- 2nd Draft -
CONSIDERATIONS IN DEVELOPING AND USING
METHODS FOR ESTIMATING DIFFUSE
OR FUGITIVE AIR EMISSIONS
OF RADIONUCLIDES AT DOE FACILITIES
Prepared by
S. Cohen & Associates, Inc.
1355 Beverly Drive
Suite 250
McLean, Virginia 22101
Under Contract No. 68D20185
Work Assignment 2-09
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Radiation and Indoor Air
Washington, DC
Albert Colli
Work Assignment Manager
July 1994
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Dear Rad NESHAPs folks,
This document is a revision of a document we previously sent
to you, "METHODS FOR ESTIMATIONG DIFFUSE OR FUGITIVE AIR
EMISSIONS OF RADIONUCLIDES AT DOE FACILITIES," dated Oct, 1992.
Part of the reason for the revisions was to answer the comments
made by the DOE in their letter from Raymond Pelletier to William
Gunter, dated Dec 30, 1993.
The following is a summary of the major changes:
Executive Summary--added
Chapt 2 - -added
Chapt 3--expanded to include DOE work; table 3-1 added.
Chapt 4--some new material
Chapt 5--5.1.2; 5.3.3, .4, & .5--added
Chapt 6 - -new
Chapt 7- - reworked
NOTE THAT THERE ARE NO CHANGES TO THE ATTACHMENTS.
THEREFORE, I AM SENDING THIS BY POSTMAN TO REDUCE THE PAPER
BURDEN. YOU CAN PRINT OUT A COPY THERE IP YOU WISH.
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TABLE OF CONTENTS
page
EXECUTIVE SUMMARY ES-1
1. INTRODUCTION 1
1.1 Background and Objectives 1
1.2 Evaluation of Methodologies 2
1.2.1 Methods for estimating fugitive
emissions of air pollutants 2
1.2.2 Structure of report 2
2. IDENTIFICATION AND CHARACTERIZATION OF
EMISSION SOURCES 3
2.1 Types of Emission Sources 4
2.2 Types of Radiological Emissions 5
2.3 Characterization of Emission Sources 8
3. RESUSPENSION OF PARTICULATES 10
3.1 Measures of Resuspension 10
3.2 Research on Resuspension 12
3.3 Studies of Wind Erosion 14
3.3.1 Mechanisms of wind erosion 14
3.3.2 Characterizing wind erosion studies
prior to 1984 15
3.3.3 DOA Wind Erosion Equation 16
4. EPA-ADOPTED PARTICULATE EMISSION MODELS 17
4.1 Natural Occurrence: Wind Erosion 17
4.1.1 Open Areas 17
4.1.2 Open waste and storage piles (except
uranium ore and mill tailings) 19
4.1.3 Uranium ore and mill tailings 20
4.1.4 EPA Soil Screening Guidance 20
4.2 Soil and Material Handling 21
4.2.1 Soil removal and haulage 22
4.2.2 Grading and shaping of soil 22
4.2.3 Agricultural tillage and seeding 22
4.2.4 Building demolition and material
disposal 22
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TABLE OF CONTENTS, Cont'd
page
4.3 Non-intrusive Action 23
4.3.1 Vehicular traffic on unpaved roads 23
4.4 Control Methods 24
5. MECHANISMS OTHER THAN RESUSPENSION 25
5.1 Evaporation From Ponds and Lagoons 25
5.1.1 Evaporation models 25
5.1.2 Wet-Cooling Towers 27
5.2 Evaporation from contaminated soil 28
5.2.1 Saturated soil 28
5.2.2 Subsurface contamination 30
5.3 Gaseous and Other Types of Emissions 31
5.3.1 Re-entry drilling 31
5.3.2 Ground seepage of noble gases 31
5.3.3 Emissions from buildings 32
5.3.4 Emissions from tank venting 33
5.3.5 Emissions from equipment 34
6.0 GUIDANCE ON ENVIRONMENTAL MONITORING PROGRAMS
TO DEMONSTRATE COMPLIANCE WITH THE DOE NESHAPS 35
6.1 Summary of NESHAPS Requirements 35
6.2 Sampling and Analytical Methodology 35
6.2.1 Radionuclide as particulates 37
6.2.2 Radionuclide as gases 37
6.3 Criteria for Environmental Monitoring
Programs 38
6.3.1 Measurements made at critical
receptor locations 38
6.3.2 Continuous sampling at the point of
measurements 39
6.3.3 Sampling and measurements of major
radionuclide contributor 39
6.3.4 Radionuclide concentrations causing
an effective dose equivalent of
1 mrem/yr must be readily detectable
and distinguishable from background 40
ii
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TABLE OF CONTENTS, Cont'd
page
6.3.5 Radionuclide concentrations that would
cause an effective dose equivalent of
1 mrem/yr must be readily
distinguishable from background 41
6.4 Quality Assurance Program in Response to the
Performance Requirements of Appendix B,
Method 114, 40 CFR 61 43
7. RECOMMENDED METHODS FOR ESTIMATING FUGITIVE AIR
EMISSIONS OF RADIONUCLIDES 45
7.1 Estimation of Radionuclide Emissions Using
Fugitive Dust Emission Models 45
7.2 Calculating Effluent Releases From Sampling
Data 46
7.2.1 Calculation of gaseous releases at NTS 46
7.2.2 Critique of methods used at other DOE
sites 47
7.2.3 Estimating fugitive particulate
emissions from environmental sampling
and monitoring 48
7.3 Summary of Recommended Methods 49
8. REFERENCES 52
9. BIBLIOGRAPHY 58
Attachment 1 Excerpts from Control of Open Fugitive
Dust Sources
Attachment 2 Excerpts from Compilation of Air Pollutant
Emission Factors
Attachment 3 Excerpts from Hazardous Waste TSDF (Treatment,
Storage, and Disposal Facilities): Fugitive
Particulate Matter Air Emissions Guidance
Document
Attachment 4 Excerpts from National Agronomy Manual
Attachment 5 Excerpts from NUREG-0570
Attachment 6 Excerpts from Superfund Exposure Assessment
Manual
iii
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List of tables
Page
Table 1-1 Radionuclide Classification and Radioactivity
Distribution Using DOE Categories 6
Table 1-2 Radioactive Releases from DOE Diffuse Sources
- 1992 7
Table 3-1 Reported Resuspension Factors 13
Table 3-2 Wind Erosion Mechanisms vs Particle Size 14
Table 4-1 Summary of AP-42 Emissions Control Measures 24
Table 6-1 Physical Parameters of Selected Primary
Radionuclides 36
Table 6-2 Examples of Backgrounds and Sensitivities of
Some Principal Airborne Radionuclides Released
from DOE Facilities 42
Table 7-1 Summary of Methods for Estimating Fugitive
Emissions 50
IV
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EXECUTIVE SUMMARY
ES.l Background and Objectives
The Environmental Protection Agency (EPA) has promulgated in 1989
a national emission standards for radionuclides (codified in 40 CFR
61, National Emission Standards for Hazardous Air Pollutants;
Radionuclides). Subpart H to Part 61 addresses emissions of
radionuclides from Department of Energy (DOE) facilities.
Emissions from DOE facilities include those from point sources
(i.e., stacks or vents), and those from diffuse or fugitive
sources. Subpart H provides guidance on monitoring, test
procedures and calculation of effective dose equivalents for
emissions from point sources. However, Subpart H does not provide
any guidance for radionuclide emissions from diffuse or fugitive
sources. At present, DOE sites address diffuse or fugitive
emissions on a site-specific basis. The purpose of this study is
to provide initial technical assistance to EPA regional offices in
identifying generic methods for estimating annual air emissions of
radionuclides from diffuse or fugitive sources.
It should be noted that because of various activities, it is
difficult to identify a comprehensive set of methods applicable to
assess a broad range of conditions found at DOE sites. It is
necessary to identify the unique conditions of each case and
identify or develop the methodology that best suits each case. The
use of default values should be carefully considered as they may
not be appropriate for the site or conditions being evaluated. This
aspect is especially important if multiple default values are
assumed for several of the model parameters, since they may
introduce, in the aggregate, an unrealistic degree of conservatism.
ES.2 Evaluation of Methodologies
The literature on fugitive or diffuse airborne radioactive
emissions provides limited information on methods for estimating
offsite releases. For the sake of simplicity, the term "fugitive
emissions" is used here to denote fugitive or diffuse emissions.
An exception is the methodology developed for calculating
particulate releases from uranium ore pads and mill tailings piles,
issued by the Nuclear Regulatory Commission.
By contrast, fugitive emissions of air pollutants (other than
radioactivity) have been extensively studied, notably by the EPA.
EPA guidance documents recommend methods for estimating many types
of fugitive emissions from hazardous waste treatment, storage and
disposal facilities, and from other sources. Since resuspension
models for windblown dust do not distinguish the type of
contamination carried by particulates, the same methodologies used
for estimating the releases from hazardous waste sites may be
applied to releases from areas contaminated by radioactivity.
ES-1
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ES.3 Types of Emission Sources
In the context of this report, fugitive emissions include both
point and diffuse sources. For example, point sources may include
stacks, vents, buildings, releases from discrete equipment, vented
tanks, wet-cooling towers, etc. On the other hand, diffuse
emissions originate from large area sources, e.g., spills, waste
piles, salvage yards, large areas of contaminated soil, etc.
The mechanisms resulting in the generation of airborne contaminants
are also expected to vary. The amounts or emission rates will
fluctuate depending upon whether the mechanisms are man-made (e.g.,
surface grading or drilling), or natural in origin (e.g., wind
erosion). Some mechanisms may involve dynamic processes which may
also result in varying emission rates. Dynamic processes may
include such effects as biotic activity, growth of vegetative
covers, migration of contaminants to greater soils depths, etc.
Emission rates can vary and be continuous or intermittent, as well.
Some types of releases may be mitigated by man-made or natural
processes. For example, the demolition of a building may first
require that the facility be decontaminated, which would remove
some of the contaminants, thereby reducing the total amounts of
radioactivity which might be released. Natural processes may
include the migration of contaminants to greater soil depths due to
surface water infiltration. As a result, surface soil contamination
levels would decrease, yielding lower emissions rates from wind
resuspension.
ES.4 Types of Radiological Emissions
Since DOE facilities conduct a broad range of activities, the
levels of radioactivity and radionuclide distribution may also vary
significantly. The physical and chemical forms of the
radioactivity are also dependent upon the process being conducted.
Materials may be released as particulates, gases, or vapors.
Particulates may be associated with radioactivity attached or
incorporated in resuspended soil grains. Gases may originate from
the venting of tanks or emanations from waste disposal sites.
Vapors may be released by plant processes or decomposition or
degradation of materials.
ES-2
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ES.5 Characterization of Emission Sources f?(<_/v-
In characterizing emissions, the information may come ^ several
sources. For example, the contaminants may have been characterized
as part of earlier field studies. In this case, the information
may present results obtained by direct measurements and sample
analysis. However, this information would only present a
characterization at a specific time, without the benefit of
assessing temporal changes and dynamic conditions.
In other cases, the data may be generated during the conduct of
routine environmental surveillance activities during which samples
are periodically collected and analyzed. Depending upon the scope
of the environmental surveillance program, the data may provide
some information about the distribution of the radioactivity in
multiple environmental media, provide the means to identify and
characterize environmental transport mechanisms, and reveal
contamination profiles as a function of time and location.
ES.6 Resuspension Processes and Other Release Mechanisms
Resuspension is the process of introducing particulate matter into
the atmosphere that were once deposited on the ground from a plume
or cloud. On the other hand, suspension is the process of
atmospheric entrainment of particles present on the ground from
other events or sources. In both cases, the entrainment process
takes place by the same mechanisms. Three processes have been
identified, saltation, suspension, and surface creep. ^Saltation
jrefers to the movement of particles which jump or bounce a few
inches above the surface. These particles are ejected from the
soil surface, fly a short distance and then fall back down. Upon
impact, they are likely to bounce and also dislodge other
particles, which may saltate, creep, or become suspended, depending
on the size of the target particle. Suspension refers to the
atmospheric entrainment of relatively smaller particles, which can
remain in the atmosphere and be carried over large distances. It
is believed that wind-induced suspension is caused entirely by
saltating particles. Creep refers to the sliding or rolling motion
of particles that are too heavy to leave the ground but are pushed
by the wind and the impact of smaller particles.
A review of the literature indicates that it is difficult to
predict resuspension factors with any accuracy. Typically,
mechanically-induced resuspension factors vary over eight orders of
magnitude, from 10"10 to 10"2 m"1. For wind-caused resuspension
factors vary over seven orders of magnitude, 10"10 to 10"3 m"1. The
major factors known to have a direct impact on resuspension
mechanisms include weathering and physical and chemical properties,
including particle chemical composition, solubility, size and
shape, density, moisture contents, erodible fraction, and threshold
velocity. Weathering and migration to greater soil depths have the
tendency to reduce resuspension factors.
ES-3
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Test have also shown that it is not uncommon to have resuspension
factors decrease by 2 to 3 orders of magnitude over a relatively
short time, e.g., several days.
Other release mechanisms include water evaporation from ponds and
lagoons. Evaporation is governed by air temperature, vapor
pressure, dew point, wind speed, and insolation. Complex
relationships have been developed to estimate the evaporation rates
for lakes and the so-called "pan" evaporation rates. Evaporation
or volatilization also could be a significant release mechanism of
radioactivity from moisture saturated soils.
Another types of unique releases include the release of noble gases
from the Nevada Test Site. These releases occur during the
re-entry drilling of cavities left after underground nuclear
detonations. In ground seepage, noble gases emanate out of the
soil and rocks via fissures and cracks caused by the detonation.
Emissions may also occur from buildings through vents, stacks or
through natural ventilation. The mechanisms leading to such
releases may be induced mechanically (e.g., exhaust fans) or via
natural means (e.g., convection and stack effect). Other releases
may be associated with the operation of specific types of equipment
(e.g., compactors, cooling towers) and venting of process equipment
(e.g., tanks).
ES.7 EPA-Adopted Emission Models
Over the past decade, the EPA has conducted numerous field
investigations to characterize particulate emissions from various
sources. These studies have led to the development of emission
models for a number of sources. Most recently, the EPA has issued
standardized guidance to support the planning of remedial action
activities at NPL sites. One of the documents provides the
methodology for deriving particulate emission factors. Finally,
additional models are presented in documents addressing the control
of open fugitive dust sources. Many of the models are reproduced
in guidance documents targeting hazardous waste sites and temporary
storage facilities. A number of the models have been incorporated
into the Compilation of Air Pollution Emission Factors, AP-42, also
issued by the EPA. Relevant excerpts from the cited documents are
described or attached to this report.
It should be noted that all of the models are complex and require
extensive information about specific parameters. The models must
therefore be carefully evaluated to justify their use in specific
applications. Many models also rely on default parameters with
little or no information as to their justification. In some
instances, site specific data may not be readily available, unless
specific studies are launched to obtain such information.
ES-4
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Given that various sources of information may be available, it is
important to identify, screen, and use the most appropriate data in
characterizing the presence of contaminants. In decreasing order
of desirability, possible sources of information include:
Emission data as measured at the source.
Results from specific site characterization studies.
Results from routine sampling and monitoring.
Process or activity related information.
Default data.
The use of environmental monitoring stations for demonstrating
compliance with the NESHAPS rule or for the purpose of deriving
release rates are similarly fraught with uncertainties. However,
these approaches must be carefully evaluated for its technical
merits. The development of such models requires that complex
factors be considered, including, among others:
validation of the deployment strategy, locations, and numbers
of environmental sampling stations.
validation of the selected sampling and analytical methods for
the expected radionuclides.
representativeness of the field data to the emission sources.
atmospheric behavior of contaminants while in transient from
the source of emission to the sampling station.
atmospheric dispersion and concurrent meteorological data for
the site.
site specific features (e.g., terrain, ground cover,
obstructions, control measures to mitigate releases, etc.).
radiological, physical, and chemical characteristics of the
contaminants.
physical characteristics of emission sources and temporal and
spatial distributions of emission rates.
ES-5
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Chapter 1
INTRODUCTION
1.1 Background and Objectives
On December 15, 1989, the Environmental Protection Agency (EPA)
promulgated national emission standards for radionuclides (see 54
FR 51654 [EPA89b, c]), which are codified in 40 CFR 61, "National
Emission Standards for Hazardous Air Pollutants; Radionuclides".
Subpart H to Part 61 addresses emissions of radionuclides other
than radon from Department of Energy (DOE) facilities.
Specifically, Subsection 61.92 states:
"Emissions of radionuclides to the ambient air from Department
of Energy facilities shall not exceed those amounts that would
cause any member of the public to receive in any year an
effective dose equivalent of 10 mrem/yr."
Emissions from DOE facilities include those from point sources
(i.e., stacks or vents), and those from diffuse or fugitive
sources. A diffuse source is defined as an area source from which
emissions are continuously distributed over a given area or emanate
from a number of points randomly distributed over the area.
Examples of diffuse sources include resuspension of dust deposited
on open fields, evaporation from ponds, and ground seepage of gases
following underground nuclear tests. A fugitive source is defined
as an undesigned localized source, such as a leaking seal during
fe-entry drilling following an underground nuclear test. Subpart
H provides guidance on monitoring, test procedures and calculation
of effective dose equivalents for emissions from point sources.
Subpart H does not provide guidance for radionuclide emissions from
diffuse or fugitive sources.
At present, each of the DOE sites addresses diffuse or fugitive
emissions on a site-specific basis. The purpose of this study is
to provide technical assistance to EPA regional offices in
identifying generic methods for estimating annual air emissions of
radionuclides from diffuse or fugitive sources.
It should be noted that because of various activities, it is
difficult to identify a comprehensive set of methods applicable to
assess a broad range of conditions found at DOE sites. It is
necessary to identify the unique conditions of each case and
identify or develop the methodology that best suits the conditions.
The use of default values should be carefully considered as they
may not be appropriate for the site or conditions being evaluated.
This aspect is especially important if multiple default values are
assumed for several of the model parameters, since they may
introduce, in the aggregate, an unrealistic degree of conservatism.
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1.2 Evaluation of Methodologies
1.2.1 Methods for estimating fugitive emissions of air
pollutants
The literature on fugitive or diffuse airborne radionuclide
emissions contains a limited number of methods for estimating
off-site releases during normal operations. (The term "fugitive
emissions" will be used to denote fugitive or diffuse emissions.
A notable exception is the methodology developed for calculating
particulate releases from uranium ore pads and mill tailings piles
which is described in Regulatory Guide 3.59 (NRC87), issued by the
U.S. Nuclear Regulatory Commission (NRC).
By contrast, fugitive emissions of hazardous air pollutants (other
than radioactivity) have been extensively studied, notably by the
EPA and its contractors. EPA guidance documents recommend methods
for estimating many types of fugitive emissions from hazardous
waste treatment, storage and disposal facilities (TSDF), as well as
other sources of air pollutants. Since the models for the
resuspension of windblown dust, for instance, do not distinguish
the type of contamination carried by the dust, the same
methodologies used for estimating the releases from hazardous waste
sites can be applied to releases from areas contaminated by
radionuclides.
1.2.2 Structure of report
In this report, fugitive emissions are grouped into two general
categories: resuspension of particulates, and emissions of gases or
vapors.
Chapter 2 presents a general descriptions and discussions
about various types of fugitive and diffuse emission sources.
Given the potentially broad range of conditions and sites,
this chapter addresses only general considerations in
identifying and characterizing such emission sources.
Chapter 3 presents a general discussion of resuspension,
including research studies aimed at understanding this
phenomenon as well as some early predictive models.
Chapter 4 describes a few selected models for estimating
particulates releases along with references to the relevant
sources in the literature and any proposed improvements to the
models. Methods that are presented in EPA guidance documents
are recommended when they are applicable to emissions from DOE
sites. Relevant excerpts from the cited documents are
attached to this report.
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Chapter 5 addresses emissions of radioactive gases and vapors.
The recommended method for estimating releases of tritiated
water vapor is based on a water evaporation model given in
NUREG-0570, an Nuclear Regulatory Commission document (NRC79) .
The document is used to assess the hazards posed by accidental
releases of toxic chemicals. This chapter also presents
additional information from an OSWER directive, the Superfund
Exposure Assessment Manual (EPA88c). This document presents a
model for vapor emission from contaminated soil. This method
is recommended for estimating the evaporation of tritiated
water from unsaturated soil. Methods currently used by some
DOE sites for estimating the emissions of radioactive gases
and vapors are described in this chapter.
Chapter 6 presents a summary of methods and general
considerations in using environmental monitoring for
demonstrating compliance with the NESHAPS rule.
Chapter 7 presents a summary of method and alternative
procedures for estimating the various types of releases
addressed in this report. They are grouped by mechanisms and
sources.
The enclosed six attachments contain specific sections
extracted from cited literature sources. They are included
here for additional information and facilitate the evaluation
of the selected methods.
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Chapter 2
IDENTIFICATION AND CHARACTERIZATION OF EMISSION SOURCES
There are potentially numerous types of diffuse and fugitive
emission sources. Given the potentially broad range of conditions
and sites, this chapter addresses only general considerations in
identifying and characterizing emission sources.
2.1 Types of Emission Sources
In the context of this report, fugitive emissions include both
point and diffuse sources. For example, point sources may include
stacks, vents, buildings, releases from discrete processes or
equipment, vented tanks, wet-cooling towers, etc. On the other
hand, diffuse emissions originate from large area sources, e.g.,
landfills, spills, waste piles, salvage yards, large areas of
contaminated soil, etc.
The mechanisms resulting in the generation of airborne contaminants
are also expected to vary. The amounts and emission rates will
fluctuate depending upon whether the mechanisms are man-made (e.g.,
surface grading or drilling), or natural in origin (e.g., wind
erosion). Some mechanisms may involve dynamic processes which may
also result in varying emission rates. Dynamic processes may
include such effects as biotic activity on soils, growth of
vegetative covers, migration of contaminants to greater soils
depths, etc.
Regardless of the types of sources, emissions could also be
continuous or intermittent in nature. Continuous emissions may
include gaseous emanations from landfills, evapo-transpiration,
etc. Intermittent releases may be due to the operation of
equipment, building exhausts, tank vents, etc. In all cases,
emissions rates and total amounts of materials released could also
vary when compared to past or similar operations conducted
elsewhere on the site.
Some types of releases may be mitigated by man-made or natural
processes. For example, the demolition of a building may first
require that the facility be decontaminated in response to
administrative requirements. The decontamination process would
remove some or all of the contaminants, thereby reducing the total
amounts of material (and radioactivity) which could be released.
Natural processes may include the migration of contaminants to
greater soil depths due to surface water infiltration. As a result,
surface soil contamination levels would decrease, yielding lower
emissions rates from wind resuspension. However, should the deeper
soil layers be disturbed by mechanical means, the emission rates
might potentially increase depending upon the amounts of soil
exposed, size of area involved, and resuspension mechanisms.
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In terms of relative importance, releases from diffuse and fugitive
emissions may vary depending upon other types of on-going
activities. For example, for sites with operating facilities, the
amounts of radioactivity due to normal plant operations may
dominate over that produced by diffuse and fugitive emissions. For
sites undergoing remediation, the amounts of radioactivity released
by remedial activities may be the sole source of airborne
emissions. Although such emissions are relatively much smaller
than those emitted by normal operations, they would nevertheless be
considered in demonstrating compliance with Subpart H.
2.2 Types of Radiological Emissions
Since DOE facilities conduct a broad range of activities, the
levels of radioactivity and distribution of radionuclides may also
vary significantly. The physical and chemical forms of the
radioactivity are also dependent upon the process or activity
causing the releases. Materials may be released as particulates,
gases, or vapors. Particulates may be associated with
radioactivity attached or incorporated in resuspended soil grains.
Gases may originate from the venting of tanks or emanations from
landfills. Vapors may be released by plant processes or
decomposition or degradation of materials.
Given the diverse range of activities taking place at DOE
facilities, it is not possible to list all radionuclides that may
be present in fugitive and diffuse emissions. However, such
radionuclides may include H-3, Mn-54, Co-60, Sr-90, Cs-134, Cs-137,
Ce-144, Pu-239, Pu-241, U-238, Th-234, Ra-226, Rn-222, depleted
uranium, among others. Obviously, this listing is not
comprehensive, but is believed to be representative of some of the
major alpha, beta, and gamma emitters contained in DOE waste or
present at contaminated sites. Table 2-1 presents an aggregate
distribution of radionuclides contained in waste classified by the
DOE (DOE92a).
Some insight about the amounts of radioactive emissions may be
obtained from the DOE's 1992 NESHAPS Summary Report (DOE94). Table
2-2 presents a summary of radionuclide releases from diffuse
sources emitted by production sites, research laboratories, and
remedial action, storage, and disposal sites. As can be noted,
most of the radioactivity is associated with tritium (96.6%) and
noble gases (3.1%). The balance of the radioactivity is due to
transuranics and others nuclides. As a category, "Others" does not
include radon gases (Rn-220 or Rn-222). The 1992 DOE report does
not provide emission data for radon released for all cited sources.
This information is presented for illustrative purposes and does
not imply that these radionuclides, either singly or in groups,
will always be present in airborne emissions, and if present, the
relative distribution need not follow that shown in Tables 2-1 and
2-2.
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Table 2-1 Radionuclide Classification and Radioactivity
Distribution Using DOE Categories**0
Fission Activat.
U/Th Product Product Alpha Other
Nuclide Percent Nuclide Percent Nuctide Percent Nuclide Percent Nuclide Percent
Tl-208
Pb-212
Bi-212
Po-212
Po-216
Ra-224
Ra-228
Ac-228
Th-228
Th-231
Th-232
Th-234
Pa-234m
Pa- 234
U-23S
U-238
0.0017
0.0045
0.0045
0.0029
0.0045
0.0045
0.0269
0.0269
0.0045
0.0259
0.273
33.197
33.197
0.0034
0.0258
33.197
100
Co- 60
Sr-90
Y-90
Zr-95
Nb-95
Tc-99
Sb-125
Te-125m
Ru-106
Rh-106
Cs-134
Cs-137
Ba-137m
Ce-144
Pr-144
Pm-147
Sm-151
Eu-152
Eu-154
Eu-155
0.08
7.77
7.77
1.27
2.83
0.02
2.93
0.73
6.39
6.39
0.38
17.31
16.38
14.67
14.67
0.06
0.11
0.09
0.09
0.06
100
Cr-51 4.95
Mn-54 38.10
Co-58 55.40
Fe-59 0.49
Co-60 0.87
Zn-65 0.19
100
Pu-238
Pu-239
Pu-240
Pu-241
Am- 241
Cm- 242
Cm- 244
2.62
0.20
0.70
96.4
0.004
0.056
0.02
100
H-3
C-14
Mn-54
Co-58
Co-60
Sr-90
Y-90
Tc-99
Cs-134
Cs-137
Ba-137n
U-238
1.22
0.06
6.76
6.24
18.03
8.48
8.48
0.12
13.98
18.45
17.45
0,73
100
(a) Extracted from the 1992 Integrated Database, Table C.5 (DOE92a>. Totals may not
exactly add up to 100X due to rounding off. "Alpha" are for nuclides of <100 nCi/g.
"Other" includes unknown radionuclide compositions or mixtures.
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Table 2-2 Radioactive Releases from DOE Diffuse Sources - 1992(a)
Production Sites and Research Laboratories(b)
Tritium Gases TRU Others Total
Activity (Ci) 8.6E+03 2.8E+02 2.8E-03 1.6E+00 8.9E+03
Percent 96.6 3.1 <0.01 0.02 100
Remedial Action, Storage, and Disposal Sites
Tritium Gases TRU Others Total
Activity (Ci) l.OE-04 l.OE-04
Percent 100 100
(a) Extracted from Table 4, DOE94.
(b) Radon releases include 4,234 Ci for Rn-220 and 23.4 Ci
for Rn-222.
7
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2.3 Characterization of Emission Sources
In characterizing emissions, the information may come several
sources. For example, the contaminants may have been characterized
as part of earlier field studies. In this case, the information
may present results obtained by direct measurements and sample
analysis. However, this information would only present a
characterization at a specific time, without the benefit of
assessing temporal changes and dynamic conditions.
In other cases, the data may be generated during the conduct of
routine environmental surveillance activities during which samples
are periodically collected and analyzed, e.g., when characterizing
radionuclides concentrations in water, soils, sediments, vegetation
samples. Depending upon the extent and duration of the
environmental surveillance program, the data may provide some
information about the distribution of the contaminants in multiple
environmental media, provide the means to identify and characterize
environmental transport mechanisms, and reveal contamination
profiles as a function of time and location.
The EPA has issued some guidance for the characterization of
radioactivity in contaminated soils (EPA92). The guidance
identifies requirements for characterizing the radiochemical and
petrographic properties of soils. The guidance addresses the
following major aspects:
Soil grain distribution as a function of weigh, particle
size and shape, and density.
Radioactivity and soil/contaminant relationship as a function
of weigh, particle size and shape, and density.
Mineral and physical properties as a function of size
fractions of the contaminants (e.g., contaminants) and host
material (e.g., soils).
Soil/contaminant chemical properties as a function of weigh,
particle size and shape, and density.
The EPA guidance uses a multi-tiered approach and presents a flow-
chart with which to conduct the characterization of contaminants
and soils.
Some types of releases may be characterized by evaluating the
process resulting in the emissions. For example, the amount of
radioactivity could be determined from knowing the concentration of
a specific radionuclides and applying factors representing the
distribution of the radioactivity between specific phases of a
process, e.g., liquid to gas, filtration efficiency, release
fraction from waste treatment processes, resuspension factor, etc.
