United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park, NC 27711
EPA-454/R-92-009r
August 1997
& EPA
GUIDANCE FOR SITING AMBIENT AIR
MONITORS AROUND STATIONARY LEAD
SOURCES
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EPA-454/R-92-009
GUIDANCE FOR SITING AMBIENT AIR MONITORS
AROUND STATIONARY LEAD SOURCES
Emissions, Monitoring, & Analysis Division
Office of Air Quality Planning and Standards
Office of Air and Radiation
United States Environmental Protection Agency
Research Triangle Park, NC 27711
August 1997
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TABLE OF CONTENTS
1.0 INTRODUCTION 1
1.1 Summary of the Part 58 Lead Air Monitoring Regulations 1
2.0 BACKGROUND 4
2.1 Airborne Lead Forms 4
2.2 Sources of Lead Emissions 6
3.0 RECOMMENDATIONS FOR MONITORING LEAD NEAR POINT SOURCES .. 10
4.0 SITE SELECTION METHODOLOGY 12
4.1 Monitoring Objectives and Spatial Scale of Representativeness 13
4.2 Source Characteristics/Emissions Types 14
4.2.1 Source Characteristics 14
4.2.2 Types of Emissions 15
4.3 Topographical and Land Use Influences 17
4.4 Meteorological Influences 18
4.5 Analysis of Monitoring Data 20
4.5.1 Single Station Analysis 21
4.5.2 Multi-Station/Mapping Analysis 22
4.5.3 Adequacy of Analysis of Existing Monitoring and Meteorological
Data 23
4.6 Air Quality Modeling 24
4.6.1 Dispersion Modeling - - 25
4.6.2 Recommended Modeling Approach 27
.4.6.3 Screening Procedures 28
4.6.4 Refined Modeling 29
4.6.5 ISC Model Application for Lead Sources 30
4.6.6 Interpretation of Model Results 32
4.6.7 Receptor Modeling 32
4.7 Saturation Monitoring • • • 33
4.7.1 Saturation Monitoring Applications for Lead Point Source Monitoring
Network Design 34
4.7.2 Saturation Monitoring Study Design 34
4.8 Site Selection Without Monitoring or Modeling Data 36
4.9 Number and Location of Monitors 37
4.10 Site Selection Study Report 38
5.0 LEAD MONITORING 40
5.1 Placement of Lead Samplers 40
5.2 Sampling Method 41
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5.3 Sampling Schedule
5.4 Analytical Method *
5.5 Quality Assurance Requirements ~~
5.6 Data Completeness and Reporting Requirements 43
6.0 CASE STUDIES 1?
6.1 General Considerations
6.2 Case #1 - Lead Acid Battery Plant ^
6.3 Case #2 - Secondary Lead Smelter • • • •; -J°
6.4 Case #3 - Primary Lead Smelter
7.0 REFERENCES 71
8.0 BIBLIOGRAPHY 73
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LIST OF FIGURES
Figure 1 Locations of the 30 Largest Lead Emitters in the United States 8
Figure 2 Maximum Quarterly Average Lead Concentrations, 19954 9
Figure 3 Gaussian Plume Dispersion Model 26
Figure 4 Annual Wind Rose - Case 1 52
Figure 5 Four Quarterly Wind Roses - Case 1 53
Figure 6 SCREEN Model Run - Case 1 56
Figure 7 Model Results - Case 1 57
Figure 8 Source Configuration - Case 2 59
Figure 9 Annual Wind Rose - Case 2 60
Figure 10 Model Results - Case 2 62
Figure 11 Source Configuration - Case 3 64
Figure 12 Monitoring Data - Case 3 66
Figure 13 Model Results - Case 3 68
LIST OF TABLES
Table 1 EPA Regions & Current Largest MSA/CMSAs (using 1995 Census data) 2
Table 2 MSA/CMSAs or Counties With lor More Lead NAAQS
Violations in 1995-1996 3
Table 3 Wind Speed and Stability Class Combinations Used by Screen 49
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GUIDANCE FOR CONDUCTING AMBIENT AIR MONITORING
FOR LEAD AROUND POINT SOURCES
1.0 INTRODUCTION
The Environmental Protection Agency (EPA) is revising the Ambient Air Quality Surveillance
regulations (40 CFR Part 58) as they pertain to lead monitoring to better reflect the change in lead
emissions sources from on-road mobile sources (emissions have essentially been eliminated due to
the removal of lead from gasoline) to stationary lead sources. The purpose of this document is to
provide supplemental information and guidance to State and local agencies for monitoring for lead
around point sources. EPA's existing guidance for such monitoring1 is replaced by this document.
For clarity, references to the lead regulations in this document generally pertain to the recent
revisions to the lead regulations. Currently promulgated lead monitoring requirements are
referred to explicitly.
1.1 Summary of the Part 58 Lead Air Monitoring Regulations
The most significant change to the Part 58 monitoring regulations is the change in emphasis from
monitoring primarily to determine the impact of automotive emissions, to establishing a network
focused around point sources. This change reflects the drastic reduction in lead emissions from
automotive sources over the last decade and the concurrent increase in the relative importance of
lead air pollution emitted from point sources.5
The original regulations required two National Air Monitoring Stations (NAMS) in urbanized
areas with populations greater than 500,000. Both of these stations were designed to measure the
impact from roadways. One of the stations was intended to measure in areas of maximum lead
concentrations, while the other station focused on measuring lead concentrations in highly
populated, high traffic density areas. Because of the significant reductions in ambient lead
concentrations due to the removal of lead from gasoline, the regulations have been modified to
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require only one NAMS as a maximum concentration type site in one of the two most populous
Metropolitan Statistical Areas/Consolidated Metropolitan Statistical Areas (MSA/CMSA) within
each of the ten U.S. EPA Regions. Descriptions of each Region and the current most populous
MS A/CMS As within each are listed in Table 1.
Table 1. EPA Regions & Current Largest MSA/CMSAs (using 1995 Census data)
•--• ^^^^^^
Region (States)
I (Connecticut, Massachusetts, Maine, New
Hampshire, Rhode Island, Vermont)
n (New Jersey, New York, Puerto Rico, U:S.
Virgin Islands)
HI (Delaware, Maryland, Pennsylvania, Virginia,
West Virginia. Washington, D.C.)
IV (Alabama, Florida, Georgia, Kentucky,
Mississippi, North Carolina, South Carolina,
Tennessee)
V (Illinois, Indiana, Michigan, Minnesota, Ohio,
Wisconsin)
VI (Arkansas, Louisiana, New Mexico, Oklahoma,
Texas)
VII (Iowa, Kansas, Missouri, Nebraska)
Vin (Colorado, Montana, North Dakota, South
Dakota, Utah, Wyoming)
IX (American Samoa, Arizona, California, Guam,
Hawaii, Nevada)
X (Alaska, Idaho, Oregon, Washington)
Largest MSA/CMSA II
Boston-Worcester-Lawrence CMSA
Hartford MSA
New York-Northern New Jersey-Long Island,
CMSA
San Juan-Caguas-Arecibo CMSA
Washington-Baltimore CMSA
Philadelphia- Wilmington- Atlantic City CMSA
Miami-Fort Lauderdale CMSA
Atlanta MSA
Chicago-Gary-Kenosha CMSA
Detroit- Ann Arbor-Flint CMSA
Dallas-Fort Worth CMSA
Houston-Galveston-Brazoria CMSA
St. Louis MSA
Kansas City MSA
Denver-Boulder-Greeley CMSA
Salt Lake City-Ogden MSA
Los Angeles-Riverside-Orange County CMSA
San Francisco-Oakland-San Jose CMSA
Seattle-Tacoma-Bremerton CMSA
Portland-Salem CMSA
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In addition, one NAMS site must be located in each of the MSA/CMSAs (or in the city or
county if outside a MSA/CMSA) where one or more violations of the quarterly lead NAAQS
have been recorded over the most recent eight calendar quarters with available lead air quality
data. This NAMS site must be located within a populated area, apart from the lead source, to
assess area-wide lead air pollution levels. These NAMS sites should represent the maximum lead
concentrations measured within the MSA/CMSA, city, or county that is not directly impacted
from a single stationary lead source, and it may be either a mobile source oriented site or a
neighborhood type site with adjacent heavily trafficked roadways. Data from this site will be
used to assess national trends and progress toward continued attainment of the lead NAAQS with
regard to area-wide sources such as mobile sources. The 1995-1996 Lead NAAQS Attainment
Strategy monitoring report is included within Appendix A for a reference of the currently tracked
stationary lead sources. Table 2 contains a listing of the MSA/CMSAs, cities, or counties that
have one or more quarterly Pb NAAQS violations over the 1995-1996 period.
Table 2. MSA/CMSAs or Counties with 1 or more Lead NAAQS Violations in 1995-1996.
Philadelphia-Wilmington- Atlantic City CMSA
Tampa-St. Petersburg-Clearwater MSA
Memphis MSA
Nashville MSA
St. Louis MSA
Cleveland- Akron CMSA
Iron County, MO
Omaha MSA
Lewis and Clark County, MT
Contributing Lead Source(s)
Franklin Smelter in Philadelphia County, PA
Gulf Coast Lead in Hillsborough County, FL
Refined Metals in Shelby County, TN
General Smelting in Williamson County, TN
Chemetco in Madison County, IL, and Doe
Run in Jefferson County, MO
Master Metals in Cuyahoga County, OH
ASARCO in/near Hogan, MO
ASARCO in Douglas County, NE
ASARCO in/near East Helena, MT
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2.0 BACKGROUND
This section presents general information related to lead air quality and trends in lead air
pollution. Specifically, Section 2.1 describes the chemical and physical forms of atmospheric lead;
Section 2.2 discusses the major source categories of lead emissions; and Section 2.3 describes
observed lead air quality patterns and trends.
2.1 Airborne Lead Forms
The chemical and physical form of airborne lead is directly related to the type of emission source.
Chemical forms of lead emitted into the atmosphere by anthropogenic sources include elemental
lead (Pb), lead oxides (PbO, PbO2, and PbO3), lead sulfates and sulfides (PbSO4 and PbS), lead
chlorides and bromides (PbCl2, PbClBr, and PbBr^, and lead-alkyl compounds [Pb(CH3)4 and
Pb(C2H5)J.
The dominant forms of atmospheric lead from mines and smelters are lead sulfates and sulfides
(PbSO4, PbOPbSO4, and PbS). The main chemical forms of lead from ore handling and fugitive
dust from open mounds of ore concentrate are lead sulfate, and the major chemical forms from
sintering and blast furnace operations are PbSO4 and PbOPbSO4.2 In contrast to the chemical
forms of lead emitted by point sources, the chemical forms of airborne lead from mobile sources
are in the form of lead halides (PbCl2, PbClBr, and PbBr2), and as double salts (e.g.,
PbBrCl«2NH4Cl, and alpha-2PbBrCl«NH4Cl). Lead alkyl compounds are emitted from petroleum
refineries.
Combustion and smelting processes emit submicron size particles due to the high temperatures at
which these processes take place. Lead particles emitted by handling and mechanical processes
(e.g., ore processing) are several times larger (greater than 2 microns) than particles emitted by
combustion sources.
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Airborne lead particles can be placed into three size ranges: (1) the nuclei mode (less than 0.1
pm); (2) the accumulation mode (0.1-2 pm); and (3) the large particle mode (greater than 2 pm).
Lead particles are generally in the nuclei and large particle size range as emitted at the source.
The large particles are removed by deposition close to the source, and the particles in the nuclei
size range either diffuse to the earth's surface or agglomerate in the air to form particles in the
accumulation size range which are then transported over long distances.2
Atmospheric lead particles found in both urban and rural areas are sized predominantly in the
accumulation mode and appear mostly in the size range between 0.2 and 0.3 pm. Many studies of
atmospheric lead concentrations in Europe, South America, and Asia have confirmed that ambient
urban and rural air contains predominantly fine particles. A study of six U.S. cities showed that
fine particle lead mass appears to be greater than coarse lead mass by approximately a factor of
five.2
During atmospheric dispersion, lead transformations may occur and include physical changes in
particle size distribution, organic to inorganic chemical phase changes, and chemical changes in
the inorganic phase of lead particles. Within a few hundred meters of the source, the particle size
distribution of lead stabilizes and remains roughly constant with transport into remote
environments. The atmospheric concentration continues to decrease with distance from the
source. Ambient concentrations of organic lead decrease more rapidly than inorganic lead. This
suggests that the organic lead converts to the inorganic form during atmospheric transport.
