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2020 National Emissions Inventory Technical
Support Document: Dust - Construction -Non-
Residential


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EP A-454/R-23 -00 lu
March 2023

2020 National Emissions Inventory Technical Support Document: Dust - Construction -Non-

Residential

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

21 Dust - Construction -Non-Residential	21-1

21.1	Sector Descriptions and Overview	21-1

21.2	EPA-developed estimates	21-1

21.2.1	Activity data	21-1

21.2.2	Allocation procedure	21-2

21.2.3	Emission factors	21-5

21.2.4	Controls	21-6

21.2.5	Emissions	21-6

21.2.6	Sample calculations	21-6

21.2.7	Improvements/Changes in the 2020 NEI	21-7

21.2.8	Puerto Rico and Virgin Islands	21-8

21.3	References	21-8

List of Tables

Table 21-1: SCCs in the Construction Dust sector	21-1

Table 21-2: Ranges and midpoints for data withheld from State and County Business Patterns	21-3

Table 21-3: Example CBP for NAICS 2361	21-4

Table 21-4: Sample calculations for non-residential construction	21-6

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21 Dust-Construction -Non-Residential

21.1 Sector Descriptions and Overview

Construction dust refers to residential and non-residential construction activity, which are functions of
acreage disturbed for construction. This sector will be divided below when describing the calculation of
EPA's emissions. Table 21-1 lists the nonpoint SCCs associated with this sector in the 2020 NEI. The SCC
level 1 and 2 descriptions is "Industrial Processes; Construction: SIC 15 -17" for all SCCs.

Table 21-1: SCCs in the Construction Dust sector

SCC

SCC Level Three

SCC Level Four

2311010000

Residential

Total

2311020000

Industrial/Commercial/Institutional

Total

2311030000

Road Construction

Total

21.2 EPA-developed estimates

The calculations for estimating the emissions from non-residential construction involve first estimating
the acres disturbed from non-residential construction in each county. The value of national-level non-
residential construction spending is available from the U.S. Census Bureau and is converted to acreage
disturbed using a conversion factor from a report by the Midwest Research Institute (MRI). The national-
level acres disturbed are distributed to counties based on the proportion of non-residential construction
employment in each county. Emissions factors for PM10 and PM25 are calculated based on
precipitation-evaporation values and dry silt content in each county. The total amount of acres
disturbed is multiplied by these emissions factors to estimate emissions of PM from non-residential
construction.

21.2.1 Activity data

The activity data for this source category 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 (NAICS 2362) 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 the next section.

Empr

EmpFrc =		(1)

Empus

CSc = EmpFrc X CSus	(2)

Where:

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

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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. 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 PD-iaa2

2020 = Xj T7T. X ——	(3)

$1 TTiillioYi PD2020

Where:

Apd202o = 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.

Ac = CSc X Apd2o20	(4)

Where:

Ac = Acres disturbed from non-residential construction in county c
CSc = Non-residential construction spending in county c
Apd2o2o = Acres disturbed per million dollars in 2020

21.2.2 Allocation procedure

Employment data are obtained from the U.S. Census Bureau's County Business Patterns (CBP) [ref 4],
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 This is the case if a
particular county has 2 or fewer establishments under a given North American Industrial Classification
Standard (NAICS) code. In prior years, the County Business Patterns data reported the counties where
data was withheld, along with dataset ranges for the withheld data (e.g., 20-99 employees). A gap-filling
procedure was implemented using state-level data, which did not feature withheld data, to estimate
employment counts in all counties.

Beginning in 2018, the Census Bureau stopped reporting dataset ranges for counties with withheld data.
As such, the prior gap-filling methods required updating. For all post-2017 inventories, year-specific
employment data from the County Business Patterns dataset is used to determine the total amount of

21-2


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withheld data in each state. The 2017 version of the County Business Patterns is then used to determine
the counties for which withheld data exist and the data ranges for those counties, and it is to these
counties that the difference between the state-level total employment and county-level total
employment are allocated.

To gap-fill withheld state-level employment data:

1.	The 2017 version of CBP is used to determine the states for which data is withheld and the
employment size range in those states.

2.	State-level data for states with known employment in the NAICS code are summed to the
national level.

3.	The total sum of state-level known employment from step b is subtracted from the national
total reported employment for the NAICS code in the national-level CBP to determine the
employment total for the withheld states.