Alternatively, such emissions could be monitored by installing
8
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sampling equipment and monitoring each release as it occurs.
Given that various sources of information may be available, it is
important to identify and use the most appropriate data in
characterizing the presence of contaminants. In decreasing order
of desirability, possible sources of information include:
Emission data as measured at the source.
Results from specific site characterization studies.
Results from routine sampling and monitoring.
Process or activity related information.
Default data.
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Chapter 3
RESUSPENSION OF PARTICULATES
Resuspension is the process of re-injecting particulates into the
atmosphere that have been deposited on the ground from an
atmospheric plume or cloud; suspension is the process of
atmospheric entrainment of particles which have been deposited on
the ground in some other manner. Since the entrainment in both
cases takes place by the same mechanisms, the two terms are often
used interchangeably, depending on the context. Pollution studies
usually refer to resuspension while discussions of agricultural
soil losses use the term suspension.
3.1 Measures of Resuspension
Early resuspension studies involved measuring the airborne
concentrations of contaminants in particulate form at some height
above the ground and relating those concentrations to the putative
source term, i.e., the level of contamination on the ground. The
result of this analysis was a irersuspension farrhp'f]. the ratio of the
concentration in the air to that on the ground: .. / ?
c. 2/^ * ^
K = C + a L * -tl -^7^3-1)
K = Resuspension factor, m"1.
C = Concentration in air, g/m3.
a = Surface concentration, g/m2.
Resuspension factors have been determined for a wide range of
natural stresses (i.e., wind erosion) as well as mechanical
stresses due to human activity. Resuspension factors due to_
mechanical stresses vary over more than eight orders of magnitude
while those due to wind alone vary over more than six orders
(NIC88). Such variation aside, a resuspension factor describes a
static situation and is therefore useless in predicting an emission
rate.
A [j-qguspenslop rata is the ratio of the vertical flux of a
contaminant to its surface concentration:
R = * -5- a |<-- ^- (3-2)
R = Resuspension rate, s"1.
* = Vertical flux, g/m2-s.
a = Surface concentration, g/m2.
In principle, if R and a were known, the emission rate could be
calculated. Since Eq. (3-2) yields:
* = R a, , (3-3)
10
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and since the total emission rate is equal to the flux multiplied
.
<
by the area:
E = * A = R a ' A r* "* > (3-4)
E = Emission rate, g/s. £" X. ^ C ^ f~ «^~;
A = Area of source, m2 .
In reality, such a procedure is encumbered with numerous problems.
Sehmel (DOE84, Ch. 12) has reported experimental determinations of
resuspension rates as a function of wind speed, particle size and
surface roughness. Rate measurements are reproducible under
carefully controlled conditions. However, resuspension rates
observed in a single field location varied over four orders of
magnitude, while other reported rates varied over almost six
orders . Another problem is inherent in the determination of a.
'kddionuclide contamination of exposed soil often extends below the
surface, especially in loose or disturbed soil where the
contamination has "weathered in" and may be exponentially
distributed. The contamination profile may then be subject to
dynamic processes which may result in varying resuspension rates.
Dynamic processes may include such effects as biotic activity on
soils, growth of vegetative covers, etc. The question then arises
as to the depth of the soil layer which should be used in this
calculation. Since different thicknesses of the soil layer can
become resuspended, depending on such factors as the degree of
compaction, moisture, resuspension mechanisms, and the duration and
speed of the wind, a is not a uniquely determined quantity.
\
This problem may be further complicated by the use of mitigating
measures to reduce the resuspension rate. In some instances it is
required to apply an agent to reduce the amount of dust to limit
exposures to workers or meet environmental protection standards.
Water is most commonly used for this purpose. Accordingly, the
application of water may result in a lower resuspension rate.
The methodology identified earlier would then be redefined as:
E = R a A (3-5)
/ .
R = Mitigated resuspension rate, s .
Where the other terms are as previously defined.
3.2 Research on Resuspension
Recent studies on the resuspension of particulate radionuclides
include those by Langer (DOE86) , Nielsen et al. (NIE90) , Pettersson
and Koperski (PET91) , and Finder et al. (PIN90) . Guidance for
calculating particulate releases from uranium ore pads mill
tailings piles has been issued by the Nuclear Regulatory Commission
(NRC87) and is embodied in the computer model MILDOS-AREA (ORNL92) .
11
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Other resuspension studies include those of Reeks et al. (REE88),
and Nicholson and Branso (NIC90). The Fourth International
Conference on Precipitation Scavenging, Dry Deposition, and
Resuspension, sponsored by the Department of Energy (DOE), dealt
extensively with this and related subjects (PRU83). Earlier
research on resuspension has been summarized by Sehmel (DOE84, Ch.
12) . Later updates were conducted by Nicholson (NIC88), Pye
(PYE87), and the Nuclear Regulatory Commission (NRC92).
A review of the work sponsored by the DOE indicates that it is
difficult to predict resuspension rates with any accuracy (DOE84,
SEH80). The work has shown that mechanically-induced resuspension
rates vary over eight orders of magnitude, from 10'1U to 10'* m'1. For
wind-caused resuspension, rates vary over seven orders of
magnitude,10"IU to 10"-1 m1. The major factors known to have a
direct impact on resuspension mechanisms include weathering and
physical and chemical properties, including particle chemical
composition, solubility, size and shape, density, moisture
contents, erodible fraction, and threshold velocity. Table 3-1
presents a summary of resuspension factors.
Weathering and migration to greater soil depths have the tendency
to reduce resuspension factors. The reduction is primarily
dependent upon the surface characteristics, weathering processes,
and mechanism causing resuspension. Test have shown that it is not
uncommon to have resuspension factors decrease by 2 to 3 orders of
magnitude over a relatively short time, e.g., typically after 30
days. Resuspension factors have been developed to reflect this
aspect (NRC83). A model developed by the NRC includes an
exponential time component and retains a minimal value for the
resuspension factor when the exponential term vanishes to zero
(NRC83).
The proposed expression is:
K(t) = [10'9 -I- 10'5 exp-(0.6769t) ] (3-6)
K(t) = time dependent resuspension factor, m"1.
t = time, year.
As can noted, after about 15 years the exponential term becomes
insignificant and the resuspension factor effectively remains
constant thereafter at 10~9 m .
Other expressions have been developed which include multiple
exponential components, each with its own constant for specific
time intervals.
o
12
- (o
O
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Table 3-1 Reported Resuspension Factors
Condition
Resuspension factor
range (m-l)(b)
Comments
Wind-caused
Mechanically-
caused
2E-11 to 8E-09
9E-08 to 1E-07
9E-08 to 5E-07
1E-04 to 1E-09
9E-11 to 3E-04
2E-13 to 6E-10
<2E-09
<5E-10
4E-09 to 5E-08
2E-06 to 3E-04
IE-OS to 1E-02
1E-10 to 4E-02
Bare soil, Y-90
Po-210 oxide
U3°8
Pu in soil, time
dependent model
literature review
NTS, Pu aerosols
Test debris, 13 y
after deposition
Test debris, 22 y
after deposition
Cs-137, Chernobyl
Pu
ZnS, per event
literature review
(a) Extracted from Table 6.4, NUREG/CR-5512 (NRC92).
(b) Exponential notation, 2E-11 means 2.0 x 10"11.
13
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3.3 Studies of Wind Erosion
3.3.1 Mechanisms of wind erosion
Wind erosion of soil and other finely divided materials is caused
by three processes: saltation, suspension, and surface creep.
Saltation refers to the movement of particles which jump or bounce
a few inches above the surface. These particles are ejected from
the soil surface at a steep vertical angle, fly a short distance
and then fall back down. Particles subject to saltation are
generally between 0.1 and 0.5 mm in diameter. Upon impact, they
are likely to bounce and also dislodge other particles, which may
saltate, creep or become suspended, depending on the size of the
target particle. The resulting avalanche increases the rate of
erosion as the cascade proceeds downwind.
Suspension refers to the atmospheric entrainment of particles less
than about 0.1 mm in diameter. Such particles constitute an
aerosol which can remain in the atmosphere and be carried for large
distances. It is belj-eved that Minri-inrhintsH g^gpgns-irm ig r-aiig«a^
^ntirely by saltating particles. Creep refers to the sliding or
"rolling "motion of particles greater than about 0.5 mm in diameter,
which are too heavy to leave the ground but are pushed by the wind
and the impact of smaller particles. These mechanisms are
summarized in Table 3-2, below.
Table 3-2 Wind Erosion Mechanisms vs Particle Size
Mechanism: Suspension Saltation Creep
Particle Size (mm): < 0.1 0.1 - 0.5 0.5 - 1.0
14
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3.3.2 Characterizing wind erosion studies prior to 1984
Earlier wind erosion studies laid the groundwork for the
intensified efforts to develop methodologies to predict emissions
of wind-blown particulates during the past ten"years. Smith et al.
(SMI82) reviewed but did not evaluate 15 models developed prior to
1982. Smith and Whicker (SMI83) performed a quantitative
comparison of five models, using a hard-rock thorium ore stockpile
as a hypothetical source. The models were judged on the basis of
availability of required data and sensitivity to critical input
parameters. No comparisons of model predictions with measured
emissions were performed. The combined suspension model of Travis,
a version of which was incorporated into the NRC codes UDAD, FGEIS
and MILDOS, as well as these three codes (treated as a single
model), were found to be the most suitable ones for the particular
case studied.
Gillette (GIL83a) summarized determinations of the minimum wind
stresses, expressed as threshold friction velocities, necessary to
initiate wind erosion events in arid soils. (Friction velocity is
an abstract concept used to characterize the vertical wind speed
profile in the lower atmosphere. A detailed explanation is
presented by Randerson in DOE84, Ch. 5). Gillette concludes that
the threshold velocity in non-crusted soils is related to the
aggregate size distribution of particles on the soil surface. He
also discusses the behavior of crusts on soil surfaces and the
mechanisms by which such crusted soils become erodible.
Gillette and Cowherd (GIL83b) discuss the role of resuspension
rates in estimating fugitive dust emission and soil erosion and
present a simple model based on this concept. The model assumes a
simple form when applied to emissions from rapidly depletable
sources such as dust deposited on paved roads or piles of coal
dust. In determining long-term emissions from a source with a deep
layer of erodible material, such as agricultural soils, the
resuspension rate concept no longer applies and a different
formulation is presented. This latter model is a simplified form
of the Wind Erosion Equation developed by the U.S. Department of
Agriculture (DOA), and is a forerunner of the "unlimited" erosion
potential model discussed below.
A study by the Dynamac Corporation (EPA83) concluded that, as of
1983, no model had been validated for predicting chronic windblown
particulate emissions. The report had the most optimism about the
DOA Wind Erosion Equation, but cautioned that further work was
needed to determine the input parameters that would be applicable
to waste sites.
15
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3.3.3 DOA Wind Erosion Equation
The Soil Conservation Service (SCS) of the DOA has developed a
procedure for estimating annual soil loss due to wind erosion, the
aforementioned Wind Erosion Equation. This equation expresses the
soil loss as a function of five empirical factors, each of which is
determined either by algebraic relationships between measurable
parameters or by means of maps, nomograms or numerical tables. The
Wind Erosion Equation combines soil losses due to the three
processes of saltation, suspension, and creep.
The National Agronomy Manual (DOA88) presents detailed
instructions, including the necessary charts, tables and graphs,
for determining or estimating each of the parameters that enter
into the equation. The form of the functional relationship between
the soil loss and the five parameters is not presented in the
manual - the actual erosion estimates may be obtained from tables
generated by the Agricultural Research Service's (ARS) WEROS
computer program. (Given the large range of values of the input
parameters, several hundred individual tables might be required.)
Alternatively, soil losses can be calculated with a slide-rule
calculator called the Wind Erosion Calculator, which was developed
by SCS, ARS and the Graphic Calculator Company for this purpose.
A simplified version of the equation is shown as eq. (4-1), below.
16
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Chapter 4
EPA-ADOPTED PARTICULATE EMISSION MODELS
During the past decade, Cowherd et al., with the Midwest Research
Institute (MRI) ; have conducted field investigations of particulate
emissions from various sources. These studies, sponsored by the
EPA, have led to the development of emission models for a number of
sources. An early discussion of these models appears in a report
on the rapid assessment of exposure to particulates (EPASSa). Most
recently, the EPA has issued standardized guidance to support the
planning of remedial action activities at NPL sites (EPA93a, b).
One of the documents provides the methodology for deriving
particulate emission factors. Finally, additional models are
presented in Control of Open Fugitive Dust Sources (EPA88a). Many
of the models from the latter report are reproduced in the guidance
document for hazardous waste TSDF (EPA89a). A number of the models
have been incorporated into the Compilation of Air Pollution
Emission Factors, AP-42 (EPA85b, EPA88b, EPA90, EPA91). Relevant
excerpts from the cited documents are attached to the present
report.
4.1 Natural Occurrence: Wind Erosion
4.1.1 Open Areas
In the course of their studies of particulate emissions, Cowherd et
al. have developed models for the release of fugitive dust caused
by wind erosion of open areas (EPA85a, EPA88a, EPA89a). Areas are
characterized as having either a "limited" or an "unlimited" wind
erosion potential. An example of an area with an "unlimited"
potential would be a smooth field, devoid of vegetation, and
covered with a thick layer of loose sandy soil. In such a field,
relatively low wind speeds will cause suspension by the action of
saltating particles, as described in para. 3.3.1, above. Because
of the large reservoir of erodible particles, the erosion rate will
vary as a function of the wind speed, and will not appreciably
decrease with time. An example of an area with "limited" potential
would be an inhomogeneous field covered with a high density of
gravel, rocks or clumps of vegetation. Because they are partially
sheltered from the wind and from the cascade of saltating
particles, the fine particles interspersed among these non-erodible
elements require higher wind speeds for suspension. Once such
winds occur, the supply of erodible particles is quickly exhausted
and emissions stop until the area is disturbed and a fresh supply
of suspensible particles is brought to the surface.
A detailed procedure for determining the erosion potential of a
particular area is presented in EPA88a, pp. 6-1 to 6-7. Note that
in the next to last paragraph on p. 6-2, in the line beginning
"catch amounts, following ... ", "Figure 6-1" should be "Figure
6-2". A more legible version of Fig. 6-1 (p. 6-3) can be found in
17
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EPA89a, p. 4-9. Appendix C.3, EPA88b, presents a silt analysis
procedure that might also be used for the soil analysis discussed
in EPA88a.
The methodology for determining emissions from areas of "limited"
erosion potential is described in EPA88a, para. 6.1.1. Numerical
values for the erosion potential function described in Eg. 6-3 on
p. 6-11 are tabulated on p. 4-8. An identical discussion is found
in EPA89a, para. 4.2.1. This material was also incorporated into
AP-42 (EPA90, para. 11.2.7). It is nonetheless necessary to refer
to EPA89a, since the graph (EPA89a, p. 4-9) needed for determining
the threshold friction velocity was omitted from AP-42.
The methodology for areas of "unlimited" erosion potential is a
simplified version of the DOA Wind Erosion Equation. A detailed
explanation of the procedure appears in EPA88a, para. 7.1.2, where
the following version of the equation is presented:
E = k a I K C L' V (4-1)
E = soil particles lost to wind erosion, tons/acre/year.
k = particle size factor, unitless.
a = TSP fraction of soil lost to wind erosion, unitless.
I = soil erodibility, tons/acre/year.
K = surface roughness factor, unitless.
C = climatic factor, unitless.
L' = unsheltered field width factor, unitless.
V' = vegetative cover factor, unitless.
The specific sections of EPA88a are contained in Attachment 1.
The following modifications to the procedure are recommended.
Eq. (4-1) should be modified by the inclusion of three
additional factors; R, A, and c. The new form of the
equation is then:
E =k-a-I-K-C-L'-V'-R-A'C (4-2)
E = annual emission of total suspended particulates (TSP), kg.
R = knoll erodibility adjustment factor, unitless.
A = site area, m2.
c = conversion factor, ton/acre to kg/m2 = 0.224.
Where the other terms are as previously defined.
Retaining R, the knoll erodibility adjustment factor, in the
original DOA equation enables a more accurate estimate of TSP
emissions from short, windward-facing slopes which have an
increased wind erosion potential. Values for R are listed in
DOA88, Table 502-1 (p. 502-18) - their use is explained in the
accompanying text (pp. 502-16/18) (see Att. 4 to the present
18
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report) . R = 1 if a knoll erodibility adjustment is not
appropriate or if the required data is not available.
Inserting A, the area of the site, converts the emission rate
per unit area to total annual emissions.
The factor c is a calculated value which converts tons/acre to
kg/m2 . The value k = 1 should be used to calculate the annual
emission of TSP. (The value of k = 0.5 is recommended in
EPA88a to determine the respirable fraction.)
Fig. 7-4 (EPA88a, p. 7-13), a map for determining the climatic
factor (C) , should be replaced with the updated - and much
more legible - four-color map in DOA88, Exhibit 502.63(a). To
calculate C from local meteorological data using Eq. 7-2
(EPA88a, p. 7-10), the formula for the factor PE
(Thornthwaite's precipitation - evaporation index) might be
more easily evaluated by the formula on p. 502-24 (DOA88),
than by the one on p. 7-12 (EPA88a) - both formulae should
yield the same value.
The factor V, a function of the amount of vegetative cover,
may be the most difficult to estimate, especially since the
amount of cover will usually vary during the course of a year.
Assigning this factor a value V = 1 (i.e., no cover) will
result in a conservative estimate of the emissions.
The DOA is currently developing a new Wind Erosion Prediction
System to replace the Wind Erosion Equation. The MRI is currently
updating para. 11.2 of AP-42, which deals with fugitive dust. The
MRI has advised the EPA to wait for the DOA to complete the
development of the new system before including the soil erosion
model in AP-42 (KIN92).
4.1.2 Open waste and storage piles (except uranium ore and mill
tailings)
The guidance for calculating fugitive dust emissions due to wind
erosion of open waste piles (EPA89a) presents methods which are
identical to the ones for open aggregate storage piles described in
EPA88a and reproduced in EPA90. These methods include the method
used for open areas with "limited" erosion potential, with the
additional consideration of the height and contour of the pile, as
well as a separate method for continuously active piles. The
methods are described in detail in EPA88a, para. 4.1.2 and para.
4.1.3 as well as in EPA89a, para. 3.2.2 and para. 3.2.3. As
mentioned before, these methods also appear in AP-42 (EPA90, para.
11.2.7) but the omission of a needed figure, as well as some errors
in the formulation of mathematical expressions in the discussion of
waste piles, make this document impractical to use.
19
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4.1.3 Uranium ore and mill tailings
NRC Regulatory Guide 3.59 (NRC87, pp. 3.59-11/14) presents the
methodology for estimating fugitive radionuclide emissions from
uranium mill tailings and ore pads. This release model was
validated by measurements at uranium storage sites and is therefore
preferable to the generic dust release models discussed above.
The principal parent radionuclide (U-238) in ores is assumed to be
in secular equilibrium with its progenies. The following
radionuclides are assumed to be in secular equilibrium with U-238,
they include: Th-234, U-234, Th-230, Ra-226, Pb-210, Bi-210, Po-
210. Radioactive daughter products with half-lives of less than
five minutes as well as Pa-234, which has a branching ratio of
0.16%, are excluded from this list, since they are unlikely to pose
significant health risks in comparison to the more abundant or
longer lived species. Radon (Rn-222), which is exempt from the
regulations of 40 CFR 61, Subpart H, is also excluded; however,
radionuclides resulting from the decay of Rn-222 in piles which are
subsequently blown offsite are not exempt and are included in the
list. Not all of the radionuclides listed pose a significant
health risk, for example, Regulatory Guide 3.59 mentions only U-
234, Th-230, Ra-226, Pb-210, and Po-210.
4.1.4 EPA Soil Screening Guidance
In support of its activities on remedial action at NPL sites, the
EPA issued standardized guidance establishing soil screening levels
(SSL) for various exposure pathways and contaminants (EPA93a). The
primary purpose of the SSL is to accelerate the decision making
process by determine whether a contaminated site requires further
considerations under CERCLA. One of the SSL criteria provides the
methodology for deriving particulate emission factors (PEF). The
PEF represents an annual average emission rate for sites of varying
sizes and aspect ratios for rectangular sites. The PEF were
derived using normalized (via regression analysis at the 95% upper
confidence level) mean concentrations of the contaminants based on
unit soil concentration (mg/kg) . The PEF also reflect the
configuration of the site, size, receptor location, and
representative meteorological data. The methodology, look up
tables, and factors for various sites are described in a companion
document (EPA93b).
The particulate emission factor is derived as follows:
PEF = (Q/C) 3600 -5- [0.036 (1-G) (U,/Ut)3 F(x) ] (4-3)
PEF = particulate emission factor, m3/kg.
Q/C = inverse of mean concentration at the center of a site,
g/m2-s per kg/ra3.
G = fraction of vegetative cover, unitless.
Um = mean annual wind speed, m/s.
20
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Ut = equivalent threshold value of wind speed at 10 m, m/s.
F(x) = wind function dependent upon the ratio of uyu^ based
on Cowherd (EPA85a).
3600 = seconds per hour.
0.036 = assumed respirable fraction, g/m2-h.
For a 30-acre site, particulate emission factor is:
3.85 x 10*8 m3/kg, or equivalent to: 2.6 /*g/m3;
assuming the following arbitrary parameters:
Q/C = 40.7 g/m2-s per kg/m3.
G = 0.5
Um =4.9 m/s
Ut =11.3 m/s
F(x) = 0.259
It should be noted that this methodology does not provide the means
to directly derive PEFs even if site contaminant levels are known.
However, the method does provide the means to evaluate the relative
impact of site configuration, size, and receptor location on
particulate emission rates. The evaluations may be conducted by
using look up tables, equations, and regression curves contained in
the companion document (EPA93b) to the SSL guidance fact sheet.
Finally, the results could be used to approximate airborne
concentrations by multiplying the particulate emission factor by
the average specific activity of each radionuclide. For example,
assuming a soil 226Ra specific activity of 1 pCi/g and the PEF
derived above, would yield an average airborne concentration of:
CRa =2.6 /xg/m3 10'6 g//zg 1 pCi/g
CRa = 2.6 x 10'6 pCi/m3
4.2 Soil and Material Handling
The methodology for estimating fugitive dust emissions from soil
and material handling operations is based on actual measurements
taken while the activity was in progress. Many of the studies were
performed by the MRI, under contract with the EPA. Most of the
data were taken using the exposure profiling technique. Multipoint
near-source ambient measurements are made over 90% of the effective
cross-section of plume at a location typically five meters downwind
of the source. In the case of a virtual point source (a stationary
activity confined to a small area), a two-dimensional array of
samplers was employed, while for a line source (e.g., an unpaved
road with vehicular traffic), a one-dimensional vertical array was
used. Simultaneous measurements of wind velocities were made at
various points to produce a wind profile, assuming a logarithmic
wind speed distribution. After the data was gathered, each
21
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individual concentration value was combined with the calculated
wind speed at the sampler location and converted to an exposure
value in units of g/m'-s. The total mass flux from the source was
determined by performing a numerical integration, spatially
integrating the concentration over the effective cross-section of
the plume (KIN92).
The fugitive dust emission factors cited in the following
paragraphs, most of which appear in AP-42, represent the latest
published information (Note: The AP-42 report is scheduled for
revision in late 1994). The MRI is in the process of carrying out
many more tests, so that a large increase in the data base is
expected in the next few years.
The AP-42 report also presents information on control methods, see
Section 4.4, below.
4.2.1 Soil removal and haulage
Cowherd et al. (EPA89a, p. 3-5) cite a formula for estimating
fugitive dust emissions from adding or removing materials from an
open waste pile. This is the same formula that is presented in
AP-42 for aggregate handling and for continuously active storage
piles (EPA88b, para. 11.2.3.3). This general procedure should be
applied to estimating fugitive emissions from the operation of
removing soil from storage piles. The movement of trucks on site
should be modeled by the emission factors for unpaved roads, a
discussed in para. 4.3.1, below.
4.2.2 Grading and shaping of soil
Cowherd et al. (EPA89a, para. 5.2.1) recommend an emission factor
for lift construction at hazardous waste landfills which was based
on field measurements of emissions from bulldozing the overburden
at Western coal mines. The emission factors for different size
particles are found in EPA91, p. 8.24-4. These emission factors
should be applied to the grading and shaping of soil on site.
4.2.3 Agricultural tillage and seeding
AP-42 (EPA85b, para. 11.2.2) describes the methodology for
estimating fugitive dust emissions from agricultural tilling. This
method should be used to estimate the emissions during the phase of
site reclamation when soil is being prepared for seeding. Grading
operations are discussed in the preceding section.
4.2.4 Building demolition and material disposal
In the absence of specific data for dismemberment of buildings,
Cowherd et al. (EPA88a, p. 5-3), recommend the use of the materials
handling equations cited in para. 4.2.1, above. The loading of the
debris following demolition is modeled in EPA88a, para. 5.1.2.3 by
22
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the following equation:
Ed = 0.029 L (4-4)
E = TSP emission, kg/m2.
L = waste material load per floor space unit area, Mg/m2.
= 0.45 (default value in absence of site-specific data)
0.029 = default average emission factor, kg/Mg.
Any pushing operations (e.g., use of a bulldozer) related to the
demolition should be modeled by the method described in para.
4.2.2, above. Default values are presented in EPA88a, para.
5.1.2.5. The emissions resulting from the on-site movement of
trucks should be estimated according to the methods for unpaved
roads, described in para. 4.3.1, below. EPA88a, para. 5.1.2.4
lists default values to be used if site-specific data are
unavailable.
In practice, however, emission rates may be mitigated to reduce
fugitive releases to limit exposures to workers or meet
environmental protection standards. The facility may be
decontaminated before the onset of the demolition work. A temporary
containment may be erected over the facility being demolished, or
water may be used as a wetting agent to reduce dust loadings.
Accordingly, these measures may result in lower emission rates.
The emission rate can also be modified to account for the total
area of the facility being demolished. Equation (4-4) is then
modified as follows:
Edm = °*029 ' L M A (4-5)
Edm = TSP emission, kg/event.
M = Mitigation factor, unitless.
A = building or total floor space area, m2 .
Where the other terms are as previously defined.
4.3 Non-intrusive Action
4.3.1 Vehicular traffic on unpaved roads
The methodology for calculating fugitive dust emissions from
vehicular traffic over unpaved roads is presented in AP-42 (EPA88b,
para. 11.2.1.2). Cowherd et al. (EPA88a, p. 3-4), while agreeing
that the AP-42 method is acceptable for continuous traffic,
recommend using a value of zero for the number of days with
measurable precipitation to arrive at a conservative estimate of
annual emissions due to intermittent traffic. A good general
discussion of this topic is presented in EPA88a, Ch. 3, while a
similar discussion, focused on hazardous waste TSDFs, is found in
EPA89a, para. 2.2.
23
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4.4 Control Methods
The AP-42 (Sect. 11.2) report also presents information on the use
of control methods to reduce emissions rates (EPA85b). Typical
control methods include the use of water, chemical binders,
vegetation covers, windbreaks, and enclosures. Water, as a wetting
agent, is most commonly used, but the reduction is short-lived.
Water acts as dust suppressant by forming cohesive moisture films
among grains of soil. Chemical binders, however, provide longer
lasting reductions. Between applications, the effectiveness of
such dust suppressants decreases with increasing traffic. Other
competing forces include evaporation and drainage or migration to
deeper soil layers. The use of binders may be problem as it may
have adverse effects on soils, plants and result in the
introduction of other contaminants. The use of windbreaks and
enclosures are relatively more expensive and their effectiveness
must be evaluated for each application. Table 4-1 summarizes some
of the information presented in the AP-42 report.
Table 4-1 Summary of AP-42 Emissions Control Measures'8'
Conditions
Methods
Effectiveness
Reduction
Unpaved roads
Agricultural
soil
Storage piles
Heavy construc-
tion
Paved roads
water
chemicals
vegetation,
windbreaks
watering &
chemicals
twice daily
watering
watering
twice/wk
short-lived
longer-lived
varying
good
fair
fair
not significant
some benefit
not significant
up to 90%
up to 50%
up to 50%
(a) Extracted from Section 11.2 of AP-42 report (EPA85b).
24
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Chapter 5
MECHANISMS OTHER THAN RESUSPENSION
5.1 Evaporation From Ponds and Lagoons
The evaporation of water from ponds and lagoons is governed by the
air temperature, vapor pressure, dew point, wind speed, and
insolation. Complex relationships have been developed to estimate
the evaporation rates for lakes and the so-called "pan evaporation
rates." Tables have been developed to provide the information by
state or climatic regions of the U.S. Among other sources, this
information is available in the Water Encyclopedia (Table 2-48 and
Fig. 2-11, LEE90) and in an EPA report (Fig. 5-1, EPA88a) . The
Water Encyclopedia also provides the methodology to calculate lake
and pan evaporation rates when site specific data are available
(Table 2-49, LEE90).
AP-42 and other EPA documents provide guidance for estimating the
emissions of volatile organic compounds from waste-water treatment
facilities and other sources. These models are not applicable to
the emission of some nuclides, .e.g., tritium, which is not an
organic compound.
5.1.1 Evaporation models
Tritium, in the form of tritiated water (HTO), is the principal
radionuclide which can be released by evaporation or volatilization
from open bodies of water such as ponds and lagoons. (HTO is
simply H20 with one of the hydrogen atoms, 1H replaced by tritium,
3H). Since HTO is chemically almost indistinguishable from water
(there are some very slight differences in the chemical properties
of different isotopes), the most appropriate way to model its
release is to assume that the water vapor emitted from the surface
of a pond has the same specific activity of tritium as the water in
the pond itself.
