United States
Environmental Protection
Agency
Environmental Sciences Research
Laboratory
Research Triangle Park NC 277 1 1
EPA-600/4-78-042
July 1978
Research and Development
Regional Air
Pollution Study
Point Source
Methodology and
Emission Inventory
-------
RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U S. Environmental
Protection Agency, have been grouped into nine series These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields
The nine series are-
1 Environmental Health Effects Research
2. Environmental Protection Technology
3 Ecological Research
4 Environmental Monitoring
5, Socioeconomic Environmental Studies
6 Scientific and Technical Assessment Reports (STAR)
7 Interagency Energy-Environment Research and Development
8 "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the ENVIRONMENTAL MONITORING series
This series describes research conducted to develop new or improved methods
and instrumentation for the identification and quantification of environmental
pollutants at the lowest conceivably significant concentrations. It also includes
studies to determine the ambient concentrations of pollutants in the environment
and/or the variance of pollutants as a function of time or meteorological factors.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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REGIONAL AIR POLLUTION STUDY
Point Source Methodology and Emission Inventory
by
F. E. Littman
Rockwell International
Air Monitoring Center
11640 Administration Drive
Creve Coeur, MO 63141
Contract 68-02-2093
Task Order 108A
Project Officer
Charles C. Masser
Office of Air Quality Planning and Standards
Office of Air and Water Management
U.S. Environmental Protection Agency
Research Triangle Park, N.C. 27711
ENVIRONMENTAL SCIENCES RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
RESEARCH TRIANGLE PARK, N.C. 27711
-------
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 re-
flect 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
The development of the point source emission data inventory for the
Regional Air Pollution Study at St. Louis is discussed. To meet the unusual
requirements of this study, which specified the acquisition of hourly
measured emission data for the St. Louis Air Quality Control Region for a
period of two years, a unique methodology was developed and put into
practice. The result is a data base containing over 20 million pieces of
information in a readily accessible form.
in
-------
CONTENTS
Abstract iii
Figures vii
Tables viii
Acknowledgments ix
A. Raps Point Source Methodology 1
1.0 Introduction 2
2.0 The Saint Louis Interstate Air Quality Control Region 4
3.0 Historical Inventory Data 6
4.0 Size Distribution of Sources 8
5.0 Sensitivity Analysis 15
6.0 Sources of Air Pollution 20
6.1 Classification 20
6.2 Pollutants of Interest 20
6.2.1 Sulfur Dioxide 20
6.2.2 Carbon Monoxide 22
6.2.3 Particulate Matter 23
6.2.4 Hydrocarbons 24
6.2.5 Oxides of Nitrogen (NOY) 24
A
6.2.6 Heat Emissions 25
7.0 Emission Data Acquisition 27
7.1 Survey 27
7.2 Acquisition of Data 28
7.2.1 Fuel Consumption and Process Data 29
7.2.2 Operating Data 31
7.2.3 Stack Gas Measurements 31
8.0 Data Handling 37
B. RAPS Point Source Emission Inventory 39
1.0 Data Acquisition 40
1.1 Major and Minor Sources 40
-------
1.2 Industrial Area Sources 43
1.3 Emission Factor Verification Studies 55
1.3.1 Background 55
1.3.2 Test Methods 57
1.3.3 Results and Discussion 57
1.3.4 Sulfuric Acid Mist 62
1.3.5 Particle Size Distribution 67
2.0 Data Handling 72
2.1 Coding Procedures 72
2.2 Editing of Data 79
2.3 RAPS Emission Inventory Calculations 79
3.0 Presentation of Data 87
References 95
Appendix A Point Source Data Handling Instructions 96
Appendix B Point Sources at Which Source Tests Were Made 113
Appendix C Point Sources for Which Process or Fuel Consumption
Data Were Obtained 115
Appendix D Point Sources for Which Operational Data Were Obtained
for Temporal Allocation of Annual Emissions 126
Appendix E Point Sources for Which no Temporal Data Were Recorded 136
VI
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FIGURES
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Metropolitan Saint Louis Interstate Air Quality Control
Region
S02 Emissions for the Saint Louis Air Quality Control Region
Relationship Between a,, and QK/Q
RAPS Major Point Sources
Input Data: Printout of Hourly Data
Input Data: Summary of Load Data
Input Data: Steam Chart
Input Data: SO^ Concentrations in Stack
Input Data: Daily Log
Sulfur Trioxide Collector
Sulfuric Acid Mist Sampling Train
Percentage Conversion of S02 to S03 in Utility Boilers
Andersen Stack Sampler
RAPS Point Source Coding Form - Hourly Data
RAPS Point Source Coding Form - Annual Data
RAPS Point Source Coding Form - Non-Criteria Pollutants
RAPS Point Source Coding Form - Emission Factors
Point Source Listing - Hourly
Point Source Listing - Daily
Point Source Listing - Annual
Modeler's Tape
Point Source Summary Report - County (Madison)
Point Source Summary Report - State (Illinois)
Point Source Summary Report - AQCR 70
Page
5
9
17
42
44
45
46
47
48
64
65
66
70
75
76
77
78
88
89
90
91
92
93
94
VII
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TABLES
Number Page
1 Qualifications of Selected SMSA's 4
2 Sources of Pollutants in the St. Louis AQCR 10
3 Sources of Pollutants in the St. Louis AQCR Emitting
in Excess of 1000 Tons/Year 11
4 Values of 6 for Selected Pairs (a, 1-C) 18
5 Maximum Allowable Error for Point Sources of Various Sizes 18
6 Classification of Sources for Emission Inventory 21
7 Classification of SOp Sources 29
8 Classification of CO Sources 29
9 Classification of Sources of Particulates 30
10 Classification of NOX Sources 30
11 Classification of Hydrocarbon Sources 30
12 Distribution of Large Sources in the St. Louis AQCR
by SCC Codes 34
13 Distribution of Large Sources in the St. Louis AQCR
by SCC Codes 35
14 Minimum Test Schedule 36
15 Hourly Point Source Summary 41
16 Annual Point Source Summary 41
17 Annual Emissions from Industrial Area Sources 50
18 Emission Limits for Industrial Area Sources 49
19 Comparison of Measured and Calculated Flows 58
20 Comparison of SO- Emissions Based on Calculated and
Measured Flow Rates 60
21 Comparison of AP-42 and Experimental Emission Factors 61
22 Sulfur Oxide Analyses and Ratios 68
23 Particle Size Distributions 71
vn i
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ACKNOWLEDGMENTS
Many people contributed to the success of this study. The cooperation
of the almost one hundred companies in the St. Louis area is greatfully
acknowledged. Officials of the Illinois Environmental Protection Agency,
the Missouri Air Conservation Commission, the St. Louis County and St. Louis
City Air Pollution Control Agencies, and, last but not least, the EPA
Project Officers, Mr. Chuck Masser and Mr. Jim Souther!and, were most
helpful and cooperative.
At the St. Louis Regional Office, staff members R. W. Griscom, 0. C.
Klein, John Piere and Kevin Isam were among the chief contributors to the
technical effort. But the work could not have been successfully accom-
plished without the faithful labors of the data clerks, Mss. S. Piere,
S. Wosmansky, B. V. Kruse and A. J. Haspert.
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A. RAPS POINT SOURCE METHODOLOGY
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1.0 INTRODUCTION
An emission inventory constitutes the starting point for any attempt to
control emissions to the atmosphere. As long as such controls deal with
average yearly concentrations, inventories giving total annual emissions of
the various sources of pollutants are sufficient. The Regional Air Pollution
Study had, however, as its first goal the validation of atmospheric dispersion
models, which attempt to predict ambient pollutant concentrations on an hourly
basis. Emission values derived from total annual emissions were therefore
largely inadequate, and the RAPS emission inventory was conceived to provide
the needed resolution and accuracy by measuring and recording hourly emissions
(or parameters directly related to hourly emissions) for the principal sources
of pollution. Thus, the emission inventory for the Regional Air Pollution
Study (RAPS) at St. Louis is distinguished from existing emission inventories
by two factors: its resolution and its accuracy.
Although ultimately all pollutants of importance were included in this
inventory, as a matter of priority emphasis of the data collection was placed
on S0? as an indicator of pollution originating from stationary sources.
Hourly measurement provided the needed time resolution and, at the same time,
increased the accuracy of the emission inventory by updating it. Later, the
inventory was expanded to include hydrocarbons, oxides of nitrogen, partic-
ulate matter, heat emissions and others.
Any attempt to obtain measured values for a large number of sources is a
complex and expensive undertaking. Within the usual constraints of air
pollution studies, such an approach is not feasible, and the use of algorithms
or models has been generally resorted to for estimation of emissions. Since
such emission models describe assumed conditions, their use in the RAPS was
less desirable; they were used only where it did not impair the overall
accuracy of the inventory, as indicated by a sensitivity analysis.
This section of the report discusses the approach to the problem of
-------
assembling a "precision" inventory for the St. Louis Interstate Air Quality
Region. It states the nature of the problem and the rationale for choosing
the St. Louis area as a "test chamber"\ the pollutants of interest are also
discussed briefly. Using an approach suggested by EPA's Weighted Sensitivity
Analysis Program, limits were placed on the scope of the investigation, which
were then applied to the actual situation in St. Louis. The mechanism for the
acquisition of data and their preparation prior to entry into a data bank are
also described.
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2.0 THE SAINT LOUIS INTERSTATE AIR QUALITY CONTROL REGION (AQCR #070)
The St. Louis area was selected on the basis of careful considerations
of the various factors of importance for a regional air pollution study (1).
Standard Metropolitan Statistical Areas (SMSA's) were used as a basis for the
analysis, and all SMSA's with population in excess of 400,000 were examined.
The primary factors considered in the selection were:
Geographic isolation from other SMSA's
Location within the Continental climate zone
Significant level and density of pollutant emissions
Presence of a rural fringe with substantial crop lands
Existence of control programs and historical data
The final selection of St. Louis was made by the Assistant Administrator
for Research and Monitoring, EPA, from the four considered sites on the basis
of the following rating (Table 1):
TABLE 1. QUALIFICATIONS OF SELECTED SMSA'S
Criterion Birmingham Cincinnati Pittsburgh St. Louis
Surrounding area Fair Poor Good Good
Heterogeneous emissions Fair Fair Fair Good
Area size Good Good Good Good
Control program Poor Good Good Good
Information Poor Good Fair Good
Climate Good Fair Fair Good
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100 km
r-
j CRAWFORD \WASHINGTON I ^
\ v
| ! \ ! \STE.GENEVIEVE
I _, I \ I \ ./
1 j . ;^J FRANCOIS \ /
t.-J >-- V . V
0 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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3.0 HISTORICAL INVENTORY DATA
Air pollution studies have been conducted in the St. Louis area for many
years, and several emission inventories have been developed. In 1964, an
"Interstate Air Pollution Study, Saint Louis-East Saint Louis Metropolitan
Areas" was undertaken by the U.S. Public Health Service. Questionnaires were
sent out to determine fuel use and combustible waste disposal practices in
the area as well as manufacturing activities. A revised emission inventory,
still based on 1963 data, was published in December 1966 as Phase II of the
Interstate Study.
After the Metropolitan Saint Louis Interstate Air Quality Control Region
had been established, the first comprehensive inventory was taken in 1968,
to serve as a basis for the Implementation Planning Program (IPP). Since
then, four more inventories have been compiled:
IBM Emission Inventory-1970
DAQED Emission Inventory-1971
NATO Emission Inventory-1971
NEDS Emission Inventory-1973
In addition, the following traffic and transportation inventories exist:
Streets and highways
Railways and vessels
The emission inventories in current use by the Missouri and Illinois
regulatory agencies were recently (Summer 1973) acquired and transferred to
the NEDS files.
These inventories are described in detail in SRI Report "A Regional Air
Pollution Study Preliminary Emission Inventory" (2).
Most of these inventories are only of historical interest. Current data
are contained in the National Emission Data System (NEDS) (3) inventory,
administered by the Federal EPA, which is based on inventories kept by the
-------
Illinois EPA and the Missouri agencies.
The NEDS inventory contains information on annual emissions of the five
"criteria" pollutants (particulates, SCL, NOX, hydrocarbons (HC) and CO)
from stationary point and area sources, as well as a listing of selected
industrial materials emitted by chemical process, food, agriculture, chemical
and mineral products industries, petrochemical operations, wood processing,
and incinerators.
From the point of view of the Regional Air Pollution Study, the NEDS
inventory had two major uses: it contained emission data for those sources
for which detailed data were unavailable, and it provided a basis for an
analysis of the problem of obtaining measured data. It therefore served as
an interim data base for the St. Louis study until the RAPS inventory became
operational.
-------
4.0 SIZE DISTRIBUTION OF SOURCES
The situation in St. Louis lent itself to a direct attack on the problem
of direct measurement of emissions because of the relatively limited number
of major point sources. In terms of S02, the current (1973) National Emission
Data System (NEDS) inventory listed about 300 sources emitting over ten tons
of S0? per year. Of these, only 62 emit in excess of 1000 tons/year, an
additional 120 over 100 tons/year. The 62 largest sources, representing 15
companies, are concentrated at 20 locations. Thus, the sheer physical mag-
nitude of the problem of collecting hourly data for the major sources of
pollution appeared to be manageable within a reasonable budget (Figure 2).
