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Risk ,
Pollutit

in purr

DC

Risk Assessment for Selecting
Pollution Prevention Alternatives

By

Souad Benromdhane

Volume I

A Ph.D. Dissertation

Submitted to
Michigan State University
in partial fulfillment of the Requirements
for the Degree of

DOCTOR OF PHILOSOPHY

Department of Civil and Environmental Engineering

1998

 

for Selecting

.

Ilnnactning process emis

 

nih'de \' ofthe Clean Air .-\C
reigned b} the LS. Em no:
32} 3e utilized to reduce emissi

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ABSTRACT
Risk Assessment
for Selecting Pollution Prevention Alternatives

By
Souad Benromdhane

Manufacturing process emissions from foundries are regulated under Title I, Title III,
and Title V of the Clean Air Act Amendments (CAAA) and implementing regulations
promulgated by the US. Environmental Protection Agency (EPA). Several strategies
may be utilized to reduce emissions. Under established principles of pollution prevention,
the preferred hierarchy for emissions control is : (1) source reduction, (2) recycling, and
(3) treatment. The traditional approach to emissions control has focused on treatment.
This approach has been driven by the pre-1990 regulatory scheme. This ofien results in
transfer of pollutants from one medium to another, i.e., a collected air pollutant becomes
a solid waste or water pollutant. Risks are not reduced but rather are transferred. Costs
are multiplied rather than being minimized.

Currently, priorities are being given to a more preventive approach. The costs
involved with treatment solutions, waste disposal and management, and control
technologies have driven industries to pollution prevention. Risk assessment techniques
are now being used to evaluate the impact and risk associated with the hazards generated
by different industrial operations to study the feasibility of control options before
implementation.

The foundry industry, a major source of hazardous air pollutants (HAPs), has been

designated as one of the industries to be regulated under the Maximum Achievable

Control Technology (MACT) provisions of Title V of the Clean Air Act. Thus, in

 

f.

._... ‘

n
.

 

 

'5

conjunction with representatives from the General Motors Corporation (GM) and the
Ford Motor Company, the GM Powertrain Saginaw Malleable Iron Foundry was selected
as the site to test the application of a new risk assessment modeling technique.

A four component model of risk assessment is proposed. It involves: 1) identifying
and quantifying of the source input; 2) modeling transport to the receptor; 3) evaluating
the risks to the receptor; and, 4) assessing the impact of pollution prevention on the
source. This last step allows refining the selection of the pollutants of concern and
ranking of the pollution prevention alternatives.

One of the important priorities to be addressed in the foundry industry is the
emissions of HAPs from the casting process at the pouring cooling and shakeout steps.
These emissions result from about 19 different families of resin binders in commercial
use. A mass balance approach to the molding operations was selected as the method to
estimate some of the HAP emissions. Alternate methods were also explored.

The US. Environmental Protection Agency Industrial Source Complex dispersion
model (lSC-3) was selected as the transport model. Exposure concentrations and their
variability around the facility at the ground level for short term exposure were calculated
to estimate the impact on the risk evaluation.

A probabilistic approach as opposed to the “point estimate” approach was undertaken
to assess the exposure and toxic effects of the pollutants of concern. A sampling strategy
that defines exposure assessment as an inherently statistical problem was made to include
randomness and representative situations. This is expected to lead to a better description

of the risk, and to establish pollution prevention priorities based on realistic scenarios.

Copyright by
Souad Benromdhane
1 998

 

 

To my parents, who alw
u h

 

To my parents, who always prayed for my success, in particular, my father
who did not live to see this day.

 

AC

 

h‘l praises be to Alla.
ruse for presiding me With
lam appreciatis'e for
.J the Manufacturing R.
Zuluuuld not hate been new
luould like to express
l. hits for his continued su
shes. He has been like a in
ermine. Dr. M. Kamn'n. D

are;
\m

h‘SUOnSI

l ush to express mi

run-ed prayers. suppon an
iii to my sister Saida and l
sne-

mle. lam also crate-ti

hull.

 

ACKNOWLEDGMENTS
‘19).” 01-413)." LLll page

In the Name Of Allah
Most Gracious. Most Merciful

All praises be to Allah Subhanahu-wa-Taala, the creator and sustainer of this
universe for providing me with the strength and the patience to complete this work.

I am appreciative for the financial support from Michigan State University,
through the Manufacturing Research Consortium. Without this support, this research
work would not have been accomplished.

I would like to express my sincere gratitude to my major advisor, Dr. Mackenzie
L. Davis for his continued support and guidance throughout the years of my Doctoral
studies. He has been like a father to me. I would like also to thank the members of my
committee, Dr. M. Kamrin, Dr. S. Masten, and Dr. S. Selke for their valuable criticism
and suggestions.

I wish to express my greatest thanks and appreciation to my mother for her
continued prayers, support and encouragement. The warmest feelings of gratitude are
owed to my sister Saida and her husband Izzat, who stood by me and made this dream
.come true. I am also grateful to my sister Sonia, my brothers Lassaad, Sofiane and
Souheil. They have always prayed for my success and supported my pursuit of this
Doctoral degree.

Thanks to all the people who by a means or another, contributed to the success of
this work, namely, Dr. S. S. Farinwata from Ford, Mr. J. Touma from EPA, NC., and Mr.
M. Jabbur from the Air Force W. DC. for their precious help and availability.

Last but not least, special thanks to all the staff at Michigan State University for
all their services, in particular those in the Department of Civil and Environmental

Engineering for their indefinite kindness and availability.

vi

Table of Contents

 

List of Tables .................................................................................................................. xi
List of Figures ................................................................................................................. xii

CHAPTER 1 Introduction

 

1.1 Background ........................................................................................ 1
References ................................................................................................ 3

CHAPTER 2 Foundry Emissions

 

2.1 Foundry Overview ............................................................................. 4
2.1.1 Description of Production Steps ......................................... 5
2.1.2 Raw Materials Handling, Storage, and Preparation ............ 5
2.1.3 Metal Melting ..................................................................... 8
2.1.4 Mold and Core Production .................................................. 11
2.1.5 Pouring, Cooling, and Shakeout ......................................... 13
2.1.6 Pollutants of Concern in the Casting Operations ................ 14
2.1.7 Finishing ............................................................................. 15
2.1.8 Waste Handling and Storage ............................................... 17
2.2 Hazardous Air Pollutant Emissions from Foundry ............................ 18
2.2.1 Binders: Major Source of Hazardous Air Pollutants .......... 18
2.2.2 Organic Resin Binders Chemistry ....................................... 21
2.2.3 Binder Resin Composition .................................................. 24

2.2.4 Measured HAPs from Molding and Core Making Process.25
2.2.5 Estimated HAPs from Molding and Core Making Process 27
2.2.6 Data Used in the Transport Model ...................................... 27
References ................................................................................................ 30

CHAPTER 3 Proposed Research

 

3.1 Problem Statement ............................................................................. 32
3.2 Modeling Approach ........................................................................... 33
3.3 Source Model ..................................................................................... 33
3.4 Processes Selection and Research Scope ........................................... 35
3.5 Transport Model ................................................................................ 35
3.5.1 Selection of a Dispersion Model ......................................... 36
3.5.2 Source Characterization ...................................................... 36
3.5.3 Selection of Receptors ........................................................ 36
3.6 Risk Model ......................................................................................... 36
3.7 Feed Back Loop ................................................................................. 37
References ................................................................................................ 37
vii

CHAPTER 4 Mass Balance Model

 

4.1 Methods for Developing a Source Model ......................................... 38
4.1.1 Material Balance ................................................................. 39
4.1.2 Emission Factors ................................................................. 39
4.1.3 Stack Tests .......................................................................... 39
4.1.4 Engineering Equations ........................................................ 40

4.2 Factors Affecting Emission Rate Estimates ....................................... 40
4.2.1 Capture Efficiency .............................................................. 40
4.2.2 Control Efficiency ............................................................... 41

4.3 Mass Balance as a Modeling Tool ..................................................... 42
4.3.1 Mass Balance Variables ...................................................... 43
4.3.2 Chemical Input Calculation ................................................ 44
4.3.3 Chemical Output Calculation .............................................. 44
4.3.4 Mass Balance Equation for Casting .................................... 45

4.4 Mass Balance Results and Discussions .............................................. 47

4.5 Comparison of Mass Balance and Emission Factors ......................... 55

References ................................................................................................ 57

CHAPTER 5 Transport Model

 

5.1 Introduction to Dispersion Modeling ................................................. 58
5.2 Selection of the Dispersion Model ..................................................... 59
5.3 Role of Atmospheric Turbulence in Air Pollution ............................. 61
5.4 Theoretical Background ..................................................................... 62
5.5 Downwash effects and Building Profile Input Program (BPIP) ....... 66
5.5.1 Input Preparation ................................................................. 68
5.6 Dispersion Modeling Implementation - Simulation Strategy ............ 69
5.6.1 Source Information ............................................................. 69
5.6.2 Selection of Receptors ........................................................ 70
5.6.3 Meteorological Data ............................................................ 71
5.6.4 Emission Rates -Variability ................................................. 71
5.7 Dispersion Modeling Simulation-Results .......................................... 74
5.7.1 Identification of Critical Zones ............................................ 74
5.7.2 Sensitivity of Area of Exposure to Emission Increase .......... 76
5.7.3 Influence of Production Variation on Exposure
Concentrations ...................................................................... 76
5.7.4 Sensitivity of Exposure Concentrations to Stack Height
Increase ................................................................................. 82
5.8 Synthesis PDF of Emission Rates ...................................................... 83
References .................................................................................................. 86
viii

CHAPTE

lHlPll

CHAPTER 6 Exposure Assessment

 

6.1 Theoretical Background ...................................................................... 87
6.1.1 Fundamental Equations ....................................................... 88
6.1.2 “Point Estimate” Approach to Exposure Assessment ......... 93
6.1.3 Probabilistic Approach to Exposure Assessment ............... 94
6.1.3.1 Computational Methods for Estimating Dose PDFs..95
6.2 Selection of an Exposure Model ......................................................... 96
6.2.1 Review of Existing Models ................................................. 97
6.2.2 Sensitivity Analysis ............................................................. 98
6.3 Review of Probability Distribution Function (PDF) ......................... 100
6.3.1 Contaminant Concentration in Air (Cair) ............................ 101
6.3.2 Inhalation Rate IR or (IU 31,) ............................................... 101
6.3.3 Exposure Frequency (EF) ................................................... 103
6.3.4 Exposure Time (ET) ........................................................... 104
6.3.5 Exposure Duration (ED) ..................................................... 105
6.3.6 Body Weight (BW) ............................................................. 106
6.3.7 Averaging Time (AT) ......................................................... 107
6.4 Selection of PDFs ................................................................................. 109
6.5 Exposure Assessment .......................................................................... 114
6.5. 1 Implementation ....................................................................... 1 14
6.5.2 Intake Results ......................................................................... 117
References ................................................................................................... 119

CHAPTER 7 Toxicity Assessment

 

7.1 Inhalation Toxicology ......................................................................... 120
7.2 Source of Toxicological Data ............................................................. 121
7.3 Adverse Effects .................................................................................... 124
7.4 Carcinogenicity .................................................................................... 125
7.5 Threshold ............................................................................................. 127
7.6 Exposure to Multiple Compounds ...................................................... 128
References .................................................................................................. 129

CHAPTER 8 Risk Characterization

 

8.1 Point Estimate of Cancerous and Non-Cancerous Risk ..................... 130
8.2 Probabilistic Risk Evaluation ............................................................. 131
8.3 Total Integrated Risk ........................................................................... 132
8.4 Uncertainty Analysis ........................................................................... 133
8.5 Results And Discussion ...................................................................... 134
8.6 Risk Sensitivity To Exposure Factors ................................................ 140
References .................................................................................................. 143

ix

CHAPTER 9 Risk Assessment Techniques as a Tool for
Selecting Pollution Prevention Alternatives

 

9.1 The Decision Making Process ............................................................ 144

9.1.1 Difficulty in Decision Making ............................................ 144

9.2 Decision Making Under Uncertainty .................................................. 145
9.3 Application of Risk Assessment to the Selection of a

Resin Binder ...................................................................................... 146

9.3.] Definition of the Problem .................................................... 146

9.3.2 Listing of Options ................................................................ 147

9.3.3 Definition of Criteria ........................................................... 147

9.3.4 Analysis of the Options ....................................................... 151

9.3.4.1 Lowest Emission ..................................................... 152

9.3.4.2 Comparison with Acceptable Risk Values ............. 152

9.3.4.3 EPA Combined Cancer Risk and HI ....................... 153

9.3.5 Selection of Course of Action ............................................. 154

References ................................................................................................ 155

CHAPTER 10 Summary, Conclusions, and

 

 

Recommendations
10.1 Summary of Research Work Completed ......................................... 156
10.2 Conclusions ...................................................................................... 157
10.3 Recommendations for Reducing Risk .............................................. 158
10.4 Recommendations for Future Work ................................................. 159
10.5 Future Research ................................................................................. 160
References .................................................................................................. 161
Appendices .............................................................................................................. 162
Bibliography .......................................................................................................... 331

 

 

 

11.13111 Induction Furnacel

3.33 Some Foundry-Am
. ‘vlold and Core Ma}
(Gschssandtnet. 19"

1111233 1151 of the 113101 q
Industrx (SCOIL l9?

3111231 Measured foundrsa
(Ma’s'silles. 19931

1111231 Selected binder HA
(Mosher. 1991; Mr

71113.6 ileuured data of H
prosided bx G.\l .....

1111211 1ssumptioiis C DIN

.111 13 \lass Balance Resu

ll? ‘1 Stlttitd Stack Dd‘.

1.1261 Selected Cumulatii
Rates in m das b\

ted Probabilits Densits

2163 Summn of Distri

@164 PDF storExrsosurr

lil~65 Predicted First 111.
“#11911

 

..............

11.1 Resin Binders

Listed toucm c1331
5 Stimates fr
51811 Time
3.111 Slimates 11011

11
16¢}:

List of Tables

 

Table 2.1 Induction Furnace Emissions for Malleable Iron (Gschwantdner, 1990) ..... 10
Table 2.2 Some Foundry-Atmosphere Contaminants Evolved During

Mold and Core Making, Casting, and Cooling

(Gschwandtner, 1990; McKinley, 1990) ....................................................... 16
Table 2.3 List of the Major Sand Binder Systems Used in the Foundry

Industry (Scott, 1976; Carey, 1990) .............................................................. 20
Table 2.4 Measured foundry available emission data (lb/ton of iron)

(Maysilles, 1993) .......................................................................................... 26
Table 2.5 Selected binder HAP emission factors

(Mosher, 1994; Maysilles, 1993) .................................................................. 28
Table 2.6 Measured data of HAP emissions from the casting process

provided by GM ............................................................................................. 29
Table 4.1 Assumptions Considered in the Mass Balance Calculations ......................... 49
Table 4.2 Mass Balance Results for Naphthalene ........................................................... 54
Table 5.1 Selected Stack Data ....................................................................................... 72
Table 6.1 Selected Cumulative Distribution Percentiles of Inhalation

Rates in m3/day by Age (Finley, 1994) ......................................................... 102
Table 6.2 Probability Density Functions for Inhalation Rates (U.S.EPA, 1996) .......... 102
Table 6.3 Summary of Distribution Factors for Body Weight by Age and Gender ....... 108
Table 6.4 PDFs for Exposure Factors ............................................................................ 110
Table 6.5 Predicted First Highest Concentrations PDFs, peg/m3 (Lowest Scenario-

4PM/9l) ........................................................................................................ 116
Table 6.6 Predicted First Highest Concentrations in ug/m3 at R3 for Alternate

Resin Binders. ............................................................................................... 1 18
Table 7.1 Listed toxicity data ......................................................................................... 123
Table 8.1 Risk Estimates from Benzene Inhalation at Receptor R3 Based on

Shift Start Time .............................................................................................. 135
Table 8.2 HI Estimates from Phenol inhalation at Receptor R3 Based on Shift

Start-Time ...................................................................................................... 136
Table 8.3 Comparison of Risk Estimates from Benzene inhalation at Receptor R3

for Women, Children, and Men ..................................................................... 137
Table 9.1 List of Resin Binders Ranked Based on Their Total Yearly Emissions

of the Selected HAPs ..................................................................................... 148
Table 9.2 Combined Risk from Exposure to Multiple Chemicals ................................. 153

—

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7 Naphthalene Stack

 

Typical Layout ot
Hypothetical strut

.3 An example oftlt'

(Bate, 1975;Scot1
General Formula 7
.,. . I
(Bate. 19, 3; Scott
Risk Assessment .‘
Desegregation of:

Variable Exhaust ll

   
   
  

 

Variable Exhaust '
Naphthalene Stack
at 95% Control Et‘.

a10°o Control Elli
Coordinate S) stem

.leteorological Dal.
Average benzene is
maximum emission
0 erage benzene is
for 1991 uith 10°.oi
Selected Receptors

a\ cage be!-
Ines “1th 1t
ual aierage ber

Production for 1hr
11PM m “

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1

 

Figure 2.1
Figure 2.2
Figure 2.3

Figure 2.4

Figure 3.1
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6

Figure 5.7
Figure 5.8

Figure 5.9

Figure 5.10

Figure 5.11

Figure 6.1
Figure 6.2
Figure 6.3

Figure 6.4
Figure 6.5

List of Figures

Typical Layout of a Foundry ....................................................................... 6
Hypothetical structure containing ricinoleic acid in castor oil .................... 21
An example of the phenolic resin ( R) in a phenolic system

(Bate, 1975; Scott, 1976) ........................................................................... 22
General Formula for Polyisocyanate for both systems

(Bate, 1975; Scott, 1976) ........................................................................... 22
Risk Assessment Model for Pollution Prevention ...................................... 34
Desegregation of the Casting Process - Emissions Mass Balance ............. 46
Naphthalene Stack Concentrations vs. Capture Efficiency for

Variable Exhaust Rates [m3/hr] with 95% Control ..................................... 50
Naphthalene Stack Concentrations vs. Capture Efficiency for

Variable Exhaust Rates [m3/hr] with 0% Control ....................................... 51
Naphthalene Stack Concentrations vs. Exhaust Flow Rates

at 95% Control Efficiency ........................................................................... 52
Naphthalene Stack Concentrations vs. Exhaust Flow Rates

at 0% Control Efficiency ............................................................................. 53
Coordinate System showing Gaussian Distributions

in the Horizontal and Vertical Directions ................................................... 63
Stack Configuration .................................................................................... 73
Receptor Location-Preliminary Grid ........................................................... 73
Average Benzene Concentrations in (pg/m3) for four years of

Meteorological Data with Maximum Emission Rates .................................. 75
Average benzene isoconcentrations in (mg/m3) for 1991 with

maximum emission rates ............................................................................. 77
Average benzene isoconcentrations in (mg/m3)

for 1991 with 10% increased ....................................................................... 77
Selected Receptors for Sensitivity Analysis ................................................. 79
Annual average benzene profiles for full production for each line

and all lines with 1991 meteorological data ................................................ 80
Annual average benzene concentrations in (pg/m3) for 16 hour

production for three operating schedules: 7AM-11PM, 3PM-7AM,

llPM-3PM .................................................................................................. 81
Sensitivity of average annual benzene concentrations in

(ug/m3) to stack height with 15 feet increase .............................................. 84
Probability Distribution for Measured Emission Rates .............................. 85

Potential interactions among source term, environmental media,
exposure media, and exposure routes that must be addressed in

multimedia, multiple pathway, exposure assessment ................................. 89
Illustration of Exposure Pathways .............................................................. 90
Desegregation by exposure route and by exposure medium for the

average daily dose to hazardous air pollutants of multimedia exposure ..... 99
PDF for Inhalation Rates in m3/hr ............................................................... 11 1
PDF for Body Weight ................................................................................. 111

xii

Figure 6.6 Exposure Duration in years ......................................................................... 112

Figure 6.7 Exposure Time in hours/day ....................................................................... 112
Figure 6.8 Exposure Frequency in days/year ................................................................ 113
Figure 6.9 Receptor ID and Location ........................................................................... 115
Figure 8.1 Risk Distribution for Benzene Inhalation ................................................... 139
Figure 8.2 Cumulative Risk for Benzene from 1987 to 1991 ...................................... 141
Figure 8.3 Sensitivity Analysis Risk-Exposure Factors ............................................... 142

)ciii

 

 

J"

 

 

 

 

1.1 Background

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Chapter 1

INTRODUCTION

1.1 Background

Risk assessment has been used as part of the remediation process for cleaning soil and
groundwater contaminated by uncontrolled releases of hazardous waste. It has been used
to analyze the existing situation and evaluate the health risk of alternative clean-up
scenarios. The major goal of the risk assessment is to help develop risk management
decisions that are more systematic, more comprehensive and accountable than has often
been the case in the past. In a generic sense, risk assessment is a systematic process for
making estimates of all the significant risk factors that prevail over an entire range of
failure modes and/or exposure scenarios due to the presence of some type of hazard
(Asante-Duah 1993). It provides not only a quantitative but also a qualitative evaluation
of the consequences arising from the hazard that could initiate a specific response,
outcome, exposure and consequence. The process of risk assessment can establish case
sPecific responses that will insure justifiable, defensible, and cost effective decisions.
HYPOthetical cases and “what if” scenarios can be investigated via risk assessment
models to evaluate the impact on human health of not only clean-up approaches but also

pollution Prevention techniques. In particular, the risk assessment technique can be used

 

artool to assess pollution p

of hurts. to the industrial co

 

‘ .

,.- . tv ‘

inimi-
6

theolthe most significant .1;

‘ '
‘5‘ afar,
..

tart-rent of organic binde

 

rerecorn licated cores and r
at developed for other indus

:eaze‘; 1976). Hence. little co

are lot many years. in this i:

rotate of the casting. As a e

I

en the cores. \shich a

mister used to determine I

s'rcot'erlooked. 0nl\ after

itili‘djtll source. a maior e

:fiit‘ “ a I
tic clkiillCdlS alld i0 d’iic
\

F

A

Taste" .
search demonstrates
"°1iitndttstn'tthe we r
. L

‘ ail-Dir“

. chtoidentif} thel

. s.

Eli‘s t

he» .

as a tool to assess pollution prevention alternatives. The results of this technique will be

of interest to the industrial community in facing the challenge of increasingly stringent

regulations.

One of the most significant advances in the foundry industry in the last thirty years is the
development of organic binders. This class of binders made it possible to manufacture
more complicated cores and more dimensionally accurate molds. Often the technology
was developed for other industries and updated to metal casting applications (Scott and
Feazel 1976). Hence, little consideration was given to the environmental impacts of their
use. For many years, in this industry the focus was on how to obtain a better quality
surface of the casting. As a consequence, several chemicals were added to the binders to
strengthen the cores, which are the most fragile part of the molding assembly. Trial and
error was used to determine the best ratios of mixing but for a long time toxic emissions
were overlooked. Only after foundry, and in particular the casting process, was found to
be a major source, a major effort was undertaken to identify and quantify the release of

toxic chemicals and to determine their health impact.

This research demonstrates the use of risk assessment as a pollution prevention tool. The
foundry industry (the case of a steel foundry) has been selected as the example for testing
an approach to identify the hazard, assess and estimate the human health risk associated

with the exposure to the hazard, and evaluate some preventive steps to reduce the

   
  

erssiorrs at the source. A n.

pith for the risk assesses.

References

isnteDuh K. D. (1993 1. ll.
Scottli'. D. and C. E.
Chemistry. Birminghar

 

 

 

emissions at the source. A major innovation in this research is to use a probabilistic

approach for the risk assessment.

References

AsanteDuah, K. D. (1993). Hazardous Waste Risk Assessment. Boca Raton, Florida.
Scott, W. D. and C. E. Feazel (1976). A Review of Organic Sand Binder
Chemistry. Birmingham, Alabama, Southern Research Institute.

’1'

 

1.1 Foundry Oren

iicugh each foundr} 39C
air; of metal products. 5
bribes the foundn proce

likable Iron Plant \thifih

.115. manufactures and ii:

mission parts for Gene
£“t '
uttnsler Corporation.

rerun quotas.

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to. iron is produced

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I

turn of coke 1.

ml.‘

{£10221 - '
utilise. Malleable rr

:1.

slim ”1de A
utthll lumdi‘es l

Chapter 2

Foundry Emissions

2.1 Foundry Overview

Although each foundry accomplishes the same basic tasks as shown in Figure 2.1, the
casting of metal products, specific materials, equipment, and processes vary. This chapter
describes the foundry process implemented at the General Motors Powertrain Saginaw
Malleable Iron Plant which is located in Saginaw, Michigan. The plant, established in
1917, manufactures and distributes finished automotive pieces such as pistons and
transmission parts for General Motors Corporation as well as for Ford Motor Company
and Chrysler Corporation. It operates 24 hours per day, 5 to 7 days per week to meet

production quotas.

Malleable iron is produced by the inoculation of the molten metal with carbon (which is
added in the form of coke), and small amounts of silicon and magnesium during the
melting phase. Malleable iron treatment also requires annealing of cooled pieces.

Electric induction furnaces (EIFs) are used for melting at the Saginaw foundry.

retestmp and are pumpe
ritually to tuo drainage c
ears the plant for reuse. l
feetot‘foundry sand. Daily
are million gallons of this

chaired from recycled plant

1.1.1 Description of

“at . .. - '
.r pant produces castings

res '
,. 11 rats materials handi

liltinr ~ ' '
Neil casting luhich in

I; 1.1; .
tau. ntludes annealing e

5

if”

i...

y .
it the mayor pollutants

l
.11 Raw Materials

11].:

enter a sump and are pumped to a primary lagoon which drains first to a secondary lagoon
and finally to two drainage ditches for settling. After settling is complete, the water then
renters the plant for reuse. The primary pond is dredged daily of approximately 15 cubic
feet of foundry sand. Daily water usage is approximately nine million gallons. Two to
three million gallons of this water is purchased from the city. The remaining water is

obtained from recycled plant water.

2.1.1 Description of Production Steps

The plant produces castings from 100% recycled scrap. There are five major production
steps: 1) raw materials handling and preparation; 2) mold and core production; 3) metal
melting; 4) casting (which includes pouring, cooling and shakeout); and 5) finishing
(which includes annealing, grinding, and shot blasting). A description of each production

step and the major pollutants emitted follows.

2.1.2 Raw Materials Handling, Storage, and Preparation

Handling operations include receiving, unloading, storing and conveying raw materials
for both furnace charging and mold and core preparation. Raw materials are received in
railroad cars, trucks and containers, then transferred by truck, loaders and conveyors to

open piles. When needed, the raw materials are transferred from storage to process areas

by similar means.

 

 

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lienilics and fluxes are the m.
was include iron and stee.
limestone. dolomite). fluoride

ix are added to the steel to in

i. plan: melts approximatelj
.c product is produced. l
xcjclng. Additional metal 5
wedded and is stored in c
weir contains zinc. altir
m5nnpers and other chron‘

cap material in accordance

addition to the raw mater
3:zneeded to prepare the sa
kT'Clcd Sand and chemical
$0331: of receiving areas.

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ii 18 recycled [0 ii‘
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‘5 1‘ ‘-
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Metallics and fluxes are the major materials required for furnace charging. Metallic raw
materials include iron and steel scrap and foundry returns. Fluxes include carbonates
(limestone, dolomite), fluoride (fluospar), and carbide compounds (calcium carbide), and

they are added to the steel to improve the quality of the cast.

This plant melts approximately 1000 tons of iron per day. Close to 500 tons per day of
usable product is produced. The remaining 500 tons per day is returned to the furnace for
recycling. Additional metal scrap is obtained from various sources. It is delivered
preshredded and is stored in open piles in a designated room in the foundry. This scrap
frequently contains zinc, aluminum (which does not mix with iron), and chromium (from
car bumpers and other chrome-plated scrap pieces). The foundry sets specifications for

scrap material in accordance with their part-order specifications.

In addition to the raw materials used to produce the molten metal, a variety of materials
are needed to prepare the sand cores and molds that form the iron castings. Lake sand,
recycled sand and chemical additives are combined in a sand handling system that
COnsists of receiving areas, conveyors, storage silos, mixers (sand mullers), core and mold

making machines, shakeout grates, sand cleaners, and sand screening.

Ten tons of sand are used per hour during normal production. Used sand from castings
Shilkeout is recycled to the sand preparation area and cleaned to remove any clay or
Carbonaceous buildup. The sand is then screened and reused to make new molds. The

plant Currently has the ability to reclaim the sand to the quality required for molding, but

 

lesnot hare the technology it
lineup sand is added to repla.

rand.

43$; Us

.mUOILi generated during hat

 

ignieparticulate matter gene

it ran materials. These emissi

2.13 Metal Melting

ixtnee most common furna

193‘, l

nan electric arc. and e

so our Elis with a capae it\

‘
‘I

laid ,. -
weal shaped retracton l

xiii ;. ‘
hgh trequency altemat

lanai-r
‘5 he metal charge in

ESL: 4' .

We

. Any 011 or moisture

511" '
. u . \l
1‘!" :th
‘a mace l ,-
aaIO“) OXV‘Q‘ 1 ~
. tin i0

does not have the technology to reclaim it to the quality required for core production.
Makeup sand is added to replace process losses and contaminated sand that must be

discarded.

Emissions generated during handling and preparation of the raw materials include
fugitive particulate matter generated from the receiving, unloading, storage and conveying

of raw materials. These emissions are currently uncontrolled.

2.1.3 Metal Melting

The three most common furnaces used for metal melting in the gray iron foundry industry
are cupolas, electric arc, and electric induction firrnaces (EIF). The GM Saginaw Plant
uses four EIFs with a capacity of 28 tons of metal per furnace. Each furnace is a
cylindrical shaped refractory lined vessel surrounded by electrical coils. When energized
with a high frequency alternating current, a fluctuating electromagnetic field is produced
that heats the metal charge in approximately 2 minutes. For safety reasons, the scrap
metal added to the furnace charge is cleaned and heated before being introduced into the
furnace. Any oil or moisture on the scrap metal could cause an explosion in the furnace.
EIFS are kept closed except when charging or tapping. A permanent opening in the top of

the filrnace allows oxygen to enter, and is a continuous source of emissions.

The basic melting process operations are: (1) preheating of the scrap material to evaporate

any moisture or oil; (2) furnace charging, in which metal, scrap, alloys, carbon, and flux

raided to the furnace; (3 l m
Waging. uhich incolres '
nt‘ulau'on during which the c
areal specifications of mi".

Emacs {which stores the molt:

 

 

 

: molten metal into a Ro

an‘nald set'en tons of molten i

no; preheating of the scrap
in.“ ‘ ‘
tend trom incomplete con

at crap on the charge scale.

an

...-d but are frequently pla

3:5“ A
mers can be doun anti

Th: in.
highest concentration of f

n
rMaon remot'al. and (an.
i l

teased f” '
c mm the melting furr

lCO‘ n '
.orgamccompounds su'

are added to the furnace; (3) melting, during which the furnace remains closed; (4)
backcharging, which involves the addition of more metal and alloys as needed; (5)
inoculation, during which the carbon, silicon, and magnesium are added to meet the
chemical specifications of malleable iron; (6) transfer of molten metal into a ROD
furnace (which stores the molten metal and maintains the metal’s temperature); and (7)
tapping molten metal into a Rotary Motor Iron Pouring Station (RMIPS). Each RMIPS

can hold seven tons of molten metal.

During preheating of the scrap metal, particulate matter, chlorides, and fluorides are
produced from incomplete combustion of coke, carbon additives, flux additions, and dirt
and scrap on the charge scale. Afierburners are installed to decrease the pollutants
emitted, but are frequently plugged with sand from the scrap metal. Once plugged, these

afterbumers can be down anywhere from several days to several months.

The highest concentration of furnace emissions occurs during charging, backcharging,
inoculation, removal, and tapping operations when the furnace is open. Emissions
released from the melting furnaces include: particulate matter (PM), carbon monoxide
(CO), organic compounds, sulfur dioxide (802), nitrogen oxides (N Ox), and small
quantities of chloride and fluoride compounds. Fine particulate fumes emitted from the
melting furnaces come from the condensation of volatilized metal and metal oxides. CO
emissions are generated by the combustion of organic material on scrap and carbon added

to the chill’ge. The furnace temperature will also affect the amount of CO emitted.

indication emissions ( acrolc
rcncaporization and partial e

iage. Measured emissions o

 

lalielliGschu'andtner and F

iahle 2.1 Induction Furnac

r%‘=1=
Emrssro

\

CPO:
C a0
M20

/

/

¢

 

10

Hydrocarbon emissions (acrolein, benzene, formaldehyde, phenol, toluene, xylene) come
from vaporization and partial combustion of any oil remaining on the scrap iron used as a

charge. Measured emissions of inorganics from melting operations are summarized in

Table 2.1(Gschwandtner and Fairchild 1990).

Table 2.1 Induction Furnace Emissions for Malleable Iron Iron (Gschwandtner and
Fairchild 1990).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Emission mg of contaminant/Mg of metal
melted
SiOz 6.5 x 104
ZnO 5.2 x 104
A1203 2.6 x to4
Cr203 1.3 x 103
CaO 6.5 x 102
MnO 1.3 x 103
M00 2.6 x 102
TiO 1.3 x 102
NiO 1.3 x 102
8203 2.6 x 101
PbO 1.3 x 101
Sn02 2.6
3903 26
V205 7.8
CuO 1.3
CoO 2.6
BaO 2.6

 

 

mg = milligram, Mg = megagram (lOOOKg)

 

 

lose emissions are depender.
cold chage or continuous). t‘r
lanes of contamination max
is. oil and fuel breakdown p: I
cairn or graphite or other add“

cor no in zinc die castings.

 

Mi lie. chromium. alumir

meis for fugitive furnace er
loop} hoods or special hood:

or: vi. '
i... ten to pollution control

2.1.4 Mold and Core

ll

and are forms Used to shape

n? a
..rare of wet Sand. clay and

and mold. the most cor

4
5 percent \
lplgfl}. . -
.. in thong coal)

1]

These emissions are dependent on the charge material composition, the melting method
(cold charge or continuous), the melting rate, and the purity of the materials used.
Sources of contamination may include paint on the scrap, various deposits on the scrap
(i.e., oil and fuel breakdown products), molding materials adhering to foundry returns,
carbon or graphite or other additions in powder form, zinc on galvanized scrap or
contained in zinc die castings, and iron and steel scrap containing nonferrous alloys or

plating (i.e., chromium, aluminum).

Controls for fugitive furnace emissions include updraft ventilation to a wet scrubber.
Canopy hoods or special hoods near the furnace doors and taps capture emissions and

route them to pollution control equipment.

2.1.4 Mold and Core Production

Molds are forms used to shape the exterior of the castings. They are prepared using a
mixture of wet sand, clay and organic additives. They are usually dried with hot air. The
green-sand mold, the most common type, and the one used at the Saginaw Malleable Iron
Plant, uses moist sand mixed with 4 to 6 percent clay (bentonite) for bonding. The
mixture has a 4 to 5 percent water content. About 5 percent sea coal (a pulverized high
VOIatilitY bituminous coal) is added to the mixture to prevent casting defects from sand
expansion when the hot metal is poured. This makes the lake sand a dark gray color.

Potential organic substitutes for sea coal include wood flour, oat hulls, pitch or similar

Organic matter.

 

lures are molded sand shapes

 

 

halting sand (mulling) vvitl‘.
to. idle the green sand n:
fact. 1990). Resins are ma;T

"v together until the iron i~

is: core making processes are

 

0 Hot-box core: The so
phosphoric acid activ'
305t0315 °C for 0.5
“Dual for furan bindc
GSChuandtner and F a

' Cold-box core: The

resrns. activated bv a

gassed with polvisoe

gas is injected as. a c;

(Carey 1986; Gschvv.

Lidnf'
eSe Processes is con“
3th "ifb' ‘
ind
ers and resins. Qua
:3;;

l- .
iv th -
O ‘ e
srze and number c

%J
Sucm

went

12

Cores are molded sand shapes used to make the internal voids in castings. They are made
by mixing sand (mulling) with organic binders and resins and molding the sand into a
core. Unlike the green sand molding process, cores are chemically bonded by resins
(Carey, 1990). Resins are made to withstand heat so that the sand particles are held
tightly together until the iron is shaped. The resin then decomposes and gases are

released.

Two core making processes are used at the Saginaw Malleable Iron Foundry:

0 Hot-box core: The sand binder is typically 3 to 5 percent furan resin with a
phosphoric acid activator. It is cured as a solid core in a heated metal pattern at
205 to 315 °C for 0.5 to 1.5 minutes. 802 gas is injected as a catalyst (this is
typical for furan binder use). The catalyst induces rapid cure (Carey 1986;
Gschwandtner and Fairchild 1990); and

0 Cold-box core: The sand binder is typically 1 to 3 percent of each of two
resins, activated by a nitrogen diluted gas. It hardens when the green core is
gassed with polyisocyanate. It is cured for 10 to 30 seconds. Triethylamine
gas is injected as a catalyst (this is typical for phenolic isocyanate binder use)

(Carey 1986; Gschwandtner and Fairchild 1990).

Each of these processes is controlled by a computer that insures the mixing of the proper

who of binders and resins. Several binders and resins are used due to variations in
hmmdlty, the size and number of cores or molds required, production rates, and

e‘illipment.

 

 

lleoreanic binders undergo ‘
apertures of catings topic
iii} in: ptrolvsis of the then

aical which will recombine

 

|
only dttienng concentration:

fineness basis. the major pol
:at‘iculate matter. Emissions
liittene. xylene) begin with th
to. containers of binder mat:

3 :b‘nia ‘ I .
nouns. Emrssrons conttr

pkg-1's «
~th compounds (acrolein.

an '
nude and particulates are

tn: mold drunk.

1 - .
"1'5 Pounug, Coolin

1.
'1 i".

lten

13

The organic binders undergo thermal decomposition when exposed to the very high
temperatures of castings (typically 1400 °C for iron castings). At these temperatures it is
likely that pyrolysis of the chemical binder will produce a complex mixture of free

radicals which will recombine to form a wide range of chemical compounds that have

widely differing concentrations.