4.	Each of the withheld states is assigned the midpoint of the range code reported for that
state. Table 21-2 lists the range codes and midpoints.

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

6.	An adjustment factor is created by dividing the number of withheld employees (calculated in
step 3 of this section) by the sum of the midpoints (step 5).

7.	For the states with withheld employment data, the midpoint of the range for that state
(step 4) is multiplied by the adjustment factor (step 6) to calculate the adjusted state-level
employment for the NAICS code.

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

1.	The 2017 version of CBP is used to determine the counties for which data is withheld and
the employment size range in those counties.

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

3.	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).

4.	Each of the withheld counties is assigned the midpoint of the range code (Table 21-2).

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

6.	An adjustment factor is created by dividing the number of withheld employees (step 3) by
the sum of the midpoints (step 3).

7.	For counties with withheld employment data, the midpoints (step 4) are multiplied by the
adjustment factor (step 6) to calculate the adjusted county-level employment.

Note that step 6 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 21-2: Ranges and midpoints for data withheld from State and County Business Patterns

Range Letter

Ranges

Midpoint

A

0-19

10

B

20-99

60

C

100-249

175

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Range Letter

Ranges

Midpoint

E

250-499

375

F

500-999

750

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+



An example of this gap-filling method is provided for NAICS 2362 in Table 21-3. The values in the table
and subsequent steps are for demonstration purposes and are not representative of any specific NEI
year or county.

Table 21-3: Example CBP for NAICS 2361

FIPS county

NAICS

empflag

emp

001

2362

A

withheld

003

2362

B

withheld

005

2362



177

007

2362



11

009

2362

A

withheld

011

2362

H

withheld

012

2362

A

withheld

013

2362



7,945

015

2362



47

017

2362



79

019

2362



2,220

021

2362



112

023

2362

A

withheld

025

2362



171

027

2362



359

1.	The total of employees not including withheld counties is 11,121.

2.	The state-level CBP reports 13,952 employees for NAICS 2362. The difference is 2,831.

3.	County 001 is given a midpoint of 10 (since range code A is 0-19) and County Oil is given a
midpoint of 3,750.

4.	State total for these all withheld counties is 3,850.

5.	2,831/3,850 = 0.74.

6.	The adjusted employment for county 001 is 10 x 0.74 = 7.35. County 011 has an adjusted
employment of 3,750 x 0.74 = 2,757.47.

The county-level employment data are used to allocate the national-level non-residential construction
spending data to the county-level (see equations 1 and 2).

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21.2.3 Emission factors

Due to regional variances in soil moisture and silt content, emissions factors for PM10 and PM25 are
calculated for each county. The initial PM10 emissions factor from non-residential construction is 0.19
tons/acre-month [ref 5], The duration of construction activity for non-residential construction is
assumed to be 11 months.

To account for the soil moisture level, the PM10 emissions are weighted using the 30-year average
precipitation-evaporation (PE) values from Thornthwaite's PE Index. Average precipitation evaporation
values for each state are estimated based on PE values for specific climatic divisions within a state [ref
5], The average PE value for the test sites from which the PM10 emissions factor was developed is 24.
Equation 5 adjusts the county-level emissions factor based on this PE value.

To account for the silt content, the PM10 emissions are weighted using average silt content for each
county. EPA uses the National Cooperative Soil Survey Microsoft Access Soil Characterization Database
to develop county-level, average silt content values for surface soil [ref 6], The U.S. Department of
Agriculture and the National Cooperative Soil Survey define silt content of surface soil as the percentage
of particles (mass basis) of diameter smaller than 50 micrometers (nm) found in the surface soil. Note
that this definition is different than the U.S. Environmental Protection Agency's definition that includes
all particles (mass basis) of diameter smaller than 75 micrometers. This database contains the most
commonly requested data from the National Cooperative Soil Survey Laboratories including data from
the Kellogg Soil Survey Laboratory and cooperating universities. The average silt content for the test
sites from which the PM10 emissions factor was developed is 9%. Equation 5 adjusts the county-level
emissions factor based on this silt content value.

