United States
Environmental Protection
Agency
Air Pollution Training Institute (APTI)
Mail Drop E14301
Research Triangle Park, NC 27711
September 2004
¦ Preparation of Fine
Particulate Emission
Inventories
Instructor's Manual
APTI Course 419B
Developed by
ICES Ltd.
EPA Contract No. 68D99022
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Notice
This is not an official policy and standards document. The opinions
and selections are those of the authors and not necessarily those of the
Environmental Protection Agency. Every attempt has been made to
represent the present state of the art as well as subject areas still under
evaluation. Any mention of products or organizations does not constitute
endorsement or recommendation by the United States Environmental
Protection Agency.
This project has been funded wholly or in part by the United States
Environmental Protection Agency under Contract No. 68D99022 to ICES, Ltd.
Availability
This document is issued by the Air Pollution Training Institute, Education
and Outreach Group, Office of Air Quality Planning and Standards, USEPA.
This workbook was developed for use in training courses presented by the
U.S. EPA Air Pollution Training Institute and others receiving contractual or
grant support from the Institute. Other organizations are welcome to use
the document.
This publication is available, free of charge, to schools or governmental
air pollution control agencies intending to conduct a training course on the
subject matter. Submit a written request to the Air Pollution Training
Institute, USEPA, Mail Drop E14301, Research Triangle Park, NC 27711.
Sets of slides designed for use in the training course of which this
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Course Description
APTI 419B: Preparation of Fine Particulate Emission Inventories is a two-
day, resident instructional course designed to present an advanced view of all
major, practical aspects of developing an emission inventory for fine particulate
matter. The course is intended primarily for employees that have a working
knowledge of emission inventory terminology and techniques. The course
focuses on the principal stationary nonpoint area and nonroad mobile source
categories emitting PM fine particles. For select categories, the course provides
a brief summary of how emissions are estimated for EPA's National Emissions
Inventory (NEI), and how state/local/tribal agencies can improve upon those
estimates. Case studies are used to provide real-world examples of how state or
lo9cal agencies collected their own data to prepare inventories that are
improvement to the NEI methods. The lessons include information on an
overview of fine PM, an overview of the NEI, onroad mobile inventory
development, onroad mobile inventory development, point source inventory
development, area sources, fugitive dust area sources, combustion area
sources, and other related topics.
The course is taught at an instructional level equivalent to that of an
advanced, undergraduate university course. The Air Pollution Training Institute
curriculum recommends APTI 419B: Preparation of Fine Particulate Emission
Inventories as an advanced course for all areas of study. The student should
have minimally completed a college-level education and APTI Course SL419A-
Introduction to Emission Inventories or have a minimum of six months of
applicable work experience.
How to Use This Manual
This manual is to be used during classroom instruction and telecourse
sessions. The workbook contains instructional objectives and materials for each
of the thirteen subject areas.
Each chapter provides a lesson goal, instructional objectives, subject
narrative, and reference materials that may guide your study. Each chapter
also contains a reproduction of selected lecture slides intended to guide
your notetaking. The slides are presented to generally follow the course
outline; however, the instructor may on occasion vary the order of presen-
tation or present material not included in the workbook. Each student,
therefore, should take thorough notes of the lecture content throughout the
course, but not rely solely upon graphic reproductions for the course
content.
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DISCLAMER
This document does not constitute U.S. Environmental Protection Agency policy.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
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TABLE OF CONTENTS
Table of Contents v
Chapter 1: PM2.5 Overview 1-1
Chapter 2: The National Emissions Inventory and Emissions Inventory Tools 2-1
Chapter 3: Onroad Mobile Inventory Development 3-1
Chapter 4: Nonroad Mobile Inventory Development 4-1
Chapter 5: Point Source Inventory Development 5-1
Chapter 6: Area Sources 6-1
Chapter 7: Fugitive Dust Area Sources 7-1
Chapter 8: Ammonia Emissions from Animal Husbandry 8-1
Chapter 9: Combustion Area Sources 9-1
Appendix Final Exam A-1
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Instructor's Manual
Chapter 1 - PM 2.5 Overview
1 -1
Preparation of Fine Particulate
Emissions Inventories
Chapter 1 - PM 2A Overview
After this lesson, participants will be able to
describe:
the general composition of fine particulate
matter in the atmosphere,
how fine particulate matter are formed,
and
sources that contribute to the formation of
fine particulate matter.
1 -2
PM2 5 In Ambient Air - A Complex Mixture
Prtmaty Panicles
O Hi?-
e
o
This graphic illustrates the difference
between primary, or directly emitted
particles and secondary particles, which are
formed in the atmosphere from precursor
gases.
Primary particles contain:
elemental carbon (EC)
primary organic aerosol (POA)
small amounts of crustal matter and other
materials.
Secondary particles contain:
secondary organic aerosol (SOA) formed
from volatile organic compounds
ammonium sulfate formed from SO2 and
ammonia gases
ammonium nitrate, formed from NOx and
ammonia gases.
The term total carbonaceous matter is
used to describe the combined mass of EC,
POA, and SOA.
Preparation of Fine Particulate Emissions Inventories 1-1
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1 -3
Urban PM Sites
¦ Eastern U.S. data is very homogenous
¦ Comprised mostly of carbon
¦ Ammonium and sulfate components
combined are comparable to carbon
¦ Crustal component is very small
Data from EPA's urban speciation trends
network show that:
particulate matter in the eastern half of
the United States is homogenous in
composition
particulate matter in eastern sites
comprises mainly carbonaceous aerosol
and ammonium sulfate in comparable
amounts
the crustal component of PM2.5 is very
small in both western and eastern urban
monitoring sites (with the exception of
some places in the southwest and the
central valley of California)
1 -4
MSA to Non MSA Comparison of PM
Emissions
This graph depicts percentages of primary
PM emissions (and their precursors)
throughout the 37 states of eastern and
central United States.
The data indicate that:
roughly half of the primary PM is emitted
in the Metropolitan Statistical Areas and
about half in the rural areas.
ammonia (NH3) is the only precursor with
greater emissions in rural areas than in
urban areas;
Higher rural ammonia emissions are related
to agricultural emissions. In urban areas,
these emission can be attributed to
agricultural and mobile sources.
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1 -5
Comparison of Urban and Rural Data
¦ More sulfate than carbon in non-urban sites
¦ Sulfate concentration slightly higher in urban
areas
¦Carbon concentrations substantially higher in
urban areas
¦Conclusions
¦ Sulfate is a regional problem
¦ Carbon has a regional component with urban
excess
¦Urban Excess definition
Ambient monitoring data from both urban
and rural sites in the speciation trends
network show:
higher quantities of sulfate than carbon in
the non-urban sites
slightly higher sulfate concentrations in
urban areas compared with surrounding
non-urban areas
substantially higher carbon
concentrations in urban areas
Conclusions to be drawn:
The monitoring data highlights the
regional scope of the problem of sulfate
emissions.
There is a significant excess of carbon in
the urban areas, as evidenced by its
marked increase from rural to urban
areas.
Urban air quality data is often compared to
rural air quality data by noting the amount of
"urban excess" for a particular component.
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1 -6
Example of "Urban Excess"
Data from Atlanta, GA is presented in this
graph to illustrate the "urban excess"
concept, as depicted on the top part of
the bars.
Note that:
Nearly all sulfate is associated with the
regional contribution; this indicates that
the sulfate in Atlanta is only 10-15%
higher in concentration than the sulfate
that you find in the surrounding rural
sites.
Since most of the ammonium is
associated with sulfate, ammonium
concentrations follow a similar pattern.
Nitrate and carbon concentrations are
nearly twice as high in urban areas as in
rural areas, a significant "excess".
The concentration of total carbonaceous
material is greater than the sulfate
concentrations in Atlanta and the
concentration of crustal material is very
small.
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1 -7
Comparison of Urban~Rural Ratios
I I
This bar chart compares emission densities
with ambient concentrations for urban and
rural areas.
The density of NOx emissions per square
mile is about four times higher in urban
areas than in rural areas
Nitrate concentrations are roughly twice
as high in urban areas as in the rural
areas.
The data suggest that the higher
concentration of ammonium nitrate in
urban areas is associated with the higher
NOx emissions in the urban areas.
Sulfate has a higher density of emissions
in urban areas, but this ratio is not
reflected in the ambient data.
The lack of an urban excess of sulfate in
Atlanta is typical throughout the eastern
United States.
Reasons for the difference between urban
excesses for nitrates and sulfates:
the NOx to nitrate reaction occurs fairly
quickly, before it can be transported very
far
nitrate is less stable and may revert to
other compounds during transport
sulfate has a very long lifetime; once
converted from SO2, sulfate particles can
last for weeks and be transported long
distances.
Although the emission density of SO2 is
much higher in urban areas than in rural
areas, the concentrations are fairly
uniform over broad geographic areas.
Consequently, sulfate is considered a
regional pollutant in terms of the impact
on PM2.5.
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1 -8
National NOx Emissions
Highway Vehicles
OffroadMobile
Ind. & C omm Fuel C omb.
1
Other
¦mi
0
/o 5% 10% 15% 20% 25% 30% 35% 40%
Preparation ofFine Particulate Emissionsln^rrtories
National data indicate that NOx emissions are
about 23 million tons a year.
This graph identifies NO emission sources as
follows:
35% are from highway vehicles
25% are from electric utilities
18% are from mobile sources, and
15% are from industrial and commercial
fuel combustion.
All of these NOx emission sources are
associated with fuel combustion. The
"Other" category represents emissions from
industrial processes.
1 -9
S02 National Emissions
Electric Utiilities
Other FuelComb.
1
1
Industrial Processes
^1
Mobile Sources
2
Other
J
0
/o 10% 20% 30% 40% 50% 60% 70% 80%
Preparation ofFine Particulate Emissionsln^rrtories
This graph identifies electric utilities as the
source of 70-75% of national SO2 emissions.
Although electric utility emissions tend to be
more highly concentrated in urban areas,
sulfate emissions impact large geographical
areas.
This impact is due to the long lifetime of
sulfate particles and their transportability
over long distances.
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1 - 10
NH3 National Emissions
Animal Husbandly
1
1
Highway Vehicles
~
Industrial Processes
]
Waste Disposal
]
Other
~
0% 10% 20% 30% 40% 50% 60% 70% 80%
This graph identifies sources of national
ammonia emissions as follows:
Animal husbandry, specifically associated
with cattle, hogs, and poultry, represents
a significant source of ammonia
emissions.
Smaller amounts of ammonia emissions
are associated with animal waste and
waste processing procedures.
Fertilizer application is a source of
approximately 15-20% of the ammonia.
Highway vehicles represent a small
percentage of the emissions, which can
be important in an urban area.
Ammonia emissions are dispersed across
large portions of the east and the Midwest,
regions with a higher concentration of farms
where animals are raised.
This is consistent with the pattern of
measured ammonium ion deposits from the
National Atmospheric Deposition Program.
1 -11
Crustal Material
¦ Main Sources:
¦ Unpaved roads
¦ Agricultural tilling
¦ Construction
¦ Wind-blown dust
¦ Fly ash (less significant)
Crustal material mainly comes from fugitive
dust.
The main sources of fugitive dust are
unpaved roads, agricultural tilling,
construction, and wind-blown dust, which
primarily occurs in the arid areas of the
western United States.
A less significant source of crustal material is
fly ash from coal- or oil-fired boilers, which is
chemically similar to crustal material.
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1 -12
Crustal Material (cont.)
Huge Disparity Between El & Ambient Data
¦ Ambient Data
¦ < 1 ug/m3 in most of US
¦ Exception: > 1 ug/m3 in much of Southwest
¦ Emissions: 2.5M TPY (comparable to Carbon Emissions)
A disparity exists between the crustal data in
an emissions inventory and the crustal
material found in ambient air quality samples.
Ambient data indicate that less than one
microgram per cubic meter of crustal material
exists in the U.S., with the exception of the
southwest.
Emissions inventory data indicate that PM2.5
emissions are about 2.5 million tons a year,
which is comparable to the carbon
emissions.
1 -13
Crustal Material (cont.)
¦ Fugitive Dust has low "Transportable Fraction
¦ Crustal materials are a relatively small part of
PM2.5 in the ambient air
¦ Fugitive dust is released near the ground and
surface features often capture the dust near its
source
¦ As much as 50-90% may be captured locally
Fugitive dust emissions:
are emitted very close to the ground and
get trapped in shrubbery, vegetation,
buildings, etc.
may not be transported far from where
they are released
air quality dispersion models fail to
recognize that much fugitive dust will be
deposited within a few hundred yards to a
few miles of the source
Estimates indicate that about half of the
fugitive dust emitted in eastern metropolitan
areas is removed by surface features near
the source.
This inventory adjustment only applies to
regional chemical transport modeling. Thus,
this adjustment is made in the emissions
processor, not in the emissions inventory.
In summary:
crustal materials are a relatively small
part of PM2.5 in the ambient air
fugitive dust is released near the ground,
and surface features often capture the
dust near its source
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1 -14
Carbon Particles: Composition <
Terminology
C Primary A f Secondary A
Particles J V Particles J
)f\Primary OrganicN f Secondary ^ f A
I Aerosol i ^Organic A erosolJ ^ )
Primary Particles
¦ Elemental (Black) Carbon
¦ Primary Organic Aerosol (POA)
¦ Primary Carbon = EC (BC) + Primary Organic
Aerosol (POA)
Carbon is a major component of PM2.5 in the
ambient air.
You may recall that carbon particles can be
either primary (or directly emitted), or
secondary organic aerosol particles that are
formed in the atmosphere primarily from
volatile organic compounds.
Primary carbon particles are made of:
elemental or black carbon,
primary organic aerosols.
Approximately 20% of the primary carbon
emissions are EC and the other 80% are
POA.
1 -15
Primary Carbon in PM2.5
Wildland Fire
Ind. &Comm. Processes
Agricultural Burning
Transportable Fugitive Dust
0% 5% 10% 15% 20% 25% 30% 35%
% of PM2.5 Primary Carbon Emissions
(National Emissions -2M TPY)
1"15 Preparation of Fine Particulate Emissions Inventones
Data show that sources of primary carbon
emissions nationwide are:
wildfires
mobile sources
industrial and commercial combustion
residential heating and open burning
burning of construction debris
industrial and commercial processes,
agricultural burning
fugitive dust
Nationally, crustal material is emitted at
about 2.5 million tons per year, as compared
to about 2 million tons per year of primary
carbon emissions.
However, carbon emissions are more
abundant in the ambient air.
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1 -16
POA & EC Characteristics of Primary
Carbon Emissions
The ratio of POA mass to EC mass for most
sources is roughly 10 to 1.
Elemental carbon represents a higher ratio
than organic carbon in diesel engines, diesel-
powered vehicles, ships, trains, and planes.
The higher elemental to organic carbon ratio
in diesels is due partially to the higher
combustion temperatures in diesel-fueled
engines, which tend to combust the organic
carbon more completely.
Conversely, the lower temperature
combustion processes will emit more organic
matter, as a result of less complete
combustion.
1 -17
Primary Organic Aerosols (POA)
Certain organic carbon excluded
Organic carbon matter = primary organic
aerosol (POA).
The OC to POA multiplier for "fresh" POA in
the emissions is usually estimated
Particles "age" through oxidation.
A different "multiplier" is applied to the POA by
the chemical transport models to account for
the "aging"
Organic carbon reported from analysis of a
source or ambient sample does not include
the oxygens, hydrogens and other elements
that comprise the organic carbonaceous
matter.
Organic carbon matter is often called primary
organic aerosol.
To approximate the amount of oxygen and
hydrogen found in POA emissions use the
formula:
POA = OCx 1.2
Since particles in the atmosphere "age"
through oxidation, a different "multiplier" is
often applied to the POA to account for the
further oxidation of the POA emissions:
POA = OCx 1.4 to 2.4
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1 -18
Primary Organic Aerosols (cont.)
Models only apply the additional multiplier to
the POA, not the ECorSOA
Multiplier is not related to the model's estimate
of secondary organic aerosol formed in the
atmosphere from precursor gases
Only accounts for further oxidation of primary
particle emissions as the aerosol "ages"
Transport models contain a separate module
to simulate the amount of secondary organic
carbon formed in the atmosphere from
precursor
Atmospheric transport and transformation
models contain the additional multiplier, but
only apply it to the POA rather than the EC or
SOA.
The multiplier is not related to the model's
estimate of secondary organic aerosol
formed in the atmosphere from precursor
gases. It is only used to account for further
oxidation of primary particle emissions as the
aerosol ages.
Transport models contain a separate module
to simulate the amount of secondary organic
carbon formed in the atmosphere from
precursor gases. The OCM of those particles
is estimated directly by that module.
1 -19
Primary Organic Aerosols (cont.)
¦ The derivation of a multiplier for ambient OC is
much more complicated
¦ Use of a single multiplier introduces error
¦ A multiplier of 1.4 to 2.4 is often used for
ambient data
¦ No agreed upon standard adjustment
Deriving a multiplier for ambient OC is more
complicated because the sample usually
contains both POA and SOA, but the relative
proportions of each are not known.
A single multiplier is applied to ambient OC,
to adjust both primary and secondary OC in
the sample.
Using a single multiplier introduces error,
since multipliers probably would not be the
same for both fractions.
A multiplier of 1.4 to 2.4 is often used for
ambient data.
To date, there is no agreed upon standard
adjustment that is consistently applied in
either monitoring and modeling studies.
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1 -20
Primary Carbon Emissions Emission
Density Ratios
:
I
ff ¦
1
h,
it
1
L
Prtnary Cwbon
Mrs POA
mac
* Eastern US
This graph compares emission density ratios
for urban and rural carbon emissions.
Primary carbon emissions are approximately
three times higher in urban areas than in
rural areas.
1 -21
Carbon Particles: Composition <
Terminology
C ELeTntal 1 ^Primary Organic\ f Secondary f ^
v Carbon J ^ Aerosol J ^OrgankAerosol^ ^ J
df£_.> f-
Organic Carbon
Primary Particles
¦ Elemental (Black) Carbon
¦ Primary Organic Aerosol (POA)
¦ Primary Carbon = EC (BC) + Primary Organic
Aerosol (POA)
The primary particles comprise
approximately 80% primary organic aerosols
and 20% elemental carbon.
1 -22
Carbon Particles: Composition <
Terminology (cont.)
C Primary f Se
Particles J V P:
)f\Primary OrganicN f Secondary ^ f A
I Aerosol i ^OrganicAerosolJ ^ )
^ t
Organic Carbon
Secondary Particles
¦ Secondary Organic Aerosol (SOA)
Organic Carbon = POA & Secondary Organic
Aerosols
The condensable part of some EPA emission
factors represents the vapor of organics
when they are measured at stack
temperatures.
The vapor condenses to form particles when
the plume cools.
The condensable part of the emission factors
is included in the POA emissions estimate.
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1 -23
Comparison of Emission Density Ratios
~ Emissions Density
* Eastern US
Aromatics:
VOC precursors that react to produce
secondary organic aerosols
Mobile sources produce 70 percent of
aromatics (including benzene, toluene,
and xylene)
Toluene and xylene are the two aromatics
that are generally associated with
secondary aerosol formation
The emission density of aromatics is
about five times higher in urban areas
than in the rural areas.
The formation of SOA from these
aromatic precursors is another potential
cause of urban excess.
Terpenes:
Major source of secondary organic
aerosols
Biogenic in origin as they are emitted by a
variety of vegetation
Emissions roughly equal when comparing
a square mile of urban area to a square
mile of rural area
Trees in urban areas account for
emissions there
1 -24
Summary of Important PM2.5 Source
Categories
This chart summarizes the larger source
categories of PM2.5 direct and precursor
emissions.
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Instructor's Manual
1 -25
PM2.5 Primary Emissions Sources -
Summary
Directly Emitted (Primary) PM2.5
Emisson Sour cos ol CarborwcAous 4 Crustal Materials
PM2.5 emissions:
Combustion sources provide the majority
of both elemental and organic carbon
Most crustal materials are associated with
fugitive dust
Very little of the total carbon is associated
with fugitive dust
About 2 million tons of PM2.5 emissions
per year, one-fourth of which is elemental
carbon.
Similarly, the emissions of crustal
materials is about 2.5 million tons per
year.
Due to El adjustments for carbon and
crustal materials, (carbon emissions are
increased while crustal emissions are
reduced), carbon is usually found in much
greater quantity on ambient PM2.5
samples.
1 -26
PM2 5 In Ambient Air - A Complex Mixture
A Review of Precursor InlecreJatiociships
-nzrirzr O
Secondary organics:
form from terpenes associated with VOC
emissions (vegetation) occurs quickly
form from aromatics associated with VOC
emissions (mobile sources) occurs slower
than the terpene reaction
reducing aromatics can reduce SOA
levels
Ammonium sulfate:
forms from S02 emitted from the
combustion of sulfur containing fuels
forms and deposits slower than ozone
can be transported much longer
distances than either ozone or nitrate
insufficient ammonia produces partially
neutralized particles of ammonium
bisulfate, or possibly sulfuric acid
reducing emissions of SO2 will lower
ammonium sulfate concentrations
Ammonium nitrate:
forms relatively quickly from NOx
emissions from fuel combustion
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Instructor's Manual
insufficient ammonia reacts to form
ammonium sulfate before forming
ammonium nitrate
higher temperatures and a lower relative
humidity result in formation of less nitrate
and more nitric acid
reducing NOx emissions may reduce
nitrates, sulfates and secondary organic
aerosols
outcomes are complicated, involve ozone
chemistry and can not be generalized
In conclusion, a reduction in VOC emissions
would reduce ozone levels, resulting in less
secondary organic aerosols, sulfate and
nitrate formation.
The complex interactions among ozone
formation, ozone precursors, sulfates,
nitrates, and secondary organics must be
collectively considered.
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Chapter 2 - The National Emissions Inventory and Emission
Inventory Tools
2 -1
Preparation of Fine Particulate
Emissions Inventories
Chapter 2 - The National Emissions
Inventory and Emission Inventory Tools
After this lesson, participants will be able to:
explain the National Emissions Inventory
and the process by which it was
developed.
describe current and future emissions
inventory preparation tools.
2-2
Information Included in the NEI
¦ National tabulation of emissions of PM2.5, S02,
NOx, Ammonia, and VOC
¦ Point sources by lat-long: 52,000 facilities, each
containing multiple emission points
¦ Over 4,500 types of processes represented
¦ Area & Mobile by County
¦ 400 categories of Highway & Non-Road Mobile
¦ Over 300 categories of Area sources
¦ Annual emissions, start/end dates, stack
parameters
¦ Also, in the NEI
¦ HAP emissions for over 6,000 types of processes
NEI information includes:
data on 52,000 point sources by latitude
and/or longitude, with over 4,500 types of
processes represented.
approximately 400 categories of highway
and non-road mobile sources
approximately 300 categories of area
sources in the NEI
annual emissions (area and mobile
sources are allocated by county)
dates that sources started or stopped
operations
stack parameters
HAP emissions for over 6,000 types of
processes
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2-3
This graph represents a timeline showing the
evolution of the National Emissions
Inventory.
First NEI PM inventory - 1985 National
Acidic Precipitation Assessment Program.
Represented inventory for PM10 and was
developed without input from the states
(states have become involved in NEI
development)
Early 1990s, minimal activity on developing
PM inventories; National Particle Inventory
was prepared in 1993.
In 1996 it was called the National Emissions
Trends Inventory.
The NET was updated in 1999 and was
renamed the NEI.
Integration of the National Air Toxics
Assessment Inventory began with the 1999
NEI and was completed with the 2002 NEI.
Improving and updating the NEI is a
continuing process.
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2-4
Wildfires in the National Emission
Inventory
¦ Will be included as point sources
¦ Data on location, and start and stop dates
¦ Currently handled as areas sources
¦ Allocated by county and season
¦ Impossible to determine impact under the
current approach
Wildfires:
2002 NEI began to include large fires as
point sources for some areas of the
United States.
Data on when they started and ended,
and the location are essential to
accurately model impact on air quality.
Smaller fires may continue to be treated
as area sources and are allocated to
county by using forested land area.
Emissions are assigned to months of the
year using temporal allocation factors.
Treating fires as point sources is important.
For example:
A fire may have a major impact on a
Class 1 area; it could relate to the 20%
worst days at that area.
When fires are treated as area sources, it
is impossible to know where or when the
fire occurred.
Consequently, it is impossible to
determine if the particulate matter
emissions in the Class 1 area are
attributable to the fire.
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2-5
NEI Development ~ Cooperative,
Iterative
The process of developing the NEI includes
the following:
data on emission factors and models,
various databases for source activity
levels,
default values for emissions related
variables,
existing point source data, and
growth factors for source categories.
This data is combined to form what is called
the preliminary NEI, which is provided to
state and local air agencies for refinement
and improvement.
The preliminary NEI becomes the improved
NEI by:
working with stakeholders,
using factor and model improvements,
and
using local activity levels and variables
provided by state and local agencies.
This process is repeated yearly but,
emphasized every three years.
2-6
Inventory Preparation Tools
¦ Emission Factors & Activity Data
¦ www.epa.gov/ttn/chief
¦ (~ 20,000 factors in FIRE)
¦ Processes vary overtime ~ Factor
representiveness issue
One of the tools used to prepare the NEI is
the Factor Information and Retrieval
database, accessible at the Web site shown.
Approximately 20,000 emission factors in this
database are used in developing the NEI.
Industrial processes vary over time and
according to facility; therefore, exercise
caution when using the database.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
2-7
Inventory Preparation Tools (cont.)
¦ Emissions Models
¦ TANKS
¦ NONROAD
¦ Others
Some of the inventory preparation tools used
are:
Process-based emission models.
TANKSa model used for estimating
storage tank emissions of VOCs.
NONROADa model used to estimate
emissions from non-road vehicles.
BEISa model used to estimate biogenic
emissions.
2-8
Inventory Preparation Tools (cont.)
¦ Spatial Characterization & Locator Aids
¦ GIS
¦ GPS
¦ Satellites
¦ Emissions Processing, including Speciation
Other tools include special characterization
and locator aids such as, Geographic
Information Systems and Global Positioning
Systems.
In Mexico and Canada, satellites are being
used to locate fires.
Satellites are limited in their ability to locate
certain fires, such as those below a certain
size or masked by cloud cover.
The purpose of an emissions processor is to
provide an efficient tool for converting
emissions inventory data into the file format
required by an air quality dispersion model.
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2-9
Overview of Emissions Processing
¦ Processors include:
¦ SMOKE, EPM
¦ Processor output
¦ Gridded, hourly emissions file
¦ Speciation of Primary Emissions (EC, Organics,
S04, Nitrates)
¦ Model-ready
¦ Processor inputs
¦ Annual, county-level area source El
¦ Annual point source data (except for CEM data)
Emissions processing:
After developing NEI emissions data, it is
processed by a modeling system, such as
the Sparse Matrix Operator Kernel
Emissions (SMOKE) system.
The modeling system applies speciation
factors to the emissions data when the
inventory is to be used by air quality
dispersion modelers.
Emissions modeling depends on
speciation factors, temporization factors,
and species allocation factors.
The data flow is from the NEI to the
emissions processor and then into the air
quality model.
The output is a gridded, hourly emissions
file speciated into elemental carbon,
organics, primary sulfates, and primary
nitrates.
The speciated inventory data is especially
useful in modeling for regional haze. For
example:
Carbon particles absorb and scatter light
with a different efficiency than other
particles.
Consequently, it is necessary to consider
different types of particles separately
when doing regional haze work.
Data input:
Area source data is input to the emissions
processor as an annual county level
inventory.
Point source data is input as annual data,
located by latitude and longitude.
CEM data feeds into the emission
processor through a separate database.
Preparation of Fine Particulate Emissions Inventories
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2 -10
Overview of Emissions Processing
(cont.)
¦ Processor contains default factors & profiles,
including:
¦ County-to-Grid Allocation Factors
¦ Temporal Allocation Profiles (hourly & seasonal)
¦ Speciation Profiles
Emissions processor default factors and
profiles:
county to grid allocation factors
temporal allocation profiles
speciation profiles
The emissions processor transforms annual
or county level inventory into a gridded,
hourly emission file.
The data is speciated into EC, POA, Primary
S04, Primary Nitrate and Other (crustal
materials/fugitive dust and unidentified
species).
It is then ready to be used as input to a
dispersion model.
2 -11
Speciation of EC & POA
Speciation Profiles ~ estimate of the EC &
POA portion of each PM2.5 source's
emissions
¦ All PM2.5 sources "assigned" to 1 of 73 "profiles"
EC, POA
¦ Derived within the Emissions Processor from
PM2.5 using speciation profiles
¦ NOT part of the NEI
Current Issues
¦ EC - POA Split, carbon analysis methods
¦ OC - POA compound adjustment
The emissions processor assigns all of the
PM2.5 sources to one of several dozen
speciation profiles.
Elemental carbon and the primary organic
aerosols are derived within the emission
processor from PM2.5 data using speciation
profiles.
They are not part of the NEI inventory.
Carbon inventory issues:
Analytical uncertainties surrounding the
split between elemental carbon and
primary organic aerosols
Operational definition of what to call
elemental carbon and what to call organic
carbon under analysis
Data provided for organic carbon, not the
organic carbonaceous matter that
accounts for all the oxygens and
hydrogens; must use a multiplier or
compound adjustment to go from organic
carbon to primary organic aerosols
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2 -12
Process-based Emissions Models
Space- & time- sensitive emissions reflective of
real time conditions
¦ wind, temperature
¦ RH, vegetation types
¦ soil type & moisture
Linkage:
¦ MM5
¦ GIS coverages
¦ Emission algorithms
Currently ~ BEIS3, MOBILE6
¦ No other categories linked to real time conditions
Process-based emission models models
consider space- and time-sensitive
emissions in an effort to reflect real world
conditions such as:
wind,
temperature,
relative humidity,
vegetation type,
soil type, and
moisture.
These models will eventually include
algorithms to develop a wind-blown dust
inventory by examining the wind fields for the
whole modeling domain and deciding when
the wind is going to blow fast enough to
create dust emissions.
Another model under development will
estimate fire emissions by considering
relative humidity, moisture, and wind speed.