Inorganic lead forms also appear to undergo chemical transformations from lead halides and
oxides to lead sulfates. Lead compounds may be removed from the atmosphere by wet or dry
deposition.2
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2.2 Sources of Lead Emissions5
The major sources of lead emissions and ambient lead levels are combustion sources and industrial
sources. Combustion sources include mobile gasoline combustion sources and stationary
combustion sources, with the latter category being dominant among combustion sources.
Industrial sources include metallurgical and manufacturing processes.
Lead emissions from on-road and non-road mobile sources currently comprise approximately 10
percent of total lead emissions nationwide, with non-road sources (e.g., aircraft) contributing
nearly all of these emissions. The lead content of gasoline has undergone a series of reductions
beginning in the 1970s and continuing through 1995 when all lead was required to be removed as
an on-road mobile source fuel in accordance with the 1990 Clean Air Act Amendments.
Currently, ambient lead levels near roadways and in urban areas are extremely unlikely to exceed
the current lead NAAQS.
Stationary combustion sources that emit lead include coal and oil combustion, waste oil
combustion, and municipal waste and sewage sludge incinerators. Taken together, this source
category amounts to somewhat more than 33 percent of nationwide emissions. Waste oil
combustion, municipal waste incineration, and coal combustion comprise the majority of
emissions in this category. Recent modeling studies3 suggest that sources in this category are
unlikely to pose a threat to the NAAQS.
Industrial manufacturing sources of lead now account for about 5 percent of total lead emissions.
This category includes production of lead alkyl, lead-acid batteries, lead oxide and pigments,
leaded glass, portland cement, solders and coatings, and other miscellaneous products. Some
larger lead-acid battery manufacturing facilities may have the potential to cause high enough
ambient lead concentrations to threaten the NAAQS.3
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Metallurgical processes that may be sources of lead emissions include lead ore mining;
smelting/refining of lead, copper, and zinc; and production of iron and steel, gray iron, brass and
bronze. Taken together, this category of sources currently comprises about 50 percent of
nationwide lead emissions. As industries, the largest contributors are primary and secondary lead
smelters and producers of iron, gray iron, and steel.
Results of dispersion modeling around various point sources suggest that metallurgical processes
are the greatest contributors to high ambient concentrations of lead.3 Observed maximum
quarterly average lead concentrations near primary and secondary lead smelting and refining
plants have exceeded the former NAAQS concentration of 1.5 ug/m3. For primary lead smelters,
both stack and fugitive emissions contribute to high predicted ambient impacts while fugitive
emissions contribute the most at secondary smelters.
Overall, about 85 percent of the primary lead produced in the U.S. is from native mines which are
often associated with minor amounts of zinc, cadmium, copper, bismuth, gold, silver, and other
minerals.2 In addition, a new source of lead emissions emerged in the mid-1960s when the
"Viburnum Trend" or "New Lead Belt" was opened in southeastern Missouri. This area consists
of eight mines and three accompanying lead smelters which makes it the largest lead-producing
district in the world. This area has also made the U.S. the world's leading lead-producing nation.
The Missouri lead ore mining operations account for about 80 to 90 percent of the domestic
production of lead.
Figure 1 shows the relative locations of major lead operations in the U.S. including mines, primary
and secondary smelters, refineries and alkyl lead plants. Maximum quarterly average lead
concentrations for the nation in 1995 are illustrated in Figure 2. Sources of lead emissions are
found throughout the entire U.S. Both mobile and point sources of lead emissions are found
mostly in areas of high population density with the exception of lead smelters. Primary lead
smelters are located mostly in rural areas. Secondary lead smelters are located mostly near large
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urban areas that produce large quantities of scrap lead (mostly used automobiles and truck
batteries).
Pleura• 1. Location* of tho SO Largest Load Emitter* In'tha UnHod States
SouroM or* located in >hod*d vtates
ESTIMATED ANNUAL EMISSIONS
(EMISSIONS INVENTOtn- YEAR)
TOKS
1 BB7 (86) GRANTTE CJTY STEEL COMPANY
2 122 (83) THE DOE RUN COMPANY
3 115 (83)ASARCO
* 10B (06) CHCyETCO. MC
5 81.8 (M) NORTHWESTERN STEEL* WIRE
6 05.B (BS) USSfTEEL CO OARY WORKS
7 34J (S3) FUMOOA POWEK
B SIX) (B3) TECO-«6 BEND STA.
fi 85.0 (••) ASAKCO MOOMPORATED
10 21J (83}SEMI»MLEEIECTRICCOOP
11 1QJ <»5) NORTHWEST WASTE TO ENERGY
12 1B.1 (83) US FOUNDRY UANUFACTUmNC
13 1741 (««)ICBY«TOHe«TEEl.*«««eHV
1* 16.7
IS 10.0
10 1B.B
17 14.7
18 14.S
19 14.6
20 14,0
ai IM
22 13J
23 -\3JO
24 12.0
28 12J
20 11>
27 11J
2» 1141
29 10*
30 0.13
(88) J-POT STEEL MELT SHOP. INC
(84) ASARCO. MC.
(88) WIRCO CASTWO
(86) ACME STEEL COMPANY
(BS) NORTHERN STATES POWER
(83) OULF POWER CO
(•2}GENERN.fXCCCO.
(M) ACME REEL CaMPANr-CHICAOO
(>3)TECO OANMON STA.
(03) JACKCONAAOE ELECTMC AUTH
<•«) COM CD - »OMCA» OEMDMTMO
(M) NONTH STAK STEEL MN
(8S) ACME WTEEL COMPANY
(«2)Am-PWKTlHB
(M) R. LAVM * SONS, INC.
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Figure 2. Maximum Quarterly Avaraga Load Conomtnttom. IMS (ratawnoa 7)
Concentration (ug/m3)
.55-3.04
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3.0 RECOMMENDATIONS FOR MONITORING LEAD NEAR POINT SOURCES
State and Local Air Monitoring Station (SLAMS) monitoring networks are now encouraged to
establish monitoring sites around point sources which have the potential to exceed the level of
the lead standard (40 CFR Part 58). At a minimum, two sites are recommended for each
source. Emissions from point sources which may emit over 5 tons per year (tpy) of actual
lead including both stack and fugitive emissions are generally considered to have the potential
to cause an NAAQS exceedance, and other smaller sources may need to be investigated,
particularly if their facility boundaries are small and they are located in heavily populated
areas where exposure may be high. Modeling may be necessary to determine if sources that
emit between 1 and 5 tpy pose a threat to the standard.
More than two monitoring sites may be needed in order to adequately monitor the areas of
expected maximum impact in the vicinity of a point source. The need for additional sites will
depend on the level and degree of variability of the ambient lead concentrations attributable to
the source as determined through monitoring and modeling analyses. Factors such as
meteorological and topographical influences, and the spatial scale of representativeness of lead
impacts should be considered.
When reviewing sources to determine the need for monitoring, total lead emissions should be
based on the maximum allowable emission rate for the source as stated in the operating permit
issued by the local air pollution control agency. If a source is not permitted or if permit
information is unavailable or incomplete (e.g., does not account for fugitive emissions), total
emissions should be based on the maximum emission rates achievable by the source. Emission
data should be based on the current operating status of the source and should take into account
any pending source modifications.
There may be situations when there is some question whether a source area should be
considered for monitoring. For example, an area might contain several sources whose
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emissions are below the threshold where monitoring is recommended, but whose combined
impacts might pose a threat to the air quality standard. Calculation of total lead emissions for
such a source area should be based on expected ambient impacts. That is, if multiple lead
sources are located within the same general area, total lead emissions should be summed
unless the impacts of those sources are clearly separate.
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4.0 SITE SELECTION METHODOLOGY
The goal of devising a site selection methodology for monitoring lead impacts near point
sources is to optimize placement of monitors such that the probability of capturing maximum
concentrations is maximized while the number of monitors, and associated cost, is niinimized.
Achieving this goal requires understanding the spatial distribution of airborne lead
concentrations at the scale of representativeness appropriate to dispersion of lead in the
ambient atmosphere. This entails acquiring knowledge of actual or predicted lead
concentrations over the area of concern with resolution at the micro-, middle-, and
neighborhood-scales. This knowledge is gained from a series of analyses utilizing available
emissions, monitoring, and meteorological data combined with knowledge of local influences
and an understanding of atmospheric dispersion and lead deposition processes.
A basic procedure has been traditionally outlined for this purpose and consists of the following
steps:
• Determine the monitoring objectives and the spatial scale of representativeness
• Identify unique source characteristics
• Characterize topographic and land use influences
• Analyze available meteorological data
• Analyze existing monitoring data
• Conduct air quality modeling analysis
• Select monitoring locations
In practice, this is not a step-by-step procedure, but one in which information gathered or
results obtained during one activity might provide insight or demonstrate a need that suggests
that an earlier activity be revisited. An iterative process is thus established. In addition,
saturation monitoring studies have been employed more recently as an additional option in this
process.
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In every case, monitoring objectives must be defined and source characteristics and local
influences identified before other steps in the process can proceed. At that point, a site
selection study plan should be established based on available data, resources, logistical and
time constraints, and regulatory requirements. Such plans should reflect a careful study of the
factors to be considered and an understanding of the methods available. They should allow for
some dynamic elements, or midstream redirection, where appropriate. Ultimately, a decision
is made based on the analyses conducted and the professional judgment of those involved.
4.1 Monitoring Objectives and Spatial Scale of Representativeness
The objective of monitoring for lead near point sources is primarily directed by the need to
achieve and maintain the lead ambient air quality standard. Data collected will be used for
making NAAQS attainment/nonattainment decisions, determining required levels of control,
and obtaining data needed for control strategy development and SIP revisions. These data uses
all imply a monitoring strategy where samplers are located to record maximum lead
concentrations.
The spatial scale of representativeness of a monitor refers to an area where the expected
variation in concentration in the area surrounding the monitor is small (less than 20 percent,
for example). Lead concentrations tend to decline rapidly with distance from the source due to
dispersion, settling, and deposition processes. Therefore, the spatial extent of the most
significant lead impacts tends to encompass relatively small areas at a given time.
Under unstable or fumigation conditions, or when a plume impinges on elevated terrain, the
ambient impact of a lead point source may vary significantly at the spatial scale affecting an
area less than 100 meters across, or micro-scale. Such conditions, however, are unlikely to
persist for long periods. More frequently, point sources are responsible for relatively
consistent impacts over areas from 100 to 500 meters across, or at the middle-scale. Impacts
may also occur at the neighborhood scale which encompasses an area extending from 500 to
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4000 meters. Because the area representative of maximum lead impacts is likely to be
relatively small, the precise location of lead samplers may be more critical.
4.2 Source Characteristics/Emissions Types
Once the need for monitoring near a point source has been established, certain source-specific
data should be compiled to aid hi the analyses necessary to select representative sampling
locations. This also applies to sources where modeling is needed to establish the need for
monitoring, since similar analyses will be conducted. Although data on total lead emissions
were used to determine that a source be considered for monitoring, lead emissions data should
be reviewed hi detail before proceeding with site selection so that emission rates are verified
and the contributions from various emission types (point, background, fugitive) are
understood.
In addition to the amount and type of lead emissions, information should be obtained
identifying source-specific influences on how the lead emitted is dispersed into the atmosphere.
These influences include physical stack parameters, and the dimensions and locations of
nearby buildings, and may also include process descriptions, and chemical and physical
characteristics of the lead-containing emissions.
4.2.1 Source Characteristics
Stack height and diameter, and effluent exit temperature and velocity are used by air quality
models to determine the effective plume centerline. Emission rates and other characteristics
for roof vents, reentrainment areas, and other fugitive sources must also be provided to the
model.