The situation for other pollutants is somewhat similar. The data are
summarized in Table 2.
Thus, if direct measurements of emission were to be limited to sources
emitting in excess of 1000 tons/year, we needed to obtain data from 62 sources
at 20 locations for S02, 13 sources at 9 locations for CO, 28 sources at 12
locations for particulates, and so on. Many of these sources overlap, thus
further reducing the data collection (but not the data recording) problems.
For example, of the 26 major sources of NO,,, 21 are also major emitters of
SO,,. The extent of the overlap is shown in Table 3, which lists all major
sources of pollutants in matrix form.
-------
100
70
60
o
u.
O 50
t-
ui
O
oc
Ul
Q.
40
30
20
10
r< 100,000 TON/YEARS
TOTAL AQCR
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
I
0 50 100
SOURCE: NEDS Inventory (1973).
150 200 250
NUMBER OF POINT SOURCES
300
350
400
SA-2579-14
FIGURE 2. S02 EMISSIONS FOR THE SAINT LOUIS AIR QUALITY CONTROL REGION
-------
OL
O
O
OO
o
Q.
O
00
o
00
cc
U~l
0
CO
Q£
O
O
Qi
Q
>-
^
POLLUTANT S02 CO PARTICULATE NOX
T3
-t-J
O
1
O
A
rM
O
A
O
A
o
1
o
A
0
I
o
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fM
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h-
O
A
(M
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A
(T3
*->
0
1
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O
A
CO
O
A
Tons/Year
en
r^
C\J
c_n
10
o
00
oo
CM
IT)
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in
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LT)
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en
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LO
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No. of Locations
LT>
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oo
U3
en
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un
CSJ
en
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oo
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^
in
No. of Companies
oo
r^
CTi
i.
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_Q
CJ
O
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C1J
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CO
10
-------
TABLE 3. SOURCES OF POLLUTANTS IN THE ST. LOUIS AQCR
EMITTING IN EXCESS OF 1000 TONS/YEAR
Source Name
Allied Chemicals
Alpha Cement Co.
Alton Box Co.
Amoco
Anheuser-Busch Co.
Anlin Corp.
Chrysler Corp.
Clark Oil Co.
Columbia Quarry
East St. Louis Stone
Ford Motor Co.
Point
No.
01
01
01
02
03
01
02
03
04
05
06
01
01
01
02
01
02
03
04
01
02
01
01
02
Pollutant
so2
X
X
X
X
X
X
X
X
X
X
X
CO
X
X
Partic-
ulates
X
X
X
X
X
X
NO,
X
X
HC's
X
X
X
X
X
X
X
X
X
X
X
Proposed
Stack
Sampling
X
X
X
X
(continued)
11
-------
TABLE 3 (continued)
Source Name
GMAC
Granite City Steel
Highland Electric Co.
Illinois Power Co.
Laclede Steel
Mississippi Lime Co.
Mississippi Portland
Cement
Monsanto Chemical Co.
Point
No.
01
02
03
04
01
02
03
04
05
01
01
02
03
04
05
06
07
08
09
10
11
01
02
03
01
01
02
Pollutant
so2
X
X
X
X
X
X
X
X
X
X
X
X
CO
X
X
X
X
X
X
X
X
X
Partic-
ulates
X
X
X
X
X
X
X
X
X
X
X
NOX
X
X
X
X
X
X
X
X
X
X
X
X
X
HC's
X
X
X
X
X
Proposed
Stack
Sampling
X
X
X
(continued)
12
-------
TABLE 3 (continued)
Source Name
Monsanto Chemical Co.
Municipal Incinerator
NL Titanium Div.
PPG Glass
St. Joseph Lead Co.
Shell Oil Co.
Point
No.
02
03
04
05
06
07
08
09
01
02
01
02
03
04
01
01
02
01
02
03
04
05
06
07
08
09
10
11
12
Pollutant
so2
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
CO
X
X
X
X
Partic-
ulates
X
X
X
X
X
X
X
NOX
HC's
X
X
X
X
X
Proposed
Stack
Sampling
X
X
X
X
X
X
X
X
X
(continued)
13
-------
TABLE 3 (continued)
Source Name
Socony
Stolle Quarry
Texaco
Union Electric
TOTALS
Point
No.
01
01
02
01
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
96
Pollutant
so2
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
62
CO
13
Partic-
ulates
X
X
X
X
X
X
28
NO,
X
X
X
X
X
X
X
X
X
X
X
26
HC's
X
X
23
Proposed
Stack
Sampling
X
X
17
14
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5.0 SENSITIVITY ANALYSIS
An important aspect of every inventory is its accuracy. While no inven-
tory can be better than the numbers supplied by the data acquisition process,
a statistical estimate of the overall quality and probable error helps to
place the uncertainties on a quantitative basis.
As a first approach to this problem, the National Air Data Branch of EPA
commissioned a study which produced a Weighted Sensitivity Analysis Program
(4). While this program does not supply any estimates of the absolute
accuracy, it does help evaluate the maximum permissible error of any part of
the inventory, given a maximum permissible error for the whole system. In
doing so, it keeps the inventory at an equivalent level of accuracy and points
out areas where accuracy has to be improved to provide a desired overall
accuracy. In addition, it also provides an approach to establish confidence
levels for the emission inventory.
The basic theoretical development proceeds as follows. The linear model:
79
qV = z <
k=l
9
E
where Q = total amount of pollutant emitted
100 e = percentage error associated with Q
Qk = amount of pollutant emitted by subclass k
100 a. = percentage error associated with Q.
is postulated as an appropriate model to analyze the propagation of errors
through the emission inventory.
If each subclass contributes to the error an amount proportional to its
relative physical contribution, it can be shown that
15
-------
The analysis demonstrates that to obtain a predetermined level of precision
for a source class, not all subclasses need to be measured with the same
precision; the greater the ration of Q:Q, becomes, the greater becomes the
allowable value of a, . Conversely, a, approaches the value of G as the ratio
approaches unity (Figure 3).
The authors also developed a method for predicting the confidence level
for the inventory; that is, the probability that the actual overall error will
not exceed 0, using Chebyshev's theorem (5). The results for selected pairs
of (a and 1-c) are shown in Table 4, where a = 26 and 1-c is the confidence
level .
Thus, a two-step procedure was suggested. First, the overall allowable
error 8 was established, either from user's (modeler's) requirements, or as
a tradeoff between confidence level and acceptable error interval; secondly,
the values of ov for the components of interest were computed.
Applying these considerations to our case suggested that, in the absence
of any definite information about the modeler's requirements for the accuracy
of emission data, a fairly stringent set of conditions would be a confidence
level of 95 percent and an acceptance interval of 10 percent (these conditions
are probably stricter than the accuracy of the emission data). This would
lead to a permissible maximum error 6 of 2.24%.
The allowable error for source classes of various sizes, such as 100
tons/year, 1000 tons/year, etc. could tnen be calculated. For example, the
allowable error for a 100 ton source of SO,, was
alOOT = 03-
- 9 9* 1.187.296
~ *'^ 100
= 244%
The data are tabulated in Table 5.
The very large OY for SU0, CO and NO^, even for the relatively stringent
statistical conditions, suggested that there probably was no need to obtain
16
-------
100
90
30
70
60
^ 50
40
30
20
10
0 = 5%
1 1 1 1 I 1
I 1 1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Source: Reference 4
QK/Q
FIGURE 3. RELATIONSHIP BETWEEN OK AND QK/Q
17
-------
TABLE 4. VALUES OF 6 FOR SELECTED PAIRS (a, 1-C)
Confidence Level
i.
O)
CD
o
O.
ai
u
\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%
Source: Reference 4
TABLE 5. MAXIMUM ALLOWABLE ERROR FOR POINT SOURCES OF VARIOUS SIZES
Acceptance Interval 10%, Confidence Level 95%, -0 = 2.24%
Pollutant
Particulates
so2
NOX
HC
CO
Total Emissions
Tons/Year
45,224
1,007,530
322,730
47,610
164,331
Al lowable
100 Tons/Yr
48%
225%
127%
49%
91%
Error a, for S
1000 Tons/Yr
15%
71%
40%
15%
29%
ources of
10,000 Tons/Yr
--
23%
--
--
--
18
-------
measured hourly values for sources smaller than 100 tons/year of these
pollutants, since annual data provided this accuracy. Thus, the collection
of hourly data was limited to sources of 1000 ton/year and larger. As
indicated in Table 2, this reduced the number of S00 sources to be measured
L.
to 62, the stationary CO sources to 13, and the NOV sources to 26. Hourlv
v A
values for the remaining sources were then calculated as discussed in Section
7.2.2.
19
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6.0 SOURCES OF AIR POLLUTION
6.1 CLASSIFICATION
Virtually every human activity results in some form and degree of air
pollution. For practical purposes, it is convenient to classify the sources
of emission; a general classification is shown in Table 6. There, sources
are divided into stationary and mobile, since these present significantly
different problems. Stationary sources are further divided into Point and
Area sources. The division between the two is arbitrary: individually
identified sources are considered "Point Sources". For the RAPS inventory,
sources emitting less than 0.01% of the total emissions of a pollutant were
not considered as individual points but rather assigned to and distributed
over the appropriate area. Resultant point source values were approximately
100 tons/year for SO , 30 tons/year for NO , 25 tons/year for particulates,
X X
10 tons/year for hydrocarbons, and 10 tons/year for carbon monoxide. Of
course, even a very small point source can be a major contributor to a given
local or nearby receptor (monitoring station), but the investigation of this
problem constitutes a localized, special situation which needs to be dealt
with separately from the overall inventory.
The division of sources into combustion and non-combustion is again a
matter of convenience; however, combustion sources constitute a specific
group of emitters which in some cases, such as SO^ for stationary sources,
constitute the overwhelming fraction of these pollutants.
6.2 POLLUTANTS OF INTEREST
The RAPS inventory initially emphasized "criteria" pollutants (for which
Air Quality Standards exist). Ultimately the inventory also included other
pollutants such as trace and hazardous contaminants.
6.2.1 Sulfur Dioxide
Sulfur dioxide (S02) was the pollutant initially emphasized in the
20
-------
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inventory since it occupied the position of highest priority within the
Regional Air Pollution Study. In St. Louis, virtually all of it (98.9%) was
estimated to originate from listed point sources. Most of the S0? is
produced by the combustion of coal and fuel oil, although some of it resulted
from ore roasting, steel production, and petroleum refining operations. The
largest contributors are the power generating stations of the utility compa-
nies. The six generating stations in the St. Louis area produce over 855,000
tons of S0? per year, or about 85% of all the S0? produced by point sources
in the area.
Sulfur dioxide is relatively non-reactive in the atmosphere, at least
over the time interval of a few hours, which is likely to be of interest to
modelers. Removal from the atmosphere occurs by several mechanisms, some of
which involve oxidation to sulfur trioxide with subsequent formation of
sulfuric acid mist or sulfates by reaction with basic materials in the atmo-
sphere (e.g., ammonia). These processes will have to be considered for long-
term (24 hours or longer) modeling.
Available evidence indicates that the ratio of S0? to S0_ in ambient air
L- O
is between 50:1 to 100:1. Recent health data (6) indicate that (at least in
the case of elderly patients with heart and lung diseases, as well as
asthmatics) it is the level of suspended sulfates that correlates with
adverse health effects rather than the S0? level. Best estimates indicate
that sulfates are about an order of magnitude more irritating than S0?. At
this time, it is not clear whether sulfuric acid mist or sulfates are
implicated, and the importance of atmospheric transformation products of
S0? is not certain.
Ambient concentrations of S09 in the St. Louis atmosphere typically
3
range from 20 to 40 micrograms/m (annual average) (6).
6.2.2 Carbon Monoxide
Carbon monoxide (CO) is closely linked with automotive traffic. Sta-
tionary combustion sources normally generate only minor amounts of CO. There
are, however, a few important industrial sources of CO: the catalytic cracker
regenerators in petroleum refineries, blast furnaces in steel mills, and
certain chemical processes. Because of the tremendous volume of stack gases
generated by electric utilities, the relatively low concentrations of CO in
22
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these gases also contribute to the overall CO concentration.
Carbon monoxide is chemically inert. It is removed slowly by contact
with certain soil bacteria, which maintain the natural balance of CO in the
air, but the rates of these processes are not significant on the time scale
of interest.
Carbon monoxide combines with hemoglobin 200 times more readily than
oxygen; it thus prevents the blood hemoglobin from transporting oxygen from
the lungs to the tissues. Exposure to low concentrations (below 100 ppm or
115 mg/m ) causes headaches and dizziness. Its actions are most likely to
affect persons living at high altitudes and people with chronic heart and
lung diseases. Cigarette smokers commonly have 5 - 10% carboxy-hemoglobin,
an amount that corresponds to 30 to 60 ppm of CO in ambient air (35 to 70
mg/m ).