These models would link to various
databases such as the meteorological data
processor (MM5). They would also link to
GIS coverage of soil and vegetation types,
and would contain emission algorithms that
respond to these variables.
The MOBILE 6 model and the BEIS 3 model
contain some aspects of process-based
emission models, such as temperature.
Consideration of temperature is critical for
estimating biogenic emissions.
Preparation of Fine Particulate Emissions Inventories
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2 -13
Process-based Emissions Models (cont.)
¦ Process-based emission model needs
¦ Ammonia (fertilizer application, animal husbandry,
removal)
¦ Fugitive Dust (wind, unpaved roads, construction,
tilling, removal)
¦ Wildland Fires (fuels, fuel consumption, plume rise)
¦ Residential Wood Burning
¦ Evaporative Loss
¦ Others ?
There are a number of other needs for
process-based emission models.
Some examples are listed here and include
estimating emissions of ammonia and
residential wood burning.
2 -14
Status of Process-based Emissions Models
(Integrated w/ Emissions Processor)
¦ Biogenics (always integrated w/ EP)
¦ On-Road (optional integration w/ EP)
¦ Ammonia (development just began)
¦ Fugitive Dust (under development)
¦ Wildland Fire (underdevelopment)
In the future, some of the process-based
models will be integrated with the emissions
processor and some will be stand-alone
models.
The biogenics model (BEIS) is always
integrated with the emissions processor.
The onroad model MOBILE 6 can optionally
be integrated with the emissions processor.
The development of process-based
emissions models for ammonia, fugitive dust
and wildland fires are currently underway.
2 -15
Wildland Fire Emissions Module
(under development)
¦ Modular input to Emission Models (e.g.,
SMOKE, OpEM) to interface with the CMAQ
modeling system.
¦ User Inputs: Fire locations, duration, size
The inputs for the wildland fire model include
fire locations, duration, and size.
Preparation of Fine Particulate Emissions Inventories
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2 -16
Wildland Fire Emissions Module
(under development) (cont.)
¦ Model Components
¦ Fuel loading default: NFDRS / FCC map
¦ Fuel Moisture: Calculates using MM5 met data
¦ Fuel Consumption: CONSUME2.1 / FOFEM
¦ Emissions, Heat Release & Plume Rise: EPM &
Briggs (modified)
This model will access meteorological data
for wind speed and moisture. It also uses
fuel-loading defaults from a one-kilometer
resolved national map of fuel loadings.
Although a fuel map currently exists, a
project to develop a map with better fuel-
loading data is being funded.
Fuel moistures are calculated using the MM5
data. Fuel consumption will be done using
CONSUME or the First Order Fire Effects
Model (FOFEM) for fuel consumption.
The emissions projection model will combine
with the Briggs' Plume Rise equation
modified for fires calculates emissions, heat
release, and plume rise.
2 -17
Wildland Fire Emissions Module
(under development) (cont.)
¦ Outputs: Gridded hourly emissions, plume
characteristics
¦ Integrate, Test & Release Module (late 2004
earliest-w/ funding)
When complete, the wildland emissions
module will provide a grid of hourly
emissions and plume characteristics,
reflecting real world meteorological
conditions that effect fire behavior and
emissions.
Preparation of Fine Particulate Emissions Inventories
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2 -18
Fugitive Dust Emissions Module
(under development)
¦ Modular input to Emission Models (e.g.,
SMOKE, OpEM) to interface with the CMAQ
modeling system. It will:
¦ establish consistent database of resource info (soil
map, land use, vegetation cover, moisture,
precipitation, wind speed) for making emission
estimates for use with grid models.
¦ demonstrate proof-of-concept of emission models
for wind erosion, unpaved roads, construction,
other dust sources.
A fugitive dust model is currently under
development.
The goal is to establish a consistent
database of resource information such as
soil, land use, vegetation, moisture,
precipitation, and wind speed that can be
used to estimate emissions for use with grid
models.
Currently, a proof of concept of this emission
model is being demonstrated for wind-
erosion, unpaved roads, construction, and
other dust sources.
2 -19
Receptor Models
¦ Inventory refinement, bounding uncertainties
¦ Fossil vs Contemporary Carbon
¦ Gasvsdiesel
¦ Cold starts, smokers
Receptor modeling is an important tool to
use in developing an emissions inventory.
Types of models:
Radiocarbon analysis can be used to
obtain an estimate of the amount of fossil
versus contemporary carbon.
Some receptor models use tracers to
examine the organic compounds of gas
and diesel particles.
The tracers can identify whether the
carbon particles are from gas or diesel
engines, and whether they are emitted
from cold starts or smokers.
Another commonly used tracer is the
Chemical Mass Balance, a dedicated
weighted least squares model.
This model infers source contribution
estimates from ambient, speciated data
and source profiles.
Multivariate models are the Positive
Matrix Factorization model and UNMIX,
which is somewhat similar to PMF.
Preparation of Fine Particulate Emissions Inventories
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2-20
Specific PM2.5 Categories Needing Input
from Federal / State / Local / Tribes
¦ Wildland Burning
¦ Forests, Rangeland & especially private & State /
tribal burners
¦ (acreages burned, fuel loadings for largest fires,
timing)
¦ Residential Open Burning
¦ Household Waste, Yard waste (volumes &
burning practices)
¦ Regulations & their effectiveness, local surveys of
burn activities)
Several specific PM2.5 categories need better
emissions models and emissions data. This
includes wildland burning of forests and
rangeland.
Data on acreage burned, fuel loadings for the
largest fires, and the timing of those fires are
needed.
Another source category that needs better
emissions data concerns_residential open
burning (household and yard waste).
Data on the volumes, burning practices,
regulations and their effectiveness, and local
surveys of burn activities are needed.
2-21
Specific PM2.5 Categories Needing Input
from Federal/ State /Local/ Tribes (cont.)
¦ Construction Debris & Logging Slash
¦ Regulations & their effectiveness, local surveys of
burn activities
¦ Agricultural Field Burning
¦ Acreages, fuel loadings, timing
¦ Residential Wood Combustion
¦ Fireplaces, Wood Stoves
¦ local surveys of fuel burned, fireplace vs wood
stoves, local regulations
Data on the effectiveness of regulations for
construction debris and logging slash are
needed. This includes local surveys of burn
activities.
For agricultural field burning, data on
acreages, fuel loadings, and timing of the
burn events are needed.
Data from local surveys of fuel burn are
needed for residential wood combustion,
fireplaces and wood stoves. This includes
data on whether the wood is being burned in
a fireplace or a wood stove.
Preparation of Fine Particulate Emissions Inventories
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2-22
Specific PM2.5 Categories Needing Input
from Federal / State / Local / Tribes (cont)
¦ Area-specific industrial process sources
¦ Fugitive Dust as indicated by local conditions
Area-specific industrial process sources are
another category for which better data are
needed.
However, since these sources constitute a
small percentage of the industrial process
sources, it is important to pick those sources
that have the largest errors associated with
them.
Finally, data on local conditions contributing
to fugitive dust are needed in some cases.
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Chapter 3 - Onroad Mobile Sources
3-1
Preparation of Fine Particulate
Emissions Inventories
Chapter 3 - Onroad Mobile Sources
After this lesson participants will be able to:
describe the EPA's MOBILE 6 model and
the National Mobile Inventory Model
explain how vehicle mileage is used to
calculate emissions from onroad vehicles.
3-2
MOBILE 6 Overview
Use MOBILE 6 model for emission factors
¦ PM2 5, S02l NOx, NH3i PM10) VOC, and CO
¦ PM25 and PM10 emission factors are for primary
emissions (PM2.5-PRI and PM10-PRI)
Use vehicle miles traveled (VMT) data for
activity
Map VMT data to corresponding MOBILE 6
emission factors
EPA's Office of Transportation and Air
Quality or OTAQ (pronounced O-TAG) has
developed MOBILE 6 to estimate emissions
from mobile sources.
The MOBILE 6 model:
includes emission factors for PM2 5, SO2,
NH3, PM10, VOC and CO.
can be downloaded with its User Guide
from www.epa.gov/otaq/m6.htm.
uses PM2.5 and the PM10 emission factors
to represent primary emissions.
matches data on vehicle miles traveled to
the corresponding MOBILE 6 emission
factors to form the basis of emission
calculations.
Preparation of Fine Particulate Emissions Inventories
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3-3
MOBILE 6 Overview (cont.)
Data and algorithms previously in PARTS
(with updates where applicable) have been
integrated into the MOBILE 6 model
Fugitive dust emission factors included in
PARTS (i.e., re-entrained road dust) removed
from MOBILE 6
MOBILE 6 also includes emission estimates
for Gaseous S02 and Ammonia (NH3)
PART 5 was EPA's prior model for modeling
PM emissions. Its data and algorithms have
been integrated into the MOBILE 6 model,
with some updates.
The fugitive dust emission factors included in
PART 5 have been excluded from MOBILE
6.
Consequently, the calculation of emissions
from re-entrained road dust is done
separately outside the model.
Also, MOBILE 6 also includes emission
estimates for gaseous S02 as well as
ammonia.
3-4
MOBILE 6 Modeling Inputs
Use same inputs for MOBILE 6 model as
used for previous MOBILE 6 model for same
time period
¦ Registration distribution
¦ Ambient conditions
¦ Speeds/speed distribution
¦ Fuel parameters
¦ Control programs
¦ VMT mix
In most cases MOBILE 6 uses the same type
of inputs that were required for prior
versions.
This includes registration, distribution,
ambient conditions such as temperature and
humidity, speeds and speed distributions,
and fuel parameters such as the Reid Vapor
Pressure of gasoline and oxygenated fuel.
It also includes control programs such as
Stage II or Inspection and Maintenance
programs, and data on VMT by vehicle type.
Preparation of Fine Particulate Emissions Inventories
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3-5
MOBILE 6 Modeling Inputs (cont.)
¦ Additional data required for MOBILE 6
¦ Diesel sulfur content (in parts per million [ppm])
¦ Additional commands needed for MOBILE 6
¦ Described in MOBILE User's Guide
¦ PM2.5 and PM10 emission factors cannot be
calculated in same scenarioparticle size
must be specified in each scenario
One additional data input required for
MOBILE 6 modeling that was not required in
the past is the diesel sulfur content
expressed in parts per million.
Also, there are additional commands needed
for generating PM25 inventories in MOBILE
6.
3-6
National Mobile Inventory Model
(NMIM)
¦ Creates national or sub-national emission
inventories
¦ Consolidated emissions modeling system
¦ Combines a graphical user interface,
MOBILE, NONROAD, and a data base
¦ Data base contains most recent information
used in the NEI
The commands are described in the MOBILE
user's guides developed by OTAQ.
Note that when you develop a PM inventory,
you cannot do a PM2.5 and a PM10 inventory
simultaneously.
As a result, it is necessary to specify just one
particle size per each run.
National Mobile Inventory Model:
a tool developed by OTAG to create
national or sub-national emission
inventories for any calendar year
uses county-specific input parameters
a consolidated emissions modeling
system for EPA's MOBILE and
NONROAD models
combines a graphical user interface,
MOBILE, NONROAD, and a database
with modeling information for each county
in the United States
Currently this database contains the most
recent information (e.g., fuel parameters,
registration data, temperatures, etc.) used by
EPA to generate the default National
Emission Inventory estimates for each
county.
Preparation of Fine Particulate Emissions Inventories
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3-7
National Mobile Inventory Model
(NMIM) (cont.)
¦ Calculates criteria pollutants and HAP emissions
¦ All estimates based on same input parameters
¦ Used to generate preliminary 2002 NEI for nonroad
engines
¦ Optional for states
¦ Available for general use in 2004
¦ Produces same results as MOBILE and NONROAD
NMIM:
can calculate both criteria (including
ammonia) and HAPs for the source
categories included in the MOBILE6 and
NONROAD models.
consolidates all the model inputs into a
single data base such that all the
estimates are based on the same input
parameters in each county (e.g., fuel
programs, inspection/maintenance,
humidity, temperatures).
draft version used to generate the
preliminary EPA default 2002 NEI
inventories for nonroad engines
is an optional tool for states to use in
estimating mobile source inventories by
organizing and automating emission
inventory development for highway
vehicles and NONROAD categories.
is not a substantively different approach
than directly using MOBILE 6 and
NONROAD2002.
The EPA expects to complete NMIM and
release it for general use in 2004 but states
will not be required to use it to generate
inventory estimates.
This tool was developed to make creating
inventories easier and does not change the
answers that are obtained from running
MOBILE or NONROAD individually.
State use:
States may wish to customize all or part
of their own inventory generation process
to the NMIM model approach.
This will allow them to take advantage of
its efficiency and transparency, and to
align the NEI inventory results more
closely with their own inventory
estimates.
State and local agencies will be able to
use the database to view the county-level
default values and to replace them with
data that better represents their
geographic areas.
Preparation of Fine Particulate Emissions Inventories
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3-8
Sources of VMT Data
State Department of Transportation
Metropolitan Planning Organization
1999 NEI VMT Data based on:
¦ State-provided VMT (8 States)
¦ FHWA HPMS data summaries
¦ By roadway type and State
¦ By roadway type and Urban Area
¦ Nationally by Vehicle Type
State departments of transportation typically
provide VMT data.
Metropolitan planning organizations track
these data for certain areas. However, VMT
data should be used from whatever source it
is available.
As a case in point:
The 1999 NEI included VMT data that
was provided by eight states and this
data was used in conjunction with
MOBILE6 emission factors. VMT data for
the remaining states were obtained from
the Federal Highway Administration's
data summaries.
The FHWA data contain vehicle miles
traveled by roadway type, by state, as
well as VMT by roadway type for specific
urban areas.
The 1999 NEI relied upon a national
distribution for the VMT mix by vehicle
type.
As a result, the same mix of vehicles was
assumed for all areas unless the state
provided their own data.
Documentation for the 1999 NEI can be
found at:
www.epa.gov/ttn/chief/net/1999inventory.htm
Preparation of Fine Particulate Emissions Inventories
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3-9
VMT Approach
Distributions of VMT by roadway type, vehicle
type, by hour of day can be applied directly to
VMT or included within MOBILE 6 input files
Also need to have speeds matched to
roadway types either as average speeds or as
speed distributions by speed ranges
In the case of the NEI, the VMT data was
developed for use in conjunction with
MOBILE 6 by using the distributions of VMT
by roadway type and vehicle type. In some
cases this activity data may be available by
hour of the day.
Regardless of the format, these fractions can
be applied directly to the total VMT, or they
can be included within the MOBILE 6 input
files in order to generate a weighted
emission factor in MOBILE 6.
It is important to have speeds matched to the
roadway types, either as an average speed
or as speed distributions by speed ranges.
This latter approach is the approach needed
for link-based VMT development and some
transportation demand models.
3-10
Level of Detail of VMT Data
¦ By county
¦ By roadway type (or link level)
¦ By vehicle type
¦ Appropriate time period
Ideally, the level of VMT data that should be
used is by county and by the various
roadway types or link level if modeling at that
level is planned.
Using data by vehicle type is important since
emission rates can vary greatly among the
different vehicle types.
Using vehicle type data will allow the
adjustments to be made to the national
defaults that are typically used.
It is important to match the VMT data (daily
or hourly) to the appropriate time period for
modeling.
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3-11
Calculating Onroad Emissions
¦ Match VMT to corresponding MOBILE 6
emission factor
¦ Map according to speed, roadway type (RT),
vehicle TYPE (VT), time period
¦ Emis= VMT * EF * K
¦ Emis = emissions in tons by RT, VT
¦ VMT = vehicle miles traveled on RT by VT in
miles
¦ EF = emission factor in grams/mile by RT, VT
¦ K = conversion factor
VMT data should be matched to a
corresponding MOBILE 6 emission factor
and mapped according to speed, roadway
type, vehicle type, and time period.
Emissions are calculated by multiplying the
VMT data by an emissions factor as shown
in the equation on this slide.
3-12
Additional Resources
User's Guide to MOBILE6.1 and MOBILE6.2: Mobile
Source Emission Factor Model, EPA420-R-02-028,
October 2002
http://www.epa.gov/otaq/m6.htm
MOBILE6.1 Particulate Emission Factor Model
Technical Description, Draft, EPA420-R-02-012,
March 2002
http://www.epa.gov/OMS/models/mobile6/r02012.pdf
Links to MOBILE6 Training Materials
http://www.epa.g0v/0taq/m6.htm#m6train
There are a number of online resources to
consult when developing an emissions
inventory for onroad sources.
This includes EPA's online user's guide for
using MOBILE 6.1 and 6.2, as well as
technical documentation describing how the
defaults were developed.
There are also links to training materials that
have been developed as MOBILE 6 has
been updated.
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Instructor's Manual
Chapter 4 - Nonroad Mobile Sources
4-1
Preparation of Fine Particulate
Emissions Inventories
Chapter 4 - Nonroad Mobile Sources
After this lesson, participants will be able to:
describe EPA's NONROAD model
explain the approaches for estimating PM
emissions from aircraft, commercial
marine vessels, and locomotives
The discussion of EPA's National Mobile
Inventory Model presented in Chapter 3 is
also applicable to the nonroad category, but
will not be repeated.
4-2
What Sources are Included?
SCCs (4-digit SCC denotes engine type)
2260xxxxxx
2-Stroke Gasoline
2265xxxxxx
4-Stroke Gasoline
2267xxxxxx
Liquefied Petroleum Gasoline (LPG)
2268xxxxxx
Compressed Natural Gas (CNG)
2270xxxxxx
Diesel
Two exceptions
2282xxxxxx
Recreational Marine
2285xxxxxx
Railroad Maintenance
Preparation o,Fine Palate Emissions In Tories
This slide lists the source categories that are
included in the NONROAD model.
The four-digit source classification code
generally denotes the engine type, or fuel
that is used in the nonroad equipment.
The four-digit SCC denotes the equipment
type instead of engine type in two categories:
recreational marine, and
railroad maintenance.
4-3
What Sources are Included? (cont.)
Equipment Category (7-digit SCC denotes equipment)
Airport ground support
Logging
Agricultural
Recreational marine
Construction
vessels
Industrial
Recreational equipment
Commercial
Oilfield
Residential/commercial
Underground mining
Lawn and garden
Railway m ai nten an ce
10-digit SCC generally denotes specific application within
equipment category
Preparation o,Fine Palate Emissions Intones
There are 12 different equipment categories
denoted by the seven-digit SCC in the
NONROAD model.
Each category may have multiple
applications that are specified at the 10-digit
SCC level.
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4-4
What Sources are Included? (cont.)
¦ Pollutants
¦ PM10-PRI, PM2.5-PRI, CO, NOx, VOC, S02, and
co2
¦ PM10 and PM25 emission factors represent Primary PM
¦ NH3 not a direct output of NONROAD, can be estimated
based on fuel consumption and EPA emission factors
derived from light-duty onroad vehicle emission
measurem ents
¦ Model estimates exhaust and evaporative VOC
components
The pollutants included in the NONROAD
model are PM10 and PM25 (representing
primary PM ), CO, NOx, VOC, S02 and C02.
Although ammonia is not a direct output of
the NONROAD model, it can be estimated
from fuel consumption estimates.
These fuel consumption estimates come
from the model and the EPA emission factors
are derived from light-duty onroad vehicle
emission measurements.
In addition to exhaust pollutants, the
NONROAD model estimates evaporative
VOC components from crankcase emissions,
spillage, and vapor displacement.
4-5
NONROAD Model Emission Equation
Uh - Eexh * A * L * P * N
where: /exh = Exhaust emissions, (ton/year)
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Instructor's Manual
4-6
NONROAD Model
Emission Equation (cont)
¦ Emission Factors
¦ Dependent on engine type and engine size
(horsepower)
¦ Future year emission controls or standards reflected in
emission factor value
¦ S02, C02, and evaporative VOC emissions based
on fuel consumption
¦ PM10 assumed to be equivalent to total PM
¦ For gasoline and diesel-fueled engines, PM2 5 = 0.92 *
PM10
¦ For LPG and CNG-fueled engines, PM25 = PM10
The emission factors are dependent on the
engine type as well as the engine size, or
horsepower.
Future year emission controls or standards
are reflected in revised emission rates, so
that as older engines are scrapped and new
engines replace them, revised emission rates
are applied to the new engines to reflect the
standards that they need to meet.
SO2, CO2 and evaporative VOC emissions
are based on fuel consumption.
In the NONROAD model:
PM10 is assumed to be equivalent to total
PM and for gasoline and diesel engines
PM2.5 is assumed to be 0.92 times PM10.
all PM is assumed to be less than PM2 5
for liquefied petroleum gas and
compressed natural gas engines
4-7
Geographic Allocation
¦ County-level allocation of equipment
population
¦ National or state-level equipment populations from
PSR or alternate sources, reported by equipment
type (SCC) and horsepower range
¦ Allocates populations to counties using surrogate
indicators that correlate with nonroad activity for
specific equipment types
Because there are no estimates of county
level populations, the NONROAD model
estimates those populations using surrogate
indicators.
Using national or state level equipment
populations (either by equipment type or
horsepower range), the model allocates them
to the county level by using surrogate
indicators.
These indicators correlate with nonroad
activity for a specific equipment type.
Preparation of Fine Particulate Emissions Inventories
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4-8
Temporal Allocation
NONROAD accounts for temporal variations
in activity
¦ Monthly activity profiles by equipment category
according to 10 geographic regions
¦ Typical weekday and weekend day activity profiles
by equipment category; do not vary by region
The NONROAD model also accounts for
temporal variations in activity.
The temporal profiles vary by month and
depend on the equipment category and the
geographic region of the country.
The model contains typical weekday and
weekend day activity profiles by equipment
category, however, those do not vary by
region.
4-9
Improving Inputs
Specify local fuel characteristics and ambient
temperatures
Replace NONROAD model default activity
inputs with State or local inputs
¦ Perform local survey
Obtain local information to improve
geographic allocation indicators and temporal
profiles
Suggested methods for improving EPA's
latest 2002 model results:
specify local fuel characteristics and the
ambient temperatures specific to the area
being modeled.
replace the NONROAD default activity
inputs with state or local data, if possible.
It can be resource intensive to obtain
reasonable estimates to replace the default
values. To obtain this data, it would be
necessary to perform a local survey of
equipment owners and users.
Another way to improve the model results is
to obtain local information to improve the
geographic allocation (i.e., going from state
to county). Obtaining local data used for the
temporal profiles can also improve the model
results.
Preparation of Fine Particulate Emissions Inventories
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4-10
Improving Inputs (cont.)
Significant PM Fine Equipment Categories
include:
¦ Diesel construction
¦ Diesel farm
¦ Diesel industrial
¦ Gasoline lawn and garden
¦ Gasoline recreational marine
Another approach to improve the model
results is to focus on priority categories and
obtain better data for those categories.
For example, for fine PM, priority categories
would be diesel construction, diesel farm,
diesel industrial, gasoline lawn and garden,
and gasoline recreational marine.
4-11
Resources
http://www.epa.gov/otaq/nonrdmdl.htm
From this web site, there are links to:
¦ Downloadable version of NONROAD2002a
model
¦ Documentation
¦ User's Guide
¦ Technical Reports to describe the sources and
development of all model default input values
The latest version of EPA's NONROAD
model can be accessed at the web address
listed here.
This web site contains documentation, a
user's guide, as well as technical reports to
describe the sources and development of all
the default input values (e.g., equipment
populations, geographic allocations, growth
factors, and emission rates).
4-12
AIRCRAFT - Overview
¦ SCCs
¦ 2275020000 - Commercial Aircraft
¦ 2275050000 - General Aviation
¦ 2275060000 - Air Taxis
¦ 2275001000 -Military Aircraft
¦ Activity Data - landing and take-off
operations (LTOs)
¦ Emission Factors - aircraft/engine-specific
or fleet average
The SCCs representing the aircraft
categories that have been historically
reported in the NEI are listed here.
The activity data used for aircraft are known
as a landing and takeoff operations, or LTO.
Emissions are estimated by applying
emission factors to the LTO data that are
either specific to an aircraft or engine type.
If the make-up of the aircraft fleet is
unknown, fleet averages can be applied to
the emission factors.
Preparation of Fine Particulate Emissions Inventories
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4-13
AIRCRAFT - Overview (cont.)
¦ Definitions of Aircraft Categories:
¦ Commercial - Aircraft used for scheduled service
to transport passengers, freight, or both
¦ Air taxis - Smaller aircraft operating on a more
limited basis to transport passengers and freight
¦ General aviation - aircraft used on an
unscheduled basis for recreational flying, personal
transportation, and other activities, including
business travel
¦ Military aircraft - aircraft used to support military
operations
This slide lists the definitions of the aircraft
categories that have been historically
reported in the NEI.
4-14
AIRCRAFT - Overview (cont.)
Aircraft operations are defined by landing and
take-off operation (LTO) cycles, consisting of
five specific modes:
¦ Approach
¦ Taxi/idle-in
¦ Taxi/idle-out
¦ Take-off
¦ Climb-out
The operation time in each of these modes (TIM)
is dependent on the aircraft category, local
meteorological conditions, and airport
operational considerations
The LTO cycle consists of different modes
including: the approach, taxi idle in, taxi idle
out, take off, and climb out.
Operation time in each of these modes is
dependent on the aircraft category,
meteorological conditions, as well as how the
airport is operating (e.g., the length of time
waiting to take off).
There can be substantial variations in these
modes from airport to airport. Because
different emission rates result when the
aircraft are operating in each of these
modes, it is important to consider all of these
factors in estimating emissions from aircraft.
4-15
COMMERCIAL AIRCRAFT
NEI Method
¦ Activity/Emissions Developed at National
Level
¦ Commercial Aircraft Emissions
¦ Calculated using national-level FAA LTO data by aircraft
type and emission rates from Emissions and Dispersion
Modeling System (EDMS) Version 4.0.
¦ Used default engines for each aircraft type and default
time-in-mode values.
The NEI estimated emissions for commercial
aircraft by using national-level FAA LTO data
by aircraft type and emission rates from the
Emissions and Dispersion Modeling System
version 4.0.
Preparation of Fine Particulate Emissions Inventories
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4-16
General Aviation, Air Taxi and
Military Aircraft - NEi Method
¦ National Emissions for General Aviation, Air
Taxi, and Military Aircraft calculated using
equation:
National Emissionscp = National LTOsc * EFcp
where: LTOs = landing and take-off operations;
EF = emission factor;
c = aircraft category; and
p = criteria pollutant.
The NEI estimated emissions from the
general aviation, air taxi, and military aircraft
categories by also using national LTO data;
however, data was not available for specific
aircraft types within each of the aircraft
categories.
Consequently, emissions for these three
categories were estimated by multiplying
total LTO by an emission factor as shown in
the equation on this slide.
4-17
General Aviation, Air Taxi and
Military Aircraft - NEI Method (cont.)
¦ LTO-based PM Emission Factors
¦ General Aviation
¦ PM10-PRI: 0.2367 Ibs/LTO
¦ Air Taxi and Military Aircraft
¦ PM10-PRI: 0.60333 Ibs/LTO
¦ PM2.5-PRI Emissions
¦ Estimated by applying particle size multiplier developed
for related engines to PM10 emissions estimate
¦ PM2.5-PRI = 0.92 * PM10-PRI
Using PM as an example, the emission
factors are LTO-based and represent a fleet
average emission factor for the general
aviation, air taxi, and military aircraft
categories. This slide lists these PM
emission factors.
The PM2.5 primary emissions are estimated,
as they are for many combustion sources, by
applying a particle-sized multiplier of 0.92 to
the PM10.
4-18
AIRCRAFT- NEI Method
¦ National Emissions Allocation for Each
Aircraft Category
Airport Emissionscpx = National Emissionscp *
where: AF = allocation factor; and
x = airport (e.g. La Guardia)
c = aircraft category; and
p = criteria pollutant.
AFCX = LTOsc/National LTOsc
Once national emissions are calculated for
the four aircraft categories, the NEI allocates
them to the county level based on airport
level LTO data as shown by this equation.
Using La Guardia airport as an example, the
NEI assumes that a fraction of the total LTO
is assigned to La Guardia, and the emissions
calculated from this allocation are assigned
to the corresponding county.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
4-19
AIRCRAFT- NEI Method (cont.)
¦ Documentation on the procedures used to
develop criteria pollutant (as well as HAP)
aircraft emission estimates is available at:
ftp://ftp.epa.gov/Emislnventory/finalnei99ver3/
criteria/documentation/nonroad/
99nonroad_voli_oct2003.pdf
More information on the NEI methodology for
estimating emissions from aircraft categories
can be found at the web address listed here.
4-20
AIRCRAFT - General Approach
¦ Determine the mixing height to be used to
define the LTO cycle
¦ Define the fleet make-up for each airport
¦ Determine airport activity in terms of the
number of LTOs by aircraft/engine type
¦ Select emission factors for each engine model
associated with the aircraft fleet
Although it may be acceptable to rely upon
the NEI data for smaller airports in an area, a
bottom up inventory should be developed for
the larger airports.
There are seven steps for developing an
aircraft inventory for a specific airport:
Step 1 - Determine the mixing height to
be used to define the LTO cycle. The
mixing height is important because above
the mixing height, emissions are not
expected to contribute much to ground
level pollutant concentrations.
Step 2 - Define the fleet make-up for the
airport.
Step 3 - Determine airport activity in
terms of the number of LTO by aircraft
and their associated engine-type.
Step 4 - Select emission factors for each
engine model that is associated with the
aircraft fleet at the airport being
inventoried (Instead of using defaults that
EDMS may apply for a specific aircraft
type).
Preparation of Fine Particulate Emissions Inventories
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4-21
AIRCRAFT - General Approach (cont.)
¦ Estimate the time-in-mode (TIM) for the
aircraft fleet at each airport
¦ Calculate emissions based on aircraft LTOs,
emission factors for each aircraft engine
model, and estimated aircraft TIM
¦ Aggregate the emissions across aircraft
Step 5 - Estimate the time-in-mode for
the aircraft fleet at the airport.
Step 6 - Calculate the emissions (based
on the aircraft LTO data, the emission
rates for each aircraft engine model, and
the time-in-mode data).
Step 7 - Aggregate the emissions across
aircraft to obtain a total for the airport.