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Since nearby buildings can create downwash and turbulent effects on the plume, dimensions
and locations of nearby buildings may be required for dispersion modeling. Information on
the particle size distribution characteristic of the emissions should be evaluated, if available,
for its influence on the degree of settling and deposition that might be expected near the
source. Similarly, the chemical composition of the emissions should be evaluated to determine
if chemical transformations need to be treated in the analyses. If receptor modeling is
employed, chemical and physical characteristics of the lead-containing emissions can be used
to develop source fingerprints.6
Descriptions of the industrial processes taking place at the lead-emitting facility should also be
obtained. This information should include operating schedules, material throughput rates, size
and location of storage piles and waste holding areas, and any other information needed to
characterize the resulting lead emissions. Such information is useful for interpreting
monitoring data, estimating emission rates from non-point or fugitive sources, and selecting
appropriate air quality models and modeling parameters.
4.2.2 Types of Emissions
Various types of lead emissions can be associated with a given point source or source area.
Stack emissions are those that are vented through a smokestack to encourage dilution and
rninimize direct impact on the surrounding area. Stack emissions are often associated with a
combustion process or furnace so that emissions are at elevated temperatures. Lead may also
be captured by ventilation systems and emitted through vents at ambient temperatures. Data
on emission rates for stacks and vents at sources large enough to require modeling should be
available from emissions inventories or might be obtained from operating permit files.
Alternatively, emission rates can be developed based on process information and published
emission factors.7
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Fugitive emissions refer to lead that escapes from an industrial process, storage facility, or
mine that are not intentionally vented to the atmosphere. Ground level or reentrained
emissions generally refer to lead that reenters the atmosphere due to wind action or a
disturbance such as automobile traffic after having been deposited from the air at an earlier
time. In some instances, reentrained lead alone may represent a significant emission source.
This would also apply to areas near facilities that are no longer operating.
Techniques have been published for estimating emissions from various fugitive dust sources
that may be applicable to estimating fugitive lead emissions in combination with data on the
lead content of soils, dusts, and storage piles in the area.8 Information on fugitive emissions
from industrial processes might be obtained from permit files for the source in question or
estimates could be based on the process type and size, and emission rates.7
Background lead levels are generally associated with distant sources, are regionally
representative, and most often occur at relatively low levels. A conservative estimate of 0.1
jig/m3 (monthly average) has been applied to sources across the U.S. This value represents a
nationwide average of annual urban background concentrations, plus concentrations
attributable to mobile sources, and diffuse minor point sources.3 Area-specific background
levels should be estimated if adequate monitoring data are available.
The total impact of all types of emissions on the surrounding area can result from combined
effects at single receptors or separate impacts at multiple receptors. Each of the different
emission types must be considered in the network design in order to ensure that monitors are
located in maximum concentration areas and that all such areas are identified. A workable
approach is to analyze the nature, strength, and dispersion patterns of each emission type
individually and then consider the potential for combined impacts. Separate impacts should be
considered for fugitive emissions that might be significant enough to threaten NAAQS
attainment.
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In general, stack emissions are of primary concern around point sources; however, their
impact must always be considered in combination with background levels including roadway
network emissions. The area of maximum impact from stack emissions should occur at some
distance downwind from the stack depending on effective stack height and meteorology. The
highest concentrations generally occur near the source when the plume is mixed to ground
level under unstable conditions. The greatest air quality impact of ground level emissions
should occur adjacent to the source area hi the downwind direction. Fugitive and ground level
emissions may or may not act hi combination with stack emissions depending on the
meteorology and topography of the area. Emissions from nearby sources must also be
considered as appropriate.
4.3 Topographical and Land Use Influences
Topographic influences can exert profound effects on dispersion patterns and impacts of lead
emissions from point sources. For example, plume impaction on elevated terrain can result in
very high, localized lead concentrations. Local topography affects the dispersion of pollutants
and can act to localize the air pollution climatology of the source area. Knowledge of local
topographic influences is essential to assess the representativeness of available monitoring and
meteorological data.
Topographic influences may include thermally-induced, diurnal air mass circulations as in
mountain/valley or land/sea air flow cycles. In addition, there are direct, terrain-induced
influences on wind direction and wind speed. These are related to the mechanics of
aerodynamic diversion occurring as air flows over and around obstacles. For example, air
tends to be channeled along valleys, affecting sources located near rivers. Wind speed
increases as air flows up and over hills. Turbulent eddies are set up on the leeward side of
hills.
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The extent to which the stability of an air mass is disturbed as the air passes over the earth's
surface is affected by the degree to which the surface is occupied by buildings, trees, and other
obstacles. This is often referred to as surface roughness. In general, urban areas present
more and larger obstacles to airflow than rural areas, resulting in greater instability.
Urban areas may also be subject to a "heat island" effect. Urban areas store heat and remain
warmer than surrounding rural areas. The effect is most pronounced at night. When a heat
island circulation exists, cool air from the surrounding rural areas converges toward the city,
is heated, rises to the level of the inversion, and then returns to the outlying areas. Strong
winds or more pronounced thermal circulations can destroy heat island circulation.
In complex terrain, special care must be taken in correctly applying modeling analyses and
interpreting modeled results. Where feasible, model output relating to complex terrain
situations should be substantiated with monitoring data. Saturation monitoring studies may be
used to corroborate models and can serve as an alternate source of information for site
selection purposes.
4.4 Meteorological Influences
Second to the emissions themselves, meteorology is the most important consideration in
evaluating the impact of lead emissions on the area surrounding a point source. In order to
understand source impacts, it is necessary to identify meteorological conditions associated with
high ambient lead concentrations and to understand when, and how often, those conditions
occur in the vicinity of the source. It is also important to characterize conditions occurring
most frequently in the vicinity of the source. Because the lead standard is based on a quarterly
mean concentration, the persistence of meteorological conditions associated with elevated lead
levels is the most important consideration. High concentration events that occur infrequently,
or for very short durations, will not significantly affect a quarterly standard.
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Meteorological conditions associated with the highest ambient concentrations include
fumigaiion, plume trapping by an elevabd inversion, and plume coning with low wind speeds.
Fumigation occurs when thermal turbulence occurring beneath an elevated inversion brings
high concentrations from stack height to ground level. Less severe, but more frequent,
elevated concentrations occur near point sources during periods of low wind speed under
stable or unstable conditions.
Analysis of meteorological data should first determine the completeness and representativeness
of available data. Meteorological data that may be useful for various modeling analyses may
.9,11,14
include the following measurements
• hourly wind speed and wind direction,
hourly atmospheric stability based on wind fluctuations (oe), or vertical temperature
gradient combined with wind speed,
• hourly surface temperature for climatological comparisons and plume rise calculations.
Hourly relative humidity, precipitation, and solar radiation may also be recorded. Upper air
data are useful to establish a vertical temperature profile and characterize the speed and
direction of winds aloft. Data collected at several altitudes are especially useful for dispersion
modeling in complex terrain situations. Analysis of monitoring data may then be used in
conjunction with representative meteorological data to identify conditions associated with high
ambient lead concentrations.11
Data obtained from the nearest National Weather Service (NWS) station are most often used
for air quality analyses. The representativeness of these data for the source area will depend
on the distance of the NWS station from the source and the local topography. Additional data
may be available from other meteorological stations including those operated by private
citizens, industry, State air pollution control agencies, colleges and universities, and federally
funded monitoring networks such as the National Dry Deposition Network (NDDN).
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Multi-station comparative analyses can be conducted to help determine the representativeness
of available measurements over the impact area. When representative data are not available, it
may be necessary to conduct on-site meteorological monitoring. Monitoring should preferably
be conducted over at least one year but could be conducted during periods representative of
conditions when maximum lead impacts are expected. Guidance is available for collecting and
preprocessing on-site meteorological data10 for use with air quality models.11
4.5 Analysis of Monitoring Data
Analysis of sufficient, representative monitoring data can identify general areas of maximum
impact and can help to determine the spatial scale of representativeness of those impacts.
Background concentrations and impacts from road networks and other diffuse sources can
often be best estimated by monitoring. Due to the likely spatial sparseness of lead monitoring
data near point sources, analysis of existing data is unlikely to conclusively identify maximum
receptor points at the small spatial scales of concern for lead monitoring.
Analysis of monitoring data can, however, aid in model selection and application by
supporting choices of receptor points to be modeled, and identifying circumstances that may
need to be given special treatment in the modeling analysis. These include influences of
terrain or meteorology, and source-specific emissions characteristics. In addition, the analysis
of monitoring data should help to validate, corroborate, and interpret the model results.
In evaluating monitoring data, it is important to consider the likely source of high
concentrations (stack, fugitive, background, reentrainment) as well as the meteorological and
topographical influences particular to the area. It is preferable to obtain comparable data from
multiple stations within and around the source area; however, analysis of data from single
stations can provide much useful information.
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4.5.1 Single Station Analysis
The goal of single station analysis is to identify factors that influence the observed lead
concentration levels. This is accomplished through a sequence of comparisons between
observed concentrations and concurrent emissions, and meteorological data. The analysis
should focus on those conditions that persist long enough or produce high enough
concentrations to influence monthly average concentrations. Care should be taken to ensure
that data used in comparisons are quality assured and representative of the same areas and time
intervals. Parameters to consider for comparisons include the following:
• Prevailing wind direction
• Scalar average wind speed
• Wind persistence (ratio of vector mean to scalar average wind speed)
• Height and magnitude of inversions (daytime and nocturnal)
• Range of Pasquill-Gifford stability categories11
• Emissions rates over time (if available)
• Facility operating schedules
The techniques that should be applied will depend on the available data, time, and resources.
The goal is to utilize the data to obtain as much information as possible on the spatial
distribution of lead concentrations surrounding the source. A recommended sequence of
analyses is to consider the frequency distributions of concentrations and related parameters,
then conduct time series analyses and case studies of peak values. If data are available from
more than one monitor, comparing the results of similar analyses conducted for the different
monitors can yield valuable information.
A good starting point for statistical analysis of monitoring data is to compile frequency
distributions for observed concentrations and meteorological parameters. A graphical
presentation of these distributions enables immediate understanding of the nature of any central
tendency of the various parameters. Most frequently occurring lead concentrations might be
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related to the most frequently occurring meteorological conditions. Monitors exhibiting
similar concentration frequency distributions, especially over a period of several years, might
be considered to be in homogeneous areas.
Comparative time series analysis can help to explore the relationship between lead
concentrations and the various influencing parameters. A graphical presentation of two or
more variables plotted simultaneously against tune can be extremely useful in identifying such
relationships. Because historical lead measurements are generally integrated over a 24-hour
period and are obtained only once in each six day period, it may be difficult to achieve
meaningful comparisons between concentration and meteorological data; however, significant
relationships should appear. Comparison of time series plots of data from different stations can
quickly reveal if the factors influencing lead levels are the same. Regression analysis can be
used to further explore and quantify apparent relationships.
After exploring interrelationships between concentrations and the various influencing factors,
case study analyses of peak values and consistently elevated values should be conducted to
thoroughly characterize conditions that could result in an exceedance of the monthly standard.
Ideally, a limited set of conditions can be identified that corresponds to the most persistent
conditions related to elevated concentrations and to the highest values recorded. This
information will help to focus subsequent analyses and, in some cases, may provide adequate
information for site selection.
4.5.2 Multi-Station/Mapping Analysis
If data from a sufficient number of monitoring stations are available to provide adequate
spatial coverage of the source impact area, mapping analysis can be conducted to identify
maximum impact areas and their associated spatial scales of representativeness. This can be
accomplished by estimating the locations of lines of uniform concentration (isopleths) on a
map of the area. Computer graphics packages are available that provide a convenient and
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systematic method of establishing concentration contours. Data must generally be available
from at least six sites concurrently in order to obtain useful information from a mapping
analysis.12 In order to obtain meaningful results, it is essential to ensure the comparability of
data from different stations. This includes consideration of sampling and analysis methods,
sampling intervals, and schedules. Data bases should be adequately validated and sufficient
completeness criteria applied (e.g., 75 percent).
4.5.3 Adequacy of Analysis of Existing Monitoring and Meteorological Data
Two judgments are required to determine whether analysis of monitoring and meteorological
data is sufficient to justify selection of permanent monitoring stations. The first is whether the
available monitoring data accurately reflect the spatial distribution and temporal variation of
lead concentrations over the area. The second is whether the resolution (detail) in that pattern
is adequate to allow selection of permanent monitoring locations with sufficient assurance that
they represent the maximum impact areas. Due to the steep concentration gradients typical of
lead air pollution, maximum impacts may occur over relatively small areas that may not be
represented in a more general pattern. High concentrations over a small area could occur due
to plume impaction on elevated terrain, but would not be represented if no monitor were
positioned to record those concentrations. Similar uncertainty could result if high
concentrations occurred near a source due to fumigation conditions and, likewise, there was no
monitor in position.
