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2020 National Emissions Inventory Technical
Support Document: Waste Disposal - Open
Burning - Land Clearing Debris


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EP A-454/R-23 -001 hh
March 2023

2020 National Emissions Inventory Technical Support Document: Waste Disposal - Open

Burning - Land Clearing Debris

U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC


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Contents

List of Tables	i

34	Waste Disposal - Open Burning - Land Clearing Debris	34-1

34.1	Sector Description and Overview	34-1

34.2	EPA-developed estimates	34-1

34.2.1	Activity data	34-1

34.2.2	Allocation procedure	34-10

34.2.3	Emission factors	34-12

34.2.4	Controls	34-12

34.2.5	Emissions	34-13

34.2.6	Example calculations	34-13

34.2.7	Improvements/Changes in the 2020 NEI	34-18

34.2.8	Puerto Rico and U.S. Virgin Islands	34-18

34.2.9	References	34-18

List of Tables

Table 34-1: Surface soil removed per unit type	34-5

Table 34-2: Spending per Mile and Acres Disturbed per Mile by Highway Type	34-7

Table 34-3: Fuel Loading Factors by Vegetation Type	34-9

Table 34-4: Ranges and midpoints for data withheld from State and County Business Patterns	34-10

Table 34-5: Example CBP vales for NAICS 2362 for a state	34-11

Table 34-6: Sample calculations for PM25-PRI emissions from open burning of land clearing debris. 34-13

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34 Waste Disposal - Open Burning - Land Clearing Debris

34.1	Sector Description and Overview

This source category covers intentional burning for waste disposal purposes of land clearing debris.
Open burning of land clearing debris is the purposeful burning of debris, such as trees, shrubs, and
brush, from the clearing of land for the construction of new buildings and highways. Emission estimates
from open burning of land clearing debris are a function of the amount of material or fuel subject to
burning per year. The SCC (2610000500) description for land clearing debris is "Waste Disposal,
Treatment, and Recovery; Open Burning; All Categories; Land Clearing Debris (use 28-10-005-000 for
Logging Debris Burning)".

Section 35 covers open burning for municipal solid waste. Section 36 covers open burning for leaf and
brush yard waste.

A list of agencies that submitted open burning, land clearing debris emissions is provided in Section
6.2.3.

34.2	EPA-developed estimates

The emissions from open burning from land clearing debris are estimated based on the number of acres
disturbed from non-residential, residential, and road construction. The number of acres disturbed is
multiplied by a fuel loading factor to determine the amount of land clearing debris burned in each
county. This number is multiplied by emissions factors to determine emissions of CAPs and HAPs.

34.2.1 Activity data

The amount of material burned is estimated using the county-level total number of acres disturbed by
residential, non-residential, and road construction. County-level weighted loading factors are applied to
the total number of construction acres to convert acres to tons of available fuel.

Acres Disturbed from Non-Residential Construction

The activity data for this non-residential construction is the acreage disturbed from non-residential
construction, which is estimated using data from the U.S. Census Bureau's Annual Value of Construction
Put in Place in the U.S [ref 1], and a conversion factor from MRI's Estimating Particulate Matter
Emissions from Construction Operations, Final Report [ref 2], The national-level non-residential
construction spending data are allocated to the county-level based on the proportion of non-residential
construction employees in each county. Employment data are taken from the U.S. Census Bureau's
County Business Patterns (CBP), and gaps in employment data are filled using a process described in
detail in section 34.2.2.

r, ^ EmPc

EmpFrr =	

Empus

CSc = EmpFrc x CSus

34-1

(1)

(2)


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Where:

EmpFrc =	The fraction of non-residential construction employees in county c

Empc =	The number of non-residential construction employees in county c

Empus =	The number of non-residential construction employees in the US

CSc =	Non-residential construction spending in county c

CSus =	Non-residential construction spending in the US

Non-residential construction spending is converted to acres disturbed using a conversion factor from
MRI's report [ref 2], For the average acres disturbed per million dollars of non-residential construction,
MRI reported a conversion factor of 2 acres/$l million (in 1992 constant dollars). The 1992 conversion
factor is adjusted to 2020 using the Price Deflator (Fisher) Index of New Single-Family Houses under
Construction [ref 3], In 2020 the conversion factor was 0.84 acres per million dollars spent on non-
residential construction activities.

2 acres PD1992
Apd2o2o	miinon x PD2020

(3)

Where:

Apd2o2o =	Acres disturbed per million dollars in 2020

PD1992 =	Price Deflator (Fisher) Index value in 1992

PD2020 =	Price Deflator (Fisher) Index value in 2020

County-level non-residential construction spending (from equation 2) is then multiplied by this
conversion factor to estimate county-level acreage disturbed from non-residential construction
activities.