4-22
COMMERCIAL AIRCRAFT
Improvements to NEI
¦ Determine engine types associated with local
aircraft types, to replace default
aircraft/engine assignments in EDMS
¦ Obtain information on climb-out, takeoff,
approach times, as well as taxi/idle times
Developing an emissions inventory for a local
airport involves determining the engine types
associated with the local aircraft types.
This data is an improvement over the
assumptions used in the NEI for the
commercial aircraft category.
In addition, developing information on climb-
out, take-off, approach time, and taxi idle
times will be an improvement over the
defaults used in the NEI.
4-23
COMMERCIAL AIRCRAFT
Improvements to NEI (cont.)
¦ For PM10 and PM25, match few emission
factors from EPA's 1992 Volume IV, Mobile
Sources Procedures document, to the
aircraft engines in their fleet as best as
possible
¦ EPA OTAQ working with FAA to develop
updated aircraft PM emission factors
¦ Regional inventories have used PM-10/NOX
emission factor ratios for air taxi applied to
commercial aircraft NOx emissions
Because the current version of EDMS does
not include PM emission rates, EPA
recommends that the few PM emission
factors available in the 1992 Mobile Sources
Procedures document be matched to the
aircraft engines in the local fleet as best as
possible.
EPA is aware of this limitation and work is
underway to try to get better data on PM
emission factors for commercial aircraft.
Some regional inventories have looked at
using emission factor ratios to develop the
PM emission rates for commercial aircraft.
Specifically, the ratio based on the PMi0and
NOx emission factor ratios for air toxics was
applied to the commercial aircraft NOx
emissions.
Preparation of Fine Particulate Emissions Inventories 4-9
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4-24
GA, AT and Military Aircraft
Improvements to NEI
¦ Obtain local estimates of LTOs for these
categories (to obtain LTOs not covered by
FAA data)
¦ Obtain information on the aircraft/engine types
that comprise the aircraft fleet for these
categories. Apply EPA engine-specific
emission factors or EDMS, if available
For the other categories (general aviation, air
taxis, and military aircraft) the NEI can be
improved by obtaining local LTO estimates,
such as:
data from smaller airports that may not be
reporting to the FAA
military bases (although heightened
security measures have made it harder to
obtain data from military operations)
Another improvement is to obtain information
on the aircraft/engine types that comprise the
fleet for these other categories. If data on
the mix of aircraft types in a fleet are
available, engine specific emission factors or
EDMS could be used to estimate emissions.
Finally, the NEI can be improved by
maintaining the latitude/longitude of the
airport so the emissions are not "smeared"
across the entire county.
4-25
COMMERCIAL MARINE VESSELS
Overview
¦ Commercial Marine Vessel SCCs
¦ 2280002100-Diesel, In Port
¦ 2280002200 - Diesel, Underway
¦ 2280003100-Residual, In Port
¦ 2280003200 - Residual, Underway
The SCCs representing the commercial
marine vessel categories that are currently
used in the NEI are listed on this slide.
This includes diesel activity for ships in port
and underway, as well as residual or
steamships for those two categories.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
4-26
COMMERCIAL MARINE VESSELS
NEI Method
¦ National Diesel and Residual Emissions split
into port and underway components
¦ Port and underway activity allocated
separately, assigned to counties
¦ Port emissions assigned to a single county in
port area
The NEI methodology for commercial marine
vessels is a top down method that splits
national diesel and residual emissions into
port and underway components.
The methodology makes assumptions about
what portion of the activity for both diesel and
residual ships takes place in ports and what
portion takes place underway (i.e., away
from ports or on their way between ports).
These are allocated separately since port
activity surrounds a port area, while
underway covers a larger area such as along
a river system. Both port and underway
emissions are assigned to counties,
however, port emissions are assigned to a
single county in a port area.
4-27
COMMERCIAL MARINE VESSELS
NEI Method (cont.)
¦ Documentation on the procedures used to
develop criteria pollutant (as well as HAP)
commercial marine emission estimates is
available at:
ftp://ftp.epa.gov/Emislnventory/finalnei99ver3/
criteria/documentation/nonroad/99nonroadvoli
_oct2003.pdf
More information on the NEI methodology for
estimating emissions from the commercial
marine vessel categories can be found at the
web address listed here.
Preparation of Fine Particulate Emissions Inventories
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4-28
COMMERCIAL MARINE VESSELS
Improvements to NEI
¦ Review 1999 NEI emission estimates for
representativeness
¦ Allocate port emissions to ports other than
150 largest
¦ Allocate port emissions to appropriate
counties, since port emissions assigned to a
single county in port area
One approach to improving NEI emission
estimates for the commercial marine vessel
category is to review the spatial allocation of
commercial marine emissions included in the
NEI.
Note that:
NEI examines port traffic for the 150
largest ports in the United States and only
allocates those emissions.
Additional ports are not accounted for in
the allocation method. Identifying smaller
ports that are not accounted for in the NEI
would be an improvement.
Another approach to improving the NEI
method is to allocate port emissions to the
appropriate counties.
Port emissions in the NEI are being assigned
to a single county in the port area.
Some ports along the Mississippi and the
Ohio Rivers that span multiple counties and
even state boundaries. Assigning these port
emissions to the appropriate counties and
states is another way to improve the NEI
results.
Preparation of Fine Particulate Emissions Inventories
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4-29
COMMERCIAL MARINE VESSELS
Improvements to NEI (cont)
¦ Obtain activity estimates at the local or State-
level from Department of Transportation, Port
Authority
¦ Fuel consumption
¦ Categories of vessels
¦ Number and size (hp) of vessels in each category
¦ Number of hours at each time-in-mode
¦ Cruising
¦ Reduced speed zone
¦ Maneuvering
¦ Hotelling
Another approach to improving the NEI
results is to conduct a bottom-up inventory
by obtaining activity estimates at the state or
local level from the DOT or Port Authority.
This can include:
data on fuel consumption
data to define categories and
characteristics of the vessels (number,
size and horsepower in each category)
Similar to aircraft, there are different
emission rates depending on the operating
mode of the vessels, so data on the fraction
of the time engines are spent in those modes
would also be an improvement.
4-30
COMMERCIAL MARINE VESSELS
Emission Calculation
Emissions = Pop * HP * LF * ACT * EF
where:
Pop
= Vessel Population or Ship Calls
HP
= Average Power (hp)
LF
= Load Factor (fraction of available power)
ACT
= Activity (hrs)
EF
= Emission Factor (g/hp-hr)
Preparation of Fine Particulate Emissions Inverrtones
This slide lists the equation for calculating
emissions from commercial marine vessels.
It requires data on vessel populations,
horsepower, load factor, and the time-in-
mode operation.
Applying this emission equation with this
data will produce a better inventory.
Preparation of Fine Particulate Emissions Inventories
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4-31
COMMERCIAL MARINE VESSELS
Activity
¦ 1999 EPA studies:
¦ Commercial Marine Activity for Deep Sea Ports in
the United States
¦ Commercial Marine Activity for Great Lake and
Inland River Ports in the United States
¦ Studies provide activity profiles for select
ports, and present method for an inventory
preparer to allocate detailed time-in-mode
activity data from a typical port to another
similar port
In 1999 EPA completed the two studies
shown here.
The content of these studies:
provides commercial marine activity
profiles for select ports, and
presents a method for allocating detailed
time-in-mode activity data from a typical
port to another similar port.
4-32
COMMERCIAL MARINE VESSELS
Activity (cont.)
¦ Activity profiles for typical port include:
¦ Number of vessels in each category
¦ Vessel Characterization, including propulsion size
(horsepower), capacity tonnage, and engine age
¦ Number of hours at each time-in-mode associated
with cruising, reduced speed zone, maneuvering,
and hotelling
The specific variables that are collected for
the typical ports in these studies include:
the number of vessels in each category,
the vessel characterization, including
propulsion size, capacity tonnage, and
engine age, and
the number of hours at each time-in-
mode associated with cruising, reduced
speed zone, maneuvering, and hotelling.
4-33
COMMERCIAL MARINE VESSELS
Activity (cont)
¦ Data on the number of trips and the tons of
cargo handled by vessel type are provided for
the top 95 Deep Sea Ports and top 60 Great
Lake and Inland River Ports
¦ More detailed activity for these ports can then
be estimated based on the data calculated for
a typical port
These studies also contain data on the
number of trips and the tons of cargo
handled by vessel type for the top 95 deep-
sea ports and the top 60 Great Lake and
inland river ports.
Based on the data calculated for a typical
port, more detailed activity can be estimated
for these ports.
These reports also describe how the typical
port inventories were developed and how
they can be scaled for the port activity in a
specific area.
Preparation of Fine Particulate Emissions Inventories
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4-34
COMMERCIAL MARINE VESSELS
Emission Factors
¦ Depending on activity data obtained:
¦ Horsepower-based emission factors
¦ Fuel-based emission factors
¦ EPA performing studies to develop updated
emission rates
¦ Category 3 Engine Final Rulemaking, January
2003
Horsepower-based emission factors are
available for use with activity data on the
number and size of engines.
There are also fuel-based emission factors
available for use with activity data on fuel
consumption.
EPA has been performing studies to develop
updated emission factors as part of their
rulemaking activities, such as the Category 3
engine final rulemaking that was published in
2003.
4-35
COMMERCIAL MARINE VESSELS
Emission Factors (cont.)
¦ PM10-PRI EFs for Category 1 and Category 2
Engines:
Engine Category
PM10 [g/kW-hr]
Category 1 37-75 kW
0 90
Category 1 75-225 kW
0 40
Category 1 225+ kW
0 30
Category 2 (5-30 l/cylinder)
0 32
Preparation of Fine Particulate Emissions Inverrtones
This table presents EPA recommended PM10
emission factors for specific categories of
commercial marine engines, on a gram per
kilowatt-hour basis for Category 1 and
Category 2 engines (i.e., small commercial
marine vessel engines).
4-36
COMMERCIAL MARINE VESSELS
Emission Factors (cont.)
¦ PM10-PRI EFs for Category 3 Engines (> 30
l/cylinder):
Mode: Engine
PM10 [g/kW-hr]
Cruise ana Reduced Speed Zone 2-stroke
1 73
Cruise and Reduced Speed Zone 4-stroke
1 76
Maneuvering 2-stroke
2 91
Maneuvering 4-stroke
2 98
Hotelling 2-stroke
0 32
Hotelling 4-stroke
0 32
All Modes Steam Generators
2 49
Preparation of Fine Particulate Em
ssions Inverrtones
EPA recommended PM10 emission factors
for the larger engines are listed in this table.
These factors are listed by the different
modes of operation.
Preparation of Fine Particulate Emissions Inventories
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4-37
COMMERCIAL MARINE VESSELS
Emission Factors (cont)
¦ Emission factors in grams per gallon fuel
consumed also available from Procedures for
Emission Inventory Preparation, Volume IV:
Mobile Sources, EPA-450/4-81-026d
(Revised), U.S. EPA, OAQPS, July 1989
¦ PM2.5-PRI = 0.92 * PM10-PRI emissions
Emission factors in grams per gallon of fuel
consumed are also available from the
document listed on this slide.
As with aircraft category, PM2.5 emissions
from commercial marine vessels are
estimated to be 92% of the PM10 emissions.
4-38
LOCOMOTIVES
Overview
SCCs:
¦ 2285002006 - Diesel Class I Line Haul
¦ 2285002007 - Diesel Class ll/lll Line Haul
¦ 2285002008 - Diesel Passenger (Amtrak)
¦ 2285002009 - Diesel Commuter
¦ 2285002010 - Diesel Switchyard Locomotives
The SCCs representing the locomotive
categories that are currently used in the NEI
are listed on this slide.
This includes larger Class I line haul
locomotives that travel through many states,
as well as the smaller Class II and III line
haul locomotives that tend to operate in a
smaller area.
The NEI also has information on passenger
Amtrak trains, commuter trains, and
switchyard operations.
4-39
LOCOMOTIVES
NEI Methods
¦ PM Emission Factors (represent Primary PM)
¦ Line-Haul
¦ PM10: 6.7 g/gallon
¦ PM2 5: 6.03 g/gallon
¦ Yard
¦ PM10: 9.2 g/gallon
¦ PM2 5: 8.28 g/gallon
The PM emission factors in that are used in
the NEI for the line haul and yard operations
are listed on this slide.
Preparation of Fine Particulate Emissions Inventories
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4-40
LOCOMOTIVES
NEI Methods (cont.)
¦ Activity Data (Gallons of distillate fuel oil
consumed)
¦ National Activity
¦ 1999 year U.S. distillate consumption by railroads
¦ Class I
¦ Class ll/lll
¦ Passenger
¦ Commuter
¦ Class I Line-Haul versus Yard (Switch)
Operation Activity
¦ Multiplied National Class I consumption by
estimated line-haul percentage of total fuel
consumption
The activity data are based on a national
estimate of the gallons of distillate fuel oil
consumed.
This national fuel consumption is allocated
among four of the five categories of railroads
to develop a national activity value for these
four categories.
Switchyard operation activity is estimated by
multiplying the national Class I fuel
consumption by the estimated line-haul
percentage of the total fuel consumption. It
is assumed that fuel consumption estimates
for Class I line-haul locomotives include
switchyard fuel consumption.
This assumption is based on the fact that the
larger line-haul railroads are the ones that
tend to operate in a switchyard.
4-41
LOCOMOTIVES
NEI Methods (cont.)
¦ County-level emissions allocation
¦ National emissions allocated to counties based on
ratio of county to national rail activity
¦ Rail activity measured as product of density (gross
ton miles per mile) on each rail line and mileage
for the associated rail line in county determined
through GIS analysis
The allocation of the activity data to the
county level is based on a ratio of county to
national rail activity.
This rail activity is measured as a product of
density (gross tons per mile) for each rail line
and mileage for the associated rail line in the
county.
Mileage for each rail line in the county is
measured using a GIS database that is
available from the Bureau of Transportation
Statistics.
Preparation of Fine Particulate Emissions Inventories
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4-42
LOCOMOTIVES
NEI Methods (cont.)
¦ Detailed documentation on the procedures
used to develop criteria pollutant locomotive
emission estimates for the 1999 NEI are
available at:
ftp://ftp.epa.gov/Emislnventory/finalnei99ver3/
criteria/documentation/nonroad/
99nonroad_voli_oct2003.pdf
Detailed documentation on the procedures
used to develop criteria and HAP pollutant
locomotive emission estimates for the 1999
NEI can be found at the web address listed
here.
4-43
LOCOMOTIVES
Improving the NEI
¦ Review NEI emission estimates for
representativeness
¦ Obtain more representative fuel consumption
estimates at the local or State-level
¦ Determine relative contribution of line-haul
versus yard activity at local or State-level
The first step in improving the NEI
locomotive emission estimates is to examine
the NEI data for reasonableness.
If the NEI data does not represent emissions
in a specific area, more representative fuel
consumption at the local or state level should
be obtained.
Also, because the NEI makes an assumption
to estimate switchyard emissions, an
improvement could be made by obtaining
information on the actual switchyard activity
in the study area.
4-44
LOCOMOTIVES
Case Study - Overview
¦ Case Study: County-level Locomotive
Inventory for Sedgwick County, KS
¦ See Case Study Number 4-1
This case study describes the development
of a county level locomotive inventory for
Sedgwick County, Kansas.
Direct student to Case Study Number 9-1
and discuss it with the students.
Preparation of Fine Particulate Emissions Inventories
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4-45
LOCOMOTIVES
Case Study - Solution
¦ Case Study: County-level Locomotive
Inventory for Sedgwick County, KS
Distribute the solutions (Handout 4-1) to the
case study. Review each question with the
students. Encourage discussion among the
class. Ask each group to report on the
questions that were assigned to them. Ask
the other groups to critique their responses.
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Chapter 5 - Point Source Inventory Development
5-1
Preparation of Fine Particulate
Emissions Inventories
Chapter 5 - Point Source Inventory
Development
After completing this lesson, participants will
be able to:
identify point sources for inclusion in an
emissions inventory, including the form of
the particulate matter and the particle size
describe methods used to estimate
emissions
explain overlap issues associated with
point and nonpoint source emission
inventories
5-2
How Do I Define a Point Source of PM
Fine or NH3 Emissions?
¦ Point sources are stationary sources included
in a point source inventory
Total plant (facility) emissions for a given
pollutant is usually the criterion for deciding
what sources to include in a point source
inventory
Criteria for including a stationary source in a
point source inventory are determined by:
¦ State, Local, or Tribal regulations or policy, and/or
¦ Consolidated Emissions Reporting Rule (CERR)
Point sources are stationary sources that are
included in a point source inventory.
Total plant or facility emissions for a given
pollutant is usually the criterion for deciding if
a specific source should be included in a
point source inventory or an nonpoint source
inventory.
The criteria are defined by either state, local,
or tribal regulations or policy. They may also
be defined by the reporting thresholds
contained in the Consolidated Emissions
Reporting Rule.
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5-3
Filterable vs. Condensable
Filterable PM are directly emitted
¦ Solid or liquid
¦ Captured on filter
¦ PM10 or PM2 5
Condensable PM is in vapor phase at stack
conditions
¦ Reacts upon cooling and dilution
¦ Forms solid or liquid particle
¦ Always PM25 or less
Filterable PM:
solid or liquid particles directly emitted at
stack or release conditions
captured on the filter of a stack test train
may be PM10 or PM2.5
Condensable PM:
material that is in the vapor phase at
stack conditions
condenses and/or reacts upon cooling
and dilution in the ambient air
forms a solid or a liquid particulate
immediately after discharge from the
stack
usually PM2.5 or less
5-4
Sources of Filterable versus
Condensible Emissions
¦ Combustion sources typically emit both
filterable and condensible PM emissions
¦ Boilers
¦ Furnaces/kilns
¦ Internal combustion engines (reciprocating &
turbines)
¦ Fugitive dust sources emit filterable emissions
only
¦ Storage piles
¦ Unpaved roads at industrial sites
Combustion sources typically emit both
filterable and condensable emissions.
Examples include boilers, furnaces and kilns,
and both reciprocating internal combustion
engines and turbines.
Fugitive dust sources emit filterable
emissions only.
Examples of fugitive dust sources include
storage piles and unpaved roads at industrial
sites.
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5-5
Primary vs. Secondary PM
Primary PM is directly emitted and the sum of
filterable and condensable
Secondary PM is formed through chemical
reactions and formed downwind of the source
¦ Precursors include S02, NOx, and VOC
¦ Should not be reported in the inventory
Primary PM:
the sum of the filterable and the
condensable PM
particles are emitted directly from a stack
Secondary PM:
particles that form through chemical
reactions in the ambient air after dilution
and condensation has occurred
formed downwind of the source
precursors include SO2, NOx, ammonia
and VOC
should not be reported in the emission
inventory (only precursor emissions are
reported)
5-6
Sources of NH3 Emissions
Industrial NH3 emissions can be placed into 3
broad categories related to the nature of the
emissions source:
¦ Emissions from industrial processes
¦ Use of NH3 as a reagent in NOx control
¦ Refrigeration losses
Sources of ammonia emissions fall into three
broad categories:
industrial processes,
use of ammonia as a reagent in NOx
control (e.g., selective catalytic reduction
or selective non-catalytic reduction), and
refrigeration losses.
5-7
Sources of NH3 Emissions (cont.)
¦ Examples of industrial processes that emit
NH3 include:
¦ Combustion sources
¦ Ammonium nitrate & ammonium phosphate
production
¦ Petroleum refining
¦ Pulp and paper production
¦ Beet Sugar Production
¦ These industrial processes represent the
more significant emitters of NH3 in 2000
Toxics Release Inventory (TRI)
The industrial processes shown here
contribute significant amounts of ammonia
emissions, as reported in the 2000 Toxics
Release Inventory.
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5-8
Resources for Identifying Point Sources
of PM Fine and NH3
¦ EIIP Point Source Guidance (Volume II)
¦ List documents applicable to PM fine categories
¦ AP-42
¦ Existing Inventories
¦ National Emissions Inventory
¦ Toxics Release Inventory (TRI) for NH3
Resources for identifying point sources of
fine PM and ammonia include:
Volume II of the Emissions Inventory
Improvement Program guidance
document for point sources,
AP-42 emission factors document, and
existing inventories such as the NEI and
the TRI (for ammonia).
5-9
What to Report to EPA
¦ PM2.5-PRI (or PM2.5- FIL & PM-CON
individually)
¦ Note that all PM-CON is assumed to be PM25
size fraction
¦ PM10-PRI (or PM10-FIL& PM-CON
individually)
States may report their PM2.5 primary
emissions to the EPA as either PM2.5 primary
or the PM2.5 filterable and PM condensable
components.
All PM condensable is assumed to be in the
PM2.5 size.
States may report their PM10 primary
emissions to the EPA as either PM10 primary,
or as PM10 filterable and PM condensable
components.
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5-10
Implications
¦ Use the NIF 3.0 PM pollutant code extensions
that identify the forms of PM (i.e., -PRI, -FIL,
or-CON)
¦ Verify the form ofthePM:
¦ Emission factors you use to calculate emissions;
and
¦ PM emissions facilities report to you.
¦ Update your database management system to
record these pollutant codes in NIF 3.0
The NIF 3.0 PM pollutant code extensions:
identify forms of the PM, should be used
for reporting.
include PRI for primary filterable, FIL for
filterable, and CON for condensable
The form of the PM should be verified to
ensure that PM emissions that are recorded
as PM10 or PM2.5 are correctly identified as
filterable, condensable, or primary emissions.
This may require an examination of the
emission factors.
If the emissions were reported by facilities,
the verification will require that States contact
the facilities to ask them what emission
factors were used to calculate the emissions.
Alternatively, if the emissions estimates
provided by the sources are based on stack
test data, the states must ask what method
was used to measure the emissions in order
to determine the form of PM.
The database management system should
be updated to record these pollutant code
extensions.
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5-11
How Do I Identify the PM Form?
Test Methods upon which emission factors or
emissions are based determine the form of
PM:
¦ PM-FIL:
¦ EPA Reference Method 5 series, Method 17, Method
201/201A
¦ PM10-FIL/PM2.5-FIL:
¦ Particles-size analysis of PM-FIL (e.g., AP-42 EFs)
¦ Preliminary Method 4 being developed by EPA to
measure both
¦ PM-CON:
¦ EPA Reference Method 202
Examining the test method used to collect
the data can identify the form of the PM.
EPA's Reference Method 5 series:
used to measure total PM filterable
emissions
basis for most AP-42 emission factors
(represent PM-filterable)
Method 17 is similar to the Method 5,
however it is infrequently used. Method
201/201A is designed for PM10 filterable.
To calculate or measure the PM10 filterable
or the PM2 5 filterable:
conduct a particle size analysis of the
total PM to develop the size fractions or
cut points for PM10 or PM2 5.
use this information to develop particle
size specific emission factors in AP-42
However, most of the emission factors in AP-
42 are for filterable emissions, although there
are some condensable emission factors for
combustion sources. Sum the filterable and
condensable emission factors to obtain a PM
primary emission factor.
There are some exemptions so it is important
to always understand the form of the PM that
the emission factor represents.
The EPA is developing Preliminary Method 4
to measure both PM25 filterable and
condensable.
Method 202 is a method for condensable
PM, but it is not used frequently, mainly
because regulations do not require sources
to measure condensable emissions.
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5-12
AP-42 Particle Size Data
¦ Provides particle size distribution data and
particle-size-specific emission factors
¦ Use AP-42 if source-specific data are not
available
¦ Use data in chapters for specific source categories first
¦ Use Appendix B-1 data next
¦ Use Appendix B-2 data last
AP-42 provides particle size distribution data
and particle size specific emission factors.
Some source categories (e.g., combustion)
in AP-42 have particle size specific emission
factors for PM. That data should be used
first.
Examine source specific data at the local or
state level prior to consulting AP-42, since
this is usually the best data.
If such data does not exist, consult the
resources listed.
5-13
AP-42 Particle Size Data (cont.)
AP-42 chapters not always clear on what source
test methods were used to develop particle size
data
¦ See background documents for AP-42 chapters for
details
AP-42 available on EPA/OQAPS CHIEF web site
¦ http://www.epa.gov/ttn/chief/
AP-42 chapters are not always clear on what
source test methods were used to develop
the particle size data.
You may need to consult the background
information documents that were used to
develop the chapters for AP-42.
AP-42 is available in EPA's Clearinghouse
for Inventories and Emission Factors
website.
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5-14
AP-42 Particle Size Data (cont.)
¦ Appendix B-1 (Particle Size Distribution Data
and Sized Emission Factors for Selected
Sources)
¦ Based on documented emission data available for
specific processes
¦ Appendix B-2 (Generalized Particle Size
Distributions)
¦ Based on data for similar processes generating
emissions from similar materials
¦ Generic distributions are approximations
¦ Use only in absence of source-specific
distributions
Appendix B1:
use for source categories that do not
have particle size specific emission
factors
contains particle size distribution data and
particle size emission factors for selected
sources
based on documented emissions data
available for specific processes
If Appendix B1 does not have particle size
data for the source category of interest, use
Appendix B2.
Appendix B2 contains generalized particle
size distributions that are based on data for
similar processes.
These distributions are approximations and
should only be used in the absence of source
specific particle size distribution data.
5-15
Factor Information Retrieval (FIRE)
Data System
¦ Latest version available was last updated
October 2000 (Version 6.23)
¦ Currently being updated to:
¦ Incorporate revisions to 10 AP-42 chapters
¦ Add more PM10-FIL, PM25-FIL, and PM-CON
emission factors
The Factor Information Retrieval System
(FIRE) is a compilation of emission factors
from AP-42 and other documents.
It is an electronic database that is available
on the CHIEF web site.
EPA is in the process of developing a more
complete set of PM10 and PM2.5 filterable and
PM condensable emission factors that will be
incorporated into FIRE.
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5-16
PM Calculator
EPA tool for calculating
uncontrolled/controlled filterable PM2 5 and
PM10 emissions using AP-42 particle size
distributions
For point sources only
Contains 2,359 SCCs with PM10 emissions
in 1996 NEI
The PM Calculator is a tool developed by
EPA to calculate uncontrolled and controlled
filterable PM2.sand PM10 emissions using
AP-42 particle size data.
For example, it can be used to calculate the
PM2.5 filterable emissions based on the PM10
filterable emissions contained in an
inventory.
You can also calculate PM10 and PM2 5 from
the total PM filterable emissions.
The calculator only deals with the filterable
emissions (i.e., it does not address the
condensable portion) and is for point sources
only. It contains over 2300 SCCs.
5-17
PM Calculator (cont.)
¦ Limitations
¦ AP-42 particle size data not available for many
sources; generic AP-42 profiles are used for
many source categories
¦ Available on EPA/OQAPS CHIEF web site
¦ http://www.epa.gov/ttn/chief/software/index.html
Although it contains over 2300 SCCs, the PM
calculators main limitation is that it is based
on AP-42 particle size data that is not
available for many sources.
As a result, many times it uses the generic
particle size data contained in Appendix B2
of AP-42 or other sources. It is also
available on the CHIEF web site.
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5-18
Point & Area Source Emissions
Inventory (El) Overlap Issues
¦ For categories included in Point and Area Els:
¦ Subtract total point activity from total state activity
to obtain total area activity
Total Area Activity = Total Activity - E Total Point
Activity
¦ Example for Fuel Combustion Sources:
¦ Point activity: fuel throughput from point source El
survey
¦ Total activity: fuel throughput from State/local gov.
agencies or U.S. DOE/EIA State Energy Data
reports
For categories included in the point and
nonpoint source emission inventories,
subtract total point activity from the total state
activity to obtain a total nonpoint source
activity.
Using the fuel combustion category as an
example, the point source activity is the fuel
throughput from the point source inventory.
Total activity is the statewide fuel throughput
obtained from the state or local government
agency, or from the state energy data reports
published by the Interior Energy
Administration in the U.S. Department of
Energy.
5-19
Point & Area Source El Overlap Issues
(cont.)
¦ Basis of Point Source Subtraction
¦ Activity-based calculation is preferred
¦ Emissions-based calculation is acceptable when
activity is not available:
¦ Total source category activity and point activity need to
be on same control level (usually uncontrolled)
¦ Back-calculation of uncontrolled emissions for controlled
processes may overstate uncontrolled emissions
Ideally, the point source subtraction is based
on activity data. For example, the point
source fuel throughput for a given year is
subtracted from the total statewide fuel
consumption for the same year.
However, in many cases, the activity data for
performing that calculation may not be
available. In this case, an emissions based
calculation is acceptable.
Under the emissions based approach:
the total source category activity and the
point activity should be on the same
control level
the control level should be an
uncontrolled emissions basis because
since total statewide activity represents
uncontrolled sources
In this case, it is important to ensure that the
point source emissions represent
uncontrolled levels. It is also important to
check the uncontrolled emissions to ensure
that they seem reasonable.
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5-20
Point <5 Area Source El Overlap Issues
(cont.)
¦ Geographic level of calculation may affect
results:
¦ Issue when using surrogate activity data (e.g.,
employment, housing, population) to allocate total
State activity to counties
¦ Subtracting county totals may produce negative
results due to inaccuracy of allocation method
¦ Subtracting State totals less likely to produce
negative results at county level
¦ Point source adjustments to surrogate allocation
data (e.g., employment) should be done if
available from point El survey
The geographic level of the point source
adjustment used to calculate the nonpoint
source activity is an issue when surrogate
activity data is used to allocate total state
activity to the county level.
The EIIP method:
uses employment for specific SIC codes
to allocate total statewide natural gas
combustion to the county level at
industrial and commercial institutions
requires you to sum point source
throughput for a county and subtracting it
from the total activity for the county may
produce negative results
indicates that point source consumption
fuel use is higher than that calculated for
the nonpoint sources
can be an artifact of the allocation data
used
The preferred approach:
sum up the point source fuel throughput
consumption on the state level
subtract this sum from the total
consumption for the state prior to doing
the county-level allocation
It is also preferable to obtain activity data
such as employment data for the point
sources included in the inventory.
In this way it is possible to make point source
adjustments to the surrogate allocations to
account for the amount of employment that is
associated with the point sources.
