Particulate Matter Emissions for

eGRID2020

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eGRID 1 >

PM2.5 White Paper
December 7, 2022

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Table of Contents

Introduction	3

Methodology	4

Results	5

Table of Figures

Figure 1. eGRID subregion map	6

Figure 2. eGRID Subregion-level 2020 generation, PM2 5 emissions, and PM2 5 emission rates	7

Table of Tables

Table 1. eGRID Subregion-level 2020 generation, PM2 5 emissions, and PM2 5 output emission rates	8

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Introduction

EPA's Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive source of data
on air pollution emissions and electricity generation for virtually all electric generating units in the United
States. Currently, eGRID includes emissions data on carbon dioxide (CO2), nitrogen oxides (NOx), sulfur
dioxide (SO2), methane (CH4), and nitrous oxide (N2O), but does not include information on particulate
matter (PM). PM pollution—principally fine particulate matter 2.5 microns in diameter or smaller
(PM2 5)—can lead to negative health impacts, including asthma exacerbations, heart attacks, and
premature mortality. For example, Lelieveld et al. (2015) estimated that in 2010, 55,000 premature deaths
in the United States were attributable to two types of air pollution — PM2 5 and ozone.1 Additionally,
EPA's retrospective analysis of the Clean Air Act found that approximately 85 percent of the public
health benefits of air quality regulations are due to PM2 5 reductions, rather than ozone (EPA 201 la).2
PM2 5 can also lead to reduced visibility, known as haze, which negatively affects much of the country,
including national parks.

This white paper provides an overview of the methodology used to develop the draft PM2 5 emissions rates
and the emissions rates for eGRID2020. eGRID uses CAMD's Power Sector Emissions Data reported to
EPA's Clean Air Markets Division (CAMD)3 to determine the CO2, NOx, and SO2 emissions at many
electric generating units. For electric generating units that do not report to CAMD, eGRID estimates
emissions based on fuel use, as reported to the Energy Information Administration (EIA).4 Neither
CAMD nor EIA collect data on PM2 5 emissions.5 For this reason, it is not possible to use PM data from
either CAMD or EIA to estimate the PM2 5 emissions and PM2 5 emission rates from power plants.

EPA's National Emissions Inventory (NEI) is a source of PM2 5 emissions data. The annual emissions of
air pollutants, including PM2 5, from most electric generating units get reported to the NEI.6 While EPA
has not previously used the NEI data for eGRID, EPA is proposing to use NEI data to determine PM2 5
emissions at electric generating units. The most recent data year for both eGRID and NEI data is 2020.

This paper discusses EPA's proposed methods to determine PM2 5 emission rates for each power plant,
including steps to estimate emissions for units that may not report to the NEI. The accompanying Excel
data file, "eGRID2020 DRAFT PM Emissions.xlsx," lists the unit- and plant-level heat input, plant-level
generation, and unit-, plant-, and eGRID subregion-level PM25 emissions and emission rates for 2018,
2019, and 2020.

Note that PM can be emitted in two forms—as particles (filterable PM) or as a gas that later condenses
into particles when it enters the atmosphere (condensable PM). The eGRID methodology is designed to

1	Lelieveld, J., J.S. Evans, M. Fnais, D. Giannadaki, and A. Pozzer. 2015. The contribution of outdoor air pollution sources to
premature mortality on a global scale. Nature 525: 367-371.

2	EPA. 201 la. The Benefits and Costs of the Clean Air Act from 1990 to 2020. U.S. Environmental Protection Agency Office of
Air and Radiation. Final Report - Rev. A. April. Available: https://www.epa.gov/sites/production/files/2015-
07/documents/fullreport_rev_a.pdf. U.S. Environmental Protection Agency.

3	These data are reported to EPA under chapter 40 of the Code of Federal Regulations part 75 (40 CFR part 75) for several Clean
Air Act programs, including the Acid Rain Program and Cross-state Air Pollution Rule.

4	These data are reported to EIA through form EIA-923.

5	EIA collects some data on PM emission rates, but it does not specify whether the rates are for PM2.5 or PM10 (particulate matter
10 microns in diameter or smaller).

