United States	Air Pollution Training Institute (APTI)	September 2004

Environmental Protection	Mail Drop E14301

Agency	Research Triangle Park, NC 27711

/a ¦ Preparation of Fine

f |	1

Particulate Emission
Inventories

Case Studies

APTI Course 419B

Developed by

ICES Ltd.

EPA Contract No. 68D99022


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Case Study Number 4-1

Estimating PMi0 and PM2.5 Emissions from Locomotives

Exercise Objective

This exercise will test your ability to apply the methodology used to estimate emissions
from locomotives.

Directions

•	Review the background information and data provided.

•	Convene groups of 4-5 people.

•	Answer the questions in the "Problem" section. These will guide you in your
thinking to organize the data and then using it to estimate emissions.

•	You will have 15 minutes to complete these tasks before the class reconvenes
for discussion. Each group will be assigned specific questions and asked to
present its results. Other groups will be asked if they agree or disagree with
the findings.

Background

This case study involves the development of a county level locomotive inventory for
Sedgwick County, Kansas. In developing this inventory only two SCCs (Line-Haul and
Switchyard Operations) are included. The activity data were obtained through a survey of
the two railroad companies operating in the inventory area. The purpose of this case
study is to require the student to review the activity data that was collected to calculate
fuel consumption, and then calculate PMio and PM2.5 emissions for both line-haul and
switchyard operations.

Available Data

The types of data that were obtained from the survey included locomotive fuel
consumption rates and traffic density for the large line-haul locomotives; fuel
consumption rates and percentage of the total track in the inventory area for smaller line-
haul locomotives; and the number of yard locomotives for switchyard locomotives.
Because the railroad operated outside the county, the total annual fuel consumption
represented locomotives that were operated outside of the inventory area.

The specific data provided by the railroad companies included the gross tonnage by a
specific line segment of the rail as well as an estimate of the distance and miles for each
of these segments. They also provided a fuel consumption index of 0.00139, which

Case Study Number 4-1 - Locomotives

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relates gallons consumed to gross ton-mile. This estimate is assumed to apply for all line
segments. This data is presented in the following table.

Line-haul Locomotive Data Providet

by Railroads

Line Segment

Gross Tonnage,
Million GT

Distance in Miles

1

15.0

17.0

2

8.0

15.0

3

0.0

10.5

The smaller of the two railroad companies operating in the inventory area did not have
records on the gross tonnage.

The railroad company also provided an estimate of the number of switchyard locomotives
that are operating in each switchyard. This particular railroad operates two switchyards
and provided an estimate of how often throughout the year each yard was operating. This
data is presented in the following table.

Switchyard Data

Provided by Rai

Switch

Number of

Yard

Switchyard



Locomotives

1

1.3

2

0.5

Total

1.8

roads

Problem

As the environmental engineer for the county, you are charged with developing PMio and
PM2.5 annual emission estimates for both long haul and switchyard locomotives using the
available data. Only emissions are needed for the railroad company that was able to
provide data. It is suggested that you approach the problem in the following manner.

1. Are the PM emission estimation methodologies the same for long haul and
switchyard locomotives?

2. What PM emission factors are applicable to locomotives?

Case Study Number 4-1 - Locomotives


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3. What is the basis of the activity data for locomotives?

4. What is the methodology for estimating PMio and PM2.5 emissions for line haul
locomotives? For switchyard locomotives?

5. What is your estimate of the PMi0 and PM2.5 emissions from long-haul
locomotives?

6. What is your estimate of the PM10 and PM2.5 emissions from switchyard
locomotives?

7. Why does the railroad data on switchyards show fractions of switchyard
locomotives in use in each switchyard?

8. Do emissions for each line segment and switchyard need to be calculated
individually?

9. How can PM10 and PM2.5 emissions be estimated for locomotives of the smaller
company that was not able to provide gross tonnage data?

Case Study Number 4-1 - Locomotives

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Notes

•	Conversion factors: 453.6 grams = 1 pound

0.002204 pounds = 1 gram

•	Assume that 92% of PMi0 emissions are PM2.5.

. EPA uses a default value of 82,500 gallons of fuel consumed for each switchyard
locomotive (based on 24 hours a day, 365 days a year).

Case Study Number 4-1 - Locomotives	4


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Case Study Number 7-1

Estimating PMi0 and PM2.5 Emissions from Unpaved Roads

Exercise Objective

This exercise will test your ability to apply the methodology used to estimate emissions
from unpaved roads.

Directions

•	Review the background information and data provided.

•	Convene groups of 4-5 people.

•	Answer the questions in the "Problem" section. These will guide you in your
thinking to organize the data and then using it to estimate emissions.