Ambient concentrations of CO in the downtown St. Louis area range from
o
15 to 35 mi Hi grams/in (6).
6.2.3 Particulate Matter
The fate of particulate matter in the atmosphere is becoming a major
research target. It is a particularly difficult subject because the charac-
teristics of particles are determined only partially by their chemical com-
sition and very largely by their size. Thus haziness, by far the most
obvious manifestation of air pollution, is strongly dependent on particle
size. Similarly, the health effect of particulate matter is largely dependent
on particle size, since only particles of a certain size range penetrate into
the lungs and are retained there. The particle size of interest in these
areas is of the order of less than five or six micrometers. Such particles
remain afloat virtually indefinitely and, while their contribution to the
total weight of particulate matter is small, their number is very large.
By contrast, the emission of particulate matter is determined on a weight
basis, whether by sampling or by material balance consideration. Thus the
small number of relatively large particles accounts for most of the mass of
particulate emission. Since particles in excess of 10 ym settle out rather
rapidly, these particles do not contribute much to the ambient concentration
of particulates, nor to their health and visibility effects.
23
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Thus, a really useful inventory of particulate emissions should specify
not only the mass but also the size distribution of particulate emission in
addition to their chemical compositiona difficult and expensive task which
cannot be carried out on a routine monitoring basis.
The problem is further complicated by the processes which form partic-
ulatesmainly droplets in the atmosphere. The formation of SCU leads
directly (via reaction with water vapor) to the formation of a sulfuric acid
mist and to the stabilization of fog; photochemical reactions result in the
polymerization of initially gaseous hydrocarbons, resulting again in partic-
ulate droplets. These products are only indirectly related to emission
inventories. The concentration of suspended particulates in the St. Louis
area ranges from 50 to 150 ym/m .
6.2.4 Hydrocarbons
In the air pollution literature, the term "hydrocarbons" is used loosely
to designate gaseous organic compounds. There are two major categories of
sources of hydrocarbons in urban atmospheres: incomplete combustion and
evaporation. Incomplete combustion occurs primarily in internal combustion
engines (automobiles). Evaporation results from the storage and handling of
solvents, petroleum products, etc. Additionally, methane is a normal con-
stituent of the atmosphere, the result of natural decomposition processes.
Hydrocarbons participate in photochemical reactions leading to "smog",
but their reactivity varies widely. It is therefore important to determine
not only the amount of hydrocarbons present, but also their composition.
Separation into unreactive hydrocarbons, olefins, paraffins, aromatics and
aldehydes has been carried out in the Los Angeles area and will be performed
in St. Louis. Complete analyses of samples collected in bags by means of a
gas chromatography are scheduled for samples of ambient air in St. Louis.
6.2.5 Oxides of Nitrogen (NOX)
Emission inventories of nitrogen oxides constitute a special problem
since these compoundsparticularly nitric oxide (NO)are primarily formed
by nitrogen fixation during combustion operations. Their formation during
combustion is a complex function of the time-temperature relationships and
24
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the combustion configuration. Any nitrogen compounds present in the fuel also
contribute to the formation of nitrogen oxides. Because of this, the nitrogen
oxide concentration in flue gases cannot be calculated from a theoretical
basis but must be determined experimentally for, at least, each typical situa-
tion. In addition to combustion sources, there are several specific point
sources in the area which emit nitrogen oxides, usually N0?, such as nitric
acid plants.
As mentioned previously, the importance of oxides of nitrogen and hydro-
carbons as pollutants is primarily as participants in photochemical reactions
where N0? acts as primary light absorber. These compounds will therefore be
of importance to RAPS when a study of photochemical reactions in the atmo-
sphere is carried out.
Ambient concentrations of NO,, in AQCR 70 tend to be between 35 ug and
75 yg per cubic meter of air.
6.2.6 Heat Emissions
The large amounts of energy produced and consumed by a city eventually
are converted into heat, resulting in a "heat island" which has an effect
on atmospheric stability and thus affects modeling efforts. A heat emission
inventory is required for a comprehensive understanding of this effect in
much the same way as a pollutant emission inventory forms the basis for an
understanding of the fate of the pollutants.
Point sources contribute significantly to the heat emission inventory,
since a sizeable portion of the energy consumed is wasted as stack gases.
Even in highly efficient power plants, about 15 percent of the energy con-
sumed is wasted at the plant. In some industrial operations, such as flares,
all of the heat of combustion is released to the atmosphere at the plant.
In a self-contained area such as St. Louis, not only the waste heat
turns up as heat emissions, but virtually all of the converted energy as well.
Except for minor amounts of energy stored as chemical energy (e.g., in a
primary aluminum plant) or radiated into space as visible light, all other
forms of energy, whether electrical or mechanical, are converted into heat
25
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and released into the atmosphere, spread out over the inhabited area. Thus,
as a first approximation, the total Btu content of the fuel used at St. Louis
can be assumed to be released at either point or area sources. The amount of
heat released by point sources can be calculated directly from fuel consump-
tion and known conversion efficiencies of the power boilers; it can be
verified by stack analysis and measurement of gas volume and temperature, from
which the heat content can be calculated.
Since fuel consumption figures are obtained for other purposes, a program
to calculate heat emission from point sources can be initiated. Significant
point sources, defined similar to pollution point sources, should be treated
individually. All other sources are assigned to grid squares, whose total
emission is then estimated. Power generation is treated separately, since a
large fraction of the waste heat is carried off in cooling water.
26
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7.0 EMISSION DATA ACQUISITION
A series of sequential steps leads to the eventual acquisition and
recording of point source inventory data for the RAPS inventory. The steps
are:
Survey
Classification of Sources into Acquisition Groups
Acquisition of Data by:
1) Fuel consumption or process data
2) Derivation from operational data
3) Stack analyses
Transformation of Data and Entry into Computer Bank
7.1 SURVEY
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 a one-time basis.
The requirements of the RAPS inventory for hourly measured data for a
period of a year far exceed the normal reporting routine and require special
arrangements with the management of the various facilities. Thus, personal
contact with the appropriate corporate office by mail, phone and, ultimately,
in person is essential to obtain the necessary cooperation. The requests
are made for access to data which would provide a basis for calculating
hourly emissions.
Such data could be
stack concentration measurements
fuel consumption records
process data
steam production records
power production records
27
-------
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 gather-
ed:
Management level
Operational level
At the management level, an "agreement in principle" is required;
usually operational personnel is present at these meetings since they will
later on be involved. After an agreement is reached, the details of the
data acquisition are worked out with operational personnel.
For point sources emitting less than 1000 tons/year, as well as those
major sources where detailed data are not available, hourly emissions have to
be derived by a model, as discussed further on under 7.3.2. For these sources,
the following information is necessary.
1. Source description
2. Work schedule
3. Maximum process and space heating loads
4. Monthly and shift fuel weighting
5. Fuel analysis data
7.2 ACQUISITION OF DATA
The division of sources of pollutants into major (those emitting more
than 1000 tons per year) and minor (emitting between 100 and 1000 tons/year),
which is based on sensitivity analysis discussed above, produced two broad
categories. Data from sources in Category 1, the major sources, were to be
collected on an hourly basis to the extent that they were available. Data
from all other sources, that is the minor ones and those of the larger ones
where detailed data are not available, were to be derived by a modeling or
algorithmic procedures.
In Group 1 are the utilities and the majority of sources emitting over
1000 tons/year of pollutant, as determined by the initial survey of sources.
28
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The data available from these sources permit a direct calculation of the
weight of pollutants emitted any given hour.
Although sources in Group II contribute only a minor portion of the
overall pollutant load, they may be of considerable significance locally.
Under certain conditions it may become necessary to obtain measured emission
data from some of these sources as a special project.
7.2.1 Fuel Consumption and Process Data
From the point of view of sampling methodology, there is no procedural
difference between emission data based on fuel consumption and data based on
processing of, for example, a sulfide ore. In both cases, the hourly weight
of consumed material determines the amount of gaseous discharge.
From an analysis of existing inventories and discussions with local air
pollution agencies, it appeared that the 62 point sources emitting in excess
of 1000 tons of S(L per year fell into the following categories (Table 7).
TABLE 7. CLASSIFICATION OF S02 SOURCES
SCC Code
1-01
1-02
3-05
3-xx
Category
Boilers, Electric Generation
Boilers, Industrial
Petroleum Processing
Other Industrial
Number
27
19
11
5
Thus, almost 75% (46) of the 62 sources (including all of the large ones) are
boilers; another 17% are concentrated in the petroleum industry.
Carbon monoxide, another combustion-related pollutant, has quite a
different distribution (Table 3).
TABLE 8. CLASSIFICATION OF CO SOURCES
SCC Code
1-01
3-01
3-03, 3-04
3-06
5-01
Category
Boilers, Electric Generation
Chemical Process
Metal Processing
Petroleum Processing
Incinerators
Number
1
2
6
2
2
29
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Here the largest sources are metal processing (blast furnaces, etc.), petro-
leum processing (cat. cracking) and certain chemical processes.
Particulate emissions are largely related to boilers; almost half of the
emission sources are boilers; another 25% comes from the mineral industry
(quarries, cement plants, etc.). The breakdown is shown in Table 9. The
overlap of pollutants from different sources has been indicated in Table 3.
TABLE 9. CLASSIFICATION OF SOURCES OF PARTICIPATES
SCC Code
1-01
3-03
3-05
3-06
Category
Boilers, Power
Metal Industry
Mineral Industries
Petroleum Processing
Number
13
4
7
4
TABLE 10. CLASSIFICATION OF NOY SOURCES
A
SCC Code
1-01
1-02
3-05
3-06
SCC Code
1-01
2-01
3-01
3-03
3-06
4-03
4-02
5-01
Category
Boilers, Electric generation
Boilers, Industrial
Industrial - Cement
Industrial - Petroleum
TABLE 11. CLASSIFICATION OF HYDROCARBON
Category
Boilers, Electric Generation
Internal Comb., Turbine
Chemical Industry
Primary Metals - Coking
Petroleum Industry - Processing
- Evaporation
Surface Coating - Evaporation
Municip. Incinerator
SOURCES
(0)
(0)
(1)
(1)
(4)
(11)
(6)
(0)
Number
22
2
1
1
Number*
9
2
4
2
17
44
19
2
*Bracketed numbers are sources in excess of 1000 tons/year; unbracketed are
sources greater than 100 tons/year.
30
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The significance of the high percentage of power boilers lies in the
fact that data pertaining to boiler operations are usually well kept and more
readily available than data about process operations. Since S0? emissions can
be calculated readily from fuel consumption and analysis figures, and the
emissions of other pollutants are closely related to fuel consumption and
operating conditions, the acquisition of hourly fuel consumption data will
go a long way toward the creation of an hourly emission inventory. For this
reason, considerable emphasis was placed on the acquisition of hourly fuel
consumption (and related data), particularly in the early stages of the RAPS
inventory effort.
7.2.2 Operating Data
This group includes all those point emission sources which are either
minor (emitting less than 1000 tons/year) or for which no detailed hourly
data are available.
The emissions from smaller sources is 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 oper-
atic for a 5 day week, 9 hours a day operation, less the number of days
when the plant was shut down.
7.2.3 Stack Gas 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
31
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to be monitor stack emissions.
In actuality, emissions (in terms of weight of pollutant) cannot be
directly measured. Stack gas analyzers only provide a measure of the concen-
tration of the pollutant, thus requiring another measurementstack gas
volumebefore the weight of the emitted pollutant can be determined. Stack
gas volume, in 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 calculations, each of which contributes to
the accuracy of the final figure.
Stack sampling methods for compliance purposes have been standardized.
EPA methods are described in CFR Title 40 (Protection of Environment) as an
appendix to paragraph 60.85. The methods are:
Method 1: Sample and Velocity Traverses for Stationary Sources
Method 3: Gas Analyses for C0?, Excess Air and Dry Molecular Weight
Method 4: Determination of Moisture in Stack Gases
Method 5: Determination of Particulate Emissions from Stationary Sources
Method 6: Determination of SOp Emissions from Stationary Sources
Method 7: Determination of NCL Emissions from Stationary Sources
Method 8: Determination of Sulfuric Acid Mist and S0? from
Stationary Sources
Method 9: Visual Determination of Opacity of Emissions from
Stationary Sources
Stack sampling is time consuming and expensive; for this reason, it is
used only to provide a primary calibration of emission factors which are used
in conjunction with more readily accessible data, such as fuel consumption or
processing rates. The most extensive collection of emission factors is con-
tained in EPA's "Compilation of Air Pollutant Emission Factors" (AP-42) which
is in almost universal use. Nevertheless, emission factors contained there
are averages and vary widely in accuracy. They are rated for estimated
32
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accuracy on a scale ranging from "A" to "E", depending on the number and
quality of field measurements on which they are based.
To insure the accuracy of the RAPS emission inventory, some stack test-
ing was planned. Such testing should include at least one example in each
SCC category; if budgetary constraints permit, a considerable number of impor-
tant sources should be sampled individually (the SRI report (2) suggests a
total of 65 stack tests). Tables 12 and 13 show the distribution of the major
sources by SCC categories.