On a mass basis, the major pollutant emitted in mold and core production operations is
particulate matter. Emissions of Volatile Organic Compounds (i.e., acrolein, benzene,
toluene, xylene) begin with their initial exposure to the atmosphere. This occurs from
open containers of binder material, the addition of binders to sand, and the mixing of sand
and binders. Emissions continue during mold preparation, storage, pouring, and cooling.
Organic compounds (acrolein, benzene, formaldehyde, phenol, toluene, xylene), carbon

monoxide and particulates are emitted from core baking, and organic emissions result

from mold drying.

2.1.5 Pouring, Cooling, and Shakeout

A Seven ton charge of molten iron is poured into a rotary motor iron pouring (RMIP)
Station. This station is a mechanical conveyor that transports the molten iron to the
poufing station where it is ladled into molds. Immediately after pouring, a manual quality
Comte] check on the metal temperature and composition is made by inserting a probe into

a Ir101d. Ventilation is provided in this area and the air is directed to a wet scrubber. The

|
acting molds are then mech..

cialate on an open conveyor

Sbicatut is the process of se," .
'necastings have solidified. ti.
timing grid. Sand from the

tired into the basement. P;

to truth small amounts of

hilcirculate and cool under a

mg is covered. but open 0:

if 310

Ill
'b

4 ,. .
lumahng station by ele

tilesb ' '
. 1111511133] in 3 Tune Ill L

2.1.6 Pollutants of C or

E..:. -
L 3?) ‘ 1 l ‘
u an generated from the r

dict
mild fumes. carbon mm

in». .
wall .
‘1“ and core materials we
Bibi?"
. .5; .
l el-‘Ouring station (W

.A..

In.“ 1 '

N“.

...toawet scrubber. CO I

[55‘th '
“Walton (be
' en meas
Ui't‘

be. r;
“ski-(Ed .
“We emitted durin .

l}
‘ Mai
arm and Fairchild 1t.

 

 

l4

cooling molds are then mechanically conveyed (partially covered) to an enclosed room to

circulate on an open conveyor until the casting solidifies in the mold.

Shakeout is the process of separating the casting from the sand molds and cores. After
the castings have solidified, they are separated from the cores and molds by means of a
vibrating grid. Sand from the mold, the core, and the piece (which is still hot) are
dropped into the basement. Pieces are retrieved and collected in a hopper. The hot metal
pieces (with small amounts of sand still attached) are then conveyed to the rooftop where
they circulate and cool under ambient conditions. The roof area designated for piece
cooling is covered, but open on the sides. The sand which can be recycled is returned to
the mold making station by elevator. Waste sand is transferred outside to open storage

piles for disposal in a Type III landfill.

2.1.6 Pollutants of Concern in the Casting Operations

Emissions generated from the rotary pouring station, RMIP, transfer and pouring consist
of hot metal fumes, carbon monoxide, organic compounds and particulates released from
the mold and core materials contacting the molten iron. The RMIP is not enclosed.
However, the pouring station emissions are collected by upward ventilation and are
directed to a wet scrubber. CO emissions peak during pouring and shakeout (the highest
Concentration has been measured during pouring). The highest concentrations of
hydrocarbons are emitted during shakeout, but also peak during the pouring

(GSChWandtner and F airchild 1990). Emissions continue as the molds cool.

bring pouring. 959-6 of the ;
meter and during shakeout
dccttmd Bates 1976‘). Orga:

nerpected to sorb to the par

inoculate matter emissions fr
mssions npicallv contain: ca
end silica lines and clay from
lifted in the molds. During s

nectar the metal and the me

 

15

During pouring, 95% of the particulate matter (PM) emissions are less than 5 microns in
diameter and during shakeout 50% of PM emissions are less than 5 microns in diameter
(Scott and Bates 1976). Organic compounds contained in the mold and the core materials

are expected to sorb to the particulate matter.

Particulate matter emissions from green sand molds peak during shakeout. These
emissions typically contain: carbonaceous material from burning organic matter in the
sand, silica fines and clay from the molding aggregate, metallic fumes, and other fines
present in the molds. During shakeout, a peak of organic emissions is observed when hot
sand near the metal and the metal, itself, contact cooler sand containing residual organics.

Any VOCs in the sand will continue to volatilize during the sand reclaiming process (see

Table 2.2).

2.1.7 Finishing

After the piece has cooled on the roof, it is conveyed to the kiln for annealing. Two
seParate annealing processes are utilized, depending on the piece: Malleable Annealing
and Armasteel Annealing. The first process involves heat treating of the cooled metal
piece in a kiln, allowing the piece to slowly cool, a final heat treatment and slow cooling.
Smaller pieces undergo the Armasteel annealing process which entails heating the pieces
in a kiln at 760 °C for 10 hours, quick-cooling via oil quench for 60-70 seconds and then

reheating it in a draw fumace at 540 °C for 2 to 5 hours.

 

 

Taliell Some Foundry-
hlalcing. C astin
hlclienlev. lei.

 

PROCESS BINDE

HOLbOX Forniai

Phenol
L'rea

furfur

Cold-box

' Carbo
flame-gagged)

Trietl:
Dimer
\leth)

Phent
Resin
lie. tr
iSOp‘n

'. L
\de

Table 2.2

16

Some Foundry-Atmosphere Contaminants Evolved During Mold and Core

Making, Casting, and Cooling (Gschwandtner and Fairchild 1990;
McKenley, Jefcoat et a1. 1990).

PROCESS

Hot-box

Cold-box
(amine-gassed)

BINDER INGREDIENTS

Formaldehyde
Phenol
Urea

F urfuryl alcohol

Carbon-dioxide
Triethylamine
Dimethylethylamine

Methyl Di-Isocyanite (MDI)

Phenol

Resin solvents

(i.e. trimethyl benzene,
isophorone)

Naphthalene and homologues

POTENTIAL EMISSIONS

Aromatic hydrocarbons
Phenol and homologues
Ammonia

Chlorinated hydrocarbons
Hydrogen cyanide

Hydrogen cyanide
Phenol and homologues
Aromatic hydrocarbons
Aniline and homologues
(i.e. aniline, toluidine)
Aliphatic amines

Resin solvents

(i.e. trimethyl benzene,
isophorone) Isocyanates
(i.e. methyl, phenyl isocyanate)
Benzoquinolines

 

tier annealing. the pieces at
one piece and surface imp-.3
dct-bluting. Large imperfec
radiations are removed vi.

a} be required to create deta

Tiedishig operations area h.

 

 

,.. .

:risfitng operations emit large

scrolled. Emission factor:

t
1144316.

1 y .
.18 Waste Handlrn g
lets consist of core butts in

:1. char process materials. \l

tat into open storage pile

. “S. Slag uhtch [S gene

to: 1‘

“Wild i" ‘
. “W from the lad}

vt‘
sand dumped in open pil

1.:‘;-‘I .
“can: [3

 

ocated at another G.\l

‘ 3.331310 3
“it further em t

17

After annealing, the pieces are sent to final finishing, and for removal of sand remaining
on the piece and surface imperfections occurring during the casting. Sand is removed by
shot-blasting. Large imperfections are removed manually with hammers and smaller
imperfections are removed via grinding. Depending on the piece specifications, stamping

may be required to create details (holes) in the piece which cannot be produced by cores.

The fmishig operations area has been identified as a major source of fugitive emissions.
Finishing operations emit large course particles. These emissions are currently
uncontrolled. Emission factors for surface cleaning and finishing are not currently

available.

2.1.8 Waste Handling and Storage

Wastes consist of core butts (unused cores), unrecyclable sand, slag, contaminated coke,
and other process materials. Wastes are transported by vehicle and batch-dumped outside
the plant into open storage piles. Visible particulate matter and odors are emitted from
these piles. Slag which is generated in the melting process is extremely hot. The slag is
manually scraped from the ladles in between usage. It is not quenched, but transported
outside and dumped in Open piles for cooling. It is then disposed of in a Type 1H landfill
Which is located at another GM foundry. The foundry is considering replacing open piles

with luggers to stop further emissions and odor problems.

 

ll Hazardous Air P,

hazardous air pollutants (H.‘
arts and volatile organic

. _ . i
pupal llAPs in the toundf__

0 Meals
0 Semivolatile orca:

o Volatile organics

l't'i. - i '
anon) ( Sb). arsenic (As).

4

alto

in for approximatel} 9“
nionfoundrv include: ben
slum (Se). Major volatile
act-amt methyl di-isocyanat

iii dill acetat

 

e. Typical sen
alpha
4“ ‘en ' .
6. and various polv;

l‘ M.
iii?)

jOI' i
“ van 65 of
it‘sm
u;- hl'i
" iihlldin

18

2.2 Hazardous Air Pollutant Emissions From Foundries

Hazardous air pollutants (HAPs) can be divided into three groups: metals, semivolatile
organics, and volatile organics. According to Euvrard 1992, the contributions of each

group of HAPs in the foundry are (Euvrard and Jackson 1992):

o Metals 70% of HAPs
0 Semivolatile organics 1%
o Volatile organics 9%

Antimony (Sb), arsenic (As), chromium (Cr), lead (Pb), manganese (Mn), and nickel (Ni)
account for approximately 99.8% of the metal HAPs. Other metal HAPs less common in
an iron foundry include: beryllium (Be), cadmium (Cd), cobalt (Co), mercury (Hg),
selenium (Se). Major volatile HAPs to be controlled include benzene, formaldehyde,
methanol, methyl di-isocyanate MDI, triethylamine, tetrachlorethane, toluene, xylenes,
and vinyl acetate. Typical semivolatile HAPs include phthalates, phenols, cresols,
naphthalene, and various polyaromatic hydrocarbons (PAHs) in trace amounts (Mosher,

1995).

2.2.1 Binders: Major Source of Hazardous Air Pollutants

Three main classes of resin binder systems are used in core and mold (AFS and Association

1995). Depending on variations in humidity, size and number of cores or molds required,

 

production rates. and equip".

 

inhale or cold-box system 1

ategorize sand binder syste:

 

inorganic." Table 3.3 lists :
nary The clays and term

finders have gradually gainer

 

phonhate binders have becor

tutu. l9 families of resins a
name binders are the origir
ham: as exclusive blends a

s r
alien core or mold produt

tr composition (Bates :

E34»; -
unions of volatile organ
and '.-.

to. process used. Thef
gems; .

.stly encountered in p
299i; Th

. resecond

group 0‘

TL

Utii'

19

production rates, and equipment, one of the 3 systems may be used: the heat-activated ,
nobake or cold-box system (Scott and F eazel 1976; Carey 1990). Another way to
categorize sand binder systems is by the elementary classification of “organic” and
“inorganic.” Table 2.3 lists the sand binder systems typically used in the foundry
industry. The clays and cements are the traditional water-plasticized binders. The silicate
binders have gradually gained acceptance, especially in the steel foundry. Recently,

phosphate binders have become commercially available.

About 19 families of resins are in commercial use (McKenley, Jefcoat et al. 1990). The
organic binders are the origin of the hazadous air pollutants emissions. These may be
present as exclusive blends and mixtures formulated to achieve desired properties in the
finished core or mold production. The method of making and curing them is as important

as their composition (Bates and Scott 1975; Scott and Feazel 1976; Carey 1990).

Emissions of volatile organic compounds depend on the composition of the resin binder
and the process used. The heat-activated binder systems yield phenol emissions. These
are mostly encountered in phenolic hot-box and shell processes (McKenley, J efcoat et al.
1990). The second group of binder systems (no-bake) are cured at room temperature.
This minimizes the emissions during sand preparation. Potential emissions that occur

during metal pouring include benzene, formaldhyde, toluene, xylene, and phenol.

 

Table 1.3 list of the Major ‘
leazel 1976; Carey [990)

l. [VORGNC
a Clays '
l. Bentonrte

2. Fire ClaysI

B. Cements
C. Silicates
l. Ethyl Sili.

2. Sodium S.

a. Gassed

b- bogus;

D. Phosphates

ll ORGNC
A. Oils

1. Core Oils
2. Oil-Oxyge'
B. l‘rethane - .\'o~Bal
l. Allyllsocy
2. Phenolic Is
a. Gassed
b.l'ngasse
3. Polyesterl
C. ot-Box
1. l'rea-Fomi
3. Phenolic ~f
aNovolac
bResole
. Furan
al’FFAi
bPFFAt
4 cPFLF.
- Mod".
n AcianBilgd
1. Fun“

 

 

 

20

Table 2.3 List of the Major Sand Binder Systems Used in the Foundry Industry (Scott and
Feazel 1976; Carey 1990)

I. INORGANIC
A. Clays
1. Bentonites
2. Fire Clays
B. Cements
C. Silicates
1. Ethyl Silicate
2. Sodium Silicate
a. Gassed - C02
b. Ungassed (organic, or inorganic)
D. Phosphates

II. ORGANIC
A. Oils
1. Core Oils
2. Oil-Oxygen No-Bake
B. Urethane - No-Bake
1. Alkyl Isocyanate
2. Phenolic Isocyanate
a. Gassed
b. Ungassed
3. Polyester Urethane
C. Hot-Box
1. Urea-Formaldehyde
2. Phenolic -Formaldehyde
a. Novolac
b. Resole
3. Furan
a. UF/F A (Urea-Formaldehyde/Furfuryl Alcohol)
b. PF/F A (Phenol-Formaldehyde/Furfuryl Alcohol)
c. PF/UF (Phenol-Formaldehyde/ Urea-Formaldehyde)
4. Modified
D. Acid No-Bake
1. F uran
a. H3PO4
b. TSA (Toluenesulfonic Acid)
c. BSA (Benzenesulfonic Acid)
2. Phenol-Formaldehyde
Starches and Sugars

 

 

2.2.2 Organic ResinE

according to the studies of
ill}! and Phenolic lsocyan.

whale molds) are formula

 

a form the rigid urethane re

liii; Scott and feazel 197C

ROH+R'

lien

: in the alkyl sv
gllceride that.
PTO-Up see eta:
' 111 the PilEl'TOi
solvent see the
is a h} dIOger‘. l
15 either a by, 3.:

‘

§

9
CH20CtCtt
I

9
fHOCmH

0

(“Wren

Binnie: Hip; L
. it),

21

2.2.2 Organic Resin Binders Chemistry

According to the studies of Scott, Bates and others, the no-bake resins (for example,
Alkyl and Phenolic Isocyanate resins which are primarily used in foundries for cores and
no-bake molds) are formulated to contain hydroxyl groups that react with a polycyanate
to form the rigid urethane resin film that holds the sand particles in place (Bates and Scott

1975; Scott and Feazel 1976). The basic reaction is:

OH

catalyst II II

ROH+R'NCD eR—O—C—N—R‘

 

Where

R= - in the alkyl system is a long chain compound such as a synthethetic
glyceride that contains a fatty acid component with an aliphatic hydroxyl
group see example in Figure 2.2
- in the phenolic urethane is a phenolic resin, usually dissolved in an organic
solvent see the example in Figure 2.3
R’= is a hydrogen or a substituent group
= is either a hydrogen or CHZOH

O
CH,0C(CH,), CH = CHCH2CH = CH (CH,),CH,
l

O
CH 0C(CH,), CH = CHCHZCH = CH (CH,),CH,
l

O
CH,0C(CH,), CH = CHCH2 C HCH,(CH,),CH,

OH

Figure 2.2 Hypothetical structure containing ricinoleic acid in castor oil

The polyisocyanate will be the same for either of the Alkyl and Phenolic systems, and

WI" have the general formula in Figure 2.4.

 

 

lite 3.3. An example of ti.
l9 :;Scort and Feazel 19.72.

.\'C0

 

22

OH OH OH
F— "T F'— —"
x \ CH20CH2~\ CH2 x

 

 

 

 

Figure 2.3. An example of the phenolic resin ( R) in a phenolic system (Bates and Scott
1975; Scott and Feazel 1976)

NCO NCO NCO

 

 

Figure 2.4. General Formula for Polyisocyanate for both systems (Bates and Scott 1975;
Scott and Feazel 1976)

 

lhe shell and furan hot-ho
polymerization. The shell

create the phenolic polyme.
sand in the form ofhexame-

arnonia and formaldehv d.

l
Cit

 

23

The shell and furan hot-box binders, on the other hand, were found to require heat for
polymerization. The shell sand binder uses a novalac resin that requires formaldehyde to
create the phenolic polymer that binds the sand. This binder component is added to the
sand in the form of hexamethylenetetramine, an organic compound that decomposes into

ammonia and formaldehyde when heated. The binder reaction is :

 

 

CH2 CH2 + hexamethyl-enetetramine ——'
.3

0
CH2
5 ‘ (2.1)
OH
CH2 CH2 CH2 CH2
0 H6. H’ OH

The hot-box furan resin undergoes a similar polymerization, although this resin requires

oH
/ CH2 CH2 CH2

  

no Other reactants. The reaction is one of condensation as methylene bridges from

botween the rings and water is released (Bates and Scott 1975; Scott and Feazel 1976).

The reaction is:

 

his polymer product form

ifftttlymer. urea is often a

can Urea formaldehyde

223 Binder Resin

[ML "r -
tarot these organic bind;

aerate tBates 1975). Pvt
ii The high temperature
Potted and form volati It

13;“ ..
idle eth

cine. ethylene a

24

0 .................. o
H__fl CH fl CHzOH (2.2)
o 0
_. 4 n

 

 

This polymer product forms the rigid film that bonds the sand grains. In making the furan
prepolymer, urea is often added to the furfuryl alcohol and the resulting binder may

contain urea formaldehyde polymers (Bates and Scott 1975; Scott and Feazel 1976).

2.2.3 Binder Resin Decomposition

Each of these organic binders can be expected to behave similarly at the mold-metal
interface (Bates 1975). Pyrolysis is expected to be nearly complete at the surface of the
cast. The high temperature will thermally decompose the resin compounds when castings
are Poured and form volatile compounds such as carbon monoxide, carbon dioxide,
methane, ethane, ethylene, acetylene and hydrogen. The phenolic portion is expected to

Pmduce aromatic hydrocarbons (Bates and Scott 1975; Scott and Bates 1975; Scott and

Feazel 1976; Carey 1936)-

“a“

‘-

I
24 Measured HAPi

 

lining a foundry emission .

 

dlaysilles 1993 ). A good
decomposition eftluent is p“
limeirvvorl. castings vverc
foundry binders in an ex per

haiethaust hood. Sampl'

 

sad; method. which is a rec
rtttrence gives an experime

for molds during the med

booties calculated based 0

25

2.2.4 Measured HAPs from Molding and Core Making Process

During a foundry emissions data review, Maysilles identified several useful references
(Maysilles 1993). A good source for measured concentrations of chemicals from mold
decomposition effluent is provided by Scott, Bates and James (Scott, Bates et al. 1977).
In their work, castings were poured into prepared commercial formulations of selected
foundry binders in an experimental foundry and were covered immediately after pouring
by an exhaust hood. Sampling and analysis procedures used are referred to as a quasi-
stack method, which is a recognized technique to identify point source emissions. This
reference gives an experimental description of a laboratory study of organic emissions
from molds during the one-hour period immediately after pouring. An average over one
hour was calculated based on the quantity of metal poured. These measurements did not
include emissions at the mold preparation and pouring. The data are useful for estimating
emissions and are considered in many other papers (Maysilles 1993). The data for
pouring, cooling and shakeout are summarized in Table 2.4. Wallace, et a1, Quarles,
Kielty, and Trenholm, provided estimates of uncontrolled emissions (Wallace and i
Cowherd 1979). They reviewed many test reports to present a complete range of
emission factors. In another laboratory study, Renman, Sango, and Skarping determined
values for emissions of isocyanate and amines from polyurethane (Renman, Sango et al.
1986). Klafka and Loeffler presented data on benzene and formaldehyde emissions in

actual foundries (Klaflca and Loeffler 1995).

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For me case mid)" ana

tags for the risk asses

22.5 Estimated [1
Sam} anempts have '
mid and core making
mucentaiions of differ
Scan 1975; Mosher 19
XL? Emissions Species
Wm} rcpons com

1980;. and Hamilton C (

Emsions of volatile 0:
Me process used. T.

i- . '
mt} ofbmders used ii

13% ' '
. Mfmmsxon. These'
C

‘J‘K A '
x ml»). Jefcoat et al.

3m
mlemperatme. Th-

27

For the case study, analysis results of the binders used in the foundry were acquired as the

bases for the risk assessment. The measured emission factors are represented in Table 2.5.

2.2.5 Estimated HAPs from Molding and Core Making Process

Several attempts have been made to estimate hazardous air pollutant emissions from
mold and core making operations. Mosher, for example, provided extrapolated
concentrations of different constituents of effluent from mold decomposition (Bates and
Scott 1975; Mosher 1994). Mosher used the information on HAP emissions found in the
Air Emissions Species Manual Volume I, Volatile Organic Compound Species Profiles or
in special reports commissioned by the EPA (U.S.EPA and Research Triangle Park

1980), and Hamilton County, Tennessee (Bernard 1990 ).

Emissions of volatile organic compounds depend on the composition of the resin binder
and the process used. Table 2.5 lists potential emissions from the casting operations for a
Variety of binders used in the foundry industry. The heat-activated binder systems yield a
phenol emission. These are mostly encountered in phenolic hot-box and shell processes
(MCKenley, J efcoat et al. 1990). The second group of binder systems (nobake) are cured

at room temperature. This minimizes the emissions during sand preparation.

2.2.6 Data Used in the Transport Model

For our case study, analysis results of the emissions from the binders used in the foundry
were acquired for real data considerations. The measured emission factors are

rePresented in Table 2.6.

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References

‘qu and C I. S. 351
Foundries. Y

RaesC. E. and W
Relationshi]
Iron." AFS

Bemard. \V. R. (19
pollutants.

(ml). R. (1986
(July 1986

Cam P. R. (1986
M&T(S°p1

(are). P. R. (199i
Managem

Emmi. R. and E
Foundry.

GSChW‘andmer. G
and Toxic

mItsandl.
annual .\1.

313301125. J. (199

reject.
u.
JALCUIEF. K1 D

Sun-e}. of

\

fisher, G. ( 1995
Foundde

 

 

30

References

AF S and C. I. S. Association (1995). FORM R - Reporting of Binder Chemicals Used in
Foundries. Des Plaines,IL, American Foundrymen's Association.

Bates, C. E. and W. D. Scott (1975). “The Decomposition of Resin Binders and the
Relationship Between Gases Formed and the Casting Surface Quality Part 2-Gray
Iron.” AF S Transactions 134: 793-804.

Bernard, W. R. (1990). Emission factors for Iron and Steel Sources- Criteria and toxic
pollutants. North Carolina, EPA, Research triangle Park, North Carolina.

Carey, P. R. (1986). “Updating Resin Binder Processes - Part VI.” FOUNDRY M&T
(July 1986): 84-88.

Carey, P. R. (1986). “Updating Resin Binder Processes - Part VII.” FOUNDRY
M&T(September 1986): 90-98.

Carey, P. R. (1990). “Today's Resin Binder Processes for Coremaking.” FOUNDRY
Management & Technology: 32-39.

Euvrard, R. and B. Jackson (1992). Case Study: Air Audit at a Medium Size Gray Iron
Foundry., A.F.S.

Gschwandtner, G. and S. F airchild (1990). Emission Factors for Iron Foundries - Criteria
and Toxic Pollutants. Durham, NC, E.H. Pechan & Associates, Inc.

Klafl(a, S. and J. Loeflle (1995). Measurement of Organic Toxics at Iron Foundry. 88th
annual Meeting & Exhibition, san Antonio, Texas.

MaYsilles, J. (1993). Review of Foundry Emissions Data, US EPA Foundry MACT
Project.

McKenley, M. D., L. A. Jefcoat, et al. (1990). “Air Emissions from Foundries: A Current
Survey of Literature, Suppliers and Foudrymen.” AF S Transactions: 979-990.

Mosher, G. (1995). AF S Title V Seminar, January 24 - 25, 1995, American
FOunddrymen's Society, Inc.

Mosher, G. E. (1994). “Calculating Emission Factors for Pouring, Cooling and
Shakeout.” Modern Casting: 28-31.

Rennan L. C. Sango. et
Emissions from T
American Industri

Scott W. D. and C. E. B.
Relationship Bets
Transactions 103:

Scott W. D. and C. E. B.
Eilissions. Smp
Hartford. CT. CS

Scott W. D.. C. E. Bates
immactions 98:

Seott W. D. and C. E. E.
Birmingham. Ala

ESEPA and N. Researr
Cating. North C

inns. D. and c. 1. Co
Kansas cm. in

 

31

Renman, L., C. Sango, et a1. (1986). “Determination of Isocyanate and Aromatic Amine
Emissions from Thermally Degraded Polyurethanes in Foundries.” Journal of
American Industrial Hygiene Association. 47(October): 621 -628.

Scott, W. D. and C. E. Bates (1975). “Decomposition of Resin Binders and the

Relationship Between Gases Formed and the Casting Surface Quality.” AF S
Transactions 103: 519-524.

Scott, W. D. and C. E. Bates, PhD (1976). Measurement of Iron Foundry Fugitive

EMissions. Symposium on Fugitive Emissions Measurement and Control.
Hartford, CT, US EPA.

Scott, W. D., C. E. Bates, et al. (1977). “Chemical Emissions from Foundry Molds.” AFS
Transactions 98: 203-208.

Scott, W. D. and C. E. Feazel (1976). A Review of Organic Sand Binder Chemistry.
Birmingham, Alabama, Southern Research Institute.

U.S.EPA and N. Research Triangle Park (1980). Environmental Assessment of Iron
Casting. North Carlina, Industrial Environmental Research Lab.

Wallace, D. and C. J. Cowherd (1979). Fugitive Emissions from Iron Foundries.
Kansas City, Midwest Research Institute.

 

3.1 Problem St:
Since 1970. most oft
Agency hare been ha
heir-g recommended.
missions and then. r:
tithe major sources.
moons: tool to ac?
525m-anamm and

mu. .
.4116} and Patntenh;

The inrothesis of the

mini"! Y‘ "
druids for the ca.-

51i5 ~
.nld a strategt' in

«nation tool

Chapter 3

Strategy to Risk Assessment Modeling

3.1 Problem Statement

Since 1970, most of the regulations developed by the US. Environmental Protection
Agency have been based on an end-of-pipe pollutant control strategy. A new strategy is
being recommended, that is, to weigh far in advance the risks associated with potential
emissions and then, reduce the high risk emissions at the source. This is a challenge to all
of the major sources, in particular the foundry industry. Risk assessment may be the most
appropriate tool to achieve this goal, but a major hindrance to applying this process is the
highly variability and uncertainty in many factors used in making a risk assessment

(Finley and Paustenbach 1994; Frey and Cullen 1995).

The hypothesis of the research discussed here is that one can establish a set of target
compounds for the casting process that are amenable to pollution prevention techniques,

and build a strategy for implementation of corrective action using risk assessment as an

evaluation tool.

32

3.2 Modeling APP

lienitieation of significa
recognition of the locatio
spatial transport and the
problemathree part mo

model; lb) dispersion m

33 Source Mode

So‘eral acceptable metl

p

actors. engineering eqt

emissions from the fun
M‘tlacmnng process

reared aceuracv.

l .a
iilmeSl etiectire alt:

torrid. .

‘55 A “1353 bala
one ,

ticQuits for mass
‘ii‘iibt' ch

emlcal re;

ft l,
‘N

 

33

3.2 Modeling Approach

Identification of significant risks, and their corresponding management require the
recognition of the locations of environmental releases of toxic chemicals, the pathways of
spatial transport, and the routes to human and ecological exposure. To approach this

problem, a three part model (see Figure 3.1) with a feed back loop is proposed: (a) source

model; (b) dispersion model; and (c) risk model.

3.3 Source Model

Several acceptable methods for computing emissions rates (stack testing, emission
factors, engineering equations and material balances) can be used to quantify the
emissions from the furnace, the pouring, shakeout, and other processes involved in the

manufacturing process. The approach selected is dependent on cost, available data and

required accuracy.

The most effective alternative is to apply a mass balance to every operation within the
foundry. A mass balance is the process by which, in a well contained control volume,
one accounts for mass rates of a chemical in and out of the system, including gains and
losses by chemical reactions and other means. This method can be applied to each
process separately and for each chemical used. Some areas of complexity will require
several assumptions about HAP emissions from different process stages, binder chemical

compositions and ratios, and capture and control efficiencies.

34

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3.4 Processes Selet

 

horderto obtain a tractai
including pouring. coolir
“henodeling technique. '
landous air pollutants t

tltematires that ma) be r

dried on the percentage
predominant. Their em
notification of the ma'
Milt lhereiore. tl

ttfaihtrtion to HIPS.

H595 selected for im;

orientations and tin

35

3.4 Processes Selection and Research Scope

In order to obtain a tractable problem for modeling, the casting process in a foundry
(including pouring, cooling, and shakeout) was selected as a case study for application of
the modeling technique. This process was selected because it is a major source of
hazardous air pollutants (HAPs) and because there are a number of pollution prevention

alternatives that may be evaluated.

Based on the percentages presented in the previous chapter, the metal HAPs are
predominant. Their emission reduction will not be achieved though, without
modification of the manufacturing process. This was not the intent of the present
research. Therefore, the volatile organic compounds were focused on, despite their
contribution to HAPs. Benzene, formaldehyde, phenol, toluene and xylene were the
HAPs selected for implementation in the transport model based on their high

concentrations and their toxicity levels.

3.5 Transport Model

The transport of pollutants, both chemical and biological, over distances is a complex
distributive phenomenon which reduces ambient concentrations of pollutants compared to
their concentrations at the stacks. Therefore, it is essential to carefully model the
transport of chemical releases in order to predict their fate and concentrations at the

receptor of interest.

 

3.5.1 Selection of

So'eral transport models
Environmental Protectio

selected as the most app

3.52 Source C ha

l‘ue sources selected in

prices lines in the four

lleteorological data for

one from the MM

353 Selection 0
“mm SClllare cente

“Eight
’ 5J8 aSSCSSInem

 

36

3.5.1 Selection of a Dispersion Model

Several transport models were investigated for use in the case study. The US.
Environmental Protection Agency’s Industrial Source Complex model (ISC3) was

selected as the most appropriate dispersion model.

3.5.2 Source Characterization

The sources selected in the case study are stacks collecting emissions from three casting

process lines in the foundry. They were considered to be point sources.

Meteorological data for the region of Saginaw for five years from 1987 to 1991 were

obtained from the Michigan Department of Environmental Quality.

3.5.3 Selection of Receptors

An arbitrary grid was built around the facility to screen the region on a two by two
kilometer square centered at the plant. A smaller set of receptors is considered for

CXpOSUI'C assessment.

3.6 Risk Model

During this final step, an exposure assessment, a toxicity assessment of the adverse
effects of carcinogens and noncarcinogens, and characterization of the risk is conducted

using a probabilistic approach.

37

3.7 Feed Back Loop

Using the risk approach, analysis of potential risk reductions at the source or from the use

of alternate control systems are investigated.

References

Finley, B. and D. Paustenbach (1994). “The Benefits of Probabilistic Exposure

Assessment: Three Case Studies Involving Contaminated Air, Water, and Soil.”
Risk Analysis 14(1): 53-73.

Frey, H. C. and A. C. Cullen (1995). Distribution Development for Probabilistic
Exposure Assessment. 88th Annual meeting 7 Exhibition, San Antonio, Texas.

instinct lows“ '
Hip emissions from
ascss upper and lots
en's used and we
examined to study tit

and concentrations

Vet be performed. 5
onnpuons are mat

coal to the accura

ll Methods ft

to“: '
a» o. the biggest c

hi... ~
5555055 mt'entom'

55m . .
TS. stack tcsurn

Chapter 4

Source Model

This chapter focuses on the use of a chemical mass balance to determine the potential
HAP emissions fi'om the molding process. Different scenarios have been considered to
assess upper and lower bounds on organic emissions based on records of the quantities of
binders used and their compositions. A range of capture and control efficiencies has been
examined to study their impact on the calculated potential emissions. By tracking the
sand concentrations before and after casting, a reasonable evaluation of the emitted HAP
can be performed. Since no measurements of the sand concentrations are available, some
assumptions are made in order to obtain these concentrations. These assumptions are

critical to the accuracy of the results.

4.1 Methods for Developing a Source Model

One of the biggest challenges in developing the source model is to compile a reliable
emissions inventory. Emissions can be estimated through material balances, emission

factors, stack testing, or engineering equations(Li, Karel] et al. 1995).

38

tn Material 3‘

Anass balance around
amount of the compour
eoupment to the amou
tron it or concerted t
nutter consideration 0
tecosan. Despite the
other methods. it shoui
tiers small error in a

raterial balance.

4.12 Emission l

I
nssron factors for 5]

tests. are listed in the

4.»:-
cmed from old units
o6» ' -

n o gne the \VOISI-

5dr talues can be us
J

39

4.1.1 Material Balance

A mass balance around a piece of equipment in a process can provide an estimate of the
amount of the compound exiting in the system by adding the quantity entering the
equipment to the amount formed by chemical reactions minus the amount accumulated
within it or converted to another compound. In case it is not recovered as a product,
further consideration of alternate loss pathways such as solid, water, and air emissions is
necessary. Despite the fact that material balance is relatively inexpensive compared to
other methods, it should be used with care and be verified by other means. For instance,

a very small error in a concentration measurement can result in a large error in the

material balance.

4.1.2 Emission Factors

Emission factors for specific operations, on a pound of pollutant per operational unit
basis, are listed in the EPA’s reference manual, AP-42 (U.S.EPA 1991). These are
derived from old units and may not be representative of the ones in use today. They also
tend to give the worst-case estimated emissions (Li, Karell et al. 1995). Nevertheless,

these values can be used to compare results generated by other means.

4.1.3 Stack Tests

Stack testing is the most accurate way to measure emissions. Measurements at the part

per billion level are possible and can give very accurate information. However, as with

 

any other analytical mi
sampling and analysis

toierify the results ot~

l.l.4 Engineeri
Engineering estimate
ngineering equation
condition may be to
cotand accuracy sir

5““ ill. Karell et i

ll Factors Af
3Com] iaClDI’S can

er»:
atllom'nemal Com

minions.

4.2.1 Cathre

40

any other analytical method, results are not exempt from errors. In addition, the cost of
sampling and analysis limits the use of stack testing to selected high priority emissions or

to verify the results of other methods of calculation.

4.1.4 Engineering Equations

Engineering estimates are based on a theoretical analysis of an actual situation. Several
engineering equations that provide estimates of the volatility of a chemical under diverse
conditions may be used to estimate emissions. This method represents a balance between

cost and accuracy since no testing is needed and safety factors may be used to account for

losses (Li, Karell et al. 1995).

4.2 Factors Affecting Emission Rate Estimates

Several factors can alter emission rate calculations. Factors such as losses of input

materials at the handling and the preparation step, the capture efficiency of hoods, and the

environmental control system efficiency can significantly reduce or enlarge the rate of

emissions.

4.2.1 Capture Efficiency

One hundred percent capture of the different emissions during mold and core making,
raw material transport, and most importantly at the pouring step is a challenge to the

control system’s builders. Due to the layout of the casting process, a major part of the

 

missions escapes I
should also accoun
am of the mold. ti
mold etc. should l
1995). In this stud;
he Ventilation sy st

ltCGlH. 1974; hi-

4.2.2 Control

item are set'eral S
androlatile organi
:crtbbers and com
obeoterational t
.. i erence (Aller
production operati
thing ttith binde
for emaction of p

iiiC'a '

4]

emissions escapes the control system and is considered to be a fugitive emission. One
should also account for these losses to the atmosphere. Many factors such as the surface
area of the mold, the exhaust air flow, the temperature, the height of the hood from the
mold, etc., should be considered in determining the ventilation rate(AF S 1972; Belglund
1995). In this study, only two factors were considered in evaluating capture efficiency:
the ventilation system characteristics and the surface area covered by the process

(ACGIH. 1974; McDermott 1985).

4.2.2 Control Efficiency

There are several state-of-the-art technologies for the control of hazardous air pollutants
and volatile organic compounds. These include thermal and catalytic incineration,
scrubbers and condensers, and carbon adsorption. These technologies have been proved -
to be operational under conditions of low concentrations and low particulate matter
interference (Allen, Archibald et al. 1991). The major pollutant emitted in mold and core
production operations is particulate matter from sand reclaiming, sand preparation, sand
mixing with binders and additives. Thus, carbon adsorption is not a feasible alternative
for extraction of pollutants. Rate calculations for these control systems should account for
their efficiencies in oxidizing or scrubbing the targeted toxics. For instance,
contemporary incinerators are designed to achieve VOC destruction efficiencies from
90% to 99%, depending on their applications. But, they are often run at 95% efficiency to
reduce the use of fuel (Allen, Archibald et al. 1991), and to minimize energy consumption

requirements (Cooper and Green 1978). As discussed previously, at the Saginaw

lldleable Iron Plar
i ”ted to a sweet so

uterest are readily

43 Mass Bala

Currently. stack tes
memories. This it
The tests may indie
Will not price any in

3ft tented to one st.

the Dimitry are the

elm data do not I

has. it is not possi'

iii “1031 ctiectiye a

Di.- .1 .' .
mpetity are listec

binding {1
redarhatii
0 x ‘
anOUS by
[0 Chemic-
o 0 , .
r5311“? C0
break d0\\‘

0 .

42

Malleable Iron Plant, the emissions from the pouring step are captured by a hood and
directed to a wet scrubber. No data on the wet scrubber efficiency for the VOCs of

interest are readily available.

4.3 Mass Balance as a Modeling Tool

Currently, stack tests are used by the foundry industry to perform air emission
inventories. This method provides accurate emission data at the time of measurement.
The tests may indicate which HAP or HAPs the foundry is not in compliance but they
will not give any indication from where these chemicals come. When multiple exhausts
are vented to one stack, the stack test data will not reveal which specific operations within
the foundry are the major sources of hazardous air pollutants. Furthermore, the stack
testing data do not provide a functional relationship between the source and the emission.

Thus, it is not possible to model the effect of process changes on emissions.

The most effective alternative is to apply a mass balance approach. Some areas of
complexity are listed below:

0 multiple stages of HAP emissions that begin with the initial exposure of the
binding material to the atmosphere during mixing and continue through sand
reclamation;

0 various binding material compositions in the sand with the multiple stages due
to chemical reaction and evaporation;

0 organic compounds in question are difficult to characterize because they may
break down or react to form other related organic compounds; and

0 efficiency of the capture and control equipment is highly variable.

lithe follotting discus
presented to determine
the tariables of conce
modeling the input in
ot’idcntifying the pot

methods.