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5-21
Point & Area Source El Overlap Issues
(cont.)
¦ QA/QC Results
¦ Review county-level area source estimates for
reasonableness
¦ Make adjustments based on experience of your
agency's personnel:
¦ If allocation method places area source activity in a
county for which you know there is no activity, exclude
the county from your allocation
¦ If all of a county's activity is covered by the point El, set
the activity for the county to zero
The county level nonpoint source estimates
should be reviewed for reasonableness after
the adjustment has been made.
Adjustments should be based on the
experience of the agency personnel.
For example, if the allocation method places
nonpoint source activity in a county for which
it is known that there is no activity, that
county should be excluded from the
allocation.
Also, if all of a county's activity is covered by
the point source emission inventory, the
nonpoint source emissions should be zero.
5-22
Point <5 Area Source El Overlap Issues
(cont.)
¦ Reporting of small point sources in area
CERR submittal:
¦ If your point El includes sources with emissions
below the CERR point El reporting thresholds, you
may include the emissions for these small sources
in the area El
¦ To avoid double counting in the area El, subtract
total point source activity or emissions from total
State-level activity or emissions before rolling up
emissions for small point sources to be included in
your area El
If the point emission inventory includes
sources with emissions below the CERR
point emission inventory reporting
thresholds, the emissions for these small
sources can be included in the nonpoint
source emissions.
To avoid double counting in the nonpoint
source inventory:
subtract total point source activity from
the total state activity, then
roll up the small point source data to add
to the inventory.
In this way the emissions for the small point
sources in the area are not double counted.
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5-23
Reading List
Stationary Source Control Techniques Document for Fine
Particulate Matter, EPA/OAQPS, Oct. 1998
(http://www.epa.gov/ttn/oarpg/t1/meta/m32050.html)
Emission Inventory Guidance for Implementation of Ozone
and Particulate Matter National Ambient Air Quality
Standards (NAAQS) AND Regional Haze Regulations,
EPA/OAQPS
(http://www.epa.gov/ttn/chief/eidocs/publications.html)
Introduction to Stationary Point Source Emission Inventory
Development, EllP Vol. 2, Chapter I, May 2001
How to Incorporate Effects of Air Pollution Control Device
Efficiencies and Malfunctions into Emission Inventory
Estimates, EIIP Vol. 2, Chapter 12, July 2000
A suggested reading list for preparing point
source inventories for fine PM is presented
here.
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Chapter 6 - Nonpoint Sources
6-1
Preparation of Fine Particulate
Emissions Inventories
Chapter 6 - Nonpoint Sources
After completing this lesson, participants will
be able to:
describe the approach used to identify
nonpoint sources for inclusion in an
emissions inventory
list the methodologies for estimating
emissions from nonpoint sources
reconcile fugitive emissions data with
ambient data
6-2
How Do I Identify and Estimate Nonpoint
Sources of PM Fine or NH3 Emissions?
The nonpoint source inventory includes any
stationary source that is not included in the
point source inventory
A nonpoint source refers to any stationary
source that is not included in the point source
inventory. For emission inventory
development purposes, EPA has traditionally
used the term "area sources" to refer to
stationary air pollutant emission sources that
are not inventoried at the facility-level.
The Consolidated Emissions Reporting Rule
specifies reporting thresholds for point and
area sources of criteria air pollutants. These
will vary depending on the pollutant and the
attainment status of the county in which the
source is located.
The Clean Air Act defines area sources of
Hazardous Air Pollutants for the purpose of
identifying regulatory applicability.
The CAA defines an area HAP source as
"any stationary source that emits or has the
potential to emit considering controls, in the
aggregate, less than 10 tons per year of any
HAP or 25 tons per year of any combination
of HAPs."
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Instructor's Manual
Sources that emit HAPs above these
thresholds are categorized as "major
sources."
To reduce confusion between these two sets
of area source definitions, EPA has adopted
the term "nonpoint" to refer to all criteria air
pollutant and HAP stationary emission
sources that are not incorporated into the
point source component of the NEI.
6-3
How Do I Identify and Estimate Nonpoint
Sources of PM Fine or NH3 Emissions? (cont.)
¦ El IP Area Source Guidance (Volume III)
¦ Lists PM fine categories for which El IP guidance is
available
¦ AP-42
¦ Existing inventories
¦ National Emission Inventory (NEI)
¦ Toxics Release Inventory (TRI)
Volume III of the EIIP Area Source Guidance
lists the PM fine categories for which the EIIP
guidance is available.
AP-42 and existing emission inventories also
can help identify nonpoint source categories
that are sources of fine PM and ammonia
emissions.
Existing inventories include:
National Emissions Inventory
Toxics Release Inventory
inventories developed through regional
planning organizations or state and local
agencies.
6-4
How Do I Identify and Estimate Nonpoint
Sources of PM Fine or NH3 Emissions? (cont)
¦ EIIP Area Source Guidance (Volume III) for
Sources of PM Emissions
¦ Chapter 2: Residential Wood Combustion,
Revised Final, Jan. 2001
¦ Chapter 16: Open Burning, Revised Final, Jan.
2001
¦ Chapter 18: Structure Fires, Revised Final, Jan.
2001
¦ Chapter 24: Conducting Surveys for Area Source
Categories, Dec. 2000
The chapters of Volume III of the EIIP Area
Source Guidance that are useful for
identifying nonpoint source categories of fine
PM and ammonia are listed here.
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6-5
How Do I Identify and Estimate Nonpoint
Sources of PM Fine or NH3 Emissions? (cont.)
¦ Area Source Category Method Abstracts for
Sources of PM Emissions
¦ Charbroiling, Dec. 2000
¦ Vehicle Fires, May 2000
¦ Residential and Commercial/Institutional Coal
Combustion, April 1999
¦ Fuel Oil and Kerosene Combustion, April 1999
¦ Natural Gas and Liquefied Petroleum Gas (LPG)
Combustion, July 1999
The El IP also has "area source category
method abstracts" for charbroiling, vehicle
fires, residential and commercial/institutional
coal combustion, fuel oil and kerosene
combustion, and natural gas and liquefied
petroleum gas combustion.
6-6
PM 1-Pagers: Nonpoint Sources
¦ PM 1-Pagers: Overview
¦ Location: PM Resource Center
¦ Web site:
http://www.epa.gov/ttn/chief/eiip/pm25inventory/areasour
ce.html
¦ Purpose:
¦ Summarize nonpoint source NEI methods for specific
categories of PM10, PM2 5, and NH3
The PM2.5 Resource Center, which is
available on the CHIEF website, contains
"PM one-pagers."
These documents contain an overview of the
NEI methods and summarize nonpoint
source NEI methods for specific categories
of PM10, PM2.5, and ammonia.
6-7
PM 1-Pagers: Nonpoint Sources (cont.)
¦ Contents:
¦ Source Category Name, SCC
¦ Pollutants of Most Concern
¦ Current NEI Methodology
¦ How can States, Locals, and Tribes improve
upon methodology?
¦ Uncertainties/Shortcomings of Current Methods
¦ Activity Variables Used to Calculate Emissions:
¦ Current Variables/Assumptions Used
¦ Suggestions for Improved Variables
¦ Where can I find Additional Information and
Guidance?
¦ References
The PM one-pagers provides the source
category name and SCC, the pollutants of
most concern, current NEI method, and how
state, locals, and tribal agencies can improve
on the NEI method, uncertainties and
shortcomings.
They also contain activity variables used to
calculate the emissions, current variables
and assumptions used in the methods,
suggestions for improving the variables, and
where to find additional information and
guidance for the categories.
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6-8
PM 1-Pagers: Nonpoint Sources (cont.)
¦ Open Burning
¦ Residential Yard Waste (Leaves) and Household
Waste
¦ Residential, Nonresidential, and Road
Construction Land Clearing Waste
¦ Structure Fires
¦ Wildfires & Prescribed Burning
¦ Managed Burning - Slash
The open burning categories covered by the
one-pagers include residential yard waste for
leaves, household waste, residential,
nonresidential, and road construction land
clearing waste, structure fires, wildfires and
prescribed burning, and managed or slash
burning.
6-9
PM 1-Pagers: Nonpoint Sources (cont.)
¦ Fugitive Dust
¦ Paved and Unpaved Roads
¦ Residential Construction
¦ Mining and Quarrying
¦ Residential Combustion - Fireplaces and
Woodstoves
Fugitive dust categories covered by the one-
pagers include paved and unpaved roads,
residential construction, and mining and
quarrying.
There are also one-pagers covering
residential combustion (i.e., fireplaces,
woodstoves, and other residential home
heaters that burn natural gas or fuel oil).
6-10
Typical Source Categories of Filterable
PM Emissions
¦ Fugitive Dust Sources (Crustal PM Fine)
¦ Construction
¦ Mining and quarrying
¦ Paved/unpaved roads
¦ Agricultural tilling
¦ Beef cattle feedlots
This list represents typical area source
categories of fugitive dust sources of
filterable PM emissions.
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6-11
Typical Categories of Filterable and
Condensible PM Emissions
¦ Open Burning Sources (Carbonaceous PM
Fine)
¦ Open burning
¦ Residential municipal solid waste burning
¦ Yard waste burning
¦ Land clearing debris burning
¦ Structure fires
¦ Prescribed fires
¦ Wildfires
¦ Agricultural field burning
This list contains typical area source
categories of open burning sources of
filterable PM emissions.
6-12
Typical Categories of Filterable and
Condensable PM Emissions (cont.)
¦ External/Internal Fuel Combustion
(Carbonaceous PM Fine):
¦ Residential wood combustion
¦ Other residential fuel combustion
¦ Industrial fuel combustion
¦ Commercial/institutional fuel combustion
This list shows typical area source categories
of filterable and condensable PM emissions.
6-13
Typical Source Categories of
NH3 Emissions
¦ Typical source categories of NH3 emissions
include:
¦ Animal husbandry
¦ Agricultural fertilizer application
¦ Agricultural fertilizer manufacturing
¦ Wastewater treatment
This list presents typical area source
categories of ammonia sources.
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6-14
How Do I Estimate Emissions?
Emissions data prepared and reported by
Source Classification Code (SCC)
¦ 10-digit SCC defines an nonpoint emission source
¦ EPA SCCs located at:
http://www.epa.g0v/ttn/chief/c0des/index.html#scc
Report actual emissions; not allowable or
potential emissions
Nonpoint source inventories are prepared
and reported by the 10-digit SCC source
classification code.
EPA's master list of SCCs are available on
the CHIEF website. This is a dynamic list
that can be updated (with EPA's approval) to
add SCCs.
For example, SCCs should be added if there
are several subcategories within a general
nonpoint source category and a state or local
agency is estimating emissions at that level.
Also, actual emissions, not allowable or
potential emissions are reported for the NEI.
6-15
How Do I Estimate Emissions? (cont.)
¦ Calculate emissions using:
¦ Activity data
¦ Emission factors
¦ Control efficiency data
¦ Rule effectiveness/rule penetration
¦ Follow El IP methods when available
¦ Provides preferred and alternative methods for
collecting activity data and use of emission factors
¦ Improve on existing inventory methods
To calculate emissions from nonpoint
sources, multiply the activity data by the
emission factor, control efficiency data, rule
effectiveness, and rule penetration.
EPA guidance specifically excludes applying
default RE/RP assumption values for PM
inventories.
You should follow EI IP methods, since these
were developed with state and local input
and they reflect the most current
standardized procedures for preparing
emission inventories.
The El IP provides preferred and alternative
methods for collecting activity data and the
use of emission factors, and contains
suggested improvements on existing
inventory methods.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
6-16
How Do I Estimate Emissions? (cont.)
¦ Emission estimation equation:
CAEa= (EFa)(Q) [(1- (CE)(RP)(RE)]
CAEa = Controlled nonpoint source emissions of
pollutant A
EFa = Uncontrolled emission factor for pollutant A
Q = Category activity
CE =% Control efficiency/100
RE =% Rule effectiveness/100
RP =% Rule penetration/100
This equation is used to estimate emissions.
6-17
How Do I Estimate Emissions? (cont.)
¦ Obtain activity data from:
¦ Published sources of data
¦ National, regional, or state-level activity data often
require allocation to counties using county-level
surrogate indicator data
¦ Survey performed to obtain local estimate of
activity
Activity data is obtained from various
published sources of data or surveys.
However, the use of use national, regional
and state level activity data requires
allocation to the counties using county-level
surrogate indicator data.
Consequently, surveying is the preferred
approach to obtain the local activity
estimates(i.e., a bottom-up approach, rather
than a top-down approach).
6-18
How Do I Estimate Emissions? (cont.)
Sources of PM and NH3 emission factors
¦ Factor Information Retrieval (FIRE) System
http://www.epa.gov/ttn/chief/software/fire/index.html
¦ AP-42
http://www.epa.gov/ttn/chief/ap42/index.html
¦ Emission factor ratios
¦ PM2 5 emissions calculated from PM10 emissions using
ratio of PM2 5-to-PM10 emission factors
¦ State or local emission factors are preferred
You can obtain emission factors for PM and
ammonia from FIRE and AP-42.
As an alternative, you can use the emission
factor ratio or particle size multiplier. This
involves calculating the PM2.5 emissions from
the PM10 emissions using the ratio of PM25 to
PM10 emission factors in AP-42.
However, the use of state, local, and tribal
emission factors are preferred over any other
approach because they are always specific.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
6-19
How Do I Estimate Emissions? (cont.)
Control efficiency (CE)
¦ Percentage value representing the amount of a
source category's emissions that are controlled by
a control device, process change, reformulation, or
management practice
¦ Typically represented as the weighted average
control for an nonpoint source category
Control efficiency is the percentage value
representing the amount of a source
category's emissions that is controlled by a
control device, process change,
reformulation, or a management practice.
Typically, the value is represented as the
weighted average control for a nonpoint
source category.
6-20
How Do I Estimate Emissions? (cont.)
¦ Rule effectiveness (RE)
¦ Adjustment to CE to account for failures and
uncertainties that affect the actual performance of
the control
¦ Rule penetration (RP)
¦ Percentage of the nonpoint source category that is
covered by the applicable regulation or is
expected to be complying with the regulation
Rule effectiveness is an adjustment to the
control efficiency to account for failures and
uncertainties that affect the actual
performance of the control method.
Rule penetration represents either the
percentage of the nonpoint source category
that is covered by the applicable regulation,
or that which is expected to be in compliance
with the regulation.
Preparation of Fine Particulate Emissions Inventories
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6-21
Spatial and Temporal Allocation
¦ Available national, regional, or state-level
activity data often require allocation to
counties or subcounties using surrogate
indicators
¦ S/L/T agencies should review estimates
developed in this manner (e.g., NEI) for
representativeness
¦ Available temporal profiles to estimate
seasonal, monthly, or daily emissions for
specific categories may be limited
¦ States are encouraged to reflect local patterns
of activity in their emission inventories
The available national, regional, or state-
level activity data often require allocation to
counties or subcounties using surrogate
indicators.
State, local, and tribal agencies should
review emission estimates developed in this
manner for representativeness.
The available temporal profiles to estimate
seasonal, monthly, or daily emissions for
specific categories may be limited, so states
are encouraged to reflect local patterns of
activity in their emission inventories.
For example, residential home heating
emissions from fuel oil combustion can be
allocated to the county level by using the
number of households in each county in the
state.
6-22
El Development Approaches
¦ Approaches Available to State, Local, and
Tribal (S/L/T) Agencies:
¦S/L/T Agency develops its own inventory following
El IP procedures
¦Compare S/L/T activity data and assumptions to
NEI Defaults - Use S/L/T data to replace NEI
defaults if data will improve estimates
¦Use NEI default estimates
The approaches that are available to state,
local, and tribal agencies for developing an
emissions inventory include:
develop an emissions inventory following
the El IP procedures
compare the state, local, tribal activity
data and assumptions to the NEI defaults
and replacing the defaults, as necessary
use the NEI default estimates
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
6-23
Triage Approach to Improving the El
¦ Consider each NEI Category - Is it important ?
¦ What's its potential impact on AQ, considering
emissions, receptor modeling & other available info
¦ May give some weight to emission reductions
potential
¦ If yes, focus improvement efforts on the
important categories
¦ Review the available guidance (Course
materials, one pagers, El IP guidance)
¦ Decide what is feasible in the near and long
term
The triage approach to improving the
emissions inventory involves:
considering the importance of each NEI
category
examining the potential impact on air
quality
considering emissions, receptor
modeling, and other available information
Improvements should be made to those
categories that are determined to be
important using the suggestions and
references provided in this training course.
This includes reviewing the available
guidance and deciding on feasible
approaches.
6-24
Crustal Materials (Mainly Fugitive Dust)
¦ Main Sources:
¦ Unpaved roads
¦ Agricultural tilling
¦ Construction
¦ Windblown dust, Fly ash
The main sources of crustal materials are
unpaved roads, agricultural tilling,
construction, and wind-blown dust.
6-25
Crustal Materials (Mainly Fugitive Dust)
(cont.)
¦ Huge Disparity Between El & Ambient Data
¦ Ambient Data
¦ < 1 ug/m3 in most of US
¦ Exception: > 1 ug/m3 in much of Southwest, California
¦ Emissions: 2.5M TPY (comparable to Carbon
Emissions)
¦ Fugitive Dust has low "Transportable Fraction"
There is a huge disparity between the crustal
data in an emissions inventory and the
ambient air quality data.
The amount of crustal material on the
ambient filters is less than expected, given
the large estimates of fugitive dust emissions
in the NEI. The reason for this apparent
anomaly is that fugitive dust has a low
transportable fraction.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
6-26
Fugitive Dust Emissions in VISTAS
States
The data on this graph shows that PM2.5
inventories in the states included in the
VISTAS area have fugitive dust in the 20-
40% range.
The rest of PM in the inventory is from
sources that are primarily carbonaceous.
6-27
Urban (EPA STN) Annual Averages
Sep 2001-Aug 2002
Comparing the data in the previous slide with
the data presented in this figure shows that
the ratio of crustal PM2.5 emissions to total
carbonaceous matter emissions does not
match the ratio of crustal to total
carbonaceous PM2.5 based on the ambient
data.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
6-28
Role of Surface Cover (Vegetation &
Structures) in Fugitive Dust Removal
¦ Early work by AQ Modelers
¦ Stilling Zone - Lower 3/4 of canopy
¦ Windbreaks - wind erosion "staple"
¦ Traditionally to slow wind on leeward side
¦ Research by Raupach
¦ Entrapment effects
¦ Dust transmittance through a windbreak is close to the
optical transmittance
In the process of developing models, the
stilling zone under the canopy of vegetation
was recognized.
The air in this zone (the bottom three-fourths
of the height of the vegetation) is still. This
promotes gravitational settling and impaction
and filtration by the vegetation.
In the western states it is common to see
wind breaks. These are rows of trees or
other tall vegetation designed to slow the
wind speed on the leeward side.
The objective is to prevent the wind from
picking up the soil and causing erosion.
Another important feature of windbreaks is
the entrapment effect involving the
transmittance of dust through a wind break.
Research shows that the dust that goes
through a wind break is similar to the optical
transmittance of light through a wind break.
The remainder is trapped in the windbreak.
6-29
Role of Surface Cover (Vegetation &
Structures) in Fugitive Dust Removal (cont.)
¦ Capture Fraction (CF)
¦ Portion of Fugitive Dust Emissions (FD) removed by
nearby surface cover
¦ Transport Fraction (TF)
¦ Portion that is transported from the source area
Capture fraction is the portion of fugitive dust
emissions that are removed by nearby
surface cover.
Transport fraction is the portion that is
transported out of the source area.
Adding the two sums produces the fugitive
dust emissions inventory.
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Instructor's Manual
6-30
Capture Fraction ~ Conceptual Model
and Field Measurement Results
This graph plots a capture fraction value and
the type of vegetation qualitatively described
as going from densely forested to barren.
The test data plotted on this graph suggest a
relationship between the amount of
vegetation and the capture fraction.
Specifically, the data suggest that tall leafy
dense vegetation has a high capture fraction
and the short sparse scattered vegetation
has a low capture fraction.
6-31
Estimates of CF for Specific Surface
Conditions
Surface Cover Type
CF (Estimated)
Smooth, Barren or Water
0.03 - 0.1
Agricultural
0.1 - 0.2
Gra«.>
0.2 - 0.3
Scrub and Sparsely Wooded
0.3 - 0.5
Urban
0.6 - 0.7
Forested
0.9 -1.0
The conceptual model suggested by the data
in the previous graph has yet to be integrated
with air quality models, but it does allow for
the assignment of capture fractions to the
variety of vegetation shown here.
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Instructor's Manual
6-32
Example CPs for Counties in NV & GA
¦CF (County) = ? CF (Land Use Types) *
County Fractional Land Use
¦Types
¦tf = 1 - CF
.14 0
I
By using land use databases that contain
data on fractional land use in six different
areas (barren and water, agriculture, grass,
urban, scrub and sparse vegetation, and
forest) it is possible to compute the capture
fraction.
As shown in this table, the capture fraction
for a given area is the sum of capture fraction
by land use type times the county fractional
land use amount. The transport fraction is
equal to one minus the capture fraction.
For example, the transport fraction from the
source in Churchill County, Nevada is much
higher than the amount that gets away from
the source in Oglethorpe County, Georgia.
The main difference is the amount of trees in
those two areas. In general, the transport
fraction is fairly low in those areas of the
country that are very heavily forested, or in
cities with a lot of buildings.
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Instructor's Manual
6-33
Fugitive Dust Modeling Issues
¦ Gaussian Models
¦ Have many CF removal mechanisms built-in
¦ rarely utilized
¦ Application requires empirical coefficients ~
¦ limited data & guidance
¦ Grid Models
¦ Remix particles w/in lowest layer at each time step
(underestimates removal by gravitational settling)
¦ Ignore removal processes in initial grid
¦ Very significant omission (unless grid is VERY small)
There are modeling issues associated with
using this approach to account for different
transport characteristics of dust in different
parts of the country.
Gaussian models actually have removal
mechanisms built in to them to accommodate
capture fraction through the use of empirical
coefficients. Unfortunately, there is limited
data and guidance on how to apply these
coefficients, so they are rarely used.
Grid models on the other hand are not
equipped to handle particle transport. They
tend to remix particles within the lowest layer
during each time step, resulting in an
underestimation of gravitational settling
removal.
Within a time step of the model particles
have had a chance to settle down, but not
settle out. In the next time step they are
remixed into the whole lower mixing cell, so
they may never get out.
Also, in the initial grid removal processes,
even gravitational settling is ignored. This is
a very significant omission unless the grid is
very small. However, modeling very small
grids is not really practical.
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Instructor's Manual
6-34
Cautions on Use of the TF in Emissions
Inventory & Modeling Applications
¦ Do NOT use to reduce the emissions
inventory
¦ Do NOT use with Gaussian Models
¦ Instead, use features of model properly
¦ Use with Grid Models (with proper caveats)
¦ There ARE other issues with the inventory - the
TF concept should NOT be expected to fully
account for overestimation of crustal fraction of
ambient measurements
¦ TF concept is evolving
¦ Grid Model modifications could (over time)
eliminate need for TF concept
Transport fractions should not be used to
reduce the emission inventory, nor should
they be used with Gaussian models.
They can be used with grid models with the
proper caveats. Because there are other
issues with the inventory, there will not be
instantaneous agreement between the
fugitive dust emissions and the ambient data.
For example, there are issues with applying
the unpaved road factors properly. The
transport fraction concept is evolving and
over time grid model modifications could
eliminate the need for this approach.
6-35
Crustal Materials ~~ Conclusions
Crustal materials are a relatively small part of
PM2.5 in the ambient air
Fugitive dust is released near the ground and
surface features often capture the dust near
its source
The Capture / Transport Fraction concept
does provide a useful way to account for near
source removal when used with Grid Models
¦ This area of research offers many opportunities to
improve model performance
¦ There is much work to do to refine the concept
Crustal material is a relatively small part of
PM2.5 in the ambient air.
Fugitive dust is released near the ground and
surface features often capture the dust near
its source.
Finally, the capture/transport fraction concept
provides a useful way to account for near
source removal when used with grid models.
This area of research offers many
opportunities to improve model performance.
Preparation of Fine Particulate Emissions Inventories
6-16
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Instructor's Manual
Chapter 7 - Fugitive Dust Area Sources
7 -1
Preparation of Fine Particulate
Emissions Inventories
Chapter 7 - Fugitive Dust Area Sources
After this lesson, participants will be able to
identify fugitive dust emissions from the
following area sources:
agricultural tilling,
paved roads,
unpaved roads, and
residential, commercial, and road
construction activities.
7-2
AGRICULTURAL TILLING
Overview
¦ see
¦ 2801000003
¦ Pollutants
¦ Filterable PM10, PM25
The SCC that is contained in the National
Emissions Inventory for agricultural tilling
emissions is 2801000003.
For this category the NEI contains estimates
of filterable PM10 and PM2.5. There are no
condensibles associated with this category.
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7-3
AGRICULTURAL TILLING
NEI Method
¦ Activity Data (no. of acres of land tilled)
¦ 1998 County-Level Activity Data
¦ Acres of crops tilled in each county by crop type and by
tilling method obtained from CTIC
¦ Five tilling methods include:
¦ no till
¦ mulch till
¦ ridge till
¦ 0 to 15 percent residue
¦ 15 to 30 percent residue
The activity data for the NEI was obtained
from the Conservation Technology
Information Center, which publishes a
national crop residue management survey
every two years that contains county level
activity data.
The NEI used 1988 survey data. This
database provides acres of crops tilled in
each county by crop type and by tilling
method.
The five tilling methods included in the
database are listed here.
7-4
AGRICULTURAL TILLING
NEI Method (cont.)
¦ Emission Factor (mass of TSP per acre tilled)
¦ Emission factor comprises:
¦ Constant of 4.8 lbs/acre pass
¦ Silt content of the surface soil
¦ Number of tillings per year (conservation and
conventional use)
¦ Particle size multiplier for PM10 and PM2 5
The emission factor in the NEI is expressed
as the mass of the total suspended
particulate per acre tilled.
The emission factor comprises:
a constant of 4.8 pounds per acre pass of
PM
the silt content of the surface soil
the number of tillings per year (separated
into conservation and conventional use)
the particle size multiplier to calculate the
PM10 or the PM2.5 from the PM emissions
AGRICULTURAL TILLING
NEI Method (cont.)
Emission Factor (cont.)
¦ Silt content
Soil Type
Silt Loam
Sandy Loam
Sand
Loamy Sand
Clay
Clay Loam
Organic Material
Loam
Soil types assigned to counties by comparing
USDA surface soil and county maps
Silt Content (%)
The silt content values that are used for
various soil types in the NEI are listed here.
These soil types are assigned to counties by
using the USDA surface soil and county level
maps to match the soil types to counties.
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Instructor's Manual
7-6
AGRICULTURAL TILLING
NEI Method (cont.)
¦ Emission Factor (cont.)
¦ Number of Tillings
This chart shows the number of tillings that
are assumed by crop type for both
conservation and conventional use.
The no till, mulch till, and ridge till methods
come from the county level inventory from
the CTIC and are grouped into the
conservation use category.
The acres reported for the zero to 15 percent
residue and the 15 to 30 residue are grouped
into the conventional use category.
As the data demonstrate, the conventional
use category has more tilling passes per
acre than the conservation use.
7-7
AGRICULTURAL TILLING
NEI Method (cont.)
¦ Emission Calculation
E = c*k*s06*p*a
where: E
= PM emissions, lbs per year
c
= constant 4.8 Ibs/acre-pass
k
= dimensionless particle size multiplier (PM10= 0.21;
PM2 5 = 0.042)
s
= silt content of surface soil, defined as the mass
fraction of particles smaller than 75 |jm diameter
found in soil to a depth of 10 cm (%)
P
= number of passes or tillings in a year
a
= acres of land tilled
This equation is used in the NEI for
calculating total PM emissions from
agricultural tilling operations.
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Instructor's Manual
7-8
AGRICULTURAL TILLING
NEI Method (cont.)
¦ Emission equation used for years prior to 1999
¦ For 1999/2002, number of acres tilled for each
of the five tillage types was estimated based on
linear interpolation of national-level data
available for 1998 and 1999/2002
¦ Developed national growth factors by tillage
type for 1999/2002, using 1998 as basis
¦ Growth factors applied to county level
emissions for 1998 to estimate county level
emissions for 1999/2002
¦ Assumed no controls
The equation in the previous slide has been
used to estimate PM emissions from
agricultural operations in the NEI prior to
1999.
Since 1999 the number of acres tilled for
each of the five tillage types has been
estimated based on a linear interpolation of
national level data available for 1998, 1999
and 2002.
Using 1998 as the basis, national growth
factors were developed by tillage type for
1998, 1999 and 2002. These growth factors
were applied to county level emissions for
1998 to estimate county level emissions for
1999 and 2002.
Note that the NEI emission calculation
assumed no controls.
7-9
AGRICULTURAL TILLING
Improving the NEI
¦ Use crop-specific acreage and tilling
practice data from state/local agencies
¦ Use state/local emission factors
¦ Perform field study to determine local silt
content percentage of surface soil
¦ Crop Calendars: Develop using state/local
data to determine time and frequency of
activities (e.g., land prep., planting, and
tilling)
One way to improve upon the NEI method is
to use crop-specific acreage and tilling
practice data from the state or local agency
or tribal authority. In addition, if state or local
emission factors exist, they should be used.
Another improvement is to perform a field
study to determine the local silt content
percentage of the surface soil.
Silt values used in the NEI are based on
limited data and represent averages for the
entire country
Local or state conditions may exist that
warrant improving NEI values
Finally, the development of crop calendars to
determine the time and frequency of the
activities will be an improvement over the
NEI data.
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Instructor's Manual
7 -10
California Air Resources Board (CARB)
Study
¦ Reference
¦ Computing Agricultural PM10 Fugitive Dust Emissions
Using Process Specific Emission Rates and GIS,
Patrick Gaffney and Hong Yu, CARB
¦ Presented at 12th International Emission Inventory
Conference, San Diego, CA, April 29 May 1, 2003
¦ Paper and slides available in PDF files:
http://www.epa.gov/ttn/chief/conference/ei12/index.html
This discussion is based on the report
"Computing Agricultural PM10 Fugitive Dust
Emissions Using Process Specific Rates and
GIS" by Patrick Gaffney and Hong Yu. The
study was presented at the National
Emissions Inventory Conference in San
Diego during April 2003.