When the distribution and variability of lead concentrations are adequately defined,
meteorological variation, topographical influences, and the distribution of sources should be
consistent with the monitoring data. Any significant changes in these factors should be
reflected in the monitoring data. If the lead air quality pattern is consistent over time (peaks in
subsequent years occur under the same conditions at about the same time), this may be taken
as evidence of a stable pattern which can be used for network design purposes. If the
monitored lead air quality pattern varies over time, then these variations should be consistent
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with changes in the influencing factors. If there are unresolved inconsistencies, then more
careful or detailed analyses should be conducted. If further analyses of existing data fail to
resolve inconsistencies, then air quality modeling should be considered to explore further
relationships.
When monitoring data are available from a few, widely dispersed monitoring stations, as is
often the case, it is difficult to determine if small scale impacts are adequately represented.
In such instances, it may be possible to benefit from knowledge of emissions characteristics,
topographic influences, and prevailing meteorological conditions to draw conclusions
regarding the location and scale at which maximum impacts are likely to occur. For example,
impacts from resuspension and ground level fugitive sources will be adjacent to the source in
the downwind direction. Ground level impacts from elevated stack emissions should be
diffuse and remote from the source except under fumigation conditions. Adequate
documentation that such conditions prevail, along with corresponding data from an
appropriately located monitor, would provide sufficient justification for permanent site
selection without the need for modeling.
Air quality models designed to predict concentrations at designated receptors under a variety
of source configurations and meteorological conditions can be used to help resolve some of
this uncertainty. Agreement between modeling and monitoring analyses, though not
constituting proof, provides additional justification for decisions regarding site selection. In
addition, if time and resources permit, saturation monitoring studies can be designed to
specifically address the uncertainties particular to a given source area.
4.6 Air Quality Modeling
Air quality models are a limited mathematical representation of the processes of emission,
transport, and deposition of pollutants in the atmosphere. Dispersion models allow
computation of relative concentrations of air pollutants based on emission conditions, degree
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of atmospheric mixing, and distance from the source. Receptor models are designed to back-
calculate source impacts using variability in ambient air data and unique source characteristics.
All air quality models incorporate simplifying assumptions which produce varying degrees of
uncertainty in the modeled results depending on the extent to which the actual situation is
represented in the assumed conditions.
Dispersion models are of primary importance in network design since they yield data that can
be used immediately to determine locations where maximum impacts might be expected.
Further, dispersion models can be used to predict the degree of emissions reductions required
to achieve an air quality standard. Receptor models are useful in situations where there are
large uncertainties in dispersion model results and can be applied to help resolve
inconsistencies between dispersion modeling and monitoring data.
4.6.1 Dispersion Modeling
Detailed guidance on the regulatory use of air quality dispersion models is available in the
Code of Federal Regulations, Title 40, Part 51, Appendix W.1' The most extensively applied
method of modeling the dispersion of air pollution utilizes the Gaussian plume equations.
These equations are derived from expressions representing the concentration of air pollutants
within a cross section perpendicular to the plume at varying distances downwind from the
source. Concentrations are assumed to decrease in proportion to increasing distance from the
source, and the concentration profile along the vertical and horizontal axes within the plume is
assumed to conform to a Gaussian (normal) distribution (Figure 3). Theoretical and
experimental results support these assumptions.1
13
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Figure 3. Gaussian Plume Dispersion Model
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The spatial impact of a plume depends on wind speed and direction, and the rate and extent of
vertical and horizontal dispersion of the plume. Plume dispersion depends on atmospheric
stability as well as emission characteristics. Atmospheric stability has been categorized by
Pasquill and others into classes defined according to wind speed, insolation, and vertical
temperature gradient.11-14 Diffusion coefficients expressing horizontal and vertical.dispersion
rates at varying downwind distances have been empirically derived for each stability category
and are employed in dispersion models. In general, such models describe a cone shaped
plume spreading out downwind from a point source over level terrain.
4.6.2 Recommended Modeling Approach
Dispersion models are required for demonstrations of attainment of the lead air quality
standard around certain point sources and in areas with measured quarterly mean lead
concentrations greater than 4.0 fig/m3. Dispersion modeling is an appropriate analytical
technique for network design for lead monitoring near point sources. In order to most
effectively utilize resources, a three-phase approach to the use of dispersion models for air
quality analysis has been developed and is recommended.14
The first two phases utilize screening procedures to determine relatively quickly which sources
pose no threat to air quality and which sources might potentially generate an air quality
problem. The screening procedures are designed to provide a conservative estimate of air
quality impacts so that, if predicted concentrations are below the level of concern, no further
analysis is necessary. Refined modeling techniques are utilized in the third phase. The
refined models are designed to provide a more accurate assessment of air quality impacts by
taking into account influences that are beyond the scope of the screening procedures.1'
14
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4.6.3 Screening Procedures
In the first phase, a simplified, step-by-step, procedure is used to apply the Gaussian
dispersion equation to estimate the maximum ground level concentration near a point source.
The simplified screening procedure can be carried out by hand or with a hand-held calculator.
The procedure is primarily applicable to single point sources in level terrain; however, a
method is provided to merge nearby point sources with similar characteristics so that they can
be treated as a single source. The simplified procedure does not apply if aerodynamic
downwash or plume impaction on elevated terrain are to be considered.14
More detailed, second phase screening procedures are warranted if concentrations predicted by
the simplified procedure exceed the level of concern. Second phase computations account for
a predetermined range of meteorological situations, and can be applied to estimate the impact
at specific downwind locations. These calculations can also be done manually. The same
assumptions of level terrain and no downwash generally apply when the basic second phase
procedures are used. A computer application called SCREEN is available to help conduct
more detailed second phase screening analyses. The SCREEN model facilitates second phase
computations and is capable of providing corrections for downwash, fumigation, elevated
terrain, and area sources.14
The available screening procedures are designed to provide maximum one-hour, 3-hour, 8-
hour, 24-hour, or annual average concentrations. Maximum concentrations averaged over
intervals longer than one hour will be lower than the one-hour values. The ratio of a longer
term maximum to a one hour maximum depends on the averaging time, source characteristics,
and the local climatology, topography, and meteorology.
Screening procedures can provide estimates of the rate at which maximum impacts decrease
with distance from the source. This information is useful for designing appropriate receptor
grids for refined modeling. Screening procedures can also be applied to various sources
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within a facility to obtain initial estimates of the relative contributions from each source. This
information is useful in selecting refined model output and interpreting refined model results.
4.6.4 Refined Modeling
Where screening procedures fail to clearly eliminate a source from concern, the use of a
refined model is called for. Refined modeling is also recommended if the source configuration
is complex, emission rates are highly variable, or pollutant dispersion is thought to be
significantly affected by terrain features or large bodies of water. Since available screening
procedures do not readily predict quarterly average concentrations, refined modeling will often
be required to determine if a source's emissions pose a threat to the standard.
The additional effort to run a refined model consists primarily of that required to construct the
receptor grid and to prepare the more detailed meteorological data. The industrial source
complex (ISC) model has been used to estimate lead impacts from a variety of sources2-3-15 and
is recommended for areas with significant sources of lead emissions.16
The ISC model allows the user to select a large number of receptor locations to be modeled.
These can be located in a grid pattern or by individual coordinates. Polar or Cartesian
coordinate systems may be used. Separate sources are likewise located according to a
coordinate system. The ISC model accounts for many source/area specific influences
including the following: particle settling and dry deposition; downwash; point, area, and
volume sources; plume rise as a function of downwind distance; wind speed variation with
height; and separation of point sources.11-17 Fumigation is not treated and terrain adjustment is
limited. Short-term and long-term versions of the ISC model are available. Both versions
provide a variety of user-specified output options.
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The short-term version (ISCST3) is designed to provide concentrations averaged over 1-hour
to 1-month intervals using hourly meteorological input data. The short-term model can also
provide a "period" concentration output calculated over the number of days of meteorological
data processed. Either monthly averages, or period averages with 90 days of data, would be
used for assessing whether a source constitutes a threat to the quarterly lead standard.
The long-term version (ISCLT3) uses meteorological data hi the form of Stability Array
(STAR) summaries and is designed to provide quarterly and annually averaged concentration
outputs. STAR summaries are joint frequency distributions of wind speed, direction, and
stability category. STAR summaries are available from the National Climate Data Center
(NCDC); however, they can also be generated from site-specific data." ISCLT3 could be
used to generate quarterly averaged concentrations if quarterly STAR summaries were used.
Quarterly concentrations generated from the short-term and long-term models might not agree
precisely; however, the probable locations of maximum concentrations predicted by the two
approaches should be reasonably similar.
4.6.5 ISC Model Application for Lead Sources
As previously noted, the ISC model is applicable to combinations of point, area, and volume
sources. Thus, the model is applicable to a source configuration with separate or combined
impacts from an isolated point source with nearby roadways (line or volume sources) and
fugitive area sources. Similarly, the ISC model can address combined impacts from multiple
nearby point sources.
Background lead concentrations should be obtained independently and added to the modeled
concentrations.11 Data on regional scale background lead concentrations may be available
from monitoring data for the area of concern or an area that can reasonably be assumed to
have similar background lead levels.16 A monthly average value of 0.1 /tg/m3 has been used as
a conservative estimate of background lead concentrations across the U.S.3
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If a point source is located within a substantial highway network, lead emissions from the road
network should be added to background concentrations and the source and nearby roadways
should be modeled separately. If available, monitoring data that are representative of the
roadway network, but are not influenced by the plant emissions, can be used to estimate the
roadway background. If representative monitoring data are not available, data from a similar
network can be substituted or emission estimates can be derived based on traffic volume.16
The ISC3 model incorporates a screening model (COMPLEX-1) for complex terrain. As a
result, it may not adequately account for effects of complex terrain. Plume impaction on
elevated terrain may cause high ground level concentrations. In addition, topography and land
use can significantly alter the local wind field as compared to that predicted for level terrain.
For modeling purposes, complex terrain is defined as having topographic features that exceed
the stack height.
Other than COMPLEX-1, other screening models for complex terrain are available, including
the Rough Terrain Dispersion Model (RTDM) and CTSCREEN. These may provide
somewhat better estimates of concentrations than COMPLEX-1. The Complex Terrain
Dispersion Model (CTDM Plus) is a refined model for complex terrain. This can make use of
digitized contour data and meteorological data at several levels up to plume height. Receptor
location is highly critical. The very dense array of receptors potentially required suggests that
the models be applied in two stages. The model would first be applied to a moderate number
of carefully selected receptors in order to identify areas with potential for high concentrations.
A more dense array of receptors could then be selected within the areas of concern. The
CTDM may be too involved for site selection purposes, and even CTSCREEN will require
some detail of terrain data. A model such as Complex-1 may suffice and does not require
substantially different input data than ISC3. Screening and refined analytical techniques for
modeling in complex terrain are discussed in recent revisions to the Guideline on Air Quality
Models.11
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4.6.6 Interpretation of Model Results
Modeling is a primary analytical tool for selecting monitoring locations for lead around
existing point sources and, in some cases, may be the only practical means of formally
analyzing the distribution of lead air pollution. As noted above, results of air quality models
are subject to a relatively large degree of uncertainty. Modeled concentrations typically differ
from observed concentrations by as much as ±50 percent." Nevertheless, modeled estimates
of the highest concentrations occurring at some time, somewhere hi an area are thought to be
reasonably reliable. In general, the maximum modeled concentration is the best estimate as to
whether the quarterly NAAQS may be violated.
At present, there is no standardized means of determining and reporting modeling uncertainty
and expressing confidence levels hi decisions based on modeling. It is important, however, to
attempt to identify and document the reliability of the model estimates for each application. It
is the responsibility of the modeler to provide a reasonable justification for interpretation of
modeling results. Therefore, it is important that modeling be conducted by experienced air
pollution meteorologists.
