342,8
122,5
37,8
444,1
687,9
2277,2
1263,2
2384,8
5043,6
2093,8
149,2
3689,8
158.5
192,8
795,8
5124*2
361,8
663,7
808,7
2217,9
162.2
859,5
181,3
488.2
3 1 1 , 2
7209.9
129.4
634.0
30,8
4878,1
373,4
4173,6
630,9
2527,9
5525,5
3136, 1
2745,2
3119,5
5026,9
5295,1
4487,8
2088,8
1359,5
10385,9
3061,7
279,3
86 . 3
1012,6
1568,4
5192,1
2880,2
5437,3
11499,6
4773,8
340,2
8412,8
361,4
439,6
1 8 1 4 , 6
11683,2
825,1
1513,2
1844,0
5056,8
369 , 9
1959,8
413,4
1 1 :l. 3 , 1
709,6
16438,5
295,0
1445,5
70,2
11122,1
851,5
9515,8
1438,6
6556,5
11759,2
6674 , 3
5842,3
8090,9
10698,0
11268,8
9308.2
5417,5
3526,0
22102,9
6350,4
579,4
183,8
2155,1
4067,8
11049.7
3544,4
11277,6
30770,7
10159,5
955,0
22214,7
1014.4
1234,1
2233,0
31705,1
2140.0
3966,9
2269 , 2
13704,7
959,4
5082.9
1160,4
1369,8
873,3
42853,4
765, 3
3808,4
197, 2
30142,8
1392,5
24978,2
3731,2
2391,9
10845,6
6155 , 7
5388.4
2951,6
9866,9
10393,3
7454,6
1976,4
1286,3
20385,6
5085.8
464,0
169,5
1987,6
1484,0
10191,2
7967,2
9031 .8
17869,4
9370,2
167,1
14053,8
177,5
215,9
5019,4
21430.6
780,7
1341,7
5100,7
7330,7
350,0
1854,3
203.0
3079.1
1963,0
29677,8
279,1
1241,4
34,5
16123.4
1691,9
17226, 1
1361,2
14067,6
36616.1
."', ,", i ,"\ .-\ I*
2u/82 , 5
18192,1
17359,8
33311,9
35089,0
28252,9
11624,0
7565 , 5
68824,5
19275,2
1758,6
572,4
6710,6
8728,0
34407,0
16522,6
34230,4
79593,6
31635,0
1969,8
59559,2
2092,3
2545,5
10409,4
88176,8
4591,6
8465,4
10578,0
35688,9
2058,5
10906,0
2393,4
6385.6
4071,0
120629,5
1642,0
81.06,7
406,8
78495,5
5142,1
70164,8
8005,8
80
-------
figures, numbers of individual residences, construction costs and other
parametric information. Emission factors were available in AP-42.
Off-highway unregistered motorcycles were assumed to be 18 percent of
those registered within the county. Usage was then assumed to be uniformly
distributed over the grids within the counties, in proportion to grid popu-
lation.
For lawn and garden equipment, an average number of operating days (190)
was derived from local climatological data. Emissions were apportioned to
the twelve counties in the AQCR on the basis of housing units per county.
Within the county, grid square emissions were also apportioned on the basis
of housing units per grid.
Emissions from construction equipment were apportioned by the dollar
value of construction volume on the state level, by population to counties,
and by construction area on the grid level. Industrial equipment was dis-
tributed only over grid squares containing industrial plants, farm equipment
by farm acreage which was estimated for each grid square. Outboard motor
boat emissions were distributed over grid squares having navigable water
surfaces.
Emissions were temporally distributed over the year to reflect diurnal
and seasonal variation of usage. To accomplish this end, each equipment
category was assigned an annual operating pattern which was felt to most
closely approximate real-life use during a calendar year. The operating
patterns assumed were as follows:
Off-highway motorcycles March through October 9 AM - 7 PM
Lawn and garden equipment April through September 9 AM - 7 PM
Construction equipment March through October 6 AM - 6 PM
Industrial equipment Year round 8 AM - 6 PM
Farm equipment March through October 5 AM - 7 PM
Outboard motors April through September 9 AM - 7 PM
A typical printout is shown in Table 36.