By combining similar sources and matching categories with actual sources
in the St. Louis AQCR, the following minimum schedule (Table 14) was deter-
mined if at least one installation of each type is to be represented. The
total of 17 stack tests should really be considered as a "Phase 1" program,
to be supplemented by further tests based on inspection and review of existing
facilities.
As discussed above, S(L is the one pollutant for which adequate data can
be obtained with only minimal stack testing, at least for those facilities
which do not have any stack gas cleaning (scrubbing) equipment. At present,
none of the boilers are equipped with such scrubbers; experimental work is
being conducted with a "Catox" unit at the Wood River power plant.
Though fuel consumption and process data are potentially capable of pro-
viding quite accurate SCL emission figures, sampling for sulfur analysis has
to be adequately performed. Practices vary widely; some plants have contin-
uous, automatic samplers, but these are located at the coal-pile end of the
conveyor system. Since there are usually storage bins in the boiler-house
itself, there is an 8 to 12 hour lag between the sample and the material
burned. Most plants sample only internn'ttently--once a shift, once a day,
even once for each barge. Fortunately, the sulfur analysis of coal does seem
to be fairly constant (about ^ 10%). A statistical evaluation of the
sampling procedures is planned; the cime lag will be incorporated in the
calculations.
Accurate data for NCL hev_ to L. Lased alnr.st wholly on stack testing.
The EPA 'mission factors spa - rang^ of 3 to 5T pounds of NOY pe<" ton of
t A
coa., - AO to 105 pounds o+ Ov ~?r '0 gallo of oil.
A
33
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TABLE 12. DISTRIBUTION OF LARGE SOURCES IN THE ST. LOUIS AQCR BY SCC CODES
External Combustion Boilers
SCC Code
1-01-002-01
1-01-002-02
1-01-002-03
1-01-002-03
1-02-002-01
1-02-002-02
1-02-002-04
1-02-002-09
1-02-004-01
1-02-004-02
Description
Elect. Gen., Bitum. Coal,
> 100 x 106 Btu/hr.
Industrial, Bitum. Coal,
> 100 x 106 Btu/hr.
Residual Oil
Residual Oil
Pulv., wet
Pulv., dry
Cyclone
Stoked
Pulv., wet
Pulv., dry
Stoked
Stoked
Number
7
14
4
1
1
3
5
5
4
1
34
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TABLE 13. DISTRIBUTION OF LARGE SOURCES IN THE ST. LOUIS AQCR BY SCC CODES
Process Heaters & Processing Emissions
SCC Code
3-01-023-99
3-01-999-99
3-03-010-01
3-04-004-03
3-06-001-03
3-06-001-14
3-06-002-01
3-06-999-98
Description
Industrial Process Chemical Mfg. H2S04-Contact
Miscellaneous*
Lead Smelter
Secondary Metal Lead Smelter
Petroleum Ind. Process Heater, Oil
, Gas
Fluid Crackers
Miscellaneous
Number
2
2
1
1
2
4
4
1
'using special emission factors
35
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TABLE 14. MINIMUM TEST SCHEDULE
A. POWER GENERATION
Equipment
Ext. Combust. Boiler
M n ii
n n n
M n n
Minimum No. Suggested
Fuel Firing Mode of Tests Location
Bitum. Coal Stoked 2 Monsanto
" Pulverized 2 Wood River-
Labadie
Cyclone 2 Sioux-Baldwin
Oil 1 Shell Oil
7
B. INDUSTRIAL SOURCES
Industry
Chemical Industry
Prim. & Sec. Metals
Petroleum
Minimum No. Suggested
Type of Tests Location
Sulfuric Acid 1 N.L.
Miscellaneous 2 Monsanto-Anlin
2 St. Joseph
Heaters 2 Shell -Amoco
Crackers 2 Shell -Amoco
Others 1 Amoco
10
36
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8.0 DATA HANDLING
As indicated in Section 7.2.1, emission (or emission related) data are
provided in many different forms, ranging from computer printouts to strip
or circular charts. The raw data have to be tabulated in appropriate form
before entry into the RAPS computer bank.
A data handling system (System 2000) had been selected which is capable
of storing data elements of variable length in repeating groups. The re-
peating groups define the structure for storing multiple sets of data values
and link the hierarchical levels.
Data preparation forms will be designed to aid the data clerk in the
structuring of the data and to make it easier to use the correct syntax. It
will not be possible, however, to depend only on well-designed data prepara-
tion forms for data quality because the content as well as the form of the
data must be verified.
Data verification can be carried out in part by the data management
system, using a preliminary storage file which can be verified, proofread
and corrected before the data manager decides that it is accurate enough to
merge into the main file.
A detailed instruction sheet has to be prepared for each data sheet for
the guidance of the data clerk. This sheet stipulates the units (if not
indicated on the original record) and specifies the manipulations, if any,
which had to be performed to obtain hourly data which could be fed into the
computerized RAPS inventory. In order to avoid human error as far as possible,
only a minimum of handling will be carried out. For example, data will be
recorded in whatever unit they were supplied and the units made part of the
record. Transformation into standardized units could then be performed by
the retrieval program to meef the specific needs of the user.
37
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For most actual sources, the values stored will be consumption or other
source data, rather than measured values of emission. The format was to
accommodate emission or consumption data. For those sources for which there
are no direct emission data, emissions must be calculated using emission
factors. The emission inventory software system will be capable of assessing
the consumption data element, refer to the appropriate code, look up the
emission factor, and compute the emission values for each specified set of
pollutants.
38
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B. RAPS POINT SOURCE EMISSION INVENTORY
39
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1.0 DATA ACQUISITION
As outlined in the methodology, hourly measured data of emission related
parameters were required for all major sources.
To test the feasibility of this approach, appropriate officials of nine
of the 15 companies emitting more than 1000 tons/year (shown in Table 3) were
contacted and interviewed. These included Union Electric, Illinois Power,
St. Joseph Lead, Alton Box Board, Laclede Steel, f'tonsanto Chemicals, Anheuser-
Busch, Shell Oil Company, and Amoco Oil. These nine companies were responsible
for over 90% of the total S02 emitted from point sources in the St. Louis area;
this included the utility companies, who were emitting approximately 85% of
all the SOp in the area. All but two agreed to supply the necessary hourly
data to RAPS (later on, information was obtained from these companies, too).
Thus, even if the percentage of cooperation of the smaller companies should
drop off, it appeared that measured data for at least 90% of the total
emission of S0? would be available. The accuracy of these data is of the
order of the accounting procedures used by the companies, which is higher
than that of chemical analysis.
1.1 MAJOR AND MINOR SOURCES
Ultimately, hourly data were obtained from 14 companies, annual data
from another 85. The breakdown is shown in Tables 15 and 16. Figure 4
shows the location of the major sources in the St. Louis area. Appendix B
lists the Plant and Point ID's and SCC codes for the sources in RAPS con-
taining hourly data and annual work patterns.
The following information was then secured at the operating level:
1. Source Description: address, location (by UTM coordinates), type
of operation (SIC and SCC Codes), etc. Most of this information
was available in the NEDS printout but had to be verified (partic-
ularly location, which should be to +_ 0.01 km).
40
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TABLE 15. HOURLY POINT SOURCE SUMMARY
Companies
14
Locations
22
Number of Point Sources
Part.
113
so2
146
NOX
113
HC
113
CO
82
Heat
113
TABLE 16. ANNUAL POINT SOURCE SUMMARY
Companies
85
Locations
92
Number of Point Sources
Part.
204
so2
128
NOX
121
HC
285
CO
123
Meat
132
41
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42
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2. Data: pollutant concentration in stack, quantity and type of fuel
burned, amount of steam or power produced, fuel analysis, process
3. Collection of Data: data collection was arranged so as to minimize
the effort required by the affected companies. Data were usually
mailed to Rockwell once a month.
The actual data obtained covered a wide range of formats, from computer
printouts of hourly fuel consumption to strip and circular charts, and even
entries in logbooks. As an example, Figure 5 shows a computer printout for
the Baldwin Power plant, which gives hourly coal consumption figures.
Figure 6 shows load data in megawatts for each boiler. Figure 7 shows a
steam abort for an industrial boiler. Figure 8 gives a tabulation of S0?
concentration values for a sulfuric acid plant. Figure 9 gives a daily log
for a refinery boiler which used several types of fuel.
1.2 INDUSTRIAL AREA SOURCES
Major Stationary Point Sources include all sources which individually
contribute more than about 0.1% of the total emissions of a given pollutant;
Minor Sources, for which less detailed data were obtained, includes sources
emitting more than 0.01% of a given pollutant.
The remaining point sources were assigned to the appropriate grid square
and considered part of the Area Source inventory. However, since no reason-
able distribution function for those small industrial sources could be
developed, they were actually treated similarly to "minor sources", that is,
annual operating data and individual emission data were obtained for all of
these sources. For this reason, they are included in this report.
The first step in inventorying these sources consisted of the preparation
of a cross-referenced list of the companies involved. This was followed up
by actual contact with the appropriate officials to obtain current consump-
tion or process rates as well as work patterns.
For the purposes of the industrial area emission inventory a source is
an n'ndividual company. By the term "company" is meant a plant location of
industrial character which is treated as a separate entity. Annual 1975
43
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48
-------
information on fuel usage, incineration, paint and solvent usage, and pro-
duction was obtained from the companies. Emissions of each of the five
criteria pollutants were calculated using the 1975 data along with AP-42
emission factors. Also, each of the companies was assigned to a grid square
after determination and verification of its DIM coordinates.
In three cases where data was unobtainable due to lack of cooperation of
the companies involved, Illinois EPA Emission Inventory (IEI), or Missouri
Emission Inventory (MEI) data from 1973 were used to calculate emission.
Those cases are identified by an asterisk in Table 17, which lists all
companies included in the inventory, its grid square location and total
pounds of emissions of each of the five criteria pollutants. As a starting
point for the inventory, IEI and MEI were consulted, using the numerical
criteria shown in Table 18 for placing a source in the industrial area
category.
TABLE 18. EMISSION LIMITS FOR INDUSTRIAL AREA SOURCES
PARTICULATES SOV NOY HC CO
A A
1
-------
TABLE 17. ANNUAL EMISSIONS FROM INDUSTRIAL AREA SOURCES
COMPANY NAME
A. B. Chance
Transformer Works
Washington, Mo.
Beall Tool Mfg.
Div. of Varien
Corporation
East Alton, 111.
Coates Steel Prod.
Granville, 111.
Continental Can
Plant*
2419 Lemp
St. Louis, Mo.
Corn Sweetners
Granite City, 111.
Drug Package, Inc.
0' Fallen, Mo.
East St. Louis
Castings Co.
East St. Louis,
Illinois
Eaton Corp.
Washington, Mo.
Excelsior Foundry
Belleville, 111.
Gilster-Mary Lee
Chester, 111.
G & S Foundry
Freeburg, 111.
Havin Material
Service
St. Clair, Mo.
GRID
SQUARE
#39
#1240
#1795
#958
#1128
#2034
#1252
#2011
#1561
#1579
#1584
#2022
EMISSIONS (LBS)
PART SOX NOX HC
111 110 742 107,446
1,356 4,192 12,421 502
11,730 42,182 51,720 2,406
000 6,000
417 25 9,591 125
540 32 28,170 162
2,023 000
1,910 000
410 1,562 1,650 83
48 3 576 14
509 454 480 24
2,702 0 0 0
CO
98
2,099
3,988
0
709
918
1,725
0
110
82
136
0
(continued)
50
-------
TABLE 17 (continued)
COMPANY NAME
Havin Material
Service
Sullivan, Mo.
International
Shoe Company*
St. Clair, Mo.
J. C. George
St. Clair, Mo.
Jennison-Wright
Corporation
Granite City, 111.
Kell wood Co.
Finishing Div.
New Haven, Mo.
Kohen Concrete
Products
German town, 111.
Kurtz Concrete
St. Charles, Mo.
Mac! ay Concrete
Festus, Mo.
Mascoutah Grain
and Feed
Mascoutah, 111,
Masters Bros.
Sand Co.
Pevely, Mo.
Micro Alloy
Corporation
O'Fallon, Mo.
Missouri
Meerschaum
Washington, Mo.
GRID
SQUARE
#2002
#2022
#56
#1128
#3
#1768
#2126
#467
#1637
#292
#2034
#47
EMISSIONS (IBS)
PART SOX NOX HC CO
2,366 0000
465 1,761 1,920 93 124
00000
7,576 3,956 8,844 6,792 9,102
180 108 2,160 54 306
1,506 0 0 0 0
11,000 0000
7,200 0000
13,702 0000
4,000 0000
167 10 2,004 50 284
262 2,685 34 23,876 46
(continued!;
51
-------
TABLE 17 (continued)
COMPANY NAME
Missouri Portland
Loading Terminal
St. Louis, Mo.
Mon Clair Grain
St. Clair County
Belleville, 111.
Mon Clair Grain
Waterloo, 111.
National Mine
Service
Nashville, 111.
New Baden Grain
Company
New Baden, 111.
Peavy Flour Mills
Alton, 111.