4.6.7 Receptor Modeling
Receptor models use monitoring data to apportion the contributions of sources by comparing
observed air quality characteristics to known emissions characteristics of sources in the area.
The Chemical Mass Balance (CMB) method for receptor modeling has been in use for over
two decades. CMB8 will be released in 1998 and should be used hi conjunction with its
application and validation protocol.22 For CMB applications, data from both sources and
receptors are required, and these data should be collected hi accordance with an approved
quality assurance protocol. Ambient lead sampling for CMB work is ideally done with
membrane filters (i.e., Teflon) followed by appropriate analysis by X-ray fluorescence
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spectroscopy. Although not predictive, CMB is useful to help bridge the gap between
monitoring data and dispersion model predictions.
In situations where there are multiple contributing sources that can be differentiated chemically
or physically, CMB analysis may be used to apportion the contribution from the various
sources. This technique can also be used to differentiate between stack, fugitive, ground level
(reentrained) and background emissions. In cases where there are multiple sources and/or
source types, complex terrain, or other factors which adversely affect the degree of uncertainty
in dispersion model results, receptor models can be applied to help resolve the contribution
from the various sources without the need to directly account for the influence of complicating
factors. Receptor modeling has also been applied in air sheds for which the meteorological
regime is dominated by calm wind conditions, and for which Gaussian plume models are ill
suited.
If adequate monitoring data are available, (from properly collected and analyzed samples),
CMB analysis can be used to validate and corroborate the results of dispersion modeling. If
dispersion models fail to predict ambient concentrations, then CMB modeling may help to
resolve the differences by accounting for the contributions from sources that were not modeled
in the dispersion analysis. This usually leads to an adjustment of the emission inventory used
in the analysis, followed by a rerun of the dispersion model. When dispersion model results
do agree with measured concentrations, there is still little evidence of the dispersion model's
ability to predict the impact of a single source within a group of sources or of variations within
the source makeup without the help of some form of receptor model analysis.
4.7 Saturation Monitoring
Saturation monitoring refers to an air quality monitoring approach directed at achieving finely
detailed spatial and temporal resolution of air quality impacts hi an area. This can be
accomplished through a rigorous program design employing a large number of portable
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samplers. Saturation monitoring studies are typically conducted over relatively short intervals
during the part of the year in which maximum impacts are expected to occur. A successful
saturation study provides a wealth of data that can complete information lacking in traditional
monitoring and modeling approaches.
4.7.1 Saturation Monitoring Applications for Lead Point Source Monitoring Network
Design
Saturation data can be used to resolve spatial concentration gradients and identify distinct
impact areas associated with different source types. In addition, saturation data can provide
missing background and area/mobile source baseline characterizations. Dispersion and
receptor model performance can be evaluated using data obtained from saturation studies.
Saturation studies may be particularly valuable in complex terrain situations where existing
monitoring data are likely to lack the necessary spatial resolution, and modeling results are
subject to the greatest uncertainties. In some cases, it may be appropriate to use a saturation
study to independently provide a basis for site selection.
4.7.2 Saturation Monitoring Study Design
Saturation study designs are, by their nature, highly dependent on the area and monitoring
objectives under consideration. The design of a saturation monitoring network shares many of
the same considerations necessary for design of a network near lead point sources or any other
sampling network. That is, historical meteorological and monitoring data need to be analyzed
and area-specific source emissions and topographic influences need to be considered.
Study Interval
An appropriate study interval must be chosen to capture the time period when maximum
impacts occur and span the required temporal scales of variation. This entails review of
historical monitoring, meteorological, and emissions data. The interval must be long enough
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to allow collection of sufficient valid samples for statistical integrity of the data. Logistical
considerations, including regulatory deadlines, manpower, and resources, are also likely to
play a significant role in study design.
Schedule
The daily sampling schedule must be addressed in terms of monitoring objectives. For lead
monitoring, a 24-hour sampling period would be consistent with the reference sampling
schedule for lead. Finer time resolution is probably unwarranted and not likely to provide
useful information since the lead standard is based on a quarterly average. Potential use of a
longer sampling interval could be evaluated in terms of comparability with composite samples
collected by a reference high volume sampler. A longer sampling period might improve the
sensitivity of portable samplers which are able to achieve flow rates far lower than those
achieved by the high volume. Whatever sampling period is chosen, it is important to devise a
scheme for sample changes to ensure that samples are collected over comparable intervals at
different locations. The saturation study design can also incorporate multiple or dynamic
sampling periods, if appropriate.
Site Selection
The likelihood of small scale impacts of lead air pollution suggests a site selection strategy that
attempts to identify representative monitoring locations within relatively small areas. If such
areas have previously been identified through modeling studies or analyses of monitoring data,
saturation samplers can be clustered within a few areas thought to be the most heavily
impacted. Alternatively, saturation samplers can be scattered over a larger number of areas to
determine where maximum impacts occur. It is also possible to change the site configuration
within the study period based on the data obtained or changes in meteorological or emission
conditions. Once again, existing air quality, meteorological, and emissions data are consulted.
Previous monitoring and/or modeling studies may be available and can be studied for
suggestions of appropriate monitoring locations.
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Methods
Battery operated, portable paniculate samplers are available that are relatively inexpensive,
convenient to operate and deploy, and exhibit detection limits and operating ranges comparable
to reference methods. A limited number of intercomparison studies have been conducted to
validate portable sampling methods.18 If possible, the study design should include a set of
samples from a portable sampler collocated with a high volume so that an assessment of the
relative accuracy of the data can be obtained. In addition, a duplicate set of measurements
should be obtained from collocated portable samplers so that operational precision can be
assessed. A quality assurance plan should be prepared and adhered to as with any monitoring
program.
4.8 Site Selection Without Monitoring or Modeling Data
There may be situations when useable information from existing lead monitors will not be
available and modeling analysis is not feasible because of logistical or resource constraints, or
limitations of the available models. It may be possible to identify representative lead
monitoring sites based on information available on emission sources, and the area's
topography and climatology. In addition, measurements or modeling analyses conducted in
similar locations under similar source influences might be available and could provide some
insight into locating representative sites. Such decisions could depend rather heavily on the
best professional judgment of those involved. Location of monitors based on population
density, industrial growth expectations, or exposure to sensitive populations might also be a
consideration.
Except in the simplest and most direct of circumstances, siting monitors without substantial
supporting information is unlikely to satisfy SLAMS network design requirements. In such
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circumstances, a saturation monitoring study could be considered, or steps might be taken to
obtain the data and resources necessary to conduct a modeling study.
4.9 Number and Location of Monitors
The recent revisions to the air surveillance regulations (40 CFR 58 Appendix D) recommend
at least two monitors near sources that are believed to have the potential to cause air quality
problems.. The monitors should be located in the two locations most likely to receive
maximum lead concentrations as determined from the foregoing analyses. These might be
located, for example, at an appropriate distance downwind from the source in the first and
second most predominate wind directions that occur when meteorological conditions favor
high ground level concentrations.
In some cases, it may be determined that separate threats to the standard may be presented by
different source types in the same source area. For example, stack emissions might impinge
upon a hillside while a separate area near the source is impacted by significant fugitive and
ground level emissions. In such instances, one monitor should be dedicated to monitoring
stack impacts while the other is located to capture the fugitive impacts.
The monitoring requirements suggest that more than two monitors may be needed in some
circumstances. Generally, the number of monitors will be greater where the expected spatial
variability of lead concentrations is larger. This would typically occur when there are multiple
sources or source types located in the same area or when background concentrations are
occasionally significant and highly variable. Examples include multiple point sources;
combined impacts of point and mobile sources; and separate or combined impacts of stack and
ground level or fugitive emissions where fugitive emissions are sufficient to pose a threat to
the standard.
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number needed. After analysis options have been exhausted, a decision is made based on the
available information by the agency involved with the support and approval of the local EPA
regional office. The final determination of whether more than the minimum number of
monitors should be installed will depend on the outcome of this process.
4.10 Site Selection Study Report
In addition to the preparation of the network description, a formal record should be maintained
summarizing the study conducted to arrive at final site selections. Documentation should be
maintained that supports study activities and allows results to be reproduced if necessary.
Such documentation might include monitoring data files, model input and output files, maps,
memoranda, references consulted, and procedures followed. A recommended outline of a site
selection study report would contain the following sections.
Introduction. Goals, and Objectives
Background on the circumstances and information leading to the need to undertake the network
design study should be provided in this section. The study design should be outlined and the
specific, technical objectives that shaped the study should be summarized.
Description of Study Area
A description of the study area should be given that outlines matters and features of concern
and references maps, emissions data files, and other information sources that provide
necessary details.
Study Design/Activities
The overall study design should be described, and activities and analyses that were conducted
as part of the study should be detailed.
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s*vdy Interpretation
The reasoning that was applied to synthesize information gathered from the various study
activities and arrive at final site selections should be presented. Data interpretation and
srtainty issues and a description of how these were addressed should be included.
uncer
^Descriptions
The physical location and features of the selected sites including then- roles in satisfying
monitoring objectives should be enumerated.
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5.0 LEAD MONITORING
Sampling for ambient concentrations of lead must be done in accordance with the lead
reference method prescribed in Appendix G of 40 CFR Part 50. The sampling schedule and
reporting requirements are codified in 40 CFR Part 58. Quality assurance and siting criteria
appear in Appendices A and E to Part 58. This Section reviews existing requirements and
significant changes to the ambient lead monitoring regulations resulting from the revisions to
40 CFR Parts 50 and 58.
5.1 Placement of Lead Samplers
Lead samplers should be placed so that a representative measurement of the lead concentration
over the impact area of concern will be obtained. In order to assure optimum sampler
placement and network consistency, criteria for siting the lead sampler are codified as given in
Section 7.0 to Appendix E of Part 58. The primary concerns involve consideration of the
vertical and horizontal lead concentration gradients near roadways, airflow obstructions, and
unrepresentative localized sources or sinks. In general, considerations for locating ambient
lead monitoring stations around point sources are confined to micro-scale and middle-scale
areas.
The following summarizes the basic sampler siting criteria that State/local agencies must
consider in the proper placement of the lead sampler at lead monitoring sites (40 CFR 58,
Appendix E):
The sampler inlet must be 2-7 meters above ground level hi micro-scale areas. For
middle-scale areas, the sampler inlet must be 2-15 meters above ground level.
The distance between samplers and obstacles must be at least twice the height that the
obstacle protrudes above the sampler.
There must be an unrestricted airflow hi an arc of at least 270° around the sampler and
this must include the predominant direction for the season with the greatest pollutant
40
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concentration. This criteria applies to micro-scale, street canyon, and similar restricted
sites. This would also apply to a maximum concentration fugitive emissions site.
• No furnaces or incinerator flues should be nearby.
• Any tree(s) that act as obstructions between the source of the lead emissions and the
sampler should be at least 20 meters and must be at least 10 meters distant.
The roadway setback criteria is not generally applicable to point source oriented sites.
Given that roadway concentrations are now generally near background or even the
reference method's minimum detection limit, a good site location should not be rejected
due to proximity to a roadway unless the roadway is a significant source of lead (e.g.,
resuspension).
5.2 Sampling Method
The use of high-volume samplers for collecting lead ambient air samples continues to be
required. During the most recent review of the lead NAAQS, the EPA investigated using the
PM10 sampler as the reference method sampler. Results from this work showed that the use of
a high-volume air sampler was effective at capturing 80 to 90 percent of the total mass of lead
in a hypothetical particle size distribution air sample using the particle size distribution
specified in the PM10 methodology requirements (52 FR 24634, July 1987).19 EPA also
demonstrated that high-volume air samplers could capture on average, two times as much lead
mass as PM10 samplers.19-20-21 The PM10 sampler, by design, excludes particles larger than
respirable size. Such particle sizes may be collected, however, by the high-volume sampler.
Exposure to lead not only occurs through inhalation, but also through ingestion of particles
which may be too large to be inhaled, especially by young children. Therefore, the high-
volume sampler provides a more complete measure of exposure to airborne lead than the PM10
sampler.
The high-volume sampler remains as the monitoring option for the lead standard because (1) it
will provide a reasonable indicator for deterrnining compliance with quarterly standards;
(2) the measurement technology is in place; (3) it is simple and inexpensive to operate; and (4)
its continued use will result in historical continuity in the lead air quality database.