AN Rc — CSc X -*4p^2020

(4)

Where:

ANRC
CSc

Apd2017

Acres disturbed from non-residential construction in county c
Non-residential construction spending in county c, in million dollars
Acres disturbed per million dollars in 2020

Acres Disturbed from Residential Construction

The US Census Bureau has 2020 data for Housing Starts - New Privately Owned Housing Units Started
[ref 4, ref 5], which provides regional level housing starts based on the groupings of 1 unit, 2-4 units, 5
or more units. Regional-level results are also provided for quarterly totals and 1-unit structures [ref 5],
The 2- to 4-unit category is broken down using a ratio calculated from the 2000 US Census Bureau
National Housing Starts data for 2 and 3-4 units [ref 6], for each quarter of the NEI year. Note that 2000
is the last full year when Census housing starts data were available separately for 2-unit and 3-4-unit
homes.

34-2


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5 -f^xS

¦~>Q,n ~ \U J Q,2—4

(5)

Where:

SQ,„ = Housing starts, by quarter, Q, and number of units, n (2 units or 3-4 units), in thousand
units

Un = Number of housing starts by number of units, n, from the 2000 National Housing

Starts data, in thousand housing starts
Ut = Total number of housing starts for both 2 units and 3-4 units from the 2000 National

Housing Starts data, in thousand housing starts
Sq,2-4 = Number of 2-4 units by quarter, Q, from the New Privately Owned Housing Units
Started by Purpose and Design data, in thousand units

Ratios of the number of 2, 3 and 4, and 5-unit structures are then used to estimate the number of
structures of each type in each region. The ratios are calculated by dividing the housing starts by quarter
for each unit type by the total housing starts for buildings with more than 2 units.

$Q,n
rQ,n = T

^Q.t

Where:

rQ/„ = Ratio of structures with number of units, n, to total number of units by quarter, Q
SQ,„ = Housing starts, by quarter, Q, and number of units, n, from distributed calculation in
Step 1 for the 2-unit or 3-4-unit categories or directly from the New Privately Owned
Housing Units Started by Purpose and Design data for the 5 units or more category,
in thousand housing starts
SQ/t = Housing starts, by quarter, Q, for total number of units greater than 2 units, t (excludes
1-unit

category), in thousand housing starts

The ratio is then used to distribute the New Privately Owned Housing Units Started by Purpose and
Design [ref 5] regional data for all unit types to the 2, 3-4, or 5 or more unit categories within each
Census region - Northeast, Midwest, South, and West.

Q,n,rgn ^~Q,n {^^t,rgn RS±,rgn)	(7)

Where:

AQ,n,rgn = Number of housing units started in quarter 0, by number of units, n, and region of the

country, rgn, in thousand units
rQ/„ = Ratio of structures with number of units, n, to total number of units by quarter, Q
RSt,rgn = Total regional starts from New Privately Owned Housing Units Started by Purpose and

Design data, in thousand housing starts
RSijgn = Regional starts of structures with 1 unit from the New Privately Owned Housing Units
Started by Purpose and Design data, in thousand housing starts

Data from the Census report New Privately Owned Housing Units Authorized Unadjusted Units [ref 7] is

34-3


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used to calculate a conversion factor to determine the ratio of structures to units in the 5 or more unit
category. The conversion factor is calculated by dividing the total number of units in structures with 5 or
more units by region [ref 6] by the total number of buildings with 5 or more units by region [ref 7],

^5 ,rgn ^
CFk = —	

D

D5,rgn

Where:

CF5,rgn	=	Ratio of 5 units or more to the number of buildings with 5 units or more by region, rgn

Usjgn	=	Total number of 5 or more units by region, rgn

Bsjgn	=	Total number of buildings with 5 or more units by region, rgn

Structures started by category are then calculated at a regional level by summing the number of housing
unit starts across all four quarters and dividing the by number of units in each building type. For the 3-4-
unit type, the number of units per building is 3.5. The value is multiplied by 1,000 because the Census
data are in units of thousand building starts.

For buildings with 1, 2, or 3-4 units:

(£QQ\AQ.n.rgn) X 1,000	&

Bn,rgn ~

Where:

B„,rgn = Number of building starts by the unit number category, n, and by region, rgn
AQ,n,rgn = Number of housing units started in quarter Q, by number of units, n, and region of the

country, rgn, in thousand units
n = Number of units per building

For buildings with 5 or more units:

(10)

.n.rgn) x 1,000
&n,rgn	Qp

Where:

B„,rg„ = Number of building starts by the unit number category, n, and by region, rgn
AQ,n,rgn = Number of housing units started in quarter Q, by number of units, n, and region of the

country, rgn, in thousand units
CFs = Ratio of 5 units or more to the number of buildings with 5 units or more

Annual county building permit data were obtained from the US Census Bureau [ref 8], The County Level
Residential Building Permit dataset has data to allocate regional housing starts to the county level. This
results in county level housing starts by number of units for the NEI year.

The number of building permits for each unit number category by region is calculated by summing the
county-level Census data to the region level.