The paper and slides can be download from
the CHIEF web site.
7 -11
CARB Study (cont.)
¦ Statewide PM10 El for:
¦ Land preparation activities
¦ Harvest activities
¦ Goals:
¦ Obtain current, crop-specific acreage data
¦ Develop crop-specific temporal profiles (crop
calendars)
¦ Develop emission factors for all crops
The California Air Resources Board prepared
a statewide PM10 inventory for land
preparation activities and harvest activities at
the county level.
The goals were to:
obtain current crop-specific acreage data
develop crop-specific temporal profiles or
crop calendars, and
develop emission factors for all crops
7 -12
CARB Study (cont.)
Crop-specific Acreage Data
¦ County-level data from CA Dept. of Food and
Agriculture
¦ Data generated annually by crop and by county
¦ Includes over 200 crops and 30 million acres
In developing the inventory CARB obtained
county level crop-specific acreage data from
the California Department of Food and
Agriculture.
This department generates the crop data
every year by county, and it includes over
200 crops and 30 million acres.
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7-5
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Instructor's Manual
7 -13
CARB Study (cont)
¦ Crop Calendars
¦ Developed for 20 most important crop types
¦ Importance based on acreage and potential emissions
¦ Define temporal periods of farming operation
activities by crop type
CARB also developed crop calendars for the
20 most important crop types with
importance based on the acreage and the
potential emissions associated with each
crop type.
The crop calendars were used to define the
temporal periods of farming operation
activities for each of the crop types.
7 -14
Example Crop Calendar for Corn
- i ¦ i ¦ i i i 1 ! 1 1 11 i
7-14 Preparation of Fine Particulate Emissions Inventones
This is an example of a crop calendar for
corn. These types of calendars are very
informative in terms of identifying when
specific activities occur.
As an example, stubble disking for corn
occurred in November and December with
one pass across the field. In contrast, the
NEI assumes these emissions are annual
and does not apply any temporal
adjustments.
7 -15
CARB Study (cont)
¦ Emission Factors (EFs)
¦ Previous Els:
¦ Land Preparation: AP-42 Tilling factor (4.0 (lbs
PM10/acre-pass) applied to all operations
¦ Harvesting: Estimated for only 3 crop types for which
EFs were available
¦ Improvements:
¦ Conducted field testing to develop EFs for more
operations
¦ Crop & operation specific (for crop calendars)
Prior to preparing the statewide PM10
inventory for land preparation activities and
harvest activities, CARB used the AP-42
tilling emission factor of 4.0 lbs PMi0/acre-
pass for all land preparation activities.
For harvesting, CARB only estimated
emissions for three crop types for which
emission factors were available.
In order to improve this approach, CARB
conducted field testing over a seven-year
period to develop emission factors for
different activities that are crop specific and
operation specific. These new data allowed
CARB to develop the crop calendars.
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Instructor's Manual
7 -16
CARB Study (cont.)
Land Preparation Emission Factors
(lbs PM_in/acre-pass1
Root Cutting 0.3
Discing, Tilling, Chiseling 1.2
Ripping, Subsoiling 4.6
Land Planning & Floating 12.5
Weeding 0.8
EFs used as surrogates for other land prep, operations
These are the land preparation emission
factors that CARB developed for five different
types of activities.
These emission factors were used as
surrogates for other land preparation
activities, such as wheat cutting, where
specific factors were not available.
7 -17
CARB Study (cont.)
Harvest Emission Factors
(lbs PMin/acre-passI
Cotton Harvest 3.4
Almond Harvest 40.8
Wheat Harvest 5
Assigned to over 200 crop types and adjusted using a "division
factor" based on consultation with agricultural industry
The harvest emission factors that CARB
developed for three types of crops are shown
here.
These factors were assigned to over 200
crop types and adjusted using a division
factor developed in consultation with the
state agricultural industry.
For example, wheat harvesting was assigned
to another crop type, and then adjusted with
a division factor. The adjusted factors were
considered as the upper limit of emission
factors for other crop types.
7 -18
PAVED ROADS
Overview
SCC: 2294000000
¦ Pollutants
¦ PM10, PM25
The SCC that is contained in the National
Emissions Inventory for paved road
emissions is 2294000000. For this category
the NEI contains emission estimates for PM10
and PM2.5.
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Instructor's Manual
7 -19
PAVED ROADS
NEI Method
¦ Activity Data [vehicle miles traveled (VMT) on
paved roads]
¦ State-Level Activity Data
State/road type level VMT from paved roads =
Total State/road type-level VMT - State/road type-level unpaved
road VMT
Because of differences in methodology between the
calculation of total and unpaved VMT, there may be
cases where unpaved VMT is higher than total VMT
In these cases, unpaved VMT is reduced to total
VMT, and paved road VMT is assigned a value of
zero
Vehicle miles traveled on paved roads are
used as activity data for the NEI.
To estimate paved road VMT, subtract the
state and road type-level unpaved road VMT
from the total state road type-level VMT.
Because the Federal Highway Administration
uses different methodologies to calculate
unpaved road VMT and total road VMT,
there are times (principally in western states)
where the unpaved road VMT is higher than
the total VMT.
When this occurs, the unpaved VMT is
reduced to equal the total VMT, and the
paved roads are assumed to be zero.
7-20
PAVED ROADS
NEI Method (cont.)
¦ Activity Data [vehicle miles traveled (VMT) on
paved roads] (cont.)
¦ Paved road VMT temporally allocated by month
using NAPAP temporal allocation factors for total
VMT.
The NEI estimates monthly paved road VMT
by applying temporal allocation factors to the
annual paved road VMT estimate.
These factors were developed for the 1985
NAPAP study.
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Instructor's Manual
7-21
PAVED ROADS
NEI Method (cont.)
Emission Factor
¦ Empirical emission factor equation from AP-42
PAVED = PSDPVD * (PVSILT/2)065 * (WEIGHT/3)15 - C
/here: PAVED = paved road dust emission factor for all
vehicle classes combined (grams per
mile)
PSDPVD = constant for particles of less than 10
microns in diameter (7.3 g/mi for PM10)
PVSILT = road surface silt loading (g/m2)
WEIGHT = average weight of all vehicle types
combined (tons)
C = emission factor for 1980's vehicle fleet
exhaust, brake wear, and tire wear
The December 2003 version of the emission
factor equation in AP-42 only estimates PM
emissions from resuspended road surface
material.
PM emissions from vehicle exhaust, brake
wear, and tire wear are estimated using
EPA's MOBILE6 model and are subtracted
from the emission factor equation.
The formula shown here is used to calculate
the paved road emission factor for all vehicle
classes. The NEI used the pre-December
2003 version of the emission factor equation
for estimating paved road emissions.
7-22
PAVED ROADS
NEI Method (cont.)
Emission Factor (cont.)
¦ Paved road silt loadings assigned to each of the
twelve functional roadway classifications
¦ Road types with average daily traffic volume (ADTV) <
5,000 vehicles per day = 0.20 g/m2
¦ Freeways = 0.015 g/m2
¦ See AP-42, Section 13.2.1 for more information
¦ AP-42 emission factors for paved roads only
apply to reentrained dust
¦ Use MOBILE model for estimating PM from
tailpipe exhaust, brake wear, and tire wear.
The road surface silt loading varies
according to the 12 functional roadway
classifications contained in the NEI.
For example, for road types with an
average daily traffic volume of less
than 5,000 vehicles per day the silt
loading is 0.2 grams per square
meter.
For freeways, the silt loading is 0.015
grams per square meter.
See Ap-42, Chapter 13.2.1 for more
information on determining appropriate silt
loading factors.
Note that the AP-42 emission factors for
paved roads now only apply to reentrained
dust. EPA's MOBILE model should be used
to calculate PM emissions from tailpipe
exhaust, brake wear, and tire wear.
Preparation of Fine Particulate Emissions Inventories
7-9
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Instructor's Manual
7-23
PAVED ROADS
NEI Method (cont.)
¦ Emission Factor (cont.)
¦ Adjustments for precipitation
Emission factor multiplied by a rain correction
factor, calculated as follows:
(365-p* 12 *0.5)/365
where: p = the number of days in a given month with greater
than 0.01 inches of precipitation
¦ Precipitation data used in the paved road emission factor
calculations were taken from stations representative of
urban areas in each state
¦ Final emission factors developed by month at the State
and road type level for the average vehicle fleet
Since the amount of fugitive dust emissions
is related to the amount of rain, the NEI
makes an adjustment for precipitation.
To adjust for precipitation:
use the formula shown here to derive a
rain correction factor
multiply the emission factor by the rain
correction factor
The precipitation data for the NEI was taken
from one meteorological station
representative of an urban area for each
state.
By this method, the NEI developed emission
factors on a monthly basis at the state and
the road type level for the average vehicle
fleet.
7-24
PAVED ROADS
NEI Method (cont.)
¦ Emission Calculation
EM srm = VMTsrm * EFsrm
where: EM = PM10 emissions, tons per month
VMT = VMT, miles per month
EF = tons per mile
M = month
S = State
R = road type class
PM = PMin emissions x 0.25
The formula used in the NEI to calculate
PM10 emissions from paved roads from
resuspended road surface material is shown.
PM emissions from vehicle exhaust, brake
wear, and tire wear are estimated using
EPA's MOBILE6 model.
PM2.5 emissions are estimated by multiplying
the PM10 emissions by a particle size
multiplier of 0.25.
Preparation of Fine Particulate Emissions Inventories 7-10
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Instructor's Manual
7-25
PAVED ROADS
NEI Method (cont.)
Allocation of State Emissions to County Level
¦ Paved road emissions are allocated to the county level
according to the fraction of total State VMT in each county
for the specific road type
PVDEMISX Y = PVDEMISqTY * VMTX y/VMToT y
where: PVDEMISXY = paved road PM emissions (tons) for county x
and road type y
PVDEMISsty= paved road PM emissions (tons) for the entire
State for road type y
VMTxy = total VMT (million miles) in county x and road
typey
VMTsty = total VMT (million miles) in entire State for
road type y
The equation for allocating the monthly
paved road emissions at the state level to the
county level is shown.
7-26
PAVED ROADS
NEI Method (cont.)
¦ Controls
¦ Control efficiency of 79 percent applied to:
¦ Urban and rural roads in serious PM NAAs; and
¦ Urban roads in moderate PM NAAs
¦ Corresponds to vacuum sweeping on paved roads
twice per month
¦ Rule penetration varies by road type and NAA
classification (serious or moderate)
The NEI methodology assumes that controls
are only in place for urban and rural roads in
serious PM non-attainment areas and for
urban roads in moderate PM non-attainment
areas.
A control efficiency of 79% is applied in these
areas. This value corresponds to vacuum
sweeping on paved roads twice per month.
There is also an accounting of rule
penetration that varies by road type and the
non-attainment area classification.
7-27 PAVED ROADS
Improvements to NEI Method
¦ VMT on paved roads for local area
(Source: State Dept. of Transportation, Mobile Source
section of Environmental Dept)
¦ Local registration data representing the
average weight of vehicles (since tnis variable
is weighted most heavily)
(Source: State Dept. of Motor Vehicles, Mobile Source
Section of Environmental Dept)
One method to improve the NEI is to obtain
VMT data for both paved and unpaved
roads. This is preferable to the NEI
approach of subtracting the unpaved road
VMT from the total VMT.
Also, local registration data may be available
that represents the average weight of the
vehicles. This is preferable to the use of the
NEI default value, particularly since this
variable is weighted most heavily.
Preparation of Fine Particulate Emissions Inventories
7-11
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Instructor's Manual
7-28
PAVED ROADS
Improvements to NEI Method (cont)
¦ Perform sampling to refine value used for silt
content
¦ Only consider if you can collect enough samples
to give a good representation of roads in your
area
¦ Obtain and use local precipitation values
(Source: National Weather Bureau)
Also, you can perform sampling to refine the
value used for silt content. However, this
can be resource intensive and should only be
used if enough samples can be collected to
give a good representation of the roads in
the inventory area.
Finally, using local precipitation data is an
improvement over the NEI method.
7-29
UNPAVED ROADS
Overview
¦ SCC 2296000000
¦ PM10-PRI/FIL and PM2.5-PRI/FIL
¦ No condensible material, so:
PM-PRI = PM-FIL
The SCC in the National Emissions Inventory
for unpaved road emissions is 2296000000.
For this category the NEI contains emission
estimates for PM10 and PM25.
There is no condensable material so the PM
filterable is equivalent to PM primary.
7-30
UNPAVED ROADS
NEI Method
¦ Activity
¦ State level VMT from U.S. DOT, Federal Highway
Administration allocated to counties by population
¦ Activity Data (VMT on unpaved roads)
¦ State-level activity for urban and rural local
functional classes
The activity data used by the NEI for
unpaved roads is state level unpaved road
VMT data that is available from the Federal
Highway Administration. This data is
allocated to counties by population.
Because specific activity for the local classes
is available, this calculation is done
differently for urban and rural local functional
classes (i.e., county maintained road types)
than for the state and federally maintained
roads.
Preparation of Fine Particulate Emissions Inventories
7-12
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Instructor's Manual
7-31
UNPAVED ROADS
NEI Method (cont.)
Unpaved VMTpIyr = MileageRoMlypB * ADTV * DPY
Where:
Unpaved VMT
= road type specific unpaved VMT
(miles/year)
Mileage
= total number of miles of unpaved
roads by functional class (miles)
ADTV
= Average daily traffic volume
(vehicle/day)
DPY
= number of days per year
Preparation of Fine Particulate Emissions Inverrtones
The equation for calculating the vehicle mile
traveled by road type is shown.
7-32
UNPAVED ROADS
NEI Method (cont.)
¦ Non-local functional classes including:
¦ Rural minor collector, rural major collector, rural
minor arterial, rural other principal arterial, urban
collector, urban minor arterial, and urban other
principal arterial
¦ ADTV not available for non-local roads, estimated
from local urban and rural VMT and mileage
The non-local functional classes of roads
tracked by the Federal Highway
Administration include:
rural minor collector
rural major collector
rural minor arterial
rural other principal arterial
urban collector
urban minor arterial
urban other principal arterial
7-33
UNPAVED ROADS
NEI Method (cont.)
ADTV = VMT/Mileage
Where:
ADTV = average daily traffic volume for State
and federally maintained roadways
VMT = urban/rural VMT on county-maintained
roadways (miles/year)
MILEAGE = urban/rural state-level roadway mileage
of county-maintained roadways (miles)
Because there are no estimates of average
daily traffic volume for the non-local roads, it
is estimated from local urban and rural VMT
and mileage data for the local roads using
the equation shown.
Preparation of Fine Particulate Emissions Inventories
7-13
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Instructor's Manual
7-34
UNPAVED ROADS
NEI Method (cont.)
¦ Add Non-local functional class VMT to local
functional class VMT to determine State total
unpaved VMT by road type
¦ Unpaved road VMT temporally allocated by
month using NAPAP temporal allocation
factors for total VMT
Calculate total state unpaved VMT by road
type by adding the non-local functional class
VMT to local functional class VMT.
The total state unpaved VMT is temporally
allocated by month using NAPAP temporal
allocation factors.
7-35
UNPAVED ROADS
NEI Method (cont.)
¦ Emission Factor
¦ AP-42 emission factor equation
EF = [k*(s/12)*(S/3O)05]/[(M/O.5)°2]- C
Where:
EF = size specific emission factor (pounds per VMT)
k = empirical constant (1.8 Ib/VMTfor PM10-PRI, 0.27
for PM2.5-PRI)
s = surface material silt content (%)
M = surface material moisture content (%)
S = mean vehicle speed (mph)
C = emission factor for 1980's vehicle fleet exhaust,
brake wear, and tire wear
The unpaved road emission factor equation
only estimates PM emissions from
resuspended road surface material. This is
similar to the AP-42 emission factor equation
for paved roads.
PM emissions from vehicle exhaust, brake
wear, and tire wear are estimated separately,
using EPA's MOBILE6, and are subtracted
out of the emission factor equation.
Note that the vehicle exhaust, brake wear,
and tire wear component is relatively much
less for unpaved roads than for paved roads.
The AP-42 empirical equation that is used to
calculate the unpaved road emission factor is
shown. It has some of the same variables as
the paved road equation, but they are
weighted differently. For example, there is
more weight given to surface material silt
content.
Preparation of Fine Particulate Emissions Inventories
7-14
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Instructor's Manual
7-36
UNPAVED ROADS
NEI Method (cont.)
¦ NEI Default Emission Factor Input Values
¦ Surface material silt content(s)
¦ Average state-level values developed available at
ftp://ftp.epa.aov/Emislnventorv/finalnei99ver2/criteria/do
cumentation/xtra sources/
¦ Mean vehicle weight (W)
¦ National average value of 2.2 tons (based on typical
vehicle mix)
¦ Surface material moisture content (Mdry)
¦ 1 percent
This slide summarizes the NEI default
emission factor input values and the source
of the values.
The web address for the surface materials
silt content values links to a database for
unpaved roads that provides the supporting
documentation used. This includes a
database of state level silt content.
The calculation of unpaved road emissions in
the NEI used the pre-December 2003 AP-42
emission factor equation. This equation
considers mean vehicle weight.
7-37
UNPAVED ROADS
NEI Method (cont.)
¦ NEI Default Emission Factor Input Values
(cont.)
¦ Number of days exceeding 0.01 inches of
precipitation (p)
¦ Precipitation data from one meteorological station in
state used to represent all rural areas of the state
¦ Local climatological data available from National
Climatic Data Center at
http://www.ncdc.noaa.gov/oa/ncdc.html
Because unpaved road activity is expected to
occur in rural areas, the precipitation data is
obtained from one meteorological station that
represents rural areas.
7-38
UNPAVED ROADS
Improvements to NEI
¦ Summary
¦ Review NEI defaults for representativeness
¦ Use local data when possible for activity and
emission factor inputs
¦ If resources are limited, focus on collecting data
for:
¦ Local precipitation data
¦ Local VMT estimates
Short of developing independent estimates,
the NEI defaults should be reviewed for
representativeness.
Also, local data should be used when
possible for the activity and emission factor.
If resources are limited, the focus should be
on collecting data that represents local
precipitation as well as actual local VMT
estimates.
Preparation of Fine Particulate Emissions Inventories
7-15
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Instructor's Manual
7-39
UNPAVED ROADS
Case Study - Overview
¦ Case Study: County level emissions
inventory for unpaved roads
¦ See Case Study Number 7-1
This hypothetical case study involves
developing a local inventory using available
county level inventory data and filling the
data gaps with the NEI default data.
Direct student to Case Study Number 7-1
and discuss it with the students.
7-40
UNPAVED ROADS
Case Study - Solution
¦ Case Study: County level emissions
inventory for unpaved roads
¦ See Handout 7-1
Distribute the solutions (Handout 7-1) to the
case study. Review each question with the
students. Encourage discussion among the
class. Ask each group to report on the
questions that were assigned to them. Ask
the other groups to critique their responses.
7-41
CONSTRUCTION
Overview
¦ SCCs:
¦ Residential-2311010000
¦ Commercial - 2311020000
¦ Road-2311030000
¦ PM10-PRI/FIL and PM2.5-PRI/FIL
¦ No condensibles, so PM-PRI = PM-FIL
¦ 1999 PM2.5-PRI NEI
¦ Res -5%
¦ Comm - 40%
¦ Road - 55%
The SCCs contained in the National
Emissions Inventory for the construction
category are shown.
The NEI contains emission estimates for
PM10 and PM2.5 and there are no
condensibles, so PM-PRI is equal to PM-FIL.
The relative contribution to the 1999 NEI of
three different types of construction is also
listed on this slide.
Preparation of Fine Particulate Emissions Inventories
7-16
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Instructor's Manual
7-42
RESIDENTIAL CONSTRUCTION
NEI Method
¦ Activity Data: Number of acres disturbed per
year
¦ Estimated using housing start data
¦ Total no. of regional monthly housing unit starts
(HS)
¦ National monthly housing unit starts available for:
¦ 1-unit housing
¦ 2-unit housing
¦ 3-4 unit housing
¦ 5+ unit housing
The NEI uses the number of acres disturbed
per year as the activity data for residential
construction.
Since direct estimates are generally
unavailable, the value for this activity is
estimated through the use of housing start
data that is available from the Bureau of the
Census. These data are available as
regional monthly housing unit start values.
Data for housing unit starts for various
housing classifications are also available at a
national level. These classifications include
1-unit houses, 2-unit houses, 3-4 unit
houses, and 5+ unit housing.
7-43
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ Regional housing unit starts by housing category
estimated as follows:
Reg. HS by Category = Total Reg. HS x National HS bv Category
Total National HS
(Reference: Housing Starts Report, 1999, U.S. Department
of Commerce, Bureau of the Census, Manufacturing and
Construction Division, Residential Construction Branch.)
Housing classifications are important
because there are different numbers of acres
disturbed for each type of housing.
The regional housing unit starts for each of
these categories is estimated using the
fraction available at a national level, as
shown in the equation.
Preparation of Fine Particulate Emissions Inventories
7-17
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Instructor's Manual
7-44
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ Monthly regional housing starts by housing
category summed to obtain an annual total
¦ County Activity
¦ Annual no. of building permits in each county for:
¦ Housing structures with 1 -unit
¦ Housing structures with 2-units
¦ Housing structures with 3-4 housing units
¦ Housing structures with 5+ units
(Reference: Building Permits Survey, 1999, U.S. Department of
Commerce, Bureau of the Census, Manufacturing and Construction
Division, Residential Construction Branch.)
Regional housing starts are provided on a
monthly basis, so they are summed to obtain
an annual total.
The next step is to allocate these regional
housing starts data to the county level. This
is done by using data on the annual number
of building permits in each county for each
housing unit classification.
Note that the building permit data should not
be used to estimate housing starts but only
to allocate housing starts to the county. At
times, a building permit is issued but the
dwelling is never constructed. Consequently,
the housing start data is a more accurate
estimate of what is really being constructed.
7-45
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ Regional no. of residential structure starts
based on the reported no. of housing unit
starts:
¦ No. of 1-unit housing units = no. of 1-unit housing
structures
¦ No. of 2 unit housing units divided by 2 units per
structure
¦ No. of 3-4 unit housing units divided by 3.5 units
per structure
¦ No. of 5+ unit housing units divided by region-
specific units per structure as calculated from
building permits data
The regional housing start data actually
represents the number of units that were
started. However, the number of structures
is a better activity indicator of the number of
acres that are disturbed.
For example, the activity data for an
apartment building with multiple units should
reflect the structure as a whole (i.e., the
number of acres disturbed in the building of
the structure and not for each unit).
The information here shows the correlation
between residential structure starts and
housing unit starts.
Preparation of Fine Particulate Emissions Inventories
7-18
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Instructor's Manual
7-46
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ Estimate county no. of residential structure
starts by housing category as follows:
County Structure Starts = Regional Structure Starts x
County Blda Permits
Regional Bldg Permits
The equation shown is used to estimate the
number of county residential housing
structure starts based on the regional
number of structure starts.
7-47
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ Estimated acres disturbed from county no. of
structures:
¦ 1-unit structures:
1/4 acre per building
¦ 2-unit structures:
1/3 acre per building
¦ Apartments:
1/2 acre per building
¦ Estimated duration of construction:
¦ 1-unit structures:
6 months
¦ 2-unit structures:
6 months
¦ Apartments:
12 months
The number of acres disturbed and the
duration of the construction activity vary
depending on the size and type of the
structure.
The assumed values for both acres disturbed
and duration are listed here.
The basis for these assumptions can be
found in Estimating Particulate Emissions
from Construction Operation, 1999.
7-48
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ Estimate no. of apartment structures by
adding the no. of 3-4 unit buildings and of 5+
unit buildings
¦ Estimate no. of 1 -unit houses with and without
basements
¦ Multiply regional no. of 1-unit structures by
regional percentage of one-family houses with
basements and subtract product from total no. of
1-unit houses to estimate 1-unit houses w/out
basements
(Reference: Characteristics of New Houses ¦ Table 9. Type of
Foundation by Category of House and Location, 1998, U.S.
^^e£artment_ofCommercei_Bureau_ofthe_Censusi^^^^^^^^^^_
The number of apartment structures is
estimated by adding the number of 3-4 unit
buildings and the number of 5+ unit
buildings.
Also, the number of 1-unit houses should be
estimated separately for houses with a
basement and those without a basement.
This is because building a house with a
basement requires the removal of additional
dirt. This must be taken into account in the
emission factor equation.
The number of 1-unit houses without
basements is estimated by multiplying the
regional number of 1-unit structures by the
regional percentage of one-family houses
with basements and subtracting the product
from the total number of 1-unit houses.
Preparation of Fine Particulate Emissions Inventories
7-19
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Instructor's Manual
7-49
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ For 1 -Unit Housing with Basements
¦ Estimate cubic yards of dirt moved per house
¦ Multiply assumed 2,000 square feet per structure by
assumed average basement depth of 8 feet
¦ Add-in 10 percent of above cubic yard estimate to
account for footings and other backfilled areas adjacent
to basement
Estimate the amount of dirt moved for 1 -unit
houses with basements by multiplying the
assumed average basement depth of 8 feet
by the assumed value of 2,000 square feet of
dirt moved per structure.
Add 10 percent to this value to account for
footings and other back-filled areas adjacent
to the basement.
7-50
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ 1-Unit Housing with Basements
¦ PM10-PRI: 0.011 tons/acre/month plus 0.059
tons/1000 cubic yards of on-site cut/fill
¦ 1 -Unit Housing without Basements and all 2-
Unit Housing
¦ PM10-PRI: 0.032 tons/acre/month
¦ Apartments
¦ PM10-PRI: 0.11 tons/acre/month
¦ PM2.5-PRI = 0.2 * PM10-PRI
The emission factor data that the NEI uses to
estimate the emissions on an acre-per-month
basis is shown. Also, PM25 is assumed to
be 20% of PM10.
7-51
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ 1-Unit Structures with Basements
Emissions = (0.011 tons PM10/acre/month) x B xfx m) +
0.059 tons PM10/1000 yards3 of cut/fill)
where: 6 = no. of housing starts with
basements;
f = buildings-to-acres conversion
factor (1/4 acre per building);
m = duration of construction activity in
months.
The equation that NEI uses to estimate PM10
emissions from 1 -unit residential structures
with basements is shown.
Preparation of Fine Particulate Emissions Inventories
7-20
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Instructor's Manual
7-52
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ 1 -Unit Structures without Basements, All 2
Structures, and Apartments
Emissions = (0.032 tons PM10/acre/month) xBxfxm)
where: 6 = no. of housing starts without
basem ents;
f = buildings-to-acres conversion factor;
and
m = duration of construction activity in
months
Use this equation for one-unit structures
without basements, as well as all two-unit
structures.
The same equation is used for apartments
with the exception that the emission factor of
0.11 tons/acre/month is used instead of the
0.032 tons/acre/month value.
7-53
RESIDENTIAL CONSTRUCTION
NEI Method (cont.)
¦ Apply a control efficiency of 50 percent for
both PM10-PRI and PM25-PRI emissions for
PM-10 NAAs; all other areas 0 percent
¦ Control efficiency represents Best Available
Control Method (BACM) controls on fugitive
dust construction activities in these counties
Controls in PM10 non-attainment areas are
taken into account by applying a control
efficiency of 50% for both PM10 and PM25
emissions for all PM10 nonattainment areas.
There is no adjustment made for attainment
areas.
The 50% value represents best available
control methods on fugitive dust construction
activities in the nonattainment counties.
7-54
RESIDENTIAL CONSTRUCTION
NEI Correction Parameters
¦ Applied to final emissions for all 3 construction
categories
¦ Soil Moisture Level
Moisture Level Corrected Emissions = Base Emissions x (24/PE)
where: PE = Precipitation-Evaporation value for
county
¦ Compiled statewide average Precipitation-Evaporation (PE)
values according to Thornthwaite's PE Index
Additional adjustments for soil moisture
content and silt content are applied to the
emission estimates for all three construction
categories.
Emissions are adjusted for soil moisture
content by using average Precipitation
Evaporation values according to
Thornthwaite's Precipitation Evaporation
Index.
The formula used to make this adjustment is
shown. It accounts for precipitation and
humidity in a certain area. As shown in the
equation, the higher the PE the smaller the
adjustment.
Preparation of Fine Particulate Emissions Inventories
7-21
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Instructor's Manual
7-55
RESIDENTIAL CONSTRUCTION
NEI Correction Parameters
¦ Silt Content
Silt Content Corrected Emissions = Base Emissions x (s/9%)
where: s = % dry silt content in soil for area being
inventoried
¦ County-specific dry silt values are applied to
PM10-PRI emissions for each county
Emissions are adjusted for the dry silt
content in the soil of the area being
inventoried by using the formula shown here.
7-56
RESIDENTIAL CONSTRUCTION
Improvements to NEI
¦ Obtain local data for new construction
housing starts, permits for
additions/modifications to existing homes
Source: State Housing Agency or Real Estate Association
¦ Develop a building to acres conversion factor
for acres disturbed per construction unit
¦ Obtain information on seasonality of
residential construction practices
¦ Obtain local information on soil moisture
content, silt content, and control efficiency
Obtaining local data for new housing starts,
or permits for additions or modifications to
existing homes would be an improvement
over the use of the NEI defaults.
Another improvement is to develop a
buildings-to-acres conversion factor for acres
disturbed per construction unit. Obtaining
data on the seasonality of residential
construction practices is a third alternative.
Finally, obtaining local information on soil
moisture content, silt content, and control
efficiencies would improve the NEI default
values.
7-57
RESIDENTIAL CONSTRUCTION
Case Study - Overview
¦ Case Study: County level emissions
inventory for residential construction
¦ See Case Study Number 7-2
This case study demonstrates the approach
for developing an inventory for residential
construction at the county level in a PM
nonattainment area.
Direct student to Case Study Number 7-2
and discuss it with the students.