41
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The reference method now allows for lead samples to be collected on quartz fiber filters in
addition to glass fiber filters. Historically, glass fiber filters were used in conjunction with
high-volume total suspended paniculate (TSP) samplers. The option of using a quartz
substrate for lead sampling was added to allow such agencies to use a single filter substrate in
their overall monitoring network. In addition, X-ray fluorescence (XRF) analysis is widely
used in receptor modeling. Samples collected on glass fiber filters are inappropriate for XRF
analysis. Thus, quartz filters may also be preferred for this reason.
5.3 Sampling Schedule
Samplers intended to monitor ambient impacts from point sources are required to be operated
at a minimum schedule of one-in-6 days; however, many monitoring agencies have increased
this frequency to once every 3rd day or every day when monitoring around point sources in
order to obtain a more complete database. Roadway monitors should generally be operated on
a one-in-six day sampling schedule.
5.4 Analytical Method
The reference method procedure for determining lead in suspended paniculate matter collected
from ambient air is provided in 40 CFR Part 50 Appendix G. In general, Appendix G
requires that lead in particulate matter be solubilized using extraction procedures involving
nitric acid (HNO3) and hydrochloric acid (HC1) with subsequent analysis using atomic
absorption spectrometry. In addition, ambient lead concentrations must be determined as
elemental lead. Equivalent methods designated in accordance with 40 CFR Part 53 may be
used in lieu of the Appendix G method for determining the ambient lead concentrations. A
current list of designated reference and equivalent methods is available from the following
address:
National Exposure Research Laboratory
Dept. E. U.S. EPA, (MD-77)
Research Triangle Park, NC 27711
42
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Updated listings are also available on the U.S.EPA's Ambient Monitoring Technology
Information Center accessible through the Internet address http://www.epa.gov/ttn.
Individual filter samples or composites of as many as eight filter samples collected over a
consistent one-week, two-week, or one-month period during a calendar month may be
analyzed for lead content to derive a monthly average concentration. EPA's National
Exposure Research Laboratory (NERL) recommends the use of eight filter samples for
compositing. As such, U.S.EPA allows for individual analysis of all lead samples collected
during the quarter or an average of three monthly composite samples for determining the
quarterly average.
Compositing procedures must be approved in accordance with procedures contained in Section
2.8 of Appendix C to 40 CFR Part 58 -- Modifications of Methods by Users..
5.5 Quality Assurance Requirements
Quality assurance programs for SLAMS monitoring networks and minimum quality assurance
requirements are codified in Appendix A to 40 CFR Part 58. Complete quality assurance
procedures for determining sampling precision, sampler flow rate accuracy, and accuracy of
the lead reference method are contained in the "Quality Assurance Handbook for Air Pollution
Measurement Systems, Volume II-Ambient Air Specific Methods", EPA-600/R-94/038b.
This Quality Assurance Handbook is available through the Center for Environmental Research
Information, 26 W. Martin Luther King Drive, Cincinnati, Ohio, 45268, (513) 569-7562, fax
(513) 569-7566.
5.6 Data Completeness and Reporting Requirements
At least 75 percent of the lead samples must be available in order for the quarterly average to
be considered valid. For monthly composite sampling, the sampler must have been properly
operating on at least 75 percent of the sampling days in a month. Alternatively, if fewer than
43
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75 percent of the scheduled samples for the quarter are available, the quarterly average shall
be considered valid if this average, computed as the sum of all available measured
concentrations in the calendar quarter divided by the number of scheduled samples, exceeds
the level of the standard. That is, a NAAQS exceedance can be recorded using fewer than 75
percent valid data capture, while at least 75 percent valid data are required to demonstrate
NAAQS attainment.
The quarterly average concentration measured at any single lead ambient air monitoring station
must not exceed the level of the standard. A quarterly average concentration, rounded to one
decimal place, is considered to exceed the standard if it is greater than 1.5 /*g/m3. No
violations can be measured for a given two year period (eight quarters) in order to demonstrate
attainment of the NAAQS.
Precision and accuracy data must be submitted to the Aerometric Information Retrieval System
(AIRS) under 40 CFR Part 58.35. Previously, only data summary reports for SLAMS data
were submitted; however, SLAMS data are now also to be submitted according to the schedule
for submitting NAMS data. SLAMS and NAMS lead monitoring data, along with the
associated precision and accuracy data, should be reported to EPA within 90 days after the end
of each calendar quarter.
44
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6.0 CASE STUDIES
6.1 General Considerations
This section discusses three case studies that were developed to illustrate the application of the
general guidance for selecting monitoring locations near lead point sources. This work was
completed during the time period when the U.S.EPA was reviewing the quarterly lead NAAQS
and considering the merits of a monthly lead NAAQS; however, the basic principles within are
applicable for either the quarterly or the monthly forms. The case studies are based on emission
rates and source configurations for a hypothetical lead acid battery plant, secondary smelter, and
primary smelter. Meteorological and topographical characteristics of the hypothetical source
locations are based on actual data. The approach to determining optimum monitoring locations
for the case studies follows the general outline provided in Section 4.0. The following paragraphs
discuss general considerations for each step of the procedure for locating lead monitors near lead
sources as applied to the case studies. A discussion of the results of such analyses for each case
study follows.
Monitoring Objectives and Spatial Scale of Representativeness
For lead monitoring near point sources, the primary monitoring objective is to measure maximum
ambient impacts. This is necessary to document compliance or noncompliance with the lead
standard. If ambient concentrations and population density near the source are large enough, it
may be advisable to site an additional monitor or monitors to document population exposure.
This may be especially important when sensitive populations, such as children concentrated at a
school, are likely to be exposed. All three case studies focus on locating maximum concentration
sites. It is assumed that the Case 2 and 3 sources are located in rural, low population density
areas. For Case 1, lead concentrations decrease rapidly enough that the source could be located
in a semi-urban area and would still be unlikely to require exposure oriented monitoring.
45
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The spatial variability of point source lead impacts is such that the spatial scale of
representativeness for a lead monitor is likely to be on the order of several hundred meters. Once
modeling and/or other analyses have suggested general areas where maximum impacts are
expected, it is desirable to site the monitor to maximize the spatial coverage of the measurements.
Probe siting requirements contained in Appendix E of 40 CFR Part 58 provide guidelines for
avoiding influences of local obstructions; however, each situation should be examined
independently. For all three case studies, monitor locations are suggested by model predictions.
Details of precise probe siting are not discussed.
Smir** Characteristics
Case study source characteristics were determined uniquely for each facility. Source
configurations include emission rates and locations for point (stack), and area and volume
(fugitive) sources within each facility. Characteristics of each source within each facility were
provided as input to ISC. Model inputs for point sources included stack height, diameter,
temperature, and exit velocity. Stack downwash effects and buoyancy induced dispersion were
considered. In addition, building dimensions were provided so that building downwash effects
could be calculated by the model. Particle size distributions and associated settling velocities
were considered for Cases 2 and 3. For the case studies, background lead concentrations were
conservatively estimated at 0.1 ug/m3.
Accurate source data are most readily obtained by surveying the source directly. Organizations
representing particular industry groups may also be consulted. In many cases, especially for larger
sources, the EPA's AIRS Facilities Subsystem will be able to provide the needed information.
State and local SIP emission inventories and/or operating permits may also be used. Within the
decade, all lead sources large enough to require monitoring will be required to obtain Title V
permits. These permits should serve as a comprehensive reference for source related information.
46
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Topographic and Land Use Influences
The terrain was assumed to be rolling or flat for all three case studies. For Case 1, there were
some topographic features elevated above stack height; however, this complex terrain was too far
from the source to experience significant impacts. Analyses required to characterize significant
impacts in true complex terrain are beyond the scope of the general studies presented here.
Meteorological Data
Meteorological data for all three cases were assumed to have been collected at an airport within a
representative region containing the source. Representativeness might have been established by
examining meteorological data from surrounding stations and determining that there were no
persistent, significant spatial gradients across the area containing the source and the airport. One
year of hourly meteorological data was used for each case study. For regulatory compliance
purposes, five years of data are required; however, for network design purposes, a single year's
data can suffice provided that the year is reasonably representative of the regional climate.
Analysis of meteorological data for the case studies consisted of examination of windroses to
determine dominant wind direction(s) and consideration of statistical summaries of the frequency
of occurrence of stability classes for various wind directions. Identical meteorological data were
used for Cases 1 and 3. This year's data are characterized by two dominant wind directions.
Meteorological data for case 2 are characterized by a dominant southerly wind.
Ambient Monitoring Data
Existing ambient monitoring data should be examined for monitors likely to be affected by point
source emissions. Cases 1 and 2 represent smaller sources where monitoring is unlikely to have
been conducted in the past. Case 3 is a large source, and it is assumed that there is an existing
record of one year's data from a monitor located approximately in the predominate downwind
direction from the source. For purposes of the case study, daily lead measurements for the Case 3
monitor were simulated using ISC output.
47
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Air Qualify Modcling
ISC was applied to each case study to predict monthly average impacts for an array of receptor
locations surrounding each source. The model was set to produce a composite arithmetic mean
for each month in the year. Hourly meteorological data are used by the model to predict hourly
ambient concentrations which are then averaged into daily concentrations and, subsequently,
averaged into monthly concentrations.
Model options (ISW switches) for ISC were, for the most part, set at the regulatory defaults.
Particle settling was not considered for Case 1 since no size category data were available. Particle
settling was modeled for Cases 2 and 3. Case 3 stacks were good engineering practice (GEP)
height, so it was not necessary to consider building wake effects. Building wake effects were
modeled for Cases 1 and 2.
In each case, a screening analysis was used to examine the expected rate of decrease of predicted
concentrations with distance from the source. This information was used to aid designing
receptor grids for refined modeling and to provide estimates of the relative contributions from
various sources within each facility.
SCREEN predicts maximum hourly concentrations for a single, or appropriately grouped, source
over a range of downwind distances. SCREEN output is based on 33 categories of
meteorological conditions based on wind speed and stability class (Table 4). Alternatively, ISC
can be run under screening conditions to produce similar output. The advantage of using ISC for
the screening analysis is that it is more convenient to handle complex facilities with multiple
sources. SCREEN was run for each of the 3 stacks for Case 1. ISC was run in screening mode
for Cases 2 and 3.
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Table 3. Wind Speed and Stability Class
Combinations Used by SCREEN
Stability
Class
A
B
C
D
E
F
Wind Speed at 10 Meters
1
1
4
9
16
25
30
2
2
5
10
17
26
31
3
3
6
11
18
27
32
4
7
12
19
28
33
5
8
13
20
29
8
14
21
10
15
22
15
23
20
24
"Screening Procedures for Estimating the Air Quality Impact of Stationary Sources," Appendix A,
USEPA, Office of Air Quality Planning and Standards, EPA-450/4-88-010, August 1988.
Hourly averages predicted in the screening analyses are about 50 times as high as monthly average
concentration predicted by refined modeling. For the case study model runs, monthly averages
predicted by ISC were 2.0 to 2.5 percent of hourly averages predicted by SCREEN. Although
concentrations predicted by SCREEN cannot be compared directly with the NAAQS, SCREEN
output provides initial estimates of which sources within each facility are most likely to produce
the highest ambient impacts.
Monitoring Locations
Monitoring locations for the case studies were suggested by the refined model output. The
modeled results were interpreted based on knowledge of the source configuration and
understanding gained from examination of the meteorological and available monitoring data.
Population exposure was also considered when multiple monitoring locations were suggested
with equal weight by the model. Specific probe siting criteria should be followed to avoid
influences of local obstructions. In addition, there are practical limitations such as property
49
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ownership, availability of electrical service, and ease of operator access that must be considered in
finally siting a monitor.
6.2 Case #1 - Lead Acid Battery Plant
fionrce Characteristics
There are approximately 90 lead acid battery plants in the U.S. with emissions ranging from 0.25
to 3.5 tpy.3 In this type of facility, lead paniculate matter is generated by grid casting, lead paste
mixing, battery assembly, and lead reclamation processes. Emissions are typically captured by
ventilation systems and controlled using baghouses or impingement scrubbers.