34-4


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n,rgn / LJln,c

BP = > BP

lJln.ran / LJli

Where:

(ii)

BPn,rgn = Number of building permits by the unit number category, n, and by region, rgn
BP„/C = Number of building permits by the unit number category, n, and by county, c

The ratio of the number of building permits by county to the total number of building permits by region
in which the county is located, for each unit number category, is then calculated.

BPn,c	(12)

BP'C BPn,rgn

Where:

Rbp,c	=	Ratio building permits, BP, to total regional building permits in county c

BP„/C	=	Number of building permits by the unit number category, n, and by county, c

BPn,rgn	=	Number of building permits by the unit number category, n, and by region, rgn

The final number of building starts for each unit type category is then calculated at the county-level by
multiplying the number of structures started at the regional level and the building permit ratio.

(13)

B)i,c Bn,rgn ^BP,c

Where:

Bn,c = Number of building starts by the unit number category, n, and by county, c
B„,rgn = Number of building starts by the unit number category, n, and by region, rgn
Rbp,c = Ratio building permits, BP, to total regional building permits in county, c

The number of acres of surface area disturbed by the construction of residential buildings is calculated
for apartment buildings, buildings with 2 units, and buildings with 1 unit. Table 34-1 shows the
assumptions used for the surface area disturbed for each unit type. Buildings with unit types of 3-4 and
5 or more are grouped together as apartments in this step.

Table 34-1: Surface soil removed per unit type

Structure

Acres disturbed

1-Unit

1/4 acre/structure

2-Unit

1/3 acre/structure

Apartment

1/2 acre/structure

The acres of soil disturbed by the construction of residential buildings are calculated for apartment
buildings, buildings with 2 units, and buildings with 1 unit.

^Rn,c Bn.c ^

(14)

34-5


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Where:

AR„/C = Surface soil disturbed by building construction by county, c, and unit type category, n,
in acres

B„/C = Number of building starts by the unit number category, n, and by county, c
a„ = Acres of surface soil disturbed by each unity type category, n. See Table 34-1.

Acres Disturbed by Road Construction

The activity data for this source category is the acreage disturbed from new road construction, which is
estimated using data from FHWA's Highway Statistics, State Highway Agency Capital Outlay, Table SF-
12A [ref 9] and FLDOT's Generic Cost per Mile Models [ref 10]. From the FHWA table, the following
columns are used: New Construction, Relocation, Added Capacity, Major Widening, and Minor
Widening. These columns are also differentiated according to the following six classifications:

1.	Interstate, urban

2.	Interstate, rural

3.	Other arterial, urban

4.	Other arterial, rural

5.	Collectors, urban

6.	Collectors, rural

Construction spending for each road type is summed across all construction types to determine the total
annual highway spending for each road type.

= y

s,r /

t—lc

HSs,r = ) SSir	(15)

'ct

Where:

HSs,r = Annual highway spending for road type r in state s, in dollars
ct = Construction type

Ss,r = Annual spending per construction type for road type r in state s, in dollars

State expenditure data are converted to miles of new road and acres disturbed per mile of new road
based on conversions obtained from FLDOT [ref 10]. These conversions are shown in Table 34-2 and the
acres disturbed per mile conversions are calculated by multiplying the total affected roadway width
(including all lanes, shoulders, and areas affected beyond the road width) by one mile and converting
the resulting land area to acres.

_ HSs r	(16)

m,s,r ~ TDM

RCa>s>r = RCmiSir X ADM	(17)

Where:

RCm,s,r = Miles of FHWA road type r constructed in state s

RCaAr = Acres of land disturbed for construction of FHWA road type r in state s

34-6


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HSs,r = Annual highway spending for road type r in state s

TDM = Conversion of dollars spent to road miles constructed, in thousand dollars per mile
ADM = Conversion of road miles constructed to acres disturbed, in acres per mile

	Table 34-2: Spending per Mile and Acres Disturbed per Mile by Highway Type	

Road Type

Thousand
Dollars per mile

Total Affected
Roadway Width
(ft)*

Acres
Disturbed per
mile

Urban Areas, Interstate

9,636

94

11.4

Rural Areas, Interstate

4,796

89

10.8

Urban Areas, Other
Arterials

4,829

63

7.6

Rural Areas, Other
Arterials

2,643

55

6.6

Urban Areas, Collectors

4,829

63

7.6

Rural Areas, Collectors

2,643

55

6.6

*Total Affected Roadway Width = (lane width (12 ft) * number of lanes) + (shoulder
width * number of shoulders) + area affected beyond road width (25 ft)

The acres of land disturbed by road type can then be summed across all road types in a state to calculate
the total state-level acreage disturbed due to new road construction.