43.1 Mass Bal

a,

Emilie pérfoman

° Mold an
' Producii
' Producti
0iterates
informs
' Binder
making
Trader,
CODCEn‘
Binder
Percem
each or
l0r 33C]
Capum
Cumin;
Ail llm

lhe Slac

43

In the following discussion, a simplified application of the mass balance approach is

presented to determine concentrations at the stack. The exercise is used to demonstrate

the variables of concern and to show the form of the relationships that may be used in

modeling the input function of the risk assessment. This exercise also begins the process

of identifying the potential areas to explore for application of pollution prevention

methods.

4.3.1 Mass Balance Variables

Effective performance of a sand system mass balance requires the following:

Mold and core sand use per hour;

Production rates which include tons of metal charged per hour;

Production schedule which includes hours a day and days a year the plant
operates;

Information regarding multiple line use within the plant;

Binder system (cold-box, Hot-Box, green sand, etc...) used in mold and core
making;

Trade names and initial chemical composition of the binders (to obtain the
concentrations of the organic compounds of interest);

Binder use per hour;

Percent evaporation, percent reaction, and percent remaining in core for
each organic contained in the binders. (This will require air/sand analysis data
for each stage of the operation);

Capture technology employed and its efficiency for each stage;

Control technology employed and its efficiency for each capture device; and
Air flow rates associated with the capture, control, and ultimate exit through

the stack.

U
-‘n-

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44

Because a fundamental assumption in developing the model is that each modeled
parameter has a probability distribution. the input model must also account for the
following:

0 Variation in binder composition;
0 Variation in emission rate; and

0 Variation in production rate.

4.3.2 Chemical Input Calculation

For this work, an estimation of the initial concentration of the chemical in the molded

sand is obtained using the binder use rates given at the Saginaw Malleable Iron foundry.

It is assumed that the mass rates of sand use in and out of the control volume (Qin and

Qsand) are equal and that everything is well contained in the control volume.

4.3.3 Chemical Output Calculation

Two Pathways were considered in the output calculations: chemicals intercepted by the

e"halISt system and chemicals remaining in the wasted sand. Fugitives were accounted

for by introducing a capture efiiciency coefficient strictly related to the exhaust system.

As a first approximation to determine the concentration in the wasted sand, Csand, an

Upper limit concentration equal to the universal treatment standard for land disposal of

the Wasted sand was assumed (Federal Register 1994)-

 

 

ll'idr careful considen
the sand before and at
for the components it
can be obtained (AFS
ummhmm
control system used.

hilarid the exhaus

43.4 Mass Ba

iii material mass l

itgtre 4.1 is obtair

Acc

.\l
dCdt =
in

II

II II

C0111
DUI
sand =

Sand 2

45

With careful consideration of measured values (when available) of the concentrations in

the sand before and after molding, and knowing the percentages of the reacted amounts

for the components in the resin binders, a reasonable evaluation of the chemical emission

can be obtained (AF S and Association 1995). Two coefficients it and B were introduced

to account for the losses due to the capture efficiency of the hood and the efficiency of the

control system used. The sensitivity of the concentration at the stack, Cstack, to variations

in , l3 and the exhaust rate, ch, are of interest.

4.3.4 Mass Balance Equation for Casting

The material mass balance equation for a chemical i based on the scheme illustrated in

Figure 4.1 is obtained :

MdC

_
—‘~——
—

(it
Where

Accmnulation = Mass In - Mass Out 3: Reactions

Cin x Qin ' Cout x Qout ' Csand x Qsand (4.1)

mass of sand in control volume, g

concentration change rate of chemical, mg/g sand / unit time
initial concentration of chemical in mold, mg/g of sand

mass rate of sand used for molding, g of sand /un't time
concentration of chemical out of th3e mold, mg/m air emitted
rate of gas flow out of the mold, m /unit time

concentration of chemical remaining in the sand after pouring,
mg/g of sand

mass rate of wasted sand, g/ unit time

 

 

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47

Since the goal is to determine the concentrations out of the stack after capture and
control, Cout and Qout are converted, respectively, to the concentration out of the stack,

Csmck, and the exhaust flow rate, Qex. Equation (4.1) is then:

M dC i 1 j
: cm x in ' Csac x ex ' Csan x Qsan 4'2
where
Csmck concentration out of the stack of chemical, mg/m3 of air exhaust
Qex = flow rate of exhaust air out of the stack, m3/unit time
u = capture efficiency coefficient in %
B = control system efficiency coefficient in %

At steady state, it is assumed that no accumulation in the control volume of the chemical i
is occurring, the change in concentration with respect to time is then zero. Equation (4.2)

is reduced to:

l .
0=C. . .[———J C, .. -C .. 4.3
m an [1(1-fl) sack ch sand Qsand ( )

Solving for the concentration of chemical i out of the stack, Cstack is then:

C _ Cin Qin 'Csand Qsand
stack _ (1/ y-(l-fl)) Qex (4-4)

 

4.4 Mass Balance Results and Discussions

Naphthalene was selected for the initial trial calculations as a quick test of the mass

balance approach. It has chosen as an example of a process where major pollutants do

 

 

not react but partiallj
assumed to be emitti
lomr R pushed by
attire preparation st
iii-t eraporated dtn
Filling step. Since
thaction oi the chi
I115’i‘trsal treatment

mphMClli‘ lS assi

he binder system
to the material sat}
ditto part I and
too of binder to s
tidal concentratic
uhoming conside
Mutated to be 64
4.1;).

,l.
17“.. ,

48

not react but partially evaporate at the sand preparation step. The remaining part is
assumed to be emitted at the pouring step due to the contact with the hot melted metal.
Form R provided by the American Foundrymen Society shows no reaction of naphthalene
at the preparation step (AF S and Association 1995). It also suggests that this chemical is
50% evaporated during sand preparation. The other 50% is then in the mold at the
pouring step. Since the sand can be disposed outside of Subtitle C, it is also assumed that
a fraction of the chemical is found in the wasted sand at concentrations below the
universal treatment standards for land disposal (for calculations, the upper limit for

naphthalene is assumed to be 5.6 mg/kg).

The binder system selected for study is a phenolic urethane known as Isocure. According
to the material safety data sheet, it is the combination of a two part resin. A typical ratio
of 52% part I and 48% part II by weight of the phenolic urethane was adopted. A typical
ratio of binder to sand between 1% and 2% by weight was assumed to determine the
initial concentrations of naphthalene in molded sand. Based on the data provided and the
upcoming considerations, the initial concentration of naphthalene in the sand was

estimated to be 641.3 mg/kg of sand in the core before pouring (see detailed data in Table

4.1).

A sand-use-rate for production of cores at the Saginaw Malleable Iron foundry was
selected to study the effect of selected capture efficiencies, exhaust flow rates and

environmental controls on the concentrations of naphthalene released to the atmosphere.

The naphthalene co
flow rates are prese
control efficiencies
control has been al:
illustrated in Figure
one order ofmagni
from 15,000 to 501'.

represented in Ap

Table 4.

 

 

49

The naphthalene concentrations as a function of capture efficiency for different exhaust
flow rates are presented in Figure 4.2 and Figure 4.3, respectively for 95% and 0%
control efficiencies. The concentration of this chemical before and after environmental
control has been also examined to account for the total emissions. The results are
illustrated in Figures 4.4 and 4.5. The mass balance calculations showed a reduction of
one order of magnitude of the concentration as a result of an increase of the exhaust rate

from 25,000 to 500,000 ft3/hour (see sample results in Table 4.2). Detailed calculations

are presented in Appendix A.

Table 4.1 Assumptions Considered in the Mass Balance Calculations

 

 

 

 

 

 

 

 

Concentration of naphthalene in phenolic urethane 10.0%
Use level or percent ratio to sand 1.28%
Evaporation fraction before pouring 50.0%
Reaction rate 0.0%
Metal poured in tons/hr 112
Core sand added in lb./hr 7329
Resin added in lb./hr 94
Universal treatment standard for naphthalene in mg/kg sand [FR, 1994 #120] 5.6

 

 

 

 

50

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WMWMMM
operations for elex'e
den'Vcd in pounds 0

These were obtaine

(axerage measurec
_.\
((‘(Sand 10 met

From equation (4, 5,
“115 Equation gix'eg
Control. For naphtl‘

2x10fib.1b.ofbi

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55

4.5 Comparison of Mass Balance and Emission Factors

Mosher provided an easy way to calculate emissions of selected HAPs from molding
operations for eleven different binder systems (Mosher 1994). Emission factors were
derived in pounds of chemical released to air per pound of index or resin binder used.

These were obtained based on the following formula:

(average measured air concentration for a chemical) * (air volume sample)
(((sand to metal ratio) * (metal in mold) * (resin in mold)) * 0.95)

 

(4.5)

From equation (4.5), one may note that a 95% capture efficiency was used by Mosher.
This equation gives the total emission of the chemical before capture and environmental
control. For naphthalene, with the same binder, Mosher obtained an emission factor of

22x 1 0'6 lb./lb. of binder used (Mosher 1994).

The mass balance calculation, under the same conditions of 0% control efficiency using
the same percentage of capture (95%), generated a concentration out of the stack of 47.3
“18/1113. This concentration is calculated for an exhaust system rate (42,500 m3/hour)
approximately 708 times bigger than the sampling rate used by Mosher (60 m3/hour). For
comparison purposes, this concentration was converted to fit the conditions of the study

e“ample. An emission factor can be then determined using the following formula:

WM the chemical out of the stack) * (exhaust flow rate per hour)

(resin use rate in mold per hour) * (708)

 

(4.6)

 

.t naphthalene em
tllllCl’S from \losl
of assumptions in
llosher used one
tSRll to develop l
the mass balance.

based on regulate

At this preliminar
hetelationshipsl
1‘ ’he lnPUI mode

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PhiUOllc Urethane

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ml If] [he me

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muted t0 resnlt'e

56

A naphthalene emission factor of 66x10"6 lb./lb. of resin was obtained. This estimate
differs from Mosher’s by a factor of three. It is not unreasonable considering the number
of assumptions involved in both Mosher’s estimate and the mass balance. For example,
Mosher used one half of the concentration reported by the Southern Research Institute
(SRI) to develop his emission factor because SRI reported data at the detection limit. In
the mass balance, however, the selection of the concentration remaining in the sand was

based on regulatory standards rather than actual field data.

At this preliminary stage, it appears that the mass balance approach is sufficient to show
the relationships between the variables of concern and that with calibration it can be used
as the input module for the risk assessment. It also has value in that pollution prevention

alternatives may be examined as a function of changes in resin composition.

Of concern, however, is the production of compounds not explicitly identified as part of
the composition of input chemical. For example, benzene appears as an emission from
phenolic urethane resin but is not listed on the Material Safety Data Sheet as a
component. Two explanations are possible. One is that benzene was an impurity or
solvent in the formulations tested by SRI that no longer exists and, therefore, is not
reported on the material safety data sheet, MSDS, used for these calculations. Another
explanation is that benzene is a reaction product of the casting process. More research is

required to resolve this question.

 

 

References

AWE-“9m
Arbor-l

its (1973" "E
(.17.

us and C- I“ 5
tumdr

.hhnlllh‘J
hleufl‘

Amerl‘
Bnpund.Rul
Exhau

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Pollut
28(5):

tueuRee‘iS
48001

ltR.Xt.Ke
Chen

hcheHnOIL
Bune

hlusher. G. i
Shah

[SEPAtl9
INC.7

57

References

ACGIH. (1974). Industrial Ventillation - A Manual of Recommended Practice. Ann
Arbor, MI., Edwards Brothers Incorporated.

AF 8 (1972). “Exhaust Systems and Hood Design.” Foundry Environmental Control 1(4):
1 -1 7.

AF S and C. I. S. Association (1995). FORM R - Reporting of Binder Chemicals Used in
Foundries. Des Plaines,IL, American F oundrymen's Association.

Allen, G. R., J. J. Archibald, et al. (1991). Hazardous Air Pollutants a Challenge to the
Metal Casting Industry. 95th AF S Casting Congress, Birmingham, Alabama,
American Foundrymen's Society Inc.

Belglund, R. (1995). “Effective Ventilation During Plating - Capture Efficiency for Rim
Exhausts.” MetaL Finishing 93: 79-83.

Cooper, H. B. and W. J. Green (1978). “Energy Consumption Requirements for Air
Pollution Control Equipment at an Iron Foundry.” Control Technology News
28(5): 545-548.

FederalRegister (1994). “Rules and Regulations.” Federal Register 59(180): 47987-
48003.

Li, R., M. Karell, et a1. (1995). “Develop A Plantwide Air Emissions Inventory.”
Chemical Engineering Progress: 96-103.

McDermott, H. J. (1985). Handbook of Ventillation for Contaminent control. Boston,
Butterworth.

Mosher, G. E. (1994). “Calculating Emission Factors for Pouring, Cooling and
Shakeout.” Modern Casting: 28-31.

U-S.EPA (1991). Compilation of Air Pollutant Emission Factors. Research Triangle Park,
NC, US. EPA.

[I

 

5.1 lntroductir

llahematieal mode
lithe atmosphere it
Seigneur in 1993 (V
united into the air t
and removal mechar
atmosphere. The be
riseand buoyancy-ii
ltitular etTects. L

l
tune towards the 2

Moon and Seig
135iW011 two lere.

e a
”more rt’fined

pf "i' . .
hints of interest 1

«ahenatieal model

Chapter 5

Transport Model

5.1 Introduction to Dispersion Modeling

Mathematical models for the transport, dispersion, transformation and fate of substances
in the atmosphere for use in health risk assessment were reviewed by Venkatram and
Seigneur in 1993 (V enkatram and Seigneur 1993). The transport and fate of substances
emitted into the air depend on source characteristics, meteorology, atmospheric chemistry
and removal mechanisms. Source characteristics determine the initial dispersion in the
atmosphere. The buoyancy associated with high-temperature emissions leads to plume
rise and buoyancy-induced spreading. Momentum associated with exit gas velocity leads
to similar effects. Large buildings near the source create wakes that drive the emitted

plume towards the ground at locations near the source.

Venkatram and Seigneur recommended an overall approach to health risk assessment that
is based on two levels of analysis. The level 1 analysis provides a screening assessment,
while a more refined assessment can be conducted for the substances and exposure
pathways of interest using a level 2 analysis. A level 1 model was defined to be a

mathematical model, that is applicable to any site, uses available data, and is acceptable

58

l0

59

to regulatory agencies for specific applications. A level 2 model was defined to be a
mathematical model, that may take into account specific features of the local environment
and, therefore, may not be applicable to all sites, may require the collection of additional
site-specific and/or chemical specific data, and is acceptable to the scientific community
but is not necessarily a model recommended by regulatory agencies (V enkatram and

Seigneur 1993).

5.2 Selection of the Dispersion Model

Because of the complex nature of the release and movement of hazardous air pollutants, a
wide variety of available models have been proposed. The choice of the appropriate
model for a specific situation depends on many factors, including user familiarity with the
model, availability of model input data, desired precision of the estimate, and

acceptability of the model by regulatory agencies and the scientific community.

Venkatram and Seigneur recommend the use of the EPA’s Industrial Source Complex
Version 2 (ISC2) for level 1 analysis. This model has two versions. The short-term
version, ISCST2, can estimate concentrations averaged over one hour. The long-term
version, ISCLT2, estimates annual averages using a statistical summary of meteorological
data. This allows for short computation periods on modern microcomputers. The long-
term version is more appropriate than the short-term for chronic risk assessment. The
long-term version uses annual frequencies of meteorological variables whereas the short-

term version uses sequential hourly meteorological data. Differences in concentrations

 

(up to 30 percent) 1
resolution of the m
in although mans

loth urban and no

literature surte
of regulator)” G
the results of fiel
he “Cold heath
llodel Detelopr.
shorted that the
Tim‘s“. The c
cofi‘p-arisons of

3st on this n
Iolel llSC3) “

CanbelBedloE

urban indlBtri

to
L

Imp-3C1 ofb
la».

60

(up to 30 percent) may occur between the two versions because of the difference in
resolution of the meteorological data. The ISC model is applicable to distances up to 20
km although many studies extend this to 50 km from the source. It includes options for

both urban and rural environments, that differ by their dispersion (Gratt and Levin 1995).

A literature survey conducted by Gratt and Levin in 1995 of studies comparing the results
of “regulatory” Gaussian plume models, such as ISC2 (the current version is ISC3), with
the results of field tracer tests designed to evaluate those and other models (for instance
the “Cold Weather Plume Study” by Vaughn in 1987 and the “Green River Air Quality
Model Development: VALMET- A Valley Air Pollution Model” by Whiteman in 1985),
showed that the Gaussian models have limitations, but are quite good for predictive
purposes. The comparison was limited due to temporal and spatial variations. Recent
comparisons of concentrations generated by current models with field measurements

showed an agreement within a few percent (Gratt and Levin 1995).

Based on this literature review, the revised version of the EPA Industrial Source Complex
model (ISC3) was selected for this work. It is a steady state Gaussian plume model that
can be used to assess pollutant concentrations from a wide variety of sources associated

with an industrial source complex.

The impact of both short ( ISCST3) and long (ISCLT3) term modeling on the ground

level concentrations used for evaluation of risks due to acute, and chronic exposure were

 

 

considered. It to
particular study 1
horttenn mode
such as: settling
mint and Volurr
point sources; 3
areas, flat and c

averaging time:

53 Role 0]

‘lmOSpheric t
in 5' lhel' are
:oneemmi 0115
is i“llllillllgg &
Wing until i
Tang 10 rep
W Speed. 3
film-tonal I
honey“ hig

direction and

61

considered. It was decided that, the short term version was more appropriate in this
particular study because of the lack of meteorological data and the highr precision of the
short term model. This model in its short version considers site-specific characteristics
such as: settling and dry deposition of particles; downwash due to nearby buildings; area,
point and volume sources; plume rise as a function of the downwind distance; multiple
point sources; and limited terrain adjustment. It is appropriate for both rural and urban
areas, flat and complex terrain, transport distances smaller than 50 km, different

averaging times fiom one-hour to a year, and for continuous toxic air releases.

5.3 Role of Atmospheric Turbulence in Air Pollution

Atmospheric turbulence is the mechanism that dilutes and mixes pollutants with ambient
air as they are transported by the wind. It plays a major role in determining ground level
concentrations. Turbulence is produced mechanically by air flowing over obstacles such
as buildings and hills, and thermally (process of convection) by warmer air rising and
cooling until it reaches the temperature of its surroundings and conversely, by cooler air
sinking to replace warm air. There are three important factors influencing turbulence:
wind speed, atmospheric stability and mixing height. Concentration is inversely
proportional to wind speed, i.e., the higher the wind speed the lower the concentration.
However, high wind speeds are often associated with persistence in the downwind
direction and can result in high ground level concentrations. During unstable conditions
(e.g., stability class A) the plume from a tall stack will be mixed to ground level relatively

close to the source, resulting in high short term concentrations. These concentrations can

 

lesignifieantly in
mixing height. St
concentrations du
conjunction with
dounnard sooner
conditions. (e.g..
honzontal disper
main close to tl
ornament;

decreases.

S“hearer-

little qufi 011 j

ttttptor b}. {he C

For
av _
dead-l Stan
anossnind dig.
~u

altonodb.

62

be significantly increased when the unstable conditions occur in conjunction with a low
mixing height. Some plumes will have their greatest impact on ground level
concentrations during near neutral conditions (e.g., stability class D) and ofien occur in
conjunction with high wind speeds. The high wind speed causes the plume to bend
downward sooner and not reach as great a height as under a low wind speed. For stable
conditions, (e.g., stability class F) the plume’s vertical spread is severely restricted and
horizontal dispersion is also reduced. Pollutants released during very stable conditions
remain close to the ground. Finally, mixing height determines the volume of air available
for pollutant dispersal; as the mixing height increases, the ground level concentration

decreases.

5.4 Theoretical Background

The ISC3 short term model for point source emissions uses the steady-state Gaussian
plume equation for a continuous elevated source. The hourly concentrations for each
source at each receptor are summed to obtain the total concentration produced at each

receptor by the combined source emissions (U.S.EPA 1995).

For a steady state Gaussian plume, the hourly concentration at downwind distance, x, and
a crosswind distance, y, (Figure 5 .1) as suggested by Pasquill (1961) (F.Pasquill 1962),

and modified by Gifford (1961) is given by Equation 5.1.

 

 

All J

Fit

63

 

; Z
/

p

Q

Q
x
(x. -v. Z)

(x. 0. 0)

' (x. -v. 0)

 

Figure 5.1. Coordinate System showing Gaussian Distributions
in the Horizontal and Vertical Directions

 

H=Q

Axle! ) 271'

there

his equation iS '
that is no thrills“
continuous file“
tween the sour
for his studl'- ll

l
on; enough 00m

he etleet of stab
Structure of the at
oer uhieh the co

ll oer l967).

64

2 2 2
x(x,y,z;H)=—Q———ex —05[—}-’—] - ex 45(‘2—H] +ex 4562441) (5.1)
27m30'y0', 0', 0'2 0,

 

 

Where

I = concentration of gas or aerosols (particles less than 20 pm in diameter) at x, y, z
from a continuous source with an effective height, H, jag/m3

Q = pollutant emission rate, g/sec

K = a scaling coefficient to convert calculated concentrations to desired units
(default value of 1x 106 for Q in g/s and concentration in pig/m3)

V = vertical term accounts for the vertical distribution of the Gaussian plume.

D = decay term, accounts for pollution removal by physical or chemical
processes

0,, dz = standard deviation of lateral and vertical concentration distribution, m
u, = mean wind speed, m/s, at release height
H = effective emission height, m

This equation is valid where diffusion in the direction of the plume travel is neglected,
that is no diffusion occurs in the x direction (see Figure 5.1). This is the case of
continuous release or when at least the duration of release is greater than the travel time
between the source and the receptors of interest (Turner 1967). This is exactly the case
for this study. The release is continuous for sixteen hours, and the duration of release is

long enough compared to the travel time.

The effect of stability is considered in the values of 0y, 0;. These vary with the turbulent
Structure of the atmosphere, height above the surface, surface roughness, sampling time,

over which the concentration is to be estimated, wind speed, and distance from the source

(Turner 1967),

lhe effective stacl
physical stack heig
stack The ditlere
hased on experimt

true value (Turner

Fat

ON
00
x

lithe ground let'

Zlhllzfl‘)

Vs
~

it
the eenterline (

”100”

65

The effective stack height, H, is used to account for the sum of the plume rise and the
physical stack height, h. Rarely will this height correspond to the physical height of the
stack. The difference between those heights, AH, as developed in Holland’s equation, is
based on experimental data. It provides a slight safety factor since it underestimates the

true value (Turner 1967). Holland’s equation is:

 

Asz‘ d-(l.5+2.68x10'3p-L—TL-d] (5.2)
u 5 Ta

Where

AH = rise of the plume above the stack, m

v, = stack gas exit velocity, m sec’l

u, = wind speed, m sec'l

d = inside stack diameter, m

p = atmospheric pressure, mb

T, = stack gas temperature, °K

T. = air temperature, °K

2.68 x 10'3 = constant, mb'l m‘I

At the ground level (2 = 0), the diffusion equation (5.1) is simplified to:

 

2 2
l(x,y,O;H)=—QK—V—Q—-exp —0.5[ y] ~exp[—0.5[—Ij—) ] (5.3)
27z'u30'y0'z a 0',

y

At the centerline of the plume (y = 0), a further simplification results:

 

2 nusa ya I

Z

2
1(x,0,0;H)= QKVD “pf-0.56311) ] (5-4)

 

 

hmgmuul

gomdhtdi

10.0.0

lounhnti,

it sound 1

lUShO“€\Q
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ISC300de

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66

For a ground-level source without effective plume rise (H = 0), the concentration at the

ground level is finally:

QKVD

z(x,0,0;0) =
27ru30' ya

 

(5.5)

2

Equation (5.3) is used in this study since the region of Saginaw is flat. Concentrations at

the ground level will be considered.

It is however, necessary for this study to test the sources for possible downwash of the
emissions by the nearby buildings. Since all the stacks are on the roof of the main
building, both the main and the administration buildings will have potential downwash
effects. This will be accounted for by the Building Profile Input Program (BPIP). The
ISC3 code will then utilize BPIP to decide if the plume at any time will be influenced by

these buildings.

5.5 Downwash effects and BPIP

If the plume is caught in the turbulent wake of the stack or a building in the vicinity of the
stack, the effluent will be mixed rapidly downward toward the ground. This effect is
called downwash. Different building downwash models were investigated and evaluated
by Paine in 1996 (Paine 1996). The Building Profile Input Program (BPIP) provided by

the EPA was selected for use in the calculation of the width and the height of

influence 61'5“.

implemented e)

lhe BPIP tBuil
projected build
Practice) 513Ck
to be manual l.‘
henceforth ider
Bulletin Board
GEP and is des
from Structures
hailing height

Which stacks at

The Second par
liierent BH ar

calculations on

lttnatted for El

isset by BPIP 1

horror 5,1 be

67

influence every 10 degrees for 360 degrees. This model has the advantage of being

implemented exclusively for ISC3, which led to its selection.

The BPIP (Building Profile Input Program) is designed to calculate building heights and
projected building widths based on an implementation of GEP (Good Engineering
Practice) Stack Height and building downwash guidance. The output data from BPIP has
to be manually edited into the appropriate ISC2 input runstream. This program is
henceforth identified by its name and a Julian date; i.g., BPIP (dated 93320) in the EPA’s
Bulletin Board. The BPIP is divided into two parts. The first part is based solely on the
GEP and is designed to determine whether or not a stack is being subject to wake effects
from structures. Several values are determined such as the GEP stack height, GEP related
building height (BH) and the projected building width (PBW). Flags are set to indicate

which stacks are being affected by which structure wake.

The second part calculates building downwash BH’s and PBW’s which can lead to
different BH and PBW values than those calculated for GEP. This part performs the
calculations only if a stack is being influenced by structure wake effects. Output is
formatted for editing either into the ISC3 input runstream (U .S.EPA 1995) or, if the flag
is set by BPIP to indicate that downwash is to be considered, then ISC-3 modifies

Equation 5.1 by adjusting 0y.

 

 

5.5.11an

The structure
dhtllltl‘ash gt
building who
nultitiered b
inthis Stud}.

administrator

Sills can b
dlhlluind f.
designed to 3
“115 is com;

[Hm data to
ill) on .3

car.

68

rput Preparation

ture-source relationships must be assessed with respect to the GEP and building
h guidance. Two methods are available. The first way is as a multitiered

where each tier is treated as a stand-alone structure. The alternative is as a

d building with two towers that may be combined. The first method was used
.dy, since only two buildings were considered: the main building, and the

ation building next to it.

n be on top of roofs and also can be more than 5 times the building length, L,
d from an upwind roof edge. The main algorithms in the BPIP were not

to process these stacks if they are further than 5L downwind from a roof edge.
intrary to guidance. An algorithm was written to automatically detect when a

n a roof. In our case all stacks are on top of the main building roof.

1 to the BPIP uses normal building dimensions and orientation in all cases. The

I calculate 36 pairs of BH and PBW values for input to the ISC3 model for each

’ processes input data on a structure by structure basis. In the case study, two
were identified by a name (maximum of 8 alphanumeric characters) and base
. The number of tiers and tier heights for each structure was determined along
lumber of comers for each tier. For details on the file structure and program

e Appendix B.

 

Sillispe

lhe lndustriz
lfoPA l‘.
the source.

modelutg, ['

5.6.1 So

he data it
Mutant

' l

DQC lot}

molding

69

5.6 Dispersion Modeling Implementation - Simulation Strategy

The Industrial Source Complex (ISC3) dispersion model in the short term version
(U .S.EPA 1995) was implemented for a two kilometer by two kilometer grid centered at
the source. The following is a discussion of the data needed to perform the dispersion

modeling, the sources of these data and assumptions used in implementing the model.

5.6.1 Source Information

The data were gathered on site at the facility. A tour of the entire plant and the different
manufacturing processes was made to compile the following information:

0 Map of the plant;

0 Identification of stacks by source (e.g. mold, core, etc.);
0 Number and location of stacks;

0 Height of buildings and ground elevation;

0 Stack internal diameters;

0 Stack heights;

0 Stack temperatures;

0 Stack exit velocities;

0 Mass emission rates (g/s) by stack; and

0 Control equipment for each stack.

Due t0 the diverse types of pieces produced, three lines are in production to meet different
molding needs and capacities. Line 1 produces: gears, yokes, and pistons; Line 2
produces: housings and line 3 produces: connecting rods. These lines can function

SirnultEureously or individually depending on the furnaces’ performance, the clients’

 

demand and.<
has a specific

hinders.

All sources 0
lines in the t]
mop of Star
do:ignated i.
Ollie tastir
meted by

steels f0r p
call} Step '
items. at
htlghp lllll

”Lites CO

.:t- . ‘
{Emil lS]

70

and, of course, the specifications of the metal produced. As expected, each line

:cific mold and core make-up and very often requires different kinds of resin

:es of emissions are stacks collecting the fiunes from the three casting process
he foundry elected as the case study. They are assumed to be point sources. Each
"stacks is collecting emissions from one particular line. These stacks are also
ed to collect separately emissions from the pouring, cooling, and shakeout steps
sting process at each line. The line with the highest production rate (Line 1) is
by six stacks (1 for pouring, 2 for cooling, and 3 for shakeout). Line 2 has two
tr pouring, one for cooling, and one for shakeout. And, Line 3 has one stack for
). These stacks are equipped with different kinds of capture systems, control
and exhaust systems. They also have different physical properties such as

mer diameter, and elevation. Table 5.1 is a compilation of the data for the
:onsidered in this research. A mapping of their location with respect to the

s illustrated in Figure 5.2.

lelection of Receptors

rary 50 m x 50 m grid was built around the facility to screen the region on a two
ilometer square centered at the plant. This primary grid (Figure 5.3) was used to
the pattern of concentration variability at the ground level and refine a more

= set of receptors for investigation in the exposure assessment.

 

 

5.6.3 Mete

hleteorologic
olEntironme
used in the si

niomtation

5.6.4 Em

limited do
his dliiel’m
all mus h
it fest d3!
I“"0 orders
rain ace
he bee;

titration i

71

5.6.3 Meteorological Data

Meteorological files for the Saginaw region were acquired from the Michigan Department
of Environmental Quality (DEQ). Data for the five years between 1987 and 1991 were
used in the simulations. Only an electronic copy of these files was accessible. Detailed

information is available in the ISC-3 output Appendix C.

5.6.4 Emission Rates -Variability

Limited data are available to characterize foundry emission rates due to the complexity of
the different processes involved in the manufacturing process. Laboratory measurements
and mass balance calculations with emission factors are among the methods used to develop
the few data available. Data disagreement is, however, common and differences of one and
two orders of magnitude are typical. Obviously, rigorous stack sampling is the only way to
obtain accurate measurements of the real emissions but very few measurements have been
made because of the cost. The selection of a set of data that best represents the real
situation is a major challenge. Traditionally, a point value, most likely the maximum
emission rate, has been used in determining the atmospheric concentrations at the receptor
to be used in exposure assessment. The EPA base estimate line for exposure concentrations
assumes the individual is exposed to the 95th percentile value over their lifetime.
Historically, the only variability introduced in the risk evaluation is the meteorological data.
In the casting process, toxic emissions are highly variable. Scott, James and Bates in their
study (Scott, James et al. 1976) observed changes over time (30 to 40 min.) of these

emissions corresponding to two peaks.

 

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The first occurs at the pouring step and a less important peak is found at the mold break-up.
Furthermore, the process is cyclic and depends on the production rate which varies for
different lines in the foundry. It is then unrealistic to assume that the facility is emitting a

constant rate of air toxics for 24 hours per day 365 days a year.

5.7 Dispersion Modeling Simulation-Results

ISC3 was used to identify critical exposure zones, to determine the sensitivity to emission
variation, to examine the influence of production change, and to estimate the sensitivity
to stack height increase. To firlfill these goals, the input file was implemented as shown

in the example in Appendix D. The results are discussed in the following paragraphs.

5.7.1 Identification of Critical Zones

The first simulation was run with the maximum production rate assuming all three lines
were running for 24 hours per day for 5 different years from 1987 to 1991. As mentioned
previously, site specific meteorological data including ambient temperature, wind speed
and direction, atmospheric stability and mixing height on an hourly basis for the region
were acquired from the Michigan Department of Environmental Quality (see sample in
Appendix C). This screening step was undertaken to identify critical zones of exposure.
As can be seen in Figure 5.4 for benzene emissions, three critical regions with three
Peaks are observed and are persistent over the five years of the meteorological
information. Results for all years from 1987 to 1991 are illustrated graphically in

Appendix E.

 

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5.7.2 Sensitivity of Area of Exposure to Emission Increase

To better visualize the area impacted by a particular concentration, isoconcentration lines
are illustrated in Figure 5.5, showing where the 3 zones mentioned previously are
observed. The same scenario with an increase of 10% for the emission rates of all point

sources was run for sensitivity analysis (see Figure 5.6).

By comparing Figure 5.5 and 5.6, one can see that not only the exposure concentration
increases 10% as expected but also that the area of influence for a given concentration
expands dramatically. For example, the area encompassed by the 0.6 rig/m3 contour

increases 100% in comparison to its original size.

5.7.3 Influence of Production Variation on Exposure
Concentrations

A sensitivity analysis was conducted to study the effect of production rate on the
predicted atmospheric concentration. Three lines implemented in the manufacturing
process were used to produce different pieces of cast. Benzene was selected as the model
pollutant as discussed in Chapter 3 Section 3.4 based on its toxicity and predominant
level in the measured data. Benzene was also the model pollutant as a carcinogen. The
emission rates ranged between 6.08E-02 and 6.21E-01 g/sec. The hazardous air

Pollutants were released into the atmosphere through multiple stacks.

77

 

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To explore realistic scenarios, the stacks were grouped in different combinations and
simulations were run to study the effect on exposure concentrations at specific receptors.
Considering the patterns observed in the first results, a transect was established to follow
the dispersion of the pollutant moving southwest and northeast from the source. The

selected receptors are illustrated in Figure 5.7. The plant is located at the mid point.

Different emission rates were investigated based on various scenarios obtained from
combining appropriate sources grouped in the three production lines. From Figure 5.8,
one can observe a shift of the highest concentration. From this figure, it can be seen that
the maximum concentration results from the combination of lines 1, 2, and 3 but does not
necessarily derive from the sum of the three independent maxima. This is due to
differences in stack heights and emission rates. This means that a potentially critical

receptor in one scenario may be safer in another scenario.

Three conservative scenarios were investigated to simulate the influence of production
schedules. These were based on operational constraints where the maximum production
can only be sustained for 16 hours. Eight hours are required for recovery. Then, three
scenarios of 16 hour maximum production starting at 7 AM, 3 PM and 11 PM and allowing

“”0 8 hour shifts separated by an 8 hour recovery periodwere selected. The results are

Presented in Figure 5.9.

 

 

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This simulation indicates that a 16 hour shift beginning at 3 PM would result in the lowest

ground level concentrations.

A more comprehensive hour by hour shift on the start time revealed that starting the 16 hour
shift at 4 PM would result in the lowest concentrations. Starting at 4 PM would result in a

30% reduction in the highest concentration observed on the 7 AM shifi.

As the sun begins to heat the ground surface by mid-morning, wind speed increases, and
the inversion layer built during the night breaks down. By early afiemoon, the solar
radiation has reached its maximum. The mixing height is extremely high and the winds
are, generally high due to convection. At dusk, the ground begins to cool and an
inversion layer begins to form. Due to reduced convection, the wind speed is very light.
This effect intensifies as the ground cools further until sunrise when the process repeats
itself. This could explain the lower concentrations observed in the scenarios starting in
the afiernoon, such as 3 and 4 PM. At this time the mixing height is large and favors the

dilution of the emitted chemicals producing lower concentrations at the ground level.

5.7.4 Sensitivity of Exposure Concentrations to Stack Height

Increase
As one can expect, an increase in stack height increases the dispersion and reduces the

Pollutant concentration at the ground level. As an example, a 15 foot increase in the stack

heights was simulated with the 24 hour- maximum and the second highest production rates

 

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83

representing respectively all the three lines together and Line 1 separately. The results are

shown in Figure 5.10.

A 15 foot increase in the stack height, increased the stack height approximately 20%. As
anticipated the concentration decreased about 30%. This implies that small changes in the

stack height do not greatly influence the aerodynamic downwash effect of the building.

5.8 Synthesized PDF of Emission Rates

Based on these simulations and the need for probability distributions to develop a
probabilistic exposure estimate, a probability distribution was synthesized to describe the
emission rates compiled from stack sampling analysis. The mean and standard deviation
were determined to build a distribution function (Finley, Proctor et al. 1994; Frey and
Cullen 1995). Using Monte Carlo simulations, and a log-normal distribution, a PDF was
constructed to describe the emission rate variability see Figure 5.11. Full concentration
results with PDF characteristics (e. g., mean, and standard deviation) at the selected

receptors for the chosen scenarios are in Appendix F.

84

 

 

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References

F .Pasquill, D. S. (1962). Atmospheric Diffusion - The Dispersion of Wind-borne Material
from Industrial and other Sources. London W. 14, England.

Finley, B., D. Proctor, et al. (1994). “Recommended Distributions for Exposure Factors
Frequently Used in Health Risk Assessment.” Risk Analysis 14(4): 533-553.

Frey, H. C. and A. C. Cullen (1995). Distribution Development for Probabilistic
Exposure Assessment. 88th Annual meeting 7 Exhibition, san Antonio, Texas.

Gratt, L. B. and L. Levin (1995). An Analysis of Dispersion Model Options in the
Comprehensive Risk Evaluation of Electric Utility Air Toxic Emissions. 88th
Annual Meeting & Exhibition, San Antonio, Texas.

Paine, R. J. (1996). Evaluation of Building Downwash Models with Several Data Bases.
89th Annual meeting & Exhibition, Nashville, Tennessee.

Scott, W. D., R. H. James, et al. (1976). “Foundry Air Contaminants from Green Sand
Molds.” American Industrial Hygiene Association: 335-343.

Turner, D. B. (1967). Workbook of Atmospheric Dispersion Estimates. Cincinnati, Ohio,
Department of Health and Welfare.