6	Electric generating units and other point sources of air pollution emissions do not report emissions directly to the NEI. Rather
they report to state, local, or tribal agencies, which then report the data to the NEI.

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include both types of PM, also known as primary PM2.5.7

Methodology

The most recent year in which both eGRID and NEI data are available is 2020.8 To estimate PM2 5
emissions and emission rates for 2020, unit-level PM2 5 emission rates (lb/MMBtu) were developed using
the 2020 NEI PM2 5 mass data and the 2020 EIA heat input data. The 2020 unit-level PM2 5 emissions
were summed to the plant-level, state-level, and eGRID subregion-level to estimate 2020 plant-level
PM2 5 emissions and state-level and eGRID subregion-level PM2 5 output emission rates.

The following methodology discusses how the 2020 unit-, plant-, and eGRID subregion-level PM2 5
emissions and emission rates were calculated. The NEI contains annual PM2 5 emissions data for electric
generating units, but the first step in integrating NEI data is to match the electric generating units to the
eGRID data. The NEI uses Emissions Inventory System (EIS) codes to identify facilities and units, while
eGRID uses Office of Regulatory Information Systems Plant (ORISPL) codes to identify facilities and
units. EPA's Office of Air Quality Planning and Standards (OAQPS), which compiles the NEI, has
matched electric generating units between EIS identifiers used in the NEI and the ORISPL identifiers
used in eGRID.9 The EIS and ORISPL systems do not always have a one-to-one relationship; in some
cases, multiple EIS IDs are used to refer to a single unit in eGRID (or vice versa). In order to use the NEI
data in eGRID, the NEI data are mapped to the appropriate ORISPL plant and unit ID. Multiple units in
the NEI that are reported as matching to one eGRID unit are grouped and summed to determine the total
emissions for each eGRID unit. For units that cannot be matched directly to the NEI, EPA estimated the
PM2 5 emissions using a series of emissions factors. In general, the process of determining the PM2 5
emissions for each unit follows a four-step process:

1.	Direct match. First the EPA matched operational combustion units with positive heat input
directly between the 2020 NEI and 2020 eGRID. Units that could not be matched directly
between the NEI and eGRID either do not report to the NEI as point sources or an adequate
match between NEI and eGRID could not be determined.

2.	Average emissions factors by fuel type, unit firing type, and prime mover. EPA developed
average emissions factors by grouping the units from the NEI that can be matched to eGRID by
fuel type (e.g., bituminous coal), unit firing type (e.g., wall-fired), and prime mover (e.g., steam
turbine). The PM2 5 emissions and heat input, expressed as million British thermal units (MMBtu),
for all units in each group were summed. The emissions factor was calculated by dividing the
total emissions in each group by the total heat input in each group. This emissions factor was
multiplied by the heat input reported by EIA for all units that could not be matched to a unit in the
NEI, but which have the same fuel type, firing type, and prime mover.

3.	Average emissions factors by fuel type and prime mover. For units that could not be matched
directly between eGRID and the NEI or that could not be matched using the emissions factors
developed under step 2, EPA next developed more general average emissions factors by grouping

7	In addition to primary PM2.5 emitted by electric generating units, secondary PM2.5 can form in the atmosphere based on
reactions of gases, such as NOx, SO2, and ammonia. This proposed method only addresses primary PM2.5.

8	The National Emissions Inventory is compiled for all sources every three years, and the most recent release is for 2017 data.
However, data on point sources, including electricity generating units, is collected annually, and the most recent data for the point
source emissions is 2020.

9	This analysis uses the data from the 2016vl air emissions modeling platform (available at fattps://www.epa.gov/airemissions-
modeling/201.6v 1.-platform) to identify PM2.5 emissions from power plants.

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the units from the NEI by fuel and prime mover. To capture more units, firing type was not
included in this step because not all units have firing type data.