ARC,

-1,

RC,

(18)

a,s

Where:

ARCS = Acres of land disturbed for all road construction in state s

RCa,s = Acres of land disturbed for construction of FHWA road type r in state s

Similar to residential construction, county-level building permits data from the U.S. Census Bureau are
used to allocate the state-level acres disturbed by road construction to the county [ref 8], Specifically,
the ratio of the county-to state-level number of building starts is calculated and multiplied by the state-
level acreage disturbed (from equation 18) to estimate the county-level acreage disturbed by road
construction.

Buildr
BFracc = ———
Buildc

ARCc = ARCS x BFracc

(19)

(20)

Where:

BFracc	=	The fraction of building starts in county c

Buildc	=	The number of building starts in county c

Builds	=	The number of building starts in state s

ARCc	=	Acres of land disturbed for road construction in county c

ARCS	=	Acres of land disturbed for all road construction in state s

34-7


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Converting Acres Disturbed to Tons of Land Clearing Debris Burned

The total acres disturbed by all construction types is calculated by summing the acres disturbed from
residential, non-residential, and road construction.

TADC = Total acres disturbed in from nonresidential, residential, and road construction in
county c

ANRC = Acres disturbed from non-residential construction in county c

AR„/C = Acres of surface soil disturbed from residential construction in county c and unit type

category n (summed to one value for residential construction for the county)

ARCc = Acres of land disturbed for road construction in county c

Version 2 of the Biogenic Emissions Land cover Database (BELD2) within EPA's Biogenic Emission
Inventory System (BEIS) [ref 11] is used to identify the acres of hardwoods, softwoods, and grasses in
each county.

Because BELD2 does not contain data on Alaska and Hawaii, the acres of hardwoods, softwoods, and
grasses in each county is estimated by using the state-level land cover statistics from the USGS National
Land Cover Database on the percent land cover under each vegetation type [ref 12]. These percentages
are multiplied by the county area (acres), from the U.S. Census Bureau [ref 13],

Aak/huj = Total acres of each fuel type,/, for each county, c, in Alaska or Hawaii
LAak/hi,c, = County acres from the U.S. Census Bureau of each fuel type,/, for each county, c, in
Alaska or Hawaii

ICAk/hi,%j = Land cover percentages for each fuel type (hardwood, softwood, grass) in Alaska or
Hawaii

Table 34-3 presents the average fuel loading factors by vegetation type. The average loading factors for
slash hardwood and slash softwood are adjusted by a factor of 1.5 to account for the mass of tree that is
below the soil surface that would be subject to burning once the land is cleared [ref 14], Weighted
average county-level loading factors are calculated by multiplying the average loading factors by the
percent contribution of each type of vegetation class to the total land area for each county.

(21)

Where:

(22)

AaK/HI.cJ — LAAk/HI,c X LCAK/HI,°/o,f

Where:

(23)

c,total

Where:

WFLFcj = Weighted average fuel loading factor by for fuel type/in county c

Acj = Acres of land cover in county c, by fuel type/(from BELD2 for continental U.S. [ref.

34-8


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11]; from equation 22 for Alaska and Hawaii)

Ac,total = Total acres of land cover of all fuel types in county c
LFf = Fuel loading factor by fuel type, f, in tons/acre, from Table 34-3

	Table 34-3: Fuel Loading Factors by Vegetation Type

Vegetation Type

Unadjusted Average
Fuel Loading Factor
(Ton/acre)

Adjusted Average
Fuel Loading Factor
(Ton/acre)

Hardwood

66

99

Softwood

38

57

Grass

4.5

Not Applicable

The weighted average county-level loading factors for each fuel type are then summed across fuel types
to calculate a single weighted average loading factor for each county.

WFLFr

"I,

WFLF,

(24)

C,f

Where:

WFLFc = Weighted average fuel loading factors for county c

WFLFcj = Weighted average fuel loading factor by for fuel type/in county c

The county-level total acres disturbed are then multiplied by the weighted average loading factor to
derive tons of land clearing debris.

(25)

LCDc = TADC X WFLFC

Where:

LCDc = Land clearing debris in county c, in tons

TADC = Total acres disturbed in county c

WFLFc = Weighted average fuel loading factors for county c

The total land clearing debris burned per county is calculated by multiplying acres of land clearing debris
by county by a control factor, based on the percent of urban land from the 2010 U.S. Census data [ref
13], See Section 34.2.4 for more information on the control factor.

(26)

BLCDc = LCDc X CFc

Where:

BLCDc = Land clearing debris burned in county c, in tons
LCDc = Land clearing debris in county c, in tons

CFc = Control factor. The control factor is 1 for counties with less than 80% urban population
and 0 for Colorado or in counties with an urban population of 0.8% or more based on
the 2010 U.S. Census data [ref 13] as no burning occurs in these counties. See

34-9


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Section 34.2.4 for more information on the control factor.