U.S.EPA (1995). User's Guide for the Industrial Source Complex (ISC3) Dispersion
Models. Research Triangle Park, NC., Office of Air Quality Planning and

Standards.

U-S.EPA (1995). User's Guide for the Industrial Source Complex (ISC3) Dispersion
Models (Revised), EPA.

Venkatram, A. and C. Seigneur (1993). “Review of Mathematical Models for Health Risk
Assessmentzll. Atmospheric Chemical Concentrations.” Environmental Software

8: 75-90.

 

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Chapter 6

Exposure Assessment - Probabilistic Model

Exposure assessment is the translation of the transport and fate of environmental
contaminants from their source or point of formation to the point of their biological
impact. The human body can take up toxic chemicals through one or more of the three
common routes: inhalation, dermal contact, and ingestion. The uptake by a particular
route will depend essentially on the properties of the toxic chemical, its concentrations in

one or more environmental media (air, water, and soil) and its human behavior.

6.1 Theoretical Background

The objective of an exposure assessment is to estimate how chemicals travel to a
receptor, how those chemicals traverse epithelial barriers to gain entrance to the body of
the receptor, and whether those chemicals present in the environmental medium at a
Particular concentration be present within a receptor at a level presumed to cause
Significant adverse effects (Finley and Paustenbach 1994; Finley, Proctor et al. 1994).
Estimating the magnitude of exposure to those chemicals of potential concern can be
done in two ways: the point estimate and the probabilistic assessment. To date , exposure
has most often been calculated using point estimates recommended by the EPA.

However, some scientists suggest that the probabilistic method over the point estimate

87

 

 

 

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approach is more appropriate because it provides more meaningful information, avoids

disputes over the best point estimates, and associates the risk estimates with a quantitative

measure of the uncertainty.

6.1.1 Fundamental Equations

Human exposure to chemicals can result from contact with contaminated soils, water, air,
and food as well as with drugs and consumer products. Exposure may be dominated by

contacts with chemicals in a single medium or may reflect concurrent contacts with

multiple media.

The nature and the extent of multimedia exposures depends largely on two things: (1)
human factors and (2) the concentrations of the chemical substance in the contact media.

The human factors include all behavioral, sociological, and physiological characteristics

of an individual that directly or indirectly affect the contact with the substance of concern.

Important factors in this regard are contact rates with air, soil, and water. The activity
Patterns, which are defined by an individual’s allocation of time spent at different
activities and locations, are also significant because they directly affect the magnitude of

inhalation, ingestion and dermal exposure to substances present in different indoor and

outdoor environments.

 

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Based on the logic illustrated in the Figure 6.1, and the pathway scheme in Figure 6.2, the

total exposure can be constructed as a function of time, pathway, environmental medium,

and concentration as follows:

H(()=Cs(0)x< Z Z Z [QJ(LADDU,.)X[LA_IC)D£]x¢[Cs(o)—>Ck(t)]]
j k i k

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where

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v

 

is the distribution of individual carcinogenic lifetime risk at some
time t in the future within a population exposed for an exposure
duration, ED, to a contaminant in a medium at an initial time zero
the contaminant concentration in mg/m3 measured at time zero

is the dose-response function that relates the potential dose,
ADDijk, by route j to the lifetime probability of detriment per
individual within the population, mg/kg-d

the unit dose factor (average daily potential dose over a specific
averaging time) from exposure medium i by route j ( inhalation,
ingestion, or dermal uptake) attributable to environmental
department k divided by Ck when Ck is constant over the duration
ED

the multimedia dispersion function converting the contaminant
concentration C,(0) in mg/m3 measured today, into contaminant
concentration Ck at a time t in the future for a duration of exposure
ED in environmental medium k (units of Ck are in mg/kg for soil,
mg/m3 for air, and mg/L for water).

The equation 6.1 applies only to carcinogenic compounds. Instead, a hazard Index is

calculated using an average daily dose (see equation 8.3). The distribution of the

 

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92

individual dose is developed by summing the dose and effects over exposure routes, over

environmental media, and over exposure pathways.

When an environmental concentration is assumed constant over the exposure duration,
ED, the population-average potential dose is the lifetime average daily dose rate

(LADDjk)f0r carcinogens or average daily dose (ADDjk) for non-carcinogens. It is given

in mg/kg.d by:

 

Ck (6.2)

k

Where

[Ci/Ck] = the intermedia-transfer ratio, which expresses the ratio of
contaminant concentration in the exposure medium i to the
concentration in an environmental medium k

[IUU /BW] = is the intake or uptake factor per unit body weight associated with
the exposure medium i and route j such as m3(air)/kg-hour

EF = is the exposure frequency for the exposed individual, in days per
year

ET = is the exposure time in hours per day

ED = is the exposure duration for the exposed population in years

AT = is the averaging time for the exposed population, in days

Ck = is the contaminant concentration in the environmental medium k

After a sensitivity analysis, it was found that the foundry pollutants of concern are most
likely to be found in the air; the percentage of their partition to the air is higher than 90%
(USEPA, 1993). For a single medium, for example air, the average daily dose (ADD) in

mg/kgd for inhalation will be then calculated by the following equation (U.S.EPA 1989).

LiDDUkOT‘

Were

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ET
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AT

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93

(6.3)

 

LADDij-korADDm-r = Cap, [”10”]. EF 'ET-ED

BW AT

Where

Q
ll

01” Contaminant concentration in the air, mg/m3.
IUair/Bw = Intake factor by unit body weight of the exposure medium, m3/kg-hr.

EF = The exposure frequency of the individual, days/year.

ET = The exposure time, hours/day.

ED = The exposure duration for the exposed population, years.
AT = Averaging time for the exposed population, days.

Frequently, the environmental concentration C 0,, is considered constant over the
exposure duration. A general limitation of the current models is that they do not account
for dynamic concentration variations in microenvironmental control volumes in which the
exposure actually occurs. Despite the level of sophistication with respect to the
description of environmental fate and transport, most exposure models are relatively
simplistic with respect to the description of the microenvironment and population

dynamics.

Probabilistic exposure models account for population dynamics and human activity
patterns at various level of sophistication, considering time-space distributions and
sensitive subpopulations. Often these models treat exposure concentration dynamics in a

simplified manner (Georgopoulos, Walia et al. 1996).

6.1.2 “Point Estimate” Approach to Exposure Assessment

Historically, regulatory decisions based on risk assessment used a point estimate approach,

Where a uIlique value was selected for each of the variables involved in the risk equation. In

 

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94

addition to the fact that it is readily accepted by state and federal regulatory agencies, it also
has the advantage of being a straight forward calculation of the health risk that requires little
knowledge about the scientific foundation of the different exposures (Finley and
Paustenbach 1994). Two other major reasons drove decision makers to this conservative
method rather than a probabilistic method. The first was the lack of consensus on the
proper distributions for key exposure factors. In addition, regulatory agencies were
reluctant to accept the probabilistic approach as a methodology for exposure and risk

assessment due to the lack of guidance and policy (Finley, Proctor et al. 1994).

The pitfalls of the point estimate method are, however, numerous. The repeated use of
upper-bound point estimates, as recommended by the US. EPA to determine a reasonable
maximal exposure, leads typically to unrealistic estimates of health risk and most likely
unreasonable clean-up goals. Because the resulting exposure estimate is applicable to
individuals well above the intended 95th percentile, it is overly protective for the vast
majority of the population. Another major disadvantage in the point estimate method in
Which a single value of the risk to the entire population is determined is that it provides a
limited amount of useful information to the risk manager (Finley and Paustenbach 1994).
Nevertheless, this method has the merit as an easy screening method to obtain a worst case

Scenario of a potentially exposed population.

6.1.3 Probabilistic Approach to Exposure Assessment

In contrast with the point estimate method, the probabilistic method will result in a more

comPlete characterization of the exposure information available for decision-making.

 

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The probabilistic approach offers the possibility of an associated quantitative measure of the
uncertainty around the value of interest. A sensitivity analysis is also possible based on the
variance determined from a probability distribution function (PDF). This will determine the
degree of confidence with which the variables are known and will also indicate whether

additional data are needed to increase reliability of the results.

A probabilistic approach means that decision-making is no longer based on a comparison of
a standard desirable limit with a worst case undesirable value. It is instead based on a
recognition of the most likely occun'ing value that should be given more weight in the
evaluation process. In addition, a better understanding of the risk is provided by a graphical
representation of the probability distribution describing the variables of interest. Instead of
a single number assigned to the variable, the full range of possible values and some measure

of the probability of occurrence of each value can be included.

6.1.3.1 Computational Methods for Estimating Dose PDFs

To perform the probabilistic exposure evaluation, a computer program (entitled
“@RISK”) that was developed to employ random sampling was implemented (Palisade

1 997). @RISK provides two sampling techniques: Monte Carlo random sampling and
Latin Hypercube random sampling. Both methods of generating ADD probability
distribution functions (PDFs) were investigated. Despite the fact that Monte Carlo
simulations are more widely used in this kind of practice, some runs were conducted with

both for comparison. The Latin Hypercube method has two advantages. The first is that

it forces 5
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till recrt
without r
number t
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96

it forces sampling to represent values in each interval. By stratifying the input probability
distribution into equal intervals in the cumulative probability scale (0 to 1.0), sampling
will recreate the input probability distribution. Also, the technique used is “sampling
without replacement”. This means that the number of stratifications will equal the
number of iterations so that no value can be considered twice. Second, this method

converges to a stable outcome more quickly than the Monte Carlo method which, of

course, means shorter run times.

Simulation results, in particular, the mean and the standard deviation of the outcome,
stabilized after about 5,000 iterations. However, with Monte Carlo sampling, 10,000

iterations were required for convergence.

6.2 Selection of an Exposure Model

An overall total exposure assessment approach is being developed by the EPA’s Risk and
Exposure Assessment Group (REAG). It has been designated as the Total Risk Integrated
Model (TRIM). A complete residual risk evaluation will include short-term acute
exposure from air and non-air sources, urban area toxic exposures from pollutant
mixtures, and use of explicit dose estimates as alternatives to the Reference Doses
(Rst). This model will be capable of assessing risks to both humans and sensitive
ecosystems resulting from multimedia contamination in air, water, soil, and food and

multi-pathway exposure via inhalation, ingestion, and absorption exposure routes to

Pollutants of concern.

 

6.2.1 R

1111995. i
were four

W35 narrt

97

6.2.1 Review of Existing Models

In 1995, investigations of EPA and non-EPA approaches were made. Only two models
were found to have sufficient capabilities for potential use by TRIM. The list of models
was narrowed based on the following factors:

0 availability of distributional data for input parameters;
a distinction between environmental concentrations and exposure concentrations;
a use of human activity and microenvironmental data; and

- consideration of uncertainty in a systematic fashion.

The two models retained for more in-depth evaluation and comparison are :

o EPA’s multimedia risk methodology, the Indirect Exposure Methodology
(IEM2) with an addendum completed in 1993; and
0 California Department of Toxic Substance Control’s multimedia risk

computerized model, CalTOX updated in 1995, with continual enhancement

underway.
IEMZ was retained primarily because it represents current EPA choice for indirect
exposure assessment, and so any other model selected should at least be compared to it.
On the other hand, CalTOX was retained as the model-of-choice, primarily because it
SuI'passed all other models reviewed in a level of sophistication. In addition to that, the
deveIOper of CalTOX claims that sophisticated regional atmospheric
disPersion/deposition models such as ISC3-ST and LT could be added to CalTOX

without much difficultY-

ll

CalIOX, 2
includes ts
uith the a
air. surfac
exposure
Contact m

6.2.2 S

The defa
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98

CalTOX, an overall model for multimedia, multiple pathway exposure assessment,
includes twenty-three potential exposure pathway scenarios. CalTOX analysis begins
with the assumption that through modeling or measurement, concentrations for ambient
air, surface water, ground water, surface soil, and root-zone soil can be determined. The
exposure assessment process consists of relating contaminant concentrations in the
contact media (personal air, tap water, foods, household dusts and soil, etc.) to the intake

in the population of concern.

6.2.2 Sensitivity Analysis

The default values provided in CalTOX were used to asess multimedia multiple pathway
exposure from the compounds of interest released fi'om the casting resins. The
investigation showed that there was no need to consider multimedia exposure models for
the hazardous air pollutants. Due to their chemical and physical properties, in particular
their high volatility, the inhalation route was predominant in terms of its major
contribution in the intake values. This was mostly due to the high partitioning of these
chemicals to the air. Figure 6.3, based on a multi media, multiple pathway, and multiple
route assessment, indicates where it is most valuable to focus research resources to more

thoroughly characterize distributions of population exposure.

99

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6.3 R

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100

6.3 Review of Probability Distribution Function (PDF)

The probabilistic approach to exposure assessment involves performing iterative
calculations using probability distributions of each of the appropriate exposure factors.
However, a major problem with probabilistic modeling is the agreement upon “standard”
data distributions. Since this is a relatively new practice there is limited information
available. There are several suggestions, though , including those published in
“Recommended Distributions for Exposure Factors Frequently Used in Health Risk
Assessment” (Finley, Proctor et al. 1994) and the Ohio EPA’s “Support Document for the
Development of Generic Numerical Standards and Risk Assessment Procedures”

(U.S.EPA 1996).

It should be noted that there are a number of technical issues that should be addressed
When characterizing a “standar ” distribution. One of these issues is factor
interdependence, such as for body weight and skin surface area. To minimize errors,
Certain age-specific distributions are used to account for these (Lioy 1990). The only
interdependence between exposure factors considered in this research are age-body

Weight and age-inhalation rates. All other parameters are assumed to be independent.

It is also important to select the appropriate data set(s) for characterizing a distribution.
Often there are several published estimates that differ widely in data quality, credibility,
accuracy, collection/analysis techniques, measured end points, and data collection

Objectives (Lioy 1990).

#l

 

6.3.1 C(

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101

6.3.1 Contaminant Concentration in Air (C31,)

Contaminant concentration in air is chemical and scenario specific. The industrial Source
Complex Short Term air dispersion model along with annual emission rates takes into
account meteorological conditions, source type, geography, and the effects of nearby

buildings when estimating airborne concentrations (Copeland, Holbrow et al. 1994).

Since ISC3 was not developed to introduce emission rates as an explicit function of time,
emission rate variability determinations were possible only from one hour to another.
Hence, hourly-averaged emission rates were used and no generation of distribution
functions of the predicted concentrations at the receptors was possible from the ISC3
output. Based on an analysis of the variability of emissions (Chapter 5) the Ca, data were
fitted to a log-normal distribution. A graphical illustration of an example of this behavior

is presented in Figure 6.9. Detailed results for all scenarios are shown in Appendix F.

6.3.2 Inhalation Rate IR or (IUair)

IR is dependent on the activity being performed. It is described by inhalation rates that are
given in m3/day. Some examples of proposed PDFs are shown in Table 6.1 and 6.2.

Detailed analysis of some of the existing PDFs is offered in Appendix H.

Table 6.1 Selected Cumulative Distribution Percentiles of Inhalation Rates in m3/day by

Age (Finley, Proctor et al. 1994)

102

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Percentile (m3/day)
Age 5th 10th 25th 50th 75th 90th 95th 99th
42/0
<3 3.3 3.6 4.1 4.7 5.5 6.2 6.7 7.8
3-10 6.1 6.5 7.3 8.4 9.7 10.9 11.8 13.8
10-18 9.1 9.8 11.2 13.1 15.3 17.7 19.3 22.5
18-30 10.5 11.3 12.8 14.8 17.1 19.5 21.0 24.6
30—60 8.4 9.1 10.2 11.8 13.6 15.4 16.7 19.2
<60 8.5 9.2 12.4 11.9 13.7 15.6 16.7 19.6

 

 

 

Table 6.2 Probability Density Functions for Inhalation Rates (U.S.EPA 1996)

 

 

Pathway Distribution Mean SD Min. Max.
Inhalation Rate (m3/hr) Residential Triangular 0.80 - 0.52 1.02
Adult

 

Thalation Rate (m3/hr) Residential
Child

Triangular 0.47 1.8 0.38 0.56

 

 

 

 

 

 

 

 

 

6.3.3

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103

6.3.3 Exposure Frequency (EF)

EF is site- and scenario-specific. It could be influenced by season, activity and age. The
EF is measured in units of days per year, or events per year. For example, the PDF
recommended by the Ohio EPA for commercial land use is: (U .S.EPA 1996)

EFwork ~ Triangular[Min = 125 d/yr, Likeliest value = 214 d/yr, Max = 290 d/yr]

These parameters are defined by a triangular distribution based on the climate patterns in
different regions of Ohio, and assumptions about vacation leave, sick leave, holidays, and
part-time/full-time status of workers. This distribution will not be the same in all parts of
the US due to different weather conditions affecting exposure. For instance, the Oregon
Department Environmental Quality adopted different distributions. These were :

EFwork ~ Triangular[Min = 125 d/yr, Likeliest value = 214 d/yr, Max = 290 d/yr]

For residential land use, two EF distributions were given by the Ohio EPA:

EFrcsidemia] ~ Triangular[Min = 323 d/yr, Likeliest value = 351 d/yr, Max = 365 d/yr]
and

EFmsidcmia. ~ Triangular[Min = 261 d/yr, Likeliest value = 330 d/yr, Max = 365 d/yr]

This distribution based upon best professional judgment assumes a maximum value for
an adult/child receptor to be home every day of the year. However, the Oregon DEQ
assumes:

EFrcsidcmia. ~ Uniform[Min = 50 week/yr, Max = 52 week/yr]

Finley an
El dislri
constant

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104

Finley and Paustenbach (Finley and Paustenbach 1994) do not recommend a “standard’
EF distribution. In the three case studies involving air, water, and soil, they proposed a

constant EF value of 350 days. This is an upper bound point estimate that is in agreement

with the US. EPA.

California’s approach to EF is a uniform distribution with a maximum value of l and a
minimum value of 0.58. They define EF as a fraction of a year as used in inhalation, soil
ingestion, and dermal absorption pathways. Again, this would not be a good assumption

in all parts of the United States based on climate differences.

For the purpose of this study, a central tendency was adopted. A simulated average of the
different distributions recommended by the Ohio EPA was implemented based on the

climate similarities with the region of Michigan.

6.3.4 Exposure Time (ET)

ET is defined by an hour per day or hour per event that a person is exposed. It is
measured as by time spent in shower, bathroom, household, or at work. Some examples
of distribution that can be used are:
0 Values defined by a custom distribution based on the assumption that some
residents spend 8 hr/d at work, reducing the time spent at home:
EThouschold = uniform, minimum value of 8 hr/d, maximum value of 20 hr/d,

arithmetic mean of 14 (McKone and Bogen 1992; Finley, Proctor
et al. 1994);

105

ETmsidcmiaHdun = 50 percent probability: 16 hr/d, 50 percent probability: 24 hr/d;

ETresidcmiathd = 25 percent probability: 16 hr/d, 75 percent probability:
24hr/d (U .S.EPA 1996).

o A custom distribution based on the assumption that children may spend 8 hr/d

away from home.

ETcommcrcial = minimum value of 4 hr/d, likeliest value of 8hr/d, maximum
value of 12 hr/d (U .S.EPA 1996).

o A triangular distribution based on best professional judgment.

ETindustrial = 0.90 relative frequency: 8 hr/d, 0.10 relative frequency: 9-
12hr/d (U .S.EPA 1996).

o A custom distribution based on best professional judgment and the assumption
that most industrial workers are at the property for 8 hr/d and 10% are present as

long as 12 hr/d.

The EThousehom, was used for calculations to focus on residential health risk as it was

intended in the scope of this study.

6.3.5 Exposure Duration (ED)

Exposure duration will vary with each different scenario and pathway of exposure. For
example, ED for a residential-adult is a custom probability distribution based on

residency occupancy periods. The PDF’s for Ohio are listed below (U .S.EPA 1996).

 

 

 

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106

The exposure duration values for the commercial/industrial land use scenario were

derived from a Bureau of Labor Statistics survey using workers that are 25 and older in

all work categories and are as follows:

Year at One Job
1
2t05
6to9
10to 14
15to 19
20 to 40

6.3.6 Body Weight (BW)

Relative Probability
0.209
0.317
0.134
0.14
0.08
0.12

There have been several recommended distributions for the body weight factor. One such

distribution was described by Copeland et al [Copeland, 1993 #193] as a normal

distribution with the following parameters:

0-1.5 year Mean= 10kg
1.5-5year Mean= 14kg
5 - 12 year Mean=26 kg
12 - 70 year Mean=62 kg

SD = 0.12
SD = 0.13
SD = 0.75
SD = 0.30

Another BW distribution from the same author, (Copeland, Paustenbach et al. 1993)

describes BW as a normal distribution with a mean of 62.4 kg and a standard deviation of

13.49 kg.

The Ohio EPA defines BW as a normal distribution for an equal population of residential

adult men and women, with an arithmetic mean of 71 kg and a standard deviation of 15.9

 

1

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107

kg. The minimum body weight was set at 32 kg to account for elderly residents who may
weigh less than other adults considered. The residential child BW is described by a
normal distribution for an equal population of male and female children with a mean of

15 kg with a standard deviation of 1.95 kg (U.S.EPA 1996).

On the other hand, Finley, Proctor, and Scott, Harrington, Paustenbach, and Price

recommend the BW distribution shown in Table 6.3 (Finley, Proctor et al. 1994).

Each of these distributions is very similar, differing only by a factor of about 4 kg.
However, for a conservative approach, the Ohio EPA’s distribution is recommended for
“standar ” use since it tends to agree with other studies and will most likely be adopted in

the future. Review of a second study can be examined in Appendix H.

6.3.7 Averaging Time (AT)

According to the U.S.EPA (U.S.EPA 1989) and Finley, Scott and Paustenbach (Finley,
Scott et al. 1993), AT is specific to each exposure scenario. For non-carcinogens it is
equal to ED x 365 d/y. For carcinogens AT is represented by a point value of 25550 days
(70 yrs x 365 d/yr). This is a very conservative estimate because it is assumed that both
children and adults are exposed to the contaminant 365 days per year for 70 years (Finley,

Scott et al. 1993).

 

108

Table 6.3 Summary of Distribution Factors for Body Weight by Age and Gender

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Age (yr) Gender Mean (kg) SD(kg)
0.5-1 Both 9.4 1.2
1-2 Both 1 1.8 1.4
2-3 Both 13.6 1.6
3—4 Both 15.7 1.7
4-5 Both 17.8 2.3
5-6 Both 20.1 2.8
6-7 Both 23.1 3.5
7-8 Both 25.1 3.8
8-9 Both 28.4 5.2
9-10 Both 31.3 5.0
10-11 Both 37.0 7.5
11-12 Both 41.3 10.5
12-13 Both 44.9 10.0
13-14 Both 49.5 10.5
14-15 Both 56.6 10.3
15-16 Both 60.5 9.7
16-17 Both 67.7 11.6
17-18 Both 67.0 11.5
>18 Men 78.7 13.5
>18 Women 65.4 15.3
>18 Both 71.0 15.9

 

 

6.4 Sr

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109

6.4 Selection of PDFs

The recommended distributions selected were chosen using the following criteria:

a consistency with other studies
a derivation of the distribution from a survey population representative
of the general population,
0 minimization of confounding variables, and sufficient data to reasonably

characterize the variability and extremes of the distribution.

Despite the lack of consensus on the proper distributions for key exposure factors, the
PDFs illustrated graphically in Figures 6.4 to 6.8 was selected. These distribution
functions, for the different parameters affecting the intake value or precisely the average
daily dose (ADDair) were chosen based on advice from several experts in the field (e. g.,
EPA office experts in Washington DC., Dr. M. Kamrin from the Institute of Toxicology
at Michigan State University). They may be introduced into the exposure calculations as

shown in Table 6.4.

110

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Figure 6.7 Exposure Time in hours/day

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Figure 6.6 Exposure Dura

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114

6.5 Exposure Assessment

The need for using explicit time profiles of exposure concentrations in order to calculate
meaningful estimates of biologically effective doses rather than average or cumulative
exposures has been stated and justified by Smith (Smith 1992; Georgopoulos, Walia et al.
1996). These distributions were picked for the best possible fit to our population of

interest and to realistically simulate the case study conditions.

6.5.1 Implementation

The concentrations of emitted hazardous air pollutants in the air in a surrounding area of
two kilometers on two kilometers centered at the source were estimated using the
transport model (chapter 5). These simulations showed three critical zones of high
atmospheric concentrations that were persistent for a period of five years (from 1987 to
1991) despite the meteorological data variation. These three zones are designated by their
centers as point receptors R1, R2, R3. Four other receptors at each comer of the physical

limits of the study area, designated R4, 5, 6, and 7 were specified for control purposes

(see Figure 6.9).

The predicted concentrations for selected chemicals (benzene, formaldehyde, phenol,
t01uene, and xylene) were calculated for different scenarios based on real production

rates. Their variations were fitted to log-normal distributions that are statistically

characterized in Table 6.5.

115

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117

6.5.2 Intake Results

Intake evaluation for deifferent populations was performed for all the selected receptors.
Investigation of different scenarios at different starting times were done to determine the
lowest average daily dose. Simultaneously, evaluations were performed on eleven resin
binders as comparisons for the binder system used in the case study (see Table 6.6). All
results are illustrated in Appendix I. These are further used in Chapter 8 for risk

characterization.

Based on the dispersion results of chapter 5, the receptor R3 exhibited the highest

predicted concentrations. Therefore, it was targeted in most of the investigations.

Table

5

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118

Table 6.6 Predicted First Highest Concentrations in ug/m3 at R3 for Alternate Resin

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Binders.
Cone. of Benzene Receptor R3 —‘]
in ug/mJ Benzene Formaldehyde Phenol Toluene Xylene
Case Study Resin Binder 3.755 4.982 25.957 1.518 0.196
Phenolic NoBake 30.025 0.027 2.612 1.698 0.260
Phenolic Urethane 14.334 0.059 10.457 2.231 1.176
Phenolic HotBox 2.684 0.016 10.457 0.487 0.324
Green Sand 1.637 0.011 0.351 0.169 0.056
Core oil 6.279 0.257 0.153 1.280 0.640
Shell 17.859 0.094 6.579 7.519 1.567
Low Nitrogen Furan 1.736 0.715 0.064 0.324 5.965
Med. Nitrogen Furan-TSA 12.145 0.174 0.270 23.642 0.651
F uran HotBox 1.438 0.024 0.043 0.086 0.086
Alkyl lsocynate 14.293 0.284 0.005 0.068 6.756
Sodium Silicate-Ester 3.777 0.453 0.731 0.755 0.252

 

 

 

 

 

 

 

 

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119

References

Copeland, T. L., A. M. Holbrow, et al. (1994). “Use of Probabilistic Methods to
understand the Conservatism in California’s Approach to Assessing Health Risks
Posed by Air Contaminants.” Air & Waste Management Association
44(December): 1399—1413.

Copeland, T. L., D. Paustenbach, et a1. (1993). “Comparing the Results of a Monte Carlo
Analyis with the EPA's Reasonable Maximum Exposed Individual (RMEI): A .
Case Study of a Former Wood Trestment Site.” Regulatory Toxicology and
Pharmacology( 1 8): 275-312.

Finley, B. and D. Paustenbach (1994). “The Benefits of Probabilistic Exposure
Assessment: Three Case Studies Involving Contaminated Air, Water, and Soil.”

Risk Analysis 14(1): 53-73.

Finley, B., D. Proctor, et al. (1994). “Recommended Distributions for Exposure Factors
Frequently Used in Health Risk Assessment.” Risk Analysis 14(4): 533-553.

Finley, B. L., P. Scott, et al. (1993). “Evaluating the Adequacy of Maximum Contaminant
Levels as Health-Protective Cleanup Goals: An Analysis Based on Monte Carlo
Techniques.” Regulatory Toxicology an Pharmacology 18: 43 8-455.

Georgopoulos, P. G., A. Walia, et a1. (1996). “Integrated Exposure and Dose Modeling
and Analysis System. 1. Formulation and Testing of Microenvironmental and
Pharmacokinetic Components.” Enviro. Science & Technology 31(1): 17-27.

Lioy, P. J. (1990). “Assessing total human exposure to contaminants.” Environ. Sci.
Technol. 24(7): 938-945.

McKone, T. and K. Bogen (1992). “Uncertainty in Health Risk Assessment: An
Integrated Case Study Based on Tetrachloroethylene in California Groundwater.”
Regulatory Toxicological Pharmacology 15: 86-103.

Palisade (1997). @RISK. Newfield, NY, Palisade Corporation.
Smith, T. J. (1992). American Journal Ind. Med.(21): 35-51.

U.S.EPA (1989). Risk Assessment Guidance for Superfund - Human Health Evaluation
Manual. Washington DC., EPA.

U~S-EPA (1993). CalTOX, A Multimedia Total Exposure Model for Hazardous-Waste
Sites. Sacramento, California. Ca EPA.

U~S-EPA (1996). Support Document for the Development of Generic Numerical
Standards and Risk Assessment Procedures. Ohio, Ohio EPA.

 

ln th
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7.1

alier

Chapter 7

Toxicity Assessment

In the process of selecting the pollutants of concern for the case study, different
manufacturing processes were explored, and it was found that the binders used to form
the cores and the molds for the casting Operation were the major source of the most
volatile hazardous air pollutants. Based on the data available and discussions with the
operating personnel, five compounds were selected for investigation. These were
benzene, formaldehyde, phenol, toluene, and xylene. Benzene and formaldehyde were

selected based on their carcinogenicity and abundance. The rest were chosen based on

their non-carcinogenic toxicity and abundance.

7.1 Inhalation Toxicology

The potential toxic effects that need to be considered following inhalation exposure
include irritation of the respiratory tract, behavioral changes, pathologic change to vital
organs or tissues within and distal to the respiratory tract, immune system responses,
pulmonary function alterations, metabolic disturbances; carcinogenicity, and even death
(Hayes 1994). Studies to measure the effects of chemical agents on the biological system

after entering the respiratory tract must follow carefiilly designed protocols. Regulatory

120

 

agent
inhal:
EPA,
and tl.

Stand:

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121

agencies have adopted standardized protocols for both short-term and long-term
inhalation testing. Different regulatory institutions all over the world, such as the US.
EPA, the organization for European Economic Cooperation and Development (OECD),
and the Japanese Ministry of Agriculture, Forest and Fisheries (MAF F) have agreed on

standardized protocols alayes 1994).

Reproducing, experimentally, the atmosphere in a form that can be inhaled by a test
species requires very sophisticated equipment. In addition to that, it is inherently difficult
to relate the inhaled dose to the retained one. The total dose depends on the physical and
chemical properties of the compound, the physiological characteristics of the tested
animal, and the numerous factors involved in deposition and clearance in the lungs.
Therefore, it is very difficult, if not impossible, to find consistent research results for all
chemicals. In addition to quality issues, the need to study a huge number of chemicals

leads to deficiencies in the amount of pertinent toxicological data.

7.2 Sources of Toxicological Data

A review of different databases revealed a large number of on-line resources, and written
documentation. Among the sources available, the following are always highlighted
(Wexler 1995):

0 TOXNET (the Toxicology Data Network developed by the National Library of
Medicine’s Toxicology and Environmental health Information Program in
1985 as an expansion of the former Toxicology Data Bank TDB) covers the

broad areas of toxicology, hazardous chemicals, and the environment;

 

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122

o ATSDR (Agency for Toxic Substances and Disease Registry) provides
information about toxic chemicals including their classification, and a

summary of research results; and

o IRIS (Integrated Risk Information System) contains an EPA file with data
related to carcinogenicity and non-carcinogenic risk assessment, including oral
reference doses (Rst), inhalation reference concentrations (Rsz), and cancer

slope factors and wit risks.

The Integrated Risk Information System (IRIS) presents the most reliable source. It is
considered a starting point in the risk assessment data sources hierarchy. IRIS updates are
posted by EPA on a monthly basis. Gaps are, however, observed in some of the data due

to the lack of studies and inconsistency of results.

The results of the toxicity data search are summarized in Table 7.1 for the selected HAPs.

123

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7.3 Adverse Effects

In the following paragraphs, the adverse effects (non-cancerous) of the five compounds

selected for study are summarized.

The major systemic effect associated with chronic benzene exposure in humans is
depression of bone marrow resulting in pancytopenia, which is a decrease in numbers of
erythrocytes, leukocytes and thrombocytes, sometimes progressing to aplastic anemia.
Some evidence of impaired immune system is observed in humans who have been
exposed to chronically. Decreased serum complement, IgG, and IgA, levels were found
in workers exposed to benzene, IgM level was slightly higher (Calabrese and Kenyon

1991; U.S.EPA 1994).

Based on the Agency for Toxic Substances and Diseases Registry (ATSDR) Public
Health Statement, formaldehyde is an irritant to the skin, eyes and mucous membranes in
humans. At high concentrations, it may cause bronchitis, pneumonia or laryngitis. Skin
contact can lead to whitening and anesthetic effects due to superficial coagulation

necrosis (Calabrese and Kenyon 1991; U.S.EPA 1994).

The effects on inhalation of phenol by humans are unknown. However, when it is
ingested in food or water it produces diarrhea and mouth sores in hmnans. Laboratory

animal experiments yielded effects such as muscle tremors and loss of coordination. At

 

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higher concentrations for longer periods of time, exposure resulted in paralysis and severe
injury to the heart, kidney, liver, and sometimes the lungs, followed by death (Calabrese

and Kenyon 1991; U.S.EPA 1994).

Short term exposure to toluene at high concentrations is associated with irritation of the
skin, nose, and throat, difficulty in breathing, and impaired function of the lungs. The
primary target is the nervous system and symptoms include headaches, lack of

coordination and loss of balance (Calabrese and Kenyon 1991; U.S.EPA 1994).

Very limited data are available on xylene. At 200 ppm, xylene is a definite irritant of the
eyes, nose, and throat. At higher concentrations pulmonary edema, anorexia, nausea,
vomiting, and abdominal pain are observed (Calabrese and Kenyon 1991; U.S.EPA

1994)

7.4 Carcinogenicity

Benzene is a known human carcinogen, under the EPA weight-of-evidence classification.
Mutagenicity of benzene is observed in both humans and animals. It induces
chromosomal aberration in bone marrow cells and peripheral lymphocytes. Benzene was
noted as genotoxic and fetotoxic at doses that are also maternally toxic. However,
because of limited data, the ATSDR stated that there is not sufficient evidence to show

reproductive effects result from exposure to benzene. In epidemiological studies of

 

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people exposed primarily to benzene, statistically significant excesses of leukemia were

observed (Calabrese and Kenyon 1991; ATSDR 1992).

Formaldehyde is a probable human carcinogen under the EPA weight-of-evidence
classification. It is a mutagen but requires bioactivation. It causes chromosomal
aberrations and forms adduct with DNA and protein in vivo and vitro. It also inhibits
DNA repair in cultured human cells. Although no evidence of teratogenicity is reported,
fetotoxicity in rodents resulted from inhalation exposure. It is carcinogenic via the
inhalation route in experimental animals based on the nasal squamous cell carcinomas (a
rare tumor type) in both sexes of F344 rats, multiple rat strains, and mice. Generally,
however, tumors were not observed beyond the initial site of nasal contact (Calabrese and

Kenyon 1991, Stine, 1996 #304).

Phenol is not classified a carcinogen in the EPA weight-of-evidence classification. It is a
mutagen that most likely acts on DNA synthesis, replication and repair. Non-teratogenic
effects were reported, including a decrease in fetal weight due to maternal exposure to
phenol. Cancer was produced in animals through the skin route but it has not been

associated with cancer in humans (Calabrese and Kenyon 1991).

Toluene is not classified a carcinogen in the EPA weight-of-evidence classification.
Although, studies in animals suggest, it may produce adverse effects in the fetus in

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fetal deaths, decreased weight, changes and delay in the skeletal development. It is not

however considered teratogenic (Calabrese and Kenyon 1991; ATSDR 1993).

Xylene is not classified as a carcinogen in the EPA weight-of-evidence classification. No
associated cancer cases have been reported. The xylene isomers are not genotoxic,
whether administred in combined form or individually. They are considered also non-
teratogenic, although delayed development, decreased fetal weight, and altered enzyme

activities were observed (Calabrese and Kenyon 1991).

7.5 Threshold

No no-observed adverse effect level (N OAEL) or lowest observed adverse effect level
(LOAEL) were proposed for reproductive toxicity due to benzene. However, NOAEL of
10 ppm for both maternal and fetal toxicity in rats has been suggested (Calabrese and

Kenyon 1991; ATSDR 1992).

At concentrations between 0.1 and 3 ppm of formaldehyde, irritation of the eyes, skin,
nose and throat are observed. Slightly higher concentrations, around 5 ppm, result in
coughing and chest tightness (asthmatic’s problem). More than 50 ppm can cause severe

injury, and lead to pneumonia and pulmonary edema (Calabrese and Kenyon 1991).

 

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Based on systemic toxicity, OSHA has established a threshold limit value (TLV) for
phenol of 0.024 ppm (0.009 mg/m3) for an 8-hour time-weighted average (TWA)

(Calabrese and Kenyon 1991; ATSDR 1992).

Based on systemic toxicity, the ACGIH has established a TLV for toluene of 0.38 ppm

(1.4 mg/ m3) based on an 8-hour TWA (Calabrese and Kenyon 1991; ATSDR 1993).

Based on systemic toxicity, the ACGIH has established a TLV for the combined isomers
(o-, m-, p-) for xylene of 1.2 ppm (5.2 mg/m3) based on an 8-hour TWA (Calabrese and

Kenyon 1991; ATSDR 1992).

7.6 Exposure to Multiple Compounds

It is apparent that toxicity testing under realistic environmental conditions is a much more
complex enterprise than determining the effects of a unique chemical on a unique specie
under controlled laboratory settings. The insult to the total organism fi'om multiple
compounds is difficult to assess. For example from a toxicological point of view, all
carcinogens do not cause the same form of cancer. Thus, the burden from different
chemicals should not be combined linearly without specific information that the same
target organ is affected. None-the-less, one would expect that exposure to multiple
compounds is a greater burden than to one compound and that, potentially, from a holistic

point of view, the total impact is greater than the sum of the individual impacts.

 

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References

ATSDR (1992). Case Studies in Environmental Medecine, U.S.Departrnent of Health and
Human Services.

ATSDR (1993). Case Studies in Environmental Medecine, U.S.Department of Health and
Human Services.