Permaneer Corp.
Union, Mo.
Pre-Coat Metals
St. Louis, Mo.
Koesch Enamel Mfg.
Belleville, 111.
Ruprecht Quarry
Lemay, Mo.
St. Louis Grain
Corporation
Duncan Street
St. Louis, Mo.
Washington Metal
Products
Washington, Mo.
GRID
SQUARE
#972
#1484
#1146
#1829
#1714
#1020
#2015
#852
#1511
#744
#858
#59
EMISSIONS (LBS)
PART S0y NOY HC
A A
1,800 000
16,977 000
15,400 000
0 0 1,500 750
6,616 000
12,750 000
600 593 4,011 15,100
3,400 204 40,800 16,700
1,780 107 40,940 534
48,000 000
179 0 0 0
1,080 4,090 4,320 216
CO
0
0
0
2,500
0
0
531
5,780
3,026
0
0
288
(continued)
52
-------
TABLE 17 (continued)
COMPANY NAME
St. Louis Grain
Corporation
Foot of E. Grand
St. Louis, Mo.
St. Louis Grain
Corporation
Cahokia, 111.
St. Louis Steel
Casting
St. Louis, Mo.
Spartan Alum-
inum Products
Sparta, 111.
Sterling Steel
Casting Co.
Sauget, 111.
Sunoco Petroleum
255 E. Monroe
Kirk wood, Mo.
Sunoco Petroleum
1252 E. Road
Manchester, Mo.
Thompson Asphalt
Alton, 111.
Trautman Quarry
Pevely, Mo.
Troy Grain Co.
Troy, 111.
Vitro Products*
St. Louis, Mo.
Weber, Inc., Fred
O'Fallon Plant
O'Folloi 3 Mo.
GRID
SQUARE
#1008
#997
#849
#2448
#1095
#404
#789
#2281
#355
#1624
#1072
#118
EMISSIONS (LBS)
PART SOV NOV HC
A A
1,607 000
535 0 0 0
13,520 0 2,080 0
6,840 000
1,414 988 5,460 162
000 4,105
0 0 0 728
7,025 4 870 22
13,336 000
4,300 000
000 22,400
3,250 5,680 6,000 300
CO
0
0
0
0
697
0
0
123
0
0
0
400
(con4"in, -i
53
-------
TABLE 17 (continued)
COMPANY NAME
Weber, Inc., Fred
Festus Asphalt
Plant
Festus, Mo.
Weber, Inc., Fred
South Asphalt Plant
Lemay, Mo.
Weber, Inc. , Fred
North Asphalt Plant
Creve Coeur, Mo.
Western Litho
Plate
St. Louis, Mo.
Wirco Castings,
Inc.
New Athens, 111.
GRID
SQUARE
#467
#2245
#2147
#281
#1683
EMISSIONS (IBS)
PART SOV NOV HC CO
A A
3,590 6,305 6,660 333 444
13,230 41,350 43,680 2,184 2,972
16,565 93 18,600 465 2,635
240 14 2,880 72 96
1,050 0000
TOTALS - Ibs.
- Tons
265,264 116,508 298,113 211,698 39,219
133 58 149 106 20
* ASTERISK INDICATES DATA DERIVED FROM STATE AGENCY INVENTORIES
54
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1.3 EMISSION FACTOR VERIFICATION STUDIES
1.3.1 Background
Emission estimates are based on consumption or production figures from
which emissions are calculated using an emission factor. Emission factors
are averaged numbers relating emissions to consumption or process data. In
some cases, the relationship is direct and relatively uncomplicated. For
example, for every ton of bituminous coal burned, a total of 38S^ pounds of
sulfur dioxide is produced, where S^ indicates the sulfur content of the fuel,
on a weight percent basis. Thus, if a plant burns 100 tons of 3% sulfur coal
per hour, it emits
100 x 38 x 3 = 11,400
11,400 Ibs of S02 per hour. Since in this particular case the sulfur is
contained in the fuel and is converted virtually completely (95%) to S0?, the
numbers resulting from the use of the emission factor are quite accurate and
reliable.
If, on the other hand, we wish to determine the amount of oxides of
nitrogen produced by the same operation, a somewhat different situation
ensues. The emission factor for a boiler burning bituminous coal, as given
in the EPA publication AP-42 "Compilation of Air Pollution Emission Factors",
varies with both boiler type and size, from 6 to 55 Ibs. of NOv per ton of
coal. This is because the factors affecting NO,, production include flame
and furnace temperature, residence time of the combustion gases, rate of
cooling, amount cf excess air, as well as the amount of nitrogenous com-
pounds in the fuel. Thus, the emission factor of 18, which is applicable
to a pulverized coal boiler of this size, is an averaged value. Actual
values may depart significantly from the numbers obtained by such a factor.
In order to improve the accuracy of the emission inventory gathered at
55
-------
St. Louis, a number of representative sources were sampled and their stack
effluents analyzed. 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 catalyst recovery units in a petroleum refinery, and
cement kilns, known or suspected of being major sources of pollution.
The following sources were sampled in 1975 and 1976:
Illinois Power's Wood River Power Plant, Uood River, Illinois
Boiler No. 1, operated on gas
Boiler No. 1, 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
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
Hitric Acid Production Unit
General Motors Assembly Plant (Re-test), St. Louis, Missouri
Boiler No. 2, operated on coal
In Appendix B is a list of Plant and Point ID's and SCC codes for which source
test emission factors are being used in the RAPS inventory.
56
-------
1.3.2 Test Methods
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 C02, Excess Air and Dry Molecular Weight
4 - Determination of Moisture in Stack Gases
5 - Determination of Particulate Emissions
6 - Determination of S0? Emissions
7 - Determination of Nitrogen Oxide Emissions
8 - Determination of Sulfuric Acid Mist Emissions
1.3.3 Results and Discussion
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 (7)
indicated that values of 104 to 150% of the rated value can be obtained.
Grove (8) 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.
A comparison of measured and calculated flows is shown in Table 19. The
flow rate was calculated from known fuel consumption, fuel composition and
excess air data.
57
-------
TABLE 19. COMPARISON OF MEASURED* AND CALCULATED** FLOWS
Location Flow, SCFH
Measured Calculated
Wood River #1
Wood River #4
Highland Power
Stag Brewery
Monsanto
General Motors
10,086.750
17,981,280
1,386,070
1,394,990
1,687,655
1,598.005
8,237,263
13,089.200
910,920
782,900
1,563,000
1,434,847
+22.5
+37.4
+52.2
+78.2
+ 8.0
+11.4
* Using S-type Pitot tube, EPA Method 2
** Based on stoichiometry and excess air
One way of ascertaining the correctness of the data is by comparing the
mass flow S02 calculated from fuel consumption and sulfur analysis of the
fuel, on one hand, with the value obtained from stack gas flow and analysis,
on the other. The former is calculated according to Equation 1
W,n = Wr x 38 x S , (1)
OUo V*
where
WQn - weight of S09 produced, Ibs/hr
bU2 L
W - weight of coal consumed, tons/hr
S - % sulfur in coal, dry basis
This value should be equal to one obtained from Equation 2
cr\ ~ ^cn ^ We '
bUp oU? b
where
Ccn - Concentration of S09 in stack gas, Ibs/SCF
bU2 i
Qs - Stack gas flow rate, SCF/hr
58
-------
For example, the flow rate for Boiler #4 at Wood River was calculated thusly:
Assumed combustion reactions:
(1) C + 02 -* C02
(2) 2 H2 + Q9 -> 2 H20 (Excluded from calculation for dry flue gas)
(3) S + 02 + S02
(4) Oxidation reaction uncertain
Composition of Coal
Oxygen Required for
Lb-mols/100 Ibs Coal Combustion, moIs
Combustion Reaction
C
H2
S
°2
N2
H^O (moisture)
Ash
Chlorides
61.43%
4.38%
3.21%
9.67%
1.11%
11.82%
8.55%
0.02%
5.12
(2.19)
0.10
0.30
0.04
(0.66)
(1)
(2)
(3)
(4)
(2)
5.12
(1.09)
0.10
-.30
-
-
100.19% 6.01 mols
oxygen
Average Excess Air: 40% 2.40
Total 8.41
Corresponding Nitrogen @ 3.76 x 02 31.77
Dry flue gases per 100 Ibs. coal, Ib-mols:
C02 + S02 + 02 +
+ Air Nitrogen
5.12 + 0.10 + 2.40 + 0.04 + 31.77 = 39.43 lb-mols/100# coal
SCF
Ib-mols x 386
Ib-mol
= SCF
39.43 x 386 = 15,220 SCF/100# coal
@ 43 tons coal/hr. = 13,089,200 SCFH
59
-------
A comparison of results is shown in Table 20. As can be seen from Table
20, 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 Pitot measurements.
TABLE 20. COMPARISON OF S02 EMISSIONS BASED ON CALCULATED AND
MEASURED FLOW RATES
Location
WSQ - Weight of S0? Produced, Based on
AP-42 Emission Factor Calculated Flow Measured Flow
Wood River #1 (oil)
Wood River #4
Highland Power
Stag Brewery
General Motors
153 Ibs/hr
5245
414
75
479
178 Ibs/hr
5104
433
82
472
217 Ibs/hr
7035
658
125
546
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 manom-
eter.
Using the most reliable available results, experimental emission factors
were calculated for S02> NOX, CO, HC, and particulates for the sources tested.
These emission factors are compared in Table 21 with the standard emission
factors from AP-42.
Even though relatively few source tests have been run, certain conclu-
sions 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 the 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
60
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emissions factors for SQ^ for coal burning installations come out
to 38.755^ compared with 385^ suggested in AP-42.
3. The emission factors in Table 21 are applicable only to the specific
installations for which they were obtained. However, definite
patterns appear to exist, which seem to have more general validity:
a) Emission facotrs of NOw for large combustion sources (utilities)
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 instal-
lations which do not have precipitators. In the presence
of the latter, their assumed efficiency becomes the deter-
mining 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.
1.3.4 Sulfuric Acid Mist
An alternate method was used for determining sulfuric acid mist. The
current standard method for SCL 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 perioxide. There is
a filter between the first and second impinger to retain entrained particu-
lates. 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 S03 (sulfuric acid mist) will be retained
in the first impinger and filter (both of which are analyzed together).
However, Hillenbrand (9) found that substantial amounts of S0? are retained
in the first impinger, some of which is subsequently oxidized to SO.,, thus
62
-------
contributing to high results. For this reason a different technique was
used, which was first described by Goks^yr and Ross (10) and subsequently
verified by Lisle and Sensenbaugh (11). This method is generally referred
to as the Shell method, as it was developed in their laboratories. The
method 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 (Figure 10). The condensate is washed
out and titrated.
Data presented in references 10 and 11 indicate that adsorption of SO-
is essentially complete, repeatability is excellent, S0? in concentrations
up to 2000 pprn does not interfere, and a precision of +_ 0.3 ppm of SO., can
be readily attained.
The method was then evaluated in our laboratories. The results of the
evaluation indicate an average 100.1 +_ 6.5% recovery with no significant
interference from any of the variables tested (12).
The gas sampling train consists of water-cooled coil condenser maintained
below the dew point of sulfuric acid at 140°-194°F, followed by a fritted
glass plate and chilled impingers containing an isopropanol and hydrogen
peroxide mixture, followed by an impinger containing silica gel for drying.
This setup is shown in Figure 11.
The condensed sulfuric acid mist in the coil condenser is water washed
from the condenser. The final determination is made by titrating the solu-
tion with barium chloride, using a thorin indicator. Isopropanol is added
*"3 the solution to improve the rapidity with which the barium sulfate pre-
cipitates during titration.
Sulfur dioxide in the gas sample is oxidized to sulfur trioxide in the
impingers containing the hydrogen peroxide. Sulfur dioxide is then deter-
mined by titrating the hydrogen perioxide solution with barium chloride,
using a thorin indicator.
The SO., concentrations of large and small boilers were investigated
first. The concentrations ranged from 2.7 to 44.3 ppm, well within the range
indicated by other investigators. As indicated in Figure 12, there appears
63
-------
Stopper
Gas Sample
In
Spherical
Boll
Joint
Spiral Tube
Grade 4
Sintered
Glass
Disc
Gas Sample
Out
FIGURE 10. SULFUR TRIOXIDE COLLECTOR
64
-------
REVERSE-
TYPE
PITOT TUBE
1'' i
VELOCITY
PRESSURE
GAUGE
ORIFICE
GAUGE
FIGURE 11. SULFURIC ACID MIST SAMPLING TRAIN
65
-------
so
3 H
2 -
1
I
4
I
10
12 °7o 02
FIGURE 12. PERCENTAGE CONVERSION OF S02 TO S03 IN UTILITY BOILERS
66
-------
to be a marked dependence on excess oxygen. The percentage of SCL 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 correlation with the sulfur content of the fuel nor did there
appear to be any marked effect of boiler capacity on the amount of concen-
tration of SO., produced. Data are presented in Table 22.