34.2.2 Allocation procedure

Acres disturbed by Non-residential Construction - County business patterns allocation

Employment data are obtained from the U.S. Census Bureau's County Business Patterns (CBP) [ref 15].
Due to concerns with releasing confidential business information, the CBP does not release exact
numbers for a given NAICS code if the data can be traced to an individual business. Instead, a series of
employment flags is used. To estimate employment in counties and states with withheld data, the
following procedure is used for NAICS code 2362 (non-residential construction).

To gap-fill withheld state-level employment data:

1.	State-level data for states with known employment are summed to the national level.

2.	State-level known employment is subtracted from the national total reported in the national-
level CBP.

3.	Each of the withheld states is assigned the midpoint of the employment flag. Table 34-4 lists the
employment flags and midpoints.

4.	The midpoints for the states with withheld data are summed to the national level.

5.	An adjustment factor is created by dividing the number of withheld employees (calculated in
step 2 of this section) by the sum of the midpoints (step 4)

6.	For the states with withheld employment data, the midpoint of the range for that state (step 3)
is multiplied by the adjustment factor (step 5) to calculate the adjusted state-level employment
for non-residential construction.

These same steps are then followed to fill in withheld data in the county-level business patterns.

1.	County-level data for counties with known employment are summed by state.

2.	County-level known employment is subtracted from the state total reported in state-level CBP
(or, if the state-level data are withheld, from the state total estimated using the procedure
discussed above).

3.	Each of the withheld counties is assigned the midpoint of the employment flag (Table 34-4).

4.	The midpoints for the counties with withheld data are summed to the state level.

5.	An adjustment factor is created by dividing the number of withheld employees (step 2) by the
sum of the midpoints (step 4).

6.	For counties with withheld employment data, the midpoints (step 3) are multiplied by the
adjustment factor (step 5) to calculate the adjusted county-level employment for non-
residential construction.

Note that step 5 adjusts all counties within each state with withheld employment data by the same
state-based proportion. It is unlikely that actual employment corresponds exactly with this smoothed
adjustment method, but this method is the best option given the availability of the data.

Table 34-4: Ranges and midpoints for data withheld from State and County Business Patterns

Employment Flag

Employment Range

Midpoint

A

0-19

10

B

20-99

60

C

100-249

175

E

250-499

375

F

500-999

750

34-10


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Employment Flag

Employment Range

Midpoint

G

1,000-2,499

1,750

H

2,500-4,999

3,750

1

5,000-9,999

7,500

J

10,000-24,999

17,500

K

25,000-49,999

37,500

L

50,000-99,999

75,000

M

100,000+



For example, take the CBP data for NAICS 2362 (nonresidential construction) in an example state
provided in Table 34-5. The values in this table are only for example purposes and are not
representative of any specific NEI year or state.

Table 34-5: Example CBP vales for NAICS 2362 for a state

County
FIPS

NAICS

Employment
Flag

Employment

001

2362

B

withheld

003

2362



125

005

2362



166

007

2362



24

011

2362

B

withheld

012

2362

A

withheld

013

2362



8,580

015

2362



64

017

2362



53

019

2362



2,085

021

2362



115

023

2362



16

025

2362



260

027

2362



233

1.	The total of employees not including withheld counties is 11,831.

2.	The state-level CBP reports 11,721 employees for NAICS 2362. The difference is 110.

3.	Withheld counties are given the midpoint of the employment range. County 001 is given a
midpoint of 60 (since employment flag A is 20 - 99) and County 012 is given a midpoint of 10
(since employment flag H is 0 - 19).

4.	State total for these all-withheld counties is 130.

5.	110/130 = 0.846.

6.	The adjusted employment for county 001 is 60 x 0.846 = 51.36 employees. County 012 has an
adjusted employment of 10 x 0.846 = 8.46 employees.

The county-level employment data are used to allocate the national-level non-residential construction
spending data to the county-level (see equation 1).

34-11


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Acres disturbed by Residential Construction - Building permits allocation

Annual county building permit data were obtained from the U.S. Census Bureau [ref 8] and used to
allocate regional housing starts to the county level. This results in county level housing starts by number
of units. See equations 11-13 in section 34.2.1.

Acres Disturbed by Road Construction - Building permits allocation

State-level estimates of acres disturbed by road construction is distributed to the counties based on
county-level data on residential building starts from the U.S. Census Bureau [ref 4], See equations 19
and 20 in section 34.2.1.

34.2.3	Emission factors

Emissions factors are provided in the "Wagon Wheel Emission Factor Compendium" on the 2020 NEI
Supporting Data and Summaries site. Emissions factors for CAPs and HAPs are from the AP-42 and U.S.
EPA Emissions Inventory Improvement Program [ref 16, ref 17]. The PM25 to PM10 emissions factor
ratio for brush burning (0.7709) is multiplied by the PM10 emissions factors for land clearing debris
burning to develop PM25 emissions factors. Emission factors for NH3 were derived from the 2002 NEI
crop residue emission estimates using the ratio of NH3/NOx for pasture grass from Pouliot et al. (2017)
[ref 18] and the NOx emission factor from AP-42 [ref 16]. Emissions factors for HAPs are from an EPA
Control Technology Center report [ref 19].