Wing (NRC79) surveyed several evaporation models and compared the
published experimental observations on the evaporation of water
from drying trays with the model predictions (see Attachment 5 in
this report.) Only the equation from a work by Eckert and Drake
yielded a rate that was within 10% of the experimental results.
Wing then used this equation to calculate annual evaporation rates
at ten different locations in the U.S., using annual average values
of wind speed, temperature and relative humidity, and compared the
results with measured evaporation rates. Although the model
calculations were lower than the published data in nine of the
cases, the worst prediction was only 47% below the actual value.
Using annual average meteorological conditions rather than using
hourly data and integrating the evaporation rate over the entire
year may have contributed to the discrepancy.
25
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Several factors which appear in the published equation have been
combined, while other factors, representing physical properties of
air and water, have been replaced with accepted values of these
properties. The result is the following formula for the
evaporation rate from a circular pool:
E = 20.73 Ps A0'9 U°'8 4- T1'47 (5-1)
E = evaporation rate of water, g/s.
A = surface area of pool, m2 .
PS = equilibrium vapor pressure of water at ambient
temperature, mm Hg.
U = wind speed, m/s.
T = absolute temperature, in *K.
= ambient temperature, in *C + 273.2.
This model assumes that the water and air are at the same
temperature, ignoring that evaporative cooling would tend to reduce
the vapor pressure and hence the evaporation rate. The net
evaporation rate is a balance of evaporation from the surface and
condensation onto the surface from the ambient water vapor in the
atmosphere. However, only the one-way process, which will be called
the surface volatilization rate, is the pathway for the release of
tritium, as ambient water vapors are assumed to be free of tritium.
This rate corresponds to evaporation under zero ambient humidity
and is conservative, since in reality some of the tritiated vapor
will recondense, reducing the net flux.
To calculate the emission rate of tritium, the evaporation rate
multiplied by the specific activity of tritium in the water.
R = E a (5-2)
R = emission rate of tritium, pCi/s.
E = evaporation rate of water, g/s.
a = specific activity of tritium in water, pCi/g.
Ideally, the annual emissions should be calculated by integrating
the emission rate, using hourly average wind speeds and
temperatures and specific activities measured at various times
during the year.
The concentration of tritium in the atmosphere is governed by the
presence of airborne water vapors. There is no significant
fractionation when mixing natural and tritiated water. However,
some fractionation may occur when tritiated and natural water pass
across a liquid-gas interface. Because of the difference in mass,
the vapor pressure of tritiated water is about 90% that of normal
water at environmental conditions. The concentration of tritium in
the atmosphere (pCi/m3) is dependent on the concentration of
tritium in atmospheric water (pCi/L) and absolute humidity. This
relationship is expressed as:
26
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Cfl = Cw Hfl 10'3 (5-3)
Ca = tritium concentration in the atmosphere, pCi/m3.
Cu = tritium concentration in atmospheric water vapor, pCi/L.
H = absolute humidity, g/m3.
10"? = conversion factor, L/g for water.
The concentration of tritium in atmospheric water may be obtained
by sampling and analysis (NRC83) or estimated to reflect specific
processes or release mechanisms. Absolute humidity values are
known to vary significantly depending upon geographical locations
and seasons, ranging from 3.0 to 16.5 g/m3 in the continental U.S.
(NRC83, Fig. 2.15). The NRC uses a default value of 8.0 g/m3, when
site specific data are not available (NRC79). More information on
the behavior of tritium is provided in NCRP Report No. 62, Tritium
in the Environment (NCRP79).
Some DOE facilities, notably the Nevada Test Site, calculate
tritium emissions by assuming that the entire tritium activity that
is discharged into the pool during the year evaporates during the
same year (DOE92) . This method is said to be conservative in that
loss of tritium through ground seepage is neglected. This would be
a valid method if neither the volumes nor the specific activities
of the liquid effluents varied from year to year, and if the volume
of the pool and specific activity of the water in the pool remained
constant. Both methods may be used and the results that yield the
more conservative estimate should be reported as the emission rate.
Another method would be to estimate the evaporation rate by using
evaporation rates from the Climatic Atlas of the United States,
published by the U.S. Department of Commerce. These data may not
be current, however, nor sufficiently site-specific. Furthermore,
they represent only the net evaporation rate - their use may
therefore produce an underestimate of the tritium release rate, as
discussed above.
5.1.2 Wet-Cooling Towers
Wet-cooling towers are heat-exchangers used to dissipate large heat
loads from industrial processes. Water is used as the medium to
transfer heat away from coils, in which process fluids flow. Under
normal conditions, the two fluids never mixed. In the event of a
leak, the cooling fluid may become contaminated by the process
fluid. Within the tower, some of the cooling fluid are drawn up as
droplets by convection currents and are released as "drift"
droplets. The droplets are then carried downwind. On the other
hand, the larger droplets settle out of the air and deposit near
the tower. Some towers are equipped with drift or mist eliminators
to minimize such emissions.
27
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As the water evaporates, the droplets leave behind fine particulate
matter formed by the crystallization and agglomeration of dissolved
solids. Dissolved solids may include minerals, chemicals from
corrosion and algae inhibitors, etc. Emissions from cooling
towers, therefore, might be modeled as PM.0 particulates (EPA91).
Given that the size of the droplets vary, it also follows that the
fine particulate matter formed by dissolved solids would have its
own particle size distribution.
In Supplement D to AP-42, the EPA has estimated that the overall
emission rate to be about 2.3 x 10"3 g per L of circulating water
flow, based on limited data for induced draft cooling towers (Sect.
11.4, EPA91). However, no data were provided for natural draft
cooling towers. The rate given by the EPA is also believed to be
typical of older towers with less efficient mist eliminators.
The emission of radioactivity from wet-cooling towers is further
complicated by the possible speciation of radioactivity in the
circulating water. For example, some radionuclides, such as
uranium, cesium, iodines, etc., may chemically bind with minerals
or chemical inhibitors. Furthermore, it is not clear if further
nuclide speciation takes place once the fine particulate matter is
formed by the dissolved solids left by the droplets. On the other
hand, tritium and noble gases (e.g., xenon, argon, radon, etc.),
may be most efficiently dispersed, since by design, cooling towers
work as very effective aerators.
Given these various considerations, estimating release rates from
wet-cooling towers (either mechanically induced or by natural
draft) may have to addressed on a case-by-case basis.
5.2 Evaporation from contaminated soil
Evaporation or volatilization could be a significant release
mechanism of radioactivity from contaminated soils where water
contaminated by tritium or carbon-14 has been spilled or otherwise
released.
5.2.1 Saturated soil
The Superfund Exposure Assessment Manual (EPA88c) recommends that
spills of liquid contaminants where liquid pools are visible on the
soil surface or where the soil is saturated from the surface on-
down be modeled in the same manner as open liquid storage pools.
This is also the most conservative model - models for the release
of contaminants from the pore spaces in the soil predict lower
release rates. Furthermore, the soil release models require data
or assumptions regarding the time-dependent contaminant
concentrations and depth profiles.
28
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It is therefore recommended that atmospheric releases of tritium
from soils contaminated with tritiated water be modeled in the same
way as pools containing tritiated water (see para. 5.1.1).
Another model used to assess the amounts of radioactivity released
from contaminated soils relies on the evapo-transpiration rate of
water. This model, developed by DOE, is used for both tritium and
carbon-14. The model is documented in the RESRAD computer code
(DOE93, App. E and L). The model assumes that tritium exhaled by
plants is negligible. However, for carbon-14, plants are the sink
since atmospheric UCO2 is incorporated during photosynthesis.
The model for tritium and carbon are similar, the only difference
being on how the tritium and carbon flux rates are derived. The
following equation applies to both, tritium and carbon-14.
Ci = Wfd FJ /A 3.17 X 10'8 + Hmjx Uw (5-4)
Ci = Average airborne concentration over area, pCi/m3.
Wfd = Wind frequency for receptor location, unitless.
Fj = Contaminant flux rate from soil, pCi/m2-y.
A = Size of contaminated area, m2.
Hmjx = Mixing height within which contaminant is uniformly
distributed, 2 m for human inhalation.
Ug = Annual average wind speed, m/s.
3.17 x 10"8 = conversion factor, y/s.
For tritium, the flux rate, F,, is derived as follows:
Fj = WT Et 106 (5-5)
Fj = Contaminant flux rate from soil, pCi/m2-y.
WT = Tritium concentration in soil water, pCi/m3.
106 = Conversion factor, cm3/ro3.
Et = Ce [(l-Cr) Pp + Ir] (5-6)
Et = Evapo-transpiration rate, m/y.
Ce = Evaporation coefficient, unitless.
Cr = Runoff coefficient, unitless.
Pp = Annual rainfall rate, m/y.
Ir = Irrigation rate, m/y.
DOE assumes a default evaporation coefficient of 0.5. For the
runoff coefficient, values range from 0.1 to 0.4 for agricultural
soils and woodlands and 0.4 to 0.65 for urban environments.
29
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For carbon-14, the flux rate, F., is derived as follows:
F, = Sc Ec Pb ds 106 (5-7)
F; = Contaminant flux rate from soil, pCi/m2-y.
Sc = Carbon-14 concentration in waste, pCi/g.
pb = Soil bulk density, g/cm3.
ds = Soil depth, m.
106 = Conversion factor, cm3/m3.
Additional information of the behavior of tritium and carbon-14 may
be obtained from NCRP Reports No. 62 (Tritium in the Environment)
and No. 81 (Carbon-14 in the Environment) (NCRP85, 79).
5.2.2 Subsurface contamination
In cases where the surface layer of the soil is dry and devoid of
tritium, but tritiated water remains below the surface, Eq. 2-3 in
EPA88c (p. 16) can be used to calculate a more realistic release
rate than that produced by the surface evaporation model. The
value of the diffusion coefficient of water vapor in air, required
in Eq. 2-3, is 0.2 cm2/sec. The.soil porosity and the saturation
vapor concentration must also be determined. The expression is as
follows:
E, = D, Csj A (Pt)4/3 [M, + dsc] (5-8)
EJ = Emission rate, g/s.
Dj = Diffusion coefficient, 0.2 cm2/s.
A = Contamination area, exposed, cm2.
Mj = mole fraction of contamination in soil, unitless.
dsc = Effective depth of soil cover, cm.
Pt = Total soil porosity, unitless.
= 1 - ft -5- p (5-9)
/? = Soil bulk density, g/cm3.
p = Particle density, g/cm3.
C . = Saturation vapor concentration, g/cm3.
SI
= P MW. -5- R T (5-10)
P = Vapor pressure of contaminant, mm Hg.
MWj = Molecular weight of contaminant, g/mole.
R = Molar gas constant, 62,361 mm Hg-cm3/mole-°K.
T = Absolute temperature, °K.
30
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5.3 Gaseous and Other Types of Emissions
Radionuclides are released at the Nevada Test Site (NTS) during
re-entry drilling and by ground seepage of noble gases following
underground nuclear detonations. (Some of the following
information is based on informal telephone conversations with a
number of EPA staff and DOE contractor personnel at NTS.)
5.3.1 Re-entry drilling
Within one to two days of an underground nuclear test, a hole is
drilled into the hollow chamber created by the explosion to sample
the non-fissioned material and determine the fission yield. During
this process, called "drillback", radioactive halogens in gaseous
form (principally 1-131) and noble gases (Xe-133 and Kr-85) are
sometimes released. Although emanating from a small area (a
virtual point source), these releases are uncontrolled and not
directly monitored. They may therefore be classified as fugitive
(though not necessarily diffuse) emissions.
Apps. 1 and 2, DOE92, describe the drillback operations conducted
by the Lawrence Livermore (LLNL) and Los Alamos National
Laboratories (LANL). LLNL uses measurements of the radiation field
in the vicinity of the drill pipe and ambient air samples to
estimate the effluent activity. This estimate is then verified by
the alternative method of measuring radionuclide concentrations in
downwind air samples and using local wind data to calculate the
release rate.
LANL, which uses a different drillback system, samples the ambient
air in the work area on top of the drillback platform (LANL92) . If
leakage of radioactive gases is suspected, samples are also taken
from the "cellar", the subsurface excavation housing the
containment equipment. Data collected during the LOCKNEY
drillback, at which time a large amount of activity was released,
were used to derive a procedure for inferring effluent activities
from air sampling measurements. LANL estimates that the releases
calculated by this procedure are within a factor of three of the
actual amounts for modest releases, and within an order of
magnitude for small ones.
5.3.2 Ground seepage of noble gases
Seepage of radioactive noble gases is sometimes observed in the
Pahute Mesa test area, beginning a week or more after an
underground nuclear explosion. An analytical model to explain and
quantify this seepage is being developed (NIL91, NIL , BUR89).
According to the current understanding of the release mechanisms,
the collapse of the cavity created by a nuclear detonation creates
a rubblized zone, called the chimney, immediately above the cavity.
If the volcanic rock above the chimney contains fractures,
31
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radioactive noble gases can leak to the surface. The normal
cyclical changes in barometric pressure cause the atmosphere to act
as a piston, driving air into the fractures or drawing gases
contained in these fissures. The rock thus breathes, inhaling air
and exhaling gaseous radionuclides. However, the observed seepage
is inconsistent and the phenomenon is not yet fully understood.
The purpose of the model is to quantify the releases due to this
natural mechanism and thus ascertain the integrity of the
containment, in compliance with regulatory standards and
international treaties.
It should be noted that the seepage of noble gases may best be
characterized by sampling, followed by analysis. Some of the major
limitations in conducting this type of sampling include the proper
selection of sampling locations, orientation of the samplers to
reflect local atmospheric dispersion, effect of terrain on
dispersion, distances from seepage points to sampling locations,
and integration of the results over the area being evaluated.
5.3.3 Emissions from buildings
Emissions from buildings may occur through vents, stacks or through
natural ventilation. The mechanisms leading to such releases may be
induced mechanically (e.g., exhaust fans) or via natural means
(e.g., convection and stack effect). In simple terms, emissions can
be estimated by determining the volume of material (air, gas,
vapor, etc.) released, its concentration, and application of a
mitigation factor, if warranted. The expression is:
n
R, T, C, M, (5-11)
Eb = Sum of all releases over all events i, Ci.
R. = Release rate for event i, nr/sec.
= Duration of release i, sec./event.
0,. = Concentration of contaminants for event i, Ci/m3.
MJ = Mitigation factor, unitless.
For puff releases, the above expression is reduced to:
E = 2 V, C. M, (5-12)
Vi = Volume released in each event i, m3.
The other terms are as defined previously.
32
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The mitigation factor may be used to account for the use of devices
or process which reduce the amount of materials released. Such
devices may include HEPA filters, baghouses, scrubbers, adsorber
beds, etc.
If the release is monitored downstream of such devices to determine
actual concentrations, the mitigation factor is set to equal one.
5.3.4 Emissions from tank venting
The emission of materials released during the venting of tanks may
be estimated by determining the displaced volume of the overhead
space above a liquid. As before, the emission takes into account
the concentration of the contaminants, partition factor between the
liquid and gaseous phases, and application of a mitigation factor,
if warranted. The expression is:
Et = £ V, C. P, M, (5-13)
Et = Sum of all releases over all venting events i, Ci.
Vj = Volume released for each venting i, as displaced by
the amount of liquid added to the tank, m3, and where
Vj cannot exceed the tank's rated capacity.
Cj = Concentration of contaminants for event i, Ci/m3.
Pj = Partition factor for each contaminant, unitless.
MJ = Mitigation factor, unitless.
The partition factor may vary depending upon the contaminants,
being typically one for noble gases and less than one for
contaminants that are miscible or soluble in the liquid phase.
The mitigation factor may be used to account for the use of devices
or process which reduce the amount of materials released. Such
devices may include filters, adsorber beds, traps, etc.
If the release is monitored downstream of such devices to determine
actual concentrations, the mitigation and partition factors are
each set to equal one.
For tanks holding gaseous contaminants, the above expression is
redefined in terms of the gas volume released:
Eg = 2 V. C, P. M, (5-14)
Vj = Gas volume released in each event i, m3, adjusted to
normal temperature and pressure or as measured during
each release.
33
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The other terms are as defined previously.
The partition factor is set to equal one for noble gases and less
than one for vapors or reactive gases which may plate-out in tanks.
The mitigation factor may be used to account for the use of devices
or process which reduce the amounts of materials released. Such
devices may include filters, adsorber beds, traps, etc.
If the release is monitored downstream of such devices to determine
actual concentrations, the mitigation and partition factors are
each set to equal one.
5.3.4T1 Emissions from equipment
Emissions can also be associated with equipment used to process
radioactive materials. The emissions may be associated with built-
in system features (e.g., filtration systems) or inherent in the
process (e.g., air displaced by a waste compactor ram). The
mechanisms leading to such releases are similar to that modelled in
para. 5.3.3. As before, the expression is:
E = Z Rj T. C,. MJ (5-15)
i
Ep = Sum of all releases over all processes i, Ci.
Rj = Release rate for process i, vr/sec.
Tj = Duration of release i, sec./process.
Cj = Concentration of contaminants for process i, Ci/m3.
MJ = Mitigation factor, unitless.
The mitigation factor may be used to account for the use of devices
or process which reduce the amount of materials released. Such
devices may include HEPA filters, baghouses, scrubbers, adsorber
beds, etc.
If the release is monitored downstream of such devices to determine
actual concentrations, the mitigation factor is set to equal one.
34
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Chapter 6
GUIDANCE ON ENVIRONMENTAL MONITORING PROGRAMS
TO DEMONSTRATE COMPLIANCE WITH THE DOE NESHAPS
6.1 Summary of NESHAPS Requirements
Paragraph (b)(5) of 40 CFR 61.93 states that the use of
environmental measurements at critical receptor locations to
demonstrate compliance with the standard is subject to prior
approval of the EPA (EPA89a). Applications for approval must:
1) include a detailed description of the sampling and
analytical methodology, and
2) show how the following criteria will be met:
i) Measurements shall be made at locations of the
critical receptor.
ii) The air at the point of measurement shall be
continuously sampled for the collection of
radionuclides.
iii) The radionucl ides released that are the major
contributors to the effective dose equivalent must
be collected and measured.
iv) Radionuclide concentrations that would cause an
effective dose equivalent greater than or equal to
10 percent of the standard shall be readily
detectable and distinguishable from background.
v) A quality assurance program shall be conducted that
meets the requirements described in Appendix B,
Method 114, 40 CFR 61.
6.2 Sampling and Analytical Methodology
The stack monitoring and sample collection methods described in
Method 114, Section 2, and the radionuclide analytical methods
listed in Method 114, Section 3, can, in general, be applied to
environmental measurement of airborne radionuclides. If the method
provided in the application does not conform to Method 114, the
procedure for the alternate method must be submitted to the EPA for
further review and decision on its applicability.
Table 6-1 lists the half-lives and modes of decay of the principal
radionuclides released at DOE facilities and identifies the
physical state of each. Consideration of these physical parameters
is necessary to establish whether environmental monitoring for
determining compliance will be feasible.
35
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Table 6-1 Physical Parameters of Selected Primary Radionuclides
Decay Mode
Particulates
U-234
U-235
U-238
Pu-238
Pu-239
Am-241
K-40
Co-60
Sr-90
Sb-125
Pb-212
H-3 (H2)
C-ll
N-13
C-14 (C02)
0-15
Ar-41
Kr-88
Xe-133
H-3 (H20)
2.4 E+5 yr
7.1 E+8 yr
4.5 E+9 yr
8.8 E+l yr
2.4 E+4 yr
4.3 E+2 yr
1.3 E+9 yr
5.3 E+0 yr
2.9 E+l yr
2.7 E+0 yr
1.1 E+l hr
Gases
1.2 E+l yr
2.0 E+l min
1.0 E+l min
5.7 E+3 yr
1.2 E+2 sec
1.8 E+0 hr
2.8 E+0 hr
5.3 E+0 day
Liquids/Vapors
1.2 E+l yr
Alpha
Alpha
Alpha
Alpha
Alpha
Alpha
Beta, Gamma
Beta, Gamma
Beta
Beta, Gamma
Beta, Gamma
Beta
Positron
Positron
Beta
Positron
Beta, Gamma
Beta, Gamma
Beta, Gamma
Beta
(a) See text for details.
(b) Exponential notation, 2.4 E+5 means 2.4 x 10"
36
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6.2.1 Radionuclides as particulates
The radionuclides of greatest concern at many DOE facilities, often
uranium-234 and/or uranium 238, are particulates. To sample
particulates, air is pulled through a high-efficiency particulate
filter using a calibrated high-volume air sampler. The sampling
rates (volume of air per unit of time) should be recorded
periodically, and the total volume of air sampled is based on the
average of the recorded flow rates.
For radionuclide analysis, the air filter may be equally split into
at least two halves, and each analyzed separately: 1) as a
duplicate analysis; 2) as a cross-check analysis for the QA
program; or 3) to be retained for re-analysis or conducting other
types of analyses. The volume of air sampled may be assumed to be
proportional to the mass of the filter fraction of each filter
section, unless data and filter conditions show otherwise. Also,
composite filter samples can be used for measuring long-lived
radionuclides.
6.2.2 Radionuclides as gases
Tritium, as water vapor, can be collected by the methods described
in Section 2.2.1 of Method 114. To measure total tritium in air
sample (tritiated water vapor plus elemental tritium, 2H), the
sampling system requires an oxidizing bed to convert any elemental
tritium into water followed by a zeolite bed, for example, to
absorb the tritiated water that was initially present in the air
and that formed from the oxidation of 2H in the sampling system.
Because elemental tritium may remain as 2H for extended periods of
time, the method should:
1) measure both chemical forms of tritium in the
environment; or
2) increase the measured environmental tritiated water vapor
concentration by the activity ratio (total tritium vs
tritiated water vapor), measured at the point of release;
or
3) show that concentrations of elemental tritium are
insignificant at the environmental sampling location
relative to the tritium present as water vapor.
Carbon-14 in environmental airborne samples can be considered to be
in the form of carbon dioxide (C02) and sampled as carbon dioxide,
see Method 114, Section 2.2.4.
37
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Applying cryogenic techniques to sample radioactive noble gases are
usually impractical at most locations in the environment and away
from the plant. Therefore, a sampler that collects a controlled
volume of air at specific time intervals may be acceptable and
considered a continuous sample for this purpose. Cryogenic
techniques, along with liquid scintillation counting, may be used
to separate and measure noble gases, as see Method 114, Section
2.2.3.
Again, it may not be practical, nor possible, to collect and
measure short-lived gaseous radionuclides in environmental samples.
These radionuclides are primarily oxygen-15, carbon-11, and
nitrogen-13 (see Table 6-1). Although the half-lives of argon-41
and krypton-88 are much longer (2-3 hours), their measurement in
the environment on a continuous basis is also impractical. As the
sample collects, the radioactivity rapidly decays, and in a short
time an equilibrium is established when the collection is equal to
the decay rate. Thus, there is a limit to the quantity of
radioactivity that can be collected, as well as that which occur
during dispersion from the source to sampler. For these reasons,
demonstrating compliance by measuring the following short-lived
radionuclides in situ is usually not a practical option:
Radionuclide Half-life
oxygen-15 120 seconds
nitrogen-13 10 minutes
carbon-11 20 minutes
argon-41 1.8 hours
krypton-88 2.8 hours
Except for possibly a few DOE facilities, radiation exposures to
the maximum exposed individuals due to these short-lived gaseous
radionuclides are not significant when compared to the 10 mrem/yr
limit.
6.3 Criteria for Environmental Monitoring Programs
6.3.1 Measurements made at critical receptor locations:
How should locations be selected?
For facilities with continuous emissions, the critical receptor
locations may be either:
(a) the location of the highest X/Q on the facility perimeter
fence line; or
(b) the location of the highest off-site X/Q where a
residence, business, or school exists.
38
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In case b) , the sampling location may be placed at any site between
the highest off-site X/Q and the fence line, if this would make
sampling easier, more convenient, and cost-effective.
Acceptable dispersion models (e.g., AIRDOS-PC, CAP88-PC, COMPLY)
may be used to determine the highest X/Q location(s) . If the
highest X/Q location is represented by several sites with similar
values, measurements should be required at all such sites until the
location with maximum X/Q can be definitely identified (one year
minimum sampling). Sampling is then required at only the maximum
X/Q site, unless conditions change. The same procedure should be
followed when fence-line measurements are used (case (a) above) and
the highest concentrations are computed to be similar within two or
more of the 16 sectors.
For facilities with intermittent or variable emissions, many
locations around the facility (at least one within each of the 16
sectors) should be monitored.
6.3.2 Continuous sampling at the point of measurements:
What represents continuous sampling?
There may be valid and acceptable reasons for the sampling systems
at a facility to be off line for short periods of time (e.g.,
filter or sample changes, maintenance, calibration, etc.). Under
many circumstances, the requirement for continuous sampling can be
satisfied when the 95 percent data completeness requirement is met.
This means that the time the sampling system is not in satisfactory
operations should not exceed 5 percent of the sampling period.
The 95 percent figure is intended to provide uniformity in dealing
with various co-located facilities or multiple release points.
More restrictive conditions may be required if a facility is
approaching the dose limit or when in non-compliance. If
necessary, a backup sampler may be placed in operation to insure
that sampling is accomplished during the balance of the time (i.e.,
5 percent).
6.3.3 Sampling and measurements of major radionuclide contributor:
What radionuclides does this include?
The radionuclides that contribute significantly to the effective
dose equivalent typically include particulate and gaseous
radionuclides and tritium (see Table 6-1). All of the listed
particulate radionuclides and tritium can be readily collected and
measured by routine sampling methods. For all other gases listed,
only xenon-133 has a half-life sufficiently long to permit it to be
collected in the environment and analyzed in a laboratory.
39
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6.3.4 Radionuclide concentrations causing an effective dose
equivalent of 1 mrem/yr must be readily detectable and
distinguishable from background: How should this be
determined?
Environmental monitoring programs are typically judged to meet this
criterion if the "lower limit of detection" (LLD) of the sampling
and analysis methods is 10 percent or less of the Concentration
Levels for Environmental Compliance listed in Table 2, Appendix E
of the 40 CFR part 61. The LLD is defined that nuclide
concentration that is discernable from background at a confidence
level of 95 percent (i.e., the net activity value is greater than
a specified value above the random fluctuation of the background
count-rate). The LLD is calculated as follows:
4.66 Sb
LLD = (6-1)
2.2 x 1012 E V Y em
LLD = lower limit of detection, Ci/m3.
Sb = standard deviation of the background or blank count
rate, cpm.
E = counting efficiency, cpm/dpm.
V = sample volume, m3.
Y = radiochemical yield, if applicable, unitless.
A, = radioactive decay constant, time '}.
At = time elapsed between midpoint of sample collection and
time of counting, time.
2.2 x 1012 = conversion factor, dpm/curie.
The value of Sb should be based on the standard deviation of a
series of blank measurements using the same type of sample
collection media (e.g., an air-particulate filter) carried through
the complete analytical procedure.
If the application for approval does not list an LLD for the
sampling and analytical methods, nor a description of the
computation used for its determination, the applicant should be
requested to provide this information or provide the information
necessary to perform the computation. If this occurs, the reviewer
may calculate the LLD using the information provided.
Detection limits may be expressed as a "minimum detectable
activity" (MDA) or "minimum detectable concentration" (MDC).
Calculating the MDA or MDC requires determination of the standard
deviation of the background count rate (Sb) . This value can be
used in the above equation to compute the MDA or MDC.
The following EPA document provides more details on the basis and
derivation of the LLD: Upgrading Environmental Radiation Data, EPA
520/1-80-012, pp.6-14 to 6-34, August 1980.
40
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Table 6-2 lists typical sensitivities and examples of actual
procedural sensitivities for some of the major radionuclides
released by DOE facilities. The required sensitivities are one-
tenth the concentrations listed in 40 CFR 61, Appendix E, Table 2.
The procedural sensitivities are based primarily on airborne
radionuclide measurement program results conducted at the EPA's
National Air and Radiation Environmental Laboratory (NAREL),
formerly the Eastern Environmental Radiation Facility (EERF)
(BROS3). The information in Table 6-2 indicates that the
sensitivities for measuring all particulate radionuclides, tritium,
and carbon-14 are quite adequate to satisfy the requirements of the
rule. On the other hand, the sensitivities associated with argon-
41 and krypton-88, are not low enough to satisfy the sensitivities
required by the rule.
6.3.5 Radionuclide concentrations that would cause an effective
dose equivalent of 1 mrem/yr must be readily distinguishable
from background: How should this be determined?
The background radionuclides concentrations are typically low such
that nearly all nuclides released by DOE facilities can be readily
distinguished from background levels at concentrations that would
cause an effective dose equivalent of 1 mrem/yr (see Table 6-2).
However, there are two notable exceptions, radon-222 and external
exposure rates:
1) radon-22 concentrations in air that cause an effective
dose equivalent to the lung of 1 mrem/yr cannot be
distinguished from a background concentration of less 0.5
pCi/L; and
2) submersion dose rates of 1 mrem/yr caused by radionuclide
concentrations in air cannot be distinguished from
background external exposure rates due to photons.
Therefore, any DOE applications proposing to measure radon-222 or
external exposure rates should be carefully evaluated for its
technical merits.