Calabrese, E. J. and E. M. Kenyon (1991). Air Toxics and Risk Assessment. Chelsea,
Lewis Publishers, Inc.

Hayes, A. W. (1994). Principles and Methods of Toxicology. New York, Raven Press.

U.S.EPA (1994). Instant EPA's Air Toxics. Austin, Texas, Instant Sources, Inc and
Digital Liaisons.

Wexler, P. (1995). TOXNET: An Online Resource for environmental and Toxicological
Inforrmation.

Chapter 8

Risk Characterization

Risk characterization is a summary of the information gathered during the exposure and

toxicity assessment to present qualitative and quantitative conclusions about the risk. The
risk characterization should contain not only a risk estimate for a given exposure scenario
but also a summary of the relevant biological information, the assumptions used and their

limitations, and a discussion of uncertainties in the risk assessment (Paustenbach 1989).

8.1 Point Estimation of Cancerous and Non-cancerous Risk

For low-dose cancer risk (smaller than 0.01), the quantitative risk for a single compound

by a single route is calculated as (Hertz and Thomas 1983; U.S.EPA 1991):

Risk = Intake x Slope Factor (8.1)

Where

Intake

SIOpe factor — characteristic of the contaminant obtained from Integrated

LADD calculated in the exposure assessment.

Risk Information System, IRIS.

For higher carcinogenic risk levels (greater than 0.01), the following equation is used:

130

131

Risk 1 - exp [-Intake x Slope Factor] (8.2)
To describe potential non-carcinogenic effects occurring in individuals due to hazardous
air pollutants, a noncancerous hazard quotient or hazard index (HI) is used instead of the

risk expression. The EPA recommends the use of the following equation (Hertz and

Thomas 1983; U.S.EPA 1991):

Intake
RfD

HI=

 

(8.3)

where
RfD = Reference Dose , mg/kg-day
(RfC is used for inhalation)

Risk interpretations from the hazard index are quite different from those of the cancer
risk. While the latter is interpreted as a statistical probability, the former is a reference of

the level of concern. The greater the value above the unity, the greater the concern.

8.2 Probabilistic Risk Evaluation

As it is shown in Equations 8.1 and 8.2, the cancer risk value is derived from the product
of two parameters. In the point estimate approach, a single value is assumed for each of
them. For example the slope factor is the 95% upper confidence limit of the slope

calculated from the dose response curve. Only a single, unique value (as opposed to a

 

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PDF) is available due to the lack of information (slope factor literature review is available

in chapter 7).

A variety of intake values associated with different pathways and population ages as well
as exposure duration were explored in the present research. A PDF for the “Intake” value
was developed and, because there was no alternative, a point estimate of the “slope
factor,” “RfC” or “RfD,” depending upon the compound, was used. Hence, rather than a
single value of the risk corresponding to a certain exposure to the chemical, a range of
risk values is given that describes the risk to the associated population with the specified

pathway and route of exposure.

8.3 Total Integrated Risk

In terms of risk evaluation, a value integrated over multiple chemicals is not always
appropriate. One can not simply add the risk values of individual chemicals to calculate
the overall risk. With the new risk assessment guidelines and the weight of evidence
procedure (U.S.EPA 1997), a new approach to carcinogenic risk characterization is being
implemented. This includes the idea that all chemicals act in the same fashion. Thus
even though several chemicals may be shown to induce cancer, they don’t necessarily act
on the same organ. For example, benzene and formaldehyde are both carcinogenic.
Formaldehyde induces nasal cancer (Andjelkovitch, J anszen et al. 1995), while benzene

causes leukemia (Hayes 1994). Thus, their residual cancer risk is not cumulative.

 

 

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On the other hand, considering the foundry as the only source in the risk evaluation
without investigating possible other sources that might contribute to the atmospheric
concentration at the ground level or in the indoor environment might lead to an
underestimation of the hazard of some of these chemicals. It has been reported, for
example, that homes, especially mobile homes, release pollutants such as formaldehyde
into the indoor air. Formaldehyde is an indoor air pollutant of particular concern for
houses with low indoor air exchange and urea-formaldehyde foam insulation (Stock
1987). Because individuals are inside their homes 60-75 percent of the time (Sexton, Liu
et al. 1986), in-home concentrations are often the single best determinant of individual
exposure. Concentrations of formaldehyde in indoor air ranged from 0.05 to 0.18 ppm in
some studies (Sexton, Liu et al. 1986; Stock 1987). Thus, cumulative risk from indoor

and outdoor formaldehyde exposure must be considered in future risk assessment.

The same situation holds for benzene. Exposure from smoking, consumer products in the
home, and personal activities such as driving or painting have been estimated to account
for more than 80% of the nationwide exposure to benzene (Lioy 1990 304 ). It is,
therefore, very important to recognize the potential contributions from other sources and

perform exposure assessment for air pollutants within a frame that takes consideration of

this issue.

8.4 Uncertainty Analysis

Another key component of the risk characterization is the discussion and analysis of the

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estimate approach in describing the dose, a phrase such as “it is plausible that someone
might absorb as much as 5 mg/kg-d” might be used to exposure uncertainty (Paustenbach
1989). A more rigorous analysis provides a distribution of exposure assessment. This
may be expressed as a PDF, a CDF or a verbal description of the distribution such as
“0.5% of the population might absorb as much as 5 mg/kg-d, 70% might absorb 0.5

mg/kg-d and 29.5% is likely to absorb less than 0.1 mg/kg-d” (Paustenbach 1989).

The probabilistic risk assessment used in this research explicitly includes uncertainty
because the final output is presented as a PDF. Thus, risk values incorporate both a

measure of uncertainty and results from uncertainty in the input parameters.

8.5 Results And Discussion

Risk values for the five identified chemicals were determined for different times during
the day from 7:00AM to 11:00PM, and at different receptors (R1, R2, and R3). Results
for both the case study resin binder, and the selected comparison binders are summarized
here. The assessment for only the carcinogen benzene, and the noncarcinogen phenol for
three particular scenarios for men are presented (see Table 8.1 and Table 8.2). Different
target populations: children, women, and men were also examined and the assessment
results are shown in Table 8.3. Detailed risk and HI results for all chemicals, different
scenarios, and binders are available in Appendix J. The numbers in tables 8.1, 8.2 and
8.3 represent the mean values corresponding to distribution functions of the risk for the

particular scenarios.

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These distributions are all lognorrnal as shown in the example of Figure 8.1 As it can be
seen from these numbers, the case study resin produces a risk similar to the one produced
by Silicate-'-Ester. Results from other binders show a potential risk reduction up to 60%
by using an alternate binder such as Furan 'HotBox instead of the current binder. This is
based, of course, on the potential emissions. Other factors have to be considered to
determine if such an alternative is feasible. While all risk values are in the range of
acceptable risk 1045 to 104, it is important to note that only the residual risk from
exposure to a single chemical, in a single medium, and through a single route was
considered in this study. Thus, cumulative effects from other sources, and other
chemicals can lead to increased cancer risk values that could exceed the acceptable

value.

Risk values for different populations did not show major variability. Children’s risk was
lower due to the shorter exposure time in comparison to the adult exposure time. A slight
difference was also observed between women and men and that was due to the difference

in body weight.

The Figure 8.1 illustrates the risk distribution function for benzene averaged over the five
years of meteorological data for production that started at 7:00AM. The risk distribution
was fitted to a log-normal distribution that was characterized by a mean of 7 E-OS and a

95% value of 2.5 E-O4. For comparison purposes, the EPA point estimate risk value was

139

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to another on the risk values (Figure 8.2).

8.6 Risk Sensitivity To Exposure Factors

As mentioned before, exposure assessment efficiency and accuracy depend largely on the
choice of the key factors involved and their representative distributions. Selecting one
distribution rather than another will probably affect the results. Therefore, a sensitivity
analysis was carried out to determine the key factors that have the most impact on the risk
values. This will allow for future exploration of the influence of different distribution
functions on the risk. The tornado graph shown in Figure 8.3 gives a sense of the
magnitude of the sensitivity of the risk to each of these factors. The sensitivity analysis
uses either a multivariate regression or a rank order correlation, which are both linear.
The sensitivity results showed a strong correlation of the risk with the exposure duration,
ED, with a coefficient higher than 0.9. The second highest correlation is with the
inhalation rate, IR, third is the exposure time, ET, and the fourth is the body weight, BW.
This ranking suggests the parameters that should be focused on in future investigations,
and for which more elaborate sensitivity analysis should be performed to study their

impact on the risk evaluation.

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References

Andjelkovitch, D. A., D. B. Janszen, et al. (1995). “Formaldehyde Exposure Not
Associated with Cancer of the Respiratory Tract in Iron Foundry Workers.”
Chemical Industry Institute of Toxicology (CHT) 15(7): 1-9.

Hayes, A. W. (1994). Principles and Methods of Toxicology. New York, Raven Press.

Hertz, D. B. and H. Thomas (1983). Risk Analysis and its Applications. Chichester . New
York . Brisbane . Singapore, The Pitman Press Ltd.

Lioy, P. J. (1990). “Assessing total human exposure to contaminants.” Environ. Sci.
Technol. 24(7): 938-945.

Paustenbach, D. J. (1989). Risk Assessment of Environmental and Human Health
Hazards: A Textbook of Case Studies. Alameda, John Wiley & Sons.

Sexton, K., K. S. Liu, et al. (1986). “Formaldehyde Concentrations Inside Private
Residences: A Mail-Out Approach to Indoor Air Monitoring.” Journal of the Air
Pollution Control Association 36(6): 699-704.

Stock, T. H. (1987). “Formaldehyde Concentrations Inside Conventional Housing.”
JAPCA 37(8): 913-918.

U.S.EPA (1991). Risk Assessment Guidance for Superfund - Human Health Evaluation
Manual(Part B, Development of Risk-based Preliminary Remediation Goals).
Washington DC., EPA.

U.S.EPA, Ed. (1997). Integrated Risk Information System.

Chapter 9

Risk Assessment Techniques as a Tool
for Selecting Pollution Prevention Alternatives

9.1 The Decision Making Process

A rational approach to decision making is based on an educated, documented, open
judgment. Most importantly, the process of decision making must be explicit and
communicable, objective and self-correcting, and verifiable and reproducible (Baird

1989)

The minimum critical steps in decision making are:

0 Definition of the problem;
0 Listing of options;

0 Definition of criteria;

0 Analysis of the options;

a Choice of course of action.

9.1.] Difficulty in Decision Making

Some decisions cannot, of course, be reduced to simple deterministic algorithms. In

some cases, the number of solution alternatives may be infinite, and consideration of all

144

145

options is, hence, impossible. Also, multiple objectives to be achieved as an outcome to
the process might be difficult to weigh, in particular the ones that are in direct conflict
with others. Ofien, more than one decision maker is involved in the process and this
raises the problem of group dynamics and interpersonal relationships. On the other hand,
what is “ best” for one individual, one organization, or one society is not necessarily best
for others. Finally, a “good” judgment is still based on the information available to the
decision maker and might not be the “correct” one based on the outcome of the decision.

Therefore, any decision should remain open to verification and correction (Lindley 1985;

Baird 1989).

9.2 Decision Making Under Uncertainty

A decision problem occurs whenever there is a choice between at least two courses of
action. Having all the information regarding decision choices does not lead, necessarily,
to making the right decision, because decisions are based on judgments (Lindley 1985).
It is a precept of decision making that a more educated choice results when a good

knowledge of the risk involved is available.

The natural reaction of decision makers to uncertainty is to acquire more information to
minimize as much as possible these uncertainties. The problem, however, is how much it

costs to obtain the information and how useful this knowledge will be in reducing the

uncertainty.

146

One of the most valuable benefits of the probabilistic risk approach is the broad range of
choices that this method can give to the decision maker. With a better description of the
risk and the different factors that impact on its value, it is possible to consider different
options instead of merely a “yes” or “no” to a particular scenario. A risk PDF allows not
only an assessment of the range of importance but also provides a symbolic measure of

the risk.

An essential goal in decision making is to choose an action that can be carried out. It is
rather useless to select an option that can not be realized. Hence, feasibility is an
essential factor to consider in decision making. Feasibilty may reflect the availability of

monetary or technical resources or the chance of public acceptance.

9.3 Application of Risk Assessment to the Selection of a Resin
Binder

The following discussion outlines the “critical steps in decision making” for applying

risk assessment to the selection of a binder that minimizes atmospheric pollution from

VOCs.

9.3.1 Definition of the Problem

As one may note from the investigations reported in previous chapters, alternative means
of reducing the risk from the air emissions from the casting process have been examined.

Some of these options, such as schedule alternation and stack height alteration, do not

147

prevent pollution but only redistribute the pollutants so that the risk is reduced. For the

selection of pollution prevention alternatives the “problem definition” must be narrowed.

The simple statement of the problem is: “how can the foundry prevent pollution of the air
by emissions from the casting process?” For the purpose of this research, the problem is
bounded, that is to say: “of the pollution prevention alternatives, based on risk, which is
the most desirable?” The problem is further bounded, from an outsiders point of view, in
that the alternatives for pollution prevention are restricted to the selection of resins from
those commercially available. Reforrnulations or new formulations, innovative casting

techniques, new metals (such as aluminum), etc. are beyond the purview of this work.

9.3.2 Listing of Options

Under the bounded problem definition, the options for pollution prevention are limited to
the 19 binder formulations commercially available. Of these, the risks of only eleven
could be analyzed because of the availability of data. These alternative resins and their

estimated emissions are listed in Table 9.1.

9.3.3 Definition of Criteria

Although the criteria for selection of a resin include such things as the type and size of the
cast, the characteristics of the sand, and the cost, the bounded problem for this research

limits the criteria to selection of the resin that minimizes the total inhalation risk.

 

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149

Even with this great simplification, selecting the best alternative is not easy to resolve

because the “total inhalation risk” is difficult, if not impossible, to define based on

fundamental scientific principles. For example, as discussed in Chapter 8, because

different compounds have different target organs, combinations of quantitative estimates

of risk are not scientifically defensible. On the other hand, risk managers must make

decisions based on the information available and independent risk calculations may result

in a “no effect” decision that is highly erroneous as well as one that is unacceptable to the

public.

For the purpose of this research, four alternative decision criteria were considered:

Lowest emission
The lowest emission criteria is to simply select the binder resin with

lowest total estimated mass emission;

Comparison with acceptable cancer risk value or hazard index

In this alternative those compounds whose risk or hazard index is above
some threshold risk or hazard criteria are screened out. For example all
resins with no compounds exceeding a cancer risk of 10'4 and a HI of 1.0

would be deemed acceptable;

Total risk

This will be calculated using the protocol for combining risk and hazard
indexes published by the EPA (U .S.EPA 1989) with the exception that the
mean values of risk are utilized rather than the risk corresponding to the

reasonable maximum exposure (RME) scenario.

150

0 Relative marginal increase in risk
In effect, a risk calculation based on single source is an estimate of the
marginal increase in risk. Since this estimate is out of context of the total
exposure an individual may experience, the relative impact of the risk is
not assessed. A relative risk assessment requires an estimate using the total
exposure assessment methodology, TEAM, proposed by the EPA in a
multimedia, multiple pathway, multiple routes model. The risk from an
individual compound is compared to the total risk and then minimized

over all compounds by selection of the appropriate resin.

It is self-evident that a number of other decision criteria may exist. A cursory review of
texts on management decision theory (Baird 1989) revealed a number of techniques that
appear appropriate for making risk management decisions. Because of the scope of this
work and the vast potential scope of an exploration of management decision theory, it is

left for future research.

The lowest emission criterion is the easiest to use but it may not result in the lowest risk

because risk is not considered.

The comparison approach assumes that risks below a threshold do not contribute to the
body burden. However, given the uncertainty of toxicological assessments, this may
appear to be acceptable management decision criterion. In addition, it has the potential

advantage of leaving several alternatives to which other selection criteria may be applied

   

F.

151

as well as not violating the toxicological “rule” that contraindicates combining risks.

However, it may not be perceived as acceptable by the public.

The EPA combined risk technique has the decided advantage of providing the decision
maker with two lumped parameters: risk and HI. The disadvantages of the technique are
that it violates the toxicological “rule” and the selection of mean ( or the RME for that

matter) discards most of the information gained from using a probabilistic approach.

While the relative marginal risk approach appears to provide a method for assessing all
the components of risk without violating the toxicological “rule,” it does, in fact, measure
the marginal elements to be combined in some fashion. Furthermore, its implementation

requires either an extensive environmental audit or a large number of assumptions about

other exposures.

9.3.4 Analysis of the Options

For the purpose of comparison, the selection of a resin (or resins) to prevent pollution was

made based on each of the first three criteria discussed in 9.3.3.

Possible actions must now be carefully studied in terms of the desired outcomes, after
risk evaluation and comparison of the different results. Also reliability of the information

source should be assessed objectively. Analysis of the solution trade-offs can then be

performed.

152

9.3.4.1 Lowest Emission

A mass balance analysis of emission rates allowed the identification of the resin with
lowest emissions per year. Computations were performed using emission factors of the
toxic chemicals compiled by Mosher. A ranking of the different resins available on the
market could then be made and used to select of the system binder with lowest emissions.
Based on the results of mass balance shown in Table 9.1, the Furan Hotbox resin
presented an alternative considering its low yearly emissions of the selected chemicals

lumped together and for the HAP of concern (benzene) as well.

9.3.4.2 Comparison with Acceptable Risk Values

Using the risk techniques presented in the previous chapters, one can eliminate some of
the system binders which exhibit higher levels of risk compared to the system in use in
the case study. As can be seen in Table 8.1, the risk values for some of the binders
explored in this research were higher than the case study. For example phenolic no bake,
phenolic urethane could be eliminated at the screening level to allow more time and effort

to be dedicated to the ones that present a potential reduction of the actual emissions.

Another way of performing the screening is to use an acceptable risk value beyond which
the binder should be rejected. In this case, an E—06 was chosen as a safe level of risk to
accept. All resin binders that generated a higher risk should be eliminated. Seven of the

binders investigated for benzene emissions will then not be considered. These are:

153

phenolic no bake, phenolic urethane, core oil, shell, nitrogen furan TSA, alkyl isocyanate,

and silicate ester. Furan hot box is again a potential selection from the rest of binders.

9.3.4.3 EPA Combined Cancer Risk and HI

One can also use the EPA approach to lump the risk from all chemicals to sum their
impacts on human health. Combined risk from simultaneous exposure to carcinogenic

compounds, benzene, and formaldehyde is presented in Table 9.2.

Table 9.2 Combined Risk from Exposure to Multiple Chemicals

 

 

Resin Binders Benzene Formaldehyde Total Risk
Case Study 8.38E-06 1.11E-05 2.13E-05
Phenolic 'NoBake 6.71E-05 5.98E—08 6.71E-05
Phenolic 'Urethane 3.20E-05 1.32E-07 3.21E—05
Phenolic ' HotBox 5.995-06 3.59E-08 5.02E-06
Green 'Sand 3.65E-06 2.39E-08 3.67E-06
Core Oil 1.40E-05 5.74E-07 1.46E-05
Shell 3.98E-05 2.09E-07 4.00E-05
Nitrogen Furan 3.88E-06 1.6E-06 5.48E-05
Nitrogen 'Furan-TSA 2.71 E-05 3 .89E-07 2.75E-05
Furan ' HotBox 3.21 E-06 5 .3 8E-08 3.26E-06
Alkyd 'lsocyanate 3.19E-05 6.34E-07 3.25E—05
Silicate-'-Ester 8.43E-06 l .01E—06 9.44E-O6

Based on the total risk, FuranHotbox is again a potential for risk reduction. It is also clear
that this binder is not the only alternative for investigation. Phenolic Hotbox and green

sand should be considered as well.

154

9.3.5 Selection of Course of Action

Making the decision, which course should be taken, is yet the most critical exercise to
enterprise. An immediate action to be made is to investigate the possible use of binders
predicted to involve less risk based on their emission factors. A closer look to their
chemical composition is to be made to determine if they satisfy the casting requirement as
a comparison with the one in use right now. This, of course, will eliminate the additional
cost that could be generated in taking a different course such as change in stack heights,

or implementation of better control systems.

Favoring solutions that will reduce the risks at the source, the course of action of choice
will be to reduce the emissions by optimizing the use of binders. Furan hotbox is, a priori,
the best alternative to the current resin. But, this will remain open to verification of its

chemical composition and whether, it meets quality requirements of the cast.

As it can be seen from the previous exercise, different choices are possible. A unique
course of action is, however, not necessarily the right choice. A combination of two or
more alternatives may be a more appropriate way of solving the problem. In this case, a
decision only based on a simple estimate of risk is not going to be sufficient to prevent
pollution, since this risk value is not integrative of all the chemicals, all the sources, all
the media, and the routes of exposure. Also, in addition to risk reduction total emissions

per year should be also reduced.

155

References

Baird, B. F. (1989). Managerial Decisions Under Uncertainty An Introduction to the
Analysis of Decision Making. Toronto, CAN, John Willey & Sons, Inc.

Lindley, D. V. (1985). Making Decisions. London . New York . Toronto . Singapore.

U.S.EPA (1989). Risk Assessment Guidance for Superfund - Human Health Evaluation
Manual. Washington DC., EPA.

Chapter 10

Summary, Conclusions, and Recommendations

10.1 Summary of Research Work Completed

In May 1995, a literature review was initiated. About 300 references were abstracted and
investigated. For a better understanding of the different processes involved in the foundry
industry two field trips were made: one to the selected GM foundry in Saginaw; and one
to the Ford Motor Company plant in Windsor, Canada. As a result of the literature search
and these site visits, an initial scope for the study was selected. Casting (pouring,
cooling, and shakeout) was identified as the primary generator of Hazardous Air
Pollutants. These investigations also suggested a preliminary list of target compounds of

concern: benzene, formaldehyde, phenol, toluene, and xylene.
Emission calculations were initiated using a computer code provided by Mr. Oviatt from
the Michigan Department of Environmental Quality, MDEQ. Calculations were made for

casting emissions based on emission factors provided by Mosher (Mosher 1994).

An attempt was made to reproduce one of the emission factors obtained by Mosher

through a mass balance process. A mass balance model for the casting steps was built to

156

157

account for mass rates in and out of the control volume including losses and gains by
chemical reactions. Reasonable stack concentrations for a selected pollutant were

obtained.

For predicting the ambient impact of these pollutants, the selection of a suitable
dispersion model was based on the application and the available data. The Industrial
Source Complex, ISC-3, was selected to perform atmospheric dispersion of the

prioritized list of HAPs and predict their fate at the receptors of interest.

Source and meteorological data were gathered and utilized to generate atmospheric
concentrations of the contaminants. Transport of these chemicals was investigated far

from the source and an area of two kilometers was selected for exposure assessment and

intake evaluation.

A risk assessment was conducted using probability distributions for the exposure factors

used in health risk assessment. Computer simulations were used to facilitate statistical

risk analysis.

10.2 Conclusions

An essential goal of this study was to use risk assessment to help develop risk
management decisions that are more systematic, more comprehensive and accountable

than before. Hypothetical cases and “what if” scenarios can be investigated via risk

158

assessment models to evaluate the impact on human health of pollution prevention. In
particular, the risk assessment technique can be used as a tool to assess pollution
prevention alternatives. One of the important findings of this research was the influence
of the variability of the ISC3 source term on the exposure estimates. From the risk
values, it is obvious that several binders are likely to emit much less HAPs than the
current one in use, and as a consequence a potential reduction of the risk is available by
changing binders. Finally, a major conclusion of this research is that the classic point risk

method overestimates the risk for the general population exposed.

10.3 Recommendations for Reducing Risk

The recommendations are divided into two categories: those that reduce risk and those
that reduce risk by pollution prevention. Based on the evaluated risk, analysis of potential
reduction at the source or through use of alternate control systems have been investigated.

Some of the corrective measures that could substantially minimize the risk are:

0 Changing the heights of stacks in different lines and increasing the hight of the
one that has the most impact on exposure levels to improve their dispersion;

- Optimizing of the scenario (combination) of operating lines during the day to
give the lowest concentration at the ground level. For example, since the 3PM
scenario seems to give the lowest concentrations; it may be appropriate to run
the line that produces most of the emissions at that time. The other lines could
be run at a different times since their emissions are lower;

0 Finally, alternate control systems should be implemented to achieve lower
emissions.

159

Based on the production rates of the facility under study, it is recommended that the lines
with higher rates be scheduled at the favorable times (3 - 4 PM start) and the lines with
lower production rates be run during the morning shifi. This will allow reduction of the
ground level concentrations at this critical time when the atmospheric conditions are not

favorable.

10.4 Recommendations for Future Work

In future work, concentrations of the different chemicals in the binder and the sand before
and after pouring should be explored. This will minimize the uncertainties due to
multiple assumptions in makeup ratios and the concentrations in the remaining sand.
Another problem faced in the mass balance process is that most HAPs are generated from
reactions of the binder chemicals. For instance, the highest emission rate is for benzene;
however, it does not appear in the initial binder component list. Investigation of the
chemical reactions and the pathways that generate the HAPs is an obvious next step.

Other work that should be conducted includes:

0 Monitoring of the emission rates at the stacks for a more accurate

distribution fimction of the concentrations at the receptors;

0 Study of the Reactions of the toxic chemicals of concern to more

accurately determine their fate at the receptors;

0 Mass balance model calibration for more accurate analysis of the emissions

before their capture; and

160

Investigating Resins with lower emissions than the one in current use.

10.5 Future Research

The scope of this research was, largely, dictated by the available data, the financial

resources, and the time constraints. It does, however provide the basis for more elaborate

research that promises to greatly improve the risk assessment. As was hypothesized,

previously, the intent in this study was to demonstrate that risk assessment techniques

can be used to develop a strategy for pollution prevention. Some alternatives were

explored as solutions to pollution reduction. Some of the recommended research efforts

to be continued are:

Pollution Prevention based on cost and risk
Cost-risk-benefit analysis to define acceptable risks and industry
willingness to pay for their reduction. This will depend essentially on

financial resources, expectations, and time;

Development of more realistic ED, EF, and ET PDFs

To achieve acceptance of the probabilistic exposure model, standardized
PDFs need to be identified. More efforts need to be deployed to develop
reliable and representative probability distribution functions for the key

exposure factors;

Alternative metal (alminum) for casting
Investigate and compare the risks from the use of a different metal with
lower melting temperature such as aluminum that could potentially reduce

emissions;

161

0 Design of a new resin to minimize major components of risk
Since environmental have not been considered in developing the resin
binders used in this industry, more research is required to understand the
emissions evolving from their decomposition under the high temperature

of the cast;
0 Marginal risk minimization
Investigation of the risk contribution from industry emissions to the
total risk from all the sources. This will give a better idea about the risk in
order to Optimize the costs for it reduction;
0 Explore population risk vs. point individual risk

0 Apply management decision theory

0 Develop a technifitnre for graphical display of the risk modelinmsults.

References

Mosher, G. E. (1994). “Calculating Emission Factors for Pouring, Cooling and
Shakeout.” Modern Casting: 28-31.

Risk Assessment for Selecting
Pollution Prevention Alternatives

By

Souad Benromdhane

Volume II

A Ph.D. Dissertation

Submitted to
Michigan State University
in partial fulfillment of the Requirements
for the Degree of

DOCTOR OF PHILOSOPHY

Department of Civil and Environmental Engineering

1998

APPENDICES

Appendix A

162

Appendix A
Sample Mass Balance Calculation

In support of the proposed mass balance approach to the sand operations of a foundry, a
sample solution has been performed using data from the General Motors Powertrain
Saginaw Malleable Iron Plant. Line 1, which produces gears, yokes and pistons, was
chosen for analysis. Line 1 uses Sea Coal as the binder for the green sand molding process

and both Hotbox and Coldbox processes are used in core production.

Many of the items required for a thorough mass balance of this line were not available.
This resulted in a simplified mass balance that required several key assumptions. A
detailed account, including a process flow diagram, of the mass balance and its
assumptions is provided along with a graph that presents yearly HAP emissions with

varied capture efficiencies.

The calculation used the following steps:
1. Use Coldbox cores of Line 1 only (neglecting the molds and Hotbox cores)

2. Coldbox process utilizes half of the core sand 0.5x7329 = 3665 lb/hr and half of the
total metal charged (1/2)x(7 tons/charge) x (4 charges/hr.fu.rnaces) = 56 tons/hr

3. Isocure cores use a two part resin binder:
52% part I - 23.82 lb/hr
48% part II - 23.75 lb/hr

4. Naphthalene was chosen as the organic within the binder the binder to follow for
HAP emissions. A Material Safety Data Sheet was obtained for isocure, Part I and
Part II, which revealed :

Part I is 1-9% Naphthalene by weight ===> chose 9.0%
Part II is 1-5% Naphthalene by weight ===> chose 5.0%

Therefore, the initial amount of naphthalene available should be:

163

23.82*(.09)= 2.14lb/hr
23.75*(o.05) = 1.19 lb/hr

5. Pouring operation was selected for HAP emission analysis within the mass balance

6. Percent evaporation, percent reaction, and percent remaining in core information is
only available for the stage where the sand is mixed with the binder. The following
was obtained for naphthalene from a Form R reporting of foundry binder chemicals
(AF S, 1 995).

Partl(2.14lb/hr) Part II (1.19lb/hr)
0% reacts 0% reacts

50% evaporates 50% evaporates
50% remains in core 50% remains in core

There, the amount of naphthalene entering the pouring operation is what remains in the

core. For the two parts:
2.14(.5) + 1.19(.5) = 1.67 lb/hr

7. In determining how much of this remaining naphthalene will enter the air as a HAP
during pouring, the worst case is to assume 100% volatilization. However, it was
assumed that the Universal Treatment Standard amount of naphthalene remained in
the sand for disposal.

Universal Treatment Standard (5.6 x10—6 lb naphthalene/lb sand)

(3665 lb Sand/hr)
= 0.021 lb naphthalene/hr

There , 1.67 - 0.021 = 1.65 lb/hr of naphthalene is assumed to volatilize during pouring.

8. This 1.65 lb/hr is to be captured by a hood with a variable capture efficiency, u, with
an air flow rate of Q = 25,000 fi3/min.

9. The amount captured, 1.65*u, is assumed to be controlled with a variable control
efficiency, 13.

10. The amount of naphthalene emitted from the stack as a HAP from the pouring
operation is then Mout*u*(l-B) lb/ft3 air.

1 1. The foundry operates 16 hours a day, 5 days a week, and 260 days a year

164

POURING PROCESS
All values are on an hour basis
Sand: Sin = Sout = 3665 lbs
Nap = Naphthalene

1.65*C*( l -T)

 

1.65*C*T I

 

Control Device

 

 

 

 

 

 

1.67 lb Nap. Evap. 1.65 (l-C) 4 1. 65*C
f t 1.65 lb Nap. Evap.
3.33 lb Nap. Core Making 1.67 lb Nap. remains Pouring
’ Process process

Sin

 

¢ 0.02 lb Nap. with Sout

(to landfill)

 

165

Table 1 Effect of Varied Capture Efficiency on yearly Emissions

 

 

 

 

 

 

 

 

 

 

HAP Emission Capture Control Foundry Stack
During Efficiency Efficienccy Operation Emission
Pouring Schedule of HAP
(lb/hr) % % (hrs/year) (tons/yr)
Naphthalene 1.65 50% 95% 4160 0.0858
1.65 60% 95% 4160 0.1030
1.65 70% 95% 4160 0.1201
1.65 80% 95% 4160 0.1373
1.65 90% 95% 4160 0.1544
1.65 100% 95% 4160 0.1716
Figure 2 Yearly Naphthalene Stack Emissions
Stack Emlsslons from the Pourlng stage
3 0.1800
7; 0.1600 . y
g.“- 0.1400 -
«a
'saLOJZOO :
==l§°i°°°
33:» 0.0800 .
3.... 0.0600 -, . , -
:5 0.0400
E 0.0200
.3 0.0000

50%

70%

80%

90%

 

100%

Naphthalene emltted [tons/year]

 

 

 

APPENDIX B

166

Appendix B

BPIP File Structure for Downwash Calculations

Buildings considered in the BPIP calculations were multitiered. Each tier corner's

location was identified by a pair of X - and Y - Cartesian or respective UTM coordinates.

Each stack or source structure name used was the same name as used for the source
emissions data in the respective ISC3 input runstream. The ISC3 programs restrict the
stack or source name to a maximum 8 characters and so does BPIP. A stack base
elevation and stack height were determined for each stack along with its X - and Y -
Cartesian or UTM Easting and Northing coordinates. The direction for UTM 'north' is
assumed to be the same as True North, but there can be a slight difference that the user

can adjust for by setting a value other than 360.0 for 'plant north'.

The case study plant plots are not oriented to True North. The direction of 'plant north'
was determine with respect to True North. BPIP will adjust the plant coordinates to True
North coordinates after any UTM adjustments are made but before any GEP processing
begins. Once this data has been determined, an input file to BPIP was written (Table 5.1a)

Data entry is in 'Free F orrnat'.

'Foundry Building'

081‘!

'METERS' 1.0
'UTMY‘ 105.5

2

'L - Blg' 1 10.0

8 8.9
78988.27
79045.96
79027.74
79031.94
78993.47
78972.68
78990.86
78935.80
's -Blg' 1
6 5.0
78661.08
78661.08
7891 1.86
7891 1.86
78934.17
78934.17
13

1465971 . l 7
146589787
146587918
146587503
1465835 .05
146585582
146586864
146592258
10.0

146588845
1465821.70
146581 1.16
146589881
146589881
146588799

Table 5.1a Input file for BPIP calculations

'FFF5845’ 18.90 25.91 78988.27 1465971.l7
'FFF5946' 18.90 22.86 79045.96 146589787
'FFF6047' 18.90 22.86 79027.74 1465879.]8
'FSC1953' 3.96 39.01 78990.86 146586864
'FSC2155' 3.96 39.01 78942.40 146591551
'FSC2256' 3.96 39.01 78950.84 146592435
'FEF5437' 18.9 21.95 78992.81 146595439
'FEF5640' 18.9 21.34 78985.52 1465927.83
'FSC2783' 16.76 19.51 78983.10 146592539
'FEF2094' 13.11 22.86 78982.36 146592601
'FEF1774' 9.14 14.94 78999.81 146592028
'FSC3199' 13.11 19.51 78992.55 146592028
'FSC32100'13.11 20.12 78993.86 1465921.74

0

 

lull?
BPI}

incrcI
proce
value
definj

is set 1

 

14513

param

InitialP

MB
MT

MT
MS]

ML

8P0,”
Was “T11

N0OPE:

168

Initial Program Settings

BPIP has been programmed with parameters that the user can set to accommodate
increases in the number of structures, tiers per structure, or stacks that need to be
processed without changing the dimensions of over two dozen arrays. The parameter
values are arguments in PARAMETER statements that are located shortly afier the
definitions in the main program and at the beginning of each subroutine. Initially, BPIP

is set up to process a maximum of 8 buildings with a maximum of 4 tiers per building and
14 stack locations. In order to change the dimensions of these variables, the following

parameters need to be changed:

lnitialParameter Definition Setting
0 MB Maximum Number of Buildings 8
0 MT Maximum Number of Tiers/Building 4
o MBT Maximum Building-Tier Number (MB*MT) 32
o MTS Maximum Number of Sides/Tier 8
- MSK Maximum Number of Stacks 14
0 MD Number of Sectors - ISCST2 36
0 ML Number of Sectors - ISCLT2 16

BPIP will need to be recompiled after changing any one of the above parameters. BPIP
was written to F ortran 77 standards and compiled with Microsoft's Fortran 5.0 compiler.

No OPEN statements were used in the source code.

Execution of BPIP

 

0m

The‘

BPU

3m

BPIP 1

 

 

 

 

data SL
lSCSl
file see

Which :

169

Once the input file has been prepared and saved to disk, BPIP is ready to be executed.

The execution line is as follows:

BPIP input_filename output_filename summary_filename

BPIP output files and formats

BPIP runs generate two output files. A primary output file contains the essential output
data such as the preliminary GEP stack height values and the BH and PBW for an
ISCST2 or ISCLT2 input runstream file. This file is considered to be the primary output
file see Table 5. A second file is a summary file and it contains detailed output such as

which tier(s) are affecting which stack for a particular wind flow direction (Table 5.1b).