4.8 FUGITIVE DUST EMISSIONS INVENTORY
As part of the RAPS a methodology was developed to calculate an hourly
81
-------
TABLE 36. SAMPLE OF EMISSIONS FROM OFF-HIGHWAY MOBILE SOURCES
GRID
NO- ."'OLT
i HC 8
CO 1
NGX 7
FART 1
SOX 2
2 HC S
CO 1
NOX 6
r'AKT 1
SCX 2
3 HC 1
CO 2
NOX 1
PART 1
SOX 4
4 HC 2
CO 4
NOX 2
r'AB. F J
SOX 7
5 HC 6
CO 1
NOX 3
i-V-tRT 9
SOX 2
6 HC 3
CO 6
NOX 2
PART 4
SCX 1
7 HC 3
CO 6
NOX 2
PART 4
GGX 1
tt-CrCL
.6717+01
.6a01+02
.0311-01
.1713+00
.5290-01
.4603+01
.0.197+02
.8397-01
.1433+00
.4771-01
.3729+02
.623:1+02
.1131+00
.8jd.:+oo
,0197-01
.3740+01
.927^+01
.OB7">-01
.4 733-01
.''..364-02
.9774+01
.335S+02
.6573-01
.42=17-01
. 0427-01
.4323+01
.5713+01
.763.3-01
.638 ?-01
.0051-01
.4321! -i-Ol
,571!3+01
.733'.:-01
.638<>-Gl
.005.1-01
3 HC 3.432H+01
CO 6
NOX 2
PART 4
SOX 1
M-CYCL
LWN&GDN
FRM EQ
CONSTR
IND EQ
OUTBD
UNITS
.571B1-01
.7333-01
.633v>-01
.0051-01
LUN3GDN
4. 7226+02
3.6794-1-03
3.S'.40fOt
9. 7593+00
2.4679+00
4.C435+02
3.C437+G3
3. 6731+01
9.3995+CO
2.3769+00
7.1818+02
5.5?53i-03
5.8000+01
1.4841+01
3.7530+00
1.3275+02
1.0343+03
1. 0721+01
2.7-T34+00
6.9373-01
4.0044+02
3.1179+03
3.2340+01
8.27^1iOO
2. 0926+00
1.7410+02
1. 3364+03
1.4061+01
3.5977+00
9.C9S1-01
1.7410+02
1.3564+C3
1.4061+01
3.5779+00
9.0931-01
1.7410+02
1.3364+03
1.4061+01
3.5V79+00
9.0981-01
FRM £0
7.1641-03
8.3016+04
1 .2173+04
1 .4408-1-03
9.4990+02
7.1341+03
8.3016+04
1.2173+04
1.4408*-03
9.4990+02
7.1841+03
8.3016+04
1.2173+04
1 .4408+03
9.4990+02
1 .7960+03
2.0754+04
3.0433+03
3.602C+02
2.3747+02
1 .7960+03
2.0754+04
3.0433r03
3.6020+02
2.3747+02
1.7960+03
2.0754+04
3.0433f03
3.6020f02
2.3747+02
1.7960+03
2.0754+04
3.04331-03
3.6020t02
2.3747+02
1.7960+03
2.0754+04
3.0433+03
3.6020+02
2.3747+02
CONSTR
1.
1.
9.
6.
7.
1.
1.
9.
7.
7.
1 .
1.
9.
7.
7.
->
~i
1 .
1.
1 .
4.
4.
n
n
2.
4.
4.
->
1
1
4.
4.
-->
~>
2.
4.
4.
~>
2 0
2.
3690+03
4099+04
1471+03
9642+02
100Bf02
4248+03
4675f04
5225+03
2483+02
3906+02
4523+03
4962r04
7G72t-03
3906+02
5355+02
1419+02
2040+03
4315+03
0896+02
1110f02
2339+02
4120+03
8630+03
1793+02
2220+02
2333+02
4120+03
S630+03
1793+02
2220+02
2320+02
4120+03
8630+03
1793 f02
2220+02
2338+02
4120+03
S630-rC3
1793+02
2220+02
a
0
0
0
0
0
0
0
0
0
~t
7
1
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
IND ECJ
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3312+02
.4375+03
.1312+03
.7375+01
.2500+01
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.GOCO
.0000
.0000
.0000
.0000
.ooco
.0000
.0000
.0000
.0000
.0000
.0000
OUTBD
c
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
1
3
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.cooo
.0000
.0000
.0000
.0000
.0000
.croo
.TO 00
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3770+03
.5930+04
.1462+01
.0000
.0759+01
.0000
.0000
.^000
.cooo
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
TOTAL
9.
1.
")
k *
1.
9.
1 .
">
n
1*
9.
1
-) t
->
1.
7.