34.2.4	Controls

Controls for land clearing debris burning are generally in the form of a ban on open burning of waste in a
given municipality or county. Counties that are more than 80% urban by land area determined by the
2010 U.S. Census data [ref 13], are assumed not to practice any open burning of land clearing debris.
Therefore, CAP and HAP emissions from open burning of land clearing debris are zero in these counties.

Additionally, it is assumed that even in counties that are less than 80% urban by land area, open burning
will only be practiced in areas that are rural. Therefore, the total land clearing debris burned per county
(from equation 26) will be scaled based on the fraction of rural land area in each county from the 2010
Census.

RLandc	(27)

BLCDr c = BLCDc X —— ——

TLandr

Where:

BLCDr,c	= Land clearing debris burned in rural areas by county, c, in tons

BLCDc	= Land clearing debris burned by county, c, in tons

RLandc	= Amount of rural land by land area in county c

TLandc	= Total amount of land in county c

Further controls on burning (i.e., burn bans in rural areas) are represented by multiplying the land
clearing debris burned in rural counties by a burn ban's effectiveness; effectiveness is a value between 0
and 1.

34-12


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BLCDr c = BLCDr c X BEC

(28)

Where:

BLCDr,c = Land clearing debris burned in rural areas by county, c, in tons

BEC = Burn ban effectiveness in county c

In this methodology, burn ban effectiveness is represented by a single value between 0 and 1 that is
multiplied by the amount of land clearing debris burned in the rural areas of each county. In practice,
the burn ban effectiveness is a function of both a rule's penetration and effectiveness. Rule penetration
refers to the extent to which a regulation covers emissions for a specified controlled area, and
effectiveness concerns the ability of the regulatory program to achieve emissions reductions compared
to full compliance. By default, the burn ban effectiveness for each county is 1 (i.e., the methodology
assumes no burn bans in each county), although this may be updated by state, local, or tribal agencies.

34.2.5 Emissions

County-level criteria pollutant and HAP emissions are calculated by multiplying the mass of land clearing
debris burned in rural areas per year (from equation 28) by an emissions factor.

1 ton

Er„ = BLCDr r X EF„ X „„„„ ,,

c'v r'c v 2000 lb

(29)

Where:

Ec,p = Emissions by county, c, and pollutant, p, in tons

BLCDr,c = Land clearing debris burned in rural areas by county, c, in tons

EFP = Emissions factor by pollutant, p, in pounds/ton

34.2.6 Example calculations

Table 34-6 shows sample calculations for PM25-PRI emissions from open burning of land clearing debris
for a county in the Midwest. The values in these equations are demonstrating program logic and are not
representative of any specific NEI year or county. Equations 5 through 7 use the first quarter (Ql) for 2-
unit structures as an example. However, these calculations would need to be repeated to calculate
values for all 4 quarters for all 3 unit sizes. Note that structures with 5 or more units and structures with
1 unit with or without a basement have additional steps not shown in the sample calculations here.
Equations 15 through 20 use urban roads as an example for acres of land disturbed from road
construction. For full calculations of acres of land disturbed from road construction the calculations for
rural roads would also need to be incorporated.

Ta

lie 34-6: Sample calculations for PM25-PRI emissions from open burning of land clearing debris

Eq.#

Equation

Values

Result









0.000241

1

EmpFrc

Empc

140 nonres construction employees

fraction on
non-residential
construction
employees

Empus

581,963 nonres construction employees

34-13


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Eq.#

Equation

Values

Result

2

CSc = EmpFrc x CSus

0.000241/ractjon of employees
x $ 347,666 million in nonres
construction spending in the US

$83.79 million
in non-
residential
construction
spending

3

2 acres
^Pd2017~ $1 million

pd1992

x	

PD2017

2 acres disturbed 57 in 1992
$1 million 113 in NEI year

1.009 acres
disturbed per
million dollars
spent on non-
residential
construction
spending,
nationally

4

ANRC = CSc X ^4pdy

acres disturbed

$83.79 million x 1.009					

million $

84.4 acres
disturbed from
non-residential
construction

5

5 -(^1x5

~ \u J Q>2~4

/14 two unit housing starts in 2002\
V 38 total housing starts in 2002 J

X

2 two to four unit housing starts inQ 1

0.74 thousand
housing starts
for 2-unit
structures in
Q1 2017,
nationally

6

Sq,11

0.74 two unit housing starts

0.01 ratio of
buildings with
2 units to all
units greater
than 2 for Ql,
nationally

1Q,n o

^Q.t

72 two or more unit housing starts

7

Q,n,rgn ^ ^>l)