170

Table 5.1b BPIP Output file
BPIP (Dated: 95086)

DATE : 3/ 5/97
TIME : 23:13:52

Foundry Building
BPIP output is in meters

SO BUILDHGT FFF5845 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5845 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5845 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5845 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5845 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5845 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDWID FFF5845 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FFF5845 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FFF5845 110.49 106.16 102.48 113.84 125.10 132.56
SO BUILDWID FFF5845 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FFF5845 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FFF5845 110.49 106.16 102.48 113.84 125.10 132.56

SO BUILDHGT FFF5946 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5946 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5946 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5946 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5946 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FFF5946 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDWID FFF5946 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FFF5946 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FFF5946 110.49 106.16 102.48 113.84 125.10 132.56
SO BUILDWID FFF5946 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FFF5946 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FFF5946 110.49 106.16 102.48 113.84 125.10 132.56

SO BUILDHGT F FF6047 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT F F F6047 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT F F F6047 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT F FF6047 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT F FF6047 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT F FF 6047 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDWID FFF6047 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FFF6047 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID F FF6047
SO BUILDWID FFF6047
SO BUILDWID FFF6047
SO BUILDWID FFF6047

SO BUILDHGT FSC1953
SO BUILDHGT FSC1953
SO BUILDHGT FSC1953
SO BUILDHGT FSC1953
SO BUILDHGT FSC1953
SO BUILDHGT FSC1953
SO BUILDWID F SC1953
SO BUILDWID F SC1953
SO BUILDWID F SC1953
SO BUILDWID FSC1953
SO BUILDWID F SC1953
SO BUILDWID FSC1953

SO BUILDHGT F SC21 55
SO BUILDHGT FSC2155
SO BUILDHGT F SC21 55
SO BUILDHGT FSC2155
SO BUILDHGT FSC2155
SO BUILDHGT FSC2155
SO BUILDWID FSC2155
SO BUILDWID FSC2155
SO BUILDWID FSC2155
SO BUILDWID FSC2155
SO BUILDWID FSC2155
SO BUILDWID FSC2155

SO BUILDHGT FSC2256
SO BUILDHGT FSC2256
SO BUILDHGT FSC2256
SO BUILDHGT F SC2256
SO BUILDHGT F SC2256
SO BUILDHGT FSC2256
SO BUILDWID FSC2256
SO BUILDWID FSC2256
SO BUILDWID F SC2256
SO BUILDWID F SC2256
SO BUILDWID FSC2256
SO BUILDWID FSC2256

110.49
135.99
84.08

1 10.49

8.90
8.90
8.90
8.90
8.90
8.90
135.99
84.08
110.49
135.99
84.08
110.49

8.90
8.90
8.90
8.90
8.90
8.90
135.99
84.08
110.49
135.99
84.08
110.49

8.90
8.90
8.90
8.90
8.90
8.90
135.99
84.08
110.49
135.99
84.08
110.49

171

106.16 102.48 113.84 125.10 132.56
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84

106.16 102.48 113.84 125.10 132.56

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90
8.90

135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56

 

 

 

rn (A (,5 rn_m 'A "

(I?

mmmmmmmmrnrnrnrn

172

SO BUILDHGT FEF5437 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5437 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5437 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5437 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5437 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5437 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDWID FEF5437 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FEF5437 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FEF5437 110.49 106.16 102.48 113.84 125.10 132.56
SO BUILDWID FEF5437 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FEF5437 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FEF5437 110.49 106.16 102.48 113.84 125.10 132.56

SO BUILDHGT FEF5640 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5640 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5640 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5640 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5640 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FEF5640 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDWID FEF5640 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FEF5640 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FEF5640 110.49 106.16 102.48 113.84 125.10 132.56
SO BUILDWID FEF5640 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FEF5640 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FEF5640 110.49 106.16 102.48 113.84 125.10 132.56

SO BUILDHGT FSC2783 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FSC2783 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FSC2783 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FSC2783 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FSC2783 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDHGT FSC2783 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDWID FSC2783 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FSC2783 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FSC2783 110.49 106.16 102.48 113.84 125.10 132.56
SO BUILDWID FSC2783 135.99 135.29 130.48 121.71 109.23 93.44
SO BUILDWID FSC2783 84.08 88.79 99.55 107.28 111.76 112.84

SO BUILDWID FSC2783 110.49 106.16 102.48 113.84 125.10 132.56

SO BUILDHGT FEF 2094 8.90 8.90 8.90 8.90 8.90 8.90
SO BUILDHGT F EF2094 8.90 8.90 8.90 8.90 8.90 8.90
SO BUILDHGT FEF2094 8.90 8.90 8.90 8.90 8.90 8.90
SO BUILDHGT FEF 2094 8.90 8.90 8.90 8.90 8.90 8.90
SO BUILDHGT FEF 2094 8.90 8.90 8.90 8.90 8.90 8.90
SO BUILDHGT FEF 2094 8.90 8.90 8.90 8.90 8.90 8.90

SO BUILDWID FEF2094
SO BUILDWID FEF2094
SO BUILDWID FEF2094
SO BUILDWID FEF2094
SO BUILDWID FEF2094
SO BUILDWID FEF2094

SO BUILDHGT FEF1774
SO BUILDHGT FEF1774
SO BUILDHGT FEF1774
SO BUILDHGT FEF1774
SO BUILDHGT FEF1774
SO BUILDHGT F EF1774
SO BUILDWID F EF1774
SO BUILDWID F EF1774
SO BUILDWID FEF 1774
SO BUILDWID FEF1774
SO BUILDWID FEF1774
SO BUILDWID FEF1774

SO BUILDHGT FSC3199
SO BUILDHGT FSC3199
SO BUILDHGT F SC3199
SO BUILDHGT FSC3199
SO BUILDHGT FSC3199
SO BUILDHGT FSC3199
SO BUILDWID FSC3199
SO BUILDWID F SC3199
SO BUILDWID FSC3199
SO BUILDWID FSC3199
SO BUILDWID FSC3199
SO BUILDWID FSC3199

SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDWID FSC32100
SO BUILDWID FSC32100
SO BUILDWID FSC32100
SO BUILDWID F SC32100
SO BUILDWID FSC32100
SO BUILDWID FSC32100

135.99
84.08
110.49
135.99
84.08
110.49

8.90
8.90
8.90
8.90
8.90
8.90
135.99
84.08
110.49
135.99
84.08
1 10.49

8.90
8.90
8.90
8.90
8.90
8.90
135.99
84.08
110.49
135.99
84.08
110.49

8.90
8.90
8.90
8.90
8.90
8.90
135.99
84.08
110.49
135.99
84.08
110.49

173

135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56

8.90
8.90
8.90
8.90
8.90 8.90 8.90 8.90 8.90

8.90 8.90 8.90 8.90 8.90
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90

8.90

8.90

8.90

8.90

8.90 8.90 8.90 8.90 8.90

8.90 8.90 8.90 8.90 8.90
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90
8.90 8.90 8.90 8.90 8.90

8.90 8.90 8.90 8.90 8.90
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56
135.29 130.48 121.71 109.23 93.44
88.79 99.55 107.28 111.76 112.84
106.16 102.48 113.84 125.10 132.56

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90

8.90
8.90
8.90
8.90

APPENDIX C

NOTE: MET
IS INCLUDE ‘

>
as
(5

U

('1':

*1

W)»
a

O

”7171

 

  

STAE

CATE
.7000OE
-700001
.IOOOOE
~150001
.35000E
.55000E

STAB
CATE
-00000E
-OOOOOE
’OOOOOE.
~000005.

NOTE: METEOROLOGICAL DATA ACTUALLY PROCESSED WILL ALSO DEPEND ON WHAT

174

Appendix C

Sample of Meteorological Data

PROCESSED BETWEEN
START DATE: 9] / l /1 I] AND END DATE: 91/ 12 /31/ 24

IS INCLUDED IN THE DATA FILE.

mmg0w>

mmonw>

m UPPER BOUND OF FIRST THROUGH FIFTH WIND SPEED CATEGORIES m
(METERS/SEC)

1.54, 3.09, 5.14, 8.23, 10.80,

*" WIND PROFILE EXPONENTS *"

STABILITY WIND SPEED CATEGORY

CATEGORY 1 2 3 4 5 6
.70000E-0 1 .70000E-01 .70000E-01 .70000E-0 1 .70000E-0 1 .70000E-01
.70000E-01 .70000E-01 .70000E-01 .70000E-01 .70000E-01 .70000E-01
. 10000E+00 . 10000E+00 . 10000E+00 . 10000E+00 . 10000E+00 . 10000E+00
.15000E+00 .15000E+00 .15000E+00 .15000E+00 .15000E+00 .15000E+00
.3 5000E+00 .35000E+00 .35000E+00 .35000E+00 .35000E+00 .35000E+00
.55000E+00 .55000E+00 .55000E+00 .55000E+00 .55000E+00 .55000E+00

*** VERTICAL POTENTIAL TEMPERATURE GRADIENTS "*
(DEGREES KELVIN PER METER)

STABILITY
CATEGORY
.00000E+00
.00000E+00
.00000E+00
.00000E+00
.20000E-0 1
. 35000E-0 1

1

.00000E+00
.00000E+00
.00000E+00
.00000E+00
.20000E-0 1
.3 5000E-0 1

WIND SPEED CATEGORY
2 3 4 5 6
.00000E+00 .00000E+00 .00000E+00
.00000E+00 .OOOOOE+00 .00000E+00
.00000E+00 .00000E+00 .00000E+00
.00000E+00 .00000E+00 .00000E+00
.20000E-0 l .20000E-0 1 .20000E-0 1
.35000E-01 .35000E-01 .3 5000E-01

.00000E+00
.00000E+00
.00000E+00

.00000E+00

.20000E-0 l
.35000E-01

 

M

—_l.

FILE: I
SURF‘

LENGTH 21'
YEAR MONT
(mmrHR)

 

175

*** THE FIRST 24 HOURS OF METEOROLOGICAL DATA *"

FILE: C:\AC\SIMULA~1\MET1\MBS91_T.TRI

SURFACE STATION NO.: 72639
NAME: UNKNOWN
YEAR: 1991

FORMAT: UNFORM
UPPER AIR STATION NO.: 14826
NAME: UNKNOWN

YEAR: 1991

FLOW SPEED TEMP STAB MIXING HEIGHT (M) USTAR M-O
LENGTH Z-O IPCODE PRATE
YEAR MONTH DAY HOUR VECTOR (M/S) (K) CLASS RURAL URBAN (M/S) (M) (M)
(mm/HR)

91 1 1 1 351.0 4.63 264.3 4 742.9 742.9 .0000 .0 .0000 0 .00
91 1 1 2 8.0 4.63 265.4 4 729.9 729.9 .0000 .0 .0000 O .00
91 1 l 3 24.0 4.12 265.9 4 716.9 716.9 .0000 .0 .0000 0 .00
91 1 1 4 23.0 5.66 265.9 4 703.9 703.9 .0000 .0 .0000 0 .00
91 1 1 5 13.0 5.66 265.9 4 691.0 691.0 .0000 .0 .0000 0 .00
91 1 1 6 22.0 6.17 265.9 4 678.0 678.0 .0000 .0 .0000 0 .00
91 1 1 7 15.0 5.14 266.5 4 665.0 665.0 .0000 .0 .0000 O .00
91 1 1 8 13.0 3.09 267.0 5 652.0 426.0 .0000 .0 .0000 0 .00
91 1 1 9 347.0 3.60 266.5 4 74.1 445.1 .0000 .0 .0000 0 .00
91 l 1 10 1.0 5.14 267.6 4 174.1 470.9 .0000 .0 .0000 0 .00
91 l 1 11 354.0 5.14 268.7 4 274.0 496.7 .0000 .0 .0000 0 .00
91 1 1 12 346.0 6.17 269.3 4 374.0 522.4 .0000 .0 .0000 0 .00
91 1 l 13 3.0 6.17 270.9 4 474.0 548.2 .0000 .0 .0000 0 .00
91 1 l 14 9.0 4.12 270.9 4 574.0 574.0 .0000 .0 .0000 0 .00
91 1 l 15 32.0 5.66 272.6 4 574.0 574.0 .0000 .0 .0000 0 .00
91 l l 16 24.0 6.69 272.0 4 574.0 574.0 .0000 .0 .0000 0 .00
91 l l 17 1.0 4.12 271.5 4 574.0 574.0 .0000 .0 .0000 0 .00
91 1 1 18 27.0 5.14 271.5 4 579.5 579.5 .0000 .0 .0000 0 .00
91 l 1 19 54.0 6.17 272.0 4 585.4 585.4 .0000 .0 .0000 0 .00
91 1 1 20 57.0 4.63 272.0 4 591.2 591.2 .0000 .0 .0000 0 .00
91 1 l 21 80.0 2.06 271.5 4 597.1 597.1 .0000 .0 .0000 0 .00
91 1 1 22 92.0 5.14 272.6 4 603.0 603.0 .0000 .0 .0000 0 .00
91 1 l 23 80.0 4.12 272.6 4 608.9 608.9 .0000 .0 .0000 0 .00
91 1 1 24 100.0 5.66 272.0 4 614.7 614.7 .0000 .0 .0000 O .00

APPENDIX D

176

Appendix D

Sample of Dispersion Simulations Input File

ISCST3 - (DATED 96113)
IBM-PC VERSION (3.04) ISCSTBR
Run Began on 2/21/1998 at 10:21:11

" BREEZE AIR ISCST3 - C:\AC\SIMULA~1\SIM\FULL\FULL91.DAT
" Trinity Consultants Incorporated, Dallas, TX

CO STARTING

CO TITLEONE TRIAL

CO MODELOPT DFAULT CONC RURAL
CO AVERTIME 24 ANNUAL

CO POLLUTID BENZENE

CO TERRHGTS FLAT

CO RUNORNOT RUN

CO FINISHED

SO STARTING

SO ELEVUNIT METERS

SO LOCATION FFF5845 POINT 78971.8 14659508 18.9

" SRCDESCR POURINGI

SO LOCATION FFF5946 POINT 78967.02 146594984 18.9

‘”" SRCDESCR COOLINGll

SO LOCATION FFF6047 POINT 78964.3 14659464 18.9

" SRCDESCR COOLING12

SO LOCATION FSC1953 POINT 78958.68 14659388 3.96

" SRCDESCR SHAKEOUTII

SO LOCATION FSC2155 POINT 78963.3 146593983 3.96

“ SRCDESCR SHAKEOUT12

SO LOCATION FSC2256 POINT 78965.25 146593783 3.96

" SRCDESCR SHAKEOUT13

SO LOCATION FEF5437 POINT 78992.81 146595489 18.9

" SRCDESCR POURING-RMIP2

SO LOCATION FEF5640 POINT 78985.52 1465927.83 18.9

" SRCDESCR MAIN PURING2

SO LOCATION FSC2783 POINT 78983.1 146592589 16.76

“ SRCDESCR COOLINGZ

SO LOCATION FEF2094 POINT 78982.36 146592601 13.11

" SRCDESCR SHAKEOUT2

SO LOCATION FEF1774 POINT 78999.81 146592028 9.14

‘”" SRCDESCR MAIN POURING3

SO LOCATION FSC3199 POINT 78992.55 146592028 13.11

" SRCDESCR SHAKEOUT3

SO LOCATION FSC32100 POINT 78993.86 1465921.74 13.11

” SRCDESCR COOLING3

SO SRCPARAM FFF5845 8.681209E-02 25.91 302.5889 26.68 1.22
SO SRCPARAM FFF5946 6.205366E-01 22.86 310.9278 8.98 1.07
SO SRCPARAM FFF6047 6.205366E-01 22.86 310.9278 12.14 1.07
SO SRCPARAM FSC1953 1.319192E-01 39.01 310.9278 16.9 1.07
SO SRCPARAM FSC2155 1.319192E-01 39.01 310.9278 16.98 1.22
SO SRCPARAM FSC2256 1.319192E-01 39.01 310.9278 16.98 1.22

177

SO SRCPARAM FEF5437 1.335578E-02 21.95 302.5889 49.7 0.76

SO SRCPARAM FEF5640 1.335578E-02 21.34 302.5889 21.83 1.22
SO SRCPARAM FSC2783 3.817736E-01 19.51 310.9278 16.17 1.22
SO SRCPARAM FEF2094 1.215880E-01 22.86 373.15 17.25 0.91

SO SRCPARAM FEF1774 1.335578E-02 14.94 302.5889 19.41 1.22
SO SRCPARAM FSC3199 6.079398E-02 19.51 310.9278 16.17 1.22
SO SRCPARAM FSC32100 1.908868E-01 20.12 310.9278 10.11 1.22

SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID

F FF5946
F FF5946
FF F5946
FFF5946
F FF5946
F FF5946
F FF 5946
F FF5946
FFF 5946
FFF5946
F F F5946
FFF5946
F SC 1 953
FSC1953
FSC1953
F SC 1 953
FSC1953
FSC1953
FSC 1953
FSC1953
F SC 1 953
FSC1953
FSC1953
FSC1953
FSC2155
FSC2155
FSC2155
FSC2155
FSC2155
FSC2155
FSC2155
FSC2155
FSC2155
FSC2155
FSC2155
FSC2155
F SC2256
FSC2256
F SC2256
F SC2256
F SC2256
F SC2256
FSC2256
FSC2256
F SC2256
FSC2256
FSC2256
F SC2256

10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0

10.0 10.0 10.0 10.0
10.0 10.0 10.0 10.0
10.0 10.0 10.0 10.0
10.0 10.0 10.0 10.0
10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55

 

SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDWID
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDHGT
SO BUILDWID

FEF5437
FEF5437
FEF5437
F EF5437
FEF5437
FEF5437
FEF5437
FEF5437
F EF5437
FEF5437
F EF5437
FEF5437
FEF5640
FEF5640
FEF5640
FEF5640
FEF5640
FEF5640
F EF5640
FEF5640
FEF5640
FEF5640
F EF5640
FEF5640
FSC2783
F SC2783
FSC2783
FSC2783
FSC2783
FSC2783
FSC2783
FSC2783
FSC2783
FSC2783
FSC2783
FSC2783
FEF2094
FEF2094
FEF2094
FEF2094
FEF2094
FEF2094
FEF2094
FEF2094
FEF2094
FEF2094
FEF2094
FEF2094
FEF] 774
FEF 1774
FEF 1774
FEF1774
FEF 1774
FEF1774
FEF1774

10.0
10.0
10.0
10.0

10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0 10.0
10.0 10.0 10.0

178

10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0

10.0
10.0
10.0
10.0
10.0
10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55

10.0 10.0 10.0
10.0 10.0 10.0
10.0 10.0 10.0
10.0 10.0 10.0
10.0 10.0 10.0
10.0 10.0 10.0

10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0

10.0
10.0
10.0
10.0
10.0
10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55

10.0 10.0 10.0
10.0 10.0 10.0
10.0 10.0 10.0
10.0 10.0 10.0
10.0 10.0 10.0

10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0
10.0 10.0

10.0
10.0
10.0
10.0
10.0

10.0 10.0 10.0 10.0 10.0 10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

135.98 135.28 130.47 121.69 109.22 93.43

SO BUILDWID FEF1774
SO BUILDWID FEF1774
SO BUILDWID FEF1774
SO BUILDWID FEF1774
SO BUILDWID FEF1774
SO BUILDHGT FSC3199
SO BUILDHGT FSC3199
SO BUILDHGT FSC3199
SO BUILDHGT FSC3199
SO BUILDHGT FSC3199
SO BUILDHGT FSC3199
SO BUILDWID FSC3199
SO BUILDWID FSC3199
SO BUILDWID FSC3199
SO BUILDWID FSC3199
SO BUILDWID FSC3199
SO BUILDWID FSC3199

SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDHGT FSC32100
SO BUILDWID FSC32100
SO BUILDWID FSC32100
SO BUILDWID FSC32100
SO BUILDWID F SC32100
SO BUILDWID FSC32100
SO BUILDWID FSC32100

SO EMISFACT FFF5845
SO EMISFACT FFF5845
SO EMISFACT FFF5845
SO EMISFACT FFF5845
SO EMISFACT FFF5946
SO EMISFACT FFF5946
SO EMISFACT FFF5946
SO EMISFACT FFF5946
SO EMISFACT FFF6047
SO EMISFACT FFF6047
SO EMISFACT FFF6047
SO EMISFACT FFF6047
SO EMISFACT FSC1953
SO EMISFACT FSC1953
SO EMISFACT FSC1953
SO EMISFACT FSC1953
SO EMISFACT FSC2155
SO EMISFACT FSC2155
SO EMISFACT FSC2155
SO EMISFACT FSC2155
SO EMISFACT F SC2256
SO EMISFACT FSC2256
SO EMISFACT FSC2256
SO EMISFACT FSC2256
SO EMISFACT FEF5437

179

84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

10.0 10.0 10.0 10.0 10.0 10.0

135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55
135.98 135.28 130.47 121.69 109.22 93.43
84.09 88.79 99.55 107.28 111.76 112.84
110.49 106.17 102.48 113.83 125.09 132.55

HROFDY 0.08681 0.08681 0.08681 0.08681 0.08681 0.08681

HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY 0.6205
HROFDY 0.6205
HROFDY 0.6205
HROFDY 0.6205
HROFDY 0.6205
HROFDY 0.1319
HROFDY 0.1319
HROFDY 0.1319
HROFDY 0.1319
HROFDY 0.1319
HROFDY 0.1319
HROFDY 0.1319
HROFDY 0.1319
HROFDY 0.1319

0.6205
0.6205
0.6205

0.6205
0.6205
0.6205
0.6205
0.6205
0.6205
0.6205
0.6205 0.6205

0.1319 0.1319 0.1319 0.1319 0.1319
0.1319 0.1319 0.1319 0.1319 0.1319
0.1319 0.1319 0.1319 0.1319 0.1319
0.1319 0.1319

0.1319 0.1319 0.1319 0.1319 0.1319
0.1319 0.1319 0.1319 0.1319 0.1319
0.1319 0.1319 0.1319 0.1319 0.1319
0.1319 0.1319

0.1319 0.1319 0.1319 0.1319 0.1319
HROFDY 0.1319 0.1319 0.1319 0.1319 0.1319 0.1319
HROFDY 0.1319 0.1319 0.1319 0.1319 0.1319 0.1319
HROFDY 0.1319 0.1319 0.1319

0.6205 0.6205 0.6205 0.6205
0.6205 0.6205 0.6205 0.6205
0.6205 0.6205 0.6205 0.6205
0.6205

0.6205 0.6205 0.6205 0.6205
0.6205 0.6205 0.6205 0.6205
0.6205 0.6205 0.6205 0.6205

0.08681 0.08681 0.08681 0.08681 0.08681 0.08681
0.08681 0.08681 0.08681 0.08681 0.08681 0.08681
0.08681 0.08681 0.08681 0.08681 0.08681 0.08681

0.6205
0.6205
0.6205

0.6205
0.6205
0.6205

0.1319
0.1319
0.1319

0.1319
0.1319
0.1319

0.1319
0.1319
0.1319

HROFDY 0.01336 0.01336 0.01336 0.01336 0.01336 0.01336
SO EMISFACT FEF5437 HROFDY 0.01336 0.01336 0.01336 0.01336 0.01336 0.01336

SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISF ACT
SO EMISFACT
SO EMISFACT
SO EMISF ACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT
SO EMISFACT

FEF5437
F EF5437
FEF5640
FEF5640
FEF5640
FEF5640
FSC2783
FSC2783
F SC2783
FSC2783
FEF2094
FEF2094
FEF2094
FEF2094
FEF 1 774
FEF 1 774
FEF I 774
FEF 1 774
FSC3 199
F SC3 199
FSC3 199
FSC3 199

HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY
HROFDY

0.01336
0.01336
0.01336
0.01336
0.01336
0.01336
0.38177
0.38177
0.38177
0.38177
0.12159
0.12159
0.12159
0.12159
0.01336
0.01336
0.01336
0.01336
0.06079
0.06079

180

0.01336 0.01336
0.01336 0.01336
0.01336 0.01336
0.01336 0.01336
0.01336 0.01336
0.01336 0.01336
0.38177 0.38177
0.38177 0.38177
0.38177 0.38177
0.38177 0.38177
0.12159 0.12159
0.12159 0.12159
0.12159 0.12159
0.12159 0.12159
0.01336 0.01336
0.01336 0.01336
0.01336 0.01336
0.01336 0.01336
0.06079 0.06079
0.06079 0.06079

0.01336
0.01336
0.01336
0.01336
0.01336
0.01336
0.38177
0.38177
0.38177
0.38177
0.12159
0.12159
0.12159
0.12159
0.01336
0.01336
0.01336

0.01336
0.01336
0.01336
0.01336
0.01336
0.01336
0.38177
0.38177
0.38177
0.38177
0.12159
0.12159
0.12159
0.12159
0.01336
0.01336
0.01336 0.01336
0.01336 0.01336 0.01336
0.06079 0.06079 0.06079
0.06079 0.06079 0.06079

0.01336
0.01336
0.01336
0.01336
0.01336
0.01336
0.38177
0.38177
0.38177
0.38177
0.12159
0.12159
0.12159
0.12159
0.01336
0.01336

0.06079 0.06079 0.06079 0.06079 0.06079 0.06079
HROFDY 0.06079 0.06079 0.06079 0.06079 0.06079 0.06079

SO EMISFACT
SO EMISF ACT

FSC32100 HROFDY 0.19089 0.19089 0.19089 0.19089 0.19089 0.19089
FSC32100 HROFDY 0.19089 0.19089 0.19089 0.19089 0.19089 0.19089
SO EMISFACT FSC32100 HROFDY 0.19089 0.19089 0.19089 0.19089 0.19089 0.19089
SO EMISFACT FSC32100 HROFDY 0.19089 0.19089 0.19089 0.19089 0.19089 0.19089
SO SRCGROUP LINE] FF F5845 FFF5946 FFF6047 FSC1953 FSC2155 F SC2256

SO SRCGROUP LINEZ FEF5437 FEF5640 FSC2783 F EF2094

SO SRCGROUP LINE3 FEF1774 FSC3199 FSC32100

SO SRCGROUP LINE12 FFF5845 FFF5946 FFF6047 FSC1953 FSC2155 F SC2256 FEF5437
SO SRCGROUP LINE12 FEF5640 F SC2783 FEF2094

SO SRCGROUP LINE23 FEF5437 FEF5640 FSC2783 FEF2094 FEF1774 F SC3 199 FSC32100
SO SRCGROUP LINE13 FFF5845 FFF5946 FFF6047 FSC1953 FSC2155 FSC2256 FEF1774
SO SRCGROUP LINE13 FSC3199 FSC32100

SO SRCGROUP ALL

SO FINISHED

RE STARTING

RE GRIDCART CART] STA

RE GRIDCART CARTl XYINC 78000 41 50 1465000 41 50
RE GRIDCART CARTI END

RE FINISHED

ME STARTING

ME INPUTFIL C:\AC\SIMULA~1\MET1\MBS91_T.TRI UNFORM
MEANEMHGHT 10.0 METERS

ME SURFDATA 72639 1991

MEUAIRDATA 14826 1991

MESTARTEND 91 01 01 1 91 12 31 24

ME FINISHED

OU STARTING

OU RECTABLE 24 FIRST

OU FINISHED

"" SETUP Finishes Successfully ***

APPENDIX E

181

Appendix E
Sample of Dispersion Simulations Output File
Concentrations of Benzene for 1991

THE ANNUAL ( 8760 HRS) AVERAGE CONCENTRATION VALUES
FOR SOURCE GROUP: LINE] INCLUDING SOURCE(S):
FF F5845 , FFF5946 , FFF6047 , FSC1953 , F SC2155 , FSC2256 ,

*** NETWORK ID: CART] ; NETWORK TYPE: GRIDCART ***

** CONC OF BENZENE IN MICROGRAMS/M**3 **

Y-COORD | X-COORD (METERS)
(METERS)| 78000.00 78050.00 78100.00 78150.00 78200.00 78250.00 78300.00
78350.00 78400.00

146700000 | .20165 .20159 .201 13 .2027] .20765 .21596 .22656 .23 768
21540686995000 I .2094] .21 100 .21069 .21037 .21254 .21831 .22736 .23 832
21540656290000 | .21737 .21979 .22102 .22045 .22032 .22314 .22970 .23934
21540686585000 | .22730 .22890 .23098 .23176 .23090 .23 102 .2345] .24175
21541696180000 I .24004 .24015 .2414 1 .24304 .24326 .24209 .24250 .24662
21544636975000 | .25578 .25447 .25418 .25498 .25603 .25554 .25402 .25473
21548696270000 | .27427 .27216 .27032 .26948 .26970 .26996 .26859 .26610
21646626255000 | .29442 .29297 .29029 .28772 .28618 .28560 .28417 .28079
217636760000 | .31440 .31548 .31385 .31042 .30685 .30374 .30120 .29779
21942646455000 | .33326 .33747 .33906 .33728 .33234 .32640 .32133 .3167]
31140666850000 I .35110 .35796 .36318 .36522 .36237 .35535 .34674 .3389]
3134126645000 | .3662] .37646 .3 8503 .39080 .3929] .38940 .37996 .36746
31545656540000 I .3 7683 .39082 .403 12 .41280 .41959 .42222 .41813 .405 7 1
312768625000 | .3 8488 .401 13 .41644 .43019 .44135 .44903 .45190 .4472]
.41341656530000 | .39085 .40884 .42636 .443 1 1 .45819 .47025 .47807 .48025
.4124616625000 | .39016 .41095 .43 151 .45140 .46999 .48629 .49890 .50579
5125616220000 | .38125 .40330 .42623 .44952 .47232 .4934] .51 143 .52438

.52962

182

 

146615000 I .36690 .3 8861 .41 150 .43538 .45978 .48412 .50705 .5265]
51349656710000 | .34595 .36674 .38875 .41 181 .43555 .45958 .48300 .5045]
51242606705000 I .32258 .34086 .3602] .3805] .40149 .42284 .443 82 .46333
41749::00000 I .30747 .32348 .34016 .35734 .37466 .39176 .40788 .42196
.41342655295000 I .29729 .31 193 .32705 .34243 .3577] .37246 .38590 .39694
.41?6056900.00 I .27850 .2899] .30120 .31204 .32195 .33034 .33630 .33868
3133695985000 I .2410] .24695 .25205 .25597 .25832 .25870 .25667 .25184
2:43695780000 I '. 19636 . 19904 .20129 .203] 1 .20453 .20568 .20659 .20722
21317635875000 I . 16558 .1693] . 17343 . 17797 . 18293 .1883] . 19425 .201 19
21140605070000 I . 15249 .15818 . 16450 . 17170 . 18025 . 19084 .20414 .22007
21346645665000 I . 14916 . 15722 . 16699 . 17899 . 19344 .20974 .22585 .23 842
211425460000 I .15476 .16693 .1808] .19576 .21006 .22147 .22857 .23187
2133655955000 I . 16749 .18084 .1930] .20280 .20946 .21364 .21667 .21854
21117685150000 I . 17607 . 18453 . 19048 . 19457 . 19817 .20176 .20423 .20498
2947655745000 I . 17247 .17630 . 17997 .183 89 . 18775 . 19039 . 19240 . 19772
213968520000 I . 16287 .1666] .1709] . 17474 . 17742 . 18053 . 18767 .20063
21141685435000 I . 15503 . 15907 .1625] . 16545 . 16952 . 17776 . 19073 .20414
.2122645630000 I . 14845 .1514] . 15434 .1592] . 16817 . 18067 . 19305 .20099
21215655825000 | .14147 . 14452 .14979 .15874 .17060 .18207 .18949 .19387
21041615920000I .1358] .14135 .15007 .16093 .17118 .17816 .18246 .18856
21211635015000 I .13374 .14209 .15198 .16112 .1674] .17130 .17654 .18709
21944625710000 I .13474 .14372 .15187 .15754 .16105 .16542 .17397 .18825
2138605205000 I .13609 .14338 .14849 .15167 .15535 .16236 .17410 .19082
.21 103

Y-COORD I X-COORD (METERS)
(METERS) I 78450.00 78500.00 78550.00 78600.00 78650.00 78700.00 78750.00

78800.00 78850.00

146700000 I
.37750
146695000 I
.39134
146690000 I
.40460
146685000 I
.41770
146680000 I
.43052
146675000 I
.44257
146670000 I
.45303
146665000 I
.46106
146660000 I
.46505
146655000 I
.46297
146650000 I
.45224
146645000 I
.42994
146640000 I
.39378
146635000 I
.34375
146630000 I
.28315
146625000 I
.21598
146620000 I
.14228
146615000 I
.08193
146610000 I
.04844
146605000 I
.03242
146600000 I
.02774
146595000 I
.01655
146590000 I
.01448
146585000 I
.02327
146580000 I
.04438
146575000 I
.09308
146570000 I
. 14489

.27003

.26792

.2659]

.26492

.26517

.26623

.26997

.27706

.28785

.3028]

.3218]

.34383

.36979

.40646

.45482

.49493

.52397

.54245

.53240

.49028

.43745

.40517

.32643

.2330]

.20680

.22142

.24844

.29083

.29100

.28886

.28532

.28110

.27796

.27730

.28000

.28647

.29659

.31073

.32955

.35202

.37927

.42070

.47040

.50499

.5305]

.53060

.49154

.43395

.39752

.30806

.21904

.20558

.23389

.25100

.30644

.31130

.31385

.3131]

.30793

.30046

.29320

.28865

.28852

.29340

.30218

.31435

.33138

.35274

.38047

.42587

.47036

.49900

.51032

.47855

.41849

.3778]

.27924

.20203

.2047]

.24095

.24283

183

.31673

.32429

.33141

.33616

.33709

.33337

.32472

.31289

.30190

.29575

.29642

.30240

.31100

.3237]

.34140

.36834

.41379

.4462]

.46510

.44515

.38702

.34272

.23949

.18175

.20449

.23377

.22367

.33078

.33700

.34395

.35007

.35562

.35917

.35860

.3516]

.33767

.31938

.30297

.29449

.29433

.29695

.30174

.31213

.33639

.37298

.39152

.38526

.33598

.29016

.19068

.15870

.19886

.20847

.19674

.35219

.35868

.36424

.36817

.37174

.37517

.3780]

.3790]

.37518

.36293

.34058

.31220

.2878]

.27480

.26767

.26033

.25957

.27842

.29663

.29516

.26057

.2180]

.13583

.1348]

.17437

.16672

.17696

.36869

.37924

.38828

.39501

.39967

.40200

.40189

.39992

.39620

.39003

.37854

.3566]

.32085

.27740

.24207

.21946

.19730

.18365

.19050

.18826

.17107

.13868

.08414

.10965

.12286

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.37428

.38786

.40059

.41223

.42259

.43087

.43592

.43684

.43234

.42172

.40498

.38257

.35366

.31378

.25840

.1978]

.15397

.11750

.09645

.09755

.08685

.06594

.04262

.06953

.06909

.10032

.15635

146565000 I
.19048
146560000 I
.22616
146555000 I
.25303
146550000 I
.27174
146545000 I
.28387
146540000 I
.29096
146535000 I
.29414
146530000 I
.29422
146525000 I
.29180
146520000 I
.28738
146515000 I
.28146
146510000 I
.27435
146505000 I
.26640
146500000 I
.25790

Y-COORD I
(METERS) I

.2448]

.22988

.21659

.21742

.22433

.22326

.21718

.21450

.2169]

.22185

.22678

.22979

.22904

.22420

78900.00

79250.00 79300.00

146700000 I
.37810
146695000 I
.38950
146690000 I
.40296
146685000 I
.41768
146680000 I
.43405
146675000 I
.45170
146670000 I
.46943
146665000 I
.48523
146660000 I
.49687
146655000 I
.50226

.38510

.39897

.4120]

.42493

.43760

.44962

.46052

.4696]

.47579

.47761

.23932

.22374

.2221]

.23122

.23232

.2280]

.22822

.23383

.24103

.24653

.24812

.24466

.23637

.22465

78950.00

.38932

.40396

.41773

.43144

.44487

.45756

.46914

.47875

.4853]

.48738

.22715

.22198

.23342

.23793

.23693

.24177

.25160

.26095

.26603

.26478

.2573]

.24553

.2319]

.21804

184

.21448

.22780

.23743

.24215

.25390

.26884

.27981

.28294

.27769

.2664]

.25232

.23799

.22470

.2128]

.21030

.22705

.24087

.26220

.28272

.29415

.29417

.2853]

.27207

.25802

.24478

.23290

.22234

.21285

X-COORD (METERS)
79000.00 79050.00 79100.00 79150.00 79200.00

.38656

.40114

.41485

.42842

.44163

.4540]

.46516

.47422

.48012

.48140

.38222

.39625

.40937

.42224

.43478

.44663

.4573]

.46634

.47277

.47542

.38364

.39845

.41278

.42709

.44150

.45568

.46899

.48084

.48992

.4946]

.20216

.22909

.2623]

.28814

.29907

.29680

.28739

.27576

.26420

.25339

.24340

.23395

.22497

.21638

.39433

.4105]

.42626

.44126

.45559

.46862

.47956

.48799

.49337

.49562

.20198

.24740

.27774

.28965

.29000

.28544

.2790]

.27169

.2638]

.25558

.24723

.23890

.23079

.22299

.40236

.4163]

.42923

.44033

.45045

.4598]

.4688]

.47833

.48876

.49984

.20967

.24472

.26437

.27523

.28046

.28153

.27955

.27550

.27016

.26408

.25763

.2509]

.24407

.23714

.3940]

.40432

.41483

.42497

.43639

.44963

.46475

.48140

.49775

.51048

A
J-

 

146650000 I
.50263
146645000 I
.50335
146640000 I
.50674
146635000 I
.50829
146630000 I
.51297
146625000 I
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146620000 I
.54669
146615000 I
.52181
146610000 I
.42135
146605000 I
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146600000 I
.34494
146595000 I
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146590000 I
.32918
146585000 I
.36810
146580000 I
.38693
146575000 I
.40290
146570000 I
.39050
146565000 I
.34628
146560000 I
.33 185
146555000 I
.341 15
146550000 I
.33320
146545000 I
.31 198
146540000 I
.29503
146535000 I
.28576
146530000 I
.27802
146525000 I
.26722
146520000 I
.25332

.47307

.4595]

.43366

.39199

.33198

.25502

.16617

.08984

.03875

.01181

.00567

.00354

.00084

.00994

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.32343

.31507

.48298

.46956

.40327

.34496

.27014

.18079

.10185

.04690

.02107

.00649

.00000

.0029]

.01367

.0476]

.09976

.16003

.2213]

.26993

.30543

.32824

.34032

.34415

.34200

.33570

.32666

.31589

.47614

.46193

.43594

.39536

.33855

.26704

.18298

.10836

.05310

.02097

.0112]

.00462

.00974

.01269

.0452]

.09862

.15460

.20712

.24779

.2780]

.2982]

.30972

.31432

.31370

.3093]

.30232

.29360

185

.47268

.46239

.44183

.40786

.35779

.29133

.20900

.12846

.06683

.05078

.02594

.01709

.0225]

.02918

.05859

.09806

.14772

.19902

.24044

.26966

.28709

.2954]

.29737

.29502

.28980

.28270

.27439

.4929]

.48272

.46232

.43087

.38784

.32959

.24710

.16270

.12746

.10285

.05136

.03995

.03906

.0612]

.08122

.12743

.17413

.21088

.23244

.25003

.26647

.2791]

.28602

.28725

.28396

.27755

.26919

.4950]

.49155

.48334

.46344

.41989

.35289

.2937]

.25258

.22062

.1652]

.09908

.08506

.0810]

.10186

.12903

.16240

.21248

.23995

.26482

.27740

.27594

.27068

.26731

.26580

.26440

.26176

.25735

.50947

.51235

.50023

.46919

.43136

.40118

.37654

.35760

.31822

.22074

.16727

.15944

.14617

.17479

.21286

.22425

.24388

.27995

.28832

.29230

.29856

.29610

.28444

.27010

.25765

.24814

.24087

.51496

.50806

.4939]

.48198

.47174

.46313

.46215

.45599

.39056

.27994

.25646

.25903

.2360]

.27977

.30030

.32007

.29985

.30043

.3220]

.31918

.3061]

.30010

.29644

.28750

.2731]