The RMS average SO, emission appears to be about 1.85% of the SCL emis-
sion. This factor will be incorporated in the data handling system output
program, which will report SCL emissions based on the corresponding SCL
emissions. Using the current figures for SCL, this amounts to an annual
emission of 22,585 tons of SCL per year for the St. Louis AQCR.
1.3.5 Particle Size Distribution
Particle size testing was performed with an Andersen Stack Sampling
head coupled with the apparatus used for the standard EPA method for particu-
lates. The Andersen is a fractionating inertial impactor which separates
particles according to aerodynamic characteristics.
The Mark II sampling head consists of a stainless housing plate holder
and nine jet plates. The plates have a pattern of precision-drilled orifices.
The nine plates, separated by 2.5 millimeter stainless steel spacers, divide
the sample into eight fractions or particle size ranges. The jets on each
plate are arranged in concentric circles which are offset on each succeeding
plate. The size of the orifices is the same on a given plate, but is smaller
for each succeeding downstream plate. Therefore, as the sample is drawn
through the sampler at a constant flow rate, the jets of air flowing through
any particular plate direct the particulates toward the collection area on
the downstream plate directly below the circles of jets on the plate above.
Since the jet diameters decrease from plate to plate, the velocities increase
such that whenever the velocity imparted to a particle is sufficiently great,
its inertia will overcome the aerodynamic drag of the turning airstream and
the particle will be impacted on the collection surface.
The Mark III is identical to the Mark II except the location of the
offices in the plates have been modified to permit the use of a special
rillection substrate (glass fiber in our tests). This permits lighter tare
67
-------
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weights for gravimetric analyses and a collection of material for chemical
analysis. Figure 13 illustrates the Andersen sampling head and an exploded
view of the plate holder and plates.
At one location fourteen tests were performed to evaluate variations
of testing methods consisting of placing the Andersen in-stack, out of stack
(in oven), using Mark II plates and Mark III plates with filters. The main
objective of these tests was to arrive at a testing arrangement to be used
on all subsequent tests. However, there was no clearcut single method which
proved better than the other. Each of these methods has its advantages which
may make it desirable for any one individual test.
Sampling in the stack avoids any problems with extracting a sample and
having some of it deposited in the probe. Also, the sample head is at the
same temperature as the stack gases which avoids any problems of condensation.
In-stack sampling, however, means the impaction surface is vertical and is
subject to having the sample dislodged in handling. When sampling must be
done vertically in a duct, from the top down, this method cannot be used.
Sampling with the Andersen sampler in the sample oven at the end of a
heated probe affords much better handling. The sample head can be kept
vertically with the plates horizontal at all times. The sample head can be
clamped in place and not threaded on to the probe, thus minimizing handling.
Isokinetic sampling rates can be determined more readily when the
Andersen sampler is in the oven since the probe in the stack has a Pitot
attached. There are two problems with sampling this way: (1) the oven can
be heated only to 350°F, which may not be as high as the temperature in the r
stack; and (2) larger particles tend to be deposited in the probe, which
lowers the weight of the deposit on the first two plates.
Parallel sampling with both the Mark II plates and the Mark III plates
with filters indicated that there was no significant difference in the weight
of sample obtained or the size distribution between these two methods. If
the Mark II model is used, the number of tests is limited by sets of plates
available. With the Mark III plates and filters more runs can be performed
by changing the filters between runs with the available time being the only
Jife
constraint on the number of runs. More care must be taken in assembling the
69
-------
AIR FLOW
FIGURE 13. ANDERSEN STACK SAMPLER
70
-------
Mark III, since the filters are pre-cut to match the plates and must be
properly aligned to avoid blocking any holes.
As a result of these comparison tests, it was decided that testing
would be performed with the Mark III plates and filters and that the
Andersen sample head would be placed in the oven for ease in handling
and subsequent analysis.*
TABLE 23. 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
Weight
>7y
22.5
26.6
37.4
14.3
13.9
5.0
29.0
% vs.
3-7y
22.8
18.9
16.0
24.4
0.9
8.0
38.4
Particle Size
l-3y
18.5
10.0
7.6
18.5
22.0
41.0
14.2
0.5-ly
8.3
12.7
18.3
9.2
18.3
34.2
8.4
<0.5y
27.9
31.8
20.7
33.6
36.4
11.0
10.0
Most measurements at boiler stacks indicate a bimodal distribution,
one peak at 3-7 micron and the other at less than 1 micron. The test of a
glass furnace at Owens - Illinois, however, indicated a single peak at
approximately ly.
re details, see Reference 12.
71
-------
2.0 DATA HANDLING
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.
2.1 CODING PROCEDURES
The input and output programs were 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 appropriate manipulation as part of the output program. The
advantage of this system is its flexibility, since new or additional emissions
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 is made up for each source, which details the entry method
(see Appendix A). A uniform coding form is then made out, which serves as a
basis for the keypunchings of computer cards.
Five different types of input cards are used: Type^ I is used to record
hourly fuel consumption process information data. Type II is for recording
stack gas pollutant concentration and stack gas volumes; hourly values of
concentration and flow are on alternate fields. Type III is used to record
the annual data and corresponding patterns of minor sources. Type IV is used
for non-criteria pollutants (annual data). Finally, Type V is used for the
recording of special emission factors for non-criteria pollutants, heat
emission, etc. The various coding forms are shown in Figures 14 - 17.
The following information is recorded:
72
-------
Card 1
STATE CODE
COUNTY CODE
PLANT ID
PLANT NAME
STREET ADDRESS
ZIP CODE
SIC
OWNERSHIP CODE
Card 2
STACK ID
UTM ZONE
UTM COORDINATES
AREA ID
TEMPERATURE
STACK HEIGHT
BOILER DESIGN CAPACITY
STACK DIAMETER
FLOW RATE
Card 3
CONTROL EQUIPMENT AND EFFICIENCY
FOR ALL CRITERIA POLLUTANTS
Card 4
POINT ID
FUEL HEAT CONTENT
SULFUR
ASH
ESTIMATION METHOD
POLLUTANT
UNITS
TIME INCREMENT
START DATA
START HOUR
STOP DATE
STOP HOUR
THERMAL EFFICIENCY
SCC NUMBER
Card 5/Type 1 - Hourly Process Data
HOURLY DATA
73
-------
Card 5/Type 2 - Hourly Emission Data
HOURLY DATA
Card 5/Type 3 - Annual Pattern
ANNUAL DATA
OPERATING PATTERN
Card 5/Type 4 - Non-Criteria Pollutants
ANNUAL DATA
EMISSION FACTOR
Card 5/Type 5 - Emission Factors
EMISSION FACTOR
Card 6
COMMENTS
74
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2.2 EDITING OF DATA
The data are edited at several levels. The coding sheets are edited
visually for numerical errors. The punched cards are verified for keypunch
errors. They are then checked by editing programs, which check for correct
syntax and proper limit of the entered values.
Every card is 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 is checked for proper ID codes for State (14 or 26), County,
Plant, and City (>0).
Card II is checked for Stack No. (>0), UTM zone (15 or 16) UTM
coordinate 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 is examined for proper Method code (0-8), Pollutant name
(both criteria and non-criteria, including heat emissions)
appropriate Unit and Time Intervals and legal SCC number.
Card V is checked only if it is Type 3, when the program checks
for correct syntax* of operating patterns.
Card VI is checked only for "housekeeping data."
2.3 RAPS EMISSION INVENTORY CALCULATIONS
The data entered into the memory bank of the Univac 1100 at EPA-NERC
are 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 a few exceptional cases are emission concentrations recorded.
Data for major sources are calculated on an hourly basis; the inputs are
usually based on hourly values, though in some cases only shift, daily,
weekly or even monthly data are available. In all these cases an average
hourly value is calculated by the computer.
c" refers to a finite system of rules determining a rational
jciure of operating patterns.
79
-------
Annual data are recorded for minor sources (producing between 1000 and
100 Tons SO,;, per year). The operating pattern (e.g. 5 days per week, 8 hours
per day, closed for Holidays and a two week vacation period) is also recorded.
An hourly average value is calculated by the computer based on the actual
operating hours for each facility.
Base data are recorded in four different ways:
1. Fuel consumption (in tons, 10 gal, 103 or 106 cu/ft)
3
2. Steam production (10 Ibs steam per hour)
3. Power production (kw or mw)
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 explicity or by
reference to AP-42, "A compilation of Air Pollution Emission Factors".
Typical sample calculations for mass emission of S02 in Ib/hour are
shown below. All data are recorded in the units in which they are supplied;
conversions to metric units, where desired, are performed by the computer as
part of the output routine.
80
-------
Example 1
SCC 1-01-002-02 A coal fired utility boiler
The following pertinent data are recorded: Example
FR .... Firing rate, tons/hour 30
S Aver. Sulfur analysis, % 3.27
Calculation Method
(referring to AP-42) 3
Calculation:
Ec-« = FR x 38 x S where
ESQ = mass emission of S02 in Ibs/hour, FR = firing rate (in tons/hour)
and S = sulfur analysis in percent.
In this case
= 30 - 38 x 3.27 = 3,727.8 Ibs/hour
81
-------
Example 2
SCO 1-01-005-01 An oil-burning utility boiler
The following data are recorded:
Example
FR ---- Firing rate, gal x 10 /hr 1.9
S ---- Sulfur analysis, % 0.40
Calculation Method 3
Calculation
ESQ = FR x 144 x S where
ESQ = mass emission of SOp, Ibs/hour, FR = firing rate, 10 gal/hour,
and S = sulfur analysis in percent.
In this case,
E - 1.9 x 144 x 0.4 « 109.4 Ib/hr
If the firing rate is expressed in units other than 10 gal/hour, such
as, for example, 10 gal/day, the computer will first convert this to
an hourly rate, then proceed with the above calculation.
82
-------
Example 3
SCC 1-01-004-01 A residual-oil burning utility boiler
Only Power output is available
In some cases the firing rate has to be calculated from secondary data.
In this case, the power output in megawatts per hour is the primary
input.
The following data are recorded:
Example
P ---- Power output, megawatt-hour/hour 57
H .... Heat content of fuel, 106Bt-u/103gal 152.30
TE ---- Thermal efficiency, overall, percent 20.1
S ---- Sulfur analysis of fuel % 3.47
Calculation Method 3
Since the theoretical conversion of MW hr to Btu is:
1 MW hr ~ 3.413 x 106 Btu,
the amount of fuel consumed per hour is
413 x
_____
- P x 3.413 x 106 x 100
____
Method 3 then gives the mass emission of S02 as
F - P x 3.413 x 1Q8 . .
fcS02 " H x TE x ID/ x b,
or in this case,
g
57 x 3.413 x 10
-
2 152.3 x 10b x 20.1 S(yhr
83
-------
Example 4
SCC 1 - 02-002-01 A coal-fired utility boiler.
Only power, output is available.
This case is similar to example 3, but coal is the fuel. The recorded
data are:
Example
P .... Power output, megawatt hours/hr 507
H Heat content of fuel, 106 Btu/Ton 22.12
TE .... Thermal efficiency, overall, percent 38.1
S Sulfur analysis of fuel 2.5
Calculation method 3
Again, the theoretical conversion rate is:
1 MWhr = 3.413 x 106 Btu
Therefore, the amount of fuel consumed is
FR P " V"TE '°6 " '°2 *»» P" hour.
and the mass emission of SOp according to AP-42 (Method 3) is
F - P x 3.413 x 108 ,««.
hS02 ~ H x TE x 38 x 5
In our example
- 507 x 3.413 x 1Q8 x 38 x 2.5 _ 1Q ,-n, ,. ,.
= 2 = 19,505 Ib/hr
2 22.12 x 10° x 38.1
84
-------
Example 5
SCC 1-02-002-09 An industrial coal fired boiler
Only steam production data are available.
The following data are recorded:
Example
SP .... Steam production, Ib/hour x 10 42
H Heat content of fuel, 106 Btu/ton 21.39
TE .... Thermal efficiency (boiler only), % 80.01
S .... Sulfur analysis of fuel, percent 2.9
Calculation method 3
The conversion from steam production to the weight of coal fired (FR) is
FR . SP x 12£02 x 100 tons coa]/hr
(The conversion factor 1202 converts pounds of steam (300 psi, saturated)
to BTU. The thermal efficiency factor, as recorded on the cards, Includes
an adjustment to the actual steam conditions.)
The mass emission of sulfur dioxide, using method 3 (AP-42) is
_ SP x 1202 x 100 x 38 x S
ES02 TE x H
In our example
= 42xl03xl202xl02x38x2.9 _ ,,K n
80.1x21.39x106 325'°
85
-------
Example 6
SCC 3-01-023-99 Sulfuric Acid manufacture. S(L concentration and
exhaust gas volume are avajlaDle.
In this case, the concentration of SOg in the stack gases in con-
tinuously recorded. The flow of stack gases is nominally constant
but no record is available.
The recorded data are
Exampl e
C .... S02 concentration, ppm (vol) 1400
F .... stack gas flow, cu ft/min x 10 31
Thus, hourly mass emission of S02
F x 60 x C x 0.178
,, cn ,.
Ibs S0/hr
2 1Q
S0 6
The factor 0.178 is the weight of 1 cu ft of S02 (in Ibs) under standard
conditions.