3.
4 .
4 ,
3.
2.
2.
J *
5.
4.
")
"1
5.
5.
4.
2.
1120+03
0096+05
1361r04
1482+03
6627+03
1484+03
0140+05
1733+04
1762+03
6916+03
7754+03
1127+05
3073+04
2759+03
7801+03
5457+03
9974+04
5171V03
7226+02
8010+02
6946+03
9419+04
9391+03
8735+02
0197+02
4323+03
6588+04
9206+03
8219+02
6069+02
4328+03
2.6583+04
5 .
o «
4.
T
9206+03
3219+02
6069+02
4323+03
2.6598+04
5.9206+03
5.8219+02
4.6069+02
OFF HIWAY MOTORCYCLES
LAWN &
GARDEN EQUIPMENT
FARM EQUIPMENT
CONSTRUCTION EQU
IPMENT
INDUSTRIAL EQUIPMENT
OUTBOARD MOTORS
KG/YR
82
-------
fugitive dust emission inventory for the Metropolitan St. Louis Air Quality
Control Region (29).
The following six categories of fugitive dust sources were addressed:
1. Unpaved roads;
2. Agricultural land tilling;
3. Wind erosion of agricultural land;
4. Construction sites;
5. Aggregate storage piles; and
6. Unpaved airstrips.
For each of 1989 RAPS grid areas, data were compiled on annual emissions
of fugitive dust. This required, in addition to basic emission factors
adjusted for local climatic and surface conditions, annual measures of source
extent for each grid area. For unpaved roads, vehicle-miles traveled were
estimated for each grid containing unpaved roads. For agricultural land
tilling the acreage of harvested crops for five major crops (corn, soybeans,
wheat, milo and hay) was determined for each grid square. Wind erosion of
agricultural land is proportional to the average exposed (unvegetated)
acreage, which was assumed to be vulnerable to wind erosion from the time
of primary tilling to about 1 month after planting. Fugitive dust emissions
from construction sites are directly related to the land area being worked,
over a specific time period, therefore, annual acres of construction for each
grid square were determined. Emissions from aggregate storage are propor-
tional to the quantity of aggregate stored, therefore, the quantity of
aggregate stored annually within each grid is determined. Dust emissions
from unpaved airstrips was estimated from the number of landing/takeoff
cycles. Finally, hourly apportioning factors were derived to account for
emission variation by hour of the day, day of the week, and season of the
year. The temporal apportioning factors for agricultural tilling are shown
in Figure 18.
The basic emission factor equations were developed as part of EPA's
emission factor development program (30). These factors refer to dust parti-
cles smaller than 30 microns in diameter, the approximate effective cut-off
diameter of a standard high-volume sampler (based on a particle density of
2.5 g/cm3).
83
-------
Source Type: Agricultural Tilling
N
T
s.
N
\
S
&
01 234 5678 91011121314151617181920212223
Hour of Day
20
V 15
/0 10
5
55
50
45
40
35
% 30
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FIGURE 18. TEMPORAL APPORTIONING FACTORS
84
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Available retrievals include temporal apportioning factors, county
totals of annual source extent and annual emissions for each source category.
(Table 37).
85
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5.0 SUMMARY
The goal of the emission inventory for the Regional Air Pollution Study
at St. Louis was an ambitious one: to produce a high resolution inventory
for AQCR 70 for all pollutants of interest. This goal has been largely
achieved.
The RAPS emission inventory have been arranged in a uniform format and
stored on EPA's Univac 1110 computer at Research Triangle Park, NC. The
following inventory data are available for the St. Louis area:
A. Point Sources
1) Criteria Pollutants - TSP, SO. NO , THC, CO
A X
2) Hydrocarbon breakdown
3) Non-Criteria Pollutants
4) Heat
5) Sulfur trioxide
6) Particle Size Distribution
B. Area Sources
1) Criteria Pollutants - TSP, SO , NO , THC, CO
A X
2) Hydrocarbon Breakdown
3) Heat
Hourly values are obtainable for all point sources in excess of 10 tons
per year (in some cases 1 ton per year). Location of point sources on UTM
coordinates (Zone 15) are available to +_ 10 m. Area sources are assigned to
a network of grid squares of variable size.
This data base, which contains complete information for 1975 and 1976
complements the RAMS (ambient) data collected over the same period. Together,
they form an extensive, accurate data base suitable for many uses in the
field of air pollution modeling and control.