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Table 6-2 Examples of Backgrounds and Sensitivities of Some
Principal Airborne Radionuclides Released from DOE
Facilities
Required Representative Example
Radionuclide Sensitivity'13 Background Sensitivity
Concentration (aCi/m3)
U-234
U-238
Pu-238
Pu-239
Am-241
770
830
210
200
190
25(2>
25<2>
<4(2)
<4(2>
<4
13<3)
13(3)
13(3)
13(3)
13(3)
Concentration (pCi/m3)
Ar-41
Kr-85
Kr-88
C-14
H-3
170
100,000
50
NL<7>
150
0
40<5>
0
1.3<8)
<1.1(9>
600<4>
ND(&)
ND
1-1
<1.1
(1) These sensitivities are 1/10 the concentrations listed in 40
CFR 61, Appendix E, Table 2.
(2) Average of January - December 1986 airborne measurements in 63
U.S. cities (EERF87a, b).
(3) Based on a weekly sample, average collection rate of 26 cfm,
analysis of 1/2 filter, and a measurement sensitivity of 0.05
pCi/sample.
(4) Estimated from an EPA report on airborne radionuclides at the
Savannah River Plant (BLA84).
(5) Average concentration measured in air at 12 U.S. cities in
1983, Environmental Radiation Data (ERD) filed, Eastern
Environmental Radiation Facility, EPA.
(6) ND - Not Determined.
(7) NL - Not Listed.
(8) Concentration taken from pp. 61-62 of NCRP85 and ORP73. This
concentration relates to 7.5 pCi/g carbon.
(9) This estimate assumes 30 percent humidity at 20 °C and a
background <200 pCi/L of water vapor.
42
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Background levels are defined as general ambient radionuclide
concentrations that are not related to an emission source. In some
cases, sources other than the facility of interest may contribute
to the radionuclide concentrations at the critical receptor
location. Uranium mining and milling facilities are potential
examples of multiple emission source contributing to the
measurements made at a single receptor location. Also, this
situation can exist when several different facilities releasing
similar contaminants are in the same area. In these cases, it may
be difficult to distinguish individually the contributions of the
various sources at receptor locations.
Similarly, when the radionuclide being monitored also occurs in
nature (e.g., potassium-40), the contribution to airborne
concentrations from natural sources during high-wind conditions may
not be distinguishable from the amount of the radionuclide released
from the facility. Therefore, because of these uncertainties, no
correction (subtraction) of concentrations resulting from other
sources to the concentration measured at the receptor location
should be allowed (i.e., the total measured radionuclide
concentration shall be used to determine compliance).
Monitoring programs that include subtractions from other emission
sources should be critically reviewed as the proposed method may be
technically incorrect. Rather, the total airborne concentration
(from all sources) should be compared to the concentration levels
of Table 2, Appendix E of 40 CFR 61, to determine compliance.
6.4 Quality Assurance Program in Response to the Performance
Requirements of Appendix B, Method 114, 40 CFR 61:
How is the validity of a QA Program evaluated?
The application should include a statement that the applicant is
conducting or is in the process of developing a quality assurance
(QA) program in general conformance with the requirements of Method
114. Specifically, the applicant should provide the information
required by Section 4 of Method 114, including the following:
1) the requirements for precision, accuracy, and
completeness of the environmental measurements; and
2) the number of replicates, spiked samples, split samples,
and blank samples to be analyzed.
Applications that do not indicate the conduct of a QA program in
conformance to the requirements of Method 114 should not be
approved. A certain degree of technical judgment may be allowed in
judging whether a QA program is in conformance with Method 114.
Quality assurance programs that meet the general intent of Method
114 should be judged to be in conformance, when properly justified
and documented.
43
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If any information required by Section 4 of Method 114 is not
available, the applicant should be informed that this information
must be provided. In reviewing these requirements, the following
guidelines should be used:
1) the accuracy and precision of the measurements should be
within 20 percent at the concentration levels listed in
Table 2 of Appendix E;
2) completeness should be at least 95 percent, that is, 95
percent of the samples collected should provide valid
data; and
3) 20 percent of the sample analyzed should be replicates,
blank, split, or spiked samples. Usually 10 percent are
duplicate or split samples, 5 percent are blank samples,
and 5 percent are spiked samples.
44
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Chapter 7
GUIDANCE ON METHODS FOR ESTIMATING FUGITIVE
RADIONUCLIDE AIR EMISSIONS
7.1 Estimation of Radionuclide Emissions Using Fugitive Dust
Emission Models
Chapters 3 and 4 of this report present a number of fugitive dust
emission models which are described in EPA guidance documents or
have been endorsed by the EPA by way of other EPA-sponsored
publications. To use these models for calculating the fugitive
emissions of radionuclides, it is necessary to characterize the
radionuclide concentrations in the emitted dust.
Two measures commonly used to characterize the soil contamination
- the specific activity (pCi/g) of the bulk material in situ or the
surface concentration (pCi/m2) - are not satisfactory for this
purpose. The bulk specific activity method will lead to errors for
two reasons.
First, radioactive contamination is not always uniformly
distributed in the soil layer. If the contamination had been
deposited on the ground from an atmospheric plume or cloud, it
will initially be concentrated on the surface. After a period
of weathering, the activity in the underlying soil layers will
increase, while decreasing at the surface. However, the fine
soil or dust particles available for resuspension typically
reside in a one millimeter-thick layer on the surface. Thus,
the average specific activity in, say, the top six inches of
the soil (the layer which is generally sampled) will not
generally be representative of the suspensible soil fraction.
Second, as Langer found at Rocky Flats, specific activity
varies with particle size (LAN83). The fugitive dust,
consisting predominantly of fine particles, will have a size
distribution very different from that of the particles in the
soil layer. Therefore, even if the bulk specific activity of
the sampled soil layer did not vary with depth, this activity
will generally be different from the specific activity of the
resuspended particles.
Surface concentrations are usually calculated by determining the
total activity of a given soil sample and then dividing by the area
of the sampled surface. They are thus a measure of the average
activity over the depth of the sample. The use of such a value
leads to the same errors in estimating the activity of the fugitive
dust as does the use of the bulk specific activity.
45
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A more acceptable method involves collecting samples of the
suspensible fraction from the surface of the contaminated area and
determining the specific activity (pCi/g of dry weight) of each
radionuclide. A simple method of accomplishing this is to collect
that portion of the surface soil that passes through a 200 mesh
screen upon dry sieving (EPA85a, p. 17). More sophisticated
sampling devices, such as a dust collector may also be used. One
drawback of these techniques is that the process of sample
collection may distort the distribution of radionuclides among the
variously sized particles. Another is that samples may be
collected from a deeper soil layer than actually becomes
resuspended. If the contamination had originally been deposited
from the atmosphere, it will tend to be more concentrated on the
surface. Collecting subsurface dust will dilute the sample and
will usually lead to an underestimate of the emissions.
A still better method involves the use of portable wind tunnels to
suspend the dust and collect samples. There is some controversy,
however, as to whether such sub-scale testing develops a flow field
that is indicative of what it would be in the atmospheric boundary
layer. The MRI is currently preparing to carry out such studies at
the Rocky Flats site. From a theoretical standpoint, the best
method is to measure the specific activity of the particles
collected by ambient air samplers. An overview of environmental
monitoring is presented in Chapter 6.
The annual effluent radionuclide activity is calculated by
multiplying the predicted annual emission of TSP from a particular
source by the specific activity of each radionuclide in that
source.
7.2 Calculating Effluent Releases From Sampling Data
7.2.1 Calculation of gaseous releases at NTS
Effluent releases from both drillback systems at NTS, discussed in
para. 5.3.1, could be determined by a variant of the exposure
profiling method employed by MRI to measure fugitive dust
emissions, with air samplers designed to collect gaseous
radionuclides being substituted for the ones used to trap dust
particles. Such a method would quantitatively intercept the
effluent plume and not be dependent on atmospheric dispersion
calculations, which are of questionable validity at short distances
from the source, nor on the current validity of historical data on
effluent emissions and radioactivity measurements. An alternative
method would be to attempt to pinpoint the source of the
uncontrolled emissions and to measure both the activity and the
volumetric release rate of the effluent gases from that point. Not
enough information about the drillback operation is available to
enable us to determine if the latter procedure is feasible. Either
of these proposed methods would yield more accurate release
estimates than the ones currently employed.
46
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Releases due to the ground seepage of noble gases at NTS are
estimated by collecting downwind air samples at two or three
locations and using the CAP88-PC code to back-calculate the
emission rate. Although the samples are collected over periods of
one week, historical annual-average meteorological data are used
as input for the code. This approach might present specific
problems in assessing its inherent variability and degree of
conservatism, if any.
Other methods of quantifying effluent concentrations include the
use existing environmental monitoring stations at appropriate
locations and in a sufficient numbers to intercept the plume over
its entire cross-section. The other method involves the use of a
combination of upwind and downwind air samplers, including at least
two downwind distances, along with concurrent wind velocity
measurements and stability class determinations.
An overview of environmental monitoring is presented in Chapter 6
in the context of demonstrating compliance with Subpart H of 40 CFR
61.
7.2.2 Critique of methods used at other DOE sites
The preceding critique of the methods used to estimate releases of
radionuclides at NTS applies to other DOE sites where effluent
emissions from area sources, whether gaseous or particulate, are
estimated on the basis of downwind or perimeter air samples.
Several sites report using the EPA model CAP88-PC to estimate
releases.
There are a number of drawbacks to using the computer code CAPS8
for this purpose. This model was developed for calculating the
annual effective dose equivalent to the population and to the
maximally exposed individual. Known or estimated release rates are
input to the model along with annual-average meteorological data.
The model then calculates radionuclide concentrations at the
receptor site and uses the appropriate dose conversion factors to
calculate the effective dose equivalent.
One obvious source of error in using CAP88 to calculate the source
term is the possible use of a different dose conversion factor than
one used by the code. (Such an error may have occurred at NTS,
where a dose factor cited in a DOE document has been used to
calculate a dose from a measured concentration. This dose was then
compared to a CAP88-calculated dose equivalent).
A more fundamental error may result from the use of annually-
averaged meteorological data to calculate short-term, time-varying
releases. Samples are collected over weekly or monthly periods, and
annually-averaged meteorological data does not accurately
characterize such a- short collection period. Another problem
results in the calculation of sector-averaged atmospheric dilution
47
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factors. According to the Guideline on Air Quality Models (EPA86,
p. 8-6) , sector averaging is "acceptable only to determine long
term averages." While this model assumption is valid for annual
dose calculations, errors can occur if it is used for short-term
calculations.
Perhaps the most significant criticism of using measured
concentrations and CAP88 to calculate emissions is that it begs the
question that this study was designed to answer. The purpose of
calculating emissions is to provide the EPA with a basis of "7
calculating risks to the public other than that provided by
measured off-site radionuclide concentrations. If the same °
concentration measurements, the same meteorological data and the
same model are used to calculate both the dose and the source term,
no new information is gained; the calculations are merely a
numerical exercise.
7.2.3 Estimating fugitive particulate emissions from environmental ,
sampling and monitoring .^ ^ -fW
A case might be made that by using the results of environmental
sampling and monitoring, fugitive emissions, in effect, could be
derived by applying an atmospheric dispersion factor to the field
data. However, the EPA has not analyzed this approach, and
therefore it cannot be recommended for this purpose. The
development of such a model requires that complex factors be
considered, including, among others:
validation of the deployment scheme, locations, and numbers of
environmental sampling stations.
validation of the selected sampling and analytical methods for
the expected radionuclides.
representativeness of the field data to the emission sources.
atmospheric behavior of contaminants while in transient from
the source of emission to the sampling station.
atmospheric dispersion and concurrent meteorological data for
the site.
site specific features (e.g., terrain, ground cover,
obstructions, control measures to mitigate releases, etc.).
radiological, physical, and chemical characteristics of the
contaminants.
physical characteristics of emission sources and temporal and
spatial distributions of emission rates.
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7.3 Summary of Guidance and Methods
A summary of the methods to estimate fugitive emissions, water
vapor, and radioactive gaseous effluents is given in Table 7-1.
For each release mechanism, the methodology for estimating emission
rates is tabulated, along with the current status of the procedure.
Procedures included in AP-42 are for the purpose of estimating the
emission of air pollutants. Other procedures appear in an EPA
guidance document on particulate emissions from TSDF (EPA89a).
Still others are from EPA, NRC, and DOE documents used for various
applications (EPA88a, DOE93, NRC92).
In cases of release mechanisms for which no EPA-approved models
exist, alternative methods used by the NRC, DOE, or the MRI (an EPA
contractor) may be proposed, given that they are technically
justified and fully documented.
To estimate the effluent radionuclide activities, it is necessary
to combine the procedures in Table 7-1 with the sampling and
calculational methods described in this report.
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Table 7-1 SUMMARY OF METHODS FOR ESTIMATING FUGITIVE EMISSIONS
Mechanism
WIND EROSION
Open areas
Limited
Unlimited
Waste piles
Intermittent
Continuous
Uranium ore &
mill tailings
MATERIAL HANDLING
Soil removal
Soil grading &
shaping
Agriculture
Demolition
UNPAVED ROADS
EVAPORATION
Open ponds
Soil
Saturated
Subsurface
GASEOUS (NTS)
Procedure
AP-42 method using "fastest mile"
Modified Wind Erosion Equation
AP-42 method using "fastest mile",
modified for geometry of pile
AP-42 aggregate handling emission
factor
NRC Regulatory Guide 3.59 methodology
Same as continuous waste piles
AP-42 emission factor for bulldozing
overburden at Western coal mines
AP-42 emission factor
Same as continuous waste piles
AP-42 methodology
Evaporation equation from NUREG-0570
Same as open ponds
Superfund Exposure Assessment Manual
Proposed air sampling protocol
combined with short-term site-
specific dispersion calculations
Status
Adopted by EPA (AP-42)
Approved by EPA (EPA88a)
Adopted by EPA (AP-42)
Adopted by EPA (AP-42)
Adopted by EPA
EPA guidance for TSDF
EPA guidance for TSDF
Adopted by EPA (AP-42)
Approved by EPA (EPA88a)
Adopted by EPA (AP-42)
Used by NRC Staff
Based on EPA88c
EPA: OSWER Directive
Proposed
50
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Table 7-1 SUMMARY OF METHODS FOR ESTIMATING FUGITIVE EMISSIONS, Cont'd
Mechanism
Procedure
Status
EQUIPMENT &
FACILITIES
Buildings
Tank venting
Equipment
Wet-cooling
tower
Proposed method based on measurement
or estimated source term
Same as above
Same as above
Same as PM10 particulates
Proposed
Proposed
Proposed
Proposed
CONTAMINATED
SOILS
Tritium
Carbon-14
Proposed based on DOE model
Proposed based on DOE model
Proposed
Proposed
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8.0 REFERENCES
BLA84 Blanchard, R.L., Broadway, J.A. and Sensintaffar, E.L.,
"An Airborne Radioactive Effluent Study at the Savannah
River Plant," U.S. Environmental Protection Agency
Report, EPA 520/5-84-012, June 1984.
BR083 Broadway, J.A., and Mardis, M., "Analytical Capability of
the Environmental Radiation Ambient Monitoring System,"
U.S. Environmental Protection Agency Report, EPA 520/5-
83-024, September 1983.
BUR89 Burkhard, N.R., et al, 1989. "Containment of Cavity Gas
in Fractured or Rubblized Emplacement Media." Presented
at the 5th Symposium on the Containment of Underground
Nuclear Detonations, Santa Barbara, CA, Sept. 19-21,
1989.
DOA88 U.S. Department of Agriculture, 1988. National Agronomy
Manual, 2nd ed., (Parts 500-509). U.S. Department of
Agriculture, Soil Conservation Service, Washington, D.C.
DOE84 Randerson, D., ed., 1984. Atmospheric Science and Power
Production. U.S. Department of Energy, Office of
Scientific and Technical Information, Technical
Information Center, Oak Ridge, TN.
DOE86 Langer, G. , 1986. Dust Transport - Wind Blown and
Mechanical Resuspension, July 1983 to December 1984.
Rockwell International, Energy Systems Group, Rocky Flats
Plant, Golden, CO.
DOE92 Black, S.C., and W^G. Phillips, 1992. National Emission
Standards for Hazardous Air Pollutants Submittal-1992.
Reynolds Electrical & Engineering Co., Inc., Las Vegas,
NV.
DOE92a Integrated Database for 1992: U.S. Spent Fuel and
Radioactive Waste Inventories, Projections, and
Characteristics, DOE/RW-0006, Rev. 8., Department of
Energy, Office of Civilian Radioactive Waste Management,
Oak Ridge, TN, October 1992.
DOE93 U.S. Department of Energy, Manual for Implementing
Residual Radioactive materials Guidelines Using RESRAD,
Ver. 5.0, Argonne National Laboratory, Argonne IL,
September 1993.
DOE94 Summary of Radionuclide Air Emissions from Department of
Energy Facilities for CY 1992, DOE/EH-0360, Department of
Energy, Office of Environmental Guidance, Washington, DC,
February 1994.
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EERF87a Eastern Environmental Radiation Facility, "Environmental
Radiation Data: Report 47, "U.S. Environmental Protection
Agency Report, EPA 520/5-87-006, June 1987.
EERF87b Eastern Environmental Radiation Facility, "Environmental
Radiation Data: Report 48, "U.S. Environmental Protection
Agency Report, EPA 520/5-87-017, June 1987
EPA83 Dynamac Corp., 1983. Methods for Assessing Exposure to
Windblown Particulates, EPA-600/4-83-007. U.S.
Environmental Protection Agency, Office of Health and
Environmental Assessment, Washington, D.C.
EPA85a Cowherd, C., G.E. Muleski, P.J. Englehart, and D.A.
Gillette, 1985. Rapid Assessment of Exposure to
Particulate Emissions from Surface Contamination Sites,
EPA/600/8-85/002. U.S.Environmental Protection Agency,
Office of Health and Environmental Assessment,
Washington, D.C.
EPA85b U.S. Environmental Protection Agency, 1985. Compilation
of Air Pollutant Emission Factors, vol. 1, AP-42, 4th Ed.
U.S. Environmental Protection Agency; Office of Air,
Noise and Radiation; Office of Air Quality Planning and
Standards; Research Triangle Park, NC.
EPA86 U.S. Environmental Protection Agency, 1986. Guideline on
Air Quality Models, EPA-450/2-78-027R. U.S.
Environmental Protection Agency, Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
EPA88a Cowherd, C., G.E. Muleski, and J.S. Kinsey, 1988.
Control of Open Fugitive Dust Sources, EPA-450/3-88-008.
U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, Research Triangle Park,
NC.
EPA88b Joyner, W.M., 1988. Compilation of Air Pollutant
Emission Factors, vol. 1: Stationary Point and Area
Sources, 4th ed. Supplement B, AP-42-SUPPL-B,
Environmental Protection Agency, Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
EPA88c U.S. Environmental Protection Agency, 1988. Superfund
Exposure Assessment Manual, U.S. Environmental Protection
Agency, Office of Remedial Response, Washington, D.C.
53
-------
EPA89a Cowherd, C., P. Englehart, G. E. Muleski, and J. S.
Kinsey, 1989.Hazardous Waste TSDF (Treatment, Storage,
and Disposal Facilities) : Fugitive Particulate Matter Air
Emissions Guidance Document, EPA-450/3-89/019. U.S.
Environmental Protection Agency, Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
EPA89b U.S. Environmental Protection Agency, 1989. 40 CFR Part
61: National Emission Standards for Hazardous Air
Pollutants; Radionuclides; Final Rule and Notice of
Reconsideration, Federal Register, Part II, Vol. 54, No.
240, December 15, 1989.
EPA89c Environmental Protection Agency, "Background Information
Document - Volumes 1 and 2, NESHAPS for Radionuclides,"
US Environmental Protection Agency Report, EPA 520/1-89-
006-2, September 1989.
EPA90 Joyner, W.M., 1990. Compilation of Air Pollutant
Emission Factors, vol. 1: Stationary Point and Area
Sources, 4th ed. Supplement C, AP-42-SUPPL-C,
Environmental Protection Agency, Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
EPA91 Joyner, W.M., 1991. Compilation of Air Pollutant
Emission Factors, vol. 1: Stationary Point and Area
Sources, 4th ed., Supplement D, AP-42-SUPPL-D,
Environmental Protection Agency, Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
EPA92 Environmental Protection Agency, 1989. Characterization
Protocol For Radioactive Contaminated Soils, Office of
Solid Waste and Emergency Response, Office of Emergency
and Remedial Response, and Office of Radiation Programs,
Publication 9380.1-10FS, May 1992, Washington, DC.
EPA93a Environmental Protection Agency, 1993. Draft Soil
Screening Level Guidance, Quick Reference Fact Sheet,
Office of Solid Waste and Emergency Response, Office of
Emergency and Remedial Response, and Hazardous Site
Control Division, September 29, 1993, Washington, DC.
EPA93b Environmental Protection Agency, 1993. Evaluation of the
Dispersion Equations in the Risk Assessment Guidance
Document for Superfund (RAGS): Volume I - Human Health
Evaluation Manual (Part B) , Development of Preliminary
Remediation Goals), Office of Emergency and Remedial
Response and Toxics Integration Branch, April 1993,
Washington, DC.
54
-------
GIL83a Gillette, D.A., 1983. "Threshold Velocities for Wind
Erosion on Natural Terrestrial Arid Surfaces (A
Summary)", in Precipitation Scavenging, Dry Deposition,
and Resuspension: Proceedings of the Fourth
International Conference, Santa Monica, California, 29
November - 3 December 1982, vol. 2, H. R. Pruppacher et
al., eds., Elsevier, New York.
GIL83b Gillette, D.A., and C. Cowherd, 1983. " The Concept of
Resuspension Rates Applied to Problems of Fugitive Dust
Emissions and Wind Erosion" in Precipitation Scavenging,
Dry Deposition, and Resuspension: Proceedings of the
Fourth International Conference, Santa Monica,
California, 29 November - 3 December 1982, vol. 2, H. R.
Pruppacher et al., eds., Elsevier, New York.
KIN92 Kinsey, J. S., 1992. Private communication.
LAN83 Langer, G., 1983. "Activity, Size and Flux of
Resuspended Particles From Rocky Flats Soil" in
Precipitation Scavenging, Dry Deposition, and
Resuspension: Proceedings of the Fourth International
Conference, Santa Monica, California, 29 November - 3
December 1982, vol. 2, H.R. Pruppacher et al., eds.,
Elsevier, New York.
LANL92 Henderson, R.W., 1992 (Unpublished). "Operational Area
Monitoring Plan for the Los Alamos National Laboratory
Testing Area and Facilities, Nevada Test Site".
LEE90 Leeden, F., Troise, F., and Todd, D., The Water
Encyclopedia, Lewis Publishers, 2nd Ed., Chelsea, MI,
1990.
NCRP85 National Council on Radiation Protection and
Measurements, "Carbon-14 in the Environment," NCRP Report
No. 81, May 15, 1985.
NCRP79 National Council on Radiation Protection and
Measurements, "Tritium in the Environment," NCRP Report
No. 62, March 9, 1979.
NIC88 Nicholson, K.W., 1988. "A Review of Particle
Resuspension." Atmospheric Environment, 22, 2639-2651.
NIC90 Nicholson, K.W., and J.R. Branso, 1990. "Factors
Affecting Resuspension by Road Traffic." The Science of
the Total Environment, 93, 349-358.
NIE90 Nielsen, S.P., et al., 1990. "Dry Deposition of MRb and
137Cs From a- Boiling Water Reactor Plume." Health Physics
58, 283-289.
55
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NIL Nilson, R.H., et al. (Unpublished). "Field Measurements
of Gas Transport Induced by Atmospheric Pumping".
NIL91 Nilson, R.H., et al., 1991. "Atmospheric Pumping: A
Mechanism Causing Vertical Transport of Contaminated
Gases Through Fractured Permeable Media." Journal of
Geophysical Research, 96, 21.933-21.948.
NRC79 Wing, J., 1979. Toxic Vapor Concentrations in the
Control Room Following a Postulated Accidental Release,
NUREG-0570. U.S. Nuclear Regulatory Commission, Office
of Nuclear Reactor Regulation, Division of Site Safety
and Environmental Analysis, Washington, D.C.
NRC83 U.S. Nuclear Regulatory Commission. Radiological
Assessment, NUREG/CR-3332, Nuclear Regulatory Commission,
Office of Nuclear Regulatory Research, Washington, D.C.,
September 1983.
NRC87 U.S. Nuclear Regulatory Commission, 1987. Regulatory
Guide 3.59:Methods for Estimating Radioactive and Toxic
Airborne Source Terms for Uranium Milling Operations.
U.S. Nuclear Regulatory Commission, Office of Nuclear
Regulatory Research, Washington, D.C.
NRC92 U.S. Nuclear Regulatory Commission. Residual Radioactive
Contamination from Decommissioning: Vol. 1, Technical
Basis for Translating Contamination Levels to Annual
Total Effective Dose Equivalent, NUREG/CR-5512, Nuclear
Regulatory Commission, Office of Nuclear Regulatory
Research, Washington, D.C., September 1992.
ORP73 Office of Radiation Programs,.US Environmental Protection
Agency, "Carbon-14 in Total Diet and Milk, 1972-1973,"
Radiation Health Data Reports 14. 679, November 1973.
56
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ORNL92 Yuan, Y.C., J.H.C. Wang, and A. Zielen, 1992.
MILDOS-AREA: An Enhanced Version of MILDOS for
Large-Area Sources. Oak Ridge National Laboratory,
Radiation Shielding Information Center, Oak Ridge, TN.
PET91 Pettersson, H.B.L., and J. Koperski, 1991.
"Investigation of Aerial Dispersion of Radioactive Dust
from an Open-Pit Uranium Mine by Passive Vinyl
Collectors." Health Physics 60, 681-690.
PIN90 Finder, J.E., et al., 1990. "Atmospheric Deposition,
Resuspension, and Root Uptake of Pu in Corn and Other
Grain-Producing Agroecosystems Near a Nuclear Fuel
Facility". Health Physics, 59, 853-867.
PRU83 Pruppacher, H.R., et al., eds., 1983. Precipitation
Scavenging, Dry Deposition, and Resuspension:
Proceedings of the Fourth International Conference, Santa
Monica, California, 29 November - 3 December 1982, 2
vols. Elsevier, New York.
PYE87 Pye, K., 1987. Aeolian, dust and dust deposits. Academic
Press, New York.
REE88 Reeks, M.W., J. Reed, and D. Hall, 1988. "On the
Resuspension of Small Particles by a Turbulent Flow."
Journal of Physics D: Applied Physics, 21, 574-589.
SEH80 Sehmel, G.A., Particle Resuspension: A Review,
Environmental International, Vol. 4, pp.107-127, 1980.
SMI82 Smith, W.J., F.W. Whicker and H.R. Meyer, 1982. "Review
and Categorization of Saltation, Suspension and
Resuspension Models". Nuclear Safety 23(6).
SMI83 Smith, W.J., and F.W. Whicker, 1983. "Quantitative
Comparison of Five Suspension Models", in Precipitation
Scavenging, Dry Deposition, and Resuspension:
Proceedings of the Fourth International Conference, Santa
Monica, California, 29 November - 3 December 1982, vol.
2, H.R. Pruppacher et al., eds., Elsevier, New York.
57
-------
Attachment 1
Excerpts from Control of Open Fugitive Dust Sources (EPASSa)
61
-------
PB89-103691
EPA-450/3-88-008
CONTROL OF OPEN FUGITIVE OUST SOURCES
FINAL REPORT
by
C. Cowherd, 6. E. Muleskl. tnd J. S. Klnsey
Midwest Research Institute
425 Volker Boulevard
Kansas City. Missouri 64110
EPA Contract No.- 68-02-4395
Work Assignment 14
MRI Project 8985-14
Hllllan L. Elaore, Project Officer
Emission Standards Division
Office of Air Quality Planning and Standards
U. S. Environmental Protection Agency
Research Triangle Park, North Carolina
September 1988
US. DEPARTMENT OF COMMERCE
NATDNM.TECHMCAL
tFORMATDNSEfMCC
SPRMGFELD.VA221I1
-------
1. Loading of aggregate onto storage piles (batch or continuous droo
operations).
2. Equipment traffic tn storage area.
3. Wind erosion of pile surfaces and ground areas around piles.
4. Loadout of aggregate for shipment or for return to the process
strew (batch or continuous drop operations).
4.1.1 Materials Handling
.Adding aggregate material to a storage pile or removing It usually
Involves dropping the material onto a receiving surface. Truck dumping on
the pile or loading out fron the pile to a truck with a front-end loader
re examples of batch drop operations. Adding material to the pile by a
conveyor stacker Is in example of a continuous drop operation.
The following equation Is recomnended for estimating emissions from
transfer operations (batch or'continuous drop):
E - k(0.0016) *« A>4 (kg/Mg)
(4-1)
(U) ^ '
E k(0.0032) 5 . , (Ib/ton)
<&^
where: E emission factor
k particle size multiplier (dimensionless)
U mean wind speed, m/s (mph)
M material moisture content, percent
The particle size multiplier k varies with aerodynamic particle diameter
as shown below:
Aerodynamic Particle Size Multiplier, k
<30 um . <15 um <10 -ja <5 um
fl.ll
Based on the criteria presented 1n AP-42, the above equation 1s rated A.
J-3
-------
For emissions from equipment traffic (trucks, front-end loaders,
dozers, etc.) traveling between or on piles. It 1s recommended that the
equations for vehicle traffic on unpaved surfaces be used (see
Section 3-0). For vehicle travel between storage piles, the silt value(s)
for the areas among the piles (which may differ fron the silt values for
the stored materials) should be used.