.25698

.24214

146515000 I
.23824

146510000 I
.22398

146505000 I
.21 144
146500000 I
.20074

‘YJCCKDRIDI

(METERS) I

.30511

.29434

.28298

.27150

79350.00

79700.00 79750.00

146700000 I
.32959
146695000 I
.34000
146690000 I
.35036
146685000 I
.36173
146680000 I
.37626
146675000 I
.39562
146670000 I
.41978
146665000 I
.44787
146660000 I
.48091
146655000 I
.51888
146650000 I
.55262
146645000 I
.56856
146640000 I
.56159
146635000 I
.53858
146630000 I
.50938
146625000 I
.48741
146620000 I
.48983
146615000 I
.5 1349
146610000 I
.53 184
146605000 I
.54309

.37006

.38482

.40128

.41853

.43542

.45148

.46548

.47612

.4838]

.49120

.50125

.51190

.52038

.53412

.55885

.59305

.60364

.54456

.43976

.41660

.30416

.29214

.27988

.26779

.36903

.38438

.40020

.41600

.43017

.44277

.45363

.46398

.47613

.49060

.50418

.51672

.53588

.56590

.60688

.64096

.61898

.53783

.46456

.47324

.2838]

.27360

.26300

.2524]

186

.2654]

.25605

.24650

.23698

.25983

.24993

.23993

.23008

X-COORD (METERS)
79400.00 79450.00 79500.00 79550.00 79600.00 79650.00

.36697

.38066

.3943]

.40767

.42002

.43138

.44386

.45868

.47449

.48877

.50360

.52556

.55761

.60022

.64585

.65302

.60482

.52386

.49324

.51394

.36142

.37353

.38552

.39738

.41006

.4239]

.43940

.45497

.46944

.48523

.5082]

.54033

.5818]

.63077

.65794

.6367]

.57653

.51590

.51828

.53988

.35426

.36515

.37634

.3889]

.40354

.41926

.43395

.44800

.46430

.48738

.51823

.55729

.60502

.64336

.64346

.60558

.54723

.51498

.53547

.55397

.25132

.24389

.23557

.22675

.34649

.35723

.36956

.38340

.39785

.41207

.42602

.44222

.46469

.49420

.53034

.57468

.61727

.63318

.61355

.56984

.52412

.51714

.54409

.55917

.23482

.22903

.22307

.21672

.3398]

.35163

.36439

.37736

.39009

.40332

.41975

.44168

.46937

.50253

.54282

.58573

.61190

.60708

.5776]

.53636

.50829

.51868

.54524

.55792

.22978

.21972

.21145

.20439

.33488

.34646

.35807

.36952

.38176

.3972]

.41807

.44452

.47523

.51143

.55213

.58384

.59110

.57368

.54182

.50865

.49768

.51762

.54060

.55210

 

146600000 I
.56866
146595000 I
.58907
146590000 I
.57856
146585000 I
.54206
146580000 I
.51698
146575000 I
.53588
146570000 I
.56433
146565000 I
.55738
146560000 I
.53624
146555000 I
.52173
146550000 I
.50401
146545000 I
.47315
146540000 I
.43040
146535000 I
.38186
146530000 I
.33316
146525000 I
.29129
146520000 I
.26150
146515000 I
.24296
146510000 I
.23152
146505000 I
.22282
146500000 I
.21363

Y-COORD I
(METERS) I

146700000 I
146695000 I
146690000 I
146685000 I
146680000 I
146675000 I
146670000 I

.42153

.44437

.41244

.43037

.4671]

.4685]

.46503

.42424

.3669]

.34370

.34418

.33420

.31111

.28819

.27277

.26297

.25424

.24360

.23107

.21787

.20506

79800.00

.32312
.33255
.34316
.35684
.37483
.39696
.42230

.48260

.51066

.48010

.47245

.52612

.52039

.51660

.48952

.43096

.36979

.34258

.33727

.32659

.30470

.2801]

.26075

.24779

.23855

.22989

.22018

.20914

79850.00

.31600
.32596
.33884
.35558
.3759]
.39889
.42483

.52725

.55758

.53007

.50210

.55736

.56094

.54969

.53253

.48766

.42125

.36189

.33367

.32477

.31398

.29445

.27079

.24968

.23417

.22344

.21502

.20695

187

.55704

.58752

.56363

.52339

.56614

.58799

.57022

.55708

.52535

.47012

.40283

.34803

.32047

.30952

.29897

.28174

.26015

.23920

.22232

.2099]

.20095

.57448

.60377

.58343

.53784

.56183

.59876

.58335

.56757

.54649

.50412

.4446]

.3805]

.33127

.30534

.29335

.28293

.26786

.24869

.22880

.21156

.19835

X-COORD (METERS)
79900.00 79950.00 80000.00

.31002
.32217
.33778
.3565]
.37744
.4006]
.42762

.30672
.32129
.33860
.35775
.37857
.40257
.42953

.30602
.32205
.33964
.35846
.37983
.40423
.42848

.58220

.60958

.59240

.54619

.55183

.59437

.59015

.56972

.55408

.52398

.47552

.41607

.35720

.31358

.28940

.27703

.26692

.25354

.23657

.21852

.20198

.58258

.60769

.59320

.54913

.54016

.57923

.58937

.56803

.55203

.53178

.49497

.44414

.38714

.33416

.29578

.27355

.26110

.25114

.23934

.2247]

.20864

.57755

.60029

.58800

.54747

.52842

.55836

.58036

.56406

.54502

.53002

.50397

.4629]

.41237

.35934

.31227

.27842

.25780

.24566

.23638

.22602

.21335

146665000 I
146660000 I
146655000 I
146650000 I
146645000 I
146640000 I
146635000 I
146630000 I
146625000 I
146620000 I
146615000 I
146610000 I
146605000 I
146600000 I
146595000 I
146590000 I
146585000 I
146580000 I
146575000 I
146570000 I
146565000 I
146560000 I
146555000 I
146550000 I
146545000 I
146540000 I
146535000 I
146530000 I
146525000 I
146520000 I
146515000 I
146510000 I
146505000 I
146500000 I

.45150
.48575
.51980
.54208
.54403
.52895
.50546
.48198
.47123
.48254
.50618
.52049
.53207
.55725
.57546
.56637
.53388
.50582
.51393
.54322
.5468]
.52713
.51049
.49719
.47550
.44150
.39838
.35256
.30867
.27207
.24603
.2293]
.21855
.21039

.45526
.48732
.51217
.52157
.51452
.49687
.47602
.45980
.4585]
.47487
.49632
.50730
.5195]
.54394
.56009
.5521]
.5234]
.49452
.49370
.51964
.53219
.51747
.49872
.48617
.47079
.44493
.40910
.36794
.32598
.28684
.25478
.23189
.21677
.20667

.45720
.48285
.49687
.49608
.48363
.46649
.45035
.44206
.4477]
.46632
.48457
.49318
.5061]
.52954
.54380
.53669
.51139
.48305
.47547
.49564
.51403
.5065]
.48705
.4725]
.46063
.44242
.41449
.37895
.34040
.30222
.26734
.23918
.21895
.20523

188

.45483
.4718]
.47624
.46876
.4546]
.43967
.4282]
.42632
.43693
.45623
.47139
.47865
.49223
.5145]
.52705
.52063
.49833
.47135
.45897
.4722]
.49265
.49243
.47522
.45902
.44810
.43535
.41473
.38570
.35132
.3156]
.28097
.24989
.22506
.20709

.44708
.45554
.45280
.44189
.42854
.41662
.41004
.41308
.42625
.44438
.45620
.46275
.47698
.49806
.50910
.50323
.48347
.45814
.44273
.44976
.46988
.47654
.46375
.44672
.43496
.42519
.41054
.38808
.35884
.32619
.29332
.26194
.23422
.21224

Y-COORD I
(METERS) I

THE ANNUAL ( 8760 HRS) AVERAGE CONCENTRATION
VALUES FOR SOURCE GROUP: LINE2
FEF5437 , FEF5640 , FSC2783 , F EF2094 ,

** CONC OF BENZENE IN MICROGRAMS/M**3 **

78350.00 78400.00

146700000 I
.04108
146695000 I
.04081
146690000 I
.04062
146685000 I
.04053
146680000 I
.04068
146675000 I
.04124
146670000 I
.04220
146665000 I
.04376
146660000 I
.04596
146655000 I
.04857
146650000 I
.05144
146645000 I
.05497
146640000 I
.06009
146635000 I
.0671 1
146630000 I
.07416
146625000 I
.07992
146620000 I
.08530
146615000 I
.08849

.03418

.03548

.03674

.03827

.04026

.0428]

.0459]

.04937

.05282

.05603

.05914

.06212

.06454

.06633

.06766

.06803

.06674

.06423

INCLUDING SOURCE(S):

.03394
.03552
.03697
.0384]
.04013
.04236
.04522
.0487]
.05256
.05632
.05980
.06317
.0662]
.06856
.07029
.07113
.07022

.06767

*** NETWORK ID: CART]

189

X-COORD (METERS)

.03362

.0352]

.03693

.03856

.04018

.04212

.04463

.04786

.0518]

.05610

.06018

.06395

.06754

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.0749]

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.07283

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; NETWORK TYPE: GRIDCART ***

78000.00 78050.00 78100.00 78150.00 78200.00 78250.00

.03714
.03710
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.0457]
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.0847]

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78300.00

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.05315

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.06375

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.0814]

.08574

.08737

190

146610000 I .0608] .06405 .06745 .07097 .07452 .07813 .08160 .0847]
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'01:?61510000 I .02853 .02913 .02976 .03037 .03079 .03098 .03 120 .03205
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‘01:?645820000 I .02398 .02448 .02539 .02682 .02856 .03014 .03] 17 .03189
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01345615610000 I .0230] .02434 .02582 .0271 1 .02798 .02862 .02959 .03 135
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Y-COORD | X-COORD (METERS)

(METERS) I 78450.00 78500.00 78550.00 78600.00 78650.00 78700.00 78750.00
78800.00 78850.00

146700000 I .04402 .04718 .04949 .05097 .05300 .05656 .05995 .06157

.06218

 

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146605000 I
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146565000 I
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.0449]

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.07263

.0788]

.08365

.08109

.07186

.06513

.05540

.03670

.03137

.03442

.03895

.03765

191

.05190

.05274

.0533]

.05309

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.0485]

.04657

.0453]

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.06833

.0623]

.0544]

.04290

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.03423

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.0571]

.05758

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.05791

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.0581]

.05785

.0569]

.05474

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.0515]

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.0587]

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.0308]

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.03930

.04052

.03483

.02375

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.02336

.02203

.02303

.02706

.06332

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.06613

.06700

.06745

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.06655

.06510

.06300

.0604]

.05747

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.02352

.01992

.02506

.02654

.02366

.01438

.01246

.01568

.01578

.02016

.02812

.08020

192

146560000I .03827 .03699 .0349] .03337 .03299 .03214 .03306 .03590
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“0134962520000 I .0341 I .03494 .03628 .03676 .03 770 .04094 .0439] .04339
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“0:45:5130000 I .03 500 .03684 .04019 .04402 .04622 .04576 .04479 .04454
01445615825000 I .03 502 .03790 .04163 .04469 .04533 .04432 I .04378 .04385
.01445635820000 I .03 563 .03905 .04249 .04424 .043 72 .0428] .04266 .04300
01315605515000 | .03647 .03992 .04250 .04289 .04192 .04136 .04148 .04207
.03655210000 I .03 725 .04026 .04163 .04108 .04015 .03998 .04029 .041 12
.033685305000 I .03 769 .03989 .0401 1 .03915 .03 852 .03 865 .03908 .04016
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.04208

Y-COORD I X-COORD (METERS)
(METERS) I 78900.00 78950.00 79000.00 79050.00 79100.00 79150.00 79200.00
79250.00 79300.00

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11646616185000 I .0687] .06998 .06986 .06857 .0683] .07034 .07165 .06992
11648616780000 I .07030 .07164 .07156 .07010 .06995 .07220 .0730] .07103
0110616175000 I .07178 .07318 .07313 .07147 .07149 .0739] .07413 .07228
'011226870000 I .07300 .07447 .07443 .07258 .07286 .07533 .07499 .07376

D1146st’is65000 I .07403 .07554 .07552 .07345 .07406 .07643 .07569 .07552
£57636160000 | .07463 .07620 .07618 .0739] .07493 .07704 .07634 .07755
.(1196‘6455000 I .07462 .07628 .07625 .073 80 .07530 .07705 .07700 .07953
0184065250000 I .07375 .07559 .07552 .07297 .07497 .07639 .07770 .08086

146645000 I
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146615000 I
.0800]
146610000 I
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146585000 I
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146565000 I
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146560000 I
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146555000 I
.05362
146550000 I
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146540000 I
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146535000 I
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146530000 I
.04696
146525000 I
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146520000 I
.04260
146515000 I
.04006

.07169

.06804

.06242

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.0245]

.01472

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.0315]

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.00000

.00375

.0078]

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.0274]

.03483

.04105

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.04892

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.05198

.05220

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.05100

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.04854

193

.0712]

.06827

.0639]

.05789

.05007

.0403]

.02967

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.03075

.0118]

.01749

.0182]

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.0472]

.0479]

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.0476]

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.03404
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.04232
.04519
.04669
.04708
.04669
.0458]
.04465

.04335

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.0351]

.03930

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.0416]

.04176

.0425]

.04335

.04386

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.04342

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.0436]

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.04150

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.08059

.07813

.07459

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.07252

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.04165

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.04128

.04708

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.04510

.04493

.04987

.05066

.04948

.04916

.04869

.04703

.04453

.04199

.03986

.03826

194

146510000 I .04782 .04933 .04709 .04364 .0420] .04148 .03922 .03706
.03777
146505000I .04658 ‘ .04770 .04558 .04236 .04063 .0402] .03850 .03612
.03586
146500000 I .04522 .04602 .04402 .04105 .03926 .03886 .0376] .0353]
.03432
Y-COORD I X-COORD (METERS)
(METERS)| 79350.00 79400.00 79450.00 79500.00 79550.00 79600.00 79650.00 ‘

79700.00 79750.00

.0853]

146700000 I .06160 .06141 .06173 .06142 .06042 .05909 .05777 .05688
31546626995000 I .06355 .0638] .0640] .06332 .06199 .06048 .05934 .05 869
.01548606590000 I .065 85 .06637 .06625 .065 10 .06347 .06202 .0612 1 .06062
01549676785000 I .06844 .06900 .06836 .06673 .06496 .06388 .06330 .06254
.012136680000 I .07099 .07 133 .07017 .06823 .06673 .06607 .06544 .0644]
.0133636175000 I .07355 .07325 .07145 .06966 .06894 .06843 .0675] .06640
.01645676270000 I .075 85 .07465 .0725 1 .07139 .071 18 .07068 .06966 .06894
.01648696465000 I .07755 .07548 .07377 .073 56 .0734] .0727] .07223 .07239
”122696260000 I .07833 .07606 .0756] .07589 .07553 .07520 .07562 .07656
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01842656950000 | .07829 .07905 .08005 .08045 .08152 .08312 .08488 .08665
01847696345000 I .07915 .081 18 .08238 .0839] .08599 .08837 .09066 .09210
11941696240000 I .08085 .08330 .08562 .08839 .09143 .0943] .09589 .09530
0194268635000 I .08260 .08608 .08978 .09380 .09736 .09884 .09773 .09474
.0194065330000 | .08463 .08955 .0948] .09908 .10044 .09887 .09529 .09065
3184566225000 I .08685 .0936] .09857 .09986 .09796 .09393 .08889 .08388
.0119666220000 I .08907 .0947] .09612 .09394 .08957 .08477 .08079 .0780]
.0131662671 50.00 | .08633 .08778 .08529 .08163 .07895 .07776 .07758 .07779
o1317696510000 I .07296 .07195 .07235 .07435 .0769] .07915 .08067 .08140
1184126105000 | .05966 406631 .07233 .07698 .08018 .08213 .08307 .08325
.01842686700000 I .05969 .06697 .07294 .07759 .08100 .0833] .08470 .0853]

 

195

146595000 I .05819 .06744 .07500 .08078 .0849] .08760 .08913 .0897]
.08957
146590000 I .06033 .06972 .07725 .08287 .08680 .08927 .09058 .09095
.0906]
146585000 I .05674 .06415 .0707] .0761 1 .08024 .08316 .0850] .08595
.08616
146580000 | .06693 .07364 .0774] .07928 .08019 .08066 .08090 .08093
.08074
146575000 I .0675] .07546 .08226 .08679 .08865 .08834 .0867] .08452
.08222
146570000 I .06819 .0752] .08062 .08505 .08858 .09085 .09149 .09054
.08838
146565000 I .0643] .07359 .07970 .08366 .08620 .0879] .08912 .08975
.08959
146560000 I .05685 .06714 .0756] .081 14 .08444 .08609 .08666 .08669
.08655
146555000 | .05394 .05879 .06742 .07505 .08019 .08324 .08465 .08486
.08430
146550000 I .0552] .05512 .05888 .06601 .07279 .07762 .0806] .08213
.08237
146545000 I .05456 .0552] .05487 .05776 .06359 .06955 .07410 .07712
.07877
146540000 I .05146 .05424 .05416 .05368 .0559] .06066 .06588 .0701 1
.07309
14653 50.00 | .04837 .051 14 .05300 .05247 .05196 .05369 .05756 .06207
.06596
146530000 I .04647 .04754 .0501 1 .05122 .05046 .04994 .05129 .05444
.0583]
146525000 I .04528 .04482 .04652 .04865 .04913 .04826 .04777 .04883
.05132
146520000 I .04398 .04318 .04337 .04524 .04684 .04690 .0460] .0455]
.04624
146515000 I .04214 .04194 .041 16 .04195 .04374 .04485 .04458 .04365
.04317
146510000 I .03989 .04057 .03968 .03938 .04054 .04203 .04267 .04224
.04139
146505000 I .03756 .03887 .03 847 .03756 .03773 .03 897 .04019 .04055
.04004
146500000 I .03539 .03687 .0371 1 .03620 .03563 .03620 .03744 .03 839
.03854
Y-COORD | X-COORD (METERS)
(METERS) I 79800.00 79850.00 79900.00 79950.00 80000.00
146700000 | .05559 .05465 .05360 .05277 .05239
146695000 I .05714 .05606 .05515 .0547] .05472
146690000 I .05868 .05770 .05720 .05720 .05745
146685000 I .06042 .05986 .05984 .06013 .06042
146680000 I .06269 .06268 .06300 .06334 .06358
146675000 I .0657] .06608 .06648 .0668] .06713
146670000 I .06938 .06987 .0703] .07074 .07100
146665000 | .073 52 .0741 1 .07466 .07490 .07452
146660000 I .07823 .07890 .07907 .07840 .07686
146655000 I .08347 .08348 .0824] .08033 .07753

 

 

146650000 I
146645000 I
146640000 I
146635000 I
146630000 I
146625000 I
146620000 I
146615000 I
146610000 I
146605000 I
146600000 I
146595000 I
146590000 I
146585000 I
146580000 I
146575000 I
146570000 I
146565000 I
146560000 I
146555000 I
146550000 I
146545000 I
146540000 I
146535000 I
146530000 I
146525000 I
146520000 I
146515000 I
146510000 I
146505000 I
146500000 I

.08804
.09007
.08919
.08585
.08089
.07628
.07517
.0779]
.08089
.08212
.08485
.08889
.08976
.0858]
.08035
.0801]
.08556
.08849
.08618
.0834]
.08170
.07924
.07490
.06885
.0617]
.05449
.04824
.04377
.04098
.03928
.0380]

.08644
.08692
.08493
.08117
.07675
.07382
.07435
.07749
.0799]
.08106
.0840]
.08780
.08852
.0850]
.07972
.07816
.08242
.08650
.08547
.08246
.08050
.07876
.07556
.07053
.06428
.05754
.05097
.04543
.04147
.03894
.03733

.08372
.08286
.08029
.07677
.07336
.07199
.07360
.07673
.0786]
.07979
.08287
.08640
.08698
.08385
.07886
.0764]
.07929
.08384
.08427
.08149
.07904
.07736
.07508
.07128
.06606
.06000
.05372
.04778
.04287
.03935
.03705

196

.08032
.07859
.07580
.07266
.0704]
.07042
.07277
.07566
.07710
.07837
.08152
.08478
.08523
.08245
.0778]
.07479
.07637
.08074
.08238
.08018
.07736
.07558
.07394
.07122
.06708
.06183
.05606
.05025
.04488
.04052
.03739

.07657
.0744]
.07173
.06920
.06800
.06888
.07150
.07404
.07522
.07666
.07986
.08287
.0832]
.08070
.0764]
.07303
.07342
.07718
.0798]
.07865
.07585
.07377
.07237
.07048
.06738
.06303
.05788
.05245
.04710
.04226
.03837

 

 

197

THE ANNUAL ( 8760 HRS) AVERAGE CONCENTRATION
VALUES FOR SOURCE GROUP: LINE3

INCLUDING SOURCE(S): FEF1774 , FSC3199 , F SC32100,

*** NETWORK ID: CART] ; NETWORK TYPE: GRIDCART ***

** CONC OF BENZENE IN MICROGRAMS/M**3 **

.02954

Y-COORD I X-COORD (METERS)

(METERS)| 78000.00 78050.00 78100.00 78150.00 78200.00 78250.00 78300.00
78350.00 78400.00

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3114860610000 I .01877 .01946 .02008 .02058 .02096 .02117 .02108 .02056
01149611525000 I .01920 .02003 .02085 .02157 .02217 .02264 .02291 .02281
'012526830000I .01956 .02048 .02141 .02231 .02316 .02389 .02437 .02472
.01244676325000 I .01970 .02076 .02182 .02287 .02391 .02489 .02566 .02632
.0124662920000 I .01938 .02057 .02179 .02305 .02432 .02545 .02663 .02766
01248616115000 I .01876 .01994 .02121 .02256 .02401 .02542 .02693 .02837
11249666210000 I .01787 .01901 .02024 .02157 .02300 .02448 .02613 .02784

198

 

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3123635590000 I .01459 .01533 .01611 .01693 .01773 .01863 .01956 .02047
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01142685155000 I .00783 .00849 .00921 .00996 .01064 .011 18 .01 153 .01181
31142605350000 I .00845 .00908 .00965 .01009 .01040 .01065 .01090 .01109
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0140:5305000 I .00666 .00709 .00749 .00778 .00796 .00818 .00858 .00923
01140615500000 I .00671 .00707 .00733 .00749 .00767 .00800 .00854 .00931
.01027

Y-COORD I X-COORD (METERS)
(METERS)I 78450.00 78500.00 78550.00 78600.00 78650.00 78700.00 78750.00
78800.00 78850.00

146700000 I .01333 .01433 .01515 .01568 .01621 .01718 .01819 .01869
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.01955

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146620000 I
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146615000 I
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146605000 I
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146585000 I
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146565000 I
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146560000 I
.01722

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.0311]

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.0172]

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.0286]

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.0160]

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.0154]

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.0305]

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.0155]

.01272

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.0148]

.01406

.0128]

199

.01640

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.01668

.01649

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.0182]

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.0174]

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.0179]

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.0198]

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.0181]

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.0317]

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.02150

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.02012

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.01939

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.01663

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.0232]

.02726

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.01935

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.01454

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.01190

.01345

.01476

.01999

.02054

.02103

.02144

.02165

.0218]

.02183

.02175

.02159

.02133

.02076

.0195]

.01748

.01554

.01486

.01474

.01637

.02205

.02756

.02282

.01612

.01156

.01218

.01017

.0124]

.01464

.01669

146555000 I
.01718
146550000 I
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146545000 I
.01667
146540000 I
.01630
146535000 I
.01587
146530000 I
.01543
146525000 I
.01498
146520000 I
.01454
146515000 I
.01410
146510000 I
.01371
146505000 I
.01328
146500000 I
.01286

Y-COORD I
(METERS) I

.01210

.01135

.01129

.01153

.01140

.01103

.01078

.01078

.01092

.01110

.01122

.01122

78900.00

79250.00 79300.00

146700000 I
.01951
146695000 I
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146690000 I
.02059
146685000 I
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146680000 I
.02193
146675000 I
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146670000 I
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146665000 I
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146660000 I
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146655000 I
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146650000 I
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146645000 I
.02821

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.01207

.01186

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.01176

.01157

.01170

.01192

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.01222

.01212

.01177

78950.00

.01954

.02029

.02105

.02180

.02258

.02338

.0241]

.02493

.02574

.02650

.02716

.02769

200

.01234 .01314 .01380
.01274 .01339 .01395
.01280 .01327 .01446
.01252 .01345 .01509
.01248 .01387 .01545
.01272 .01423 .01537
.01303 .01433 .01486
.01324 .01413 .01408
.01321 .01358 .01329
.01291 .01286 .01251
.01238 .01210 .01181
.01171 .01137 .01120
X-COORD (METERS)

.01449

.01544

.01626

.01653

.01618

.01543

.01456

.01373

.01304

.01239

.01183

.01131

.01625

.01720

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.01588

.01507

.01435

.01369

.01312

.01256

.01203

.01154

.01764

.01750

.01693

.01630

.01568

.01508

.01448

.0139]

.01336

.01288

.01240

.01196

79000.00 79050.00 79100.00 79150.00 79200.00

.01953

.0203]

.02110

.02189

.02270

.02354

.02430

.02517

.0260]

.02679

.02749

.02803

.01929

.02004

.0208]

.02155

.02232

.02309

.02379

.02456

.02530

.02596

.0265]

.0269]

.01916

.01990

.02066

.02140

.02216

.02295

.02369

.02453

.02536

.02617

.02692

.02755

.01946

.02027

.02112

.02195

.0228]

.02370

.0245]

.02539

.0262]

.02694

.02756

.02807

.02007

.02090

.02175

.02252

.02326

.02396

.02460

.02519

.02584

.02656

.0274]

.02843

.0202]

.02085

.02149

.02205

.02260

.02319

.02388

.02463

.02565

.02684

.02807

.02910

 

146640000 I
.02853
146635000 I
.02930
146630000 I
.03056
146625000 I
.03231
146620000 I
.03463
146615000 I
.03670
146610000 I
.03597
146605000 I
.02861
146600000 I
.02642
146595000 I
.02670
146590000 I
.02854
146585000 I
.02912
146580000 I
.03116
146575000 I
.02926
146570000 I
.02760
146565000 I
.02328
146560000 I
.02106
146555000 I
.02077
146550000 I
.01946
146545000 I
.01778
146540000 I
.01670
146535000 I
.01603
146530000 I
.01530
146525000 I
.01436
146520000 I
.01335
146515000 I
.01244
146510000 I
.01164

.02658

.0259]

.02445

.02218

.01938

.01618

.0119]

.00956

.01265

.01906

.00755

.00600

.00879

.01282

.01483

.01568

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.01687

.01720

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.01737

.01722

.01694

.01655

.01608

.01556

.01505

.0280]

.02804

.02769

.02687

.0254]

.02308

.01944

.0145]

.00788

.00708

.00357

.00733

.01044

.01215

.0148]

.01714

.01882

.01979

.02022

.02022

.0199]

.01939

.01873

.01800

.01722

.01644

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.02835

.02835

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.0255]

.02344

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.01297

.00007

.00000

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.02009

.02069

.02080

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.02018

.01959

.0189]

.01816

.01737

.01659

.01582

.01513

201

.02708

.02697

.02653

.02576

.0246]

.02314

.02143

.02040

.02090

.01226

.01368

.01335

.01364

.01384

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.01832

.01884

.01879

.01852

.0181]

.01762

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.02802

.02821

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.0261]

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.0247]

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.02164

.01982

.01640

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.01702

.01696

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.01752

.01711

.01653

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.01519

.01452

.01386

.01329

.0285]

.02899

.0295]

.02969

.0287]

.0276]

.02979

.03017

.0247]

.0190]

.02109

.02313

.02136

.01882

.01928

.01906

.01887

.01772

.01659

.01595

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.01507

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.0303]

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.02933

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.02753

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.0183]

.0171]

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.03460

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.0241]

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.02337

.02111

.02104

.01964

.01833

.01768

.01694

.01585

.01465

.01358

.01274

.01213

.01165

 

202

.03044

146505000 I .01449 .01500 .01442 .01339 .01270 .01249 .01201 .01125
0114160500000 I .01392 .01430 .01375 .01282 .01215 .01194 .01159 .01089
.01047

Y-COORD I X-COORD (METERS)
(METERS)I 79350.00 79400.00 79450.00 79500.00 79550.00 79600.00 79650.00
79700.00 79750.00

146700000 I .01872 .01843 .01839 .01825 .01796 .01761 .01722 .01688
.(11416156295000 I .01935 .01921 .01917 .01894 .01858 .01816 .01776 .01747
.01147626090000 I .02010 .02007 .01997 .01964 .01919 .01873 .01838 .01811
.01147676785000 I .02097 .02099 .02078 .02033 .01981 .01938 .01908 .01876
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01148696675000 I .02290 .02277 .02226 .02165 .02125 .02094 .02054 .02007
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3124067565000 I .02491 .02428 .02357 .02322 .02294 .02252 .02214 .02201
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01245616250000 I .02681 .02646 .02632 .02600 .02594 .02632 .02670 .02712
112476215000 I .02755 .02756 .02740 .02743 .02780 .02835 .02896 .02934
11249626140000| .02861 .02873 .02891 .02944 .03013 .03085 .03131 .03100
31249696935000 I .02985 .03032 .03108 .03201 .03291 .03322 .03258 .03127
01249656030000 I .03151 .03260 .03384 .03492 .03505 .03402 .03224 .03015
01248616825000 I .03386 .03546 .03663 .03646 .03500 .03286 .03052 .02833
01246656120000 I .03656 .03770 .03714 .03526 .03276 .03026 .02818 .02662

11245656515000 I .03764 .03667 .03427 .03153 .02935 .02793 .02707 .02652
012425710000 I .03416 .03134 .02939 .02859 .02840 .02836 .02823 .02791
.23‘762305000 I .02762 .02822 .02917 .02982 .03004 .02989 .02946 .02885
'(1‘31861200000 I .02833 .02964 .03041 .03077 .03080 .03058 .03016 .02961
'23::5950001 .02884 .03051 .03163 .03224 .03241 .03223 .03180 .03118

 

 

203

146590000 I .03047 .0319] .0328] .03323 .03323 .03292 .03237 .03165
.03082
146585000 | .02927 .02973 .03026 .03066 .03082 .03075 103045 .02997
.02936
146580000 I .03316 .03345 .03265 .03 151 .03043 .0295] .02875 .02807
.02742
146575000 I .031 19 .03288 .03395 .03407 .03330 .03196 .03042 .02892
.02757
146570000 I .02998 .03135 .03218 .03268 .03287 .03265 .03195 .03082
.02943
146565000 | .02710 .02958 .03085 .03137 .0314] .03120 .03088 .0304]
.02976
146560000 I .02264 .02587 .0283] .02962 .03014 .03009 .02969 .02914
.02863
146555000 I .0205] .02164 .02427 .02656 .02792 .02854 .02859 .02822
.02769
146550000 I .02003 .01964 .02046 .02256 .0246] .02599 .0267] .02699
.02678
146545000 I .01890 .01905 .01862 .0192] .02088 .02267 .02405 .02489
.02526
146540000 I .0172] .01808 .01796 .01754 .01798 .01930 .0209] .02218
.02304
146535000 I .01583 .01655 .01712 .01685 .01648 .01686 .01793 .01925
.02042
146530000I .01497 .01508 .01580 .0161] .01584 .01553 .01579 .01664
.01777
146525000 I .01434 .01402 .01445 .01507 .01519 .01485 .01459 .01479
.01547
146520000 I .01372 .01339 .01330 .01379 .01426 .01426 .01393 .01371
.O 1385
146515000| .01295 .01285 .01251 .01264 .01313 .01346 .01337 .01306
.01286
146510000 I .01212 .01227 .01195 .01177 .01204 .01247 .01267 .01254
.01226
146505000I .01133 .01163 .01148 .01116 .01113 .01146 .01182 .01193
.01 178
146500000 I .01062 .01095 .01098 .01069 .01046 .01057 .0109] .01120
.0] 125
Y-COORD I X-COORD (METERS)
(METERS) I 79800.00 79850.00 79900.00 79950.00 80000.00
146700000 I .01635 .0160] .01565 .01534 .01516
146695000 I .01686 .01648 .01613 -01593 .01587
146690000 I .01738 .01700 .01676 .01669 .01673
146685000 I .01794 .01767 .01758 .01762 .01768
146680000 I .01865 .01855 .01859 .01866 .01869
146675000 I .01960 .01965 .01973 .01979 .01984
146670000 I .0208] .02090 .02099 .02108 .021 15
146665000 I .02220 .02232 .02245 .02252 .02243
146660000 I .023 80 .02397 .02404 .023 85 .02337
146655000 I .02567 .02569 .02537 .02470 .02376
146650000 | .02747 .02697 .02605 .02486 .023 55

 

146645000 I
146640000 I
146635000 I
146630000 I
146625000 I
146620000 I
146615000 I
146610000 I
146605000 I
146600000 I
146595000 I
146590000 I
146585000 I
146580000 I
146575000 I
146570000 I
146565000 I
146560000 I
146555000 I
146550000 I
146545000 I
146540000 I
146535000 I
146530000 I
146525000 I
146520000 I
146515000 I
146510000 I
146505000 I
146500000 I

.02854
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.0263]
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.0188]
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.01298
.01210
.01153
.01109

.02738
.02688
.02586
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.0266]
.02754
.0288]
.02907
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.02745
.02638
.0256]
.02487
.02364
.02179
.01959
.0173]
.01517
.01342
.01219
.01140
.01087

.02592
.02520
.02427
.02340
.02308
.0236]
.02468
.02543
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.02577
.0249]
.02427
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.02013
.01806
.01598
.01408
.01256
.01148
.01076

204

.02440
.02362
.02283
.02230
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.02309
.02409
.02465
.02503
.02600
.02707
.02723
.02639
.02494
.02385
.0241]
.02537
.02587
.02514
.02418
.02354
.0229]
.02190
.02042
.0186]
.01668
.0148]
.01312
.01178
.01083

.02293
.02223
.02162
.02138
.02169
.0225]
.02339
.02382
.02424
.0252]
.02619
.0263]
.02556
.02425
.0231]
.02303
.02406
.02484
.02445
.02353
.0228]
.02229
.02159
.02047
.01896
.01723
.01546
.01376
.01226
.01109

 

205

THE ANNUAL ( 8760 HRS) AVERAGE CONCENTRATION
VALUES FOR SOURCE GROUP: ALL
F F F5845 , FFF5946 , F F F6047 , FSC1953 , FSC2155 , FSC2256 ,
FEF5437 ,
FEF5640 , FSC2783 , FEF2094 , F EF 1774 , FSC3199 , FSC32100,

INCLUDING SOURCE(S):

*** NETWORK ID: CART] ; NETWORK TYPE: GRIDCART ***

** CONC OF BENZENE IN MICROGRAMS/M**3**

Y-COORD I X-COORD (METERS)
(METERS)I 78000.00 78050.00 78100.00 78150.00 78200.00 78250.00 78300.00
78350.00 78400.00

.63868

146700000 I .24588 .24558 .24475 .24642 .25220 .26216 .27498 .28853
3134616695000 I .25530 .25703 .25640 .25572 .25809 .26489 .27575 .28904
3133686190000 I .26489 .26769 .26896 .26798 .2675] .27066 .27843 .29004
31214606185000 | .2768] .27865 .28103 .28173 .28036 .28017 .28413 .29275
312560380000 I .29214 .29215 .293 56 .29537 .29536 .29359 .29373 .29846
31047676675000 I .31 120 .30939 .30886 .30970 .31077 .30988 .30766 .30816
31143606970000 I .3337] .33082 .32829 .32707 .32716 .32726 .32526 .32196
31241686065000 I .35833 .35615 .35249 .34903 .34689 .34598 .3441] .33966
31345606960000 I .38274 .38363 .38118 .37655 .37180 .36777 .3644] .36002
31543616955000 I .40573 .41046 .41 193 .40924 .4028] .39508 .3 8848 .38255
.3115606050000 I .42759 .43 545 .44132 .44333 .43932 .43018 .41913 .40912
.3129666145000 I .4465] .45829 .46805 .47455 .47646 .47155 .45944 .44366
.41248656840000 I .46014 .47650 .49073 .50164 .50906 .51 149 .5058] .4900]
.41641586g5000 I .47040 .48972 .50776 .52360 .53617 .54449 .54707 .54053
5124069680000 I .47807 .4996] .5204] .54014 .55764 .57129 .57957 .58123
517336425000 I .47790 .50285 .52732 .55090 .57285 .59186 .60618 .61352
.61141676‘20000 I .46737 .49409 .52168 .54957 .57671 .60156 .62277 .63 778
61443621 50.00 I .44988 .47622 .50395 .53285 .56235 .59155 .61905 .64225
61547666810000 I .42463 .4498] .47644 .50435 .53307 .56218 .59072 .61705

206

146605000 I .39609 .41830 .44179 .46646 .49192 .51792 .54360 .56767
“51318616700000 I .37710 .39649 .41669 .43752 .45846 .47931 .49909 .51661
51340615995000 I .36486 .38264 .40099 .41968 .43819 .45622 .47275 .48653
.41945685190000 I .34304 .35715 .371 17 .38475 .39724 .40816 .41636 .42051
41148685885000I .29873 .30631 .31290 .31813 .32143 .32247 .32058 .31522
3316605180000 I .24394 .24721 .24987 .25192 .25333 .25431 .25485 .25495
21544615475000 I .20444 .20861 .21319 .21823 .22374 .22975 .23640 .24409
2153675070000 I .18669 .19329 .20059 .20888 .21863 .23058 .24556 .26361
21842675165000 I .18162 .19095 .20214 .21585 .23242 .25134 .27066 .28672
21946615560000 I .18708 .20120 .21737 .23508 .25254 .26720 .27715 .28242
2184162155000 I .20165 .21772 .23280 .24546 .25461 .26060 .26469 .26712
“21641661350000 I .21296 .22397 .23210 .23778 .24246 .24682 .24987 .25081
21543615315000 I .21054 .21581 .22056 .22528 .22990 .23319 .23551 .24088
2154363440000 I .19984 .20437 .20952 .21421 .21754 .22102 .22857 .24272
2154352525000 I .19023 .19512 .19941 .20304 .20758 .21648 .23099 .24692
2154875930000 I .18224 .18596 .18949 .19493 .20485 .21909 .23398 .24435
21540615725000I .17389 .17747 .18336 .19340 .20715 .22110 .23079 .23663
21445605020000I .16678 .17300 .18290 .19563 .20819 .21732 .22306 .23013
23655315000 I .16366 .17325 .18496 .19623 .20444 .20966 .21585 .22786
21447665910000 I .16438 .17509 .18520 .19260 .19731 .20252 .21230 .22883
21542605605000] .16597 .17505 .18172 .18598 .19042 .19845 .21207 .23168
21545675300000| .16570 .17173 .17559 .17940 .18607 .19736 .21384 .23502
.25747