Case 1 represents a large, lead acid battery plant. Total emissions are 3.4 tpy. Because total
emissions are between 1 and 5 tpy, modeling is needed to determine the need for monitoring. If
model results indicate that the source poses a threat to the standard, then monitoring should be
established.
All emissions are from three stacks located on one building. Stack heights are low (11, 11, and
13 meters) and are not elevated significantly above the building height. Stack temperatures are
near ambient for stacks #1 and #2, suggesting that these sources emit ambient air ventilated from
the building. The largest emission rate is from stack #1. Stack #3 has an elevated stack
temperature and a relatively low emission rate. There are no lead emissions from nearby sources.
Tnpngraphir and Land TTse Influences
The Case 1 facility is located in rolling terrain in a rural to suburban area. Land use near the
source is primarily agricultural; however, some land is used for residential and recreational
purposes. Because of the relatively sparse population density near the source, no special
consideration of siting an exposure oriented monitor is warranted. The area is not heavily
industrialized; therefore it is not necessary to consider combined impacts from nearby sources.
There is some terrain elevated above stack height 4 kilometers from the source; however,
50
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SCREEN indicates that concentrations fall off rapidly enough with distance from the source that
no special modeling is needed to account for complex terrain effects. ISC truncates elevated
terrain to the level of stack height.
Meteorological Data
One year of hourly meteorological data was obtained from a nearby airport. The data are
considered to be representative of conditions near the source since there is no record of a
significant spatial gradient in meteorological conditions in the area and there are no terrain
features that are likely to create such a gradient. The data are also considered to be representative
of the air quality climate for the area.
The annual wind rose indicates bi-directional predominant winds (Figure 4). This bi-directionality
persists for three quarters of the year. Southerly winds clearly dominate during the second
quarter of the year; however, during the remaining quarters, northerly winds make up a substantial
proportion. Figure 6 provides the four quarterly wind roses. The model predictions reproduce
the wind rose. Highest impacts are predicted for receptors both North and South of the source.
51
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Figure 4. Annual Wind Rose • Case 1
^2*
— E
52
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Figure 5. Four Quarterly Wind Roses - Case 1
w--
1-3 «-6 7-16 11-16 \<-
VEED CLASSES
53
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No ambient monitoring data are available for the area impacted directly by this facility. Roadway
lead monitors at the nearest urban area suggest that background concentrations are lower than the
0.1 ug/m3 default background.
Screening analysis shows that stack #1 is the most significant contributor to ambient impacts. In
addition, ambient impacts predicted by SCREEN indicate that the concentration gradient begins
to level off at a distance of 900 meters from the source. At this distance, predicted impacts are
one fifth of the highest impacts near the source and should be well below the NAAQS.
Concentrations were one tenth of their highest values at a distance of 4000 meters. A sample of
SCREEN output for source #1 is provided as Figure 6.
The receptor grid for refined modeling was spaced at 100 meter intervals beyond the fenceline out
to 500 meters and at 500 meter intervals out to 4000 meters. Elevations exceeding stack height
are located more than a kilometer from the plant. Since concentrations at this distance are quite
low, complex terrain was not treated in the analysis. Paniculate settling was not modeled for
Case 1 since no size distribution data were available. Note however, that had settling been
included, the maximum impacts might have been nearer to the plant.
The highest predicted impact was 0.73 ng/m3 excluding background and occurs south of the
facility at coordinates [-200, -200]. When very modest background concentrations are added,
NAAQS exceedances are predicted. Figure 8 shows ISC-ST model results for Case 1. In this
Figure, a high non-exceedance is defined as a maximum monthly concentration between 0.50 and
0.75 ug/m3 or a concentration greater than 0.25 ug/m3 persisting for more than 6 months of the
year. The three stacks are located very near to one another at the center of the Figure
(coordinates [0,0]). A low concentration is defined as a maximum month below 0.5 ug/m3 and
monthly averages below 0.25 ug/m3 for more than half the year. If a background concentration as
54
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low as 0.05 ug/m3 is added, a receptor located north of the facility at coordinates [100,300], in
addition to the maximum receptor, will exceed the standard.
Given a maximum acceptable predicted impact (including background) at 90 percent of the
standard, 4 of the 7 high, non-exceedances illustrated in Figure 6 might be considered to pose a
threat to the standard. The majority of these are located south of the plant. It is interesting to
note that, while the predominant wind direction is from the south, the highest concentrations are
downwind from the plant in the secondary, northerly wind direction. This is because the southern
fenceline is nearer to the stacks than the northern fenceline and concentrations are decreasing
rapidly in this region.
Monitoring Locations
Monitoring will be needed for this facility since predicted impacts, including background, clearly
pose a threat to the standard. The monitors should be placed at the locations representative of
areas where maximum impacts are expected. Based on the modeling analysis, these are on the
fenceline in the primary and secondary predominant wind directions. Monitors should be placed
at or near coordinates [-200, -200], and [ 100, 300].
55
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Figure 7. Model Results - Case 1
1000
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X Coordinate (m)
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57
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6.3 Case #2 - Secondary Lead Smelter
,SmirT» Characteristics
There are approximately 23 secondary lead smelters in the U.S. Total emissions (stack and
fugitive) per plant range from 3 to 60 tpy. Secondary lead smelters process lead bearing scrap
and residue to produce lead ingots, battery lead oxide, and lead pigments. Processing typically
involves scrap pretreatment, smelting, refining, and casting. Reverberatory and blast furnaces
account for the majority of lead emissions from secondary smelters; however, fugitive emissions
from loading and holding areas, or reentrainment can contribute significantly to ambient impacts.
The Case 2 smelter is a small smelter with total emissions of 3.9 tpy. Fugitive lead from area and
volume sources accounts for 20 percent of total emissions; however, fugitive emission sources are
the largest contributors to ground level ambient impacts. The source configuration for the Case 2
facility is shown in Figure 8. There are 3 stacks located on the west side of the controlling
building. Sources numbered 4 through 6 are treated as volume sources and are related to various
processing and handling areas. Sources numbered 7 and 8 are area sources such as storage piles,
or resuspension areas surrounding the site.
-and Use Influences
The terrain surrounding the Case 2 facility is flat Land use is primarily agricultural. In addition
to the smelter, there is some other heavy industry in the area; however, lead emissions from these
other sources are not significant, and they are located outside of the maximum impact area for
emissions from the Case 2 smelter. Thus, combined emissions do not present any added impact.
Background emissions are expected to be within the assumed 0.1 ng/m3.
58
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I)
1
-so
-190
fi
iH
U
-as e ao
Figure 8. Source Configuration - Case 2
Meteorological Data
One year's meteorological data was obtained from the nearest airport. These data may be
considered to be spatially representative and consistent with the prevailing air quality climate.
The annual wind rose is uni-directional with predominant winds from the south (Figure 9).
Ambient Monitoring Data
There are no existing measurement data available for the Case 2 impact area.
59
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Figure 9. Annual Wind Rose - Case 2
Air Quality Modeling
Screening analyses show that area source #8 is clearly the largest contributor to ambient impacts
and is responsible for perhaps 50 percent of the total. While accounting for only 20 percent of
emissions, area and volume sources account for 90 percent of ambient impacts. This is because
the greatest impacts are close to the source where fugitive emissions are most important. Except
under fumigation conditions, the ground level impact from elevated stack emissions will be
diffuse, and distant from the source. With increasing distance from the source, fugitive emissions
become relatively less important compared to stack emissions.
The rate of decline of ambient impacts begins to level off within 500 meters from the source.
Thus, the receptor grid for refined modeling was designed to be most dense (100 meter intervals)
within 500 meters from the source and less dense (250 meter intervals) beyond 500 meters from
the source. Between 2000 and 4000 meters from the source, receptors were added at 1000 meter
intervals to account for any potential long range impacts.
60
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Figure 10 shows the results of refined modeling for Case 2. Exceedances are defined as predicted
monthly maximum concentrations greater man 0.75 ug/m3 (excluding background). High
nonexceedances are defined as monthly maximum concentrations exceeding 0.5 ug/m3 or monthly
averages exceeding 0.25 ug/m3 for more than 6 months of the year. Otherwise, the point is
plotted as a low concentration receptor. The highest predicted impact was 2.3 ug/m3 which
occurred during July at the fenceline receptor with coordinates [0,200]. Concentrations at this
receptor exceed the standard for all 12 months of the year. Other receptors where exceedances
are predicted are clustered around this receptor. These model predictions are consistent with a
predominant southerly wind direction and major impacts from ground level, fugitive sources.
Highest impacts are close to the source in the predominant downwind direction.
Figure 10 shows some high nonexceedances on the southern and western fencelines. The highest
predicted impact is 0.72 ug/m3 at the receptor with coordinates [-200,100]. If a background
value of 0.1 ug/m3 is added to this concentration, an exceedance would be predicted. Thus,
concentrations at receptors on the southern fenceline may also pose a threat to the standard.
Concentrations predicted for receptors on the western fenceline were slightly below the 90
percent threshold (0.68 ug/m3 including background) required to pose a threat to the standard.
Monitoring Locations
Total emissions for the Case 2 facility are between 1 and 5 tpy; however, modeling clearly shows
that the source may pose a threat to the standard. Thus, monitoring should be established at a
minimum of two locations. The primary monitoring location should represent the area of
maximum impact. This is on the northern fenceline near the point with coordinates [0, 200].
61
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Figure 10. Model Results - Case 2
1000
500
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62
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Two options present themselves for locating the second monitor. The first option is to back up
the first monitor by locating either beyond the fenceline in the same direction, or elsewhere on the
northern fenceline. The northerly receptor where the second highest impacts were predicted is
located at [100,200]. This option provides additional confidence that maximum impacts will be
recorded throughout the year.
The second option is to locate the second monitor on the southern fenceline where
concentrations, while significantly lower than on the northern fenceline, were high enough to pose
a threat to the standard. This second option provides greater spatial coverage. The decision
where to locate the second monitor may ultimately depend on additional considerations, such as
proximity and direction of populated areas.
6.4 Case #3 - Primary Lead Smelter
Source Characteristics
Four primary lead smelters operating in the U.S. in 1989 had lead emissions ranging from 21 to
291 tpy.3 Primary smelters take mined lead ore containing 3 to 8 percent elemental lead and
produce a refined concentrate containing 55 to 70 percent lead (ref AP-42). Processing involves
sintering, reduction, and refining. Lead is emitted from the sintering machines and furnaces used
in the reduction and refining processes. Fugitive lead is emitted from ore handling, crushing and
storage, and resuspension.
Case 3 addresses locating monitors for a hypothetical primary lead smelter emitting 160 tpy.
Similar considerations might be applied to a large secondary smelter. Figure 11 shows the source
configuration for Case 3. Case 3 is a large and complex facility with very high ambient impacts.
There are 4 point sources comprising 62.3 percent of emissions (100.1 tpy); 24 volume sources
comprising 19.9 percent of emissions (32 tpy); and 10 area sources comprising 17.8 percent of
emissions (28.6 tpy).
63
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600
-500
XCooidinate
Point Sources
1.
2.
3.
4.
Sinter Machine (Main Stack)
Sinter Crushing
Sinter Preparation
Blast Furnace/Dross Kettles
Volume Sources
5.9, Concentrate Storage/Handling
10-11. Sinter Building
12-13. Blast Furnace Area
14-18. Dressing Area
19-28. Refinery Area
Sources
29.
30-32.
33-38.
Concentrate Truck Unloading
Granulated Storage Pile
Resuspension
Figure 11. Source Configuration - Case 3
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Topographic and Land Use Influences
The Case 3 facility is located in flat terrain. Land use is rural, largely undeveloped, and consists
primarily of lead mining operations. There are no specific topographic or land use influences that
would effect the dispersion of lead emissions from the source and demand special treatment in the
modeling analysis.
Meteorological Data
The same meteorological data were applied to Cases 1 and 3. Once again, it is assumed that there
is adequate justification for concluding that data collected from a nearby airport are representative
of the meteorology near the source both spatially and temporally. The annual wind rose is bi-
directional with predominant southerlies and a significant proportion of northerlies except in the
second quarter of the year (see Figures 5 and 6).