In our example
r - 31 X 103 X 60 x 1400 X 0.178 _ .-, , 1Kc/f,miy.
Ecn = ? = 463*5 Ibs/hour
S02 1Q6
86
-------
3.0 PRESENTATION OF DATA
The inventory data are available in a number of formats. Several of
these were designed to parallel NEDS formats: Point Source Listings, Annual
County, State and AQCR Summaries. Others are unique to RAPS, such as Hourly,
Daily and Monthly Point Source listings. In addition, a basic modeler's tape
output is available, which contains information of particular interest to
modelers in an unlabeled 80 character per record format.
Examples of the various output formats are shown in Figure 18 to 24.
Figure 18 shows a complete point source listing, giving all descriptive in-
formation for a single source, as well as hourly emissions for five criteria
pollutants. Figure 19 gives similar descriptive information, but is a sample
of a daily summary for pollutants. Figure 20 is similar giving the annual
output of the same point source; Figure 21 shows a modeler's tape. It shows
hourly outputs for five pollutants, Particulates, S02, NO.,, hydrocarbons, and
CO in pounds per hour as well as stack gas flow in SCFM, for three points for
Julian day 75 001 (1 Jan 1975). The plant is uniquely identified by State
(26), County (1680) and number (03). Each of the three points is further
identified by another number (01-03), Other information contained on the
same line as grid ID (74) SIC code (4911), UTM coordinates in Zone 15 (688.37,
4270.23), stack height (700 ft.), stack diameter (20.5 ft.) and stack temp-
erature (285°F). The information is repeated for each point. Daily totals
for the plant appear on the last line. Figure 22 gives total annual emissions
for a county (Madison County, 14-4680) for all criteria pollutants, broken
down into major categories. Figure 23 gives all criteria pollutants for the
major categories for a State, (Illinois). Finally, Figure 24 shows a listing
of emissions for the whole Air Quality Control Region 70, again aggrated into
the major source categories.
37
-------
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74
26168003 01
95.44
95.1o
95.16
94.89
95.44
95.72
95.44
95.44
96.27
96,54
96.62
96.82
9o,82
97,10
117.23
117. 23
118,61
118,06
117.78
118,34
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26166003 02
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26168003 03
243.84
244,39
244.39
244.39
242. 19
244.12
239,43
238.60
238.33
238.05
241 .64
242.46
243.57
236.40
243.02
240,81
232.26
221.78
222.88
234.74
238.60
239.43
239.71
238.88
109909
255.08
75001 7
744911 688.374
14546.08
14504.04
14004.04
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14672.20
14714.25
14756.29
14756.29
14756.2°
14798.33
17867.30
17867.30
18077.50
17993.42
17951.38
18035.46
18077.00
17951 .38
17825.26
17909.34
5001 JPRINT
270.23 70020.5
4284.98
4272.60
4272.60
4260.21
4284.98
4297.37
4284.98
4284.98
4322.13
4334.52
4346.90
4346.90
4346.90
4359.29
5263.34
5263.34
5325.27
5300.50
5238.11
5312.88
5325.27
5288.11
5250.96
5275.73
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37248.06
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37206.02
36491 .33
36365.21
36323.17
36281.12
36827.65
36953.78
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36028.88
37037.86
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33968.89
35776.64
36365.21
36491.33
36533.37
36407.25
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10947.76
10972.52
10972.52
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10749.61
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12-16-76
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42.85
42.73
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42.85
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42.85
43.22
43.35
43.47
43.47
43.47
43.59
52.63
52.63
53.25
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109.48
109.73
109.73
109.73
108.73
109.60
107.50
107.12
107.00
106.88
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108.86
109.35
106.13
109.11
108.12
104.28
99.57
100.07
105.39
107.12
107.50
107.62
107.25
3706,26
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142.83
142.42
142.42
142.01
142.83
143.25
142.83
142.83
144.07
144.48
144.90
144.90
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175.44
175.44
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176.27
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FIGURE 21. MODELER'S TAPE
91
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92
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EOF!65
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N I* ****
FIGURE 23. POINT SOURCE SUMMARY REPORT - STATE (ILLINOIS)
93
-------
********************************************************
* IVm-j: I'UINI "vUUKGF SUMMARY FMir.nilWU hl-PUKl v? M // *
********************************************************
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FIGURE 24. POINT SOURCE SUMMARY REPORT - AQCR 70
94
-------
REFERENCES
1. Collis, R. T. H., and D. R. Scheuch, The Regional Air Pollution Study:
A Prospectus, Stanford Research Institute, Menlo Park, CA. Contract
No. 68-02-0207, APTD-1122.
2. Littmarij, F. E, , et a], A RAPS Preliminary Emission Inventory, Stanford
Research Institute, Menlo Park, CA. Contract No. 68-02-1026. EPA
450/3-74 -030,-
3, Guide f.-'i i'-O'iip-l-'ng :> Comprehensive Emission Inventory, MDAD, APTD I1 35,
4, Ditto, - '".,,: '^, al.. We'jnled Sen,. Jee' :-- J.v ..--.e-css o^ 5,.'!fi,r Ox-;c:es: CHESS T970-/1, U.S. Environ-
.tiertci! ""c^L^irs Acen-'i- , EPA 650. ': -74-004.
o^?"ton. ~ -, ^u:ji*r:-;t3 ". ;un o^ iij:ack Qa^ Flow,, J, Air Poll, Contrc"
3.. Grc^f C j.. -'uj i-i, '.-. , S;nitn Pitot Tube Errors Due to Misalignment
ana Nori-s*..-e6iTl ~:ned Flow, Stack Sampling News, 1974.,
9. hrl lenbr-ijid. c-:f. al , l!Cheinical Composition of Partlculate Air Pollutants
frort1 cos? : } -Fue1 Combustion Sources", Battelle Columbus Labs, March 1973,
EPA-K2-743-2T6, PB219.009.
10. Goks0yr, H. , and K. Ross, "Determination of Sulphur Trioxide in Flue
Gases", j. Inst. Fuel 35, 177 (1962).
11. Lisle, E. S.. and J. D. Sensenbaugh, "Determination of Sulfur Trioxide
and Acid Dew Point in Flue Gases", Combustion 36, 12, (1965).
":?. Littman, F, E-a et al , "Sulfur Compounds and Particulate Size Distri-
b'j-^-M Inventory", Rockwell International, Air Monitoring Center, Creve
.-i , MO, Contract No. 68-02-1081-T056. EPA 600/4-77-017.
95
-------
APPENDIX A
96
-------
GENERAL INSTRUCTIONS
1. Cards 1, 2 and 3 need to be made out only once; after that a point
source is fully identified by the first 15 spaces on each card,
2. A card 4 must precede each set of cards 5. The Time Interval (TI)
on card 4 defines the period (hour, shift, date, etc.) covered by
each entry is in card 5. Similarly, the Start Date and Stop Date
entries define the time covered by the entries in card 5. All
hours included between Start Date and Stop Date must be accounted
for as one or more ca^o 5's.
3, If a value is repeated more than once, the spaces under R may
be used to indicate the number o* repetitions, followed by the
value which is to be repeated: e.g.,
for 560,2 repeated 4 times
4. Daily data, indicated by a p_ in time inters/a* .-p-ace on Card 45
may be repeated just l.ke hourly data, with the number of
repetitions under R, -V>so; a f.;11 month of data may be entered
on a single sheet by using the start and step date to span
the period and accounting for <-:-ach day with '"dividual entries
or with repeats.
5, On each data card (ca- ,: '>} the type of data (Process data,
Emission data, etc.) a.v the action (Add or Change) should be
entered.
6. Any appropriate comment.^ <:an be entered on card 6, which should
be preceded by both a card 4 and card 5.
7. The sequence number will be entered just prior to delivery for
key punching.
97
-------
AMAX Zinc Co. - Sauget, Illinois (618) 274-5000
Illinois - 14, St. Clair Co. - 6900, Sauget - 9010, Plant - 38
Location: Rt. 3, just south of Poplar Street bridge
Personnel: Ken Carpenter - engineer - our contract
0. Gorman - plant manager
Data Acquisition: by mail monthly
Data Description: table of hourly S02 stack concentration
Data Handling: 1) On each coding form, record appropriate ID's,
method-!, pollutant-SO.., units-ppm, time
interval of data, appropriate Julian dates,
SCC number and action. Data type - 2
2) Data is entered piggy-back style with the S02
concentration first followed by the flow rate - 31.0
for 31,000 s.c.f.m., e.g.: R R
I hl*|0|0io| I I I |
3) If the entry is repeated, the number of repe-
titions is only necessary in front of the
concentration, not the flow rate.
98
-------
AMOCO Oil Refinery - (618) 254-7351
Illinois - 14, Madison Co. - 4680, Wood River - 8520, Plant - 04
Location: Int. 270 East, north on 111. 3 to Wood River
Personnel: Mr. Bob Yoder, engineer - our contact
Mr. Ed Sullivan, plant environmental engineer
Mr. F. K. Webb, plant manager
Data Acquisition: monthly, by mail
Data Description: daily totals of fuel consumption by unit, fuel
consumption at power house, thruput for fluid
cracker, SO,, emissions at flares
Data Handling: 1) See attached sheets for explanation of each unit.
2) On each coding form; record appropriate ID's;
current average fuel analysis; method - 3,5 or 8;
pollutant - S0X; data units - CFT, GAL, BBL, LB;
time interval of entered data; appropriate Julian
date, SCC number, and action
3) Most data are daily values, therefore a whole month
may be put on one sheet by recording each daily
amount.
4) Gas analysis heat content is 106 Btu/103ft3, e.g.:
992.5 Btu/ft3 -* 0.99 106 Btu/KTft1*
Data Handling - by Unit:
Power Station: Boilers 1, 3, 5 burn all four fuels simultaneously
Boilers 2, 4, 6 do not burn slop oil
Boiler 1 Boiler 2 Boiler 3 Boiler 4 Boiler 5 Boiler 6
% of Total 12.2 14.2 15.0 17.2 21.0 20.4
% of Slop Oil 25.3 31.1 . 43.6
For each boiler, multiply given percentage by daily fuel consumption
figure and record. Use separate sheets for each fuel and each unit.
For hydrogen gas and slop oil, determine a monthly average and use
this figure rather than the daily numbers. For these fuels use
the start and stop dates to cover the whole month with one coding
form.
99
-------
Determine a monthly average fuel analysis for each fuel. For
gaseous fuels, the volumetric percent of H^S must be converted
to weight percent sulfur. Use the conversion:
1% H2S = 1.577 %S
A special emission factor (method 5) will be used with the
gaseous fuels:
F = 1.069 S
#5 Pipe Still (stack 07)
This unit burns No. 5 PS AOG (Absorber Off-Gas) Refining Gas
and Asphalt. The No. 5 PS AOG is assumed to be 679,100 cf/day
with a heat content the same as refinery gas.
The Refinery Gas usage is determined by subtracting 679,100
from the given daily amount of Refinery Gas. This will have
the same analysis as the gas used at the Power Station.
The asphalt usage is converted from barrels/day to thousands
of gallons/day and recorded. Asphalt is recorded as if it
was fuel oil. Use method 5 for asphalt.
Reformer and Desulfurizer (stacks 09. 10. 12, 14)
Stack #14
The amount of Ultraformer AOG (Hydrogen Gas) burned at the
Desulfurizer is determined by using the graph in the file
and an average Naptha flow for the month. This will give an
average daily rate for the entire month. The fuel analysis
and the special emission factor is the same as for the
Hydrogen Gas at the Power Station. This is also method-5.
Stacks #15
A monthly average is also used for the reformer furnaces.
This is determined by subtracting the gas used at the de-
sulfurizer and the hydrogen gas used at the power station
from the amount of AOG produced and then determining a
monthly average figure and dividing it equally among the
three furnaces.
Fuel oil from tank 420 is also used in the reformer furnaces.
This is assumed constant and is recorded as 1.4 x 10^ gallons
per day on each of the three furnaces. Method-3 is used and
the amount is recorded as a daily figure.
100
-------
Asphalt Plant
Furnace (Stack 17):
This is figured by assuming fuel is burned 50% of the time at
6800 cubic feet/hour. This number will always be recorded as
$1.6 x 103ft3 per day. Gas burned here is refinery gas; same
analysis as at power station, method-5.
Fume Burner (Stack 23): SCC 30600999
Refinery gas is burned here also, at two different rates
during the year which will be specified on the monthly
data; either 62.4 x 10J ftvday or 230.4 x 103 ft3/day.
Method-5.
South Flare (Stack 21): SCC 30600999
Until notified differently, this flare burns 15.5 x 103 ft3/day
of Refinery Gas and emits 10.0 tons per day SOp every day.
For the straight S0? emission there is no fuel analysis,
Method-8, units - TflN.
B-2 Flare (Stack 22): SCC30600999
This depends upon which processes are in operation, which will
be specified in the data.
A-198 emits 9475 pounds S02 per day
A-202 emits 4300 pounds S02 per day
A-463 emits 5532 pounds S02 per day
A-464 emits 4820 pounds S0£ per day
Depending upon what is in operation, the daily total is
divided by 24 and the hourly emission in pounds is entered.