Y-COORD I X-COORD (METERS)

(METERS)I 78450.00 78500.00 78550.00 78600.00 78650.00 78700.00 78750.00
78800.00 78850.00

146700000 I .32737 .35234 .37108 .38337 .39999 .42593 .44683 .45453
.4112995000 I .32469 .35232 .37668 .39225 .40728 .43327 .45896 .47053

.4750]

 

146690000 I
.49091
146685000 I
.50638
146680000 I
.52144
146675000 I
.53552
146670000 I
.54750
146665000 I
.55665
146660000 I
.56090
146655000 I
.55788
146650000 I
.54466
146645000 I
.51809
146640000 I
.47594
146635000 I
.41864
146630000 I
.34993
146625000 I
.27285
146620000 I
.18594
146615000 I
.11410
146610000 I
.07579
146605000 I
.05701
146600000 I
.06264
146595000 I
.05268
146590000 I
.03440
146585000 I
.04242
146580000 I
.06149
146575000 I
.11635
146570000 I
.18051
146565000 I
.23695
146560000 I
.27870

.32214

.32076

.32085

.32195

.32613

.33442

.34725

.36517

.38783

.41405

.44535

.49008

.54909

.59869

.63570

.66088

.65168

.60218

.53747

.49820

.40938

.29277

.25306

.26605

.29794

.2981]

.28119

.34955

.34514

.34010

.33611

.33502

.33793

.3454]

.35733

.37424

.39670

.42352

.45670

.50755

.56872

.61236

.64625

.65024

.6055]

.53528

.49057

.3897]

.2755]

.25088

.27983

.30345

.29276

.27379

.37948

.37837

.37202

.36288

.35403

.34839

.34794

.35348

.36383

.37838

.3987]

.42438

.45869

.51503

.57085

.6083]

.62686

.59239

.51961

.46920

.35779

.25425

.24880

.28878

.29659

.27886

.26969

207

.40055

.40612

.40686

.40204

.3914]

.37714

.36389

.35645

.3571]

.36416

.37452

.3898]

.41146

.44577

.5034]

.54582

.57372

.55544

.48583

.43028

.31276

.22893

.24715

.28349

.27587

.26242

.27385

.41544

.42273

.42907

.43296

.43186

.4231]

.4062]

.38439

.36497

.35503

.35505

.35852

.36464

.37823

.41085

.4597]

.48682

.48715

.42967

.37136

.25626

.20036

.24028

.25808

.24390

.25402

.27328

.43963

.44407

.44806

.45195

.45517

.45602

.45115

.43625

.40952

.37599

.34756

.33289

.32538

.31753

.31868

.34714

.37562

.38250

.34543

.28986

.19117

.17066

.21504

.21143

.21679

.24394

.27510

.46933
.47671_
.48160
.48380
.48328
.48067
.47632
.46914
.45556
.42948
.38711
.33621
.29575
.27094
.24609
.23264
.25020
.25482
.24233
.19838
.12724
.13935
.16076
.16166
.19444
.24250

.29522

.48553

.4989]

.51063

.51976

.52486

.52520

.51928

.50647

.48698

.46137

.42826

.38182

.31666

.24595

.19670

.15576

.13274

.14466

.14094

.11243

.07312

.09355

.09695

.12627

.18892

.25242

.2973]

 

146555000 I
.30945
146550000 I
.33070
146545000 I
.34430
146540000 I
.35204
146535000 I
.35533
146530000 I
.35513
146525000 I
.35216
146520000 I
.34697
146515000 I
.34008
146510000 I
.33189
146505000 I
.32269
146500000 I
.31284

Y-COORD I

(METERS) I

.26448

.26287

.27020

.27033

.26406

.26054

.26272

.26826

.27417

.27814

.27795

.27302

78900.00

79250.00 79300.00

146700000 I
.46106
146695000 I
.47434
146690000 I
.48996
146685000 I
.50705
146680000 I
.52609
146675000 I
.54685
146670000 I
.56810
146665000 I
.58741
146660000 I
.60231
146655000 I
.60986
146650000 I
.61064
146645000 I
.61032

.46755

.48406

.49967

.51496

.52997

.54424

.55708

.56800

.57555

.57805

.57318

.55787

.2689]

.27802

.28074

.27664

.27634

.28225

.29063

.29750

.30017

.29713

.28837

.27514

78950.00

.47313

.49056

.50702

.5232]

.53908

.55412

.5677]

.57922

.58724

.59015

.58573

.5711]

.28038

.28695

.28677

.29172

.30265

.31386

.32070

.3205]

.31302

.30007

.28440

.26796

208

.28590

.29230

.30515

.32234

.33600

.34119

.33672

.32478

.30880

.29193

.27595

.26150

.28994

.31385

.33815

.35323

.35547

.34689

.33225

.31582

.29999

.28556

.27268

.26110

X-COORD (METERS)
79000.00 79050.00 79100.00 79150.00 79200.00

.47014

.48758

.50403

.52016

.53589

.55066

.56389

.57490

.58230

.58443

.57914

.5637]

.46464

.48139

.49712

.51236

.52719

.54119

.55367

.56435

.57196

.57517

.57215

.56050

.46554

.48307

.50006

.51680

.53360

.55012

.56554

.57943

.59020

.59607

.59479

.58393

.31299

.3445]

.36005

.35997

.35032

.33695

.32308

.30993

.29779

.28632

.27545

.26506

.47776

.49696

.51577

.53354

.55059

.56623

.57940

.5898]

.59662

.5996]

.59896

.59468

.33340

.35075

.35318

.34832

.34054

.33155

.32193

.31192

.30183

.29175

.28190

.27242

.48830

.5052]

.52105

.53449

.5467]

.55790

.56840

.57920

.59093

.60339

.61457

.61893

.32290

.33612

.34197

.34287

.3402]

.3351]

.32849

.32098

.31305

.30492

.29663

.28829

.47999

.49246

.50508

.51694

.53002

.54510

.56239

.58154

.60095

.61684

.62388

.61774

 

 

1wmmmn
.61302
146635000 I
.61588
146630000 I
.62299
146625000 I
.64033
146620000 I
.66201
146615000 I
.63852
146610000 I
.52944
146605000 I
.43069
146600000 I
.42264
146595000 I
.43368
146590000 I
.40718
146585000 I
.44627
146580000 I
.47544
146575000 I
.49123
146570000 I
.47639
146565000 I
.42196
146560000 I
.40364
146555000 I
.41553
146550000 I
.40615
146545000 I
.38068
146540000 I
.36073
146535000 I
.34984
146530000 I
.34028
146525000 I
.32666
146520000 I
.30927
146515000 I
.29074
146510000 I
.27338

.52829
.48032
.41103
.32208
.21990
.13052
.06538
.03085
.02455
.03439
.01341
.02200
.05146
.10495
.16643
.22990
.28408
.32811
.36073
.38229
.39420
.39830
.39641
.39017
.38087
.36955

.3572]

.54290

.49750

.43243

.34860

.24784

.15614

.08837

.05195

.02450

.00926

.00782

.03053

.07105

.12878

.19926

.27118

.32888

.37111

.39850

.41326

.41822

.41599

.40875

.39816

.38544

.37150

.3572]

.53494

.48968

.4260]

.34544

.25005

.1633]

.09713

.0582]

.03598

.00469

.01349

.03014

.07535

.13555

.20090

.26204

.30952

.34447

.36773

.38086

.38589

.38480

.37929

.37070

.36006

.34817

.33582

209

.53717

.49874

.4422]

.36716

.27393

.18126

.10895

.08936

.07759

.04116

.05367

.06074

.09019

.12676

.18617

.24827

.29736

.33120

.35122

.36073

.36290

.36007

.35387

.34543

.33554

.3248]

.31368

.56138

.52594

.47685

.4106]

.31869

.2240]

.18247

.16119

.09966

.07794

.07977

.10533

.11540

.17052

.22124

.25905

.28343

.30568

.32643

.34182

.34982

.35086

.34652

.33855

.32836

.31704

.30523

.58495

.56277

.51563

.44220

.37356

.32707

.29573

.23334

.14813

.12427

.12108

.15222

.17678

.20737

.26686

.2983]

.32528

.33710

.33414

.32840

.32545

.32453

.32333

.32028

.3149]

.30747

.29844

.60738

.57428

.53065

.49412

.46826

.45007

.40686

.29888

.22140

.20784

.19649

.23619

.2757]

.28244

.30085

.34419

.35365

.35808

.36446

.35987

.34490

.32764

.31318

.30242

.29424

.28735

.28067

.6016]

.58602

.57399

.56662

.56716

.56087

.49177

.36212

.32208

.31973

.29964

.35005

.37528

.39630

.3683]

.36647

.39292

.38948

.37392

.36693

.36206

.35038

.33229

.31254

.29473

.28017

.26843

 

146505000 I
.25829

146500000 I
.24553

Y-COORD I

(METERS) I

.34405

.33065

793 50.00

79700.00 79750.00

146700000 I
.40251
146695000 I
.41524
146690000 I
.42791
146685000 I
.44154
146680000 I
.45853
146675000 I
.48106
146670000 I
.50948
146665000 I
.54286
146660000 I
.58199
146655000 I
.62689
146650000 I
.66801
146645000 I
.68971
146640000 I
.68441
146635000 I
.65862
146630000 I
.62319
146625000 I
.59357
146620000 I
.59164
146615000 I
.61751
146610000 I
.64068
146605000 I
.65408
146600000 I
.68292
146595000 I
.70907

.45038

.46772

.48723

.50793

.52830

.54793

.56526

.57858

.58780

.59582

.60635

.61860

.62983

.64656

.67498

.71376

.72926

.66852

.54687

.50387

.50955

.53139

.34258

.3281]

79400.00

.44886

.46739

.48664

.50599

.52340

.53879

.55185

.56374

.57699

.59329

.60969

.62547

.6479]

.68229

.72903

.77002

.75138

.66227

.56784

.56777

.57920

.60860

210

.32300 .30225 .29327
.31018 .29085 .28149
1K4CCXDRI)(LIETTHKS)

.28827

.27755

.27358

.26592

.25882

.25059

79450.00 79500.00 79550.00 79600.00 79650.00

.44708

.46384

.48053

.49680

.51174

.52508

.53926

.55602

.57448

.59185

.60995

.63534

.67214

.72106

.77449

.78822

.73807

.6434]

.59497

.61543

.63060

.66420

.44109

.45579

.47026

.48444

.49930

.51522

.53315

.55175

.56949

.58838

.61465

.65167

.69963

.75657

.79193

.77303

.70572

.62906

.6212]

.64667

.66540

.70053

.43263

.44572

.45899

.47368

.49074

.50944

.5272]

.54435

.56362

.59013

.62569

.67107

.72657

.77363

.77894

.73854

.66955

.62328

.64077

.66420

.68627

.72108

.42318

.43586

.45030

.46665

.48404

.50144

.51844

.53744

.56330

.5975]

.63977

.69139

.74242

.76524

.74643

.69662

.63914

.62282

.65159

.67118

.69609

.7294]

.41479

.42873

.44398

.45973

.47534

.49138

.51069

.53604

.56828

.60725

.65439

.70534

.73909

.73739

.70513

.65577

.61725

.62333

.65414

.67045

.69743

.7286]

.40865

.42263

.43679

.45082

.46557

.48367

.50790

.5389]

.57522

.61777

.66590

.70527

.71739

.69968

.66262

.62086

.60231

.62193

.6499]

.66419

.69246

.72118

 

 

21]

146590000 I .50324 .58173 .64012 .67972 .70346 .71459 .71613 .71060
.69998
146585000 I .51637 .56632 .60307 .63015 .64890 .66009 .66458 .66339
.65758
146580000 I .56720 .6332] .66742 .67692 .67243 .66199 .64979 .6374]
.62514
146575000 I .5672] .62872 .67714 .70884 .72070 .71467 .69635 .67179
.64567
146570000 I .56320 .62315 .66249 .68795 .70480 .71364 .71280 .7017]
.68214
146565000 I .51565 .59269 .64307 .67210 .68517 .68883 .68803 .6842]
.67673
146560000 I .44640 .52396 .59157 .6361 1 .66107 .67026 .66837 .66084
.65142
146555000 | .41815 .45022 .51294 .57173 .61222 .63 576 .64502 .64309
.63372
146550000 I .41942 .41734 .44123 .49139 .5420] .57912 .60229 .61308
.61316
146545000 I .40765 .41 153 .40716 .42500 .46497 .50829 .54229 .56492
.57717
146540000 I .37978 .3989] .39689 .39170 .40516 .43716 .47392 .50466
.52652
146535000 I .35239 .37239 .38409 .37884 .37378 .38413 .40965 .44066
.46824
146530000 I .3342] .34273 .36036 .36630 .35965 .35486 .36285 .38334
.40923
146525000 I .32259 .31959 .33176 .34546 .34725 .34014 .3359] .34204
.35808
146520000 | .31193 .30436 .30634 .31917 .32895 .32808 .32104 .31702
.32159
146515000 I .29869 .29333 .28783 .29379 .30556 .31186 .30910 .30237
.29899
146510000 | .28308 .28274 .27507 .27347 .28138 .29107 .29469 .291 15
.28516
146505000 I .26676 .27068 .26497 .25863 .26042 .26895 .27672 .2785]
.27464
146500000 I .25107 .25695 .25504 .24783 .24444 .24875 .25699 .26293
.2634]
Y-COORD I X-COORD (METERS)
(METERS) | 79800.00 79850.00 79900.00 79950.00 80000.00
146700000 I .39505 .38666 .37926 .37483 .37356
146695000 I .40655 .39849 .39346 .39193 .39264
146690000 | .4192] .41354 .41 173 .41249 .41382
146685000 I .43519 .43310 .43393 .43549 .43655
146680000 I .45617 .45714 .45903 .46057 .46210
146675000 I .48226 .48462 .48682 .48916 .4912]
146670000 I .51249 .51560 .51892 .52135 .52062
146665000 I .5472] .55169 .5543] .55224 .54402
146660000 I .58778 .59020 .58595 .57406 .55577
146655000 I .62894 .62134 .60465 .58127 .55409
146650000 I .65759 .63497 .60585 .57394 .54200

 

146645000 I
146640000 I
146635000 I
146630000 I
146625000 I
146620000 I
146615000 I
146610000 I
146605000 I
146600000 I
146595000 I
146590000 I
146585000 I
146580000 I
146575000 I
146570000 I
146565000 I
146560000 I
146555000 I
146550000 I
146545000 I
146540000 I
146535000 I
146530000 I
146525000 I
146520000 I
146515000 I
146510000 I
146505000 I
146500000 I

.66264
.64667
.61894
.58918
.57259
.58248
.60978
.62826
.64158
.67037
.69399
.68610
.6484]
.61302
.6205]
.65682
.66423
.64137
.62092
.60517
.57997
.5399]
.4885]
.43307
.37955
.33470
.30278
.28239
.26937
.25949

.6288]
.60868
.58304
.56126
.55627
.57337
.59902
.61338
.62718
.65548
.67670
.66970
.63639
.60046
.59734
.62866
.64653
.63039
.60755
.59228
.57442
.54412
.5014]
.45180
.40083
.35297
.31363
.28556
.2671]
.25487

.5924]
.57198
.55138
.5388]
.54278
.56353
.58598
.5972]
.6117]
.63918
.65813
.6518]
.62244
.58750
.57649
.60022
.62450
.61751
.5943]
.57646
.56226
.54088
.50775
.46514
.41846
.37193
.32920
.29460
.26979
.25303

212

.55759
.53908
.52370
.51902
.52970
.55209
.57113
.58039
.59563
.62202
.63889
.63308
.60716
.57409
.55760
.57268
.59875
.60067
.58054
.56056
.54722
.53219
.50785
.4732]
.43176
.38836
.34603
.30789
.27736
.25532

.52588
.51058
.50086
.50244
.51682
.53839
.55363
.56179
.57787
.60313
.61815
.61275
.58973
.55879
.53887
.5462]
.5711]
.58119
.56684
.54609
.53153
.51986
.5026]
.47593
.44083
.40129
.36122
.32280
.28874
.26170

 

 

 

*** THE SUMMARY OF MAXIMUM PERIOD ( 8760 HRS) RESULTS ***

213

** CONC OF BENZENE IN MICROGRAMS/M**3 **

NETWORK

GROUP ID AVERAGE CONC RECEPTOR (XR, YR, ZELEV, ZFLAG) OF TYPE GRID-ID

LINE] lST HIGHEST VALUE IS
2ND HIGHEST VALUE IS
LINE2 lST HIGHEST VALUE lS
2ND HIGHEST VALUE 1S
LINE3 lST HIGHEST VALUE IS
2ND HIGHEST VALUE IS
LINE12 lST HIGHEST VALUE IS
2ND HIGHEST VALUE IS
LINE23 lST HIGHEST VALUE IS
2ND HIGHEST VALUE IS
LINE13 1ST HIGHEST VALUE IS
2ND HIGHEST VALUE IS
ALL 1ST HIGHEST VALUE IS
2ND HIGHEST VALUE IS

.65794 AT (
.65302 AT (

.10044 AT (
.09986 AT (

.03770 AT (
.03764 AT (

.75701 AT(
.75159 AT (

.13632 AT(
.13549 AT(

.69285 AT (
.68966 AT (

.79193 AT (
.78822 AT (

”‘" RECEPTOR TYPES: GC = GRIDCART

GP = GRIDPOLR
DC = DISCCART
DP = DISCPOLR
BD = BOUNDARY

79500.00,
79450.00,

146630000,
146625000,

79550.00,
79500.00,

146630000,
146625000,

79400.00,
793 50.00,

146620000,
146615000,

79500.00,
79450.00,

146630000,
146625000,

79500.00,
79550.00,

146625000,
146630000,

79500.00,
79450.00,

146630000,
146625000,

79500.00, 146630000,
79450.00, 146625000,

.00,

.00,

.00,
.00,

.00,
.00,

.00,
.00,

.00,
.00,

.00,
.00,

.00,

.00,

.00) GC
.00) GC

.00) GC
.00) CC

.00) GC
.00) GC

.00) CC
.00) CC

.00) GC
.00) GC

.00) GC
.00) CC

.00) GC

CART]
CART]

CART]
CART]

CART]
CART]

CART]
CART]

CART]
CART]

CART]
CART]

CART]

.00) GC CART]

 

214

*** THE SUMMARY OF HIGHEST 24-HR RESULTS ***
** CONC OF BENZENE IN MICROGRAMS/M**3 **

DATE NETWORK
GROUP ID AVERAGE CONC (YYMMDDHH) RECEPTOR (XR, YR, ZELEV, ZFLAG)

LINE] HIGH 1ST HIGH VALUE 1S 9.62033 ON 91051724: AT ( 78400.00, 146565000, .00, .00)
LINE2 HIGH 1ST HIGH VALUE IS 1.60427 ON 91051724: AT ( 78400.00, 146560000, .00, .00)
LINE3 HIGH lST HIGH VALUE IS .60768 ON 91051724: AT ( 78600.00, 146570000, .00, .00)
LINE12 HIGH lST HIGH VALUE 1S 10.95743 ON 91051724: AT ( 78400.00, l465650.00,.00,.00)
LINE23 HIGH 1ST HIGH VALUE IS 2.13750 ON 91051724: AT ( 78400.00, 146560000, .00,.00)
LINE13 HIGH lST HIGH VALUE 1S 10.04036 ON 91051724: AT ( 78400.00, 14656500000, .00)

ALL HIGH 1ST HIGH VALUE IS 11.37746 ON 91051724: AT ( 78400.00, 146565000, .0000)

 

 

215

*" ISCST3 - VERSION 96113 "* "* TRIAL
02/21/98

it!

"MODELOPTS: CONC RURAL FLAT

**"‘ Message Summary : ISCST3 Model Execution *"
------- Summary of Total Messages -----

A Total of 0 Fatal Error Message(s)

A Total of 0 Warning Message(s)

A Total of 721 Informational Message(s)

A Total of 721 Cairn Hours Identified

"*""‘"‘ FATAL ERROR MESSAGES """**
an NONE "It

#******* WARNING MESSAGES ******#*
it! NONE tit

t###¥#***#*¢##¥**¥**ttittfititttttttt

"“" ISCST3 Finishes Successfully *"

itt##0##itti*tttttittittttfit*ttll*tt

**#

DFAULT

10:21:11
PAGE 212

**#

 

 

216

Appendix F

Graphical Results of Dispersion Modeling under Full Production Rates

distance-north (m)

 

Benzene 24hr—average concentrations in 11ng - 1991

conc (ug/m3)
_o
0'!

._L
8
0°

 

1000

 

 

 

 

O
distance—east (m)

217

Graphical Results of Sensitivity Analysis to a 10% Increase in Emission Rates

distance-north (m)

—1(1010000 —800 -600 -400 —200 0 200 400 600 800 1000

Benzene 24hr—average concentrations in ug/m3 — 1991 +10%ER

conc (ug/ma)
.0
01

_A
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Predicted Concentrations - PDF Characteristics Development

 

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APPENDIX H

 

307

Appendix H

Selected Exposure Factor Studies

1.0 INTRODUCTION

Two parameters which Table 1 Body Weight of Adults“ (kilograms)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

are widely used in Men and
exposure assessment, Men women women
relatively constant Age gears) Mean (kg) Std. Dev. Mean (kg) Std. Dev. Mean 92
throughout the general

mummies“ iii? £3“; 13'} 23'? ii"; 7,13
numerous Stud"?s 0f 45—55 80.9 13.6 68.0 15.3 74.5
reasonable quallty are: 55—65 78.8 12.8 67.9 14.7 73.4
body weight (BW) and 65-75 74.8 12.8 66.6 13.8 70.7
inhalation rate (IR). 1&75 78.1 13.5 65.4 14.6 71.8
The EPA’s standard Note: 1kg = 22046 11>

 

weight and inhalation Percentile data provided in Appendix A (A-1 and A-2)
rate are 70 kg and 20 Source: Adapted from National Center for Health Statistics (NCHS), 1987

m3/ day, respectively. These values may be inaccurate for the national population and
may be inappropriate for the sub-populations under consideration.‘

 

assumptions for body I. Includes clothing weight, estimated as ranging from .09 to .28 kg

The purpose of this report is to provide additional choices and considerations for
selecting body weight and inhalation values for use in probability distributions. This
will allow the reader to develop a critical viewpoint based on a compilation of relevant
facts.

2.0 BODY WEIGHT

The body weight of individuals is not expected to vary significantly from setting to
setting. For this reason, one would suspect a standard distribution to be derived quite
easily. Several distributions have been developed based on the enormous amounts of
body weight data published by the US Public Health Service. The following body
weight studies utilized data taken from the second National Health and Nutrition
Examination Survey (N HANES II), conducted from February 1976 through February
1980. This survey represents the most comprehensive and reliable data set for body
weight in the United States.2

308

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

StUdX 1 Table 2 Body Weight of Children3 (kilograms)
This study consisted of W
20,322 subjects, based Boys J Girls Girls
on a civilian {1011— Age Mean (kg) Std. Dev. Mean (kg) Std. Dev. Mean gag);
institutionalized
population aged 6 6-11 months 9.4 1.3 8.8 1.2 9.1
2...... 1;: :2 :2: 1: :1:
Ultmiely ““3 Study 3 years 15.7 2.0 14.9 2.1 15.3
résults 11} the normal 4 years 17.8 2.5 17.0 2.4 17.4
dismbutlons 5 years 19.8 3.0 19.6 3.3 19.7
summarized in Tables 1 6 years 23.0 4.0 22.1 4.0 22.6
and 2. 7 years 25.1 3.9 24.7 5.0 24.9
, , 8 years 28.2 6.2 27.9 5.7 28.1
Exmnatlon 0f Table 1 9 years 31.1 6.3 31.9 8.4 31.5
shows a mean body 10 years 36.4 7.7 36.1 8.0 36.3
weight of 71.8 kg (for 11 years 40.3 10.1 41.8 10.9 41.1
adult men and women 12 years 44.2 10.1 46.4 10.1 45.3
combined). This 13 years 49.9 12.3 50.9 11.8 50.4
closely compares to the 1: years 21(1) :18 3‘5”: 19181 :2?
. years . . . . .
70 kg EPA.assumpt.‘°n' 16 years 67.1 12.4 58.1 10.1 62.6
meta“, Ems Study ‘5 17 years 66.7 11.5 59.6 11.4 63.2
given 3 111811 order 0f 18 years 71.1 12.7 59.0 11.1 65.1
confidence by the US 19 years 71.7 11.6 60.2 11.0 66.0
EPA. It is Note: 1kg = 2.2046 lb
recommended for use, ‘ Includes clothing weight, assumed as ranging from .09 re .28 kg
due to high peer review Percentile data provided in Appendix A (A-3 and A4)
of the NH ANES I] Source: Adapted from National Center for Health Statistics (NCHS), 1988

 

 

source data, large sample size (28,000 persons), lack of bias, and low measurement
error.

Study 22
The second study focuses on persons less than 18 years of age, due to the greatest

variance in body weight existing within this group. Burmaster, examined a group of
4,079 females and 4,379 males between the ages of 6 months and 20 years, extracted
from the N HANES II data. The data employed was statistically adjusted for non-
response and probability of selection, as well as stratified by age, sex and race to reflect
the entire United States population prior to reporting. Burmaster et. al. (1994) found
that lognormal distributions give strong fits to body weight for persons between 6
months and 20 years.1 Corresponding statistics for the lognormal probability plots are
presented in Table 3.

This particular study is listed by the EPA as a relevant alternative information source
on body weights and is believed to give a good approximation for a young subject

group.

 

309

Table 3 Statistics for Probability Plot Regression Analysis

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Male Fem ale
Afl MeanII (kg) Std. Dev.’ Mean' (kg) Std. Dev.‘

6 months - 1 year 2.23 0.132 2.16 0.145

1 to 2 years 2.46 0.119 2.38 0.128

2 to Lyears 2.60 0.120 2.56 0.112

3 to 4 years 2.75 0.114 2.69 0.137

4 to 5 years 2.87 0.133 2.83 0.133

5 to 6 years 2.99 0.138 2.98 0.163

6 to 7 years 3.13 0.145 3.10 0.174

7 to Slears 3.21 0.151 3.19 0.174

8 to 9years 3.33 0.181 3.31 0.156

9 to 10 years 3.43 0.165 3.46 0.214

10 to 11 years 3.59 0.195 3.57 0.199

11 to 12 years 3.69 0.252 3.71 0.226

12 to 13 years 3.78 0.224 3.82 0.213

13 to 14 years 3.88 0.215 3.92 0.216

14 to 15 years 4.02 0.181 3.99 0.187

15 to 16jears 4.09 0.159 4.00 0.156

16 to 17 years 4.20 0.168 4.06 0.167

17 to 18 years 4.19 0.167 4.08 0.165

18 to 19 years 4.25 0.159 4.07 0.147

19 to 20 years 4.26 0.154 4.10 0.149

Note: 1kg = 2.20461b
‘ Mean, Std. Dev. - correspond to the mean and standard deviation
of the lognormal distribution of body weight (kg).

Source: Burmaster etal., 1994.

 

 

 

3.0 INHALATION RATE

Breathing rates (inhalation rates) are dependent on various individual characteristics,
including: age, gender, weight, level of health and degree of activity. Since inhalation
rates are unlikely to be influenced by site-specific conditions, standard distributions
should be appropriate. The inhalation studies discussed in the following pages, present
several interpretations based on different choices of individual characteristics for a
population.

Study 1
This study utilizes measured values gathered by the US EPA (1985) for inhalation rates

based on various age and gender categories from early studies. The data was compiled
at multiple activity levels as summarized in Table 4. Activity patterns (hours spent at a
particular activity) were determined for three microenvironments by activity level for
all age groups (Table 5). From the data presented in Tables 4 and 5, daily inhalation
rates were calculated using a time-activity ventilation approach. These rates are
summarized in Table 6.

310

 

Table 4 Summary of Hurmn Inhalation Rates for Men, Women and Children by Activity Level (m3/hr)‘
F

 

 

 

 

 

 

n Restingc n Ugh“ n Morbrate' n [Envy'
Adlt Male 454 0.7 102 0.8 102 2.5 267 4.8
Adlt Fm 595 0.3 786 0.5 106 1.6 211 2.9
£39 Adlt - 0.5 - 0.6 -- 2.1 -- 3.9
‘ Clild, sf 6 8 0.4 16 0.8 4 2 5 2.3
Clild, 10 10 0.4 40 l 29 3.2 43 3.9

 

 

 

 

 

 

 

 

 

 

 

“ Values ofinhalationrates forrmles, ferrules andchildrermnleandfennle)presentedinthistablerepresent
themeenofvaluesreportedforeachactivitylevelin 1985. (SeeAppendixBGH) foradetailedlisting
ofthe data from US. EPA, 1985.)

n = mrberofobservationsateachactivity level.

Includes watching television, reading and sleeping

Includesmostdomestic work, attendingtopersomlmdsandeere, hobbiesandoondrctingmirmintbor
repairs andhome improvements.

Includes heavy indoor cleemp, perforrmme of major indoor repairs and alterations and climbing stairs.
Includes vigorms physical exercise and climbing stairs earrying a load.

 

 

Source: Adapted from US EPA, 1985.

The main limitation of this study arises due to values based on early studies. The US
EPA neglects to discuss the accuracy and validity of the values used. This introduces a
degree of uncertainty. Actual measurements taken for a large nmnber of subjects and
data presented for both adults and children are among the advantages.

Table 5 Activity Pattern Data For All Age Groups

 

 

 

 

 

 

 

 

 

Average Houra Per Day
Activity In Each Mlcroenvlronment
M lcroenvlronment Level at Each Activity Level
Indoors Resting 9.82
Light 9.82
Moderate 0.71
Heavy 0.098
TOTAL 20.4 ,
iOutdoora Resting 0.505
Light 0.505
Moderate 0.65
Heavy 0.12
TOTAL . 1.77
In Transportation Resting 0.86
Vehicle Light 0.86
Moderate 0.05
Heavy 0.0012
TOTAL 1.77
Source: Adapted from U.S. EPA, 1985.

 

311

 

 

 

 

  
  
 

 

 

 

       

 

 

 

   

Table 6 Summary of Daily Inhalation Rates Grouped By Age and Activity Level
Daily Inhalation Rate (m3/day)' Total Dally
Subject Restin Li ht Moderate Heavy m" (m3/daL)
Adult Male 7.83 8.95 3.53 1.05 21.40
Adult Female 3.35 5.59 2.26 0.64 11.80
5.60 ' 6.71 ' 2.96 0.85 16.00
Child (Age 6) 4.47 8.95
Child (A e 10) 4.47 11.19

 

 

 

 

 

In this table, inhalation rate was calculated by using the following equation:
1 l
[R = 7'21“ 1R iti

where: IR, = inhalation rate at ith activity (Table 4)

ti = hours spent per day during ith activity (Table 5)
= number of activity periods
= total time of the exposure period (e.g., a day)

 

Total daily inhalation rate was calculated by summing the individual
daily inhalation rate

 

Source: Generated Mata from Tables 4 and 5.

 

 

 

 

 

 

 

 

 

 

 

Study 2

The International Commission Table 7 Daily Inhalation Rates Estimated From Daily Activities‘
on Radiological Protection Inhalation Rate. (“9

(ICRP) also estimated daily Rei‘mg “fl" ”mi,”

. ti n rates for a“ a CS Subject (in lhr) (111 /hr) IR (in /day)
lnhala o , , g Adult Male 0.45 1.20 22.80
and genders usmg a time- Adult Female 0.36 1.14 21.10
activity ventilation approach. _Adult Avera e -- -- 21.95
Using compiled reference Child (10 years) 0.29 0.78 14.80
values for inhalation rates Infant (1 year) 0.09 0.25 3.76
from various sources ANCWbT“ d 0'03 b d 0:: t. 3:681)

. ssump ions ma e were ase on ours res lng an ours
(Appendix B)’ the ¥CRP light activity for adults and children (10 yrs); 14 hours resting
preceded by 353mg the and 10 hours light activity for infants (1 yr); 23 hours resting
hours spent for various age and 1 hour light activity for newborns. Compiled reference

and gender categories within

Hp values in Appendix B(B-2).
specific activities (described

1

in Table 7 footnotes). Table IR = 71:2 :1 IR,ti
7 presents the results of this
study. where: [kg = inhalation rate at ith activity

t, = hours spent per day during ith activity
The obvious limitation Of this lt = number of activity periods
study lies within the accuracy T = total time of the exposure period
and validity of employed ("°" 3 “a”

inhalation data- Assuming the Source: International Commission on Radiological Protection
hours spent in specific (ICRP). 1981.

 

 

 

312

activities may also limit this study’s precision. Overall, these assumptions may over or
under-estimate calculated inhalation rates. Therefore, this approach poses a large
degree of uncertainty.

Study 3
The Exposure Assessment Group (EAG) of the EPA’s Office of Research and

Development conducted a study on ventilation rates between March 1984 and January
1985. For each age-sex activity level for which data were available, minimum,
maximum and mean rates were determined. Some values were presented without
minimum and maximum values from the original data, making them representative only

of available individual measurements.4 Table 8 summarizes the ventilation ranges
determined by the EAG.

Table 8 Ventilatim Sex and Level

 

Adllts

Fermle 0.25“ 0.70 0.34 0.25"I 1.76 0.49 1.24* 2.05“ 0.16 1.40" 6.89 2.87
Male 0.14 1.13”“ 0.73 0.14 L66" 0.83 86 4. 45 2.08 1.

-— any or

Some of the values in Table 8 are marked as means, indicating that the true minimum
or maximum for the group may lie outside the value given. This presents a large
amount of uncertainty and potential error in final inhalation calculations. Inhalation
rates may be calculated by choosing times spent at each activity level, similar to that
summarized in Table 5.

This study is included to allow experiment with assigning data, such as that in Table 8,
to a triangular distribution. Typically triangular distributions are used as a conservative
estimate of the actual distribution, taking into account large amounts of uncertainty in
available data.3

Summary Of EPA Recommendations

The EPA (1989) summarizes the recommended inhalation rate values in Table 9. The
values for males and females differ slightly from the EPA’s original 20 m3/day
assumption. These recommendations consider many key studies, including some
discussed in this report. As in all cases, assessors are encouraged to use values which
most accurately reflect the population in question.

 

4.0 CONCLUSION

The application of Monte Carlo analyses to environmental health risk assessment is far
from routine. One of the primary obstacles of probabilistic modeling is the lack of
consensus on proper distributions to use for key exposure factors.3 This report focused
lightly on two exposure factors (body weight and inhalation rate) along with some
relevant studies. Body weight values were shown to fall between 70 and 72 kilograms

313

for adults, suggesting consistency among studies. Inhalation rates ranged from 11.3 to
21 m3/day for females and from 15.2 to 23 m3/day for males, posing a sensitive
outcome based on distribution selection. Due to the large numbers of studies, the risk
assessor must make critical decisions as to which distribution best represents the
population in question. In some cases a “best guess” may have to be employed where
data is limited and unavailable.

The purpose of EPA’s Risk Assessment Handbooks is to provide users with acceptable
distributions for exposure factors, applicable to a general population. In most cases,
the recommended distribution contains a high level of confidence based on peer review.
Risk assessors should use caution when adopting these distributions. Typically they are
grossly generalized for a large population.

Table 9 Summary of Recommended Values for Inhalation

 

 

 

 

 

 

Jl’opulation Mean
Long-term exposures 4.5 m3/day
Children (1 year)
Children (1-12 years) 8.7 m3/day
Adults ‘
Males ‘ 15.2 ins/day
Females 11.3 m3/day
Short-term exposures
Adults and Children
Rest 0.3 m3/hr
Sedentary Activities 0.4 m3/hr
Light Activities 1.0 m3/hr
Moderate Activities 1.2 malhr
Heavy Activities 1.9 m3/hr
Outdoor Workers
Hourly Average 1.3 m3/hr
Slow Activities 1.1m3/hr
Moderate Activities 1.5 m3/hr
Heavy Activities 2.3 ms/hr

 

 

Source: EPA Exposure Factors Handbook,
Volume I, General Factors, M ay 1989.

 

 

 

The goal of this report was to provide the decision maker with an opportunity to see the
wealth of studies on probability distribution characterizations as an option to:5
0 Increase flexibility
9 Consider more possibilities and;
0 Provide alternatives which might not be allowed or feasible if the
risk characterization was a “bright line” or “single number”.

314

REFERENCES

1. US EPA (May 1989). Exposure Factors Handbook, Volume 1 of 3, General
Factors, Washington DC., EPA.

2. Burmaster, D.E.; Lloyd, K.J.; Crouch, E.A.C., “Lognormal Distributions of Body
Weight as a Function of Age For Female and Male Children In The US”, 1994.

3. Finley, Brent; Proctor, Deborah; Scott, Paul; Harrinton, Natalie; Paustenbach,

Dennis; Price, Paul, “Recommended Distributions for Exposure Factors Frequently
Used in Health Risk Assessment”, Risk Analysis, Vol. 14, No. 4, 1994.

4. Anderson, 5.; Browne, N.; Duletsky, S.; Ramig, J.; and Warn, T., “Devolopment
of Statistical Distributions or Ranges of Standard Factors Used In Exposure
Assessments”, Office of Health and Environmental Assessment, US EPA, Washington
DC, 1985.

5. Hairnes, Yacov Y.; Barry, Timothy; and Lambert, James H., “When and How Can
You Specify a Probabiltiy Distribution When You Don’t Know Much?”, Risk Analysis,
Vol. 14, No. 5, 1994.

 

 

 

 

APPENDIX I

Intake Results from Selected Chemicals for all Binders

 

315

 

 

 

 

 

 

 

 

 

 

 

 

 

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