Preparation of Fine Particulate Emissions Inventories
7-22
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Instructor's Manual
7-58
RESIDENTIAL CONSTRUCTION
Case Study - Solution
¦ Case Study: County level emissions
inventory for residential construction
¦ See Handout 7-2
Distribute the solutions (Handout 7-2) to the
case study. Review each question with the
students. Encourage discussion among the
class. Ask each group to report on the
questions that were assigned to them. Ask
the other groups to critique their responses.
7-59
COMMERCIAL CONSTRUCTION
NEI Method
¦ Activity data: No. of acres disturbed per year
¦ National-Level Activity
¦ Dollar Value of Construction Put in Place, 1999
¦ National data allocated to Counties
(Reference: Table 1. Annual Value of Construction Put in Place in the
United States for Nonresidential buildings: 1996 - 2000, Millions of
constant dollars, U.S. Department of Commerce, Bureau of the
Census.)
Similar to the residential construction
category, the NEI uses the number of acres
disturbed each year to represent fugitive dust
emissions from commercial construction.
The NEI developed a top-down inventory by
using national level activity data on the dollar
value of commercial construction. These
data were then allocated to the county level.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
7-60
COMMERCIAL CONSTRUCTION
NEI Method (cont.)
¦ Allocation of National Data to Counties
¦ National level activity allocated to counties using 2
data sources:
¦ Annual Average Employment for SIC 154, Data Series
ES202, Bureau of Labor Statistics, 1999
¦ Annual Average Employment for SIC 154, Marketplace
3.0, Dun & Bradstreet, 1999
¦ Applied Dun & Bradstreet county proportion of the
State total to the BLS State total to estimate
employment for counties where data were
withheld
The allocation of the national level
expenditure data was performed by using the
two data sources shown here.
The Dunn & Bradstreet database was used
to fill in the gaps for data missing from the
first database.
Specifically, the county proportion of the
state total from the Dunn & Bradstreet
database was applied to the state total from
the BLS database to estimate employment
for counties where data were missing.
7-61
COMMERCIAL CONSTRUCTION
NEI Method (cont.)
¦ Activity Data Conversion
¦ Converted dollar value to acres disturbed using a
conversion factor of 1.6 acres/106 dollars applied
to the estimated county-level construction
valuation data
The dollar value activity data were converted
to acres disturbed using a conversion factor
of 1.6 acres/106 dollars. This conversion
factor was applied to the estimated county-
level construction valuation data.
7-62
COMMERCIAL CONSTRUCTION
NEI Emission Calculations
¦ PM10-PRI Emission Factor = 0.19
tons/acre/month
¦ PM2.5-PRI = 0.2 * PM10-PRI
The PM10-PRI emission factor for
commercial construction is 0.19 tons per
acre month. The PM25 is assumed to be
20% of the PM10.
Preparation of Fine Particulate Emissions Inventories
7-24
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Instructor's Manual
7-63
COMMERCIAL CONSTRUCTION
NEI Emission Calculations (cont.)
¦ Emission formula for calculating the emissions
is:
Emissions = ("0.19 tons/acre/month) x $ xfx m
where: $ = dollars spent on nonresidential
construction in millions
f = dollars-to-acres conversion factor
m = duration of construction activity in
months (assumed 11 months)
The emission formula used in the NEI to
calculate the PM emissions from commercial
construction is shown.
The calculated emissions are adjusted to
reflect control measures that are in place in
PM10 non-attainment areas.
In addition to accounting for the control
measures, adjustments are applied for soil
moisture content and silt content.
7-64
COMMERCIAL CONSTRUCTION
Improvements to NEI
¦ Obtain local information on number of acres
disturbed per construction event or per
construction dollars spent
Source: Construction Industry Association
¦ Obtain information on location, average
duration, and seasonality of commercial
construction practices
¦ Obtain local information on soil moisture
content, silt content, and control efficiency
The NEI results can be improved by
obtaining local information on the number of
acres disturbed per construction event or per
construction dollar spent.
Also information on location, average
duration, and seasonality of commercial
construction practices would be an
improvement over the NEI default values.
Finally, local information on soil moisture
content, silt content, and control efficiency
would result in improved emission estimates.
7-65
ROAD CONSTRUCTION
NEI Method
¦ Activity data: Number of acres disturbed
¦ State-Level Activity
¦ Obtained State expenditure data for capital outlay
for six classifications
¦ Interstate, urban
¦ Interstate, rural
¦ Other arterial, urban
¦ Other arterial, rural
¦ Collectors, urban
¦ Collectors, rural
(Reference: Highway Statistics, Section IV- Finance, Table SF-12A,
"State Highway Agency Capital Outlay - 1999." Federal Highway
Administration.)
The NEI uses the number of acres disturbed
as the activity data indicator for road
construction.
State level expenditure data for capital outlay
for the six road construction classifications
listed are available.
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Instructor's Manual
7-66
ROAD CONSTRUCTION
NEI Method (cont.)
¦ State-Level Activity (Continued)
¦ Expenditures include all improvement types
except for:
¦ Minor widening
¦ Resurfacing
¦ Bridge rehabilitation
¦ Safety
¦ Traffic operation and control
¦ Environmental enhancement and other
Because some of the activities included in
the total state level expenditure data do not
contribute to PM emissions, the expenditures
for these activities have been removed.
These activities include minor widening,
resurfacing, bridge rehabilitation, safety,
traffic operation and control, and
environmental enhancement and other.
7-67
ROAD CONSTRUCTION
NEI Method (cont.)
¦ Estimate miles of new road constructed
¦ $4 million/mile for interstate roads
¦ $1.9 million/mile for other arterial and collector
roads
(Reference: Personal Communication with North Carolina Department of
Transportation)
To obtain the activity data in terms of acres
disturbed, it was necessary to first convert
the expenditure data to mileage and then to
acreage.
The NEI estimated the miles of new road
constructed by applying conversion factors of
$4 million dollars per mile of interstate, and
$1.9 million dollars per mile for other arterial
and collector roads.
These conversion factors were based on
information obtained from the North Carolina
Department of Transportation.
7-68
ROAD CONSTRUCTION
NEI Method (cont.)
¦ Estimate acres for each road type using
estimates of acres disturbed per mile:
¦ Interstate, urban and rural; Other arterial, urban -
15.2 acres/mile
¦ Other arterial, rural - 12.7 acres/mile
¦ Collectors, urban - 9.8 acres/mile
¦ Collectors, rural - 7.9 acres/mile
(Reference: Estimating Particulate Matter Emissions from Construction
Operations, prepared by Midwest Research Institute for U.S.
Environmental Protection Agency, 1999.)
The NEI then applied the conversion factors
listed on this slide to convert to acres
disturbed per mile of road activity level.
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Instructor's Manual
7-69
ROAD CONSTRUCTION
NEI Method (cont.)
¦ Sum across road types to yield state total of
acres disturbed
¦ Activity Data Allocation to Counties
¦ Distributed state-level estimates of acres
disturbed to counties according to housing starts
¦ see residential construction for description of
development of county-level housing start data
The estimated acres disturbed are summed
across all road types to estimate the total
acres disturbed.
The NEI allocates these state-level estimates
to the county-level by using housing start
data. These are the same data that were
developed for the residential construction
category. The assumption is that new road
development is directly proportional to new
housing starts.
7-70
ROAD CONSTRUCTION
NEI Emission Calculations
¦ PM10-PRI Emission Factor =
0.42 tons/acre/month
¦ PM2.5-PRI = 0.2 * PM10-PRI
The PM10-PRI emission factor for road
construction is 0.42 tons per acre month. The
PM2.5 is assumed to be 20% of the PM10.
7-71
ROAD CONSTRUCTION
NEI Emission Calculations (cont.)
¦ The formula for calculating emissions is:
Emissbns = (0.42 tons PM10/acre/month) x $ x f1 xf2xd
where: $ = State expenditures for capital outlay on road
construction
f1 = $-to-miles conversion factor
f2 = miles-to-acres conversion factor
d = duration of roadway construction activity in
months (assumed 12 months)
The NEI uses the emission formula shown to
calculate the PM emissions from road
construction.
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Instructor's Manual
7-72
ROAD CONSTRUCTION
Improvements to NEI
¦Obtain information on location and timing of
road construction practices in area
(Source: State Department of Transportation)
¦ Obtain local data on the number of miles
constructed and the number of acres
disturbed per project or per mile of road
constructed
¦ Obtain local estimate for duration of projects
Obtaining information on location and timing
of road construction practices in the area is
one way to improve NEI results.
Also, obtaining local data on the number of
miles constructed and the number of acres
disturbed per project or mile of road
constructed is better than using the NEI
default values based on expenditure data.
Using local data on the duration of the
projects would also improve the NEI.
7-73
ROAD CONSTRUCTION
Improvements to NEI (cont.)
¦Obtain information on private road
construction activity
(Source: Construction Industry Association)
¦Obtain local information on soil moisture
content, silt content, and control efficiency
Information on private road construction
activity (not included in the NEI) would also
serve to improve the NEI.
Obtaining information for making
adjustments for soil moisture content, silt
content, and control efficiency also improve
NEI default values.
7-74
ROAD CONSTRUCTION
Case Study - Overview
¦ Case Study: County level emissions
inventory for road construction activities
¦ See Case Study Number 7-3
This hypothetical case study involves
developing a local inventory using available
county level inventory data and filling the
data gaps with the NEI default data.
Direct student to Case Study Number 7-3
and discuss it with the students.
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Instructor's Manual
7-75
ROAD CONSTRUCTION
Case Study -Solution
¦ Case Study: County level emissions
inventory for road construction activities
¦ See Handout 7-3
Distribute the solutions (Handout 7-3) to the
case study. Review each question with the
students. Encourage discussion among the
class. Ask each group to report on the
questions that were assigned to them. Ask
the other groups to critique their responses.
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Instructor's Manual
Chapter 8 - Ammonia Emissions from Animal Husbandry
8-1
Preparation of Fine Particulate
Emissions Inventories
Chapter 8 - Ammonia Emissions from
Animal Husbandry
This lesson presents the issues associated
with estimating ammonia emissions from
animal husbandry operations and some of
the efforts that are being undertaken to
address these issues.
8-2
NH3 - Precursor to Ammonium Sulfate &
Nitrate (National Emissions ~ 4.8 M TPY)
Highway Vehicles
~
Industrial Processes
]
Waste Disposal
]
Other
~
0% 20% 40% 60% 80%
8-2 Preparation ofFine Particulate Emissions inventories
Almost 5 million tons a year of ammonia are
emitted nationally. As the graph
demonstrates, animal husbandry is the
source of the majority of ammonia emissions
nationally.
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Instructor's Manual
8-3
Update to Ammonia from Animal
Husbandry is Timely
¦ Inverse modeling suggests overestimation of
ammonia.
¦ Shortcomings of old NEI
¦ Probable errors in emission factor selections, especially for
beef.
¦ Does not use information on variability of emissions due to
different manure handling practices within a given animal
industry.
¦ Does not make total use of information of available National
Agricultural Statistics Service (NASS) data on different
animal populations, by average live weight.
It is important to address ammonia emissions
from animal husbandry because inverse
modeling suggests that ammonia emissions
may be overestimated.
Inverse modeling:
involves doing a complete chemical
transformation and transport modeling of
an area
requires accounting for all of the
ammonia through transformation and
deposition processes.
results indicate that ammonia may be
overestimated nationally when compared
to ammonia in the ambient air
Some problems associated with the old NEI:
probable errors in the emission factor
selections, especially for beef.
does not use information on variability of
emissions due to different manure
handling practices within a given animal
industry
does not make total use of the National
Agricultural Statistic Service data on
different animal populations by weight
does not take temperature into account,
which would greatly increase the
temporal variation in ammonia emissions.
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Instructor's Manual
8-4
Update to Ammonia from Animal
Husbandry is Timely (Cont'd)
¦ Effluent Guidelines project provided
information on production & waste handling
practices (new).
¦ National Academy of Science (NAS)
committee recommended a long data
gathering effort.
¦ Old NEI estimates are not the best we can do in
the interim (while this data gathering is
undertaken).
In addition, EPA's water emission effluent
guidelines project has provided some new
information on animal production and waste
handling practices.
Also, the National Academy of Sciences, at
the behest of the agricultural community, has
reviewed EPA's inventory work, and
recommended a long-term data-gathering
effort.
8-5
Improved Basis for Interim NEI Update
¦ Provides improved data on populations,
practices, and emissions.
¦ Allows a switchover to a process-based
framework that is common, transparent and that
allows partial updating as more data becomes
available.
¦ Motivates and provide structure for relevant
data collection.
¦ Opportunity to educate users about data
limitations, proper use.
¦ Goal: Higher animal production States will begin
to adopt / offer improvements to new method.
A recent EPA report provides a basis for
making interim improvements to the NEI
through improved data on populations,
practices, and emissions.
It is the beginning of a switch-over to a
process-based framework that is a consistent
and transparent way of estimating emissions.
Advantages:
will allow for partial updating as better
data become available
provides motivation and a structure for
making data-collection improvements
provides an opportunity to educate users
about data limitations and the proper use
of the data.
The goal is for the higher animal production
states to begin to adopt and offer
improvements to the NEI using this new
method.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
8-6
Overview of New Estimation
Methodology
¦ Step 1: Estimate average annual animal
populations by animal group, state, and
county.
¦ Step 2: Identify Manure Management Trains
(M MT) used by each animal group and then
estimate the distribution of the animal
population using each MMT.
¦ Step 3: Estimate the amount of nitrogen
excreted from the animals using each type of
MMT, using general manure characteristics.
Let's review the six steps that comprise this
new methodology for estimating ammonia
emissions from animal husbandry
operations.
8-7
Overview of New Estimation
Methodology (Cont'd)
¦ Step 4: Identify or develop emission factors
for each component of each MMT.
¦ Step 5: Estimate ammonia emissions from
each animal group by MMT and county for
2002.
¦ Step 6: Estimate future ammonia emissions
for years 2010, 2015, 2020, and 2030.
8-8
Step 1: Population Estimates
Animals: Dairy, beef, swine, and poultry.
¦ Keep weight groups & animal types distinct.
State-level population: 2002 NASS.
County apportionment: using 1997 Census of
Agriculture.
¦ Privacy Issue - Where state and/or county is not
disclosed, divide equally.
The first step in this process is estimating
average animal populations by animal group,
state, and county.
This step uses 2002 NASS data for state-
level populations, and the 1997 census of
agricultural to apportion the state-level NASS
data to the county level. However, there are
some privacy issues with regard to animal
populations.
For example, a county with only one large
facility would create an industrial privacy
issue since that facility will not want their
competition to know how many animals they
are raising.
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Instructor's Manual
8-9
Step 2: Manure Management Trains
15 MMT's plus permutations (similar to
"model farms" used in past approaches).
¦ e.g., Housing, waste storage, land application
type.
¦ Non-feedlot outdoor confinement (e.g. pasture)
is one of the trains for swine, dairy, and beef.
¦ MMT's represent different pathways for escape
of ammonia to the air.
¦ MMT "mix" varies by state, not within a State.
¦ Another "opportunity" for improvement
The second step is using Manure
Management Trains (MMTs) for each animal
group to estimate the distribution of the
animal population.
Fifteen MMTs have been identified.
Some of the variables that affect the different
trains include:
the way animals are housed,
waste storage methods, and
the land application methods that are
used.
For example, the non-feedlot outdoor
confinement is one of the trains for swine,
dairy, and beef. The MMTs represent
different pathways for the escape of
ammonia into the air.
In applying the MMT approach to estimate
the 2002 ammonia inventory, the mix of
MMTs is assumed to vary by state, but not
within a state.
8-10
Step 2: Manure Management Trains
(Cont'd)
¦ Animal population, etc. is allocated among
the applicable trains.
¦ Note: Final stage in each train is land
application.
Animal population is allocated among the
applicable trains.
For example, in a given state 20% of the
hogs may be handled using manure
management train 3, another 60% may be
using manure management train 7, and the
rest of them may be using manure
management train 14.
Finally, it should be noted that the final stage
on every train is land application.
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Instructor's Manual
8-11
Advanced Example of Manure
Management Train
This graphic illustrates an advanced MMT,
one of several such trains for the dairy
industry.
This MMT begins with the amount of nitrogen
excreted by dairy cows.
The train traces the manure through the
different handling options and shows how
much is handled in different ways. The train
also shows the nitrogen and ammonia
emissions at the various handling points.
For example, there is nitrogen loss in the
flush barn and the lagoon, and ammonia loss
in the dry lot. There are other trains that
provide similar information for other farm
industries. These trains characterize a type
of industry, and the general way that manure
would be handled in a facility.
8-12
Step 3: Nitrogen Excreted
Typical animal weights (within a type and
weight range)
Nitrogen per 1000 kg of live weight from
NRCS Agricultural Waste Management Field
Handbook
Local agriculture experts could help improve
this
¦ Land Grant University Researchers / Extension
Agents
The third step involves using each type of
MMT to estimate the amount of nitrogen
excreted from the animals.
This step involves examining typical animal
weights and data on the amount of nitrogen
per thousand kilos of live weight. The data
on the nitrogen amounts can be obtained
from NRCS Agricultural Waste Management
Field Handbook.
Another useful source of information is land
grant university researchers and local
agricultural extension agents. It is important
to include experts in the agricultural industry
in the inventory development efforts.
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8-6
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Instructor's Manual
8-13
Step 4: Emission Factors
¦ Select the emission factor for each stage of
each manure management train.
¦ Some are lb/animal, some are percent air release
of input ammonia.
¦ Both kinds also determine ammonia transferred to
next stage.
¦ Air emissions can never be higher than original
manure content.
¦ Using stage-specific emission factors sets the
stage for applying temporal profiles and
process-related variability later.
Step four involves identifying or developing
the emission factors for each component of
each MMT. Some of these factors are in
pounds per animal, and some are percent air
release of the input ammonia.
These factors are used to determine the
amount of ammonia that goes to the next
stage of the manure train process.
Under this approach, the air emissions could
never be higher than the original manure
content. Also, using this approach sets the
stage for applying temporal profiles and
process-related variables such as moisture
and rainfall.
8-14
Step 5: Apply for 2002
¦ Track ammonia release through each
manure management train for each animal
type, calculating air releases and transfers
to next stage.
¦ Assumes no air emission controls at this
time.
¦ But can add control assumptions later, and see
downstream consequences.
¦ Emissions are summed up to animal type
and county
¦ Database is preserved with full detail for
transparency and later revisions.
The next step involves applying this
methodology to estimate annual ammonia
emissions from each animal group by MMT.
This includes:
tracking the ammonia release through
each MMT for each animal type and
county, and
calculating ammonia releases to the air
and transfers to the next stage.
This whole process assumes no air emission
controls at this time, but control assumptions
could be added later. Emissions are
summed up to animal type and county, but
the database is preserved with full detail for
transparency so that changes and
improvements can be made.
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8-7
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Instructor's Manual
8-15
Step 6: Future Years Projections
2010, 2013, 2020, and 2030.
USDA and Food and Agricultural Policy
Research Institute.
Accounts for past observed cyclical
populations.
State-by-state population pattern.
¦ Changes with time for dairy.
¦ Fixed for others.
The last step involves estimating ammonia
emissions for future years.
8-16
Comparison of 1999 and 2002
Ammonia NEIs
Animal
GroupO
1999 NEI
2002 NEI
Population
Emission
lb/head
Emissions
Tons/year
Emission
lb/head/yr
Emissions
Tons/year
Cattle ana
Calves
Compos ite
100,126,106
50 5
2,476,333
100,939,728
23 90
1,205,493
Compos ite
63,095,955
20 3
640,100
59,978,850
1432
429,468
Poultry ana
Chickens
Compos ite
1,754,482,225
0 394
345,325
2,201,945,253
0 60
664,238
Total
1,917,704,286
N/A
3,461,758
2,362,863,831
N/A
2,299,199
Preparation of Fine Particulate Emissions Inverrtones
A comparison of the 1999 NEI version 3 with
the 2002 NEI version 1 shows significant
differences in the ammonia emissions.
As shown on this chart, about half of the
emissions from all animals come from calves
and cattle.
Also, total ammonia emissions from animal
husbandry operations decreased significantly
from 3.4 million in 1999 to 2.3 million in 2002.
8-17
Ongoing Additional Improvements
Plan to incorporate emission estimates for
sheep, ducks, goats, and horses.
Looking at more recent manure production
and excretion rates by animal types and
weight (may provide lower overall estimates
than currently indicated in draft report).
Looking into ways to better address spatial,
seasonal, and regional differences in
emissions.
Other improvements that are being made to
the NEI for animal husbandry operations:
incorporating emission estimates for
sheep, ducks, goats, and horses
examining additional data sources to
provide recently data on manure
production and excretion rates by animal
type and weight.
examining ways to better address special,
seasonal, and regional differences in
emissions
Preparation of Fine Particulate Emissions Inventories
8-8
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Instructor's Manual
8-18
CMU Model and the NEI
Carnegie Mellon University (CMU) has prepared
a model for estimating ammonia emissions from
agricultural activities, humans, wastewater
treatment, wildfires, domestic and wild animals,
transportation sources, industrial activities, and
soils.
Includes an improved methodology for fertilizer
application when compared to the methodology
used in previous versions of the NEI.
EPA is evaluating the methodologies used for
other source categories in the CMU model.
Carnegie Mellon University has prepared a
model for estimating ammonia emissions
from agricultural activities, humans,
wastewater treatment, wildfires, domestic
and wild animals, transportation sources,
industrial activities, and soils.
The Carnegie Mellon model includes an
improved methodology for fertilizer
application when compared to the
methodology used in previous versions of the
NEI.
EPA is evaluating the methodologies used
for other source categories in the Carnegie
Mellon model.
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Chapter 9 - Combustion Area Sources
9-1
Preparation of Fine Particulate
Emissions Inventories
Chapter 9 - Combustion Area Sources
After this lesson, participants will be able to:
Describe the methodologies for
calculating emissions from residential
wood combustion, residential and land
clearing debris burning, agricultural field
burning, and wildland fires.
9-2
MANE-VU 2002 RWC Emission
Inventory
¦ Objective
¦ Prepare 2002 El based on survey of household
equipment usage and wood consumption patterns
¦ Survey Method - stratified, random-sampling
¦ Data Collected for Each Household
¦ Wood consumption at equipment level (both real
wood and artificial logs)
¦ Wood type for real wood
¦ Temporal activity to calculate monthly, weekly,
and daily emissions
The MANE-VU View Regional Planning
Organization conducted a residential wood
combustion survey to develop an emissions
inventory for the year 2002.
A survey is the EIIP preferred method for this
category.
The objective of the MANE-VU project is to
prepare a 2002 inventory based on a survey
of household equipment usage and wood
consumption patterns, using a stratified
random sampling approach.
The data collected for each household
consists of:
wood consumption at the equipment level
for both real wood and artificial logs;
the type of real wood; and
the temporal activity to calculate monthly,
weekly, and daily emissions.
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Instructor's Manual
9-3
Sample Frame Construction
Sampling designed to address major sources
of variability in activity (i.e., wood
consumption)
Sources of variability include:
¦ Location and type of housing
¦ Heating demand (expressed as heating degree
days (HDDs))
¦ Availability of wood
The sampling was designed to address
major sources of variability in wood
consumption activity.
These sources include:
the location and type of housing;
the heating demand expressed as
heating degree days; and
the availability of wood.
9-4
Sample Frame Construction (cont.)
¦ Sample Stratification
¦ Housing Data - 2000 Census tract data used to
stratify sample by:
¦ Urban, suburban, and rural single-family and "other"
homes (other homes = multi-family units such as
apartments, condos, mobile homes)
¦ Rural category stratified by forested and non-forested
areas using USGS GIS data (i.e., Forest Fragmentation
Index Map of North America)
¦ Heating Demand - Total annual HDDs used to
stratify sample into 3 zones
Housing data from the 2000 census covers
four categories:
urban
Suburban
rural single family
stratified into forested versus non-
forested areas using USGS-GIS data,
other homes
includes multi-family units:
Apartments
condominiums
mobile homes
Total annual heating degree days were used
to further stratify the sample into three zones:
low,
medium and
high.
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Instructor's Manual
9-5
Sample Frame
Rural-Forested
Ru
Non-Fo
rested
Sub
rban
Urt
Geographic
Zone
Single-
Other
Single-
Family
Other
Single-
Other
Sngle-
Other
Ugh HDD
Cell 1
(173)
Cell 2
(64)
Cell 3
(87)
Cell 4
(66)
CellS
(61)
CellS
(72)
Cell?
(69)
CellS
(69)
Lou HDD
Cell 9
(150)
Cell 10
(62)
Cell tt
(118)
Cell 12
(69)
Cell 13
(76)
Cell 14
(67)
Cell 15
(75)
Cell 16
(62)
Mid HDD
Cell 17
(87)
Cell 1$
(60)
Cell 19
(91)
Cell 20
(64)
Cell 21
(71)
Cell 22
(60)
Cell 23
(63)
Cell 24
(68)
9-5
Pr
paration of
ne Partiail
e Emission
Inventories
This slide shows a sample frame shown in a
grid.
61 is the minimum sample size
determined based on calculations for the
precision desired from the survey
The numbers in parentheses represent the
number of surveys that were actually
collected or completed.
Surveys for which the respondents did not
categorize correctly were removed from the
sample.
9-6
Survey Instrument
¦ Questionnaire developed to gather activity
data for:
¦ Indoor equipment (fireplaces, woodstoves, pellet
stoves, furnaces, and boilers)
¦ Outdoor equipment (fire pits, barbeques,
fireplaces, and chimineas)
¦ Pilot survey performed to test the instrument
¦ Survey conducted using computer-assisted
telephone interviewing
¦ Completed 1,904 surveys across all 24 cells
The survey instrument is a questionnaire
developed to gather the activity data on
indoor equipment and outdoor equipment.
A pilot survey was conducted to test the
questionnaire.
Questions were rephrased in order to collect
the information that was needed to
characterize the activity.
The final survey was conducted using
computer-assisted telephone interviewing.
Completed over 1,900 surveys across all 24
cells
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Instructor's Manual
9-7
Survey Data Reduction/Analysis
¦ QA reviewed each survey
¦ Calculated/summarized for each cell:
¦ User fraction (fraction of total household
population that burns wood in indoor and
outdoor equipment)
¦ Annual activity (cords of wood by equipment
and wood types)
¦ Temporal data
¦ Conducted statistical analyses to identify
significant differences between cells for:
¦ User fraction
¦ Annual Activity
Surveys were quality assured to make sure
that the data collected made sense.
Cell summaries included:
the user fraction,
the annual activity, and
temporal data.
Statistical analyses identified significant
differences between cells:
the user fraction
annual activity
9-8
Indoor Wood-Burning Equipment
Preliminary Survey Results (% Burners)
s
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This table is the same table that was shown
on an earlier slide with the exception that the
grid cells have the fraction of indoor wood
burning equipment on a percentage basis.
Observations:
In some cases the fractions add up to
more than 100% because some houses
were using more than one piece of
equipment.
Rural forested areas within a high heating
demand zone have a higher diversity of
equipment.
More rural households are using wood
burning equipment than the urban areas.
Preparation of Fine Particulate Emissions Inventories
9-4
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Instructor's Manual
9-9
Preliminary Results/Observations
¦ Indoor Equipment
¦ Geographic distribution of equipment
¦ Rural Areas:
¦ Higher diversity of equipment types than in urban areas
¦ Higher percentage of stoves and furnaces than in urban
areas
¦ Urban/Suburban Areas:
¦ Lower diversity of equipment types than in rural areas
¦ Higher percentage of fireplaces than in rural areas
¦ Heating Demand
¦ High HDD Zone:
¦ Rural Areas - higher percentage of stoves and furnaces
¦ Low HDD Zone:
¦ Rural Areas - higher percentage of fireplaces
Rural areas have a higher percentage of
stoves and furnaces and boilers than urban
areas.
Urban and suburban areas:
lower diversity of equipment types
higher percentage of fireplaces
Rural areas:
higher percentage of stoves and furnaces
in the higher HDD zone
higher percentage of fireplaces in the
lower HDD zone
9-10
Preliminary Results/Observations
(cont.)
¦ Indoor Equipment
¦ For urban areas, it was difficult to find households
that burned wood for the sample size taken
¦ The urban sample size was not increased
because of budget constraints and priorities for
obtaining a representative sample for three
instead of two HDD zones
¦ The equipment- and fuel-based survey results
were used to estimate emissions (e.g., lb
PM25/household-yr) for each household surveyed
¦ A household-based statistical model is being
developed to estimate emissions for each cell
For indoor equipment, because of the
sample size of the survey, it was hard to find
households that burned wood in urban areas.
Urban sample size was not increased for two
reasons:
Budget, and
Priorities.
As a result, emissions were not calculated for
each piece of indoor equipment in urban
areas.
Equipment and fuel-based survey results
were used to estimate average emissions.
Household-based statistical model was used
to estimate emissions for each cell for indoor
equipment.
Preparation of Fine Particulate Emissions Inventories
9-5
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Instructor's Manual
9-11
Preliminary Results/Observations
(cont.)
¦ Outdoor Equipment
¦ Equipment-based emissions will be estimated
using survey results
Annual Emissions = Fraction of outdoor equipment
users per cell x annual activity x emission factor
Emissions were estimated for outdoor
equipment using the survey results.
The emissions are the product of:
the fraction of outdoor equipment users
per cell,
the annual activity, and
the emission factor.
This is the first attempt to estimate emissions
from outdoor wood burning equipment at the
household level.
NEI only includes indoor equipment.
9-12
Emission Inventory Development
Emissions were:
¦ Estimated for all criteria pollutants/precursors and
several dozen toxic air pollutants
¦ Estimated atthe census tract level (summed to
county, State, region)
¦ Temporally allocated to support modeling using
profiles developed from the survey
Emissions were estimated for:
criteria pollutants and precursors, and
several dozen toxic air pollutants.
They were estimated at census track level
Summed to the county, state and region.
Emissions were temporally allocated to
support modeling using profiles that were
developed from the survey.