4.1.2 Wind Erosion
Oust emissions may be generated by wind erosion of open aggregate
storage piles and exposed areas within an Industrial facility. These
sources typically art characterized by nonhonogeneous surfaces Impregnated
with nonerodlble elements (particles larger than approximately 1 cm 1n
diameter). Field testing of coal piles and other exposed materials using
a portable wind tunnel has shown that (a) threshold wind speeds exceed
5 m/s (11 mph) at 15 cm above the surface or 10 m/s (22 mph) at 7 above
the surface, and (b) part1culate emission rates tend to decay rapidly
(half life of a few minutes) during an erosion event. In other words,
these aggregate material surfaces are characterized by finite availability
of credible material (mass/area) referred to as the erosion potential.
Any natural crusting of the surface binds the credible material, thereby
reducing the erosion potential.
4.1.2.1 Emissions and Correction Parameters. If typical values for
threshold wind speed at 15 cm are corrected to typical wind sensor height
(7-10 m), the resulting values exceed the upper extremes of hourly mean
wind speeds observed 1n most areas of the country. In other words, mean
atmospheric wind speeds are not sufficient to sustain wind erosion from
aggregate material surfaces. However, wind gusts may quickly deplete a
substantial portion of the erosion potential. Because erosion potential
has been -found to Increase rapidly with Increasing wind speed, estimated
emissions should be related to the gusts of highest magnitude.
The routinely measured meteorological variable which best reflects
the magnitude of wind gusts 1s the fastest mile. This quantity represents
the wind speed corresponding to the whole mile of wind movement which has
passed by the l-m1 contact anemometer 1n the least amount of time. Daily
measurements of the fastest mile are presented in the monthly Local
CUmatologlcal Data (LCD) summaries. The LCD summaries can be obtained
4-4
-------
from the National Climatic Center, Ashevllle. North Carolina. The
duration of the fastest mile, t^lcally about 2 *1n (for a fastest «lle of
30 «ph), matches well with the half life of the erosion process, which
ranges between 1 and 4 »1n. It should be noted, however, that peak winds
can significantly exceed the dally fastest mile.
The wind speed profile In the surface boundary layer 1s found to
follow a logarithmic distribution:
u(2) - £y ln(|-) (2 > 20) (4-2)
o
where: u wind speed, cm/s
u* friction velocity* a/s
2 height above test surface, or
ZP roughness height, em
0.4 von (Carman's constant, dlaenslonless
The friction velocity (u*) Is a measure of wind shear stress on the
credible surface, as determined from the slope of the logarithmic velocity
profile. The roughness height (z0) 1s a measure of the roughness of the
exposed surface as determined from the y-Intercept of the velocity
profile. I.e., the height at which the wind speed 1s zero. These
parameters are Illustrated 1n Figure 4-1 for a roughness height of 0.1 cm.
Emissions generated by wind erosion are also dependent on the
frequency of disturbance of the credible surface because each time that a
surface 1s disturbed. Us erosion potential 1s restored. A disturbance 1s
defined as an action which results In the exposure of fresh surface
material. On a storage pile, this would occur whenever aggregate material
Is either added to or removed from the old surface. A disturbance of an
exposed area may also result from the turning of surface material to a
depth exceeding the size of the largest pieces of material present.
4.1.2.2 Predictive Emission Factor Equation*. The emission factor
for wind-generated paniculate emissions from mixtures of credible and
nonerodlble surface material subject to disturbance may be expressed 1n
units of g/m*-yr as follows:
N
Emission factor » k J PI (4-3)
4-5
-------
tarn
Srteo AT 2
WtttO -S/Veo Ar IOm
Figure 4-1. Illustration of logarithmic velocity profile.
-------
This distribution of particle sUt within tht < 30 * fraction 1s
comparable to the distributions rtporttd for other fugitive dust sources
where wind SP0^ 1« factor. This Is Illustrated, for example. 1n tht
distributions for batch and continuous drop operations encompassing 4
number of test aggregate Mterlals (see AP-42 Section 11.2.3).
In calculating Mission factors, each area of an credible surface
that Is subject to a different frequency of disturbance should be treated
separately. For a surface disturbed dally. N 365/yr, and for a surface
disturbance once every 6 BO, N 2/yr.
The erosion potential function for a dry, exposed suiface has the
following font:
P 58 (u* - u*)t «- 25 (u* - up . (<-3)
P 0 for u* s u£
where: u* friction velocity («/s)
u* threshold friction velocity («/$)
Because of the nonlinear fora of the erosion potential function, each
erosion event oust be treated separately.
Equations 4-2 and 4-3 apply only to dry. exposed Materials with
Halted erosion potential. The resulting calculation Is valid only for a
t1«e period as lor A? or longer than the period between disturbances.
For uncrusted surfaces, the threshold friction velocity 1s best
estimated from the dry aggregate structure of the soil. The threshold
-. t
friction velocity for erosion can be determined from the code of the
aggregate size distribution, following a relationship derived by Gillette
(1980) as shown In Figure 4-4.» A simple hand-sieving test of surface
soil Is highly desirable to determine the mode of the surface aggregate .
size distribution by Inspection of relative sieve catch aaounts, follow-
ing the procedure specified in Figure 4-5.
A more approximate basis for determining threshold friction velocity
would be based on hand sieving with just one sieve, but otherwise follows
the procedure specified 1n Figure 4-5. Based the relationship
4-8
-------
I
to
tr 1000 .
Threshold Friction Velocity, u« t (cm/set
o 8
M W * CM «* M U * (M »«*
^
^
.^
X
X
^^
.^^
^^p'
X*
X
X
^
.
_^
<^
^
^
+
4
4
.
2 348 67*B 2 345 67M 2 S 4 S 7it
0.1
1 10
Aggregate Size Distribution Mode (mm)
100
Figure 4-4. Relationship of threshold friction velocity to size distribution node.
-------
Attachment 2
Excerpts from Compilation of Air Pollutant Emission Factors
(EPA85b, et seq)
62
-------
AP-42
Fourth Edition
September 1985
COMPILATION
OF
AIR POLLUTANT
EMISSION FACTORS
Volume I:
Stationary Point
And Area Sources
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office Of Air And Radiation
Office Of Air Quality Planning And Standards
Research Triangle Park. North Carolina 27711
September 1985
-------
11.2.1 UNPAVED ROADS
11.2.1.1 General
Dust plumes trailing behind vehicles traveling on unpaved roads are a
familiar sight in rural areas of the United States. When a vehicle travels an
unpaved road, the force of the wheels on the road surface causes pulverization
of surface material. Particles are lifted and dropped from the rolling wheels,
and the road surface is exposed to strong air currents in turbulent shear with
the surface. The turbulent wake behind the vehicle continues to act on the
road surface after the vehicle has passed.
11.2.1.2 Emissions Calculation And Correction Parameters
The quantity of dust emissions from a given segment of unpaved road varies
linearly with the volume of traffic. Also, field investigations have shown
that emissions depend on correction parameters (average vehicle speed, average
vehicle weight, average number of wheels per vehicle, road surface texture and
road surface moisture) that characterize the condition of a particular road and
the associated vehicle traffic.1'4
Dust emissions from unpaved roads have been found to vary in direct.
proportion to the fraction of silt (particles smaller than 75 micrometers in
diameter) in the road surface materials.1 The silt fraction is determined by
measuring the proportion of loose dry surface dust that passes a 200 mesh
screen, using the ASTM-C-136 method. Table 11.2.1-1 summarizes measured silt
values for industrial and rural unpaved roads.
The silt content of a rural dirt road will vary with location, and it
should be measured. As a conservative approximation, the silt content of the
parent soil in the area can be used. However, tests show that road silt con-
tent is normally lower than in the surrounding parent soil, because the fines
are continually removed by the vehicle traffic, leaving a higher percentage
of coarse partttles.
Unpaved roads have a hard, generally nonporous surface that usually dries
quickly after a rainfall. The temporary reduction in emissions caused by
precipitation may be accounted for by not considering emissions on "wet" days
(more than 0.254 millimeters [0.01 inches] of precipitation).
The following empirical expression may be used to estimate the quantity of
size specific particulate emissions from an unpaved road, per vehicle kilometer
traveled (VKT) or vehicle mile traveled (VMT), with a rating of A:
/s\ /S\ /W\0-7 /w\0.5 /365-p\
E-kC1.7> (_) () ( ( ) Ug/VTCT)
W V»8/ \2.7j VA/ \365/
/s\ /S\ /W*.7 /tf>P.5 /365_pV
E-k(5.9> () [ -) () ) ) (Ib/VMT)
W W w w \3*v
9/88 Miscellaneous Sources 11.2.1-1
-------
K»
K>
TABLE 11.2.1-1. TYPICAL SILT CONTENT VALUES OP SURFACE MATERIAL
ON INDUSTRIAL AND RURAL UNPAVED ROADS*
Industry
Copper smelting
Iron and steel production
Sand and gravel processing
Stone quarrying and processing
Taconlte alnlng and processing
Western surface coal mining
Rural roads
i
Road use or
surface material
Plant road
Plant road
Plant road
Plant road
Haul road
Service road
Access road
Haul road
Scraper road
Haul road
. (freshly
graded)
Gravel
Dirt
Crushed limestone
Plant
sites
1
9
1
1
1
1
2
3
3
2
1
2
2
Test
samples
3
20
3
5
12
8
2
21
10
5
1
5
8
Silt (in
Range I
-
15.9 - 19.1
4.0 - 16.0
4.1 - 6.0
10.5 - 15.6
3.7 - 9.7
2.4 - 7.1
4.9 - 5.3
2.8 - 18
7.2 - 25
18 - 29
NA
5.8 - 68
7.7 - 13
t. X)
Mean
17.0
8.0
4.8
14.1
5.8
4.3
5.1
8.4
17
24
5.0
28.5
9.6
PI
M
M
O
£
00
oo
References 4-11. NA - Not available.
-------
where: E emission factor
particle size Multiplier (dimensionless)
silt content of road surface material (Z)
mean vehicle speed, km/hr (mph)
mean vehicle weight, Mg (ton)
mean number of wheels
number of days with at least 0.254 mm
(0.01 in.) of precipitation per year
The particle size multiplier, k, in the equation varies with aerodynamic particle
size range as follows:
Aerodynamic Particle Size Multiplier For Equation
PO urn*
1.0
_OO urn
0.80
<15 urn
0.50
jCIO urn
0.36
<[5um
0.20
<2.5 urn
0.095
Stokes diameter
The number of wet days per year, p, for the geographical area of interest
should be determined from local climatic data. Figure 11.2.1-1 gives the
geographical distribution of the mean annual number of wet days per year in the
United States.
The equation retains the assigned quality rating, if applied within the
ranges of source conditions that were tested in developing the equation, as
follows:
Ranges Of Source Conditions For Equation
Road silt
content
(wgt. Z)
4.3 - 20
Mean vehicle weight
Mg
2.7 - 142
ton
3 - 157
Mean vehicle speed
km/hr
21 - 64
mph
13 - 40
mean no.
of wheels
4-13
Also, to retain the quality rating of the equation when addressing a specific
unpaved road, it is necessary that reliable correction parameter values be
determined for the road in question. The field and laboratory procedures for
determining road surface silt content are given in Reference 4. In the event
that site specific values for correction parameters'cannot be.obtained, the
appropriate mean, values from Table 11.2.1-1 may be used, but the quality rating
of the equation is reduced to B.
The equation was developed for calculating annual average emissions, and
thus, is to be multiplied by annual vehicle distance traveled (VDT). Annual
average values for each of the correction parameters are to be substituted for
the equation. Vorst case emissions, corresponding to dry road conditions, may
be calculated by setting p - 0 in the equation (equivalent to dropping the last
9/88
Miscellaneous Sources
11.2.1-3
-------
Reproduced from
bail valUbl* copy.
. . I
t/1
o
z
K
MUIS
CD
00
Figure 11.2.1-1. Hean number of days with 0.01 Inch or more of precipitation in United States.
)0
-------
tera from Che equation). A separate set of nonclimatic correction parameters
and a higher than normal VDT value may also be justified for the worst case
average period (usually 24 hours). Similarly, in using the equation to calcu-
late emissions for a 91 day season of the year, replace the term (365-p)/365
with the term (91-p)/91, and set p equal to the number of wet days in the 91 day
period. Also, use appropriate seasonal values for the nonclimatic correction
parameters and for VDT.
11.2.1.3 Controls
Common control techniques for unpaved roads are paving, surface treating
with penetration chemicals, working into the roadbed of stabilization chemicals,
watering, and traffic control regulations. Chemical stabilizers work either by
binding the surface material or by enhancing moisture retention. Paving, as a
control technique, is often not economically practical. Surface chemical treat-
ment and watering can be accomplished with moderate to low costs, but frequent
retreatments are required. Traffic controls, such as speed limits and traffic
volume restrictions, provide moderate emission reductions but may be difficult
to enforce. The control efficiency obtained by speed reduction can be calcu-
lated using the predictive emission factor equation given above.
The control efficiencies achievable by paving can be estimated by comparing
emission factors for unpaved and paved road conditions, relative to airborne
particle size range of.interest. The predictive emission factor equation for
paved roads, given in Section 11.2.6, requires estimation of the silt loading
on the traveled portion of the paved surface, which in turn depends on whether
the pavement is periodically cleaned. Unless curbing is to be installed, the
effects of vehicle excursion onto shoulders (berms) also must be taken into
account in estimating control efficiency.
The control efficiencies afforded by the periodic use of road stabilization
chemicals are ouch more difficult to estimate. The application parameters
which determine control efficiency include dilution ratio,- application intensity
(mass of diluted chemical per road area) and application frequency. Other
factors that affect the performance of.chemical stabilizers include vehicle
characteristics (e. g., traffic volume, average weight) and road characteristics
(e. g., bearing strength).
Besides water, petroleum resin products have historically been the dust
suppressants most widely used on industrial unpaved roads. Figure 11.2.1-2
presents a method to estimate average control efficiencies associated with
petroleum resins applied to unpaved roads. Several items should be noted:
1. The term "ground inventory" represents the total volume (per
unit area) of petroleum resin concentrate (not solution)
applied since the start of the dust control season.
2. Because petroleum resin products must be periodically reapplied
to unpaved roads, the use of a time-averaged control efficiency
value is appropriate. Figure 11.2.1-2 presents control effi-
ciency values averaged over two common application intervals,
two weeks and one month. Other application intervals will
require interpolation.
9/88 Miscellaneous Sources 11.2.1-5
-------
S
M
vt
to
O
Z
100
0.25
0.5
GROUND INVENTORY
(liters/square meter)
0.75 1 0 0.25
0.5
0.75
o
z
UJ
o
u.
u.
LJ
_J
O
cc
H
o
o
LLJ
O
<
GC
UJ
T
T
T
T
Note: Averaging periods (2 weeks or 1 month)
refer to time between applications
80 -
60
40
20
0
2 weeks
1 month
TOTAL PARTICULAR
I
T
1 month
PARTICLES = 10 |imA
0.05 0.1 0.15 0.2 0.25 0 0.05
(gallons/square yard)
GROUND INVENTORY
0.1
O.t5
0.2
0.25
oo
oo
Figure 11.2.1-2. Average control efficiencies over common application Intervals.
-------
11.2.2 AGRICULTURAL TILLING
11.2.2.1 General
the two universal objectives of agricultural tilling are the creation
of the desired soil structure to be used as the crop seedbed and the eradi-
cation of weeds. Plowing, the most common method of tillage, consists of
some form of cutting loose, granulating and inverting the soil, and turning
under the organic litter. Implements that loosen the soil and cut off the
weeds but leave the surface trash in place have recently become more popu-
lar for tilling in dryland farming areas.
During a tilling operation, dust particles from the loosening and pul-
verization of the soil are injected into the atmosphere as the soil is
dropped to the surface. Dust emissions are greatest during periods of dry
soil and during final seedbed preparation.
11.2.2.2 Emissions and Correction Parameters
The quantity of dust from agricultural tilling is proportional to the
area of land tilled. Also, emissions depend on surface soil texture and
surface soil moisture content, conditions of a particular field being
tilled.
Dust emissions from agricultural tilling have been found to vary di-
rectly with the silt content (defined as particles < 75 micrometers in di-
ameter) of the surface soil depth (0 to 10 cm [0 to 4 in.]). The soil silt
content is determined by measuring the proportion of dry soil that passes a
200 mesh screen, using ASTM-C-136 method. Note that this definition of
silt differs from that customarily used by soil scientists, for whom silt
is particles from 2 to 50 micrometers in diameter.
Field measurements2 indicate that dust emissions from agricultural
tilling are not significantly related to surface soil moisture, although
limited earlier data had suggested such a dependence.1 This is now be-
lieved to reflect the fact that most tilling is performed under dry soil
conditions, as were the majority of the field tests.1"2
Available test data indicate no substantial dependence of emissions on
the type of tillage implement, if operating at a typical speed (for exam-
ple, 8 to 10 km/far [5 to 6 mph]).1'2
11.2.2.3 Predictive Emission Factor Equation
The quantity of dust emissions from agricultural tilling, per acre of
land tilled, may be estimated with a rating of A or B (see below) using the
following empirical expression2:
E = k(5.38)(s)°'6 (kg/hectare) (1)
E = ktt.SOXs)0'6 (Ib/acre)
5/83 Miscellaneous Sources 11.2.2-1
-------
where: E = emission factor
k = particle size multipler (
s - silt content of surface soil (I)
The particle size multiplier (k) in the equation varies with aerodynamic
particle size range as follows:
Aerodynamic Particle Size Multiplier for Equation 1
Total
particulate
1.0
< 30 M°>
0.33
< 15 MB
0.25
< 10 [in
0.21
< 5 M»
0.15
< 2.5 [ao
0.10
Equation 1 is rated A if used to estimate total particulate emissions,
and B if used for a specific particle size range. The equation retains its
assigned quality rating if applied within the range of surface soil silt
content (1.7 to 88 percent) that was tested in developing the equation.
Also, to retain the quality rating of Equation 1 applied to a specific ag-
ricultural field, it is necessary to obtain a reliable silt value(s) for
that field. The sampling and analysis procedures for determining agricul-
tural silt content are given in Reference 2. In the event that a site spe-
cific value for silt content cannot be obtained, the mean value of 18 per-
cent may be used, but the quality rating of the equation is reduced by one
level.
11.2.2.4 Control Methods3
In general, control methods are not applied to reduce emissions from
agricultural tilling. Irrigation of fields before plowing will reduce
emissions, but in many cases, this practice would make the soil unworkable
and would adversely affect the plowed soil's characteristics. Control
methods for agricultural activities are aimed primarily at reduction of
emissions from wind erosion through such practices as continuous cropping,
stubble mulching, strip cropping, applying limited irrigation to fallow
fields, building windbreaks, and using chemical stabilizers. No data are
available to indicate the effects of these or other control methods on
agricultural tilling, but as a practical matter, it may be assumed that
emission reductions are not significant.
References for Section 11.2.2
1. C. Cowherd, Jr., et al., Development of Emission Factors for Fugitive
Dust Sources, EPA-450/3-74-037, U. S. Environmental Protection Agency,
Research Triangle Park, NC, June 1974.
2. T. A. Cuscino, Jr., et al., The Role of Agricultural Practices in
Fugitive Dust Emissions, California Air Resources Board, Sacramento,
CA, June 1981.
3. G. A Jutze, et al., Investigation of Fugitive Dust - Sources Emissions
And Control, EPA-450/3-74-036a, U. S. Environmental Protection Agency,
Research Triangle Park, NC, June 1974. uJ
11.2.2-2 EMISSION FACTORS 5/83
-------
11.2.3 AGGREGATE HANDLING AND STORAGE FILES
11.2.3.1 General
Inherent in operations that use mineral! in aggregate form is the
maintenance of outdoor storage piles. Storage piles are usually left uncovered,
partially because of the need for frequent material transfer into or out of
storage.
Dust emissions occur at several points in the storage cycle, such as
during material loading onto the pile, disturbances by strong wind currents,
and loadout from the pile. The movement of trucks and loading equipment in the
storage pile area is also a substantial source of dust.
11.2.3.2 Emissions And Correction Parameters
The quantity of dust emissions from aggregate storage operations varies
with the volume of aggregate passing through the storage cycle. Also, emis-
sions depend on three parameters of the condition of a particular storage pile:
age of the pile, moisture content and proportion of aggregate fines.
When freshly processed aggregate is loaded onto a storage pile, its
potential for dust emissions is at a maximum. Fines are easily disaggregated
and released to the atmosphere upon exposure to air currents, either from aggre-
gate transfer itself or from high winds. As the aggregate weathers, however,
potential for dust emissions is greatly reduced. Moisture causes aggregation
and cementation of fines to the surfaces of larger particles. Any significant
rainfall soaks the interior of the pile, and the drying process is very slow.
Silt (particles equal to or less than 75 microns in diameter) content is
determined by measuring the portion of dry aggregate material that passes
through a 200 mesh screen, using ASTM-C-136 method. Table 11.2.3-1 summarizes
measured silt and moisture values for industrial aggregate materials.
11.2.3.3 Predictive Emission Factor Equations
Total dust emissions from aggregate storage piles are contributions of
several distinct source activities within the storage cycle:
1. Loading of aggregate onto storage piles (batch or continuous drop
operations).
.2. Equipment traffic in storage area. /
3. Wind erosion of pile surfaces and ground areas around piles.
A. Loadout of aggregate for shipment or for return to the process stream
(batch or continuous drop operations).
Adding aggregate material to a storage pile or removing it both usually
involve dropping the material onto a receiving surface. Truck dumping on the
pile or loading out from the pile to a truck with a front end loader are exam-
ples of batch drop operations. Adding material to the pile by a conveyor
stacker is an example of a continuous drop operation.
9/88 Miscellaneous Sources 11.2.3-1
-------
TABLE 11.2.3-1. TYPICAL SILT AND MOISTURE CONTENT VALUES
OF MATERIALS AT VARIOUS INDUSTRIES
Industry
Iron and steel
production*
Stone quarrying
and processing0
Taconlte mining
and processing0
Western surface
coal ralnlngd
.
Coal fired power
generation6
Material
Pellet ore
Lump ore
Coal
Slag
Flue dust
Coke breeze
Blended ore
Sinter
Limestone
Crushed
limestone
Pellets
Tailings
Coal
Overburden
Exposed ground
Coal
Silt (Z)
No. of test
samplers Range Mean
10
9
7
3
2
I
1
1
1
2
9
2
15
IS
3
60
1.4 - 13
2.8 - 19
2 - 7.7
3 - 7.3
14 - 23
1.3 - 1.9
2.2 - 5.4
NA
3.4 - 16
3.8 - 15
5.1 - 21 ,
0.6 - 4.8
4.9
9.5
5
5.3
18.0
5.4
15.0
0.7
0.4
1.6
3.4
11.0
6.2
7.1
15.0
2.2
Moisture '(Z)
No. of test
samplers Range Mean
8
6
6
3
0
1
1
0
0
2
7
1
7
0
3
59
0.64 - 3.5
1.6 - 8.1
2.8 - 11
0.25 - 2.2
NA
NA
NA
0.3 - 1.1
0.05 - 2.3
2.8 - 20
NA
0.8 - 6.4
2.7 - 7.4
2.1
5.4
4.8
0.92
NA
6.4
6.6
NA
NA
0.7
0.9
0.35
6.9
NA
3.4
4.5
ro
.
u»
I
N>
tn
en
I
'References 2-5. NA - not applicable.
Reference 1.
r*
'Reference 6.
Reference 7.
09
nei ei diets i ,
eference 8. Values reflect "as received" conditions of a single power plant.
-------
The quantity of particulate emissions generated by either type of drop
operation, per ton of material transferred, may be estimated, with a rating of
A, using the following empirical expression^:
k(0.0016)
(kg/Mg)
E - k(0.0032)
(Ib/ton)
where: E - emission factor
k - particle size multiplier (dimensionless)
U . mean wind speed, m/s (mph)
M - material moisture content (Z)
The particle size multiplier, k, varies with aerodynamic particle diameter, as
shown in Table 11.2.3-2.
TABLE 11.2.3-2. AERODYNAMIC PARTICLE SIZE MULTIPLIER (k)
<30 urn
0.74
<15 urn
0.48
<10 urn
0.35
<5 urn
0.20
<2.5 urn
0.11
The equation retains the assigned quality rating if applied within the
ranges of source conditions that were tested in developing the equation, as
given in Table 11.2.3-3. Note that silt content is included in Table 11.2.3-3,
even though silt content does not. appear as a correction parameter in the equa-
tion. While it is reasonable to expect that silt content and emission factors
are interrelated, no significant correlation between the two was found during
the derivation of the equation, probably because most tests with high silt
contents were conducted under lower winds, and vice versa. It is recommended
that estimates from the equation be reduced one quality rating level, if the
silt content used in a particular application falls outside the range given in.
Table 11.2.3-3.
9/88
Miscellaneous Sources
11.2.3-3
-------
TABLE 11.2.3-3. RANGES OF SOURCE CONDITIONS FOR EQUATION 1
Silt
Content
0.44 - 19
*
Moisture
Content
0.25 - 4.8
Wind Speed
(n/s) (mph)
0.6 - 6.7 1.3 - 15
Alto, to retain the equation's quality rating when applied to a specific
facility, it is necessary that reliable correction parameters be determined for
Che specific sources of interest. The field and laboratory procedures for
aggregate sampling are given in Reference 3. In the event that site specific
values for correction paraaeters cannot be obtained, the appropriate mean
values from Table 11.2.3-1 may be used, but, in that case, the quality rating
of the equation is reduced by one level.
For emissions from equipment traffic (trucks, front end loaders, dozers,
etc.) traveling between or on piles, it is recommended that the equations for
vehicle traffic on unpaved surfaces be used (see Section 11.2.1). For vehicle
travel between storage piles, the silt value(s) for the areas among the piles
(which may differ from the silt values for the stored materials) should be used.
Worst case emissions from storage pile areas occur under dry windy condi-
tions. Worst case emissions from materials handling operations may be calcu-
lated by substituting into the equation appropriate values for aggregate material
moisture content and for anticipated wind speeds during the worst case averaging
period, usually 24 hours. The treatment of dry conditions for vehicle traffic
(Section 11.2.1), centering on parameter p, follows the methodology described
in Section 11.2.1. Also, a separate set of nonclimatic correction parameters and
'source extent values corresponding to higher than normal storage pile activity
may be Justified for the worst case averaging period.
'11.2.3.4 Controls
Watering and chemical wetting agents are the principal means for control
of aggregate storage pile emissions. Enclosure or covering of inactive piles
to reduce wind erosion can also reduce emissions. Watering is useful mainly to
reduce emissions from vehicle traffic in the storage pile area. Watering of
the storage piles themselves typically has only a very temporary slight effect
on total emissions. A much more effective technique is to apply chemical wet-
ting agents for better vetting of fines and longer retention of the moisture
film. Continuous chemical treatment of material loaded onto piles, coupled
with watering or treatment of roadways, can reduce total participate emissions
from aggregate storage operations by up to 90 percent.9
References for Section 11.2.3
1. C. Cowherd, Jr., et al., Development Of Emission Factors For Fugitive Dust
Sources. EPA-450/3-74-037, U. S. Environmental Protection Agency, Research
Triangle Park, NC, June 1974.
11.2.3-4 EMISSION FACTORS
9/88
-------
APPENDIX C.3
SILT ANALYSIS PROCEDURES
1. Select the appropriate 8 inch diameter 2 inch deep sieve sizes.
Recommended standard series sizes are 3/8 inch No. 4, No. 20, No. 40,
No. 100, No. 140, No. 200, and a pan. Jhe No. 20 and the No. 200 are
mandatory. Comparable Tyler Series sizes can also be used. '
2. Obtain a mechanical sieving device such as a vibratory shaker or a
Roto-Tap (without the tapping function).
3. Clean'the sieves with compressed air and/or a soft brush. Material lodged
in the sieve openings or adhering to the sides of the sieve should be
removed without handling the screen roughly, if possible.
»
4. Obtain a scale with capacity of at least 1600 grams, and record its make,
capacity, smallest increment, date of last calibration, and accuracy.
5. Record the tare weight of sieves and pan, and check the zero before every
.weighing.
6. After nesting the sieves in decreasing order of hole size, and with the
pan at the bottom, dump dried laboratory sample into the top sieve,
preferably immediately after moisture analysis. The sample should weigh
between 800 end 1600 grams (1.8 and 3.5 pounds). Brush fine material
adhering to the sides of the container into the top sieve, and cover the
top sieve with a special lid normally purchased with the pan.
7. Place nested sieves into the mechanical device, and sieve for 10 minutes.
Remove pan"containing minus No. 200 and weigh its contents. Repeat the
sieving in 10 minute intervals until the difference between two successive
pan sample weights is less than 3.0 percent when the tare of the pan has
been substracted. Do not sieve longer than 40 minutes.
8. Weigh each sieve and its contents, and record the weight. Remember to
check the zero before every weighing.
9. Collect the laboratory sample, and place it in a separate container if
further analysis is expected.
10. Calculate the percent of mass less than the 200 mesh screen (75 micro-
meters). This is the silt content.
9/88 Appendix C.3 C.3-1
-------
Attachment 3
Excerpts from
Hazardous Waste TSDF (Treatment, Storage, and Disposal Facilities) :
Fugitive Particulate Matter Air Emissions Guidance Document
(EPA89a)
63
-------
PB90-103250
Hazardous Waste TSDF (Treatment, Storage, and Disposal Facilities)
Fugitive Participate Matter Air Emissions Guidance Document
Midwest Research Inst., Kansas City, MO
Prepared for:
Environmental Protection Agency, Research Triangle Park,, NC
May 89
-------
.Items 1 and 4 (pile formation and removal) fall under the general cate-.
gory of. materials handling as discussed below. Equipment traffic In tw
waste pile area occurs primarily In association >1tn pile formation and'
removal.