Some or all of these emissions will be eliminated if
sufficient DEA is available to AMOCO.
BOT Flare (Stack 24)
When the A-150 process is running, as indicated on the data
sheets, this will have an emission of 2379.5 pounds per day
S02-
This also may be eliminated by having enough DEA.
101
-------
FLUID CRACKER (Stack 25): SCC 306Q02P1
This is recorded directly as thousands of barrels per day
from the data. There is no fuel analysis, method-5, units - BBL.
Special Emission Factors
Refinery Gas F = 1.069 S
Hydrogen Gas F = 1.069 S
Slop Oil F = 144 S
Asphalt F = 164 S
No. 5 PS AOG F = 1.069 S
Fluid Crackers F = 347.5
102
-------
CLARK OIL REFINERY (618) 254-7301
Illinois - 14, Madison Co. - 4680, Hartford^- 9020, Plant - 05
Location: Int. 270 east, north on 111. 3 to Hartford, right on Hawthorne
Personnel: Mr. Seward VanPetten - Manager, Construction - our contact
Data Acquisition: monthly by mail
Data Description: monthly totals of oil and gas consumption copies
of any fuel analyses
Data Handling: 1) Using percentages on file, distribute total fuel
consumption to each process heater and power
house boiler. Fluid Crack thruput remains unchanged.
2) Determine an average fuel analysis for the month,
unless there is a definite- time period given with
each fuel analysis.
3) On each coding form, record appropriate ID's, current
fuel analysis, method-5 for gas, 3 for oil, pollutant
- S0X, data units - CFT for gas, GAL for oil, time
interval - D, appropriate Julian dates, SCC number
and action.
4) For Fluid Cracker there is no fuel analysis, method-3,
units - BBL, time interval - D.
B) In data spaces, divide monthly figures for each unit
by days in the month and record the number of days
under R followed by the daily amount. For the fluid
cracker record the number of days under R followed by
the daily thruput.
6) Gas should be recorded as thousands of cubic feet,
oil as thousands of gallons and fluid cracker thru-
put as thousands of barrels.
7) On each data card (card 5) indicate type of data
and action.
Data Prnctssing: For the oil usage and the fluid cracker thruput the
;,,ered figure will be used directly with AP-42 emission factovs
determine emissions. For gas usage, a special emission factor
: 1 be entered separately and used with the entered data to
Jeternrine emissions. F = 0.98 S.
103
-------
GENERAL MOTORS ASSEMBLY (314) 679-5152
Missouri - 26, St. Louis City - 4280, St. Louis - 4280, Plant - 06
Location: 3809 N. Union Street
Personnel: Dick Dumont - Engineer - our contact
Data Acquisition: by mail, monthly, from the plant
Data Description: Steam charts from 4 boiler, coal analysis
Data Handling: 1) On each coding form, record appropriate ID'S, cur-
rent fuel analysis, method-3, pollutant-S^X, data
units - LBS, time interval of entered data, appropriate
Julian date, efficiency, SCC number, and action-A.
Data type-1.
2) In data spaces, record hourly thousands of pounds
of steam produced. Readings are taken at the
half hour, i.e. 7:30 for the 7-8 AM reading.
104
-------
HIGHLAND ELECTRIC AND LIGHT (618) 654-4101
Illinois - 14, Madison Co. - 4680, Highland - 3340, Plant - 22
Location: Int. 70 east, south on 111. 143, cross U.S. 40, right
after crossing R.R. tracks, right on 9th street
Personnel: Mr. Allen Schulte - plant superintendent - our contact
Mr. Oliver Bishop - city manager
Data Acquisition: pickup monthly from the plant
Data Description: Xerox of monthly log and daily log sheets
Data Handling: 1) On each coding form, record appropriate ID's,
current coal analysis, method, pollutant, data
units of measure, time interval of entered data,
appropriate Julian date, thermal efficiency, SCC
number, and action.
2) In data spaces, record Kilowatt (KW) production
every other hour and then repeat each number for
the next hour.
3) On each data card (card 5) indicate type of data
and action.
4) The sequence number will be entered just before
coding forms are sent out for keypunching.
-------
MONSANTO - WGK - Sauget, Illinois (314) 621-4075
Illinois - 14, St. Clair Co. - 6900, Sauget - 9010, Plant - 06
Location: Monsanto Ave., Sauget, Illinois
Personnel: Clarrie Buckley - Environmental Specialist - our contact
Paul Hodges - Corporate Headquarters
Data Acquisition: by mail from the plant monthly
Data Description: Tabulated, hourly, steam rate from power house and
hourly emission rate of S02 from a contact sulfuric
acid plant.
Data Handling: 1) On each coding form, record appropriate ID's,
method - 3 or 8, pollutant - S0X, data units -
LB, time interval of entered data, appropriate
Julian date, SCC number and action. Data type - 1.
2) In data spaces, record hourly steam load or
hourly SOp emissions.
106
-------
MONSANTO - Sauget, Illinois (314) 621-4075
Illinois - 14, St. Clair Co. - 6900, Sauget - 9010, Plant - 06
Location: Monsanto Ave, Sauget, Illinois
Personnel'; Clarn'e Buckley - Environmental Specialist - our contact
Paul Hodges - Corporate Headquarters
Data Acquisition: by mail from the plant monthly
Data Description; Tabulated, hourly, mass emission rates of S02 from
6 boilers and a contact sulfuric acid plant.
Data Handling: 1) On each coding form, record appropriate ID's,
method-8, pollutant-S$X, data units - LB, time
interval of entered data, appropriate Julian date,
SCC number and action. Data type - 1.
2) In data spaces, record hourly emission values.
107
-------
MONSANTO - 1700 S. Second Street (314) 621-4000
Missouri - 26, St. Louis City - 4280, St. Louis - 4280, Plant - 23
Location: 1700 S. Second St., St. Louis
Personnel: Al Peterson - Environmental Specialist - our contact
Paul Hodges - Corporate Headquarters
Data Acquisition: by mail from the plant monthly
Data Description: Steam charts from K street boiler (coal) and Geyer
St. Boilers (when using oil), daily ratio of high
to low sulfur coal burned. The boilers on gas are
included for heat inventory.
Data Handling: 1) On each coding form, record appropriate ID'S, cur-
rent fuel analysis, method-3, pollutant-S^X, data
units - LBS, time interval of entered data, approp-
riate Julian date, SCC number and action, and ef-
ficiency.
2) In data spaces, record hourly thousands of pounds
of steam produced. Readings are taken at the half
hour, i.e., 7:30 for the 7-8 am reading.
Charts are changed at 11:30 pm. Last hour of day
is on next day's chart.
Data Processing:
1) The daily fuel analysis must be calculated from the
high/low ratio and the current coal analysis.
108
-------
ILLINOIS POWER - Baldwin (618) 785-2294
Illinois - 14, Randolph Co - 6460, Baldwin - 9000, Plant 01
Location: South on 111. 3 to Red Bud, continue straight on 111. 154
to Baldwin, left on 5th Street
Personnel: Mr. Kane - Plant Manager
Mr. Mason - Plant Engineer - our contact
Mr. Jim May - Our contact in Decatur
Data Acquisition: by mail, from Decatur, weekly. We supply stamped
envelopes with return addresses.
Data Description: Xerox of daily computer log sheets, copy of monthly
coal analyses.
Data Handling: 1) On each coding form, record appropriate ID'S, current
coal analysis, method, pollutant, data units of
measure, time interval of entered data, appropriate
Julian date, SCC number and action.
2) In data spaces, record hourly coal usage, as is.
109
-------
UNION ELECTRIC
(314) 621-2637
Labadie:
Sioux :
Meramec:
Venice:
Cahokia:
Ashley:
«
Missouri-26, Franklin Co - 1680, Labadie-5200, Plant
Missouri-26, St. Charles - 4160, W. Alton, 5220, Plant
Missouri-26, St. Louis Co. - 4300, Plant
Illinois-14, Madison Co. - 6900, Venice - 7760 Plant
Illinois-14, St. Clair Co. 6900, Sauget - 9010 Plant
Missouri-26, St. Louis City - 4280, St. Louis - 4280, Plant
03
01
10
03
14
38
Location: Data from main office - 1901 Gratiot Street, St. Louis
Personnel: Gerald Smith - Environmental Manager
John Wooten - Engineer - our contact
Data Acquistiion: by mail from main office
Data Description:
Data Handling: 1)
2)
Computer printout for Meramec, xerox of UE generation
summary for Venice, Sioux, and Labadie and Cahokia.
Boiler hours for Cahokia.
On each coding form, record appropriate ID's,
current coal analysis, method, pollutant, data
units of measure, time interval of entered data,
appropriate Julian date, thermal efficiency, SCC
number, and action.
In data spaces, record megawatt (MW) production
for each hour, as is, for Labadie, Sioux, and
Meramec. For Venice, when the production is
95 MW or less, record as if #7 was only running
and when more than 95 MW the production is equally
split and recorded as noted on the data worksheet.
When oil is burned at Venice, this is also noted
on the data worksheet. Cahokia stack output is
determined from plant output and boiler service
charts.
no
-------
STAG BREWERY (CARLING) (618) 234-1234
Illinois - 14, St. Clair County - 6900, Belleville - 0320, Plant - 01
Location: 1201 West E. Street, Belleville
Personnel: Don Hoageson - Chief Engineer, our contact
Data Acquisition: Steam charts from 2 boilers
Data Handling: 1) On each coding form, record appropriate ID's, current
fuel analysis, method-3, pollutant-S^X, data units -
IBS, time interval of entered data, appropriate Julian
date, efficiency, SCC number, and action - A. Data
type - 1.
2) In data spaces, record hour-ly thousands of pounds
of steam produced. Readings are taken at the
half hour, i.e. 7:30 for the 7-8 am reading.
Ill
-------
PVO INTERNATIONAL - 3400 N. Wharf St. (314) 621-4345
Missouri - 26, St. Louis City - 4280, Plant - 58
Location: Wharf St. at Angelrodt, St. Louis
Personnel; Don Mueller - Engineering manager - our contact
Data Acquisition; by mail, monthly, from the plant
Data Description; Steam charts from all 3 boilers.
Data Handling; 1) Convert chart readings from percent to steam out-
put (thousands of pounds per hour)
2) On each coding form, record appropriate ID's.
current fuel analysis, method-3, pollutant-SjDX,
data units - LBS, time interval of entered data,
appropriate Julian date, SCC number, efficiency,
and action. Data type - 1.
3) In data spaces, record hourly thousands of pounds
of steam produced. Readings are taken at the
half-hour, i.e. 7:30 for the 7-8 am reading.
Charts are changed at 7:30 am. First 8 hours of
a day are on the previous day's chart.
112
-------
APPENDIX B
113
-------
The following list identifies the point sources in the RAPS data base
that have been source tested, resulting in point specific emission factors.
State
26
14
26
26
14
la
14
14
14
26
14
County
4300
4680
4300
2230
4680
4680
4680
4680
6900
4280
4680
Plant ID
02
13
01
06
01
01
01
22
01
06
04
Stack ID
08
04
01
10
01
01
02
03
01
01
64
SCO Code
1-02-006-01
3-05-014-01
3-05-007-05
3-01-013-01
1-01-006-01
1-01-005-01
1-01-002-01
1-01-002-08
1-02-002-08
1-02-002-02
3-06-002-01
Plant Name
Chrysler Motors
Owens-Illinois
Alpha Cement
USS Agri Chen
Illinois Power- Wood
Illinois Power-Wood
Illinois Power- Wood
Highland Power
Stag Brewery
General Motors
Arnoco Refinery
River
River
River
114
-------
APPENDIX C
115
-------
The following three lists identify point sources in the RAPS data base
for which actual process or fuel data have been gathered on an hourly, daily
or monthly basis.
116
-------
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APPENDIX D
126
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The following list identifies the point sources in the RAPS data base
for which actual working pattern data has been collected. Calculated
yearly emissions were then apportioned uniformly to this working pattern.
127
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136
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/4-78-042
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
REGIONAL AIR POLLUTION STUDY
Point Source Methodology and Emission Inventory
5. REPORT DATE
July 1978
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
F.E. Littman
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Rochwell International
Air Monitoring Center
11640 Administration Drive
Creve Coeur, MO 63141
10. PROGRAM ELEMENT NO.
1AA603 AA-07 (FY-77)
11. CONTRACT/GRANT NO.
68-02-2093
Task Order 108A
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Sciences Research Laboratory - RTP, 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
The development of the point source emission data inventory for the Regional
Air Pollution Study at St. Louis is discussed. To meet the unusual requirements
of this study, which specified the acquisition of hourly, measured emission data
for the St. Louis Air Quality Control Region for a period of two years, a unique
methodology was developed and put into practice. The result is a data base con-
taining over 20 million pieces of information in a readily accessible form.
17.
KEY WORDS AND DOCUMENT ANALYSIS
a.
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
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)
IINP.IASSIFIED
21. NO. OF PAGES
160
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION is OBSOLETE
150
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