9-13
Lessons Learned
Survey Instrument: for regional surveys, tailor
it to suit the usage patterns in rural, suburban,
urban areas
Difficult to find wood burners in urban areas -
minimum sample sizes need to reflect this
A number of lessons were learned from the
MANE-VU study:
Survey instrument for regional surveys
should be tailored to suit the usage patterns
on rural and suburban and urban areas.
It is difficult to find wood burners in the urban
areas, and the sample size may need to be
increased to locate these sources.
Preparation of Fine Particulate Emissions Inventories
9-6
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Instructor's Manual
9-14
Lessons Learned (cont.)
¦ For indoor equipment, to keep resources
manageable:
¦ Consider the use of a statistically-derived
emissions-based model (household level) instead
of an equipment-specific method
¦ Concern: Approach aggregates emissions for
different types of wood burning equipment needed
to support control measure analysis
For indoor equipment, keep resources
manageable.
Consider the use of statistically derived
emissions based model (household level)
instead of an equipment specific method.
The concern with this MANE-VU approach is
that it aggregates emissions for different
types of wood burning equipment.
Should be disaggregated in order to conduct
a control strategy analysis.
9-15
Documentation for MANE-VU El
¦ Technical memoranda and Work Plan for a
Survey to Determine Residential Wood
Combustion and Open Burning Activity
(July 31, 2001)
(MANE-VU Web Site:
http://www.manevu.org/pubs/index.asp)
Documentation for the MANE-VU project can
be obtained at the web address listed here.
Technical memoranda
Work plan including equations for calculating
the sampling precision
9-16
How are RWC Emissions Estimated in
the '02 NEI?
¦ SCCs
¦ FIREPLACES
¦ 2104008001 Without Inserts
¦ 2104008002With Inserts; Non-EPA Certified
¦ 2104008003With Inserts; Non-Catalytic, EPA Certified
¦ 2104008004 With Inserts; Catalytic, EPA Certified
¦ WOODSTOVES
¦ 2104008010 Non-EPA Certified
¦ 2104008030 Catalytic, EPA Certified
¦ 2104008050 Non-Catalytic, EPA Certified
NEI categorization:
Fireplaces - Four SCCs
Woodstoves - Three SCCs
Preparation of Fine Particulate Emissions Inventories
9-7
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Instructor's Manual
9-17
How are RWC Emissions Estimated in
the '02 NEi? (cont.)
¦ Pollutants
¦ PM10-PRI, PM25-PRI, NOx, CO, VOC, SOx
¦ HAPs (number of pollutants)
NEI pollutants for residential wood
combustion:
PM10 primary
PM2.5 primary
NOx
CO
. SOx
HAPs.
The emission factors that are used for
residential wood combustion represent
primary emissions.
There is no breakout of the filterable and
condensable portions of the emission factor.
9-18
Emission Factors for Fireplaces Without
Inserts (lbs pollutant/ton of dry wood)
¦ NOx, SOx, VOC, & HAPs
¦ AP-42, Chapter 1.9, Table 1.9-1
¦ PM10-PRI, PM25-PRI, & CO
¦ Houck, J.E., etal, "Review of Wood Heater
and Fireplace Emission Factors," NEI
Conference, May 1-3, 2001
¦ Based on test data more current than AP-
42
¦ PM25-PRI assumed to be same as PM10-
PRI
The emission factors for fireplaces without
inserts obtained from AP-42 except:
PM and CO obtained from the reference
listed on this slide.
The PM2.5 emission factor assumed to be
the same as PM10 primary emission
factor.
Emission factors for all pollutants from
woodstoves and fireplaces without inserts
are obtained from AP-42.
9-19
Emission Factors for Woodstoves & Fireplaces
With Inserts (lbs pollutantAon of dry wood)
¦ Criteria Pollutants: AP-42, Chapter 1.10,
Table 1.10-1
¦ PM10-PRI, PM25-PRI, & CO EFs are average
for all woodstoves
¦ PM25-PRI assumed to be same as PM10-PRI
¦ HAPs: AP-42, Chapter 1.10, Tables 1.10-
2, -3, & -4
¦ AP-42 EFs for Polycyclic Aromatic
Hydrocarbons (PAH) reduced by 62% based
on recent test data (Houck, et al, 2001)
¦ Conversion Factor: One cord of wood
equals 1.163 tons
This slide shows the information for emission
factors for woodstoves and fireplaces with
inserts.
Preparation of Fine Particulate Emissions Inventories
9-8
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Instructor's Manual
9-20
Activity Data
¦ Develop separate national wood consumption
estimates for fireplaces with inserts, fireplaces
without inserts, & woodstoves to account for:
¦ Different emission factors
¦ Different usage patterns (climate zones; urban vs.
rural)
¦ National wood consumption estimated using:
¦ Number of combustion units
¦ Average wood consumption rates
¦ Spatial allocation of wood consumption to
county level performed to reflect usage
patterns
To account for the different emission factors
and different usage patterns, the NEI
developed separate national wood
consumption estimates and emission
estimates for:
fireplaces with inserts,
fireplaces without inserts,
and woodstoves.
The methodology is different for fireplaces
without inserts than it is for fireplaces with
inserts and woodstoves.
9-21
Estimating Emissions from Fireplaces
Without Inserts
¦ Stepl: Determine national number homes
with usable fireplaces (with and without
inserts)
¦ Reference: Table 2-25 of 2001 American
Housing Survey (AHS) for the United States
(U.S. Census Bureau)
¦ Step 2: Adjust to account for homes with
more than one fireplace (multiply Step 1 by
1.17)
¦ Reference: 1989 U.S. Consumer Product
Safety Commission report
Estimating emissions from fireplaces without
inserts
Step 1: determine the number of homes with
fireplaces in the United States using data
obtained from the US Department of Census.
Step 2: adjust to account for the fact that
some homes have more than one fireplace.
9-22
Estimating Emissions from Fireplaces
Without Inserts (cont)
¦ Step 3: Adjust for fireplaces that burn wood
(74% wood, 26% gas)
¦ References: Industry trade associations/experts,
market surveys (Houck, et al, 2001)
¦ Step 4: Subtract out fireplaces not being
used (42% not used)
¦ References: Local surveys, industry market
surveys, government publications (Houck, et al,
2001)
Estimating emissions from fireplaces without
inserts
Step 3: adjust to account for the fact that not
every home burns wood.
Step 4: subtract the number of fireplaces not
being used.
Preparation of Fine Particulate Emissions Inventories
9-9
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Instructor's Manual
9-23
Estimating Emissions from Fireplaces
Without Inserts (cont)
¦ Step 5: Determine number of homes with
usable fireplaces with inserts used for heating
¦ Used to determine the number of homes with
usable fireplaces without inserts
¦ Reference: Table 2-4 of 2001 AHS
¦ Step 6: Adjust to account for homes with
more than one fireplace (multiply Step 5 by
1.10)
¦ Reference: 1989 U.S. Consumer Product Safety
Commission report
Estimating emissions from fireplaces without
inserts
Step 5: subtract number of fireplaces with
inserts.
Step 6: adjust for homes with more than one
fireplace.
9-24
Estimating Emissions from Fireplaces
Without Inserts (cont)
¦ Step 7: Determine number of fireplaces
without inserts used for heating and aesthetic
purposes
¦ The amount of wood burned in each device is
determined by assuming wood consumption
rates
¦ 0.656 cords burned /unit/year for fireplaces used
for heating
¦ 0.069 cords/unit/year for fireplaces used for
aesthetics
Estimating emissions from fireplaces without
inserts
Step 7: Separated fireplaces without inserts
into 2 categories: those used for heating and
those used for aesthetics.
The amount of wood burned in each device
is determined by assuming wood
consumption rates:
0.656 cords burned /unit/year for
fireplaces used for heating and
0.069 cords/unit/year for fireplaces used
for aesthetics.
9-25
Estimating Emissions from Fireplaces
Without Inserts (cont)
¦ In 1997, EPA estimated that 2.94 million
cords of wood were burned in the former and
0.483 million cords of wood were burned in
the latter
In 1997, EPA estimated:
2.94 million cords of wood were burned
for heating
0.483 million cords of wood were burned
for aesthetics
Preparation of Fine Particulate Emissions Inventories
9-10
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Instructor's Manual
9-26
Spatial Allocation of National Residential
Wood Consumption to Counties
¦ National activity is allocated to counties
using:
¦ Climate zone (i.e., temperature)
¦ Demographics/population (i.e., number of single-
family homes)
¦ Usage patterns for each device (i.e., urban
versus rural)
Calculated consumption is allocated to
counties based on:
1 of 5 climate zones,
demographics/population, and
usage patterns.
9-27
Spatial Allocation of National Residential
Wood Consumption to Counties (cont.)
Climate Zone Percent of Wood
ConsumecT
1 (>7000 HDD) 36
2 (5500-7000 HDD) 19
3 (4000-5499 HDD) 21
4 (<4000 HDD and <2000 CDD) 15
5 (<4000 HDD and >2000 CDD) 9
Climate zones defined by:
ranges of heating degree day and cooling
degree day values
amount of national consumption allocated
to each zone
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
9-28
Spatial Allocation of National Residential
Wood Consumption to Counties (cont.)
¦ Urban/Rural Apportionment
¦ Designate each county as either urban or rural,
sum activity for climate zone, and adjust county
activity so climate zone total matches the following
proportions :
Rural Urban
Woodstoves 65% 35%
Fireplaces with inserts 43% 57%
Fireplaces without inserts 27% 73%
The census data classifies counties as either
urban or rural.
Urban = 50 percent of the county's
population located in cities and towns
Rural = less than 50 percent of the
population located in cities and towns
The total wood consumption for all the urban
counties are summed for each climate zone,
and the same is done for the rural counties.
The data is adjusted if the percentage
proportion between urban and rural areas
does not match the percentage in the
number of units that are reported in the 2001
census.
For example, if the total wood consumption
for woodstoves in climate zone 1 is 60
percent for rural and 40 percent for urban,
then each urban and rural county within zone
1 receives a percent increase or decrease in
cordwood consumption to obtain the correct
percent split to reach the 65 percent rural
and 35 percent urban split for zone 1.
Finally, AP-42 factors are used to determine
county emissions from fireplaces without
inserts.
Preparation of Fine Particulate Emissions Inventories
9-12
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Instructor's Manual
9-29
Estimating Emissions from Fireplaces
With Inserts and Woodstoves
¦ Determine the number of woodstoves and
fireplaces with inserts
¦ Data obtained from the Department of Census
¦ Adjust for homes with more than one stove
¦ Obtain total cords of wood consumed by
residential section
¦ Energy Information Administration (EIA)
¦ Adjust for use - heating or aesthetics
Estimating emissions from fireplaces with
inserts and woodstoves
Determine the number of woodstoves and
inserts in the United States.
These data are obtained from the DOC.
Adjust for the fact that some homes have
more than one stove.
The total cords of wood consumed by the
residential section for 1997 are obtained from
the Energy Information Administration.
Subtract the cords of wood used in fireplaces
for aesthetic purposes.
Units used for main heating purposes are
considered different from units that are used
for other heating purposes.
9-30
Estimating Emissions from Fireplaces
With Inserts and Woodstoves (cont.)
¦ Allocate to climate zones
¦ Allocate to individual counties
¦ Sum wood consumption and compare to
urban/rural split
Allocate consumption to 1 of 5 climate zones.
Within each climate zone, allocate
consumption to the individual counties using
the relative percent of detached single family
homes in the county to the total number of
detached single family homes in the entire
climate zone.
After allocating to the climate zones, the
wood consumption in each zone is summed
and compared the urban and rural split.
The total is adjusted until the desired split is
achieved.
The split is 65 percent rural and 35 percent
urban.
For inserts, the split is 43/57.
Preparation of Fine Particulate Emissions Inventories
9-13
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Instructor's Manual
9-31
Estimating Emissions from Fireplaces
With Inserts and Woodstoves (cont.)
¦ Wood consumption for woodstoves and
fireplaces with inserts were apportioned as
follows:
Percent of Total
Tvne of Device
Wood Consumntion
Non-certified
92
Certified non-catalytic
5.7
Certified catalytic
2.3
Preparation of Fine Particulate Emissions Inverrtones
Wood consumption for woodstoves and
fireplaces with inserts are allocated to one of
the three SCCs.
Fireplaces without inserts are recorded on
one SCC.
Once the amount of wood consumed per
residential wood combustion type is
obtained, AP-42 emission factors are used to
calculate emission estimates.
9-32
Temporal Allocation of Residential
Wood Consumption Emissions
¦ Default temporal allocation profiles by climate
zone
¦ S/L/T agencies should adjust allocations to better
fit seasonal usage patterns
¦ Seasonal throughput percentages assigned to
each climate zone are:
Climate
Zone
Winter
Snrina
Summer
Fall
5
100
0
0
0
4
70
15
0
15
3
50
25
0
25
2
40
30
0
30
1
33.33
33.33
0
33.33
NEI seasonal activity is allocated by climate
zone.
The seasonal throughput percentages
assigned to each climate zone are listed on
this slide.
Zone five is the warmest zone, so all the
activity was placed into the winter category.
Summer has no activity with the NEI default
method.
The activity is distributed across the seasons
for zones two, three and four.
9-33
How Can You Improve the NEI for Your
Area?
¦ Preferred Method: Residential Wood Survey
¦ Obtain locally representative information on the
amount of wood fuel use specifically for
woodstoves & fireplaces (with and without
inserts)
¦ This will require a local survey, or activity data
generated by State & local governments
¦ Reduces uncertainties in estimates associated
with allocating national activity to counties
¦ Alternative Method: Census Bureau and EIA Data
Method
¦ Use if resources are limited or emphasis is on
preparing summer season inventory
Improving on the NEI method can be
accomplished by:
Conducting a local survey
Allocating emissions within the seasons.
It is preferable to use local data and the
preferred collection method is to do a local or
statewide survey.
The EIIP provides an alternative method
using census bureau data and the EIA data
method.
Preparation of Fine Particulate Emissions Inventories
9-14
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Instructor's Manual
9-34
How Can You Improve the NEI for Your
Area? (cont.)
¦ Rule Effectiveness/Rule Penetration
¦ Incorporate effects of S/L/T rules and level of
compliance
¦ NEI methodology does not account for S/L/T rules
Any assumptions other than 100% for rule
effectiveness and rule penetration should be
incorporated into the emissions estimation
methodology
NEI method does not account for the effect
of state and local rules.
9-35
Comparison of MANE-VU Approach to
NEI Method
¦ MANU-VU El is a bottom-up methodology
¦ NEI is a top-down methodology
¦ MANE-VU El provides for:
¦ Better estimates by geographic area (rural,
suburban, urban) and census tract (sub-county)
level
¦ Accounts for differences in housing type (single- and
multi-family homes)
¦ Better estimates of usage patterns based on HDDs
¦ Includes outdoor equipment not included in NEI
estimates
¦ Provides temporal data
The MANE-VU inventory is a bottom-up
methodology.
NEI is top down.
MANE-VU:
Provides better estimates by geographic
area and census.
Accounts for differences in housing type
Provides better estimates of usage
patterns based on heating demand.
Includes outdoor equipment not included
in the NEI estimates.
Provides some temporal data that can be
used to allocate emissions.
NEI emission estimates for residential wood
combustion are generally within the ballpark
of, but on the low end of the range of,
emissions estimated for the MANE-VU
inventory.
Preparation of Fine Particulate Emissions Inventories
9-15
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Instructor's Manual
9-36
Residential Wood Combustion
Case Study - Overview
¦ Case Study: County level emissions
inventory for residential wood combustion
¦ See Case Study Number 9-1
This hypothetical case study involves
developing a local inventory using survey
data and filling the data gaps with the NEI
default data.
Direct student to Case Study 9-1 and discuss
it with the students.
9-37
Residential Wood Combustion
Case Study - Solution
¦ Case Study: County level emissions
inventory for residential wood combustion
¦ See Handout 9-1
Distribute the solutions (Handout 9-1) to the
case study. Review each question with the
students. Encourage discussion among the
class. Ask each group to report on the
questions that were assigned to them. Ask
the other groups to critique their responses.
9-38
Residential Open Burning
What Sources are Included?
SCCs:
2610030000 - Residential Municipal Solid Waste
(MSW) Burning
Pollutants: PM10, PM2.5, CO, NOx, VOC, S02,
32 HAPs
2610000100 - Residential Leaf Burning
2610000400 - Residential Brush Burning
Pollutants: PM10, PM 2.5, CO, VOC, 6 HAPs
Residential open burning includes:
household waste burning
yard waste burning (includes brush waste
and leaf waste).
This slide lists the SCCs and the pollutants
for residential open burning that are included
in the NEI.
Preparation of Fine Particulate Emissions Inventories
9-16
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Instructor's Manual
9-39
Residential Open Burning
NEi Methods for Residential MSW
¦ Activity Data (tons of waste burned)
¦ Step 1 - Estimate 2002 rural population by
county
¦ County-level rural population estimated by
applying rural/urban percentages from 2000
Census data to 2002 population
¦ Step 2 - Multiply per capita waste factor by
rural population
¦ Used national average per capita waste
generation factor of 3.37 Ibs/person/day
(noncombustibles and yard waste subtracted
out).
Developing activity data for residential
municipal solid waste:
Step 1: estimate the rural population by
county by applying percentages of rural and
urban population from the census data.
Step 2: multiply the rural population by a per
capita household waste factor of 3.37
pounds per person per day.
9-40
Residential Open Burning
NEI Methods for Residential MSW (cont.)
¦ Step 3- Estimate amount of waste burned
¦ Assume 28% of total waste generated is burned
¦ Step 4 - Account for burning bans
¦ For counties where urban population exceeds
80 percent of the total population, the amount of
waste burned was assumed to be zero, therefore
zero open burning assigned to these counties
Developing activity data for residential
municipal solid waste:
Step 3: estimate the amount of waste burned
Assume that 28% of the household waste
generated is burned.
Step 4: account for burning bans.
Ideally this is done by knowing exactly which
areas have instituted a burning ban and the
time period over which the ban applies.
The NEI assumes that if a county has an
urban population that exceeds 80% of the
total population the amount of waste burned
is zero.
Preparation of Fine Particulate Emissions Inventories
9-17
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Instructor's Manual
9-41
Residential Open Burning
NEi Methods for Residential Yard Waste
¦ Activity Data (tons of waste burned)
¦ Step 1 - Estimate 2002 rural population by
county
¦ County-level rural population estimated by
applying rural/urban percentages from 2000
Census data to 2002 population
¦ Step 2 - Multiply per capita waste factor by
rural population
¦ Used national average per capita yard waste
generation factor of 0.54 Ibs/person/day.
Developing activity data for residential yard
waste:
Step 1: estimate the rural population by
county by applying percentages of rural and
urban population from the census data.
Step 2: multiply the rural population by a per
capita household waste factor of 0.54
pounds per person per day.
9-42
Residential Open Burning
NEi Methods for Residential Yard Waste (cont.)
¦ Step 3 - Estimate amount of leaf, brush
and grass yard waste
¦ Multiply total yard waste mass by 25% to
estimate leaf waste, 25% for brush waste,
and 50% for grass waste
¦ Step 4 - Estimate amount of waste burned
¦ Assume 28% of total leaf and brush waste
generated is burned; assume 0% of grass is
burned
Developing activity data for residential yard
waste:
Step 3: estimate the percentage of total yard
waste that corresponds to leaf, brush, and
grass waste.
The NEI assumed:
25% was leaf waste
25% was brush waste
50% was grass waste.
Step 4: estimate amount of waste burned.
Assume that 28% of the total leaf and brush
waste is burned.
Assume that 0% of the grass waste is
burned.
Preparation of Fine Particulate Emissions Inventories
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Instructor's Manual
9-43
Residential Open Burning
NEi Methods for Residential Yard Waste (cont.)
¦ Step 5 - Adjust for variations in vegetation
¦ Used the following ranges to make adjustments
to the amount of yard waste generated per
county:
Percent forested acres per county Adjustment for yard waste generated
Developing activity data for residential yard
waste:
Step 5: adjust to account for the variation in
vegetation among the counties.
Use an estimate of the percent of the
forested acres per county that was obtained
from the biogenic emissions land cover
database from the Biogenic Emission
Inventory System.
For example, if the BEIS data indicates that a
county has less than 10% forested acres, the
NEI assumes that there is no yard waste
generated.
9-44
Residential Open Burning
NEI Methods for Residential Yard Waste (cont.)
¦ Step 6 - Account for burning bans
¦ For counties where urban population exceeds
80 percent of the total population, the amount
of waste burned was assumed to be zero,
therefore zero open burning assigned to these
counties.
Developing activity data for residential yard
waste:
Step 6: account for burning bans in the same
manner that was used for household waste.
Preparation of Fine Particulate Emissions Inventories
9-19
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Instructor's Manual
9-45
Residential Open Burning
NEI Methods for Residential MSW and Yard Waste
E = A * EF* (1 - CE * RP * RE)
where: E = Controlled Emissions, lbs pollutant per year
A = Activity, tons of MSW or leaves/brush burned
per year
EF = Emission Factor, lbs per ton burned
CE = % Control Efficiency/100
RP = % Rule Penetration/100
RE = % Rule Effectiveness/100
¦ 100% CE assumed for counties where urban population
exceeds 80% of the total population
¦ Assumed 100% RE and RP
¦ All other counties, assumed 0% CE, RE, and RP
Once the activity data is estimated for both
solid waste and yard waste, emissions are
calculated by the use of the equation shown
here.
A 100% CE is assumed for counties that
have an urban population greater than 80%
of the total population.
The NEI also assumes that RE and RP are
100% for these areas.
The NEI assumes that all other counties are
uncontrolled.
9-46
Residential Open Burning
EiiP Alternative for Yard Waste
¦ Identify records of burning permits or
violations, coupled with data (or assumptions)
on typical volumes and material composition
The EIIP document for open burning
contains an alternative approach for
estimating emissions for yard waste.
This approach involves:
obtaining records of burning permits or
violations, and
data on typical volumes and material
composition.
9-47
Residential Open Burning
Improvements to NEI Methods
¦ Review EIIP Volume III, Ch. 16 Open
Burning
¦ Obtain State/local estimates of per-capita
waste generation
¦ Use State/local estimates for amount or
percentage of waste burned
¦ Obtain State/local estimates of months
when yard wastes are burned
The open burning EIIP contains alternative
methods for estimating activity data for this
category.
Another approach is to use the NEI
methodology coupled with state or local
estimates of the per capita waste generation
and the amount or percentage of waste
burned.
Also, state/local data on the months when
yard waste is burned would be an
improvement.
The NEI does not make any temporal
adjustment for yard waste burning.
Preparation of Fine Particulate Emissions Inventories
9-20
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9-48
Residential Open Burning
Improvements to NEI Methods (cont.)
¦ Sources
¦ Solid Waste Agency
¦ Air Agency
¦ Health Department
¦ Solid Waste Management Organization
¦ Local Survey
Some of the sources for this type of
information include:
the Solid Waste agency;
the Air Agency;
the Health Department;
the Solid Waste Management agency;
and
the use of local surveys.
9-49
Residential Open Burning
Improvements to NEI Methods (cont)
¦ Identify rules prohibiting or limiting open
burning, and the organization that enforces
those rules
¦ For areas that have burning prohibitions,
consider performing rule effectiveness (RE)
surveys
¦ Level of enforcement/compliance can be a
significant variable in calculating controlled
emissions
¦ Rule penetration (RP) to reflect duration of
seasonal bans relative to annual activity
profile, exempt activities
The NEI can also be improved by obtaining
better estimates of control measures that are
applied to open burning.
This involves identifying the rules that limit or
prohibit open burning and the organization
that enforces those rules (e.g., fire marshal,
health department).
For areas that have burning prohibitions, a
rule effectiveness survey can be performed
to estimate the compliance rate with the rule.
This is critical in rural areas where there are
few complaints about open burning.
Also, rule penetration is critical since many
open burning rules have exemptions that are
listed (e.g., firefighting training activities,
recreational campfires).
Rule penetration is also important for
seasonal bans.
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9-50
Residential Open Burning
MANE-VU Example
¦ Development of 2002 residential open burning
inventory for MANE-VU States
¦ Multi-state RPO developed inventory following
El IP procedures
This example examines the development of
a 2002 residential open burning inventory for
the MANE-VU states.
Developed by a multi-state Regional
Planning Organization
Followed the procedures in the El IP
document (i.e., conducting a survey) to
obtain activity data
9-51
Residential Open Burning
MANE-VU Example (cont.)
¦ Developed survey instrument to collect:
¦ Number/percentage of households that burn
waste
¦ Burn frequency
¦ Amount per burn
¦ Seasonal Activity
¦ 3 separate surveys for:
¦ Residential MSW
¦ Brush
¦ Leaf
A survey instrument was developed to collect
data on:
the number of households burn waste,
the burn frequency,
the amount burned, and
the seasonal nature of the burning.
Three separate surveys were performed:
residential municipal solid waste,
brush waste, and
leaf waste.
9-52
Residential Open Burning
MANE-VU Example (cont.)
¦ Survey results were used to estimate
emissions for each survey jurisdiction
¦ For non-surveyed areas, default activity data
derived from survey responses were applied
The data collected from these surveys were
used to estimate:
emissions for each survey area, and
default activity data for those areas not
included in the surveyed areas.
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9-53
Residential Open Burning
MANE-VU Exam pie (cont.)
¦To estimate the mass of waste burned for
residential MSW and yard waste, the following
equation was used:
Wt= HH * Bt* M
where: Wt = Mass ofwaste burned per time period
HH = Number of households that burn
Bt = Number of burns per time period
M = Mass ofwaste per burn
This is the equation that was used to
estimate the amount ofwaste burned based
on the data collected from the surveys.
9-54
Residential Open Burning
MANE-VU Example (cont.)
¦ Developed control database to establish area-
specific control efficiency (CE), rule
effectiveness (RE), and rule penetration (RP)
¦ Performed rule effectiveness (RE) survey to
determine level of compliance with state or
local open burning prohibitions
¦ To estimate default RE values, the survey
data was statistically analyzed resulting in one
value for all non-surveyed areas
A control database was developed that
established area-specific control efficiency,
rule effectiveness, and rule penetration.
Rule effectiveness and rule penetration can
vary significantly depending on enforcement
and the rule applicability.
A rule effectiveness survey was conducted to
determine the level of compliance with the
state or local open burning prohibitions.
This data was also used to estimate default
RE values for use in the non-surveyed areas.
9-55
Residential Open Burning
MANE-VU Example (cont.)
¦ Emissions estimated for all criteria
pollutants/precursors and several toxic air
pollutants
¦ Emissions estimated at the census tract level
(summed to county, State, region)
¦ Emissions temporally allocated to support
modeling using profiles developed from the
survey
Using the activity data and the control
information, emissions were estimated for:
all criteria pollutants and precursors, and
several HAPs.
The emissions were estimated at the census
track level and summed to the county, state,
and regional level.
Finally, the data on the occurrence of the
burning activities were used to temporally
allocate the emissions to support modeling
using profiles that were developed from the
survey.
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9-56
Lessons Learned
If leaf burning is significant, perform separate
surveys in targeted areas for leaf waste and
brush waste burning
Perform MSW surveys separate from yard
waste surveys, instead of combined to reduce
survey length
A larger sample may have allowed for greater
geographic distinction
A number of lessons were learned from
conducting the survey:
Separate surveys should be performed in
targeted areas where leaf burning is
significant.
Household waste and yard waste surveys
should be performed separately simply to
reduce the length of the survey.
A larger sample may have allowed for
greater geographic distinction.
9-57
Lessons Learned (cont.)
¦ Sub-county emissions estimates serve as the
basis for a more spatially refined inventory
¦ Regional survey provides greater consistency
¦ Better accounting of controls results in
decreased emissions relative to NEI
A number of lessons were learned from
conducting the survey:
Sub-county emissions estimates serve as
the basis for a more spatially refined
inventory.
A regional survey provides greater
consistency that allows for easier
comparison of emission estimates from
different areas.
Better accounting of controls results in a
decrease of the NEI emissions.
9-58
Land Clearing Debris Burning
What Sources are Included?
SCCs:
2610000500 - Land Clearing Debris Burning
Pollutants: PM10, PM 2.5, CO, VOC, 6 HAPs
Land clearing debris burning is covered
under SCC 2610000500.
The NEI contains emission estimates for
PM10, PM2.5, CO, VOC, and 6 HAPs from this
category.
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9-59
Land Clearing Debris Burning
NEl Method
Activity Data
Estimate the county-level total number of
acres disturbed by residential, non-residential
and roadway construction
¦ Used number of acres disturbed from fugitive
dust construction emissions activity calculations
Apply loading factor to number of acres to
estimate the amount of material or fuel
subject to burning
The activity data for this category is the
number of acres disturbed for the different
types of construction categories.
Step 1: estimate of the county-level total
number of acres disturbed.
Step 2: Apply loading factor to the number of
acres disturbed to estimate the amount of
material burned.
9-60
Land Clearing Debris Burning
NEl Method (cont.)
¦ Weighted, county-specific loading factors
developed based on acres of hardwoods,
softwoods, and grasses (BELD2 data base in
BEIS)
¦ Multiplied average loading factors by percent
contribution of each type of vegetation class
to the total county land area
Step 3: develop weighted county-specific
loading factors based on the acres of
hardwood, softwoods, and grasses.
Step 4: Multiply average loading factors by
the percent contribution of each type of
vegetation class to the total county land area.
9-61
Land Clearing Debris Burning
NEl Method (cont.)
¦ Average loading factors for hardwood and
softwood further adjusted by 1.5 to account
for mass of tree below the surface
Fuel Type Fuel Loading
(tons/acre)
Hardwood 99
Softwood 57
Step 5: adjust average loading factors for
hardwood and softwoods by an additional 1.5
to account for the mass of tree below the
surface.
The emission factors presented in the table
reflect this adjustment.
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9-62
Land Clearing Debris Burning
NEI Method (cont.)
¦ Fuel Loading Factor Equation
L=Fh*Lh + Fs*Ls + Fg*Lg
where: Lw = County-specific weighted loading factor
Fh = Fraction of county acres classified as hardwoods
Lh = Average loading factor for hardwoods
Fs = Fraction of county acres classified as softwoods
Ls = Average loading factor for softwoods
Fg = Fraction of county acres classified as grasses
Lg = Average loading factor for grasses
This slide shows the equation for developing
the loading factors.