•	You will have 15 minutes to complete these tasks before the class reconvenes
for discussion. Each group will be assigned specific questions and asked to
present its results. Other groups will be asked if they agree or disagree with
the findings.

Background

This case study involves developing a PMio inventory for unpaved roads in a
hypothetical county. The method is to develop a local PMio inventory using county level
data where available, and filling in the gaps with NEI default data.

Available Data

In this case study, daily vehicle miles traveled (VMT) data was provided by a local
metropolitan planning organization, and VMTs were calculated using TransCAD GIS-
based modeling software.

The emission factor input values for surface material silt content were obtained from
samples taken on dirt roads in the county for which the inventory was conducted. Default
values were used for the mean vehicle weight value and the surface material moisture
content. The number of days that were exceeding the precipitation threshold of 0.01
inches was obtained from a local meteorological station. The inventory is a county level
inventory with a temporal resolution of monthly.

Case Study Number 7-1 - Unpaved Roads

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The following table shows a summary of the data that are available for use in the case
study.

Data for Unpaved Road Case Study

VMT for the Month of
June

2.964 million miles

Surface Material Silt
Content

7.5 percent

Problem

You have been asked by your supervisor to develop an estimate of resuspended road
surface material from unpaved roads in a county for the month of June. It is suggested
that you approach the problem in the following manner.

1. How is the PM emission factor for unpaved roads calculated?

2. What emissions from unpaved roads are accounted for by the emission factor?

3. What is the basis of the activity data for unpaved roads?

4. What is the methodology for estimating PMio emissions from unpaved roads?

5. What is the value for the empirical constant in the emission factor equation?

6. What is the value for the default surface material moisture content?

Case Study Number 7-1 - Unpaved Roads

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7. How is mean vehicle weight considered in the estimation of PM emissions from
unpaved roads?

8. What is your estimate for the PMi0 emission factor for unpaved roads in the
hypothetical county?

9. What is your estimate of the PMi0 emissions from unpaved roads in the county for
the month of June?

10. How would PM2.5 emissions be estimated if this case study required that an
estimate of PM2 5be developed?

11. How would annual PMi0 emissions from unpaved roads be calculated?

Notes

•	Assume that the mean vehicle speed for vehicles on unpaved roads is 35 mph.

•	Assume that PM emissions from vehicle exhaust, brake wear, and tire wear are
equal to 0.2819 lbs/VMT.

. 1 lb/VMT = 281.9 g/VMT

Case Study Number 7-1 - Unpaved Roads

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Case Study Number 7-2

Estimating PMi0 and PM2.5 Emissions from Residential Construction

Activities

Exercise Objective

This exercise will test your ability to apply the methodology used to estimate PMio and
PM2.5 emissions from residential construction activities.

Directions

•	Review the background information and data provided.

•	Convene groups of 4-5 people.

•	Answer the questions in the "Problem" section. These will guide you in your
thinking to organize the data and then using it to estimate emissions.

•	You will have 15 minutes to complete these tasks before the class reconvenes
for discussion. Each group will be assigned specific questions and asked to
present its results. Other groups will be asked if they agree or disagree with
the findings.

Background

This hypothetical case study involves developing a PM10 and PM2.5 inventory for
residential construction at the county level in a PM nonattainment area. In this example,
local officials provided data that represent actual housing unit starts for single unit
houses, duplexes, and apartment buildings. NEI default values are used where local level
data is not available.

Available Data

The following table shows a summary of the data provided by local officials that are
available for use in the case study.

Case Study Number 7-2 - Residential Construction

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Data for Residential Construction Case Study



Single Family
Houses (No
Basements)

Duplexes

Apartments

Housing Structure
Starts (B)

251

2

44

Acres Disturbed per
building (f)

0.184

0.184

0.07

Duration (m) (months)

6

6

12

In addition, the Thornthwaite Precipitation Evaporation Index for the soil in the county
being inventoried is 6, and the dry silt content of the county is 40 percent.

Problem

You have been asked by your supervisor to develop an estimate of fugitive dust
emissions from the residential construction activities in the past year. Furthermore, the
emissions estimates need to be categorized by single-family homes, duplexes, and
apartments. It is suggested that you approach the problem in the following manner.

1. What PM emission factors are applicable to residential construction activities?

2. What is the basis of the activity data for residential construction activities and
how is it measured?

3. What is the methodology for estimating PMio and PM2.5 emissions from
residential construction activities?

Case Study Number 7-2 - Residential Construction

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4. What is your estimate of the PMio and PM2.5 emissions from the residential
construction activities in the county within the past year without accounting for
rule effectiveness, rule penetration, soil moisture, and silt content?

5. What is your estimate of the PM10 and PM2.5 emissions from the residential
construction activities in the county within the past year accounting for control
efficiency and rule penetration, but not for soil moisture and silt content?