24 Sc

EFpMio,c = efpMW x x	(5)

Where:

EFpmio,c =

efpMio =

PES
Sc

PMioemission factor corrected for soil moisture and silt content in state s and county

c, in tons/acre-month
Initial PMio emissions factor for non-residential construction, 0.19 tons/acre-month
Precipitation-evaporation value for state s
Percent dry silt content in soil for county c

Once PMio adjustments have been made, PM2.5 emissions are set to 10% of PMi0.[ref 7]

EFpM25,c = 0.10 X EFpmio,c	(6)

Where:

EFpmio,c = PMio emission factor corrected for soil moisture and silt content in state s and county c, in
tons/acre-month

EFPm25,c = PM2.5 emission factor corrected for soil moisture and silt content in county c, in tons/acre-
month

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Primary PM emissions are equal to filterable emissions as there are no condensible emissions from dust
from non-residential construction. Emission factors for these sources are provided in the "Wagon Wheel
Emission Factor Compendium" on the 2020 NEl Supporting Data and Summaries site.

21.2.4	Controls

There are no controls assumed for this category.

21.2.5	Emissions

The total annual PM emissions from non-residential construction in each county are calculated by
multiplying the acres disturbed by the emissions factors calculated in equations 5 and 6 and by the
duration of construction activity.

EP,C = AC x EFPiC X M	(7)

Where:

Ep,c = Annual emissions of pollutant p in county c

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

EFpmio,c = PMioemission factor corrected for soil moisture and silt content in state s and county
c, in tons/acre-month

EFPm25,c = PM2.5 emission factor corrected for soil moisture and silt content in county c, in
tons/acre-month

M = Duration of construction activity in months, assumed to be 11 months

21.2.6 Sample calculations

Table 21-4 lists sample calculations to determine the dust emissions from non-residential construction.
The values in these equations are demonstrating program logic and are not representative of any
specific NEI year or county.

Table 21-4: Sample calculations for non-residential construction

Eq.

#

Equation

Values

Result

1

EmpFrc
Empc

Empus

120 nonres construction employees
582,574 nonres construction employees

0.000206
fraction of
non-
residential
construction
employees

2

CSc =
EmpFracr x

csus

0.000206 fraction of employees in Grand Traverse x
$ 347,666 million in nonres construction spending in the US

$71.61
million in
non-
residential
construction
spending

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Eq.

#

Equation

Values

Result

3

Apdy =
2 acres

7	TT— x

2 acres disturbed 57 in 1992

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

$1 million

pd1992

PDy

$1 million 113 m 2020

4

Ac

= CSc x Apdy

acres disturbed

$ 71.61 million x 1.009					

million $

72.25 acres
disturbed
from non-
residential
construction

5

EFpmio.c =

24

efpMW X x

h.

9%

24 21.95%

0.19 tons per acre month x	x	

F 103.6 9%

0.1073 tons
PM10 per
acre-month
of non-
residential
construction

6

EFpM2S,c
0 1 o X

EFpMio.c

0.10 x 0.1073 tons per acre month

0.0107 tons
PM25 per
acre month
on non-
residential
construction

7

F

P,c

= Ac x EFp c
xM

tons

72.25 acres x 0.1073	x 11 months

acre — month

85.3 tons
PM10
emissions
from non-
residential
construction

tons

72.25 acres x 0.0107		 x 11 months

acre — month

8.5 tons
PM25
emissions
from non-
residential
construction

21.2.7 Improvements/Changes in the 2020 NEI

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

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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 employment 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. Except for
activity data updates, the CBP gap-filling method update is the only significant change made to the
methodology for this source used in the 2020 NEI.

21.2.8 Puerto Rico and Virgin Islands

Since insufficient data exists 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 emission factor. For each
Puerto Rico and US Virgin Island counties, the tons per capita emission factor is multiplied by the county
population (from the same year as the inventory's activity data) which serve as the activity data. In these
cases, the throughput (activity data) unit and the emissions denominator unit are "EACH".

21.3 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. Table 5-2.

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

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

5.	Midwest Research Institute. 1996. Improvement of Specific Emission Factors (BACM Project No.
1). Prepared for South Coast Air Quality Management District.

6.	U.S. Department of Agriculture, National Cooperative Soil Survey, NCSS Microsoft Access Soil
Characterization Database.

7.	Midwest Research Institute. 2006. Background Document for Revisions to Find Fraction Ratios
Used for AP-42 Fugitive Dust Emissions Factors. Prepared for Wester Governors 'Association.

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

Environmental Protection	Air Quality Assessment Division	March 2023

Agency	Research Triangle Park, NC


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