4. Emissions factors from AP-42. For the remaining units, EPA estimated the PM2 5 emissions
using an emissions factor reported in EPA's AP-42.10 The emissions factors from AP-42 are
specific to the unit's fuel, firing type, and prime mover. For some of these units, EPA was able to
match the units to a PM control efficiency reported in the EIA-923. Therefore, for these units, the
PM2 5 emissions estimated using the emissions factor were adjusted to account for the control
efficiency. Since the NEI emissions included in step 1 and the average emissions factors
developed in steps 2 and 3 are based on reported emissions to the NEI, the control efficiency is
already accounted for in these emissions factors. The emissions in steps 1 through 3 therefore did
not need to be further adjusted for any control efficiencies.

There are some fuel types for which there are no emissions factors in AP-42 or another source. For these
factors, an emissions factor from a similar fuel type was applied. For example, there is no emission factor
for other gas (OG), so the emissions factor for natural gas (NG) was used. For some fuel types, including
lignite coal, petroleum coke, and waste oil, the PM2 5 emissions factors depend on the ash content of the
fuel. For the units combusting these fuels that could not be directly matched to the NEI, an ash content of
the fuel was first estimated. EIA-923 reports ash content at the unit-level for each month. A weighted
average ash content was calculated for each unit that uses lignite, petroleum coke, or waste oil, weighted
by the amount of heat input for each unit in each month, which were used with equations from AP-42 to
determine unit-specific emissions factors for those three fuel types.

Results

Figure 1 displays the 27 eGRID subregions for eGRID2020. The 2020 subregion-level annual net
generation, PM2 5 emissions, and PM2 5 output emission rates are shown in Figure 2 and in Table 1. The
2018, 2019, and 2020 unit-, plant-, and subregion-level annual net generation, PM2 5 emissions, and PM2 5
output emission rates are included in the Excel data file "eGRID2020 DRAFT PM Emissions.xlsx."

The 2020 subregion-level PM2 5 output emission rates range from 0.017 lbs/MWh in the NYUP subregion
to 0.917 lbs/MWh in the HIMS subregion. The highest output emission rates are in the subregions in
Alaska and Hawaii, which have a higher percentage of generation from oil and a lower percentage of
generation from natural gas when compared to the subregions in the contiguous United States. These
higher output emission rates are explained by the fact that oil has a high PM2 5 output emission rate
compared to natural gas.

10 United States Environmental Protection Agency, Office of Air Quality Planning and Standards. Compilation of Air Pollutant
Emission Factors, AP-42, Fifth Edition, Volume I: Stationary Point and Area Sources

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USEPA, eGRID, January 2022

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Figure 2. eGRlD Snbregion-level 2020 generation, PM2.5 emissions, and PM2.5 emission rates

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Table 1. eGRlD Snbregion-level 2020 generation, PM2.5 emissions, andPM2.5 output emission rates

Subregion

Annual Generation
(MWh)

Annual PM2.5 Emissions
(short tons)

Annual PM2.5 Output
Emission Rate
(lbs/MWh)

AKGD

4,659,917

583

0.2500

AKMS

1,496,130

589

0.7873

AZNM

165,394,893

4,029

0.0487

CAMX

196,129,978

2,778

0.0283

ERCT

414,911,396

8,743

0.0421

FRCC

241,403,268

8,073

0.0669

HIMS

2,450,535

1,123

0.9167

HIOA

6,618,705

1,768

0.5344

MROE

20,714,885

430

0.0416

MROW

227,120,021

5,106

0.0450

NEWE

96,795,891

2,237

0.0462

NWPP

286,004,986

5,403

0.0378

NYCW

39,727,442

1,224

0.0616

NY LI

10,559,287

658

0.1246

NYUP

84,654,342

713

0.0168

PRMS

18,215,878

812

0.0892

RFCE

283,765,353

5,741

0.0405

RFCM

85,325,787

3,212

0.0753

RFCW

496,410,171

16,449

0.0663

RMPA

64,065,081

838

0.0261

SPNO

70,017,393

1,243

0.0355

SPSO

152,288,973

3,389

0.0445

SRMV

159,992,962

3,940

0.0492

SRMW

108,641,153

3,798

0.0699

SRSO

246,150,586

4,128

0.0335

SRTV

211,540,764

5,227

0.0494

SRVC

326,493,675

7,328

0.0449

U.S.

4,021,549,452

99,562

0.0495

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