0.01

x (21 total Q1 housing starts in Midwest
— 14 one unit housing starts in Midwest

0.07 thousand
housing starts
for 2-unit
structures for
Ql in the
Midwest

8

Us.rgn
CFk = „

#5,r

N/A

Equation is for
5 or more unit
buildings;
example is for
2-unit buildings

9

(2 Aq n.rgn) x 1,000

"n,rqn

a n

0.775 two unit structures x 1,000
2 units per building

388 2-unit
structures
constructed in
the Midwest

34-14


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Eq.#

Equation

Values

Result

10

(2 Aq n.rgn) x 1,000

&n,rgn Qp

N/A

Equation is for
5 or more unit
buildings;
example is for
2-unit buildings

11

BP =7 BP

LJln,rgn / LJln,c

^ Midwest two unit building permits

1,571 2-unit
structure
building
permits in the
Midwest

12

BPn,c

1 County building permits

0.000637 ratio

of county-level

building

permits to

regional-level

building

permits

1BP,c

Ul n,rgn

1,571 Midwest building permits

13

Bn,c Bn,rgn ^ ^BP,c

388 two unit building starts in the
Midwest x 0.000637

0.25 total 2-
unit structure
building starts

14

^Rn,c Bn.c ^

0.25 two unit structures
x 0.33 acres per structure

0.08 acres
surface soil
disturbed by 2-
unit structures

15

HSs r / ^s.r
£—'ct

$20,399,000 + $33,029,000
+ $93,892,000

$147,320,000
spent on urban
interstate
construction in
the state

$58,519,000 + $2,626,000

+ $35,1367,000
+ $206,057,000
+ $17,193,000

$319,532,000
spent on urban
other arterial
construction in
the state

$16,093,000 + $338,000 + $355,000

$16,786,000
spend on urban
collector
construction in
the state

16

HSs r

PC —

m's'r TDM

$147,320,000
6,895,000 $ per mile

21.4 miles of
urban
interstate
constructed in
the state

34-15


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Eq.#

Equation

Values

Result





$319,532,000
4,112,000 $ per mile

77.7 miles of
urban other
arterial

constructed in
the state

$16,786,000
4,112,000 $ per mile

4.1 miles of
urban collector
constructed in
the state

17

RCa,s,r = RCm,s,r x ADM

21.4 miles x 11.4 acres per mile

242.9 acres
disturbed from
urban
interstate
construction in
the state

77.7 miles x 7.6 acres per mile

589.6 acres
disturbed from
urban other
arterial

construction in
the state

4.1 miles x 7.6 acres per mile

31 acres
disturbed from
urban collector
construction in
the state

18

ARCS = ^ RCa s

242.9 acres + 589.6 acres + 31 acres

863.5 acres
disturbed from
urban road
construction in
the state

19

Buildr

246 building starts in County

0.012 fraction
of building
starts

L) 1 1

Builds

20,578 building starts in State

20

ARCc = ARCS x BFracc

863.5 acres x 0.012

10.4 acres
disturbed from
urban road
construction

34-16


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Eq.#

Equation

Values

Result

21

TADC = ANRC + Snc)
+ ARCc

84.4 acres + 62.02* acres

+ 13.95** acres

* note that the value for residential
construction is for all unit types, not just 2-
unit buildings as shown in example above
** note the value for road construction is
for all road types, not just urban roads as
shown in the example above

160.4 total
acres disturbed

22

AaK/HI.cJ = LAAk/HI,c

X LCAK/HI,°/o,f

N/A

Equation is for
Alaska or
Hawaii

23

WFLFcJ = -^xLFf

™total

17,516 acres

—	x 99 tons per acre

758,793 acres

2.3 tons/ acre
weighted
factor for
hardwood fuel

0 acres

—	x 57 tons per acre

758,793 acres

0.0 tons/ acre
weighted
factor for
softwood fuel

741,276 acres

—	x 4.5 tons per acre

758,793 acres

4.4 tons/ acre
weighted
factor for grass
fuel

24

WFLFC = ^ WFLFcj

tons tons tons

2.3	+0.0	+ 4.4	

acre acre acre

6.7 tons/acre
weighted
factor for all
fuels

25

LCDc = TADC X WFLFC

tOTLS

160.4 acres x 6.7	

acre

1,071 tons of
land clearing
debris

26

BLCDc = LCDc X CFc

1,071 tons x 1 control factor

1,071 tons of
land clearing
debris burned

27

RLandr

BLCDr c — BLCDc X

TLandc

1,071 tons

2,923,414,473 m2 rural land
X 3,064,933,852 m2 total land

1,022 tons of
land clearing
debris burned
in rural areas

28

Fc,p = BLCDr c X EFV

1 ton

X 2000 lb

lb 1 ton

1,022 tons x 13.1053	x	—

ton 2000 lb

6.7 tons PM25-
PRI emissions

34-17


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34.2.7	Improvements/Changes in the 2020 NEI

For the 2020 NEI, ammonia emissions factors were developed so that ammonia emissions are estimated
for land clearing debris. The ammonia emissions factors are developed by applying the ratio of NH3 to
NOx emissions from pasture grass, using emissions data from Table 1 in Pouliot et al. (2011) [ref 18] to
the NOx emissions factor used for these sources, from AP-42 [ref 16].