6. What is your estimate of the PMi0 and PM2.5 emissions from the residential
construction activities in the county within the past year accounting for control
efficiency, rule penetration, and soil moisture?

7. What is your estimate of the PM10 and PM2.5 emissions from the residential
construction activities in the county within the past year accounting for control
efficiency, rule penetration, and silt content (but not soil moisture)?

8. What is your estimate of the PM10 and PM2.5 emissions from the residential
construction activities in the county within the past year accounting for control
efficiency, rule penetration, soil moisture, and silt content?

9. Explain the significance of the adjustments that are made for soil moisture content
and silt content.

Case Study Number 7-2 - Residential Construction

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Notes

•	Assume that none of the houses in the inventory area include basements

•	Assume a Rule Effectiveness of 100%

•	Assume a Control Efficiency of 50%

•	Assume a Rule Penetration of 75%

•	Assume PM2.5 is 20 percent of PMio

Case Study Number 7-2 - Residential Construction

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Case Study Number 7-3

Estimating PMi0 and PM2 5 Emissions from Road Construction

Activities

Exercise Objective

This exercise will test your ability to apply the methodology used to estimate emissions
from road construction activities.

Directions

•	Review the background information and data provided.

•	Convene groups of 4-5 people.

•	Answer the questions in the "Problem" section. These will guide you in your
thinking to organize the data and then using it to estimate emissions.

•	You will have 15 minutes to complete these tasks before the class reconvenes
for discussion. Each group will be assigned specific questions and asked to
present its results. Other groups will be asked if they agree or disagree with
the findings.

Background

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. In this case study
the county officials have provided estimates of the miles of roadway constructed in the
county.

Available Data

The following table shows a summary of the data that are available for use in the case
study.

Data for Road Construction Case Study

Miles of roadway
constructed

12.3 miles

Duration

12 months

In addition, the Thornthwaite Precipitation Evaporation Index for the soil in the county
being inventoried is 6, and the dry silt content of the county is 40 percent.

Case Study Number 7-3 - Road Construction

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Problem

You have been asked by your supervisor to develop an estimate of fugitive dust
emissions from the road construction activities in the past year. It is suggested that you
approach the problem in the following manner.

1. What PM emission factors are applicable to road construction?

2. What is the basis of the activity data for road construction?

3. What is the methodology for estimating PMi0 and PM2.5 emissions from road
construction?

4. What is your estimate of the PMio and PM2.5 emissions from the road construction
activities in the county within the past year without accounting for rule
effectiveness, rule penetration, soil moisture, and silt content?

5. What is your estimate of the PMio and PM2.5 emissions from the road construction
activities in the county within the past year accounting for control efficiency and
rule penetration, but not for soil moisture and silt content?

Case Study Number 7-3 - Road Construction

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6. What is your estimate of the PMio and PM2.5 emissions from the road construction
activities in the county within the past year accounting for control efficiency, rule
penetration, and soil moisture?

7. What is your estimate of the PMi0 and PM2.5 emissions from the road construction
activities in the county within the past year accounting for control efficiency, rule
penetration, and silt content (but not soil moisture)?

8. What is your estimate of the PM10 and PM2.5 emissions from the road construction
activities in the county within the past year accounting for control efficiency, rule
penetration, soil moisture, and silt content?

Notes

•	Assume that all roads fall into the urban collectors category.

•	Assume a Rule Effectiveness of 100%

•	Assume a Control Efficiency of 50%

•	Assume a Rule Penetration of 75%

Case Study Number 7-3 - Road Construction

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Case Study Number 9-1

Estimating PMi0 Emissions from Residential Wood Combustion

Exercise Objective

This exercise will test your ability to apply the methodology used to estimate PMio
emissions from residential wood combustion.

Directions

•	Review the background information and data provided.

•	Convene groups of 4-5 people.

•	Answer the questions in the "Problem" section. These will guide you in your
thinking to organize the data and then using it to estimate emissions.

•	You will have 20 minutes to complete these tasks before the class reconvenes
for discussion. Each group will be assigned specific questions and asked to
present its results. Other groups will be asked if they agree or disagree with
the findings.

Background

This case study involves the development of a PMio emissions inventory for a
hypothetical county. In developing this inventory, the preferred method of using a
residential wood combustion survey was employed. The purpose of this case study is to
require the student to review the survey data that was collected to calculate wood
consumption and then PMio emissions.

The hypothetical county is classified as urban since more than 50 percent of the
population is located in cities and towns. The latest Census data indicates that the county
has a population of 1.3 million people living in 380,000 homes. The survey was sent to
500 homes in the county.

The hypothetical county is located in the Mid-Atlantic region and the number of heating
degree days falls between 5,500 and 7,000.