3.2.1 Materials Handling
Adding waste material to a storage pile o~ removing 1t usually
Involves dropping the material onto a receiving surface. Truck dumping
on the pile or loading out from the pile to a truck with a front-end
loader are examples of batch drop operations. Adding material to the
pile by a conveyor stacker 1s an example of a continuous drop operation.
The following equation 1s recommended for estimating emissions from
transfer operations (batch or continuous drop):
Uvl-3
k(0.0016) y-*/L4 (kg/Mg)
6) *
(3-1)
1.3
e k(0.0016))5/ (Ib/ton)
where: e emission factor
k particle size multiplier (d1mens1onless)
. U mean wind speed, m/s (mph)
M material moisture content, X
The particle size multiplier k varies with aerodynamic particle diameter
as shown below:
Aerodynamic Particle Size Multiplier, k
<30 UM <1S ua <10 um *S urn <2.5
u u * u . um
TT3T ~OT "OB 0.11
3-5
-------
Attachment 4
Excerpts from National Agronomy Manual (DOA88)
64
-------
Subpart D - WEQ Factors
SUBPART D - WEQ FACTORS
502.31(a)
5502.30 The Wind Erosion Estimate - £.
The wind erosion estimate £ is the estimate of average annual tons
of soil per acre that the vind vill erode from an area represented by
an unsheltered distance L and for the soil, climate, and site
conditions represented by J, £, £, and 2. The equation is an
empirical formula developed by relating vind tunnel data to observed
field erosion during a 3-year period in the mid-1950's [86]. The
field data was normalized to reflect long-term average annual erosion
assuming given conditions during the critical period without reference
to change in those-conditions through the year. The estimate arrived
at by using the critical wind erosion period method does not track
specific changes brought about by management and crop development; nor
does it assume that critical period conditions exist all year. The
calibration procedure accounted for minor changes expected to occur
during a normal crop year at that time in history. However, these
changes were not quantified nor described in the documentation and
field publications. The WEQ annual £ is based on an annual £ and
field conditions during the critical wind erosion period of the year.
This procedure does not account for all the effects of management.
The management period method of estimating wind erosion involves
assigning factor values to represent field conditions expected to
occur during specified time periods. Using annual wind energy
distribution data, erosion can be estimated for each period of time
being evaluated. The time period estimates can be summed to arrive at
an annual estimate. Cropping sequences involving more than one year
can be evaluated using this procedure. It also allows for a more
thorough analysis of a management system and how management techniques
affect the erosion estimate.
§502.31 Soil credibility index - I.
(a) I is the erodibility factor for the soil on the site. It is
expressed as the average annual soil loss per acre that would occur
from wind erosion, assuming the site were
Isolated (incoming saltation is absent).
. Level (knolls are absent).
Smooth (ridge roughness effects are absent).
. Unsheltered (barriers are absent).
. At a location where the £ factor is 100.
. Bare (vegetative cover is absent).
. "Wide" (the distance at which the flow of eroding soil
reaches its maximum and does not increase with field size).
502-15
(190-V-HAM, Second Ed., March 1988)
-------
Part 502 - Wind Erosion
502.31(a)
. Loose and noncrusted (aggregates not bound together, and
surface not sealed. However, clods may be present).
(b) This factor is related to the percentage of nonerodible
surface soil aggregates larger than 0.84 am in diameter. For most SCS
uses, the I value is assigned for named soils based on wind
credibility groups (WEG). The WEG is included on SCS soil
interpretation records. If the soil name is not known, Exhibit
502.61(a) can be used to determine the WEG from the surface soil
texture.
(c) To determine erodibility for field conditions during a
management period, follow sieving instruction in Exhibit 502.61 (b).
(Do not use this procedure to determine average annual I values).
(d) A soil erodibility index based solely on the percentage of
aggregates larger than 0.84 mm has several potential sources of
error. Some of these are:
(1) The relative erodibility of widely different soils may
change with a change in wind velocity over the surface of the soil.
(2) Calibration of the equation is based on the volume of soil
removed, but the erodibility index is based on weight.
(3) Differences in size of aggregates have considerable
influence on erodibility but no distinction for this influence is made
in Table 1, Exhibit 502.61(b).
(4) Stability of surface aggregates influences erodibility;
large durable aggregates can become a "surface armor"; less stable
aggregates can be abraded into smaller, more erodible particles".
(5) Surface crusting can greatly reduce erodibility;
erodibility may increase again as the crust deteriorates [20].
(e) Knoll erodibility.Knolls are topographic features
characterized by short, abrupt windward slopes. Wind erosion
potential is greater on knoll slopes than on level or gently rolling
terrain because wind flowlines are compressed and wind velocity
increases near the crest of the knolls. Erosion that begins on knolls
often affects field areas downwind (figure 502-3).
Adjustments of the Soil Erodibility Index (I) are used where
windward-facing slopes are less than 500 feet long and the increase in
slope gradient from the adjacent landscape is 3 percent or greater.
Both slope length and slope gradient change are determined along the
direction of the prevailing erosive wind (figure 502-4).
502-16
(190-V-HAM, Second Ed., March 1988)
-------
Part 502 - Wind Erosion
502.31(e)
Table 502-1. Knoll credibility adjustment factor for I.
Slope Change
in Prevailing Wind
Erosion Direction
3
4
5
6
8
10
10 - 15*
15 - 20
20+
A
Knoll
Adjustment
to 1
1.3
1.6
1.9
2.3
3.0
3.6
2.0
1.4
1.0
B
Increase
at Crest Area
Where Erosion
Is Most Severe
1.5
1.9
2.5
3.2
4.8
6.8
*Factors above 10 percent slope change based on SCS judgment. Ho
research data available.
No adjustment of I for knoll erodibility is made on level fields, or
on rolling terrain where slopes are longer and slope changes are less
abrupt. Where these situations occur, the wind flow pattern tends to
conform to the surface and does not exhibit the flow constriction
typical of knolls (figure 502-5).
Wind direction *"
Figure 502-5 Windflow over Level or Rolling Surfaces
-------
Subp*r« D - WEQ Factors
502.31(e)
?,
Figure 502-3 Downwind Effect of Knolls
Prevailing wind
t ration direction
Knoll credibility odjuttment
applies here
Deposition occurs her*
Compressed air flow
Greatest credibility
Slope change 23% o""'8 here
Windword slope < 500 feet
Figure 602-4 Knoll Erodlbllity
Table 502-1 contains knoll erodibility adjustment factors for the Soil
Zrodibility Index I. The I value for the Wind Erodibility Group is
multiplied by the factor' shown in Column A. This adjustment expresses
the average increase in credibility along the knoll slope. For
comparison, Column B shows the increased erodibility near the crest
(about the upper 1/3 of the slope), vhere the effect is most severe.
(190-V-RAM, Second Ed., March 1988)
502-17
-------
Subpart D - WEQ Factors
SUBPAET D - WEQ FACTORS
502.31(a)
SS02.30 The Wind Erosion Estimate - £.
The wind erosion estimate £ is the estimate of average annual tons
of soil per acre that the vind vill erode from an area represented by
an unsheltered distance It and for the soil, climate, and site
conditions represented by I, g, £, and 2. The equation is an
empirical formula developed by relating vind tunnel data to observed
field erosion during a 3-year period in the mid-1950's [86]. The
field data vas normalized to reflect long-term average annual erosion
assuming given conditions during the critical period without reference
to change in those-conditions through the year. The estimate arrived
at by using the critical vind erosion period method does not track
specific changes brought about by management and crop development; nor
does it assume that critical period conditions exist all year. The
calibration procedure accounted for minor changes expected to occur
during a normal crop year at that time in history. However, these
changes were not quantified nor described in the documentation and
field publications. The WEQ annual £ is based on an annual £ and
field conditions during the critical vind erosion period of the year.
This procedure does not account for all the effects of management.
The management period method of estimating vind erosion involves
assigning factor values to represent field conditions expected to
occur during specified time periods. Using annual vind energy
distribution data, erosion can be estimated for each period of time
being evaluated. The time period estimates can be summed to arrive at
an annual estimate. Cropping sequences involving more than one year
can be evaluated using this procedure. It also allows for a more
thorough analysis of a management system and hov management techniques
affect the erosion estimate.
{502.31 Soil credibility index - I.
(a) I is the credibility factor for the soil on the site. It is
expressed as the average annual soil loss per acre that vould occur
from vind erosion, assuming the site vere
. Isolated (incoming saltation is absent).
. Level (knolls are absent).
. Smooth (ridge roughness effects are absent).
. Unsheltered (barriers are absent).
. At a location vhere the £ factor is 100.
. Bare (vegetative cover is absent).
. "Vide- (the distance at which the flov of eroding soil
reaches its maximum and does not increase vith field size).
502-15
(190-V-KAM, Second Ed., March 1988)
-------
Attachment 4
Excerpts from National Agronomy Manual (DOA88)
64
-------
Subpart D - VZQ Factors
502.31(e)
Flour* 602-3 Downwind Effect of Knolls
Prevailing wind
trosion direction
Knoll erodibility odjuttment
opplles here
Deposition occur* here
Compressed oir flow
Greatest trodibility
occurs here
Windward slope < 500 feet
Flgurt) 502-4 Knoll Erodlbllity
Table 502-1 contain* knoll erodibility adjustment factors for the Soil
Krodibility Index I. The I ralue for the Wind Erodibility Group is
tniltiplied by the factor* shown in Column A. This adjuatnent ezpreases
the averafe increase in erodibility along the knoll slope. For
comparison, Column B shows the increased erodibility near the crest
(about the upper 1/3 of the slope), where the effect is most severe.
(190-V-RAM, Second Ed., March 1988)
502-17
-------
Attachment 5
Excerpts from NUREG-0570 (NRC79)
65
-------
NUREG-0570
TOXIC VAPOR CONCENTRATIONS IN THE
CONTROL ROOM FOLLOWING A
POSTULATED ACCIDENTAL RELEASE
Prepared by
James Wing
Manuscript Completed: May 19V»
Date Published: June 1979
Division of Site Safety and Environmental Analysis
Office of Nuclear Reactor Regulation
U. S. Nuclear Regulatory Commission
Washington, D.C. 20555
-------
D.R = 0.0018583 (T + 273)3/2 (n + i)1/2
MD J MA MB (2.1.-14)
P(JAB2 QAB
where M. = molecular weight of gas A (g/mole)
Mg = molecular weight of gas B (g/mole)
P = atmospheric pressure (atm)
a = Lennard - Jones parameter
fi.g = dimensionless function of temperature and intermolecular
potential field E.g
The Lennard-Jones parameter is empirically estimated to be an arithmetic
mean of the two gases:
OAB = (OA+ OB) /2 (2.1-15)
The intermolecular potential field is empirically estimated to be a
geometric mean of the two gases:
EAB = (EA Eg)1* . . (2.1-16)
The values of a, E, and fi for a few compounds are available in the
literature (Bird et al, p. 744; Reid and Sherwood, p. 524, p. 632).
Eventually, the air space in the confined building will be saturated
with the toxic vapor of the spill, and the vapor concentration will .
approach the following value (assuming ideal-gas behavior of the vapor):
Cs = a Ps M
Rg (Ta * 273) (2.1-17)
where Cs = saturation concentration (g/cm )
R = universal gas constant
9
-11-
-------
M = molecular weight of the liquid (g/mole)
Tg = ambient temperature (°C)
PS = saturation vapor pressure of the liquid (mm Hg)
*
This would be the maximum vapor concentration of a liquid that is spilled in
a confined space, such as a basement.
2.1.3.2 .Mass Transfer by Forced Convection
The evaporation of a liquid in an open space with wind or in a
confined area with good ventilation can be described as a mass transfer
process by forced convection.
The evaporation rate may be calculated by the "following formulas
(Eckert and Drake, pp. 470-476):
(dmy/dt) = hd M A(t) (Ps - Pa)/Rg (Tfl + 273) (2.1-18)
where, for a laminar flow (Eckert and Drake, pp. 176, 177, 475):
hd = 0.664 £ (Re)1/2 (Sc)1/3 (2.1-19)
and for a turbulent flow (Eckert and Drake p. 215):
hd = 0.037 £ (Re)0'8 (Sc)1/3 (2.1-20)
Re = Reynold number = L u p/u
Sc = Schmidt number = u/Dp
hrf = mass transfer coefficient (cm/sec)
R = universal gas constant
2
D = diffusion coefficient (cm /sec.)
u = wind speed (cm/sec)
o
p = density of air (g/cm )
-12-
-------
L = characteristic length (cm)
u = viscosity of air (g/cm sec)
M = molecular weight of the liquid (g/mole)
PS = saturation vapor pressure of the liquid at temperature
Ta (mm Hg)
Pa = actual vapor pressure of the liquid in air (mm Hg)
For water, P may be computed from the relative humidity (List,
a
p. 347). For other liquids, P_ would normally be zero.
a
The diameter of the spill area may be taken as the characteristic
length, L. The spill area and thus the characteristic length vary with
time (eq. (2.1-1)).
"2.1.4 Comparison of Calculations With Empirical Data
A few calculations of evaporation rates are compared with the available
empirical data. However, the empirical observations were made on evapora-
tion of watery from confined sources, such as pools and ponds, where the
surface areas are already fixed. Therefore, it is not possible to check
the dynamic process of simultaneous spreading and evaporation of the
liquid.
2.1.4.1 Annual Evaporation of Water
The annual evaporation of water from a standard evaporation pan of
the Weather Bureau has been measured at many stations throughout the
United States. The data for the period of 1946-1955 have been collected
(Kohler et al.).
-13-
-------
The annual mean temperatures, wind speeds, and relative humidities
of several geographic regions of the United States have been compiled
(Lerner, pp. 182-193). Using this information, it is possible to calculate
the annual evaporation of water by eqs. (2.1-18) and (2.1-19). A
comparison of the calculated annual evaporation using eq. (2.1-19) (laminar
flow) with the experimental data for ten widely separated locations is
presented in Table 1. In the calculations, the arithmetic mean of the
relative humidities was used for each location, and the characteristic
length was 1.2m (diameter of the standard evaporation pan of the National
Weather Service). The agreement is good, with the maximum variation
being within a factor of 2.
2.1.4.2 Empirical Formulas for Evaporation of Water
Several formulas for the prediction of evaporation of water from
pools have been developed empirically (Chow, 11-4; Merritt; Patterson, et
al.; Davis and Sorensen). These are summarized in Table 2 together with
the calculated*evaporation rates for water at 38°C with a relative humidity
of 10% and wind speed of 1 m/sec. The experimental datum obtained under
this condition for evaporation on drying trays (Bolz and Tuve) is also
shown. Using L = 1.2 m, the evaporation rates computed from eqs. (2.1-18)
and (2.1-19) (laminar flow) are also presented in Table 2.
The empirical formulas predict evaporation rates ranging from 0.070
o 2
to 0.44 g/m sec, with an average of 0.26 g/m sec. The agreement between
the empirical formulas and the experimental value, and hence eqs. (2.1-18)
and (2.1-19), is within a factor of 2 in most cases. The empirical
-14-
-------
Table 1 Comparison of Calculated Evaporation Rates
cf Water With Experimental Data*
Location Annual
Average
Temperature
(°C)
Phoenix, AR
Los Angeles, CA
Denver, CO
Louisville, KY
New Orleans, LA
Portland, ME
Albuquerque, NM
Blsmark, ND
El Passo, TX
Seattle, WA
21.3
16.5
11.1
13.1
20.2
7.2
13.8
5.2
17.4
10.6
Annual Average Annual
Average Relative Evaporation
Wind Speed Humidity (cm)
(cm/sec) (X) Experimental Eq. (2.1-19)
High-Low
268
277
402
371
375
393
398
478
434
420
53-32
75-53
69-41
81-59
88-63
80-60
57-37
78-56
52-35
83-74
183
117
81
91
124
61
137
86
183
61
179
85
91
66
84
46
126
49
177
43
*Data for annual average temperatures, wind speeds, and relative humidity obtained
from Lerner, pp. 182-193. Data for annual evaporation from Kohler et al.
- 15 -
-------
Table 2 Comparison of Calculated Evaporation Rates
of Water by Several Formulas
Ambient Temperature = 38°C
Relative Humidity = 10%
Wind Speed = 1 m/sec
Formula
FitzGerald
Meyer, small pool:
large reservoir:
Morton
Rohwer
Harbeck
Patterson
Kohler, Davis
Equation
E = (0.4 + 0.199 w) (ps-pa)
E = 0.5 (1 + 0.1 w) (ps-pa)
E = 0.37 (1 + 0.1 w) (ps-pa)
E = 0.4 {ps C2-exp(-0.2w)l - pa}
E = 0.771 (0.44 + O.llSw) (1.465-0.0186P)
(ps-pa)
E = 0.0599 w (ps-pa)
E = 0.345 (1 + O.lw) (ps-pa)
E = (0.37 +0.1 w) (ps-pa)0'88
Evaporation Rate;
2
(q/m sec)
0.44
0.32
0.23
0.29
0.26
0.070
0.22
0.29
Experimental
Bolz and Tuve
Eq. (2.1-19)
Drying trays
0.15
0.14
E = evaporation rate (inches/day)
w = wind speed (miles/hr)
P, ps, pa in inches Hg.
-16-
-------
expressions do not account for the ambient temperature, except through
its influence on vapor pressures, or for the reservoir size. Both of
these factors are accounted for in eqs. (2.1-18) and (2.1-19). Under
these circumstances, it is not expected that the agreement between the
empirical expressions and eqs. (2.1-18) and (2.1-19) to be better than a
factor of 2.
2.2 Vapor Dispersion
The vapor from instantaneous flashing (puff) and from continuous
vaporization or evaporation (plume) moves in the direction of the wind
and disperses by diffusion into the atmosphere. The dispersion is assumed
- to follow a Gaussian distribution for short travel times (a few minutes
to one hour). That is, an individual puff may or may not be well-described
by a Gaussian formulation, but an ensemble of puffs is assumed to disperse
in a Gaussian function. This diffusion model is applicable only to the
*-
vapors whose densities do not differ greatly from that of air (Slade).
The wind is assumed to be in the direction from the source of spill to
the control room air intake. It should be noted that topological variations
of the terrain between the source and receptor are ignored in this treatment.
2.2.1 Instantaneous Puff Release
The diffusion equation for an instantaneous puff with a finite initial
volume and a receptor at the air intake is given by the following equation
(Yanskey, et al., 3-2; Slade, p. 115):
-17-
-------
x(x, y, z, h) =
/ 9
(2rt
- exp { - 1
+ yl_ 1
O 2J
{ exp! _ 1
J
exp
1
1
(2.2-1)
Where x = concentration (g/m )
Q = source strength (g) = m
°XI' °YI* CTZI = adJusted standard deviations of the puff concentration
in the horizontal-along-wind (X), horizontal cross-wind (Y), and vertical
cross-wind directions (Z), respectively (m).
x, y, z = distances from the puff center in the X, Y, and Z directions,
respectively (m). z is also the effective above-ground elevation of the
receptor, e.g., the fresh-air intake of a control room.
h = effective above-ground elevation of the source.
To account for the initial volume of the puff, it is assumed that
2
=
2
'
-2
XI
-2
YI * °o
rr
- °XI = oYI
and letting x = XQ - ut
°o =
3/2 _ 31/3
(2.2-2)
(2.2-3)
(2.2-4)
(2.2-5)
(2.2-6)
(2.2-7)
where
o = initial standard deviation of the puff (m)
o
aXI' °YI* °ZI = standard deviation of puff concentration in the
-18-
-------
where: k panicle size multiplier
N number of disturbances per year
P1 erosion potential corresponding to the observed (or probable)
fastest Bile of wind for the 1th period between disturbances.
The particle size Multiplier (k) for Equation 4-3 varies with
aerodynamic particle size, as follows:
AERODYNAMIC PARTICLE SIZE MULTIPLIERS FOR EQUATION 4-3.
<30 urn <15 urn <10 urn <2.5 un
urn <15 urn <1D urn
JoO57T
0.2
This distribution of particle size within the <30 urn fraction 1s
comparable to the distributions reported for other fugitive dust sources
where wind speed 1s a factor. This 1s Illustrated, for example. 1n the
distributions for batch and continuous drop operations encompassing a
number of test aggregate materials (see AP-42 Section 11.2.3).
In calculating emission factors, each area of an credible surface
that Is subject to a different frequency of disturbance should be treated
separately. For a surface disturbed dally, N 365/yr, and for a surface
disturbance once every 6 no, N - 2/yr.
The erosion potential function for a dry, exposed surface has the
following fora:
P 58 (u* - u*)» * 25 (u* - u*)
(4-4)
P » 0 for u* 5 uj
where: u* » friction velocity (m/s)
u£ threshold friction velocity (m/s)
Table 4-2 presents the erosion potential function in matrix form.
Because of the nonlinear form of the erosion potential function, each
erosion event must be' treated separately.
4.7
-------
TABLE 4-2. EROSION POTENTIAL FUNCTION
«..
/
0.2
0.4
0.6
0.8
.0
.2
.4
.6
.8
2.0
2.2
2.4
2.6
2.8
3.0
0
7
19
36
37
83
114
149
188
233
282
336
394
457
525
0
0
7
19
36
37
83
114
149
188
233
282
336
394
457
0
0
0
7
19
36
57
83
114
149
188
233
282
336
394
0
0
0
0
7
19
36
57
83
114
149
188
233
282
336
0
0
0
0
0
7
19
36
57
83
114
149
188
233
282
P <«
0
0
0
0
0
0
7
19
36
57
83
114
149
188
233
/^)
0
0
0
0
0
0
0
7
19>
36
37
83
114
149
188
0
0
0
0
0
0
0
0
7
19
36
57
83
114
149
0
0
0
0
0
0
0
0
0
7
19
36
57
83
114
0
0
0
0
0
0
0
0
0
0
7
19
36
57
83
0
0
0
0
0
0
0
0
0
0
0
7
19
36
37
0
0
0
0
0
0
0
0
0
0
0
0
7
19
36
4-8
-------
Equations 4-3 and 4-4 apply only to dry, exposed materials with
Halted erosion potential. The resulting calculation 1s valid only for a
tloe period as long or longer than the period between disturbances.
Calculated enlsslons represent Intermittent events and should not be Input
directly Into dispersion models that assune steady state emission rates.
For uncrusted surfaces, the threshold friction velocity 1s best
estimated from the dry aggregate structure of the soil. A simple hand
sieving test of surface soil (adapted from a laboratory procedure
published by W. S. Chepll*) can be used to determine the mode of the
surface aggregate size distribution by Inspection of relative sieve catch
amounts, following the procedure specified 1n Section 6. The threshold
friction velocity for erosion can be determined from the mode of the
aggregate size distribution, as described by Gillette.'° This conversion
1s also described 1n Section 6.
Threshold friction velocities for several surface types have been
determined by field measurements with a portable wind tunnel.i°-»J These
values are presented 1n Tables 4-3 and 4-4 for Industrial aggregates and
Arizona sites. Figure 4-2 depicts these data graphically.
The fastest mile of wind for the periods between disturbances may be
obtained from the monthly LCD summaries for the nearest reporting weather
station that 1s representative of the site 1n question.'- These summaries
report actual fastest mile values for each day of a given month. Because
the erosion potential 1s a highly nonlinear function of the fastest mile,
mean values of the fastest mile are Inappropriate. The anemometer heights
of reporting weather stations are found 1n Reference 15, and should be
corrected to a 10 m reference height using Equation 4-2.
To convert the fastest mile of wind
-------
TABLE 4-3. THRESHOLD FRICTION VELOCITIESINDUSTRIAL AGGREGATES
Threshold wind
Material
Overburden*
Scoria (roadbed
ateHal)*
Ground coal*
(surrounding coal
Pile)
Uncrusted coal pile*
Scraper tracks on
coal pile**6
Fine coal dust on
concrete padc
Threshold
friction
velocity,
n/s
1.02
1.33
0.55
1.12
0.62
0.54
velocity at
Roughness
height,
on
0.3
0.3
0.01
0.3
0.06
0.2
10 m
actual
21
27
16
23
15
11
(a/s)
fcf,.
19
25
10
21
12
10
Ref.
7
7
7
7
7
12
*Westem surface coal «1ne.
°Llghtly crusted.
CEastern power plant.
4-10
-------
TABLE 4-4. THRESHOLD FRITION VELOCITIESARIZONA SITES"
Location
Threshold
friction
velocity,
/sec
Roughness
height,
(a)
Threshold
wind velocity
t 10 B.
/sec
Mesa - Agricultural site 0.57
Glendale - Construction site 0.53
Narlcopa - Agricultural site 0.58
Yona - Disturbed desert 0.32
Yuma Agricultural site 0.58
Algodones - Dune flats 0.62
Yuna - Scrub desert 0.39
Santa Cruz River, Tucson 0.18
Tucson - Construction site 0.25
AJo Nine tailings 0.23
Hayden - Nine tailings 0.17
Salt River. Mesa 0.22
Casa Grande - Abandoned 0.25
agricultural land
0.0331
0.0301
0.1255
0.0731
0.0224
0.0166
0.0163
0.0204
0.0181
0.0176
0.0141
0.0100
0.0067
16
15
14
8
17
18
11
5
7
7
5
7
8
4-11
-------
Fof narrowly «l»ed, finely divided materials only
1
A00'*0*l* *
dlilrlbiillon mod* n' U«MM»H
Gravel ^
__
Coarsa
Sand H
Fine
Sand "
X-
03
02
di
w
005
001
(In) (mm) (cnVs)
0
7
.6
6
4
3
-
2
_
1
05
t
01
002
-
-
-
-
-
-
-
-
-
ISO
"S""-"**
IM***!.*,*.
U« ^.
100 C-1.TJ_1
a**«<»4tt.
ssrr*^
so f^S3*«i"
0
Figure 4-2. Scale of threshold friction velocities.
-------
This assumes a typical roughness height of 0.5 on for open terrain.
Equation 4-5 Is restricted to large relatively flat piles or exposed areas
with Uttle penetration -Into the surface wind layer.
If the pile significantly penetrates the surface wind layer (I.e.,
with a helght-to-base ratio exceeding 0.2). It Is necessary to divide the
pile area Into subareas representing different degrees of exposure to
wind. The results of physical odellng show that the frontal face of an
elevated pile 1s exposed to wind speeds of the sane order as the approach
wind speed at the top of the pile.
For two representative pile shapes (conical and oval with flat-top,
37 degree side slope), the ratios of surface wind speed (u$) to approach
wind speed (ur) have been derived from wind tunnel studies." The results
are shown 1n Figure 4-3 corresponding to an actual pile height of 11 . a
reference (upwind) anemometer height of 10 «, and a pile surface roughness
height (2Q) of 0.5 OB. The Measured surface winds correspond to a height
of 25 en above the surface. The area fraction within each contour pair 1s
specified 1n Table 4-5.
The profiles of us/ur 1n Figure 4-3 can be used to estimate the
surface friction velocity distribution around.similarly shaped piles,
using the following procedure: ' *
1. Correct the fastest alle value (u*) for the period of Interest
from the anemometer height (z)'to a reference height of 10 n
(uf0) using a variation of Equation 4-2, as follows:
« . * in go/o.oos)
U'o " u In (z/6.6fl5J
where a typical roughness height of 0.5 en (0.005 ) has been
assumed.' If a site specific roughness height 1s available, 1t
should be used.
2. Use the appropriate part of Figure 4-3 based on the pile shape
and orientation to the fastest mile of wind, to obtain the
corresponding surface wind speed distribution (U), I.e.,
4-13
-------
Flow
, Direction
Pile A
Pile B1
Pile B2
Pile B3
Figure 4-3. Contours of normalized surface wind
speeds, us/ur.
-------
TABLE 4-5. SUBAREA DISTRIBUTION FOR REGIMES OF us/ur
Percent of pile surface area (Figure 4-3)
Pile subarea Pile A ' Pile Bl Pile 82 Pile B3
0.2a 5533
0.2b 35 2 28 25
0.2c 29
0.6a 48 26 29 28
~0.6b - 24 22 26
0.9 12 14 15 14
1.1 3 4
4-15
-------
3. For any subarea of the pile surface having a narrow range of
surface wind speed, use e variation of Equation 4-2 to calculate
the equivalent friction velocity (u*). as follows:
0.4 u*
u* K* 0.10 u* (4-8)
From this point on, the procedure 1s Identical to that used for a
flat pile, as described above.
Implementation of the above procedure 1s carried out 1n the following
steps:
1. Determine threshold friction velocity for credible material of
Interest (see Tables 4-3 and 4-4 or Figure 4-2 or determine from mode of
aggregate size distribution).
2. Divide the exposed surface area Into subareas of constant
frequency of disturbance (N).
3. Tabulate fastest mile values (u*) for each frequency of
disturbance and correct them to 10 m (ut«) using Equation 4-6.
4. Convert fastest mile values (ut«) to equivalent friction
velocities (u*), taking Into account (a) the uniform wind exposure of
nonelevated surfaces, using Equation 4-5, or (b) the nonunlfonn wind
exposure of elevated surfaces (piles), using Equations 4-7 and 4-8.
5. For elevated surfaces (piles), subdivide areas of constant N Into
subareas of constant u* (I.e., within the Isopleth values of u$/ur 1n
Figure 4-3 and Table 4-5) and determine the size of each subarea.