87
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REFERENCES
1. Collis, R. T. H., and Don R. Scheuch, Regional Air Pollution Study: A
Prospectus, Stanford Research Institute, Menlo Park, CA, Contract No.
68-02-0207.
2. Littman, F. E., S. Rubin, K. T. Semrau and W. F. Dabberdt, A RAPS Prelim-
inary Emission Inventory, Stanford Research Institute, Menlo Park, CA,
Contract No. 68-02-1026, EPA Report number EPA-450/3-74-020, January 1974.
3. Ditto, F. H., L. Gutierrez, T. Lewis, and L. Rushbrook, Weighted Sensi-
tivity Analysis of Emission Data, IBM Corporation, Gaithersburg, MD,
Contract No. 68-01-0398, EPA Report number EPA-450/3-74-022, July 1973.
4. Littman, F. E., RAPS Point Source Methodology and Inventory, Air Monitor-
ing Center, Rockwell International, Creve Coeur, MO, Contract No. 68-02-
2093, T0108A, December 1977.
5. Littman, F. E., R. W. Griscom and 0. Klein, Criteria and Non-Criteria
Pollutant Source Testing Program, Air Monitoring Center, Rockwell Inter-
national, Creve Coeur, MO, Contract No. 68-02-2093, Task Order 108B
EPA Report No. EPA-600/4-77-044, November 1977.
6. Burton, C. S., Quantisation of Stack Gas Flow, J. Air Poll. Control
Assoc. 22, 731 (1972).
7. Grove, D. J. and Smith, W. S., Pi tot Tube Errors Due to Misalignment and
Non-streamlined Flow, Stack Sampling News, 1974.
8. Hillenbrand, et al., Chemical Composition of Particulate Air Pollutants
from Fossil-Fuel Combustion Sources, Battelle Columbus Labs, March 1973,
EPA-R2-73-216, PB219.009.
9. Gokstfyr, H., and K. Ross, Determination of Sulphur Trioxide in Flue Gases,
J. Inst. Fuel 35, 197 (1962).
88
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10. Lisle, E. S. and J. D. Sensenbaugh, Determination of Sulfur Trioxide
and Acid Dew Point in Fuel Gases, Combustion 36, 12, (1965).
11. Littman, F. E., R. W. Griscom, and H. Wang, Sulfur Compounds and Partic-
ulate Size Distribution Inventory, Air Monitoring Center, Rockwell
International, Creve Coeur, MO, Contract No. 68-02-1081-T056, EPA Report
number EPA-600/4-77-017, April 1977.
12. Littman, F. E., R. W. Griscom and G. Seeger, Hydrocarbon Emission Inven-
tory, Air Monitoring Center, Rockwell International, Creve Coeur, MO,
Contract No. 68-02-2093, T0108F, March 1977.
13. Griscom, R. W., Point and Area Source Organic Emission Inventory, Air
Monitoring Center, Rockwell International, Creve Coeur, MO, Contract No.
68-02-2093, T0108I, EPA Report number, EPA-600/4-78-028, June 1978.
14. Trijonis, J. C. and K. W. Arledge, Utility of Reactivity Criteria in
Organic Emission Control Strategies, TRW Environmental Services, Redondo
Beach, CA. Contract No. 68-02-1735, EAP Report number, EPA-600/3-76-091,
August 1976.
15. National Emission Inventory of Sources and Emissions
EPA-450/3-74-008
EPA-450/2-73-001
EPA-600/6-75-003
APTD-68, 69, 70
APTD-1129, 1130, 1139, 1140, 1159
APTD-1507 to 1511, 1543
Preferred Standard Path Report for POM - 1974
16. Littman, F. E., H. H. Wang and J. Piere, Non-Criteria Pollutant Inventory
for the St. Louis AQCR, Air Monitoring Center, Rockwell International,
Creve Coeur, MO. Contract No. 68-02-1081, T054, EPA Report number,
EPA-600/4-77-018, April 1977.
17. Littman, F. E., R. W. Griscom and H. H. Wang, Sulfur Compounds and
Particulate Size Distribution, Air Monitoring Center, Rockwell Inter-
national, Creve Coeur, MO. Contract No. 68-02-1081, T056, EPA Report
number, EPA-600/4-77-017, April 1977.
89
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18. Weast, T. E., et al., Fine Particulate Emission Inventory and Control
Survey, Midwest Research Institute, Kansas City, MO. Contract No. 68-02-
1324, EPA Report number, EPA-450/3-74-040, January 1974.