Case Study Number 9-1 - Residential Wood Combustion

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Available Data

The following table shows a summary of the data that was obtained as a result of a survey
that was conducted in the county.

Data Obtained From the Residential Wood Combustion Survey

Number of homes with a fireplace without an insert

110

Number of homes with a fireplace with an insert

30

Number of homes with a wood stove

40

Average number of cords of wood burned in fireplaces without an insert

1/4

Average number of cords of wood burned in fireplaces with an insert

1/4

Average number of cords of wood burned in wood stoves

1/8

The data on the number of cords of wood burned are for an average winter week. The
survey also asked respondents to estimate how many weeks during the year they used
their fireplaces or woodstoves as well as the amount of wood that was burned during the
non-winter weeks in which they used their fireplaces and woodstoves. However, the data
on the temporal usage of wood was determined to be invalid.

Problem

You have been tasked with developing an annual PMio emissions inventory for
residential wood combustion within a hypothetical county. It is suggested that you
approach the problem in the following manner.

1. What PMio emission factors are applicable to residential wood combustion?

2. What is the methodology for estimating PMio emissions from residential wood
combustion?

Case Study Number 9-1 - Residential Wood Combustion

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3. What is your estimate of the PMio emissions from residential wood combustion
in the county within the past year without accounting for rule effectiveness or rule
penetration?

4. What is your estimate of the PMi0 emissions from residential wood combustion
in the county within the past year accounting for rule effectiveness and rule
penetration?

5. If the residential wood combustion survey failed to collect data on the amount of
wood burned, how could emissions from fireplaces without inserts be calculated?

6. How would you propose to estimate PM2.5 emissions from residential wood
combustion in the county?

Case Study Number 9-1 - Residential Wood Combustion

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Notes

•	Assume that the entire county is located in the same climate zone.

•	Assume different types of wood are burned with an average density of 23.9
pounds per cubic feet

•	Conversion factor: 1 cord =128 cubic feet

•	PMio emission factor for residential fireplaces without inserts is 23.2 pounds per
ton dry wood burned.

. AP-42 PMio emission factor for residential fireplaces with inserts is 30.6 pounds
per ton dry wood burned.

. AP-42 PMio emission factor for residential woodstoves is 34.6 pounds per ton dry
wood burned.

•	Assume each season is 13 weeks long.

•	Assume a Rule Effectiveness of 100%

•	Assume a Rule Penetration of 75%

Case Study Number 9-1 - Residential Wood Combustion

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Case Study Number 9-2

Estimating PMi0 and PM2.5 Emissions from Agricultural Field Burning

Exercise Objective

This exercise will test your ability to apply the methodology used to estimate emissions
from agricultural field burning operations.

Directions

•	Review the background information and data provided.

•	Convene groups of 4-5 people.

•	Answer the questions in the "Problem" section. These will guide you in your
thinking to organize the data and then using it to estimate emissions.

•	You will have 10 minutes to complete these tasks before the class reconvenes
for discussion. Each group will be assigned specific questions and asked to
present its results. Other groups will be asked if they agree or disagree with
the findings.

Background

This hypothetical case study involves developing a PMi0 and PM2.5 inventory for burning
a field of wheat stubble. The method is to develop a local PMio and PM2.5 inventory
using county level data where available, and filling in the gaps with NEI default data.

Available Data

This case 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. Also,
the fuel loading for wheat stubble was obtained from the county agricultural extension
office.

Case Study Number 9-2 - Agricultural Field Burning

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The following table shows a summary of the data that are available for use in the case
study.

Data for Agricultural Burning Study

Number of Acres Burned
in June

1,950

Wheat Stubble Fuel
Loading

1 ton/acre

Problem

You have been asked by your supervisor to develop an estimate of PMio and PM2.5
emissions from these activities during the month of June. The spatial resolution for this
inventory is the county and the temporal resolution is monthly. It is suggested that you
approach the problem in the following manner.

1. What is the basis of the activity data for agricultural burning?

2. What does the loading factor represent?

3. What is the methodology for estimating PM10 emissions from agricultural burning
operations?

4. What is your estimate of the PM10 emissions from wheat stubble burning in the
county for the month of June?

5. How would PM2.5 emissions be estimated if this case study required that an
estimate of PM2 5be developed?

Case Study Number 9-2 - Agricultural Field Burning

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6. How would annual PMio emissions from agricultural burning be calculated?

Notes

• The emission factor for wheat stubble burning is 8.82 pounds per tons of wheat
stubble burned for PMi0.

. The emission factor for wheat stubble burning is 8.34 pounds per tons of wheat
stubble burned for PM2.5.

Case Study Number 9-2 - Agricultural Field Burning

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