6. Treating each subarea (of constant N and u*) as a separate
source, calculate the erosion potential (P^) for each period between
disturbances using Equation 4-4 and the emission factor using
Equation 4-3.
7. Multiply the resulting emission factor for each subarea by the
size of the subarea. and add the emission contributions of all subareas.
Note that the highest 24-h emissions would be expected to occur on the
windiest day of the year. Maximum emissions are calculated assuming a
single wind event with the highest fastest mile value for the annual
period.
4-16
-------
The reconnended emission facto- equation presented above assumes that
all of the erosion potential corresponding to the fastest mile of wind Is
lost during the period between disturbances. Because the fastest mile
event typically lasts only about 2 Bin, which corresponds roughly to the
half-life for the decay of actual erosion potential, 1t could be argued
that the emission factor overestimates part 1cu late Missions. However,
there are other aspects of the wind erosion process which offset this
apparent conservatism:
1. The fastest Bile event contains peak winds which substantially
exceed the mean value for the event.
2. Whenever the fastest mile event occurs, there are usually a
number of periods of slightly lower mean wind speed which contain peak
gusts of the same order as the fastest mile wind speed.
Of greater concern 1s the likelihood of overpredlctlon of wind
erosion emissions 1n the case of surfaces disturbed Infrequently 1n
comparison to the rate of crust formation.
4.1.3 Wind Emissions From Continuously Active Piles
For emissions from wind erosion of active storage piles, the
following total suspended part 1cu late (TSP) emission factor equation 1s
recommended:
(4-9)
where: E total suspended paniculate emission factor
s silt content of aggregate, percent
p number of days with ±0.25 am (0.01 in.) of precipitation per
year
f percentage of time that the unobstructed wind speed exceeds
5.4 m/s (12 mph) at the mean pile height
The fraction of TSP which Is PM,0 is estimated at 0.5 and 1s
consistent with the PM;-8/TSP ratios for materials handling (Section 4.1.1)
and wind erosion (Section 4.1.2). The coefficient 1n Equation (4-9) 1s
taken from Reference 1, based on sampling of emissions from a sand and
4-17
-------
gravel storage pile area during periods when transfer and maintenance
equipment was not operating. The factor from Reference 1, expressed 1n
ass per unit area per day. 1s wort reliable than the factor expressed 1n
ass per unit mass of Mterial placed 1n storage, for reasons stated 1n
that report. Note that the coefficient has been halved to adjust for the
estimate that the wind speed through the emission layer at the test site
was one half of the value measured above the top of the piles. The other
terns 1n this equation were added to correct for silt, precipitation, and
frequency of high winds, as discussed 1n Reference 2. Equation (4-9) 1s
rated In AP-42 as C for application In the sand and gravel Industry and 0
for other Industries (see Appendix A).
Worst case emissions from storage pile areas occur under dry windy
conditions. Worst case emissions from materials handling (batch and
continuous drop) operations may be calculated by substituting Into
Equation (4-9) appropriate values for aggregate material moisture content
and for anticipated wind speeds during the worst case averaging period,
usually 24 h. The treatment of dry conditions for vehicle traffic
(Section 3.0) and for wind erosion (Equation. 4-9), centering around
parameter p. follows the methodology described 1n Section 3.0. Also, a
separate set of none11mat1c correction parameters and source extent values
corresponding to higher than normal storage pile activity may be justified
for the worst case averaging period*
4.2 DEMONSTRATED CONTROL TECHNIQUES
The control techniques applicable to storage piles fall Into distinct
categories as related to materials handling operations (Including traffic
around piles) and wind erosion. In both cases, the control can be
achieved by (a) source extent reduction, (b) source Improvement related to
work practices and transfer equipment (load-In and load-out operations),
and (c) surface treatment. These control options are summarized In
Table 4-6. The efficiency of these controls ties back to the emission
factor relationships presented earHer.in this section.
. *
In most cases, good work practices which confine freshly exposed
material provide substantial opportunities for emission reduction without
the need for Investment 1n a control application program. For example,
pile activity, loading and unloading, can be confined to leeward
(downwind) side of the pile. This statement also applies to areas around
4-18
-------
Topsoll removal: 5.7 kg./^T for pan scrapers
Earthmovlng: 1.2 kg/VICT for pan scrapers
Truck haulage: ' 2.8 kg/VKT for haul trucks
PH10 emissions due to Materials handling and wind erosion of exposed areas
can be calculated using the emission factors presented In Sections 4.0 and
6.0, respectively.
5.1.2 Demolition Emissions
For demolition sites, the operations Involved 1n demolishing and
removing structures from a site are:
Mechanical or explosive dismemberment
Debris loading
Onslte truck traffic
Pushing (dozing) operations
5.1.2.1 Dismemberment. Since no emission factor data are available
for blasting or wrecking a building, the first operation 1s addressed
through the use of the revised AP-42 nateHals handling equation:3,*
Ej - k(0.0016) 1 4 (5-1)
(?) '
where EQ PM10 emission factor In kg/Mg of material
k particle size multiplier'- 0.35 for PH10
U mean wind speed In «/s (default 2.2 m/s)
M material moisture content 1n percent (default « 2 percent)
and EQ 0.00056 kg/Mg (with default parameters)
The above factor can be modified for waste tonnage related to
structural floor space where 1 m* of floor space represents 0.45 Mg of
waste material (0.046 ton/ft:).' The revised emission factor related to
structural floor space (using default parameters) can be obtained by:
En 0.00056 kg/Mg °'45 **
'
- 0.00025 kg/m:
5-3
-------
5.1.2.3 Debris Loading. Tht Emission factor for debris loading 1s
based on two tests of the filling of trucks with crushed limestone using a
front-end loader which Is part of the test basis for the batch drop
equation 1n AP-42, § 11.2.3.* The resulting emission factor for debris
loading 1s:J
0.45
E. * k(0.029) kg/Mg
* »
0.0046 kg/m»
where 0.029 kg/Mg 1s the average measured TSP emission factor and k 1s the
particle size multiplier (0.35 for PM10).
5.1.2.4 Ons 1te Truck Traffic. Emissions from onslte truck traffic
1s generated from the existing AP-42 unpaved road equation presented 1n
Section 3.0 above. >
E 1-7 k (HjHry) ((S} (5-2)
where E PM,0 emission factor 1n kg/vehicle kilometer traveled (VKT)
k particle size multiplier 0.36 for PN10 .
s « silt content In percent (default 12 percent)
S truck speed 1n km/h (defaglt » 16 kra/h)
U - truck weight 1n Mg (default 20 Hg)
w - number of truck wheels (default 10 wheels)
p « number of days with measurable precipitation
(default « 0 days)
and ET 1.3 kg/VKT (with default values)
The above factor 1s converted from kg/VKT to kg/m* of structural
f-loor space by:J
£ m 0.40 km . 1 m3 waste . 7.65 mi volume . 1.3 kg
23 mJ waste 4 m' volume 0.836 m* floor space VKT
« 0.052 kg/mi
5.1.2.5 Pushing Operations. For pushing (bulldozer) operations, the
AP-42 emission factor equation for overburden removal at Western surface
5-4
-------
coal mines can be used.' Although this equation actually relates to par-
tlculate <15 umA, 1t would be expected that the PM10 emissions from such
operations would be generally comparable. The AP-42 dozer equation 1s:
,
P (M)1'4
where Ep PM,0 emission rate 1n kg/h
S silt content of surface material 1n percent
«
(default « 6.9 percent)
M moisture content of surface material 1n percent
(default 7.9 percent)
and Ep 0.45 kg/h (with default parameters)
Finally. PH,0 emissions due to wind erosion of exposed areas can be
calculated as discussed 1n Section 6.0. In general, these emissions are
expected to be minor as compared to other sources.
5.1.3 Mud/Dirt Carryout Emissions
Finally, the Increase 1n emissions on paved roads due to mud/dirt
carryout have been developed based on surface loading measurements at
eight sites.* Tables 5-2 and 5-3 provide these emission factors in terms
of gm/vehlcle pass which represent PM;0 generated over and above the
background* for the paved road sampled. Table 5-2 expresses the emission
factors according to the volume of traffic entering and leaving the site
whereas Table 5-3 expresses the same data according to type of
construction.
5.2 DEMONSTRATED CONTROL TECHNIQUES
As discussed above, similar generic open dust sources "exist at both
construction and demolition sites. Therefore, similar types of controls
would also apply. In this section, a' discussion 1s provided on the
various tecnnlques available for the control of open dust sources
associated with construction and demolition. Detailed Information on
control efficiency, Implementation cost, etc., will be presented in
Section 5.3 below.
5-5
-------
TABLE 5-2. EMISSIONS INCREASE UE) BY SITE TRAFFIC VOLUME* '
Sites with >25
Particle
size .
fraction"
<-30 uB
<10 UB
<2.5 u>
Mean,
X
52
13
5.1
Standard
devia-
tion, 0
28
6.7
2.6
veh1de/d
Range
15-80
4.4-20
1.7-7.8
Sites
Mean,
X
19
5.5
2.2
with <25 veh1cle/d
Standard
devia-
tion, v
7.8
2.3
0.88
Range
14-28
4.2-8.1
1.6-3.2
*aE expressed In g/veh1c1e pass.
Aerodynamic diameter.
TABLE -5-3.
Particle
size
fraction0
<-30 uB
<10 uB
Mean,
X
65
16
<2.5 uffl 6.3
EMISSIONS
Conmerdal
Standard
devia-
tion, 0
39
9.3
3.6
INCREASE* (AE)
Range
15-110
4.2-25
1.6-9.7
BY CONSTRUCTION TYPE*
Mean,
X
39
10
3.9
Residential
Standard
devia-
tion, o
»
22
5.4
2.1
Range
10-72
2.8-19
1.1-7.3
*iE expressed 1n g/vehlcle pass.
"Aerodynamic diameter.
5-6
-------
6.0 OPEN AREA WIND EROSION
Oust emissions My be generated by wind erosion of open agricultural
land or exposed ground areas on public property or within an Industrial
facility.
With regard to estimating part1culate emissions from wind erosion of
exposed surface material, site Inspection can be used to determine the
potential for continuous wind erosion. The two basic requirements for
wind erosion are that the surface be dry and exposed to the wind. For
example. 1f the contaminated site lies 1n a swampy area or 1s covered by
unbroken grass, the potential for wind erosion 1s virtually nil. If. on
the other hand, the vegetative cover 1$ not continuous over the exposed
surface, then the plants are considered to be nonerodlble elements which
absorb a fraction of the wind stress that otherwise acts to suspend the
Intervening soil.
For estimating emissions from wind erosion, either of two emission
factor equations are recommended depending on the credibility of the
surface material. Based on the site survey, the exposed surface must be
placed 1n one of two credibility classes described below. -The division
between these classes 1s best defined 1n terms of the threshold wind speed
for the onset of wind erosion.
Nonhomogeneous surfaces Impregnated with nonerodlble elements
(stones, clumps of vegetation, etc.) are characterized by the finite
availability ("limited reservoir") of credible material. Such surfaces
have high threshold wind speeds for wind erosion, and part 1culate emission
rates tend to decay rapidly during an erosion event. On the other hand,
bare surfaces of finely divided material such as sandy agricultural soil
are characterized by an "unlimited reservoir" of credible, particles. Such
surfaces have low threshold wind speeds for wind erosion, and partlculate
emission rates are relatively time independent at a given wind speed.
For surface areas not covered by continuous vegetation, the
classification of surface material as either having a "limited reservoir"
or an "unlimited reservoir' of erodlble surface particles Is determined by
estimating the threshold friction velocity. Based on analysis of wind
erosion research, the dividing line for the two credibility classes 1s a
6-1
Preceding page blank
-------
threshold friction velocity of about 50 on/s. This somewhat arbitrary
division 1s based on the observatlcr. that highly credible surfaces;
usually corresponding to sandy surface soils that are fairly deep, -have
threshold friction velocities below 50 cn/s. Surfaces with friction
velocities larger than 50 cn/s tend to be composed of aggregates too large
to be eroded «1xed 1n with a snail amount of credible Material or of
crusts that are resistant to erosion.'
The cutoff friction velocity of 50 on/s corresponds to an ambient
wind speed of about 7 /$ (15 mph), measured at a height of about 7 m. In
turn, a specific value of threshold friction velocity for the credible
surface Is needed for either wind erosion emission factor equation
(model).
Crusted surfaces are regarded as having a 'limited reservoir" of
credible particles. Crust thickness and strength should be examined
during the site Inspection, by testing with a pocket knife. If the crust
1s more than 0.6 cm thick and not easily crumbled between the fingers
(modulus of rupture >1 bar), then the soil may be considered non-
erodlble. If the crust thickness Is less than 0.6 cm or 1s easily
crumbled, then the surface should be treated as having a limited reservoir
of credible particles. If a crust 1s found beneath a loose deposit, the
amount of this loose deposit, which constitutes the limited erosion
reservoir, should be carefully estimated.
For uncrusted surfaces, the threshold friction velocity 1s best
estimated from the dry aggregate structure of the soil. A simple hand*
sieving test of surface soil 1s highly desirable to determine the mode of
the surface aggregate size distribution by Inspection of relative sieve
catch amounts, following the procedure specified 1n Figure 6-1. The
threshold friction velocity for erosion can be determined from the mode of
the aggregate size distribution, following a relationship derived by
Gillette (1980) as shown 1n Figure 6-1.-'
A more approximate basis for determining threshold friction velocity
would be based on hand sieving with just one sieve, but otherwise follows
the procedure specified in Figure 6-2. Based on the relationship
developed by Blsal and Ferguson (1970), 1f more than 60 percent of the
5-2
-------
I
CJ
u
01
U
o
I
u
r-
U.
a
*o
i/»
i
IOOO
lop
10
I «*!
0.1
I 10
Aggregate Size Distribution Mode (m)
100
Figure 6-1. Relationship of threshold friction velocity to size distribution node.
-------
FIELD PROCEDURE FOR DETERMINATION OF THRESHOLD FRICTION VELOCITY*
1. PREPARE A NEST OF SIEVES WITH THE FOLLOWING OPENINGS: 4 on, 2 ».
1 on. 0.5 on. 0.25 m. PLACE A COLLECTOR PAN BELOW THE BOTTOM SIEVE
(0.25-MB OPENING).
2. COLLECT A SAMPLE REPRESENTING THE SURFACE LAYER OF LOOSE PARTICLES
(APPROXIMATELY 1 on IN DEPTH FOR AN UNCRUSTEO SURFACE). REMOVING ANY
ROCKS LARGER THAN ABOUT 1 on IN AVERAGE PHYSICAL DIAMETER. THE AREA
TO BE SAMPLED SHOULD NOT BE LESS THAN 30 Oi x 30 ou
3. POUR THE SAMPLE INTO THE TOP SIEVE (4-on. OPENING). AND PLACE A LID ON
THE TOP.
4. ROTATE THE COVERED SIEVE/PAN UNIT BY HAND USING BROAD SWEEPING ARM MO-
TIONS IN THE HORIZONAL PLANE. COMPLETE 20 ROTATIONS AT A SPEED JUST
NECESSARY TO ACHIEVE SOME RELATIVE HORIZONTAL MOTION BETWEEN THE SIEVE
AND THE PARTICLES. - '
5. INSPECT THE RELATIVE QUANTITIES OF CATCH WITHIN EACH SIEVE AND
DETERMINE WHERE THE MODE IN THE AGGREGATE SIZE DISTRIBUTION LIES.
I.E.. BETWEEN THE OPENING SIZE OF THE SIEVE WITH THE LARGEST CATCH AND
THE OPENING SIZE OF THE NEXT LARGEST SIEVE.
ADAPTED FROM A LABORATORY PROCEDURE PUBLISHED BY W. S. CHEPIL (1952).*
Figure 6-2.
6-4
-------
soil passes a 1-an sieve, the "unlimited reservoir" node! will apply; If
not, the 'United reservoir" model iill apply.* This relationship has
been verified by Gillette (1980) on desert soils.*
If the soil contains nonerodlble elements which are too large to
Include In the sieving (I.e., greater than about 1 OB In diameter), the
effect of these elements must be taken Into account by Increasing the
threshold friction velocity. Marshall (1971) has employed wind tunnel
studies to quantify the Increase 1n the threshold velocity for differing
kinds of nonerodlble elements." His results are depicted In terms of a
graph of the rate of corrected to unconnected friction velocity versus Lc
(Figure 6-3). where 1^ 1s the ratio of the silhouette area of the
roughness elements to the total area of the bare loose soil. The
silhouette area of a nonerodlble element Is the projected frontal area
normal to the wind direction.
A value for LC Is obtained by marking off a l-m x l-« surface area
and determining the fraction of area, as viewed from directly overhead,
that 1s occupied by nonerodlble elements. Then the overhead area should
be corrected to the equivalent frontal area; for example. If a spherical
nonerodlble element 1s half embedded 1n the surface, the frontal area 1s
one-half of the overhead area. Although 1t 1s difficult to estimate Lc
for values below 0.05, the correct1on-to-fr1ct1on velocity becomes less
sensitive to the estimated value of Lg.
The difficulty 1n estimating Lg also Increases for small nonerodlble
elements. However, because small nonerodlble elements are more likely to
be evenly distributed over the surface, 1t 1s usually acceptable to
examine a smaller surface area, e.g., 30 cm x 30 cm.
Once again, loose sandy soils fall into the high credibility
("unlimited reservoir") classification. These soils do not promote crust
formation, and show only a brief effect of moisture addition by
rainfall. On the other hand, compacted soils with a tendency for crust
formation fall Into the low ("limited reservoir") credibility group. Clay
content In soil, which tends to promote crust formation, 1s evident from
crack formation upon drying.
6-5
-------
2 3 45*7891 . 2 J 4S67I9I
2 3 4 3 7 B4l
TJ
OJ
4J
U
O»
(U
o
c
13
10
Figure 6-3. Increase in threshold friction velocity with LC.
-------
The roughness height, z0, which 1s related to the size and spacing of
surface roughness elements, 1s needcu to convert the friction velocity to
the equivalent wind speed at the typical weather station sensor height of
7 above the surface. Figure 6-4 depicts the roughness height scale for
various conditions of ground cover.* The conversion to the 7-9 value 1s
discussed below.
6.1 ESTIMATION OF EMISSIONS
6.1.1 'Limited* Erosion Potential
In the case of surfaces characterized by a *11i1ted reservoir" of
credible particles, even the highest Kan atmospheric wind speeds are
usually not sufficient to sustain wind erosion. However, wind gusts Bay
quickly deplete a substantial portion of the erosion potential. Because
erosion potential has been found to Increase rapidly with Increasing wind
speed, estimated emissions should be related to the gusts of highest
agnltude.
The routinely measured Meteorological variable which best reflects
the Magnitude of wind gusts 1s the fastest Mile. This quantity represents
the wind speed corresponding to the whole Mile of wind Movement which has
passed by the 1-«1 contact anemometer 1n the least amount of tine. Dally
Measurements of the fastest Mile are presented 1n the monthly Local CUma-
tologlcal Data (LCD) summaries. The LCD summaries May be obtained from
the National Climatic Center, Ashevllle, North Carolina. The duration of
the fastest mile, typically about 2 «1n (for a fastest «1le of 30 nph).
matches well with the half life of the erosion process, which ranges
between 1 and 4 irln. It should be noted, however, that peak winds can
significantly exceed the dally fastest «1le.
The wind speed profile 1n the surface boundary layer 1s found to
follow a logarithmic distribution:
u(2) o 1n {- (z " V <6-^
-*. o
where: u wind speed, on/s
u* friction velocity, cm/s
%
z height above test surface, cm
_Z0 roughness height, en
0.4 von (Carman's constant, dlmenslonless
6-7
-------
High Rise Buildings
(30+Floors)
Suburban
Medium Buildings
(Institutional)
u
O
Suburban
Residential Dwellings '
Wheat Field.
O-
Plowed Field
Zo (cm)
1000
Natural Snow
*
BOO
600
100
200
100 .
80
60
-40
-20
io
^*o»
mt^^^3
2
1.
0.
0
0.
0.
0.
.0
.0
.0
.oJ
.0
0
0
0
8
4
2
1
Urban Area
Woodland Forest
Grassland
Figure 6-4. Roughness heights for various surfaces.
6-8
-------
The friction velocity (u+) 1r a measure of wind shear stress on the
credible surface, as determined from the slope of the logarithmic velocity
profile. The roughness height (zo) 1s a Measure of the roughness of the
exposed surface as determined from the y-Intercept of the velocity
profile. I.e., the height at which the wind speed 1s zero. These
parameters are Illustrated 1n Figure 6-5 for a roughness height of 0.1 on.
Emissions generated by wind erosion are also dependent on the
frequency of disturbance of the credible surface because each time that a
surface 1s disturbed. Its erosion potential 1s restored. A disturbance 1s
defined as an action which results 1n the exposure of fresh surface
material. On a storage pile, this would occur whenever aggregate material
1s either added to or removed from the old surface. A disturbance of an
exposed area may also result from the turning of surface material to a
depth exceeding the size of the largest pieces of material present.
The emission factor for wind-generated partleulate emissions from
mixtures of credible and nonerodlble surface material subject to
disturbance may be expressed In units of g/m*-yr as follows:
N
Emission factor k. J P< (6-2)
where: k particle size multiplier
N number of disturbances per year
Pj erosion potential corresponding to the observed (or probable)
fastest mile of wind for the 1th period between disturbances,
g/m'
The particle size multiplier (k) for Equation 6-2 varies with
aerodynamic panicle size, as follows:
AERODYNAMIC PARTICLE'SIZE MULTIPLIERS FOR EQUATION 6-2
<3D tun <15 urn <10 um <2.5 K»
TTo 57? 575 572
6-9
-------
AatTHMK nc
to*
torn
Srceo AT Z
Sreeo AT tOm
Figure 6-5. Illustration of logarithmic velocity profile.
-------
This distribution of particle s1?^ within the <30 urn fraction 1s
comparable to the distributions reported for other fugitive dust sources
where wind speed 1s factor. This 1s Illustrated, for example. 1n the
distributions for batch and continuous drop operations enconpasslng a
number of test aggregate materials (see AP-42 Section 11.2.3).
In calculating emission factors, each area of an credible surface
that Is subject to a different frequency of disturbance should be treated
separately. For a surface disturbed dally, N 365/yr. and for a surface
«
disturbance once every 6 mo, N 2/yr.
The erosion potential function for a dry, exposed surface has the
following font:
P 58 (u* - u*)» * 25 (u* - u*)
P 0 for u* s uj
where: u* friction velocity (/$)
u£ threshold friction velocity (/$)
Because of the nonlinear fora of the erosion potential function, each
erosion event must be treated separately.
Equations 6-2 and 6-3 apply only to dry, exposed materials with
limited erosion potential. The resulting calculation 1s valid only for a
time period as long or longer than the period between disturbances.
Calculated emissions represent Intermittent events and should not be Input
directly Into dispersion models that assume steady state emission rates.
For uncrusted surfaces, the threshold friction velocity Is best
estimated from the dry aggregate structure of the soil. A simple hand
sieving test of surface soil (adapted from a laboratory procedure
published by W. S. Chepll*) can be used, to determine the mode of the
surface aggregate size distribution by Inspection of relative sieve catch
amounts, following the procedure specified 1n Figure 6-2. The threshold
friction velocity for erosion can be determined from the mode of the
aggregate size distribution, as described by Gillette.* This conversion
1s presented 1n Figure 6-1.
6-11
-------
Threshold friction velocities s'or several surface types have been
determined by field measurements with a portable wind tunnel. These
values are presented 1n Tables 6-1 and 6-2 and Figure 6-6.
The fastest «11e of wind for the periods between disturbances Bay be
obUlned froo the Monthly LCD suonaHes for the nearest reporting weather
station that 1s representative of the site In question.7 These summaries
report actual fastest mile values for each day of a given month. Because
the erosion potential 1s a highly nonlinear function of the fastest alle.
wan values of the fastest mile are Inappropriate. The anemometer heights
of reporting weather stations are found 1n Reference 8, and should be
corrected to a 10 reference height using Equation 6-1.
To convert the fastest mile of wind (u*) from a reference anemometer
height of 10 m to the equivalent friction velocity (u*), the logarithmic
wind speed profile Bay be used to yield the following equation:
u* 0.053 uto (6-4)
where: u* friction velocity (/$)
uf0 fastest mile of reference anemometer for period between
disturbances (a/s)
This assumes a typical roughness -height of 0.5 on for open terrain.
Equation 6-4 Is restricted to large relatively flat areas with little
penetration Into the surface wind layer.
Implementation of the above procedure 1s carried out 1n the following
steps:
1. Determine threshold friction velocity for erodlWe material of
Interest (see Tables 6-1 and 6-2 and Figure 6-6 or determine from
mode of aggregate size distribution).
2. Divide the exposed surface area Into subareas of constant
frequency of disturbance (N).
3. Tabulate fastest mile values (u*) for each frequency of
disturbance and correct them to 10 m (uto) using Equation 6-5.
6-12
-------
TABLE 6-1. THRESHOLD DICTION VELOCITIES
Material
Overburden*
Scoria (roadbed
material)*
Ground coal
(surrounding coal
pile)
Uncrusted coal p11ea
Scraper tracks on
coal pile*'6
Fine coal dust on
concrete padc
Threshold
friction
velocity
(/$)
1.02
1.33
0.55
1.12
0.62
0.54
Roughness
height
0.3
0.3
0.01
0.3
0.06
0.2
Threshold
wind
velocity at 10 n (m/s)
Z0 Actual Zg
21
27
16
23
15
11
0.5 cm
19
25
10
21
12
10
Ref.
2
2
2
2
2
3
fwestem surface coal «1ne.
"Lightly crusted.
Eastern power plant.
6.13
-------
TABLE 6-2. THRESHOLD FRICTION VELOCITIESARIZONA SITES
Location
Threshold
friction
velocity,
/sec
Roughness
height.
Threshold
velocity
at 10 m.
/sec
Mesa - Agricultural site 0.57
Glendale - Construction site 0.53
Marlcopa - Agricultural site 0.53
Yuaa Disturbed desert 0.32
YUM - Agricultural site 0.58
Algodones - Dune flats 0.62
YUM - Scrub desert 0.39
Santa Cruz River, Tucson 0.18
Tucson - Construction site 0.25
Ajo - Nine tailings 0.23
Nayden - Mine tailings 0.17
Salt River. Mesa 0.22
Casa Grande - Abandoned 0.25
agricultural land
0.0331
0.0301
0.1255
0.0731
0.0224
0.0166
0.0163
0.0204
0.0181
0.0176
0.0141
0.0100
0.0067
16
15
14
8
17
18
11
5
7
7
5
7
8
6-14
-------
For narrowly sited. (Inety divided malarial* only
I
t^
01
dlilflbullon
^
Gravel ^
-
M
_ j
Coarse
Sand ~*
» ^_ w. - .. .^ M^ ..^ _ .«
Flno
Snnd "
03
02
01
k*
DOS
001
^~
(In)
- -.'«
7
6
5
4
3
-
2
1
0.6
-
,01
002
(mm)
-
-
-
_
-
-
"
Ja_ MeaiMted
- ISO
- 100
- so
ri«tlwMp«Mii
41M4
Jfl
Figure 6-6. Scale of threshold friction velocities.
-------
4. Convert fastest mile values (uf0) to equivalent friction
velocities (u*), using Equation 6-4.
5. Treating each subarea (of constant H and u*) as a separate
source, calculate the erosion potential (Pj) for each period
between disturbances using Equation 6-3 and the emission factor
using Equation 6-2.
6. Multiply the resulting emission factor for each subarea by the
size of the subarea. and add the emission contributions of all
subareas. Note that the highest 24-h ealsslons Mould be expected
to occur on the windiest day of the year. Max1BUD emissions are
calculated assualng a single wind event with the highest fastest
He value for the annual period.
The recoonended emission factor equation presented above assumes that
all of the erosion potential corresponding to the fastest rile of wind 1s
lost during the period between disturbances. Because the fastest mile
event typically lasts only about 2 Bin. which corresponds roughly to the
half-life for the decay of actual erosion potential. It could be argued
that the emission factor overestimates partlculate emissions. However,
there-are other aspects of the wind erosion process which offset this
apparent conservatism:
1. The fastest mile event contains peak winds which substantially
exceed the mean value for the event.
2. Whenever the fastest mile event occurs, there are usually a
number of periods of slightly lower mean wind speed which contain
peak gusts of the same order as the fastest mile wind speed.
Of greater concern 1s the likelihood of overpred1ct1on of wind
erosion emissions 1n the case of surfaces disturbed Infrequently 1n
comparison to the rate of crust formation.
6.1.2 "Unlimited' Erosion Potential
For surfaces characterized by an "unlimited reservoir* of credible
particles, partlculate emission rates are relatively time Independent at a
given wind speed. The technology currently used for predicting
agricultural wind erosion 1n the United States 1s based on variations of
the Wind Erosion Equation.*;,»» This prediction system uses erosion loss
estimates that are Integrated over large fields and long-time scales to
6-16
-------
7.0 AGRICULTURE
Fugitive dust from agricultural operations Is suspected of
contributing significantly to the ambient part1culate levels of many
agricultural counties. Such agricultural operations Include (a) plowing,
(b) disking, (c) fertilizing, (d) applying herbicides and Insecticides,
(e) bedding, (f) flattening and fining beds, (g) planting, (h) culti-
vating, and (1) harvesting. These operations can be generlcally
classified as soil preparation, soil maintenance, and crop harvesting
operations. As discussed In Section 6« dust Missions are also generated
by wind erosion of bare or partially vegetated soil. This section will
focus on emissions fron both wind erosion and agricultural tilling opera-
tions that are designed to (a) create the desired soil structure for the
crop seed bed and (b) to eradicate weeds.