9-63
Land Clearing Debris Burning
NEI Method (cont.)
¦ Emission Calculation
E = A * LF * EF
where: E = Emissions, lbs pollutant per year
A = No. of acres of land cleared per county
(residential + commercial + road construction)
LF = County-specific loading factor, tons per acre
EF = Emission factor, lbs pollutant per ton
¦ Represents an upper-bound emissions estimate
¦ Assume all fuel loading on land cleared is
burned; no controls or bans
Emissions are estimated from the activity
data as shown by this equation.
This formula multiplies:
the activity data,
the number of acres of land, and
the county-specific loading factor.
Since the loading factor does not vary by the
types of construction, the number of acres
cleared for all three types of activities are
summed before the loading factor is applied.
The NEI assumes that all the fuel loading on
the land cleared is burned and that no
controls or bans are in place.
For estimating these emissions, the NEI
takes a similar approach as to that used for
Residential Yard Waste (see Slide 42), in
that it removes emissions from counties that
are considered mostly urban.
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9-64
Land Clearing Debris Burning
Improvements to NEI Method
¦ Review El IP section on Open Burning
¦ EIIP Volume III, Ch. 16
¦ Preferred methods rely on direct measure of mass
of waste or debris burned
¦ Mass amounts may be available from permits
issued
¦ Improve estimates of the acres cleared
¦ Develop improved estimate of the "average
loading factor"
A good place to begin is to Review the EIIP
section on open burning.
The EIIP methods rely on a direct measure
of mass of waste or debris burned, which
may be obtainable from local officials that
track this activity for permitting purposes.
Also, obtaining a better estimate of the acres
cleared for the fugitive dust construction
category would improve the inventory for the
land clearing debris burning category.
Other approaches for improving the NEI
include developing an improved loading
factor.
9-65
Land Clearing Debris Burning
Improvements to NEI Method (cont)
¦ Identify specific counties with burning bans,
and specification of counties where wastes
are burned
¦ State or local estimates of the percentage or
amount of waste burned per construction
event
Other ways to improve on the NEI include:
Identifying specific counties with burning
bans and specifying counties where
wastes are burned.
Obtaining state or local estimates of the
percentage or amount of waste burned
per construction event (The NEI assumes
that the fuel loading associated with the
land that is cleared is being burned).
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9-66
Land Clearing Debris Burning
Northern Virginia Example
¦ Performed RE survey to determine the level
of compliance with rules for:
¦ Land clearing debris burning
¦ Residential waste burning
¦ Developed RE to apply to ozone season open
burning emission estimates for the Virginia
portion of the Washington DC-MD-VA Ozone
Nonattainment Area
This Northern Virginia area study involved a
RE survey to determine the level of
compliance with rules for land clearing debris
burning and residential waste burning.
The objective of the study was to develop a
defensible RE value for use in the State
Implementation Plan.
Current EPA guidelines requires the
application of an 80% rule effectiveness.
9-67
Land Clearing Debris Burning
Northern Virginia Example (cont)
¦ Reviewed conditions of existing open burning
rules
¦ Time period of ban
¦ Exemptions and special provisions
¦ Surveyed local open burning officials
responsible for tracking and enforcing open
burning rules
The study reviewed the existing conditions of
the open burning rules to determine:
the time period of the ban, and
the exemptions that apply.
A survey of local open burning officials
responsible for tracking and enforcing open
burning rules was conducted.
9-68
Land Clearing Debris Burning
Northern Virginia Example (cont)
¦ Started with EPA questionnaire from RE
guidance, modified for open burning
¦ Responses to questions are assigned specific
point values that add up to a maximum of 100
points, considered equivalent to a RE
percentage value
The survey form was derived from an EPA
questionnaire that is available from the rule
effectiveness guidance.
Responses to the questions on the survey
were assigned a specific point value that
adds up to a maximum of 100 points.
This point value is considered equivalent to
the RE percentage value.
If all the questions were answered with the
highest rating, an RE value of 100% was
assigned.
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9-69
Land Clearing Debris Burning
Northern Virginia Example (cont)
¦ RE values analyzed by county and for 5-
county region
¦ Estimated regional RE of 93 percent
¦ If area comprised of counties and jurisdictions
with significantly different population densities,
analyze responses by urban and rural areas
The RE values were analyzed by county as
well as for the five-county region.
A regional RE value of 93% was estimated.
Although not done in this case study,
separate RE values could be developed for
urban and rural area in cases where there
are significantly different population
densities.
9-70
Lessons Learned
¦ Local officials may defer to higher officials
(e.g., county or state-level) for enforcing open
burning rules
¦ RE may be high for time period that ban is in
effect, but need to account for duration of ban
(RP) if less than annual or seasonal
¦ It is important to account for when the ban is
taking place
Some of the lessons learned from this study
are:
Local officials tend to defer to the county
or state level officials for enforcing the
open burning rules.
In developing an annual emissions
inventory, it is important to note that RE
may be high only for the time period that
the ban is in effect.
The duration of the ban needs to be taken
into account if it is less than annual or
seasonal.
Account for when the ban is taking place and
if it overlaps with when the activity occurs.
For example, a ban in place for the summer
months for brush waste burning will have
minimal impact if the majority of the brush
burning occurs in the fall.
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9-71
Agricultural Burning - Overview
¦ SCC 2801500000
¦ PM10-PRI and PM2.5-PRI
¦ Both condensibles and filterables
Agricultural burns create particulate matter
pollution and their inventory is important to
the overall particulate matter air quality
analysis.
The SCC for agricultural burning is
2801500000.
EPA encourages States to inventory both
PM10 and PM2.5-PRI.
Since agricultural burning is a combustion
process, both condensibles and filterables
are included in the PM-PRI estimate.
9-72
Agricultural Burning - General Method
Activity
¦ Acres of crop burned
Loading Factor (tons of biomass or vegetation
per acre burned)
Emission Factor
¦ Pounds PM 25 per ton of vegetation burned (crop-
specific)
EPA develops emission estimates for most
source categories in the NEI and States
submit any improved information that they
have for those particular categories.
EPA does not at this time prepare an
estimate of emissions from agricultural
burning.
EPA encourages each State to develop their
own inventories and submit them.
In 1999 ten States (Alabama, California,
Delaware, Georgia, Idaho, Kansas, Maine,
Oregon, Texas, and Utah) developed their
own agricultural burning inventory.
In general, these States developed the
inventories by:
characterizing the activity or acres of the
crop burned,
the loading factor, and
the emission factor.
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9-73
Wheat Stubble Burning Example
¦ Method - Develop inventory using county-
specific data when available
¦ Activity
¦ Acres of wheat burned by month obtained from burn
permits issued by county fire department
¦ Fuel loading for wheat stubble from county agricultural
extension office
This study involves wheat stubble burning
and uses county-specific data.
The activity data that was obtained are the
acres of wheat burned by month.
This was obtained from burn permits that are
usually issued by the county fire department.
The fuel loading for wheat stubble was
obtained from the county agricultural
extension office.
9-74
Wheat Stubble Burning Example (cont.)
¦ Emission Factors
¦ PM10: 8.82 pounds perton of wheat stubble
burned
¦ PM2.5: 8.34 pounds perton of wheat stubble
burned
¦ Resolution
¦ Spatial - county
¦ Temporal - monthly
The emission factors are from a study done
by CARB
(Jenkins, B.M. et al., Atmospheric Pollutant
Emission Factors from Open Burning of
Agricultural and Forest Biomass by Wind
Tunnel Simulations, Volume 2, Results,
Cereal Crop Residues, California Air
Resources Board Project Number A932-
126).
The spatial resolution = county
The temporal resolution = monthly.
9-75
Wheat Stubble Burning Example (cont.)
¦ Sample Calculation
¦ PM2.5-PRI Emissions
= Acres Burned per month * Loading Factor * Emission Factor
Annual PM2.5-PRI Emissions = ? Monthly Emissions
This slide shows the formula for calculating
PM2.5-PRI emissions.
This calculation would be repeated for each
month during the burning season and
summed to give an annual emissions
estimate.
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9-76
Agricultural Burning - Improvements
Preferable to inventory larger fires (> 100
acres) as events with a start and stop date
and time; lump smaller fires into monthly
acreages
Requires coordination with burners and permit
authorities
Start building a system and relationships with
the burners/ permitting authorities to enable
such an inventory in the future
EPA encourages all states to develop their
own agricultural burning inventory.
For fires larger than 100 acres, EPA
suggests:
locate at a specific latitude and longitude,
and
record stop and start date and time of the
fire.
Smaller fires should be lumped into overall
monthly acreage like in the previous case
study example.
Obtaining information on agricultural burning
requires coordination with the burners and
the permitting authorities.
In order to develop an agricultural burning
inventory, states needs to build a system and
a relationship with the burners and permitting
authorities.
Chances are pretty good that the first time a
State tries to obtain this information they will
find that records are not kept or are not kept
in a way that can easily be understood.
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9 - 77 Agricultural Burning - Improvements
(cont.)
The local acres of crops burned are obtained
from:
burn permits,
a survey of county agricultural extension
¦ Obtain local acres of crops burned data
from:
¦ Burn permits
¦ Survey of county agricultural extension offices
¦ Verify that burns actually occurred
¦ Obtain fuel loading data
offices, or
a combination of both.
¦ Local data preferred from county agricultural
extension offices, local Natural Resources
Conservation Service Center
¦ National defaults available from Chapter 2.5
in AP-42
It is important that States verify that the
burns actually occurred.
Often a burner will get a permit to burn a lot
more acreage than they actually are able to
burn in a particular day.
In many cases a burner is limited by the
weather or other factors that keep them from
burning the acreage that they are permitted
to burn.
States need to obtain local fuel loading data.
Obtainable from the local county agricultural
extension office or the local Natural
Resources Conservation Service Center.
Preferable to using the national defaults that
are available in Chapter 2.5 of AP-42.
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9-78
Agricultural Burning
Case Study - Overview
¦ Case Study: County level emissions
inventory for burning of wheat stubble
¦ See Case Study Number 9-2
This hypothetical case study involves
developing a local inventory using survey
data and filling the data gaps with the NEI
default data.
Direct student to Case Study 9-2 and discuss
it with the students.
9-79
Agricultural Burning
Case Study - Solution
¦ Case Study: County level emissions
inventory for burning of wheat stubble
¦ See Handout 9-2
Distribute the solutions (Handout 9-2) to the
case study. Review each question with the
students. Encourage discussion among the
class. Ask each group to report on the
questions that were assigned to them. Ask
the other groups to critique their responses.
9-80
Overview of Wildland Fire Inventory
¦ Wildland Burning
¦ Types: Wildfires, Managed (Prescribed) Burns
¦ Burners:
¦ NPS, USFS, BLM, USFWS, State & Tribal Forests,
Private burners
¦ Prescribed Burning
¦ Habitat improvement
¦ Managing undergrowth and understoring of the
forest
¦ Reducing risk of wildfires
Fires have become a major issue in:
visibility impairment
creation of high concentrations of PM2.5
that could result in health problems.
The problems have been mainly in the West,
but also wildfires from the Southeast, the
Central States, Canada, and Mexico have
become a concern.
EPA's wildland burning inventory includes
both wild and managed burns.
The typical agencies that burn are:
the National Park Service,
the United States Forest Service,
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the Bureau of Land Management,
the United States Fish and Wildlife
Service,
State & Tribal Forests, and
private burners.
Prescribed burns are those burns that are
ignited intentionally:
for habitat improvement of the wildlife;
for managing the overall under growth
and understoring of the forest; and
to reduce the risk of wildfires later on by
removing the fuels from the forested area.
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9-81
How were Wildfire Emissions Estimated
in the '99 - V2V1 NEI?
¦ Pollutants
¦ PM10, PM25, NOx, CO, VOC, S02, 30 HAPS
¦ Emission Factors (AP-42)
¦ State-specific fuel consumed per acre burned
¦ Annual Activity Data ~ State (or regional) level
¦ USFS, BIA, BLM, NPS, FWS
¦ Some States provide private / State burn data
¦ Spatial allocation to counties using forested area
¦ Emissions Processor ~ Allocates Diurnal &
Monthly
The approach used to estimate wildfire
emissions in the NEI is a very rudimentary
approach.
It should be noted that this discussion
focuses on the technique for estimating
emissions from wildfires; however, emissions
from prescribed or managed fires are
estimated in a similar fashion.
The pollutants that are included in the NEI
inventory for wildland fire emissions are:
PM-io,
PM2.5,
NOx,
CO,
VOC,
SO2, and
about 30 HAPS.
The emissions factors for estimating fire
emissions and the state-specific fuel
consumed per acre burned are found in the
NEI documentation.
The technique is to merge the factor and fuel
consumption information with annual activity
data obtained at either the state or regional
level from the main burning agencies.
Most of the federal burners keep fairly good
records of the burns that they conduct mostly
because these fires end up being watched
and/or fought by personnel.
Some states also provide burn data as do
some private burners
The data obtained from the burners is at the
state level or regional level and it is allocated
to the state or county level using the amount
of forested area in a state.
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The amount of acreage that was burned
during a year in a particular state is allocated
across the state to the forested lands
The NEI allocates the emissions annually
and the emissions processor allocates the
emissions diurnally and monthly.
This allocation is important because certain
areas of the country have different fire
seasons and fire seasons are different for
prescribed burns and managed burns.
9-82
What are the RPO's Doing?
¦ The Regional Planning Organizations (RPOs)
are working on:
¦ Treating most fires as point sources
¦ Using fire-specific fuel consumption
¦ Providing a much improved emission estimate
RPOs are working on treating most fires as
point sources, using fire-specific fuel
consumption, and providing a much
improved emission estimate.
9-83
What are Future Plans for Improving the
Approach to Estimating Fire Emissions?
¦ Future plans include the following:
¦ Incorporate satellite observations
¦ Improve locational data
¦ Improve fuel characterization
¦ Use actual fire weather conditions that effect
emissions
Future plans include incorporating satellite
observations, improving locational data,
improving fuel characterization, and using
actual fire weather conditions that effect
emissions.
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9-84
What Needs to Happen Nationally / Regionally
to Improve Wildland Fire Emissions?
¦ Improve Regional / National Databases &
Models
¦ Fire Event: area burned, when, where
¦ Develop, refine national & regional models &
databases to estimate pre-burn fuel loading
¦ Refine, expand use of fuel consumption models
¦ Provide guidance on estimating impact of
mitigation measures on emissions
In order to improve wild land fire emissions,
national and regional databases and models
must be improved.
Fires need to be treated as events.
Large fires need to be entered into the
databases as point sources, including:
particular location,
start date,
end date, and
the time of day.
National regional models and databases
need to be developed and refined to improve
the pre-burn fuel loading information.
The information in AP-42 is very general,
very dated, and averaged over large regions
of the country.
Use of fuel consumption models needs to be
to refined and expanded.
Guidance on estimating the impact of
mitigation measures on emissions needs to
be provided.
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9-85
What Needs to Happen Nationally / Regionally
to Improve Wildland Fire Emissions? (cont)
¦ Fire Events Database Development
¦ Federal MOU
¦ Includes: EPA, DOI, USDA
¦ Broad Scope: Fire Management Activities
¦ Status: In Progress
¦ Investigation of the role of national
databases
¦ USDA /DOI efforts
¦ NEISGEI http://capita.wustl.edu/NEISGEI/
¦ B-RAINS (Pacific NW Database)
¦ Much more work is needed to move toward real
time data collection, QA & sharing
Fire Events Database Development
There is a Memorandum of Understanding in
effect between the EPA, Department of
Interior, and the United States Department of
Agriculture to develop a fire events database.
It is a broad scope MOU that covers fire
management activities including ways to
improve the national databases.
There is a similar effort called nice guy, being
conducted at Washington University in St.
Louis.
There currently exists a database for
recording fire events in the Pacific NW called
the B-RAINS system.
Although these types of projects are moving
toward real time data collection, quality
assurance and data sharing, there is much
more work needed in these areas.
9-86
What Needs to Happen Nationally / Regionally
to Improve Wildland Fire Emissions? (cont)
¦ Investigating the Potential Use of Satellites
¦ EPA
¦ EllP-funded Overview of Using Satellites in AQ
¦ http://www.epa.gov/ttn/chief/eiip/pm25inventory/
remsens.pdf
¦ Collaboration w/NASA
¦ Interagency
¦ NIFC
¦ Work at Missoula Fire Research Center & Salt Lake City
¦ Collaboration w/NASA
¦ Others
¦ CAMFER
EPA is also investigating the potential use of
satellites to improve wildland fire inventories.
EPA has funded a report entitled Overview of
Using Satellites in AQ Management.
There is also collaboration going on with
NASA to take advantage of their skills in
aerial surveillance with satellites.
There are several interagency groups
working on the use of satellites including the
National Interagency Fire Center (a jointly
funded effort of all the Federal burners) in
Boise, Idaho, the Missoula Fire Research
Center, and Salt Lake City.
Another project includes CAMFER, which is
a project underway at University of California
Berkeley.
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9-87
What Needs to Happen Nationally / Regionally
to Improve Wildland Fire Emissions? (cont)
¦ Emission Estimation Tools & Inventories
¦ EPA
¦ Recent Report: Fire Emission Estimation Methods
¦ USFS
¦ Work at the Fire Sciences Lab (Missoula)
¦ Work at Pacific NW Research Station (Corvallis)
¦ Collaboration
¦ WRAP - Fire Emissions Joint Forum
¦ RPO-led 2002 Wldland Fire El development
¦ Nat'l Fire Emissions Workshop
¦ Nat'l FCC coverage @ 1 km2 resolution
¦ Emissions model to interface with grid models
EPA recently published a report entitled Fire
Emission Estimation Methods that is
available on the CHIEF web site that
contains a lot of good background
information on wildland fire emission
estimation.
In addition, there is a lot of ongoing work to
improve emission estimation tools for
wildland fires.
The US Forest Service has ongoing work on
the development of fuel consumption and fire
behavior models at the Fire Sciences Lab in
Missoula and also at the Pacific NW
Research Station in Corvallis.
Also, there is also a lot of emission factor
testing occurring in the Fire Sciences Lab in
Missoula.
There is also collaboration going on between
all the different burn agencies, EPA, and the
Regional Planning Organizations.
The Western Regional Air Partnership
conducts a fire emissions joint forum and
EPA and the burn agencies participate in that
forum.
There is a RPO project to refine the 2002
wildland fire emissions inventory.
There was a national fire emissions
workshop held in May of 2004 that focused
on the latest ideas and methodologies for
estimating fire emissions.
Also, the US Forest Service with assistance
and funding from EPA is developing a
geographic coverage of the fuel types and
fuel conditions for burning at a 1 km
resolution.
Preparation of Fine Particulate Emissions Inventories
9-40
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Instructor's Manual
A map of the country that will be useful in
GIS systems will be developed out of this
project.
Finally there will be further work on
developing an emissions model that will
estimate fire emissions in real time using real
time meteorological data.
Output from this model will be fed directly
into the grid models for estimating ambient
air concentrations associated with fire
emissions.
9-88
Wildland Fire Emissions Module
(under development)
¦ Modular input to Emission Models (e.g.,
SMOKE, OpEM) to interface with the CMAQ
modeling system
¦ User Inputs: Fire locations, duration, size
¦ Model Components (Modules from the
BlueSky system)
¦ Fuel loading default: NFDRS / FCC map
¦ Fuel Moisture: Calculates using MM5 met data
¦ Fuel Consumption: CONSUME/FOFEM
¦ Emissions, Heat Release & Plume Rise: EPM &
Briggs (modified)
The emissions model that is under
development is the Wildlands Fire Emissions
Model.
It will interface with SMOKE and OpEMs, and
the CMAQ modeling system.
The user will need to input:
fire locations,
durations, and
size of the fire.
The model components, which will be drawn
from the Blue Sky system being developed in
the Pacific NW, are:
1) A fuel loading default that will use
either the national fire danger rating
system or, as it becomes available,
the FCC map.
2) Fuel moisture will be calculated using
actual metrological data for the period
during, and immediately before the
fire.
This is a significant improvement over the
past and an important improvement since
fuel moisture is critical in determining the
amount of fuel that will burn and the
emissions from that fuel.
Preparation of Fine Particulate Emissions Inventories
9-41
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Instructor's Manual
3) Fuel consumption models are being
built into the model.
Both the CONSUME / FOFEM are such
models that have recently been improved
significantly.
The CONSUME model is developed in the
Corvallis lab and the FOFEM has been
developed by the Missoula Fire Lab.
These models compliment each other and
have strengths and weakness that, when
used together properly, give a pretty good
handle on fuel consumption.
4) The emission heat release and plume
rise is being handled through the EPM
model and the modified Briggs plume
rise equation.
There is an improvement to the EPM model
called FAR, which is about to be released in
beta test form.
9-89
Wildland Fire Emissions Module
(under development) (cont)
¦ Outputs: Gridded hourly emissions, plume
characteristics
¦ Integrate, Test & Release Module (late 2004)
The output of the model will be:
a gridded hourly emission estimate, and
plume characteristics.
The output will be able to be interfaced with
grid models to provide a regional scale
estimate of the effects of fires.
For instance, this new wildland fire model will
be able to estimate the NOx plume from a
wildland fire and the effects of that increased
NOx on ozone formation.
The integration, testing, and release of the
model are anticipated for late 2004.
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Instructor's Manual
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Preparation of Fine Particulate Emissions Inventories
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Preparation of Fine Particulate Emission
Inventories
Final Exam
1. are composed mostly of carbonaceous particles, but will also contain
crustal materials and a few other materials.
a. Primary particles
b. Secondary particles
c. VOCs
d. Sulfates
2. Twenty-five percent of total emissions are associated with electric utilities,
the second largest contributor.
a. VOC
b. SOx
c. NOx
d. PM
3. are currently under development for estimating emissions from
ammonia sources, fugitive dust and wildland fires.
a. Emission processors
b. Process-based emission models
c. Receptor Models
d. FIRE databases
4. The includes data on 52,000-point sources and about 400
categories of highway and nonroad mobile sources and 300 categories of area
sources.
a. National Acidic Precipitation Assessment Program
b. National Particle Inventory
c. National Emissions Trends Inventory
d. National Emissions Inventory
5. In calculating emissions from onroad sources, data needs to be matched
to a corresponding MOBILE 6.2 emission factor and mapped according to speed,
roadway, and vehicle type.
a.
VMT
b.
FHA
c.
LTO
d.
RMS
APTI 419B Final Exam
-------
6. Which of the following pollutants do not have an emission factor included in the
NONROAD model?
a. CO
b. C02
c. VOC
d. None of the above
7. Which of the following is a measure of equipment activity that is used by the
NONROAD model to estimate exhaust emissions?
a. Load factor
b. Horsepower
c. Equipment population
d. All of the above
8. Which of the following statements about primary PM is true?
a. It is emitted directly from a stack.
b. It is formed downwind of the source.
c. It consists of filterable PM only.
d. It is almost always PM2.s or less.
9. Secondary PM precursors should
a. not be reported in a particulate matter emission inventory.
b. always be reported in a particulate matter emission inventory.
c. be reported in a particulate matter emission inventory for nonattainment areas.
d. be totaled with primary PM in a particulate matter emission inventory.
10. Which of the following is not a source for obtaining information for identifying area
sources for inclusion in an emissions inventory?
a. EIIP Area Source Guidance
b. AP-42
c. SCC Handbook
d. Toxics Release Inventory
11. Which type of emissions are reported for PM in the NEI?
a. Actual
b. Allowable
c. Potential
d. All of the above
12. Which of the following variables is not included in the NEI emissions methodology
for estimating emissions from agricultural tilling operations?
a. silt content of soil
b. acres of land tilled
c. control measures
d. number of passes
APTI 419B Final Exam
-------
13. In the unpaved roads category, the NEI contains emission estimates for
a. PM10
b. PM2.5
c. Condensable PM
d. A and B
14. True or False - A smaller Precipitation Evaporation value represents low
precipitation and humidity and results in larger adjustment to the base emissions
estimate for fugitive emissions from construction activities.
a. True
b. False
15. Which of the following statements about ammonia emissions from animal
husbandry operations is false?
a. Animal husbandry operations are the largest emitter of ammonia nationally.
b. There are probable errors in some of the ammonia emission factors in the NEI.
c. The NEI does not take temperature into account in estimating ammonia
emissions.
d. All of these statements are false.
16. For which of the following categories did the NEI not develop a methodology?
a. agricultural field burning
b. agricultural tilling
c. wood stoves
d. land clearing debris burning
17. The NEI methodology for residential municipal solid waste burning assumes that if
a county has an urban population that exceeds 80% of the total population, the
amount of waste burned is percent.
a. 0
b. 25
c. 50
d. 75
18. The activity data for land clearing debris burning is the same that is used for the
category.
a. agricultural burning
b. unpaved roads
c. agricultural tilling
d. construction
APTI 419B Final Exam
-------
19. How many manure management trains have been identified for estimating
ammonia emissions from animal husbandry operations?
a. one
b. one for each type of animal
c. six
d. fifteen
20. Developing VMT data for use in conjunction with MOBILE 6.2 can be done by
using distributions of VMT.
a. roadway type
b. time-weighted
c. speed
d. population-weighted
APTI 419B Final Exam
-------
Name:
PREPARATION OF FINE PARTICULATE EMISSION INVENTORIES
FINAL EXAM ANSWER SHEET
Instructions: Circle the appropriate answer on this Answer Sheet
1.
A
B
C
D
2.
A
B
C
D
3.
A
B
C
D
4.
A
B
C
D
5.
A
B
C
D
6.
A
B
C
D
7.
A
B
C
D
8.
A
B
C
D
9.
A
B
C
D
10.
A
B
C
D
11.
A
B
C
D
12.
A
B
C
D
13.
A
B
C
D
14.
A
B
C
D
15.
A
B
C
D
16.
A
B
C
D
17.
A
B
C
D
18.
A
B
C
D
19.
A
B
C
D
20.
A
B
C
D
Final Exam Answer Sheet
-------
Preparation of Fine Particulate Emission
Inventories
Final Exam- Instructor's Version
1. are composed mostly of carbonaceous particles, but will also contain
crustal materials and a few other materials.
a. Primary particles
b. Secondary particles
c. VOCs
d. Sulfates
2. Twenty-five percent of total emissions are associated with electric utilities,
the second largest contributor.
a. VOC
b. SOx
c. NOx
d. PM
3. are currently under development for estimating emissions from
ammonia sources, fugitive dust and wildland fires.
a. Emission processors
b. Process-based emission models
c. Receptor Models
d. FIRE databases
4. The includes data on 52,000-point sources and about 400
categories of highway and nonroad mobile sources and 300 categories of area
sources.
a. National Acidic Precipitation Assessment Program
b. National Particle Inventory
c. National Emissions Trends Inventory
d. National Emissions Inventory
5. In calculating emissions from onroad sources, data needs to be matched
to a corresponding MOBILE 6.2 emission factor and mapped according to speed,
roadway, and vehicle type.
a.
VMT
b.
FHA
c.
LTO
d.
RMS
APTI 419B Final Exam
-------
6. Which of the following pollutants do not have an emission factor included in the
NONROAD model?
a. CO
b. C02
c. VOC
d. None of the above
7. Which of the following is a measure of equipment activity that is used by the
NONROAD model to estimate exhaust emissions?
a. Load factor
b. Horsepower
c. Equipment population
d. All of the above
8. Which of the following statements about primary PM is true?
a. It is emitted directly from a stack.
b. It is formed downwind of the source.
c. It consists of filterable PM only.
d. It is almost always PM2.s or less.
9. Secondary PM precursors should
a. not be reported in a particulate matter emission inventory.
b. always be reported in a particulate matter emission inventory.
c. be reported in a particulate matter emission inventory for nonattainment areas.
d. be totaled with primary PM in a particulate matter emission inventory.
10. Which of the following is not a source for obtaining information for identifying area
sources for inclusion in an emissions inventory?
a. El IP Area Source Guidance
b. AP-42
c. SCC Handbook
d. Toxics Release Inventory
11. Which type of emissions are reported for PM in the NEI?
a. Actual
b. Allowable
c. Potential
d. All of the above
12. Which of the following variables is not included in the NEI emissions methodology
for estimating emissions from agricultural tilling operations?
a. silt content of soil
b. acres of land tilled
c. control measures
d. number of passes
APTI 419B Final Exam
-------
13. In the unpaved roads category, the NEI contains emission estimates for
a. PM10
b. PM2.5
c. Condensable PM
d. A and B
14. True or False - A smaller Precipitation Evaporation value represents low
precipitation and humidity and results in larger adjustment to the base emissions
estimate for fugitive emissions from construction activities.
a. True
b. False
15. Which of the following statements about ammonia emissions from animal
husbandry operations is false?
a. Animal husbandry operations are the largest emitter of ammonia nationally.
b. There are probable errors in some of the ammonia emission factors in the NEI.
c. The NEI does not take temperature into account in estimating ammonia
emissions.
d. All of these statements are false.
16. For which of the following categories did the NEI not develop a methodology?
a. agricultural field burning
b. agricultural tilling
c. wood stoves
d. land clearing debris burning
17. The NEI methodology for residential municipal solid waste burning assumes that if
a county has an urban population that exceeds 80% of the total population, the
amount of waste burned is percent.
a. 0
b. 25
c. 50
d. 75
18. The activity data for land clearing debris burning is the same that is used for the
category.
a. agricultural burning
b. unpaved roads
c. agricultural tilling
d. construction
APTI 419B Final Exam
-------
19. How many manure management trains have been identified for estimating
ammonia emissions from animal husbandry operations?
a. one
b. one for each type of animal
c. six
d. fifteen
20. Developing VMT data for use in conjunction with MOBILE 6.2 can be done by
using distributions of VMT.
a. roadway type
b. time-weighted
c. speed
d. population-weighted
APTI 419B Final Exam
-------
Name:
PREPARATION OF FINE PARTICULATE EMISSION INVENTORIES
FINAL EXAM ANSWER SHEET - INSTRUCTOR VERSION
Instructions: Circle the appropriate answer on this Answer Sheet
Final Exam Answer Sheet
------- |