19. Littman, F. E., R. W. Griscom and E. Puronen, Heat Emission Inventory
for the St. Louis AQCR, Air Monitoring Center, Rockwell International,
Creve Coeur, MO. Contract No. 68-02-2093, T0108G, EPA Report number,
EPA-600/3-78-029, June 1978.
20. Haws, R. C., R. E. Paddock and C. C. Masser, The RAPS Grid System,
Research Triangle Institute, Research Triangle Park, NC. EPA Report
number, EPA-450/3-76-021. December 1975.
21. Holden, R. E., Residential and Commercial Area Source Emission Inventory
for the RAPS, Environmental Science and Engineering, Gainsville, FL.
Contract No. 68-02-1003, EPA Report number, EPA-450/3-75-078, September
1975.
22. Haefner, L. E., Methodology for the Determination of Emissions from
Line Sources, Washington University, St. Louis, MO. Contract No.
68-02-1417, EPA Report number, EPA-450/3-76-035, February 1975.
23. Haefner, L. E., Methodology for Determination of Line and Area Source
Emissions from Motor Vehicles, Washington University, St. Louis, MO.
Contract No. 68-02-2060, EPA Report number, EPA-450/3-77-019, June 1976.
24. Sturm, J. C., An Estimation of River Towboat Air Pollution in St. Louis,
MO. U. S. Dept. of Transportation, Report No. DOT-TSC-OST-75-42,
February 1976.
25. Petterson, R. M., et al., Airport Emission Inventory Methodology, GCA
Corporation, Bedford, MA. Contract No. 68-02-0041, EPA Report number,
EPA-450/3-75-048, December 1974.
26. Wiltsee, K. W., et al., Assessment of Rail Fuel Use and Emissions for the
RAPS, Walden Research, Wilmington, MA. Contract No. 68-02-1895, EPA
Report number, EPA-450/3-71-025, April 1977.
27. Hare, C. T., Methodology for Estimating Emissions from Off-Highway
Mobile Sources. Southwest Research Institute, San Antonio, TX.
Contract No. 68-02-1397, EPA Report number, EPA-450/3-75-002, October 1974.
90
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28. Littman, F. E., and K. Isam, Off-Highway Mobile Source Emission Inventory,
Air Monitoring Center, Rockwell International, Creve Coeur, MO. Contract
No. 68-02-2093, T0108E, EPA Report number, EPA-600/4-77-041, October 1977.
29. Cowherd, C. and C. Guenther, Development of a Methodology and Emission
Inventory for Fugitive Dust for RAPS, Midwest Research Institute, Kansas
City, MO. Contract No. 68-02-2040, EPA Report number, EPA-450/3-76-003,
January 1976.
30. Cowherd, C., et al., Development of Emission Factors For Fugitive Dust
Sources, Midwest Research Institute, Kansas City, MO. Contract No.
68-02-0619, EPA Report number, EPA-450/3-74-037, June 1974.
91
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1.
RT NO
4-79-004
2.
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
REGIONAL AIR POLLUTION STUDY
Emission Inventory Summarization
5. REPORT DATE
January 1979
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Fred E. Littman
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Rockwell International
Environmental Monitoring and Services Center
11640 Administration Drive
Creve Coeur, MO 63141
10. PROGRAM ELEMENT NO.
1AA603
11. CONTRACT/GRANT NO.
68-02-2093
Task Order 1080
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Sciences Research Laboratory - RTF, NC
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
EPA/600/09
15. SUPPLEMENTARY NOTES
16. ABSTRACT
As part of the Regional Air Pollution Study (RAPS), data for an air pollution
emission inventory are summarized for point and area sources in the St. Louis Air
Quality Control Region. Data for point sources were collected for criteria and non-
criteria pollutants, hydrocarbons, sulfur trioxide, particle size distribution, and
heat. For area sources, data were collected on criteria pollutants, hydrocarbons
and heat.
All the data have been entered into the RAPS Data Bank. Hourly values are
available for all point sources; locations are identified by UTM coordinates (zone 15)
to within +10 m. Area sources are assigned to a network of 1989 grid squares of
variable size. The emission inventory is applicable for the years 1975 and 1976 and
complements the RAPS aerometric data.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
*Air pollution
*Emission
*Environmental surveys
*Sources
St. Louis, MO
13B
05J
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (This Report)
UNCLASSIFIED
21. NO. OF PAGES
102
20 SECURITY CLASS (This page)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDI TION i s OBSOLETE
92
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