7.1 ESTIMATION OF EMISSIONS
7.1.1 Tilling
The mechanical tilling of agricultural land Injects dust particles
Into the ataosphere as the soil Is loosened or turned under by plowing.
disking, harrowing, one-way1ng, etc. AP-42 presents a predictive emission
factor equation for the estimation of dust emissions from agricultural
tilling:*
E - k(5.38)(s)«-« kg/ha
E k(4.80)(s)o*« Ib/acre
where: s silt content (percent) of surface soil (default value of
18 percent)
k particle size multiplier (dimenslonless)
The particle size multiplier, k Is given as 0.21 for PM,O. The above
equations are based solely on field testing information cited 1n AP-42.
Silt content of tested soils ranged from 1.7 to 88 percent.
7-1
-------
7.1.2 Hind Erosion
The technology currently used for predicting agricultural wind
erosion 1n the United States 1s based on variations of the Wind Erosion
Equation.».» This prediction system uses erosion loss estimates that are
Integrated over large fields and long time scales to produce average
annual values.
* 7.1.2.1 Simplified Version of Wind Erosion Equation. Presented
below 1s a procedure for estimating windblown or fugitive dust emissions
from agricultural fields. The overall approach and much of the data have
been adapted from the wind erosion equation, which was developed as the
result of nearly 40 yr of research by the U.S. Department of Agriculture
to predict topsoll losses from agricultural fields.
Several simplifications have also been Incorporated during the
adaptation process. The simplified format 1s not expected to affect
accuracy 1n Its present usage, since wind erosion estimates using the
simplified equation are almost always within 5X of those obtained with the
original USOA equation. Most of the Input data are not accurate to ±5X.
7.1.2.1.1 Windblown dust equation. .The modified equation 1s of the
form:
E « kalKCL'V (7-1)
where: E PMi0 wind erosion losses of tilled fields, tons/acre/yr
k 0.5, the estimated fraction of TSP which 1s PM10
a portion of total wind erosion losses that would be measured
as suspended part1culate, estimated to be 0.025
I « soil credibility, tons/acre/yr
K surface roughness factor, d1mens1on1ess
C climatic factor, dlmenslonless
L' unsheltered field width factor, dlmenslonless
V » vegetative cover factor, dlmenslonless
As an aid In understanding the mechanics of this equation, "I" may be
thought of as the basic credibility of a flat, very large, bare field 1n a
climate highly conducive to wind erosion (I.e.. high wind speeds and
temperature with little precipitation) and 1C, C. L', and V as reduction
7-2
-------
factors for ridged surface, a c11mat« less conducive to wind erosion,
smaller-sized fields, and vegetative cover, respectively.
The sane equation can be used to estimate emissions from: (1) a
single field, (2) a medium-sized area such as a valley or county, or
(3) an entire AQCR or state. Naturally, more generalized Input data must
be used for the larger land areas, and the accuracy of the resulting
estimates decreases accordingly.
7.1.2.1.2 Procedures for compiling Input data. Procedures for
quantifying the five variable factors 1n Equation (7-1) are explained 1n
detail below.
Soil Credibility. I. Soil credibility by wind Is a function of the
amount of credible fines In the soil. The largest soil aggregate size
normally considered to be erodlble 1s approximately 0.84 ma equivalent
diameter. Soil credibility. I. 1s related to the percentage of dry
aggregates greater than 0.84 mm as shown 1n Figure 7-1. The percentage of
nonerodlble aggregates (and by difference the amount of fines) 1n a soil
sample can be determined experimentally by a standard dry sieving
procedure, using a No. 20 U.S. Bureau of Standards sieve with 0.84-mm
square openings.' .
For areas larger than can be field sampled for soil aggregate size
(e.g.. a county) or In cases where soil particle size distributions are
not available, a representative value of I for use 1n the windblown dust
equation can be obtained from the predominant soil type(s) for farmland 1n
the area. Measured credibilities of various soil textural classes are
presented 1n Table 7-1.
If an area Is too large to be accurately represented by a soil class
or by the weighted average of several soil classes, the map In Figure 7-2
and the legend In Table 7-2 can be used to Identify major soil deposits
and average soil credibility on a national basis. Other soil maps are
available from*the Soil Conservation Service branch of the U.S. Department
of Agriculture.
Values of I obtained from Figure 7-1, from Table 7-1. or from soil
maps can be substituted directly into Equation (7-1).
7-3
-------
-
' i
;
i
i
m**t\
j 'B°
1
1 ...»
< i
"
.-r
i
M
10
T
;
\
i\
:
i
-
t
..; .
-f-}.'
1
: 1
1
\
^
._...
-
; . .
: ! ! ' '- !
: i " 1'.
i i '
; '
i |
-
.
! ,
...j : .
i ; 1 "~ !
t
. , ~
»
; i
. _ i .. i
1 ' '
. : ...
1 1
\ !i""
\
1
\
:
V
: ! N
i
-
i
t
...
) 20 36 [_ 4
] rttctU or
.
OTM1M
;
.
j 1 : 1
'"
v~i':'
h.. ..
. :.f~|:---.;
.... -J _... . . _..
i...
\ ' !
V *
l\
. . ..
;
\
..
...
X ...
.
3 SO 6t> 71
* wf . . . f *
MT' idu «oeito«Tti
- I .
\
> ; ft
1
_. !_
...J _.
i
*
:
:
:
;
' ;
1
i
.. .
,
'
"T
fffv
^
>_[-«b i- id
! .
:' ;
: ... L :-
j
..u_(.._ .
1
1
i
.
"!' !"";
"H i
.. . .: .J
fe. L.I. ;
1 i
I-
Figure 7-1. Soil credibility as a function of particle size.
7-4
-------
TABL- 7-1. SOIL ESSOIBILITY F33. WRIuL'S
SOIL TEXTURAL CLASSES
Predominant soil textural class
Sand*
Loamy sand*
Sandy loan*
Clay
SUty clay
Loam
Sandy clay loam8
Sandy clay*
S1lt loam
Clay loan
Silty clay loam
sm
Credibility, I,
tons/acre/yr
220
134
36
86
86
»
56
56
56
47
47
28
38
*Very fine, fine, or Medium sand.
7-5
-------
CtNtBAt SOIL MAP Of THC UNtTtO STATES
Figure 1-2. Generalized soli map of the United States.
i.
-------
TABLE 7-2. LEG-NO rop SOIL MAP IN ?IGUR£ '-2
Al, A2 Season*!!./ wet soils with subsurface clay accumu-at'c-
A3- AS Cool or cold soils with subsurface clay accumulation
A6- A8 Clays
A9. AID Burnt clay soils
Al)- A13 Dry clay soils with some cementation
01- 06 Arid soils with clay and alkali or caroonate
accumulation
El Poorly.drained loamy sands
E2 Loamy or clayey alluvial deposits
E3- £8 Shallow clay loan deposits on bedrock
E9 Loamy sands 1n cold regions
E10. E12 Loamy sands 1n warn regions
Ell. E13, E14 Loamy sands 1n warn, dry regions
HI, H2 Wet organic soils; peat and muck
II Ashy or amorphous soils 1n cold regions
12 Infertile soils with large amounts of amorphous material
13 Fertile soils of weathered volcanic ash
14 Tundra; frozen soils
15, 16 Thin loam surface horizon soils
*-
17 Clay loams in cool regions
18- 110 Wide varying soil material with some clay t'or
111 Rocky soils shallower than 20 in, to oeorocK
112 Clay loams in warm, moist regions
113 Clay loams In cold regions
(continued)
7-7
-------
LLL/dl401-7at. D. 3
LE »-» 'Continued
114 Clay loams 1n temperate climates
Ml- M4 Surface loam horizon underlain by clay
MS Shallow surface loams with no underlying clays
M6- MS Surface loamy soils
M9- M14 Semlarld loams or clay loams
M15. M16 Dry loams
01, 02 Clays and sandy clays
SI- S4 Sandy, clay, and sandy clay loams
Ul Wet silts with some subsurface clay accumulation
U2- U6 S1lty loams with subsurface clay accumulation
U7 Dry silts with thin subsurface clay accumulation
VI- V2 Clays and clay loams
V3- V5 SUty clays
XI- X5 Barren areas, mostly rock with some Included soils
7-3
-------
Surface Roughness Factor. K. Th!s factor accounts for the resistance
to wind erosion provided by ridges and furrows or large clods In the
field. The surface roughness factor. K. 1s a function of the height and
spacing of the ridges, and varies fron 1.0 (no reduction) for a field'with
a saooth surface to a minimum of 0.5 for a field with the optimum ratio of
ridge height (h) to ridge spacing (w).
The relationship between K and h*/w 1s shown 1n Figure 7-3. The
value of K to be used 1n Equation (7-1) should be rounded to the nearest
0.1 because of the large variations Inherent In ridge measurement data.
In cases where there are extreme variations of h or w within a field.
determination of the K value should be United to either 0.5 for a ridge
surface or 1.0 for an unrldged surface.
For county or regional areas, K can best be determined as a function
of crop type, since field preparation techniques are relatively uniform
for a specific crop. Average K values of coonon field crops are shown 1n
Table 7-3. When the K (or L* or V) factors are based on crop type,
separate calculations of windblown dust emissions must be made for each
major crop In the survey area. This procedure Is explained and
demonstrated later 1n this presentation. ' -
Climatic Factor. C. Research has Indicated that the rate of soil
movement by wind varies directly as the cube of wind velocity and
Inversely as the square of soil surface moisture. Surface moisture 1s
difficult to measure directly, but precipitation-evaporation Indices can
be used to approximate the amount of moisture 1n soil surface particles.
Therefore, readily available climatic data can provide a quantitative
Indicator of relative wind erosion potential at any geographic location.
The C factor has been calibrated using the climatic conditions at the
site of much of the researchGarden City, Kansasas the standard base
(C « 1.00). At any other geographic location, the C factor for use 1n
Equation (7-1) can be calculated as:
u'
C - 0.345 -=r (7-2)
(PE)
7-9
-------
ff
o
CO
CO
IU
u
ti-
ff
CO
INCHES
Figure 7-3. Determination of surface roughness factor.
7-10
-------
TASLi T-3. VAL'JES *" *., '.. AMH v FOP. CCMMCN -H.D CHOPS
Crop
Alfalfa
Barley
Beans
Corn
Cotton
Grain hays
Oats
Peanuts
Potatoes
R1ce
Rye
Saf flower
Sorghum
Soybeans
Sugar beets
Vegetables
Wheat
K
1.0
0.6
0.5
0.6
0.5
0.8
0.8
0.6
0.8
0.8
0.6
1.0
0.5
0.6
0.6
0.6
0.6
. L. ft
1000
2000
1000
2000
20CO
2000
2000
1000
1000
1000
2000
2000
2000
2000
1000
500
200C
V, "3/JCrs
30CO
11CO
:so
5:0
2:0
1250
1250
250
400
1000
1250
- 1500
300
250
100
100
1350
Ml
-------
where: W ««ean annual wind velocity, -in mph, corrected to a standard
height of 30 ft
PE * Thomthwalte's precipitation-evaporation Index
0.83 (sun of 12 monthly ratios of precipitation to actual
evapotransplratlon)
Monthly or seasonal climatic factors can be estimated from
Equation (7-2) by substituting the Man wind velocity of the period of
Interest for the Man annual wind velocity. The annual PE value 1s used
for all calculations of C.
CIlMtlc factors have been computed froa Heather Bureau data for many
locations throughout the country. Figure 7-4 1s a up showing annual
climatic factors for the USA. C values for use 1n Equation (7-1) may be
taken from appropriate maps like this when preparing regional emission
surveys. For emission estimates covering smaller areas. Equation (7-2)
may be used to obtain C.
Unsheltered Field Width Factor. L'. Soil erosion across a field 1s
directly related to the unsheltered width along the prevailing wind
direction. The rate of erosion 1s zero at the windward edge o.f the field
and Increases approximately proportionately with distance downwind until.
If the field 1s large enough, a maximum'rate of soil movement 1s reached.
Correlation between the width of a field and Its rate of erosion 1s
also affected by the soil credibility of Us surface: the more credible
the surface, the shorter the distance 1n which maximum soil movement 1s
reached. This relationship between the unsheltered width of a field (L),
Us surface credibility (IK), and Us relative rate of soil erosion (L1)
1s shown graphically 1n Figure 7-5. If the curves of Figure 7-5 are used
to obtain the L1 factor for the windblown dust equation, values for the
variables I and K must already be known and an appropriate value for L
must be determined.
L 1s calculated as the distance across the field 1n the prevailing
wind direction minus the distance from the windward edge of the field that
1s protected from wind erosion by a barrier. The distance protected by a
barrier 1s equal to 10 times the height of the barrier, or 10 H. For
example, a row of 30-ft high trees along the windward side of a field
reduces the effective width of the field by 10 x 30 or 300 ft. If the
7-12
-------
SMT|$
ANNUAL CLIMATIC FACTOR C
ORIGINAL DRAWING 4-17-68. 0. V. ARMBRUST.
ARK.. IA., KV., LA.. TCNN.. W. VA. ADDED
11-24-71. N. P. WOODRUFF.
Figure 7-4. Climatic factor used In wind erosion equation.
-------
Figure 7-5. Effect of field length on relative emission rate.
-------
prevailing wind direction differs significantly (more than 25 degrees)
from perpendicularity with the field, L should be Increased to account for
this additional distance of exposure to the wind. The distance across the
field. L 1s equal to the field width divided by the cosine of the angle
between the prevailing wind direction and the perpendicularity to the
field:
For aultlple fields or regional surveys, measurement and calculation
of L values become unwieldy. In region-wide emission estimates, average
field widths should be used. Field width 1s generally a function of the
crop being grown, topography of the area, and the amount of trees and
other natural vegetation 1n or adjacent to the faming areas that would
shelter fields from erosive winds. Since the windblown dust calculations
are already split Into Individual crop type to accurately consider
variations 1n K by crop, average L values have also been developed by
crop; they are presented 1n Table 7-3. These values are representative of
field sizes In relatively flat terrain devoid of tall natural vegetal ton,
such as found 1n large areas of the Great Plains. The L values 1n
Table 7-3 should be divided by 2 1n areas with moderately uneven terrain
and by 3 1n h11ly areas. Additionally, the average field width factors
should be divided by 2 to account for wooded areas and fence thickets
Interspersed with farmland.
Vegetative Cover Factor. V. Vegetative cover on agricultural fields
during periods other than the primary crop season greatly reduces wind
erosion of the soil. This cover most commonly Is crop residue, either
standing stubble or mulched Into the soil. The effect of various amounts
of residue, V, 1^ reducing erosion 1s shown quantitatively In Figure 7-6,
where IKCL' 1s the potential annual soil loss (1n tons/acre/yr) from a
bare field, and V Is the fractional amount of this potential loss which
results when the field has a vegetative cover of V, In lb of a1r-dr1ed
residue/acre. Obviously, the other four variables 1n Equation (7-1) I,
K, C, and L' must be known before V can be determined from Figure 7-6.
7-15
-------
I
t-t
O»
Figure 7-6. fffect of vegetative cover on relative emission rate.
-------
The amount of vegetative cover en a single field can DS esce-tainec
by collecting and weighing clean res'Jje from a representative plot sr oy
visual comparison with calibrated onotographs. The weight obtained by
either measuring method oust then be converted to an equivalent weight of
flat small-grain stubble before entering Figure 7-6, since different crop
residues vary 1n their ability to reduce wind erosion. Detailed
descriptions of the Measuring methods or conversion procedures are tso
complex for this presentation. Interested readers are referred to the
USOA for these descriptions.
The residue left on a field when using good soil conservation
practices 1s closely related to the type of crop. Table 7-3 presents
representative values of V for common field crops-when stubble or mulch 1s
left after the crop. These values should be used 1n calculating windblown
dust emissions unless a knowledge of local farming practices indicates
that some Increase or decrease Is warranted. Note that three of the five
variables 1n the windblown dust equation are determined as functions of
the crop grown on the field.
7.1.2.1.3 Summary. The estimated emissions 1n tons/acre/yr nay now
be calculated for each field or group of fields as the product of the five
variables times the constant "a* estimated to be 0.025, and the particle
size multiplier for PH10 estimated to be 0.5.
For regional emission estimates, the acreage 1n agriculture should be
determined for each jurisdiction (e.g.,' county) toy croc. "I" and "C"
values can be determined for Individual jurisdiction, with the remaining
three .variables being quantified as functions of crop type. The emission
calculations are best performed 1n a tabular format such as the one snown
in Table 7-4. The calculated emissions from each crop are summed to get
agricultural wind erosion emissions by jurisdiction and these are totalec
to get emissions for this source category for the entire region.
. 7.1.2.1.4 appropriate Usage of Results. Inherent variabilities in
the many parameters used in the windblown dust equation cause the results
to be less accurate than emission estimates for most other sources.
However, the rough estimates provided by the proposed procedure are better
than not considering this source at all 1n particulate emission Inventory
7-17
-------
TABLE 7-4. CALCULATION SHEET FOR ESTIMATION OF DUST FROM MIND EROSION
Juris- I,C. K, L, V, L'. V . E, » Tot.ll
diction Based on Climatic Surface Field Veget. Length Vegot. alKC- Kmi:isi<>i>:
(County) Soil Typo Factor Crop Acres Roughness Length Cover Factor Factor I.'V* hy rrnii
Alfalfa
Oarlcy
Deans
Corn
Cotton
Potatoes
Sorghum
Soybeans
Sugar
Deets
Vcgets.
Wheat
Etc' _
- TotnT
_-- - _^_^^^^-_J^^-^^-^^^ i .^^,^^m^tm mt ,^^__^^^^^^ ^ » -. .
(List of
Crops
Crown in
Juris-
diction)
Tot.il
-------
work. Inclusion of this source category, possibly with some qualifying
statement as to Its relative accuracy, gives an Indication of Its
contribution to regional air quality.
The estimation procedure 1s not Intended for use In predicting
emissions for short t1«e periods, nor can 1t be used 1n determining
edsslon rates for enforcement purposes.
7.1.2.2 Hew Wind Erosion Prediction Technology. New technology for
prediction of agricultural wind erosion Is currently being developed by
the U.S. Department of Agriculture. This undertaking was recently
described by L. J. Hagen as follows.'
Currently, the U.S. Department of Agriculture 1s taking a
leading role 1n combining erosion science with data bases and
computers to develop what should be a significant advancement
1n wind erosion prediction technology. In 1986 an Initial
group composed of Agricultural Research Service (ARS) and Soil
Conservation Service (SCS) scientists was formed to begin
development of a new Wind Erosion Prediction System (HEPS).
Additional scientists are now being added to the group to
strengthen specific research and technology development
areas. The objective of the project 1s to develop replacement
technology for the Wind Erosion Equation.
The primary user of wind erosion prediction technology Is
the USDA Soil Conservation Service, which has several major
applications. First, as a part of the periodic National
Resource Inventory, 1t collects data at 300,000 primary
sampling points, and at central locations, calculates the
erosion losses occurring under current land use practices.
The analyzed results are used to aid In developing regional
and national policy.
Second, SCS does conservation planning of wind erosion
control practices to assist farmers and ranchers In meeting
erosion tolerances. Implementation of adequate conservation
plans preserves land productivity and reduces both onsfte and
offslte damages. Conservation planning requires a prediction
system that will operate on a personal computer and produce
answers 1n a relatively short time. In addition, WEPS must
serve as a communication cool between conservation planners
and those who implement the plans.
Various users also undertake project planning in which
erosion prediction 1s used to evaluate erosion and deposition
1n areas Impacted by the project. In this aopHcation, more
time and resources may be expended than in conservation
planning to collect input data and make analyses. Project
7-19
-------
planning 1s typically carried out by mult1d1sc1pHnary teams
Including field personnel w.^- collect needed Input data.
Other users of wind erosion prediction technology
represent a wide range of problem areas. Often their problems
will require development of additional models to supplement
WEPS 1n order to obtain answers of Interest. Some of these
diverse problem areas Include evaluating new erosion control
techniques, estimating long-term soil productivity changes,
calculating onslte and offslte economic costs of erosion,
finding deposition loading of lakes and streams, computing the
effects of dust on add rain processes, determining Impact of
management strategies on public lands, and estimating
visibility reductions near airports and highways.
From the preceding survey of user needs, 1t 1s apparent
that the prediction technology must deal with a wide range of
soil types and management factors. Wind erosion prediction
technology also must cover a broad range of climatic and
geographic regions 1n the United States. The major Impact of
wind erosion 1s 1n the Great Plains, but credible areas 1n the
Great Lakes region, the semlarld western United States, and
windy coastal regions are all affected.
7.2 DEMONSTRATED CONTROL TECHNIQUES
7.2.1 Tilling
Operational modifications to tilling of the soil Include the use of
novel Implements or the alteration of cultural techniques to eliminate
some operations altogether. All operational modifications will affect
soil preparation or seed planting operations. Furthermore, the suggested
operational modifications are crop specific. Estimated PM10 efficiencies
for agricultural controls are presented In Table 7-5.
The punch planter 1s a novel Implement which might have applications
for emissions reduction from planting cotton, corn, and lettuce. The
punch planter 1s already being used In sugar beet production. The punch
planter punches a hole and places the seed Into It, as opposed to
conventional planters which make a trough and drop the seeds 1n at a
specified spacing. The advantage 1s that punch planters can leave much of
the surface soil and surface crop residues undisturbed. Large-scale use
of the punch planters would require Initial capital Investments by the
farming Industry for new equipment.
7-20
-------
Attachment 6
Excerpts from Superfund Exposure Assessment Manual (EPA88c)
66
-------
EP A/540/1 -« 8/001
OSWER Directive 928S.S-1
April 1888
Superfund Exposure Assessment
Manual
U.S. Environmental Protection Agency
Office of Remedial Response
Washington, DC 20460
-------
quantified property values. These data are available
for many chemicals that may be present at
uncontrolled hazardous waste sites, and are found in
various chemical reference texts. In cases where
chemical data are missing, the analyst must estimate
the property values. This section provides equations
for estimating certain requisite chemical properties.
Comprehensive guidance for chemical property
estimation is providdd in reference materials such as
Lyman et al. (1982). Readily accessible computerized
systems are available to predict a range of pertinent
chemical properties. The computerized Graphic
Exposure Modeling System (GEMS), and its
subsystem CHEMEST. is an example. The EPA
Office of Toxic Substances in Washington, O.C. has
developed and is managing this system. Essentially a
computerized version of Lyman et al. (1982), it can
be rapidly accessed to estimate the chemical
characteristics necessary for volatilization estimation.
The user of this manual can refer to Farino et al.
(1983) for a detailed review and evaluation of existing
equations for estimating volatilization from
uncontrolled hazardous waste sites. This report
presents a survey of available air release models for
volatile substances and a critical analysis of the
applications and limitations of each.
(1) Landfills Without Internal Gas Generation
Equation 2-3 can be used to estimate volatile
releases from covered landfills containing toxic
materials alone, or toxic materials segregated from
other landfilled nonhazardous wastes. Equations 2-4
through 2-7 are used to calculate certain input
variables that are required to apply Equation 2*3.
Farmer et al. (1978) developed an equation to
estimate the effectiveness of various landfill cover
types and depths in controlling volatile releases'. This
equation, based .on Pick's First Law of steady state
diffusion, assumes that diffusion into the atmosphere
occurs at a plane surface where concentrations
remain constant It ignores biodegradation, transport
in water, adsorption, and production of landfill gas.
Diffusion of the toxic vapor through the soil cover is
the controlling factor. It also assumes that there is a
sufficient mass of toxicant in the landfill so that
depletion of the contaminant will not reduce the
emission rate.
Equation 2-3, simplified by Farmer et al. (USEPA
1980b), incorporates a number of assumptions (see
Farino et al. 1983 for a complete discussion), such as
completely dry soil (worst case) and zero
Although computerized dispereion modeing can be uMd to
obtam contaminant release me*, tt is primarily a tool tor
determine contaminant atmospheric fat*. Thus, rater to
Chapter 3. Environmental Fate Anaiyaia. tor detailed
discussions ol a* dispersion models applicable to unoontroaad
hazardous waste tacMea.
concentration of volatilizing material at the soil
surface. Shen (1981) converted Farmer's simplified
equation for calculating the vapor flux rate to a form
that provides a toxic vapor emission rate by
multiplying the basic equation by the exposed
contaminated surface area. In addition. Shen modified
the equation to allow calculation of the volatilization
rate of a specific component of the overall toxic
mixture by multiplying by the weight fraction of the
component in the mixture. However, as pointed out
by Farino et al. (1983), a more accurate approach
would be to multiply by the mole fraction of the toxic
component in the buried mixture. Thus. Farmer's
equation, as modified by Shen (1981) and Farino et
al. (1983). is:*
Ei=Di
(2-3)
where
A
Pt
Mi
mission rate of component i. (g/sec).
diffusion coefficient of component i in air.
(cm2/sec).
saturation vapor concentration of component
i, (g/cm3).
exposed area. (cm?).
total soil porosity, (dimensionless).
mole fraction of toxic component i in the
wast«,(gmote/gmole).
effective depth of soil cover, (cm).
Note that total soil porosity, rather than air-filled sofl
porosity, is used in this equation. The presence of
water in a soil cover will tend to decrease the flux rate
of a volatile compound by effectively decreasing the
porosity, and also by increasing the geometric
complexity of the sofl pore system (because water
adheres to soil particles), thus effectively increasing
the vapor path (USEPA 1980b). Farmer et al.
suggest however, that when using their equation to
design a landfill cover, the total porosity value be
used (USEPA 1980b), thereby designing for the worst
case (i.e.. dry conditions). In most instances, it wiP be
appropriate to apply this same worst-case logic to
the analysis of volatilization release from landfilled
wastes, assume that landfill cover soils are dry. and
use a value for total porosity in Equation 2-3. It is
recognized, however, that there may be situations
where it can be shown that cover soils exist in a wet
condition more often than in a dry one. In these
cases, the air-filed sol porosity (Pa) may be more
appropriate, and this value can be substituted for Pt
in Equation 2-3 when analyzing volatilization release.
N not providec in existing literature. DI, a compound's
diffusion coefficient (required for the above equation),
can be calculated by Fuller's Method (Perry and
Chilton 1973):
-------
0.001TtTO-
sfe
MW
T
MWt;MWa
P.
ZVi;£Va
(2-4)
absolute temperature. (*K).
.molecular weights of toxic
substance and air (28.8).
respectively, (g/mole).
absolute pressure, (atm).
molecular diffusion volumes of
toxic substance and air (20.1).
This is the sum of the atomic
diffusion volumes of the
compound components.
(cm3/moie).
To estimate short-term (maximum) release rates.
use a value for the temperature that reflects the
expected summer maximum temperatures. Annual
average temperatures should be used to initially
estimate long-term (average) release rates. This
initial estimated long-term release value will be
revised as described in Section 2.3.3 to develop final
long-term release estimates.
Relevant atomic diffusion volumes for use
estimating D{ are (Perry and Chiton 1973):
in
C 16.5
H - 1.98
0 « 5.48
N « 5.69
Cl
Br
F
S <
19.5
35.0
25.0*
17.0
Aromatic ring -20.2
Heterocyclic ring 20.2
Table 2-3 presents diffusion coefficients that have
been calculated for a variety of compounds, some of
which may be present at abandened sites.
An alternative method (Shen 1981) for approximating
DI involves the identification of a compound listed in
Table 2-3 that has a molecular weight and molecular
diffusion volume (calculated) similar to those of the
toxic substance under evaluation. The unknown
diffusion coefficient can then be calculated using:
0,=]
where
D*
MW,
(2-5)
diffusion coefficient of the compound to
be estimated from the known D'.
diffusion coefficient of a compound that
can be found in the table, the molecular
weight and atomic diffusion, volume of
which are close to that of the unknown.
MW* molecular weight of the selected
compound D'.
MW( « molecular weight of the compound to
be estimated.
Total sofl porosity,
(USEPA 19806):
can be calculated as follows
(2-6)
where
total soil porosity, (dimensionless).
sod bulk density.* (g/cm3): generally
between 1.0 and 2.0 g/crn3.
particle density, (g/cm3): usually 2.65
g/cm3 used for most mineral material.
For estimation, Pt can be assumed to be
approximately 0.55 for dry, non-compacted soils,
and about 0.35 for compacted soils. This same value
(0.35) is also appropriate for use as a generic air-
filled soil porosity (Pa) when analyzing the
volatilization release from soils with a high moisture
content (Shen 1981). Alternatively, the local Soil
Conservation Service office can be contacted to
obtain she-specific estimated air-filled soil porosity
values for specific locations.
Saturation vapor concentration, C.j, can be
determined by (USEPA 1980b):
(2-7)
where
Ctf » saturation vapor concentration of
component i, (g/cm3).
p vapor pressure of the chemical," (mm
Hg).
MWj mole weight of component i, (g/mole).
R » molar gas constant. (62.361 mm Hg-
cm3/mole-*K).
T « absolute temperature. (K).
Again, use maximum summer temperatures to
estimate short-term release and annual average
temperatures to initially estimate long-term release.
Th» value from Snan (1881).
Valuee tor aoi buk danafty for (pacified locations can be
obtained from ttte U.S. Sol Conaervaton Service, 8oM* S Fie
data DAM.
If th» vapor pretture of chemicel under consideration ia not
available in standard reference teat, eatimaie it ai deacnoad in
Lyman et al. (1882).
17
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