For prior inventory years, the U.S. Census Bureau denoted counties for which County Business Patterns
(CBP) data was withheld and reported an employment size range. A gap-filling procedure was
implemented using state-level data, which was used to estimate the number of employees not reported
in the county-level dataset. An average value for number of employees for each employment size range
was used to allocate the difference to the counties with withheld data. Beginning in reference year
2018, data are still only published for a county and NAICS code if there are three or more
establishments. However, the CBP data no longer includes an employment size range for counties in
which data is withheld for a NAICS code. For the 2020 NEI, the gap-filling method was updated. 2020
employment data from the CBP dataset is used to determine the total amount of withheld data in each
state. The 2017 version of the CBP is then used to determine the counties for which data is withheld and
the employment size range in those counties. The difference between the state-level total employment
and the county-level total employment is allocated to the counties identified using 2017 CBP.

34.2.8	Puerto Rico and U.S. Virgin Islands

Since insufficient data exist to calculate emissions for the counties in Puerto Rico and the US Virgin
Islands, emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico
and 12087, Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida
counties are divided by their respective populations creating a tons per capita emissions factor. For each
Puerto Rico and US Virgin Island County, the tons per capita emissions factor is multiplied by the county
population (from the same year as the inventory's activity data) which served as the activity data. In
these cases, the throughput (activity data) unit and the emissions denominator unit are "EACH".

34.2.9	References

1.	U.S. Census Bureau. 2020. Value of Construction Put in Place.

2.	Midwest Research Institute. 1999. Estimating Particulate Matter Emissions from Construction
Operations, Final Report (prepared for the Emission Factor and Inventory Group, Office of Air
Quality Planning and Standards, U.S. Environmental Protection Agency).

3.	U.S. Census Bureau. 2020. Price Deflator (Fisher) Index of New Single-Family Houses Under
Construction.

4.	U.S. Census Bureau. New Privately Owned Housing Units Started by Purpose and Design in 2020.
Annual Data, accessed December 2021.

5.	U.S. Census Bureau. New Privately Owned Housing Units Started by Purpose and Design in 2020.
Quarterly Data, accessed December 2021.

6.	U.S. Census Bureau. 2001. Housing Starts. Table 1. New Privately-Owned Housing Units Started.

7.	U.S. Census Bureau. New Privately-Owned Housing Units Authorized - Unadjusted Units for
Regions. Divisions, and States. Annual 2020.

8.	U.S. Census Bureau. Annual Housing Units Authorized by Building Permits. ASCII files by State.
MSA. County or Place. co2020a.

34-18


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9.	Federal Highway Administration. Table SF-12A, State Highway Agency Capital Outlay -2020.

10.	Florida Department of Transportation. Generic Cost per Mile Models for 2020.

11.	U.S. Environmental Protection Agency. 2023. Biogenic Emission Inventory System (BElSl

12.	U.S. Geological Survey (USGS). 2015. National Land Cover Database.

13.	U.S. Census Bureau, Decennial Censuses. 2010. Census: Summary File 1.

14.	D.V. Sandberg, D.E. Ward, R.D. Ottmar, C.C. Hardy, T.E. Reinhardt, and J.N. Hall. 1989. Mitigation
of Prescribed Fire Atmospheric Pollution through Increased Utilization of Hardwoods, Piled
Residues, and Long-Needled Conifers. Final Report. USDA Forest Service, Pacific Northwest
Research Station, Fire and Air Resource Management.

15.	U.S. Census Bureau, County Business Patterns. 2020. CBP Tables.

16.	U.S. Environmental Protection Agency. 1992. AP-42, Fifth Edition, Volume 1, Chapter 2: Solid
Waste Disposal.

17.	U.S. Environmental Protection Agency. 2001. Emission Inventory Improvement Program,

Volume III, Chapter 16, Open Burning.

18.	G. Pouliot, V. Rao, J.L. McCarty, and A. Soja. 2017. Development of the crop residue and
rangeland burning in the 2014 National Emissions Inventory using information from multiple
sources. Journal of the Air & Waste Management Association, 67(5), 613-622.
https://doi.org/10.1080/10962247.2Q16.1268982.

19.	U.S. Environmental Protection Agency. 1997. Evaluation of Emissions from the Open Burning of
Household Waste in Barrels, EPA-600/R-97-134a.

34-19


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United States	Office of Air Quality Planning and Standards	Publication No. EPA-454/R-23-001hh

Environmental Protection	Air Quality Assessment Division	March 2023

Agency	Research Triangle Park, NC


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