Ambient Monitoring Data
Due to the large emissions from the Case 3 facility, a monitor was placed in the predominant
downwind direction, north of the facility, just over a year before the study was conducted. Thus,
one complete year's monitoring data were available for examination. Daily measurements were
obtained. The measurements show that ambient concentrations near the plant consistently exceed
the standard by a very large margin.
Figure 12 is a boxplot comparison of the daily measurements data for each month of the year.
The dotted line is plotted at the level of the standard (0.75 |Jg/m3). The boxplots show the range
and variability of the data using statistics that are not sensitive to the distribution of the data. This
is important since monitoring data are unlikely to be normally distributed. The center of each box
represents the median. The upper and lower boundaries of each box represent the 75th and 25th
percentiles respectively. The upper and lower extensions (whiskers) represent the 90th and 10th
percentiles. Values exceeding 1.5 times the interquartile range (VERIFY) are plotted as outliers.
Monthly median concentrations exceed the standard during all but three months of the year
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(February, September, and October). Monthly mean concentrations exceed the standard during
all months of the year.
20
i—i—i—i—i—i—i
L2.3.4. Sift.?. & SL 10. U. 12.
Figure 12. Monitoring Data - Case 3
Air Quality Modeling
The relative contributions of stack, volume, and area sources to ambient impacts depends on the
prevailing meteorological conditions. Tall stacks contribute maximally to ambient impacts under
conditions of low wind speed and strong instability (strong insolation). Volume sources and short
stacks contribute maximally under conditions of low wind speed and moderately unstable to stable
conditions. Contributions from ground level area sources may be somewhat independent of wind
speed and are maximized under stable overcast, or night-time conditions. Resuspension area
sources are maximized with high wind speed and stable conditions.
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For Case 3, ISC was used under screening conditions to provide the 50 highest hourly
concentrations for the total facility, and for stack, volume, and area sources. Each predicted value
is associated with one of 33 meteorological categories depending on stability class and wind speed
(see Table 4). Analysis of this output shows that area and volume sources are the most important
contributors to ambient impacts under all conditions.
Although stacks comprise over 60 percent of total emissions, they contribute less than 10 percent
of the total ambient impact under the most favorable conditions (unstable and low wind speed).
Except with high wind speeds, area and volume sources contribute equally to ambient impacts.
Resuspension sources dominate with high wind speeds.
Figure 13 summarizes the ISC-ST model results for Case 3. Because of the large number and
wide distribution of exceedances, concentrations greater than 1.5 and 5.0 ug/m3 are plotted
separately. The highest concentrations are along the eastern side of the northern fenceline.
Maximum monthly average concentrations in this area are predicted to be in the range from 6 to 9
(ig/m3, or one order of magnitude above the standard. The receptor where the highest
concentrations are predicted is located at coordinates [200,600].
The second most heavily impacted area is along the southern portion of the eastern fenceline.
Predicted maximum concentrations in this area are 4 to 5 |ag/m3. The highest concentration in this
area is predicted at coordinates [-500, -400].
67
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Figure 13. Model Results - Case 3
-4
-3
-2
_1 Thousands 1
X Coordinate
Predicted Exceedance
o Exceedance > 1.5
High Non-Exceedance
Exceedance > 5.0
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Exceedances are predicted in every direction as far as 1000 meters from the source. Some
exceedance may occur as far as 3000 meters from the source. Maximum concentrations along a
square at 1000 meters from the center of the facility range from 1.4 to 3.8 jig/m3. The highest
concentrations at this range are northeast of the source [500,1000], with the second highest
concentrations occurring to the southeast [-500, -1000]. This is approximately in line with the
highest and second highest concentrations occurring at the fenceline, suggesting that the more
distant impacts are attributable to the same sources within the facility.
Concentrations on a square perimeter at 2000 meters from the source range from 0.5 to 1.4
Hg/m3. The highest concentrations are predicted north and northeast of the facility.
Concentrations high enough to pose a threat to the standard are also predicted at southeastern
receptors. The maximum impact receptor is located at coordinates [0,2000].
Monitoring Locations
Due to the extremely high concentrations and the large number of receptors where exceedances
are predicted for this facility, more than the minimum of two monitors should be sited. In order
to ensure sufficient spatial coverage, it is recommended that 3 to 5 monitors be sited around this
facility.
The first two monitors would be located in the areas of maximum and second highest
concentration on the fenceline. These locations are predicted to occur at coordinates [200, 600]
and [-500, -400], respectively. The existing monitor is currently located near coordinates [300,
800] and is not far from the coordinates where maximum impacts were predicted. This monitor
should be relocated to the maximum impact location.
The third and possibly the fourth monitor should be located at a distance of 1000 meters from the
source near where the maximum and second highest concentrations are predicted. The fifth
monitor should be located at a distance of 2000 meters from the facility at the coordinates where
the maximum impact is predicted for this distance.
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This monitoring strategy is designed to measure maximum concentrations and to account for
different degrees of reduced impact with distance from the plant as emissions from different
sources come under control. For example, if stack emissions are reduced, then impacts further
from the plant should be affected. If area source emissions are controlled, then impacts nearer the
plant should be reduced. The strategy also provides data that can be used to determine human
and environmental exposure with increasing distance.
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7.0 REFERENCES
1. "Guideline for Conducting Ambient Air Monitoring for Lead Around Point Sources,"
U.S. EPA, Office of Air Quality Planning and Standards, EPA-454/R-92-009, January
1992.
2. "Air Quality Criteria for Lead (Vol. I - IV)," U.S. EPA, Environmental Criteria and
Assessment Office, EPA/600/8-83/028 a-d, June 1986, and "Supplement to the 1986
Lead Criteria Document Addendum," 1990.
3. "Cost Assessment of Regulatory Alternatives for Lead National Ambient Air Quality
Standards," Prepared by Alliance Technologies Corporation for U.S. EPA, Draft Final
Report, May 1991.
4. "National Air Quality and Emissions Trends Report, 1995," U.S. EPA, Office of Air
Quality Planning and Standards, EPA 454/R-96-005, October 1996.
5. "National Air Pollutant Emission Trends, 1900-1995," U.S. EPA, Office of Air
Quality Planning and Standards, EPA 454/R-96-007, October 1996.
6. "Receptor Model Technical Series (Vol I and II)," U.S. EPA, Office of Air Quality
Planning and Standards, EPA-450/4-81-016a,b, 1981.
7. "Compilation of Air Pollutant Emission Factors, 5th edition" U.S. EPA, AP-42, 1995,
Government Printing Office #055-000-005-00-1.
8. "Control of Open Fugitive Dust Sources," U.S. EPA, Office of Air Quality Planning
and Standards, EPA-450/3-88-008, September 1988.
9. "Ambient Monitoring Guidelines for Prevention of Significant Deterioration (PSD),"
U.S. EPA, Office of Air Quality Planning and Standards, EPA-450/4-87-007, May
1987.
10. "On-Site Meteorological Program Guidance for Regulatory Modeling Applications,"
U.S. EPA, Office of Air Quality Planning and Standards, EPA-450/4-87-013, June
1987.
11. Appendix W to 40 C.F.R. Part 51, "Guideline on Air Quality Models," U.S. EPA,
Office of Air Quality Planning and Standards, 1997.
12. "Network Design and Optimum Site Exposure Criteria for Paniculate Matter," U.S.
EPA, Office of Air Quality Planning and Standards, EPA-450/4-87-009, May 1987.
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13. Noll, K. and T. Miller, "Air Monitoring Survey Design," Ann Arbor Science
Publishers, Inc., 1977.
14 "Screening Procedures for Estimating the Air Quality Impact of Stationary Sources,
Revised, "U.S. EPA, Office of Air Quality Planning and Standards, EPA-454/R-92-
019, October 1992.
15. "Extended Blood Lead Analysis of Alternative Lead National Ambient Air Quality
Standards," MathTech, Inc., December 1990.
16. "Draft - Updated Information on Approval and Promulgation of Lead Implementation
Plans," U.S. EPA, July 1983.
17 "User's Guide for the Industrial Source Complex (ISC3) Dispersion Models," U.S.
EPA, Office of Air Quality Planning and Standards, EPA-454/B-95-003a,b, September
1995*.
18 "Evaluation of Comparability Between Portable Saturation Monitors and Total
Suspended Particulate/Lead Monitors - Project Report," (prepared by J. Kelly, Region
VH), U.S. EPA, Environmental Services Division, March 1991.
19 "Use of the High Volume Sampler for the Determination of Lead in Ambient Air,"
Technical Memorandum to J. Haines from L. Purdue, U.S. EPA, Office of Air Quality
Planning and Standards, September 1988.
20 "Data Analysis of TSP and PM10 Filters in East Helena with Regard to the Chemet's
Contribution," Memorandum to file (Steinberg, S.), Dept. of Health and
Environmental Sciences, Air Quality Bureau, Helena, MT, June 1988.
21 "Collocated PM10/Hi-Vol Monitoring Results for E. Helena," Memorandum to file
(Brion, G.), U.S. EPA, Office of Air Quality Planning and Standards, July 1988.
22 "Chemical Mass Balance (CMB8) User's Manual" and "Protocol for Applying and
Validating the CMB Model" (Under development for release hi 1998 - For additional
information contact Tom Coulter, U.S. EPA, Air Quality Modeling Group, (MD-14),
Research Triangle Park, NC 27711, 919-541-0832.)
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8.0 BIBLIOGRAPHY
• "Guideline for Lead Monitoring in the Vicinity of Point Sources", U.S. EPA, Office of
Air Quality Planning and Standards, EPA-450/4-81-006, January 1981.
• "Optimum Site Exposure Criteria for SO2 Monitoring," U.S. EPA, Office of Air
Quality Planning and Standards, EPA-450/3-77-013, April 1977.
• "Optimum Sampling Site Exposure for Lead," U.S. EPA, Office of Air Quality
Planning and Standards, EPA-450/4-84-012, February 1984.
• Dattner, S., J. Moneysmith, and R. Tropp, "Minimum Sampling Frequency
Requirements for Sampling Toxic Pollutants Dominated by a Major Point Source,"
Presented at 80th Annual APCA Meeting, paper 87-66.5, 1987.
• Sledge, Donna J., Size Distribution of Lead Particles at Major Lead Stationary
Sources: Source Sampling and Ambient Monitoring MEMO to John Haines, September
1987.
• Plants Reporting Lead/lead Compounds Emissions in Excess of 500 Ibs., TRIS
database - 1988.
• CASAC Lead Review, EPA-SAB-CASAC-89-018, April 1989.
• Brion G., "Secondary Lead Smelters and Other Sources," Memo to J. Haines. August
1989
• "Strategy for Reducing Lead Exposure - Final Draft," U.S. EPA September 1990.
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing/
1. REPORT NO. 2.
FPA-454/R-P.?-On9
4. TITLE AND SUBTITLE
Guidance for Siting Ambient Air Monitors Around
Stationary Lead Sources
7. AUTHOR(S)
Revision: L. Byrd, D. Atkinson, R. Lee, T. Coultei
Original: Prepared Under Contract for USEPA in 19(
9. PERFORMING ORGANIZATION NAME AND ADDRESS
U.S. Environmental Protection Agency
Office of Air and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
1 2. SPONSOR! NG AGENCY NAME AND ADDRESS
•
15. SUPPLEMENTARY NOTES
3. RECIP/ENT'S ACCESSIOt+NO.
5. REPORT DATE
August 1997
6. PERFORMING ORGANIZATION CODE
8. PERFORMING ORGANIZATION REPORT NO.
f
32
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
13. TYPE OF REPORT AND PERIOD COVERED
Revision-Current in 1997
14. SPONSORING AGENCY CODE
16. ABSTRACT
This document provides technical guidance to air montoring agencies on how
to determine the network design for measuring lead air pollution concentrations
emitted from stationary lead sources.
17. KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS b.lDENTIFI
Ambient Air Quality Surveillance
Air Pollution
Lead Air Pollution Monitoring
Lead
Lead Stationary Sources
18. DISTRIBUTION STATEMENT 19. SECURI
Release Unlimited 2o.sEcuR
ERS/OPEN ENDED TERMS C. COSATI Field/Group
.
TY CLASS (This Report) 21. NO. OF PAGES
79
TY CLASS (This page) 22. PRICE
EPA Form 2220-1 (9-73)
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